Detecting Important Features and Predicting Yield from Defects Detected by SEM in Semiconductor Production.

A key step to optimize the tests of semiconductors during the production process is to improve the prediction of the final yield from the defects detected on the wafers during the production process. This study investigates the link between the defects detected by a Scanning Electron Microscope (SEM) and the electrical failure of the final semiconductors, with two main objectives: (a) to identify the best layers to inspect by SEM; (b) to develop a model that predicts electrical failures of the semiconductors from the detected defects. The first objective has been reached by a model based on Odds Ratio that gave a (ranked) list of the layers that best predict the final yield.

Robust k-means-based Clustering of Independent Components Estimated from the EEG data.

In this study, we present a comprehensive investigation into the robust clustering of independent components (ICs) related to anticipatory postural control tasks in individuals with traumatic brain injury (TBI) using EEG data. Given the significance of accurately clustering the neural sources, our research evaluates the performance of various k-means clustering algorithms, including traditional, our modified approach of repeated k-means, and global k-means. This study aims to identify the optimal clustering approach that accurately locates the cortical sources germane to balance dysfunction and is computationally efficient.

Identifying neural correlates of balance impairment in traumatic brain injury using partial least squares correlation analysis.

Balance impairment is one of the most debilitating consequences of traumatic brain injury (TBI). To study the neurophysiological underpinnings of balance impairment, the brain functional connectivity during perturbation tasks can provide new insights. To better characterize the association between the task-relevant functional connectivity and the degree of balance deficits in TBI, the analysis needs to be performed on the data stratified based on the balance impairment.

Effects of Computerized Biofeedback-based Balance Intervention on the Muscle Coactivation Patterns during Dynamic Postural Control in Traumatic Brain Injury.

Balance Dysfunction (BDF) is a severe conse-quence of Traumatic Brain Injury (TBI) that significantly increases the falls risk. However, the neuromuscular mecha-nisms of the BDF are not adequately researched. Therefore, in this study, our objective was to investigate the effects of a Computerized Biofeedback-based Balance Intervention (CBBI) on the muscle coactivation patterns in a group of TBI participants.

Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders.

This article presents the design and validation of an accurate automatic diagnostic system to classify intramuscular EMG (iEMG) signals into healthy, myopathy, or neuropathy categories to aid the diagnosis of neuromuscular diseases. First, an iEMG signal is decimated to produce a set of "disjoint" downsampled signals, which are decomposed by the lifting wavelet transform (LWT). The Higuchi's fractal dimensions (FDs) of LWT coefficients in the subbands are computed.

EEG Database of Seizure Disorders for Experts and Application Developers.

This article presents an online accessible electroencephalogram (EEG) database, where the EEG recordings comprise abnormal patterns such as spikes, poly spikes, slow waves, and sharp waves to help diagnose related disorders. The data, as of now, are a collection of EEGs from a diagnostic center in Coimbatore, Tamil Nadu, India, and the data samples pertain to an age-group ranging from 1 to 107 years. Eventually, the EEG data concerning other disorders as well as those from other institutions will be included.