Recent works on brain functional analysis have accentuated the importance of distributed functional networks and synchronized activity between networks in mediating cognitive functions. The network perspective is important to relate mechanisms of brain functions and the basis for classifying brain states. In this work, the network patterns related to neural tasks based on synchronization measure phase-locking value (PLV) in an electroencephalogram (EEG) are analyzed. Based on network dissimilarities between the rest and motor imagery tasks, important nodes and channel pairs corresponding to motor tasks are identified. A framework is developed to identify these most reactive channel pairs that form the subject-specific functional network. The identified functional network corresponding to tasks demonstrate significant PLV variation in line with the experiment protocol. With the selection of subject-specific reactive band, these channel pairs provide even higher variation corresponding to tasks. To demonstrate the potential of the developed framework to brain-computer interface, identified network patterns are employed as features for classification of tasks. Analysis performed with the two classes (left-hand and right-hand motor imagery EEG data) showed that the proposed approach yielded better classification results compared to earlier band-power based approaches for single trial analysis.

In the domain of printed circuit board (PCB) defect detection and classification, the availability of diverse and comprehensive datasets is fundamental for developing effective detection models. However, existing datasets often lack comprehensive labeling and focus on specific defect types, limiting their applicability to real-world scenarios. To address this gap, we introduce a new dataset named ‘dataset for Mixed Defect Detection in PCB’ (MDD_PCB), which includes intentionally induced mixed PCB defects to provide a more realistic representation of practical scenarios. We evaluate the MDD_PCB dataset using YOLO models and implement it successfully for real-time inference on Jetson Nano, achieving enhanced detection capabilities. Our optimized YOLOv5n model trained on the MDD_PCB dataset achieves impressive metrics (accuracy 93%, precision 95%, recall 96%, mAP 95%, F1-score 94%) with a detection speed of 120.69 frames per second (FPS). Real-time deployment on the Jetson Nano demonstrates practical usability with a detection speed of 30 frames per second (FPS). These results underscore the significance of the diverse dataset proposed, which contributes to robust detection solutions and advances in PCB defect detection methodologies.

Structural MRI (sMRI) provides valuable information for understanding neurodegenerative illnesses such as Alzheimer's Disease (AD) since it detects the brain's cerebral atrophy. The development of brain networks utilizing single imaging data—sMRI is an understudied area that has the potential to provide a network neuroscientific viewpoint on the brain. In this paper, we proposed a framework for constructing a brain network utilizing sMRI data, followed by the extraction of signature networks and important regions of interest (ROIs). To construct a brain network using sMRI, nodes are defined as regions described by the brain atlas, and edge weights are determined using a distance measure called the Sorensen distance between probability distributions of gray matter tissue probability maps. The brain signatures identified are based on the changes in the networks of disease and control subjects. To validate the proposed methodology, we first identified the brain signatures and critical ROIs associated with mild cognitive impairment (MCI), progressive MCI (PMCI), and Alzheimer's disease (AD) with 60 reference subjects (15 each of control, MCI, PMCI, and AD). Then, 200 examination subjects (50 each of control, MCI, PMCI, and AD) were selected to evaluate the identified signature patterns. Results demonstrate that the proposed framework is capable of extracting brain signatures and has a number of potential applications in the disciplines of brain mapping, brain communication, and brain network‐based applications.