Efficient and accurate license plate recognition (LPR) in unconstrained environments remains a critical challenge, particularly when confronted with skewed imaging angles and the limited computational capabilities of edge devices. In this study, we p...
Cyber-Physical Systems are experiencing a paradigm shift in which processing has been relocated to the distributed sensing layer and is no longer performed in a centralized manner. This approach, usually referred to as Edge Computing, demands the use...
This paper presents an edge-to-cloud orchestrator capable of supporting services running at the edge on heterogeneous nodes based on general-purpose processing units and Field Programmable Gate Array (FPGA) platform (i.e., AMD Kria K26 SoM) in an urb...
Event-based vision sensors offer microsecond temporal resolution and low power consumption, making them attractive for edge robotics and simultaneous localization and mapping (SLAM). Contrast maximization (CMAX) is a widely used direct geometric fram...
Image noise degrades image quality in applications such as medical imaging, surveillance, and remote sensing, where real-time processing and high accuracy are critical. Software-based denoising can be flexible, but often suffers from latency and low...
The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design s...
Deep learning (DL) has revolutionized image classification, yet deploying convolutional neural networks (CNNs) on edge devices for real-time applications remains a significant challenge due to constraints in computation, memory, and power efficiency....
The breakthrough of deep learning has started a technological revolution in various areas such as object identification, image/video recognition and semantic segmentation. Neural network, which is one of representative applications of deep learning,...
Due to its advantages of low latency, low power consumption, and high flexibility, FPGA-based acceleration technology has been more and more widely studied and applied in the field of computer vision in recent years. An FPGA-based feature extraction...
With the rapid evolution toward autonomous vehicles, securing in-vehicle communications is more critical than ever. The widely used Controller Area Network (CAN) protocol lacks built-in security, leaving vehicles vulnerable to cyberattacks. Although...
Brain–computer interfaces (BCIs) have gained popularity in recent years. Among noninvasive BCIs, EEG-based systems stand out as the primary approach, utilizing the motor imagery (MI) paradigm to discern movement intentions. Initially, BCIs were...
Agricultural pest and disease monitoring has recently become a crucial aspect of modern agriculture. Toward this end, this study investigates methodologies for implementing low-power, high-performance convolutional neural networks (CNNs) on agricultu...
Breast cancer detection is a critical task in healthcare, requiring fast, accurate, and efficient diagnostic tools. However, the high computational demands and latency of deep learning models in medical imaging present significant challenges, especia...
This paper proposes efficient implementations of robot audition systems, specifically focusing on deployments using HARK, an open-source software (OSS) platform designed for robot audition. Although robot audition systems are versatile and suitable f...
Detection of object boundaries and significant features within an image is one of the most important processes in image processing and computer vision, as it allows the identification of object boundaries and significant features within an image. In...
Neuromorphic computing has emerged as a promising paradigm for edge AI systems owing to its event-driven operation and high energy efficiency. However, conventional spiking neural network (SNN) architectures often suffer from redundant computation an...
Cellular Nonlinear Networks (CNN) are a concept introduced in 1988 by Leon Chua and Lin Yang as a bio-inspired architecture capable of massively parallel computation. Since then, CNN have been enhanced by incorporating designs that incorporate memris...
This paper presents NeuroAdaptiveNet, an FPGA-based neural network framework that dynamically self-adjusts its architectural configurations in real time to maximize performance across diverse datasets. The core innovation is a Dynamic Classifier Sele...
Real-time systems for measuring structural cracks are of great significance due to their computational and cost efficacy, inherent hazards, and detection discrepancies associated with the manual visual assessment of structures. The precision and effe...
This paper presents an FPGA-based architecture for real-time spline-based signal reconstruction, targeted at multimedia signal processing applications. Leveraging the multi-resolution properties of B-splines, the proposed design enables efficient ups...
This research presents a sleep posture monitoring system designed to assist the elderly and patient attendees. Monitoring sleep posture in real time is challenging, and this approach introduces hardware-based edge computation methods. Initially, we d...
The constant false-alarm rate (CFAR) algorithm is essential for detecting targets during radar signal processing. It has been improved to accurately detect targets, especially in nonhomogeneous environments, such as multitarget or clutter edge enviro...
Hardware Trojans (HTs) are malicious hardware components designed to leak confidential information or cause the chip/circuit on which they are integrated to malfunction during operation. When we deploy such hardware platforms for edge computing, FPGA...
Hough Transform has been widely used for straight line detection in low-definition and still images, but it suffers from execution time and resource requirements. Field Programmable Gate Arrays (FPGA) provide a competitive alternative for hardware a...
In recent years, convolutional neural networks (CNNs) have gained widespread adoption in remote sensing image processing. Deploying CNN-based algorithms on satellite edge devices can alleviate the strain on data downlinks. However, CNN algorithms pre...
This study presents a comprehensive performance evaluation of field-programmable gate array (FPGA), graphics processing unit (GPU), and central processing unit (CPU) platforms for implementing finite impulse response (FIR) filters in semiconductor-ba...
Deep neutral networks (DNNs) are complex machine learning models designed for decision-making tasks with high accuracy. However, DNNs require high computational power and memory, which limits such models to fitting on edge devices, resulting in unnec...
Multi-FPGA systems can form larger and more powerful computing units through high-speed interconnects between chips, and are beginning to be widely used by various computing service providers, especially in edge computing. However, the new computing...
Reconfigurable intelligent surfaces (RIS) offer the potential to customize the radio propagation environment for wireless networks, and will be a key element for 6G communications. However, due to the unique constraints in these systems, the optimiza...
Edge AI accelerators are utilized to accelerate the computation in edge AI devices such as image recognition sensors on robotics, door lockers, drones, and remote sensing satellites. Instead of using a general-purpose processor (GPP) or graphic proce...
To address the problems of large storage requirements, computational pressure, untimely data supply of off-chip memory, and low computational efficiency during hardware deployment due to the large number of convolutional neural network (CNN) paramete...
Advancements in technology have driven the miniaturization of embedded systems, making them more cost-effective and energy-efficient for wireless applications. As a result, the number of connectable devices in Internet of Things (IoT) networks has in...
As AI workloads move to edge devices, the von Neumann architecture is hindered by memory- and power-wall limitations We present an SRAM-based compute-in-memory binary convolution accelerator that stores and transports only 1-bit weights and activatio...
In the evolving landscape of computer vision, the integration of machine learning algorithms with cutting-edge hardware platforms is increasingly pivotal, especially in the context of disruptive healthcare systems. This study introduces an optimized...
Particle detector systems require data acquisition systems (DAQs) as their back-end. This paper presents a new edge-computing DAQ that is capable of handling multiple pixel detectors simultaneously and was designed for particle-tracking experiments....
12 December 2021
Medication safety administration is a complicated process involving the information of patients, drugs, and data storage. The sensitive data transmitted through wireless sensor networks (WSNs) from Internet of things (IoT) over an insecure channel is...
Graph-based neural networks have proven to be useful in molecular property prediction, a critical component of computer-aided drug discovery. In this application, in response to the growing demand for improved computational efficiency and localized e...
Over the last decade, various deep neural network models have achieved great success in image recognition and classification tasks. The vast majority of high-performing deep neural network models have a huge number of parameters and often require sac...
Object detection is an important task for many applications, like transportation, security, and medical applications. Many of these applications are needed on edge devices to make local decisions. Therefore, it is necessary to provide low-cost, fast...
The development of computation technology and artificial intelligence (AI) field brings about AI to be applied to various system. In addition, the research on hardware-based AI processors leads to the minimization of AI devices. By adapting the AI de...
Using convolutional neural networks (CNNs) to recognize forest fires in complex outdoor environments is a hot research direction in the field of intelligent forest fire recognition. Due to the storage-intensive and computing-intensive characteristics...
Currently, the trend of elderly people living alone is rising due to rapid aging and shifts in family structures. Accordingly, the efficient implementation and management of monitoring systems tailored for elderly people living alone have become para...
With the increasing demand for efficient deep learning models in resource-constrained environments, Binary Neural Networks (BNNs) have emerged as a promising solution due to their ability to significantly reduce computational complexity while maintai...
With high real-time performance and a good filtering effect, the wavelet transform-based real-time filter is more commonly used in the fusion magnet power supply. Considering the signal characteristics of the fusion magnet power supply, the sliding t...
27 September 2022
This paper proposes a novel approach suitable to design energy-efficient approximate multipliers using both ASIC and FPGAs. The new strategy harnesses specific encoding logics based on bit significance and computes the approximate product performing...
With the increasing number of edge devices connecting to the cloud for storage and analysis, concerns about security and data privacy have become more prominent. Homomorphic encryption (HE) provides a promising solution by not only preserving data pr...
Recently, with the rapid development of underwater resource exploration and underwater activities, underwater acoustic (UA) target recognition has become crucial in marine resource exploration. However, traditional underwater acoustic recognition sys...
The increasing demand for energy-efficient hardware platforms to support artificial intelligence (AI) and machine learning (ML) algorithms in edge computing has driven the adoption of system-on-chip (SoC) architectures. Implementing neural network (N...
This article addresses the growing need in resource-constrained edge computing scenarios for energy-efficient convolutional neural network (CNN) accelerators on mobile Field-Programmable Gate Array (FPGA) systems. In particular, we concentrate on reg...
The Zhuang ethnic minority in China possesses its own ethnic language and no ethnic script. Cultural exchange and transmission encounter hurdles as the Zhuang rely exclusively on oral communication. An online cloud-based platform was required to enha...
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