In this current technological world, the application of machine learning is becoming ubiquitous. Incorporating machine learning algorithms on extremely low-power and inexpensive embedded devices at the edge level is now possible due to the combination of the Internet of Things (IoT) and edge computing. To estimate an outcome, traditional machine learning demands vast amounts of resources. The TinyML concept for embedded machine learning attempts to push such diversity from usual high-end approaches to low-end applications. TinyML is a rapidly expanding interdisciplinary topic at the convergence of machine learning, software, and hardware centered on deploying deep neural network models on embedded (micro-controller-driven) systems. TinyML will pave the way for novel edge-level services and applications that survive on distributed edge inferring and independent decision-making rather than server computation. In this paper, we explore TinyML's methodology, how TinyML can benefit a few specific industrial fields, its obstacles, and its future scope.
Integration of TinyML and LargeML: A Survey of 6G and Beyond
Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava（弗拉迪斯拉夫技术大学电子工程与计算机科学学院）；Faculty of Information Technology, Van Lang School of Technology, Van Lang University（范-lang大学信息科技学院）
TinyML Towards Industry 4.0: Resource-Efficient Process Monitoring of a Milling Machine
Tim Langer Chair of Highly-Parallel VLSI Systems and Neuro-Microelectronics TU Dresden（高等并行非常规集成电路系统与神经微电子学教授，德累斯顿技术大学）；Fraunhofer Institute for Machine Tools and Forming Technology IWU Dresden（机械加工与成形技术弗劳恩霍夫研究所，德累斯顿）；Fraunhofer Institute for Integrated Circuits IIS Division Engineering of Adaptive Systems EAS Dresden（集成电路弗劳恩霍夫研究所，适应性系统工程部门，德累斯顿）
Atrial Fibrillation: A Medical and Technological Review
Jadavpur University（贾达普大学）
房颤（AF）是美国最常见的导致住院治疗的心律失常类型（希腊语a-，损失+心律失常，心律失常=心律失常）。虽然有时AF是无症状的，但它除了降低健康相关生活质量（HRQOL）外，还增加了患者中风和心力衰竭的风险。AF相关护理每年花费医疗保健系统$6.0 to $ 260亿美元。房颤的早期发现和临床关注可以帮助改善患者的症状和HRQOL，并降低护理成本。然而，AF检测的流行范例依赖于在单个时间点记录的心电图（ECG），并且没有阐明症状与心律或AF的关系。在最近十年中，由于健康监测器的民主化和高性能计算机的出现，机器学习算法已被证明在从患者的ECG识别AF方面是有效的。本文概述了房颤的症状、诊断以及该领域研究的未来前景。
Systematic Literature Review of Machine Learning Models and Applications for Text Recognition
Faculty of Electrical Engineering, Universiti Teknologi Malaysia（马来西亚工艺大学电气工程学院）；Faculty of Artificial Intelligence, Universiti Teknologi Malaysia（马来西亚工艺大学人工智能学院）；Department of Electrical and Electronic Engineering, American International University-Bangladesh（孟加拉国美国国际大学电气与电子工程系）；Department of Electrical Engineering, Balochistan University of Information Technology, Engineering and Management Sciences（巴基斯坦俾路支省信息技术、工程与管理科学大学电气工程系）
