By regarding a Solid-state drive(SSD) as a black box and observing its external behavior instead of peeping into its internal details, we investigate how the factors of I/O granularity and I/O queue depth influence the throughput of an SSD through a series of experiments and relate to the internal parallelism of an SSD, and then propose the concept of Combination equivalence class (CEC) as the set of combination pairs of these two factors. A novel buffer allocation scheme for hash join over SSDs is invented by taking both factors into account. Extensive experiments demonstrate the effectiveness of our scheme.

We propose a robust fuzzy time series forecasting method based on multi-partition approach and outlier detection for forecasting market prices. The multipartition approach employs a specific partition criterion for each dimension of the time series. We use a Gaussian kernel version fuzzy C-means clustering to construct the fuzzy logic relationships and detect the outliers by calculating the grade of membership. We apply an additional model, which is trained on the set of outliers by Levenberg-Marquardt algorithm, for forecasting the outliers in testing set. The experiment results show that the proposed method improves the robustness and the average forecasting accuracy rate.

Collaborative filtering recommender systems (CFRSs) are known to be highly vulnerable to profile injection attacks, in which malicious users insert fake profiles into the rating database in order to bias the systems' output, since their openness, and attack detection is still a challenging problem in CFRSs. In order to provide more accurate recommendations, many schemas have been proposed to detect such shilling attacks. However, almost all of them are proposed to detect one or several specific attack types, and few of them can handle hybrid attack types, which usually happen in practice. With this problem in mind, we propose a novel L2, 1-norm regularized matrix completion incorporating prior information (LRMCPI) model to detect shilling attacks by combining matrix completion and L2, 1-norm. The proposed LRMCPI formalizes the attack detection problem as a missing value estimation problem, and it is appropriate because the user-item rating matrix is approximately low-rank and attack profiles could be considered as structural noise. The proposed LRMCPI model not only can better recover the rating matrix using correct rating value but also can detect the positions where the attackers are injected. We evaluate our model on three well-known data sets with different density and the experimental results show that our model outperforms baseline algorithms in both single and hybrid attack types.

A thermopile-based microwave power sensor and a double-channel microwave power sensor are compared in order to research the measurement accuracy of microwave power. The relationship of the displacement of MEMS cantilever beam with the measured microwave power is researched, and the reason that the microwave power consumed by the MEMS cantilever cannot be ignored at low power level is explained. The ratio of microwave power consumed by the MEMS cantilever with the microwave frequency is obtained, and the measured results show that the percentage is 51.96%@8GHz, 52.31%@10GHz and 55.11%@12GHz on the average, respectively. There is an important reference value to achieve the accurate microwave power measurement of the double-channel microwave power sensors.

We investigated the transmission characteristics of Cu/CNT composite Through-silicon via (TSV) interconnects. The equivalent lumped-element circuit model was established, with the effective conductivity employed for impedance extraction. The impacts of CNT filling ratio, temperature, and other geometrical parameters on the performance were examined.The sensitivity analysis of Cu/CNT composite TSVs was carried out. The electrical performance of Cu/CNT composite TSVs were optimized by utilizing low-permittivity dielectrics or even air-gap.

Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of programs. The problem of data redundancy exists in software defect data set, which has great influence on prediction effect. We propose a defect distribution prediction model (Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network (DBN) technology, which is an effective deep learning technique in image processing and natural language processing, whose features are similar to defects in source program. Experiment results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine (SVM), Locally linear embedding SVM (LLE-SVM), and Neighborhood preserving embedding SVM (NPE-SVM).

By converting the problem of preimage distribution of a perfect nonlinear function with three binary outputs to that with two binary outputs, this paper presents the preimage distributions of perfect nonlinear functions with three binary outputs. This paper also characterizes the fundamental characterization of the preimage distribution of vectorial plateaued functions with single amplitude and its component functions being all unbalanced, and gives the preimage distributions of such functions with two or three binary outputs.

Non-malleable extractor is an important tool for studying the problem of privacy amplification in classical and quantum cryptography with an active adversary. The randomness of the weakly-random source X before privacy amplification always depends on the information adversary has, called side information. We study properties of such extractors in the presence of classical and quantum side information, and show that any non-malleable extractor is essentially secure in the case where the adversary has classical side information. We also prove that non-malleable extractors are quantumproof with uniform seed, or only require the seed to be weakly random.

Security-sensitive operations in Android applications (apps for short) can either be benign or malicious. In this work, we introduce an approach of static program analysis that extracts fisecond-step behavior featuresfl, i.e., what was triggered by the security-sensitive operation, to assist app analysis in differentiating between malicious and benign operations. Firstly, we summarized the characteristics of malicious operations, such as spontaneity, independence, stealthiness and continuity, which can be used to classify the malicious operations and benign ones. Secondly, according to these characteristics, Second step behavior features (SSBFs for short) have been presented, including structural features and semantic features. Thirdly, an analysis prototype named SSdroid has been implemented to automatically extract SSBFs of security-sensitive operations. Finally, experiments on 9285 operations from both benign and malicious apps show that SSBFs are effective and usefulness. Our evaluation results suggest that the second-step behavior can greatly assist in Android malware detection.

The Boolean Satisfiability (SAT) problem is the key problem in computer theory and application. Field-programmable gate array (FPGA) has been addressed frequently to accelerate the SAT solving process in the last few years owing to its parallelism and flexibility. We have proposed a novel SAT solver based on an improved local search algorithm on the reconfigurable hardware platform. The new software preprocessing procedure and hardware architecture are involved to solve large-scale SAT problems instances. As compared with the past solvers, the proposed solver has the following advantages: the preprocessing technology can strongly improve the efficiency of solver; the strategy of strengthening the variable selection can avoid the same variable flipped continuously and repeatedly. It reduces the possibility of search falling into local minima. The experimental results indicate that the solver can solve problems of up to 32K variables/128K clauses without off-chip memory banks, and has better performance than previous works.

When the number of snapshots used to estimate the Sample covariance matrix (SCM) approaches infinity and the array steering vector is accurately known, the Standard Capon beamformer (SCB) can better suppress spatial noises than data-independent beamformers. On the contrary, the performance of the SCB may decrease. To solve this problem, we propose a two-stage shrinkage scheme for the SCM. Specifically, in the first stage, the SCM is enhanced by the General linear combination (GLC) method, which will be referred to as GLC-SCM; and in the second stage, the GLCSCM is further improved with the Exponential matrix (EM) method, which will be referred to as GLC-EM-SCM. Compared with the conventional methods, the proposed method can achieve higher signal-to-interference-noise ratio output and more accurate signal power estimate.

The linear phase is a major characteristic of digital differentiators in many signal processing applications. This study presents a sequential partial optimization method for designing a fullband infinite impulse response digital differentiator with a near linear phase. To achieve a near linear phase, the group delay is treated as an optimization variable, and the maximum phase error is minimized within a constrained domain. During each iteration of the algorithm, in addition to the whole numerator and group delay, only one secondorder denominator factor is optimized. The necessary and sufficient stability triangles are applied to insure the stability of the differentiators, and the Gauss-Newton strategy is used to handle the nonconvexity of the design problems. Design examples show that the proposed method outperforms several state-of-the-art methods in terms of the maximum phase deviation from the desired linear phase.

We focus on the Direct position determination (DPD) of a moving narrowband source based on Doppler frequency shifts of signals with known waveforms. Two common motion models are considered: the Constant velocity (CV) model and the Constant acceleration (CA) model. The DPD cost function is obtained after some algebraic manipulations using the Maximum likelihood (ML) criterion. To develop a computationally efficient optimization algorithm, we present a preliminary mathematical result that plays a fundamental role in development of the proposed algorithm. Subsequently, a Newton iterative algorithm is tailored to the two motion models to determine the moving transmitter's trajectory. When compared with multidimensional grid searching, the proposed algorithm is more computationally attractive without compromising its estimation accuracy.Simulation results confirm the superiority of the proposed algorithm.

In this work, we report on non-invasive observation of human esophagus, liver, and uterus samples with Full field optical coherence tomography (FFOCT). ln imaging process, fresh human samples were fixed in formalin immediately after excision and then imaged directly without staining and cutting the samples into a serial of thin slices. Tissue microstructures of each type of normal or cancerous tissue as well as their changes with the increase of the depth beneath tissue surface can be identified in the depth-resolved images. The results demonstrate the potential of applications of the en face images in clinic practice.

In conventional 3D shape retrieval and classification, they differentiate each other in their final stages. We propose a unified feature representation and learning framework for the instance-based shape retrieval and classification. Firstly, we render every 3D model in several directions and use the produced view-sets to represent the 3D models. In this way, both tasks can be tackled by measuring the distances between rendered views of 3D models. Secondly, we construct the viewsets as Symmetric positive definite matrices (SPDMs), which are points on a Riemannian manifold. Thus, the shape retrieval and classification tasks are reduced to a problem of measuring the distances between projected views and SPDMs. To solve this heterogeneous problem, we map them to a Hilbert space using a method of point-to-set matching. In this Hilbert space, the distances are surprisingly easy to calculate. Finally, we use a robust nearest-neighbor approach to unify the instancebased shape retrieval and classification. Our framework combines the state-of-the-art deep learning approaches with traditional mathematical optimization method, makes full use of both advantages, which is much more flexible than pure deep learning methods. Experimental results show the efficiency of our approach.

Ultrasound computed tomography (USCT) is a promising approach for early breast cancer screening. Existing studies that estimate breast images using Waveform inversion (WI) methods utilize a Circular ultrasonic transducer element array (CUTA) to collect the measurement data. However, very accurate transducer element positioning and directivity are required for signal calibration in the hardware system of these studies, thereby causing difficulties in hardware manufacturing. The purpose of this study is to estimate high-resolution USCT images using a WI method with a Square border ultrasonic transducer element array (SUTA), which reduces the difficulty of hardware manufacturing and creates accurate correspondence between the continuous and discrete forms of the transducer element positions. Therefore, in this study, an SUTA is adopted to collect the measurement data of breast imaging using the WI method. A penalized least-squares optimization problem is constructed to obtain the numerical solution of a sound speed distribution. Computer simulations are conducted to compare images reconstructed from the measurement data that are collected using an SUTA and a CUTA. The performance is evaluated using a numerical breast phantom. Results suggest that the biases of the images reconstructed are less than 1% with the evenly distributed SUTA and 1% with the CUTA, under a noise condition.

This paper proposes a flexible design scheme for H.265/HEVC encoding based on FPGA, which allows an easy incorporation of a variety of algorithms applied to different scenarios. In particular, we present an H.265/HEVC intra encoder as an instantiation of our proposed scheme. The key idea is to develop an encoder system by configuring basic Processing elements (PEs) of fundamental algorithms. Our intra encoder using the flexible framework is structured with fourstage CTU based pipeline. Pixel-level PEs are designed to unify the intra prediction of 35 modes and a multiscale compatible transform array is proposed to process variable size transform. 32 PEs are paralleled for intra mode decision to support 35 combinations of modes and partitions. In the reconstruction stage, 16 PEs are paralleled for intra prediction and a 16 x 16 multiplier array is configured for transforms of variable sizes with a constant 16 pixels/cycle throughput. Implementation results show that our proposed architecture costs about 63K Lookup tables and 62KB on-chip memories on Xilinx Kintex-7 platform with the maximum working frequency at 175MHz, which is sufficient for real-time encoding of 1920 x 1080 @60fps video at 160MHz. The flexibility and extension capability of our framework provides a great potential for future FPGA solutions serving for different purposes.

In this paper, we study double circulant codes of length 2n over the non-chain ring R = 𝔽 _q + v𝔽 _q + v ²𝔽 _q ; where q is an odd prime power and v ³ = v. Exact enumerations of self-dual and LCD double circulant codes of length 2n over R are derived. When n is an odd prime, using random coding, we obtain families of asymptotically good self-dual and LCD codes of length 6n over 𝔽 _q .

Ultralightweight mutual authentication protocols (UMAP) of Radio frequency identification (RFID) systems have attracted much attention from researchers. Many studies reveal that most of UMAP suffer malicious attack. To improve security of UMAP, formal analysis is performed with Simple promela interpreter (SPIN). Two typical UMAPs, which are RCIA and RAPP, are selected as our case study. A protocol abstract modeling method is presented to make UMAP can be formalized simply. Using SPIN, verification results show that RCIA and RAPP are both vulnerable against desynchronization attack. A Generalized model of UMAP (G-UMAP) and a general patching scheme are presented for resisting the attack. To validate the patching scheme, formal verification is then performed for the improved protocol. SPIN verification shows that the improved RCIA and RAPP both gain higher security. The above proposed modeling method has great significance for similar UMAP analyzing, and the proposed patching scheme is proved to be practical and reliable.

A variety of web services have emerged with the rapid development of the Internet. These services are often of a single function. The value-added services can be achieved by combing with multiple services. The processing speed and stability of existing methods in service composition were not very well and seldom consider the fault diagnosis and handling methods for the service, which results in a greater probability of the service composition failure at run time. We use spiking neural P systems with colored spikes to model the fault of available service, component, and connector in the service composition. The proposed model can be used to locate a fault and handle it correctly when the service combination fails, the advantage of efficiency and stability of proposed method has been proved by comparing with the method of Petri net.

Declassification and endorsement can efficiently improve the usability of mobile applications. However, both declassify and endorse operations in practice are often ad-hoc and nondeterministic, thus, being insecure. From a new perspective of threat assessments, we propose the Threat-based typed security π-calculus (π^TBTS) to model declassification and endorsement in mobile computing. Intuitively, when relaxing confidentiality policies and/or integrity policies, we respectively assess threats brought by performing these two relaxes. If these threats are acceptable, the declassification and/or endorsement operations are permitted; Otherwise, they are denied. The proposed assessments have explicit security conditions, results and less open parameters, so our approach solves the problem of the ad-hoc and nondeterministic semantics and builds a bridge between threat assessments and declassification/endorsement.

This paper proposes and studies a novel M-ary chirp modulation scheme adopting quasi-orthogonal waveforms. The Symbol error probability (SEP) of M-ary modulation over Additive white Gaussian noise (AWGN) channel is derived based on coherent detection, which is a function of modulation factor and waveform period. Moreover, the SEP is investigated over a channel limited by a nominal bandwidth. Both theoretical analysis and simulations prove that the SEP performance of the proposed 8-ary chirp modulation outperforms that of conventional 8 Phase shift keying (8PSK) with the same bandwidth efficiency.

Accurate parasitic parameter extraction of high-frequency transformers is one of the key techniques to efficiently design a Switched-mode power supply(SMPS). An approach to accurately and efficiently extract the parasitic parameters of high-frequency transformers is proposed. The high-frequency transformer is first decomposed into first-order RL and second-order RLC circuits according to inherent step response characteristics. Through the measured discharging pulse and damped oscillation responses of the high-frequency transformer excited by a square wave, we can extract the equivalent circuit parameters through fitting the responses with Particle swarm optimization(PSO). The equivalent circuits of the high-frequency transformer with the desired parameters are obtained for the SMPS design. We validate the effectiveness and accuracy of the proposed method with simulations and measurements.

A staggered grid scheme is proposed to reduce both the total memory requirement and the CPU time of generating the corrected near matrix in the FFTbased methods. Two sets of Cartesian grids are used to project the source points and the field points, respectively. The proposed method does not lower the efficiency of computing far matrix-vector products, compared with the traditional uniform Cartesian grid scheme. Some numerical experiments are provided to demonstrate both the correctness and the efficiency of the proposed method.

Synthetic aperture radar Tomography (TomoSAR) provides scene reflectivity estimation of vegetation along elevation coordinates. However, the more multi-baselines acquisition, the longer the time span of acquisition, which will result in serious temporal decorrelation in forested area. In this way, we expect to use as smaller number of baselines as possible to obtain high estimation accuracy in elevation direction. We thus investigate the performance of Polarimetric SAR tomography (Pol-TomoSAR) with small number of baselines in forested areas. The results show that compressive sensingbased Pol-TomoSAR has higher estimation accuracy in elevation direction than conventional Pol-TomoSAR with small number of baselines.

The Cat's eye effect target recognition method based on visual attention (CTRVA) is proposed. The difference image can be processed by a designed second-directional derivative filter at eight directional channels. Morphological method is employed to deal with the filtered image in all directions, which ensures that target can be easily distinguished from background. The salient maps for each channel where the potential targets exist are calculated through the spectral residual approach, and the "target-saliency" map is computed by a designed saliency fusing method. The coarse detection is performed by the adaptive threshold to extract candidate targets from the "target-saliency" map. The real target region is identified by the characteristics of the cat's eye effect target. Experimental results show that the proposed method is efficient and has an outstanding performance for cat's eye effect target detection.