Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Support vector machine & Transconductance. The organization has 581 authors who have published 1045 publications receiving 8345 citations.

A new scheme for reducing breathing trouble through MEMS based capacitive pressure sensor

Abstract: In this paper, a treatment of respiratory disorders with brushless DC motor (BLDC) driven positive airway pressure (PAP) respirator is developed. The proposed schema of the system consists of a BLDC motor driven blower fan aided by a MEMS capacitive type pressure sensor to measure respiration rate (RR). To measure the respiration rate, the array of such sensors are mounted below right nostril (RN) and left nostril (LN), in such a way that the nasal airflow during inspiration and expiration impinge on the sensor diaphragms directly. Due to irregularities in nasal airflow in some respiratory diseases, the RR varies from the normal rate (12---20). Thus, a supporting airflow regulatory system has been designed to reduce abnormalities in RR. The supporting system consists of a positive airway pressure (PAP) respirator with a blower fan to relieve patients breathing abnormalities. The MEMS based RR sensors help to monitor patients breathing rate continuously and finally maintain the required air pressure/flow by regulating the rpm of the blower fan through BLDC motor drive. In order to implement such a control action, we have chosen the sensorless drive of BLDC motor to construct a transportable as well as noise immune system. An algorithm has been developed to sustain normal RR for both bradypnea (RR 20), which puts into operation through ATmega 328 to facilitate high level precision controlled action. The control system alongwith the sensory part occupies limited space in few centimeters with light weight. As a result, the electronics of the whole system can be easily mounted at the outer surface of the tube connected with the nasal mask. The scheme of such a portable and cost effective system is described in this present work.

Real-time Implementation of Electromyography for Hand Gesture Detection Using Micro Accelerometer

Abstract: This paper focuses on the development of a novel approach for identification of various hand movements of a person that involves actions like fist opening, closing, and arm roll. The system consists of an electromyogram (EMG) sensor coupled with a digital MEMS accelerometer (full scale range of ±2 g, ±4 g, ±8 g, and ±16 g) for detection of hand gestures; this system is mounted over a strip strapped on the limb of its user. Based on the analysis of the EMG signals that are coupled with the MEMS accelerometer data from the limb, innumerous hand gestures are identified. Six-point-based calibration of the accelerometer data is done to eliminate mounting errors. The hand movements involving roll are better identified using this sensor topology, which is based on EMG sensor coupled with MEMS accelerometer than a system which just uses an EMG sensor to find out hand gestures because the accelerometer data gives precise information about the orientation of the limb in three-dimensional spaces.

Influence on the analog/RF performance in graded channel Gate Stack DG-MOSFETs

Abstract: It is presently established that Double Gate MOSFET has better immunity to SCE. However in underlap MOSFET structure on current reduced significantly. Graded Channel Double Gate MOSFET (GC DG MOSFET) with Gate Stack architectures show better performance for its enhanced immunity to short channel effects. In this work, low-high and high-low channel doping of the Double Gate MOSFET with Gate Stack configurations have been compared for improved subthreshold analog performance in the 20nm gate length regime. For analysis the parameters studied are the on current (Ids), the transconductance (gm), the gain per unit current (gm/Ids), the total gate capacitance (Cgg) and, the cut off frequency (fT). Analysis suggested that the average on current, the transconductance and, the cut off frequency increases for low-high graded channel doping architecture however, the total gate capacitance decreases for this structure.

Analysis of Product Purchase Patterns in a Co-Purchase Network

Abstract: Real world social networks are often temporal in nature. They evolve with time as new nodes may appear, old nodes may cease to exist and the relationships between the entities may also change. In this paper, we have studied a publicly available dynamic network dataset, namely Amazon co-purchase network dataset. We have analyzed the network data to understand the significance of nodes with high in-degree and high out-degree. We subsequently analyze the evolution of communities in the network by observing the change of association among nodes and by observing communities to see how many members they could retain over time. We show some frequent item sets from the co-purchase market basket in terms of the item categories and subcategories. We finally select some central entities and group of such entities from the network to recommend how promoting some of the co-purchased items may increase the sales of the selected items.