arXiv subjects

Joyeta Saha

Publications and source records attributed to Joyeta Saha.

4 recordsLinked to original sources

arXiv2024

Partitioning of total charge in matter from geometric phases of electrons

Based on geometric phases of Bloch electrons computed from first-principles, we propose a scheme for unambiguous partitioning of charge in matter, derivable directly from the Kohn-Sham states. Generalizing the fact that geometric phases acquired by electrons due to evolution of their crystal momentum $\vec k$ in a direction through out the Brillouin zone(BZ), provide position of their localization with net minimum spread along the corresponding direction in real space. We find that the total charge can be meaningfully distributed into charge centres simultaneously contributed by triads of electrons with their crystal momentum evolving linearly independently through each unique $\vec k$ across the BZ. The resultant map of charge centres readily renders not only the qualitative nature of inter-atomic as well as intra-atomic hybridization of electrons, but also unbiased quantitative estimates of electrons on atoms or shared between them, as demonstrated in a select variety of isolated and periodic systems with varying degree of sharing of valence electrons among atoms, including variants of multi-centered bonds.

cond-mat.other↗

arXiv2022

Internet of Things (IoT) based ECG System for Rural Health Care

Nearly 30% of the people in the rural areas of Bangladesh are below the poverty level. Moreover, due to the unavailability of modernized healthcare-related technology, nursing and diagnosis facilities are limited for rural people. Therefore, rural people are deprived of proper healthcare. In this perspective, modern technology can be facilitated to mitigate their health problems. ECG sensing tools are interfaced with the human chest, and requisite cardiovascular data is collected through an IoT device. These data are stored in the cloud incorporates with the MQTT and HTTP servers. An innovative IoT-based method for ECG monitoring systems on cardiovascular or heart patients has been suggested in this study. The ECG signal parameters P, Q, R, S, T are collected, pre-processed, and predicted to monitor the cardiovascular conditions for further health management. The machine learning algorithm is used to determine the significance of ECG signal parameters and error rate. The logistic regression model fitted the better agreements between the train and test data. The prediction has been performed to determine the variation of PQRST quality and its suitability in the ECG Monitoring System. Considering the values of quality parameters, satisfactory results are obtained. The proposed IoT-based ECG system reduces the health care cost and complexity of cardiovascular diseases in the future.

eess.SP↗

arXiv2021

A Machine Learning Approach for Early Detection of Fish Diseases by Analyzing Water Quality

Early detection of fish diseases and identifying the underlying causes are crucial for farmers to take necessary steps to mitigate the potential outbreak and thus to avert financial losses with apparent negative implications to the national economy. Typically, fish diseases are caused by viruses and bacteria; according to biochemical studies, the presence of certain bacteria and viruses may affect the level of pH, DO, BOD, COD, TSS, TDS, EC, PO43-, NO3-N, and NH3-N in water, resulting in the death of fishes. Besides, natural processes, e.g., photosynthesis, respiration, and decomposition, also contribute to the alteration of water quality that adversely affects fish health. Being motivated by the recent successes of machine learning techniques, a state-of-art machine learning algorithm has been adopted in this paper to detect and predict the degradation of water quality timely and accurately. Thus, it helps to take preemptive steps against potential fish diseases. The experimental results show high accuracy in detecting fish diseases specific to water quality based on the algorithm with real datasets.

cs.LG↗

arXiv2020

Real Time Multi-Class Object Detection and Recognition Using Vision Augmentation Algorithm

The aim of this research is to detect small objects with low resolution and noise. The existing real time object detection algorithm is based on the deep neural network of convolution need to perform multilevel convolution and pooling operations on the entire image to extract a deep semantic characteristic of the image. The detection models perform better for large objects. The features of existing models do not fully represent the essential features of small objects after repeated convolution operations. We have introduced a novel real time detection algorithm which employs upsampling and skip connection to extract multiscale features at different convolution levels in a learning task resulting a remarkable performance in detecting small objects. The detection precision of the model is shown to be higher and faster than that of the state-of-the-art models.

cs.CV↗