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This paper describes the design and implementation of an ECG signal monitoring system that is compatible with existing mobile telecommunications networks. First, a two-dimensional ECG signal compression scheme is presented using the distributed source coding (DSC). Also proposed is a modified BCJR algorithm which further enhances the DSC decoding by exploiting the symbol-level a priori information. Finally, we implement a novel integrated system comprising a mobile telephone, with sensors for patient monitoring, and a remote receiver linked by cellular networks. The system, when fully developed, will become powerful aids to monitoring a patient for mobile telemedicine and for emergency situations. [ABSTRACT FROM PUBLISHER]

We present a extended algorithm to design an optimal scalar quantizer for distributed source coding in order to achieve a coding rate close to joint conditional entropy of the quantized frames given the side information. A simple model is considered in this paper. A tabu search method was combined with the Lloyd algorithm to select initial quantizers, which aims at minimizing the Lagrangian cost function and avoiding many poor local optima. Experimental results show the proposed method can improve rate-distortion performance for Gaussian scalar asymmetric case. [ABSTRACT FROM PUBLISHER]

Distributed source coding (DSC) applications range from sensor networks to video coding. In this paper, DSC principles are integrated in a biometric system to provide secure template storage. The proposed biometric recognition system uses the palmprint as a biometric trait together with a state-of-the-art feature extraction technique that produces binary templates. The hand images are captured in an unconstrained way, i.e., the user can place his hand freely within the camera's field of view and the background is not required to be constant. The proposed system was tested in indoor environments and promising recognition accuracy results were achieved, when performing identification on a small database, acquired using the proposed system. [ABSTRACT FROM PUBLISHER]

We propose a segmented PCA based distributed framework exploiting the high correlation and dimensionality reduction properties of PCA in conjugation with the distributed source coding paradigm for onboard hyperspectral image compression. The hybrid scheme deploying Lossy Wyner Ziv coding in conjunction with statistically segmented PCA strategy exploits the two fold intra group and inter group correlation for establishment of multiple independent processing elements evenly distributing the cumulative encoder load over the entire system. The net performance of this flexible infrastructure scheme is further increased by utilizing the superior rate distortion performance of JPEG2000 for high fidelity reconstruction of side information crucial for corresponding Wyner Ziv decoding. The inherent parallelism and low encoder complexity of the proposed scheme not only makes it a viable candidate for onboard implementation but also conforms it to subsequent multi-node transmission. Experimental analysis demonstrates that the proposed scheme offers a novel perspective and competitive performance as compared to other techniques in the same domain. [ABSTRACT FROM PUBLISHER]

Source coding with a side information "vending machine" is a recently proposed framework in which the statistical relationship between the side information available at the decoder and the source sequence can be controlled by the decoder based on the message received from the encoder. In this paper, the characterization of the optimal rate-distortion performance as a function of the cost associated with the control actions is extended from the previously studied point-to-point set-up to two multiterminal models. First, a distributed source coding model is studied, in which two encoders communicate over rate-limited links to a decoder, whose side information can be controlled based on the control actions selected by one of the encoders. The rate-distortion-cost region is characterized under the assumption of lossless reconstruction of the source encoded by the node that does not control the side information. Then, a three-node cascade scenario is investigated, in which the last node has controllable side information. The rate-distortion-cost region is derived for general distortion requirements and under the assumption of "causal" availability of side information at the last node. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a data collection network (DCN) system where sensors take samples and transmit them to a Fusion Center (FC). Signal correlation is modeled with signal sparseness. The number of compressed measurements which allows correct signal recovery at FC is investigated. This is done by studying the probability of signal recovery failure. The joint typical decoder (JT decoder) similar to the one proposed by Akcakaya and Tarokh is used to avoid dependence on particular choice of recovery routines. The following interesting results have been obtained: 1) The detection failure probability linearly converges to zero as the number of sensors increases. 2) The number of compressed measurements per sensor (PSM) needed for successful recovery converges to sparsity as the number of sensors increases. [ABSTRACT FROM PUBLISHER]

Among the emerging video coding schemes, the effectual solution for the separate-encoding and joint-decoding architecture is the distributed source coding (DSC). In the DSC-based video system, the side information is available in the Wyner-Ziv (WZ) decoder and video reconstruction. Theoretically, the quality of side information (SI), usually referring to the difference between SI and source, dominates the coding efficiency. In order to improve the coding efficiency of DVC with temporal group of blocks, we proposed three SI generations after analyzing the correlation between the SI and original information. In contrast to the traditional improvement in the DVC decoder, the complexity of SI generator was dramatically reduced in our schemes. Experimental results show that the best method can reduce 0.7% bit-rate with less computation and no codec delay compared to the bi-linear interpolation (BLI) method. [ABSTRACT FROM PUBLISHER]

We present a compressive sensing (CS) approach for secret key agreement based on UWB channel reciprocity. More generally, we show that the CS problem can be used for solving the distributed source coding problem. We also show that the proposed CS-based secret key agreement protocol (CS-SKAP) provides perfect secrecy, a better key agreement probability, and sometimes a longer secret key length, compared to the traditional syndrome-based distributed source coding techniques. [ABSTRACT FROM PUBLISHER]

We introduce a framework to study fundamental limits of sequential coding of Markov sources under an error propagation constraint. An encoder sequentially compresses a sequence of vector-sources that are spatially i.i.d. but temporally correlated according to a Markov process. The channel erases up to B packets in a single burst, but reveals all other packets to the destination. The destination is required to reproduce all the source-vectors instantaneously and in a loss less manner, except those sequences that occur in a window of length B+W following the start of the erasure burst. We define a rate-recovery function R(B, W), the minimum compression rate that can be achieved in this framework, and develop upper and lower bounds for first-order Markov sources. For the special class of linear diagonally correlated deterministic sources, we propose a new coding technique -- prospicient coding -- that achieves the rate-recovery function. Finally, a lossy extension to the rate-recovery function is also studied for a class of Gaussian sources where the source is temporally and spatially i.i.d. and the decoder aims to recover a collection of past K sources with a quadratic distortion measure. The optimal rate-recovery function is compared with the sub-optimal techniques including forward error correction coding (FEC) and Wyner-Ziv coding, and performance gains are quantified. [ABSTRACT FROM PUBLISHER]

In this paper, an implementation friendly distributed version of parallel concatenated convolutional codes (D-PCCC) with a novel bi-modal iterative decoder is proposed to estimate a binary data source observed by two sensors. The convergence of the iterative decoder is analyzed by defining a modified EXIT chart. It is shown that iterative method is not always the best decoding algorithm for D-PCCC with two encoders. The system quality conditions in terms of channel SNR and sensors' observation accuracy required for usefulness of iterative algorithm is found. This provides a frame by frame mode selection criterion for decoder. The decoder chooses an appropriate iterative or non-iterative decoding mode that yields higher BER performance in both modes and reduces decoding complexity by avoiding destructive or useless iterations. [ABSTRACT FROM PUBLISHER]

The problem of correlated information multicasting is considered in this paper, i.e., from multiple sources to multiple destinations through a network. It can be solved by jointly optimizing the distributed source coding and network coding. Previous research found that optimal performance can be achieved with unlimited decoding complexity, which cannot be applied in practical implementations. Some practical methods were also presented, which shows sub-optimal performance. To improve the coding performance and keep the low complexity of the algorithm, an adaptive coding and transmission scheme according to the conditional entropy of the sources is proposed. It has been demonstrated that the proposed scheme can achieve better performance compared with the existing ones. [ABSTRACT FROM PUBLISHER]

The minimum cost subgraph for joint distributed source and network coding has been studied using a linear programming approach. This problem considers some statistically correlated information sources in a network, where sending over each link has a defined cost. The desired solution to this problem is a set of coding rates over the graph links, that minimizes the total communication costs. But in this problem, the placement of the sources is pre-defined, which is not necessarily optimal. In this paper, for the first time, we find a “good” placement for the sources using a genetic programming approach. This problem can be seen as the generalized mirror sites problem, where sources are not restricted to replication of data and transmission by routing. [ABSTRACT FROM AUTHOR]