State-of-the-art background subtraction and foreground detection methods still face a variety of challenges, including illumination changes, camouflage, dynamic backgrounds, shadows, intermittent object motion. Detection of foreground elements via the robust principal component analysis (RPCA) method and its extensions based on low-rank and sparse structures have been conducted to achieve good performance in many scenes of the datasets, such as Changedetection.net (CDnet); however, the conventional RPCA method does not handle shadows well. To address this issue, we propose an approach that considers observed video data as the sum of three parts, namely a row-rank background, sparse moving objects and moving shadows. Next, we cast inequality constraints on the basic RPCA model and use an alternating direction method of multipliers framework combined with Rockafeller multipliers to derive a closed-form solution of the shadow matrix sub-problem. Our experiments have demonstrated that our method works effectively on challenging datasets that contain shadows.

A general method for generating multiple two-dimensional frequency-hopping pilot signals with limited mutual interference, for propagation channel estimation in time and frequency with equidistant sampling, is presented. Each pilot signal uses a different generic frequency-hopping pilot pattern that is repeated in frequency domain, with repetition period equal to the desired sampling interval in frequency domain. Some interesting special cases of the general construction are considered as well. The practical applicability and usefulness of the proposed solution are demonstrated by the numerical evaluation of a set of frequency-hopping pilot patterns in a typical multi-cell scenario of the future evolved third generation cellular systems.

In this paper, operator-based reset control for a class of nonlinear systems with unknown bounded disturbance is considered using right coprime factorization approach. In detail, firstly, for dealing with the unknown bounded disturbance of the nonlinear systems, operator-based reset control framework is proposed based on right coprime factorization. By the proposed framework, robust stability of the nonlinear systems with unknown bounded disturbance is guaranteed by using the proposed reset controller. Secondly, under the reset control framework, an optimal design scheme is discussed for minimizing the error norm based on the proposed operator-based reset controller. Finally, for conforming effectiveness of the proposed design scheme, a simulation example is given.

The stopping distance and stopping redundancy of a linear code are important concepts in the analysis of the performance and complexity of the code under iterative decoding on a binary erasure channel. In this paper, we studied the stopping distance and stopping redundancy of Finite Geometry LDPC (FG-LDPC) codes, and derived an upper bound of the stopping redundancy of FG-LDPC codes. It is shown from the bound that the stopping redundancy of the codes is less than the code length. Therefore, FG-LDPC codes give a good trade-off between the performance and complexity and hence are a very good choice for practical applications.

In this paper, we propose an online antenna-pulse selection method in space time adaptive processing, while maintaining considerable performance and low computational complexity. The proposed method considers the antenna-pulse selection and covariance matrix estimation at the same time by exploiting the structured clutter covariance matrix. Such prior knowledge can enhance the covariance matrix estimation accuracy and thus can provide a better objective function for antenna-pulse selection. Simulations also validate the effectiveness of the proposed method.

This paper focuses on the research of a new high-speed DC switch repulsion mechanism with experimental and simulation methods. Multi-physical equations reflecting the transient electromagnetic field, electric circuit, mechanical motion and material deformation are coupled in the calculation. For the reason of accuracy, skin effect and the proximity effect caused by the current in the coil are also taken into account. According to the simulation results, which indicate several key parameters severely affecting the mechanism speed, a high-speed DC switch repulsion mechanism is developed. By the test of mechanism motion, its average speed can be up to 8.4 m/s and its mechanism response time is 250 µs, which verifies the simulation results. Furthermore, during high speed motion the stress on the metal plate and moving contact is also discussed. It is noticed that the influence of the material deformation on the mechanical motion is very important.

Boolean functions and vectorial Boolean functions are the most important components of stream ciphers. Their cryptographic properties are crucial to the security of the underlying ciphers. And how to construct such functions with good cryptographic properties is a nice problem that worth to be investigated. In this paper, using two small nonlinear functions with t-1 resiliency, we provide a method on constructing t-resilient n variables Boolean functions with strictly almost optimal nonlinearity >2n-1-2n/2 and optimal algebraic degree n-t-1. Based on the method, we give another construction so that a large class of resilient vectorial Boolean functions can be obtained. It is shown that the vectorial Boolean functions also have strictly almost optimal nonlinearity and optimal algebraic degree.

Two adaptive energy detectors are proposed for cognitive radio systems to detect the primary users. Unlike the conventional energy detector (CED) where a decision is made after receiving all samples, our detectors make a decision with the sequential arrival of samples. Hence, the sample size of the proposed detectors is adaptive. Simulation results show that for a desired performance, the average sample size of the proposed detectors is much less than that of the CED. Therefore, they are more agile than the CED.

This letter proposes a representation learning framework of convolutional neural networks (Convnets) that aims to rectify and improve the feature representations learned by existing transformation-invariant methods. The existing methods usually encode feature representations invariant to a wide range of spatial transformations by augmenting input images or transforming intermediate layers. Unfortunately, simply transforming the intermediate feature maps may lead to unpredictable representations that are ineffective in describing the transformed features of the inputs. The reason is that the operations of convolution and geometric transformation are not exchangeable in most cases and so exchanging the two operations will yield the transformation error. The error may potentially harm the performance of the classification networks. Motivated by the fractal statistics of natural images, this letter proposes a rectifying transformation operator to minimize the error. The proposed method is differentiable and can be inserted into the convolutional architecture without making any modification to the optimization algorithm. We show that the rectified feature representations result in better classification performance on two benchmarks.

Traditional low-rank feature lose the temporal information among action sequence. To obtain the temporal information, we split an action video into multiple action subsequences and concatenate all the low-rank features of subsequences according to their time order. Then we recognize actions by learning a novel dictionary model from concatenated low-rank features. However, traditional dictionary learning models usually neglect the similarity among the coding coefficients and have bad performance in dealing with non-linearly separable data. To overcome these shortcomings, we present a novel similarity constrained discriminative kernel dictionary learning for action recognition. The effectiveness of the proposed method is verified on three benchmarks, and the experimental results show the promising results of our method for action recognition.

In this letter, the structural analysis of nonbinary cyclic and quasi-cyclic (QC) low-density parity-check (LDPC) codes with α-multiplied parity-check matrices (PCMs) is concerned. Using analytical methods, several structural parameters of nonbinary cyclic and QC LDPC codes with α-multiplied PCMs are determined. In particular, some classes of nonbinary LDPC codes constructed from finite fields and finite geometries are shown to have good minimum and stopping distances properties, which may explain to some extent their wonderful decoding performances.

In this review paper we showcase recent activities on silicon photonics science and technology research in Hong Kong regarding two important topical areas--microresonator devices and optical nonlinearities. Our work on silicon microresonator filters, switches and modulators have shown promise for the nascent development of on-chip optoelectronic signal processing systems, while our studies on optical nonlinearities have contributed to basic understanding of silicon-based optically-pumped light sources and helium-implanted detectors. Here, we review our various passive and electro-optic active microresonator devices including (i) cascaded microring resonator cross-connect filters, (ii) NRZ-to-PRZ data format converters using a microring resonator notch filter, (iii) GHz-speed carrier-injection-based microring resonator modulators and 0.5-GHz-speed carrier-injection-based microdisk resonator modulators, and (iv) electrically reconfigurable microring resonator add-drop filters and electro-optic logic switches using interferometric resonance control. On the nonlinear waveguide front, we review the main nonlinear optical effects in silicon, and show that even at fairly modest average powers two-photon absorption and the accompanied free-carrier linear absorption could lead to optical limiting and a dramatic reduction in the effective lengths of nonlinear devices.

Montgomery modular multiplication is one of the most efficient algorithms for modular multiplication of large integers. On resource-constraint embedded processors, memory-access operations play an important role as arithmetic operations in the modular multiplication. To improve the efficiency of Montgomery modular multiplication on embedded processors, this paper concentrates on reducing the memory-access operations through adding a few working registers. We first revisit previous popular Montgomery modular multiplication algorithms, and then present improved algorithms for Montgomery modular multiplication and squaring for arbitrary prime fields. The algorithms adopt the general ideas of hybrid multiplication algorithm proposed by Gura and lazy doubling algorithm proposed by Lee. By careful optimization and redesign, we propose novel implementations for Montgomery multiplication and squaring called coarsely integrated product and operand hybrid scanning algorithm (CIPOHS) and coarsely integrated lazy doubling algorithm (CILD). Then, we implement the algorithms on general MIPS64 processor and OCTEON CN6645 processor equipped with specific multiply-add instructions. Experiments show that CIPOHS and CILD offer the best performance both on the general MIPS64 and OCTEON CN6645 processors. But the proposed algorithms have obvious advantages for the processors with specific multiply-add instructions such as OCTEON CN6645. When the modulus is 2048 bits, the CIPOHS and CILD outperform the CIOS algorithm by a factor of 47% and 58%, respectively.

This letter proposes an efficient Two-stage Resource scheduling algorithm for cloud based Live Media Streaming system (TRLMS). It transforms the cloud-based resource scheduling problem to a min-cost flow problem in a graph, and solves it by an improved Successive Short Path (SSP) algorithm. Simulation results show that TRLMS can enhance user demand satisfaction by 17.1% than mean-based method, and its time complexity is much lower than original SSP algorithm.

With more digital home appliances and network devices having OSGi as the software management platform, the power-saving capability of the OSGi platform has become a critical issue. This paper is aimed at improving the power-efficiency of the OSGi platform, i.e. reducing the energy consumption with minimum performance degradation. The key to this study is an efficient power-saving technique which exploits the runtime information already available in a Java virtual machine (JVM), the base software of the OSGi platform, to best determine the timing of performing DVFS (Dynamic Voltage and Frequency Scaling). This, technically, involves a phase detection scheme that identifies the memory phase of the OSGi-enabled device/server in a correct and almost effortless way. The overhead of the power-saving procedure is thus minimized, and the system performance is well maintained. We have implemented and evaluated the proposed power-saving approach on an OSGi server, where the Apache Felix OSGi implementation and the DaCapo benchmarks were applied. The results show that this approach can achieve real power-efficiency for the OSGi platform, in which the power consumption is significantly reduced and the performance remains highly competitive, compared with the other power-saving techniques.

In this letter we design a new family of space-time block codes (STBC) for multi-input multi-output (MIMO) systems. The complex orthogonal STBC achieves full diversity and full transmission rate with fast maximum-likelihood decoding when only two transmit antennas are employed. By combining the Alamouti STBC and the multidimensional signal constellation rotation based on the cyclotomic number field, we construct cyclotomic orthogonal space-time block codes (COSTBCs) which can achieve full diversity and full rate for multiple transmit antennas. Theoretical analysis and simulation results demonstrate excellent performance of the proposed codes, while the decoding complexity is further reduced.

Object detection is one of the most important aspects of computer vision, and the use of CNNs for object detection has yielded substantial results in a variety of fields. However, due to the fixed sampling in standard convolution layers, it restricts receptive fields to fixed locations and limits CNNs in geometric transformations. This leads to poor performance of CNNs for slender object detection. In order to achieve better slender object detection accuracy and efficiency, this proposed detector DFAM-DETR not only can adjust the sampling points adaptively, but also enhance the ability to focus on slender object features and extract essential information from global to local on the image through an attention mechanism. This study uses slender objects images from MS-COCO dataset. The experimental results show that DFAM-DETR achieves excellent detection performance on slender objects compared to CNN and transformer-based detectors.

This letter is concerned with incorrigible sets of binary linear codes. For a given binary linear code C, we represent the numbers of incorrigible sets of size up to ⌈3/2d - 1⌉ using the weight enumerator of C, where d is the minimum distance of C. In addition, we determine the incorrigible set enumerators of binary Golay codes G23 and G24 through combinatorial methods.

Speech emotion recognition (SER) has been a complex and difficult task for a long time due to emotional complexity. In this paper, we propose a multitask deep learning approach based on cascaded attention network and self-adaption loss for SER. First, non-personalized features are extracted to represent the process of emotion change while reducing external variables' influence. Second, to highlight salient speech emotion features, a cascade attention network is proposed, where spatial temporal attention can effectively locate the regions of speech that express emotion, while self-attention reduces the dependence on external information. Finally, the influence brought by the differences in gender and human perception of external information is alleviated by using a multitask learning strategy, where a self-adaption loss is introduced to determine the weights of different tasks dynamically. Experimental results on IEMOCAP dataset demonstrate that our method gains an absolute improvement of 1.97% and 0.91% over state-of-the-art strategies in terms of weighted accuracy (WA) and unweighted accuracy (UA), respectively.

Gaze following is the task of estimating where an observer is looking inside a scene. Both the observer and scene information must be learned to determine the gaze directions and gaze points. Recently, many existing works have only focused on scenes or observers. In contrast, revealed frameworks for gaze following are limited. In this paper, a gaze following method using a hybrid transformer is proposed. Based on the conventional method (GazeFollow), we conduct three developments. First, a hybrid transformer is applied for learning head images and gaze positions. Second, the pinball loss function is utilized to control the gaze point error. Finally, a novel ReLU layer with the reborn mechanism (reborn ReLU) is conducted to replace traditional ReLU layers in different network stages. To test the performance of our developments, we train our developed framework with the DL Gaze dataset and evaluate the model on our collected set. Through our experimental results, it can be proven that our framework can achieve outperformance over our referred methods.