7907486
Joint source-channel with side information coding error exponents
In this paper, we study the upper and the lower bounds on the joint source-channel coding error exponent with decoder side-information. The results in the paper are non-trivial extensions of the Csiszár's classical paper [6]. Unlike the joint source-channel coding result in [6], it is not obvious whether the lower bound and the upper bound are equivalent even if the channel coding error exponent is known. For a class of channels, including the symmetric channels, we apply a game-theoretic result to establish the existence of a saddle point and hence prove that the lower and upper bounds are the same if the channel coding error exponent is known. More interestingly, we show that encoder side-information does not increase the error exponents in this case.
2546960
The Source Coding Game With a Cheating Switcher
The problem of finding the rate-distortion function of an arbitrarily varying source (AVS) composed of a finite number of memoryless subsources is revisited. Berger's 1971 paper “The Source Coding Game” solves this problem when the adversary is allowed only strictly causal access to the subsource realizations. The case when the adversary has access to the subsource realizations non-causally is considered. This new rate-distortion function is determined to be the maximum of the rate-distortion function over a set of independent and identically distributed (IID) random variables that can be simulated by the adversary. The results are extended to allow for partial or noisy observations of subsource realizations. The model is further explored by attempting to find the rate-distortion function when the `adversary' is actually helpful. Finally, a bound is developed on the uniform continuity of the IID rate-distortion function for finite-alphabet sources. The bound is used to give a sufficient number of distributions that need to be sampled to compute the rate-distortion function of an AVS to within a desired accuracy. The bound is also used to give a rate of convergence for the estimate of the rate-distortion function for an unknown IID source.
9272685
Genetic algorithm for affine point pattern matching
Point pattern matching (PPM) is an important topic in the fields of computer vision and pattern recognition. According to if there exists a one to one mapping between the two point sets to be matched, PPM can be divided into the case of complete matching and the case of incomplete matching. According to if utilizing information other than 2-D image coordinates, PPM can be divided into labelled point-matching case and unlabelled point-matching case. Using partial Hausdorff distance, this paper presents a genetic algorithm (GA) based method to solve the incomplete unlabelled matching problem under general affine transformation. Since it successfully reduces the solution space of GA by constructing 'feature ellipses' of point sets, the method can achieve high computing efficiency and good matching results. Theoretical analysis and simulation results show that the new algorithm is very effective.
12575767
Universal Quadratic Lower Bounds on Source Coding Error Exponents
We consider the problem of block-size selection to achieve a desired probability of error for universal source coding. While Baron, et al. (2004; 1973) studied this question for rates in the vicinity of entropy for known distributions using central-limit-theorem techniques, we are interested in all rates for unknown distributions and use error-exponent techniques. By adapting a technique of Gallager from the exercises of Gallager (1971), we derive a universal lower bound to the source-coding error exponent that depends only on the alphabet size and is quadratic in the gap to entropy.
1424357
Lossless Coding for Distributed Streaming Sources
Distributed source coding is traditionally viewed in a block coding context wherein all source symbols are known in advance by the encoders. However, many modern applications to which distributed source coding ideas are applied, are better modeled as having streaming data. In a streaming setting, source symbol pairs are revealed to separate encoders in real time and need to be reconstructed at the decoder with subject to some tolerable end-to-end delay. In this paper, a causal sequential random binning encoder is introduced and paired with maximum likelihood (ML) and universal decoders. The latter uses a novel weighted empirical suffix entropy decoding rule. We derive a lower bounds on the error exponent with delay for each decoder. We also provide upper bounds for the special case of streaming with decoder side information and discuss when upper and lower bounds match. We show that both ML and universal decoders achieve the same (positive) error exponents for all rate pairs inside the Slepian-Wolf achievable rate region. The dominant error events in streaming are different from those in block-coding and result in different exponents. Because the sequential random binning scheme is also universal over delays, the resulting code eventually reconstructs every source symbol correctly with probability one.
16124776
Interference channel capacity region for randomized fixed-composition codes
The randomized fixed-composition codes with optimal decoding error exponents are recently studied in [11], [12] for the finite alphabet interference channel with two transmitter-receiver pairs. In this paper we investigate the capacity region for randomized fixed-composition codes. A complete characterization of the capacity region of the said coding scheme is given. The inner bound is derived by showing the existence of a positive error exponent within the capacity region. A simple universal decoding rule is given. The tight outer bound is derived by extending a technique first developed in [10] for single input output channels to interference channels. It is shown that even with a sophisticated time-sharing scheme among randomized fixed-composition codes, the capacity region of the randomized fixed-composition coding is not bigger than the known Han-Kobayashi [24] capacity region. This suggests that the study of the average behavior of randomized codes are not sufficient in finding new capacity regions.
1776041
The design of distributed real-time video analytic system
Video has become an indispensable carrier of information and requires extremely computation expensive analysis to understand high level semantics. The computational difficulties in extracting embedded information and bridging the semantic gap present the major challenges in interoperability, scalability and real-time response of video analytic systems. In this paper, we propose a distributed scalable infrastructure VAP (Video Analytic Platform) for supporting real-time video stream analysis. In VAP, the application requirements are represented as a Directed Acrylic Graph (DAG), where nodes stand for video analysis computation modules and links show data flow and dependencies between nodes. VAP leverages UIMA (Unstructured Information Management Architecture) framework as the data flow control engine and multiple commodity databases as the storage and computation resources. The actual executions of video analysis computation modules have been pushed down into database engines to minimize the data movement cost. We initially choose video surveillance in retail store as a representative application domain. As a case study, a prototype system has been developed to achieve fundamental functions of real-time video surveillance, including video capture/store, human detection/tracking and customer shopping trajectory analysis.
6881358
Probabilistic real-time guarantees for component-oriented phased array radars
In recent years, many modern phased array radars are built with commercial-off-the-shelf components, and the functions of many hardware components are also re-implemented by software modules. In such systems, radar tasks could be modelled as distributed real-time tasks, which require end-to-end deadline guarantees and have precedence constraints. Different from most previous work on either algorithms with restrictions in resource utilization or heuristics without analytical ways for schedulability guarantees, the objective of This work is to propose a joint real-time scheduling algorithm for both transmitter/receiver and signal processor workloads with an analytical framework for off-line probabilistic analysis and online admission control. The strength of our approach is verified by analysis results and a series of experiments based on a real phased array radar for air defense frigates [A. G. Huizing et al. (1996)].
782406
Data Compression
6876954
A New Backoff Algorithm for IEEE 802.11 Distributed Coordination Function
DCF (Distributed Coordination Function) of IEEE802.11 MAC protocols uses binary exponential increasing mechanism, called BEB (Binary Exponential Backoff) algorithm. Due to the lack of a mechanism evaluating the channel condition, it can not tune the value of contention windows adaptively, according to the actual network traffic. By introducing the network allocation vector count in routing protocol to approximate the intensity of surrounding traffic of nodes, the algorithm has been improved, so that the contention windows of backoff mechanism are adjusted reasonably according to the traffic of WLAN. Simulation experiments are done and the results prove that the improved algorithm provides a better channel utilization and reduces the average transmission delay.
30762676
Kernel particle filter: iterative sampling for efficient visual tracking
Particle filter has recently received attention in computer vision applications due to attributes such as its ability to carry multiple hypotheses and its relaxation of the linearity assumption. Its shortcoming is increase in complexity with state dimension. We present kernel particle filter as a variation of particle filter with improved sampling efficiency and performance in visual tracking. Unlike existing methods that use stochastic or deterministic optimization procedures to find the modes in a likelihood function, we redistribute particles by invoking kernel-based representation of densities and introducing mean shift as an iterative mode-seeking procedure, in which particles move towards dominant modes while still maintaining as fair samples from the posterior. Experiments on face and limb tracking show that the algorithm is superior to conventional particle filter in handling weak dynamic models and occlusions with 60% fewer particles in 3-9 dimensional spaces.