Spiking-physformer: Camera-based remote photoplethysmography with parallel spike-driven transformer
Artificial neural networks (ANNs) can help camera-based remote photoplethysmography
(rPPG) in measuring cardiac activity and physiological signals from facial videos, such as …
(rPPG) in measuring cardiac activity and physiological signals from facial videos, such as …
Advancements in affective disorder detection: Using multimodal physiological signals and neuromorphic computing based on snns
Currently, the integration of artificial intelligence (AI) techniques with multimodal
physiological signals represents a pivotal approach to detect affective disorders (ADs). With …
physiological signals represents a pivotal approach to detect affective disorders (ADs). With …
Snn and sound: a comprehensive review of spiking neural networks in sound
S Baek, J Lee - Biomedical Engineering Letters, 2024 - Springer
The rapid advancement of AI and machine learning has significantly enhanced sound and
acoustic recognition technologies, moving beyond traditional models to more sophisticated …
acoustic recognition technologies, moving beyond traditional models to more sophisticated …
Spiking Neural Network for Ultralow-Latency and High-Accurate Object Detection
Spiking Neural Networks (SNNs) have attracted significant attention for their energy-efficient
and brain-inspired event-driven properties. Recent advancements, notably Spiking-YOLO …
and brain-inspired event-driven properties. Recent advancements, notably Spiking-YOLO …
Spikingvit: a multi-scale spiking vision transformer model for event-based object detection
Event cameras have unique advantages in object detection, capturing asynchronous events
without continuous frames. They excel in dynamic range, low latency, and high-speed …
without continuous frames. They excel in dynamic range, low latency, and high-speed …
EventAugment: Learning Augmentation Policies from Asynchronous Event-based Data
Data augmentation is an effective way to overcome the over-fitting problem of deep learning
models. However, most existing studies on data augmentation work on frame-like data (eg …
models. However, most existing studies on data augmentation work on frame-like data (eg …
EEG-based Auditory Attention Detection with Spiking Graph Convolutional Network
Decoding auditory attention from brain activities, such as electroencephalography (EEG),
sheds light on solving the machine cocktail party problem. However, effective representation …
sheds light on solving the machine cocktail party problem. However, effective representation …
STCSNN: High energy efficiency spike-train level spiking neural networks with spatio-temporal conversion
Brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest
due to their low power features, high biological plausibility, and strong spatiotemporal …
due to their low power features, high biological plausibility, and strong spatiotemporal …
A dynamic decoder with speculative termination for low latency inference in spiking neural networks
Abstract Spiking Neural Networks (SNNs) represent a novel class of biologically inspired
neural networks. Their distinctive event-driven feature provides superior energy efficiency …
neural networks. Their distinctive event-driven feature provides superior energy efficiency …
Spike-HAR++: an energy-efficient and lightweight parallel spiking transformer for event-based human action recognition
X Lin, M Liu, H Chen - Frontiers in Computational Neuroscience, 2024 - frontiersin.org
Event-based cameras are suitable for human action recognition (HAR) by providing
movement perception with highly dynamic range, high temporal resolution, high power …
movement perception with highly dynamic range, high temporal resolution, high power …