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SpikeNAS-Bench: Benchmarking NAS Algorithms for Spiking Neural Network Architecture
Genchen Sun;Zhengkun Liu;Lin Gan;Hang Su;Ting Li;Wenfeng Zhao;Biao Sun
IEEE Transactions on Artificial Intelligence
Year: 2025 | Early Access Article |
In recent years, Neural Architecture Search (NAS) has marked significant advancements, yet its efficacy is marred by the dependence on substantial computational resources. To mitigate this, the development of NAS benchmarks has emerged, offering datasets that enumerate all potential network architectures and their performances within a predefined search space. Nonetheless, these benchmarks predomi...Show More
Compression of Respiratory Sound Signals via TranscoderQVAE
2024 IEEE Biomedical Circuits and Systems Conference (BioCAS)
Year: 2024 | Conference Paper |
Respiratory diseases are one of the most common causes of death worldwide. At present, digital stethoscopes are valuable tools for diagnosing respiratory diseases, but they face limitations in storage, computation, and transmission capabilities. Therefore, developing respiratory sound compression algorithms is crucial. In this work, we propose TranscoderQVAE (TQVAE). TQVAE adopts the idea of varia...Show More
SGDE-Net: SAM-Guided Dual-Path Encoding NetWork for Medical Image Segmentation
2024 43rd Chinese Control Conference (CCC)
Year: 2024 | Conference Paper |
Accurate segmentation is crucial in medical image diagnosis and treatment planning. Currently, segmentation methods in medical images primarily utilize Transformer and Convolutional Neural Networks (CNN) architectures for feature learning The former leverages its modeling capability for long-range dependencies to capture global information, while the latter is more adept at learning local informat...Show More
EEG-Based Emotion Recognition Using Dynamic Spatial-Spectral-Temporal Network
2024 43rd Chinese Control Conference (CCC)
Year: 2024 | Conference Paper |
EEG-based emotion recognition has demonstrated significant potential in various domains, for example, emotion detection of patients with disorders of consciousness, cognitive load assessment, emotion detection of drivers and so on. In the realm of brain-computer interface research and development, the ability to accurately and effectively recognize emotions is of significant importance. In this pa...Show More
Graph Convolution Neural Network Based End-to-End Channel Selection and Classification for Motor Imagery Brain–Computer Interfaces
Classification of electroencephalogram-based motor imagery (MI-EEG) tasks is crucial in brain–computer interface (BCI). EEG signals require a large number of channels in the acquisition process, which hinders its application in practice. How to select the optimal channel subset without a serious impact on the classification performance is an urgent problem to be solved in the field of BCIs. This a...Show More
A Multiscale Feature Extraction Network Based on Channel-Spatial Attention for Electromyographic Signal Classification
Biao Sun;Beida Song;Jiajun Lv;Peiyin Chen;Xinlin Sun;Chao Ma;Zhongke Gao
The applications of myoelectrical interfaces are majorly limited by the efficacy of decoding motion intent in the electromyographic (EMG) signal. Currently, EMG classification methods often rely substantially on handcrafted features or ignore key channel and interfeature information for classification tasks. To address these issues, a multiscale feature extraction network (MSFEnet) based on channe...Show More
WSQ-AdderNet: Efficient Weight Standardization based Quantized AdderNet FPGA Accelerator Design with High-Density INT8 DSP-LUT Co-Packing Optimization
Yunxiang Zhang;Biao Sun;Weixiong Jiang;Yajun Ha;Miao Hu;Wenfeng Zhao
2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD)
Year: 2022 | Conference Paper |
Cited by: Papers (5)
Convolutional neural networks (CNNs) have been widely adopted for various machine intelligence tasks. Nevertheless, CNNs are still known to be computational demanding due to the convolutional kernels involving expensive Multiply-ACcumulate (MAC) operations. Recent proposals on hardware-optimal neural network architectures suggest that AdderNet with a lightweight ℓ1-norm based feature extraction ke...Show More
Speed Imagery EEG Classification with Spatial-temporal Feature Attention Deep Neural Networks
2022 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Decoding continuous brain intentions is a major challenge for the research and application of brain-computer interfaces (BCI). Neuronal activity has been experimentally observed through various brain activity measuring techniques, of which electroencephalography (EEG) is the most widely used as it is noninvasive, practical, and has high time resolution. Here we propose a spontaneous speed imagery ...Show More
Coupling Convolution, Transformer and Graph Embedding for Motor Imagery Brain-Computer Interfaces
2022 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2022 | Conference Paper |
Cited by: Papers (5)
Over the past ten years, convolution neural network (CNN) and self-attention based models (e.g., transformer) have shown extremely competitive performance in the classification of motor imagery (MI) tasks based on electroencephalogram (EEG) signals. CNN exploits local features effectively, while self-attention based models are good at capturing long-distance feature dependencies. In this paper, we...Show More
Classification of olfactory-induced electroencephalogram (EEG) signals has shown great potential in many fields. Since different frequency bands within the EEG signals contain different information, extracting specific frequency bands for classification performance is important. Moreover, due to the large inter-subject variability of the EEG signals, extracting frequency bands with subject-specifi...Show More
Training-Free Deep Generative Networks for Compressed Sensing of Neural Action Potentials
Energy consumption is an important issue for resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) is a promising framework for addressing this challenge because it can compress data in an energy-efficient way. Recent work has shown that deep neural networks (DNNs) can serve as valuable models for CS of neural action potentials (APs). Howe...Show More
Multi-Stream Fusion Network With Generalized Smooth L1 Loss for Single Image Dehazing
Single image dehazing is an important but challenging computer vision problem. For the problem, an end-to-end convolutional neural network, named multi-stream fusion network (MSFNet), is proposed in this paper. MSFNet is built following the encoder-decoder network structure. The encoder is a three-stream network to produce features at three resolution levels. Residual dense blocks (RDBs) are used ...Show More
Classification of electroencephalogram-based motor imagery (MI-EEG) tasks is crucial in brain computer interfaces (BCI). In view of the characteristics of non-stationarity, time-variability and individual diversity of EEG signals, a novel framework based on graph neural network is proposed for MI-EEG classification. First, an adaptive graph convolutional layer (AGCL) is constructed, by which the e...Show More
EEG Motor Imagery Classification With Sparse Spectrotemporal Decomposition and Deep Learning
Classification of electroencephalogram-based motor imagery (MI-EEG) tasks raises a big challenge in the design and development of brain-computer interfaces (BCIs). In view of the characteristics of nonstationarity, time-variability, and individual diversity of EEG signals, a deep learning framework termed SSD-SE-convolutional neural network (CNN) is proposed for MI-EEG classification. The framewor...Show More
AxC-CS: Approximate Computing for Hardware Efficient Compressed Sensing Encoder Design
2019 32nd IEEE International System-on-Chip Conference (SOCC)
Year: 2019 | Conference Paper |
In this paper, we present an approximate computing framework for hardware-efficient compressed sensing encoder design exploiting application-level error-resiliency, termed as AxC-CS (Approximate Computing for Compressed Sensing). We consider a 2-stage scalar quantization scheme during CS encoding process for physiological signals in sensor nodes and demonstrate numerically that bringing forward th...Show More
A Steganographic Scheme based on Perceptual Loss and Feature Fusion
2019 Chinese Control Conference (CCC)
Year: 2019 | Conference Paper |
The purpose of steganography is to convey information in public that no one else pays attention to. With the development of communication technique, the communication in the public channels also causes security problems.This is why the study of steganography has been concerned. Traditional steganographic algorithms usually extract the embedded information from the image itself. In this paper, a st...Show More
Objective: Pupillometer plays a key role in a variety of research areas, including disease diagnosis, human-machine interaction, and education. Here, we set out to leverage the deep learning theory to develop a remote binocular vision system for pupil diameter estimation. Approach: the system consists of three parts: eye detection, eye tracking, and pupil diameter estimation. We first train a conv...Show More
A Hardware Platform for Wireless Structural Health Monitoring Based on Compressed Sensing
2018 13th World Congress on Intelligent Control and Automation (WCICA)
Year: 2018 | Conference Paper |
In this paper, a novel hardware platform is proposed for wireless structural health monitoring in real time based on compressed sensing. The platform consists of a three-axis accelerometer sensor, a high-precision multi-channel analog-to-digital converter, a wireless communication module and a Field Programmable Gate Array (FPGA) chip. Firstly, the three-axis acceleration signal of the structure a...Show More
Probability Model Based Clustering for Gene Activity States
Xu Han;Yongiun Qian;Xinshan Zhu;Biao Sun;Chao Ren;Mingshuang Wang
2018 13th World Congress on Intelligent Control and Automation (WCICA)
Year: 2018 | Conference Paper |
As the quantity and diversity of transcriptomics data increase and the prices to obtain data continue to decline, numerous approaches which attempt to classify the gene activity into two states (active or inactive) appeared. These approaches suffer from many limitations, such as failing to overcome the limitation of data distribution. To address these limitations, we propose an improved Bayesian a...Show More
On-Chip Neural Data Compression Based On Compressed Sensing With Sparse Sensing Matrices
On-chip neural data compression is an enabling technique for wireless neural interfaces that suffer from insufficient bandwidth and power budgets to transmit the raw data. The data compression algorithm and its implementation should be power and area efficient and functionally reliable over different datasets. Compressed sensing is an emerging technique that has been applied to compress various ne...Show More
Improving Classification of Slow Cortical Potential Signals for BCI Systems With Polynomial Fitting and Voting Support Vector Machine
IEEE Signal Processing Letters
Cited by: Papers (28)
Classification of slow cortical potential (SCP) signals is crucial for brain-computer interface (BCI) systems. This letter presents a new scheme to improve the classification performance of SCP signals. It consists of two parts: first, by fitting the wavelet coefficients of SCP signals with a second-order polynomial, the SCP trends are extracted; and second, a voting system based on the optimal tr...Show More
Using Spike-Based Bio-Inspired Olfactory Model for Data Processing in Electronic Noses
Ying-Jie Liu;Qing-Hao Meng;Pei-Feng Qi;Biao Sun;Xin-Shan Zhu
Conventional electronic noses need complicated data preprocessing and tedious feature reduction steps for different sensors and applications. To overcome the drawbacks, a bio-inspired data processing method using a spike-based olfactory model is proposed in this paper, which consists of spike encoding by virtual olfactory receptor neurons (VORNs) and subsequent processing in a bionic olfactory bul...Show More
Efficient Compressed Sensing for Wireless Neural Recording: A Deep Learning Approach
IEEE Signal Processing Letters
Cited by: Papers (23)
Data compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and compressed sensing (CS) theory has successfully demonstrated its potential in neural recording applications. Based on deep learning theory, this paper presents a binarized autoencoder scheme for CS, in which a binary sensing matrix and a noniterative recovery solver are join...Show More
A Training-Free One-Bit Compressed Sensing Framework for Wireless Neural Recording
IEEE Communications Letters
Cited by: Papers (6)
Signal compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and compressed sensing (CS) has successfully demonstrated its potential in this field. This letter proposes a training-free one-bit compressed sensing framework for wireless neural recording applications. By adopting the analysis model to enforce the signal sparsity and constr...Show More
Training-free compressed sensing for wireless neural recording
2016 IEEE Biomedical Circuits and Systems Conference (BioCAS)
Year: 2016 | Conference Paper |
Cited by: Papers (3)
Signal compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and Compressed Sensing (CS) has successfully demonstrated its potential in this field. However, the conventional CS approaches rely on data-dependent and computationally intensive dictionary learning processes to find out the sparse representation of neural signals, and dictio...Show More
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