arXiv AI By Fu Wang, Chi Yang, Qi-Feng Lu, Rui-Xia Liu, Xiao-Fei Yang, Xiao-Fang Liu, Bo Li, Lin Chen

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites

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arXiv:2607. 16270v1 Announce Type: cross Abstract: Multilayer cloud detection from active--passive observation is vital for numerical weather prediction.

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arXiv Machine Learning
Sep 2

Predicting Subsurface Abnormalities Growth using Physics-Informed Neural Networks

The paper introduces a Physics‑Informed Neural Network (PINN) tailored for predicting Ground‑Penetrating Radar (GPR) data, integrating electromagnetic wave propagation physics into a deep learning framework. The architecture combines a CNN, spatial feature channel attention, ConvLSTM, and temporal feature frame attention to extract relevant visual and temporal features. Results show improved accuracy in forecasting GPR data, aiding assessments of bridge deck conditions and other civil infrastructure evaluations.

By Mehrdad Shafiei Dizaji, Hoda Azari
arXiv Computer Vision
6d ago

CSCWD: Cross-Scale Channel-wise Knowledge Distillation for Lightweight Tiny Object Detection on Edge Devices

The paper introduces Cross-Scale Channel-wise Knowledge Distillation (CSCWD), a training-time framework that transfers high‑resolution spatial representations from a YOLO11m‑P2 teacher to a lightweight YOLO11n student without changing the student’s inference architecture. CSCWD aligns teacher P2 features with student P3 while also applying same‑scale distillation at deeper pyramid levels, yielding a 2.92‑point mAP@0.5 improvement over the baseline and a 2.09‑point gain over same‑scale distillation alone. In zero‑shot tests on DUT‑Anti‑UAV and on a Raspberry Pi 5, the 2.58‑million‑parameter student reaches 50.32% mAP@0.5 at 82.32 ms latency (12.15 fps) with negligible runtime or memory increase.

By Amir Zamani, Zeinab Ghasemi-Naraghi
arXiv Machine Learning
Aug 18

A Low-Cost IoT Device for Environmental Monitoring and Embedded Solar Forecasting with On-Device Incremental Learning

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.

By Erick Michel Lara Pinal, Abhinav Das, Stephan Schl\"uter