Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 6,331 to 6,340 of 205,745 articles

BioGAP-Ultra: A Modular Edge-AI Platform for Wearable Multimodal Biosignal Acquisition and Processing.

IEEE transactions on biomedical circuits and systems Jun 1, 2026
The growing demand for continuous physiological monitoring and human-machine interaction in real-world settings calls for wearable platforms that are flexible, low-power, and capable of on-device intelligence. This work presents BioGAP-Ultra, an adva... read more 

Optimal Tracking Control of Uncertain Nonlinear Systems Using Simplified Reinforcement Learning.

IEEE transactions on cybernetics Jun 1, 2026
This article investigates the optimal tracking control problem for high-order uncertain nonlinear systems by developing a simplified reinforcement learning (RL) framework with minimal neural networks (NNs). In contrast to conventional RL-based scheme... read more 

Evolutionary Multiobjective Neural Architecture Search for Binary Neural Networks by Two-Stage Optimization.

IEEE transactions on cybernetics Jun 1, 2026
Binary neural networks (BNNs) have been applied in limited resources and mobile devices because of their extreme model compression ability. However, manually designing suitable architectures is challenging given the specialized structure of binarized... read more 

Cardiovascular Disease Classification System With ECG-Gating PCG Algorithm and Programmable AI Accelerator Design.

IEEE transactions on biomedical circuits and systems Jun 1, 2026
Cardiovascular diseases (CVDs) are among the leading causes of mortality. Traditional diagnostic methods require hospital visits and professional medical personnel, but the timely detection of cardiac conditions can significantly improve survival rat... read more 

Safe Optimal Control Framework for Cooperative Manipulation of Objects in Human-Robot Teams.

IEEE transactions on cybernetics Jun 1, 2026
This article introduces a distributed deep neural network (NN)-based adaptive control framework for cooperative object manipulation in human-robot teams with unknown agent dynamics by using three distinct multilayer NN observers (MNNOs). The first ob... read more 

Skill Information Representation Imitation Learning for Long-Horizon Dexterous Robot Micromanipulation of Deformable Cell.

IEEE transactions on cybernetics Jun 1, 2026
Robots performing collaborative long-horizon dexterity cell micromanipulation tasks are challenging and practically significant, such as peeling cell membranes, which is considered one of the most technically demanding procedures. The imitation learn... read more 

ATRNet-STAR: A Large Dataset and Benchmark Toward Remote Sensing Object Recognition in the Wild.

IEEE transactions on pattern analysis and machine intelligence Jun 1, 2026
The absence of publicly available, large-scale, high-quality datasets for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has significantly hindered the application of rapidly advancing deep learning techniques, which hold huge potent... read more 

Dual Adaptive Disentangled Representation Learning With Multimodal Data for Disease Diagnosis.

IEEE transactions on pattern analysis and machine intelligence Jun 1, 2026
The use of imaging and genetic data for biomarker detection and disease diagnosis can deepen the understanding of disease pathogenesis and assist in clinical diagnosis. However, current methods face two major challenges: 1) the significant heterogene... read more 

Perch Like a Bird: Bio-Inspired Optimal Maneuvers and Nonlinear Control for Flapping-Wing Unmanned Aerial Vehicles.

IEEE transactions on cybernetics Jun 1, 2026
This research endeavors to design the perching maneuver and control in ornithopter robots. By analyzing the dynamic interplay between the robot's flight dynamics, feedback loops, and the environmental constraints, we aim to advance our understanding ... read more 

Neural networks learn forward dynamics when freed from numerical integration

bioRxiv Jun 1, 2026
Seamless interaction between humans and machines requires interfaces that remain robust to the variability inherent in biological signals and physical environments. Advanced human-machine interfaces (HMIs) increasingly rely on machine learning to pre... read more 
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