In some video compressive sensing (CS) applications, the sparsity of original signals is unknown to the sampling device. The computing power, memory space and power consumption of the sampling device are also limited, which makes it difficult to achieve adaptive rate compressive sensing (ARCS). A new blocked ARCS method for surveillance videos is proposed, which fully considers the limitations mentioned above. By observing the result of CS measurement, the statistical characteristics of the original signal are estimated. The sparsity of the original signal is reasonably estimated by using these statistical characteristics. Therefore, blocks can be divided into more classes with higher accuracy. The proposed method has the advantages of low computational complexity, small memory footprint and low power consumption, which makes it suitable for implementing in applications such as wireless video sensor networks (WVSN) and single pixel cameras (SPC). The experiment results show that the proposed method can well adapt to the change of sparsity, allocate appropriate sampling rate for each block, effectively reduce the sampling rate, and improve the quality of the reconstructed image. Meanwhile, the amount of calculation in the sampling process is much lower, and the sampling speed is obviously accelerated. The overall performance of the proposed method is better than the previous state-of-the-art method.

This paper presents an Improved PSO Algorithm (IPSO) to solve the problem of optimal Phasor Measurement Unit (PMU) placement. The aim of Optimal PMU Placement problem is to guarantee both full observability of the power grid and minimal number of PMU. In the Improved PSO Algorithm, the point of genetic algorithm and simulated annealing process is involved into basic particle swarm optimization. To deal with the constraints, an improved Algorithm is developed and it can avoid costing much time and trapping local optimal solution. IEEE systems are tested to show the feasibility and effectiveness of the algorithm.

This paper addresses the secure communication problem in a distributed active-passive reconfigurable intelligent surface (RIS) assisted multiple-input single-output (MISO) scenario with a single-antenna malicious eavesdropper. The objective is to maximize the secrecy rate of the single-antenna legitimate user under constraints on the base station (BS) power and RIS amplification coefficients. The study investigates a joint optimization algorithm for BS beamforming and RIS phase-shift matrices. To address the complex non-convex fractional programming problem with coupled variables, an alternating optimization (AO) algorithm based on semi-definite relaxation (SDR) and the method of minorization maximization (MM) is proposed to obtain a suboptimal solution to the optimization problem. Simulation results demonstrate that the proposed AO algorithm achieves better secrecy performance with fewer iterations compared to traditional approaches. The design of the distributed active-passive RIS effectively mitigates the "double fading" effect, leading to a significant improvement in secrecy rate compared to existing active RIS and passive RIS designs.

An adaptive rate Compressive Sensing (CS) method for video signals is proposed. The Blocked Compressive Sensing (BCS) scheme is adopted in this method. Firstly, each video frame is blocked and measured by the BCS scheme, and then the mean and variance of each image block are estimated by observing the CS measurement results. Using the mean and variance of each image block, the sparsity of the block is estimated and then the block can be classified. Adaptive rate sampling is realized by assigning different sampling rates to different classes. At the same time, in order to make better use of the correlation between video frames, a reference block subtraction method is also designed in this paper, which uses the estimates of the sparsity of image blocks as the basis for the reference block update. All operations of the proposed method only depend on the CS measurement results of image blocks and all calculations are simple. Thus, the proposed method is suitable for implementation in CS sampling devices with limited computational performance. Experiment results show that, compared with the actual values, the sparsity estimates and block classification results of the proposed method are accurate. Compared with the latest adaptive Compressive Video Sensing methods, the reconstructed image quality of the proposed method is better.

In this paper, the design and implementation of a high speed optical ring topology based free space optical interconnect is described. This interconnect system operates at 500 MHz and consists of 16 laser transmitters, a four channel free space interconnect, and a fast speed receiver. A Nearest Neighbor interconnect has been successfully demonstrated. At the data rate of 500 MHz, the total system throughput is 8 Gbps. The system can easily be operated at much higher data rates since the rate was only limited by the electronic circuitry. A discussion is given about device issues such as optical switching devices and practical system design issues such as integration and interface with current electronic systems is considered. This interconnect is very promising in the implementation of ultra fast massively parallel SIMD machines.

To address the issues of edge blurring and texture loss in image compressed sensing (CS) reconstruction, this letter proposes an edge-preserving reconstruction method (EP-CS). Building on the observation that reconstruction errors of the L1-TV model mainly occur in edge regions, EP-CS incorporates an edge detection mechanism to locate key image structures and imposes stronger sparsity constraints on the corresponding wavelet coefficients, thereby embedding edge structural prior into the reconstruction model. The proposed model achieves simultaneous edge enhancement and artifact suppression while mitigating the under-estimation of large-magnitude coefficients. Meanwhile it remains globally convex, an efficient solver can be derived within the framework of the alternating direction method of multipliers (ADMM). Experimental results show that the proposed method outperforms state-of-the-art baselines in both visual quality and objective metrics, significantly reducing artifacts and structural blurring while improving detail fidelity.