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• Emotion <scp>AI</scp> on the Edge: A <scp>TinyML</scp> ‐Driven Framework for Speech Emotion Recognition in Social Environments
  ABSTRACT The rise of Emotion AI is transforming how human emotions are detected, interpreted and responded to in real‐time social systems. Speech emotion recognition (SER) enables machines to understand human emotions from voice, fostering more empathetic and …
  crossref Md. Sakib Bin Alam, Aiman Lameesa, Barsha Roy, Shams Forruque Ahmed 等 2026-06-19T00:32:17Z 置信度 0.82 computer sciencecomputer and information scienceselectrical engineering, electronic engineering, information engineeringartificial intelligencebrain-computer interface
• Design and Implementation of a Low-Cost Device to Monitor the Real-Time Presence of CO and CO2 in Ambient Air and Prediction of its Pollution Level using TinyML
  crossref Abir Lal Dutta, Tapajit Mukherjee, Jayee Sinha 2026-01-20T16:09:46Z 置信度 0.66
• Intelligent Control System for Life Support and Energy Management in Moonal Habitats Using TinyML and Dual Microcontrolers
  crossref Julio Cesar Tello Rojas, Luigy Zidane Moreno Perez, Marisol Ramos Camacho, Omar Obregón Cevallos 等 2026-01-16T14:18:11Z 置信度 0.66
• Fusing Multi-sensor Input with State Information on TinyML Brains for Autonomous Nano-drones
  crossref Luca Crupi, Elia Cereda, Daniele Palossi 2024-12-31T18:01:08Z 置信度 0.66
• A Novel Active RFID and TinyML based system for livestock Localization in Pakistan
  Localization of livestock is a vital component of good livestock management in Pakistan. This abstract describes a unique method for livestock localization in Pakistan that makes use of Active RFID technology and Tiny Machine Learning (TinyML) approaches. The …
  crossref Syed Atir Raza Shirazi, Maham Fatima, Abdul Wahab, Sadaf Ali 2024-06-24T09:02:22Z 置信度 0.82 computer scienceelectrical engineering, electronic engineering, information engineeringscalabilityagriculturesignal processing
• Lightweight AI for IoT Security: A Survey of IoT Threats, Model Compression, TinyML, Federated Learning, and Edge-Based Intrusion Detection
  crossref Abdelmalek Bakiri, Samir Fenanir, Lakhdar Goudjil 2026-07-21T19:08:10Z 置信度 0.66
• A Secure Framework for MIoT: TinyML-powered Emergency Alerts and Intrusion Detection for Secure Real-time Monitoring
  The healthcare industry is shifting towards a patient-centered model, with remote monitoring at its core. The Medical Internet of Things (MIoT) is central to this shift, leveraging sensors to collect real-time health data and enhance patient care. However, man…
  crossref Saranya T, Jeyamala D, Suseela Sellamuthu, Indra Priyadharshini S 2024-10-23T17:40:23Z 置信度 0.82 computer sciencecloud computinghealth careartificial intelligencedata science
• BSC: Block-based Stochastic Computing to Enable Accurate and Efficient TinyML
  Along with the progress of AI democratization, machine learning (ML) has been successfully applied to edge applications, such as smart phones and automated driving. Nowadays, more applications require ML on tiny devices with extremely limited resources, like i…
  crossref Yuhong Song, Edwin Hsing-Mean Sha, Qingfeng Zhuge, Rui Xu 等 2022-02-21T22:39:17Z 置信度 0.79 computer and information sciencescomputer sciencecs.lgelectrical engineering, electronic engineering, information engineeringcryptography and security
• CNN-based prediction of multi-variables for lithium-ion batteries optimized with TinyML and deployed on edge devices
  crossref Yuqin Weng, Wenkai Guan, Cristinel Ababei 2025-11-20T15:29:49Z 置信度 0.66
• A TinyML approach to non-repudiable anomaly detection in extreme industrial environments
  Unforeseen failures of industrial assets may lead to unexpected downtime with a huge impact on critical business processes. Therefore, modern assets usually include several embedded sensors and processing units, allowing to monitor certain operational paramete…
  crossref Mattia Antonini, Miguel Pincheira, Massimo Vecchio, Fabio Antonelli 2022-07-22T16:42:05Z 置信度 0.79 computer sciencecomputer and information sciencescloud computinginternet of thingsmachine learning
• Toward Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions
  CubeSats have revolutionized access to space by providing affordable and accessible platforms for research and education. However, their reliance on Commercial Off-The-Shelf (COTS) components and open-source software has introduced significant cybersecurity vu…
  crossref Yasamin Fayyaz, Li Yang, Khalil El-Khatib 2026-03-26T19:51:14Z 置信度 0.82 computer sciencecomputer securityengineeringcomputer and information sciencesdata science
• Myoelectric Control based on Machine Learning of a Low-Cost 7-DOF Transhumeral Prosthetic Arm through TinyML
  Upper-limb amputations are a significant source of disability, especially in resource-limited environments where expensive commercial-grade prosthetics (often costing over USD 70,000) are unaffordable. This paper describes the design, optimisation and pilot te…
  crossref Misbah Anwer, Muhammad Fahad, Anusha Hasan, Abdul Karim Hasan 等 2026-07-16T08:08:03Z 置信度 0.82 ai safetycs.clmulti-agent systemseess.ascs.sd
• An empirical evaluation of tinyML architectures for Class-Incremental Continual Learning
  Neural networks excel at addressing real-world tasks, yet their computational demands often confine them to cloud-based platforms. Recent literature has responded with compute-efficient neural architectures for edge devices (e.g. microcontroller units). Noneth…
  crossref Matteo Tremonti, Davide Dalle Pezze, Francesco Paissan, Elisabetta Farella 等 2024-04-23T18:10:48Z 置信度 0.82 computer sciencecomputer and information sciencescs.lgartificial intelligencecs.ai
• TinyML in Telemedicine: Noise and Latency Reduction for Optimized Embedded Execution
  crossref Djeneba Sangare, Fatima-Ezzahraa Ben-Bouazza, Youssef Ait Bigane, Aymane Edder 等 2026-08-06T01:24:01Z 置信度 0.66
• ECG-Sense: Demonstration of a Wearable ECG Patch with an On-Device TinyML Engine for Real-Time Arrhythmia Detection
  crossref Nagarajan S, Chandra Shekhar Jha, Satya Prakash Chandra, Kurian Polachan 2026-07-17T19:43:30Z 置信度 0.66
• Mapping explainability and energy efficiency in TinyML-based real-time health monitoring using wearable and Internet of medical things devices: A scoping review
  Wearable and Internet of Medical Things devices increasingly support continuous health monitoring, but cloud-dependent analytics remain constrained by latency, connectivity, privacy, and battery requiirements. Tiny machine learning shifts inference toward reso…
  crossref Elly Warni, Muhammad Rizal H 2026-06-08T10:10:08Z 置信度 0.82 artificial intelligencemedicinecomputer sciencescience policymedline
• Unlocking Doors: A TinyML-based Approach for real-time Face Mask Detection in Door Lock Systems
  In response to the rapid spread of coronaviruses, including COVID-19 and seasonal common cold viruses, this article introduces a proposed system for enhancing door lock systems using TinyML technology for real-time face mask detection. The research project foc…
  crossref Azzedine El Mrabet, Ayoub Tber, Mohamed Benaly, Laamari Hlou 等 2023-07-01T14:31:57Z 置信度 0.79 computer scienceartificial intelligencecomputer and information sciencesbiotechnologymachine learning
• CookSafe AI: An Edge–AI Framework for Intelligent Gas Knob Monitoring and Autonomous Safety Actuation Using ESP32 with TinyML
  The ignition of gas stoves being unattended has been one of the major causes of deaths in residential fires around the globe. In 2017-2021, 158,400 domestic cooking fires took place on average each year in the United States, resulting in the deaths of 470 civi…
  crossref G Nivethika, V Pragiyaa, E Rohithaa, Y Padmaja 等 2026-05-11T19:44:21Z 置信度 0.82 computer scienceartificial intelligencebrain-computer interfaceelectrical engineering, electronic engineering, information engineeringsignal processing
• SafePath: A TinyML-based on-device edge intelligence framework for real-time protection of vulnerable road users
  crossref Debashis Das, Sourav Banerjee, Uttam Ghosh 2026-04-13T20:44:19Z 置信度 0.66
• Explainable TinyML-Based Energy Efficient Secure Telesurgery Framework for Healthcare with 6G
  crossref Krisha Shastri, Mohammad S. Obaidat, Sneh Shah, Heli Shah 等 2026-08-13T19:17:07Z 置信度 0.66
• Performance of Low-Cost TinyML Embedded Systems for Real-Time Classification of Table Tennis Strokes
  The integration of sensor technology and artificial intelligence is revolutionizing athletic training. This paper presents a novel cost-effective smart table tennis racket embedded with a nine-axis inertial measurement unit (IMU) for real-time stroke classific…
  crossref Yung-Hoh Sheu, Shu-Hung Lee, Chen-Bin Wu, Sheng K. Wu 等 2026-06-17T08:22:56Z 置信度 0.82 computer sciencecomputer and information sciencesartificial intelligencemachine learningelectrical engineering, electronic engineering, information engineering
• UAV-Assisted TinyML Transfer Learning for Edge Soil-Moisture Forecasting in Urban and Peri-Urban Smart Farming
  Distributed irrigation intelligence is increasingly relevant to smart-city systems because urban and peri-urban food production now depends on reliable sensing, low-power communications, and resource-efficient water management. This paper presents a UAV-assist…
  crossref Wenping Wang, Rolf Brühl 2026-03-11T20:16:17Z 置信度 0.82 artificial intelligencecomputer sciencecomputer and information sciencesmachine learningcs.ai
• A Comprehensive Android App Based Solution for Automated Attendance and Management in Institutions Using IoT and TinyML
  The Internet of Things (IoT) has emerged as a revolutionary technology, transforming the way we interact with our surroundings and enabling a new era of connectivity. The idea of IoT has led to numerous applications across numerous domains. In the educational …
  crossref Arnob Paul, Nainaiu Rakhaine, Nusrat Jahan Ohee, Arif Ahammad 2023-11-06T19:08:41Z 置信度 0.79 computer sciencecloud computinginternet of thingsedge computingcomputer security
• Systematic Pruning and Acceleration for TinyML on Extremely Weak IoT Devices
  crossref Jiani Cao, Lixiang Han, Kun Wang, Zhen Xiao 等 2026-07-10T19:41:06Z 置信度 0.66
• HoloTiny-AD: A Trustworthy Anomaly Detection in Resource-Constrained IoT Devices Using Holographic TinyML and Deep Metaheuristics
  Mobile Edge Computing (MEC) security and performance may be threatened by network traffic anomalies such as illegal data transfers or access. Traditional detection methods, such as machine learning, statistical models, and signature-based methods, are ineffect…
  crossref Yue Zhao, Gautam Srivastava, Farhan Ullah 2025-12-09T18:34:44Z 置信度 0.82 computer sciencecomputer and information sciencesartificial intelligenceelectrical engineering, electronic engineering, information engineeringmachine learning
• Nightfall-EX Advanced: An Offline TinyML-Based Portable ECG Monitoring and Emergency Alert System
  crossref Anupama Shetter, Sneha M, Shobith. B. R, Prathiba M K 2026-07-15T20:01:11Z 置信度 0.66
• A Plug-and-Play TinyML-based Vision System for Drone Automatic Landing
  Automatic landing is a feature that allows aerial robotic platforms to safely and accurately land without human intervention. This paper presents a plug-and-play tiny machine learning vision-based system for automatic landing compatible with the Pixhawk flight…
  crossref Luca Santoro, Andrea Albanese, Marco Canova, Matteo Rossa 等 2023-07-18T17:29:39Z 置信度 0.79 cs.roelectrical engineering, electronic engineering, information engineeringroboticscomputer sciencecomputer and information sciences
• Comparison of Cloud Computing and TinyML Methods for Brain-Computer Interface in Motor Imagery Problems
  In the modern day, several different innovations have been created with the sole purpose of improving existing models, products, and systems. One such innovation would be the improvement of brain-computer interfaces (BCI). BCIs tend to require high processing …
  crossref Jonathan Daniel S. Ong, Robert Kerwin C. Billones, Ronnie Concepcion, Nilo T. Bugtai 2023-06-19T17:49:27Z 置信度 0.79 computer sciencecloud computingelectroencephalographybrain-computer interfacedistributed computing
• An analysis of TinyML@ICCAD for implementing AI on low-power microprocessor
  crossref Guoqing Li, Jingwei Zhang, Meng Zhang, Tuo Li 等 2024-04-01T11:01:38Z 置信度 0.66
• Adaptive Gradient Methods for Differentially Private TinyML in 6G
  The sixth-generation (6G) of wireless systems is poised to enable a hyper-connected world of intelligent devices, where tiny machine learning (TinyML) will drive pervasive, real-time applications. However, this paradigm, built on distributed data from billions…
  crossref Chen Hou, Tao Huang, Qingyu Huang, Xu Yang 等 2026-01-01T18:39:09Z 置信度 0.82 computer and information sciencesmachine learningcomputer sciencecs.aielectrical engineering, electronic engineering, information engineering
• Transmission Line Anti-External Damage Detection for Ultra-Low Power MCUs Based on TinyML
  crossref Jiahui Yang, Wei Cao 2026-07-29T19:13:51Z 置信度 0.66
• TinyML Acoustic Classification using RAMAN Accelerator and Neuromorphic Cochlea
  The growing use of acoustic classification systems in edge computing and Internet of Things (IoT) devices has created a demand for innovative technologies and methods that can deliver high performance and energy efficiency. In this work, we introduce a novel a…
  crossref Adithya Krishna, H Shankaranarayanan, Hitesh Pavan Oleti, Anand Chauhan 等 2024-11-06T18:37:24Z 置信度 0.82 computer sciencecomputer and information sciencescloud computingedge computingcs.lg
• Avaliação de Desempenho e Consumo Energético de TinyML em Dispositivos de Borda para Previsão de Precipitação
  No ecossistema da Internet das Coisas (IoT), dispositivos de borda operam com recursos computacionais e energéticos limitados, exigindo modelos de aprendizado de máquina eficientes. Neste trabalho, é desenvolvido um sistema de predição de chuva baseado em mode…
  crossref Clariele Almeida, Rafael José Moura, Danilo Araújo, Ermeson Andrade 2026-08-19T20:03:57Z 置信度 0.82 convolutional neural networkphilosophyinternet of thingsartificial neural networkstat.ml
• Advancing Air Quality Monitoring: TinyML-Based Real-Time Ozone Prediction with Cost-Effective Edge Devices
  crossref Huam Ming Ken, Mehran Behjati, Ahmad Sahban Rafsanjani, Saad Aslam 等 2025-04-03T01:50:56Z 置信度 0.66
• Crowdsourcing Through TinyML as a Way to Engage End-Users in IoT Solutions
  crossref Pietro Manzoni, Marco Zennaro, Fredrik Ahlgren, Tobias Olsson 等 2023-07-16T23:02:12Z 置信度 0.64
• Model theft attack against a tinyML application running on an Ultra-Low-Power Open-Source SoC
  With the advent of tinyML, IoT devices have expanded their range of operations from simple data gathering and transmission to full-fledged inference. This expansion has been further enabled by the rise in popularity of open-source hardware, with the RISC-V arc…
  crossref Antonio Porsia, Annachiara Ruospo, Ernesto Sanchez 2024-07-01T06:19:21Z 置信度 0.82 computer securitycomputer scienceinternet of thingscryptography and securityedge computing
• Energy-Efficient TinyML-Based Fall Detection for Wearable Healthcare Devices
  Falls and irregular heart rhythms are the main causes of injury among kids and the elderly, always overwhelming healthcare systems, and thus making privacy-aware, real-time monitoring a necessity. This work unveils a TinyML wrist-worn prototype based on Arduin…
  crossref P Prathap, H Hibafathima, Sufaira Shamsudeen, M Selin 等 2026-07-31T17:37:17Z 置信度 0.82 computer and information sciencesphysical sciencescomputer sciencematerials sciencemedicine
• Evaluation of AI-Driven Autonomous and TinyML Applications for Enterprise Automatic Shopping Bills Management
  The shopping mall Billing industry is changing due to artificial intelligence (AI). The next big thing with actual bills is AI-Powered Automatic Shop Bills. With these retail bills, consumers are dealing with entirely automated machinery. Examining the factors…
  crossref Mudra Narasimharao, Aditya Kumar Lenka, Biswaranjan Swain, Praveen Priyaranjan Nayak 等 2024-12-24T19:09:48Z 置信度 0.82 psychologyknowledge managementbusinesstransparencyindustry 4.0
• Guest Editorial: SPECIAL ISSUE ON TinyML for Edge-Driven 6G Networks
  crossref Ali Hassan Sodhro, Hien Quoc Ngo, Rui Luis Aguiar, Praveen Kumar Donta 等 2026-03-26T19:52:20Z 置信度 0.66
• Low-Cost TinyML for Pneumonia Detection Using Custom AutoKeras Architecture on Low Resolution Pneumoniamnist Images
  crossref Darla Gempita Darris Purba, Disa Fajar Aidha, Auralius Manurung, Husneni Mukhtar 等 2026-08-13T19:14:21Z 置信度 0.66
• Toward Secure TinyML on a Standardized AI Architecture
  crossref Muhammad Yasir Shabir, Gianluca Torta, Andrea Basso, Ferruccio Damiani 2023-11-16T13:02:49Z 置信度 0.64
• Development of Speech Command Control Based TinyML System for Post-Stroke Dysarthria Therapy Device
  Post-stroke dysarthria (PSD) is a widespread outcome of a stroke. To help in the objective evaluation of dysarthria, the development of pathological voice recognition and technology has a lot of attention. Soft robotics therapy devices have been received as an…
  crossref Bambang Riyanta, Henry Ardian Irianta, Berli Paripurna Kamiel 2024-02-28T06:54:05Z 置信度 0.82 computer and information sciencesartificial intelligencecomputer sciencebrain-computer interfacehuman-computer interaction
• Assessing the Effects of Post-Quantum Cryptography on Edge Intelligence of TinyML-Based IoMT Healthcare Systems
  crossref M Ramamoorthy, Carmel Mary Belinda M J, Gnanajeyaraman Rajaram, U. Arul 2026-07-17T19:43:33Z 置信度 0.66
• TinyML with Meta-Learning on Microcontrollers for Air Pollution Prediction
  Tiny machine learning (tinyML) involves the application of ML algorithms on resource-constrained devices such as microcontrollers. It is possible to improve tinyML performance by using a meta-learning approach. In this work, we proposed lightweight base models…
  crossref I Nyoman Kusuma Wardana, Suhaib A. Fahmy, Julian W. Gardner 2024-04-08T04:00:18Z 置信度 0.82 cs.lgcomputer and information sciencescs.aimachine learningstat.ml
• Energy-aware dynamic programming scheduler for TinyML workloads on energy-harvesting CubeSat-IoT platforms: a comprehensive system modelling and performance analysis
  The integration of Tiny Machine Learning (TinyML) algorithms into CubeSatInternet of Things (IoT) platforms presents a transformative opportunity for autonomous space-based sensing and decision-making. However, the energyconstrained nature of CubeSats necessit…
  crossref Mfonobong Uko, Choice Amaizu, Sunday Ekpo, Gloria Iyawa 等 2026-02-28T05:43:44Z 置信度 0.82 computer and information sciencescomputer scienceelectrical engineering, electronic engineering, information engineeringcs.aisignal processing
• An energy efficient TinyML model for a water potability classification problem
  crossref Emanuel Adler Medeiros Pereira, Jeferson Fernando da Silva Santos, Erick de Andrade Barboza 2024-06-26T17:20:40Z 置信度 0.66
• SPARK: An Efficient Hybrid Acceleration Architecture with Run-Time Sparsity-Aware Scheduling for TinyML Learning
  Currently most TinyML devices only focus on inference, as training requires much more hardware resources. In this paper, we introduce SPARK, an efficient hybrid acceleration architecture with run-time sparsity-aware scheduling for TinyML learning. Besides a st…
  crossref Mingxuan Li, Qinzhe Zhi, Yanchi Dong, Le Ye 等 2024-11-07T19:27:22Z 置信度 0.82 computer and information sciencescs.aics.clcomputer scienceelectrical engineering, electronic engineering, information engineering
• TinyML and IoT in Agriculture
  The convergence of Tiny Machine Learning with the Internet of Things in agriculture brings real-time precision, enhanced autonomy, and sustainable practices-all under one umbrella. This chapter looks at how innovation through this integration can solve the maj…
  crossref M. Shanthalakshmi, N. Deepika, R.M. Avvudaiyappan, J. Prince Raj 2026-06-26T23:10:18Z 置信度 0.82 computer scienceagricultureinternet of thingssustainabilitycloud computing
• A Comparison of Several MCU-Oriented TinyML Models for Skin Lesions Classification
  crossref Radu Dogaru, Ioana Dogaru, Robert-Cristian Tecaru 2026-04-30T05:46:40Z 置信度 0.66
• A TinyML device for risk identification for people with hearing loss
  This paper presents an innovative approach to enhancing the quality of life for people with hearing impairment by implementing a portable and discreet TinyML device. The Support System for Identifying Emergency Sounds (SSIES) is designed to recognize four char…
  crossref Cristian Bautista-Villalpando, Victor Lomas-Barrie 2025-12-08T07:58:19Z 置信度 0.82 computer sciencebrain-computer interfacecs.lginterfacehuman-computer interaction
• Multimodal Interface for Games: A Case Study with TinyML
  Multimodal interfaces go beyond the traditional interaction through keyboard and mouse by incorporating multiple modes of interaction, such as touch, voice, gesture, and even gaze, to create more intuitive and immersive user experiences. This paper investigate…
  crossref Haoxuan Xie, Lam Chi Hou, Lap Tou Chau, Lei Ka Weng 等 2024-03-18T18:53:49Z 置信度 0.82 human-computer interactionembodied cognitioninterfacewearable computerrobotics
• Live Demonstration: Real-time audio and visual inference on the RAMAN TinyML accelerator
  The setup includes a host PC, camera and microphone sensors, and a Pynq-Z2 FPGA board. The neuromorphic cochlear model and RAMAN accelerator for neural network inference are deployed on the FPGA. The ARM processor on the FPGA sends the image received, cochleag…
  crossref Adithya Krishna, Ashwin Rajesh, Hitesh Pavan Oleti, Anand Chauhan 等 2024-07-02T17:22:52Z 置信度 0.80 convolutional neural networkcs.dcplasma physicscryptography and securitymechanical engineering
• Towards Reliable DTMF Recognition: A TinyML Approach
  crossref Hadiza Yusuf, Mike Perkins, Chukwuemeka Nkama 2024-12-17T15:47:25Z 置信度 0.66
• Test the Capability of Arduino TinyML for Machine Learning
  crossref Valentin Caravella, Sahar Yassine, Seifedine Nimer Kadry 2024-08-19T07:05:26Z 置信度 0.66
• RedMule: A mixed-precision matrix–matrix operation engine for flexible and energy-efficient on-chip linear algebra and TinyML training acceleration
  crossref Yvan Tortorella, Luca Bertaccini, Luca Benini, Davide Rossi 等 2023-07-05T21:04:21Z 置信度 0.64
• TinyML Implementation on Microcontrollers: The Case of Maize Leaf Disease Identification
  Maize, a crucial global food crop, faces various diseases affecting agricultural yield and quality, which must be monitored. This study employs advanced Convolutional Neural Network (CNN) architecture for efficient maize leaf disease detection and classificati…
  crossref Fortunatus Aabangbio Wulnye, Ewura Abena Essanoah Arthur, Dennis Agyemanh Nana Gookyi, Derek Kwaku Pobi Asiedu 等 2024-04-25T17:39:17Z 置信度 0.82 cs.cvconvolutional neural networkcs.lgcomputer sciencebiotechnology
• Modified AMBER Alerts System Using TinyML Processing
  crossref Mohammed Umair Khan, Ahmed Al Shamrani, Ahmad Al Shami 2023-11-01T03:02:20Z 置信度 0.64
• K-Means Based Tinyml Anomaly Detection and Distributed Model Reuse via the Distributed Internet of Learning (DIoL)
  This paper presents a lightweight K-Means anomaly detection model and a distributed model-sharing workflow designed for resource-constrained microcontrollers (MCUs). Using real power measurements from a mini-fridge appliance, the system performs on-device feat…
  crossref Abdulrahman Albaiz, Fathi Amsaad 2026-06-05T19:37:46Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringcomputer sciencesignal processingcs.ai
• Special Session: Approximate TinyML Systems: Full System Approximations for Extreme Energy-Efficiency in Intelligent Edge Devices
  Approximate computing (AxC) has advanced from being an emerging design paradigm to becoming one of the most popular and effective methods of energy optimization for applications in the domains of computer vision, image/video processing, data mining, analytics,…
  crossref Arnab Raha, Soumendu Ghosh, Debabrata Mohapatra, Deepak A. Mathaikutty 等 2021-12-20T19:12:28Z 置信度 0.79 computer scienceartificial intelligencecs.lgcomputer and information sciencescs.ai
• Mapping explainability and energy efficiency in TinyML-based real-time health monitoring using wear-able and Internet of medical things devices: A scoping review
  Wearable and Internet of Medical Things devices increasingly support continuous health monitoring, but cloud-dependent analytics remain constrained by latency, connectivity, privacy, and battery requirements. Tiny machine learning shifts inference toward resou…
  crossref Elly Warni, Muhammad Rizal H 2026-06-08T10:10:08Z 置信度 0.82 artificial intelligencemedicinecomputer sciencescience policymedline
• A TinyML-Based Out-of-Distribution Security Monitor for Energy-Constrained Industrial and Medical IoT Systems
  The sudden increase in industrial and medical Internet of Things (IoT) systems deployment has created serious cybersecurity and reliability problems under tight energy and resource constraints, where unknown attacks and device failures are to be detected at th…
  crossref Viswanathan Ranganathan, Arun Kumar Elengovan, Venkat Nutalapati, Deepak Kole 等 2026-06-02T20:03:34Z 置信度 0.82 computer sciencecomputer and information sciencesinternet of thingscloud computingartificial intelligence
• Using TinyML to Classify the Flight Phases of an Unmanned Aerial Vehicle
  During the development and study of an unmanned aerial vehicle (UAV) the relation between collected data and the flight phase can be a precious information. This work presents a TinyML solution for a real-time classification of flight phases, achieving an accu…
  crossref Paulo H. A. Andrade, Matheus V. Silva Jales, Daut J. N. P. Couras, Victor W. F. De Azevedo 等 2024-12-03T18:55:33Z 置信度 0.82 computer and information sciencescomputer scienceelectrical engineering, electronic engineering, information engineeringartificial intelligencesignal processing
• Effective real-time TinyML-based system for early detection and blister quantification of grape leaf blister mite (Eriophyes vitis (Pagst.)) damage
  Aim of study: To develop a real-time detection system for grape leaf blister mite (Eriophyes vitis (Pagst.)), a significant pest in vineyards, using deep learning models. Early detection of pests and diseases is essential to mitigate agricultural losses, espec…
  crossref Tahsin Uygun, Mehmet Metin Ozguven, Ziya Altas 2026-08-25T10:40:51Z 置信度 0.82 medicinecomputer scienceconvolutional neural networkartificial intelligencebrain-computer interface
• A two-layer TinyML approach aided by metaheuristics optimization for leveraging agriculture 4.0 and plant disease classification
  crossref Vesna Radojcic, Nebojsa Bacanin, Luka Jovanovic, Milos Dobrojevic 等 2025-11-10T10:17:41Z 置信度 0.66
• Channel State Information based Device Free Wireless Sensing for IoT Devices Employing TinyML
  The channel state information (CSI) of the sub-carriers employed in orthogonal frequency division multiplexing (OFDM) systems has been employed traditionally for channel equalisation. However, the CSI intrinsically is a signature of the operational RF environm…
  crossref Ali M. Hayajneh, Sami Aldalahmeh, Syed Ali R. Zaidi, Des McLernon 等 2023-01-02T19:15:45Z 置信度 0.79 computer sciencecomputer and information sciencessignal processingelectrical engineering, electronic engineering, information engineeringcloud computing
• NeuroLens: A Multimodal Mobile Platform for Early Parkinson's Disease Screening Using TinyML and Cross-Modal Feature Fusion
  crossref Niwanka Pathirathna, Chanuthi Savithma, Yasasvi Vilochana, Sriharan Saravanan 等 2026-07-16T21:47:53Z 置信度 0.66
• Implementation of Tiny Machine Learning (TinyML) as Pre-distorter for High Power Amplifier (HPA)Linearization of SDR-based MIMO-OFDM
  This paper implements a new pre-distorter (PD) scheme using Tiny Machine Learning (TinyML) to compensate for the non-linear High-Power Amplifier (HPA) in the Software Defined Radio (SDR)-based Multiple-Input Multiple-Output Orthogonal Frequency Division Multip…
  crossref Melki Mario Gulo, I Gede Puja Astawa, Amang Sudarsono, Naufal Ammar Priambodo 等 2023-09-12T17:26:59Z 置信度 0.79 electrical engineering, electronic engineering, information engineeringcomputer and information sciencessignal processingcryptography and securityinformation theory
• TinyML-based Edge Intelligence With Real-Time Earth Tremor Detection In Low-Power IoT Devices
  crossref Shaik Faizul Gaffar, D M Deepak, M Sai Sumanth, Y Bharath Babu 等 2026-08-17T19:23:18Z 置信度 0.66
• TinyTNAS: Time-Bound, GPU-Independent Hardware-Aware Neural Architecture Search for TinyML Time-Series Classification
  We present TinyTNAS, a hardware-aware Neural Architecture Search (NAS) framework optimized for efficient execution on CPUs, eliminating the need for costly GPUs. Traditional NAS methods often depend on reinforcement learning or evolutionary algorithms, requiri…
  crossref Bidyut Saha, Riya Samanta, Ram Babu Roy, Soumya K. Ghosh 2025-04-17T13:41:43Z 置信度 0.82 cs.lgcomputer and information sciencescomputer sciencecs.aiartificial intelligence
• Trees have Ears: An Acoustic Surveillance and TinyML-Based for Detecting Illegal Logging
  This study presents an innovative acoustic surveillance system utilizing TinyML to detect illegal logging in the rainforests of the Philippines. To overcome the limitations of traditional methods, the researchers integrated acoustic data capture with machine l…
  crossref Ariel M Lorenzo, Rodrigo Barien, Neil Darwin Favila, Dennis Basa 等 2025-02-27T18:44:08Z 置信度 0.82 artificial intelligencecomputer sciencecs.lgcomputer and information sciencesmachine learning
• Edge Computing Enabled Battery State of Charge(SoC) Estimation Using TinyML Techniques
  Redox flow batteries (RFBs) are electrolyte-based batteries that have become a research hotspot due to their modular nature and decoupled power-energy characteristics. Household and microgrid environments extensively utilize RFBs as storage systems. The nonlin…
  crossref Anshul Kumar Yadav, Rajesh Kumar, Anil Kumar Saini, P Ragupathy 等 2025-04-21T17:35:35Z 置信度 0.82 computer sciencephysical sciencesartificial intelligencebatteryenergy storage
• The Future of PHM Could be Tiny under Cloud: Exploring Potential Application Patterns of TinyML in PHM Scenarios
  Deep learning has shown impressive performance acrosshealth management and prognostics applications. Nowadays, an emerging trend of machine learning deployment on resource constraint hardware devices like micro-controllers(MCU) has aroused much attention. Give…
  crossref Xingyu Zhou, Zhuangwei Kang, Robert Canady, Shunxing Bao 等 2021-11-24T17:12:24Z 置信度 0.79 computer scienceartificial intelligencecs.lgmachine learningcomputer and information sciences
• TinyML-Enabled MIoT-Based Real-Time Edge Health Monitoring Using EfficientDet-Lite
  This paper introduces an innovative approach in the realm of Intelligent Edge Computing and Industrial Internet of Things (IIoT) for medical applications. An automated IIOT-based adaptive infant monitoring system is proposed that combines lightweight deep lear…
  crossref R. Jansi, Aayush Sinha, Prasit Kumar Dutta 2026-05-13T12:47:26Z 置信度 0.82 computer sciencecloud computingedge computinginternet of thingscomputer security
• An Efficient Signal Processing using EfficientNetB4-Lite Supported Deepfake Detection in Spectral Sensing TinyML Environment
  crossref V. Saranya Devi, Mo'Ath Alluwaici, P. Adi Lakshmi, S.A. Shifani 等 2026-03-23T20:02:17Z 置信度 0.66
• FPGA-Based AI Accelerator for Real-Time Voice Command Recognition Using TinyML for Smart Home Applications
  The intersection of TinyML and reconfigurable computing forms the foundation of a hardware-accelerated architecture proposed in this paper for offline voice command recognition in smart home control applications. Eliminating dependence on cloud connectivity wh…
  crossref Nikam Pawan Anil, Rane Digamber Bhasker, Shaikh Shakil A., Mungase Shubham Balasaheb 等 2026-05-05T13:09:02Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringcs.lgcomputer sciencecs.ai
• TinyML-Powered Gesture Wizardry: Low-Cost, Low-Power Two-Stage CNN for Static Hand Gesture Classification on MCU in Appliance Control
  crossref Bidyut Saha, Riya Samanta, Soumya Kanti Ghosh, Ram Babu Roy 2025-03-05T11:49:51Z 置信度 0.66
• EdgeSense: A Hybrid TinyML and Deep Learning Framework for SNR-Aware Adaptive Data Rate in LoRaWAN
  crossref Muhammad Ali Lodhi, Xiaobing Sun, Khalid Mahmood, Anum Lodhi 2026-05-22T19:37:41Z 置信度 0.66
• TinyML Datasets as Enablers of 6G Edge Intelligence: Key Insights and Research Gaps
  Tiny Machine Learning (TinyML) enables intelligent inference on resource-constrained edge devices, positioning it as a cornerstone for sixth-generation (6G) wireless systems demanding ubiquitous, low-latency, distributed intelligence at unprecedented scale. Ho…
  crossref Nordine Quadar, Abdellah Chehri, Benoit Debaque 2026-02-02T20:46:29Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringcomputer sciencesignal processingartificial intelligence
• TinyML-Based Concept System Used to Analyze Whether the Face Mask Is Worn Properly in Battery-Operated Conditions
  As the COVID-19 pandemic emerged, everyone’s attention was brought to the topic of the health and safety of the entire human population. It has been proven that wearing a face mask can help limit the spread of the virus. Despite the enormous efforts of people …
  crossref Dominik Piątkowski, Krzysztof Walkowiak 2022-01-05T20:42:16Z 置信度 0.79 computer sciencecomputer and information sciencescs.lgartificial intelligencecs.ai
• TinyML for Smart Libraries
  This study is about Tiny Machine Learning (TinyML) as an affordable device, but it transforms the library systems across the developing nations. Nowadays artificial intelligence (AI) very much influences the library systems, and it has a very high cost and dep…
  crossref Payel Saha, Pradipta Dutta, Volina Podder 2026-08-26T21:03:56Z 置信度 0.82 artificial intelligenceknowledge managementcomputer sciencetransformative learningbusiness
• Survey on implementation of TinyML for real-time sign language recognition using smart gloves
  Communication is a major part of life. For people who are deaf and dumb, sign language is imperative. All sign languages contain various hand movements and finger placement which in turn form different gestures. Learning any sign language can be challenging. W…
  crossref Santosh Kumar B, Rachna P, Ritika Basavaraj Hiremath, Vanshika Sai Ramadurgam 等 2023-03-15T13:27:00Z 置信度 0.79 computer sciencecs.lgartificial intelligencebrain-computer interfacecs.ai
• iAirGuard: A Modular IoT Architecture with Dynamic Sampling and TinyML-Based Fault Detection for Air Quality Monitoring
  The paper iAirGuard is introduced, the open-source, modular, and wireless sensor network intended for large-scale and affordable air quality scrutiny. Conventional high-precision stations, which are scattered, still set a high price and are not widely maintain…
  crossref Molay Mondal, Arko Datta, Prashanth Durgam, Astom Mondal 等 2026-03-10T19:50:41Z 置信度 0.82 computer sciencecomputer and information scienceselectrical engineering, electronic engineering, information engineeringnetworking and internet architecturephysical sciences
• Neural Signal Compression using RAMAN tinyML Accelerator for BCI Applications
  High-quality, multi-channel neural recording is indispensable for neuroscience research and clinical applications. Large-scale brain recordings often produce vast amounts of data that must be wirelessly transmitted for subsequent offline analysis and decoding,…
  crossref Adithya Krishna, Sohan Debnath, Madhuvanthi Srivatsav, André van Schaik 等 2026-03-17T20:23:05Z 置信度 0.82 computer sciencecomputer and information sciencesbrain-computer interfaceelectrical engineering, electronic engineering, information engineeringneuroscience
• Capacitive Sensing Based On-board Hand Gesture Recognition with TinyML
  Although hand gesture recognition has been widely explored with sensing modalities like IMU, electromyography and camera, it is still a challenge of those modalities to provide a compact, power-efficient on-board inferencing solution. In this work, we present …
  crossref Sizhen Bian, Paul Lukowicz 2021-09-24T17:56:03Z 置信度 0.79 computer and information sciencesroboticssignal processinginterfaceconvolutional neural network
• An SRAM Optimized Approach for Constant Memory Consumption and Ultra-fast Execution of ML Classifiers on TinyML Hardware
  With the introduction of ultra-low-power machine learning (TinyML), IoT devices are becoming smarter as they are driven by Machine Learning (ML) models. However, any increase in the training data results in a linear increase in the space complexity of the ML m…
  crossref Bharath Sudharsan, Piyush Yadav, John G. Breslin, Muhammad Intizar Ali 2021-11-15T22:58:02Z 置信度 0.79 computer and information sciencescomputer sciencecs.lgartificial intelligencecs.ai
• An Intelligent IoT Sensing System for Rail Vehicle Running States Based on TinyML
  Real-time identification of the running state is one of the key technologies for a smart rail vehicle. However, it is a challenge to accurately real-time sense the complex running states of the rail vehicle on an Internet-of-Things (IoT) edge device. Tradition…
  crossref Shaoze Zhou, Yongkang Du, Bingzhi Chen, Yonghua Li 等 2022-09-15T19:32:43Z 置信度 0.79 computer and information sciencescomputer scienceelectrical engineering, electronic engineering, information engineeringcs.lginternet of things
• A Review of the Transition from Industry 4.0 to Industry 5.0: Unlocking the Potential of TinyML in Industrial IoT Systems
  The integration of artificial intelligence into the Industrial Internet of Things (IIoT), supported by edge computing architectures, marks a new paradigm of intelligent automation. Tiny Machine Learning (TinyML) is emerging as a key technology that enables the…
  crossref Margarita Terziyska, Iliana Ilieva, Zhelyazko Terziyski, Nikolay Komitov 2026-01-07T16:14:12Z 置信度 0.82 computer scienceindustry 4.0sustainabilitydata sciencemanufacturing engineering
• Revisiting the Inference Time Optimization of TinyML for ARM-Based Microcontrollers
  As AI research advances, various performance optimization techniques have been studied for TinyML models on microcontrollers with very limited resources. In TinyML frameworks such as TFLM (TensorFlow Lite Micro) and NNOM (Neural Network on Microcontrollers), t…
  crossref Chan-Kyu Lee, Seung-Ryeol Ohk, Young-Jin Kim 2026-07-08T16:16:18Z 置信度 0.82 computer and information sciencescomputer sciencecs.lgelectrical engineering, electronic engineering, information engineeringcryptography and security
• Energy-Aware Compression and Consumption Algorithms for Efficient TinyML Model Using Aquila Optimization in Industrial IoT
  With the proliferation of Industrial Internet of Things (IIoT) applications, the demand for energy efficient compression and clustering algorithms has escalated along with reliable routing algorithms. This paper proposes a novel approach: an energy aware compr…
  crossref Chaitanya Thuppari, Srikanth Jannu, Damodar Reddy Edla, Ankit Vidyarthi 等 2024-10-07T17:44:13Z 置信度 0.82 computer sciencecomputer and information sciencescs.lgdistributed computingartificial intelligence
• Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification
  Semantic and goal-oriented (SGO) communication is an emerging technology that only transmits significant information for a given task. Semantic communication encounters many challenges, such as computational complexity at end users, availability of data, and p…
  crossref Eslam Eldeeb, Mohammad Shehab, Hirley Alves, Mohamed-Slim Alouini 2025-04-03T14:11:18Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringcomputer vision and pattern recognitionmachine learningcomputer science
• Enhancing Automotive Products with TinyML and MEMS Sensors: A Preliminary Approach
  crossref Lídia Sousa, Rui Silva, Hugo Peixoto, Pedro Melo-Pinto 等 2024-11-08T05:47:15Z 置信度 0.66
• TinyML models for SoH estimation of lithium-ion batteries based on Electrochemical Impedance Spectroscopy
  As battery systems become more widespread, the need for accurate and fast estimation of State of Health (SoH) in lithium-ion cells is increasingly critical. This study analyzes two data-driven model methods that leverage Electrochemical Impedance Spectroscopy …
  crossref Spyridon Giazitzis, Abdisamad Ahmed Isse, Nicola Blasuttigh, Alessandro Massi Pavan 等 2025-06-24T12:34:52Z 置信度 0.82 computer sciencecs.lgartificial intelligencecomputer and information sciencescs.ai
• RIOT-ML: toolkit for over-the-air secure updates and performance evaluation of TinyML models
  Abstract Practitioners in the field of TinyML lack so far a comprehensive, “batteries-included” toolkit to streamline continuous integration, continuous deployment and performance assessments of executing diverse machine learning models on various low-power Io…
  crossref Zhaolan Huang, Koen Zandberg, Kaspar Schleiser, Emmanuel Baccelli 2024-05-22T04:02:09Z 置信度 0.82 computer scienceartificial intelligencecomputer and information sciencescloud computingscalability
• Deployment of TinyML-Based Stress Classification Using Computational Constrained Health Wearable
  Stress has become a common mental health issue in modern society, causing individuals to experience acute behavioral changes. Exposure to prolonged stress without proper prevention and treatment may cause severe damage to one’s physiological and psychological …
  crossref Asma Abu-Samah, Dalilah Ghaffa, Nor Fadzilah Abdullah, Noorfazila Kamal 等 2025-02-12T08:05:42Z 置信度 0.82 computer scienceartificial intelligencecs.lgcomputer and information sciencesmachine learning
• Demo Abstract: Shared-Column Im2col for Energy-Efficient TinyML Convolution on Microcontrollers
  This work presents a shared-column im2col design for energy-efficient TinyML Conv2D. By storing overlapping input regions once and using shifted indexing, it reduces redundant SRAM traffic while preserving identical outputs. Implemented on Arduino Nano 33 BLE,…
  crossref Seung-Ryeol Ohk, Won-Seok Chang, Young-Jin Kim 2026-08-10T19:23:31Z 置信度 0.82 computer and information sciencesnetworking and internet architecturecomputer vision and pattern recognitioncryptography and securityartificial neural network
• Every second counts for search and rescue: A systematic review of TinyML drone
  This study explores the transformative potential of TinyML in unmanned aerial vehicles (UAVs) to address key inefficiencies in traditional search and rescue (SAR) operations, especially in the context of increasingly severe climate-related disasters. By analyz…
  crossref Liu Junchang, JosephNg Poh Soon, Phan Koo Yuen, Wong See Wan 2026-02-10T10:41:46Z 置信度 0.82 computer sciencecomputer and information sciencesartificial intelligenceknowledge managementbusiness
• A review on TinyML: State-of-the-art and prospects
  Machine learning has become an indispensable part of the existing technological domain. Edge computing and Internet of Things (IoT) together presents a new opportunity to imply machine learning techniques at the resource constrained embedded devices at the edg…
  crossref Partha Pratim Ray 2021-11-30T18:13:32Z 置信度 0.79 computer sciencekeycloud computingartificial intelligencereliability
• Toward Attention-Based TinyML: A Heterogeneous Accelerated Architecture and Automated Deployment Flow
  One of the challenges for Tiny Machine Learning (tinyML) is keeping up with the evolution of Machine Learning models from Convolutional Neural Networks to Transformers. We address this by leveraging a heterogeneous architectural template coupling RISC-V proces…
  crossref Philip Wiese, Gamze İslamoğlu, Moritz Scherer, Luka Macan 等 2025-01-08T15:24:32Z 置信度 0.82 computer and information sciencescs.lgartificial intelligencecs.ainetworking and internet architecture
• Software engineering approaches for TinyML based IoT embedded vision
  Internet of Things (IoT) has catapulted human ability to control our environments through ubiquitous sensing, communication, computation, and actuation. Over the past few years, IoT has joined forces with Machine Learning (ML) to embed deep intelligence at the…
  crossref Shashank Bangalore Lakshman, Nasir U. Eisty 2023-02-03T23:05:47Z 置信度 0.79 computer scienceinternet of thingscomputer and information sciencescloud computingartificial intelligence
• TinyML-powered pothole detection on edge devices: Optimizing speed, memory, and accuracy
  crossref H. M. Ramalingam, N. I. Avinash, S. Bhat Vinayambika, Deepthi Shetty 等 2025-10-17T12:40:02Z 置信度 0.66