3263120
The price of ignorance: The impact of side-information on delay for lossless source-coding
Inspired by the context of compressing encrypted sources, this paper considers the general tradeoff between rate, end-to-end delay, and probability of error for lossless sou rce coding with side-information. The notion of end-to-end delay is made precise by considering a sequential setting in which source symbols are revealed in real time and need to be reconstructed at the decoder within a certain fixed latency requirement. Upper bounds are derived on the reliability functions with delay when side-informatio n is known only to the decoder as well as when it is also known at the encoder. When the encoder is not ignorant of the side-information (including the trivial case when there is no sideinformation), it is possible to have substantially better t radeoffs between delay and probability of error at all rates . This shows that there is a fundamental price of ignorance in terms of end-to-end delay when the encoder is not aware of the side information. This effect is not visible if o nly fixed-block-length codes are considered. In this way, side-information in source-coding plays a role analogous t o that of feedback in channel coding. While the theorems in this paper are asymptotic in terms of long delays and low probabilities of error, an example is used to show that the qualitative effects describ ed here are significant even at short and moderate delays.
12856888
On the rate distortion function of Bernoulli Gaussian sequences
We study the rate distortion function of the Bernoulli-Gaussian random variable which can be used to model sparse signals. Both lower and upper bounds on the rate distortion function are given.We show that the bounds are almost tight in the low distortion regime for sparse signals. Interestingly, a naive coding scheme is near-optimal in this scenario.
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.
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.
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.
16280666
Component-oriented radars with probabilistic timing guarantees
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 reimplemented by software modules. In such systems, radar tasks could be modeled 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 paper is to propose a joint real-time scheduling algorithm for both transmitter/receiver and signal processor workloads with an analytical framework for offline 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
42929157
Real-Time Digital Signal Processing of Phased Array Radars
With the advance of hardware and software technology, modern phased array radars are now built with commercial-off-the-shelf (COTS) components, and it opens up a new era in real-time resource scheduling of digital signal processing. This paper targets the essential issues in building a component-oriented signal processor (SP), which is one of the two major modules in modern phased array radars. We propose a simple but effective task allocation policy and a real-time scheduling algorithm to address the design objectives of SPs. We are able to bound the number of processing units needed for a component-oriented SP in the design time, while everything was done empirically in the past. A series of experiments was done to demonstrate the strength of our methodology.
27390874
Color Sectors and Edge Features for Content-Based Image Retrieval
We provide a new image retrieval model which integrates the spatial distributing information of colors and the shape features. The color features are represented by an amelioration of HSV color model: color sector. To describe the relative position and orientation of objects within the image, hue edges in the image, computed using Canny approach, are processed by Hough transform to generate the spatial histogram which is used as shape representation. To address matching of image, we define two global distance measures based on the accumulative distance and vector-based distance to compare the similarity of two images. Experimental results indicate that our approach provides higher retrieval rate than that of single feature (color), is a better tradeoff between high efficiency and reasonable precision.
10631202
Real-time scheduling in a programmable radar signal processor
The new trend of designing radar digital signal processors is not only to improve their processing speed by adopting the parallel multi-processor architecture but also to make their infrastructure flexible so that one generic digital signal processor framework can be applied to a variety of different radar systems. The key technology to make such radar digital signal processor architecture programmable, or flexible, is the scheduler which manages resources of digital signal processing in real-time according to system requirements. In this paper, the architecture of the programmable radar signal processor (PRSP) is presented, and a real-time scheduling algorithm used in PRSP is thoroughly discussed.
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.