AIMC Topic: Pattern Recognition, Automated

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Surface EMG Pattern Recognition Using Long Short-Term Memory Combined with Multilayer Perceptron.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Motion classification based on pattern recognition of surface EMG (sEMG) signals is a promising approach for prosthetic control. We present a pattern recognition model that combines long short-term memory (LSTM) network with multiplayer perceptron (M...

Pattern Analysis in Physiological Pulsatile Signals: An Aid to Personalized Healthcare.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We present a system to analyze patterns inside pulsatile signals and discover repetitions inside signals. We measure dominance of the repetitions using morphology and discrete nature of the signals by exploiting machine learning and information theor...

Classification of electroencephalogram signals using wavelet-CSP and projection extreme learning machine.

The Review of scientific instruments
Brain-computer interface (BCI) systems establish a direct communication channel from the brain to an output device. As the basis of BCIs, recognizing motor imagery activities poses a considerable challenge to signal processing due to the complex and ...

Machine Learning in Rehabilitation Assessment for Thermal and Heart Rate Data Processing.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Multimodal signal analysis based on sophisticated noninvasive sensors, efficient communication systems, and machine learning, have a rapidly increasing range of different applications. The present paper is devoted to pattern recognition and the analy...

Action-Driven Visual Object Tracking With Deep Reinforcement Learning.

IEEE transactions on neural networks and learning systems
In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control track...

An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition.

IEEE transactions on biomedical circuits and systems
Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim t...

Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, long short-term memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynami...

Effect of conductance linearity and multi-level cell characteristics of TaO-based synapse device on pattern recognition accuracy of neuromorphic system.

Nanotechnology
To improve the classification accuracy of an image data set (CIFAR-10) by using analog input voltage, synapse devices with excellent conductance linearity (CL) and multi-level cell (MLC) characteristics are required. We analyze the CL and MLC charact...

Hybrid gray wolf optimizer-artificial neural network classification approach for magnetic resonance brain images.

Applied optics
Automated and accurate classification of magnetic resonance images (MRIs) of the brain has great importance for medical analysis and interpretation. This paper presents a hybrid optimized classification method to classify the brain tumor by classifyi...