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Usage of online educational courses by undergraduate engineering students in Karnataka
Increasing availability of low-cost technology has enabled many students to use online courses to supplement their studies. The emergence of MOOCs (Massively Open Online Courses) has also brought about a great revolution in the teaching and learning methods. In case of Indian students, since most of the online courses available are not customized according to the syllabus, the students do not find them completely useful. In this case, Massively Empowered Classrooms (MEC) provides curriculum based video lectures and quizzes to students free of cost. The students are able to gain a good understanding of the subject and also score well in exams. This paper is based on an exploratory study conducted to analyze the usage of online courses and MEC by the undergraduate engineering students in Karnataka, India. The paper also describes some expectations from students and teachers to improve the reach and impact of online education. 2013 IEEE. -
FinTech: Answer for Financial Literacy and Financial Inclusion in India
In India, financial literacy and financial inclusion is the need of the hour. Though economic growth of the country is growing in positive direction, it derailed by many factors such as financial literacy, accountability, and stability of the common public. It could be due to the deprived accessibility to the financial services in India. This study addresses the two key elements in economic growth of the country namely financial literacy and financial inclusion and how it could be handled by financial technology. This study sets up the platform in which it is trying to include perception and attitude of both the provider and the user of the fintech services and compiling its impact on both financial literacy and financial inclusion. A sample size of 644 respondents have been selected using multi-stage sampling technique and distributed with structured questionnaire. The study result gives implication for fin-tech service providers in understanding the consumer perspectives and government for policy making. The Electrochemical Society -
Process Optimization Using Value Stream Mapping in PCB Manufacturing
PCB Manufacturing process is a complex process and has several processes and sub-processes. Adopting a lean manufacturing system will help to increase the efficiency of the system. This study aims to optimize the process for PCB manufacturing using value stream mapping. Observation method has been used to collect the cycle time of different processes from a PCB manufacturing plant in India. Pareto charts, why-why analysis and Ishikawa diagrams have been used to do the analysis and optimize the process and create a value stream mapping for the entire process. Standard Operating Procedures have been framed and solutions to increase the efficiency has been proposed. 2022 IEEE. -
Primary mirror active control system simulation of Prototype Segmented Mirror Telescope
The upcoming large astronomical telescopes are trending towards the Segmented Mirror Telescope (SMT) technology, initially developed at the W M Keck Observatory in Hawaii, where two largest SMTs in the world are in use. SMT uses large number of smaller hexagonal mirror segments aligned and positioned by the use of three position/force actuators and six intersegment edge sensors. This positioning needs to be done within nanometer range to make them act like a monolithic primary mirror in the presence of different disturbances like wind, vibration & thermal effects. The primary mirror active control system of SMT does this important task at two levels. First at a global scale, by measuring edge sensor information continuously and commanding actuators to correct for any departure from the reference surface. And second at local actuator level, where all the actuators maintain their position to the reference control inputs. The paper describes our novel approach of primary mirror active control simulation of Prototype Segmented Mirror Telescope (PSMT) under design and development at Indian Institute of Astrophysics (IIA), Bangalore. The PSMT is a 1.5m segmented mirror telescope with seven hexagonal segments, 24 inductive edge sensors, and 21 soft actuators. The state space model of the soft actuator with Multiple-Input Multiple-Output (MIMO) capability is developed to incorporate dynamic wind disturbances. Further, a segment model was developed using three such actuators which accept actuator position commands from the global controller and telescope control system and yields tip-tilt-piston (TTP) of a single segment. A dynamic wind disturbance model is developed and used to disturb the mirror in a more realistic way. A feed forward PID controller is implemented, and gains are appropriately tuned to get the good wind rejection. In the last phase, a global controller is implemented based on SVD algorithm to command all the actuators of seven segments combined to act as a single monolithic mirror telescope. In this paper, we present the progress of PSMT active control system simulation along with the simulation results. 2017 IEEE. -
Design and Simulation of 6.2m Wide-Field Telescope for Spectroscopic Survey
The upcoming large astronomical telescopes are trending towards the Segmented Primary Mirror due to technological advancements & manufacturing feasibility. We have designed a wide-field optical IR spectroscopic survey telescope that can deliver spectra of several millions of astronomical sources. The baseline design of this telescope is a 6.2 m segmented primary mirror with hexagonal mirror segments of 1.44m size, intersegment Edge sensors, and soft positioning actuators. The telescope is designed to provide a 2.5deg FOV achieved through a system of wide field corrector lenses with a design residual ~0.2". Also, it delivers an f/3.61 beam suitable for directly feeding optical fibres. A mechanical concept of the telescope is designed with a truss-based mirror cell to support the segmented primary mirror and keep the deformation to a minimum. As the primary mirror is segmented, the deformation due to different disturbances like wind, vibration and thermal effects must be corrected to a nanometer accuracy to make it act like a monolithic primary mirror. This is achieved through an active control system using three actuators and six inter-segment edge sensors. A simulation tool, codeSMT, is built based on the state-space model of a soft actuator with Multiple-Input Multiple-Output (MIMO) capability to incorporate dynamic wind disturbance from the IAO Hanle site and vibration effects. A detailed error multiplier analysis is performed numerically using this tool and is in good agreement with analytical calculations. A parameter sensitivity analysis is performed to fine-tune the primary mirror control system variables. This paper presents the Optical, Mechanical and Active Control system design approach of a 6.2m wide-field telescope currently under conceptual design. 2024 SPIE. -
Exploring Ethical Considerations: Privacy and Accountability in Conversational Agents like ChatGPT
In recent years, advances in artificial intelligence (AI) and machine learning have transformed the landscape of scientific study. Out of all of these, chatbot technology has come a long way in the last few years, especially since ChatGPT became a well-known artificial intelligence language model. This comprehensive review investigates ChatGPT's background, applications, primary challenges, and possible future advancements. We first look at its history, progress, and fundamental technology before delving into its many applications in customer service, health care, and education. We also discuss potential countermeasures and highlight the major challenges that ChatGPT faces, including data biases, moral dilemmas, and security threats. Finally, we go over our plans for ChatGPT's future, outlining areas that need further research and development, improved human-AI communication, closing the digital gap, and ChatGPT integration with other technologies. This study offers useful information for scholars, developers, and stakeholders interested in the rapidly evolving subject of artificial intelligence-powered conversational bots. This study looks at the ways that ChatGPT has changed scientific research in several domains, such as data processing, developing hypotheses, collaboration, and public outreach. In addition, the paper examines potential limitations and ethical quandaries associated with the use of ChatGPT in research, highlighting the importance of striking a balance between human expertise and AI-assisted innovation. The paper addresses multiple ethical issues with the state of computers today and how ChatGPT can cause people to oppose this notion. This study also has a number of ChatGPT biases and restrictions. It is noteworthy that in a very short period, ChatGPT has garnered significant interest from academics, research, and enterprises, notwithstanding several challenges and ethical issues. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Remote Diabetic Retinopathy Screening with IoT and Machine Learning on Edge Devices
This study presents a novel method of screening for diabetic retinopathy using edge devices the Internet of Things and machine learning. The developed remote screening system ensures broad accessibility as well as affordability by overcoming geographical barriers. While edge computing maximizes real-time analysis, the integration of sophisticated machine learning algorithms improves diagnostic accuracy. The investigation of socio-technical subtleties is guided by the interpretivist philosophy. The outcomes show a strong architecture, effective models, as well as revolutionary effects on accessibility. A critical assessment finds the good points and continuous improvements. Suggestions place a strong emphasis on scaling issues and the ongoing improvement of machine learning models. In order to secure data management and keep up with changing healthcare needs, future research suggests combining blockchain technology with sophisticated imaging modalities. This study advances early detection, enhances accessibility to healthcare, and advances remote screening technologies. 2023 IEEE. -
Content-Restricted Boltzmann Machines for Diet Recommendation
Nowadays, society is leading towards an unhealthy and inactive and lifestyle. Recent studies show the rapid growth of people suffering from diseases caused due to unhealthy lifestyles and diet. Considering this, recognizing the right type and amount of food to eat with a suitable exercise set is essential to obtain good health. The proposed work develops a framework to recommend the proper diet plans for thyroid patients, and medical experts validate results. The experiments results illustrate that the proposed Content-Restricted Boltzmann Machines (Content-RBM) produces more relevant recommendations with content-based information. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Industry Internet of Things (IIoT) Adoption Pressures in SME OEMs
Small and Medium Original Equipment Manufacturers (SME OEMs) face challenges in IIoT adoption due to a lack of technical expertise, additional costs, and preferences of the end-users and significant institutional pressures. This research investigates the influence of Environmental Attitude on the Adoption Intention of Industry Internet of Things (IIoT) technologies among Small and Medium Enterprises and Original Equipment Manufacturers (SME-OEMs). This research analyses the effects of End-user Pull and Institutional Pressure in this relationship. A survey of 263 SME OEMs from 11 industrial sectors across 67 cities was conducted using purposive sampling. Structural Equation Modeling (SEM) was used to analyze data, assessing direct and indirect effects. Results show a significant positive relationship between Environmental Attitude and IIoT Adoption Intention. Mediation analysis reveals significant indirect effects through End-user Pull and Institutional Pressure, with complete mediation as the direct effect becomes insignificant. Findings highlight the crucial role of environmental attitude in shaping IIoT adoption intentions among SMEs. A positive environmental attitude drives SMEs to explore IIoT benefits. End-user Pull and Institutional Pressure are key mediators in this process. These insights are valuable for industry stakeholders, policymakers, and SMEs aiming to promote IIoT adoption. Fostering a positive environmental attitude and leveraging End-user Pull and Institutional Pressure can facilitate IIoT adoption. Policymakers can create initiatives to raise environmental awareness and encourage sustainable practices through IIoT. Industry players can form strategic partnerships to support SME OEMs in IIoT adoption. 2024 IEEE. -
Enhancing Small and Medium OEMs' Adoption of IIoT Technologies
Small and Medium Original Equipment Manufacturers (SME OEMs) face challenges of high initial costs, lack of skilled workforce, data security concerns, and limited infrastructure for IIoT implementation. This research explores the crucial factors influencing the successful integration of Industry Internet of Things (IIoT) technologies into products and processes of SME OEMs. The study investigates the impact of IIOT Manufacturers' operational and business support, training effectiveness, and awareness of benefits on SME OEMs' adoption intention of IIoT solutions. A survey was conducted among 263 firms operating in 103 different equipment manufacturing operations across 67 cities, representing 11 industry sectors. The participants were SME OEMs, and data were collected to assess the influence of various factors on their willingness to adopt IIoT technologies. The study revealed significant insights into adopting IIoT solutions among SME OEMs. Training provided by IIoT manufacturers was found to have the most substantial impact on the adoption intention. Moreover, awareness of benefits and business and operational support had an equal and notable influence on the adoption intention of SME OEMs. These findings underline the importance of effective training programs and comprehensive support from IIoT manufacturers in facilitating successful IIoT integration. The study's outcomes emphasize the value of fostering strategic partnerships between Small and Medium Original Equipment Manufacturers and Industry IoT Manufacturers. Such collaborations can be pivotal in enhancing IIoT adoption rates among SME OEMs, enabling them to stay competitive in the fast-paced market. 2024 IEEE. -
Effective Techniques Non-linear Dynamic Model Calibration using CNN
This paper proposes an efficient method to estimate nonlinear dynamic models using convolutional neural networks (CNNs). The proposed method combines the power of statistical optimization and machine learning to obtain more accurate and efficient estimates of complex models by training CNNs to recognize maps featuring input models and between results, thereby reducing the computational cost of measurements and then using the trained CNN to generate surrogate models -The method can determine accuracy for a range of exposed cases in various nonlinear dynamic models, including differential equation model of chemical reactor and stochastic model of biological systems The results show that the proposed methods are effective for measuring these models, if at most with such accuracy and reducing the computational cost in terms of both frequency and magnitude, the proposed method represents a promising method for estimating nonlinear dynamic models, offering significant advantages in terms of accuracy, efficiency and in scalability 2024 IEEE. -
Performance Evaluation of OTFS Under Different Channel Conditions for LEO Satellite Downlink
Orthogonal Time Frequency Space (OTFS) modulation scheme is being actively pursued as a viable alternative to Orthogonal Frequency Division Multiplexing (OFDM) modulation scheme in future wireless standards due to the inherent ability of OTFS to mitigate the Doppler effects in high mobility scenarios. The inclusion of Non Terrestrial Network (NTN) in Release 17 of 3GPP (3rd generation partnership project) New Radio (NR) standard signifies the vital role of Satellite Communications to achieve coverage extension, capability augmentation and seamless global connectivity. In this context, it becomes important to study the suitability of OTFS modulation scheme with respect to satellite channel scenarios. In this paper, we consider the downlink channel scenarios defined by 3GPP NR NTN for Low Earth Orbit (LEO) satellites at sub-6 GHz and millimetre wave frequencies for evaluating the performance of OTFS modulation schemes. Simulation results using LMMSE (Linear Minimum Mean Square Error) and MRC (Maximum Ratio Combining) detection algorithms confirm that OTFS modulation is highly robust against Doppler effects and performs consistently across all channel conditions. From simulation results, it is observed that the performance of iterative MRC detection is better than LMMSE for 16QAM and 64QAM modulation schemes by achieving respective gains of around 5 dB and 10 dB for corresponding Bit Error Rate (BER) values of 0.01 and 0.1. 2023 IEEE. -
An Experimental Investigation on Flexural Strength of Ferrocement Slab Made of Slag Sand Partially Replaced with Iron Ore Tailings
Effective use of slag sand and Iron Ore Tailings and other waste obtained from the manufacturing industry and mining industry like waste foundry sand, will reduce the negative impact on the environment and also will provide opportunities for effective use of natural resources and contribute to sustainability. The aim of this research project is to study the flexural strength of ferrocement slab made of slag sand partially replaced with iron ore tailings with sustainability point of view. Investigation of 48 slab panels of 700mm 300 mm size with thickness 25 mm and 30 mm was conducted using 1 and 2 layers of weld mesh reinforcement casted with different percentage of iron ore tailings. Slabs were tested in Universal Testing Machine, which showed good results with 15% of iron ore tailings. Published under licence by IOP Publishing Ltd. -
Classification of fibroid using novel fully connected CNN with back propagation classifier (NFCCNNBP)
In this phase, we utilize features extracted from a prior stage to classify uterine fibroids. We employ a predefined dataset with feature values as our training set for a novel classifier called the "Novel Fully Connected CNN with Back Propagation Classifier."This classifier learns from the training set. We then put this method to the test with new images not included in the training dataset. Its primary objective is to assess the extent of infection across the entire uterine surface. Through the adoption of a Convolutional Neural Network (CNN) combined with Back Propagation (BP), we have achieved an impressive accuracy rate of 98.3% for predictions. When we compare this accuracy to existing classifiers like Fuzzy Logic, Naive Bayes, and SVM, our proposed model, NFCCNNBP, outperforms them significantly. 2024 Author(s). -
Pulse Shaper Design for UWB-Based Medical Imaging Applications
In this paper, a pulse shaping filter is designed to shape the higher-order derivatives of the basic UWB Gaussian pulse for efficient pulse transmission through human tissues for medical imaging applications. The shaped pulse for the desired center frequency fits the FCC mask and power spectral density (PSD) specifications with higher spectral efficiency being achieved. It is observed that the ringing effect of Gaussian pulse is reduced by using the proposed bandpass FIR shaping filter. The low ringing effect observed in the shaped pulse ensures better antenna power distribution and improved location accuracy which is critical factor for medical imaging applications. The pulses synthesized are highly orthogonal which aids in multi-access communication, improved bit error rate (BER) performance and short duration UWB pulses leading to higher data rate transmission. The drooping frequency response characteristics of the synthesized pulse have reduced clutter hence tightly focused image obtained for imaging applications. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Synthesis of UWB Pulse Shaper for Efficient Pulse Propagation in Human Tissue
In this paper, a filter based pulse shaper is proposed for efficient Ultra-wideband (UWB) pulse transmission through human tissues. A bandpass Finite Impulse Response (FIR) filter is synthesized and its closed form expression for the impulse response coefficients is obtained. The filter shapes the basic UWB pulse, to closely fit the desired Federal Communication Commission (FCC) mask specifications, to achieve high spectral utilization efficiency. In this approach, the effects due to Gibb's phenomenon are minimized thereby resulting in lower dominant sidelobe of the resultant UWB pulse. The interference between adjacent pulses of the UWB data stream is minimized thus it allows shorter duration UWB pulses to be synthesized leading to higher data rate transmission compared to some techniques in literature. 2020 IEEE. -
Approach for Preprocessing in offline Optical Character Recognition (OCR)
offline optical character recognition (offline OCR) is one of the important applications of pattern recognition. To achieve a better recognition result, the input character images must have good quality. That is why the preprocessing step be-comes essential for any image identification task. Lots of research has been performed in numerous jobs towards this preprocessing in the literature. Here, an attempt has been made to summarize different procedures and aspects of preprocessing adopted in implementing these preprocessing techniques. This is done in the hope that this may help the research community towards the gaining of knowledge of different preprocessing techniques used in offline OCR. offline OCR has several applications, such as old manuscript digitization, signature authentication, bank cheque automatic clearance and postal letter sorting, etc. Finally, an overall summary in a concise way has been presented based on different preprocessing techniques used in offline OCR. 2022 IEEE. -
Forecasting Bitcoin Price During Covid-19 Pandemic Using Prophet and ARIMA: An Empirical Research
Bitcoin and other cryptocurrencies are the alternative and speculative digital financial assets in today's growing fintech economy. Blockchain technology is essential for ensuring ownership of bitcoin, a decentralized technology. These coins display high volatility and bubble-like behavior. The widespread acceptance of cryptocurrencies poses new challenges to the corporate community and the general public. Currency market traders and fintech researchers have classified cryptocurrencies as speculative bubbles. The study has identified the bitcoin bubble and its breaks during the COVID-19 pandemic. From 1st April 2018 to 31st March 2021, we used high-frequency data to calculate the daily closing price of bitcoin. The prophet model and Arima forecasting methods have both been taken. We also examined the explosive bubble and found structural cracks in the bitcoin using the ADF, RADF, and SADF tests. It found five multiple breaks detected from 2018 to 2021 in bitcoin prices. ARIMA(1,1,0) fitted the best model for price prediction. The ARIMA and Facebook Prophet model is applied in the forecasting, and found that the Prophet model is best in forecasting prices. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Analysing Crypto Trends: Unveiling Ethereum and Bitcoin Price Forecasts Through Analytics-Driven Weighted Moving Averages
This research meticulously analyses the performance dynamics of two paramount cryptocurrencies, Bitcoin and Ethereum, over 2,682 observations. Preliminary findings indicate a near alignment in the mean returns of both assets, with Ethereum marginally outperforming Bitcoin. Interestingly, Ethereums superior returns are accompanied by heightened volatility, underlined by its more significant standard deviation. Both cryptocurrencies manifest negative skewness, hinting at a proclivity for negative returns, with Bitcoin showing a sharper skew. Their pronounced kurtosis values attest to the potential for extreme price swings. Regarding forecasting efficacy, the Weighted Moving Average (WMA) method emerges as superior for both assets, yielding the most accurate predictions. At the same time, the Exponential Moving Average (EMA) demonstrates the highest forecast errors. Further, the Relative Strength Index (RSI) evaluation suggests Ethereum may be oversold, alluding to potential investment opportunities. In contrast, Bitcoin, with its mid-range RSI, resides in a neutral zone devoid of clear market signals. The findings shed light on the nuanced performance and forecasting landscape of these leading cryptocurrencies, offering pivotal insights for potential investors. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
KnowSOntoWSR: Web Service Recommendation System Using Semantically Driven QoS Ontology-Based Knowledge-Centred Paradigm
Web services have significantly expanded and become a key enabling technology for online data, application and resource sharing. Designing new methods for efficient and reliable web service recommendation has been of tremendous importance with the growing usage and prominence of web services. It would be ideal for a system to suggest online services that are in line with consumers preferences without requesting specific query information from them. Quality of Service (QoS) is vital for characterising non-functional aspects of Web services as they become more prevalent and widely used on the World Wide Web. The KnowSOntoWSR framework, which is built on a knowledge-driven and semantically inclined model that adheres to QoS ontology, is proposed in this research. AWS and WebSphere are employed as knowledge tags, and the powerful machine learning classifier XGBoost is applied. The features and recommendations are computed using the Twitter semantic similarity. The proposed framework outperforms the baseline models estimates with an accuracy of 95.94% and average F-measure of 95.93%. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.