cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.25734075
title: Design and investigation of computation-in-memory based low power hybrid MTJ/CMOS logic gates

description: Hybrid magnetic tunnel junction (MTJ)/CMOS circuits based on the computation-in-memory (CIM) architecture are contemplated as the future generation of digital integrated circuits. It overcomes the limitations of von-Neumann architecture by offering solutions to problems such as memory wall and standby power dissipation. In this work, we have developed hybrid logic gates, such as AND/NAND, OR/NOR, and XOR/XNOR, for CIM architecture by integrating three terminal spin-Hall effect assisted spin transfer torque (SHE + STT) MTJs with standard CMOS. To write the MTJs an auto-write-stopping (AWS) circuit is adopted, whereas to perform the logic operations and produce the corresponding outputs, an improved sense amplifier circuit (ISA) is employed. All the hybrid logic gates are investigated for key performance indicators such as power, delay, device count, and power delay product (PDP). The results are compared with their conventional counterparts. The comparison reveals that the ISA + AWS-based hybrid gates dissipate 50.52% lower total power. The worst-case read delay of ISA + AWS hybrid AND/NAND, OR/NOR, and XOR/XNOR gates are 27.41%, 13.4%, and 21.28% lower. Meanwhile, the reduction of read PDP (write PDP) is 47.64% (37.09%), 25.78% (36.29%), and 39.31% (35.48%) observed with ISA + AWS hybrid AND/NAND, OR/NOR, and XOR/XNOR gates in comparison with the conventional counterparts. Hence the ISA + AWS gates are superior in terms of total power dissipation, worst read delay, and read/write PDP. Further, we have conducted Monte-Carlo simulations on all the logic circuits to study the parameter variations during fabrication.
DOI: 10.6084/m9.figshare.25734075.v1
title: Design and investigation of computation-in-memory based low power hybrid MTJ/CMOS logic gates

description: Hybrid magnetic tunnel junction (MTJ)/CMOS circuits based on the computation-in-memory (CIM) architecture are contemplated as the future generation of digital integrated circuits. It overcomes the limitations of von-Neumann architecture by offering solutions to problems such as memory wall and standby power dissipation. In this work, we have developed hybrid logic gates, such as AND/NAND, OR/NOR, and XOR/XNOR, for CIM architecture by integrating three terminal spin-Hall effect assisted spin transfer torque (SHE + STT) MTJs with standard CMOS. To write the MTJs an auto-write-stopping (AWS) circuit is adopted, whereas to perform the logic operations and produce the corresponding outputs, an improved sense amplifier circuit (ISA) is employed. All the hybrid logic gates are investigated for key performance indicators such as power, delay, device count, and power delay product (PDP). The results are compared with their conventional counterparts. The comparison reveals that the ISA + AWS-based hybrid gates dissipate 50.52% lower total power. The worst-case read delay of ISA + AWS hybrid AND/NAND, OR/NOR, and XOR/XNOR gates are 27.41%, 13.4%, and 21.28% lower. Meanwhile, the reduction of read PDP (write PDP) is 47.64% (37.09%), 25.78% (36.29%), and 39.31% (35.48%) observed with ISA + AWS hybrid AND/NAND, OR/NOR, and XOR/XNOR gates in comparison with the conventional counterparts. Hence the ISA + AWS gates are superior in terms of total power dissipation, worst read delay, and read/write PDP. Further, we have conducted Monte-Carlo simulations on all the logic circuits to study the parameter variations during fabrication.
DOI: 10.6084/m9.figshare.25953172
title: Exploring large language models as an integrated tool for learning, teaching, and research through the Fogg Behavior Model: a comprehensive mixed-methods analysis

description: Large language models (LLMs) are a recent advancement in artificial intelligence that has the potential to revolutionize learning, teaching, and research. Still, there is room for improvement regarding how effectively LLMs could be incorporated into these environments. This study investigated the role of LLMs, specifically ChatGPT, in learning, teaching, and research contexts. To understand how motivation, ability, and triggers influence the behavior of undergraduate students, teachers, and research scholars toward LLMs, the Fogg Behavior Model (FBM) is adopted. The study revealed that the behavior of students and researchers to apply LLMs in their respective domains was greatly influenced by their motivation and ability. However, teachers exhibited little interest in incorporating any LLMs into their pedagogical strategies. In addition to these results, participants identified limitations of ChatGPT in learning, teaching, and research fields. These insights contribute valuable perspectives on the practical implementation and effectiveness of LLMs in diverse academic and research contexts. The study sheds light on the potential benefits and challenges of integrating LLMs into educational and research environments. The findings emphasize the importance of accounting for motivational factors and individual abilities when applying such models. The study’s findings offer invaluable insights for educators and researchers to harness the potential of LLMs in educational and research environments while mitigating their limitations.
DOI: 10.6084/m9.figshare.25046406.v1
title: A weighted Weibull detection model for line transect sampling: application on wooden stake perpendicular distance data

description: The line transect survey method is commonly used to estimate the population size. However, recent developments in this field have tended to prefer practical mathematical models for this purpose. In this study, a new model called the weighted Weibull detection model was introduced to specifically address certain criteria related to line transect data. This study thoroughly explored the characteristics of this new model, including its shapes, moments, and probability density functions. The maximum likelihood estimation and Bayesian estimation methods were employed to ensure accurate parameter estimation. To gauge the performance of this model, population size estimates were generated and compared with various existing parametric estimation methods that are commonly used in the field. Through simulations, the resulting estimates were assessed and compared to widely adopted approaches for estimating population size. Additionally, this mathematical model was applied to real-world data involving perpendicular distances, allowing for a direct comparison of its performance against both traditional and contemporary methods using various measures of goodness of fit. Moreover, the study calculates statistical metrics, such as the variance–covariance matrix, parameter confidence intervals, and estimated population size, were obtained using the proposed detection model. These metrics provide valuable insights into the precision and uncertainty associated with estimated parameters and population size estimates. Confidence intervals offer a range of plausible parameter values, whereas the variance–covariance matrix quantifies the relationships and uncertainties between the estimated parameters.
DOI: 10.6084/m9.figshare.25271288
title: Performance analysis of path selection routing protocol for UVANET based on geographical multicast routing algorithm

description: Urban Vehicular Ad hoc Networks (UVANETs) are the most important form of mobile ad hoc networks (MANETs). It has been broadly used in intelligent transportation system (ITS) to supply safety and comfort services to the passengers and drivers and also provide security/privacy using vehicle-to-vehicle and vehicle-to-road side unit (RSU) communications. In UVANETs, this research which maintains the security of the sensitive information shared between vehicles and end-users and tested on different types of attacks and provide possible solutions. And different emerging open challenges along with the security threats are identified and detected and the power of each node are also managed. This research proposed geographical multi cast network mode to analysis the performance of path selection routing protocols (PSRP) for UVANET because geographical multicast network mode uses central coordinator or it constructs virtual central coordinates. Due to these benefits geographical multicast routing algorithm (GMRA) is used to handle security issue, fault detection technique and power management. To simulate the proposed scheme, for performance analysis network simulator-3(NS-3) version-3.23 is used. Simulation results show that, the PDR for five CBR connections, GMRA is 4% and 7% better than GPSR and AODV routing protocols, respectively. Similarly one can observe the same advantages of GMRA at 10 CBR and 15 number of CBR connections. The packet loss of GMRA is enhanced by 10% and 7% than GPSR and AODV routing protocols respectively. The delay of GMRA for the three CBR connections are lower than GPSR and AODV routing protocols. Over the past few years, the technology of vehicular ad hoc networks (VANETs) has been a significant topic of research. The idea of creating a network of cars for a particular need gave rise to the ad-hoc nature of VANET networks. VANETs are widely recognized as dependable networks used by cars in urban settings for communication. Security for VANET should be prioritized on a par with security for other computing networks. All apps created for vehicular networks must be safeguarded from malicious manipulation because to the very sensitive nature of the information broadcast <i>via</i> VANET. Thus, creating a dynamic routing protocol that can assist in spreading information from one node (vehicle) to another is one of the key issues in the design of vehicular ad-hoc networks. This research presented Path Selection Routing Protocol for UVANET based on geographical multicast routing algorithm to overcome this problem and analyze UVANET performance.
DOI: 10.6084/m9.figshare.25271288.v1
title: Performance analysis of path selection routing protocol for UVANET based on geographical multicast routing algorithm

description: Urban Vehicular Ad hoc Networks (UVANETs) are the most important form of mobile ad hoc networks (MANETs). It has been broadly used in intelligent transportation system (ITS) to supply safety and comfort services to the passengers and drivers and also provide security/privacy using vehicle-to-vehicle and vehicle-to-road side unit (RSU) communications. In UVANETs, this research which maintains the security of the sensitive information shared between vehicles and end-users and tested on different types of attacks and provide possible solutions. And different emerging open challenges along with the security threats are identified and detected and the power of each node are also managed. This research proposed geographical multi cast network mode to analysis the performance of path selection routing protocols (PSRP) for UVANET because geographical multicast network mode uses central coordinator or it constructs virtual central coordinates. Due to these benefits geographical multicast routing algorithm (GMRA) is used to handle security issue, fault detection technique and power management. To simulate the proposed scheme, for performance analysis network simulator-3(NS-3) version-3.23 is used. Simulation results show that, the PDR for five CBR connections, GMRA is 4% and 7% better than GPSR and AODV routing protocols, respectively. Similarly one can observe the same advantages of GMRA at 10 CBR and 15 number of CBR connections. The packet loss of GMRA is enhanced by 10% and 7% than GPSR and AODV routing protocols respectively. The delay of GMRA for the three CBR connections are lower than GPSR and AODV routing protocols. Over the past few years, the technology of vehicular ad hoc networks (VANETs) has been a significant topic of research. The idea of creating a network of cars for a particular need gave rise to the ad-hoc nature of VANET networks. VANETs are widely recognized as dependable networks used by cars in urban settings for communication. Security for VANET should be prioritized on a par with security for other computing networks. All apps created for vehicular networks must be safeguarded from malicious manipulation because to the very sensitive nature of the information broadcast <i>via</i> VANET. Thus, creating a dynamic routing protocol that can assist in spreading information from one node (vehicle) to another is one of the key issues in the design of vehicular ad-hoc networks. This research presented Path Selection Routing Protocol for UVANET based on geographical multicast routing algorithm to overcome this problem and analyze UVANET performance.
DOI: 10.6084/m9.figshare.25038428
title: Effectiveness verification framework for coupon distribution marketing measure considering users’ potential purchase intentions

description: In recent years, web marketing has thrived, and online coupon distribution has become a significant marketing measure that leads to increased sales. However, randomly distributing coupons risks lowering the profit ratio of companies. Therefore, it is important to estimate the effect of coupons and analyze the causal relationship between coupons and results. The potential purchase intention (PPI) of users is believed to influence the effect of coupons. For example, distributing coupons to users with a low PPI is likely to increase the gross profit of companies, whereas distributing coupons to users with a high PPI is likely to decrease the gross profit. Therefore, by analyzing the relationship between PPI and the effect of coupons, highly effective targeting can be conducted based on the PPI. In this paper, we propose an experimental design based on machine learning to analyze the effect of coupons, which varies depending on the PPI. We propose a method to predict users’ PPI based on their purchase history data using machine learning and analyze the relationship between PPI and the effect of coupons. Finally, we demonstrate the effectiveness of the proposed framework by applying it to real-world data.
DOI: 10.6084/m9.figshare.25038428.v1
title: Effectiveness verification framework for coupon distribution marketing measure considering users’ potential purchase intentions

description: In recent years, web marketing has thrived, and online coupon distribution has become a significant marketing measure that leads to increased sales. However, randomly distributing coupons risks lowering the profit ratio of companies. Therefore, it is important to estimate the effect of coupons and analyze the causal relationship between coupons and results. The potential purchase intention (PPI) of users is believed to influence the effect of coupons. For example, distributing coupons to users with a low PPI is likely to increase the gross profit of companies, whereas distributing coupons to users with a high PPI is likely to decrease the gross profit. Therefore, by analyzing the relationship between PPI and the effect of coupons, highly effective targeting can be conducted based on the PPI. In this paper, we propose an experimental design based on machine learning to analyze the effect of coupons, which varies depending on the PPI. We propose a method to predict users’ PPI based on their purchase history data using machine learning and analyze the relationship between PPI and the effect of coupons. Finally, we demonstrate the effectiveness of the proposed framework by applying it to real-world data.
DOI: 10.6084/m9.figshare.25046406
title: A weighted Weibull detection model for line transect sampling: application on wooden stake perpendicular distance data

description: The line transect survey method is commonly used to estimate the population size. However, recent developments in this field have tended to prefer practical mathematical models for this purpose. In this study, a new model called the weighted Weibull detection model was introduced to specifically address certain criteria related to line transect data. This study thoroughly explored the characteristics of this new model, including its shapes, moments, and probability density functions. The maximum likelihood estimation and Bayesian estimation methods were employed to ensure accurate parameter estimation. To gauge the performance of this model, population size estimates were generated and compared with various existing parametric estimation methods that are commonly used in the field. Through simulations, the resulting estimates were assessed and compared to widely adopted approaches for estimating population size. Additionally, this mathematical model was applied to real-world data involving perpendicular distances, allowing for a direct comparison of its performance against both traditional and contemporary methods using various measures of goodness of fit. Moreover, the study calculates statistical metrics, such as the variance–covariance matrix, parameter confidence intervals, and estimated population size, were obtained using the proposed detection model. These metrics provide valuable insights into the precision and uncertainty associated with estimated parameters and population size estimates. Confidence intervals offer a range of plausible parameter values, whereas the variance–covariance matrix quantifies the relationships and uncertainties between the estimated parameters.
DOI: 10.6084/m9.figshare.24814231
title: Revisiting multi-domain empirical modelling of light-emitting diode luminaire

description: In this work, an empirically derived multi-domain model of a light-emitting diode (LED) luminaire is proposed. The optical, electrical, and thermal characteristics of LEDs are obtained from the data sheets provided by manufacturers. Both transient and steady-state performance of LED luminaire were realized theoretically and validated against experimental findings from earlier investigations. The difficulties encountered in creating an ideal LED luminaire are discoursed and examined. Most of the studies on LED luminaires described in prior works ignored the influence of luminaire housing and optics on the thermal management of the luminaire. Knowledge of thermal time constants of LED luminaires is important, as they decide the rate of luminous flux decay when LED lighting systems are used for long periods of operation. Thermal time constants also decide the time taken by the LED junction to reach steady state with its surrounding. From the study it is inferred that the increase in junction temperature and deterioration of luminous flux is controlled by the product of the single most dominant thermal resistance from the junction to a point along the heat conduction path and sum of all the downstream thermal capacitances. This is true as in most cases LED device thermal capacitance is very less related to the thermal capacitance of the heat-sink, hence the product of single most dominant thermal resistance and thermal capacitance of heat-sink decide the rate of rising of junction temperature or luminous flux deterioration.
DOI: 10.6084/m9.figshare.24814231.v1
title: Revisiting multi-domain empirical modelling of light-emitting diode luminaire

description: In this work, an empirically derived multi-domain model of a light-emitting diode (LED) luminaire is proposed. The optical, electrical, and thermal characteristics of LEDs are obtained from the data sheets provided by manufacturers. Both transient and steady-state performance of LED luminaire were realized theoretically and validated against experimental findings from earlier investigations. The difficulties encountered in creating an ideal LED luminaire are discoursed and examined. Most of the studies on LED luminaires described in prior works ignored the influence of luminaire housing and optics on the thermal management of the luminaire. Knowledge of thermal time constants of LED luminaires is important, as they decide the rate of luminous flux decay when LED lighting systems are used for long periods of operation. Thermal time constants also decide the time taken by the LED junction to reach steady state with its surrounding. From the study it is inferred that the increase in junction temperature and deterioration of luminous flux is controlled by the product of the single most dominant thermal resistance from the junction to a point along the heat conduction path and sum of all the downstream thermal capacitances. This is true as in most cases LED device thermal capacitance is very less related to the thermal capacitance of the heat-sink, hence the product of single most dominant thermal resistance and thermal capacitance of heat-sink decide the rate of rising of junction temperature or luminous flux deterioration.
DOI: 10.6084/m9.figshare.27766585
title: Systematic analysis of video tampering and detection techniques

description: Video tampering detection is a crucial research area that aims to identify and mitigate the manipulation and tampering of video content. With the widespread use of videos in various applications, ensuring the integrity and truthfulness of video data has become paramount. This research provides a thorough examination of video tampering detection strategies, encompassing both active and passive approaches. The article discusses several different kinds of tampering attacks, such as spatial, temporal, and spatiotemporal tampering, and explores the techniques used to detect these tampering instances. Moreover, the article highlights the challenges and limitations of existing techniques, such as the need for specialized hardware, the vulnerability to owner tampering, and the impact of compression artifacts. The article concludes by highlighting the future directions for research in video tampering detection, including the exploration of deepfake detection and adversarial techniques. The findings of this research provide valuable insights for developing robust and effective video tampering detection systems, thereby enhancing the trustworthiness and reliability of video content in diverse domains.
DOI: 10.6084/m9.figshare.27766585.v1
title: Systematic analysis of video tampering and detection techniques

description: Video tampering detection is a crucial research area that aims to identify and mitigate the manipulation and tampering of video content. With the widespread use of videos in various applications, ensuring the integrity and truthfulness of video data has become paramount. This research provides a thorough examination of video tampering detection strategies, encompassing both active and passive approaches. The article discusses several different kinds of tampering attacks, such as spatial, temporal, and spatiotemporal tampering, and explores the techniques used to detect these tampering instances. Moreover, the article highlights the challenges and limitations of existing techniques, such as the need for specialized hardware, the vulnerability to owner tampering, and the impact of compression artifacts. The article concludes by highlighting the future directions for research in video tampering detection, including the exploration of deepfake detection and adversarial techniques. The findings of this research provide valuable insights for developing robust and effective video tampering detection systems, thereby enhancing the trustworthiness and reliability of video content in diverse domains.