The Experts below are selected from a list of 93 Experts worldwide ranked by ideXlab platform
Alvis C. M. Fong - One of the best experts on this subject based on the ideXlab platform.
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A review of the state-of-the-art distributed compressive video sensing architectures
Journal of Computer Applications in Technology, 2014Co-Authors: Noreen Imran, Boon-chong Seet, Alvis C. M. FongAbstract:Low complexity video coding that provides efficient compression with reasonable reconstruction quality has been a Desired Requirement for resource-constrained video sensors in distributed vision-based sensing applications. In this paper, we present a review of the state-of-the-art codec architectures based on distributed compressive video sensing (DCVS), which is a relatively new video coding paradigm that integrates the techniques of distributed video coding (DVC) and compressive sensing (CS). The review includes a comparative discussion of several well-known DCVS architectures in literature with a focus on their functional aspects, and suggests a number of possible enhancements to the design of these architectures.
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Distributed compressive video sensing: A review of the state-of-the-art architectures
2012Co-Authors: Noreen Imran, Boon-chong Seet, Alvis C. M. FongAbstract:Low complexity video coding that provides efficient compression with reasonable reconstruction quality has been a Desired Requirement for resource-constrained video sensors in distributed vision-based sensing applications. In this paper, we present a review of the state-of-the-art codec architectures based on distributed compressive video sensing (DCVS), which is a relatively new video coding paradigm that integrates the techniques of distributed video coding (DVC) and compressive sensing (CS). The review includes a comparative discussion of several well-known DCVS architectures in literature with a focus on their functional aspects, and suggests a number of possible enhancements to the design of these architectures.
Noreen Imran - One of the best experts on this subject based on the ideXlab platform.
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A review of the state-of-the-art distributed compressive video sensing architectures
Journal of Computer Applications in Technology, 2014Co-Authors: Noreen Imran, Boon-chong Seet, Alvis C. M. FongAbstract:Low complexity video coding that provides efficient compression with reasonable reconstruction quality has been a Desired Requirement for resource-constrained video sensors in distributed vision-based sensing applications. In this paper, we present a review of the state-of-the-art codec architectures based on distributed compressive video sensing (DCVS), which is a relatively new video coding paradigm that integrates the techniques of distributed video coding (DVC) and compressive sensing (CS). The review includes a comparative discussion of several well-known DCVS architectures in literature with a focus on their functional aspects, and suggests a number of possible enhancements to the design of these architectures.
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Distributed compressive video sensing: A review of the state-of-the-art architectures
2012Co-Authors: Noreen Imran, Boon-chong Seet, Alvis C. M. FongAbstract:Low complexity video coding that provides efficient compression with reasonable reconstruction quality has been a Desired Requirement for resource-constrained video sensors in distributed vision-based sensing applications. In this paper, we present a review of the state-of-the-art codec architectures based on distributed compressive video sensing (DCVS), which is a relatively new video coding paradigm that integrates the techniques of distributed video coding (DVC) and compressive sensing (CS). The review includes a comparative discussion of several well-known DCVS architectures in literature with a focus on their functional aspects, and suggests a number of possible enhancements to the design of these architectures.
Lianfeng Shen - One of the best experts on this subject based on the ideXlab platform.
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GLOBECOM - Node Selection Based on Equal-REB Contour for Wireless Network Localization under Desired Accuracy
2019 IEEE Global Communications Conference (GLOBECOM), 2019Co-Authors: Yaping Zhu, Feng Yan, Weiwei Xia, Song Xing, Yueyue Zhang, Lianfeng ShenAbstract:Considering the scenarios where the localization accuracy of the agent is required to meet a Desired Requirement rather than achieve the best result, it is not necessary for all nodes to participate in positioning the agent. In this paper, a reference node (RN) selection algorithm for wireless network localization under Desired accuracy is proposed. A robust error bound (REB) is derived as the RN selection metric and the concept of equal-REB contour is given, based upon which the searching region (SR) for selecting RNs is defined. In REB, the measurement errors of distances are taken into consideration and modeled as a Gaussian noise whose variance is proportional to the square of the distance. The proposed RN selection strategy selects nodes from the SR instead of the whole network region iteratively until the localization accuracy meets the Desired Requirement. Simulations show that the RN-selection algorithm can select the RN sets providing better localization accuracy when using REB metric. Moreover, the improved performance in terms of power conservation of the proposed algorithm is evaluated through simulation results.
Boon-chong Seet - One of the best experts on this subject based on the ideXlab platform.
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A review of the state-of-the-art distributed compressive video sensing architectures
Journal of Computer Applications in Technology, 2014Co-Authors: Noreen Imran, Boon-chong Seet, Alvis C. M. FongAbstract:Low complexity video coding that provides efficient compression with reasonable reconstruction quality has been a Desired Requirement for resource-constrained video sensors in distributed vision-based sensing applications. In this paper, we present a review of the state-of-the-art codec architectures based on distributed compressive video sensing (DCVS), which is a relatively new video coding paradigm that integrates the techniques of distributed video coding (DVC) and compressive sensing (CS). The review includes a comparative discussion of several well-known DCVS architectures in literature with a focus on their functional aspects, and suggests a number of possible enhancements to the design of these architectures.
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Distributed compressive video sensing: A review of the state-of-the-art architectures
2012Co-Authors: Noreen Imran, Boon-chong Seet, Alvis C. M. FongAbstract:Low complexity video coding that provides efficient compression with reasonable reconstruction quality has been a Desired Requirement for resource-constrained video sensors in distributed vision-based sensing applications. In this paper, we present a review of the state-of-the-art codec architectures based on distributed compressive video sensing (DCVS), which is a relatively new video coding paradigm that integrates the techniques of distributed video coding (DVC) and compressive sensing (CS). The review includes a comparative discussion of several well-known DCVS architectures in literature with a focus on their functional aspects, and suggests a number of possible enhancements to the design of these architectures.
Yaping Zhu - One of the best experts on this subject based on the ideXlab platform.
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GLOBECOM - Node Selection Based on Equal-REB Contour for Wireless Network Localization under Desired Accuracy
2019 IEEE Global Communications Conference (GLOBECOM), 2019Co-Authors: Yaping Zhu, Feng Yan, Weiwei Xia, Song Xing, Yueyue Zhang, Lianfeng ShenAbstract:Considering the scenarios where the localization accuracy of the agent is required to meet a Desired Requirement rather than achieve the best result, it is not necessary for all nodes to participate in positioning the agent. In this paper, a reference node (RN) selection algorithm for wireless network localization under Desired accuracy is proposed. A robust error bound (REB) is derived as the RN selection metric and the concept of equal-REB contour is given, based upon which the searching region (SR) for selecting RNs is defined. In REB, the measurement errors of distances are taken into consideration and modeled as a Gaussian noise whose variance is proportional to the square of the distance. The proposed RN selection strategy selects nodes from the SR instead of the whole network region iteratively until the localization accuracy meets the Desired Requirement. Simulations show that the RN-selection algorithm can select the RN sets providing better localization accuracy when using REB metric. Moreover, the improved performance in terms of power conservation of the proposed algorithm is evaluated through simulation results.