This letter proposes a YOLO-based framework for spatial bearing fault diagnosis using time-frequency spectrograms derived from continuous wavelet transform (CWT). One-dimensional vibration signals are first transformed into time-frequency spectrograms using Morlet wavelets to capture transient fault signatures. These spectrograms are then processed by YOLOv9, v10, and v11 models to classify fault types. Evaluated on three benchmark datasets, including Case Western Reserve University (CWRU), Paderborn University (PU), and Intelligent Maintenance System (IMS), the proposed CWT-YOLO pipeline achieves significantly higher accuracy and generalizability than the baseline MCNN-LSTM model. Notably, YOLOv11 reaches mAP scores of 99.4% (CWRU), 97.8% (PU), and 99.5% (IMS). In addition, its region-aware detection mechanism enables direct visualization of fault locations in spectrograms, offering a practical solution for condition monitoring in rotating machinery.

The field of AI-generated text detection has evolved from supervised classification to zero-shot statistical analysis. However, current approaches share a fundamental limitation: they aggregate token-level measurements into scalar scores, discarding positional information about where anomalies occur. Our empirical analysis reveals that AI-generated text exhibits significant non-stationarity, statistical properties vary by 73.8\% more between text segments compared to human writing. This discovery explains why existing detectors fail against localized adversarial perturbations that exploit this overlooked characteristic. We introduce Temporal Discrepancy Tomography (TDT), a novel detection paradigm that preserves positional information by reformulating detection as a signal processing task. TDT treats token-level discrepancies as a time-series signal and applies Continuous Wavelet Transform to generate a two-dimensional time-scale representation, capturing both the location and linguistic scale of statistical anomalies. On the RAID benchmark, TDT achieves 0.855 AUROC (7.1\% improvement over the best baseline). More importantly, TDT demonstrates robust performance on adversarial tasks, with 14.1\% AUROC improvement on HART Level 2 paraphrasing attacks. Despite its sophisticated analysis, TDT maintains practical efficiency with only 13\% computational overhead. Our work establishes non-stationarity as a fundamental characteristic of AI-generated text and demonstrates that preserving temporal dynamics is essential for robust detection.

--The prevalence of Autism Spectrum Disorder (ASD) has surged rapidly over the past decade, posing significant challenges in communication, behavior, and focus for affected individuals. Current diagnostic techniques, though effective, are time-intensive, leading to high social and economic costs. This work introduces an AI-powered assistive technology designed to streamline ASD diagnosis and management, enhancing convenience for individuals with ASD and efficiency for caregivers and therapists. The system integrates transfer learning with image transforms derived from eye gaze variables to diagnose ASD. This facilitates and opens opportunities for in-home periodical diagnosis, reducing stress for individuals and caregivers, while also preserving user privacy through the use of image transforms. The accessibility of the proposed method also offers opportunities for improved communication between guardians and therapists, ensuring regular updates on progress and evolving support needs. Overall, the approach proposed in this work ensures timely, accessible diagnosis while protecting the subjects' privacy, improving outcomes for individuals with ASD.

The classification of harmful brain activities, such as seizures and periodic discharges, play a vital role in neurocritical care, enabling timely diagnosis and intervention. Electroencephalography (EEG) provides a non-invasive method for monitoring brain activity, but the manual interpretation of EEG signals are time-consuming and rely heavily on expert judgment. This study presents a comparative analysis of deep learning architectures, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and EEGNet, applied to the classification of harmful brain activities using both raw EEG data and time-frequency representations generated through Continuous Wavelet Transform (CWT). We evaluate the performance of these models use multimodal data representations, including high-resolution spectrograms and waveform data, and introduce a multi-stage training strategy to improve model robustness. Our results show that training strategies, data preprocessing, and augmentation techniques are as critical to model success as architecture choice, with multi-stage TinyViT and EfficientNet demonstrating superior performance. The findings underscore the importance of robust training regimes in achieving accurate and efficient EEG classification, providing valuable insights for deploying AI models in clinical practice.

This paper introduces a robust stress detection system utilizing a Convolutional Neural Network (CNN) designed for the analysis of Photoplethysmogram (PPG) signals. Employing the WESAD dataset, we applied Continuous Wavelet Transform (CWT) to extract informative features from wrist PPG signals, demonstrating enhanced stress detection and learning compared to conventional techniques. Notably, the CNN achieved an impressive accuracy of 93.7% after five epochs, post-implementation on a resource-constrained microcontroller. The optimization process, including pruning and Post-Train Quantization, was crucial to reduce the model size to 1.6 megabytes, overcoming the microcontroller's limited resources of 2 megabytes of Flash memory and 512 kilobytes of RAM. This optimized model not only addresses resource constraints but also outperforms traditional signal processing methods, positioning it as a promising solution for real-time stress monitoring on wearable devices.

Quite a few people in the world have to stay under permanent surveillance for health reasons; they include diabetic people or people with some other chronic conditions, the elderly and the disabled.These groups may face heightened risk of having life-threatening falls or of being struck by a syncope. Due to limited availability of resources a substantial part of people at risk can not receive necessary monitoring and thus are exposed to excessive danger. Nowadays, this problem is usually solved via applying Human Activity Recognition (HAR) methods. HAR is a perspective and fast-paced Data Science field, which has a wide range of application areas such as healthcare, sport, security etc. However, the currently techniques of recognition are markedly lacking in accuracy, hence, the present paper suggests a highly accurate method for human activity classification. Wepropose a new workflow to address the HAR problem and evaluate it on the UniMiB SHAR dataset, which consists of the accelerometer signals. The model we suggest is based on continuous wavelet transform (CWT) and convolutional neural networks (CNNs). Wavelet transform localizes signal features both in time and frequency domains and after that a CNN extracts these features and recognizes activity. It is also worth noting that CWT converts 1D accelerometer signal into 2D images and thus enables to obtain better results as 2D networks have a significantly higher predictive capacity. In the course of the work we build a convolutional neural network and vary such model parameters as number of spatial axes, number of layers, number of neurons in each layer, image size, type of mother wavelet, the order of zero moment of mother wavelet etc. Besides, we also apply models with residual blocks which resulted in significantly higher metric values. Finally, we succeed to reach 99.26 % accuracy and it is a worthy performance for this problem.