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Found 14 papers, 0 papers with code
Multi-Modal Wireless Flexible Gel-Free Sensors with Edge Deep Learning for Detecting and Alerting Freezing of Gait in Parkinson's Patients
The inference of the DL model is performed on a low-power microcontroller in the central node.
A 21.3%-Efficiency Clipped-Sinusoid UWB Impulse Radio Transmitter with Simultaneous Inductive Powering and Data Receiving
State-of-the-art power efficiency of 21. 3% was achieved at a data-rate of 230Mbps and energy consumption of 21pJ/b.
Edge Deep Learning Enabled Freezing of Gait Detection in Parkinson's Patients
In a validation using a public dataset, the prototype developed achieved a FoG detection sensitivity of 88. 8% and an F1 score of 85. 34%, using less than 20 k trainable parameters per sensor node.
A CNN-Transformer Deep Learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Device
This paper proposes a deep learning (DL) model for automatic sleep stage classification based on single-channel EEG data.
A Closed-loop Sleep Modulation System with FPGA-Accelerated Deep Learning
The DL model has been validated using a public sleep database containing 81 subjects, achieving a state-of-the-art classification accuracy of 85. 8% and a F1-score of 79%.
ECE496Y Final Report: Edge Machine Learning for Detecting Freezing of Gait in Parkinson's Patients
Parkinson's disease is a common neurological disease, entailing a multitude of motor deficiency symptoms.
Classify Respiratory Abnormality in Lung Sounds Using STFT and a Fine-Tuned ResNet18 Network
Our key contributions include the bench-marking of different audio feature extractors and neural network based classifiers, and the implementation of a complete pipeline using STFT and a fine-tuned ResNet18 network.
On-CMOS High-Throughput Multi-Modal Amperometric DNA Analysis with Distributed Thermal Regulation
Over 95% of the circuit blocks are synergistically shared among the four operating modes, including CPA, CV, IS, and the proposed temperature regulation mode.
M-FasterSeg: An Efficient Semantic Segmentation Network Based on Neural Architecture Search
In the search process, combine the self-attention network structure module to adjust the searched neural network structure, and then combine the semantic segmentation network searched by different branches to form a fast semantic segmentation network structure, and input the picture into the network structure to get the final forecast result.
A System-on-Chip for Closed-loop Optogenetic Sleep Modulation
Stimulation of target neuronal populations using optogenetic techniques during specific sleep stages has begun to elucidate the mechanisms and effects of sleep.
Study on Compressed Sensing of Action Potential
Compressive sensing (CS) is a signal processing technique that enables sub-Nyquist sampling and near lossless reconstruction of a sparse signal.
Edge Deep Learning for Neural Implants
For seizure event detection, the sensitivity and FPR (h-1) for the DNN, CNN, and LSTM models were 87. 36%/0. 169, 96. 70%/0. 102, and 97. 61%/0. 071, respectively.
An Energy-efficient Wireless Neural Recording System with Compressed Sensing and Encryption
This paper presents a wireless neural recording system featuring energy-efficient data compression and encryption.
Fractional spectral graph wavelets and their applications
One of the key challenges in the area of signal processing on graphs is to design transforms and dictionaries methods to identify and exploit structure in signals on weighted graphs.
