Chinese Journal of Electronics

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

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Abstract:
Vehicles on the road exchange data with base station frequently through vehicle to infrastructure (V2I) communications to ensure the normal use of vehicular applications, where the IEEE 802.11 distributed coordination function 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 parameter MCW is privacy information and each vehicle is not willing to share it with other vehicles. In this uncertain setting, age of information (AoI), which measures the freshness of data and is closely related with fairness, has become an important communication metric. On this basis, we design an intelligent vehicular node to learn the dynamic environment and predict the optimal MCW, which can make the intelligent node 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 deep-Q-learning (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.

A V2V Emergent Message Dissemination Scheme for 6G-Oriented Vehicular Networks

CHEN Chen, WANG Chenyu, LI Cong, XIAO Ming, PEI Qingqi

, Available online , doi: 10.23919/cje.2022.00.337

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Abstract:
To ensure traffic safety and improve traffic efficiency, vehicular networks come up with multiple types of messages for safety and efficiency applications. In sixth-generation (6G) systems, these messages should be timely and error-free disseminated through vehicle-to-vehicle (V2V) communication to ensure traffic safety and efficiency. V2V supports direct communication between two vehicle user equipments, regardless of whether a base station is involved. We propose a packet delivery ratio (PDR)-based message dissemination scheme (PDR-MD) between V2V in 6G-oriented vehicular networks to select relay vehicles when broadcasting emergent messages. This scheme grasps the balance between vehicle distance and PDR so as to reduce transmission delay while ensuring reliable PDR. We compared the PDR-MD scheme with other probabilistic broadcasting schemes. The experimental results show that the PDR-MD protocol can maintain close to 95% and above PDR in transmitting emergent messages, and the transfer rate stays below 40%.

Mobility-Aware Multi-Task Migration and Offloading Scheme for Internet of Vehicles

LI Xujie, TANG Jing, XU Yuan, SUN Ying

, Available online , doi: 10.23919/cje.2022.00.333

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Abstract:
In Internet of vehicles, vehicular edge computing (VEC) as a new paradigm can effectively accomplish various tasks. Due to limited computing resources of the roadside units (RSUs), computing ability of vehicles can be a powerful supplement to computing resources. Then the task to be processed in data center can be offloaded to the vehicles by the RSUs. Due to mobility of the vehicles, the tasks will be migrated among the RSUs. How to effectively offload multiple tasks to the vehicles for processing is a challenging problem. A mobility-aware multi-task migration and offloading scheme for Internet of vehicles is presented and analyzed. Considering the coupling between migration and offloading, the joint migration and offloading optimization problem is formulated. The problem is a NP-hard problem and it is very hard to be solved by the conventional methods. To tackle the difficult problem, the idea of alternating optimization and divide and conquer is introduced. The problem can be decoupled into two sub-problems: computing resource allocation problem and vehicle node selection problem. If the vehicle node selection is given, the problem can be solved based on Lagrange function. And if the allocation of computing resource is given, the problem turns into a 0-1 integer programming problem, and the linear relaxation of branch bound algorithm is introduced to solve it. Then the optimization value is obtained through continuous iteration. Simulation results show that the proposed algorithm can effectively improve system performance.

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

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Abstract:
With the development of the mobile communication and intelligent information technologies, the intelligent transportation systems 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 intelligent transportation system networks in the presence of imperfect successive interference cancellation (SIC) and non-ideal channel state information. 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) Imperfect SIC 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.

PLC for In-Vehicle Network: A DRL-Based Algorithm of Diversity Combination of OFDM Subcarriers

CHEN Zhixiong, ZHANG Zhikun, CAO Tianshu, ZHOU Zhenyu

, Available online , doi: 10.23919/cje.2022.00.331

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Abstract:
For low latency communication service of vehicles, it is critical to improve the delay performance of power line communication (PLC) for in-vehicle network, which can decrease the weight and cost of the vehicle. In order to minimize the total time slots used in a transmission task, an orthogonal frequency-division multiplexing (OFDM) subcarrier diversity combination algorithm of PLC based on the deep reinforcement learning (DRL) is proposed herein. The short packet communication theory is used to develop an optimal combination model with constraints on short packet reliability, transmitting power and the amount of data. The state, action, and reward function of double deep Q-learning network (DDQN) are defined, and diversity combination for OFDM subcarriers is performed using DDQN. An adaptive power allocation algorithm based on the thresholds of error rate and the data amount is used. Simulation results show that the proposed algorithm can effectively improve the delay performance of PLC under the constraints of power and data amount.

Collaborative Caching in Vehicular Edge Network Assisted by Cell-Free Massive MIMO

WANG Chaowei, WANG Ziye, XU Lexi, YU Xiaofei, ZHANG Zhi, WANG Weidong

, Available online , doi: 10.23919/cje.2022.00.294

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Abstract:
The 6G mobile communications demand lower content delivery latency and higher quality of service for vehicular edge network. With the popularity of content-centric networks, mobile users are paying more and more attention to the delay and reliability of fetching cached content. For reducing communication costs, increasing network capacity and improving the content delivery, we propose a collaborative caching scheme based on deep reinforcement learning for vehicular edge network assisted by cell-free massive multiple-input multiple-output (MIMO) system, in which the macro base station is considered as the central processor unit, and the roadside units are treated as roadside access points (RSAPs). The proposed scheme can effectively cache contents in edge nodes, i.e., RSAPs and vehicles with caching capability. We jointly consider the mobility of vehicles and the content request preferences of users, then we use deep Q-networks algorithm to optimize the caching decisions. Simulation results show that the proposed scheme can significantly reduce the content delivery average latency and increase the content cache hit ratio.

Multi-Objective Coordinated Optimization for UAV Charging Scheduling in Intelligent Aerial-Ground Perception Networks

ZHOU Yi, CHENG Xiang, SHI Huaguang, JIN Zhanqi, NING Nianwen, LIU Fuqiang

, Available online , doi: 10.23919/cje.2022.00.334

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Abstract:
The unmanned aerial vehicles (UAVs)-assisted intelligent traffic perception system can provide effective situation awareness. However, UAVs are required to be recharged before the energy is exhausted, which may cause task interruption. To address this concern, the charging UAV (CUAV) is employed to provide wireless charging for the mission UAVs (MUAVs). This paper studies the charging scheduling problem of the CUAV under the premise of optimizing the MUAVs deployment. We first model the MUAVs deployment problem considering the energy consumption and data transmission and establish the CUAV charging model. Then, the above problem is formulated as a multi-objective multi-agent stochastic game process to simplify the decisions-making of MUAVs and CUAV, based on which we propose the utility-based Pareto optimal deployment and charging algorithm, which reduces the computing complexity by equivalent utility of the MUAVs while using Kullback-Leibler divergence to constrain solutions. Next, to ensure the effectiveness of policy update, the multi-agent communication protocol is adopted to improve policy exploration efficiency. Simulation results show that the proposed algorithm outperforms existing works in terms of energy efficiency and charging by comparing with the Pareto front of different methods, endurance anxiety of the MUAVs, and charging utilization under different task modes.

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

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The 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 the resource-constrained device 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. 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).

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

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Abstract:
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. 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. The proposed algorithm works better on manifold data sets and uneven density data sets.

Fine-Grained Cross-Modal Fusion Based Refinement for Text-to-Image Synthesis

SUN Haoran, WANG Yang, LIU Haipeng, QIAN Biao

, Available online , doi: 10.23919/cje.2022.00.227

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Abstract:
Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel fine-grained text-image fusion based generative adversarial networks (FF-GAN), which consists of two modules: Fine-grained text-image fusion block (FF-Block) and global semantic refinement (GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given texts.

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

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Music recommendation algorithms, from the perspective of real-time, can be classified into two categories: offline recommendation algorithms and online recommendation algorithms. To improve music recommendation accuracy, especially for the new music (users have no historic listening records on it), and real-time recommendation ability, and solve the interest drift problem simultaneously, we propose a hybrid music recommendation model based on personalized measurement and game theory. This model can be separated into two parts: an offline recommendation part (OFFLRP) and an online recommendation part (ONLRP). In the offline part, we emphasize users personalization. We introduce two metrics named user pursue-novelty degree (UPND) and music popularity (MP) to improve the traditional items-based collaborative filtering algorithm. In the online part, we try to solve the interest drift problem, which is a thorny problem in the offline part. We propose a novel online recommendation algorithm based on game theory. Experiments verify that the hybrid music recommendation model has higher new music recommendation accuracy, decent dynamical personalized recommendation ability, and real-time recommendation capability, and can substantially mitigate the problem of interest drift.

Rating Text Classification with Weighted Negative Supervision on Classifier Layer

ZHANG Jun, QIU Longlong, SHEN Fanfan, HE Yueshun, TAN Hai, HE Yanxiang

, Available online , doi: 10.23919/cje.2021.00.339

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Abstract:
Bidirectional encoder representations from transformers (BERT) gives full play to the advantages of the attention mechanism, improves the performance of sentence representation, and provides a better choice for various natural language understanding (NLU) tasks. Many methods using BERT as the pre-trained model achieve state-of-the-art performance in almost various text classification scenarios. Among them, the multi-task learning framework combining the negative supervision and the pre-trained model solves the issue of the model performance degradation that occurs as the semantic similarity of texts conflicts with the classification standards. The current model does not consider the degree of difference between labels, which leads to insufficient difference information learned by the model, and affects classification performance, especially in the rating classification tasks. Based on the multi-task learning model, this paper fully considers the degree of difference between labels, which is expressed by using weights to solve the above problems. We supervise negative samples on the classifier layer instead of the encoder layer, so that the classifier layer can also learn the difference information between the labels. Experimental results show that our model can not only performs well in 2-class and multi-class rating text classification tasks, but also performs well in different languages.

Stability Improvement for One Cycle Controlled Boost Converters Based on Energy Balance Principle

WANG Lei, WU Qinghua, MA Wei, TANG Wenhu

, Available online , doi: 10.23919/cje.2021.00.204

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Boost converters with one cycle control (OCC) are prone to exhibit oscillations as the Hopf bifurcation, which may degrade performances and limit the parameter stable region of converters. This work proposed a novel control strategy for suppressing such bifurcations and enlarging the parameter stability region of the boost system on the basis of the principle of energy balance in the circuit. Through analyzing of the stability and bifurcation condition, the results reflect that, the energy-based OCC can adjust the poles of the system transfer function, which ensures the stable operation of the system in an extended range of circuit parameters. Moreover, the orders of the transfer function will not be increased by such adjustments, thus the computational complexity of the transfer function will be increased. The theoretical analysis demonstrates the ability of the energy-based OCC for suppressing the bifurcations and enlarging the stable region of the system parameters. The results by simulation and experiment further prove the effectiveness of the proposed control strategy.

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

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Abstract:
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 Co-60 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 Dynamic Hysteresis Model of Piezoelectric Ceramic Actuators

DONG Ruili, TAN Yonghong, XIE Yingjie, LI Xiaoli

, Available online , doi: 10.23919/cje.2021.00.273

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A modified Prandtl-Ishlinskii (PI) model with rate-dependent thresholds for describing the hysteresis characteristics of piezoelectric actuators is proposed. Based on the classical PI model, a novel threshold depending on the input rate is constructed. With the novel rate-dependent threshold, the play operator has the capability to track the frequency variation of the input signal. Hence, the proposed modified PI model can be used to depict the rate-dependent hysteresis of piezoelectric actuators. Experimental results are presented to illustrate the model validation results of the proposed modeling method.

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

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In this paper, the single-walled carbon nanotube (SWCNT) with graphene nanoribbon (GNR) inside, namely GNR@SWCNT, is proposed as alternative conductor material for the interconnect applications. The equivalent circuit model is established, and the circuit parameters extracted analytically. By virtue of the equivalent 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. Moreover, 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.

SiC Double Trench MOSFET with Split Gate and Integrated Schottky Barrier Diode for Ultra-low Power Loss and Improved Short-circuit Capability

ZHANG Jinping, WU Qinglin, CHEN Zixun, ZOU Hua, ZHANG Bo

, Available online , doi: 10.23919/cje.2022.00.394

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A silicon carbide (SiC) double trench metal-oxide-semiconductor field effect transistor (DTMOS) with split gate (SG) and integrated Schottky barrier diode (SBD) is proposed for the first time. The proposed device features two enhanced deep trenches in the surface, in which a source-connected SG with a thicker dielectric layer is located at the bottom of the deep gate trench and an integrated SBD is located at the sidewall of the deep source trench (DST). Combined with shielding effect provided by the p+ shield layer under the DST and integrated SBD, the proposed structure not only reduces the reverse transfer capacitance ($ C $$ _{\rm rss} $) and gate-drain charge ($ {Q} $$ _{\rm gd} $) but also restrains the saturation drain current ($ {I} $$ _{\rm d, sat} $) and improves the diode performance of the device. Numerical analysis results show that compared with the Con-DTMOS and Con-DTMOS with external SBD diode, the turn-on loss ($ {E} $$ _{\rm on} $) and turn-off loss ($ {E} $$ _{\rm off} $) for the proposed device are reduced by 56.4%/70.4% and 56.6%/69.9%, respectively. Moreover, the $ {I} $$ _{\rm d, sat} $ at the $ {V} $$ _{\rm gs} $ of 18V for the proposed device is reduced by 74.4% and the short-circuit withstand time ($ {t} $$ _{\rm SC} $) is improved by about 7.5 times. As a result, an ultra-low power loss and improved short-circuit capability is obtained for the proposed device.

A Design of 2-stage Voltage Ramp-up SRAM Physical Unclonable Function

SONG Minte, LIU Nan, ZHOU Shuaiyang, WANG Zhengguang, RU Zhanqiang, DING Peng, HUANG Wei, SONG Helun

, Available online , doi: 10.23919/cje.2022.00.406

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Silicon physical unclonable function (PUF) implemented by SRAM exists inherent demerit of unstable cells due to noise of environment and circuits, which significantly restricts its reproducibility. In this paper, a 16T SRAM cell with reset-delay circuit and a 2-stage voltage ramp up is fabricated and reported. Compared to conventional SRAM structure, each PUF cell adds a pair of pull-up PMOS and pull-down NMOS controlled by reset and delayed-reset signals respectively, resulting in two positive feedback stages with different amplification coefficients when the voltage is ramped up. PUF array consists of 4064 cells, 322 dummy cells and a group of 8 series-connected inverters with an area of 304 um×650 um to match the digital post-processing module. PUF test chip was fabricated in HHGrace 110 nm platform with total area 1140×1140 um². The average HDintra (intra-chip hamming distance, also bit error rate, BER) and HDinter (inter-chip hamming distance) value of the 50 PUF chips in SOP16 package measured at normal point (1.5V/25 ℃) was 1.92% and 49.85%, respectively.

Design of High Performance MXene/Oxide Structure Memristors for Image Recognition Applications

LIAN Xiaojuan, SHI Yuelin, SHEN Xinyi, WAN Xiang, CAI Zhikuang, WANG Lei

, Available online , doi: 10.23919/cje.2022.00.125

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Recent popularity to realize image recognition by memristor-based neural network hardware systems has been witnessed owing to their similarities to neurons and synapses. However, the stochastic formation of conductive filaments inside the oxide memristor devices inevitably makes them face some drawbacks, represented by relatively higher power consumption and severer resistance switching (RS) variability. In this work, we design and fabricate the Ag/MXene (Ti₃C₂)/SiO₂/Pt memristor after considering the stronger interactions between Ti₃C₂ and Ag ions, which lead to a Ti₃C₂/SiO₂ structure memristor owning to much lower ‘SET’ voltage and smaller RS fluctuation than pure SiO₂ memristor. Furthermore, the conductances of the Ag/Ti₃C₂/SiO₂/Pt memristor have been modulated by changing the number of the applied programming pulse, and two typical biological behaviors, i.e., long-term potentiation and long-term depression, have been achieved. Finally, device conductances are introduced into an integrated device-to-algorithm framework as synaptic weights, by which the MNIST hand-written digits are recognized with accuracy up to 77.39%.

Design of Low-Power Turbo Encoder and Decoder for NB-IoT

ZHANG Chong, LIN Yuhang, WANG Deming, HU Jianguo

, Available online , doi: 10.23919/cje.2022.00.225

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Turbo code is an error correction coding scheme close to the Shannon limit, usually used in wireless data transmission. Based on the parallel Turbo code algorithm, a parallel Turbo code circuit design scheme is proposed. In the encoder, the recursive systematic convolutional encoder is multiplexed. The decoder is divided into branch metric, recursive, maximum likelihood ratio, and external information calculation modules. The decoding algorithm is based on Max-Log-MAP, controlling the component decoder in parallel. And the state metric calculation in the decoding circuit is combined to reduce the overall power consumption effectively, enabling the encoder and decoder to be used in NarrowBand Internet of Things(NB-IoT). Finally, the hardware scheme of the main functional modules of Turbo code encoding and decoding is designed and implemented. The results show that the dynamic power consumption is less than 50 mW. The overall on-chip power consumption is reduced by 40% at the frequency of 125 MHz compared with previous jobs.

A Single-Event-Transient Hardened Phase Locked Loop for Clock and Data Recovery

YUAN Hengzhou, LIANG Bin, SANG Hao, XU Weixia, GUO Yang, CHEN Xi

, Available online , doi: 10.23919/cje.2022.00.017

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A radiation-hardened phase-locked loop is proposed for phase interpolator clock and data recovery purposes. A sensitive node-compressed charge pump and multi-node cross coupling voltage-controlled oscillators are proposed in this phase-locked loop with the goal of achieving good jitter performance and improving anti-SET capability. The phase-locked loop RMS jitter is reduced from 3.7 ps to 2.58 ps@2 GHz, while the laser threshold is improved from 120 pJ to 370 pJ compared to the unhardened phase-locked loop. The hardened phase-locked loop also does not lose its lock state from LETs of 3.3 to 37.3 MeV·cm²/mg.

Low Loss and Low EMI Noise Trench IGBT with Shallow Emitter Trench Controlled P-type Dummy Region

ZHANG Jinping, LI Xiaofeng, ZHU Rongrong, WANG Kang, ZHANG Bo

, Available online , doi: 10.23919/cje.2022.00.080

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A novel trench insulated gate bipolar transistor (TIGBT) with a shallow emitter trench controlled p-type dummy region (STCP-TIGBT) is proposed. Compared with the conventional TIGBT with floating p-type dummy region (CFP-TIGBT) and TIGBT with floating p-type dummy region and normally on hole path (HFP-TIGBT), the proposed STCP structure not only speeds up the extraction of excessive holes in the turn-off process but also reduces the miller plateau charge (Q_gc). Therefore, both the power loss and electromagnetic interference (EMI) noise are significantly reduced. Simulation results show that the Q_gc of the proposed device is only 501 nC/cm$ ^{\rm 2} $, which is reduced by 58.5% and 26.4% when compared to the CFP-TIGBT and HFP-TIGBT, respectively. At same on-state voltage drop (V_ceon) of 1.02 V, the turn-off loss (E_off) of the proposed device is 13.49 mJ/cm², which is 64.6% and 67.6% less than those of the CFP-TIGBT and HFP-TIGBT, respectively. Moreover, the reverse recovery dV_ak/dt of the FWD at same turn-on loss (E_on) of 31.8 mJ/cm² for the proposed STCP-TIGBT is only 2.15 kV/μs, which is reduced by 91.3% and 57.2% when compared to 24.69 kV/μs and 5.02 kV/μs for the CFP-TIGBT and HFP-TIGBT, respectively. The reduced dV/dt significantly suppresses the EMI noise generated by the proposed device.

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

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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.

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

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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 Compact Filtering Antenna System with Wide-Angle Scanning Capability for V2I Communication

HAN Chuang, LI Tong, ZHANG Zhaolin, WANG Ling, YANG Guangwei

, Available online , doi: 10.23919/cje.2023.00.039

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A compact filtering antenna system with wide-angle scanning is proposed for vehicle to infrastructure (V2I) communication which would handle complex communication scenarios. In this work, a wide beam filtering antenna is realized by using some inductive resistance structures such as metal pins and pillars, and capacitive structures such as slots, parasitical patches to produce the radiation nulls at two sides of the operating frequency band and improve the impedance matching in the passband. Meanwhile, the wide beam capability is also realized by the above structure. Furthermore, two H- and E-plane linear arrays are designed for the beam scanning capability with filtering characteristics based on the proposed antenna. To verify the proposed design concept, a prototype is fabricated and measured. The measurement and simulation agree well, demonstrating an excellent filtering characteristic with the operating frequency band from 3.18 to 3.45 GHz (about 8.1%), the high total efficiency of about 88%, and 3-dB-beamwidth of more than 100° and 120° in the above two arrays, respectively. Additionally, the proposed arrays can realize the beam scanning up to the coverage of 112° and 120° with a lower gain reduction and a good filtering characteristic, respectively.

A Recursive DRL-based Resource Allocation Method for Multibeam Satellite Communication Systems

MENG Haowei, XIN Ning, QIN Hao, ZHAO Di

, Available online , doi: 10.23919/cje.2022.00.135

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Abstract:
Optimization-based radio resource management (RRM) has shown significant performance gains on high-throughput satellites (HTSs). However, as the number of allocable on-board resources increases, traditional RRM are difficult to apply in real satellite systems due to its intense computational complexity. DRL is a promising solution for the resource allocation problem due to its model-free advantages. Nevertheless, the action space faced by DRL increases exponentially with the increase of communication scale, which leads to an excessive exploration cost of the algorithm. In this paper, we propose a recursive frequency resource allocation algorithm based on long-short term memory (LSTM) and proximal policy optimization (PPO), called PPO-RA-LOOP, where RA means resource allocation and LOOP means the algorithm outputs actions in a recursive manner. Specifically, the PPO algorithm uses LSTM network to recursively generate sub-actions about frequency resource allocation for each beam, which significantly cut down the action space. In addition, the LSTM-based recursive architecture allows PPO to better allocate the next frequency resource by using the generated sub-actions information as a prior knowledge, which reduces the complexity of the neural network. The simulation results show that PPO-RA-LOOP achieved higher spectral efficiency and system satisfaction compared with other frequency allocation algorithms.

Architecture Design of Protocol Controller Based on Traffic-Driven Software Defined Interconnection

LI Peijie, SHEN Jianliang, LYU Ping, DONG Chunlei, CHEN Ting

, Available online , doi: 10.23919/cje.2022.00.094

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Abstract:
To solve the problems of redundant logic resources and poor scalability in protocol controller circuits among communication networks, we propose a traffic-driven software defined interconnection (TSDI) mechanism. The unified software defined interconnection interface standards and the normalized interconnection topology are designed to implement the architecture of TSDI-based protocol controller. The key power-performance-area (PPA) indicators can be realized while resolving the flexible interconnection of the controller. We designed a TSDI-based RapidIO controller as an example. Compared to traditional designs, the design could achieve more protocol scalability, and RapidIO protocol standards of Gen4 could be supported directly. The key PPAs, such as a lower delay of 56.1ns and more than twice throughput of 98.1Gbps, were achieved at the cost of a 23.4% area increase.

Optical Space Time Pulse Position Modulation over Exponential Weibull Turbulence Channel

ZHANG Yue, WANG Huiqin, MA Xuemei, CAO Minghua, PENG Qingbin

, Available online , doi: 10.23919/cje.2022.00.097

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Abstract:
An optical space time pulse position modulation (OSTPPM) scheme is proposed to satisfy the communication requirement of high transmission rate and better reliability. A low-complexity near optimal performance, called improved orthogonal matching pursuit based on threshold judgment (IT-OMP) algorithm, is proposed for OSTPPM scheme. The average bit error rate of OSTPPM-IT-OMP scheme is investigated over the Exponential Weibull channel, and its analytical expression is verified via the Monte Carlo simulation. With the same simulation parameters, the signal to noise ratio (SNR) of (4,4,2)-OSTPPM-IT-OMP is 3.75 dB and 8.5 dB better than that of spatial pulse position modulation (SPPM) and generalized spatial pulse position modulation (GSPPM) schemes at BER = 1 × 10⁻³. With the same transmission bits per symbol, the computational complexity of (3,4,2)-OSTPPM-IT-OMP scheme is reduced by 90.47% and 75.4% compared with (16,4,2)-SPPM and (5,4,2)-GSPPM schemes.

Performance Study of MIMO-OSTBC Parallel Relay FSO System Based on GFDM

ZHOU Rui, WANG Yong, WANG Yi

, Available online , doi: 10.23919/cje.2022.00.069

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Abstract:
This paper investigates the performance of a new generalized frequency division multiplexing (GFDM) parallel relay free space optical (FSO) communication system using multi-input multi-output (MIMO) orthogonal space-time block codes (OSTBC) scheme. Under the M distribution atmospheric turbulence, taking into account the triple effects of irradiance, pointing errors and path loss, the mathematical expression of system symbol error rate (SER) is derived with the help of Meijer-G function. The symbol error performance of GFDM is compared with on-off keying (OOK), gaussian minimum shift keying (GMSK), polarization shift keying (PolSK) and orthogonal frequency division multiplexing (OFDM) modulation methods. The effects of the MIMO-OSTBC parallel relay scheme on the GFDM system including filter roll down coefficient, the number of transmitting and receiving antennas, the number of relays, normalized beamwidth and jitter variance are analyzed, and the numerical results are verified by Monte Carlo simulation. This work provides a good foundation for engineering applications.

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

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Abstract:
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.

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

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Abstract:
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.

Joint Transmit and Reflective Beamforming Design for Active IRS-aided SWIPT Systems

SHI Weiping, WU Qingqing, WU Di, SHU Feng, WANG Jiangzhou

, Available online , doi: 10.23919/cje.2022.00.287

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Abstract:
To further improve the performance of passive intelligent reflecting surface (IRS)-assisted communication systems and mitigate the serious path loss due to “double-fading” of IRS-assisted links, an active IRS-aided simultaneous wireless information and power transfer (SWIPT) system is investigated. This paper jointly optimizes the transmit beamforming at the base station (BS) and the phase shifts at the active IRS in order to maximize the power collected by the energy harvesting receiver (EHR) under both perfect channel state information (CSI) and imperfect CSI states, subject to the signal-to-interference-noise ratio (SINR) constraint of the information decoding receiver (IDR), and the power constraints of the BS/IRS. Under perfect CSI, the alternating optimization (AO) algorithm is utilized for obtaining the transmit beamforming at the BS and the phase shifts at the active IRS. For each subproblem, we first transform non-convex objective function and constraints into convex ones by performing a first-order Taylor expansion. Then, each subproblem is solved by using the interior point method. Given that obtaining perfect CSI is impractical, two robust beamforming designs are proposed for imperfect CSI case. Under the bounded CSI error model, we first transform the non-convex optimization problem into two semidefinite programming (SDP) subproblems, and then solve each subproblem based on S-procedure and sequential rank-one constraint relaxation (SROCR) techniques. Under the stochastic CSI error model, the AO method is applied in an iterative manner based on Bernstein-type inequality and SROCR technique. Simulation results show that both robust and non-robust schemes for active IRS-assisted SWIPT systems can achieve extremely superior performance over conventional passive IRS-assisted systems under the same overall power budget.

A Tightly Coupled Dipole Array with Diverse Element Reflection Phases for RCS Reduction

GOU Yuewen, CHEN Yikai, YANG Shiwen

, Available online , doi: 10.23919/cje.2022.00.121

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Abstract:
This paper proposes a novel low scattering tightly coupled dipole array (TCDA), aiming to reduce the radar cross section (RCS) of phased antenna arrays under a certain oblique incident wave. First, according to the theoretical analysis, we develop three types of antenna elements with consistent radiation performance but diverse reflection phase differences. The required reflection phase difference is achieved by using different dielectric superstrates for each antenna element. Then, by arranging the three types of subarrays next to each other, a low scattering TCDA (8×9) is designed. Meanwhile, a reference antenna array with a single type of antenna element is also constructed. To demonstrate the effectiveness of the proposed RCS reduction technique, simulated and measured results of the reference and proposed antenna array are compared. Both antenna arrays operate over the 6~18 GHz frequency band and can scan up to ±45° in the E-/H-planes. However, the proposed antenna array achieves a significant monostatic RCS reduction over 8~12 GHz, with a maximum reduction value of 7.55 dB. It indicates that this diverse element reflection phase technique is a good candidate for RCS reduction of wideband phased antenna arrays.

Drug-target Interactions Prediction based on Signed Heterogeneous Graph Neural Networks

CHEN Ming, JIANG Yajian, LEI Xiujuan, PAN Yi, JI Chunyan, JIANG Wei

, Available online , doi: 10.23919/cje.2022.00.384

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Abstract:
Drug-target interactions (DTIs) prediction plays an important role in the process of drug discovery. Most computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target. In this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks (SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI, which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions (DDIs) and protein-protein interactions (PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from DrugBank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

The Exchange Attack and the Mixture Differential Attack Revisited: From the Perspective of Automatic Evaluation

QIAO Kexin, ZHANG Zhiyu, NIU Zhongfeng, ZHU Liehuang

, Available online , doi: 10.23919/cje.2023.00.008

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Abstract:
Recent results show that the differential properties within quadruples boom as a new inspiration in cryptanalysis of AES-like constructions. These methods include the exchange attack proposed in Asiacrypt’19 and the mixture differential attack proposed in ToSC’18 etc., where the essential properties are obtained by manually scrutinizing the structures of the AES-like constructions. This paper presents a novel framework and an automatic tool based on Mixed Integer Linear Programming to search for mixture differential distinguishers for general constructions. This framework considers what equality patterns among quadruples can make a distinguisher and traces how the patterns propagate through cipher components with accurate probability estimation. With this tool, a 5-round AES distinguishing attack with lower complexity and more 6-round distinguishing attacks in the chosen plaintext scenarios are deduced. We prove that no exchange-type or mixture differential distinguisher exists for 7 and above rounds AES if the details of the Sbox and mix column matrix are not taken into account.

Energy-Efficient Driving Strategy for High-Speed Trains with Considering the Checkpoints

ZHANG Zixuan, CAO Yuan, SU Shuai

, Available online , doi: 10.23919/cje.2022.00.174

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Abstract:
With rising energy prices and concerns about environmental issues, energy-efficient driving strategies (EEDS) for high-speed trains have received a substantial amount of attention. In particular, energy-saving schemes play a huge role in reducing the energy and operating costs of trains. This article studies the EEDS of high-speed trains at a given time. A well-posed model is formulated, in which the constraints of the checkpoints, in addition to the speed limits, vehicle dynamics, and discrete control gears, are first considered. For a given control sequence, the Karush-Kuhn-Tucker (KKT) conditions are used to obtain the necessary conditions for an EEDS. According to several key equations of the necessary conditions, the checkpoint constraints are satisfied. Some case studies are conducted based on the data of the Beijing-Shanghai high-speed line to illustrate the effectiveness of the proposed approach.

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

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Abstract:
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.

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

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Abstract:
Considering uncertain knowledge of target aspect angle (TAA), this paper copes with the joint optimization of transmit sequences and receive filter array for the detection of extended target in the presence of clutter disturbance. We consider joint transmit-receive design in multiple-input multiple-output (MIMO) structure to optimize the worst Signal to interference plus noise ratio (SINR) at the output of the receive filter array. Through a suitable reformulation, we propose a sequential optimization algorithm which monotonically enhances the worst SINR value. Each iteration of the process, includes a convex and a worst-case optimization problem which can be handled by the generalized Dinkelbachs method with a lower computational burden. In addition, resorting to several mathematical manipulations, the original problem is transformed into an equivalent convex problem, which can also be solved via interior-point techniques. Finally, the usefulness of two optimization techniques is confirmed through experimental simulation, emphasizing the detection capability improvement generated by the proposed approaches.

Sensing Matrix Optimization for Random Stepped-Frequency Signal Based on Two-Dimensional Ambiguity Function

LYU Mingjiu, CHEN Hao, YANG Jun, WU Xia, ZHOU Ming, MA Xiaoyan

, Available online , doi: 10.23919/cje.2022.00.046

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Abstract:
Compressive sensing technique has been widely applied to achieve range-Doppler reconstruction of high frequency radar by utilizing sparse random stepped-frequency (SRSF) signal, which can suppress the complex electromagnetic interference and greatly reduce the coherent processing interval. An important way to improve the performance of sparse signal reconstruction is to optimize the sensing matrix (SM). However, the existing research on the SM optimization needs to design a measurement matrix with superior performance, which needs a large amount of computation and does not consider the influence of the waveform parameters design. In order to improve the superior reconstruction performance, a novel SM optimization approach for SRSF signal is proposed by using two-dimensional ambiguity function (TDAF) in this paper. Firstly, based on the two-dimensional sparse reconstruction model of the SRSFs, the internal relationship between the waveform parameters and the SM was derived. Secondly, the SM optimization problem was directly transformed into the waveform design of SRSFs. Furthermore, on the basis of analyzing the relationship between the mutual coherence matrix of SM and the TDAF matrix of SRSFs, the purpose of optimizing the SM can be achieved by designing the TDAF of the SRSFs. Based on this analysis, a sparse waveform optimization method with joint constraints of maximum and mean sidelobes of the TDAF by using the genetic algorithm was derived. Compared with the traditional SM optimization method, our method not only avoids generating a new measurement matrix, but also further reduces the complexity of the waveform optimization. Simulation experiments verified the effectiveness of the proposed method.

Formal Verification of Data Modifications in Cloud Block Storage Based on Separation Logic

ZHANG Bowen, JIN Zhao, WANG Hanpin, CAO Yongzhi

, Available online , doi: 10.23919/cje.2022.00.116

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Abstract:
Cloud storage is now widely used, but its reliability has always been a major concern. Cloud block storage (CBS) is a famous type of cloud storage. It has the closest architecture to the underlying storage and can provide interfaces for other types. Data modifications in CBS have potential risks such as null reference or data loss. Formal verification of these operations can improve the reliability of CBS to some extent. Although separation logic is a mainstream approach to verifying program correctness, the complex architecture of CBS creates some challenges for verifications. This paper develops a proof system based on separation logic for verifying the CBS data modifications. The proof system can represent the CBS architecture, describe the properties of the CBS system state, and specify the behavior of CBS data modifications. Using the interactive verification approach from Coq, the proof system is implemented as a verification tool. With this tool, the paper builds machine-checked proofs for the functional correctness of CBS data modifications. This work can thus analyze the reliability of cloud storage from a formal perspective.

No Reference Image Sharpness Assessment Based on Global Color Difference Variation

SHI Chenyang, LIN Yandan

, Available online , doi: 10.23919/cje.2022.00.058

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Abstract:
Image quality assessment (IQA) model is designed to measure the image quality in consistent with subjective ratings by computational models. In this research, a valid no reference IQA (NR-IQA) model for color image sharpness assessment is proposed based on local color difference map in a color space. In the proposed model, the absolute color difference variation and relative color difference variation are combined to evaluate sharpness in YIQ color space (a color coordinate system for the development of the United States color television system). The difference between sharpest and blurriest spot of an image is represented by the absolute color difference variation, and relative color difference variation expresses the variation in the image content. Extensive experiments are performed on five publicly available benchmark synthetic blur databases and two real blur databases, and the results prove that the proposed model work better than the other state-of-the-art and latest NR-IQA models for the prediction accuracy on blurry images. Besides, the model maintains the lowest computational complexity.

Gmean Maximum FSVMI Model and Its Application for Carotid Artery Stenosis Risk Prediction

ZHANG Xueying, GUO Yuling, LI Fenglian, WEI Xin, HU Fengyun, HUI Haisheng, JIA Wenhui

, Available online , doi: 10.23919/cje.2020.00.185

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Abstract:

Carotid artery stenosis is a serious medical condition that can lead to stroke. Using machine learning method to construct classifier model, carotid artery stenosis can be diagnosed with transcranial doppler data. We propose an improved fuzzy support vector machine (FSVMI) model to predict carotid artery stenosis, with the maximum geometric mean (Gmean) as the optimization target. The fuzzy membership function is obtained by combining information entropy with the normalized class-center distance. Experimental results showed that the proposed model was superior to the benchmark models in sensitivity and geometric mean criteria.

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