In this paper, we present a design for a wearable computational DSP system that alleviates the issues of a previous design and provides a much smaller and lower power solution for the overall BMI goals. The system first acquires the neural data through a high speed data bus in order to train and evaluate prediction models. Then it wirelessly transmits the predicted results to a simulated robot arm. This system has been built and successfully tested with real and simulated data.
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http://dx.doi.org/10.1109/IEMBS.2006.260423 | DOI Listing |
A SbS-based reconfigurable diffractive optical neural network (RDONN) for on-chip integration is proposed. The RDONN can be integrated into standard silicon-on-insulator systems, offering a compact, passive, all-optical solution for implementing machine learning functions. The weights of the proposed optical chip are reconfigurable without the need to modify hardware structures or re-fabricate the chip.
View Article and Find Full Text PDFFront Neurosci
January 2025
Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Background And Purpose: Irritable bowel syndrome (IBS) is a common bowel-brain interaction disorder whose pathogenesis is unclear. Many studies have investigated abnormal changes in brain function in IBS patients. In this study, we analyzed the dynamic changes in brain function in IBS patients using a Hidden Markov Model (HMM).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.
To address the challenges of missed detections caused by insufficient shape and texture features and blurred boundaries in existing detection methods, this paper introduces a novel moving vehicle detection approach for satellite videos. The proposed method leverages frame difference and convolution to effectively integrate spatiotemporal information. First, a frame difference module (FDM) is designed, combining frame difference and convolution.
View Article and Find Full Text PDFMicromachines (Basel)
December 2024
Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China.
Reconfigurable processor-based acceleration of deep convolutional neural network (DCNN) algorithms has emerged as a widely adopted technique, with particular attention on sparse neural network acceleration as an active research area. However, many computing devices that claim high computational power still struggle to execute neural network algorithms with optimal efficiency, low latency, and minimal power consumption. Consequently, there remains significant potential for further exploration into improving the efficiency, latency, and power consumption of neural network accelerators across diverse computational scenarios.
View Article and Find Full Text PDFNanophotonics
January 2025
Key Laboratory for Information Science of Electromagnetic Waves, School of Information Science and Technology, Fudan University, Shanghai 200433, China.
Gesture recognition plays a significant role in human-machine interaction (HMI) system. This paper proposes a gesture-controlled reconfigurable metasurface system based on surface electromyography (sEMG) for real-time beam deflection and polarization conversion. By recognizing the sEMG signals of user gestures through a pre-trained convolutional neural network (CNN) model, the system dynamically modulates the metasurface, enabling precise control of the deflection direction and polarization state of electromagnetic waves.
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