Pub Date : 2022-10-22DOI: 10.5121/csit.2022.121715
Zineb Machrouh, A. Najid, Iyad Lahcen-Cherif
Wireless communications evolved in a remarkable way during the last decade and is well on its way to surpass wired internet. The demand shifted towards higher transmission speed for more users and heavier traffics. In this paper, we present an IEEE 802.11ax scenario, in which we study the energy efficiency for the key metrics of the MAC layer. In this latest edition, called High Efficiency WLAN (HEW) energy is a main concern in order to satisfy scenarios of internet of things and wireless sensor networks. we prove that some of the new features such as the higher order modulation and coding schemes enhance remarkably the energy efficiency. We also show the impact of an increase in the number of users on the system and prove the payoff of using IEEE 802.11ax. We evaluate the contention window size performance as one of the most important metrics, on which throughput highly depends.
{"title":"On the Energy Efficiency of IEEE 802.11Ax High Density WLANS","authors":"Zineb Machrouh, A. Najid, Iyad Lahcen-Cherif","doi":"10.5121/csit.2022.121715","DOIUrl":"https://doi.org/10.5121/csit.2022.121715","url":null,"abstract":"Wireless communications evolved in a remarkable way during the last decade and is well on its way to surpass wired internet. The demand shifted towards higher transmission speed for more users and heavier traffics. In this paper, we present an IEEE 802.11ax scenario, in which we study the energy efficiency for the key metrics of the MAC layer. In this latest edition, called High Efficiency WLAN (HEW) energy is a main concern in order to satisfy scenarios of internet of things and wireless sensor networks. we prove that some of the new features such as the higher order modulation and coding schemes enhance remarkably the energy efficiency. We also show the impact of an increase in the number of users on the system and prove the payoff of using IEEE 802.11ax. We evaluate the contention window size performance as one of the most important metrics, on which throughput highly depends.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126954004","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}
Pub Date : 2022-10-22DOI: 10.5121/csit.2022.121708
Feihong Liu, Yu Sun
Many people believe that the crouch start was the best way to start a sprint [1]. While it seems intuitive, when the process of running is dissected using specific physical and mathematical representations, the question of “what is the best starting position” becomes harder to answer [2]. This paper aims to examine this phenomenon through a computer science approach inspired by gradient descent. Specifically, this paper aims to maximise the distance covered by a runner in ten steps. Assuming that runners do their best on every step and that their motion is not slowed by friction or air resistance, we will generate a hypothetical environment to study what the best strategy is for reaching the furthest distance within ten steps.
{"title":"A Gradient Descent Inspired Approach to Optimization of Physics Question","authors":"Feihong Liu, Yu Sun","doi":"10.5121/csit.2022.121708","DOIUrl":"https://doi.org/10.5121/csit.2022.121708","url":null,"abstract":"Many people believe that the crouch start was the best way to start a sprint [1]. While it seems intuitive, when the process of running is dissected using specific physical and mathematical representations, the question of “what is the best starting position” becomes harder to answer [2]. This paper aims to examine this phenomenon through a computer science approach inspired by gradient descent. Specifically, this paper aims to maximise the distance covered by a runner in ten steps. Assuming that runners do their best on every step and that their motion is not slowed by friction or air resistance, we will generate a hypothetical environment to study what the best strategy is for reaching the furthest distance within ten steps.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114861706","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}
Pub Date : 2022-10-22DOI: 10.5121/csit.2022.121710
Mu Xiangwei, Zhuang Mingjie, Liu Shuxin, Li Kequan, You Lianlian, Che Shuang
Predict and analyze key features of all-cause death in maintenance hemodialysis patients to provide guidance for later diagnosis and treatment. Four machine learning methods were used to establish an all-cause death prediction model for maintenance hemodialysis patients and compare their performance. Analyze the key characteristics that have an important impact on all-cause death, and conduct user portraits for patients of different ages and genders. After comparison, the random forest algorithm works best, and an important factor affecting the all-cause death of patients is obtained. Among them, the all-cause death of all patients is related to factors such as albumin, blood potassium, blood magnesium, and urea; With age, the importance of factors such as blood sodium and phosphorus increases, and the importance of factors such as cardiac ultrasound ejection fraction decreases. Finally, there were also differences in the importance of analyzing patients of different ages and different sexes affecting their all-cause death. It is useful for residents to adjust their dialysis index timely.
{"title":"Prediction and Key Characteristics of All-Cause Mortality in Maintenance Hemodialysis Patients","authors":"Mu Xiangwei, Zhuang Mingjie, Liu Shuxin, Li Kequan, You Lianlian, Che Shuang","doi":"10.5121/csit.2022.121710","DOIUrl":"https://doi.org/10.5121/csit.2022.121710","url":null,"abstract":"Predict and analyze key features of all-cause death in maintenance hemodialysis patients to provide guidance for later diagnosis and treatment. Four machine learning methods were used to establish an all-cause death prediction model for maintenance hemodialysis patients and compare their performance. Analyze the key characteristics that have an important impact on all-cause death, and conduct user portraits for patients of different ages and genders. After comparison, the random forest algorithm works best, and an important factor affecting the all-cause death of patients is obtained. Among them, the all-cause death of all patients is related to factors such as albumin, blood potassium, blood magnesium, and urea; With age, the importance of factors such as blood sodium and phosphorus increases, and the importance of factors such as cardiac ultrasound ejection fraction decreases. Finally, there were also differences in the importance of analyzing patients of different ages and different sexes affecting their all-cause death. It is useful for residents to adjust their dialysis index timely.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122053522","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}
Pub Date : 2022-10-22DOI: 10.5121/csit.2022.121716
César Fernández, M. A. Vicente, S. Lorenzo, I. Carrillo, M. Guilabert
Failure to follow do-not-do recommendations (also known as low-value practices) is one of the causes of the lack of quality care in all health systems in all countries. Healthcare professionals must be provided with information about these low-value practices that are still frequently performed and their implications for patients and the healthcare system. Continuous education is a key factor in this scenario, so that health students, health professionals, and even patients are kept updated with the main do-not-do recommendations. Gamified platforms are one of the most valuable options for continuous education, as they combine learning efficiency with a high level of engagement for the students. Besides, the effectiveness of gamification platforms can be improved by adding artificial intelligence techniques. In this paper, a novel gamified platform focused on improving knowledge about low-value practices is proposed. AI techniques, as well as NLP tools are used to optimize the effectiveness of learning by adapting the platform to each user, at an individual level. Besides, the engagement of students is encouraged by their participation in a common project, namely the creation of a specialized dictionary for do-not-do terms. Hardware development is currently in progress. A basic gamification platform has already been developed for the two main mobile operating systems. Developing IA and NLP techniques to analyse the training outputs and make the platform adaptable to each student is progressing. The proposed learning tool can significantly improve healthcare quality and be applied to many other learning fields, particularly when continuous training is required.
{"title":"Reconfigurable Gamification Platform for the Autonomous Learning of Low Value Medical Practices","authors":"César Fernández, M. A. Vicente, S. Lorenzo, I. Carrillo, M. Guilabert","doi":"10.5121/csit.2022.121716","DOIUrl":"https://doi.org/10.5121/csit.2022.121716","url":null,"abstract":"Failure to follow do-not-do recommendations (also known as low-value practices) is one of the causes of the lack of quality care in all health systems in all countries. Healthcare professionals must be provided with information about these low-value practices that are still frequently performed and their implications for patients and the healthcare system. Continuous education is a key factor in this scenario, so that health students, health professionals, and even patients are kept updated with the main do-not-do recommendations. Gamified platforms are one of the most valuable options for continuous education, as they combine learning efficiency with a high level of engagement for the students. Besides, the effectiveness of gamification platforms can be improved by adding artificial intelligence techniques. In this paper, a novel gamified platform focused on improving knowledge about low-value practices is proposed. AI techniques, as well as NLP tools are used to optimize the effectiveness of learning by adapting the platform to each user, at an individual level. Besides, the engagement of students is encouraged by their participation in a common project, namely the creation of a specialized dictionary for do-not-do terms. Hardware development is currently in progress. A basic gamification platform has already been developed for the two main mobile operating systems. Developing IA and NLP techniques to analyse the training outputs and make the platform adaptable to each student is progressing. The proposed learning tool can significantly improve healthcare quality and be applied to many other learning fields, particularly when continuous training is required.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124037882","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}
Pub Date : 2022-10-22DOI: 10.5121/csit.2022.121702
Wadnerson Boileau
Insurance has been around for more than centuries. This risk mitigation strategy has been utilized in maritime commerce as early thousand years ago, where Asian merchant seafarers were pooling together their wares in collective funds to pay for damages of individual’s capsized ship. In 2018, insurance industry made up 6% of global GDP while financial industry amounted to about 7-9% of the US GDP. In 2020, the industry net premiums written totaled $1.28 trillion, created 2.9 million jobs, and recorded $2.0 trillion investments. Despite of growing reform, the insurance market is dominated by intermediaries assisting people to match their insurance needs. While many predictions focused on artificial intelligence, cloud computing, blockchain stands out as the most disruptive technology that can change the driving forces underlying the global economy. We will focus on presenting blockchain use cases in insurance, demonstrating how the sector can turn blockchain threat into innovative opportunities.
{"title":"Blockchain in Insurance Industry: Turning Threat into Innovative Opportunities","authors":"Wadnerson Boileau","doi":"10.5121/csit.2022.121702","DOIUrl":"https://doi.org/10.5121/csit.2022.121702","url":null,"abstract":"Insurance has been around for more than centuries. This risk mitigation strategy has been utilized in maritime commerce as early thousand years ago, where Asian merchant seafarers were pooling together their wares in collective funds to pay for damages of individual’s capsized ship. In 2018, insurance industry made up 6% of global GDP while financial industry amounted to about 7-9% of the US GDP. In 2020, the industry net premiums written totaled $1.28 trillion, created 2.9 million jobs, and recorded $2.0 trillion investments. Despite of growing reform, the insurance market is dominated by intermediaries assisting people to match their insurance needs. While many predictions focused on artificial intelligence, cloud computing, blockchain stands out as the most disruptive technology that can change the driving forces underlying the global economy. We will focus on presenting blockchain use cases in insurance, demonstrating how the sector can turn blockchain threat into innovative opportunities.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133982219","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}
Pub Date : 2022-10-22DOI: 10.5121/csit.2022.121705
J. Dai, Yu Sun
The aim of this paper is to provide a solution to the growing need for fresh music to use in media, as adding music can greatly enhance the media’s atmosphere and the viewers’ experience [6]. Our solution to this issue was the creation of a mobile application named MFly that can output music using the sentiment from an inputted message. To test the effectiveness of this new music-generating method, an experiment was conducted in which twenty-three participants inputted a message with a positive and negative sentiment each and recorded whether each outputted musical piece accurately represented the sentiment from the message [7]. A post-experiment survey was also provided to each of the participants to gauge the convenience and practicality of the application. The results indicated that MFly was largely successful at conveying messages into appropriately fitting music. However, the practicality of the application could use some work, as generating music based on the sentiment does not always seem to match up with the original inputted message's sentiment, especially with messages that have a negative sentiment. Furthermore, feedback from participants indicated that the application could still improve with the addition of more features, such as the ability to save the generated music for later use.
{"title":"An Efficient AI Music Generation mobile platform Based on Machine Learning and ANN Network","authors":"J. Dai, Yu Sun","doi":"10.5121/csit.2022.121705","DOIUrl":"https://doi.org/10.5121/csit.2022.121705","url":null,"abstract":"The aim of this paper is to provide a solution to the growing need for fresh music to use in media, as adding music can greatly enhance the media’s atmosphere and the viewers’ experience [6]. Our solution to this issue was the creation of a mobile application named MFly that can output music using the sentiment from an inputted message. To test the effectiveness of this new music-generating method, an experiment was conducted in which twenty-three participants inputted a message with a positive and negative sentiment each and recorded whether each outputted musical piece accurately represented the sentiment from the message [7]. A post-experiment survey was also provided to each of the participants to gauge the convenience and practicality of the application. The results indicated that MFly was largely successful at conveying messages into appropriately fitting music. However, the practicality of the application could use some work, as generating music based on the sentiment does not always seem to match up with the original inputted message's sentiment, especially with messages that have a negative sentiment. Furthermore, feedback from participants indicated that the application could still improve with the addition of more features, such as the ability to save the generated music for later use.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126306788","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}
Pub Date : 2022-08-20DOI: 10.5121/csit.2022.121706
Tong Wang
The research on the brain mechanism of creativity mainly has two aspects, one is the creative thinking process, and the other is the brain structure and functional connection characteristics of highly creative people. The hundreds of millions of nerve cells in the brain connect and interact with each other. The human brain has a high degree of complexity at the biological level, especially the rational thinking ability of the human brain. Starting from the connection of molecules, cells, neural networks and the neural function structure of the brain, it may be fundamentally impossible to study the rational thinking mode of human beings. Human's rational thinking mode has a high degree of freedom and transcendence, and such problems cannot be expected to be studied by elaborating the realization of the nervous system. The rational thinking of the brain is mainly based on the structured thinking mode, and the structured thinking mode shows the great scientific power. This paper studies the theoretical model of innovative thinking based on category theory, and analyzes the creation process of two scientific theories which are landmarks in the history of science, and provides an intuitive, clear interpretation model and rigorous mathematical argument for the creative thinking. The structured thinking way have great revelation and help to create new scientific theories.
{"title":"Research on Creative Thinking Mode based on Category Theory","authors":"Tong Wang","doi":"10.5121/csit.2022.121706","DOIUrl":"https://doi.org/10.5121/csit.2022.121706","url":null,"abstract":"The research on the brain mechanism of creativity mainly has two aspects, one is the creative thinking process, and the other is the brain structure and functional connection characteristics of highly creative people. The hundreds of millions of nerve cells in the brain connect and interact with each other. The human brain has a high degree of complexity at the biological level, especially the rational thinking ability of the human brain. Starting from the connection of molecules, cells, neural networks and the neural function structure of the brain, it may be fundamentally impossible to study the rational thinking mode of human beings. Human's rational thinking mode has a high degree of freedom and transcendence, and such problems cannot be expected to be studied by elaborating the realization of the nervous system. The rational thinking of the brain is mainly based on the structured thinking mode, and the structured thinking mode shows the great scientific power. This paper studies the theoretical model of innovative thinking based on category theory, and analyzes the creation process of two scientific theories which are landmarks in the history of science, and provides an intuitive, clear interpretation model and rigorous mathematical argument for the creative thinking. The structured thinking way have great revelation and help to create new scientific theories.","PeriodicalId":170432,"journal":{"name":"Signal & Image Processing Trends","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126917092","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：