Amad Zafar

TL;DR: In this paper, Li-ion batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost, and a smart battery management system is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life.

TL;DR: It is revealed that fNIRS-based BCI using initial dip detection can reduce the command generation time from 7 sec to 2.5 sec while the classification accuracy is a bit sacrificed from 65.9% to 57.5% for three mental tasks.

TL;DR: The experimental results show that the active brain locations of the two tasks were quite distinctive and spatially specific if using the initial dip map at 4 s in comparison to the map of HRs at 14 s and the average classification accuracy was improved from 59% to 74.9% when using the phase diagram of dual threshold circles.

TL;DR: The existence and elusive nature of the initial dip duration of HR in intrinsic signal optical imaging (ISOI), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS) is reviewed.

TL;DR: A hybrid features based support vector machine (SVM) model is proposed using infrared thermography technique for hotspots detection and classification of photovoltaic panels using a novel hybrid feature vector consisting of RGB, texture, the histogram of oriented gradient, and local binary pattern as features.