Just Accepted

Just Accepted Papers are peer-reviewed and accepted for publication. They will soon (normally in 1–3 weeks) transform into Typeset Proofs when initial checks such as language editing and reference cross-validation are completed and typesettings of the papers are done. Note that for both types of papers under this directory, they are posted online prior to technical editing and author proofing. Please use with caution.
Display Method:

Coming soon.....

Optimized Design of Multi-layer Absorber for Human Tissue Surface
TU Botao, YE Mengqiu, YANG Zhen, LI Jinfeng, LI Guanghui, ZHANG Yuejin
, Available online  , doi: 10.23919/cje.2021.00.385
Abstract(192) HTML (94) PDF(29)
In order to avoid the damage of electromagnetic wave to human tissue, a structural optimization scheme of human tissue surface is designed, main absorbing material-graphite was calculated under different concentrations of electromagnetic parameters, the reflection coefficient calculated by the equivalent transmission line theory, and finally through the establishment of reflection coefficient $ \digamma $ objective function and the genetic algorithm to optimize the absorbing device design. The experimental results show that when the material thickness error is within ±0.005 mm, the microwave absorption performance error of the multilayer absorber is about 5%, and the concentration and thickness of the graphite layer in the absorber have a great influence on the performance of the absorber, while the sensitivity of the other four layers is low. The performance of multi-layer absorber is successfully optimized, so that it can not only have a wide frequency band, but also ensure low reflectivity.
Blind Signal Reception in Downlink Generalized Spatial Modulation Multiuser MIMO System Based on Minimum Output Energy
WU Wei-Chiang
, Available online  , doi: 10.23919/cje.2022.00.113
Abstract(73) HTML (35) PDF(13)
This paper considers downlink multiuser MIMO (MU-MIMO) system with parallel spatial modulation (PSM) scheme, in which base station transmitter (BSTx) antennas are separated into K groups corresponding to K user terminals (UT). Generalized spatial modulation (GSM) is employed, where a particular subset of transmit antennas in each group is activated and the activation pattern itself conveys spatial information symbols. Different from the existing precoding-based algorithms, we develop a two-stage detection scheme at each UT: In the pre-processing stage, a “Minimax” algorithm is proposed to identify the indices of active antennas, where the key idea is that that the minimum output energy of the detector is maximized. A constrained Minimum Output Energy (MOE) algorithm is proposed in the post-processing stage to mitigate multiuser interference (MUI) and extract temporal symbols. Compared with existing precoding-based algorithms, the complexity is significantly reduced. Moreover, the proposed algorithm is semi-blind in that only a small subset of channel state information (CSI) is required to identify active antennas as well as eliminate MUI. Simulation results demonstrate that the proposed algorithm is near-far resistant and the capacity is extensively increased compared to the conventional spatial modulation (SM) scheme.
Research Article
Multi-sensor Fusion Adaptive Estimation for Nonlinear Under-observed System with Multiplicative Noise
CUI Yongpeng, SUN Xiaojun
, Available online  , doi: 10.23919/cje.2022.00.364
Abstract(36) HTML (18) PDF(10)
The adaptive fusion estimation problem was studied for the multi-sensor nonlinear under-observed systems with multiplicative noise. A one-step predictor with state update equations was designed for the virtual state with virtual noise first of all. An extended incremental Kalman filter (EIKF) was then proposed for the nonlinear under-observed systems. Furthermore, an adaptive filtering method was given for optimization. The fusion adaptive incremental Kalman filter weighted by scalar was finally proposed. The comparison analysis was made to verify the optimization of the state estimation using adaptive filtering method in the filtering process.
Transformer-Based Under-Sampled Single-Pixel Imaging
YE Tian, FU Ying, JUN Zhang
, Available online  , doi: 10.23919/cje.2022.00.284
Abstract(94) HTML (46) PDF(16)
As an innovative imaging technique, single-pixel imaging has attracted much attention during the last decades. However, it is still a challenging task for single-pixel imaging to reconstruct high-quality images with fewer measurements. Recently, deep learning based methods have shown great potential in under-sampled single-pixel imaging. In despite of the better performance than traditional model-based methods, the existing deep learning based methods usually adopt fully convolutional networks to model the imaging process which ignores the long-range dependencies of measurements, leading to limited reconstruction performance. In this paper, we present a transformer-based single-pixel imaging method to realize high-quality image reconstruction in under-sampled situation. By taking advantage of self-attention mechanism, the proposed method is good at modeling the imaging process and can reconstruct high-quality object image directly using the measured one-dimensional light intensity sequence reflected from the object. Numerical simulations and real optical experiments demonstrate that our proposed method has better reconstruction performance and noise robustness compared with the state-of-the-art single-pixel imaging methods.
Improvement of Quaternion Non-local Means Denoising Algorithm by Coupling Quasi-Chebyshev Distance and Quaternion Bilateral Filtering
XU Xudong, ZHANG Zhihua, M. James C. Crabbe
, Available online  , doi: 10.23919/cje.2022.00.138
Abstract(28) HTML (13) PDF(3)
The Quaternion non-local means (QNLM) denoising algorithm makes full use of high degree self-similarities inside images to suppress the noise, so the similarity metric plays a key role in its denoising performance. In this study, two improvements have been made for QNLM: (a) for low level noise, the use of quaternion Quasi-Chebyshev distance is proposed to measure the similarity of image patches and it has been used to replace the Euclidean distance in the QNLM algorithm. Since the Quasi-Chebyshev distance measures the maximal distance in all color channels, the similarity of color images measured by Quasi-Chebyshev distance can capture the structural similarity uniformly for each color channel; (b) for high level noise, Quaternion bilateral filtering (QBF) has been proposed as the preprocessing step in the QNLM algorithm. Denoising simulations were performed on 110 images of landscape, people and architecture at different noise levels. Compared with QNLM, Quaternion non-local total variation (QNLTV) and non-local means (NLM) variants (NLTV, NLM After wavelet threshold preprocessing, the color adaptation of NLM), our novel algorithm not only improves PSNR/SSIM and figure of merit values by an average of 2.77 dB/8.96% and 0.0491, but also reduces processing time.
Decision Intelligence Empowered Resource Management for 6G Vehicle-to-Everything Communications
ZHAO Junhui, NIE Yiwen, ZHANG Huan, WANG Dongming
, Available online  , doi: 10.23919/cje.2022.00.290
Abstract(66) HTML (33) PDF(12)
To meet more requirements for the future communications, the sixth generation (6G) wireless communication networks are expected to provide the better coverage, spectra/energy efficiency, intelligence, and security than the fifth generation (5G) networks. With the recent advances in artificial intelligence (AI), the 6G vehicle-to-everything (V2X) focus on the further AI applications for intelligent transportation systems (ITS), especially for extending decision intelligence (DI) of resource management. Specifically, a new V2X framework integrated 6G techniques and AI approaches is proposed in this article. We introduce some typical 6G techniques to assist the vehicular intelligence as alternative schemes. Moreover, we provide the intelligent resource managements based on edge intelligence, integrated intelligence, distributed intelligence, and secure intelligence to overcome challenges of efficiency, delay, mobility, and privacy, respectively. We also propose a federated reinforcement learning (FRL) based algorithm to validate the privacy-aware secure intelligence with effective resource management. Simulation results show that the proposed algorithm significantly outperforms the non-AI baseline schemes in the novel network structure. To enlighten future topics for 6G vehicular resource management, we discuss some potential scenarios with the promising AI-based solutions.
The Squeeze & Excitation Normalization based nnU-Net for Segmenting Head & Neck Tumors
XIE Juanying, PENG Ying, WANG Mingzhao
, Available online  , doi: 10.23919/cje.2022.00.306
Abstract(58) HTML (29) PDF(2)
Head and neck cancer is one of the most common malignancies in the world. We propose SE-nnU-Net by adapting SE (Squeeze and Excitation) normalization into nnU-Net, so as to segment head and neck tumors in PET/CT images by combining advantages of SE capturing features of interest regions and nnU-Net configuring itself for a specific task. The basic module referred to convolution-ReLU-SE is designed for SE-nnU-Net. In the encoder it is combined with residual structure while in the decoder without residual structure. The loss function combines Dice loss and Focal loss. The specific data preprocessing and augmentation techniques are developed, and specific network architecture is designed. Furthermore, the deep supervised mechanism is introduced to calculate the loss function using the last four layers of the decoder of SE-nnU-Net. This SE-nnU-net is applied to HECKTOR 2020 and HECKTOR 2021 challenges, respectively, using different experimental design. The experimental results show that SE-nnU-Net for HECKTOR 2020 obtained 0.745, 0.821, and 0.725 in terms of Dice, Precision, and Recall, respectively, while the SE-nnU-Net for HECKTOR 2021 obtains 0.778 and 3.088 in terms of Dice and median HD95, respectively. This SE-nnU-Net for segmenting head and neck tumors can provide auxiliary opinions for doctors’ diagnoses.
A Bus Planning Algorithm for FPC Design in Complex Scenarios
WU Haoying, ZOU Sizhan, XU Ning, XIANG Shixu, LIU Mingyu
, Available online  , doi: 10.23919/cje.2022.00.399
Abstract(55) HTML (28) PDF(5)
Flexible printed circuit (FPC) design in complex scenarios has a list of pin concentration areas, which lead to extremely congested intersection regions while connecting the pins. Currently, it is challenging to explore the routability and to find topologically non-crossing and routable paths manually for the nets timely. The existing bus planning methods cannot offer optimal solutions concerning the special resource distribution of FPC design. To investigate an effective way to shorten the routing time of FPC and achieve enhanced performance, a bus planning algorithm is proposed to tackle complex area connection problems. On the basis of the pin location information, the routing space is partitioned and generally represented as an undirected graph, and the topological non-crossing relationship between different regions is obtained using the dynamic pin sequence. Considering the routability and electrical constraints, a heuristic algorithm is proposed to search the optimal location of the crossing point on the region boundary. Experimental results on industrial cases show that the proposed algorithm realize better performance in terms of count and routability in comparison with numerous selected state-of-the-art router and methods.
A Model for Chinese Named Entity Recognition Based on Global Pointer and Adversarial Learning
ZHANG Yangsen, LI Jianlong, XIN Yonghui, ZHAO Xiquan, LIU Yang
, Available online  , doi: 10.23919/cje.2022.00.279
Abstract(131) HTML (64) PDF(25)
To solve the problem that the Chinese NER models have poor anti-interference ability and inaccurate entity boundary recognition, this paper proposes the RGP-with-FGM model which is based on global pointer and adversarial learning. Firstly, the RoBERTa-WWM model is used to optimize the semantic representation of the text, and fast gradient method is used to add perturbation to the word embedding layer to enhance the robustness of the model. Then, BiGRU is used to focus on the timing information of Chinese characters to enhance the semantic connection. Finally, the global pointer is constructed in the decoding layer to obtain more accurate entity boundary recognition results. In order to verify the effectiveness of the model proposed in this paper, we construct Uyghur names dataset UHND to train the Chinese NER model, and perform extensive experiments with public Chinese NER data sets. Experiment results show that for UHND, the F1 value is 95.12%, which is 3.09% higher than the RoBERTa-WWM-BiGRU-CRF model. For the Resume data set, the Precision and F1 value are 96.28% and 96.10% respectively.
A time-area-efficient and compact ECSM processor over GF(p)
HE Shiyang, LI Hui, LI Qingwen, LI Fenghua
, Available online  , doi: 10.23919/cje.2022.00.267
Abstract(54) HTML (27) PDF(7)
Elliptic curve scalar multiplication (ECSM) is the core of Elliptic curve cryptography (ECC), which directly determines the performance of ECC. In this paper, a novel time-area-efficient and compact design of a 256-bit ECSM processor over GF($ p $) for resource-constrained devices is proposed, where $ p $ can be selected flexibly according to the application scenario. A compact and efficient 256-bit modular adder/subtractor and an improved 256-bit Montgomery multiplier are designed. We select Jacobian coordinates for point doubling and mixed Jacobian-affine coordinates for point addition. We have improved the binary expansion algorithm to reduce $ 75\% $ of the point addition operations. The clock consumption of each module in this architecture is constant, which can effectively resist side-channel attacks (SCAs). Reuse technology is adopted in this paper to make the overall architecture more compact and efficient. The design architecture is implemented on Xilinx Kintex-7 (XC7K325T-2FFG900I), consuming 1439 slices, 2 DSPs, and 2 BRAMs. It takes about 7.9 ms at the frequency of 222.2 MHz and 1763k clock cycles to complete once 256-bit ECSM operation over GF($ p $).
A Risk Prediction Model Based on Crash History Data for Railway Trams
JI Wenjiang, YANG Jiangcheng, WANG Yichuan, ZHU Lei, QIU Yuan, HEI Xinhong
, Available online  , doi: 10.23919/cje.2022.00.231
Abstract(92) HTML (47) PDF(9)
Risk prediction is the main tasks for railway trams driving safety. Although data driven based intelligent methods are proved to be effective for driving risk prediction, accuracy is still a top concern for the challenges of data quality which mainly represent as the unbalanced datasets. This study focuses on applying feature extraction and data augmentation methods to achieve effective risk prediction for railway trams, and proposes an approach based on a self-adaptive K-means and Least Squares Deep Convolution Generative Adversarial Network (LS-DCGAN). Firstly, the data preprocessing methods are proposed, which include the K-means algorithm to cluster the locations of trams and the eXtreme Gradient Boosting Recursive Feature Elimination (XGBoost-RFE) feature selection algorithm to retain the key features. Secondly, the LS-DCGAN model is designed for sparse sample expansion, aiming to address the sample category distribution imbalance problem. Finally, the XGBoost algorithm is applied to classify and output the risk prediction result. The experiments implemented with the public and real datasets show that the proposed approach can reach a high accuracy of 90.69%, which can greatly enhances the tram driving safety.
An Efficient Task Offloading Strategy based on Deep Reinforcement Learning in Edge-Cloud Collaborative Computing
SUN Ming, BAO Tie, XIE Dan, LV Hengyi, SI Guoliang
, Available online  , doi: 10.23919/cje.2022.00.202
Abstract(55) HTML (27) PDF(5)
With the development of IoT technology, the widespread use of mobile devices drives the increasing investment of resources. Mobile edge computing is an emerging paradigm that can effectively overcome the problems of long transmission distance and high response delay of traditional cloud computing by deploying communication, computing, and storage resources on edge devices. In this paper, we focus on the task offloading problem for multiple users by considering the collaboration of edge servers under the constraints on computing and communication resources. Our objective is to optimize the total cost by jointly considering the delays and energy consumption. We propose a novel task offloading strategy based on deep reinforcement learning CTOS-DRL by introducing a set updating mechanism in edge-cloud collaborative computing. Specifically, we consider avoiding the high complexity brought by the adjacent edge severs and propose a heuristic algorithm to filter out the feasible ones. Then, we propose a new task offloading strategy by training a fully connected neural network. Extensive evaluations demonstrate that the proposed task offloading strategy outperforms baselines in terms of efficiency.
Railway Track Fault Detection Using Mel Frequency Cepstral Coefficient from Acoustic Data using Chi Square
Furqan Rustam, Abid Ishaq, Muhammad Shadab Alam Hashmi, Hafeez-Ur-Rehman Siddiqui, Imran Ashraf
, Available online  , doi: 10.23919/cje.2022.00.168
Abstract(61) HTML (31) PDF(4)
Spatial, temporal, and weather elements like ballast, loose nuts, misalignment, and cracks due to rain, snow, and earthquakes may lead to railway accidents and cause human and financial loss. Manual inspection is erroneous, labor-intensive, and time-consuming. The automatic inspection provides a fast, reliable, and unbiased solution in this regard, however, ensuring high accuracy for fault detection is challenging due to the lack of public datasets, noisy data, high computer processing requirements, and inefficient models. This study presents an approach that uses Mel frequency cepstral coefficient features from the acoustic data. The dataset gathered using a customized railway cart from our previous research is used for experiments. The focus of the study is to increase the fault detection performance using selective features from the acoustic data. This study employs Chi-square (Chi2) for the selection of important features and involves performance analysis of machine learning and deep learning models using selected features. Experimental results suggest that using 60 features, 40 original features, and 20 Chi2 features, produces optimal results both regarding accuracy and computational complexity. A 100% accuracy can be obtained using the proposed approach with machine learning models. Moreover, this performance is significantly better than existing approaches.
FlowGANAnomaly: Flow-based Anomaly Network Intrusion Detection with Adversarial Learning
LI Zeyi, WANG Pan, WANG Zixuan
, Available online  , doi: 10.23919/cje.2022.00.173
Abstract(160) HTML (80) PDF(10)
In recent years, low recall rates and high dependencies on data labelling have become the biggest obstacle to developing DAD techniques. Inspired by the success of generative adversarial networks (GANs) in detecting anomalies in computer vision and imaging, we propose an anomaly detection model called FlowGANAnomaly for detecting anomalous traffic in NIDS. Unlike traditional GAN-based approaches, the architecture consists of a generator (G) and a discriminator (D), which are composed of a flow encoder, a convolutional encoder-decoder-encoder, a flow decoder, and a convolutional encoder, respectively. FlowGANAnomaly maps the different types of traffic feature data from separate datasets to a uniform feature space, thus can capture the normality of network traffic data more accurately in an adversarial manner to mitigate the problem of the high dependence on data labeling. Moreover, instead of simply detecting the anomalies by the output of D, we proposed a new anomaly scoring method that integrates the deviation between the output of two G's convolutional encoders with the outputs of D as weighted scores to improve the low recall rate of anomaly detection. We conducted several experiments comparing existing machine learning algorithms (e.g., One-Class SVM, LOF, Isolation Forest, and PCA) and existing deep learning methods (AutoEncoder and VAE) on four public datasets (NSL-KDD, CIC-IDS2017, CIC-DDoS2019, and UNSW-NB15). The evaluation results show that FlowGANAnomaly can significantly improve the performance of anomaly-based NIDS.
Some Results on Optimal Ternary Cyclic Codes with Minimal Distance Four
LI Lanqiang, LIU Li
, Available online  , doi: 10.23919/cje.2022.00.317
Abstract(81) HTML (40) PDF(12)
Cyclic codes over fnite fields have been studied for decades due to they have wide applications in communication systems, consumer electronics and data storage systems. In this paper, we investigate a family of ternary cyclic codes generated by a product of two distinct minimal polynomials. We proposed a sufficient and necessary condition such that such code has minimum distance 4 and is optimal. Based on this, four classes of optimal ternary cyclic codes are presented. Finally, our codes are compared with the previous work to make sure that they all are generated by different cyclotomic cosets and thus represent different codes.
Multi-Frequency-Ranging Positioning Algorithm for 5G OFDM Communication Systems
LI Wengang, XU Yaqin, ZHANG Chenmeng, TIAN Yiheng, LIU Mohan, HUANG Jun
, Available online  , doi: 10.23919/cje.2021.00.124
Abstract(223) HTML (115) PDF(19)

Vehicles equipped with 5th Generation(5G) wireless communication devices can exchange information with infrastructure(Vehicle to Infrastructure, V2I) to improve positioning accuracy. Vehicle location has great research value due to the problems of multipath environment and lack of Global Navigation Satellite System(GNSS) signals. This paper proposes a multi-frequency ranging method and positioning algorithm for 5G Orthogonal Frequency Division Multiplexing(OFDM) communication system. It selects specific subcarriers in the OFDM communication system to be used for transmitting ranging frames and delay observations without affecting other subcarriers used for communication. With almost no impact on communication capacity, several specific subcarriers of OFDM are used for ranging and positioning. It introduces the ranging subcarriers’ selection method and the format of the ranging frame carried by the subcarriers. The Cramero Lower Bound(CRLB) of this ranging positioning system is proved. Ranging positioning accuracy meets the requirements of vehicle location applications. The experimental simulation compares the performance with other positioning methods and proves the superiority of this system. The theory proves and simulates the relationship between ranging accuracy and channel parameters in a multipath environment. The simulation results show that the positioning accuracy about 5 cm can be achieved under the conditions of 5 GHz frequency and high signal-to-noise ratio(SNR).

Signal Processing
A Layout Method of Space-Based Pseudolite System Based on GDOP Geometry
YANG Xin, WANG Feixue, LIU Wenxiang, XIAO Wei, YE Xiaozhou
, Available online  , doi: 10.23919/cje.2022.00.013
Abstract(31) HTML (15) PDF(3)
The pseudolite system can be used to provide positioning and timing service for users in a specific area. In order to provide better positioning and timing service, a good geometric configuration needs to be formed for the pseudolite system. For the problem of pseudolite system deployment, the average and mean square values of geometric dilution of precision (GDOP) are optimized in this paper for users in a target area. We proposed a space-based pseudolite deployment method based on GDOP geometry, starting from the minimum GDOP value for users in the central area. From the simulation results, it can be seen that, compared with the empirical method and the NSGA-II method, the method in this paper has the smallest average GDOP, a better robustness, and a higher positioning accuracy in the target area, through which a pseudolite deployment proposal can be quickly obtained.
Teacher-student training approach using an adaptive gain mask for LSTM-based speech enhancement in the airborne noise environment
HUANG Ping, WU Yafeng
, Available online  , doi: 10.23919/cje.2022.00.307
Abstract(61) HTML (30) PDF(10)
Research on speech enhancement algorithms in the airborne environment is of great significance to the security of airborne systems. Recently, the research focus on speech enhancement has turned from conventional unsupervised algorithms, like the log minimum mean square error estimator (log-MMSE), to the state-of-the-art masking-based long short-term memory (LSTM) method. However, each method has its characteristics and limitations, so they cannot always handle noise well. Besides, the requirements of clean speech and noise data for training a supervised speech enhancement model are difficult to meet in the real-world airborne environment. Therefore, in this work, to fully utilize the advantages of those two different methods without any data restrictions, we propose a novel adaptive gain mask (AGM) based teacher-student training approach for speech enhancement. In our method, the AGM, as a robust learning target for the student model, is devised by incorporating the estimated ideal ratio mask (IRM) from the teacher model into the procedure of the log-MMSE approach. To get an appropriate tradeoff between the two methods, we adaptively update the AGM using a recursive weighting coefficient. Experiments on the real airborne data show that the proposed AGM-based method outperforms other baselines in terms of all essential objective metrics evaluated in this paper.
Graph Signal Reconstruction from Low-Resolution Multi-Bit Observations
LIU Zhaoting, YU Chen, WANG Yafeng, LIU Shuchen
, Available online  , doi: 10.23919/cje.2022.00.272
Abstract(60) HTML (29) PDF(9)
Low hardware cost and power consumption in information transmission, processing and storage is an urgent demand for many big data problems, in which the high-dimensional data often be modelled as graph signals. This paper considers the problem of recovering a smooth graph signal by using its low-resolution multi-bit quantized observations. The underlying problem is formulated as a regularized maximum-likelihood optimization and is solved via an expectation maximization scheme. With this scheme, the multi-bit graph signal recovery (MB-GSR) is efficiently implemented by using the quantized observations collected from random subsets of graph nodes. The simulation results show that increasing the sampling resolution to 2 or 3 bits per sample leads to a considerable performance improvement, while the energy consumption and implementation costs remain much lower compared to the implementation of high resolution sampling.
Visible Light Indoor Positioning System Based on Pisarenko Harmonic Decomposition and Neural Network
, Available online  , doi: 10.23919/cje.2022.00.161
Abstract(102) HTML (49) PDF(7)
Visible-light indoor positioning is a new generation of positioning technology that can be integrated into smart lighting and optical communications. The current received signal strength (RSS)-based visible-light positioning systems struggle to overcome the interferences of background and indoor-reflected noise. Meanwhile, when ensuring the lighting, it is impossible to use the superposition of each light source to accurately distinguish light source information; furthermore, it is difficult to achieve accurate positioning in complex indoor environments. This study proposes an indoor positioning method based on a combination of power spectral density (PSD) detection and a neural network. The system integrates the mechanism for visible-light radiation detection with RSS theory, to build a back propagation neural network model fitting for multiple reflection channels. Different frequency signals are loaded to different light sources at the beacon end, and the characteristic frequency and power vectors are obtained at the location end using the Pisarenko harmonic decomposition method. Then, a complete fingerprint database is established to train the neural network model and conduct location tests. Finally, the location effectiveness of the proposed algorithm is verified via actual positioning experiments. The simulation results show that, when four groups of sinusoidal waves with different frequencies are superimposed with white noise, the maximum frequency error is 0.104 Hz and the maximum power error is 0.0362 W. For the measured positioning stage, a 0.8 m × 0.8 m × 0.8 m solid wood stereoscopic positioning model is constructed, and the average error is 4.28 cm. This study provides an effective method for separating multi-source signal energies, overcoming background noise, and improving indoor visible-light positioning accuracies.
Monaural Speech Separation Using Dual-Output Deep Neural Network with Multiple Joint Constraint
SUN Linhui, LIANG Wenqing, ZHANG Meng, LI Ping’an
, Available online  , doi: 10.23919/cje.2022.00.110
Abstract(118) HTML (58) PDF(16)
Monaural speech separation is a significant research field in speech signal processing. To achieve a better separation performance, we propose three novel joint-constraint loss functions and a multiple joint-constraint loss function for monaural speech separation based on dual-output deep neural network (DNN). The multiple joint-constraint loss function for DNN separation model not only restricts the ideal ratio mask (IRM) errors of the two outputs, but also constrains the relationship of the estimated IRMs and the magnitude spectrograms of the clean speech signals, the relationship of the estimated IRMs of the two outputs, and the relationship of the estimated IRMs and the magnitude spectrogram of the mixed signal. The constraint strength is adjusted through three parameters to improve the accuracy of the speech separation model. Furthermore, we solve the optimal weighting coefficients of the multiple joint-constraint loss function based on the optimization idea, which further improves the performance of the separation system. We conduct a series of speech separation experiments on the GRID corpus to validate the superiority performance of the proposed method. The results show that using perceptual evaluation of speech quality, the short-time objective intelligibility, source to distortion ratio, signal to interference ratio and source to artifact ratio as the evaluation metrics, the proposed method outperforms the conventional DNN separation model. Taking the gender into consideration, we carry out experiments among Female-Female, Male-Male and Male-Female cases, which show that our method improves the robustness and performance of the separation system compared with some previous approaches.
RESS: A Reliable and Effcient Storage Scheme for Bitcoin Blockchain Based on Raptor Code
SHI Dongxian, WANG Xiaoqing, XU Ming, KOU Liang, CHENG Hongbing
, Available online  , doi: 10.23919/cje.2022.00.343
Abstract(37) HTML (18) PDF(4)
The Bitcoin system uses a fully replicated data storage mechanism in which each node keeps a full copy of the blockchain. As the number of nodes in the system increases and transactions get more complex, more and more storage space are needed to store block data. The scalability of storage has become a bottleneck, limiting the practical application of blockchain. This paper proposes a node storage scheme, called RESS, to integrate erasure coding technology into the blockchain to encode multiple blocks. Under the proposed block grouping method, nodes can reduce the times of coded block decoding. In addition, the coding scheme based on Raptor codes proposed in this paper has linear coding and decoding complexity. The rateless feature of Raptor code helps to achieve high decentralization and scalability of the Bitcoin network. RESS ensures data availability, efficiency and blockchain robustness based on achieving storage space scalability. Experimental results show that the proposed scheme reduces the storage requirements of nodes by nearly an order of magnitude.
Mobility Prediction Based Tracking of Moving Objects in Wireless Sensor Networks
TANG Chao, XIA Yinqiu, DOU Lihua
, Available online  , doi: 10.23919/cje.2021.00.365
Abstract(97) HTML (48) PDF(8)
This paper investigates the multi-sensor fused localization of moving targets in a Wireless Sensor Network (WSN). Each UWB sensor is assigned a stability weight according to its survival time prediction. The measurement accuracy of each sensor into the constraints of the weight distribution based on the IMM method, a double weight distribution algorithm that considers measurement accuracy and stability is proposed. Based on the double weight algorithm, the measurement information of each UWB sensor, the IMU-based state vector and the UWB-based state vector by federated kalman filter(FKF) are integrated to realize the correction of the IMU. Finally, several numerical simulations are performed to show that the proposed algorithm can effectively suppress the measurement dropout when tracking moving targets in a WSN, and it can also automatically adjust the weight of each sensor based on the measurement error covariance to improve the tracking accuracy.
Circuit Modeling and Performance Analysis of GNR@SWCNT Bundle Interconnects
ZHAO Wensheng, YUAN Mengjiao, WANG Xiang, WANG Dawei
, Available online  , doi: 10.23919/cje.2021.00.379
Abstract(40) HTML (20) PDF(2)
In this paper, the single-walled carbon nanotube (SWCNT) with graphene nanoribbon (GNR) inside, namely GNR@SWCNT, is proposed as alternative conductor material for interconnect applications. The equivalent circuit model is established, and the circuit parameters extracted analytically. By virtue of the circuit model, the signal transmission performance of GNR@SWCNT bundle interconnect is evaluated and compared with its Cu and SWCNT counterparts. The optimal repeater insertions in global- and intermediate-level GNR@SWCNT bundle interconnect are studied. It is demonstrated that the GNR@SWCNT interconnects could provide superior performance, indicating that GNR@SWCNT structure would be beneficial for development of future carbon-based integrated circuits and systems.
Formal Modeling of Frame Selection in Asynchronous TSN Communications
LI Ershuai, ZHOU Xuan, SUN Jinjing, XIONG Huagang, HE Feng
, Available online  , doi: 10.23919/cje.2022.00.321
Abstract(58) HTML (27) PDF(7)
The asynchronous Time-Sensitive Networking (TSN) based on IEEE 802.1Qcr is expected to be a promising solution for the asynchronous transmissions of safety-critical flows without the support of clock synchronization. When the Asynchronous Traffic Shaping (ATS) mechanism is adopted to meet the deadline requirements for transmissions of safety-critical flow, it is necessary to formally verify the real-time properties and corresponding network performance. However, it is still unclear how to build an efficient formal model to evaluate different frame selection methods during the ATS scheduling process, which originate from the dominations of priority or eligibility time. In this paper, we present a formal modeling framework to compare the impacts of different frame selection on transmission sequence under the asynchronous ATS mechanism. According to the priority level (pATS) or eligibility time (eATS) for flows, two transmission selection methods in ATS are modeled and compared. Then, we verify the real-time properties of ATS. The result shows that the shaping-for-free property can be satisfied with the pATS method but can not be fulfilled with the eATS method. Besides, the timing analysis results illustrate that the eATS method can provide more fairness than the pATS method for the transmission of low-priority flows in TSN networks.
Technology Dependency of TID Response for a Custom Bandgap Voltage Reference in 65 nm to 28 nm Bulk CMOS Technologies
LIANG Bin, WEN Yi, CHEN Jianjun, CHI Yaqing, YAO Xiaohu
, Available online  , doi: 10.23919/cje.2021.00.448
Abstract(47) HTML (23) PDF(10)
Total ionizing dose (TID) radiation response of the custom bandgap voltage reference (BGR) fabricated with 65 nm, 40 nm and 28 nm commercial bulk CMOS technologies is investigated. TID response is assessed employing Co60 gamma ray source. The measurements indicate that the voltage reference is reduced by 5.67% in 28 nm, 0.56% in 40 nm and increased by 1.28% in 65 nm devices under irradiation up to 1.2 Mrad(Si) TID. After 48 hours of annealing, the voltage reference changes are just −1.84% in 28 nm, 0.14% in 40 nm and 1.14% in 65 nm. The obtained results demonstrate that the custom BGR has naturally superior TID response due to the circuit design margins.
A Novel Approach of Electromagnetic Compatibility Conducted Susceptibility Analysis Based on Gaussian Even Pulse Signal
MENG Youwei, PENG Yanhua, ZHANG Haoyang, LI lilin, SU Donglin
, Available online  , doi: 10.23919/cje.2022.00.298
Abstract(56) HTML (28) PDF(12)
The purpose of the electromagnetic compatibility conducted susceptibility test for interconnected cables in the system is to evaluate its ability to operate acceptably when subjected to interference. In this paper, we propose a novel conducted susceptibility analysis approach: by injecting the Gaussian even pulse signal, we find that the susceptibility threshold of the system shows two different patterns with the change of signal parameters. Then we locate the cause of the susceptibility of the device by analyzing the threshold level curves (TLC). The effectiveness of the proposed approach is verified by testing with devices containing digital modules such as navigation receivers. The proposed approach facilitates a deeper understanding of the susceptibility mechanism of systems and their appropriate electromagnetic compatibility design.
A Novel Construction of Updatable Identity-based Hash Proof System and Its Applications
QIAO Zirui, ZHOU Yanwei, YANG Bo, ZHANG Wenzheng, ZHANG Mingwu
, Available online  , doi: 10.23919/cje.2022.00.203
Abstract(59) HTML (30) PDF(8)
In the previous works, to further provide the continuous leakage resilience for the identity-based encryption (IBE) scheme, a new cryptography primitive, called updatable identity-based hash proof system (U-IB-HPS), was proposed. However, most of the existing constructions have some deficiencies, they either do not have perfect key update function or the corresponding security with tight reduction relies on a non-static complexity assumption. In order to address the above problems, a new construction of U-IB-HPS is created, and the corresponding security of our system is proved based on the static complexity assumption. Also, the corresponding comparisons and analysis of performances show that our proposal not only achieves the perfect key update function and the anonymity, but also has the tight security reduction. In additional, our proposal achieves the same computational efficiency as other previous systems. To further illustrate the practical function of U-IB-HPS, a generic method of non-interactive data authorization protocol with continuous leakage resilience is designed by employing U-IB-HPS as an underlying tool, which can provide continuous leakage-resilient data authorization function for the cloud computing. Hence, the application field of U-IB-HPS is further extended through our study.
Scheduling pattern of time triggered Ethernet based on reinforcement learning
HE Feng, XIONG Li, ZHOU Xuan, LI Haoruo, XIONG Huagang
, Available online  , doi: 10.23919/cje.2021.00.419
Abstract(54) HTML (28) PDF(14)
TTEthernet(Time-Triggered Ethernet) is a deterministic and congestion-free network based on the Ethernet standard. It supports mix-critical real-time applications by providing different message classes. Time-triggered (TT) messages have strict end-to-end delay and accurate jitter requirement, and rate-constrained (RC) messages has less determinism than TT messages but with bounded end-to-end delay requirement. Traditionally, the scheduling of TT messages makes it free of conflicts for the transmission on physical links, but ignoring RC messages scheduling, so it cannot guarantee the transmission of RC messages with a bounded delay. Therefore, the design of TT schedule becomes the key to TTE network applications within avionics environment. In this paper, we propose an algorithm called RLTS based on Reinforcement Learning and Tree Search, to optimize the end-to-end delays of both TT and RC messages. Besides, its computation speed is dozens of times faster than Satisfied Modularity Theory (SMT) with asynchronous method for the calculation of the optimal scheduling table. In the case of a large network with more than 1000 TT and 1000 RC messages, the RLTS method can find a scheduling timetable in 10 seconds, and reduce the worst-case delay of RC messages averagely by 20% compared to the genetic algorithm. Meanwhile, our algorithm has a good generalization performance, in another word, it can quickly adjust itself to satisfy the scheduling with the similar performance as before. By using our method, the scheduling pattern of TTEthernet is further discussed. According to the experimental results, the uniformly distributed slots scheduling pattern, namely the porosity scheduling model which is usually recommended for TTE application, is not always suitable for general situations.
RFID-Based WSN Communication System with ESPAR Array Antenna for SIR Improvement
Md. Moklesur RAHMAN, Heung-Gyoon RYU
, Available online  , doi: 10.23919/cje.2022.00.213
Abstract(72) HTML (35) PDF(5)
To improve the received signal strength (RSS) and signal-to-interference and noise ratio (SINR), electronically steerable parasitic array radiator (ESPAR) array antennas are designed for the ultra-high frequency (UHF) radio frequency identification (RFID) communication systems that can provide very low power consumption in sensor tag edge. Higher gain, appropriate radiation pattern, and most power-efficient array antennas are completely essential in these sensor network systems. As a result, it is suggested that ESPAR array antennas be used on the RFID reader side to reduce interference, multipath fading, and extend communication range. Additionally, a system architecture for UHF- RFID wireless sensor network (WSN) communication is put forth in order to prevent interference from antenna nulling technology, in which ESPAR array antennas could be capable of generating nulls. The array antennas within the system demonstrate high efficiency, appropriate radiation patterns, and gains (9.63 dBi, 10.2 dBi, and 12 dBi) from one array to other arrays. The nulling technique using the proposed array antennas also provides better SINR values (31.63 dB, 33.2 dB, and 36 dB). Finally, the nulling space matrix is studied in relation to the channel modeling. Therefore, the suggested approach might offer better communications in sensor networking systems.
A security defense method against eavesdroppers in the communication-based train control system
YANG Li, WEI Xiukun, WEN Chenglin
, Available online  , doi: 10.23919/cje.2022.00.248
Abstract(75) HTML (39) PDF(5)
The communication-based train control system is the safety guarantee for automatic train driving. Wireless communication brings network security risks to the communication-based train control system. The eavesdropping of the transmitted information by unauthorized third-party personnel will lead to the leakage of the estimated value of the system state, which will lead to major accidents. This paper focuses on solving the problem of defense against eavesdropping threats and proposes an eavesdropping defense architecture. It includes a coding mechanism based on punishing eavesdroppers, an information upload trigger mechanism based on contribution, and a random information transmission strategy. This architecture provides a guarantee for the privacy protection of information. This research makes three contributions. First, it is the first attempt to construct an information encoding mechanism with punishing eavesdroppers as the objective function; Secondly, for the first time, an information upload trigger mechanism based on contribution is proposed; Then, the strategy of random transmission of information is proposed; Finally, the method proposed in this paper is verified by taking the medium and low-speed maglev train as the object. The experimental results show that, compared with Gaussian noise and non-Gaussian noise mechanisms, the coding mechanism proposed in this paper can not only protect the security of information but also make the estimation error of eavesdroppers tend to be infinite. Using the state estimation error as a metric, the average growth rate of the state estimation error of the system using the trigger mechanism in this paper is less than 2% while improving the security of the system. The transmission strategy in this paper does not increase the system state estimation error while improving the security of the system.
Joint Optimization of Trajectory and Task Offloading for Cellular-Connected Multi-UAV Mobile Edge Computing
XIA Jingming, LIU Yufeng, TAN Ling
, Available online  , doi: 10.23919/cje.2022.00.159
Abstract(80) HTML (40) PDF(18)
Since the computing capacity and battery energy of unmanned aerial vehicle (UAV) are constrained, UAV as aerial user is hard to handle the high computational complexity and time-sensitive applications. This paper investigates a cellular-connected multi-UAV network supported by mobile edge computing (MEC). Multiple UAVs carrying tasks fly from a given initial position to a termination position within a specified time. To handle the large number of tasks carried by UAVs, we propose a energy cost of all UAVs based problem to determine how many tasks should be offloaded to high-altitude balloons (HABs) for computing, which UAV-HAB association, the trajectory of UAV, and calculation task splitting are jointly optimized. However, the formulated problem has nonconvex structure. Hence, an efficient iterative algorithm by applying successive convex approximation (SCA) and the block coordinate descent (BCD) methods is put forward. Specifically, in each iteration, the UAV-HAB association, calculation task splitting, and UAV trajectory are alternately optimized. Especially, for the nonconvex UAV trajectory optimization problem, an approximate convex optimization problem is settled. The numerical results indicate that the scheme of this paper proposed is guaranteed to converge and also significantly reduces the entire power consumption of all UAVs compared to the benchmark schemes.
Sigma-Mixed Unscented Kalman Filter-based Fault Detection for Traction Systems in High-speed Trains
CHENG Chao, WANG Weijun, MENG Xiangxi, SHAO Haidong, CHEN Hongtian
, Available online  , doi: 10.23919/cje.2022.00.154
Abstract(41) HTML (21) PDF(4)
Fault detection (FD) for traction systems is one of the active topics in the railway and academia because it is the initial step for the running reliability and safety of high-speed trains. Heterogeneity of data and complexity of systems have brought new challenges to the traditional FD methods. For addressing these challenges, this paper designs a FD algorithm based on the improved unscented Kalman filter (UKF) with consideration of performance degradation. It is derived by incorporating a degradation process into the state-space model. The network topology of traction systems is taken into consideration for improving the performance of state estimation. We first obtain the mixture distribution by the mixture of sigma points in UKF. Then, the Lévy process with jump points is introduced to construct the degradation model. Finally, the moving average interstate standard deviation (MAISD) is designed for detecting faults. Verifying the proposed methods via a traction systems in a certain type of trains obtains satisfactory results.
Optimization on the Dynamic Train Coupling Process in High-Speed Railway
CHENG Fanglin, TANG Tao, SU Shuai, MENG Jun
, Available online  , doi: 10.23919/cje.2022.00.189
Abstract(95) HTML (48) PDF(13)
This article focuses on the driving strategy optimization problem of a scenario in which two trains come from two branches under virtual coupling, aiming at going through the junction area efficiently. Firstly, a distance-discrete optimal control model is constructed. The optimization objective is to maximize the trip time during which the two trains operate in coupled state. The line conditions, dynamic properties of the trains and the safety protection constraints are considered. Then, the nonlinear constraints are converted into linear constraints with Piecewise Affine (PWA) function and logical variables, and the proposed problem is converted into Mixed Integer Linear Programming (MILP) problem which can be solved by existing solvers (such as Cplex). Finally, four simulation experiments are conducted to verify the effectiveness of MILP. The Dynamic Programming (DP) algorithm is used as the benchmark algorithm in the case study. Compared with DP algorithm in small state space, MILP has better performance since it shortens the coupling time. Moreover, the improvement of line capacity of virtual coupling is 35.42% compared with the fixed blocking system.
A Polarization Control Operator for Polarized Electromagnetic Wave Designing
CUI Shuo, LI Yaoyao, ZHANG Shijian, CHEN Ling, CAO Cheng, SU Donglin
, Available online  , doi: 10.23919/cje.2022.00.410
Abstract(44) HTML (22) PDF(6)
To describe and control the polarization state of electromagnetic waves, a polarization control operator of the complex vector form is proposed. Distinct from traditional descriptors, the proposed operator employs an angle parameter to configure the polarization state of the polarized wave. By setting the parameter in the proposed operator, the amplitude of the field components can be modified, resulting in changes in the magnitude and direction of the field vector, and thus realizing control of the polarization state of the electromagnetic wave. The physical meaning, orthogonal decomposition, and discrete property of the proposed operator are demonstrated through mathematical derivation. In the simulation examples, the polarization control operator with the fixed and time-varying parameters are applied to the circularly polarized wave. The propagation waveform, the trajectory projection and the waveform cross section in different reception directions of the new electromagnetic waves are observed. The results show that complex electromagnetic waves with more flexible polarization states can be obtained with the aid of the polarization operator.
Levy Flight Adopted Particle Swarm Optimization-based Resource Allocation Strategy in Fog Computing
Sharmila Patil(Karpe), Brahmananda S H
, Available online  , doi: 10.23919/cje.2022.00.212
Abstract(46) HTML (22) PDF(10)
The prevalence of the Internet of Things (IoT) is unsteady in the context of cloud computing, it is difficult to identify fog and cloud resource scheduling policies that will satisfy users’ QoS need. As a result, it increases the efficiency of resource usage and boosts user and resource supplier profit. This research intends to introduce a novel strategy for computing fog via emergency-oriented resource allotment, which aims and determines the effective process under different parameters. The modeling of a non-linear functionality that is subjected to an objective function and incorporates needs or factors like Service response rate, Execution efficiency, and Reboot rate allows for the resource allocation of cloud to fog computing in this work. Apart from this, the proposed system considers the resource allocation in emergency priority situations that must cope-up with the immediate resource allocation as well. Security in resource allocation is also taken into consideration with this strategy. Thus the multi-objective function considers 3 objectives such as Service response rate, Execution efficiency, and Reboot rate. All these strategies in resource allocation are fulfilled by Levy Flight adopted Particle Swarm Optimization (LF-PSO). Finally, the evaluation is performed to determine whether the developed strategy is superior to numerous traditional schemes. However, the cost function attained by the adopted technique is 120, which is 19.17%, 5%, and 2.5% greater than the conventional schemes like GWSO, EHO, and PSO, when the number of iterations is 50.
Enhancing Network Throughput via The Equal Interval Frame Aggregation Scheme for IEEE 802.11ax WLANs
ZHU Yihua, XU Mengying
, Available online  , doi: 10.23919/cje.2022.00.282
Abstract(53) HTML (27) PDF(11)
Frame aggregation is fully supported in the newly published IEEE 802.11ax standard to improve throughput. With frame aggregation, a mobile station (STA) combines multiple subframes into an Aggregate MAC Service Data Unit (A-MSDU) or an Aggregate MAC Protocol Data Unit (A-MPDU) for transmission. It is challenging for a STA in the 802.11ax WLANs to set an appropriate Number of Subframes (NoS) being included in an A-MSDU or A-MPDU. This problem is solved by the proposed Equal Interval Frame Aggregation (EIFA) scheme that lets a STA aggregate at most k subframes at a fixed time period of T. A novel Markov model is developed for deriving the probability of number of subframes in the data buffer at the STA, resulting in the throughput and packet delay in the EIFA. The optimization problem of maximizing the throughput with the constraint on delay is formulated and its solution leads to the optimal pair of parameters k and T for improving throughput in the EIFA scheme. Simulation results show the EIFA has a higher throughput than the ones in which the STA chooses the minimum NoS, the maximum NoS, or a random NoS.
Link Prediction Method Fusion with Local Structural Entropy for Directed Network
LIU Shuxin, CHEN Hong-chang, WU Lan, WANG Kai, LI Xing
, Available online  , doi: 10.23919/cje.2022.00.166
Abstract(34) HTML (17) PDF(5)
Link prediction utilizes accessible network information to complement or predict the network links. Similarity is an important prerequisite for link prediction which means links more likely occurs between two similar nodes. Existing methods utilize the similarity of nodes but neglect of network structure. However the link direction leads to a far more complex structure and contains more information useful than the undirected networks. Most classic methods are difficult to depict the distribution of the network structure with incidental direction so the similarity characteristics of the network structure itself are lost. In this respect, a new method of local structure entropy is proposed to depict the directed structural distribution characteristics, which can be evaluate the degree of local structural similarity of nodes and then applied to link prediction methods. Experimental results on 8 real directed network show that this method is effective for both AUC and Ranking-Score measures and improved predictive capacity of the baseline methodology.
SAT-Based Automatic Searching for Differential and Linear Trails: Applying to CRAX
HAN Yiyi, WANG Caibing, NIU Zhongfeng, HU Lei
, Available online  , doi: 10.23919/cje.2022.00.313
Abstract(92) HTML (47) PDF(9)
Boolean satisfiability problem (SAT) is now widely applied in differential cryptanalysis and linear cryptanalysis for various cipher algorithms. It generated many excellent results for some ciphers, for example, Salsa20. In this research, we study the differential and linear propagations through the operations of addition, rotation and XOR (ARX), and construct the SAT models. Then we apply the models to CRAX to search differential trails and linear trails automatically. In this sense, our contribution can be broadly divided into two parts. Firstly, we give the bounds for differential and linear cryptanalysis of Alzette both up to 12 steps, by which we present a 3-round differential attack and a 3-round linear attack for CRAX. Secondly, we construct a 4-round key-recovery attack for CRAX with time complexity $ 2^{89} $ times of 4-round encryption and data complexity $ 2^{25} $.
Constructing the Impossible Differential of Type-II GFN with Boolean Function and its Application to $\mathtt{WARP}$
SHI Jiali, LIU Guoqiang, LI Chao
, Available online  , doi: 10.23919/cje.2022.00.132
Abstract(132) HTML (68) PDF(4)
Type-II generalized Feistel network (GFN) has attracted a lot of attention for its simplicity and high parallelism. Impossible differential attack is one of the powerful cryptanalytic approaches for word-oriented block ciphers such as Feistel-like ciphers. In this paper, we deduce the impossible differential of Type-II GFN by analyzing the Boolean function in the middle round. The main idea is to investigate the expression with the variable representing the plaintext (ciphertext) difference words for the internal state words. By adopting the miss-in-the-middle approach, we can construct the impossible differential of Type-II GFN. As an illustration, we apply this approach to $\mathtt{WARP}$. The structure of $\mathtt{WARP}$ is a 32-branch Type-II GFN. Therefore, we find two 21-round truncated impossible differentials and implement a 32-round key recovery attack on $\mathtt{WARP}$. For the 32-round key recovery attack on $\mathtt{WARP}$, some observations are used to mount an effective attack. Taking the advantage of the early abort technique, the data, time, and memory complexities are $2^{125.69}$ chosen plaintexts, $2^{126.68}$ 32-round encryptions, and $2^{100}$-bit. To the best of our knowledge, this is the best attack on $\mathtt{WARP}$ in the single-key scenario.
Microwave Tomographic Imaging of Anatomically Realistic Numerical Phantoms with Debye Dispersion for Breast Cancer Detection Using a Regularized Inverse Scattering Technique in the Time Domain
LIU Guangdong
, Available online  , doi: 10.23919/cje.2021.00.343
Abstract(120) HTML (58) PDF(26)
Background: Increasing attention has been given to microwave tomographic imaging in the time domain due to its high resolution, which is urgently required for the early-stage detection of small breast tumors. In recent years, three similar versions of time-domain inverse scattering (TDIS) algorithms have been proposed for the successful estimation of the dispersive dielectric properties of several single-pole Debye media. However, for practical applications in common biomedical engineering, these algorithms are not without their shortcomings, such as the lack of regularization, unfitness for multiple-pole Debye dispersive media, and inconvenience caused by the simultaneous reconstruction of overall Debye model parameters. Methods: In this paper, an improved TDIS algorithm is explicitly derived to provide a more versatile algorithm for the microwave tomographic imaging of biological tissues. Its three improvements are as follows. First, the number of poles for Debye models is extended from one to a positive integer W. The second improvement is the extension of unknowns from three to 2W + 2 for each discretized cell. The third improvement is the adoption of the first-order Tikhonov regularization scheme. Results: Based on the four classes (mostly fatty, scattered fibroglandular, heterogeneously dense, and very dense) of 2-D anatomically realistic numerical phantoms with two-pole Debye dispersion from the University of Wisconsin Computational Electromagnetics Laboratory (UWCEM) database, the performance of the developed algorithm for the detection of a 3-mm-diameter tumor implanted in the four types of breast models was investigated for three scenarios. Conclusion: The obtained results preliminarily indicate that the modified technique is feasible and promising for breast cancer screening or the quantitative reconstruction of the internal breast composition, especially for sparse breast tissues.
The investigation of data voting algorithm for train air-braking system based on multi-classification SVM and ANFIS
WANG Juhan, GAO Ying, CAO Yuan, TANG Tao
, Available online  , doi: 10.23919/cje.2021.00.428
Abstract(75) HTML (38) PDF(10)
The pressure data of the train air braking system is of great significance to accurately evaluate its operation state. In order to overcome the influence of sensor fault on the pressure data of train air braking system, it is necessary to design a set of sensor fault-tolerant voting mechanism to ensure that in the case of a pressure sensor fault, the system can accurately identify and locate the position of the faulty sensor, and estimate the fault data according to other normal data. In this paper, a fault-tolerant mechanism based on multi classification support vector machine (MSVM) and adaptive network-based fuzzy inference system (ANFIS) is introduced. Specifically, MSVM is used to identify and locate the system fault state, and ANFIS is used to estimate the real data of the fault sensor. After estimation, the system will compare the real data of the fault sensor with the ANFIS estimated data. If it is similar, the system will recognize that there is a false alarm and record it. Then the paper tests the whole mechanism based on the real data. The test shows that the system can identify the fault samples and reduce the occurrence of false alarms.
A Hybrid Entropy and Blockchain Approach for Network Security Defense in SDN-based IIoT
SU Jian, JIANG Mengnan
, Available online  , doi: 10.23919/cje.2022.00.103
Abstract(110) HTML (53) PDF(14)

In the Industrial Internet of Things (IIoT), various applications generate a large number of interactions and are vulnerable to various attacks, which are difficult to be monitored in a sophisticated way by traditional network architectures. Therefore, deploying software-defined networks (sdn) in the industrial IoT is essential to defend against various attacks. However, sdn has a drawback: there is a security problem of distributed denial-of-service attacks (DDoS) at the control layer. This paper proposes an effective solution: DDoS detection within the domain using tri-entropy in information theory. The detected attacks are then uploaded to a smart contract in the blockchain, so that the attacks can be quickly cut off even if the same attack occurs in different domains. Experimental validation was conducted under different attack strengths and multiple identical attacks, and the results show that the method has better detection ability under different attack strengths and can quickly block the same attacks.

Analytical Models of On-chip Hardware Trojan Detection based on Radiated Emission Characteristics
ZHANG Fan, ZHANG Dongrong, REN Qiang, CHEN Aixin, SU Donglin
, Available online  , doi: 10.23919/cje.2022.00.310
Abstract(72) HTML (37) PDF(8)
Nowadays, since the many third parties involved in IC manufacturing, hardware Trojans (HT) malicious implantation have become a threat to the integrated circuit (IC) industry. Therefore, varieties of reliable hardware Trojan detection methods are need. Since electromagnetic radiation is an inherent phenomenon of electronic devices, there are significant differences in the electromagnetic radiated characteristics for circuits with different structures and operating states. In this paper, a novel hardware Trojan detection method is proposed, which considers the electromagnetic radiation differences caused by hardware Trojan implantation. Experiments of detecting hardware Trojan in FPGA show that the proposed method can effectively distinguish the ICs with Trojan from the ones without Trojan by the radiated emission.
Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method
WU Qiong, SHI Shuai, WAN Ziyang, FAN Qiang, FAN Pingyi, ZHANG Cui
, Available online  , doi: 10.23919/cje.2022.00.093
Abstract(180) HTML (88) PDF(16)
Vehicles on the road exchange data with base station (BS) frequently through vehicle to infrastructure (V2I) communications to ensure the normal use of vehicular applications, where the IEEE 802.11 distributed coordination function (DCF) is employed to allocate a minimum contention window (MCW) for channel access. Each vehicle may change its MCW to achieve more access opportunities at the expense of others, which results in unfair communication performance. Moreover, the key access parameters MCW is the privacy information and each vehicle are not willing to share it with other vehicles. In this uncertain setting, age of information (AoI) is an important communication metric to measure the freshness of data, we design an intelligent vehicular node to learn the dynamic environment and predict the optimal MCW which can make it achieve age fairness. In order to allocate the optimal MCW for the vehicular node, we employ a learning algorithm to make a desirable decision by learning from replay history data. In particular, the algorithm is proposed by extending the traditional DQN training and testing method. Finally, by comparing with other methods, it is proved that the proposed DQN method can significantly improve the age fairness of the intelligent node.
Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array
ZHAN Xichao, SUN Zhongwen, SHU Feng, CHEN Yiwen, CHENG Xin, WU Yuanyuan, ZHANG Qi, LI Yifan, ZHANG Peng
, Available online  , doi: 10.23919/cje.2022.00.112
Abstract(117) HTML (61) PDF(10)
For a sub-connected hybrid multiple-input multiple-output (MIMO) receiver with $ K $ subarrays and $ N $ antennas, there exists a challenging problem of how to rapidly remove phase ambiguity in only single time-slot. First, a DOA estimator of maximizing received power (Max-RP) is proposed to find the maximum value of $ K $-subarray output powers, where each subarray is in charge of one sector, and the center angle of the sector corresponding to the maximum output is the estimated true DOA. To make an enhancement on precision, Max-RP plus quadratic interpolation (Max-RP-QI) method is designed. In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of Max-RP. Finally, to achieve the CRLB, a Root-MUSIC plus Max-RP-QI scheme is developed. Simulation results show that the proposed three methods eliminate the phase ambiguity during one time-slot and also show low-computational-complexities. In particular, the proposed Root-MUSIC plus Max-RP-QI scheme can reach the CRLB, and the proposed Max-RP and Max-RP-QI are still some performance losses 2dB~4dB compared to the CRLB.
An Optimized Fractional order PI Controller for Enhancing the Power Quality of Three-phase Solar PV, BESS and Wind Integrated UPQC
Shravan Kumar Yadav, Krishna Bihari Yadav
, Available online  , doi: 10.23919/cje.2022.00.079
Abstract(211) HTML (108) PDF(23)
This paper focuses on an optimized Fractional Order Proportional Integral (FOPI) Controller for enhancing the power quality of three-phase hybrid energy storage system integrated with Unified Power Quality Conditioner (UPQC). With a view to providing continuous electricity, Renewable Energy Sources (RES) like Photovoltaic (PV) array, Battery Energy Storage System (BESS) and wind energy are modeled. To ease the grid's power quality issues and the harmonics injected by non-linear loads there endures the UPQC model with series and shunt active filter compensator. Furthermore, PV, wind, and BESS integrated UPQC are capable of solving power quality issues in the event of long voltage interruptions. The shunt compensator of UPQC extracts the power from the hybrid energy systems whereas the load is protected by the series compensator from the grid related power quality issues. Hence, to regulate the voltage of the DC link at the desired level, this paper intends to develop a FOPI controller that exhibits iso-damping properties. Particularly, the gain of the FOPI controller is optimally tuned by a novel hybrid algorithm known as Enhanced Seagull with Rooster Update (ES-RU) algorithm that hybrid the concepts of Seagull Optimization Algorithm (SOA) and Chicken Swarm Optimization (CSO). At last, the proposed method was validated during voltage sag/swell, concerning the total harmonic distortion.
Defects Recognition of Train Wheelset Tread Based on Improved Spiking Neural Network
ZHANG Changfan, HUANG Congcong, HE Jing
, Available online  , doi: 10.23919/cje.2022.00.162
Abstract(195) HTML (98) PDF(13)

Surface defect recognition on Train wheelset is crucial for the safe operation of a train wheel system. However, existing algorithms find it difficult to make rapid and accurate recognitions, due to the diversity and complexity of such defects. Targeting this issue, a train wheelset tread defect recognition method based on an improved spiking neural network (ISNN) is proposed. Specifically, a hybrid convolutional encoding module is first designed to conduct image-to-spike conversions and to create multi-scaled sparse representations of the features. Second, a residual spiking convolutional neural network is implemented to extract spiking features optimally, and a multi-scale structure is adopted to enhance the SNN’s ability to handle the details. A channel attention module is then incorporated to re-calibrate the weights of four-dimensional spiking feature maps. Finally, effective spiking features are obtained based on which the recognition decisions is made. The experimental results showed that the proposed method improved the accuracy of defect recognition. The recognition time of a single image is only 0.0195 s on average. The overall performance of the proposed method is noticeably superior to current mainstream algorithms.

A hybrid music recommendation model based on personalized measurement and game theory
WU Yun, LIN Jian, MA Yanlong
, Available online  , doi: 10.23919/cje.2021.00.172
Abstract(160) HTML (83) PDF(10)

Recommendation algorithms, from the perspective of real-time, can be classified as offline recommendation algorithms and online recommendation algorithms. To improve music recommendation accuracy, especially the new music (users have no historic listening records on it) recommendation accuracy, and real-time recommendation ability, and solve the “interest drift” problem, we propose a hybrid music recommendation model (HMRM) based on personalized measurement and game theory, which can be separated into two parts: an offline recommendation part (OFFLRP) and an online recommendation part (ONLRP). In OFFLRP, we emphasize users personalization. We introduce two metrics: User-Pursue-Novel-Degree (UPND) and Music Popularity (MP) to improve the traditional items-based collaborative filtering algorithm. In ONLRP, we try to solve the “interest drift” problem, which is a thorny problem in OFFLRP. We propose a novel online recommendation algorithm based on game theory. Experiments verify that the hybrid music recommendation model has a higher new music recommendation accuracy, a decent dynamical personalized recommendation ability, and real-time recommendation capability, and substantially mitigating the problem of interest drift.

Dual-band flexible MIMO antenna with self-isolation enhancement structure for wearable applications
YANG Lingsheng, XIE Yizhang, JIA Hongting, QU Meixuan, LU Zhengyan, LI Yajie
, Available online  , doi: 10.23919/cje.2021.00.293
Abstract(114) HTML (60) PDF(8)

A two-element dual-band flexible MIMO antenna which can be used for wearable applications is proposed in this paper. The antenna consists of two radiating elements fed by coplanar waveguide (CPW), and a shielding layer, which are all made of flexible conductive cloth MKKTN260. Each radiating element composes of two coupled split ring-shaped bending strips. The proposed antenna shows two measured impedance bandwidth (S11 < −10 dB) of 2.39-2.48 GHz and 5.72-5.88 GHz, so that it can be used for 2.4 GHz and 5.8 GHz ISM applications. The two coupled split rings form a self-isolation enhancement structure and can realize polarization diversity at 2.4 GHz band and radiation shielding at 5.8 GHz band, respectively. High isolation (>30 dB) has been achieved for both the bands. Other characteristics for wearable applications like gain, efficiency, SAR, and bending performances were also studied.

Privacy Preserving Algorithm for Spectrum Sensing in Cognitive Vehicle Networks
LI Hongning, HU Tonghui, CHEN Jiexiong, WU Xiuqiang, PEI Qingqi
, Available online  , doi: 10.23919/cje.2022.00.007
Abstract(91) HTML (47) PDF(15)

The spectrum resources are scarce to support the increasing throughput demands in vehicular networks. It is urgent to make full use of spectrum bands in mobile network. To get the availability of spectrum bands, users should sense wireless channels and cooperate with others. The spectrum sensing data are always related to users’ privacy, such as location. In this paper, we first introduce sensing trajectory inference attack (STIA) in cognitive vehicular networks (CVN), and then propose a data confusion-based privacy-preserving algorithm (DCPPA) and a cryptonym array-based privacy-preserving aggregation (CAPPA) scheme for spectrum sensing in CVN. Unlike existing methods, the proposed schemes transmit confused data during aggregation process. It is almost impossible to infer users’ location from the data transmitted. Analysis demonstrates that the proposed scheme are resilient to STIA

Reverse-Nearest-Neighbor-Based Clustering by Fast Search and Find of Density Peaks
ZHANG Chunhao, XIE Bin, ZHANG Yiran
, Available online  , doi: 10.23919/cje.2022.00.165
Abstract(213) HTML (107) PDF(23)

Clustering by fast search and find of density peaks (CFSFDP) has the advantages of a novel idea, easy implementation, and efficient clustering. It has been widely recognized in various fields since it was proposed in Science in 2014. The CFSFDP algorithm also has certain limitations, such as non-unified sample density metrics defined by cutoff distance, the “Domino Effect” for the assignment of remaining samples triggered by unstable assignment strategy, and the phenomenon of picking wrong density peaks as cluster centers. We propose reverse-nearest-neighbor-based clustering by fast search and find of density peaks (RNN-CFSFDP) to avoid these shortcomings. We redesign and unify the sample density metric by introducing reverse nearest neighbor. The newly defined local density metric and the K-nearest neighbors of each sample are combined to make the assignment process more robust and alleviate the “Domino Effect”. Specifically, a cluster fusion algorithm is proposed, which further alleviates the “Domino Effect” and effectively avoids the phenomenon of picking wrong density peaks as cluster centers. Experimental results on publicly available synthetic data sets and real-world data sets show that in most cases, the proposed algorithm is superior to or at least equivalent to the comparative methods in clustering performance. Especially, the proposed algorithm works better on manifold data sets and uneven density data sets.

Unsupervised Video Object Segmentation via Weak User Interaction and Temporal Modulation
FAN Jiaqing, ZHANG Kaihua, ZHAO Yaqian, LIU Qingshan
, Available online  , doi: 10.23919/cje.2022.00.139
Abstract(222) HTML (115) PDF(15)

In Unsupervised Video Object Segmentation (UVOS), the whole video might segment the wrong target due to the initial prior is missing. Also, in Semi-supervised Video Object Segmentation (SVOS), the initial video frame with a fine-grained pixel-level mask is essential to good segmentation accuracy. However, it is expensive and laborious to provide the accurate pixel-level masks for each training sequence. To address this issue, in this paper, we present a weak user interactive UVOS approach guided by a simple human-made rectangle annotation in the initial frame. Specifically, we first interactively draw the region of interest by a rectangle, and then we leverage the Mask RCNN method to generate a set of coarse reference labels for subsequent mask propagations. Second, to establish the temporal correspondence between the coherent frames, we further design two novel temporal modulation modules to enhance the target representations. Then, we compute the earth mover’s distance (EMD)-based similarity between coherent frames to mine the co-occurrent objects in the two images, which is used to modulate the target representation to highlight the foreground target. Furthermore, we design a cross-squeeze temporal modulation module to emphasize the co-occurrent features across frames, which further helps to enhance the foreground target representation. Finally, we augment the temporally modulated representations with the original representation and obtain the compositive spatio-temporal information, producing a more accurate VOS model. The experimental results on both UVOS and SVOS datasets including Davis2016, FBMS, Youtube-VOS, and Davis2017, show that the presented approach yields good balance between accuracy and complexity against the state-of-the-art solutions. The related code will be released at



Mode Competition of Low Voltage Backward Wave Oscillator near 500 GHz with Parallel Multi-Beam
ZHAO Xiaoyan, HU Jincheng, ZHANG Haoran, GUO Sidou, FENG Yuming, TANG Lin, ZHANG Kaichun, LIU Diwei
, Available online  , doi: 10.23919/cje.2022.00.003
Abstract(129) HTML (65) PDF(17)

In this paper, a Backward wave oscillator (BWO) with parallel multiple beams and multi-pin Slow-wave structure (SWS) operating at the frequency above 500 GHz is studied. Both the cold-cavity dispersion characteristics and CST Particle Studio (PIC) simulation results reveal that there are obvious mode competition problems in this kind of terahertz source. Considering that the structure of the multi-pin SWS is similar to that of two-dimensional Photonic crystals (PC), we introduce the defects of photonic crystal with the property of filtering into the SWS to suppress high-order modes. Furthermore, a detailed study of the effect of suppressing higher-order modes is carried out in the process of changing location and arrangement pattern of the point defects. The stable, single-mode operation of the terahertz source is realized. The simulation results show that the ratio of the output peak power of the higher-order modes to that of the fundamental mode is less than 1.9%. Also, the source can provide the output peak power of 44.8 mW at the frequency of 502.2 GHz in the case of low beam voltage of 4.7 kV.

Study on Coded Permutation Entropy of Finite Length Gaussian White Noise Time Series
SUN Huihui, ZHANG Xiaofeng
, Available online  , doi: 10.23919/cje.2022.00.209
Abstract(113) HTML (56) PDF(11)

As an extension of PE, coded permutation entropy (CPE) improves the performance of PE by making a secondary division for ordinal patterns defined in PE. In this study, we provide an exploration of the statistical properties of CPE using a finite length Gaussian white noise time series theoretically. By means of the Taylor series expansion, the approximate expressions of the expected value and variance of CPE are deduced and the Cramér-Rao Low Bound (CRLB) is obtained to evaluate the performance of the CPE estimator. The results indicate that CPE is a biased estimator, but the bias only depends on relevant parameters of CPE and it can be easily corrected for an arbitrary time series. The variance of CPE is related to the encoding patterns distribution, and the value converges to the CRLB of the CPE estimator when the time series length is large enough. For a finite-length Gaussian white noise time series model, the predicted values can match well with the actual values, which further validates the statistic theory of CPE. Using the theoretical expressions of CPE, it is possible to better understand the behavior of CPE for most of the time series.

Overlay Cognitive Radio-assisted NOMA Intelligent Transportation Systems with Imperfect SIC and CEEs
LI Xingwang, GAO Xuesong, LIU Yingting, HUANG Gaojian, ZENG Ming, QIAO Dawei
, Available online  , doi: 10.23919/cje.2022.00.071
Abstract(368) HTML (182) PDF(50)

With the development of the mobile communication and intelligent information technologies, the intelligent transportation systems (ITS) driven by the sixth generation (6G) has many opportunities to achieve ultra-low latency and higher data transmission rate. Nonetheless, it also faces the great challenges of spectral resource shortage and large-scale connection. To solve the above problems, non-orthogonal multiple access (NOMA) and cognitive radio (CR) technologies have been proposed. In this regard, we study the reliable and ergodic performance of CR-NOMA assisted ITS networks in the presence of imperfect successive interference cancellation (ipSIC) and non-ideal channel state information (CSI). Specifically, the analytical expressions of the outage probability (OP) and ergodic sum rate (ESR) are derived through a string of calculations. In order to gain more insights, the asymptotic expressions for OP and ESR at high signal-to-noise ratio (SNR) regimes are discussed. We verify the accuracy of the analysis by Monte Carlo simulations, and the results show: i) ipSIC and channel estimation errors (CEEs) have negative impacts on the OP and ESR; ii) The OP decreases with the SNR increasing until convergence to a fixed constant at high SNR regions; iii) The ESR increases with increasing SNR and there exists a ceiling in the high SNR region.

ESE: Efficient Security Enhancement Method for the Secure Aggregation Protocol in Federated Learning
TIAN Haibo, LI Maonan, REN Shuangyin
, Available online  , doi: 10.23919/cje.2021.00.370
Abstract(239) HTML (121) PDF(21)

In federated learning, a parameter server may actively infer sensitive data of users and a user may arbitrarily drop out of a learning process. Bonawitz et al. propose a secure aggregation protocol for federated learning against a semi-honest adversary and a security enhancement method against an active adversary. The purpose of this paper is to analyze their security enhancement method and to design an alternative. We point out that their security enhancement method has the risk of Eclipse attack and that the consistency check round in their method could be removed. We give an new efficient security enhancement method by redesigning an authentication message and by adjusting the authentication timing. The new method produces an aggregation protocol secure against an active adversary with less communication and computation costs.

New Construction of Quadriphase Golay Complementary Pairs
LI Guojun, ZENG Fanxin, YE Changrong
, Available online  , doi: 10.23919/cje.2021.00.215
Abstract(146) HTML (73) PDF(16)

Based on an arbitrarily-chosen binary Golay complementary pair (BGCP)

\begin{document}$ ({\boldsymbol{c}},{\boldsymbol{d}}) $\end{document}

of even length


, first of all, construct quadriphase sequences

$ {\boldsymbol{a}} $


$ {\boldsymbol{b}} $

of length


by weighting addition and difference of the aforementioned pair with different weights, respectively. Secondly, new quadriphase sequence

$ {\boldsymbol{u}} $

is given by interleaving three sequences

$ {\boldsymbol{d}} $


$ {\boldsymbol{a}} $

, and

$ -{\boldsymbol{c}} $

, and similarly, the sequence

$ {\boldsymbol{v}} $

is acquired from three sequences

$ {\boldsymbol{d}} $


$ {\boldsymbol{b}} $

, and

$ {\boldsymbol{c}} $

. Thus, the resultant pair

$ ({\boldsymbol{u}},{\boldsymbol{v}}) $

is the quadriphase Golay complementary pair (QGCP) of length


. The QGCPs play a fairly important role in communications, radar, and so on.

Vibration-based fault diagnosis for railway point machines using VMD and multiscale fluctuation-based dispersion entropy
SUN Yongkui, CAO Yuan, LI Peng, XIE Guo, WEN Tao, SU Shuai
, Available online  , doi: 10.23919/cje.2022.00.075
Abstract(211) HTML (104) PDF(31)

As one of the most important railway signaling equipment, railway point machines undertake the major task of ensuring train operation safety. Thus fault diagnosis for railway point machines becomes a hot topic. Considering the advantage of the anti-interference characteristics of vibration signals, this paper proposes an novel intelligent fault diagnosis method for railway point machines based on vibration signals. A feature extraction method combining variational mode decomposition (VMD) and multiscale fluctuation-based dispersion entropy (MFDE) is developed, which is verified a more effective tool for feature selection. Then, a two-stage feature selection method based on Fisher discrimination and ReliefF is proposed, which is validated more powerful than signle feature selection methods. Finally, support vector machine (SVM) is utilized for fault diagnosis. Experiment comparisons show that the proposed method performs best. The diagnosis accuracies of normal-reverse and reverse-normal switching processes reach 100% and 96.57% respectively. Especially, it is a try to use new means for fault diagnosis on railway point machines, which can also provide references for similar fields.

EAODroid: Android Malware Detection based on Enhanced API Order
HUANG Lu, XUE Jingfeng, WANG Yong, QU Dacheng, CHEN Junbao, ZHANG Nan, ZHANG Li
, Available online  , doi: 10.23919/cje.2021.00.451
Abstract(284) HTML (143) PDF(20)

The development of smart mobile devices not only brings convenience to people’s lives but also provides a breeding ground for Android malware. The sharp increasing malware poses a disastrous threat to personal privacy in the information age. Based on the fact that malware heavily resorts to system APIs to perform its malicious actions, there has been a variety of API-based detection approaches. Most of them do not consider the relationship between APIs. We contribute a new approach based on Enhanced API Order for Android malware detection, named EAODroid. EAODroid learns the similarity of system APIs from a large number of API sequences and groups similar APIs into clusters. The extracted API clusters are further used to enhance the original API calls executed by an app to characterize behaviors and perform classification. We perform multi-dimensional experiments to evaluate EAODroid on three datasets with ground truth. We compare with many state-of-the-art works, showing that EAODroid achieves effective performance in Android malware detection.

A Novel Wideband Wilkinson Pulse Combiner with Enhanced Low Frequency Isolation
WANG Zitong, WU Qi, SU Donglin
, Available online  , doi: 10.23919/cje.2021.00.429
Abstract(240) HTML (120) PDF(13)

A novel Wilkinson pulse combiner(WPC) is proposed for the combination of Gaussian pulse signals. The WPC requires a very wide bandwidth, small size and high port isolation. To improve the operating bandwidth, the design adopts the form of eight-section WPC. Eight capacitors are connected in series with the isolating resistors of each section. After capacitive loading, isolation between WPC input ports is significantly improved at low frequency. Consequently, the operating bandwidth of WPC has been increased from 13:1 to 31:1. Compared with the conventional Wilkinson combiner with the same bandwidth, the proposed WPC reduces the size by 40%. In addition, all the ports are well impedance matched and the insertion loss in the operating frequency band is less than 0.5dB. To verify the feasibility of the design, a prototype was fabricated and measured. Experiment shows that the novel WPC is more advantageous to generate dual-Gaussian pulse signals.

Towards Order-preserving and Zero-copy Communication on Shared Memory for Large Scale Simulation
LI Xiuhe, SHEN Yang, LIN Zhongwei, ZHAO Shunkai, SHI Qianqian, DAI Shaoqi
, Available online  , doi: 10.23919/cje.2021.00.393
Abstract(158) HTML (82) PDF(7)

Parallel simulation generally needs efficient, reliable and order-preserving communication. In this article, a zero-copy, reliable and order-preserving intra-node message passing approach ZeROshm is proposed, and it partitions shared memory into segments assigned to processes for receiving messages. Each segment consists of two levels of index L1 and L2 that recordes the order of messages in the host segment, and the processes also read from and write to the segments directly according to the indexes, thereby eliminating allocating and copying buffers. As experimental results show, ZeROshm exhibits nearly equivalent performance to MPI for small message and superior performance for large message - ZeROshm costs less time by 43%, 40% and 55% respectively in pure communication, communication with contention and real Phold simulation within a single node. In hybrid environment, the combination of ZeROshm and MPI also shorten the execution time of Phold simulation by about 42% compared to pure MPI.

Security Analysis for SCKHA Algorithm: Stream Cipher Algorithm Based on Key Hashing Technique
Souror Samia, El-Fishawy Nawal, Badawy Mohammed
, Available online  , doi: 10.23919/cje.2021.00.383
Abstract(348) HTML (175) PDF(18)

The strength of any cryptographic algorithm is mostly based on the difficulty of its encryption key.However, the larger size of the shared key the more computational operations and processing time for cryptographic algorithms. To avoid increasing the key size and keep its secrecy, we must hide it. The authors proposed a stream cipher algorithm that can hide the symmetric key[1] through hashing and splitting techniques. This paper aims to measure security analysis and performance assessment for this algorithm. This algorithm is compared with three of the commonly used stream cipher algorithms: RC4, Rabbit, and Salsa20 in terms of execution time and throughput. This comparison has been conducted with different data types as audio, image, text, docs, and pdf. Experiments proved the superiority of SCKHA algorithm over both Salsa20 and Rabbit algorithms. Also, results proved the difficulty to recover the secret key for SCKHA algorithm. Although RC4 has a lower encryption time than SCKHA, it is not recommended for use because of its vulnerabilities. Security factors that affect the performance as avalanche effect, correlation analysis, histogram analysis, and Shannon information entropy are highlighted. Also, the ciphertext format of the algorithm gives it the ability to search over encrypted data.

On UAV Serving Node Deployment for Temporary Coverage in Forest Environment: A Hierarchical Deep Reinforcement Learning Approach
WANG Li, WU Xuewei, WANG Yanhui, XIAO Zhe, LI Liang, FEI Aiguo
, Available online  , doi: 10.23919/cje.2021.00.326
Abstract(218) HTML (108) PDF(33)

Unmanned aerial vehicles (UAVs) can be effectively used as serving stations in emergency communications because of their free movements, strong flexibility, and dynamic coverage. In this paper, we propose a coordinated multiple points (CoMP) based UAV deployment framework to improve system average ergodic rate, by using the fuzzy C-means (FCM) algorithm to cluster the ground users and considering exclusive forest channel models for the two cases, i.e., associated with a broken base station (BS) or an available one. In addition, we derive the upper bound of the average ergodic rate to reduce computational complexity. Since deep reinforcement learning (DRL) can deal with the complex forest environment while the large action and state space of UAVs leads to slow convergence, we use a ratio cut method to divide UAVs into groups and propose a hierarchical clustering DRL (HC-DRL) approach with quick convergence to optimize the UAV deployment. Simulation results show that the proposed framework can effectively reduce the complexity, and outperforms the counterparts in accelerating the convergence speed.

Multi-scale Global Retrieval and Temporal-Spatial Consistency Matching based long-term Tracking Network
SANG Haifeng, LI Gongming, ZHAO Ziyu
, Available online  , doi: 10.23919/cje.2021.00.195
Abstract(194) HTML (99) PDF(17)

Compared with the traditional short-term object tracking task based on temporal-spatial consistency, the long-term object tracking task faces the challenges of object disappearance, dramatic changes in object scale, and object appearance. To address these challenges and problems, in this paper we propose a Multi-scale Global Retrieval and Temporal-Spatial Consistency Matching based long-term Tracking Network (MTTNet). MTTNet regards the long-term tracking task as a single sample object detection task and takes full advantage of the temporal-spatial consistency assumption between adjacent video frames to improve the tracking accuracy. MTTNet utilizes the information of single sample as guidance to perform full-image multi-scale retrieval on any instance and does not require online learning and trajectory refinement. Any type of error generated during the detection process will not affect its performance on subsequent video frames. This can overcome the accumulation of errors in the tracking process of traditional object tracking networks. We introduce Atrous Spatial Pyramid Pooling to address the challenge of dramatic changes in the scale and the appearance of the object. On the experimental results, MTTNet can achieve better performance than composite processing methods on two large datasets.

Robust Beamforming Design for IRS-Aided Cognitive Radio Networks with Bounded CSI Errors
ZHANG Lei, WANG Yu, SHANG Yulong, TIAN Jianjie, JIA Ziyan
, Available online  , doi: 10.23919/cje.2021.00.254
Abstract(210) HTML (103) PDF(25)

In this paper, intelligent reflecting surface (IRS) is introduced to enhance the performance of cognitive radio (CR) systems. The robust beamforming is designed based on combined bounded channel state information (CSI) error for primary user (PU) related channels. The transmit precoding at the secondary user (SU) transmitter and phase shifts at the IRS are jointly optimized to minimize the SU's total transmit power subject to the quality of service of SUs, the limited interference imposed on the PU and unit-modulus of the reflective beamforming. Simulation results verify the efficiency of the proposed algorithm and reveal that the number of phase shifts at IRS should be carefully chosen to obtain a tradeoff between the total minimum transmit power and the feasibility rate of the optimization problem.

Unique Parameters Selection Strategy of Linear Canonical Wigner Distribution via Multiobjective Optimization Modeling
SHI Xiya, WU Anyang, SUN Yun, QIANG Shengzhou, JIANG Xian, HAN Puyu, CHEN Yunjie, ZHANG Zhichao
, Available online  , doi: 10.23919/cje.2021.00.338
Abstract(367) HTML (185) PDF(37)

There are many kinds of linear canonical transform (LCT)-based Wigner distributions (WDs), which are both very effective in detecting noisy linear frequency-modulated (LFM) signals. Among WDs in LCT domains, the instantaneous cross-correlation function type of Wigner distribution (ICFWD) attracts much attention from scholars, because it achieves not only low computational complexity but also good detection performance. However, the existing LCT free parameters selection strategy, a solution of the expectation-based output signal-to-noise ratio (SNR) optimization model, is not unique. In this paper, by introducing the variance-based output SNR optimization model, a multiobjective optimization model is established. Then the existence and uniqueness of the optimal parameters of ICFWD are investigated. The solution of the multiobjective optimization model with respect to one-component LFM signal added with zero-mean stationary circular Gaussian noise is derived. A comparison of the unique parameters selection strategy and the previous one is carried out. The theoretical results are also verified by numerical simulations.

A Novel Adaptive InSAR Phase Filtering Method Based on Complexity Factors
XU Huaping, WANG Yuan, LI Chunsheng, ZENG Guobing, LI Shuo, LI Shuang, REN Chong
, Available online  , doi: 10.23919/cje.2021.00.280
Abstract(261) HTML (132) PDF(13)

Phase filtering is an essential step in interferometric synthetic aperture radar (InSAR). For interferograms with complicated and changeable terrain, the increasing resolution of InSAR images makes it even more difficult. In this paper, a novel adaptive InSAR phase filtering method based on complexity factors is proposed. Firstly, three complexity factors based on the noise distribution and terrain slope information of the interferogram are selected. The complexity indicator composed of three complexity factors is used to guide the adaptive selection of the most suitable and effective filtering strategies for different areas. Then, the complexity scalar is calculated, which can guide the adaptive local fringe frequency (LFF) estimation and adaptive parameters calculation in different filter methods. Finally, validations are performed on the simulated and real data. The performance comparison between the other three representative phase filtering method and the proposed method have validated the effectiveness and superiority of the proposed method.

Coupling enhancement of THz metamaterials source with parallel multiple beams
ZHANG Kaichun, FENG Yuming, ZHAO Xiaoyan, HU Jincheng, XIONG Neng, GUO Sidou, TANG Lin, LIU Diwei
, Available online  , doi: 10.23919/cje.2022.00.032
Abstract(134) HTML (69) PDF(11)

In this paper, we propose a terahertz radiation source over the R-band (220-325 GHz) based on metamaterials (MTMs) structure and parallel multiple beams. The effective permittivity and permeability of the slow-wave structure (SWS) can be obtained through the S-parameter retrieval approach, using numerical simulation. Additionally, the electromagnetic properties of the MTMs structure are analyzed, including the dispersion and the coupling impedance. Furthermore, we simulate the beam-wave interaction of the backward oscillator (BWO) with MTMs structure and parallel multiple beams by 3-D particle-in-cell (PIC) code. It is observed that parallel multiple beams can highly enhance the beam-wave interaction and greatly enlarge the output power. These results indicate that the saturated (peak) output power is approximately 63W with the efficiency of roughly 6% at the operating frequency of 231 GHz, under the beam voltage of 35 kV and total current of 30 mA (6-beam) respectively. Meanwhile, the BWO can generate power of 10 W-80 W in the tunable frequency of 220 GHz-240 GHz.

Design and Implementation of a Novel Self-bias S-band Broadband GaN Power Amplifier
ZHANG Luchuan, ZHONG Shichang, CHEN Yue
, Available online  , doi: 10.23919/cje.2021.00.118
Abstract(165) HTML (82) PDF(13)

In this paper, a 3.6 mm gate width GaN HEMT with 0.35 μm gate length process and input and output matching circuits of Nanjing Electronic Devices Institute are used for broadband design respectively, and a novel high-power and high-efficiency self-bias S-band broadband continuous wave GaN power amplifier is realized. Under the working conditions of 2.2 GHz to 2.6 GHz and 32 V drain power supply, the continuous wave output power of the amplifier is more than 20 W, the power gain is more than 15 dB, and the max power added efficiency is more than 65%. The self-bias amplifier simplifies the circuit structure and realizes excellent circuit performance.

Recursive Feature Elimination Based Feature Selection in Modulation Classification for MIMO Systems
ZHOU Shuai, LI Tao, LI Yongzhao
, Available online  , doi: 10.23919/cje.2021.00.347
Abstract(287) HTML (138) PDF(25)

Feature-based (FB) algorithms are widely used in modulation classification due to their low complexity. As a prerequisite step of FB, feature selection can reduce the computational complexity without significant performance loss. In this paper, according to the linear separability of cumulant features, the hyperplane of the support vector machine is used to classify modulation types, and the contribution of different features is ranked through the weight vector. Then, cumulant features are selected using recursive feature elimination (RFE) to identify the modulation type employed at the transmitter. We compare the performance of the proposed algorithm with existing feature selection algorithms and analyze the complexity of all the mentioned algorithms. Simulation results verify that the proposed RFE algorithm can optimize the selection of the features to realize modulation recognition and improve identification efficiency.

Convolutional Neural Networks of Whole Jujube Fruits Prediction Model Based on Multi-Spectral Imaging Method
WANG Jing, FAN Xiaofei, SHI Nan, ZHAO Zhihui, SUN Lei, SUO Xuesong
, Available online  , doi: 10.23919/cje.2021.00.149
Abstract(250) HTML (122) PDF(37)

Soluble sugar is an important index to determine the quality of jujube, and also an important factor to influence the taste of jujube. The soluble sugar content of jujube mainly depends on manual chemical measurement, which is time-consuming and labor-intensive. In this study, the feasibility of multi-spectral imaging combined with deep learning for rapid nondestructive testing of fruit internal quality was analyzed. Support vector machine regression model, partial least squares regression model and convolutional neural networks (CNNs) model were established by multi-spectral imaging method to predict the soluble sugar content of the whole jujube fruit, and the optimal model was selected to predict the content of three kinds of soluble sugar. The study showed that the sucrose prediction model of the whole jujube had the best performance after CNNs training, and the correlation coefficient of verification set was 0.88, which proved the feasibility of using CNNs for prediction of the soluble sugar content of jujube fruits.

Dual Radial-Resonant Wide Beamwidth Circular Sector Microstrip Patch Antennas
MAO Xiaohui, LU Wenjun, JI Feiyan, XING Xiuqiong, ZHU Lei
, Available online  , doi: 10.23919/cje.2021.00.219
Abstract(320) HTML (155) PDF(33)

In this article, a design approach to a radial-resonant wide beamwidth circular sector patch antenna is advanced. As properly evolved from a U-shaped dipole, a prototype magnetic dipole can be fit in the radial direction of a circular sector patch radiator, with its length set as the positive odd-integer multiples of one-quarter wavelength. In this way, multiple TM0m (m = 1, 2, …) modes resonant circular sector patch antenna with short-circuited circumference and widened E-plane beamwidth can be realized by proper excitation and perturbations. Prototype antennas are then designed and fabricated to validate the design approach. Experimental results reveal that the E-plane beamwidth of a dual-resonant antenna fabricated on air/Teflon substrate can be effectively broadened to 128°/120°, with an impedance bandwidth of 17.4%/7.1%, respectively. In both cases, the antenna heights are strictly limited to no more than 0.03-guided wavelength. It is evidently validated that the proposed approach can effectively enhance the operational bandwidth and beamwidth of a microstrip patch antenna while maintaining its inherent low profile merit.

A Novel Re-weighted CTC Loss for Data Imbalance in Speech Keyword Spotting
LAN Xiaotian, HE Qianhua, YAN Haikang, LI Yanxiong
, Available online  , doi: 10.23919/cje.2021.00.198
Abstract(430) HTML (234) PDF(53)

Speech keyword spotting system is a critical component of human-computer interfaces. And Connectionist temporal classifier (CTC) has been proven to be an effective tool for that task. However, the standard training process of speech keyword spotting faces a data imbalance issue where positive samples are usually far less than negative samples. Numerous easy-training negative examples overwhelm the training, resulting in a degenerated model. To deal with it, this paper tries to reshape the standard CTC loss and proposes a novel re-weighted CTC loss. It evaluates the sample importance by its number of detection errors during training and automatically down-weights the contribution of easy examples, the majorities of which are negatives, making the training focus on samples deserving more training. The proposed method can alleviate the imbalance naturally and make use of all available data efficiently. Evaluation on several sets of keywords selected from AISHELL-1 and AISHELL-2 achieves 16%—38% relative reductions in false rejection rates over standard CTC loss at 0.5 false alarms per keyword per hour in experiments.

Two Jacobi-like algorithms for the general joint diagonalization problem with applications to blind source separation
CHENG Guanghui, MIAO Jifei, LI Wenrui
, Available online  , doi: 10.23919/cje.2019.00.102
Abstract(408) HTML (189) PDF(21)

We consider the general problem of the approximate joint diagonalization of a set of non-Hermitian matrices. This problem mainly arises in the data model of the joint blind source separation for two datasets. Based on a special parameterization of the two diagonalizing matrices and on adapted approximations of the classical cost function, we establish two Jacobi-like algorithms. They may serve for the canonical polyadic decomposition (CPD) of a third-order tensor, and in some scenarios they can outperform traditional CPD methods. Simulation results demonstrate the competitive performance of the proposed algorithms.

Review Article
Characteristic Mode Analysis: Application to Electromagnetic Radiation, Scattering, and Coupling Problems
DENG Xuan, ZHANG Di, CHEN Yikai, YANG Shiwen
, Available online  , doi: 10.23919/cje.2022.00.200
Abstract(81) HTML (40) PDF(12)
With the rapid development of the theory of characteristic modes (CMs), this analysis technique is widely accepted and its application potential to antenna engineering is identified. The theory of characteristic modes reveals the natural resonant property of an object and provides a variety of modal parameters. Using these modal parameters, many CM-based techniques are developed which cover a wide range of electromagnetic radiation, scattering, and coupling problems in antenna engineering. In this review, some state-of-the-art CM-based techniques are provided for the selected topics of wideband, circular polarization, radiation pattern control, scattering control, and coupling control in the antenna design. Some future perspectives are given that show the potential applications of this method to more complex electromagnetics problems.
Multiple plasmonic Fano resonances revisited with modified Transformation-optics theory
JIANG Jing, LIANG Mingli, LI Jiaying
, Available online  , doi: 10.23919/cje.2022.00.095
Abstract(48) HTML (24) PDF(10)
Plasmonic Fano Resonances have recently attracted a great deal of research interest due to their sharp asymmetric profile, high sensitivity to the ambient material and low radiation damping. In this paper, we extend the Plasmonic Fano Resonances that resulted from the interference between bright and dark modes in a core-shell nanoparticle system for novel biosensing, optical switching applications, by using the theory of transformation optics. To this end, we consider the optical properties of dielectric-core-metallic-shell dimer and derive full analytical formulae for different geometric configurations. Our results demonstrated that breaking the geometrical symmetry of the structure, multiple Fano resonances arise owing to the near-field coupling of the bright and dark resonant modes. Electromagnetic induced transparency (EIT)-like effects are observed when the resonance frequencies of the bright and dark modes overlap. Strong dependence of the localized surface plasmon (LSP) on the geometry renders the multiple Fano resonances highly tunable in the proposed structure through independently altering the spectral profile of each nanoparticle, which provides much feasibility since most multiple Fano resonances reported in literature are results of collective plasmonic behavior and cannot be tuned independently. Furthermore, the figure of merit for refractive index sensing of the higher-order dark modes are predicted to be up to 36% greater than that of a single nanowire. These results make the proposed nanostructure attractive for many potential applications such as multiwavelength biosensing, switching and modulation.
The Novel Instance Segmentation Method Based on Multi-Level Features and Joint Attention
XU Bowen, LU Yinan, WU Tieru, GUO Xiaoxin
, Available online  , doi: 10.23919/cje.2021.00.226
Abstract(64) HTML (31) PDF(6)
Instance segmentation is an important task in computer vision. In order to enhance the multi-level features expression ability of the segmentation networks, a novel module is proposed in this paper. Firstly, we design a weighted bi-directional feature fusion way by improving the weight distribution function of bi-directional feature pyramid network. Secondly, we propose a joint attention mechanism to effectively filter different levels of feature information by adopting serial and parallel ways to combine the channel attention and spatial attention modules with modifying the original convolution. At the same time, the module uses dynamic convolution to stabilize the calculation speed while improve the 6.7% mean average precision (mAP) of segmentation. The experiments on the COCO dataset demonstrate that the module can effectively improve the performance of the existing instance segmentation networks.
Hyperspectral Image Super-Resolution Based on Spatial-Spectral Feature Extraction Network
LI Yanshan, CHEN Shifu, LUO Wenhan, ZHOU Li, XIE Weixin
, Available online  , doi: 10.23919/cje.2021.00.081
Abstract(281) HTML (141) PDF(30)

Constrained by physics, the spatial resolution of hyperspectral images (HSI) is low. Hyperspectral image super-resolution (HSI SR) is a task to obtain high-resolution hyperspectral images (HR HSI) from low-resolution hyperspectral images (LR HSI). Existing algorithms have the problem of losing important spectral information while improving spatial resolution. To handle this problem, a spatial-spectral feature extraction network (SSFEN) for HSI SR is proposed in this paper. It enhances the spatial resolution of the HSI while preserving the spectral information. The SSFEN is composed of three parts: spatial-spectral mapping network (SSMN), spatial reconstruction network (SRN), and spatial-spectral fusing network (SSFN). And a joint loss function with spatial and spectral constraints is designed to guide the training of the SSFEN. Experiment results show that the proposed method improves the spatial resolution of the HSI and effectively preserves the spectral information coinstantaneously.

Interdisciplinary Applications/BHI
Deep Contextual Representation Learning for Identifying Essential Proteins via Integrating Multisource Protein Features
LI Weihua, LIU Wenyang, GUO Yanbu, WANG Bingyi, QING Hua
, Available online  , doi: 10.23919/cje.2022.00.053
Abstract(403) HTML (202) PDF(19)

Essential proteins with biological functions are necessary for the survival of organisms. Computational recognition methods of essential proteins can reduce the workload and provide candidate proteins for biologists. However, existing methods fail to efficiently identify essential proteins, and generally do not fully use amino acid sequence information to improve the performance of essential protein recognition. In this work, we proposed an end-to-end deep contextual representation learning framework called DeepIEP to automatically learn biological discriminative features without prior knowledge based on protein network heterogeneous information. Specifically, the model attaches amino acid sequences as the attributes of each protein node in the protein interaction network, and then automatically learns topological features from protein interaction networks by graph embedding algorithms. Next, multi-scale convolutions and gated recurrent unit networks are used to extract contextual features from gene expression profiles. The extensive experiments confirm that our DeepIEP is an effective and efficient feature learning framework for identifying essential proteins and contextual features of protein sequences can improve the recognition performance of essential proteins.

Explainable Business Process Remaining Time Prediction using Reachability Graph
CAO Rui, ZENG Qingtian, NI Weijian, LU Faming, LIU Cong, DUAN Hua
, Available online  , doi: 10.23919/cje.2021.00.170
Abstract(267) HTML (110) PDF(34)

With the recent advances in the field of deep learning, an increasing number of deep neural networks have been applied to business process prediction tasks, remaining time prediction, to obtain more accurate predictive results. However, existing time prediction methods based on deep learning have poor interpretability, an explainable business process remaining time prediction method is proposed using reachability graph, which consists of prediction model construction and visualization. For prediction models, a Petri net is mined and the reachability graph is constructed to obtain the transition occurrence vector. Then, prefixes and corresponding suffixes are generated to cluster into different transition partitions according to transition occurrence vector. Next, the bidirectional recurrent neural network with attention is applied to each transition partition to encode the (trace) prefixes, and the deep transfer learning between different transition partitions is performed. For the visualization of prediction models, the evaluation values are added to the sub-processes of Petri net to realize the visualization of the prediction models. Finally, the proposed method is validated by publicly available event logs.

Attention Guided Enhancement Network for Weakly Supervised Semantic Segmentation
ZHANG Zhe, WANG Bilin, YU Zhezhou, ZHAO Fengzhi
, Available online  , doi: 10.23919/cje.2021.00.230
Abstract(276) HTML (145) PDF(25)

Weakly supervised semantic segmentation using just image-level labels is critical since it alleviates the need for expensive pixel-level labels. Most cutting-edge methods adopt two-step solutions that learn to produce pseudo-ground-truth using only image-level labels and then train off-the-shelf fully supervised semantic segmentation network with these pseudo labels. Although these methods have made significant progress, they also increase the complexity of the model and training. In this paper, we propose a one-step approach for weakly supervised image semantic segmentation—Attention guided enhancement network (AGEN), which produces pseudo-pixel-level labels under the supervision of image-level labels and trains the network to generate segmentation masks in an end-to-end manner. Particularly, we employ Class activation maps (CAM) produced by different layers of the classification branch to guide the segmentation branch to learn spatial and semantic information. However, the CAM produced by the lower layer can capture the complete object region but with many noises. Thus, the self-attention module is proposed to enhance object regions adaptively and suppress irrelevant object regions, further boosting the segmentation performance. Experiments on the Pascal VOC 2012 dataset show that the performance of AGEN outperforms other state-of-art weakly supervised semantic segmentation with only image-level labels.

Computer Hardware & Architecture
Vector Memory-Access Shuffle Fused Instructions for FFT-like Algorithms
LIU Sheng, YUAN Bo, GUO Yang, SUN Haiyan, JIANG Zekun
, Available online  , doi: 10.23919/cje.2021.00.401
Abstract(176) HTML (78) PDF(12)

The shuffle operations are the bottleneck when mapping the FFT-like algorithms to the vector SIMD architectures. We propose six (three pairs) innovative vector memory-access shuffle fused instructions,which have been proved mathematically. Together with the proposed modified binary-exchange method,the innovative instructions can efficiently address the bottleneck problem for DIF/DIT radix-2/4 FFT-like algorithms,reach a performance improvement by 17.9%~111.2% and reduce the code size by 5.4%~39.8%.Besides,the proposed instructions fit some hybrid-radix FFTs and are suitable for the terms of the initial or result data placement for general algorithms. The software and hardware cost of the proposed instructions is moderate.

MIMO Radar Transmit-Receive Design for Extended Target Detection against Signal-Dependent Interference
YAO Yu, LI Yanjie, LI Zeqing, WU Lenan, LIU Haitao
, Available online  , doi: 10.23919/cje.2021.00.140
Abstract(314) HTML (148) PDF(32)

Assuming unknown knowledge of Target impulse response (TIR), this paper deals with the joint design of Multiple-input multiple-output (MIMO) Space-time transmit code (STTC) and Space-time receive filter (STRF) for the detection of extended targets in the presence of signal-dependent interference. To enhance the detection performance of extended targets for MIMO radar, we consider transmit-receive system optimization to maximize the worst-case Signal to interference plus noise ratio (SINR) at the output of the STRF array. The problem is formulated in terms of a non-convex max-min quadratic fractional optimization program. Relying on an appropriate reformulation, we present an alternate optimization technique which monotonically increases the SINR value and converges to a stationary point. All iterations of the procedure, involve both a convex and a max-min quadratic fractional programming problem which is globally solved resorting to the generalized Dinkelbachos process with a polynomial computational complexity. In addition, resorting to several mathematical manipulations, the original problem is transformed into an equivalent convex problem, which can also be globally solved via interior-point methods. Finally, the effectiveness of two optimization design procedures is demonstrated through experimental results, underlining the performance enhancement offered by robust joint design methods.