Stochastic forward-backward-half forward splitting algorithm with variance reduction

In this paper, we present a stochastic forward-backward-half forward splitting algorithm with variance reduction for solving the structured monotone inclusion problem composed of a maximally monotone operator, a maximally monotone and Lipschitz continuous operator and a cocoercive operator. By defining a Lyapunov function, we establish the almost sure convergence of the proposed algorithm, and obtain the linear convergence when one of the maximally monotone operators is strongly monotone. Numerical examples are provided to show the performance of the proposed algorithm

Numerical Static Analysis of Interrupt-Driven Programs via Sequentialization

International audienceEmbedded software often involves intensive numerical computations and thus can contain a number of numerical run-time errors. The technique of numerical static analysis is of practical importance for checking the correctness of embedded software. However, most of the existing approaches of numerical static analysis consider sequential programs, while interrupts are a commonly used technique that introduces concurrency in embedded systems. To this end, a numerical static analysis approach is desired for embedded software with interrupts. In this paper, we propose a sound numerical static analysis approach specifically for interrupt-driven programs based on sequentialization techniques. A key benefit of using sequentialization is the ability to leverage the power of the state-of-the-art analysis and verification techniques for sequential programs to analyze interrupt-driven programs. To be more clear, we first propose a sequen-tialization algorithm to sequentialize interrupt-driven programs into non-deterministic sequential programs according to the semantics of interrupts. On this basis, we leverage the power of numerical abstract interpretation to analyze numerical properties of the sequentialized programs. Moreover , to improve the analysis precision, we design specific abstract domains to analyze sequentialized interrupt-driven programs by considering their specific features. Finally, we present encouraging experimental results obtained by our prototype implementation

Static Analysis of Run-Time Errors in Interrupt-Driven Programs via Sequentialization

International audienceEmbedded software often involves intensive numerical computations and suffers from a number of run-time errors. The technique of numerical static analysis is of practical importance for checking the correctness of embedded software. However, most of the existing approaches of numerical static analysis consider sequential programs, while interrupts are a commonly used facility that introduces concurrency in embedded systems. Therefore, a numerical static analysis approach is highly desired for embedded software with interrupts. In this paper, we propose a static analysis approach specifically for interrupt-driven programs based on sequentialization techniques. We present a method to sequentialize interrupt-driven programs into non-deterministic sequential programs according to the semantics of interrupts. The key benefit of using sequentialization is the ability to leverage the power of the state-of-the-art analysis and verification techniques for sequential programs to analyze interrupt-driven programs, for example, the power of numerical abstract interpretation to analyze numerical properties of the sequentialized programs. Furthermore, to improve the analysis precision and scalability, we design specific abstract domains to analyze sequen-tialized interrupt-driven programs by considering their specific features. Finally, we present encouraging experimental results obtained by our prototype implementation

A lightweight CNN for multi-source infrared ship detection from unmanned marine vehicles

Infrared ship detection is of great significance due to its broad applicability in maritime surveillance, traffic safety and security. Multiple infrared sensors with different spectral sensitivity provide enhanced sensing capabilities, facilitating ship detection in complex environments. Nevertheless, current researches lack discussion and exploration of infrared imagers in different spectral ranges for marine objects detection. Furthermore, for unmanned marine vehicles (UMVs), e.g., unmanned surface vehicles (USVs) and unmanned ship (USs), detection and perception are usually performed in embedded devices with limited memory and computation resource, which makes traditional convolutional neural network (CNN)-based detection methods struggle to leverage their advantages. Aimed at the task of sea surface object detection on USVs, this paper provides lightweight CNNs with high inference speed that can be deployed on embedded devices. It also discusses the advantages and disadvantages of using different sensors in marine object detection, providing a reference for the perception and decision-making modules of USVs. The proposed method can detect ships in short-wave infrared (SWIR), long-wave infrared (LWIR) and fused images with high-performance and high-inference speed on an embedded device. Specifically, the backbone is built from bottleneck depth-separable convolution with residuals. Generating redundant feature maps by using cheap linear operation in neck and head networks. The learning and representation capacities of the network are promoted by introducing the channel and spatial attention, redesigning the sizes of anchor boxes. Comparative experiments are conducted on the infrared ship dataset that we have released which contains SWIR, LWIR and the fused images. The results indicate that the proposed method can achieve high accuracy but with fewer parameters, and the inference speed is nearly 60 frames per second (FPS) on an embedded device