The evolution in technology leads to current situation of detection and recognition of human movements which is more prevalent due to many applications in various fields. This paper provides with some recent trends in human motion capture (HMC) technology. Animation is a dynamic factor in different fields like entertainment, medicine etc. about which objects or images are manipulated to resemble as running pictures. For the animation process to speed up more, motion capture was invented, a means by which we are able to capture real-time motions and then recorded motion data into a three-dimensional model in virtual environment. Human motion capture is the method of capturing human motion from real life using various sensing objects into the computer. This paper discusses about some human motion capture methods, their processing, tracking and reconstruction of motion. A brief review of literature with different methodologies is provided. Mainly focusing on recent motion capture technologies, the utilization of easier, less time consuming and accurate motion capture methods.
{"title":"A Review on Human Motion Capture","authors":"Maya R. Das, Ranjith Ram A","doi":"10.2139/ssrn.3791097","DOIUrl":"https://doi.org/10.2139/ssrn.3791097","url":null,"abstract":"The evolution in technology leads to current situation of detection and recognition of human movements which is more prevalent due to many applications in various fields. This paper provides with some recent trends in human motion capture (HMC) technology. Animation is a dynamic factor in different fields like entertainment, medicine etc. about which objects or images are manipulated to resemble as running pictures. For the animation process to speed up more, motion capture was invented, a means by which we are able to capture real-time motions and then recorded motion data into a three-dimensional model in virtual environment. Human motion capture is the method of capturing human motion from real life using various sensing objects into the computer. This paper discusses about some human motion capture methods, their processing, tracking and reconstruction of motion. A brief review of literature with different methodologies is provided. Mainly focusing on recent motion capture technologies, the utilization of easier, less time consuming and accurate motion capture methods.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75000159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Chalkiadakis, Anna Zaremba, G. Peters, M. Chantler
This paper establishes a new framework for assessing multimodal statistical causality between cryptocurrency market (cryptomarket) sentiment and cryptocurrency price processes. In order to achieve this we present an efficient algorithm for multimodal statistical causality analysis based on Multiple-Output Gaussian Processes. Signals from different information sources (modalities) are jointly modelled as a Multiple-Output Gaussian Process, and then using a novel approach to statistical causality based on Gaussian Processes (GP), we study linear and non-linear causal effects between the different modalities. We demonstrate the effectiveness of our approach in a machine learning application studying the relationship between cryptocurrency spot price dynamics and sentiment time-series data specific to the crypto sector, which we conjecture influences retail investor behaviour. The investor sentiment is extracted from cryptomarket news data via methods developed in the area of statistical machine learning known as Natural Language Processing (NLP). To capture sentiment, we present a novel framework for text to time-series embedding, which we then use to construct a sentiment index from publicly available news articles. We conduct a statistical analysis of our sentiment statistical index model and compare it to alternative state-of-the-art sentiment models popular in the NLP literature. In regards to the multimodal causality, the investor sentiment is our primary modality of exploration, in addition to price and a blockchain technology-related indicator (hash rate). Analysis shows that our approach is effective in modelling causal structures of variable degree of complexity between heterogeneous data sources, and illustrates the impact that certain modelling choices for the different modalities can have on detecting causality. A solid understanding of these factors is necessary to gauge cryptocurrency adoption by retail investors and provide sentiment- and technology-based insights about the cryptocurrency market dynamics.
{"title":"On-chain Analytics for Sentiment-driven Statistical Causality in Cryptocurrencies","authors":"I. Chalkiadakis, Anna Zaremba, G. Peters, M. Chantler","doi":"10.2139/ssrn.3742063","DOIUrl":"https://doi.org/10.2139/ssrn.3742063","url":null,"abstract":"This paper establishes a new framework for assessing multimodal statistical causality between cryptocurrency market (cryptomarket) sentiment and cryptocurrency price processes. In order to achieve this we present an efficient algorithm for multimodal statistical causality analysis based on Multiple-Output Gaussian Processes. Signals from different information sources (modalities) are jointly modelled as a Multiple-Output Gaussian Process, and then using a novel approach to statistical causality based on Gaussian Processes (GP), we study linear and non-linear causal effects between the different modalities. We demonstrate the effectiveness of our approach in a machine learning application studying the relationship between cryptocurrency spot price dynamics and sentiment time-series data specific to the crypto sector, which we conjecture influences retail investor behaviour. The investor sentiment is extracted from cryptomarket news data via methods developed in the area of statistical machine learning known as Natural Language Processing (NLP). To capture sentiment, we present a novel framework for text to time-series embedding, which we then use to construct a sentiment index from publicly available news articles. We conduct a statistical analysis of our sentiment statistical index model and compare it to alternative state-of-the-art sentiment models popular in the NLP literature. In regards to the multimodal causality, the investor sentiment is our primary modality of exploration, in addition to price and a blockchain technology-related indicator (hash rate). Analysis shows that our approach is effective in modelling causal structures of variable degree of complexity between heterogeneous data sources, and illustrates the impact that certain modelling choices for the different modalities can have on detecting causality. A solid understanding of these factors is necessary to gauge cryptocurrency adoption by retail investors and provide sentiment- and technology-based insights about the cryptocurrency market dynamics.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79969724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We examine the quality differentiation strategy, which is widely adopted by sellers in product markets, for a two-sided ridesharing platform that matches drivers with riders. The riders have different valuations for waiting time, which is one measure of service quality, and drivers have different service costs. Unlike product markets, the ridesharing platform faces a self-scheduled supply where drivers individually decide whether to offer their service and they may also be strategic in accepting a ride request. The platform can possibly use driver wage to control driver supply, but this, in turn, affects the service quality. We show that the ridesharing platform would offer quality-differentiated services only when the rider-side cannibalization is not high, which is consistent with findings in product markets. A platform that can control driver supply through wage has more incentive to offer quality-differentiated services than a platform that cannot if the rider-side cannibalization is low, but the reverse can happen if the rider-side cannibalization is moderate. In the presence of strategic drivers, offering differential wages to control driver supply leads to adverse effects from driver-side cannibalization as well. An increase in the driver-side cannibalization diminishes the platform's incentive to practice wage differentiation as well as the incentive to offer quality-differentiated services. On the other hand, an increase in the rider-side cannibalization can enhance the platform's incentive to practice wage differentiation.
{"title":"Service Quality and Wage Differentiation in Two-Sided Ridesharing Platforms","authors":"Haozhao Zhang, Chenglong Zhang, Srinivasan Raghunathan","doi":"10.2139/ssrn.3737241","DOIUrl":"https://doi.org/10.2139/ssrn.3737241","url":null,"abstract":"We examine the quality differentiation strategy, which is widely adopted by sellers in product markets, for a two-sided ridesharing platform that matches drivers with riders. The riders have different valuations for waiting time, which is one measure of service quality, and drivers have different service costs. Unlike product markets, the ridesharing platform faces a self-scheduled supply where drivers individually decide whether to offer their service and they may also be strategic in accepting a ride request. The platform can possibly use driver wage to control driver supply, but this, in turn, affects the service quality. We show that the ridesharing platform would offer quality-differentiated services only when the rider-side cannibalization is not high, which is consistent with findings in product markets. A platform that can control driver supply through wage has more incentive to offer quality-differentiated services than a platform that cannot if the rider-side cannibalization is low, but the reverse can happen if the rider-side cannibalization is moderate. In the presence of strategic drivers, offering differential wages to control driver supply leads to adverse effects from driver-side cannibalization as well. An increase in the driver-side cannibalization diminishes the platform's incentive to practice wage differentiation as well as the incentive to offer quality-differentiated services. On the other hand, an increase in the rider-side cannibalization can enhance the platform's incentive to practice wage differentiation.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87632657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Personal Care has become a daily ritual for most individual’s world-over. With this widespread flair towards personal grooming, the search for natural remedies and natural ingredient-based products for personal care has also grown due to the health-conscious nature of most consumers. Within this context, the purpose of this paper is to examine the impact of social media marketing on the purchase intention of connected consumers towards natural ingredient based personal care products. Furthermore, ‘Perceived Trust’ is positioned as a mediator within the context of this study to enhance the understanding of whether perceived trust contributes to mediate the relationship between ‘Social Media Marketing’ and ‘Purchase Intention’. The 469 responses collected from Colombo and Gampaha districts using a structured questionnaire were analysed using SPSS to conclude that social media marketing directly has a significant positive impact on purchase intention while perceived trust mediates the relationship between ‘Social Media Marketing’ and ‘Purchase Intention’. The researcher was also able to confirm the significant positive impact of social media marketing on perceived trust as well as the significant positive impact of perceived trust on purchase intention and that social media marketing has a greater impact on the purchase intention than it does on the perceived trust.
{"title":"The Impact of Social Media Marketing on Purchase Intention of Connected Consumers: A Study Based on Natural Ingredient-Based Personal Care Products","authors":"U. Subasinghe, R. Weerasiri","doi":"10.2139/ssrn.3862937","DOIUrl":"https://doi.org/10.2139/ssrn.3862937","url":null,"abstract":"Personal Care has become a daily ritual for most individual’s world-over. With this widespread flair towards personal grooming, the search for natural remedies and natural ingredient-based products for personal care has also grown due to the health-conscious nature of most consumers. Within this context, the purpose of this paper is to examine the impact of social media marketing on the purchase intention of connected consumers towards natural ingredient based personal care products. Furthermore, ‘Perceived Trust’ is positioned as a mediator within the context of this study to enhance the understanding of whether perceived trust contributes to mediate the relationship between ‘Social Media Marketing’ and ‘Purchase Intention’. The 469 responses collected from Colombo and Gampaha districts using a structured questionnaire were analysed using SPSS to conclude that social media marketing directly has a significant positive impact on purchase intention while perceived trust mediates the relationship between ‘Social Media Marketing’ and ‘Purchase Intention’. The researcher was also able to confirm the significant positive impact of social media marketing on perceived trust as well as the significant positive impact of perceived trust on purchase intention and that social media marketing has a greater impact on the purchase intention than it does on the perceived trust.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74656709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Employees in corporate companies are frequent travellers and have insufficient time to participate in regular physical training. Thus, looking for a revolutionary technology era where they can learn at any time, anywhere. Mobile technologies and mobile devices are attached to employees’ daily lifestyles. Hence, the best solution is the use of mobile learning as a training platform to enhance employee skills and knowledge. This paper examines the readiness to adopt M-Learning solutions as the training platform for corporate companies in Sri Lanka concerning the factors Perceived Ease of Use, Perceived Usefulness, Personal Innovativeness, Performance Expectancy, Social influence, Self-Management of Learning. The factors were based on the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology and several other factors from the literature. The report discusses the problem statement, objectives and literature review. Specific attention was made to the Sri Lankan context in explaining the current practices. Then factors were analysed based on data collected from 132 participants from 30 corporate companies using a survey questionnaire. Data were analysed using Cronbach's Analysis, Pearson’s Correlation Coefficient and ANOVA. Based on the results, critical success factors were identified and the framework was developed. Finally, the recommendations were given, and future research was discussed. The Perceived Ease of Use, Performance Expectancy and Self-Management of Learning were found as the critical factors which influence improving usage of mobile learning. The educational institutions and software development companies should focus on these factors before adopting the learning method and developing educational frameworks. Researchers can focus on M-learning targeting other professions and industries.
{"title":"Readiness to Adopt M-Learning Solutions as the Training Platform among Corporate Companies in Sri Lanka","authors":"Y. L. Velananda, W. Wanninayake","doi":"10.2139/ssrn.3862961","DOIUrl":"https://doi.org/10.2139/ssrn.3862961","url":null,"abstract":"Employees in corporate companies are frequent travellers and have insufficient time to participate in regular physical training. Thus, looking for a revolutionary technology era where they can learn at any time, anywhere. Mobile technologies and mobile devices are attached to employees’ daily lifestyles. Hence, the best solution is the use of mobile learning as a training platform to enhance employee skills and knowledge. This paper examines the readiness to adopt M-Learning solutions as the training platform for corporate companies in Sri Lanka concerning the factors Perceived Ease of Use, Perceived Usefulness, Personal Innovativeness, Performance Expectancy, Social influence, Self-Management of Learning. The factors were based on the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology and several other factors from the literature. The report discusses the problem statement, objectives and literature review. Specific attention was made to the Sri Lankan context in explaining the current practices. Then factors were analysed based on data collected from 132 participants from 30 corporate companies using a survey questionnaire. Data were analysed using Cronbach's Analysis, Pearson’s Correlation Coefficient and ANOVA. Based on the results, critical success factors were identified and the framework was developed. Finally, the recommendations were given, and future research was discussed. The Perceived Ease of Use, Performance Expectancy and Self-Management of Learning were found as the critical factors which influence improving usage of mobile learning. The educational institutions and software development companies should focus on these factors before adopting the learning method and developing educational frameworks. Researchers can focus on M-learning targeting other professions and industries.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91540852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this chapter, we address a number of critical questions. Is there a problem with large technology firms, or platforms, purchasing nascent competitors and suppressing competition before they can mature into vibrant competitors? Further, if there is a problem, are the current antitrust laws and the enforcement of those laws sufficient to combat the problem? If not, is there a legislative solution? In addressing these questions, we offer a clear delineation and classification scheme to differentiate potential competition, nascent competitors, and killer acquisitions. Ultimately, while classification schemes are helpful, the assessment of all horizontal acquisitions, whether the rivalry is currently happening or will happen in the future, comes down to the core analytical considerations involving competitive effects, entry, and efficiencies.
{"title":"Potential Competition, Nascent Competitors, and Killer Acquisitions","authors":"John M. Yun","doi":"10.2139/ssrn.3733716","DOIUrl":"https://doi.org/10.2139/ssrn.3733716","url":null,"abstract":"In this chapter, we address a number of critical questions. Is there a problem with large technology firms, or platforms, purchasing nascent competitors and suppressing competition before they can mature into vibrant competitors? Further, if there is a problem, are the current antitrust laws and the enforcement of those laws sufficient to combat the problem? If not, is there a legislative solution? In addressing these questions, we offer a clear delineation and classification scheme to differentiate potential competition, nascent competitors, and killer acquisitions. Ultimately, while classification schemes are helpful, the assessment of all horizontal acquisitions, whether the rivalry is currently happening or will happen in the future, comes down to the core analytical considerations involving competitive effects, entry, and efficiencies.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86960436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examines the role of text-based direct messaging systems in online labor markets, which provide a communication channel between workers and employers, adding a personal touch to the exchange of online labor. We propose the effect of workers’ use of the direct messaging system on employers’ hiring decisions and conceptualize the information role of direct messaging. To empirically evaluate the information role of the direct messaging system, we leverage data on the direct messaging activities between workers and employers across more than 470,000 job applications on a leading online labor market. We report evidence that direct messaging with a prospective employer increases a worker’s probability of being hired by 8.9%. However, the degree to which workers benefit from direct messaging is heterogeneous, and the effect amplifies for workers approaching employers from a position of disadvantage (lacking tenure or fit with the job) and attenuates as more workers attempt to message the same prospective employer. The effects also depend on message content. In particular, we find that the benefits of direct messaging for workers depend a great deal on the politeness of the workers, and this “politeness effect” depends on several contextual factors. The beneficial effects are amplified for lower-status workers (i.e., workers lacking tenure and job fit) and workers who share a common language with the employer. At the same time, the beneficial effects weaken in the presence of typographical errors. These findings provide important insights into when and what to message to achieve favorable hiring outcomes in online employment settings.
{"title":"Just DM Me (Politely): Direct Messaging, Politeness, and Hiring Outcomes in Online Labor Markets","authors":"Y. Hong, Jing Peng, Gordon Burtch, Ni Huang","doi":"10.2139/ssrn.3151289","DOIUrl":"https://doi.org/10.2139/ssrn.3151289","url":null,"abstract":"This study examines the role of text-based direct messaging systems in online labor markets, which provide a communication channel between workers and employers, adding a personal touch to the exchange of online labor. We propose the effect of workers’ use of the direct messaging system on employers’ hiring decisions and conceptualize the information role of direct messaging. To empirically evaluate the information role of the direct messaging system, we leverage data on the direct messaging activities between workers and employers across more than 470,000 job applications on a leading online labor market. We report evidence that direct messaging with a prospective employer increases a worker’s probability of being hired by 8.9%. However, the degree to which workers benefit from direct messaging is heterogeneous, and the effect amplifies for workers approaching employers from a position of disadvantage (lacking tenure or fit with the job) and attenuates as more workers attempt to message the same prospective employer. The effects also depend on message content. In particular, we find that the benefits of direct messaging for workers depend a great deal on the politeness of the workers, and this “politeness effect” depends on several contextual factors. The beneficial effects are amplified for lower-status workers (i.e., workers lacking tenure and job fit) and workers who share a common language with the employer. At the same time, the beneficial effects weaken in the presence of typographical errors. These findings provide important insights into when and what to message to achieve favorable hiring outcomes in online employment settings.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85307880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We examine the hypothesis that the concept of symmetry augmentation is fundamentally linked to learning. Our focus in this study is on the augmentation of symmetry embedded in 1-dimensional time series (1D-TS). Motivated by the duality between 1D-TS and networks, we augment the symmetry by converting 1D-TS into three 2-dimensional representations: temporal correlation (GAF), transition dynamics (MTF), and recurrent events (RP). This conversion does not require a priori knowledge of the types of symmetries hidden in the 1D-TS. We then exploit the equivariance property of CNNs to learn the hidden symmetries in the augmented 2-dimensional data. We show that such conversion only increases the amount of symmetry, which may lead to more efficient learning. Specifically, we prove that a direct sum based augmentation will never decrease the amount of symmetry. We also attempt to measure the amount of symmetry in the original 1D-TS and augmented representations using the notion of persistent homology, which reveals symmetry increase after augmentation quantitatively. We present empirical studies to confirm our findings using reinforcement learning for financial portfolio management. We demonstrate significant improvements in learning efficiency.
{"title":"Reinforcement Learning with Symmetry Augmentation for Portfolio Management","authors":"A. Aboussalah, Chi-Guhn Lee","doi":"10.2139/ssrn.3748132","DOIUrl":"https://doi.org/10.2139/ssrn.3748132","url":null,"abstract":"We examine the hypothesis that the concept of symmetry augmentation is fundamentally linked to learning. Our focus in this study is on the augmentation of symmetry embedded in 1-dimensional time series (1D-TS). Motivated by the duality between 1D-TS and networks, we augment the symmetry by converting 1D-TS into three 2-dimensional representations: temporal correlation (GAF), transition dynamics (MTF), and recurrent events (RP). This conversion does not require a priori knowledge of the types of symmetries hidden in the 1D-TS. We then exploit the equivariance property of CNNs to learn the hidden symmetries in the augmented 2-dimensional data. We show that such conversion only increases the amount of symmetry, which may lead to more efficient learning. Specifically, we prove that a direct sum based augmentation will never decrease the amount of symmetry. We also attempt to measure the amount of symmetry in the original 1D-TS and augmented representations using the notion of persistent homology, which reveals symmetry increase after augmentation quantitatively. We present empirical studies to confirm our findings using reinforcement learning for financial portfolio management. We demonstrate significant improvements in learning efficiency.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73633940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rajeev Ratna Vallabhuni, K. C. Koteswaramma, B. Sadgurbabu, A. Gowthamireddy
The limitation of CMOS to operate under 25nm is a major concern, which is viewed in this paper. Also, scaling issues like leakage current, lower carrier mobility, higher junction capacitance limits the transistor operation is discussed. At lower voltage values, CMOS productive decreases drastically, which reflects on the performance of the circuit to achieve its criteria’s. In the present-day scenario of electronic industry, the speed of the processor is increasing, the urge for speed cache memory is also increasing. In cache memory design, SRAM cell is mainly used, CMOS SRAM Cells are producing more delay and also limitations like mentioned above. Therefore, an alternative preferred choice for SRAM Cells is FinFET, which increases the speed of the Cell by avoiding limitations caused by the bulk CMOS. By considering the advantages of the FinFET, SRAM Cells are implemented in Cadence virtuoso using FinFET 18nm Spectre. In this paper, standard SRAM Cells like 7T, 8T, 9T, and 1oT are simulated and parameters like power, delay, power delay product (PDP), energy-delay product (EDP) are calculated and respective values are presented by varying the voltage (V). SRAM Cells behavior under different voltages is graphically represented. CMOS SRAM Cell values have been taken from previous works of SRAM. By comparing the parameters, a drastic increase in the speed of the FinFET SRAM Cells can be observed over CMOS SRAM Cells.
{"title":"Comparative Validation of SRAM Cells Designed using 18nm FinFET for Memory Storing Applications","authors":"Rajeev Ratna Vallabhuni, K. C. Koteswaramma, B. Sadgurbabu, A. Gowthamireddy","doi":"10.2139/ssrn.3733530","DOIUrl":"https://doi.org/10.2139/ssrn.3733530","url":null,"abstract":"The limitation of CMOS to operate under 25nm is a major concern, which is viewed in this paper. Also, scaling issues like leakage current, lower carrier mobility, higher junction capacitance limits the transistor operation is discussed. At lower voltage values, CMOS productive decreases drastically, which reflects on the performance of the circuit to achieve its criteria’s. In the present-day scenario of electronic industry, the speed of the processor is increasing, the urge for speed cache memory is also increasing. In cache memory design, SRAM cell is mainly used, CMOS SRAM Cells are producing more delay and also limitations like mentioned above. Therefore, an alternative preferred choice for SRAM Cells is FinFET, which increases the speed of the Cell by avoiding limitations caused by the bulk CMOS. By considering the advantages of the FinFET, SRAM Cells are implemented in Cadence virtuoso using FinFET 18nm Spectre. In this paper, standard SRAM Cells like 7T, 8T, 9T, and 1oT are simulated and parameters like power, delay, power delay product (PDP), energy-delay product (EDP) are calculated and respective values are presented by varying the voltage (V). SRAM Cells behavior under different voltages is graphically represented. CMOS SRAM Cell values have been taken from previous works of SRAM. By comparing the parameters, a drastic increase in the speed of the FinFET SRAM Cells can be observed over CMOS SRAM Cells.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80856386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates the relationship between the bitcoin price and the hashrate by disentangling the effects of the energy efficiency of the bitcoin mining equipment, bitcoin halving, and of structural breaks on the price dynamics. For this purpose, we propose a methodology based on exponential smoothing to model the dynamics of the Bitcoin network energy efficiency. We consider either directly the hashrate or the bitcoin cost-of-production model (CPM) as a proxy for the hashrate, to take any nonlinearity into account. In the first examined subsample (01/08/2016–04/12/2017), the hashrate and the CPMs were never significant, while a significant cointegration relationship was found in the second subsample (11/12/2017–24/02/2020). The empirical evidence shows that it is better to consider the hashrate directly rather than its proxy represented by the CPM when modeling its relationship with the bitcoin price. Moreover, the causality is always unidirectional going from the bitcoin price to the hashrate (or its proxies), with lags ranging from one week up to six weeks later. These findings are consistent with a large literature in energy economics, which showed that oil and gas returns affect the purchase of the drilling rigs with a delay of up to three months, whereas the impact of changes in the rig count on oil and gas returns is limited or not significant.
{"title":"Does the Hashrate Affect the Bitcoin Price?","authors":"Dean Fantazzini, N. Kolodin","doi":"10.2139/ssrn.3720039","DOIUrl":"https://doi.org/10.2139/ssrn.3720039","url":null,"abstract":"This paper investigates the relationship between the bitcoin price and the hashrate by disentangling the effects of the energy efficiency of the bitcoin mining equipment, bitcoin halving, and of structural breaks on the price dynamics. For this purpose, we propose a methodology based on exponential smoothing to model the dynamics of the Bitcoin network energy efficiency. We consider either directly the hashrate or the bitcoin cost-of-production model (CPM) as a proxy for the hashrate, to take any nonlinearity into account. In the first examined subsample (01/08/2016–04/12/2017), the hashrate and the CPMs were never significant, while a significant cointegration relationship was found in the second subsample (11/12/2017–24/02/2020). The empirical evidence shows that it is better to consider the hashrate directly rather than its proxy represented by the CPM when modeling its relationship with the bitcoin price. Moreover, the causality is always unidirectional going from the bitcoin price to the hashrate (or its proxies), with lags ranging from one week up to six weeks later. These findings are consistent with a large literature in energy economics, which showed that oil and gas returns affect the purchase of the drilling rigs with a delay of up to three months, whereas the impact of changes in the rig count on oil and gas returns is limited or not significant.","PeriodicalId":13594,"journal":{"name":"Information Systems & Economics eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85958466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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