The Experts below are selected from a list of 190800 Experts worldwide ranked by ideXlab platform
Pramod K Varshney - One of the best experts on this subject based on the ideXlab platform.
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joint Collaboration and compression Design for distributed sequential estimation in a wireless sensor network
IEEE Transactions on Signal Processing, 2021Co-Authors: Xiancheng Cheng, Prashant Khanduri, Baixiao Chen, Pramod K VarshneyAbstract:In this work, we propose a joint Collaboration-compression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a Collaboration matrix) with each other. Then a subset of sensors selected to communicate with the FC linearly compress their observations before transmission. We Design near-optimal Collaboration and linear compression strategies under power constraints via alternating minimization of the sequential minimum mean square error. The objective function for Collaboration Design is generally non-convex. We establish correspondence between the sparse Collaboration matrix and the non-sparse vector consisting of the nonzero elements of the Collaboration matrix. Then, we reformulate and solve the Collaboration Design problem using quadratically constrained quadratic program (QCQP). The compression Design problem is solved using the same methodology. We propose two versions of compression Design, one centralized scheme where the compression strategies are derived at the FC and decentralized, where the local sensors compute their individual compression strategies independently. Importantly, we show that the proposed methods can also be used for estimating time-varying random vector parameters. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.
Xiancheng Cheng - One of the best experts on this subject based on the ideXlab platform.
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joint Collaboration and compression Design for distributed sequential estimation in a wireless sensor network
IEEE Transactions on Signal Processing, 2021Co-Authors: Xiancheng Cheng, Prashant Khanduri, Baixiao Chen, Pramod K VarshneyAbstract:In this work, we propose a joint Collaboration-compression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a Collaboration matrix) with each other. Then a subset of sensors selected to communicate with the FC linearly compress their observations before transmission. We Design near-optimal Collaboration and linear compression strategies under power constraints via alternating minimization of the sequential minimum mean square error. The objective function for Collaboration Design is generally non-convex. We establish correspondence between the sparse Collaboration matrix and the non-sparse vector consisting of the nonzero elements of the Collaboration matrix. Then, we reformulate and solve the Collaboration Design problem using quadratically constrained quadratic program (QCQP). The compression Design problem is solved using the same methodology. We propose two versions of compression Design, one centralized scheme where the compression strategies are derived at the FC and decentralized, where the local sensors compute their individual compression strategies independently. Importantly, we show that the proposed methods can also be used for estimating time-varying random vector parameters. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.
Christian M Becker - One of the best experts on this subject based on the ideXlab platform.
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world endometriosis research foundation endometriosis phenome and biobanking harmonization project ii clinical and covariate phenotype data collection in endometriosis research
Fertility and Sterility, 2014Co-Authors: Allison F Vitonis, Katy Vincent, Nilufer Rahmioglu, Amelie Fassbender, Germaine Buck M Louis, Lone Hummelshoj, Linda C Giudice, Pamela Stratton, David G Adamson, Christian M BeckerAbstract:Objective To harmonize the collection of nonsurgical clinical and epidemiologic data relevant to endometriosis research, allowing large-scale Collaboration. Design An international Collaboration involving 34 clinical/academic centers and three industry collaborators from 16 countries on five continents. Setting In 2013, two workshops followed by global consultation, bringing together 54 leaders in endometriosis research. Patients None. Intervention(s) Development of a self-administered endometriosis patient questionnaire (EPQ), based on [1] systematic comparison of questionnaires from eight centers that collect data from endometriosis cases (and controls/comparison women) on a medium to large scale (publication on >100 cases); [2] literature evidence; and [3] several global consultation rounds. Main Outcome Measure(s) Standard recommended and minimum required questionnaires to capture detailed clinical and covariate data. Result(s) The standard recommended (EPHect EPQ-S) and minimum required (EPHect EPQ-M) questionnaires contain questions on pelvic pain, subfertility and menstrual/reproductive history, hormone/medication use, medical history, and personal information. Conclusion(s) The EPQ captures the basic set of patient characteristics and exposures considered by the WERF EPHect Working Group to be most critical for the advancement of endometriosis research, but is also relevant to other female conditions with similar risk factors and/or symptomatology. The instruments will be reviewed based on feedback from investigators, and—after a first review after 1 year—triannually through systematic follow-up surveys. Updated versions will be made available through http://endometriosisfoundation.org/ephect.
Baixiao Chen - One of the best experts on this subject based on the ideXlab platform.
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joint Collaboration and compression Design for distributed sequential estimation in a wireless sensor network
IEEE Transactions on Signal Processing, 2021Co-Authors: Xiancheng Cheng, Prashant Khanduri, Baixiao Chen, Pramod K VarshneyAbstract:In this work, we propose a joint Collaboration-compression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a Collaboration matrix) with each other. Then a subset of sensors selected to communicate with the FC linearly compress their observations before transmission. We Design near-optimal Collaboration and linear compression strategies under power constraints via alternating minimization of the sequential minimum mean square error. The objective function for Collaboration Design is generally non-convex. We establish correspondence between the sparse Collaboration matrix and the non-sparse vector consisting of the nonzero elements of the Collaboration matrix. Then, we reformulate and solve the Collaboration Design problem using quadratically constrained quadratic program (QCQP). The compression Design problem is solved using the same methodology. We propose two versions of compression Design, one centralized scheme where the compression strategies are derived at the FC and decentralized, where the local sensors compute their individual compression strategies independently. Importantly, we show that the proposed methods can also be used for estimating time-varying random vector parameters. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.
Prashant Khanduri - One of the best experts on this subject based on the ideXlab platform.
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joint Collaboration and compression Design for distributed sequential estimation in a wireless sensor network
IEEE Transactions on Signal Processing, 2021Co-Authors: Xiancheng Cheng, Prashant Khanduri, Baixiao Chen, Pramod K VarshneyAbstract:In this work, we propose a joint Collaboration-compression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a Collaboration matrix) with each other. Then a subset of sensors selected to communicate with the FC linearly compress their observations before transmission. We Design near-optimal Collaboration and linear compression strategies under power constraints via alternating minimization of the sequential minimum mean square error. The objective function for Collaboration Design is generally non-convex. We establish correspondence between the sparse Collaboration matrix and the non-sparse vector consisting of the nonzero elements of the Collaboration matrix. Then, we reformulate and solve the Collaboration Design problem using quadratically constrained quadratic program (QCQP). The compression Design problem is solved using the same methodology. We propose two versions of compression Design, one centralized scheme where the compression strategies are derived at the FC and decentralized, where the local sensors compute their individual compression strategies independently. Importantly, we show that the proposed methods can also be used for estimating time-varying random vector parameters. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.