Journal of Switching Hub

Fingerprint Recognition using Deep Convolutional Network and Machine Learning

G.D.Khandale — Mon, 18 Dec 2023 00:00:00 +0530

This research investigates the application of Deep Convolutional Networks (DCNs) and machine learning techniques in the realm of fingerprint recognition. Fingerprint identification is a critical aspect of biometric security, and the study aims to enhance accuracy and efficiency by harnessing the power of DCNs. The research involves the development of a robust model for fingerprint feature extraction, leveraging convolutional layers for spatial hierarchies. Through comprehensive experimentation and evaluation, the study endeavours to contribute to advancements in biometric authentication systems, addressing challenges such as variability in fingerprint patterns and ensuring robust performance across diverse datasets.

ML in the Electronics Manufacturing Industry

Kazi Sultanabanu Sayyad Liyakat, Kazi Kutubuddin Sayyad Liyakat — Fri, 08 Dec 2023 00:00:00 +0530

The manufacturing of electronics is renowned for emphasizing efficiency and precision. A single error can result in a defective unit, and a defective unit is a loss. The electronic marketplace is arguably the business that has risen to the top the quickest. Particularly in the electronics manufacturing industry, AI and ML have a wide range of uses, from waste reduction to improved forecasts and quality control. Manufacturers may lower expenses, cut down on mistakes, streamline supply chains, and boost productivity across the board with machine learning. At its core, ML uses statistical and probabilistic methods to draw insights from data and make predictions. Manufacturers face some difficulties as a result of the widespread use of electronic devices and systems and the complexity of their production. AI and ML have swiftly established themselves as indispensable instruments for streamlining the production process. It can be helpful to observe how AI and ML are being used in an actual manufacturing setting to fully grasp their potential. The application of Machine Learning in the electronics industry is a revolutionary development that has opened up a world of possibilities. It has enabled manufacturers to improve productivity, reduce costs, and create products that are more tailored to consumer needs. With its ability to provide a more efficient way of analyzing data and making decisions, Machine Learning can help electronics companies to better understand consumer behaviour and develop products that are better suited to customer preferences.

Arduino-Based Weather Monitoring System

Kazi Sultanabanu Sayyad Liyakat, Kazi Kutubuddin Sayyad Liyakat — Wed, 20 Dec 2023 00:00:00 +0530

The use of technology has revolutionized our lives in many ways, making our day-to-day activities more convenient and efficient. One such technological advancement is the Arduino-based weather monitoring system. This system has become more well-known recently as a result of its ability to accurately measure and monitor weather conditions in real-time. In this article, we will explore what an Arduino-based weather monitoring system is, how it works, and its benefits. An Arduino-based weather monitoring system is a compact and cost-effective system that uses Arduino microcontrollers to collect and analyze weather data. The system consists of various sensors that measure different weather parameters such as temperature, humidity, pressure, and rainfall. The collected data is then processed and displayed on an LCD screen or transmitted to a computer or mobile device for further analysis. In this article, we're going to build an Arduino-based Weather monitor system that can provide us with real-time weather reports for your area, including air quality, temperature, humidity, atmospheric pressure, and light intensity. The Arduino-based weather monitoring system is a remarkable innovation that has made monitoring weather conditions more accessible and affordable. Its accuracy, portability, and customizable features make it a valuable tool for various industries. With the continuous development of technology, we can expect to see further advancements in this field, making weather monitoring more efficient and reliable.

Using Multipliers for Optimal Performance in FIR Filter Implementation

V. N. Mahawadiwar — Mon, 11 Dec 2023 00:00:00 +0530

Digital filters play a pivotal role in various signal-processing applications, ranging from telecommunications to audio processing and beyond. The efficiency and performance of these filters heavily rely on the underlying implementation techniques. This study focuses on leveraging multiplier-based architectures to achieve optimal performance in digital filter implementations. Traditional digital filters utilize adders and shifters in their implementations, commonly known as finite impulse response (FIR) and infinite impulse response (IIR) filters. However, advancements in integrated circuit design and hardware capabilities have enabled the integration of dedicated hardware multipliers, presenting an opportunity for more efficient filter implementations.

Cyber Security Implementation for Application of Geospatial Data

Dadi Sanyasi Naidu — Mon, 30 Oct 2023 00:00:00 +0530

Geospatial information is often seen as just being connected with guides, compasses, and areas. In any case, the application areas of geospatial information are far more extensive and even stretch out to the field of network safety. In addition to the fact that there is a capacity to show focal points and rising network traffic conditions, however, geospatial information likewise can display cybercrime development designs and demonstrate impacted regions as well as the rise of sort of digital dangers. Geospatial information can take care of knowledge frameworks, assist with investigation, and data sharing, and help make situational mindfulness. This is especially helpful in network safety. Geospatial information is exceptionally strong and can serve to meet digital dangers and recognize basic areas of concern. Already, geospatial information was essentially utilized by militaries, insight offices, weather conditions, administrations or traffic lights. At present, the utilization of geospatial information has increased, and it traverses a lot more businesses and areas. So, for network safety, geospatial information has a wide number of purposes. It very well might be hard to track down examples or patterns in huge informational collections. Nonetheless, the realistic capacities of geoplanning assist with introducing information in a more absorbable way. This might assist investigators in recognizing arising issues, dangers, and target regions. In this paper, the helpfulness of geospatial information for network safety is investigated. The paper will cover a structure of the key application regions that geospatial information can serve in the field of network safety.