Implementation of edge AI for early fault detection in IoT networks: evaluation of performance and scalability in complex applications via Discover Internet of Things: https://doi.org/10.1007/s43926-025-00196-4

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A 2025 study compares YOLOv8 across Raspberry Pi 5 and Jetson Orin hardware, revealing the trade-offs behind real-time drone vision.

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Penalized parametric change-point detection by functional pruning dynamic programming algorithm. The successive means are constrained using a graph structure with edges defining the nature of the changes These changes can be unconstrained (type std), up or down constrained (type up and down) or constrained by a minimal size jump (type abs). The type null means that the graph allows us to stay on the same segment. To each edge we can associate some additional properties: a minimal gap size, a penalty, some robust parameters (K,a) for biweight (K) and Huber losses (K and a). The user can also constrain the inferred means to lie between some minimal and maximal values. Data is modeled by a cost with possible use of a robust loss, biweight and Huber (see edge parameters K and a). These costs should have a quadratic, log-linear or a log-log representation. This includes quadratic Gaussian cost (type = 'mean'), log-linear cost (type = 'variance', 'poisson' or 'exp') and log-log cost (type = 'negbin'). More details in the paper published in the Journal of Statistical Software: &lt;<a href="https://doi.org/10.18637%2Fjss.v106.i06" target="_top">doi:10.18637/jss.v106.i06</a>&gt;.

Officials described participation in a pilot with the National Weather Service, University of Oklahoma and MITRE to develop spatial fire warnings that combine weather, humidity, wind and fuel-load data; they noted radar/satellite detection can dispatch crews before 911 calls in some pilots.

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DeKalb County's police chief told the IRPS committee that "Digital Shield" integrates cameras, license-plate readers, gunshot detection, drones and 911/CAD data to support officers, and outlined audit controls, access limits and a public transparency portal for resident review.

Department of Natural Resources officials described a move from expensive, ground‑based forest inventories to a remotely sensed, lidar‑and‑imagery approach that can produce high‑resolution data statewide at lower cost but with technical tradeoffs around canopy-only detection and IT requirements.

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8devices 8Sight T100 is a compact Long-Wave Infrared (thermal imaging) camera for UAS, robotics, and industrial applications. It combines a Lynred ATI320 LWIR sensor with an STM32N6 Arm Cortex-M55 microcontroller featuring a Neural-ART NPU and a hardware ISP for edge AI inference and real-time thermal image processing. The camera delivers 320×240 thermal video at 60 fps and operates completely offline, allowing you to deploy custom AI models for object detection, anomaly detection, or temperature-triggered events without relying on cloud resources. 8devices 8Sight T100 specifications: MCU – STMicro STM32N657X0H3Q with an Arm Cortex-M55 core up to 800MHz with a 600 GOPS Neural-ART Accelerator (NPU) and hardware ISP System Memory – 256 Mbit (32 MB) RAM @ 200 MHz (16-bit) Storage – 512 Mbit (64 MB) NOR flash Thermal Camera – Lynred ATI320 LWIR (Long-Wave Infrared) sensor Spectral range – 8 to 14 µm Resolution – 320 x 240 pixels Frame rate – [...] The post Compact Long-Wave Infrared camera pairs STM32N6 MCU with Lynred ATI320 thermal sensor appeared first on CNX Software - Embedded Systems News.