Intelligent transportation systems (ITSs) play a vital role in addressing urban traffic safety challenges. The deployment of lightweight and efficient algorithms for conducting vehicle detection and tracking on edge devices is a key requirement, due to the limited computational resources of these devices. To address this challenge, we propose YOLO-LIGHT, a real-time vehicle detection model, and Ve-Track, a lightweight tracking algorithm. YOLO-LIGHT improves the feature extraction process and reduces the incurred computational cost. It integrates a novel FastPConv convolution in its detection head, while the DySample and SCAM modules in the neck provide enhanced feature representations. Soft-NMS and AMA_SPPF further strengthen the ability of the model to capture fine details. Additionally, global channel sparsity and pruning are employed to compress the parameters of the model while preserving its detection accuracy. For tracking purposes, Ve-Track builds on ByteTrack by filtering background detection boxes using a “life value” indicator and recalculating scores. An enhanced Kalman filter (EV-KF) is also introduced to better model nonlinear vehicle motions. Experimental results obtained on the modified VisDrone dataset show that YOLO-LIGHT improves the mAP95 metric by 17.6% and exhibits a computational complexity reduction of 82.6%, achieving 51 FPS on an embedded platform. Ve-Track outperforms ByteTrack, with a 6.2% MOTA increase and a 17.2% FPS increase, demonstrating its suitability for real-time ITS applications.

With the rapid advancement of urbanization and intelligent transportation systems (ITSs), traffic surveillance has become essential for road safety, traffic management, and data-driven decision-making. However, the large amount of surveillance video data poses challenges in terms of storage, bandwidth usage, and real-time processing. Traditional compression methods struggle to achieve high compression ratios without sacrificing critical information. To address this problem, we propose a novel content-aware video compression (CA-VC) method that reduces redundant information transmission while preserving the quality of critical regions in traffic surveillance videos. Our approach employs an enhanced object detection network to identify regions of interest (ROIs) and non-regions of interest (N-ROIs), generating binary masks for ROI segmentation. The video is then compressed via a layered strategy: ROI segments are encoded at higher fidelity to preserve details, whereas N-ROI segments undergo stronger compression to reduce storage and transmission costs. In addition, we improved the YOLOv8 model by designing a lightweight PC-C2f module and introducing the Wise-IoU v3 loss function, which enhances detection accuracy and reduces the computational requirements for deployment on edge devices. The experimental results show that our method significantly improves the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) within ROIs at the same bit rate, thereby enhancing video quality in key areas.

Cryptographic Hardware Engineering (CHE) is an emerging field that amalgamates cryptography principles with hardware design and implementation. It plays an increasingly important role as secure and trustworthy computing and communication is needed in all applications. In order to introduce CHE into undergraduate curriculum to prepare the next generation workforce, students must have a solid theoretical foundation in cryptography, be proficient in digital circuit design, and have access to commercial design tools and equipment. In this paper, we report our experience in developing and teaching a CHE course for junior students. The course consists of three components that are complementary to each other: digital circuits and FPGA design fundamentals, hardware implementation of cryptographic algorithms, and security analysis of cryptographic hardware. Through this course, students get a good comprehension of CHE principles and gain hands-on experience in secure cryptographic hardware design and analysis.

Fully Homomorphic Encryption (FHE) enables computations directly on encrypted numbers, thereby preserving the privacy of sensitive information even in untrusted environments. However, the substantial computational overhead associated with homomorphic evaluations restricts the practical application of FHE schemes. To deal with the performance challenges, this paper proposes a hardware/software pipeline framework with a three-level cache architecture to accelerate the costly Key-Switching operation in FHE. This framework supports the dynamically reconfigurable processing mode, two parallelism strategies, and flexible control flow, effectively breaking the compute-bound and the memory-bound limitations. A no-stall and conflict-free memory mapping algorithm is implemented on the Xilinx U55C FPGA platform that boosts the throughput of ciphertext-ciphertext multiplication to 395 operations per second, which 1.4× and 2.5× speeds up the FPGA-based (HPCA'23) and GPU-based (HPCA'23) schemes with the same parameter set and precision.

Cheon-Kim-Kim-Song (CKKS) is a Fully Homomorphic Encryption (FHE) scheme that enables computations directly on encrypted real or complex numbers, ensuring the privacy of sensitive information even in untrusted environments. However, the processing of encrypted data incurs significant computational overhead compared to plaintext computations, making CKKS impractical for wider adoption. Field Programmable Gate Arrays (FPGAs) are a promising platform to accelerate CKKS because of parallelism, scalability, flexibility, and widespread availability across cloud providers. This paper systematically surveys the key techniques and current advancements in FPGA-based accelerators for CKKS and discusses future research trends to facilitate real-world homomorphic applications.

The goal of textual adversarial attack methods is to replace some words in an input text in order to make the victim model misbehave. This article proposes an effective word-level adversarial attack method based on sememes and an improved quantum-behaved particle swarm optimization (QPSO) algorithm. The sememe-based substitute method, which uses the words sharing the same sememes as the substitutes of the original words, is first employed to form the reduced search space. Then, an improved QPSO algorithm, called historical information-guided QPSO with random drift local attractor (HIQPSO-RD), is proposed to search the reduced search space for adversarial examples. The HIQPSO-RD introduces historical information into the current mean best position of the QPSO, for the purpose of improving the convergence speed of the algorithm, by enhancing its exploration ability and preventing the premature convergence of the swarm. The proposed algorithm uses the random drift local attractor technique to make a good balance between its exploration and exploitation, so that the algorithm can find a better adversarial attack example with low grammaticality and perplexity (PPL). In addition, it employs a two-stage diversity control strategy to enhance the search performance of the algorithm. Experiments on three natural language processing (NLP) datasets, with three commonly used nature language processing models as victim models, show that our method achieves higher attack success rates but lower modification rates than the state-of-the-art adversarial attack methods. Moreover, the results of human evaluations show that adversarial examples generated by our method can better maintain the semantic similarity and grammatical correctness of the original input.

SRAM-based FPGAs have been widely deployed in aerospace applications in recent years. However, the embedded RAM and user logic are vulnerable to Single Event Upset (SEU), which will result in misconnection or misrouting. This paper proposes a comprehensive survey on fault-tolerance methods for SRAM-based FPGAs in harsh radiation environments. First, the architecture of the Xilinx 7 serial FPGAs is provided to explain how SEU happens and why it causes malfunction. Second, we elaborate on the approaches to evaluate the reliability of SRAM-based FPGAs against SEU. Third, representative fault-tolerance methods are introduced, including Triple Module Redundancy (TMR) and configuration scrubbing. In sum, this survey can serve as a tutorial for engineers and scientists who major in designing fault-tolerance methods for SRAM-based FPGAs in aerospace devices.

In this paper, we design an energy-efficient true random number generator with ultra-high throughput for FPGA. Only four ring oscillators constructed using eight LUTs are sampled by multiple sampling points to fully exploit the randomness of the entropy source, which provides high-quality and over 275 Mbps random sequences while consuming 13 slices. An end-to-end implementation and testing framework is tailored for easy deployment and portability on Xilinx 7 serials FPGAs. The proposed architecture passes the NIST SP 800-22 and 800-90B tests without post-processing and outperforms the state-of-the-art in terms of minimum entropy and energy efficiency.

Economic dispatch problems (EDPs) can be reduced to non-convex constrained optimization problems, and most of the population-based algorithms are prone to have problems of premature and falling into local optimum when solving EDPs. Therefore, this paper proposes a hybrid quantum-behaved particle swarm optimization (HQPSO) algorithm to alleviate the above problems. In the HQPSO, the Solis and Wets local search method is used to enhance the local search ability of the QPSO so that the algorithm can find solutions that is close to optimal when the constraints are met, and two evolution operators are proposed and incorporated for the purpose of making a better balance between local search and global search abilities at the later search stage. The performance comparison is made among the HQPSO and the other ten population-based random search methods under two different experimental configurations and four different power systems in terms of solution quality, robustness, and convergence property. The experimental results show that the HQPSO improves the convergence properties of the QPSO and finally obtains the best total generation cost without violating any constraints. In addition, the HQPSO outperforms all the other algorithms on 7 cases of all 8 experimental cases in terms of global best position and mean position, which verifies the effectiveness of the algorithm.

Abstract The architectures of many state‐of‐the‐art local tempering detection models are complexity, and the training process of those models is also time‐consuming. Therefore, this paper constructs a lightweight local tampering detection method based on the convolutional network MobileNetV2 and a dual‐stream network. Specifically, the algorithm first improves the MobileNetv2, which not only reduces the multiple of its downsampling operator to retain richer traces of image tampering, but also introduces the dilated convolution in it to expand the receptive field of feature maps. The dual‐stream network uses RGB stream to extract image tampering features such as strong contrast difference and unnatural tampered boundaries, and implements spatial rich model (SRM) stream to extract image tampered area and noise features of real area. Finally, the features extracted from two streams are fused through an improved attention mechanism called parallel convolutional block attention module (CBAM), which can improve the sensitivity of the model to important features in RGB and SRM. The experimental results show that the proposed algorithm still has higher positioning accuracy than some existing algorithms, while achieving lightweight.

Bayesian networks (BNs) are probabilistic graphical models, which are regarded as one of the most effective theoretical models in the field of representing and reasoning under uncertainty. Learning BNs structure is an NP-hard problem since the search space of structure grows super-exponentially as the increasing of the number of variables. Evolutionary algorithms (EAs) are widely used to learn BNs structure while single-solution searching methods may trap into local optima. This work aims to propose an efficient knowledge-driven Genetic algorithm (EKGA-BN) to solve the BN structure learning problem. The proposed EKGA-BN uses a novel selection operator to keep population diversity in order to learn a BN structure with higher accuracy. The idea of Hill climbing algorithm (HC) is combined in the selection operator so as to accelerate the convergence rate. A novel knowledge-driven mutation procedure is proposed to enhance the local search ability of EKGA-BN. Experimental results on four well-known benchmark networks show that the proposed method outperforms state-of-the-art algorithms in both convergence rate and the accuracy of BNs structure.