A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme [PDF]
Sensors, 2023 Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable.
Amin Biglari, Wei Tang
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Gait Stride Length Estimation Using Embedded Machine Learning [PDF]
Sensors, 2023 Introduction. Spatiotemporal gait parameters, e.g., gait stride length, are measurements that are classically derived from instrumented gait analysis. Today, different solutions are available for gait assessment outside the laboratory, specifically for ...
Joeri R. Verbiest +5 more
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An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications
Sensors, 2021 Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications.
Taiwo Samuel Ajani +2 more
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Applied Sciences, 2021 Environmental Sound Recognition has become a relevant application for smart cities. Such an application, however, demands the use of trained machine learning classifiers in order to categorize a limited set of audio categories. Although classical machine
Lancelot Lhoest +6 more
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Machine Learning Based Embedded Code Multi-Label Classification
IEEE Access, 2021 With the development of Internet of Things (IoT) technology, embedded based electronic devices have penetrated every corner of our daily lives. As the brain of IoT devices, embedded based micro controller unit (MCU) plays an irreplaceable role.
Yu Zhou, Suxia Cui, Yonghui Wang
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Embedded Machine Learning Using a Multi-Thread Algorithm on a Raspberry Pi Platform to Improve Prosthetic Hand Performance. [PDF]
Micromachines (Basel), 2022 Triwiyanto T +7 more
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A survey on versatile embedded Machine Learning hardware acceleration
Journal of Systems ArchitectureThis survey investigates recent developments in versatile embedded ML hardware acceleration. Various architectural approaches for efficient implementation of ML algorithms on resource-constrained devices are analyzed, focusing on three key aspects: performance optimization, embedded system considerations (throughput, latency, energy efficiency) and ...
Julien Francq +2 more
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Rethinking the Methods and Algorithms for Inner Speech Decoding and Making Them Reproducible
NeuroSci, 2022 This study focuses on the automatic decoding of inner speech using noninvasive methods, such as Electroencephalography (EEG). While inner speech has been a research topic in philosophy and psychology for half a century, recent attempts have been made to ...
Foteini Simistira Liwicki +4 more
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Braille letter reading: A benchmark for spatio-temporal pattern recognition on neuromorphic hardware
Frontiers in Neuroscience, 2022 Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities. Recent deep learning approaches have reached outstanding accuracies in such tasks, but their implementation on conventional ...
Simon F. Müller-Cleve +13 more
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Learned protein embeddings for machine learning [PDF]
Bioinformatics, 2018 Abstract Motivation Machine-learning models trained on protein sequences and their measured functions can infer biological properties of unseen sequences without requiring an understanding of the underlying physical or biological mechanisms. Such models enable the prediction and discovery of sequences
Kevin K. Yang +3 more
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