SHA-RV: A RISC-V Accelerator for SHA-224/256 with Cycle Reduced ISA Extensions for Blockchain Applications

The Secure Hash Algorithm SHA-256 and SHA-224 are widely used for software integrity, digital signatures, and blockchain across embedded and edge platforms. Prior RISC-V accelerators still struggle to achieve low cycle counts and high system throughput on long message streams. This paper proposes a hardware-efficient RISC-V accelerator with low-latency SHA instruction extensions, named SHA-RV, to reduce cycles and improve end-to-end performance. SHA-RV integrates three optimizations: a high-bandwidth BufferSet for continuous data supply, a four-stage pipelined SHA core, a system-level double-buffering pipeline, and an FSM-orchestrated BufferSet mapping. Implemented on a Xilinx ZCU102 system on a chip, SHA-RV operates at up to 300 MHz and uses 3,146 flip-flops, 5,175 lookup tables, and 15 block RAMs. On 64-byte blocks, SHA-RV completes a block in 257 cycles, improving over related RISC-V designs by between 9.7 and 134.9 times, while reducing logic resources versus the ISOCC 2024 design by 89.4 percent in flip-flops and 85.2 percent in lookup tables. At the system level, SHA-RV achieves a throughput of 599 megabits per second and an energy efficiency of 798.7 megabits per second per watt under a real-time dynamic power assumption of 0.75 watts, outperforming representative CPUs by between 61 and 454 times in energy efficiency. These results show lower latency and superior hardware efficiency relative to prior work

Impact of Optical Crosstalk on OIRS-Assisted HAP-Based Multiuser FSO Systems over Turbulence Channels

Free-space optical communication (FSO) utilizes laser beams to transmit data through the atmosphere. However, FSO faces significant challenges, including the strict requirement for line-of-sight (LoS) communication and terrestrial obstacles, which limit its scalability to connect multiple users in diverse environments. To address these limitations and enable reliable multi-user connectivity, the integration of high-altitude platforms (HAP) and optical intelligent reflecting surfaces (OIRS) has emerged as a critical solution. To serve multiple users simultaneously, an OIRS is equipped at the HAP to dynamically control the reflected beam from a ground station to the terminals. This study analyzes the proposed FSO system performance through the outage probability. During the analysis, practically influencing factors such as optical crosstalk, i.e., interference between OIRS regions, and atmospheric turbulence, are considered. The numerical results show the feasibility of deploying OIRS on HAP to support multiuser FSO systems. In addition, properly designing the OIRS coverage could improve the overall performance of the multiuser FSO system

Non-contact Beat-to-Beat Pulse Interval Estimation from Radial Artery Micro-Movements Using mmWave Radar

This study presents a novel contactless method for beat-to-beat pulse interval estimation using 24 GHz millimeter-wave radar detection of radial artery surface motion. The proposed system employs Doppler effect principles to detect physiological micro-movements, incorporating comprehensive signal processing including band-pass filtering (0.7-3 Hz), baseline wander removal, and advanced nonlinear denoising algorithms. An adaptive peak-finding algorithm extracts inter-beat intervals (IBI) from processed radar signals. Validation was performed against simultaneous electrocardiogram (ECG) recordings in 15 healthy subjects during 60-second measurement sessions. Results demonstrated excellent agreement with reference ECG measurements, achieving a high Pearson correlation coefficient of r = 0.991, mean absolute error of 11.8 ms, and root mean square error of 16.93 ms. Bland-Altman analysis revealed 95% limits of agreement within ±33.15 ms. The mmWave radar approach provides a highly accurate, contactless alternative for cardiovascular monitoring, offering enhanced patient comfort and suitability for long-term health assessment applications while maintaining clinical accuracy comparable to traditional methods

A Design of Variable-Cutoff-Frequency CMOS High-Pass Filter using Low-Bias Current Control

This paper presents a variable-cutoff-frequency CMOS high-pass filter, which is designed for low-power analog front-end applications. The filter employs a weak-inversion MOSFET network to synthesize a GΩ-range equivalent resistance, enabling low-frequency operation without relying on large on-chip resistors. The cutoff frequency is adjusted by a low-bias current control scheme consisting of four selectable 300 pA branches, which tune the effective equivalent resistance while maintaining minimal power consumption. A comparator utilizes a folded cascode stage for common-mode stabilization and accurate bias control. Implemented in a 180 nm CMOS technology, the proposed prototype occupies 0.0045 mm2, consumes 0.749 µW, and provides five programmable high-pass corner frequencies ranging from 50 Hz to 100 kHz. The aforementioned features make the proposed architecture suitable for compact, energy-efficient front-end systems requiring reliable and tunable high-pass filtering

Joint Caching and Hovering Minimized Service Time for Cooperative Video Streaming in UAV-assisted VANETs

The rapid growth of advanced applications and services (A\&Ss), particularly video streaming, imposes stringent latency and quality-of-experience requirements on vehicular ad hoc networks (VANETs). Although edge caching at roadside units (RSUs) can reduce service time, its effectiveness is limited in highly dynamic environments characterized by uneven vehicle user (VU) distributions, constrained caching resources, and sparse roadside infrastructure. To address these challenges, this paper proposes a caching and hovering (CAHO) optimization design for cooperative video streaming in VANETs assisted by unmanned aerial vehicles (UAVs). In the proposed CAHO design, VU distributions are modeled as independently thinned one-dimensional homogeneous Poisson point processes, and RSU-to-VU and UAV-to-VU communication models together with video popularity characteristics are incorporated. Based on this modeling, the CAHO problem jointly optimizes video caching at RSUs and UAVs as well as UAV hovering positions to minimize the service time under limited storage resources. The CAHO optimization problem is efficiently solved using genetic algorithms combined with a penalty function and a divide-and-conquer strategy. Simulation results demonstrate that the proposed CAHO scheme outperforms benchmark schemes under various system scenarios in terms of service time and storage resource utilization, highlighting the effectiveness and practical potential for video streaming A\&Ss in UAV-assisted VANETs

Low-complexity chroma down/up sampling conversion using decimation/interpolation FIR filter and FPGA IP-core design

This paper proposes a low-complexity solution for chroma downsampling and upsampling, applicable to video compression. The proposed method uses decimation and interpolation FIR filters, which are known to be effective in signal processing. This approach enhances the quality of chroma recovery and minimizes computational complexity, making it suitable for hardware platforms with limited resources. An experimental design was implemented on an FPGA using a parameterized IP core that supports YCbCr and YUV color spaces, enabling flexible conversion between 4:4:4, 4:2:2, and 4:2:0 formats. This implementation contributes to improved performance, enhanced flexibility, and reduced computational complexity—an important requirement for modern video applications. The IP core was successfully verified using the Xilinx Vitis Model Composer tool, with a focus on visually inspecting the output and comparing it against reference models developed in the MATLAB/Simulink environment

Enhancing FMCW Radar-Based Human Activity Recognition using DI-ResNet

oai:ojs.rev-jec.org:article/433This paper proposes a method to enhance the quality of Human Activity Recognition (HAR) based on Frequency Modulated Continuous Wave (FMCW) radar. The method utilizes a Dual Input-ResNet (DI-ResNet) model to address the limitations associated with relying solely on isolated range or micro-Doppler (m-D) features for activity classification. Specifically, two parallel ResNet-18 backbones are employed to extract deep semantic features from two distinct data domains: the Range-Time (RT) spectrogram and the Doppler-Time (DT) spectrogram. These features are subsequently fused via a concatenation layer to synthesize global context, thereby generating a more comprehensive representation of the performed activities. Experimental results demonstrate that the proposed method achieves superior recognition performance compared to single-stream baseline networks. Notably, the proposed model effectively mitigates confusion among activities exhibiting kinematic similarity, improving the Recall metric for the “SitDown” activity by 42.1% compared to traditional single-input methods. Furthermore, the accuracy for complex hand gestures such as "Drink" significantly increased by 19.8%, substantially minimizing the high misclassification rate associated with "Grab." Finally, the model achieves near-ideal reliability for safety-critical activities, attaining a recognition accuracy of 99.5% in fall detection, thereby confirming its potential for practical deployment

Intelligent AutoEncoder-Based Modulation: Optimizing Transmission Performance over Non-Ideal Wireless Channels

This paper presents an End-to-End wireless transceiver architecture based on deep AutoEncoder (AE) networks that jointly optimizes the transmitter and receiver as a single differentiable system, replacing the conventional cascade of independently designed signal processing blocks. The channel model incorporates three concurrent non-ideal impairments: nonlinear distortion from the power amplifier (PA) characterized by the Rapp model, progressive carrier frequency offset (CFO), and flat Rayleigh fading. Through the training process and testing scenarios across three progressively evolving architectures, namely single-symbol constellation shaping, multi-symbol blind CFO compensation, and implicit neural forward error correction (Neural FEC), the obtained results confirm that the AE is capable of autonomously learning PAresilient signal constellations, performing blind CFO estimation without pilot signals, and unifying the modulation and channel coding processes into a single optimal system representation. Monte Carlo BER simulations show that the proposed architecture achieves 3–5 dB SNR gain over conventional 16-QAM with ZF equalization, provides 2–3 dB gain relative to ideally CFO-compensated 16-QAM, and the Neural FEC configuration successfully performs the channel coding function, exhibiting effective error correction performance

Partial Distance Correlation-Based Motion Pattern Detection in Pangasius Fish

In computer vision, behavioral recognition of aquatic organisms plays an important role, particularly for Pangasius catfish, a fish species commonly cultured in Vietnam. This study presents a method for detecting Pangasius motion patterns comprising 38 catfish videos with a total of 236,133 extracted frames, from which 4,593 motion windows are extracted and classified into six behavioral categories (Cruising, Burst–Coast, Escape, Schooling, Milling, and Swarming) based on Partial Distance Correlation (PDC) integrated with video processing techniques and feature extraction methods. Experimental results show that Distance Correlation (dCor) on raw data yields high correlation values (0.826–0.989) but with substantial scatter. PDC with heading angle control maintains elevated values (0.804–0.979) with tighter residual clustering. When denoising is combined with heading angle control, pdCor achieves optimal efficacy (0.852–0.973). Compared with dCor, pdCor provides consistent improvements, especially for complex behaviors (Escape: 5.1%; Swarm: 3.2%). The combined strategy detects 40% of patterns better and 60% similarly, indicating pdCor does not reduce performance for simple behaviors but substantially improves detection for nonlinear, high noise patterns