Results 71 to 80 of about 374 (124)

Efficient human activity recognition on edge devices using DeepConv LSTM architectures

Scientific Reports
Driven by the rapid development of the Internet of Things (IoT), deploying deep learning models on resource-constrained hardware has become an increasingly critical challenge, which has propelled the emergence of TinyML as a viable solution.
Haotian Zhou, Xiujun Zhang, Yu Feng, Tongda Zhang, Lijuan Xiong   +4 more
doaj   +1 more source

Expanding Applications of TinyML in Versatile Assistive Devices: From Navigation Assistance to Health Monitoring System Using Optimized NASNet-XGBoost Transfer Learning

IEEE Access
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 ...
Sreenu Ponnada, Tan Kuan Tak, Pravin R. Kshirsagar, P. Srinivasa Rao, Abhinav Dayal   +4 more
doaj   +1 more source

Energy-aware dynamic programming scheduler for TinyML workloads on energy-harvesting CubeSat-IoT platforms: a comprehensive system modelling and performance analysis

Discover Electronics
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.
Mfonobong Uko, Choice Amaizu, Sunday Ekpo, Gloria Iyawa, Obinna Amaizu   +4 more
doaj   +1 more source

Real-Time RSSI-Based Indoor Localization Using a Two-Stage TinyML Architecture on Edge Devices

IEEE Access
Indoor positioning is a critical component of navigation and control in automated guided vehicles (AGVs) and autonomous mobile robots (AMRs). Wi-Fi RSSI-based localization provides a cost-effective solution for indoor environments; however, its ...
Ahmet Gurkan Yuksek
doaj   +1 more source

Empowering IoT security: deploying TinyML ensemble techniques for cyberattack detection

Scientific African
As the Internet of Things (IoT) grows and devices connect, protecting IoT networks from vulnerabilities is crucial. Intrusion detection systems (IDS) that use machine learning (ML) techniques are vital for increasing security and preventing unauthorized ...
Abderahmane Hamdouchi, Ali Idri
doaj   +1 more source

TinyML Enhances CubeSat Mission Capabilities

CoRR
Earth observation (EO) missions traditionally rely on transmitting raw or minimally processed imagery from satellites to ground stations for computationally intensive analysis. This paradigm is infeasible for CubeSat systems due to stringent constraints on the onboard embedded processors, energy availability, and communication bandwidth.
Luigi Capogrosso, Michele Magno
openaire   +2 more sources

TinyMetaFed: Efficient Federated Meta-learning for TinyML

Accepted by the ECML PKDD 2023 workshop track: Simplification, Compression, Efficiency, and Frugality for Artificial Intelligence (SCEFA)
Haoyu, Ren, Xue, Li, Darko, Anicic, Thomas A., Runkler   +3 more
openaire   +4 more sources

tinyml-rtos-kws

TinyML keyword spotting demo running under FreeRTOS, showing real-time ML inference on Cortex-M devices.
openaire   +1 more source

TinyML-Based Lightweight AI Healthcare Mobile Chatbot Deployment

Journal of Multidisciplinary Healthcare
Anita Christaline Johnvictor,1 M Poonkodi,1 N Prem Sankar,1 Thinesh VS2 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India; 2Arista Networks Pvt Ltd, Bangalore, IndiaCorrespondence: Anita Christaline ...
Johnvictor AC, Poonkodi M, Prem Sankar N, VS T   +3 more
doaj  

TinyML – koneoppimista taskukoossa

@SeAMK, 2021
openaire   +2 more sources