15 papers · ranked by Valyu relevance
Hengjin Ke, Chang Cai, Lihua Yao, Dan Chen
1## Introduction Psychiatry stands at a pivotal turning point shaped by rapid technological advances and pressing clinical demands (1). Mental health disorders, defined by multifaceted etiologies and heterogeneous presentations, have traditionally relied on subjective assessments and qualitative interviews. However…
Pottumarthy Venkata Lahari, Sagnika Dutta, H. Deeksha, Samreen A. Patel + 2 more
Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye. However, conventional optical microscopy faces challenges such as optical aberrations, diffraction-limited resolution, low signal-to-noise ratio…
Manyun Zhang, Tianlei Wang, Zhiyuan Zhu
3## Breakthroughs and limitations of deep learning in neural data analysis As the dominant paradigm in contemporary artificial intelligence, deep learning (DL) has achieved remarkable success in the analysis of neural data owing to its hierarchical feature extraction and nonlinear approximation capabilities (; ).…
S. Deepika, V. Arunachalam
Exploiting unstructured sparsity in the hardware accelerator of a Convolutional Neural Networks (CNNs) based inference can improve energy efficiency. However, it needs a complex controller for indexing and load-balancing. A controller for managing unstructured sparsity in Fully Connected (FC) layers is designed. In a…
Wendy Flores-Fuentes, Oleg Sergiyenko, Julio C. Rodríguez-Quiñonez, Jesús E. Miranda-Vega
Information theory is the foundation for computer vision and image processing across diverse applications. It constitutes the application of sophisticated mathematical principles to electrical engineering development for advanced research in vision systems and machine learning. Within this field of study, 3D visual…
Wessam M. Salama, Moustafa H. Aly, Eman S. Amer
Both commercial and scientific underwater wireless optical communication (UWOC) systems are essential and significant for several applications with the ability to provide high data transmission rates over distances up to tens of meters. There are several research gaps for UWOC like the limitation of the UWOC…
Mingjing Li, Huihui Zhou, Xiaofeng Xu, Zhiwei Zhong + 15 more
There is a growing necessity for edge training to adapt to dynamically changing environments. Neuromorphic computing represents a significant pathway for highly efficient intelligent computation in energy-constrained edges, but existing neuromorphic architectures lack the ability of directly training spiking neural…
Jiahao Li, Ming Xu, Heng Dong, Bin Lan + 5 more
The deployment of Spiking Neural Networks (SNNs) on resource-constrained edge devices is hindered by a critical algorithm-hardware mismatch: a fundamental trade-off between the accuracy degradation caused by aggressive quantization and the resource redundancy stemming from traditional decoupled hardware designs. To…
Sheng Zhou, Chang Gao, Tobi Delbruck, Marian Verhelst + 1 more
Artificial intelligence (AI) has made significant strides towards efficient online processing of sensory signals at the edge through the use of deep neural networks with ever-expanding size. However, this trend has brought with it escalating computational costs and energy consumption, which have become major obstacles…
Zhijie Qu, Jinquan Zhang, Yuewei Zhou, Lina Ni + 4 more
The escalating complexity, density, and agility of the modern electromagnetic environment (CME) pose unprecedented challenges to radar signal deinterleaving, a cornerstone of electronic intelligence. While traditional methods face significant performance bottlenecks, the advent of artificial intelligence, particularly…
Michael Voudaskas, Jack Iain MacLean, Neale A. W. Dutton, Brian D. Stewart + 2 more
This review examines the state of spiking neural networks (SNNs) for imaging, combining a structured literature survey, a comparative meta-analysis of reported datasets, training strategies, hardware platforms, and applications and a case study on LMU-based depth estimation in direct Time-of-Flight (dToF) imaging.…
Drew E. Winters
Studying flexible, adaptive transitions between cognitive tasks and serial-parallel processing under changing task demands has been a central focus for understanding human cognition. Advances in neuroimaging analysis have improved the ability to link cognition with brain function, providing a foundation for developing…
Felix Wühler, Tim Markus Häußermann, Alessa Rache, Björn van Marwick + 5 more
Automated and comprehensive processing of hyperspectral image data is increasingly important in academic research and medical technology. This study presents an automated processing pipeline that integrates hyperspectral image acquisition, analysis, multimodal fusion, and centralized data management to improve the…
Drew E. Winters
Studying flexible, adaptive transitions between cognitive tasks and serial-parallel processing under changing task demands has been central to understanding human cognition. Advances in neuroimaging analysis have improved the ability to link cognition with brain function, motivating methods that characterize dynamic…
Anu Roopa Devi Sekar, Ruban Nersisson
Human facial emotion recognition (FER) is a vibrant research field. This research proposes a novel, biologically inspired hybrid FER framework that uniquely connects event-driven Spiking Neural Networks (SNNs) with deep learning, specifically a Spike-based Support Vector Machine (S-SVM), which is designed for its…