http://localhost:80/xmlui/handle/123456789/1060 Thu, 02 Apr 2026 08:22:29 GMT 2026-04-02T08:22:29Z http://localhost:80/xmlui/handle/123456789/10624 Title: A Comparative Study Between Micro and Millimeter Impedance Sensor Designs for Type-2 Diabetes Detection Authors: Guin, Santu; Chowdhury, Debjyoti; Chattopadhyay, Madhurima Sat, 01 Feb 2025 00:00:00 GMT http://localhost:80/xmlui/handle/123456789/10624 2025-02-01T00:00:00Z http://localhost:80/xmlui/handle/123456789/10623 Title: 4.17 - Polymer solar cell fabrication using spray coating Authors: Pal, A.K.; Saha, Subhadip Abstract: Although polymer solar cells are inexpensive, lightweight, easily assembled, scalable, and environmentally benign, their efficiency is significantly lower than that of other solar cells now on the market. The capacity to produce polymer solar cells (PSC) at a reasonable cost and on a large scale remains a fundamental challenge. Because PSC manufacture mostly requires a vacuum atmosphere, which is an expensive endeavor, the facility for this procedure is currently somewhat remote. However, for a type of thin film solar cells where the components of the solar cell can be generated in a solution, more scalable and vacuum-free fabrication techniques are being developed. Examples of materials that could be handled in solutions are thin-film solar cells that contain all or some layers of polymer, dye-sensitized, quantum dot, and copper indium gallium selenide. After the solvent has evaporated, a thin layer will form on the substrate from the components that have undergone solution processing. This can be achieved by atomizing the solution and transferring the spray droplets to the substrate. Spray coating is a challenging fabrication approach, if it is possible to achieve reasonably uniform layers with high charge carrier separation and transport capabilities. This is because the coating is applied at atmospheric pressure using moderately priced equipment that can process rolls to rolls. Large scale production is possible by spray coating and few techniques are briefly discussed in this chapter. Wed, 01 May 2024 00:00:00 GMT http://localhost:80/xmlui/handle/123456789/10623 2024-05-01T00:00:00Z http://localhost:80/xmlui/handle/123456789/8631 Title: Self-Regulating Adaptive Controller for Oxygen Support to Severe Respiratory Distress Patients and Human Respiratory System Modeling Authors: Naskar, Indrajit; Pal, Arabinda Kumar; Jana, Nandan Kumar Abstract: Uncontrolled breathing is the most critical and challenging situation for a healthcare person to patients. It may be due to simple cough/cold/critical disease to severe respiratory infection of the patients and resulting directly impacts the lungs and damages the alveoli which leads to shortness of breath and also impairs the oxygen exchange. The prolonged respiratory failure in such patients may cause death. In this condition, supportive care of the patients by medicine and a controlled oxygen supply is only the emergency treatment. In this paper, as a part of emergency support, the intelligent set-point modulated fuzzy PI-based model reference adaptive controller (SFPIMRAC) is delineated to control the oxygen supply to uncomforted breathing or respiratory infected patients. The effectiveness of the model reference adaptive controller (MRAC) is enhanced by assimilating the worthiness of fuzzy-based tuning and set-point modulation strategies. Since then, different conventional and intelligent controllers have attempted to regulate the supply of oxygen to respiratory distress patients. To overcome the limitations of previous techniques, researchers created the set-point modulated fuzzy PI-based model reference adaptive controller, which can react instantly to changes in oxygen demand in patients. Nonlinear mathematical formulations of the respiratory system and the exchange of oxygen with time delay are modeled and simulated for study. The efficacy of the proposed SFPIMRAC is tested, with transport delay and set-point variations in the devised respiratory model. Description: https://doi.org/10.3390/diagnostics13050967 Sun, 01 Jan 2023 00:00:00 GMT http://localhost:80/xmlui/handle/123456789/8631 2023-01-01T00:00:00Z http://localhost:80/xmlui/handle/123456789/8630 Title: On-Device Multi-Level Signal Quality Aware Compression for Energy-Efficient Wearable PPG Sensing Authors: Alam, Samiul Abstract: On-device computing in biomedical sensors has become attractive for developing wearable health monitoring systems. The challenge is to make a compromise between the latency and complexity in a resource-constrained implementation. This article describes an on-device implementation of multi-level signal quality aware and quality controlled compression (MSQQCC) that enhances the compression factor while preserving the clinical features in a wearable photoplethysmography (PPG) sensing application. The multi-level quality assessment (QA) provides three eligible PPG qualities, viz., “excellent,” “good,” and “average,” based on which corresponding upper limits are set for further compression using a discrete wavelet transform, while the “corrupted” segments are discarded. A pretrained multilayer perceptron neural network (MLPNN) provides the optimal quantization level of coefficients. The residual data is separately compressed using an autoencoder (AE). MSQQCC was evaluated with 300 min of PPG data from three public data sets and 110 min of data collected at a laboratory. The end-to-end pipeline was implemented in a standalone system with an ARM Cortex A53 controller, requiring 35.51 kB of memory and 1.8 s latency to process 4 s PPG data. The on-device QA achieved 98.45% overall accuracy (Ac), which outperforms published works on PPG QA. The mean deviation of PPG clinical features by 5%, with overall compression ratio (CR) and percentage root mean squared difference (PRD) were 40.85 and 2.52, which are superior to many published works. Real-time transmission over Bluetooth shows an improvement of energy efficiency by a significant factor and a 34% extended battery life for wearable PPG sensors. The results are encouraging for the adoption of MSQQCC in wearable biomedical health monitoring. Description: doi: 10.1109/JSEN.2023.3234171 Sun, 01 Jan 2023 00:00:00 GMT http://localhost:80/xmlui/handle/123456789/8630 2023-01-01T00:00:00Z