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• TinyML-based Event Detection: An Edge-Cloud Approach for Smart Agriculture over LoRa WSNs
  In modern agriculture, the capability to promptly detect and respond to specific events is crucial. This study centres on the transformative potential of TinyML for enhancing event detection in Smart Agriculture, particularly when integrated with LoRa-based Wi…
  crossref Aristeidis Karras, Christos Karras, Anastasios Giannaros, Konstantinos C. Giotopoulos 等 2024-03-21T18:08:07Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringagriculturesignal processingedge computing
• Joint Optimization Risk Factor and Energy Consumption in IoT Networks With TinyML-Enabled Internet of UAVs
  The high mobility of Internet of Unmanned Aerial Vehicles (IUAVs) has attracted attention in the field of data collection. With the rapid development of the Internet of Things (IoT), more and more data are generated by IoT networks. IUAV-aided IoT networks can…
  crossref Run Liu, Mande Xie, Anfeng Liu, Houbing Song 2024-01-01T19:55:26Z 置信度 0.82 computer sciencecomputer and information scienceselectrical engineering, electronic engineering, information engineeringcloud computingdistributed computing
• Design and Implementation of a Multi-Parameter Health Monitoring SoC using RISC-V and TinyML for Real-Time Edge-Based Diagnostics
  The increasing demand for continuous cardiac monitoring has created the need for portable, low-power, and intelligent health-tracking systems capable of operating reliably in edge environments. Traditional cloud-dependent solutions suffer from latency, bandwid…
  crossref Sahana B, Manju Devi, Neha S, Soubhagya M 2026-02-25T20:53:55Z 置信度 0.82 computer and information sciencescomputer sciencewearable computerelectrical engineering, electronic engineering, information engineeringscalability
• A Joint Survey in Decentralized Federated Learning and TinyML: A Brief Introduction to Swarm Learning
  TinyML/DL is a new subfield of ML that allows for the deployment of ML algorithms on low-power devices to process their own data. The lack of resources restricts the aforementioned devices to running only inference tasks (static TinyML), while training is hand…
  crossref Evangelia Fragkou, Dimitrios Katsaros 2024-11-12T03:53:14Z 置信度 0.82 computer sciencecomputer and information sciencesmachine learningcloud computingedge computing
• Cost-Effective TinyML-Ready Design and Field Deployment of a Solar-Powered Environmental Monitoring Data Collector Using LTE-M Communication
  Environmental monitoring is essential for smart agriculture, renewable energy assessment, and climate-aware farm management. However, deploying autonomous sensing platforms in rural environments remains challenging because of energy constraints, communication …
  crossref Emanuel-Crăciun Trînc, Valentin Niţă, Cristina Stolojescu-Crisan, Cosmin Ancuţi 等 2026-03-27T11:12:25Z 置信度 0.82 electrical engineering, electronic engineering, information engineeringcomputer and information sciencescomputer sciencesignal processingcloud computing
• Quantized ID-CNN for a Low-power PDM-to-PCM Conversion in TinyML KWS Applications
  This paper proposes a novel low-power HW accelerator for audio PDM-to-PCM conversion based on artificial neural network. The system processes samples from a digital MEMS microphone and converts them in PCM format by using a 1-Dimensional Convolutional Neural N…
  crossref Paola Vitolo, Gian Domenico Licciardo, Anna Chiara Amendola, Luigi Di Benedetto 等 2022-09-05T20:21:42Z 置信度 0.79 computer sciencecs.neartificial neural networkconvolutional neural networkcomputer and information sciences
• TinyML Fault Detection in Water Pump Systems: Leveraging Acoustic Analysis on the Edge
  crossref Thommas K. S. Flores, Thiago C. Jesus, João Carlos N. Bittencourt, Ivanovitch Silva 等 2026-08-19T19:09:26Z 置信度 0.66
• Embedded AI and TinyML: A Practical Analysis of Workflows and Libraries
  crossref Ander Garcia, Javier Tardos, Juan Luis Ferrando, Daniel Aguinaga 等 2025-02-17T18:27:57Z 置信度 0.66
• XimSwap: Many-to-Many Face Swapping for TinyML
  The unprecedented development of deep learning approaches for video processing has caused growing privacy concerns. To ensure data analysis while maintaining privacy, it is essential to address how to protect individuals’ identities. One solution is to anonymi…
  crossref Alberto Ancilotto, Francesco Paissan, Elisabetta Farella 2023-06-01T11:00:15Z 置信度 0.79 computer sciencecomputer and information sciencescs.lgartificial intelligencecryptography and security
• TinyML Custom AI Algorithms for Low-Power IoT Data Compression: A Bridge Monitoring Case Study
  The use of low-power IoT devices presents challenges for transmitting large amounts of data, particularly in remote and infrastructure-lacking environments, as is often the case in monitoring the structural health of bridges. The low transmission rates and lim…
  crossref Thaís Medeiros, Miguel Amaral, Matheus Targino, Marianne Silva 等 2023-07-18T13:29:39Z 置信度 0.79 computer sciencedata sciencecomputer and information sciencescloud computinginternet of things
• Detecting Gesture Language for Deaf and Mute People Usingon Ultra-Low-Power TinyML Model
  Sign language serves as a communication method for deaf individuals, utilizing gestures and body movements to convey messages. It includes components such as hand shapes, movements, orientations, and facial expressions, each gesture not only representing a wor…
  crossref Basel A. Dabwan, Mukti E. Jadhav, Omar A. Ismil, Eman A. Hassan 等 2024-10-01T17:23:43Z 置信度 0.82 computer sciencecomputer and information sciencesartificial intelligencebrain-computer interfacecs.lg
• A TinyML solution for an IoT-based communication device for hearing impaired
  crossref S. Sharma, R. Gupta, A. Kumar 2024-01-08T20:29:31Z 置信度 0.66
• Towards Rapid Exploration of Heterogeneous TinyML Systems using Virtual Platforms and TVM's UMA
  The rapid setup of deep learning compilation toolchains for heterogeneous TinyML systems with a processor and dedicated ML accelerator is still at an early stage. Here, achieving the most optimal combination of targets for a TinyML application on ultra-low-pow…
  crossref Samira Ahmadifarsani, Rafael Stahl, Philipp van Kempen, Daniel Mueller-Gritschneder 等 2024-06-10T12:43:58Z 置信度 0.80 drug discoveryedge computingbenchmarkcryptography and securityhabitability
• Edge AI-enabled dynamic power factor correction using TinyML, blockchain and IoT for real-time smart grid optimization and industrial applications
  This work presents a comprehensive design and implementation of an AI-enabled Smart Power Factor Correction (PFC) System that integrates advanced technologies such as Machine Learning (ML), Deep Learning, IoT, Edge Computing, and Blockchain with conventional P…
  crossref Ch. Gochhayat Prakash, Md Afam, Alam Sk Md Tanvir, K. Mallick Gaurav 等 2026-01-27T10:59:37Z 置信度 0.82 computer sciencecomputer and information sciencescloud computingengineeringelectrical engineering, electronic engineering, information engineering
• MST and MPT: Lightweight Incremental Algorithms for Multivariate Anomaly Detection and Correction on TinyML Devices
  The Internet of Things (IoT) generates massive multivariate time series data requiring real-time anomaly detection and correction for reliable monitoring. This challenges resource-constrained embedded devices due to conventional offline training and batch proc…
  crossref Morsinaldo Medeiros, Thaís Medeiros, Marianne Silva, Ivanovitch Silva 等 2025-10-21T17:07:47Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringcomputer scienceartificial intelligencecloud computing
• TinyML Motion Classification System for Low-Power Smart Bracelets: Deployment and Energy Optimization of Ultra-Lightweight DNNs on MCUs
  Most motion classification methods rely on highpower processors and are difficult to run efficiently on resourceconstrained microcontrollers (MCUs). This paper addresses the motion classification needs of low-power smart bracelets by constructing an extremely …
  crossref Junchen Hou, Kang Shao, Lianhao Wang 2026-01-28T20:57:39Z 置信度 0.82 computer sciencecomputer and information sciencescs.lgbrain-computer interfacecs.cv
• Low-Power Real-Time TinyML Approach for Pothole and Speed Bump Classification Using IMU Data on FPGA
  Monitoring the condition of road surfaces can bring benefits to drivers, ranging from increased safety and comfort, to cost reduction. In this context, accelerometer-based approaches offer a cheap and robust solution to the mitigation of common issues that ari…
  crossref César Melo, Pedro Delgado, Gabriel Carvalho, Rui Silva 等 2026-03-30T20:03:31Z 置信度 0.82 computer sciencecs.lgcomputer and information scienceselectrical engineering, electronic engineering, information engineeringcs.ai
• Bin Occupancy Estimation in Warehouses With a TinyML Vision Sensor
  Machine vision has improved logistics efficiency, but its use for estimating available space in traditional warehouses remains limited, even with Artificial Intelligence of Things (AIoT) and Tiny Machine Learning (TinyML) enabling cost-effective, energy-effici…
  crossref Rubens de A. Fernandes, Hendrio Bragança, Wallace Cavalcante, Raimundo C. S. Gomes 等 2025-07-23T18:45:39Z 置信度 0.82 computer and information sciencesartificial intelligenceelectrical engineering, electronic engineering, information engineeringmachine learningcomputer science
• R ² T-Tiny: Runtime-Reconfigurable Throughput-Optimized TinyML for Hybrid Inference Acceleration on FPGA SoCs
  The emergence of tiny machine learning (TinyML) has represented a paradigm shift toward energy efficient and on-device inference, with TinyML research primarily focusing on low-cost and energy-efficiency microcontroller units (MCUs). However, the low computati…
  crossref Georgios Mentzos, Valentin Alexander Frey, Konstantinos Balaskas, Georgios Zervakis 等 2025-11-20T18:39:34Z 置信度 0.82 computer and information sciencesartificial intelligencecs.lgcomputer scienceelectrical engineering, electronic engineering, information engineering
• PolluSenseCheck: Cost Effective TinyML-based Air Quality Monitoring System with In-Built Sensor Fault Detection
  Air pollution is a significant contributor to respiratory diseases, posing severe health risks globally. Traditional air quality monitoring stations, while effective, are too expensive, particularly in developing nations like India, where numerous monitoring s…
  crossref Arko Datta, Joyita Chakraborty, Pabitra Majhi, Astom Mondal 等 2025-02-20T20:05:58Z 置信度 0.82 medicinecs.aicomputer scienceengineeringcomputer and information sciences
• Efficient and secure <i>µ</i>-Training and <i>µ</i>-Fine-Tuning for edge-based TinyML with future-guided self-distillation
  This study presents a novel, computationally efficient training framework demonstrated through bio-signal processing on edge medical devices. The approach integrates conventional full training with an innovative µ -Training technique, wherein the encoder and d…
  crossref Zhaojing Huang, Leping Yu, Luis Fernando Herbozo Contreras, Omid Kavehei 2025-07-31T08:03:30Z 置信度 0.82 computer and information sciencescomputer sciencecs.lgcs.aielectrical engineering, electronic engineering, information engineering
• Thermal Camera Prototype for Predictive Maintenance in Photovoltaic Applications Based on TinyML Embedded System
  crossref N. Blasuttigh, A. Mellit, A. Massi Pavan, M. Zennaro 2025-01-30T17:42:30Z 置信度 0.66
• RIMIX: RISC-V Core with MIXed-Precision SIMD Instruction Extensions Supported by Oracle-Assisted Sub-Network Search for Efficient TinyML
  As the size of the deep learning model increases, mixed-precision quantization has become an efficient compression technique. However, the lack of MCU support for mixed-precision computation limits its performance in running tinyML tasks. To address this issue…
  crossref Jiyong Park, Dahoon Park, Yeeun Hong, Jaeha Kung 2025-12-03T18:39:13Z 置信度 0.82 computer and information sciencescs.lgelectrical engineering, electronic engineering, information engineeringcomputer sciencesignal processing
• TINYML: A cutting-edge technology revolutionizing machine learning in healthcare
  In the recent technological revolution, Machine learning (ML) models are hugely successful due to the availability of the resources like powerful servers with large storage capacities and multiple graphical processing units (GPUs). Eventually, this paved way f…
  crossref E Edith Esther, P Shobha Rani, N Kamal 2025-01-25T08:03:51Z 置信度 0.82 computer scienceartificial intelligencecs.lghealth carecomputer and information sciences
• Adaptive Lightweight Security for TinyML Driven Unmanned Aerial Vehicles in Smart Cities
  This paper presents the design and evaluation of a secure on-device intelligence system for Unmanned Aerial Vehicles (UAVs) using Tiny Machine Learning (TinyML) and an Adaptive Security Layer (ASL). The proposed system enables local object detection and secure…
  crossref Norziana Jamil, Kaleab Nega, Muhammad Haikal Rozaidi, Parag Kulkarni 等 2025-12-19T18:56:15Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringcomputer sciencesignal processingcryptography and security
• Quantum–Tinyml Neural Compression for Real-Time Medical Imaging
  The high rate of exponential growth of medical imaging data, combined with the growing implementation of edge devices in healthcare, requires solutions which are efficient, low-latency and high-fidelity to real-time image transmission. The classic compression …
  crossref Pinnamaraju Sahitya, M. Ramya, Sunil Manohar Reddy K, Senthilkumar Aandi 等 2026-01-13T20:55:18Z 置信度 0.82 computer and information sciencescomputer scienceartificial intelligencecs.lgelectrical engineering, electronic engineering, information engineering
• TinyML on Microcontrollers: Enabling Energy-Efficient, Real-Time, Privacy-Preserving Incremental Learning for Embedded Systems
  TinyML is transforming embedded systems by enabling machine learning models to perform real-time inference and incremental learning directly on highly constrained microcontrollers. This paper explores algorithms and architectures that empower microcontrollers …
  crossref Govarthan V, M. Thangamani, R. Aarthi, S. Satheesh 等 2025-12-30T18:35:51Z 置信度 0.82 computer and information sciencescs.lgcomputer scienceelectrical engineering, electronic engineering, information engineeringmachine learning
• FlEdge-CAN: Federated Learning with Edge-TinyML for Autonomous Internet of Vehicle Security
  Autonomous vehicles are increasing worldwide, and their security concerns are growing rapidly. Level-5 autonomy will require AVs to be network-connected for real-time data. This, along with physical port access, can make AVs vulnerable to attacks on the CAN bu…
  crossref Farhan Ahmad Nafis, Maiesha Fahomida, Sk Tahmed Salim Rafid, Nusaiba Khan 等 2026-05-06T19:37:56Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringcs.lgcs.aiedge computing
• EPIC: A Sub-6mW in-Memory Computing-Based RISC-V Microcontroller Unit with on-Chip Training Support for TinyML
  We present EPIC, a sub-6mW digital in-mEmory comPutIng-based RISC-V microController unit. We create the hardware and software systems with the optimized dataflow for both inference and training. EPIC supports the industrial software development framework based…
  crossref Chuan-Tung Lin, Seunghyun Moon, Paul Xuanyuanliang Huang, Mingoo Seok 2025-11-05T18:37:29Z 置信度 0.82 softwarecs.neartificial neural networkcs.seconvolutional neural network
• TensorFlores: An enhanced Python-based TinyML framework
  The TensorFlores framework is a Python-based tool designed to optimize machine learning deployment in resource-constrained environments. It introduces evolving clustering-based quantization, supporting both quantization-aware training and post-training quantiz…
  crossref Thommas K.S. Flores, Daniel G. Costa, Ivanovitch Silva 2025-06-23T07:27:07Z 置信度 0.82 computer and information sciencescryptography and securityartificial intelligencecomputer sciencescalability
• An edge-deployable TinyML approach enhanced by transfer learning for efficient bearing fault diagnosis
  crossref Zheng Gao, Zhichao Jiang, Zefang Dong, Xianpeng Fu 等 2025-11-08T08:14:52Z 置信度 0.66
• Decentralised Reputation Tracking using TinyML for Task Recommendation in Spatial Crowdsourcing
  Spatial crowdsourcing (SC) platforms depend on reputation systems to ensure trust and quality in the execution of tasks. However, traditional centralised reputation mechanisms face scalability, privacy, and connectivity challenges. This paper presents a decent…
  crossref Md Mujibur Rahman, Md Mehedi Hasan, Mohammad Mustaneer Rahman, Md Khalilur Rahman Farhad 等 2025-09-24T17:31:44Z 置信度 0.82 computer and information sciencescomputer scienceartificial intelligencecs.aimachine learning
• TinyML-Based Object Detection on Smart Blind Stick for Visually Impaired Person
  In our daily life, we often find people with certain disabilities or certain physical limitations, such as visually impaired people. People with visual impairment often require assistance from others to perform daily activities. We have seen people with visual…
  crossref Parama Diptya Widayaka, Pradini Puspitaningayu, Sayyidul Aulia Alamsyah, Endryansyah Endryansyah 等 2025-12-16T18:29:20Z 置信度 0.82 computer sciencehuman-computer interactionbrain-computer interfacephysical medicine and rehabilitationrobotics
• TinyML Acceleration with MAX78000
  crossref Ali Dabbous, Luca Lazzaroni, Francesco Bellotti, Sara Muñoz Presentación 等 2025-01-02T17:34:50Z 置信度 0.66
• Real-Time Stress Detection from Raw Noisy PPG Signals Using LSTM Model Leveraging TinyML
  crossref Amin Rostami, Bahram Tarvirdizadeh, Khalil Alipour, Mohammad Ghamari 2024-05-07T11:01:58Z 置信度 0.66
• TinySenseNet: A Lightweight sEMG-IMU Fusion Network Using TinyML for Mechanical Arm Control in Low-Resource Settings
  This paper introduces TinySenseNet, a lightweight and memory-efficient deep learning model for real-time robotic arm control using multimodal surface electromyography (sEMG) and inertial measurement unit (IMU) signals. Optimized for embedded deployment, the sy…
  crossref Fazlay Rabby, Md. Rifat Aknda, Mumtahina Tasnim Mahi, Shaikh Radwan Ahmed Ratul 等 2026-02-12T20:57:13Z 置信度 0.82 computer and information sciencesartificial intelligencecomputer scienceroboticshuman-computer interaction
• ĐÁNH GIÁ HIỆU SUẤT CỦA CÁC MÔ HÌNH PHÂN LOẠI HÌNH ẢNH TINYML LƯỢNG TỬ HÓA TRÊN NỀN TẢNG UAV MÔ PHỎNG CHO ỨNG DỤNG GIÁM SÁT NÔNG NGHIỆP
  UAV mang lại khả năng thu thập dữ liệu và giám sát thời gian thực trong nông nghiệp. Tuy nhiên, việc triển khai AI tiên tiến, đặc biệt là học sâu, trên UAV bị hạn chế bởi tài nguyên tính toán, bộ nhớ và năng lượng. Nghiên cứu này nghiên cứu về hiệu suất của cá…
  crossref Hoàng Trọng Nghĩa 2025-10-29T08:34:46Z 置信度 0.82 biochemistrytaxonomymicrobiologybiodiversityneuromorphic computing
• TinyML-based Secure Energy Efficient Framework for CAVs Communication in 5G-Based V2X Networks
  This work discusses a TinyML-based attack classification framework with low complexity that is aimed at V2X communication in 5G networks. The proposed model obtains the highest accuracy of 95 % for all evaluated models and shows better performance than any oth…
  crossref Parishi Shah, Mohammad S. Obaidat, Lakshin Pathak, Dhrishita Parve 等 2025-10-31T17:09:54Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringcomputer sciencecs.lgcs.ai
• Combining epsilon-greedy reinforcement learning based gradient sparsification and siamese neural networks for few-shot federated tinyML intrusion detection in IoT
  crossref Pietro Fusco, Francesco Palmieri, Massimo Ficco 2025-11-05T03:54:16Z 置信度 0.66
• Temporal Fusion Transformer-Based RUL Prediction for Battery Life with TinyML Integration
  crossref Dhanush Kotalony, S. Jagadeesh Babu, Subhasri Duttagupta 2026-05-03T23:33:50Z 置信度 0.66
• BiomedBench: A Benchmark Suite of TinyML Biomedical Applications for Low-Power Wearables
  Editor’s notes: This article introduces BiomedBench, the first benchmark suite specifically designed for evaluating TinyML applied in the context of low-power wearable biomedical devices. BiomedBench represents a valuable step toward reproducible evaluation in…
  crossref Dimitrios Samakovlis, Stefano Albini, Rubén Rodríguez Álvarez, Denisa-Andreea Constantinescu 等 2024-10-17T13:42:10Z 置信度 0.82 edge computingmulti-agent systemswearable computerbenchmarknetworking and internet architecture
• TinyML model compression: A comparative study of pruning and quantization on selected standard and custom neural networks
  crossref Muhammad Yasir Shabir, Gianluca Torta, Ferruccio Damiani 2025-10-25T09:52:35Z 置信度 0.66
• Benchmarking TinyML Encrypted Federated Learning with Secret Sharing in Medical Computer Vision
  In this chapter, we benchmark the performance impact of gradient encryption and secret sharing in federated learning (FL) through ablation. We measure the performance and computational time for training a federated ResNet18 vision model on MedMNIST medical ima…
  crossref Ruduan B. F. Plug, Putu H. P. Jati, Samson Y. Amare, Mirjam van Reisen 2025-12-30T08:09:13Z 置信度 0.82 computer and information sciencescryptography and securitymachine learningcs.lgelectrical engineering, electronic engineering, information engineering
• Online On-Device Adaptation of Linguistic Fuzzy Models for TinyML Systems
  Background: Many everyday electronic devices incorporate embedded computers, allowing them to offer advanced functions such as Internet connectivity or the execution of artificial intelligence algorithms, giving rise to Tiny Machine Learning (TinyML) and Edge …
  crossref Javier Martín-Moreno, Francisco A. Márquez, Ana M. Roldán, Antonio Peregrín 2025-12-12T15:27:20Z 置信度 0.82 computer scienceartificial intelligencecomputer and information sciencescs.aics.lg
• Neural Architecture Search for Optimized TinyML Applications
  crossref Abbas Kassem Zein, Rand Abou Diab, Mohamad Yaacoub, Ali Ibrahim 2025-03-07T23:59:24Z 置信度 0.66
• DiabSecure: Federated-TinyML-enabled unified Privacy-Preserving Framework for Diabetes Risk Assessment using Zero-Trust Blockchain
  In this paper, we introduce DiabSecure: a decentralized framework based on FL for real-time diabetes risk prediction using wearable healthcare devices. Local TinyML models deployed on each wearable device are trained on-device, with no need to share raw data, …
  crossref Parshva Shah, Prince Jayantibhai Tandel, Aparna Kumari, Sunil Kumar 等 2025-08-01T18:07:25Z 置信度 0.82 computer sciencecomputer and information sciencescs.lgmachine learningartificial intelligence
• Optimized Neural Architecture for Wearable Health Devices using Edge-Level TinyML
  Wearable health devices enable real-time monitoring of physiological signals such as heart rate, SpO₂, and ECG. However, their limited computational resources and battery life necessitate efficient machine learning models suitable for edge deployment. This pap…
  crossref Pavan Kumar Reddy Manellore, M Archana, K. Deepthi Reddy, V N V L S Swathi 等 2025-10-20T17:48:22Z 置信度 0.82 computer sciencecomputer and information scienceswearable computercs.lgelectrical engineering, electronic engineering, information engineering
• Design and Experimental Verification of a TinyML-based MPPT Controller for Wind Energy Conversion Systems
  The energy conversion efficiency of wind energy conversion systems (WECS) critically depends on the Maximum Power Point Tracking (MPPT) controller’s ability to maintain the turbine at its optimal power output under fluctuating wind conditions. Traditional cont…
  crossref Dung A. Hoang, Hoang V. Tu, Manh V. Nguyen, Hai V. Pham 2025-08-06T05:01:31Z 置信度 0.82 renewable energycomputer scienceenergy storageeess.sycontrol theory
• Quantum Variational Autoencoder with TinyML Adaptive Neural Compression for Real-Time Medical Image Transmission on Edge Devices
  The exponential growth of medical imaging data, coupled with the increasing deployment of edge devices in healthcare, demands efficient, low-latency, and high-fidelity solutions for real-time image transmission. Traditional compression and transmission methods…
  crossref Kota Venkateswara Rao, P. Namratha, B. Prasanthi, K. Anil Kumar 等 2026-03-31T19:49:17Z 置信度 0.82 computer and information sciencesartificial intelligencecomputer sciencecs.lgcs.cv
• Swarm Smarts: Enabling Real-Time Self-Healing With TinyML at the Edge
  Edge devices in 6G wireless networks are driving unprecedented levels of intelligence and autonomy, where billions of interconnected nodes collaboratively sense, learn, and adapt in real time. However, the coexistence of massive device heterogeneity, stringent…
  crossref Haotian Wu, Jiwei Zhang, Minxi Feng, Minghui Dai 等 2026-03-06T21:03:46Z 置信度 0.82 computer and information sciencescomputer scienceelectrical engineering, electronic engineering, information engineeringcs.aisignal processing
• <scp>TinyML</scp> ‐Enabled Active Stability Co‐Evolution: A Lightweight Ensemble for Fault Diagnosis on <scp>IIoT</scp> ‐Connected Ultra‐High‐Pressure Waterjet Equipment
  ABSTRACT The Industrial Internet of Things (IIoT) has enabled large‐scale heterogeneous sensor deployment on ultra‐high‐pressure waterjet (UHPW) equipment, creating urgent demand for on‐device intelligence capable of operating reliably under communication cons…
  crossref Xiangze Li, Yijun Hou 2026-06-03T00:54:09Z 置信度 0.82 computer and information sciencescomputer scienceelectrical engineering, electronic engineering, information engineeringcs.aisignal processing
• TinyML-Enabled Resource-Efficient Framework for Real-Time Network Securiy in Electric Vehicle Charging Networks
  Electric Vehicle Charging Infrastructure (EVCI) faces critical cybersecurity challenges due to its integration with external networks, making it vulnerable to attacks such as malware propagation, session hijacking, and Denial-of-Service (DoS). Traditional mach…
  crossref Fatemeh Dehrouyeh, Ibrahim Shaer, Soodeh Nikan, Firouz Badrkhani Ajaei 等 2025-12-18T18:34:35Z 置信度 0.82 computer and information sciencescomputer sciencecs.lgartificial intelligencecs.ai
• Non-Invasive Continuous Real-Time Blood Glucose Estimation Using PPG Features-based Convolutional Autoencoder with TinyML Implementation
  In this paper, we developed a convolutional autoencoder for non-invasive continuous monitoring of blood glucose levels (BGL) using four photoplethysmography (PPG) features. The model was specifically designed to account for temporal relations among consecutive…
  crossref Noor Faris Ali, Alyazia Aldhaheri, Bethel Wodajo, Meera Alshamsi 等 2024-07-02T17:22:52Z 置信度 0.82 computer sciencecs.lgconvolutional neural networkmachine learningbrain-computer interface
• (POSTER) Insights from Executing TinyML Models on Smartphones and Microcontrollers
  In this paper, we empirically compare the system-level performances of executing a machine learning task on a resource-constrained IoT device and compare its performance to offloading the task to a more capable device, a smartphone. Our results indicate that a…
  crossref Harman M. Singh, Shrishailya Agashe, Shreyans Jain, Surjya Ghosh 等 2023-09-27T17:31:41Z 置信度 0.79 cs.lgcomputer scienceartificial intelligencecomputer and information sciencestask
• Efficient Air Quality Index Prediction on Resource-Constrained Devices using TinyML: Design, Implementation, and Evaluation
  These days, mobile/handheld air quality monitoring devices primarily sense pollutants and send them to the cloud without performing intelligent processing, such as soft calibration, sensor fault detection, or any onboard predictions. This paper explores the fe…
  crossref Arko Datta, Aniruddha Pal, Ranbir Marandi, Nilanjan Chattaraj 等 2024-01-22T18:08:30Z 置信度 0.82 computer and information sciencescomputer scienceartificial intelligencecs.aics.lg
• Hyperparameters Optimization Using GridSearchCV Method for TinyML Models
  crossref Tobiasz Puślecki, Krzysztof Walkowiak 2023-11-30T10:01:54Z 置信度 0.64
• H-RT-IDPS: A Hierarchical Real-Time Intrusion Detection and Prevention System for the Smart Internet of Vehicles via TinyML-Distilled CNN and Hybrid BiLSTM-XGBoost Models
  The integration of connected vehicles into smart city infrastructure introduces critical cybersecurity challenges for the Internet of Vehicles (IoV), where resource-constrained vehicles and powerful roadside units (RSUs) must collaborate for secure communicati…
  crossref Ikram Hamdaoui, Chaymae Rami, Zakaria El Allali, Khalid El Makkaoui 2025-12-05T16:37:02Z 置信度 0.82 computer and information sciencesartificial intelligenceelectrical engineering, electronic engineering, information engineeringcomputer sciencecs.ai
• ViT-Reg: Regression-Focused Hardware-Aware Fine-Tuning for ViT on TinyML Platforms
  Editor’s notes: This article presents a hardware-aware methodology for modeling the performance and energy behavior of vision transformers (ViTs) across a wide design space relevant to TinyML platforms. It serves as a valuable framework for balancing accuracy …
  crossref Md Ragib Shaharear, Arnab Neelim Mazumder, Tinoosh Mohsenin 2024-12-23T14:24:08Z 置信度 0.82 networking and internet architectureedge computingcomputer vision and pattern recognitioncomputer and information sciencesresource
• TinyML based monitoring and diagnosis system to enable the European battery digital passport
  The European Union mandates comprehensive battery digital passports for EV and industrial batteries above 2 kWh capacity starting February 2027. This paper presents a TinyML-based monitoring system that enables real-time battery parameters estimation to suppor…
  crossref Emanuele Ogliari, Marco Mussetta, Marco Carratù, Vincenzo Paciello 等 2026-02-17T21:05:10Z 置信度 0.82 computer scienceartificial intelligencecomputer and information sciencesartificial neural networkelectrical engineering, electronic engineering, information engineering
• Development of a TinyML-based Device for Automatic Detection of Poultry Diseases using Chicken Vocalizations
  Poultry farming provides essential protein but faces challenges from diseases that affect bird welfare and farm economics. Traditional detection methods are slow and costly, underscoring the need for real-time, low-cost solutions enabled by precision livestock…
  crossref Minh Nguyen-Ngoc, Bien Nguyen-Quang, Duan Luong-Cong 2026-07-31T18:50:33Z 置信度 0.82 computer scienceinternet of thingsartificial intelligenceengineeringcs.lg
• Efficient solid waste inspection through drone‐based aerial imagery and <scp>TinyML</scp> vision model
  Abstract Solid waste management is a significant challenge in the development of smart cities. Existing approaches for solid waste monitoring are often time‐consuming and resource intensive. Therefore, this study proposes a novel approach to solid waste monito…
  crossref Timothy Malche, Priti Maheshwary, Pradeep Kumar Tiwari, Ahmed Hussein Alkhayyat 等 2023-10-23T04:39:16Z 置信度 0.79 internet of thingscloud computingedge computingcomputer sciencewireless
• Application of TinyML in Virtual Sensors for Monitoring Air Quality in Industrial Environments
  The continuous monitoring of air quality, especially in the mining sector, is essential to ensure that pollutant levels remain within regulatory standards. Traditional solutions for this monitoring face technical and economic limitations. In this context, Mach…
  crossref Tayco Rodrigues, Gabriel Germano, Karla Sophia Cruz, Frede Carvalho 等 2026-05-21T17:24:16Z 置信度 0.82 computer scienceartificial intelligencecomputer and information sciencescs.aimachine learning
• Cross-Layer Resource Optimization for Ultra-Low-Power TinyML Inference on ARM Cortex-M Microcontrollers
  Running neural networks on battery-powered Internet of Things (IoT) sensor nodes is difficult because flash memory, SRAM, latency, and energy per inference are limited at the same time. Existing TinyML co-design methods usually improve model size or memory use…
  crossref Abdulaziz G. Alanazi, Haifa A. Alanazi, Nasser S. Albalawi 2026-07-03T07:59:47Z 置信度 0.82 computer and information sciencescs.aielectrical engineering, electronic engineering, information engineeringbenchmarkcs.lg
• Edge-AI Framework for Fire Detection in Wildland-Urban Interface using TinyML
  Wildfires in the Wildland-Urban Interface (WUI) pose a significant threat to public safety and property, indicating the need for advanced early detection systems that are not only cost-effective and low-maintenance but also capable of operating in remote locat…
  crossref Rodrigo Santa Comba Coelho da Silva, João Carlos N. Bittencourt, Daniel G. Costa 2025-10-21T17:07:47Z 置信度 0.82 convolutional neural networkcs.aicomputer and information sciencesartificial intelligencecomputer science
• The Problem of Optimizing Telepresence Suit Traffic Using TinyML
  crossref Viktoria Dmitrieva, Oleg Vtorov, Alexander Brushinin, Artem Volkov 等 2026-07-14T22:04:54Z 置信度 0.66
• Classroom Activity Detection Using TinyML
  crossref Satwik Devle, Yogesh Jadhav, Vaibhav Maske, Siddheshwar Das 等 2025-10-01T01:00:28Z 置信度 0.66
• Energy-Efficient Resource Accounting for IoT-Based AMI Using Federated Reinforcement Learning and TinyML Optimization
  Advanced Metering Infrastructure (AMI) and Internet of Things (IoT) deployments require energy-efficient resource accounting to scale in power distribution systems. Existing approaches require centralized data collection, neglect edge compute and quantization …
  crossref Nitin Thapliyal, Anzar Ahmad, Jyoti Kaurav, Nozima Dusmukhammedova 等 2026-05-11T19:44:42Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringmachine learningedge computingsignal processing
• Integrated Underwater Data Transmission and Object Detection System Using TinyML and Multi-Hop Networks
  Underwater communication faces challenges like high attenuation, limited bandwidth, and energy constraints. This paper presents an underwater communication system using ultrasonic sensors for image transmission and TinyML for efficient object detection. The ar…
  crossref Ch Madhu Bhushan, S H Laskar, Jahnava Sai Garikapati, P Sai Srihitha 等 2025-08-26T19:04:22Z 置信度 0.82 computer scienceunderwatercomputer and information scienceselectrical engineering, electronic engineering, information engineeringedge computing
• A TinyML based Portable, Low-Cost Microwave Head Imaging System for Brain Stroke Detection
  Microwave Imaging (MWI) has emerged as a potential candidate for brain stroke detection due to its low cost, time efficiency and accurate nature when compared to other screening techniques. TinyML is a revolutionary technique for utilizing AI in portable and l…
  crossref Muhammad Hashir, Nazish Khalid, Nasir Mahmood, Muhammad A. Rehman 等 2023-07-21T13:19:44Z 置信度 0.79 computer scienceartificial intelligenceconvolutional neural networkcomputer and information sciencesbrain-computer interface
• A Guided Task and Obstacle Alert Robot System Based on TinyML and Augmented Reality
  Currently, the robot system has been widely applied and rapidly developed. It applies to diverse fields such as service, patrol, life, etc. Most patrol robot systems are based on augmented reality and SLAM to complete guided navigation tasks. The AI system is …
  crossref Xiangyu Zhu, Rui Li, Jia Hu, Hui Zhang 等 2022-10-12T22:13:51Z 置信度 0.79 computer sciencerobotunderwaterhuman-computer interactionartificial intelligence
• TinyML-Based Federated Learning: A Novel Framework for Privacy-Preserving Smart Healthcare Applications
  This paper presents an optimized integration framework combining Tiny Machine Learning (TinyML) and Federated Learning (FL) for privacy-preserving smart healthcare applications. While building upon established techniques, our contribution lies in their synergi…
  crossref Manas Kumar Yogi, K. V. V. L. S. Karthik, Pasupuleti Sri Durga Tanuja Gayatri 2026-04-07T03:00:13Z 置信度 0.82 computer and information sciencescomputer sciencemachine learningcryptography and securityinternet of things
• TinyML Olive Fruit Variety Classification by Means of Convolutional Neural Networks on IoT Edge Devices
  Machine learning (ML) within the edge internet of things (IoT) is instrumental in making significant shifts in various industrial domains, including smart farming. To increase the efficiency of farming operations and ensure ML accessibility for both small and …
  crossref Ali M. Hayajneh, Sahel Batayneh, Eyad Alzoubi, Motasem Alwedyan 2023-12-01T10:39:48Z 置信度 0.79 computer sciencecomputer and information sciencesartificial intelligencecs.aics.lg
• SecureQNN: Shielding the Intellectual Property of QNNs in TinyML Systems
  Building accurate Machine Learning (ML) models requires substantial expertise and large-scale datasets typically only available in big data companies. These companies have been selling their models as Machine Learning as a Service. However, concerns about data…
  crossref Miguel Costa, Tiago Gomes, Sandro Pinto 2025-08-19T18:17:12Z 置信度 0.82 computer sciencecomputer and information sciencescs.lgartificial intelligencecs.ai
• Zero-Shot NAS for TinyML Semantic Segmentation via Weight Sharing
  Enhancing the efficiency of deep neural networks enables complex computer vision applications, such as semantic segmentation, to meet latency requirements on resource-limited embedded systems. By optimizing network parameters, computation overhead can be reduc…
  crossref Zhuoran Xiong, Warren J. Gross, Brett H. Meyer 2026-01-29T21:24:07Z 置信度 0.82 computer and information sciencesartificial intelligenceelectrical engineering, electronic engineering, information engineeringcomputer sciencecs.lg
• A Novel FastKAN With Few-Shot Learning for Real-Time Driver Distraction Detection on TinyML Microcontrollers
  crossref Chaymae Yahyati, Ismail Lamaakal, Yassine Maleh, Khalid El Makkaoui 等 2026-01-21T21:11:10Z 置信度 0.66
• TeleSure: TinyML-Based Framework for Secure UAV Delivery in Telesurgery Systems with 5G
  In today’s advanced telesurgery systems, Unmanned Aerial Vehicles (UAVs) have significant importance in timely and secure conveying of important medical items. As 5G networks are integrated in these systems, their connectivity, low latency and high data rate e…
  crossref Lakshin Pathak, Mohammad S. Obaidat, Khushi Vasava, Vidhi Ruparelia 等 2025-08-01T18:07:25Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringcomputer sciencecryptography and securityrobotics
• Multipath Attention and Adaptive Gating Network for Video Action Recognition
  Abstract 3D CNN networks can model existing large action recognition datasets well in temporal modeling and have made extremely great progress in the field of RGB-based video action recognition. However, the previous 3D CNN models also face many troubles. For …
  crossref Haiping Zhang, Zepeng Hu, Dongjin Yu, Liming Guan 等 2024-03-27T07:03:24Z 置信度 0.78 computer and information sciencescs.cvcs.aics.lgcomputer vision and pattern recognition
• Lightweight TinyML-Enhanced Task Offloading in VANETs for Next-Generation Intelligent Transportation Systems
  Vehicular Ad Hoc Networks (VANETs) face resource constraints, high node mobility, and stringent latency requirements, especially in safety-critical applications such as collision avoidance, path planning, and emergency braking. Task offloading to nearby vehicl…
  crossref Muhammad Ali, Tariq Qayyum, Asadullah Tariq, Zouheir Trabelsi 等 2026-07-16T21:52:09Z 置信度 0.82 computer and information sciencescomputer scienceartificial intelligenceelectrical engineering, electronic engineering, information engineeringrobotics
• Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems
  BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artifi…
  crossref Jutarut Chaoraingern, Vittaya Tipsuwanporn, Arjin Numsomran 2024-01-14T00:05:50Z 置信度 0.82 computer vision and pattern recognitioncomputer sciencephysical sciencescomputer and information sciencesconvolutional neural network
• Smart Irrigation System for Precision Farming Using IoT, TinyML, Hybrid GSM and WiFi and Chatbot
  Water scarcity and non-efficient irrigation practices reduces the productivity in agricultural field and the conservation of resources. This paper presents a smart irrigation framework which integrates IoT-based sensing, an ESP32 microcontroller with TinyML ca…
  crossref Austy B Evangeline, Alen M Alex, R S Saran, Sharfin J 2026-01-06T18:39:57Z 置信度 0.82 cloud computinginternet of thingsbig dataengineeringcomputer science
• Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems
  BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artifi…
  crossref Jutarut Chaoraingern, Vittaya Tipsuwanporn, Arjin Numsomran 2024-01-14T03:20:04Z 置信度 0.82 computer vision and pattern recognitioncomputer sciencephysical sciencescomputer and information sciencesconvolutional neural network
• UAV-Assisted Partial Co-Operative NOMA-Based Resource Allocation in CV2X and TinyML-Based Use Case Scenario
  The evolution of Internet-of-Vehicles (IoV) from IoT has revolutionized Smart cities, enabling vehicle communication for safety and traffic information dissemination. However, fulfilling time-sensitive applications like safety alerts via Cellular Vehicle-to-Ev…
  crossref Garima Chopra, Shalli Rani, Wattana Viriyasitavat, Gaurav Dhiman 等 2024-01-09T20:42:15Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringnetworking and internet architecturesignal processingcs.ni
• TinyML: The NextGen AI Technology for Standalone Devices
  crossref Satishkumar Kataria, Pankaj Prajapati, Sachin Gajar, Amit Rathod 2026-06-17T12:05:17Z 置信度 0.66
• Resource-Efficient TinyML Framework for Real-Time Fault Diagnosis in Solar Charge Controllers using 8-bit Quantized Neural Networks
  crossref S M Shaif Mahamud Shemon, Tama Fouzder, Anopa Rani Ghosh, Farhan Shadik 2026-08-27T19:07:45Z 置信度 0.66
• iMCU: A 28-nm Digital In-Memory Computing-Based Microcontroller Unit for TinyML
  Tiny machine learning (TinyML) envisions executing a deep neural network (DNN)-based inference on an edge device for improving battery life, latency, security, and privacy. Toward this vision, recent microcontroller units (MCUs) integrate in-memory computing (…
  crossref Chuan-Tung Lin, Paul Xuanyuanliang Huang, Jonghyun Oh, Dewei Wang 等 2024-02-14T13:47:13Z 置信度 0.82 computer and information sciencescomputer sciencecs.lgcs.aielectrical engineering, electronic engineering, information engineering
• Expanding Applications of TinyML in Versatile Assistive Devices: From Navigation Assistance to Health Monitoring System Using Optimized NASNet-XGBoost Transfer Learning
  The healthcare sector receives a considerable amount of unprocessed data from wearable and portable devices. However, traditional cloud-based models used to handle this type of data can pose risks such as exposing sensitive patient data to a network environmen…
  crossref Sreenu Ponnada, Tan Kuan Tak, Pravin R. Kshirsagar, P. Srinivasa Rao 等 2024-11-12T13:44:26Z 置信度 0.82 computer scienceartificial intelligencecomputer and information sciencescs.lghealth care
• An Intelligent Microprocessor Integrating TinyML in Smart Hotels for Rapid Accident Prevention
  In the modern era of Internet of Things (IoT) and Industry 4.0 there is a growing need for intelligent microcontrollers that can collect, sense and analyse data effectively and efficiently. Such devices can be installed in large scale IoT deployments ranging f…
  crossref Angelos Zacharia, Dimitris Zacharia, Aristeidis Karras, Christos Karras 等 2022-11-03T21:41:14Z 置信度 0.79 computer scienceinternet of thingscomputer and information sciencescs.lgartificial intelligence
• TinyML-Based AQI Forecasting Using CNN with Butterworth Low-Pass Filter for Edge Devices
  Air pollution, particularly$\text{PM}_{2.5}$, is a major concern for public health in urban areas. In this paper, we propose a TinyML pipeline for$\text{PM}_{2.5}$forecasting that integrates a lightweight 1D-CNN with zero-phase Butterworth low-pass denoising t…
  crossref Ch Madhu Bhushan, Alekhya Lankavalasa, Hanchete Lahari Bai, Firoj Gazi 等 2026-05-08T19:37:10Z 置信度 0.82 computer and information scienceselectrical engineering, electronic engineering, information engineeringartificial intelligencecs.aics.lg
• PersASR: A 2.3-μJ/frame 96.8%-accurate TinyML Automatic Speech Recognition Processor with Data Augmentation-Driven Personalization in 16-nm FinFET
  We present PersASR, a 16-nm FinFET personalized ASR processor that features on-chip retraining with hardware-efficient data augmentation and a hardware-optimized beam search. PersASR achieves a state-of-the-art ASR accuracy of 96.8%. The chip delivers real-tim…
  crossref Seunghyun Moon, Jinho Park, Chuan-Tung Lin, SeongHwan Cho 等 2026-05-15T02:52:39Z 置信度 0.82 cryptography and securityeess.ascs.sdcomputer vision and pattern recognitioncs.cv
• TinyML-Enabled Unsupervised Ultrasonic Guided Wave SHM Under Varying Thermal Conditions
  Most machine learning (ML) algorithms reported for ultrasonic guided wave structural health monitoring (GW-SHM) applications, particularly for damage assessment in the presence of time-varying environmental and operating variables, use networks with a lot of p…
  crossref Pankhi Kashyap, Kajal Shivgan, Sheetal Patil, Sagar Mahajan 等 2023-10-09T18:21:54Z 置信度 0.79 cs.lgcomputer sciencemachine learningedge computingcomputer and information sciences
• Identificação de equipamentos eletroeletrônicos e monitoramento de carga não invasivo utilizando TinyML
  Este artigo apresenta o desenvolvimento e a implementação de um sistema de Monitoramento e Identificação de Equipamentos Eletrônicos Não Invasivo (NILM) para otimizar o consumo energético residencial.Utilizando uma única medição na entrada principal, o sistema…
  crossref Erick Alves Ferreira, Fábio de Oliveira da Silva, Gabriel Alves dos Anjos, Thiago Silva Ribeiro 等 2026-05-25T18:28:52Z 置信度 0.82 philosophyworkarchitecturenatural language processingmulti-agent systems
• A TinyML System for Early Screening of CHD on ESP32
  crossref Wang Hao, Hou Chengyu, Zhang Kuang, Qu Zheng 等 2026-04-28T19:49:18Z 置信度 0.66
• Microarchitecture Aware Neural Architecture Search for TinyML Devices
  Designing accurate and efficient models for tiny machine learning (TinyML) devices is challenging because of its strict power consumption limitations and accuracy requirements. This paper proposed a Microarchitecture Aware Neural Architecture Search (M-NAS) me…
  crossref Juntao Guan, Gufeng Liu, Fanhong Zeng, Rui Lai 等 2024-07-19T17:30:48Z 置信度 0.82 cs.lgcomputer and information sciencesinformation theoryenergysignal processing
• A TinyML Soft-Sensor for the Internet of Intelligent Vehicles
  The increased number of sensors in modern cars offers the opportunity to develop algorithms that can monitor and diagnose vehicle performance more efficiently. We present the results of applying and deploying a TinyML model into a typical OBD-II automotive sca…
  crossref Thommas Flores, Marianne Silva, Pedro Andrade, Jordao Silva 等 2022-08-17T19:42:21Z 置信度 0.79 environmental sciencegreenhouse gascomputer and information scienceselectrical engineering, electronic engineering, information engineeringcarbon fibers
• Self-Attention-Assisted TinyML With Effective Representation for UWB NLOS Identification
  Ultra-Wide Band (UWB) Non-Line-of-Sight (NLOS) identification is a crucial task in wireless localization systems. Various Deep Learning (DL) solutions have demonstrated promising outcomes in UWB NLOS identification by utilizing Channel Impulse Response (CIR) a…
  crossref Yifeng Wu, Xu He, Lingfei Mo, Qing Wang 2024-01-03T19:55:24Z 置信度 0.82 computer and information sciencesartificial intelligencecs.lgelectrical engineering, electronic engineering, information engineeringcomputer science
• Certainty-Based Neural Network Architecture Selection Framework for TinyML Systems
  The development of technologies related to the TinyML concept observed in recent years forces us to consider the trade-off between inference time and recognition quality. Moreover, employing data stream processing methods to analyze large volumes of data in re…
  crossref Joanna Komorniczak, Tobiasz Puślecki, Paweł Ksieniewicz, Krzysztof Walkowiak 2024-10-16T18:04:50Z 置信度 0.82 cs.lgartificial neural networkconvolutional neural networkcomputer and information scienceselectrical engineering, electronic engineering, information engineering
• A Cost-Effective, Stand-Alone, and Real-Time TinyML-Based Gait Diagnosis Unit Aimed at Lower-Limb Robotic Prostheses and Exoskeletons
  Robotic prostheses and exoskeletons can do wonders compared to their non-robotic counterpart. However, in a cost-soaring world where 1 in every 10 patients has access to normal medical prostheses, access to advanced ones is, unfortunately, extremely limited es…
  crossref Zarin Anjum Madhiha, Antar Mazumder, Sohani Munteha Hiam 2026-05-07T19:51:14Z 置信度 0.82 computer sciencecomputer and information sciencesmedicineartificial intelligencebrain-computer interface
• Hardware Deployable Edge-AI Solution for Prescreening of Oral Tongue Lesions Using TinyML on Embedded Devices
  Diagnosing oral cavity cancer (OCC) in its initial stages is an effective way to reduce patient mortality. However, current prescreening solutions are manual and the resultant clinical treatment is not cost effective for the average individual, primarily in de…
  crossref Mohammed Zubair M. Shamim 2022-03-17T16:34:33Z 置信度 0.79 artificial intelligencemedicinecomputer and information sciencescs.aiartificial neural network
• <scp>GMR</scp> ‐Based Eddy‐Current Sensing and Embedded <scp>TinyML</scp> for Advanced Crack‐Shape Characterization
  ABSTRACT A combined computational and experimental approach was used to characterize crack shapes in flawed specimens. The first step involved a three‐dimensional finite‐element method based on the (A, V–A) formulation to analyze field variations in cracked co…
  crossref Dalal Radia Touil, Ahmed Chouki Lahrech, Bachir Helifa, Ibn Khaldoun Lefkaier 2026-06-19T08:32:49Z 置信度 0.82 computer scienceunderwaterrobotcomputer and information sciencesrobotics
• Detecção de Quedas e Crises Epilépticas com Dispositivo Vestível utilizando TinyML e ESP32
  A epilepsia é uma condição neurológica crônica que afeta cerca de 50 milhões de pessoas globalmente, demandando soluções que garantam a segurança e a agilidade no atendimento emergencial. Este trabalho apresenta o desenvolvimento de um sistema vestível baseado…
  crossref Francisco Thiago Barbosa Quinto, Thiago de Sena Lima, Fábio Timbó Brito, José Daniel de Alencar Santos 2026-07-30T15:47:28Z 置信度 0.82 philosophypolitical scienceworkchemical engineeringepistemology