https://rsepress.com/index.php/jes/issue/feed Journal of Electronics & Sensor Perspective (JESP) 2020-12-18T14:19:15+00:00 Editor editor.jes@rsepress.com Open Journal Systems <p><strong>About this Journal</strong></p> <p>The <em><strong>Journal of Electronics &amp; Sensor Perspective (JESP)</strong></em>publishes papers related to all aspects of electronics and sensors, from their theory and design to the use of complete sensing devices.<span data-preserver-spaces="true"> Electronic, mechanical, aeronautical, astronomical, industrial, chemical, biochemical, biomedical, electronic, electromagnetic (including optical), proximity and thermal sensors are all covered. </span></p> https://rsepress.com/index.php/jes/article/view/31 A Review on Impedance Spectroscopy based microfluidic Technology 2020-12-18T14:19:15+00:00 Shabbir Chowdhury chowdhuryshabbir99@gmail.com <p>One of the most difficulties researchers facing in the field of microfluidic technology is labeling when characterizing the dielectric properties. Impedance based microfluidics can overcome this problem by characterizing the dielectric properties of cells, medium, and particles without labeling. Cell sorting or separating can also be done by this technology which took the highly complex image or video processing in other ways. Now, impedance spectroscopy and impedance flow cytometry are being combinedly used to measure the single-cell properties. This article presents the present condition of impedance spectroscopy in the field of microfluidic technology along with a short history.&nbsp;</p> 2021-01-05T00:00:00+00:00 Copyright (c) 2021 Journal of Electronics & Sensor Perspective (JESP) https://rsepress.com/index.php/jes/article/view/21 Signal Acquisition by Functional Near Infrared Spectroscopy and Data Analysis for Mental Aptitude Tasks 2020-10-01T22:09:18+00:00 Eng. Bader Dakhil Allah Alrashdi Eng.baader@gmail.com Dr. K. Prahlad Rao pkalyanrao@kau.edu.sa <p>General Linear Model is a statistical approach to enable an accurate analysis of NIRS signal. In this study. the fNIRS data are regressed using a linear combination of task-related regressors plus an error term. The mental tasks related regressors are obtained by convolving boxcar functions, that correspond to our experimental design with HRF. Experimentally the signal was acquired from a functional NIRS system during the brain activation from the participant while visual stimulation task. The block design for data acquisition consists of 40s rest and 60s task in repetition. From the measured data, oxy-hemoglobin was estimated and considered for parametric analysis. We observed a statistical significance of p&lt;0.9 from our analysis.</p> 2020-10-05T00:00:00+00:00 Copyright (c) 2020 Journal of Electronics & Sensor Perspective (JESP)