Results 31 to 40 of about 7,108,814 (163)

IoT Makers: A Collaborative Learning Experience with TinyML

Shodh Sari
Traditional teaching methods often fail to fully engage students in the field of IoT, particularly when it comes to applying machine learning at the edge. This paper presents an innovative pedagogical approach titled “IoT Makers,” aimed at MSc Artificial
Dr. Helen K. Joy
doaj   +1 more source

Tiny Machine Learning: Progress and Futures [PDF]

Tiny Machine Learning (TinyML) is a new frontier of machine learning. By squeezing deep learning models into billions of IoT devices and microcontrollers (MCUs), we expand the scope of AI applications and enable ubiquitous intelligence.
Han, Song, Zhu, Ligeng, Wang, Wei-Chen, Chen, Wei-Ming, Lin, Ji   +4 more
core   +1 more source

Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review [PDF]

, 2023
The field of Tiny Machine Learning (TinyML) has gained significant attention due to its potential to enable intelligent applications on resource-constrained devices.
Wolinski, Pierre, Arbel, Julyan, Lê, Minh Tri   +2 more
core   +3 more sources

A TinyDL Model for Gesture-Based Air Handwriting Arabic Numbers and Simple Arabic Letters Recognition

IEEE Access
The application of tiny machine learning (TinyML) in human-computer interaction is revolutionizing gesture recognition technologies. However, there remains a significant gap in the literature regarding the effective recognition of complex scripts, such ...
Ismail Lamaakal, Ibrahim Ouahbi, Khalid El Makkaoui, Yassine Maleh, Pawel Plawiak, Fahad Alblehai   +5 more
doaj   +1 more source

TINY MACHINE LEARNING (TINYML) ADVANCEMENTS FOR INTELLIGENT BATTERY-POWERED IOT SENSORS

Abstract Battery-powered IoT sensors are increasingly capable of on-device intelligence through Tiny Machine Learning (TinyML). Advances in ultra-low-power microcontrollers (MCUs), efficient neural kernels, model compression, and hardware-aware network design have made it practical to run speech, vision, and anomaly-detection models within tens to ...
Hayat, Muhammad Ahsan, Ahmed, Syed Affan, Fatima, Sana, Irfan, Engr. Faiza, Nizamani, Muhammad Osama, Khalil, Ammar   +5 more
openaire   +2 more sources

TinyML on-device neural network training [PDF]

, 2022
LAUREA MAGISTRALETiny Machine Learning (TinyML) è l'ambito di ricerca in cui si combinano le soluzioni di apprendimento automatico con i vincoli stringenti del hardware embedded/IoT.
Ostrovan, Eugeniu
core  

Co‐Design of Stretchable Fabric Sensors and Tiny Machine Learning for Human Interface Device‐Based Edge‐Intelligent Wearable Gloves

Advanced Intelligent Systems, Volume 8, Issue 9, September 2026.
A flexible smart glove integrated with textile sensors enables real‐time hand gesture recognition for human–machine interaction. Using edge AI inference and Bluetooth communication, the system translates finger movements into human interface device commands to control endpoint devices efficiently.
Chi Cuong Vu, Tuan Nghia Nguyen, Minh‐Thanh Le   +2 more
wiley   +1 more source

A Systematic Review of State-of-the-Art TinyML Applications in Healthcare, Education, and Transportation

IEEE Access
Tiny Machine Learning (TinyML) has emerged as a transformative paradigm enabling machine learning inference directly on ultra-low-power microcontrollers and edge devices.
Chaymae Yahyati, Ismail Lamaakal, Yassine Maleh, Khalid El Makkaoui, Ibrahim Ouahbi, May Almousa, Ahmed A. Abd El-Latif   +6 more
doaj   +1 more source

AI‐Assisted IoT‐Enabled ECG Monitoring: Integrating Foundational and Generative AI Tools for Sustainable Smart Healthcare—Recent Trends

AI &Innovation, Volume 1, Issue 2, September 2026.
ABSTRACT The rapid evolution of the Internet of Things (IoT) has significantly advanced the field of electrocardiogram (ECG) monitoring, enabling real‐time, remote, and patient‐centric cardiac care. This paper presents a comprehensive survey of AI assisted IoT‐based ECG monitoring systems, focusing on the integration of emerging technologies such as ...
Amrita Choudhury, Kandarpa Kumar Sarma, Lachit Dutta   +2 more
wiley   +1 more source

Optimization and Benchmarking of Lightweight Neural Networks for Efficient Embedded AI Deployment

Engineering Reports, Volume 8, Issue 5, May 2026.
A hardware‐aware optimization and benchmarking framework for lightweight neural networks is presented for deployment on heterogeneous embedded platforms including CPU, GPU, TPU, and MCU architectures. Model compression techniques such as quantization, pruning, knowledge distillation, and mixed‐precision computation reduce inference latency, memory ...
Vidapankal Mohammad Fridous, Abhishek Agarwal, K. B. Bhaskar, V. Nandagopal, N. Sivakamasundari   +4 more
wiley   +1 more source