This paper introduces an autonomous optical wireless receiver that uses on-device TinyML to diagnose faults in real time. Built on an ESP32‑S3 microcontroller, it extracts key channel metrics—BER, signal voltage, SNR, and ambient light—and classifies four operational conditions with a 74.68% accurate INT8 TensorFlow Lite Micro model. The approach eliminates external processing, enabling deterministic, low‑latency fault detection for optical IoT nodes.
arXiv:2606. 24173v1 Announce Type: cross Abstract: On-device fault detection enables real-time diagnostics without cloud dependency, but deploying machine learning models on resource-constrained hardware demands careful tradeoffs between accuracy, latency, and model size.
arXiv:2606. 02256v1 Announce Type: new Abstract: Our work presents a method for ECG segmentation and arrhythmia detection using Tiny Machine Learning (TinyML) models for real-time, on-device inference on resource-constrained embedded systems.
The paper presents a novel multi‑exit computational scheme for TinyML on an ultra‑low‑power GAP9 SoC, adding confidence‑based gating points to a MobileNetV2 CNN for ImageNet‑100. By allowing inference to stop early, the approach cuts average MAC operations by 41 % (from 313 MMAC to 185 MMAC), reduces inference time by 29 % (49 ms to 35 ms), and saves 24 % in energy (2.1 mJ to 1.6 mJ per frame) with only a ~1 % drop in accuracy. Compared to a state‑of‑the‑art adaptive CNN on the same hardware, the method more than doubles computational efficiency, raising MAC/cycle from 8.1 to 17.2.
arXiv:2607. 03213v1 Announce Type: cross Abstract: We present OpenGlass, an open-source, privacy-oriented, local-first system for low-latency multimodal visual assistance, with a primary focus on blind and low-vision users.
The paper introduces a Statistical Ensemble Method (SEM) for detecting radio‑frequency jamming in Open Radio Access Network (O‑RAN) environments. SEM combines adaptive Median Absolute Deviation, count‑based anomaly detection, and abrupt change detection, operating in both cold‑start and supervised modes. Experiments on 25 hours of data from eight heterogeneous 5G testbeds show that SEM retains recall under distribution shifts where machine‑learning models falter, and its parameters can be deployed as an O‑RAN rApp for continuous refinement.
arXiv:2608. 10349v1 Announce Type: cross Abstract: Machine-learning intrusion-detection studies commonly emphasize predictive accuracy while treating explanation generation as a computationally free post-processing step.
arXiv:2606. 14716v1 Announce Type: cross Abstract: Edge object detection on embedded hardware requires balancing inference latency and detection quality under changing resource pressure.
arXiv:2608. 14698v1 Announce Type: cross Abstract: Hyperlocal meteorological sensing is essential for accurate solar photovoltaic forecasting, yet professional-grade meteorological stations require investments easily exceeding 1000~USD per node, making distributed deployments economically inaccessible.
arXiv:2609.06271v1 Announce Type: cross Abstract: Some low-cost Internet of Things (IoT) sensor deployments lack device-level source authentication, leaving them vulnerable to impersonation or inject...
arXiv:2509. 16577v2 Announce Type: replace Abstract: Over-the-air (OTA) aggregation enables federated edge learning (FEEL) by exploiting the superposition property of the wireless channel to merge communication with computation, eliminating the need to schedule and decode devices individually.
arXiv:2508. 00042v2 Announce Type: replace-cross Abstract: Machine learning models deployed in non-stationary environments degrade silently, since as the input distribution drifts their accuracy decays without an error signal and without labels to reveal it.
arXiv:2601. 21293v3 Announce Type: replace-cross Abstract: Industrial Internet of Things (IIoT) systems increasingly rely on distributed vibration sensing to support predictive maintenance of rotating machinery.