J. K. Das

Bio: J. K. Das is an academic researcher from KIIT University. The author has contributed to research in topics: Memristor & MOSFET. The author has an hindex of 6, co-authored 41 publications receiving 113 citations.

Future Wireless Communication Technology towards 6G IoT: An Application-Based Analysis of IoT in Real-Time Location Monitoring of Employees Inside Underground Mines by Using BLE

Abstract: In recent years, the IoT has emerged as the most promising technology in the key evolution of industry 4.0/industry 5.0, smart home automation (SHA), smart cities, energy savings and many other areas of wireless communication. There is a massively growing number of static and mobile IoT devices with a diversified range of speed and bandwidth, along with a growing demand for high data rates, which makes the network denser and more complicated. In this context, the next-generation communication technology, i.e., sixth generation (6G), is trying to build up the base to meet the imperative need of future network deployment. This article adopts the vision for 6G IoT systems and proposes an IoT-based real-time location monitoring system using Bluetooth Low Energy (BLE) for underground communication applications. An application-based analysis of industrial positioning systems is also presented.

Odia Braille: Text transcription via image processing

Abstract: Braille-a model introduced to reduce the illiteracy rate among the visually challenged people. There has been a lot of scope for the conversion of English language to Braille but, the problem arises when the masses are unable to understand the communication by the visually challenged people. This paper focuses on the conversion of the Braille code representing Odia language(a language widely spoken in East India) into Odia word as text. For this, image processing using MATLAB technique provides a suitable platform to perform the segmentation of Braille cell for pattern selection and hence, Odia letter and word recognition. Braille Data Base creation acts as a storage system for the process and its accuracy is also tested which is explained in this paper.

Design and stability analysis of CNTFET based SRAM Cell

Abstract: Static Random Access Memory (SRAM) is one of the most crucial and critical memory devices used in today's technological environment. The continuous scaling of CMOS technology significantly limits the performance of 6T SRAM cell in terms of leakage power and stability. With remote chances to further improve the MOSFET technology in future, Carbon Nanotube Field Effect Transistors (CNTFETs) are being widely studied as the possible alternatives. In this paper, the conventional 6T SRAM cell is compared with CNTFET based SRAM cell. The conventional 6T SRAM cell is designed using Cadence Virtuoso Tool in 180nm and 45nm Technology. The Verilog-A code of CNTFET for replacing nMOS and pMOS are separately simulated in Cadence Virtuoso Tool. The CNTFET based SRAM cell is technology independent. The performances are evaluated in terms of leakage power, delay and stability to show that the CNTFET based SRAM cell can successfully replace the CMOS based SRAM cell.

Advanced protein coding region prediction applying robust SVD algorithm

Abstract: Prediction of protein coding region has been one of the challenging issues in the context of Genomic Signal Processing and bioinformatics. This paper presents a variation of the Singular Value Decomposition (SVD) algorithm based on the pseudo EIIP mapping for exon location detection. The spectral peaks at 1/3 frequency component position of DNA sequence are utilized to identify the exons location. In addition to this three distinct threshold formulations have been used to investigate the performance of the algorithm in terms of various parameters like accuracy, sensitivity, specificity, geometric-mean and execution time. Moreover several other standard numerical mapping schemes and algorithms have been modeled and tested for comparison purpose which proves the efficiency of the algorithm.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Reliable Modeling of Ideal Generic Memristors via State-Space Transformation

Abstract: The paper refers to problems of modeling and computer simulation of generic memristors caused by the so-called window functions, namely the stick effect, nonconvergence, and finding fundamentally incorrect solutions. A profoundly different modeling approach is proposed, which is mathematically equivalent to windowbased modeling. However, due to its numerical stability, it definitely smoothes the above problems away.

Making Shopping Easy for People with Visual Impairment Using Mobile Assistive Technologies

Abstract: People with visual impairment face various difficulties in their daily activities in comparison to people without visual impairment. Much research has been done to find smart solutions using mobile devices to help people with visual impairment perform tasks like shopping. One of the most challenging tasks for researchers is to create a solution that offers a good quality of life for people with visual impairment. It is also essential to develop solutions that encourage people with visual impairment to participate in social life. This study provides an overview of the various technologies that have been developed in recent years to assist people with visual impairment in shopping tasks. It gives an introduction to the latest direction in this area, which will help developers to incorporate such solutions into their research.

An Early Intestinal Cancer Prediction Algorithm Based on Deep Belief Network.

Abstract: The incidence of colorectal cancer (colorectal cancer, CRC) in China has increased in recent years, and its mortality rate has become one of the highest among all cancers. CRC also increasingly affects people’s health and quality of life, and the workloads of medical doctors have further increased due to the lack of sufficient medical resources in China. The goal of this study was to construct an automated expert system using a deep learning technique to predict the probability of early stage CRC based on the patient’s case report and the patient’s attributes. Compared with previous prediction methods, which are either based on sophisticated examinations or have high computational complexity, this method is shown to provide valuable information such as suggesting potentially important early signs to assist in early diagnosis, early treatment and prevention of CRC, hence helping medical doctors reduce the workloads of endoscopies and other treatments.