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• Low-cost air, noise, and light pollution measuring station with wireless communication and tinyML.
  europepmc J.S. Botero-Valencia, C. Barrantes-Toro, D. Marquez-Viloria, Joshua M. Pearce 2023 置信度 0.80
• Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge.
  europepmc 2022 置信度 0.80
• A Comparative Study of Lightweight, Sparse Autoencoder-Based Classifiers for Edge Network Devices: An Efficiency Analysis of Feed-Forward and Deep Neural Networks.
  europepmc 2025 置信度 0.80
• The road to commercial success for neuromorphic technologies.
  europepmc 2025 置信度 0.80
• An efficient deep CNN based BiLSTM framework with RanA optimization for accurate cardiac arrhythmia detection.
  europepmc 2026 置信度 0.80
• Artificial Intelligence Applications in Chronic Obstructive Pulmonary Disease: A Global Scoping Review of Diagnostic, Symptom-Based, and Outcome Prediction Approaches.
  europepmc 2025 置信度 0.80
• Intelligent Sports Weights.
  europepmc 2025 置信度 0.80
• Atrial Fibrillation Detection on the Embedded Edge: Energy-Efficient Inference on a Low-Power Microcontroller.
  Atrial Fibrillation (AF) is a common yet often undiagnosed cardiac arrhythmia with serious clinical consequences, including increased risk of stroke, heart failure, and mortality. In this work, we present a novel Embedded Edge system performing real-time AF de…
  europepmc Yash Akbari, Ningrong Lei, Nilesh Patel, Yonghong Peng 等 2025 置信度 0.80
• Selective Sensing of Mixtures of Gases with CMOS-SOI-MEMS Sensor Dubbed GMOS.
  europepmc 2023 置信度 0.80
• Editorial for the Special Issue on Micro and Smart Devices and Systems.
  europepmc 2023 置信度 0.80
• Identification of Post-Ictal Generalised EEG Suppression with Two-Channel EEG.
  europepmc 2025 置信度 0.80
• EHFOA-ID: An Enhanced HawkFish Optimization-Driven Hybrid Ensemble for IoT Intrusion Detection.
  europepmc 2025 置信度 0.80
• Gait Stride Length Estimation Using Embedded Machine Learning.
  europepmc 2023 置信度 0.80
• Trimodal machine learning based biometrics system.
  europepmc 2025 置信度 0.80
• A standalone computing system to classify human foot movements using machine learning techniques for ankle-foot prosthesis control.
  europepmc 2022 置信度 0.80
• Integrating AI with Biosensors and Voltammetry for Neurotransmitter Detection and Quantification: A Systematic Review.
  europepmc 2025 置信度 0.80
• P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications
  Abstract The demand to process vast amounts of data generated from state-of-the-art high resolution cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such cameras are usually captured in analog voltages by a sensor pixel array…
  europepmc Gourav Datta, Souvik Kundu, Zihan Yin, Ravi Teja Lakkireddy 等 2022 置信度 0.80
• An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments.
  Industrial assets often feature multiple sensing devices to keep track of their status by monitoring certain physical parameters. These readings can be analyzed with machine learning (ML) tools to identify potential failures through anomaly detection, allowing…
  europepmc Mattia Antonini, Miguel Pincheira, Massimo Vecchio, Fabio Antonelli 2023 置信度 0.80
• A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions.
  Vehicles are the major source of air pollution in modern cities, emitting excessive levels of CO2 and other noxious gases. Exploiting the OBD-II interface available on most vehicles, the continuous emission of such pollutants can be indirectly measured over ti…
  europepmc Pedro Andrade, Ivanovitch Silva, Marianne Silva, Thommas Flores 等 2022 置信度 0.80
• A processing-in-pixel-in-memory paradigm for resource-constrained TinyML applications.
  Abstract The demand to process vast amounts of data generated from state-of-the-art high resolution cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such cameras are usually captured in analog voltages by a sensor pixel array…
  europepmc Gourav Datta, Souvik Kundu, Zihan Yin, Ravi Teja Lakkireddy 等 2022 置信度 0.80
• Toward Energy-Efficient and Low-Carbon Intrusion Detection in Edge and Cloud Computing Based on GreenShield Cybersecurity Framework.
  europepmc 2026 置信度 0.80
• ElectraSight: Fully Onboard Eye Tracking for Smart Glasses With Hybrid EOG (hEOG)
  Smart glasses with integrated eye tracking technology are revolutionizing diverse fields, from immersive augmented reality experiences to cutting-edge health monitoring solutions. However, traditional eye tracking systems rely heavily on cameras and significan…
  datacite Schärer, Nicolas, Villani, Federico, Melatur, Aishwarya, Peter, Steven 等 2025 置信度 0.66 Smart GlassesEye TrackingEOGhEOG ContactlesstinyML
• Optimizing BFloat16 Deployment of Tiny Transformers on Ultra-Low Power Extreme Edge SoCs
  Transformers have emerged as the central backbone architecture for modern generative AI. However, most ML applications targeting low-power, low-cost SoCs (TinyML apps) do not employ Transformers as these models are thought to be challenging to quantize and dep…
  datacite Dequino, Alberto, Bompani, Luca, Benini, Luca, Conti, Francesco 2025 置信度 0.66 Transformersmodel pruningedge AIRISC-V microcontrollersedge deployment
• Raport de cercetare -Analiza comparativă a unor modele cu învățare profundă din categoria TinyML pentru recunoasterea imaginilor în platforme mobile
  datacite Bianca-Elena Negoescu 2025 置信度 0.66
• Analiza comparativă a unor modele cu învățare profundă din categoria TinyML pentru recunoasterea imaginilor în platforme mobile
  datacite Bianca-Elena Negoescu 2025 置信度 0.66
• CollectiveOS V 2.0 & The External AI Motherboard
  CollectiveOS V 2.0 & The External AI Motherboard A Modular, Patent-Free Architecture for Scalable, Local-First AI Compute Human Global Science Collective (HGSC) | Version 2.0 | 2026 Draft White Paper Author & Custodian Mark Anthony Brewer — Human Global Scienc…
  datacite Brewer, Mark Anthony 2025 置信度 0.66
• CollectiveOS & The Sovereign Mobile Super-Node
  CollectiveOS & The Sovereign Mobile Super-Node An Open-Science Architecture for Portable, Patent-Free AI Infrastructure Version 1.0 — October 2025 Author & Custodian:Mark Anthony Brewer — Human Global Science Collective (HGSC) Affiliation:Human Global Science …
  datacite Brewer, Mark Anthony 2025 置信度 0.66
• CollectiveOS & The Sovereign Mobile Super-Node
  CollectiveOS & The Sovereign Mobile Super-Node An Open-Science Architecture for Portable, Patent-Free AI Infrastructure Version 1.0 — October 2025 Author & Custodian:Mark Anthony Brewer — Human Global Science Collective (HGSC) Affiliation:Human Global Science …
  datacite Brewer, Mark Anthony 2025 置信度 0.66
• RockNet: Distributed Learning on Ultra-Low-Power Devices
  As Machine Learning (ML) becomes integral to Cyber-Physical Systems (CPS), there is growing interest in shifting training from traditional cloud-based to on-device processing (TinyML), for example, due to privacy and latency concerns. However, CPS often compri…
  datacite Gräfe, Alexander, Mager, Fabian, Zimmerling, Marco, Trimpe, Sebastian 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Raport de cercetare -Analiza comparativa si optimizarea modelelelor TinyML pentru recunoasterea simbolurilor grafice cu integrare in platforme Android
  datacite Lavinia -Ioana Vlad 2025 置信度 0.66
• Federated Learning and TinyML for Localization: Benefits, Applications, and Challenges
  datacite Akpojoto Siemuri, Elmusrati, Mohammed 2025 置信度 0.66
• NanoHydra: Energy-Efficient Time-Series Classification at the Edge
  Time series classification (TSC) on extreme edge devices represents a stepping stone towards intelligent sensor nodes that preserve user privacy and offer real-time predictions. Resource-constrained devices require efficient TinyML algorithms that prolong the …
  datacite Cioflan, Cristian, Fonseca, Jose, Wang, Xiaying, Benini, Luca 2025 置信度 0.66 Signal Processing (eess.SP)FOS: Electrical engineering, electronic engineering, information engineeringFOS: Electrical engineering, electronic engineering, information engineering
• Applying Machine Learning (TinyML) Technologies to Predictions for Lithium-ion Batteries
  datacite Yuqin Weng, Ababei, Cristinel 2024 置信度 0.66
• Benchmarking TinyML Tools: A Systematic Review and Evaluation
  datacite Rosales, José M 2024 置信度 0.66
• TinyML: IoT e Machine Learning - Introduzindo Convoluções
  datacite De Figueiredo, Felipe Augusto Pereira 2023 置信度 0.66
• TinyML: IoT e Machine Learning - Introdução ao curso
  datacite De Figueiredo, Felipe Augusto Pereira 2023 置信度 0.66
• TinyML: IoT e Machine Learning - Classificação com DNNs
  datacite De Figueiredo, Felipe Augusto Pereira 2023 置信度 0.66
• TinyML: IoT e Machine Learning - Regressão com DNNs (Parte I)
  datacite De Figueiredo, Felipe Augusto Pereira 2023 置信度 0.66
• TinyML: IoT e Machine Learning - Datasets
  datacite De Figueiredo, Felipe Augusto Pereira 2023 置信度 0.66
• Realising the Power of Edge Intelligence: Addressing the Challenges in AI and tinyML Applications for Edge Computing
  datacite Gibbs, Michael, Eiman Kanjo 2023 置信度 0.66
• tinyML for Crime Prevention: Detecting Violent Conversations
  datacite Amna Anwar, Eiman Kanjo 2023 置信度 0.66
• TinyML: IoT e Machine Learning - O Paradigma do Aprendizado de Máquina
  datacite De Figueiredo, Felipe Augusto Pereira 2023 置信度 0.66
• TinyML: IoT e Machine Learning - Minimizando o erro
  datacite De Figueiredo, Felipe Augusto Pereira 2023 置信度 0.66
• TinyML: IoT e Machine Learning - Desafios do TinyML: Sistemas Embarcados
  datacite De Figueiredo, Felipe Augusto Pereira 2023 置信度 0.66
• TinyML: IoT e Machine Learning - Prevenindo o sobreajuste
  datacite De Figueiredo, Felipe Augusto Pereira 2023 置信度 0.66
• TinyML: IoT e Machine Learning - Métricas para análise de classificadores
  datacite De Figueiredo, Felipe Augusto Pereira 2023 置信度 0.66
• Combining Multiple tinyML Models for Multimodal Context- Aware Stress Recognition on Constrained Microcontrollers
  datacite Woodward, Kieran, Eiman Kanjo, Gibbs, Michael 2023 置信度 0.66
• TinyML: IoT e Machine Learning - Medindo a precisão de um modelo de ML
  datacite De Figueiredo, Felipe Augusto Pereira 2023 置信度 0.66
• Online Learning TinyML for Anomaly Detection Based on Extreme Values Theory
  datacite Pereira, Eduardo S, Marcondes, Leonardo Dos Santos, Josemar M. Silva 2023 置信度 0.66
• TinyML: Analysis of Xtensa LX6 microprocessor for Neural Network Applications by ESP32 SoC
  datacite Md Ziaul Haque Zim 2021 置信度 0.66
• msf-CNN: Patch-based Multi-Stage Fusion with Convolutional Neural Networks for TinyML
  AI spans from large language models to tiny models running on microcontrollers (MCUs). Extremely memory-efficient model architectures are decisive to fit within an MCU's tiny memory budget e.g., 128kB of RAM. However, inference latency must remain small to fit…
  datacite Huang, Zhaolan, Baccelli, Emmanuel 2025 置信度 0.66 Machine Learning (cs.LG)Performance (cs.PF)FOS: Computer and information sciencesFOS: Computer and information sciences
• Towards smart and adaptive agents for active sensing on edge devices
  TinyML has made deploying deep learning models on low-power edge devices feasible, creating new opportunities for real-time perception in constrained environments. However, the adaptability of such deep learning methods remains limited to data drift adaptation…
  datacite Vyas, Devendra, Pižurica, Nikola, Milović, Nikola, Jovančević, Igor 等 2025 置信度 0.66 Robotics (cs.RO)Artificial Intelligence (cs.AI)Image and Video Processing (eess.IV)FOS: Computer and information sciencesFOS: Computer and information sciences
• AI-Powered Air Quality Monitoring Using ESP32 and BME280 Sensors for Incense Smoke Classification
  Air quality monitoring is crucial for understanding environmental and health impacts of pollutants. Accurate, low-cost sensors combined with machine learning models offer new possibilities for real-time detection of air quality variations caused by common sour…
  datacite Premanand, Rahul, Ziheng Sun 2025 置信度 0.66
• Ethical Dimensions of Generative and Edge AI for Participatory Citizen Science and STEAM Education, integrating Human-Centered Frameworks
  The adoption of Artificial Intelligence (AI) technologies at the edge and in participatory research settings is rapidly accelerating. Today Tiny Machine Learning (TinyML) allows ML and even Large Language Model (LLM) inference on low-power microcontrollers, en…
  datacite Pita Costa, Joao, Zennaro, Marco, Shawe-Taylor, John 2025 置信度 0.66 Citizen scienceopen educationEdge AITinyMLLLMs
• Ethical Dimensions of Generative and Edge AI for Participatory Citizen Science and STEAM Education, integrating Human-Centered Frameworks
  The adoption of Artificial Intelligence (AI) technologies at the edge and in participatory research settings is rapidly accelerating. Today Tiny Machine Learning (TinyML) allows ML and even Large Language Model (LLM) inference on low-power microcontrollers, en…
  datacite Pita Costa, Joao, Zennaro, Marco, Shawe-Taylor, John 2025 置信度 0.66 Citizen scienceopen educationEdge AITinyMLLLMs
• СТАНДАРТИЗАЦИЯ И БЕЗОПАСНОЕ КОДИРОВАНИЕ ОБЪЕДИНЕНИЕ КВАНТОВАНИЯ, ПРУНИНГА И ДИСТИЛЛЯЦИИ В ЕДИНЫЙ АДАПТИВНЫЙ КОНВЕЙЕР ДЛЯ МИКРОКОНТРОЛЛЕРОВ КЛАССА CORTEX-M
  Развертывание нейронных сетей на микроконтроллерах класса Cortex-M сопряжено с ограничениями по вычислительным ресурсам, объему памяти и энергопотреблению. Индивидуальное применение методов сжатия моделей, таких как квантование, прунинг и дистилляция знаний, д…
  datacite Худайберидева Г. Б., Кожухов Д. А., Пименкова А. А. 2025 置信度 0.66 сжатие нейронных сетейквантованиепрунингдистилляция знанийадаптивный конвейер
• НЕЙРО-АППАРАТНЫЕ СИСТЕМЫ НА КРИСТАЛЛЕ (NEUSOC) ДЛЯ МИКРО-LLM: ИНТЕГРАЦИЯ СПЕЦИАЛИЗИРОВАННЫХ АКСЕЛЕРАТОРОВ В МЕДЛЕННЫЕ ПРОМЫШЛЕННЫЕ МК
  Предложена концепция Нейро-Аппаратных Систем на Кристалле (NeuSoC), направленная на эффективное исполнение микроскопических языковых моделей (Микро-LLM) на промышленных микроконтроллерах (МК) с ограниченными вычислительными ресурсами и частотой. В отличие от п…
  datacite Худайберидева Г. Б., Кожухов Д. А., Пименкова А. А. 2025 置信度 0.66 Нейро-Аппаратные АкселераторыСистема на КристаллеМикро-LLMМикроконтроллерыЭнергоэффективность
• КЭШ-ОСОЗНАННАЯ ОПТИМИЗАЦИЯ БОЛЬШИХ ЯЗЫКОВЫХ МОДЕЛЕЙ ДЛЯ МИКРОКОНТРОЛЛЕРОВ
  Распространение больших языковых моделей (LLM) на устройства Интернета вещей (IoT) сдерживается ограниченными ресурсами микроконтроллеров (MCU), в частности, малым объемом и высокой латентностью энергонезависимой памяти (Flash) и оперативной памяти (RAM). Трад…
  datacite Худайберидева Г. Б., Кожухов Д. А., Пименкова А. А. 2025 置信度 0.66 большие языковые моделиLLMмикроконтроллерыMCUоптимизация инференса
• Design methodologies for sustainable hardware acceleration at the edge
  Η ραγδαία αύξηση του αριθμού των συσκευών που ανήκουν στο Διαδίκτυο των Πραγμάτων, σε συνδυασμό με την εμφάνιση εφαρμογών και υπηρεσιών με απαιτήσεις για χαμηλό χρόνο απόκρισης, προστασία της ιδιωτικότητας κατά την εκτέλεση και ασφάλεια κατά τη μεταφορά δεδομέ…
  datacite Κοκκίνης, Αργύριος Ι. 2025 置信度 0.66 Επιταχυντές υλικούΨηφιακά συστήματαΒιώσιμη υπολογιστικήΣχεδιαστικές ροέςHardware accelerators
• Edge Artificial Intelligence: A Systematic Review of Evolution, Taxonomic Frameworks, and Future Horizons
  Edge Artificial Intelligence (Edge AI) embeds intelligence directly into devices at the network edge, enabling real-time processing with improved privacy and reduced latency by processing data close to its source. This review systematically examines the evolut…
  datacite Ali, Mohamad Abou, Dornaika, Fadi 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Edge-Based Motor Anomaly Detection on ESP32 Using an Autoencoder
  Industrial motors are susceptible to performance degradation and unexpected failures that raise downtime and maintenance costs. This paper presents a low-cost, edge-centric anomaly detection system built on the ESP32 microcontroller that fuses vibration, tempe…
  datacite Md. Shoibe Hossain, Rifat, Antor Biswas 2025 置信度 0.66 Anomaly detectionAutoencoderESP32Predictive maintenanceTinyML
• Edge-Based Motor Anomaly Detection on ESP32 Using an Autoencoder
  Industrial motors are susceptible to performance degradation and unexpected failures that raise downtime and maintenance costs. This paper presents a low-cost, edge-centric anomaly detection system built on the ESP32 microcontroller that fuses vibration, tempe…
  datacite Md. Shoibe Hossain, Rifat, Antor Biswas 2025 置信度 0.66 Anomaly detectionAutoencoderESP32Predictive maintenanceTinyML
• 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…
  datacite Wiese, Philip, İslamoğlu, Gamze, Scherer, Moritz, Macan, Luka 等 2025 置信度 0.66 Neural networksTinyMLDeploymentTransformersAccelerators
• On The Dynamic Ensemble Selection for TinyML-based Systems -- a Preliminary Study
  The recent progress in TinyML technologies triggers the need to address the challenge of balancing inference time and classification quality. TinyML systems are defined by specific constraints in computation, memory and energy. These constraints emphasize the …
  datacite Puslecki, Tobiasz, Walkowiak, Krzysztof 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• "Gym Gesture Classification Using IMU Sensor Dataset"
  "This dataset contains raw Inertial Measurement Unit (IMU) recordings for human activity recognition in strength training exercises, collected using a custom wearable device based on the Arduino Nano 33 BLE. The device was worn on the wrist and equipped with a…
  datacite Satya Adhiyaksa Ardy 2025 置信度 0.66
• SMART CITIES ON A BUDGET: LEVERAGING TINYML PREDICTION MODELS FOR URBAN EFFICIENCY
  In spite of the fact that it is a common goal for cities around the world to become smart, communities with smallerbudgets are unable to afford the necessary infrastructure and technologies. The focus of this paper is on how citiesthat want to be efficient wit…
  datacite Takudzwa Humphreys Sambo 2023 置信度 0.66 smart cityUrban areaUrban landscapeUrban policy
• SMART CITIES ON A BUDGET: LEVERAGING TINYML PREDICTION MODELS FOR URBAN EFFICIENCY
  In spite of the fact that it is a common goal for cities around the world to become smart, communities with smallerbudgets are unable to afford the necessary infrastructure and technologies. The focus of this paper is on how citiesthat want to be efficient wit…
  datacite Takudzwa Humphreys Sambo 2023 置信度 0.66 smart cityUrban areaUrban landscapeUrban policy
• SMART CITIES ON A BUDGET: LEVERAGING TINYML PREDICTION MODELS FOR URBAN EFFICIENCY
  In spite of the fact that it is a common goal for cities around the world to become smart, communities with smallerbudgets are unable to afford the necessary infrastructure and technologies. The focus of this paper is on how citiesthat want to be efficient wit…
  datacite Takudzwa Humphreys Sambo 2023 置信度 0.66 smart cityUrban areaUrban landscapeUrban policy
• Towards Deployment of Computer Vision Neural Networks for Scene Understanding
  Scene understanding is a cornerstone of autonomous operation for robotics and edge computing platforms. However, deploying advanced computer vision neural networks on these platforms presents two central challenges: the need for vast amounts of meticulously la…
  datacite Humes, Edward Steven 2025 置信度 0.66 edgemachine learningsyn2realtinyml
• A Bimanual Gesture Interface for ROS-Based Mobile Manipulators Using TinyML and Sensor Fusion
  Gesture-based control for mobile manipulators faces persistent challenges in reliability, efficiency, and intuitiveness. This paper presents a dual-hand gesture interface that integrates TinyML, spectral analysis, and sensor fusion within a ROS framework to ad…
  datacite Bhuiyan, Najeeb Ahmed, Huq, M. Nasimul, Chowdhury, Sakib H., Mangharam, Rahul 2025 置信度 0.66 Robotics (cs.RO)FOS: Computer and information sciencesFOS: Computer and information sciences
• TinyAC: Bringing Autonomic Computing Principles to Resource-Constrained Systems
  Autonomic Computing (AC) is a promising approach for developing intelligent and adaptive self-management systems at the deep network edge. In this paper, we present the problems and challenges related to the use of AC for IoT devices. Our proposed hybrid appro…
  datacite Kalka, Wojciech, Xue, Ruitao, Faber, Kamil, Slominski, Aleksander 等 2025 置信度 0.66 Networking and Internet Architecture (cs.NI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Embedded intelligence in Internet of Things scenarios: TinyML meets eBPF
  datacite Bru-Santa, Irene, Gallego-Madrid, Jorge, Sanchez-Iborra, Ramon, Skarmeta, Antonio 2025 置信度 0.66
• Embedded intelligence in Internet of Things scenarios: TinyML meets eBPF
  datacite Bru-Santa, Irene, Gallego-Madrid, Jorge, Sanchez-Iborra, Ramon, Skarmeta, Antonio 2025 置信度 0.66
• "Fall Detection IMU Dataset for Wearable Applications"
  "This dataset contains synchronized tri-axial accelerometer and gyroscope recordings collected using a single neck-mounted IMU (LSM6DS3) during controlled trials of daily life activities and simulated falls. Approximately 1500 motion trials across 15 activity …
  datacite Nirban Roy 2025 置信度 0.66
• The Future of AI: Efficiency, Miniaturization, and the Limits of Raw Compute
  This study models and compares the effective influence of AI systems combined with human insight across multiple deployment scenarios from 2025 to 2035. We focus on four key axes: Hardware efficiency – GPU versus photonic accelerators. Model accessibility – op…
  datacite Dusk, Faith 2025 置信度 0.66 BusinessWork, Economy and OrganizationsPhysical Sciences and MathematicsComputer EngineeringComputer Sciences
• TINY MACHINE LEARNING (TINYML) ADVANCEMENTS FOR INTELLIGENT BATTERY-POWERED IOT SENSORS
  Abstract Battery-powered IoT sensors are increasingly capable of on-device intelligence through Tiny Machine Learning (TinyML). Advances in ultra-low-power microcontrollers (MCUs), efficient neural kernels, model compression, and hardware-aware network design …
  datacite Hayat, Muhammad Ahsan, Ahmed, Syed Affan, Fatima, Sana, Irfan, Engr. Faiza 等 2025 置信度 0.66
• TINY MACHINE LEARNING (TINYML) ADVANCEMENTS FOR INTELLIGENT BATTERY-POWERED IOT SENSORS
  Abstract Battery-powered IoT sensors are increasingly capable of on-device intelligence through Tiny Machine Learning (TinyML). Advances in ultra-low-power microcontrollers (MCUs), efficient neural kernels, model compression, and hardware-aware network design …
  datacite Hayat, Muhammad Ahsan, Ahmed, Syed Affan, Fatima, Sana, Irfan, Engr. Faiza 等 2025 置信度 0.66
• A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management
  Honey bee colonies are essential for global food security and ecosystem stability, yet they face escalating threats from pests, diseases, and environmental stressors. Traditional hive inspections are labor-intensive and disruptive, while cloud-based monitoring…
  datacite Sucipto, Willy, Zhou, Jianlong, Kwon, Ray Seung Min, Chen, Fang 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciencesI.2.6; I.2.9; C.3
• Cyber-Physical Systems, Artificial Intelligence in Systems, Embedded Systems, Internet of Things (IoT)
  Over the past three years, our Smart System Co‐Design (SSC) Lab has pioneered a suite of low‐cost, scalable sensing platforms and AI models to tackle diverse environmental challenges—from indoor air quality in schools to landslide detection in the Himalayas. T…
  datacite Dr. Shubhankar Majumdar 2025 置信度 0.66
• AngelNau/intelligent-embedded-system: Version 1.1 of the code used in the thesis.
  Diploma thesis on TinyML with STM32H7
  datacite Angel Naumov 2025 置信度 0.66
• AngelNau/intelligent-embedded-system: Version 1.1 of the code used in the thesis.
  Diploma thesis on TinyML with STM32H7
  datacite Angel Naumov 2025 置信度 0.66
• Real-Time Performance Benchmarking of TinyML Models in Embedded Systems (PICO: Performance of Inference, CPU, and Operations)
  This paper presents PICO-TINYML-BENCHMARK, a modular and platform-agnostic framework for benchmarking the real-time performance of TinyML models on resource-constrained embedded systems. Evaluating key metrics such as inference latency, CPU utilization, memory…
  datacite Dey, Abhishek, Srivastava, Saurabh, Singh, Gaurav, Pettit, Robert G. 2025 置信度 0.66 Software Engineering (cs.SE)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• AngelNau/intelligent-embedded-system: Published version of the code used in the thesis.
  Diploma thesis on TinyML with STM32H7
  datacite Angel Naumov 2025 置信度 0.66
• 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,…
  datacite Krishna, Adithya, Debnath, Sohan, Srivatsav, Madhuvanthi, van Schaik, André 等 2025 置信度 0.66 Hardware Architecture (cs.AR)Human-Computer Interaction (cs.HC)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• An Efficient Intrusion Detection System for Safeguarding Radiation Detection Systems
  Radiation Detection Systems (RDSs) are used to measure and detect abnormal levels of radioactive material in the environment. These systems are used in many applications to mitigate threats posed by high levels of radioactive material. However, these systems l…
  datacite Coolidge, Nathanael, Sanz, Jaime González, Yang, Li, Khatib, Khalil El 等 2025 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)Systems and Control (eess.SY)FOS: Computer and information sciences
• Securing Radiation Detection Systems with an Efficient TinyML-Based IDS for Edge Devices
  Radiation Detection Systems (RDSs) play a vital role in ensuring public safety across various settings, from nuclear facilities to medical environments. However, these systems are increasingly vulnerable to cyber-attacks such as data injection, man-in-the-midd…
  datacite Pizarro, Einstein Rivas, Zaheer, Wajiha, Yang, Li, El-Khatib, Khalil 等 2025 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)Systems and Control (eess.SY)FOS: Computer and information sciences
• Supplementary Data for the Paper entitled "Into the Wild: Reliable Physiological Sensing with on-device Autoencoder-based Anomaly Detection"
  This data set was used to present the experimental results in our paper entitled "Into the Wild: Reliable Physiological Sensing with on-device Autoencoder-based Anomaly Detection".
  datacite Penner, Kevin, Wittenfeld, Felix, Hesse, Marc, Thies, Michael 2025 置信度 0.66 004wireless body sensorwearableautoencoderecg
• Edge AI and On-Device Machine Learning
  Edge Artificial Intelligence (Edge AI) and On-Device Machine Learning (ML) represent transformative paradigms in deploying intelligent systems at the network's periphery. By processing data locally rather than relying on centralized cloud infrastructure, Edge …
  datacite Venkata, Surendra Reddy Narapareddy, Suresh, Kumar Yerramilli 2025 置信度 0.66 Edge AIOn-Device Machine LearningFederated LearningTinyMLNeuromorphic Computing
• Edge AI and On-Device Machine Learning
  Edge Artificial Intelligence (Edge AI) and On-Device Machine Learning (ML) represent transformative paradigms in deploying intelligent systems at the network's periphery. By processing data locally rather than relying on centralized cloud infrastructure, Edge …
  datacite Venkata, Surendra Reddy Narapareddy, Suresh, Kumar Yerramilli 2025 置信度 0.66 Edge AIOn-Device Machine LearningFederated LearningTinyMLNeuromorphic Computing
• On-device learning, optimization, efficient deployment and execution of machine learning algorithms on resource-constrained IoT hardware
  Edge analytics refers to the application of data analytics and Machine Learning (ML) algorithms on IoT devices. The concept of edge analytics is gaining popularity due to its ability to perform AI-based analytics at the device level, enabling autonomous decisi…
  datacite Sudharsan, Bharath 2022 置信度 0.66 Science and EngineeringEngineeringElectrical & Electronic EngineeringData ScienceTinyML
• TinyML benchmark: Executing fully connected neural networks on commodity microcontrollers
  Recent advancements in the field of ultra-low-power machine learning (TinyML) promises to unlock an entirely new class of edge applications. However, continued progress is restrained by the lack of benchmarking Machine Learning (ML) models on TinyML hardware, …
  datacite Sudharsan, Bharath, Salerno, Simone, Nguyen, Duc-Duy, Yahya, Muhammad 等 2021 置信度 0.66 IoT DevicesOffline InferenceEdge Intelligence
• 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…
  datacite Sudharsan, Bharath, Yadav, Piyush, Breslin, John G., Ali, Muhammad Intizar 2021 置信度 0.66 IoT DevicesTinyMLMicrocontrollersOffline InferenceSRAM Optimization
• EdgeMark: An Automation and Benchmarking System for Embedded Artificial Intelligence Tools
  The integration of artificial intelligence (AI) into embedded devices, a paradigm known as embedded artificial intelligence (eAI) or tiny machine learning (TinyML), is transforming industries by enabling intelligent data processing at the edge. However, the ma…
  datacite Hasanpour, Mohammad Amin, Kirkegaard, Mikkel, Fafoutis, Xenofon 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciencesI.2.168T99
• TinyML Towards Industry 4.0: Resource-Efficient Process Monitoring of a Milling Machine
  In the context of industry 4.0, long-serving industrial machines can be retrofitted with process monitoring capabilities for future use in a smart factory. One possible approach is the deployment of wireless monitoring systems, which can benefit substantially …
  datacite Langer, Tim, Widra, Matthias, Beyer, Volkhard 2025 置信度 0.66 Machine Learning (cs.LG)Computer Vision and Pattern Recognition (cs.CV)Emerging Technologies (cs.ET)Systems and Control (eess.SY)Signal Processing (eess.SP)
• TINY MACHINE LEARNING (TINYML) ADVANCEMENTS FOR INTELLIGENT BATTERY-POWERED IOT SENSORS
  datacite Muhammad Ahsan Hayat,Syed Affan Ahmed,Sana Fatima,Engr. Faiza Irfan,Muhammad Osama Nizamani,Ammar Khalil 2025 置信度 0.66
• TINY MACHINE LEARNING (TINYML) ADVANCEMENTS FOR INTELLIGENT BATTERY-POWERED IOT SENSORS
  datacite Muhammad Ahsan Hayat,Syed Affan Ahmed,Sana Fatima,Engr. Faiza Irfan,Muhammad Osama Nizamani,Ammar Khalil 2025 置信度 0.66
• tinyml-rtos-kws
  TinyML keyword spotting demo running under FreeRTOS, showing real-time ML inference on Cortex-M devices.
  datacite Mullapudi Narendra 2025 置信度 0.66 tinyml, freertos, rtos, keyword-spotting, edge-ai, cortex-m, embedded-c, wake-word
• tinyml-rtos-kws
  TinyML keyword spotting demo running under FreeRTOS, showing real-time ML inference on Cortex-M devices.
  datacite Mullapudi Narendra 2025 置信度 0.66 tinyml, freertos, rtos, keyword-spotting, edge-ai, cortex-m, embedded-c, wake-word
• TINY MACHINE LEARNING (TINYML) ADVANCEMENTS FOR INTELLIGENT BATTERY-POWERED IOT SENSORS
  datacite Muhammad Ahsan Hayat,Syed Affan Ahmed,Sana Fatima,Engr.Faiza Irfan,Muhammad Osama Nizamani,Ammar Khalil 2025 置信度 0.66
• TINY MACHINE LEARNING (TINYML) ADVANCEMENTS FOR INTELLIGENT BATTERY-POWERED IOT SENSORS
  datacite Muhammad Ahsan Hayat,Syed Affan Ahmed,Sana Fatima,Engr.Faiza Irfan,Muhammad Osama Nizamani,Ammar Khalil 2025 置信度 0.66