CVR Journal of Science and Technology

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Cover

Title Page

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Title Page

Editorial Board

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Editorial Board

Editorial

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Editorial

Complete Editorial Team

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Complete Editorial Team

Index Page

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Index Page

Specifying Trust-based Community Learning

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: while chatting with neighbours is a social behaviour, trusting a neighbour is a natural tendency. Both play subtle roles in making community knowledge. We propose Trust Based Community Learning, a process that performs asynchronous knowledge transmissions over a neighbour graph implicitly defined in a community. With a formal specification, we have shown community learning as a distributed process and it eventually achieves community knowledge. It’s observed that social learning can be facilitated by re-engineering a community neighbour-graph to a small world network.

Index Terms: community knowledge, distributed algorithm, asynchronous knowledge transmissions, neighbour-graph.

Queuing Model-based Prediction Scheme (QMPS) for Conserving Power in BWA Networks

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: In mobile Worldwide interoperability for Microwave Access (WiMAX), a mobile node is battery powered with restricted capacity. It is challenging to prolong battery lifetime by reducing the quantity of power consumed. Power saving is a significant issue which must be taken into consideration while designing the Medium Access Control (MAC) layer. Schemes are designed for saving power based on packet delay and amount of power consumed. In this paper, Prediction Scheme based on Queuing Model (PSQM) is proposed for defining Power Saving Classes (PSCs) in the MAC layer of IEEE 802.16e networks. Power is saved by turning off Mobile Stations (MSs) when they do not actively transmit or receive data. The size of Sleep Window (SW) is dynamically determined using M/M/1/N queuing model based on channel, exponential service time and Poisson arrival. The size of the buffer is restricted and is based on the First-in First-out (FIFO) queuing model. Instead of exponentially or linearly increasing the size of SW based on traffic type, Arrival Rate (λ) is taken into consideration. Time of arrival of packets is determined, thus reducing the delay involved.

Index Terms: BWA networks, WiMAX, Power, QMPS, Arrival Rate (λ), Service Rate (µ), SW size.

Design and Simulation of Doherty Power Amplifier for 2.4 GHz Frequency Applications

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: The Doherty Power Amplifiers (DPA) are specialized Radio Frequency (RF) amplifiers designed to improve efficiency, particularly at high output power levels. They are used in base stations of cellular communication systems, and other wireless infrastructure applications. This work endeavors the design and simulation of a Doherty Power Amplifier (DPA) utilizing the Cadence Virtuoso environment using 45nm technology, precisely tuned to operate at 2.4 GHz frequency. The DPAs are pivotal components in modern RF communication systems, renowned for their efficiency and linearity. The work begins with a detailed examination of DPA architecture, focusing on key components such as power splitter, main amplifier, auxiliary amplifier, transmission line, and matched network. Each component is meticulously designed and optimized to ensure precise operation. Advanced simulation techniques are employed to analyze the performance of the DPA, including power gain, and Power Added Efficiency (PAE). Leveraging 45 nm VLSI process semiconductor technology, the undertaking aims at achieving high efficiency and signal integrity while minimizing power consumption and distortion. The tuning process entails iterative adjustments to component parameters guided by simulation results to achieve optimal performance characteristics, with a focus on impedance matching, power combining, and phase alignment. The final outcomes are evaluated based on comprehensive simulation results, offering valuable insights into the design and optimization of DPAs using advanced semiconductor technology.

Index Terms: Doherty Power Amplifier, Wi-Fi Standard, RF Design, Wireless Communications, VLSI Design, Power Added Efficiency.

A 32 nm CNFET Model Voltage Controlled Oscillator based ADC Design for Computation-in-Memory Architecture using Emerging ReRAM’s

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: Applications that are becoming more and more computationally demanding are beyond the capabilities of traditional Von Neumann systems. By adopting new architectural technologies, these flaws can be rectified. Specifically, Resistive Random Access Memory (ReRAM)-based Computation-In-Memory (CiM) holds great promise for satisfying the computational demands of data-intensive applications like database searches and neural networks. In CiM, calculation is carried out analogously; the potential of CiM is hampered by the expensive, time-consuming, space and energy intensive digitalization of the results. To enhance the functionality and energy efficiency of the CiM architecture, an effective Voltage-Controlled Oscillator (VCO)–based analog-to-digital converter (ADC) designed. The proposed ADC can be used per-column rather than sharing one ADC across several columns because of its efficiency. This will increase the CiM crossbar array's overall efficiency and parallel execution. A Multiplication and Accumulation (MAC) technique used in ReRAM-based CiM crossbar arrays is used to evaluate the proposed ADC. The ADC architecture is designed in Cadence Virtuoso, CMOS 45nm technology and then the complete design is implemented using CNFET 32nm Technology, considering its advantages. Figure of Merit, Resolution, delay, signal to noise ratio and power consumption of VCO-ADC are analyzed and compared with other ADCs.

Index Terms: VCO Based ADC, ReRAM, CNFET Technology.

Performance Evaluation of UFMC for Future Wireless Communication Systems

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: One of the innovative waveforms for 5G networks is the Universal Filtered Multi-Carrier (UFMC) technology. Low latency, resilience to frequency offset, and a decrease in out-of-band (OoB) radiation are anticipated benefits of UFMC, which will raise spectral efficiency. As previously indicated, the UFMC system has numerous benefits; nevertheless, because it is a multicarrier gearbox technology, its Peak-to-Average Power Ratio (PAPR) is high. Companding, Precoding and Selected Mapping (SLM) methods are an easy and effective way to lower the PAPR of UFMC signals. This work proposes a Modified Mu-law Companding Transform (MMCT) that lowers the PAPR of UFMC approach without altering the companded signal's average power and a Discrete Sine Transform (DST) precoding PAPR reduction technique to reduce the complexity. These two techniques are combined with SLM separately and their performance is assessed in the matter of both PAPR and BER. The UFMC signal's big and small amplitudes are expanded using distinct scales in the MMCT scheme based on an inflection point. Also, the DST technique provides less PAPR by considering only the real components. As a result, there is greater freedom in selecting the compounding parameters that will yield the best results in terms of PAPR, average power level, and BER. The simulation findings verify that, in comparison to the original UFMC signal and OFDM technique, the proposed schemes offer higher PAPR and BER reduction characteristics.

Index Terms: Universal Filtered Multi Carrier, Selected Mapping, Modified Mu-Law Companding, Discrete Sine Transform, Peak to Average Power Ratio.

Design and Optimization of a Carry Speculative Adder for Enhanced Performance

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: Adders play a crucial role in arithmetic logic units and digital signal processors, forming one of the most complex arithmetic circuits in digital electronics. Traditional adder designs are often hindered by significant critical path delays and excessive power consumption. The proposed Carry Speculative Adder (CSPA) overcomes these limitations by integrating speculation techniques with a robust error correction mechanism, resulting in enhanced performance compared to conventional adders. In the CSPA, the sum generator and carry generator are decoupled, allowing the carry bit and partial sum bit to be computed in parallel, thus accelerating the overall computation process. Additionally, a carry prediction circuit is employed to minimize power usage and reduce computation time. To ensure result accuracy, an error detection and correction unit is integrated, which identifies faults within the partial sum generator and efficiently rectifies them. This dual approach of speculation and correction significantly enhances both speed and power efficiency, making the CSPA a highly optimized solution for modern digital systems.

Index Terms: Carry Speculative Adder (CSPA), Arithmetic Logic Unit (ALU), Critical path delay, Carry prediction circuit.

An IoT Enabled Real Time Communication and Location Tracking System for Vehicular Emergency

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: The concept of remotely connecting and monitoring real-world devices or things over the internet is known as the Internet of Things or IoT. Various IoT solutions and services can make our life easier and even safer. With the continuous rise in the number of accidents and criminal activities occurring on the roads every year, developing an IoT enabled special system for vehicular emergencies can make a lifesaver system. In order to detect different predefined vehicular emergency situations, an IoT-enabled real-time communication and location tracking system for vehicular emergencies is developed in this paper. It then sends the location information and emergency type to the relevant parties, such as family members, the closest hospitals, and police stations through a SMS. Immediately after meeting with an accident or any other medical or criminal emergency, the system either detects the emergency and starts automatically or maybe triggered manually, depending upon the type of emergency the system initiates communication with the central control room/ hospital/ rescue team/ po- lice and shares critical information like emergency type, location information with easy to access link to the google location, name of the owner of vehicle and also the details of the vehicle like registration number, car make and model, colour, etc. The prototype of the proposed system is developed using an Arduino Uno board, GSM modem, GPS modem and uses a MEMS accelerometer for detecting the accident automatically.

Index Terms: Industry 4.0, IoT, Tracking and Safety, Cloud Application, Wireless Communication.

IoT based Accident Preventive Model for Unmanned Railway Gate

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: Railways serve as the most extensive mode of transport across the country, reaching even the most remote areas. However, accidents have been a significant concern for the Indian Railways. This work focuses on developing an automated system for closing and opening gates at railway crossings. Typically, these gates are operated manually by a gatekeeper, who receives information from the nearest station regarding the arrival of a train. Once a train leaves the station, the station master alerts the nearest gatekeeper to get ready to close the gates. This manual process introduces human error, which can be eliminated by implementing the proposed system. For instance, if a train is delayed, the gatekeeper may not receive immediate updates, resulting in prolonged gate closures and traffic issues near the crossing, causing inconvenience to the public. The proposed system addresses these issues by using infrared sensors to detect the train's approach and departure at the railway crossing. An ESP32 microcontroller controls the opening and closing of the gates using a mini servomotor installed near the gate. Additionally, the ESP32 module communicates with an IoT server called Blynk, allowing the gate to be remotely controlled by the loco pilot upon receiving a notification from the railway gate office.

Index Terms: Automatic Railway Gate Control, Infrared Sensors, ESP32 Microcontroller, Blynk IoT Platform, Train Detection System.

Customized Convolutional Neural Network for Detection of Emotions from Facial Expressions

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: The understanding and accurate interpretation of human emotions are crucial for effective interactions and communication. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have revolutionized emotion recognition by enabling the analysis of complex facial expressions. This paper focuses on a customized CNN architecture tailored for emotion detection from facial expressions. Utilizing datasets like CK+, FER2013, and JAFFE, preprocessing techniques are employed to enhance model stability and diversity. The proposed CNN architecture comprises convolutional and fully connected layers for feature extraction and classification, respectively. Experimental results, including accuracy, F1 score, and precision, demonstrate the effectiveness of the proposed CNN in accurately identifying various emotions compared to traditional machine learning methods. These findings underscore the potential of CNNs in advancing emotion recognition systems, promising significant applications across diverse fields, from marketing research to virtual reality.

Index Terms: Emotion Recognition, Facial Expressions, Convolutional Neural Networks, Customized Architecture, Image Processing.

Enhanced Efficiency of DC-AC Grid-Tied Converters for Photovoltaic Systems using AC-Link Integration

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: As of the close of 2019, the global renewable energy generation capacity reached 2,537 GW, with solar energy showing the most substantial increase at 586 GW, which constitutes 23% of this total capacity. Over the past ten years, the levelized cost of electricity (LCOE) for utility-scale photovoltaic (PV) systems has dropped by 82%, while crystalline solar PV module prices in Europe have decreased by 90%. To overcome the limitations of conventional low-voltage inverters that depend on large, low-frequency transformers for voltage step-up, this paper introduces a novel three-phase cascaded DC-AC-AC converter featuring AC-link integration, specifically optimized for improved efficiency in medium-voltage applications. The proposed architecture includes three stages for each DC-AC-AC converter cell: a medium-frequency (MF) square voltage generator, an MF transformer with four windings, and an AC-AC converter. By cascading these DC-AC-AC converters, a multilevel structure is formed, which effectively addresses per-phase imbalance while simplifying control to focus on per-cell unbalance. The converter's operational efficiency is demonstrated through two sets of simulations conducted in both off-grid and grid-connected modes. Additionally, two initial laboratory prototypes are introduced: one validates the cascaded configuration, and the other confirms the three-phase integration, highlighting significant efficiency improvements for grid-tied PV systems.

Index Terms: DC-AC-AC converter, photovoltaic systems, AC-link integration, multilevel topology, Grid-tied, renewable energy.

Time Series-based Clustering of ECG Heartbeat Arrhythmia using Medoids

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: The symptoms of heart diseases are sparse and infrequent. So, the analysis of wearable long-term ECG recordings over hours, days and months is obligatory for detection of these infrequently occurring symptoms of heart diseases that would not be detected with short-term ECG recordings. Manual identification of these heart-beat classes by cardiologists is time consuming and cumbersome. These professionals rely on computer-based classification for deter- mination of these heart-disease types. Partitioning around medoids (PAM also known as K- medoids) clustering using dynamic time warping (DTW) distance method (PAM time series- DTW) using the unequal length (dimensional) full heart-beat time-series is proposed with no explicit feature extraction except PQRST wave detection, which saves lot of time and computation cost.

Index Terms: Clustering, ECG, Heartbeat, Time-warping, AAMI.

Stacked Autoencoder-based ELM with Unsupervised Feature Extraction for Efficient Classification of Tumors

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: Tumors may exist in the brain, lungs, esophagus, leukemia, breast, ovary and bladder. Each tumor has its pathogenesis and categorizing them is an uphill task. Appropriate features are to be extracted, and the data of tumor should be classified either as benign or malignant. This is a critical issue as treatment of patients is solely based on this. In this paper, tumor data is classified using Stacked Extreme Learning Machine (S-ELM) and features are extracted using Statistically Controlled Activation Weight Initialization (SCAWI). It involves unsupervised clustering and is based on Neural Network (NN) framework. The proposed method offers a good decision support system for classifying tumor data. The proposed scheme is applied to data obtained from hospitals of repute and publicly available domains and tested for its outperformance. Results are compared with basic ELM and Multilayer-ELM (ML-ELM). It is seen that S-ELM with SCAWI offers better performance in contrast to diverse methods of classification.

Index Terms: Breast Tumor, ELM, Multilayer-ELM, Tumor Classification.

Smart Road Safety and Vehicle Accident Prevention System for Mountain Roads

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: Roads that lead through mountains present unique challenges for drivers due to their winding paths, steep inclines, and unpredictable weather conditions. To address the heightened risk of accidents in these areas, a proposed Smart Road Safety and Vehicle Accident Prevention System (SRSP) integrates advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) to bolster road safety and prevent accidents. The SRSP deploys a network of sensors along mountainous routes to gather real-time data on various factors including road conditions, weather patterns, and traffic density. Utilizing AI algorithms, this data is then analyzed to identify potential hazards and predict areas prone to accidents. Moreover, the system incorporates vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication protocols to enable proactive safety measures such as adaptive speed control, hazard alerts, and emergency braking assistance. In the event of an impending collision, the SRSP automatically notifies drivers and nearby vehicles while also alerting emergency services for rapid response. Furthermore, the system furnishes valuable insights to road authorities, aiding in the optimization of maintenance schedules and the enhancement of road infrastructure to further bolster safety measures. Ultimately, the SRSP offers a comprehensive solution to mitigate the risks associated with mountain road travel, thereby saving lives, and diminishing the economic and social toll of accidents.

Index Terms: Mountain roads, Smart Road Safety, Vehicle Accident Prevention System, IoT, Artificial Intelligence, Machine Learning, Senslives Real-time data, Hazard prediction.

Real-Time Vehicle Emission Monitoring using LabVIEW and NI myRIO

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: The growing concern over vehicular emissions and their impact on the environment has driven the need for advanced emission monitoring systems. This paper proposes the design and implementation of a Vehicle Emission Monitoring System using LabVIEW and NI myRIO, a real-time embedded hardware platform. The system is designed to monitor and analyze key pollutants such as carbon monoxide (CO) and smoke, utilizing MQ-7 and MQ-2 gas sensors, respectively. The NI myRIO serves as the core controller for data acquisition and processing, interfacing with the sensors to gather real-time emission data. This data is processed using LabVIEW to provide a user-friendly interface, enabling the visualization of pollutant levels. A GSM module is integrated to send alerts or notifications when emissions exceed predefined thresholds, allowing for immediate corrective actions. The system is powered by an external power supply for stable and continuous operation. This monitoring system offers real-time detection, ease of deployment, and scalability for integration into various vehicular systems. By leveraging the capabilities of LabVIEW and myRIO, the proposed system provides a robust solution for managing vehicular emissions, contributing to cleaner air and compliance with environmental regulations.

Index Terms: LabVIEW, NI myRIO, Vehicle Emission Monitoring, MQ-2 Sensor, MQ-7 Sensor, GSM Module, Real-Time Monitoring, Pollution Control.

Numerical Analysis of Vibration Behaviour in a Fluid-Structure Interaction Cantilevered Thin Aluminium Plate

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: This study uses a simulation approach to replicate and validate the modal characteristics of fluid-solid coupling on a cantilever aluminum plate. Utilizing finite element analysis (FEA), the natural frequencies and vibration modes were investigated under varying fluid conditions. Results closely matched those found in previous simulation studies, confirming the effectiveness of the simulation-based approach for understanding fluid-solid interactions. This work highlights the potential of numerical simulations as a reliable tool in analyzing vibration characteristics, offering valuable insights for engineering applications where fluid-structure interactions are significant.

Index Terms: modal characteristics, fluid-solid coupling.

The Characteristics of a Diesel Engine Powered by Rubber Seed Oil as Bio-Diesel

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: Diesel engines play a predominant role in various industries, from transportation to agriculture. However, they are known for emitting higher levels of nitrogen oxides (NOx) and particulate matter, which contribute significantly to air pollution and respiratory problems. Additionally, diesel engines are a major source of greenhouse gases, exacerbating climate change. One way to reduce emissions is by implementing advanced exhaust after-treatment systems, such as diesel particulate filters and selective catalytic reduction. Another approach is to use alternative fuels like biodiesel, which produce fewer pollutants. Additionally, regular maintenance and tuning of diesel engines can improve efficiency and lower emissions.

In the Diesel engine, rubber seed oil is used very efficiently as bio diesel. Evaluations of performance and emissions were conducted. In this study, rubber seed oil was combined with diesel at different proportions, such as 10%, 20%, and 30%. A significant improvement in engine performance was observed with biodiesel. As a result, emissions were also reduced. It is more cost-effective and environmentally friendly to blend rubber seed oil with diesel than to use traditional diesel. As a result, air pollution and greenhouse gas emissions could be reduced.

Index Terms: Diesel Engine, Rubber seed oil, Alternative fuel, Performance, Emissions.

Perceptions of using FASTag at Toll Plazas: A Case Study of Hyderabad City

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: FASTag is an electronic toll collection system introduced in India by the National Highways Authority of India (NHAI) to simplify and expedite toll payments on highways. The system operates using Radio Frequency Identification (RFID) technology, enabling toll charges to be deducted directly from linked bank accounts or prepaid wallets without requiring cash transactions. FASTag can be purchased, recharged, or topped up through authorized issuers or participating banks. This initiative was launched to improve efficiency at toll plazas on national highways, ensuring smoother traffic flow and reduced congestion. The FASTag, affixed to a vehicle's windscreen, allows drivers to pass through toll plazas without stopping for manual payment. It has a validity of five years, with users needing only to recharge or top it up as required. Toll plazas, typically located on highways or expressways, often face challenges related to delays and congestion. This study focuses on evaluating the level of service provided at toll plazas by examining drivers' perceptions of delays during their travel. Data was collected using an online questionnaire, with responses from 1,200 drivers, capturing their perceptions, travel behavior, and overall experience. The study provides valuable insights into driver attitudes and vehicle movement patterns, highlighting key areas for improving toll plaza efficiency and service levels.

Index Terms: FASTag, Electronic Toll Collection, Driver Perception and Toll Plaza Efficiency.

Capital Structure and Profitability of Foreign Direct Investment Companies in the Indian Pharmaceutical Industry

Wed, 01 Jan 2025 00:00:00 +0530

Abstract: The financial performance of a company is also influenced by a good amalgamate of debt and equity portions in the financing patterns of that particular company. Using firm-level data, the current study has examined the relationship between capital structure and profitability of FDI (foreign direct investment)-based enterprises in the Indian pharmaceutical industry. 25 FDI-accepted companies in the Indian pharmaceutical sector were selected as a sample for an 11-yearlong study; from 2010–2011 to 2020–2021. To investigate the impact of capital structure on the profitability of FDI-based companies, multiple regression analysis has been utilised. The profitability was assessed using the following metrics: Return on Net Worth (RONW), Return on Total Assets (ROTA), and Return on Total Income (ROTI). In order to investigate how capital structure affects profitability of FDI firms, two debt indicators are used. These are Debt/Equity (D/E), Short Term Debt/Total Net Assets (STD/TNA), and Total Outside Liabilities/Total Net Assets (TOL/TNA). The findings show that the Debt/Equity ratio significantly effects negatively the profitability of FDI companies, but the Total Outside Liabilities/Total Net Assets (TOL/TNA) and Short-Term Debt/Total Net Assets (STD/TNA) have no discernible effect on profitability. The findings show that capital structure has a substantial effect on profitability and the success of the FDI firms in the Indian pharmaceutical sector may be dependent on the financing patterns they choose.

Index Terms: Capital Structure, Profitability, Foreign Direct Investment, Debt/ Equity Ratio, and Indian Pharmaceutical Industry.

In the next issue (vol. 28, June 2025)

Thu, 09 Jan 2025 00:00:00 +0530

In the next issue (vol. 28, June 2025)

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Template - CVRJST

About the college

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About the college

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