The inherent differences between spike cameras and traditional frame-based cameras lead to more complex and diverse noise characteristics, particularly under extremely low-light conditions. Existing noise modeling approaches for spike camera predominantly rely on inter-spike intervals (ISI) for noise quantification, which often results in inaccurate noise characterization. Moreover, current datasets for spike camera image reconstruction tasks are either synthetic or lack corresponding high-quality reference images, severely limiting rigorous evaluation of noise modeling methods. To address this limitation, we propose a multimodal noise modeling framework for spike camera that integrates insights from traditional frame-based imaging into spike imaging. Specifically, we introduce a time-interval-based quantification method inspired by the exposure-time concept used in traditional frame-based cameras, enabling accurate noise characterization for spike camera. Furthermore, we present the Spike-DSLR Multimodal Dataset (SDMD), the first real-world dataset capturing aligned multimodal data pairs from spike cameras and Digital Single-Lens Reflex (DSLR) cameras, explicitly designed for evaluating spike camera noise models. Experimental results on SDMD demonstrate that our noise modeling approach significantly enhances spike camera image reconstruction quality under low-light conditions, achieving more than 1.6 dB improvement in PSNR compared to existing state-of-the-art methods. This validates both the necessity and effectiveness of adopting a multimodal perspective in spike camera noise modeling.

Flow-based microfluidic biochips have attracted much attention over the past two decades. By integrating diverse micro-components, e.g., mixers and filters, on a miniaturized planar substrate, complicated bioassays such as protein crystallization and drug screening can be executed automatically without requiring human invention, thus becoming a promising alternative to traditional cumbersome laboratory equipment. As manufacturing technology advances, it has become possible to implement hundreds of thousands of microvalves within a single chip. This breakthrough has given rise to fully programmable valve array (FPVA) biochips, representing a next-generation platform in flow-based microfluidics that offers enhanced reconfigurability and operational flexibility. Nevertheless, the exponential increase in valve density has introduced significant design complexity when implementing sophisticated assay protocols. As a result, the design automation of FPVAs has emerged as a critical research frontier, attracting considerable attention from both academia and industry. This review article systematically examines recent advances in FPVA design automation, involving computer-aided design methods for architectural synthesis, volume management, sample preparation, automated testing, fault localization, error recovery, and washing optimization. These techniques enable FPVA users to concentrate on assay protocol development while delegating implementation-specific design and optimization tasks to design automation tools. Furthermore, we analyze emerging security implications in FPVAs, particularly focusing on bioassay accuracy and reliability that ensure experimental reproducibility. Finally, potential trajectories for future research are discussed in detail to further promote the integration level and widespread application of FPVAs.

Acquiring pairwise noisy-clean training data is challenging. Consequently, some self-supervised denoising methods utilize noisy image pairs as both input and target for network training. However, a major issue with these methods is the gap between the clean images of the input and target. In this paper, we achieve high-quality image denoising by reducing or even eliminating this gap. Our method requires no training data or prior knowledge of the noise distribution. It consists of two lightweight networks that can be trained using only a single noisy test image. Specifically, we propose a random mask-based downsampler that generates multiple pairs of downsampled noisy images, which are similar but distinct. These image pairs serve as the input for the first network, with the mean image of each pair used as the target. This initially reduces the gap between the clean images of the input and target. Particularly, in our method, the clean counterpart of the first network's target (i.e., the mean image) can be obtained. We then train a second network using the mean image as input and its clean counterpart as the target. This effectively eliminates the gap and achieves better denoising results. Extensive experiments demonstrate that our method outperforms in both denoising performance and efficiency.

Infrared and visible image fusion (IVIF) aims to generate fused images with prominent targets and rich scene information. However, in low-light conditions, visible images lose accurate texture and color, reducing their ability to provide detailed scene information for fusion. Existing IVIF methods often overlook illumination degradation and cause color distortion when incorporating infrared information. To address these problems, we propose a novel visually enhanced IVIF method tailored for low-light environments. Our method combines low-light image enhancement (LLIE) and IVIF into a single module. First, we adaptively enhance the low-light visible image, ensuring rich texture and color for fusion. Additionally, we introduce a three-channel fusion coefficient map to transform infrared information into visible image, preventing color distortion and highlighting key targets while maintaining details of the fused image. Since infrared and visible images are from different modalities, we map them into the same high-dimensional feature space. We then propose the feature difference to integrate complementary information, producing a fused image with complete content and no redundancy. Notably, our method is zero-shot, requiring only a pair of test infrared and visible images for training. This better meets the complexity of IVIF in various low-light scenes. Extensive experiments show that in low-light conditions, our method surpasses other state-of-the-art (SOTA) methods by providing more natural colors, richer textures, and better alignment with human visual perception.

Polynomial multiplication, a core component of lattice-based cryptography, has demonstrated impressive performance in lattice-based cryptographic chips. However, costly Number Theoretic Transform (NTT) makes efficient and flexible hardware design extremely challenging, particularly for ASIC/FPGA-based hardware acceleration solutions that often struggle with high resource consumption and low computational efficiency. To address these issues, we propose a lightweight reconfigurable polynomial multiplication accelerator for NTT termed RePM. The hardware architecture adheres to the principles that are applicable to various post-quantum cryptography (PQC) algorithms and operates under very strict power constraints. A conflict-free near-memory mapping scheme is adopted to reconstruct the computation topology, significantly reducing the frequency of data relocation. Moreover, this work pioneers an elastic computing array architecture for butterfly units that eliminates pre-computing requirements. By integrating the mixed radix-2/4 NTT algorithms proposed in this paper, we eliminate both pre-processing and post-processing stages in the computational workflow. This innovation achieves a 2.1 × acceleration in ML-KEM (i.e. CRYSTALS-Kyber) execution speed with ML-KEM-512/1024, positioning it as a leading implementation of NIST’s fourth-round post-quantum cryptography standard. Experimental results demonstrate that this architecture achieves up to 75% power reduction and 65.8% improvement in area efficiency compared to state-of-the-art solutions, while preserving a 17× higher computational throughput under strict power constraints.

Human trajectory prediction has become an active research area, with applications in various scenarios such as evacuation situation analysis, deployment of intelligent transportation systems, and traffic operations. Pedestrian motions exhibit uncertainty and involve complex interactions with vehicles and the environment. However, some existing methods require substantial resources and the construction of complex models to handle these issues. In contrast, we propose the Brownian LSTM Network (BLN), which combines simple deterministic models with empirical models to achieve efficient prediction. Our results show an improvement over the state of art by 19%/21% on the ADE/FDE metrics, respectively. The improvement are achieved with a 4 times faster inference speed than previously baselines and a significant reduction in parameters. We present a simple and flexible scheme that can seamlessly integrate different networks. In addition, we introduce a Brownian motion module that leverages its irregular properties to simulate movement uncertainty, support multimodal predictions, and optimize the output of deterministic networks for the best prediction results. Extensive experiments on human trajectory prediction benchmarks, including the Stanford Drone and ETH/UCY datasets, demonstrate the superiority of our method.

Artificial Intelligence (AI) has almost become the default approach in a wide range of applications, such as computer vision, chatbots, and natural language processing. These AI-based applications require computing large-scale data with sufficient precision, typically in floating-point numbers, within a limited time window. A primary target for AI acceleration is matrix multiplication, mainly involving dot products through Multiply-Accumulate (MAC) operations. Current research employs the Fused Multiply-Add (FMA) operation, based on IEEE-754 Floating Point (FP) standard, to meet these requirements. However, current research focuses more on simplifying the internal digital circuits of the Processing Elements (PEs) performing FMA operations, rather than optimizing the FMA process specifically for MAC tasks. Current PE arrays often use a two-dimensional (2-D) systolic array design, without specific optimization for MAC operations, thus their parallelism is not fully utilized. Additionally, these designs lack reconfigurability and flexibility, leading to suboptimal performance on Field-Programmable Gate Arrays (FPGAs). Moreover, some designs adopt lower precision computing in AI inference for higher performance. However, some AI models still rely on high-precision computing to maintain the accuracy. Thus, multi-precision computing is commonly used in AI accelerators. To address these challenges, this paper proposes a novel Multi-Fused Multiply-Accumulate (MFMA) scheme and a corresponding three-dimensional (3-D) scalable systolic FP computing architecture. The MFMA scheme addresses the problem of the classical FMA scheme. It optimizes FMA for MAC operations with the Fused Multiply-Accumulate (FMAC) operation. Also, it combines multi-precision and mixed-precision FP computing methods for higher accuracy and lower overflow error. The proposed architecture integrates two 2-D systolic arrays into the PE for a 3-D systolic array, achieving higher parallelism and flexibility. The proposed scalable architecture can be customized to suit various FMAC operations. Compared with existing state-of-the-art FP architectures on FPGAs, our proposed architecture achieves 47%, 10%, and 159% energy efficiency improvements in FP32, FP16, and INT8 operations, respectively. Furthermore, our proposed architecture achieves energy efficiency improvements of 105%, 54%, and 262% under efficiency saturation conditions, outperforming the existing state-of-the-art design.

Multiple quadrotor uncrewed aerial vehicles (UAVs) systems have garnered widespread research interest and fostered tremendous interesting applications, especially in multiconstrained pursuit-evasion games (MC-PEGs). The cooperative evasion and formation coverage (CEFC) task, where the UAV swarm aims to maximize formation coverage across multiple target zones while collaboratively evading predators, belongs to one of the most challenging issues in MC-PEGs, especially under communication-limited constraints. This multifaceted problem, which intertwines responses to obstacles, adversaries, target zones, and formation dynamics, brings up significant high-dimensional complications in locating a solution. In this article, we propose a novel two-level framework [i.e., consensus inference-based hierarchical reinforcement learning (CI-HRL)], which delegates target localization to a high-level policy, while adopting a low-level policy to manage obstacle avoidance, navigation, and formation. Specifically, in the high-level policy, we develop a novel multiagent reinforcement learning (RL) module, consensus-oriented multiagent communication (ConsMAC), to enable agents to perceive global information and establish consensus from local states by effectively aggregating neighbor messages. Meanwhile, we leverage an alternative training-based MAPPO (AT-M) and policy distillation to accomplish the low-level control. The experimental results, including the high-fidelity software-in-the-loop (SITL) simulations, validate that CI-HRL provides a superior solution with enhanced swarm's collaborative evasion and task completion capabilities.

Due to their flexibility and agility, unmanned aerial vehicles (UAVs) offer a promising approach to cluster planning within wireless sensor networks (WSNs). However, the limited battery capacity of a single UAV limits its application in many situations, such as searching in wild areas. In this article, we propose a computational scheme of cooperative path planning for heterogeneous UAVs based on Voronoi diagrams and intelligent swarm optimization algorithm. In this article: 1) Voronoi diagrams are used to model the field environment according to the radar sensor position; 2) an improved$K$-medoids algorithm based on the maximum empty circle property of the Voronoi diagram (Vor-$K$-medoids) is proposed to complete the reconnaissance UAVs (RUAVs) domain cooperative search; and 3) a hyperbolic tangent heuristic function intelligent optimization algorithm is proposed to calculate the minimum risk path for the attack UAV (AUAV) according to the characteristics of the attack mission. The simulation results show that the proposed scheme integrates the properties of the Voronoi diagram, clustering algorithm, and path planning algorithm commendably. Compared with the traditional ant colony optimization (ACO), under the same number of iterations, the probability of obtaining the optimal track is improved by 14%, and the running time is shortened by 50.87%.The proposed scheme offers a practical and cost-effective approach for efficiently searching areas within large-scale radar sensors in real-world scenarios.

Acoustic waves are forms of energy that propagate through various mediums. They can be represented by different modalities, such as auditory signals and visual patterns. The two modalities are often described as one-dimensional waveform in the time domain and two-dimensional spectrogram in the frequency domain. Most acoustic signal processing methods use single modal data for input and training models. This poses a challenge for black-box adversarial attacks on audio signals because the input modality is also unknown to the attacker. In fact, there currently exist no methods that explore the cross-modal transferability of adversarial perturbation. This paper investigates the cross-modal transferability from waveform to spectrogram. We argue that the data distributions in the sample space with the different modalities have mapping relations and propose a novel decision-based cross-modal and cross-medium adversarial attack method. Specifically, it generates an initial example with cross-modal attack capability by combining random natural noise, then iteratively reduces the perturbation to enhance its invisibility. It incorporates the constraints of the spectrogram sample space while iteratively optimizing adversarial perturbations for black-box audio classification models. The perturbation is imperceptible to humans, both visually and aurally. Extensive experiments demonstrate that our approach can launch attacks on classification models for sound waves and spectrograms that share the same audio signal. Furthermore, we explore the cross-medium capability of our proposed adversarial attack strategy that can target processing models for acoustic signals propagating in air and seawater. The proposed method has preeminent invisibility and generalization compared to other methods.

In wireless sensor networks (WSNs), inefficient coverage does affect the quality of service (QoS), which the minimum exposure path (MEP) is traditionally used to handle. But intelligent mobile devices are generally of limited computation capability, local storage, and energy. Present methods cannot meet the demand of multiple target intrusion, lacking the consideration of energy consumption. Based on the Voronoi diagram in computational geometry, this paper proposed an invasion strategy of minimum risk path (MRP) to such a question. MRP is the path considered both the exposure of the moving target and energy consumption. Federated learning is introduced to figure out how to find the MRP, expressed as C(ti, tj) = f(E, e). The value of C(ti, tj) can measure the success of an invasion. At the time when a single smart mobile device invades, horizontal federated learning is taken to partition the path feature, and a single target feature federated (SPF) algorithm is for calculating the MRP. Moreover, for multi smart mobile device invasion, it has imported the time variable. Vertical federated learning can partition the feature of multipath data, and the multi‐target feature federated (MFF) algorithm is for solving the multipath MRP dynamically. The experimental results show that the SPF and MFF have the dominant advantage over traditional computational performance and time. It primarily applies the complex conditions of a massive amount of sensor nodes.

In a multithreaded execution environment, race condition leads to computational errors and system hazards. Up to date, a series of task scheduling strategies have been presented in the literature to reduce the risk of race condition. Because of the increasing complexity of this problem in multi-core systems, existing task scheduling approaches are not very efficient. To deal with this challenge, in this work, we develop a race-condition-aware and hardware-oriented task partitioning and scheduling algorithm using entropy maximization model. We model uncertainty as a probabilistic occurrence within a time interval, and hence the characteristics of event ordering are analyzed through an uncertainty matrix. Next, a metric is developed to measure the uncertainty of task execution in various execution environments. Finally, a maximum entropy model is generated to ensure the lowest probability of race condition during task execution. The smallest one among maximum entropy values is chosen and used in our proposed task scheduling algorithm. Experimental results show that the proposed task scheduling strategy based on our maximum entropy model outperforms existing state-of-the-art approaches. For example, in a 128-core computing system, the task execution time, CPU utilization ratio, and throughput of our proposed task scheduling is improved by$15.3\sim 36.4$percent,$8.2\sim 17.6$percent, and$20.7\sim 41.4$percent, respectively. Moreover, our proposed scheduling algorithm exhibits low computational complexity and good adaptivity to diverse execution environments.

Race conditions in real-time systems may cause unexpected computing result. Due to the uncertainty of realtime systems, a race condition detected by many static and dynamic approaches may occur in one execution environment but may not occur in another execution environment. In this paper, an easy and practical approach based on probabilistic models is presented to analyze the uncertainties of race conditions of real-time systems in various execution environments. The approach adopts a probabilistic occurrence within a time interval to represent the uncertainty of event occurrences. The confidence level is defined to measure the accuracy of the time interval observed, and then the uncertainties of event orders are analyzed according to the relations of time intervals. We propose a T-matrix to describe the uncertainties of event orders, and a metric is presented to measure the uncertainties of executions of real-time systems in various environments. Moreover, another metric is introduced to measure the total risk of the real-time system caused by race conditions in various environments.