Abstract: Software defined network (SDN) is a promising network architecture which can simplify network management, ease deployment of network applications, and enable independent evolution of control plane and data plane. However, the separation of control plane and data plane intensifies performance concerns. In this paper, we review the performance of SDN from the perspective of I Ching which has a history of more than two and half millennia of commentary and interpretation, and provides inspiration to the worlds of business, military and religion. We utilize I Ching to model and abstract the architecture of SDN, and discuss the architecture and performance of SDN based on the statements of I Ching. Then we study current works and researches on the performance and architecture of SDN, and discuss the promising directions to the development and evolvement of SDN. Finally, we study common control functions in SDN and the deployment of SDN in different networking settings.
Abstract: We study fuzzy clustering with the structural α-entropy and present a unified framework for fuzzy clustering with fuzzy entropy, which can be regarded fuzzy clustering with fuzzy entropy as its special case. Then, aiming at weighting exponent m equal to the structural α-entropy index α in the presented unified framework, we obtain the fuzzy membership degrees and cluster centers using Lagrange method. Further, we propose the Structural α-entropy based fuzzy c-means (SEFCM) algorithm. Moreover, to solve clustering of the complicated data, we also present the Structural α-entropy based kernel fuzzy c-means (SEKFCM) algorithm. In experiment, some University of California Irvine (UCI) data sets and synthetic data sets are used to test the performance of the presented algorithms and the role of the structural α-entropy. The experimental results show that the presented algorithms obtain better clustering result.
Abstract: In real-world networks, nodes may belong to more than one community simultaneously. Overlapping community detection in complex networks is a challenging task. An adaptive overlapping community detection method based on seed selection and expansion is proposed. Depending on the restrictions on the seed selection stage, a set of seeds is generated without specified set size. The personalized PageRank algorithm is used to evaluate the community for seed expansion. The uncovered nodes could be adaptively allocated to the appropriate clusters. A thorough comparison between the proposed method and other overlapping community detection methods considered is provided to indicate the effectiveness of the former. The experimental results demonstrate that the presented method is effective.
Abstract: Traffic measurement and monitoring is crucial for network applications, such as network security, network management and so on. One central problem is to detect super nodes, which have significant change of connection degree between consecutive measurement periods. Due to weakness in massive network traffic processing for the centralized algorithm and low detection accuracy, space efficiency for super node detection algorithm based on flow sampling, we propose Parallel sketch based super node detection with traceability (PSD). It constructs parallel sketch and estimates connection degree of nodes by probabilistic counting approach, so that super nodes are identified using connection degree change between consecutive measurement periods. Moreover, IP addresses of super nodes are reconstructed by simple computing to trace attacker or victims. The experimental results illustrate that the proposed method outperforms the Compact spread estimator (CSE) and Data streaming and sampling (DSS) in terms of detection accuracy and storage utilization.
Abstract: This paper proposes an Effective biogeography-based optimization (EBBO) algorithm for solving the flow shop scheduling problem with intermediate buffers to minimize the Total flow time (TFT). Discrete job permutations are used to represent individuals in the EBBO so the discrete problem can be solved directly. The NEH heuristic and NEH-WPT heuristic are used for population initialization to guarantee the diversity of the solution. Migration and mutation rates are improved to accelerate the search process. An improved migration operation using a two-points method and mutation operation using inverse rules are developed to prevent illegal solutions. A new local search algorithm is proposed for embedding into the EBBO algorithm to enhance local search capability. Computational simulations and comparisons demonstrated the superiority of the proposed EBBO algorithm in solving the flow shop scheduling problem with intermediate buffers with the TFT criterion.
Abstract: Due to the Von Neumann bottleneck, in-memory-computing, as a new architecture, has drawn considerable attention and is becoming an candidate of next generation electronics system. It presents an inmemory-computing approach for multiplier design based on Multilevel-cell (MLC) of Resistive random access memories (RRAMs). The paper proposes a Look-up-table (LUT) operations to optimize the speed, area and power of the multiplier circuits. The proposed MLC function of RRAM revealed that RRAM could have a multilevel stable resistance by adjusting the operating voltage. The simulation results show that, taking a 16-bits multiplier as an example, the circuits of this paper has a calculation speed that is increased by 35.7 percent and an area that is decreased by 14 percent under the similar power consumption conditions when compared with other traditional 16-bits multiplier.
Abstract: A non-uniform Distributed power amplifier (DPA) is designed and implemented in a 0.18μm CMOS technology. The gradual changed gain cells work with the tapered on-chip inductors to construct non-uniform artificial transmission lines, which improves the output power and efficiency in a wide frequency band while maintaining good input and output impedance matching. The proposed DPA achieves 9dB average associated gain from 1 to 17.2GHz, and the input return loss is less than -9dB while the output return loss is less than -8.5dB in the desired frequency band. The output power at 1dB Output compression point (OP1dB) is more than 7.8dBm in the frequency band of 2 -16GHz, and the peak power-added efficiency is 6.2% with the OP1dB 12.6dBm at 4GHz.
Abstract: The extensive application of Commercial off-the-shelf (COTS) components into safety computers in train control systems has caused safety problems. Aiming at the parallel programs, a concurrent program safety management mechanism based on transactional memory is proposed. The proposed mechanism implements concurrent behaviors of the application in the safe policy. A verification framework based on invariant proof and parallel separation logic theory is designed and operating system operation semantics are given for mathematical reasoning and proving. An example of code execution process is demonstrated to explain the safety control process of concurrent safety mechanism. The results indicate that the program can meet the safety and reliability requirements of concurrent safety computer platforms.
Abstract: Although China is vigorously developing clean energy and nuclear power, the thermal power generation (mainly coal power) is still the most important power generation method at present. Economic load dispatch (ELD) is a typical optimization problem in power systems which lots of researchers are trying to explore. The purpose of ELD is to increase the efficiency of thermal power generation under the conditions of load and operational constraints. When it comes to power generation scheduling, manual operation is still the main form, which is inefficient. In order to use a large amount of historical power generation data to improve the efficiency of power generation scheduling and achieve the effect of energy conservation, we propose an intelligent power generation scheduling system based on Deep neural networks (DNN) and Ant colony optimization (ACO). Experiments show that our DNN algorithm can predict the unit coal consumption precisely. Compared with the dynamic programming algorithm and equal differential increment rate algorithm, ACO can complete power generation scheduling tasks more quickly and efficiently.
Abstract: Named entity disambiguation is presented to solve the problem of name ambiguity. Traditional disambiguation methods merely use the word frequency to calculate the weights of attributes, yet ignore the important information from low frequency words. We propose a named entity disambiguation method based on classified and structural semantic relatedness. Structural semantic relatedness is computed by capturing the explicit semantic relatedness and the implicit structural semantic knowledge. Classified semantic relatedness is computed by main attributes which can determine the domain entity identity. The experimental results show our method can significantly improve the disambiguation performance and achieve 90.5% accuracy of disambiguation.
Abstract: This paper presents a way to face alignment by Coarse-to-fine shape estimation (CFSE). Head poses, facial expressions and other facial appearance attributes are estimated coarsely as well as the main landmarks will be detected. The entire shape will be further estimated. This paper constructs an independent Head pose classification (HPC) model based on convolutional neural network to estimate and classify head poses. With the classification result, the estimated facial appearance attributes and the detected landmarks, a more accurate shape will be constructed. That shape will be used as the initialized shape and optimized by cascaded regression to approximate the ground-truth shape. Experiments on two challenging database demonstrate that CFSE outperforms the state-of-the-art methods.
Abstract: Digital rights management of the 3D contents is a crucial open issue in the 3D video industry. A novel robust fingerprinting algorithm is proposed for protecting the copyright of the 3D video. Unlike the existing algorithms extracting visual features separately from the 2D videos and the depth maps, in our algorithm a novel local stereo space is constructed according to the depth information of the pixels around the extracted local feature points in the 2D videos, and the 3D videos are processed in a holistic manner. In the proposed space, the 3D-transform-feature is extracted and aggregated into a feature matrix, and then the compact 3D video fingerprints are obtained from the eigenspace of the matrix. Our comprehensive experiments are conducted on a 3D video database, and the results have demonstrated the robustness and discrimination of the proposed algorithm. Moreover, our fingerprints cost less storage spaces than the existing approaches.
Abstract: By successively assembling genetic parts according to grammatical models, complex genetic constructs can be built. However, every category of genetic parts includes many parts. With the increasing quantity of genetic parts, the process of assembling a few sets of genetic parts can be costly, time consuming, and error prone. At the final assembly step, it is difficult to decide which part should be selected. Based on a statistical language model, a dynamic programming algorithm was designed to solve this problem. The algorithm optimizes the results of genetic designs and finds an optimal solution. In this way, redundant operations can be reduced, and the cost for assembling can be minimized.
Abstract: In order to improve the detection efficiency of Android malicious application, an Android malware detection system based on feature fusion is proposed on three levels. Feature fusion especially emphasizes on ten categories, which combines static and dynamic features and includes 377 features for classification. In order to improve the accuracy of malware detection, attribute subset selection and principle component analysis are used to reduce the dimensionality of fusion features. Random forest is used for classification. In the experiment, the dataset includes 43,822 benign applications and 8,454 malicious applications. The method can achieve 99.4% detection accuracy and 0.6% false positive rate. The experimental results show that the detection method can improve the malware detection efficiency in Android platform.
Abstract: The estimation of noise Power spectral density (PSD) is a very crucial issue for speech enhancement as a result of its significant effect on the quality and intelligibility of the enhanced speech. Most of the existing estimators for noise PSD try to employ Gaussian speech priors, which, however, have been proven inconsistent with the reality. We derived an effective solution to this problem of estimating noise PSD in the Minimum mean square error (MMSE) sense when the speech component is modeled by a Laplacian distribution. Meanwhile, the soft decision technique instead of the hard Voice activity detection (VAD) is evolved into our algorithm, which can automatically makes the estimation unbiased without requiring a bias compensation. The performance of the proposed method is tested by several objective and subjective measures under various stationary and nonstationary noise environments. The results confirm that our method achieves good performance for all the noise conditions and Signalnoise-ratio (SNR) settings.
Abstract: Speech signals are nonlinear chaotic time series. This paper proposes a novel speech signal nonlinear prediction model with the hidden phase space reconstruction method. The parameters, embedding dimension m, time delay τ and model structure are solved simultaneously, breaking the restriction of phase space, which needs to be reconstructed before modeling for the existing prediction method. Subsequently, an explicit speech signal prediction model is generated. Meanwhile, the introduction of the frame length parameter k effectively extends the prediction length. Experimental results show that the values of m and τ solved by the proposed method are consistent with the values addressed by the Cao method and mutual information method, respectively. In addition, the optimal value of k is further discussed. The prediction results obtained using the proposed model are more accurate than those of linear prediction coding, the radial basis function neural network model and the long short-term memory network.
Abstract: As an effective and low-dimension representation for speech utterances with different lengths, i-vector method has drawn considerable attentions in speaker verification. Training a Total variability space (TVS) is one of the key parts in the i-vector method. However, the traditional training method only explores the relationship between different mean supervectors, ignoring priori category information of speakers, which results in a lack of discrimination. In the proposed method, a discriminative TVS based on Partial least squares (PLS) is estimated, in which both the correlation of intra-class and the distinction of inter-class are fully utilized due to using speaker labels, and the proposed method can achieve a better performance.
Abstract: In the most of exiting Local linear embedding (LLE)-based image super-resolution methods, a Low resolution (LR) image can be represented as a linear combination of LR training samples. In these methods, the combination coefficients of the LR image are directly used to estimate the High resolution (HR) image. However, experimental results show that the LR-LLE coefficients are different from the corresponding HR-LLE coefficients. To bridge the gap between LR and HR images, a novel LLEbased face hallucination algorithm is proposed. An LLE coefficients prior model is introduced to reduce the coefficient errors. In this prior model, the LLE coefficients of the interpolated LR face image are used to constraint the reconstructed coefficients. Experimental results show that the proposed method can provide improved performance over the compared methods.
Abstract: Path planning assisted by two-dimensional medical images is an essential part of minimally invasive diagnosis and treatment for cardiovascular diseases. Due to the complex background of angiography images and intricate vascular structure with multi-branch and stenoses, creating accurate pathways from angiography image is a challenge task. We present a new path planning methodology based on angiography medical images using the steady fluid dynamics. Our novel approach is useful in many medical applications, such as for computer-assisted medical images analysis and the follow-on image-guided interventions. A graph-cuts based energy function was applied to the vessel segmentation of angiography images in order to obtain boundary information. We have adopted Finite volume method (FVM) to simulate the Newtonian fluid inside the segmented blood vessels, and a set of isobars under the steady fluid condition are obtained by Meandering Triangles algorithm. The selected center points of isobars are organized to generate the directed vessels-tree, from which the vascular stenoses are automatically detected and the final surgical path is generated with branches. Our method can be used for quantitative path analysis, and we show experimental results to demonstrate that the versatility and applicability of the algorithm in obtaining single-pixel surgical path with good performance, high accuracy and less manual interventions, especially it is robust on complex vascular structures.
Abstract: Current mode (CM) Single-input and multi-output (SIMO) and Voltage mode (VM) Multi-input and single-output (MISO) biquad filters are proposed. The presented configurations use one Current controlled current differencing transconductance amplifier (CCCDTA) and two capacitors without any external resistors, which is easy suited for integrated circuit fabrication. The currentmode filter can realize the second-order low pass, band pass, high pass and band stop responses simultaneously. And the voltage-mode filter can realize all the standard filtering functions from the same structure. The natural frequency (ω) and quality factor (Q) of the filters can be controlled electronically by transconductance of the CCCDTA. Moreover, the proposed filters does not require any impedance matching due to its two input resistance can be independently tuned through the diverse external bias currents of CCCDTA. The influences of the CCCDTA non-idealities are also analyzed and both the active and passive sensitivities are considerably low. This design was simulated in PSPICE. Simulation results showed that their performances in power, bandwidth, out-band rejection and in-band ripple are better than the traditional. Additionally, the experiment results are provided in the paper.
Abstract: The domain adaptation uses labeled source domain data to train a classifier to be used in the target domain with no or small amount of labeled data. Usually there exists discrepancy in terms of marginal and conditional distributions for both source and target domains, which is of critical importance to minimize the distribution discrepancy between domains. As a classical model in deep learning, the autoencoder is capable of realizing distribution matching and enhancing classification accuracy by extracting more abstract and effective features from data. A Domain adaptation network based on autoencoder (DANA) is proposed. The DANA structure consists of a couple of encoding layers:a feature extraction layer and a classification layer. For the feature extraction layer, the marginal distributions of source and target domains are matched by using the nonparametric maximum mean discrepancy measurement. For the classification layer, the softmax regression model is applied to encode the label information of source domains meanwhile to match the conditional distribution. Experimental results on ImageNet, Corel and Leaves datasets have shown the enhanced classification accuracy by our proposed algorithm compared with the classical methods.
Abstract: The continuous increase of city motors with the improvement of society has introduced extraordinary demanding situations for the traffic situation of the town. Intelligent traffic has proposed a diversity of solutions to relieve urban traffic accidents and congestion and other troubles. It cannot regulate the cycle of traffic light in real time in keeping with the occurring traffic flux which additionally, may produce the traffic jam to result in the increase for the range of automobiles and the longer of passage time. In purpose to surmount the shortcomings of traditional traffic light control, this paper is presenting a form of intelligent control system for traffic light founding on fog computing. It calculates and shares the traffic flux situation at the intersection and the surrounding intersection through fog computing platform. Regarding the traffic flow at the intersection and the traffic flow at the surrounding intersection as the parameters, the intelligent control algorithm of traffic light is designed for achieving mutual coordination and mutual influence between different intersections, so that the traffic efficiency of each intersection is improved and the traffic flow of the entire transport network is alleviated. The simulation results showed this intelligent control system progress the traffic efficiency of every intersection and relieve the traffic flow of the whole transport network.
Abstract: Energy efficiency (EE) is an important metric for a Cognitive radio network (CRN). We focus on the EE maximization problem in a sensing-based spectrum sharing CRN for delay-sensitive applications. With the aid of one-dimension exhaustive search, fractional programming and Lagrange duality method, two energy-efficient optimal sensing time and power allocation policies are derived with the consideration of Average/Peak transmit power (ATP/PTP) constraints and Average interference power (AIP) constraints, respectively. Simulation results show that the proposed policies can achieve higher EE compared to the conventional spectrum sharing schemes.
Abstract: DBlock is a new family of block ciphers proposed by Wu et al. in Science China in 2015, which consists of three variants specified as DBlock-128/192/256. DBlock-n employs a 20-round Feistel-type structure with n-bit block size and n-bit key size. We propose the first fault analysis on DBlock and show that no more than 2 pairs of correct/faulty ciphertexts are needed to retrieve the master key. In the attack, a byte-oriented fault is injected in round 16, and three properties including differential distribution of the Sbox, bijection nature of the linear function and Feistel-type key scheduling are fully utilized to distinguish between the correct and wrong keys. A fault position guessing strategy based on known intermediates is adopted, which efficiently makes the known-fault attack apply to the random fault model. The experimental results show that, with a pair of ciphertexts, 11.820-bit exhaustive search is needed to derive the whole 128-bit key on average. With 2 pairs of ciphertexts, the unique key can be determined within 6.5 minutes.
Abstract: The Electronic product code (EPC) network is established and maintained in worldwide-scale based on the EPC standard framework to guarantee the real-time information recognition, and provide efficient management for supply chain. In EPC network, a series of data services can be provided due to the requirements of users. Aiming to guarantee the security of data services, we propose a dynamic access control model for data services based on multiple attributes. We extract specific attribute sets from user, and calculate security level of user using certainty and uncertainty theories based on the attribute sets. The data can be provided to users according to the security level of user and data. The security of data in the supply chain can be guaranteed, and data acquisition can be dynamic and fine-grained. We deploy the proposed model to real supply chain management system we built to verify effectiveness and feasibility of the solution.
Abstract: In order to solve the problem that the difference of Received signal strength (RSS) between tags will become large when tags are close to the reader, which exists in LANDMARC system, a LANDMARC localization algorithm based on weight optimization is proposed in the paper. We optimize the weight by redefining the formulas of in-weight and ex-weight. In-weight formula is discussed under the free space propagation model. The definition of ex-weight is obtained by analyzing the relationship between RSS and distance under the log-distance path loss model. We evaluate the performance of the proposed algorithm and compare it with the algorithm based on normalized weight. The simulation results show the superior performance of the proposed algorithm in terms of location accuracy.
Abstract: Network coding is proved to improve the throughput, decrease network latency and balance the network overhead. Combined with wireless overhearing, some proposed representative schemes on network coding, such as linear network coding and Completely opportunity encoding (COPE). The former scheme is based on complicated linear operation, while the latter is implemented by the way of pairwise XOR-ing which is simple and low efficiency. In order to resolve these problems, we put forward a group-based XOR-ing network coding scheme, which has the characteristics of high efficiency and low complexity. Experimental results and theoretical analysis demonstrate that our scheme has higher throughput gains and lower delay than traditional schemes. In addition, our proposed scheme has the best characteristic of fairness among the state-of-the-art approaches.
Abstract: At present most of the Marx generator based on semiconductor switches not only need generate the same number of trigger signals as the number of switches, but also consider the isolation from output high voltage and driving the signal processing, which makes Marx generator have complex circuits, large volume. In this paper, a new type of Marx generator based on semiconductor switches is designed. This generator need only one trigger signal which is generated by an avalanche transistor, other switches are triggered by the signals which are generated by voltage dividing capacitive. In the PSPICE, a five-stage Marx generator is designed. Choosing a 400V DC voltage source charged, pulse voltage with 1.9KV amplitude and 100ns pulse width is obtained. Finally, a fourstage Marx generator is designed. The charging voltage is 200V and the pulse width of trigger signal is 500ns. A pulse voltage with amplitude 736V and pulse width 562ns is obtained.
Abstract: The detection probability for a moving target is an important issue, we aim to discuss the detection probability for a moving ground target such as military vehicles using a visible light imaging satellite. First, a basic detection model for a regional ground target using a satellite was discussed and the probability of detection was calculated. Next, a basic model for the recognition of a ground target using a visible light imaging satellite was developed based on above model. Then, a basic model of the detection probability for a moving ground target using a visible light imaging satellite was studied. As the normal distribution has the maximum entropy, the normal distribution of a moving ground target such as military vehicles was analyzed, and based on our conclusions, a basic probability model for the detection of a moving ground target using a visible light satellite was developed. Finally, the simulation was carried on.
Abstract: GF-2 (Gaofen-2) is the second high resolution imaging satellite of China high resolution earth observation system (CHEOS) and it is the first civilian high resolution imager with Ground sample distance (GSD) under 1m in China. The two 1m/4m cameras represent a breakthrough in comparison with the previous GF-1 camera. Recent advances in small relative aperture optics system design, vibration reduction, and high accuracy thermal control make it possible for this camera work perfect with high resolution, lihgtweight and high image quality performance. The in-orbit commissioning demonstrates that the performance of the cameras meets all the design requirements. The components of 1m/4m camera, the novel techniques adopted and in-orbit commissioning results are illustrated.
Abstract: An L-parallel coprime array is designed and an Off-grid sparse learning via iterative minimization (OGSLIM) algorithm is proposed in order to improve the performance of Two-dimensional direction-of-arrival (2-D DOA) estimation. The L-parallel coprime array consists of two parts, one is a parallel coprime array and the other one is a linear coprime array perpendicular to the parallel coprime array. The OGSLIM algorithm is based on sparse Bayesian framework and can learn the off-grid parameter. Theory analysis and simulation results demonstrate that 2-D DOA estimation using OGSLIM algorithm with L-parallel coprime array can lead to higher estimation accuracy and resolution, it also fits to the underdetermined signals and correlated signals.