As the study to innovate and develop a new research direction of electrostatic theory, it is known that secondary electron emission coefficient of solid material can’t be ignored as an important and incentive parameter of static electricity. In this paper, we combined the experiment with theoretical way to discuss and analyze the association mechanism between the magnitude and polarity of surface electrostatic potential with the secondary electron emission characteristic, as well as tried to explore and establish the new model based on secondary electron emission characteristics with solid surface electrostatic charging.

Model-based quantitative techniques are not commonly used for web service behaviors security evaluation, which have typically been applied to analyze small parts of an overall design. Aiming at the traits of attack behaviors for web services, this paper proposes the hierarchical Stochastic game nets (SGN) model and analysis methods. We give the definitions and important theorems of stochastic game nets, study the modeling algorithm of hierarchical SGN model. A series of simulation results are presented to show that, by applying hierarchical SGN model to describe the attack and defense behaviors in web services, quantifiable results can be successfully obtained for the evaluation of important attributes.

Automatically extracting rocks from Martian surface images is valuable for hazard avoidance and rover localization in rover missions. To extract rocks, data field on image is proposed to model the interaction between two pixels of an image under the imagery characteristics of Martian surface. Foreground rocks are firstly grouped into clusters by locating the centers and edges in data field after they are differed from background information via binarizing partition. Then target rocks are discovered for the Rover to keep healthy paths. The experiment shows the data field on image is practical and potential.

This paper is concerned with the task of automatically summarizing videos, which is important for many video-related applications. Examples include video retrieval, accessing and categorization, et al. Our approach summarizes video semantically from the texts embedded in videos. To improve the accuracy, ant colony algorithm, maximum matching method, Singular value decomposition (SVD) are employed for locating text region, segmenting words and clustering sentences, respectively. Based on these methods, a prototype is developed. In an experimental evaluation on the real world video datasets, we show that our proposed approach could provide accurate, satisfactory performance.

With the rapid development of the performance of computer systems, energy consumption has been increasing dramatically as well. Energy efficiency has been paid much attention, especially in large-scale distributed computing systems. We propose an effective approach for energy reduction, by dynamic speed scaling and task scheduling simultaneously. Markov models for distributed computing systems are proposed, and detailed analyses of the models are provided. Markov decision processes (MDP) are applied for problem formulation, and MDP algorithms for obtaining the optimal solutions are introduced. The efficacy of our approach is further validated by simulation experiments.

A digital background calibration technique which mainly corrects nonlinear errors of residue amplifiers in pipelined analog-to-digital converters (ADCs) is presented. The proposed technique extracts the nonlinear errors by splitting sampling capacitors and inserting two extra comparators, and is highly effective as long as the input signal is busy. The method of self-tracking comparator thresholds is proposed to reduce the time of convergence with low complexity for any bit cases in a pipelined ADC. Both measurement and correction are processed in digital domain. After calibration, the behavioral simulation shows that the Signal-to-noise-and-distortion ratio (SNDR) is raised from 55.4dB to 90.2dB and the Spurious-free dynamic range (SFDR) is improved from 61.6dB to 102dB in a 16-bit prototype pipelined ADC with 0.5% capacitor mismatches and 4.5% gain compression of the residue amplifier.

Concolic testing is an integrated approach of symbolic execution and dynamic analysis, which is widely adopted by security researchers for program behavior analysis. This approach fails on hidden path discovery of environment-intensive program. We investigated on existing concolic testing tools and found out that several of them does not take this issue into account while others solved this issue with overloaded working model. We proposed a systematic and unified approach of automatically identifying and modifying the output of the Data input interacting functions (DIIF) based on fine-grained taint analysis, which detects and updates the data interacting with the runtime environment and generating a new customized set of inputs to execute hidden paths, to reveal the hidden paths on only particular runtime configuration or context. A prototype was developed and evaluated with a set of complex and environment-intensive programs. The experimental result demonstrated that our approach could detect the DIIF precisely and improve the code coverage.

Spectral clustering with pairwise constraints (i.e. mustlink and cannotlink) has been a hot topic in the machine learning community in recent years. Its performances are significantly influenced by utilizing the constraints. To make full use of the constraints’ effect, pairwise constraints are integrated into an affinity matrix based on the gravitational method. In the data set as input, each point has mass property, and interacts with each other according to the universal law of gravitation. A Gravitational inspired constrained spectral clustering (GCSC) algorithm is proposed in this paper. Our algorithm is evaluated on multiple benchmark classification datasets. Compared with the existing approaches, experimental results demonstrate the effectiveness of our presented algorithm.

A novel compressive sensing-based audio semi-fragile zero-watermarking algorithm is proposed in this paper. This algorithm transforms the original audio signal into the wavelet domain and applies compressive sensing theory to the approximation wavelet coefficients. The zero-watermarking is constructed according to the positive and negative properties of elements in the measurement vector. The experimental results show that the proposed algorithm is robust against common audio signal processing and fragile to malicious tampering. Compared with the existing algorithms, the proposed algorithm improves malicious tampering detection accuracy in common audio signal processing environments.

Based on previous researches, the set of all traces and Singleton failures (SF) pairs is used to characterize SF equivalence. Since the trace information can not be obtained from the SF information, the set of all SF pairs alone can not characterize SF equivalence. We propose a Revised version of singleton failures (RSF) pair, show that the set of all RSF pairs alone can characterize SF equivalence, RSF equivalence and SF equivalence are equivalent, and discuss the difference between RSF and SF. The main conclusion of this paper is that RSF equivalence algorithm is the most efficient one among all SF equivalence algorithms. We present several examples showing RSF equivalence algorithm is computationally quite efficient.

In the solar energy systems the solar irradiance variation is partially engaged, limiting the converter’s transformation rate for a certain input amount and leaving the rest of energy unconverted. In order to utilize at its maximum the energy provided from a Photovoltaic (PVP) system, this manuscript proposed a topology that can cope with marginal input voltage variations from 60V(DC) up to 1050V(DC) for a steady output voltage of 700V(DC). This output voltage value is suitable for any interlink post-regulation architecture. Based on previous observations, this paper presents a wide-input voltage high-efficiency non-isolated single-control topology. Analysis, operating stages, simulations and experimental results for a 3kW prototype are presented.

Automatically analyzing interactions from video has gained much attention in recent years. Here a novel method has been proposed for analyzing interactions between two agents based on the trajectories. Previous works related to this topic are methods based on features, since they only extract features from objects. A method based on qualitative spatio-temporal relations is adopted which utilizes knowledge of the model (qualitative spatio-temporal relation calculi) instead of the original trajectory information. Based on the previous qualitative spatio-temporal relation works, such as Qualitative trajectory calculus (QTC), some new calculi are now proposed for long term and complex interactions. By the experiments, the results showed that our proposed calculi are very useful for representing interactions and improved the interaction learning more effectively.

Bit permutation is an important operation in many applications. A novel reconfigurable N ×N bit permutation network with a compact structure is presented. It is based on combinatorics theory by cascading two recursive N/2 × N/2 sub-networks, while each subnetwork is cascaded by two N/4×N/4 sub-networks, and so on cascaded until reaching the elementary 4 × 4 seed networks. Its routing algorithm is also established to determine each multiplexor’s status. Then the circuits of different sized permutation networks in a reconfigurable cipher co-processor were designed and implemented in 0.18μm CMOS process. The proposed circuit can achieve an arbitrary n × n permutation and support all types of bit permutations in many cryptographic algorithms. Plus, it consumes less multiplexors than commonly used BENES and OMFLIP networks.

Reversible watermarking techniques enable the extraction of the embedding bits from a watermarked image in a lossless way. It exploits the high spatial correlation among neighboring pixels. Application in reversible watermarking includes military and medical images. Images occurs overflow or underflow problems during the imbedding process since pixels value may be out of range [0:255]. Most methods require a location map to solve such problems. A location map free reversible watermarking algorithm is proposed in this study. A prediction threshold value is computed, histogram shifting scheme based on the prediction threshold value to solve overflow and underflow problems. Another threshold value is adopted to achieve capacity control, image quality is better in different payload length with this control. The experimental results reveal that the performance of proposed method outperforms that proposed by FUJIYOSHI et al.

Cloud computing services have got rapid development in the field of the lightweight terminal, especially wireless communications. The comprehensive access control system framework is proposed for the cloud. A kind of access control scheme based on attribute encryption is designed, in which lightweight devices can safely use cloud computing resources to outsource encrypt/decrypt operations, and not worry for exposing terminal sensitive data. The scheme is verified by performance evaluation about the security, computing, storage, to ensure the legitimate interests of users in the cloud.

As the Internet multimedia information grows explosively, seeking an automatic technology to realize the effective organization and management of crossmedia emergency information is significantly necessary. A novel cross-media hot topic auto-tracking model based on semantics and temporal context is proposed in this paper. According to the semantic correlations of cross-media information, we learn the image visual semantics by the text semantics based on the Latent Dirichlet Allocation probability model, and establish the unified cross-media information description on the same semantic level. Also a semantics-based two-step feature dimension-reduction scheme is proposed to establish the efficient semantic feature space. The self-adaptive learning of topic model is realized to track the dynamic changes in the topic. Experimental results demonstrate that the proposed method outperforms the existing methods, which further improves the effect of hot topic auto-tracking.

The OpenMP task directive makes it possible to efficiently parallelize irregular applications, with task granularity as one of the most critical issues. To implement OpenMP specification on multi-core architecture, a model is presented specializing in the execution of irregular applications. The model captures computation and communication within a node with host cores and accelerator cores. Based on this model, we propose an adaptive task creation pruning strategy including two stages to adjust dynamically task granularity. The first stage is task creation in breadth-first manner until getting to a threshold, which utilizes potential parallelism of multi-core processor. The second stage is starvation-triggered task regeneration once some worker thread becomes starved, which ensures work-stealing and thus achieves load balance. The evaluation is conducted with a series of typical irregular benchmarks, and the results indicate that our approach offers more effective performance in parallel execution of irregular benchmarks.

Arithmetic operations are fundamental in computing models. Novel arithmetic P systems are constructed to perform four basic operations: addition, subtraction, multiplication, division. The digits of decimal integers are directly put into hierarchical membranes, one digit one membrane, thus, the number of membranes is reduced and complexity is lowered. Core evolution rules are designed for single digit operations according to the arithmetic formula tables widely used by humans. Some examples are given to illustrate how to compute decimal integers in these P systems and the results indicates that these P systems can efficiently carry out arithmetic computations of integers.

We present a LC VCO design method of the multiple start-point global optimization. It is used to optimize VCO phase noise variation in different Process, supply voltage and temperature (PVT) conditions at the same time. Based on this method, we can design PVT tolerant LC VCO even without PVT compensation circuits. The design results of different foundry manufacturing process and oscillating frequency is shown to investigate the effect of this phase noise optimization method. The design method also can help to enhance manufacturing yield.

Interactive genetic algorithms (IGAs) are effective methods of solving optimization problems with qualitative indices. The problem of user fatigue resulting from the user’s evaluations has a negative influence on the performance of these algorithms. Employing various surrogate models to evaluate (a part of ) individuals instead of a user is a feasible approach to solve the problem. Previous studies have not fully utilized knowledge provided by users with a similar preference when constructing these models. The problem of constructing surrogate models by using the knowledge of users with a similar preference was focused in this study. Users with a similar preference participating in the evolution were identified by using the collaborative filtering algorithm based on the nearest neighbor, and the individuals evaluated by these users were chosen as a part of samples for training the surrogate model of the current user’s cognition. The proposed method was applied to an evolutionary fashion design system, and the experimental results showed that the proposed method can improve the capability in exploration on the premise of greatly alleviating user fatigue.

An identification algorithm of overlapping protein complexes is put forward by simultaneously considering the topological structural and biological functional information of Protein-protein interaction (PPI) network. Main works include: constructing the edge weight of weighted PPI network on the basis of structural and functional information of PPI network to more accurately describe the correlation between protein vertices; improving the Newman algorithm to make it applicable to weighted PPI network and thus to identify overlapping protein complexes; and providing the denoising criteria based on the structural and function information of PPI network: connections which have no contribution to the high aggregation of PPI network or which are among proteins of independent functions are judged to be false positive connections. The experimental results on the dataset of saccharomyces cerevisiae PPI network show that the proposed algorithm has higher identification accuracy and matching rate when compared with the current representative identification algorithms of protein complexes.

Electroencephalogram (EEG) signal is often contaminated by electronic noise as well as movement artifacts. This paper presented an algorithm based on Canonical correlation analysis (CCA) to estimate multichannel EEG data. Different from previous studies, in which CCA was mainly used to detect the invariant features specific to each brain state, in this paper, the canonical variates computed by CCA were used to reconstruct the multi-channel EEG data. Firstly, two data sets, EEG signals and the reference signals based on prior knowledge were constructed. Next, canonical variates were computed by projecting the two data sets onto basis vectors. Finally, a least squares solution was used to estimate the multichannel EEG data. The experiment results suggested that the algorithm is capable of reconstructing the actual specific components with high quality. We also hint future possible application of the algorithm in the estimation of functional connectivity patterns at the end of the paper.

Remote sensing(RS) multi-spectral images are usually suffered from cloud and fog cover, which can lead to analysis troubles and application limitations. A novel patch-based dark channel prior dehaze method is proposed for solving this problem. An Atmospheric light (AL) curved surface hypothesis, instead of globally invariable plane, is applied to describe AL distribution, and a patch-based approach is given to estimate curved surface. By using AL curved surface estimation, a new recovering model for RS multi-spectral images is given to obtain dehaze-free images. Comparative experiments are conducted, those results illustrate that the proposed method can produces visually impressive restored images, and the proposed method is superior to other relative methods in terms of image quality evaluations.

Audio perceptual hashing is a digest of audio contents, which is independent of content preserving manipulations, such as MP3 compression, amplitude scaling, noise addition, etc. It provides a fast and reliable tool for identification, retrieval, and authentication of audio signals. A new audio hashing scheme based on non- Negative matrix factorization (NMF) of Modified discrete cosine transform (MDCT) coefficients is proposed. MDCT coefficients, which have been widely used in audio coding, exhibit good discrimination for different audio contents and highly robustness against content preserving manipulations, especially MDCT based compression such as MP3, AAC, etc. Based on the extraction of MDCT coefficients of the audio frames firstly, NMF is used to construct hash bits. Experiment results demonstrate that, compared with methods mentioned in literature, the proposed scheme exhibits a high efficiency in terms of discrimination, perceptual robustness identification rate and time consumption.

The problem of Blind source extraction (BSE) regarding a chaotic signal is addressed in this paper, by employing the newly defined Proliferation exponent (PE). The properties of gradient pursuit algorithm based on PE are further articulated, especially the infelicity to tackle a BSE problem. Subsequently, we devise a constraint optimization algorithm called Orthogonal random searching (ORS) to accomplish the optimization task, which in essence searches for optimal solutions with the principle of Markov chain Monte Carlo (MCMC). Experimental results reveal that this PE-based random searching method can extract the desired chaotic signal among multiple Gaussian mixtures, as well as showing robustness against noise contamination.

A Fusion based particle filter track-beforedetect algorithm (FPF-TBD) is proposed for dealing with dim targets, in which the importance density function of the Particle filter (PF) is generated by means of a fusion algorithm. In order to construct an accurate approximation to the true proposal distribution, the state at each time scan is predicted according to the Extended Kalman filter algorithm (EKF) and the Unscented Kalman filter (UKF) simultaneously. The information, based on the recursion of the weights, is gathered over multiple scans, and the detection decision is made based on tracking results at the end of the processing chain. By making best use of the recent measurements, this new proposed method can obtain an accurate approximation to the system and as a result, improve the track accuracy and detection performance. Simulation results illustrate the effectiveness of this approach.

The performance of automatic music transcription seems to have reached a limit over the last decade, and a promising direction of improvements could be to incorporate music instruments’ specific parameters. We propose a novel piano-specific transcription system, using both audio and visual features for the first time. Contribution of the paper mainly includes two parts: A new onset detection method is proposed using a specific spectrum envelope matched filter on multiple frequency bands. A computer-vision method is proposed to enhance audio-only piano music transcription, through tracking the positions of the pianist’s hands on the piano keyboard. Based on the MIDI Aligned piano sounds (MAPS) database and a selfrecorded video database, we carried out comparable experiments for audio-only onset detection and overall system, respectively. The performance was compared with the best piano transcription system in Music information retrieval evaluation exchange (MIREX), and the results showed that the proposed system outperforms the state-of-art method substantially.

To suppress narrowband interferences, sparse frequency waveform can be transmitted by wideband radar systems. It suffers the high range sidelobes. A new algorithm for sparse frequency waveform design with range sidelobes constraint is proposed. The approach affords much better frequency stopband suppression by flexibly controlling sidelobes constraint using Weighted integrated sidelobe level (WISL) metric. The approach is computationally efficient and can suit the more general Peakto- average power ratio (PAR) constraint. An extension to Multiple-input multiple-output (MIMO) radar case is also presented.

The parameters of radar emitter are fast changing in the current complicated electromagnetic environment, and the radar emitter recognition rate which used single sensor method cannot satisfied. To solve the problem, an improved radar emitter recognition method based on Dezert-Smarandache theory is proposed, which can improve proportional conflict redistribution rule to solve fuzzy and conflicting information from radar emitter. Some examples are given to show the validity of the improved method.

An efficient hybrid method that combines the best uniform rational approximation with the Method of moments (MoM) based on the Volume integral equation (VIE) is presented for Radar cross section (RCS) prediction of inhomogeneous dielectric objects. Over the broad frequency band, the Chebyshev nodes are chosen. At these frequency sample points, the corresponding VIEs are built and the MoM is applied to solve the VIEs by using the SWG basis function. The Chebyshev polynomial expansion is employed to approximate the volume currents in the desired frequency band. The Maehly rational approximation is implemented to improve the solution accuracy. Numerical results testify the efficiency and accuracy of the proposed method and time cost ratio is analyzed finally.

Thresholding based on gray-gray cooccurrence matrix is a local thresholding technique. Relative entropy is usually used to gauge the relative difference of uncertainties in two physical systems, and the relative entropy-based asymmetrical co-occurrence matrix thresholding has been applied successfully. We propose to construct symmetrical co-occurrence matrix with the statistical spatial information from the mean values in object and background regions of an image. In this way, a unique relative entropy-based symmetrical co-occurrence matrix thresholding method is derived. Computer-simulation results demonstrated the higher adaptability and efficiency of the proposed method, as compared with square distance based symmetrical co-ocurrence matrix thresholding, Otsu’s and relative entropy methods.

A novel Compressed sensing (CS) method based on two-dimensional measurements is proposed that can be effectively utilized in Impulse radio ultra-wideband wireless sensor networks (IR-UWB WSNs) to significantly reduce the energy consumption and sampling rate in sensor data transferring.We start by establishing the CS measurement model by taking both spatial and temporal correlations of Wireless sensor network (WSN) data into account. Since our model incorporates a new type of measurement matrix: the block quasi-Toeplitz structured matrix, we derive the Restricted isometry property (RIP) of the block quasi-Toeplitz structured matrix to ensure the performance of the two-dimensional recovery of WSNs data. We substantiate our mathematical analysis by numerical examples in the context of ideal spares vector and realWSN data, and results demonstrate that the approach achieves significantly saving of energy and sampling rate with small reconstruction error.

In the paradigms of the Internet of things (IoT), sensor nodes could collect the data through the Wireless sensor network (WSN), and could be managed and accessed by human users through the Internet. There are many challenges remain. Taking advantage of IPv6, IPv6 over Low-power personal area networks (6LoWPANs) implemented on resource constrained devices makes the connection possible and easier. This paper is contributing on constructing architecture of the end-to-end communication system based on 6LoWPAN gateway, which enabled convergence of IPv6 network and low-power wireless networking, featuring encapsulating 6LoWPAN adapter layer in a Network adapter driver (NAD) of personal computer. Using real commercial deployment, the result of application test bed shows that the IPv6 hosts could be interactive with IP-based sensor nodes through 6LoWPAN gateway with acceptable latency and packet loss.

Aiming at the anti-eavesdrop security demand and potential safety hazard in Ad hoc, a secure random linear network coding algorithm was proposed. The algorithm has secure source coding and protecting the coding vectors. The information source secure coding method and optimizing the finite region is defined to guarantee that the information is properly protected. Different coding vectors protecting schemes were designed for adapting security path and without. We applied the secure random linear network coding algorithm in Ad Hoc Networks to meet the demand on anti-eavesdrop. The anti-eavesdrop algorithm based on network coding is simulated and analyzed in NS2 platform, the results shown that comparing with the encryption algorithm, the anti-eavesdrop algorithm based on network coding has better performance in end-to-end delay and encryption speed.

In the Software-defined networking (SDN), when multiple control domains are used in control services, the transient state problem can occur causing the service flow interruption when border switches are updated asynchronously. We analyze the uniqueness of this problem for SDN, and propose a light-weight protocol for safe reconfiguration of the border switches in order to improve the availability of the services running in SDN. Our solution is designed as a generic supervision layer added in the control plane to support different types of services. To demonstrate the benefits, we implement a prototype of Informationcentric networks (ICN) with the protocol, and conduct experiments using PlanetLab. The results show the ICN service can continuously serve high volume requests for contents despite the congestions built by the heavy background traffic. The performance gains in terms of the mean and standard deviation of the content retrieve delay are 40.5% and 21.56%.

At present it is very difficult to estimate the carrier frequency, spread spectrum code period and spread spectrum code pattern of Direct sequence spread spectrum (DSSS) signals with low Signal-to-noise ratio (SNR) accurately and simultaneously in the condition of Gaussian white noise. A novel method of estimation for carrier frequency and spread spectrum code period of DSSS signals based on four-order cumulant and eigenvalue analysis is presented. Compared with the correlative detection in time domain, square filtering detection and Hebbian rule, the proposal method in this study can realize the estimation for the carrier frequency and period of spread spectrum code of low SNR DSSS signals with a high accuracy simultaneously. In a priori condition of the spread spectrum code period the proposal method can calculate synchronization loss time between spread spectrum code and information symbol. The proposal method this paper has a strong ability to suppress Gaussian noise.

A new fast matching algorithm for remote sensing images is proposed. The algorithm adopts a coarse to fine matching process. A remote sensing image is decomposed into multi-scale images which form a wavelet image pyramid by the way of DWT (Discrete wavelet transform). Extract a low frequency sub-image from the wavelet image pyramid to carry out a rough matching operation between the sub-image and target image, and then get a rough position after cluster analysis. Get a suitable position with center at the rough position from remote sensing image to accomplish fine matching operation. The algorithm has been analyzed theoretically from the signal processing point of view, and a sufficient condition is given to select wavelet filter. Simulation results testify that proposed algorithm not only distinguishes remote sensing images precisely, but also cuts down matching time greatly. It is only 25.04% of SIFT algorithm, and 35.36% of SURF algorithm for matching time.

To further improve the efficiency of Fully homomorphic encryption (FHE), a leveled FHE scheme based on the Ring learning with errors (RLWE) problem is put forward by simultaneously applying both batch techniques available. Our scheme therefore allows double packing many plaintext values into each ciphertext to support single-instruction-multiple-data-type operations, which effectively reduces the ciphertext expansion ratio. An efficient evolutionary method for achieving arbitrary homomorphic permutation operations on a packed ciphertext is also provided by using several given key-switching hints. Further, a few new operations are introduced, with which not only to describe the key switching process in our batch setting clearly, but also to analyze the noise growth conveniently.