The Experts below are selected from a list of 260040 Experts worldwide ranked by ideXlab platform
Sajal K. Das - One of the best experts on this subject based on the ideXlab platform.
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Inhabitant guidance of smart environments
International Conference on Human-Computer Interaction, 2007Co-Authors: Parisa Rashidi, Diane J. Cook, Michael G Youngblood, Sajal K. DasAbstract:With the convergence of technologies in artificial intelligence, human-computer interfaces, and pervasive computing, the idea of a "smart environment" is becoming a reality. While we all would like the benefits of an environment that automates many of our daily tasks, a smart environment that makes the wrong decisions can quickly becoming annoying. In this paper, we describe a simulation tool that can be used to visualize activity data in a smart home, play through proposed automation schemes, and ultimately provide guidance to automating the smart environment. We describe how automation policies can adapt to resident feedback, and demonstrate the ideas in the context of the MavHome smart home.
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context aware resource management in multi Inhabitant smart homes a framework based on nash h learning
Pervasive and Mobile Computing, 2006Co-Authors: Sajal K. Das, Nirmalya Roy, Abhishek RoyAbstract:Abstract A smart home aims at building intelligent automation with a goal to provide its Inhabitants with maximum possible comfort, minimum resource consumption and thus reduced cost of home maintenance. ‘Context Awareness’ is perhaps the most salient feature of such an intelligent environment. An Inhabitant’s mobility and activities play a significant role in defining his/her contexts in and around the home. Although there exists an optimal algorithm for location and activity tracking of a single Inhabitant, the correlation and dependence between multiple Inhabitants’ contexts within the same environment make the location and activity tracking more challenging. In this paper, we first prove that the optimal location prediction across multiple Inhabitants in smart homes is an NP-hard problem. Next, to capture the correlation and interactions between different Inhabitants’ movements (and hence activities), we develop a novel framework based on a game theoretic, Nash H -learning approach that attempts to minimize the joint location uncertainty of Inhabitants. Our framework achieves a Nash equilibrium such that no Inhabitant is given preference over others. This results in more accurate prediction of contexts and more adaptive control of automated devices, thus leading to a mobility-aware resource (say, energy) management scheme in multi-Inhabitant smart homes. Experimental results demonstrate that the proposed framework is capable of adaptively controlling a smart environment, significantly reduces energy consumption and enhances the comfort of the Inhabitants.
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context aware resource management in multi Inhabitant smart homes a nash h learning based approach
IEEE International Conference on Pervasive Computing and Communications, 2006Co-Authors: Nirmalya Roy, Abhishek Roy, Sajal K. DasAbstract:A smart home aims at building intelligence automation with a goal to provide its Inhabitants with maximum possible comfort, minimize the resource consumption and thus overall cost of maintaining the home. 'Context awareness' is perhaps the most salient feature of such an intelligent environment. Clearly, an Inhabitant's mobility and activities play a significant role in defining his contexts in and around the home. Although there exists an optimal algorithm for location and activity tracking of a single Inhabitant, the correlation and dependence between multiple Inhabitants' contexts within the same environment make the location and activity tracking more challenging. In this paper, we first prove that the optimal location prediction across multiple Inhabitants in smart homes is an NP-hard problem. Next, to capture the correlation and interactions of different Inhabitants' movements (and hence activities), we develop a novel framework based on a game theoretic, Nash H-learning approach that attempts to minimize the joint location uncertainty. The framework achieves a Nash equilibrium such that no Inhabitant is given preference over others. This results in more accurate prediction of contexts and better adaptive control of automated devices, leading to a mobility-aware resource (say, energy) management scheme in multi-Inhabitant smart homes. Experimental results demonstrate that the proposed framework is capable of adaptively controlling a smart environment, thus reducing energy consumption and enhancing the comfort of the Inhabitants.
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a cooperative learning framework for mobility aware resource management in multi Inhabitant smart homes
International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2005Co-Authors: Nirmalya Roy, Sajal K. Das, Abhishek Roy, Kalyan BasuAbstract:The essence of pervasive (ubiquitous) computing lies in the creation of smart environments saturated with computing and communication capabilities, yet gracefully integrated with human users. 'Context Awareness' is perhaps the most important feature of such an intelligent computing paradigm. The mobility and activity of the Inhabitants play significant roles in forming the context at any instance of time. In order to extract the best performance and efficacy of smart computing environments, one needs a technology-independent, context-aware platform spanning over multiple Inhabitants. In this paper, we have developed a framework for mobility-aware resource (in particular, energy consumption) management in a multi-Inhabitant smart home, based on a dynamic, cooperative reinforcement learning technique. The Inhabitants' mobility creates uncertainty of his location and activity. Using the proposed cooperative game-theory based framework, all the Inhabitants currently present in the house attempt to minimize this overall uncertainty in the form of utility functions associated with them. Joint optimization of the utility function corresponds to the convergence to Nash equilibrium and helps in accurate prediction of Inhabitants' future locations and activities. This results in adaptive control of automated devices and temperature of the house, thus providing an amicable environment and sufficient comfort to the Inhabitants. Simulation results point out that our framework can adaptively control the smart environment, while reducing the energy consumption and enhancing the comfort.
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The role of prediction algorithms in the MavHome smart home architecture
IEEE Wireless Communications, 2002Co-Authors: Sajal K. Das, Diane J. Cook, A. Battacharya, Edwin O. Heierman, Tze-yun LinAbstract:The goal of the MavHome project is to create a home that acts as a rational agent. The agent seeks to maximize Inhabitant comfort and minimize operation cost. To achieve these goals, the agent must be able to predict the mobility patterns and device usages of the Inhabitants. We introduce the MavHome project and its underlying architecture. The role of prediction algorithms within the architecture is discussed, and three prediction algorithms that are central to home operations are presented. We demonstrate the effectiveness of these algorithms on synthetic and/or actual smart home data.
Diane J. Cook - One of the best experts on this subject based on the ideXlab platform.
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Inhabitant guidance of smart environments
International Conference on Human-Computer Interaction, 2007Co-Authors: Parisa Rashidi, Diane J. Cook, Michael G Youngblood, Sajal K. DasAbstract:With the convergence of technologies in artificial intelligence, human-computer interfaces, and pervasive computing, the idea of a "smart environment" is becoming a reality. While we all would like the benefits of an environment that automates many of our daily tasks, a smart environment that makes the wrong decisions can quickly becoming annoying. In this paper, we describe a simulation tool that can be used to visualize activity data in a smart home, play through proposed automation schemes, and ultimately provide guidance to automating the smart environment. We describe how automation policies can adapt to resident feedback, and demonstrate the ideas in the context of the MavHome smart home.
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a multi agent approach to controlling a smart environment
Lecture Notes in Computer Science, 2006Co-Authors: Diane J. Cook, Michael YoungbloodAbstract:The goal of the MavHome (Managing An Intelligent Versa- tile Home) project is to create a home that acts as a rational agent. The agent seeks to maximize Inhabitant comfort and minimize operation cost. In order to achieve these goals, the agent must be able to predict the mobility patterns and device usages of the Inhabitants. Because of the size of the problem, controlling a smart environment can be effectively approached as a multi-agent task. Individual agents can address a portion of the problem but must coordinate their actions to accomplish the overall goals of the system. In this chapter, we discuss the application of multi-agent systems to the challenge of controlling a smart environment and describe its implementation in the MavHome project.
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The role of prediction algorithms in the MavHome smart home architecture
IEEE Wireless Communications, 2002Co-Authors: Sajal K. Das, Diane J. Cook, A. Battacharya, Edwin O. Heierman, Tze-yun LinAbstract:The goal of the MavHome project is to create a home that acts as a rational agent. The agent seeks to maximize Inhabitant comfort and minimize operation cost. To achieve these goals, the agent must be able to predict the mobility patterns and device usages of the Inhabitants. We introduce the MavHome project and its underlying architecture. The role of prediction algorithms within the architecture is discussed, and three prediction algorithms that are central to home operations are presented. We demonstrate the effectiveness of these algorithms on synthetic and/or actual smart home data.
Abhishek Roy - One of the best experts on this subject based on the ideXlab platform.
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context aware resource management in multi Inhabitant smart homes a framework based on nash h learning
Pervasive and Mobile Computing, 2006Co-Authors: Sajal K. Das, Nirmalya Roy, Abhishek RoyAbstract:Abstract A smart home aims at building intelligent automation with a goal to provide its Inhabitants with maximum possible comfort, minimum resource consumption and thus reduced cost of home maintenance. ‘Context Awareness’ is perhaps the most salient feature of such an intelligent environment. An Inhabitant’s mobility and activities play a significant role in defining his/her contexts in and around the home. Although there exists an optimal algorithm for location and activity tracking of a single Inhabitant, the correlation and dependence between multiple Inhabitants’ contexts within the same environment make the location and activity tracking more challenging. In this paper, we first prove that the optimal location prediction across multiple Inhabitants in smart homes is an NP-hard problem. Next, to capture the correlation and interactions between different Inhabitants’ movements (and hence activities), we develop a novel framework based on a game theoretic, Nash H -learning approach that attempts to minimize the joint location uncertainty of Inhabitants. Our framework achieves a Nash equilibrium such that no Inhabitant is given preference over others. This results in more accurate prediction of contexts and more adaptive control of automated devices, thus leading to a mobility-aware resource (say, energy) management scheme in multi-Inhabitant smart homes. Experimental results demonstrate that the proposed framework is capable of adaptively controlling a smart environment, significantly reduces energy consumption and enhances the comfort of the Inhabitants.
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context aware resource management in multi Inhabitant smart homes a nash h learning based approach
IEEE International Conference on Pervasive Computing and Communications, 2006Co-Authors: Nirmalya Roy, Abhishek Roy, Sajal K. DasAbstract:A smart home aims at building intelligence automation with a goal to provide its Inhabitants with maximum possible comfort, minimize the resource consumption and thus overall cost of maintaining the home. 'Context awareness' is perhaps the most salient feature of such an intelligent environment. Clearly, an Inhabitant's mobility and activities play a significant role in defining his contexts in and around the home. Although there exists an optimal algorithm for location and activity tracking of a single Inhabitant, the correlation and dependence between multiple Inhabitants' contexts within the same environment make the location and activity tracking more challenging. In this paper, we first prove that the optimal location prediction across multiple Inhabitants in smart homes is an NP-hard problem. Next, to capture the correlation and interactions of different Inhabitants' movements (and hence activities), we develop a novel framework based on a game theoretic, Nash H-learning approach that attempts to minimize the joint location uncertainty. The framework achieves a Nash equilibrium such that no Inhabitant is given preference over others. This results in more accurate prediction of contexts and better adaptive control of automated devices, leading to a mobility-aware resource (say, energy) management scheme in multi-Inhabitant smart homes. Experimental results demonstrate that the proposed framework is capable of adaptively controlling a smart environment, thus reducing energy consumption and enhancing the comfort of the Inhabitants.
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a cooperative learning framework for mobility aware resource management in multi Inhabitant smart homes
International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2005Co-Authors: Nirmalya Roy, Sajal K. Das, Abhishek Roy, Kalyan BasuAbstract:The essence of pervasive (ubiquitous) computing lies in the creation of smart environments saturated with computing and communication capabilities, yet gracefully integrated with human users. 'Context Awareness' is perhaps the most important feature of such an intelligent computing paradigm. The mobility and activity of the Inhabitants play significant roles in forming the context at any instance of time. In order to extract the best performance and efficacy of smart computing environments, one needs a technology-independent, context-aware platform spanning over multiple Inhabitants. In this paper, we have developed a framework for mobility-aware resource (in particular, energy consumption) management in a multi-Inhabitant smart home, based on a dynamic, cooperative reinforcement learning technique. The Inhabitants' mobility creates uncertainty of his location and activity. Using the proposed cooperative game-theory based framework, all the Inhabitants currently present in the house attempt to minimize this overall uncertainty in the form of utility functions associated with them. Joint optimization of the utility function corresponds to the convergence to Nash equilibrium and helps in accurate prediction of Inhabitants' future locations and activities. This results in adaptive control of automated devices and temperature of the house, thus providing an amicable environment and sufficient comfort to the Inhabitants. Simulation results point out that our framework can adaptively control the smart environment, while reducing the energy consumption and enhancing the comfort.
Miren Lopez De Alda - One of the best experts on this subject based on the ideXlab platform.
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five year monitoring of 19 illicit and legal substances of abuse at the inlet of a wastewater treatment plant in barcelona ne spain and estimation of drug consumption patterns and trends
Science of The Total Environment, 2017Co-Authors: Nicola Mastroianni, Ester Lopezgarcia, Cristina Postigo, Damia Barcelo, Miren Lopez De AldaAbstract:Abstract Illicit and legal drugs of abuse, including alcohol, continue to be in the focus of many governmental national and international studies due to the important consequences of their consumption at both individual and social level. Estimation of drug use at the community level from the concentrations of the drugs themselves or their major metabolites measured in wastewater has become an increasingly accepted and extended tool, complementary to the methods traditionally used for this purpose. The present work describes the application of this approach, generally known as wastewater epidemiology, to investigate the latest drug consumption patterns and trends in the European city of Barcelona. To this end, a total of 19 selected drugs of abuse and metabolites were monitored at the inlet of one of the main wastewater treatment plants of Barcelona every day during one week in March between 2011 and 2015. Analysis of the selected drugs and metabolites in the wastewater samples was performed by means of two methodologies based on liquid chromatography-tandem mass spectrometry (LC-MS/MS), and the concentrations obtained were translated into consumption data. In agreement with official records, alcohol, followed by cannabis, cocaine, amphetamine-like compounds, and methadone were the most consumed drugs. Alcohol, cannabis, and cocaine consumption were on average 18 mL(14 g)/day/Inhabitant (> 15), 38 g/day/1000 Inhabitants aging 15–64, and 2.4 g/day/1000 Inhabitants aging 15–64, respectively. As for drug use trends, consumption increased over the 5 years monitored for all drugs, but for heroin and diazepam. Weekly profiles characterized by higher consumption over the weekend as compared to weekdays were observed only for alcohol, cocaine, and MDMA. Extrapolation of the data obtained for the area under study to the national Spanish territory yields consumption figures of 142 t of illicit drugs per year and > 2500 million euro turnover per year in the black market.
Damia Barcelo - One of the best experts on this subject based on the ideXlab platform.
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five year monitoring of 19 illicit and legal substances of abuse at the inlet of a wastewater treatment plant in barcelona ne spain and estimation of drug consumption patterns and trends
Science of The Total Environment, 2017Co-Authors: Nicola Mastroianni, Ester Lopezgarcia, Cristina Postigo, Damia Barcelo, Miren Lopez De AldaAbstract:Abstract Illicit and legal drugs of abuse, including alcohol, continue to be in the focus of many governmental national and international studies due to the important consequences of their consumption at both individual and social level. Estimation of drug use at the community level from the concentrations of the drugs themselves or their major metabolites measured in wastewater has become an increasingly accepted and extended tool, complementary to the methods traditionally used for this purpose. The present work describes the application of this approach, generally known as wastewater epidemiology, to investigate the latest drug consumption patterns and trends in the European city of Barcelona. To this end, a total of 19 selected drugs of abuse and metabolites were monitored at the inlet of one of the main wastewater treatment plants of Barcelona every day during one week in March between 2011 and 2015. Analysis of the selected drugs and metabolites in the wastewater samples was performed by means of two methodologies based on liquid chromatography-tandem mass spectrometry (LC-MS/MS), and the concentrations obtained were translated into consumption data. In agreement with official records, alcohol, followed by cannabis, cocaine, amphetamine-like compounds, and methadone were the most consumed drugs. Alcohol, cannabis, and cocaine consumption were on average 18 mL(14 g)/day/Inhabitant (> 15), 38 g/day/1000 Inhabitants aging 15–64, and 2.4 g/day/1000 Inhabitants aging 15–64, respectively. As for drug use trends, consumption increased over the 5 years monitored for all drugs, but for heroin and diazepam. Weekly profiles characterized by higher consumption over the weekend as compared to weekdays were observed only for alcohol, cocaine, and MDMA. Extrapolation of the data obtained for the area under study to the national Spanish territory yields consumption figures of 142 t of illicit drugs per year and > 2500 million euro turnover per year in the black market.