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The adoption of smart meters may bring new privacy concerns to the general public. Given the fact that metering data of individual homes/factories is accumulated every 15 min, it is possible to infer the pattern of electricity consumption of individual users. In order to protect the privacy of users in a completely de-centralized setting (i.e., individuals do not communicate with one another), we propose a novel protocol, which allows individual meters to report the true electricity consumption reading with a pre-determined probability. Load serving entities (LSE) can reconstruct the total electricity consumption of a region or a district through inference algorithm, but their ability of identifying individual users' energy consumption pattern is significantly reduced. Using simulated data, we verify the feasibility of the proposed method and demonstrate performance advantages over existing approaches. [ABSTRACT FROM PUBLISHER]

We propose an adaptive distributed compression solution using particle filtering that tracks correlation, as well as performing disparity estimation, at the decoder side. The proposed algorithm is tested on the stereo solar images captured by the twin satellites system of NASA's Solar TErrestrial RElations Observatory (STEREO) project. Our experimental results show improved compression performance w.r.t. to a benchmark compression scheme, accurate correlation estimation by our proposed particle-based belief propagation algorithm, and significant peak signal-to-noise ratio improvement over traditional separate bit-plane decoding without dynamic correlation and disparity estimation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a system using video cameras to perform vehicle identification. We tackle this problem by reconstructing an input by using multiple linear regression models and compressed sensing, which provide new ways to deal with three crucial issues in vehicle identification, namely, feature extraction, online vehicle identification database buildup , and robustness to occlusions and misalignment. The results show the capability of the proposed approach. [ABSTRACT FROM PUBLISHER]

Distributed video coding (DVC) is rapidly gaining popularity as a low cost, robust video coding solution, that reduces video encoding complexity. DVC is built on distributed source coding (DSC) principles where correlation between sources to be compressed is exploited at the decoder side. In the case of DVC, a current frame available only at the encoder is estimated at the decoder with side information generated from other frames available at the decoder. One of the main challenges in DVC design is that correlation among the source and side information needs to be estimated online and as accurately as possible. Since correlation dynamically changes with the scene, in order to exploit the robustness of DSC code designs, we integrate particle filtering (PF) with standard belief propagation (BP) decoding for inference on one joint factor graph to estimate correlation among source and side information. Correlation estimation is performed online as it is carried out jointly with decoding of the graph-based DSC code. Moreover, we demonstrate our joint bit-plane decoding with adaptive correlation estimation schemes within state-of-the-art DVC systems, which are transform-domain based with a feedback channel for rate adaptation. Experimental results show that our proposed system gives a significant performance improvement compared to the benchmark state-of-the-art DISCOVER codec (including correlation estimation) and the case without dynamic PF tracking, due to improved knowledge of timely correlation statistics via the combination of joint bit-plane decoding and particle-based BP (PBP) tracking. [ABSTRACT FROM PUBLISHER]

A major difficulty that plagues the practical use of Slepian-Wolf (SW) coding (and distributed source coding in general) is that the precise correlation among sources needs to be known a priori. However, belief propagation (BP) algorithm cannot adapt efficiently to the statistical change of the correlation. This paper proposes an adaptive SW decoding scheme which can perform online time-varying correlation estimation at the bit-level by incorporating expectation propagation (EP) algorithm. Moreover, we compare the proposed EP-based approach with Monte Carlo method using particle filtering (PF) algorithm. Our results show that the proposed EP estimator obtains the comparable estimation accuracy with less computational complexity than the PF method. [ABSTRACT FROM PUBLISHER]

A major difficulty that plagues the practical use of Slepian-Wolf (SW) coding (and distributed source coding in general) is that the precise correlation among sources needs to be known a priori. To resolve this problem, we propose an adaptive asymmetric SW decoding scheme using particle based belief propagation (PBP). We explain the adaptive scheme for asymmetric setup in detail and then further extend it to the non-asymmetric setup based on the code partitioning approach. Moreover, we introduce a Metropolis-Hastings (MH) algorithm in the resampling step, which efficiently decreases the number of simulation iterations. We show through experiments that the proposed algorithm can simultaneously reconstruct the compressed sources and estimate the joint correlation among sources. Further, comparing to the conventional SW decoder based on standard belief propagation, the proposed approach can achieve higher compression under varying correlation statistics. [ABSTRACT FROM AUTHOR]

Belief propagation (BP) is a powerful algorithm to decode low-density parity check (LDPC) codes over additive white Gaussian noise (AWGN) channels. However, the traditional BP algorithm cannot adapt efficiently to the statistical change of SNR in an AWGN channel. This paper proposes an adaptive scheme that incorporates a particle filtering (PF) algorithm into the BP based LDPC decoding process. The proposed scheme is capable to perform online estimation of time-varying SNR at the bit-level and enhance the BP decoding performance simultaneously. [ABSTRACT FROM PUBLISHER]