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.

Health monitoring system

Abstract: Electronics have become an essential part of biomedicine. The urge for real-time health monitoring and disease detection at an early stage has created a rapid growth of the market for smart sensors. Biosensors have investigated the prospects of point of care (POC) applications for better management of healthcare, and efforts are being made to make these more efficient. Integrating with micro-electro-mechanical systems (MEMS) and nano-electro-mechanical systems (NEMS) technology has enabled biosensors to be automated and more precise, with higher accuracy data sensing systems. The application of biosensors with POC has increased research related to nanotechnology, advanced functional sensing materials, miniaturized sensing system development, AI, and the internet of things (IoT). Breath analysis is one such form for which biosensors have been used. Diabetes, Parkinson disease, urinary tract infections, lung cancer, kidney disease, pancreas infection, etc., can be detected through breath analysis. This chapter deals with a smart sensor system for diagnosis of diseases, precisely chronologic at an early stage. The sensor system is developing for detecting volatile organic compounds. Sensor arrays are deployed to collect and process electromagnetic or acoustic signals. Health monitoring systems provides a better perception of the patient’s condition, allowing doctors to make the correct diagnosis in real time and enhance curative procedure. IoT integrated with machine learning and artificial intelligence plays a vital role here.

Biofuel generation from high-fat effluent in a biofilm reactor with backwash

Abstract: The performance of an up-flow 0.5 m high and 0.076 m diameter fixed-film bioreactor treating lipid-rich synthetic wastewater has been investigated under the present study. The initial porosity of t...

Bioremediation of Dye Using Mesophilic Bacteria: Mechanism and Parametric Influence

Abstract: For centuries, dyes have been utilized in the tannery, textile, food, paper, cosmetic, and plastic industries. As a consequence of the fast urbanization and industrialization, the uncontrolled release of dyeing agents in the effluent is increasing. Such a release causes toxicity and pollution to the whole environment. These concerns become more critical due to the biomagnification phenomenon through various trophic levels resulting in severe toxicity in higher animals and plants including aquatic flora and fauna. Mitigation of this nuisance can be achieved by the economic application of biotechnology using safe biological agents to decolorize and degrade the dye in water bodies.

A low-cost fluid-level synthesis for droplet-based microfluidic biochips integrating design convergence, contamination avoidance, and washing

Abstract: Droplet-based microfluidic biochips (or DMFBs) are rapidly becoming a revolutionizing lab-on-a-chip technology. Numerous application specific protocols bridging the cross-disciplinary fields necessitate DMFBs as their prime need. The main goal at the fluid level is to minimize bioassay schedule length. Also, for a safe assay outcome, contamination among droplet routes must be avoided. Size restriction of a chip and reconfigurable nature of the operational modules in DMFB introduce contaminated cells which necessarily require washing as an urgent need. As the sub-tasks of fluid level possess their own constraints for a successful DMFB design, rip-up and reiteration of sub-tasks may become unavoidable if all of those constraints are not satisfied mutually. To achieve a shorter time for chip realization a crucial need in fluid-level design is to avoid rip-up and re-iteration; hence, design convergence is to be incorporated that collectively considers the fluid-level sub-tasks, instead of solving them individually. Thus, this paper focuses on the fluid level of DMFBs while considering design convergence, contamination avoidance, and washing issues. Obtained results are compared with several existing benchmarks.

Automatic sleep spindle detection in raw EEG signal of newborn babies

Abstract: In this study, a novel method for automatically detecting sleep spindles from a given raw EEG (Electroencephalogram) data is proposed We do not use any feature extraction and learning technique Rather, we model the visual perception of identifying rhythmic peaks within frequency range 115–15 Hz To achieve the performance close to visual detection, we first use a Gaussian window for smoothening of the signal Then peak detection method is applied for identifying visually distinguishable peaks If the frequency of peaks lies within frequency range 115–15 Hz, then we declare existence of a sleep spindle Validity of our process is determined by visual scoring of sleep spindles and comparing it with the automatic scoring We get a specificity range of 89%–98% for a sensitivity range of 87%–96% which is better that any other automatic detection process