We propose a multi-objective optimization algorithm for cloud task scheduling based on the Analytic network process (ANP) model to solve the problems in cloud task scheduling, such as the deficiencies of mathematical description, limited optimization abilities of the traditional multi-objective optimization algorithm and the selection of the Pareto optimal solutions. Firstly, we present the mathematical description of cloud task scheduling using matrix theory. Then, the improved Nondominated sorting genetic algorithm II (NSGA-II) multiobjective evolutionary algorithm whose optimization ability is improved by Gene expression programming (GEP) algorithm has been introduced into the cloud task scheduling field to search the Pareto set among multi-objects. Finally, ANP model has been combined with the improved NSGA-II to solve the selection problems of Pareto solutions. Comparing with the multi-objective optimization algorithm based on the weighted polynomial, the proposed algorithm can optimize multiple goals at the same time, and can avoid the additional iterations due to the change of users preferences effectively. The simulation results indicate that the proposed algorithm is effective.

Differential power analysis (DPA) has become a major system security concern. To achieve high levels of security with low power and die area costs, a novel Dual-voltage single-rail dynamic logic (DSDL) design is proposed. The proposed scheme can reduce power dissipation and obtain extremely well-balanced power consumption. The charge sharing mechanism is used for voltage transfer in the internal nodes during the evaluation of the design. Dual power supply voltages are used with positive feedback to speed up the evaluation process. A 4-bit micro Substitution box (SBOX) of the Advanced encryption standard (AES) algorithm has been implemented to verify the security of the proposed logic design. The experimental results proved the security and the efficiency of the proposed DSDL, which can reduce power dissipation by up to 20% and occupies at most 83% of the silicon area when compared with previous state-of-the-art countermeasures.

We propose a novel algorithm to solve the problem of person re-identification across multiple nonoverlapping cameras by grouping similarity comparison model. We use an image sequence instead of an image as a probe, and divide image sequence into groups by the method of systematic sampling. Then we design the rule which uses full-connection in a group and non-connection between groups to calculate similarities between images. We take the similarities as features, and train an AdaBoost classifier to match the persons across disjoint views. To enhance Euclidean distance discriminative ability, we propose a novel measure of similarity which is called Significant difference distance (SDD). Extensive experiments are conducted on two public datasets. Our proposed person re-identification method can achieve better performance compared with the state-of-the-art.

This paper deals with lot merging problem in semiconductor wafer fabrication system. There is the possibility to merge two or more partial lots into single lot if their subsequent process routes are the same, an improved lot merging method is presented by grouping lots belonging to different orders. Based on job information extracted from the buffers, several bin packing and knapsack solving algorithms are used to determine which lots should be merged. An iterative improvement procedure is introduced for optimizing merging strategy through a heuristic algorithm with resetting the ready time of critical lots. The closed loop structure with global revision factor is built for minimizing the impact of uncertain events while balancing the different orders processing progress. Applied to a simulation semiconductor manufacturing fab, the proposed algorithm can reduce cycle time and tardiness compared with other methods currently.

With the explosion of information, it is becoming increasingly difficult to get what is really wanted. Dimensionality reduction is the first step in efficient processing of large data. Although dimensionality can be reduced in many ways, little work has been done to achieve dimensionality reduction without changing the inner semantic relationship among high dimension data. To remedy this problem, we introduced a manifold learning based method, named Mutual information preserving mapping (MIPM), to explore the low-dimensional, neighborhood and mutual information preserving embeddings of highdimensional inputs. Experimental results show that the proposed method is effective for the text dimensionality reduction task. The MIPM was used to develop a temporal summarization system for efficiently monitoring the information associated with an event over time. With respect to the established baselines, results of these experiments show that our method is effective in the temporal summarization.

Several special properties of Smart and Vercauteren's encryption scheme are put forward. They are all based on the special parameter, which is a recommended modulus polynomial. These properties not only show that the secret key is deduced from an N-dimensional vector into its any entry, but also produce the triplet (grade-i reduced plaintext space, grade-i reduced ciphertext space, grade-i reduced secret key) for each i, where grade-i reduced secret key can decrypt grade-i reduced ciphertexts and can be efficiently computed from grade-i delegated key. At the same time, sequentially grade-(i+1) delegated key can be efficiently computed from grade-i delegated key. This work also discusses a sequential computation in opposite direction, i.e., computing grade-i delegated key from grade-(i+1) delegated key. But the sequential computation in the opposite direction is difficult except at most the first steps of such sequential computation. Based on the properties given, we then propose a simple hierarchical encryption scheme with relatively small key and ciphertext sizes.

Cut-and-choose paradigmmakes Yao's protocol for two-party computation secure in malicious model with an error probability. In CRYPTO 2013, based on multi-phase cut-and-choose, Lindell reduced this probability to the optimal value. However, this work can only compute single-output functions with optimal error probability. We transform multi-phase cut-and-choose for singleoutput case into one that can deal with two-output functions, meanwhile maintaining the optimal error probability. Based on this new paradigm, we propose an efficient two-output secure computation protocol. Besides, by utilizing the specific property of the output garbled keys, we solve the authenticity issue of the generator's output with only symmetric cryptographic operations linear in the output length of the generator, which is the most efficient method so far in standard model without Random oracle (RO).

There are representative models for spatial topological relations of Region connection calculus and intersections model. The purpose of this paper is to study the topological relations of multiple simple regions. In this paper, the 4-intersection matrix model is extended into a 2 ⁿ -intersection representative function model to represent the spatial relations of multiple simple regions. The exclusiveness and the completeness of these topological relations are proved. Moreover, this paper also gives detailed discussion on the properties and applications of this model from a special and new perspective. And the topological relations among three simple regions in this paper are applied to forecast the impact of typhoon on the specified target areas. Compared with the extended 4-intersection model, 8-intersection cube model is more elaborate in representing the topological relations among the three simple regions.

A key exchange protocol is considered unsafe. The scheme is based on a set of m commuting square singular matrices of dimension n×n over a finite field, and its security is claimed to rely on the hardness of the matrix version discrete logarithm problem. However, the proposal's design allows for a clean attack strategy. We show that the key exchange protocol is vulnerable to a linear algebra attack which only requires polynomial time to obtain the equivalent keys for all given public keys. We conduct a detailed analysis on the attack method and provide some improved suggestions on the key exchange protocol based on commuting matrices.

scalable large-signal model of Al- GaN/GaN High electron mobility transistors (HEMTs) suitable for multi-harmonic characterizations is presented. This model is fulfilled utilizing an improved drain-source current (I _ds ) formulation with a geometry-dependent thermal resistance (R _th ) and charge-trapping modification. The I _ds model is capable of accurately modeling the highorder transconductance (g _m ), which is significant for the prediction of multi-harmonic characteristics. The thermal resistance is identified by the electro-thermal Finite element method (FEM) simulations, which are physically and easily scalable with the finger numbers, unit gate width and power dissipations of the device. Accurate predictions of the quiescent currents, S-parameters up to 40GHz, and large-signal harmonic performance for the devices with different gate peripheries have been achieved by the proposed model.

Valid authentication and security protection measures are not provided for external code and resources executed by dynamic loading technology during the runtime in Android. In this paper, a new method of detecting vulnerabilities related to dynamic loading technology is proposed. Two phases are included in the detection process. Static analysis phase determines the location information of the loading point and extracts the feature vector for each loading procedure. Identification phase classifies the extracted feature vector by means of constructed multilabel classification ensemble learning algorithm. According to the examination result on 4464 Android applications, 37.8% of all applications use the dynamic loading technology, and more than 12% of total test applications are detected with related security deficiencies. Experimental result shows that the detection method can detect vulnerabilities of dynamic loading effectively and is more comprehensive.

In order to facilitate crowdsourcing-based task solving, complex tasks are decomposed into interdependent subtasks that can be executed cooperatively by individual workers. Aiming to maximize the quality of the final solution subject to the self-interested worker's utility maximization, a key challenge is to allocate the limited budget among the subtasks as the rewards for workers having various levels of abilities. This study is the first attempt to show the value of Markov decision processes (MDPs) for the problem of optimizing the quality of the final solution by dynamically determining the budget allocation on sequentially dependent subtasks under the budget constraints and the uncertainty of the workers' abilities. Our simulation-based approach verifies that compared to some offline methods where workers' abilities are fully known, our proposed MDP-based payment planning is more efficient at optimizing the final quality under the same limited budget.

The problem of different contextual information to influence the user-item-context interactions at varying degrees in context-aware recommender systems is addressed. To improve the performance accuracy, we develop a novel attribute reduction algorithm in order to effectively extract the core contextual information using rough set. We combine collaborative filtering with contextual information significance to generate more accurate predictions. We experimentally evaluate our approach on UCI machine learning repository and two real world data sets. Experimental results demonstrate that our proposed approach outperforms existing state-of-theart context-aware recommendation methods.

A low-noise voltage reference is presented to enhance resolution of MEMS capacitive accelerometer and reduce system noise, in which the circuit uses Chopper stabilization (CHS) technique for the suppression of low-frequency noise. A 3.7V voltage reference chip is fabricated in a 0.5-μm CMOS process. Compared with the voltage reference without using CHS, the proposed design is much more superior in low-noise performance. Experimental results indicate that the output noise of reference voltage V _RP can reach 0.121μV/sqrt(Hz) at the vicinity of 3Hz.

This paper presents a Provenance-based what-if analysis approach (PBWA) for data mining processes, so decision makers can examine the latest mining result under hypothetical business contexts. It fills the gap that data mining only reveals past status of enterprises with historical data. Provenance information is a kind of metadata of data mining processes. PBWA uses it to identify relevant operation path and intermediate results that is affected by hypothetical business contexts. It refreshes the mining result by partially rerunning the affected portions. Different from previous studies only for relational operations, PBWA can take more general operations into account. Besides, it focuses on the whole mining processes. Experiments demonstrate that when the affected ratio is less than 74% and 87% in different contexts, PBWA can achieve better time performance.

The testing industry need to prioritize the limited resources and focus on testing modules whose failure is mostly likely to cause faults. This paper discusses a method that can rank modules in a software package for integrate testing using the PageRank algorithm. In this algorithm, a sequences of random walks iteratively can find a high likelihood of encountering a node, which is interpreted as it being an important performance resource. An experiment result prove that the proposed method actually can be used to prioritize testing of specific modules when testing resource are scarce.

The simplicity and interpretability of decision tree induction makes it one of the more widely used machine learning methods for data classification. However, for continuous valued (real and integer) attribute data, there is room for further improvement in classification accuracy, complexity, and tree scale. We propose a new K-ary partition discretization method with no more than K−1 cut points based on Gaussian membership functions and the expected class number. A new K-ary crisp decision tree induction is also proposed for continuous valued attributes with a Gini index, combining the proposed discretization method. Experimental results and non-parametric statistical tests on 19 real-world datasets showed that the proposed algorithm outperforms four conventional approaches in terms of both classification accuracy, tree scale, and particularly tree depth. Considering the number of nodes, the proposed methods decision tree tends to be more balanced than in the other four methods. The complexity of the proposed algorithm was relatively low.

For the frequency jittering radar, the problems of range migration and phase jittering among different pulses may occur during the long-time integration, which will affect the target energy integration. Therefore, a new coherent integration algorithm, namely, Frequency jittering based Radon-Fourier transform (FJ-RFT), is proposed. Based on jointly searching in the motion parameter space, the problems mentioned above can be simultaneously resolved and the coherent integration can be then realized. The Cramer-Rao lower bound (CRLB) and the Blind speed sidelobes (BSSL) of the proposed FJ-RFT are analyzed in detail. Finally, numerical experiments are provided to demonstrate the effectiveness of the proposed method.

We present the shearlet-based variational model for image restoration and decomposition. The new model can be seen as generalizations of Daubechies-Teschke's model. By using regularization term in shearlets smoothness spaces, and writing the problem in a shearlet framework, we obtain elegant shearlet shrinkage schemes. Furthermore, the model allows us to incorporate general bounded linear blur operators into the problem. The experiments on denoising, deblurring and decomposition of images show that our algorithm is very efficient.

A new symmetric key image encryption scheme based on hyper-chaotic Lorenz system is proposed. The encryption process and the decryption process are identical in the proposed scheme. They both include two diffusion operations, one plaintext-related scrambling operation and three matrix rotating 180 degrees operations. The hyper-chaotic Lorenz system is employed to generate the secret code streams to encrypt the plain image, and to implement the diffusion process with XOR operation. The plaintext-related scrambling is used in this scheme to make different plain images correspond to different secret code streams even when the secret keys are the same, so that the scheme can fight against the chosen/known plaintext attacks. Simulation results show that the proposed scheme has the merits of high encryption speed, large key space, strong key sensitivity, strong plaintext sensitivity, good statistical properties of cipher-text, and etc., and can be used in practical communications.

Wireless LAN controller (WLC) is used to manage and control Access points (APs) in Wireless local area network (WLAN). Proxy mobile IPv6 (PMIPv6) protocol supports network-layer mobility in WLC based WLAN. However, it introduces extra delay in delivering packets from the APs to the WLC. We use Mobile access gateway (MAG) chain to reduce packet delay. The handoff delay and packet delivery delay under the proposed scheme are derived, based on which we formulate the delay minimization problem whose solution leads to the optimal MAG chain length. Numerical analysis results indicate that the proposed scheme outperforms the existing scheme in terms of delay in the case when the delay between Local mobility anchor (LMA) andWLC is relatively greater than the delay between two neighboring WLCs. The proposed scheme is able to reduce packet loss resulting from the traditional handoff procedure introduced in the PMIPv6 protocol and that due to delay limitation.

Three clock synchronization algorithms for Wireless sensor networks (WSNs) in Pairwise broadcast synchronization (PBS) mechanism are derived. They include the joint Least squares estimator (LS), joint Least squares weighted estimator (LSW) and joint Least squares weighted Recursive estimator (R-LSW). For these estimators, the corresponding algorithms are derived and described by assuming a Gaussian random delay model. Unlike PBS, these estimators can achieve the Cramer-Rao lower bound (CRLB) for both listening node and active node without knowledge of the deterministic delay. The purpose of considering R-LSW is to reduce the use of storage space with the method of estimating while observing. Simulation and analytical results verify that the estimators are efficient.

This paper studies the outage performance of a cognitive Amplify-and-forward (AF) relay network subject to Rayleigh fading. Under the condition of imperfect Channel state information (CSI) estimations of the links from the secondary system to the Primary user (PU), the closed-form upper and lower bounds of the outage probability are derived through a geometrical analysis method. An asymptotic analysis of the outage probability is also derived in the high Signal-to-noise ratio (SNR) regime to gain additional insights on the system. The simulation results corroborate our theoretical analysis, and the effectiveness of the geometrical analysis method is verified with the conventional approach as a benchmark. The asymptotic results are very tight with the analytical lower bound in the high SNR regime. It also can be observed from the simulation results that the impact of the number of relays as well as the imperfect CSI on the outage probability and the diversity order.

According to the standard for the GSM for railway (GSM-R) wireless systems in China train control system level 3 (CTCS-3), the control data transfer delay should be no larger than 500ms with greater than 99% probability. Coverage of both non-redundant networks and intercross redundant networks and cases of single Mobile terminals (MTs) and redundant MTs on one train are considered, and the corresponding vehicle-ground communication models, delay models, and fault models are constructed. The simulation results confirm that the transfer delay can meet the standard requirements under all cases. In particular, the probability is greater than 99.996% for redundant MTs and networks, and the standard of transfer delay in CTCS-3 will be improved inevitably.

We propose two novel methods to improve the source location privacy security protection and the node energy utilization in Wireless sensor networks (WSN). A privacy preservation protocol for source location in WSN based on angles(APS) and an Enhanced protocol for source location (EAPS), which dynamically adjusts emission radius during routing. The APS protocol produces geographically dispersed phantom source nodes and utilizes the energy from the energy-abundant regions to make the routing path versatile among the entire network. In the EAPS protocol, according to the number of its own adjacent nodes, residual energy and the distance to the base station, a node adjusts its radius adaptively. Experiments show that the two novel protocols can improve the security and take advantage of the residual energy in the network balance the network life and energy consumption in comparison with the existing routing protocols based on the phantom sources.

Deep network has been proven efficient and robust to capture object features in some conditions. It still remains in the stage of classifying or detecting objects. In the field of visual tracking, deep network has not been applied widely. One of the reasons is that its time consuming made the strong method could not meet the speed need of visual tracking. A novel simple tracker is proposed to complete tracking task. A simple six-layer feed-forward backpropagation neural network is applied to capture object features. Nevertheless, this representation is not robust enough when illumination changes or drastic scale changes in dynamic condition. To improve the performance and not to increase much time spent, image perceptual hashing method is employed, which extracts low frequency information of object as its fingerprint to recognize the object from its structure. 64-bit characters are calculated by it, and they are utilized to be the bias terms of the neutral network. This leads more significant improvement for performance of extracting sufficient object features. Then we take particle filter to complete the tracking process with the proposed representation. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the state-of-the-art tracking methods.

In the user selection phrase of the conventional Multiple-input-multiple-output (MIMO) scheduling schemes, the frequent user exchange deteriorates the Quality of user experience (QoE) of the bursty data service. And the channel vector orthogonalization computation results in a high time cost. To address these problems, we propose an inertial scheduling policy to reduce the number of noneffective user exchange, and substitute self-organization policy for channel vector orthogonalization computation to reduce computational complexity. The relationship between the scheduling effectiveness and the inertia of objective function is observed in the simulation. The simulation results show that the inertial scheduling policy effectively reduce the number of potential noneffective scheduling which is inversely proportional to the Mean opinion score (MOS) that quantifies the QoE. Our proposed scheduling scheme provides significant improvement in QoE performance in the simulation. Although the proposed scheduling scheme does not consider the channel vector orthogonalization in the user selection phrase, its throughput approaches the level of the throughput-oriented scheme because of its selforganization scheduling policy.

In Orthogonal frequency division multiple access (OFDMA) cellular system, the user at the different location requires different number of subcarriers to satisfy the data rate requirement. We determine average subcarrier requirement for downlink OFDMA cellular system without power control based on the Interference to signal ratio (ISR). A typical downlink OFDMA cellular system model is proposed. Based on this model, we calculate the Cumulative distribution function (CDF) of ISR, and obtain the analytical expression of the average number of subcarriers required in the whole cell. Through numerical calculation and Monte-Carlo simulations, we analyze the impact of the various parameters on the average number of subcarriers required of the downlink OFDMA cellular system in random subcarrier allocation policy. And the results are compared with that in grouping subcarrier allocation and adaptive subcarrier allocation. These study results are useful in the performance evaluation, design, planning of resources and call admission control of OFDMA cellular networks like LTE.

A low-profile dual-polarized planar antenna with compact structure for 2.4GHz Wireless local area network (WLAN) and 2.5GHz Long term evaluation (LTE2500) base stations is proposed. A combination of a planar radiator and compact Artificial magnetic conductor (AMC) reflector is utilized to achieve a full planar structure without adding extra matching circuit, an extremely low-profile of 0.127 wavelength, as well as a high front to back ratio (F/B) of 25dB. The antenna is fabricated to test the performance such as S-parameters, radiation patterns and peak gain. Reasonable results show the suitability of the proposed antenna for 2.4GHz WLAN and LTE2500 applications.

This paper proposes a novel Through wall imaging (TWI) algorithm combined with phase compensation and Nonuniform fast Fourier transform (NUFFT). The wall effect is cancelled with phase compensation, which is implemented by multiplying the conjugated wall transmissivity with the received echoes. The NUFFT technique is implemented to calculate the imaging results. The proposed method has two merits: The phase compensation avoids a time-consuming calculation of the incidences angles; the image is reconstructed more accurately and efficiently than the conventional Interpolation-fast Fourier transform (IPFFT) method. The new method is implemented on 2D synthetic-generated data and experimental data. The results demonstrate that our method generates high quality focused images and achieves target geometrical identification within a very short time.

A novel microstrip Cascaded quadruplet (CQ) bandpass filter using quarter wavelength resonators and three-quarter wavelength resonators is proposed. Based on cross coupling and source-load coupling, three pairs of finite transmission zeros are generated at the stopband. As a result, the out-of-band rejection can be enhanced and high selectivity is achieved. Based on the proposed structure, a cascaded quadruplet filter centering at 3.0GHz is designed and fabricated. Good agreement between the measured and simulated data is obtained.

A conservative Grid ionospheric vertical delay error (GIVE) estimation would lead to conservative Protection levels (PLs) estimation and affects Satellitebased augmentation systems (SBAS) service performance. To reduce the margin in GIVE estimation, an improved spatial threat model is presented. A new parameter ofMinimum separation distance (MSD) is introduced. The new model is described with the MSD, Relative centroid metric (RCM) and fit radius as inputs. Ionospheric delay measurements during storms are used to construct the new spatial threat model. Simulation results show that, with the new model, there is at least 9.5% reduction in User ionospheric vertical delay errors (UIVEs), while the user ionospheric delay estimation errors can be bounded at the same time. Even on severe ionosphere disturbed days, at least 5% improvement of PLs (95%) could be achieved for more than 20% Conterminous united states (CONUS) area, leading to higher system service performance.