The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform

Konstantinos E Parsopoulos - One of the best experts on this subject based on the ideXlab platform.

  • Visual Information Processing and Communication - Kalai-Smorodinsky Bargaining Solution for Optimal Resource Allocation over Wireless DS-CDMA Visual Sensor Networks
    Visual Information Processing and Communication III, 2012
    Co-Authors: Katerina Pandremmenou, Lisimachos P Kondi, Konstantinos E Parsopoulos
    Abstract:

    Surveillance applications usually require high levels of video quality, resulting in high power consumption. The existence of a well–behaved scheme to balance video quality and power consumption is crucial for the system’s performance. In the present work, we adopt the game–theoretic approach of Kalai–Smorodinsky Bargaining Solution (KSBS) to deal with the problem of optimal resource allocation in a multi–node wireless visual sensor network (VSN). In our setting, the Direct Sequence Code Division Multiple Access (DS–CDMA) method is used for Channel access, while a cross–layer optimization design, which employs a central processing server, accounts for the overall system efficacy through all network layers. The task assigned to the central server is the communication with the nodes and the joint determination of their transmission parameters. The KSBS is applied to non–convex utility spaces, efficiently distributing the source Coding Rate, Channel Coding Rate and transmission powers among the nodes. In the underlying model, the transmission powers assume continuous values, whereas the source and Channel Coding Rates can take only discrete values. Experimental results are reported and discussed to demonstRate the merits of KSBS over competing policies.

  • ICASSP - Optimal power allocation and joint source-Channel Coding for wireless DS-CDMA visual sensor networks using the Nash Bargaining Solution
    2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011
    Co-Authors: Katerina Pandremmenou, Lisimachos P Kondi, Konstantinos E Parsopoulos
    Abstract:

    We consider the problem of resource allocation for a Direct Sequence Code Division Multiple Access (DS-CDMA) wireless visual sensor network (VSN).We use the Nash Bargaining Solution (NBS) from game theory in order to determine the transmission power and source and Channel Coding Rate for each node. The NBS assumes that the nodes negotiate (using the help of a centralized control unit) in order to jointly determine their transmission parameters. The transmission powers are allowed to take continuous values, whereas the source and Channel Coding Rate combination can only assume discrete values. Thus, the resulting optimization problem is a mixed-integer optimization task and is solved using Particle Swarm Optimization (PSO). Experimental results are provided and conclusions are drawn.

  • Visual Information Processing and Communication - Optimal Power Allocation and Joint Source-Channel Coding for Wireless DS-CDMA Visual Sensor Networks
    Visual Information Processing and Communication II, 2011
    Co-Authors: Katerina Pandremmenou, Lisimachos P Kondi, Konstantinos E Parsopoulos
    Abstract:

    In this paper, we propose a scheme for the optimal allocation of power, source Coding Rate, and Channel Coding Rate for each of the nodes of a wireless Direct Sequence Code Division Multiple Access (DS-CDMA) visual sensor network. The optimization is quality-driven, i.e. the received quality of the video that is transmitted by the nodes is optimized. The scheme takes into account the fact that the sensor nodes may be imaging scenes with varying levels of motion. Nodes that image low-motion scenes will require a lower source Coding Rate, so they will be able to allocate a greater portion of the total available bit Rate to Channel Coding. Stronger Channel Coding will mean that such nodes will be able to transmit at lower power. This will both increase battery life and reduce interference to other nodes. Two optimization criteria are considered. One that minimizes the average video distortion of the nodes and one that minimizes the maximum distortion among the nodes. The transmission powers are allowed to take continuous values, whereas the source and Channel Coding Rates can assume only discrete values. Thus, the resulting optimization problem lies in the field of mixed-integer optimization tasks and is solved using Particle Swarm Optimization. Our experimental results show the importance of considering the characteristics of the video sequences when determining the transmission power, source Coding Rate and Channel Coding Rate for the nodes of the visual sensor network.

Rama Chellappa - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive source-Channel subband video Coding for wireless Channels
    IEEE Journal on Selected Areas in Communications, 1998
    Co-Authors: Mandyam V. Srinivasan, Rama Chellappa
    Abstract:

    This paper presents a general framework for combined source-Channel Coding within the context of subband Coding. The unequal importance of subbands in reconstruction of the source is exploited by an appropriate allocation of source and Channel Coding Rates for the Coding and transmission of subbands over a noisy Channel. For each subband, the source Coding Rate as well as the level of protection (quantified by the Channel Coding Rate) are jointly chosen to minimize the total end-to-end mean-squared distortion suffered by the source. This allocation of source and Channel Coding Rates is posed as a constrained optimization problem, and solved using a generalized bit allocation algorithm. The optimal choice of source and Channel Coding Rates depends on the state of the physical Channel. These results are extended to transmission over fading Channels using a finite state model, where every state corresponds to an additive white Gaussian noise (AWGN) Channel. A Coding stRategy is also developed that minimizes the average distortion when the Channel state is unavailable at the transmitter. Experimental results are provided that demonstRate application of these combined source-Channel Coding stRategies on video sequences.

  • MMSP - Adaptive source-Channel subband video Coding for wireless Channels
    Proceedings of First Signal Processing Society Workshop on Multimedia Signal Processing, 1
    Co-Authors: Mandyam V. Srinivasan, Rama Chellappa, Philippe Burlina
    Abstract:

    This paper proposes an adaptive source-Channel subband Coding scheme for the transmission of video over fading wireless Channels. A three-dimensional subband decomposition followed by vector quantization of the subband coefficients forms the source Coding stRategy. For transmission over the Channel, the individual subbands are offered different amounts of protection depending on their importance in reconstruction at the receiver. For each subband, the source Coding Rate as well as the level of protection (quantified by the Channel Coding Rate) are jointly chosen to minimize the total mean-squared distortion suffered by the video coder. The choice of source and Channel Coding Rates depends on the state of the physical Channel. We use a finite state model for the fading Channel, where every state corresponds to an AWGN Channel. This results in a joint source-Channel Coding scheme that adapts in an optimal way to the current state of a fading Channel.

Katerina Pandremmenou - One of the best experts on this subject based on the ideXlab platform.

  • Visual Information Processing and Communication - Kalai-Smorodinsky Bargaining Solution for Optimal Resource Allocation over Wireless DS-CDMA Visual Sensor Networks
    Visual Information Processing and Communication III, 2012
    Co-Authors: Katerina Pandremmenou, Lisimachos P Kondi, Konstantinos E Parsopoulos
    Abstract:

    Surveillance applications usually require high levels of video quality, resulting in high power consumption. The existence of a well–behaved scheme to balance video quality and power consumption is crucial for the system’s performance. In the present work, we adopt the game–theoretic approach of Kalai–Smorodinsky Bargaining Solution (KSBS) to deal with the problem of optimal resource allocation in a multi–node wireless visual sensor network (VSN). In our setting, the Direct Sequence Code Division Multiple Access (DS–CDMA) method is used for Channel access, while a cross–layer optimization design, which employs a central processing server, accounts for the overall system efficacy through all network layers. The task assigned to the central server is the communication with the nodes and the joint determination of their transmission parameters. The KSBS is applied to non–convex utility spaces, efficiently distributing the source Coding Rate, Channel Coding Rate and transmission powers among the nodes. In the underlying model, the transmission powers assume continuous values, whereas the source and Channel Coding Rates can take only discrete values. Experimental results are reported and discussed to demonstRate the merits of KSBS over competing policies.

  • ICASSP - Optimal power allocation and joint source-Channel Coding for wireless DS-CDMA visual sensor networks using the Nash Bargaining Solution
    2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011
    Co-Authors: Katerina Pandremmenou, Lisimachos P Kondi, Konstantinos E Parsopoulos
    Abstract:

    We consider the problem of resource allocation for a Direct Sequence Code Division Multiple Access (DS-CDMA) wireless visual sensor network (VSN).We use the Nash Bargaining Solution (NBS) from game theory in order to determine the transmission power and source and Channel Coding Rate for each node. The NBS assumes that the nodes negotiate (using the help of a centralized control unit) in order to jointly determine their transmission parameters. The transmission powers are allowed to take continuous values, whereas the source and Channel Coding Rate combination can only assume discrete values. Thus, the resulting optimization problem is a mixed-integer optimization task and is solved using Particle Swarm Optimization (PSO). Experimental results are provided and conclusions are drawn.

  • Visual Information Processing and Communication - Optimal Power Allocation and Joint Source-Channel Coding for Wireless DS-CDMA Visual Sensor Networks
    Visual Information Processing and Communication II, 2011
    Co-Authors: Katerina Pandremmenou, Lisimachos P Kondi, Konstantinos E Parsopoulos
    Abstract:

    In this paper, we propose a scheme for the optimal allocation of power, source Coding Rate, and Channel Coding Rate for each of the nodes of a wireless Direct Sequence Code Division Multiple Access (DS-CDMA) visual sensor network. The optimization is quality-driven, i.e. the received quality of the video that is transmitted by the nodes is optimized. The scheme takes into account the fact that the sensor nodes may be imaging scenes with varying levels of motion. Nodes that image low-motion scenes will require a lower source Coding Rate, so they will be able to allocate a greater portion of the total available bit Rate to Channel Coding. Stronger Channel Coding will mean that such nodes will be able to transmit at lower power. This will both increase battery life and reduce interference to other nodes. Two optimization criteria are considered. One that minimizes the average video distortion of the nodes and one that minimizes the maximum distortion among the nodes. The transmission powers are allowed to take continuous values, whereas the source and Channel Coding Rates can assume only discrete values. Thus, the resulting optimization problem lies in the field of mixed-integer optimization tasks and is solved using Particle Swarm Optimization. Our experimental results show the importance of considering the characteristics of the video sequences when determining the transmission power, source Coding Rate and Channel Coding Rate for the nodes of the visual sensor network.

Mandyam V. Srinivasan - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive source-Channel subband video Coding for wireless Channels
    IEEE Journal on Selected Areas in Communications, 1998
    Co-Authors: Mandyam V. Srinivasan, Rama Chellappa
    Abstract:

    This paper presents a general framework for combined source-Channel Coding within the context of subband Coding. The unequal importance of subbands in reconstruction of the source is exploited by an appropriate allocation of source and Channel Coding Rates for the Coding and transmission of subbands over a noisy Channel. For each subband, the source Coding Rate as well as the level of protection (quantified by the Channel Coding Rate) are jointly chosen to minimize the total end-to-end mean-squared distortion suffered by the source. This allocation of source and Channel Coding Rates is posed as a constrained optimization problem, and solved using a generalized bit allocation algorithm. The optimal choice of source and Channel Coding Rates depends on the state of the physical Channel. These results are extended to transmission over fading Channels using a finite state model, where every state corresponds to an additive white Gaussian noise (AWGN) Channel. A Coding stRategy is also developed that minimizes the average distortion when the Channel state is unavailable at the transmitter. Experimental results are provided that demonstRate application of these combined source-Channel Coding stRategies on video sequences.

  • MMSP - Adaptive source-Channel subband video Coding for wireless Channels
    Proceedings of First Signal Processing Society Workshop on Multimedia Signal Processing, 1
    Co-Authors: Mandyam V. Srinivasan, Rama Chellappa, Philippe Burlina
    Abstract:

    This paper proposes an adaptive source-Channel subband Coding scheme for the transmission of video over fading wireless Channels. A three-dimensional subband decomposition followed by vector quantization of the subband coefficients forms the source Coding stRategy. For transmission over the Channel, the individual subbands are offered different amounts of protection depending on their importance in reconstruction at the receiver. For each subband, the source Coding Rate as well as the level of protection (quantified by the Channel Coding Rate) are jointly chosen to minimize the total mean-squared distortion suffered by the video coder. The choice of source and Channel Coding Rates depends on the state of the physical Channel. We use a finite state model for the fading Channel, where every state corresponds to an AWGN Channel. This results in a joint source-Channel Coding scheme that adapts in an optimal way to the current state of a fading Channel.

Lisimachos P Kondi - One of the best experts on this subject based on the ideXlab platform.

  • Visual Information Processing and Communication - Kalai-Smorodinsky Bargaining Solution for Optimal Resource Allocation over Wireless DS-CDMA Visual Sensor Networks
    Visual Information Processing and Communication III, 2012
    Co-Authors: Katerina Pandremmenou, Lisimachos P Kondi, Konstantinos E Parsopoulos
    Abstract:

    Surveillance applications usually require high levels of video quality, resulting in high power consumption. The existence of a well–behaved scheme to balance video quality and power consumption is crucial for the system’s performance. In the present work, we adopt the game–theoretic approach of Kalai–Smorodinsky Bargaining Solution (KSBS) to deal with the problem of optimal resource allocation in a multi–node wireless visual sensor network (VSN). In our setting, the Direct Sequence Code Division Multiple Access (DS–CDMA) method is used for Channel access, while a cross–layer optimization design, which employs a central processing server, accounts for the overall system efficacy through all network layers. The task assigned to the central server is the communication with the nodes and the joint determination of their transmission parameters. The KSBS is applied to non–convex utility spaces, efficiently distributing the source Coding Rate, Channel Coding Rate and transmission powers among the nodes. In the underlying model, the transmission powers assume continuous values, whereas the source and Channel Coding Rates can take only discrete values. Experimental results are reported and discussed to demonstRate the merits of KSBS over competing policies.

  • ICASSP - Optimal power allocation and joint source-Channel Coding for wireless DS-CDMA visual sensor networks using the Nash Bargaining Solution
    2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011
    Co-Authors: Katerina Pandremmenou, Lisimachos P Kondi, Konstantinos E Parsopoulos
    Abstract:

    We consider the problem of resource allocation for a Direct Sequence Code Division Multiple Access (DS-CDMA) wireless visual sensor network (VSN).We use the Nash Bargaining Solution (NBS) from game theory in order to determine the transmission power and source and Channel Coding Rate for each node. The NBS assumes that the nodes negotiate (using the help of a centralized control unit) in order to jointly determine their transmission parameters. The transmission powers are allowed to take continuous values, whereas the source and Channel Coding Rate combination can only assume discrete values. Thus, the resulting optimization problem is a mixed-integer optimization task and is solved using Particle Swarm Optimization (PSO). Experimental results are provided and conclusions are drawn.

  • Visual Information Processing and Communication - Optimal Power Allocation and Joint Source-Channel Coding for Wireless DS-CDMA Visual Sensor Networks
    Visual Information Processing and Communication II, 2011
    Co-Authors: Katerina Pandremmenou, Lisimachos P Kondi, Konstantinos E Parsopoulos
    Abstract:

    In this paper, we propose a scheme for the optimal allocation of power, source Coding Rate, and Channel Coding Rate for each of the nodes of a wireless Direct Sequence Code Division Multiple Access (DS-CDMA) visual sensor network. The optimization is quality-driven, i.e. the received quality of the video that is transmitted by the nodes is optimized. The scheme takes into account the fact that the sensor nodes may be imaging scenes with varying levels of motion. Nodes that image low-motion scenes will require a lower source Coding Rate, so they will be able to allocate a greater portion of the total available bit Rate to Channel Coding. Stronger Channel Coding will mean that such nodes will be able to transmit at lower power. This will both increase battery life and reduce interference to other nodes. Two optimization criteria are considered. One that minimizes the average video distortion of the nodes and one that minimizes the maximum distortion among the nodes. The transmission powers are allowed to take continuous values, whereas the source and Channel Coding Rates can assume only discrete values. Thus, the resulting optimization problem lies in the field of mixed-integer optimization tasks and is solved using Particle Swarm Optimization. Our experimental results show the importance of considering the characteristics of the video sequences when determining the transmission power, source Coding Rate and Channel Coding Rate for the nodes of the visual sensor network.