Machine and Deep Learning for Indoor UWB Jammer Localization

The paper presents SEPAL, a Scalable Embedding Propagation Algorithm aimed at improving the use of large knowledge graphs in machine learning. Current models face limitations in optimizing for link prediction and require extensive engineering for large graphs due to GPU memory constraints. SEPAL addresses these issues by ensuring global embedding consistency through localized optimization and message passing, evaluated across seven large-scale knowledge graphs for various downstream tasks.

Meta-SimGNN is a novel WiFi localization system that combines graph neural networks with meta-learning to enhance localization generalization and robustness. It addresses the limitations of existing deep learning-based localization methods, which primarily focus on environmental variations while neglecting the impact of device configuration changes. By introducing a fine-grained channel state information (CSI) graph construction scheme, Meta-SimGNN adapts to variations in the number of access points (APs) and improves usability in diverse scenarios.

The article discusses the concept of soft-label training in machine learning, which preserves epistemic uncertainty by treating annotation distributions as ground truth. Traditional methods often collapse diverse human judgments into single labels, leading to misalignment between model certainty and human perception. Empirical results show that soft-label training reduces KL divergence from human annotations by 32% and enhances correlation between model and annotation entropy by 61%, while maintaining accuracy comparable to hard-label training.

The paper presents a Doppler Invariant Convolutional Neural Network (CNN) designed for automatic signal classification in radio spectrum monitoring. It addresses the limitations of existing deep learning models that rely on Doppler augmentation, which can hinder training efficiency and interpretability. The proposed architecture utilizes complex-valued layers and adaptive polyphase sampling to achieve frequency bin shift invariance, demonstrating consistent classification accuracy with and without random Doppler shifts using a synthetic dataset.

Mobile jammers present a significant threat to 5G networks, especially in military settings. An innovative anti-jamming framework has been proposed, utilizing Multiple Signal Classification (MUSIC) for precise Direction-of-Arrival (DoA) estimation and Minimum Variance Distortionless Response (MVDR) beamforming for adaptive interference suppression. Machine learning enhances DoA prediction for mobile jammers. Simulations indicate an average Signal-to-Noise Ratio (SNR) improvement of 9.58 dB and a DoA estimation accuracy of up to 99.8%, showcasing the framework's effectiveness in dynamic environ…

MicroEvoEval is introduced as a systematic evaluation framework aimed at predicting image-based microstructure evolution. This framework addresses critical gaps in the current methodologies, particularly the lack of standardized benchmarks for deep learning models in microstructure simulation. The study evaluates 14 different models across four MicroEvo tasks, focusing on both numerical accuracy and physical fidelity, thereby enhancing the reliability of microstructure predictions in materials design.

A new machine learning-based multimodal framework has been developed for wearable sensor-based archery action recognition and stress estimation. This innovative system utilizes a wrist-worn device equipped with an accelerometer and photoplethysmography (PPG) sensor to collect synchronized motion and physiological data during archery sessions. The framework achieves high accuracy in motion recognition and stress estimation, marking a significant advancement in the analysis of athletes' performance in precision sports.

The Cross-Modal Compositional Self-Distillation (CCSD) framework has been proposed to enhance brain tumor segmentation from multi-modal MRI scans. This method addresses the challenge of missing modalities in clinical settings, which can hinder the performance of deep learning models. By utilizing a shared-specific encoder-decoder architecture and two self-distillation strategies, CCSD aims to improve the robustness and accuracy of segmentation, ultimately aiding in clinical diagnosis and treatment planning.