Journal of Electronics & Sensor Perspective (JESP) https://rsepress.com/index.php/jes <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 &amp; sensors, from their theory and design to the applications of complete sensing devices. All classes of electronics devices and sensors are covered, including electrical, mechanical, aeronautical, astronomical, industrial, biological, acoustic, chemical, biochemical, biomedical, electronic, electromagnetic (including optical), proximity, and thermal. </p> en-US editor.jes@rsepress.com (Editor) techstaff@rsepress.com (Technical Staff) Mon, 05 Oct 2020 16:05:18 +0000 OJS 3.2.0.3 http://blogs.law.harvard.edu/tech/rss 60 A Review on Impedance Spectroscopy based microfluidic Technology https://rsepress.com/index.php/jes/article/view/31 <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> Shabbir Chowdhury Copyright (c) 2021 Journal of Electronics & Sensor Perspective (JESP) https://creativecommons.org/licenses/by-nc-nd/4.0 https://rsepress.com/index.php/jes/article/view/31 Tue, 05 Jan 2021 00:00:00 +0000 Signal Acquisition by Functional Near Infrared Spectroscopy and Data Analysis for Mental Aptitude Tasks https://rsepress.com/index.php/jes/article/view/21 <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> Eng. Bader Dakhil Allah Alrashdi, Dr. K. Prahlad Rao Copyright (c) 2020 Journal of Electronics & Sensor Perspective (JESP) https://creativecommons.org/licenses/by-nc-nd/4.0 https://rsepress.com/index.php/jes/article/view/21 Mon, 05 Oct 2020 00:00:00 +0000