AI Medical Compendium Journal:
IEEE transactions on biomedical circuits and systems

Showing 41 to 50 of 132 articles

Low Complexity Binarized 2D-CNN Classifier for Wearable Edge AI Devices.

IEEE transactions on biomedical circuits and systems
Wearable Artificial Intelligence-of-Things (AIoT) devices exhibit the need to be resource and energy-efficient. In this paper, we introduced a quantized multilayer perceptron (qMLP) for converting ECG signals to binary image, which can be combined wi...

A Highly Energy-Efficient Hyperdimensional Computing Processor for Biosignal Classification.

IEEE transactions on biomedical circuits and systems
Hyperdimensional computing (HDC) is a brain-inspired computing paradigm that operates on pseudo-random hypervectors to perform high-accuracy classifications for biomedical applications. The energy efficiency of prior HDC processors for this computati...

A Neuromorphic Processing System With Spike-Driven SNN Processor for Wearable ECG Classification.

IEEE transactions on biomedical circuits and systems
This paper presents a neuromorphic processing system with a spike-driven spiking neural network (SNN) processor design for always-on wearable electrocardiogram (ECG) classification. In the proposed system, the ECG signal is captured by level crossing...

TripleBrain: A Compact Neuromorphic Hardware Core With Fast On-Chip Self-Organizing and Reinforcement Spike-Timing Dependent Plasticity.

IEEE transactions on biomedical circuits and systems
Human brain cortex acts as a rich inspiration source for constructing efficient artificial cognitive systems. In this paper, we investigate to incorporate multiple brain-inspired computing paradigms for compact, fast and high-accuracy neuromorphic ha...

Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks.

IEEE transactions on biomedical circuits and systems
During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal...

Early Termination Based Training Acceleration for an Energy-Efficient SNN Processor Design.

IEEE transactions on biomedical circuits and systems
In this paper, we present a novel early termination based training acceleration technique for temporal coding based spiking neural network (SNN) processor design. The proposed early termination scheme can efficiently identify the non-contributing tra...

Gas Recognition in E-Nose System: A Review.

IEEE transactions on biomedical circuits and systems
Gas recognition is essential in an electronic nose (E-nose) system, which is responsible for recognizing multivariate responses obtained by gas sensors in various applications. Over the past decades, classical gas recognition approaches such as princ...

Data-Driven Real-Time Magnetic Tracking Applied to Myokinetic Interfaces.

IEEE transactions on biomedical circuits and systems
A new concept of human-machine interface to control hand prostheses based on displacements of multiple magnets implanted in the limb residual muscles, the myokinetic control interface, has been recently proposed. In previous works, magnets localizati...

An Energy Efficient ECG Ventricular Ectopic Beat Classifier Using Binarized CNN for Edge AI Devices.

IEEE transactions on biomedical circuits and systems
Wearable Artificial Intelligence-of-Things (AIoT) requires edge devices to be resource and energy-efficient. In this paper, we design and implement an efficient binary convolutional neural network (bCNN) algorithm utilizing function-merging and block...

A 16-Channel Fully Configurable Neural SoC With 1.52 μW/Ch Signal Acquisition, 2.79 μW/Ch Real-Time Spike Classifier, and 1.79 TOPS/W Deep Neural Network Accelerator in 22 nm FDSOI.

IEEE transactions on biomedical circuits and systems
With the advent of high-density micro-electrodes arrays, developing neural probes satisfying the real-time and stringent power-efficiency requirements becomes more challenging. A smart neural probe is an essential device in future neuroscientific res...