Distributed compressed video sensing (DCVS), combining advantages of compressed sensing and distributed video coding, is developed as a novel and powerful system to get an encoder with low complexity. Nevertheless, it is still unclear how to explore the method to achieve an effective video recovery through utilizing realistic signal characteristics as much as possible. Based on this, we present a novel spatiotemporal dictionary learning (DL) based reconstruction method for DCVS, where both the DL model and the l1-analysis based recovery with correlation constraints are included in the minimization problem to achieve the joint optimization of sparse representation and signal reconstruction. Besides, an alternating direction method with multipliers (ADMM) based numerical algorithm is outlined for solving the underlying optimization problem. Simulation results demonstrate that the proposed method outperforms other methods, with 0.03-4.14 dB increases in PSNR and a 0.13-15.31 dB gain for non-key frames.

Nonnegative component representation (NCR) is a mid-level representation based on nonnegative matrix factorization (NMF). Recently, it has attached much attention and achieved encouraging result for action recognition. In this paper, we propose a novel hierarchical dictionary learning strategy (HDLS) for NMF to improve the performance of NCR. Considering the variability of action classes, HDLS clusters the similar classes into groups and forms a two-layer hierarchical class model. The groups in the first layer are disjoint, while in the second layer, the classes in each group are correlated. HDLS takes account of the differences between two layers and proposes to use different dictionary learning methods for this two layers, including the discriminant class-specific NMF for the first layer and the discriminant joint dictionary NMF for the second layer. The proposed approach is extensively tested on three public datasets and the experimental results demonstrate the effectiveness and superiority of NCR with HDLS for large-scale action recognition.

This paper proposes a fast mode decision technique for intra prediction of High Efficiency Video Coding (HEVC) based on a reliability metric for motion vectors (RMMV). Since such a decision problem can be regarded as a kind of pattern classification, an efficient classifier is required for the reduction of computation complexity. This paper employs the RMMV as a classifier because the RMMV can efficiently categorize image blocks into flat(uniform), active, and edge blocks, and can estimate the direction of an edge block as well. A local search for angular modes is introduced to further speed up the decision process. An experiment shows the advantage of our technique over other techniques.

In this paper, we construct ideal and probabilistic secret sharing schemes for some multipartite access structures, including the General Hierarchical Access Structure and Compartmented Access Structures. We devise an ideal scheme which implements the general hierarchical access structure. For the compartmented access structures, we consider three special access structures. We propose ideal and probabilistic schemes for these three compartmented access structures by bivariate interpolation.

The power grid defines one of the most important technological networks of our times and has been widely studied as a kind of complex network. It has been developed for more than one century and becomes an extremely huge and seemingly robust system. But it becomes extremely fragile as well because some unexpected minimal failures may lead to sudden and massive blackouts. Many works have been carried out to investigate the structural vulnerability of power grids from the topological point of view based on the complex network theory. This Letter focuses on the structural vulnerability of the power grid under the effect of selective node removal. We propose a new kind of node centrality called overall information centrality (OIC) to guide the node removal attack. We test the effectiveness of our centrality in guiding the node removal based on several IEEE power grids. Simulation results show that, compared with other node centralities such as degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC), our OIC is more effective to guide the node removal and can destroy the power grid in less steps.

We propose a cooperative spectrum sensing scheme based on sub-Nyquist sampling in cognitive radios. Our main purpose is to understand the uncertainty caused by sub-Nyquist sampling and to present a sensing scheme that operates at low sampling rates. In order to alleviate the aliasing effect of sub-Nyquist sampling, we utilize cooperation among secondary users and the sparsity order of channel occupancy. The simulation results show that the proposed scheme can achieve reasonable sensing performance even at low sampling rates.

We consider a problem as follows: Given unit weights arriving in an online manner with the total cardinality unknown, upon each arrival we decide where to place it on the unit circle in $mathbb{R}^{2}$. The objective is to set the center of mass of the placed weights as close to the origin as possible. We apply competitive analysis defining the competitive difference as a performance measure. We first present an optimal strategy for placing unit weights which achieves a competitive difference of $rac{1}{5}$. We next consider a variant in which the destination of each weight must be chosen from a set of positions that equally divide the unit circle. We give a simple strategy whose competitive difference is 0.35. Moreover, in the offline setting, several conditions for the center of mass to lie at the origin are derived.

This paper presents novel reconfigurable semi-systolic array architecture for the Smith-Waterman with an affine gap penalty algorithm to align protein sequences optimized for shorter database sequences. This architecture has been modified to enable hardware reuse rather than replicating processing elements of the semi-systolic array in multiple FPGAs. The proposed hardware architecture and the previously published conventional one are described at the Register Transfer Level (RTL) using VHDL language and implemented using the FPGA technology. The results show that the proposed design has significant higher normalized speedup (up to 125%) over the conventional one for query sequence lengths less than 512 residues. According to the UniProtKB/TrEMBL protein database (release 2015_05) statistics, the largest number of sequences (about 80%) have sequence length less than 512 residues that makes the proposed design outperforms the conventional one in terms of speed and area in this sequence lengths range.

This paper proposes an algorithm that solves the node-to-set disjoint paths problem in an n-Möbius cube in polynomial-order time of n. It also gives a proof of correctness of the algorithm as well as estimating the time complexity, O(n4), and the maximum path length, 2n-1. A computer experiment is conducted for n=1,2,...,31 to measure the average performance of the algorithm. The results show that the average time complexity is gradually approaching to O(n3) and that the maximum path lengths cannot be attained easily over the range of n in the experiment.

Change-aware development environments can automatically record fine-grained code changes on a program and allow programmers to replay the recorded changes in chronological order. However, since they do not always need to replay all the code changes to investigate how a particular entity of the program has been changed, they often eliminate several code changes of no interest by manually skipping them in replaying. This skipping action is an obstacle that makes many programmers hesitate when they use existing replaying tools. This paper proposes a slicing mechanism that automatically removes manually skipped code changes from the whole history of past code changes and extracts only those necessary to build a particular class member of a Java program. In this mechanism, fine-grained code changes are represented by edit operations recorded on the source code of a program and dependencies among edit operations are formalized. The paper also presents a running tool that slices the operation history and replays its resulting slices. With this tool, programmers can avoid replaying nonessential edit operations for the construction of class members that they want to understand. Experimental results show that the tool offered improvements over conventional replaying tools with respect to the reduction of the number of edit operations needed to be examined and over history filtering tools with respect to the accuracy of edit operations to be replayed.

Many selected mapping (SLM) schemes have been proposed to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signal sequences. In this paper, an efficient selection (ES) method of the OFDM signal sequence with minimum PAPR among many alternative OFDM signal sequences is proposed; it supports various SLM schemes. Utilizing the fact that OFDM signal components can be sequentially generated in many SLM schemes, the generation and PAPR observation of the OFDM signal sequence are processed concurrently. While the u-th alternative OFDM signal components are being generated, by applying the proposed ES method, the generation of that alternative OFDM signal components can be interrupted (or stopped) according to the selection criteria of the best OFDM signal sequence in the considered SLM scheme. Such interruption substantially reduces the average computational complexity of SLM schemes without degradation of PAPR reduction performance, which is confirmed by analytical and numerical results. Note that the proposed method is not an isolated SLM scheme but a subsidiary method which can be easily adopted in many SLM schemes in order to further reduce the computational complexity of considered SLM schemes.

We consider a problem of the choice of price plans offered by a telecommunications company: a “pay-as-you-go” plan and a “flat-rate” plan. This problem is formulated as an online optimization problem extending the ski-rental problem, and analyzed using the competitive ratio. We give a lemma for easily calculating the competitive ratio. Based on the lemma, we derive a family of optimal strategies for a realistic class of instances.

In this paper, we propose a novel architecture for a deep learning system, named k-degree layer-wise network, to realize efficient geo-distributed computing between Cloud and Internet of Things (IoT). The geo-distributed computing extends Cloud to the geographical verge of the network in the neighbor of IoT. The basic ideas of the proposal include a k-degree constraint and a layer-wise constraint. The k-degree constraint is defined such that the degree of each vertex on the h-th layer is exactly k(h) to extend the existing deep belief networks and control the communication cost. The layer-wise constraint is defined such that the layer-wise degrees are monotonically decreasing in positive direction to gradually reduce the dimension of data. We prove the k-degree layer-wise network is sparse, while a typical deep neural network is dense. In an evaluation on the M-distributed MNIST database, the proposal is superior to a state-of-the-art model in terms of communication cost and learning time with scalability.

This paper gives an array-based practical encoder for the lossless data compression algorithm known as Compression by Substring Enumeration (CSE). The encoder makes use of the relation between CSE and the Burrows-Wheeler transform. We also modify the decoding algorithm to accommodate to the proposed encoder. Thanks to the proposed encoder and decoder, we can apply CSE to long data of more than tens of megabytes. We show compression results obtained when we perform experiments on such long data. The results empirically validate theoretical predictions on CSE.

The Euler number is an important topological property in a binary image, and it can be computed by counting certain bit-quads in the binary image. This paper proposes a further improved bit-quad-based algorithm for computing the Euler number. By scanning image rows two by two and utilizing the information obtained while processing the previous pixels, the number of pixels to be checked for processing a bit-quad can be decreased from 2 to 1.5. Experimental results demonstrated that our proposed algorithm significantly outperforms conventional Euler number computing algorithms.

In this paper, an iterative optimal method is proposed to design the prototype filters for a fast filter bank (FFB) with low complexity, aiming to control the optimum ripple magnitude tolerance of each filter according to the overall specifications. This problem is formulated as an optimization problem for which the total number of multiplications is to be minimized subject to the constrained ripple in the passband and stopband. In the following, an iterative solution is proposed to solve this optimization problem for the purpose of obtaining the impulse response coefficients with low complexity at each stage. Simulations are conducted to verify the performance of the proposed scheme and show that compared with the original method, the proposed scheme can reduce about 24.24% of multiplications. In addition, the proposed scheme and the original method provide similar mean square error (MSE) and the mean absolute error (MAE) of the frequency response.

In this paper, we propose a workload assignment policy for reducing power consumption by air conditioners in data centers. In the proposed policy, to reduce the air conditioner power consumption by raising the temperature set points of the air conditioners, the temperatures of all server back-planes are equalized by moving workload from the servers with the highest temperatures to the servers with the lowest temperatures. To evaluate the proposed policy, we use a computational fluid dynamics simulator for obtaining airflow and air temperature in data centers, and an air conditioner model based on experimental results from actual data center. Through evaluation, we show that the air conditioners' power consumption is reduced by 10.4% in a conventional data center. In addition, in a tandem data center proposed in our research group, the air conditioners' power consumption is reduced by 53%, and the total power consumption of the whole data center is exhibited to be reduced by 23% by reusing the exhaust heat from the servers.

This paper presents a novel MEMD interval thresholding denoising, where relevant modes are selected by the similarity measure between the probability density functions of the input and that of each mode. Simulation and measured EEG data processing results show that the proposed scheme achieves better performance than other traditional denoisings.

Quantum computer simulators play an important role when we evaluate quantum algorithms. Quantum computation can be regarded as parallel computation in some sense, and thus, it is suitable to implement a simulator on hardware that can process a lot of operations in parallel. In this paper, we propose a hardware quantum computer simulator. The proposed simulator is based on the register reordering method that shifts and swaps registers containing probability amplitudes so that the probability amplitudes of target basis states can be quickly selected. This reduces the number of large multiplexers and improves clock frequency. We implement the simulator on an FPGA. Experiments show that the proposed simulator has scalability in terms of the number of quantum bits, and can simulate quantum algorithms faster than software simulators.

In this paper, we propose an efficient regularized zero-forcing (RZF) precoding method that has lower hardware resource requirements and produces a shorter delay to the first transmitted symbol compared with truncated polynomial expansion (TPE) that is based on Neumann series in massive multiple-input multiple-output (MIMO) systems. The proposed precoding scheme, named matrix decomposition-polynomial expansion (MDPE), essentially applies a matrix decomposition algorithm based on polynomial expansion to significantly reduce full matrix multiplication computational complexity. Accordingly, it is suitable for real-time hardware implementations and high-mobility scenarios. Furthermore, the proposed method provides a simple expression that links the optimization coefficients to the ratio of BS/UTs antennas (β). This approach can speed-up the convergence to the matrix inverse by a matrix polynomial with small terms and further reduce computation costs. Simulation results show that the MDPE scheme can rapidly approximate the performance of the full precision RZF and optimal TPE algorithm, while adaptively selecting matrix polynomial terms in accordance with the different β and SNR situations. It thereby obtains a high average achievable rate of the UTs under power allocation.