A5024751332- Advanced Neural Network Applications
- CCD and CMOS Imaging Sensors
- Advanced Memory and Neural Computing
- Advanced Image and Video Retrieval Techniques
- Neural Networks and Reservoir Computing
- Nursing care and research
- Coding theory and cryptography
- Human Pose and Action Recognition
- Cryptographic Implementations and Security
- Machine Learning and ELM
- Video Surveillance and Tracking Methods
- Advanced SAR Imaging Techniques
- Sparse and Compressive Sensing Techniques
- Image Processing Techniques and Applications
- Heart Failure Treatment and Management
- Cryptography and Residue Arithmetic
Xuzhou Medical College
2025
Huaian First People’s Hospital
2025
Nanjing Medical University
2025
Guangdong Polytechnic Normal University
2025
Guangdong University of Technology
2020-2022
ABSTRACT To translate and adapt the English version of Caregiver Contributions to Self‐Care Heart Failure Index (CC‐SCHFI) Chinese, further investigate psychometric properties Chinese CC‐SCHFI in a cohort caregivers people with heart failure. translation cross‐cultural adaption were performed. 240 recruited complete instrument. The internal construct reliability was evaluated by Cronbach's α item‐to‐total correlations. concurrent validity tested using CACHS which also measures caregivers'...
The You Only Look Once (YOLO) neural network has great advantages and extensive applications in computer vision. convolutional layers are the most important part of take up computation time. Improving efficiency convolution operations can greatly increase speed network. Field programmable gate arrays (FPGAs) have been widely used accelerators for networks (CNNs) thanks to their configurability parallel computing. This paper proposes a design space exploration YOLO based on FPGA. A data block...
Accelerators for convolutional neural networks (CNNs) based on field-programmable gate arrays (FPGAs) have drawn much attention in recent years. Two INT-8 multiplications are often implemented a single digital signal processor (DSP) to improve DSP efficiency and performance. However, most of these works only use DSPs implement do not involve accumulations, which requires lot look-up tables (LUTs) consequently leads greater energy consumption. Furthermore, detail how obtain two signed with...
Deep convolutional neural networks (DNNs) have been widely used in many applications, particularly machine vision. It is challenging to accelerate DNNs on embedded systems because real-world vision applications should reserve a lot of external memory bandwidth for other tasks, such as video capture and display, while leaving little accelerating DNNs. In order solve this issue, study, we propose high-throughput accelerator, called reconfigurable tiny network accelerator (ReTiNNA), the...
Abstract Deep convolutional neural networks (DCNNs) have been widely applied in various modern artificial intelligence (AI) applications. DCNN's inference is a process with high calculation costs, which usually requires billions of multiply‐accumulate operations. On mobile platforms such as embedded systems or robotics, an efficient implementation DCNNs significant. However, most previous field‐programmable gate array‐based works on accelerators for just support one DCNN convolution layers....
Elliptic curve cryptography (ECC), one of the asymmetric cryptography, is widely used in practical security applications, especially Internet Things (IoT) applications. This paper presents a low-power reconfigurable architecture for ECC, which capable resisting simple power analysis attacks (SPA) and can be configured to support all point operations modular on 160/192/224/256-bit field orders over GF(p). Point multiplication (PM) most complex time-consuming operation while (MM) division (MD)...
Convolutional neural networks (CNNs) have been widely applied in the field of computer vision due to their inherent advantages image feature extraction. However, it is difficult implement CNNs directly on embedded platforms owing excessive calculations CNNs. Field Programmable Gate Arrays popular CNN accelerators because configurability and high energy efficiency. Given highly parallel workloads CNN, a accelerator with 14 × 16 processing element array designed this study accelerate...
Convolutional Neural Networks (CNNs) have been widely used in the field of computer vision. Due to computational complexity CNNs, their efficiency has become a major concern. Field Programmable Gate Array (FPGA) is an ideal embedded device for accelerating CNNs due its parallelism and programmability. However, key challenge how efficiently deploy on platform FPGA. Based inherent this paper proposes efficient parallel accelerator architecture with two processing element (PE) arrays accelerate...
Recent researches on deep convolution neural networks have proposed some compact networks, such as MobileNet, but its main computation, depthwise separable (DWC), which reduces the reusable data and improves requirement of loading efficiency. Although DWC can effectively reduce amount network it needs a special accelerator to enhance inference speed. This paper proposes high-performance for based commonly used acceleration platform field-programmable gate array. The supports computation both...