The emerging Versatile Video Coding (VVC) standard currently adopts Triangular Partitioning Mode (TPM) to make more flexible inter prediction. Due to the motion search and motion storage for TPM, the complexity of the encoder and decoder is significantly increased. This letter proposes two simplifications of TPM for reducing the complexity of the current design. One simplification is to reduce the number of combinations of motion vectors for both partitions to be checked. The method gives 4% encoding time decrease with negligible BD-rate loss. Another one is to remove the reference picture remapping process in the motion vector storage of TPM. It reduces the complexity of the encoder and decoder without a BD-rate change for the random-access configuration.

Sorting is one of the most fundamental problems in mathematics and computer science. Because high-throughput and flexible sorting is a key requirement in modern databases, this paper presents efficient techniques for designing a high-throughput sorting matrix that supports continuous data sequences. There have been numerous studies on the optimization of sorting circuits on FPGA (field-programmable gate array) platforms. These studies focused on attaining high throughput for a single command with fixed data width. However, the architectures proposed do not meet the requirement of diversity for database data types. A sorting matrix architecture is thus proposed to overcome this problem. Our design consists of a matrix of identical basic sorting cells. The sorting cells work in a pipeline and in parallel, and the matrix can simultaneously process multiple data streams, which can be combined into a high-width single-channel data stream or low-width multiple-channel data streams. It can handle continuous sequences and allows for sorting variable-length data sequences. Its maximum throughput is approximately 1.4 GB/s for 32-bit sequences and approximately 2.5 GB/s for 64-bit sequences on our platform.

In this paper, we propose secure dictionary learning based on a random unitary transform for sparse representation. Currently, edge cloud computing is spreading to many application fields including services that use sparse coding. This situation raises many new privacy concerns. Edge cloud computing poses several serious issues for end users, such as unauthorized use and leak of data, and privacy failures. The proposed scheme provides practical MOD and K-SVD dictionary learning algorithms that allow computation on encrypted signals. We prove, theoretically, that the proposal has exactly the same dictionary learning estimation performance as the non-encrypted variant of MOD and K-SVD algorithms. We apply it to secure image modeling based on an image patch model. Finally, we demonstrate its performance on synthetic data and a secure image modeling application for natural images.

In this paper, we extend the notion of bijective connection graphs to introduce directed bijective connection graphs. We propose algorithms that solve the node-to-set node-disjoint paths problem and the node-to-node node-disjoint paths problem in a directed bijective connection graph. The time complexities of the algorithms are both O(n4), and the maximum path lengths are both 2n-1.

Herein, we analytically derive the effective index and field distribution of the LP01 mode of a step-index N-sided regular-polygonal-core fiber. To do this, we utilize the lowest-order non-anomalous approximation of the π/N expansion. These properties are also calculated numerically and the results are compared the with approximations.

We present an improved constant-round secure two-party protocol for integer comparison functionality, which is one of the most fundamental building blocks in secure computation. Our protocol is in the so-called client-server model, which is utilized in real-world MPC products such as Sharemind, where any number of clients can create shares of their input and distribute to the servers who then jointly compute over the shares and return the shares of the result to the client. In the client-aided client-server model, as mentioned briefly by Mohassel and Zhang (S&P'17), a client further generates and distributes some necessary correlated randomness to servers. Such correlated randomness admits efficient protocols since otherwise, servers have to jointly generate randomness by themselves, which can be inefficient. In this paper, we improve the state-of-the-art constant-round comparison protocols by Damgå rd et al. (TCC'06) and Nishide and Ohta (PKC'07) in the client-aided model. Our techniques include identifying correlated randomness in these comparison protocols. Along the way, we also use tree-based techniques for a building block, which deviate from the above two works. Our proposed protocol requires only 5 communication rounds, regardless of the bit length of inputs. This is at least 5 times fewer rounds than existing protocols. We implement our secure comparison protocol in C++. Our experimental results show that this low-round complexity benefits in high-latency networks such as WAN. We also present secure Min/Argmin protocols using the secure comparison protocol.

A light field, which is equivalent to a dense set of multi-view images, has various applications such as depth estimation and 3D display. One of the essential problems in light field applications is light field interpolation, i.e., view interpolation. The interpolation accuracy is enhanced by exploiting an inherent property of a light field. One example is that an epipolar plane image (EPI), which is a 2D subset of the 4D light field, consists of many lines, and these lines have almost the same slope in a local region. This structure induces a sparse representation in the frequency domain, where most of the energy resides on a line passing through the origin. On the basis of this observation, we propose a group sparsity prior suitable for light fields to exploit their line structure fully for interpolation. Specifically, we designed the directional groups in the discrete Fourier transform (DFT) domain so that the groups can represent the concentration of the energy, and we thereby formulated an LF interpolation problem as an overlapping group lasso. We also introduce several techniques to improve the interpolation accuracy such as applying a window function, determining group weights, expanding processing blocks, and merging blocks. Our experimental results show that the proposed method can achieve better or comparable quality as compared to state-of-the-art LF interpolation methods such as convolutional neural network (CNN)-based methods.

In this paper, we propose an acceleration method for the Held-Karp algorithm that solves the symmetric traveling salesman problem by dynamic programming. The proposed method achieves acceleration with two techniques. First, we locate data-independent subproblems so that the subproblems can be solved in parallel. Second, we reduce the number of subproblems by a meet in the middle (MITM) technique, which computes the optimal path from both clockwise and counterclockwise directions. We show theoretical analysis on the impact of MITM in terms of the time and space complexities. In experiments, we compared the proposed method with a previous method running on a single-core CPU. Experimental results show that the proposed method on an 8-core CPU was 9.5-10.5 times faster than the previous method on a single-core CPU. Moreover, the proposed method on a graphics processing unit (GPU) was 30-40 times faster than that on an 8-core CPU. As a side effect, the proposed method reduced the memory usage by 48%.

Particle swarm optimization (PSO) is a swarm intelligence algorithm and has good search performance and simplicity in implementation. Because of its properties, PSO has been applied to various optimization problems. However, the search performance of the classical PSO (CPSO) depends on reference frame of solution spaces for each objective function. CPSO is an invariant algorithm through translation and scale changes to reference frame of solution spaces but is a rotationally variant algorithm. As such, the search performance of CPSO is worse in solving rotated problems than in solving non-rotated problems. In the reference frame invariance, the search performance of an optimization algorithm is independent on rotation, translation, or scale changes to reference frame of solution spaces, which is a property of preferred optimization algorithms. In our previous study, piecewise-linear particle swarm optimizer (PPSO) has been proposed, which is effective in solving rotated problems. Because PPSO particles can move in solution spaces freely without depending on the coordinate systems, PPSO algorithm may have rotational invariance. However, theoretical analysis of reference frame invariance of PPSO has not been done. In addition, although behavior of each particle depends on PPSO parameters, good parameter conditions in solving various optimization problems have not been sufficiently clarified. In this paper, we analyze the reference frame invariance of PPSO theoretically, and investigated whether or not PPSO is invariant under reference frame alteration. We clarify that control parameters of PPSO which affect movement of each particle and performance of PPSO through numerical simulations.

Convolutional neural networks (CNNS) have a strong ability to understand and judge images. However, the enormous parameters and computation of CNNS have limited its application in resource-limited devices. In this letter, we used the idea of parameter sharing and dense connection to compress the parameters in the convolution kernel channel direction, thus greatly reducing the number of model parameters. On this basis, we designed Shared and Dense Channel-wise Convolutional Networks (SDChannelNets), mainly composed of Depth-wise Separable SD-Channel-wise Convolution layer. The advantage of SDChannelNets is that the number of model parameters is greatly reduced without or with little loss of accuracy. We also introduced a hyperparameter that can effectively balance the number of parameters and the accuracy of a model. We evaluated the model proposed by us through two popular image recognition tasks (CIFAR-10 and CIFAR-100). The results showed that SDChannelNets had similar accuracy to other CNNs, but the number of parameters was greatly reduced.

Linear Prediction (LP) analysis is commonly used in speech processing. LP is based on Auto-Regressive (AR) model and it estimates the AR model parameter from signals with l2-norm optimization. Recently, sparse estimation is paid attention since it can extract significant features from big data. The sparse estimation is realized by l1 or l0-norm optimization or regularization. Sparse LP analysis methods based on l1-norm optimization have been proposed. Since excitation of speech is not white Gaussian, a sparse LP estimation can estimate more accurate parameter than the conventional l2-norm based LP. These are time-invariant and real-valued analysis. We have been studied Time-Varying Complex AR (TV-CAR) analysis for an analytic signal and have evaluated the performance on speech processing. The TV-CAR methods are l2-norm methods. In this paper, we propose the sparse TV-CAR analysis based on adaptive LASSO (Least absolute shrinkage and selection operator) that is l1-norm regularization and evaluate the performance on F0 estimation of speech using IRAPT (Instantaneous RAPT). The experimental results show that the sparse TV-CAR methods perform better for a high level of additive Pink noise.

High frame rate and ultra-low delay matching system plays an important role in various human-machine interactive applications, which demands better performance in matching deformable and out-of-plane rotating objects. Although many algorithms have been proposed for deformation tracking and matching, few of them are suitable for hardware implementation due to complicated operations and large time consumption. This paper proposes a hardware-oriented template update and recovery method for high frame rate and ultra-low delay deformation matching system. In the proposed method, the new template is generated in real time by partially updating the template descriptor and adding new keypoints simultaneously with the matching process in pixels (proposal #1), which avoids the large inter-frame delay. The size and shape of region of interest (ROI) are made flexible and the Hamming threshold used for brute-force matching is adjusted according to pixel position and the flexible ROI (proposal #2), which solves the problem of template drift. The template is recovered by the previous one with a relative center-shifting vector when it is judged as lost via region-wise difference check (proposal #3). Evaluation results indicate that the proposed method successfully achieves the real-time processing of 784fps at the resolution of 640×480 on field-programmable gate array (FPGA), with a delay of 0.808ms/frame, as well as achieves satisfactory deformation matching results in comparison with other general methods.

Real-time 3D players tracking plays an important role in sports analysis, especially for the live services of sports broadcasting, which have a strict limitation on processing time. For these kinds of applications, 3D trajectories of players contribute to high-level game analysis such as tactic analysis and commercial applications such as TV contents. Thus real-time implementation for 3D players tracking is expected. In order to achieve real-time for 60fps videos with high accuracy, (that means the processing time should be less than 16.67ms per frame), the factors that limit the processing time of target algorithm include: 1) Large image area of each player. 2) Repeated processing of multiple players in multiple views. 3) Complex calculation of observation algorithm. To deal with the above challenges, this paper proposes a representative spatial selection and temporal combination based real-time implementation for multi-view volleyball players tracking on the GPU device. First, the representative spatial pixel selection, which detects the pixels that mostly represent one image region to scale down the image spatially, reduces the number of processing pixels. Second, the representative temporal likelihood combination shares observation calculation by using the temporal correlation between images so that the times of complex calculation is reduced. The experiments are based on videos of the Final and Semi-Final Game of 2014 Japan Inter High School Games of Men's Volleyball in Tokyo Metropolitan Gymnasium. On the GPU device GeForce GTX 1080Ti, the tracking system achieves real-time on 60fps videos and keeps the tracking accuracy higher than 97%.

We propose hardware-aware sum-product (SP) decoding for low-density parity-check codes. To simplify an implementation using a fixed-point number representation, we transform SP decoding in the logarithm domain to that in the decision domain. A polynomial approximation is proposed to implement an update rule of the proposed SP decoding efficiently. Numerical simulations show that the approximate SP decoding achieves almost the same performance as the exact SP decoding when an appropriate degree in the polynomial approximation is used, that it improves the convergence properties of SP and normalized min-sum decoding in the high signal-to-noise ratio regime, and that it is robust against quantization errors.

The bulk execution of a sequential algorithm is to execute it for many different inputs in turn or at the same time. It is known that the bulk execution of an oblivious sequential algorithm can be implemented to run efficiently on a GPU. The bulk execution supports fine grained bitwise parallelism, allowing it to achieve high acceleration over a straightforward sequential computation. The main contribution of this work is to present a Bitwise Parallel Bulk Computation (BPBC) to accelerate the Smith-Waterman Algorithm (SWA) using the affine gap penalty. Thus, our idea is to convert this computation into a circuit simulation using the BPBC technique to compute multiple instances simultaneously. The proposed BPBC technique for the SWA has been implemented on the GPU and CPU. Experimental results show that the proposed BPBC for the SWA accelerates the computation by over 646 times as compared to a single CPU implementation and by 6.9 times as compared to a multi-core CPU implementation with 160 threads.

Parameter estimation theorems for LFM signals have been developed due to the advantages of fractional Fourier transform (FrFT). The traditional estimation methods in the fractional Fourier domain (FrFD) are almost based on two-dimensional search which have the contradiction between estimation performance and complexity. In order to solve this problem, we introduce the orthogonal matching pursuit (OMP) into the FrFD, propose a modified optimization method to estimate initial frequency and final frequency of fractional bandlimited LFM signals. In this algorithm, the differentiation fractional spectrum which is used to form observation matrix in OMP is derived from the spectrum analytical formulations of the LFM signal, and then, based on that the LFM signal has approximate rectangular spectrum in the FrFD and the correlation between the LFM signal and observation matrix yields a maximal value at the edge of the spectrum (see Sect.3.3 for details), the edge spectrum information can be extracted by OMP. Finally, the estimations of initial frequency and final frequency are obtained through multiplying the edge information by the sampling frequency resolution. The proposed method avoids reconstruction and the traditional peak-searching procedure, and the iterations are needed only twice. Thus, the computational complexity is much lower than that of the existing methods. Meanwhile, Since the vectors at the initial frequency and final frequency points both have larger modulus, so that the estimations are closer to the actual values, better normalized root mean squared error (NRMSE) performance can be achieved. Both theoretical analysis and simulation results demonstrate that the proposed algorithm bears a relatively low complexity and its estimation precision is higher than search-based and reconstruction-based algorithms.

Person Re-Identification has received extensive study in the past few years and achieves impressive progress. Recent outstanding methods extract discriminative features by slicing feature maps of deep neural network into several stripes. Still there have improvement on feature fusion and metric learning strategy which can help promote slice-based methods. In this paper, we propose a novel framework that is end-to-end trainable, called Multi-level Slice-based Network (MSN), to capture features both in different levels and body parts. Our model consists of a dual-branch network architecture, one branch for global feature extraction and the other branch for local ones. Both branches process multi-level features using pyramid feature alike module. By concatenating the global and local features, distinctive features are exploited and properly compared. Also, our proposed method creatively introduces a triplet-center loss to elaborate combined loss function, which helps train the joint-learning network. By demonstrating the comprehensive experiments on the mainstream evaluation datasets including Market-1501, DukeMTMC, CUHK03, it indicates that our proposed model robustly achieves excellent performance and outperforms many of existing approaches. For example, on DukeMTMC dataset in single-query mode, we obtain a great result of Rank-1/mAP =85.9%(+1.0%)/74.2%(+4.7%).

Sparse arrays can usually achieve larger array apertures than uniform linear arrays (ULA) with the same number of physical antennas. However, the conventional direction-of-arrival (DOA) estimation algorithms for sparse arrays usually require the spatial smoothing operation to recover the matrix rank which inevitably involves heavy computational complexity and leads to a reduction in the degrees-of-freedom (DOFs). In this paper, a low-complex DOA estimation algorithm by exploiting the discrete Fourier transform (DFT) is proposed. Firstly, the spatial spectrum of the virtual array constructed from the sparse array is established by exploiting the DFT operation. The initial DOA estimation can obtain directly by searching the peaks in the DFT spectrum. However, since the number of array antennas is finite, there exists spectrum power leakage which will cause the performance degradation. To further improve the angle resolution, an iterative process is developed to suppress the spectrum power leakage. Thus, the proposed algorithm does not require the spatial smoothing operation and the computational complexity is reduced effectively. In addition, due to the extention of DOF with the application of the sparse arrays, the proposed algorithm can resolve the underdetermined DOA estimation problems. The superiority of the proposed algorithm is demonstrated by simulation results.

Distributed compressive video sensing (DCVS) has received considerable attention due to its potential in source-limited communication, e.g., wireless video sensor networks (WVSNs). Multi-hypothesis (MH) prediction, which treats the target block as a linear combination of hypotheses, is a state-of-the-art technique in DCVS. The common approach is under the supposition that blocks that are dissimilar from the target block are given lower weights than blocks that are more similar. This assumption can yield acceptable reconstruction quality, but it is not suitable for scenarios with more details. In this paper, based on the joint sparsity model (JSM), the authors present a Tikhonov-regularized MH prediction scheme in which the most similar block provides the similar common portion and the others blocks provide respective unique portions, differing from the common supposition. Specifically, a new scheme for generating hypotheses and a Euclidean distance-based metric for the regularized term are proposed. Compared with several state-of-the-art algorithms, the authors show the effectiveness of the proposed scheme when there are a limited number of hypotheses.

Direction of arrival (DOA) estimation as a fundamental issue in array signal processing has been extensively studied for many applications in military and civilian fields. Many DOA estimation algorithms have been developed for different application scenarios such as low signal-to-noise ratio (SNR), limited snapshots, etc. However, there are still some practical problems that make DOA estimation very difficult. One of them is the correlation between sources. In this paper, we develop a sparsity-based method to estimate the DOA of coherent signals with sparse linear array (SLA). We adopt the off-grid signal model and solve the DOA estimation problem in the sparse Bayesian learning (SBL) framework. By considering the SLA as a ‘missing sensor’ ULA, our proposed method treats the output of the SLA as a partial output of the corresponding virtual uniform linear array (ULA) to make full use of the expanded aperture character of the SLA. Then we employ the expectation-maximization (EM) method to update the hyper-parameters and the output of the virtual ULA in an iterative manner. Numerical results demonstrate that the proposed method has a better performance in correlated signal scenarios than the reference methods in comparison, confirming the advantage of exploiting the extended aperture feature of the SLA.