Feature extractor is an important component of a tracker and the convolutional neural networks (CNNs) have demonstrated excellent performance in visual tracking. However, the CNN features cannot perform well under conditions of low illumination. To address this issue, we propose a novel deep correlation tracker with backtracking, which consists of target translation, backtracking and scale estimation. We employ four correlation filters, one with a histogram of oriented gradient (HOG) descriptor and the other three with the CNN features to estimate the translation. In particular, we propose a backtracking algorithm to reconfirm the translation location. Comprehensive experiments are performed on a large-scale challenging benchmark dataset. And the results show that the proposed algorithm outperforms state-of-the-art methods in accuracy and robustness.

Traditional sparse representation-based methods for human action recognition usually pool over the entire video to form the final feature representation, neglecting any spatio-temporal information of features. To employ spatio-temporal information, we present a novel histogram representation obtained by statistics on temporal changes of sparse coding coefficients frame by frame in the spatial pyramids constructed from videos. The histograms are further fed into a support vector machine with a spatial pyramid matching kernel for final action classification. We validate our method on two benchmarks, KTH and UCF Sports, and experiment results show the effectiveness of our method in human action recognition.

Given an odd prime q and an integer m ≤ q, a binary mq × q2 quasi-cyclic parity-check matrix H(m, q) can be constructed for an array low-density parity-check (LDPC) code C (m, q). In this letter, we investigate the first separating redundancy of C (m, q). We prove that H (m, q) is 1-separating for any pair of (m, q), from which we conclude that the first separating redundancy of C (m, q) is upper bounded by mq. Then we show that our upper bound on the first separating redundancy of C (m, q) is tighter than the general deterministic and constructive upper bounds in the literature. For m=2, we further prove that the first separating redundancy of C (2, q) is 2q for any odd prime q. For m ≥ 3, we conjecture that the first separating redundancy of C (m, q) is mq for any fixed m and sufficiently large q.

This paper mainly proposes a line segment detection method based on pseudo peak suppression and local Hough transform, which has good noise resistance and can solve the problems of short line segment missing detection, false detection, and oversegmentation. In addition, in response to the phenomenon of uneven development in nuclear emulsion tomographic images, this paper proposes an image preprocessing process that uses the “Difference of Gaussian” method to reduce noise and then uses the standard deviation of the gray value of each pixel to bundle and unify the gray value of each pixel, which can robustly obtain the linear features in these images. The tests on the actual dataset of nuclear emulsion tomographic images and the public YorkUrban dataset show that the proposed method can effectively improve the accuracy of convolutional neural network or vision in transformer-based event classification for alpha-decay events in nuclear emulsion. In particular, the line segment detection method in the proposed method achieves optimal results in both accuracy and processing speed, which also has strong generalization ability in high quality natural images.

The segmentation performance of fresh tea sprouts is inadequate due to the uncontrollable posture. A novel method for Fresh Tea Sprouts Segmentation based on Capsule Network (FTS-SegCaps) is proposed in this paper. The spatial relationship between local parts and whole tea sprout is retained and effectively utilized by a deep encoder-decoder capsule network, which can reduce the effect of tea sprouts with uncontrollable posture. Meanwhile, a patch-based local dynamic routing algorithm is also proposed to solve the parameter explosion problem. The experimental results indicate that the segmented tea sprouts via FTS-SegCaps are almost coincident with the ground truth, and also show that the proposed method has a better performance than the state-of-the-art methods.

Real-time matting is a challenging research in deep learning. Conventional CNN (Convolutional Neural Networks) approaches are easy to misjudge the foreground and background semantic and have blurry matting edges, which result from CNN’s limited concentration on global context due to receptive field. We propose a real-time matting approach called RMViT (Real-time matting with Vision Transformer) with Transformer structure, attention and content-aware guidance to solve issues above. The semantic accuracy improves a lot due to the establishment of global context and long-range pixel information. The experiments show our approach exceeds a 30% reduction in error metrics compared with existing real-time matting approaches.

In this letter, we investigate the separating redundancy of binary linear codes. Using analytical techniques, we provide a general lower bound on the first separating redundancy of binary linear codes and show the bound is tight for a particular family of binary linear codes, i.e., cycle codes. In other words, the first separating redundancy of cycle codes can be determined. We also derive a deterministic and constructive upper bound on the second separating redundancy of cycle codes, which is shown to be better than the general deterministic and constructive upper bounds for the codes.

In this letter, we consider the incorrigible sets of binary linear codes. First, we show that the incorrigible set enumerator of a binary linear code is tantamount to the Tutte polynomial of the vector matroid induced by the parity-check matrix of the code. A direct consequence is that determining the incorrigible set enumerator of binary linear codes is #P-hard. Then for a cycle code, we express its incorrigible set enumerator via the Tutte polynomial of the graph describing the code. Furthermore, we provide the explicit formula of incorrigible set enumerators of cycle codes constructed from complete graphs.

The separating redundancy is an important property in the analysis of the error-and-erasure decoding of a linear block code. In this work, we investigate the separating redundancy of the duals of first-order generalized Reed-Muller (GRM) codes, a class of nonbinary linear block codes that have nice algebraic properties. The dual of a first-order GRM code can be specified by two positive integers m and q and denoted by R(m,q), where q is the power of a prime number and q≠2. We determine the first separating redundancy value of R(m,q) for any m and q. We also determine the second separating redundancy values of R(m,q) for any q and m=1 and 2. For m≥3, we set up a binary integer linear programming problem, the optimum of which gives a lower bound on the second separating redundancy of R(m,q).

This paper analyzes the limitations of the multihoming support in the Proxy Mobile IPv6 protocol, then proposes an enhanced multihoming support scheme based on a per-interface address configuration method. The proposed scheme can provide a more flexible multihoming support and also maintain application session continuity during a handoff between two interfaces by using IPv6 extension headers. Plus, flow distribution with filters is also used to realize the advantages of multihoming. Simulation results with the OPNET validate the proposed multihoming support scheme for convergent networks.

We implemented a scalar multiplication method over elliptic curves using division polynomials. We adapt an algorithm for computing elliptic nets proposed by Stange. According to our experimental results, the scalar multiplication method using division polynomials is faster than the binary method in an affine coordinate system.

Recent studies on switching fabrics mainly focus on the switching schedule algorithms, which aim at improving the throughput (a key performance metric). However, the delay (another key performance metric) of switching fabrics cannot be well guaranteed. A good switching fabric should be endowed with the properties of high throughput, delay guarantee, low component complexity and high-speed multicast, which are difficult for conventional switching fabrics to achieve. This has fueled great interest in designing a new switching fabric that can support large-scale extension and high-speed multicast. Motivated by this, we reuse the self-routing Boolean concentrator network and embed a model of multicast packet copy separation in front to construct a load-balanced multicast switching fabric (LB-MSF) with delay guarantee. The first phase of LB-MSF is responsible for balancing the incoming traffic into uniform cells while the second phase is in charge of self-routing the cells to their final destinations. In order to improve the throughput, LB-MSF is combined with the merits of erasure codes against packet loss. Experiments and analyses verify that the proposed fabric is able to achieve high-speed multicast switching and suitable for building super large-scale switching fabric in Next Generation Network(NGN) with all the advantages mentioned above. Furthermore, a prototype of the proposed switch is developed on FPGA, and presents excellent performance.

This paper shows two power analysis attacks against a software implementation of a first-order DPA resistant S-box algorithm that is based on the discrete Fourier Transform (DFT). The DPA resistant S-box algorithm based on DFT was proposed by Prouff et al. in 2006 and improved by Coron et al. in 2008, respectively. In our attacks against the improved one, we pre-process the power traces by separating them into two subgroups, so that each has a biased mask. For the separated power traces, two post analysis methods are proposed to identify the key. One is based on DPA attack against one subgroup, and the other utilizes the difference of means for two subgroups and a pattern matching. Finally, we compare these two attack methods and propose an algorithm-level countermeasure to enhance the security of S-box calculation based on the DFT.

Volleyball video analysis plays important roles in providing data for TV contents and developing strategies. Among all the topics of volleyball analysis, qualitative player action recognition is essential because it potentially provides not only the action that being performed but also the quality, which means how well the action is performed. However, most action recognition researches focus on the discrimination between different actions. The quality of an action, which is helpful for evaluation and training of the player skill, has only received little attention so far. The vital problems in qualitative action recognition include occlusion, small inter-class difference and various kinds of appearance caused by the player change. This paper proposes a 3D global and multi-view local features combination based recognition framework with global team formation feature, ball state feature and abrupt pose features. The above problems are solved by the combination of 3D global features (which hide the unstable and incomplete 2D motion feature caused by occlusion) and the multi-view local features (which get detailed local motion features of body parts in multiple viewpoints). Firstly, the team formation extracts the 3D trajectories from the whole team members rather than a single target player. This proposal focuses more on the entire feature while eliminating the personal effect. Secondly, the ball motion state feature extracts features from the 3D ball trajectory. The ball motion is not affected by the personal appearance, so this proposal ignores the influence of the players appearance and makes it more robust to target player change. At last, the abrupt pose feature consists of two parts: the abrupt hit frame pose (which extracts the contour shape of the player's pose at the hit time) and abrupt pose variation (which extracts the pose variation between the preparation pose and ending pose during the action). These two features make difference of each action quality more distinguishable by focusing on the motion standard and stability between different quality actions. Experiments are conducted on game videos from the Semifinal and Final Game of 2014 Japan Inter High School Games of Men's Volleyball in Tokyo Metropolitan Gymnasium. The experimental results show the accuracy achieves 97.26%, improving 11.33% for action discrimination and 91.76%, and improving 13.72% for action quality evaluation.

The separating redundancy is an important concept in the analysis of the error-and-erasure decoding of a linear block code using a parity-check matrix of the code. In this letter, we derive new constructive upper bounds on the second separating redundancies of low-density parity-check (LDPC) codes constructed from projective and Euclidean planes over the field Fq with q even.

Given an odd prime q and an integer m ≤ q, an array-based parity-check matrix H(m,q) can be constructed for a quasi-cyclic low-density parity-check (LDPC) code C(m,q). For m=4 and q ≥ 11, we prove the stopping distance of H(4,q) is 10, which is equal to the minimum Hamming distance of the associated code C(4,q). In addition, a tighter lower bound on the stopping distance of H(m,q) is also given for m > 4 and q ≥ 11.

Recently, broadcast services are introduced in cellular networks and macro diversity is an effective way to combat fading. In this paper, we propose a kind of distributed space-time block codes (STBCs) for macro diversity which is constructed from the total antennas of multiple cooperating base stations, and all the antennas form an equivalent multiple input multiple output (MIMO) system. This code is termed High-Dimension-Full-Rate-Quasi-Orthogonal STBC (HDFR-QOSTBC) which can be characterized as: (1) It can be applied with any number of transmit antennas especially when the number of transmit antennas is large; (2) The code is with full transmit rate of one; (3) The Maximum Likelihood (ML) decoding complexity of this code is controllable and limited to Nt/2-symbol-decodable for total Nt transmit antennas. Then, we completely analyze the structure of the equivalent channel for the kind of codes and reveal a property that the eigenvectors of the equivalent channel are constant and independent from the channel realization, and this characteristic can be exploited for a new transmission structure with single-symbol linear decoder. Furthermore, we analyze different macro diversity schemes and give a performance comparison. The simulation results show that the proposed scheme is practical for the broadcast systems with significant performance improvement comparing with soft-combination and cyclic delay diversity (CDD) methods.

Feature extractor plays an important role in visual tracking, but most state-of-the-art methods employ the same feature representation in all scenes. Taking into account the diverseness, a tracker should choose different features according to the videos. In this work, we propose a novel feature adaptive correlation tracker, which decomposes the tracking task into translation and scale estimation. According to the luminance of the target, our approach automatically selects either hierarchical convolutional features or histogram of oriented gradient features in translation for varied scenarios. Furthermore, we employ a discriminative correlation filter to handle scale variations. Extensive experiments are performed on a large-scale benchmark challenging dataset. And the results show that the proposed algorithm outperforms state-of-the-art trackers in accuracy and robustness.

Tea sprouts segmentation via machine vision is the core technology of tea automatic picking. A novel method for Tea Sprouts Segmentation based on improved deep convolutional encoder-decoder Network (TS-SegNet) is proposed in this paper. In order to increase the segmentation accuracy and stability, the improvement is carried out by a contrastive-center loss function and skip connections. Therefore, the intra-class compactness and inter-class separability are comprehensively utilized, and the TS-SegNet can obtain more discriminative tea sprouts features. The experimental results indicate that the proposed method leads to good segmentation results, and the segmented tea sprouts are almost coincident with the ground truth.

20-Gbps non return-to-zero (NRZ) – binary phase shift keying (BPSK) using the silicon Mach-Zehnder modulator is demonstrated and characterized. Measurement of a constellation diagram confirms successful modulation of 20-Gbps BPSK with the silicon modulator. Transmission performance is characterized in the measurement of bit-error-rate in accumulated dispersion range from -347 ps/nm to +334 ps/nm using SMF and a dispersion compensating fiber module. Optical signal-to-noise ratio required for bit-error-rate of 10-3 is 10.1 dB at back-to-back condition. It is 1.2-dB difference from simulated value. Obtained dispersion tolerance less than 2-dB power penalty for bit-error-rate of 10-3 is -220 ps/nm to +230 ps/nm. The symmetric dispersion tolerance indicates chirp-free modulation. Frequency chirp inherent in the modulation mechanism of the silicon MZM is also discussed with the simulation. The effect caused by the frequency chirp is limited to 3% shift in the chromatic dispersion range of 2 dB power penalty for BER 10-3. The effect inherent in the silicon modulation mechanism is confirmed to be very limited and not to cause any significant degradation in the transmission performance.