26146425
An On-Line Universal Lossy Data Compression Algorithm via Continuous Codebook Refinement - Part III: Redundancy Analysis
For pt.II see ibid., vol.42, p.822-36 (1996). The Gold-washing data compression algorithm is an adaptive vector quantization algorithm with vector dimension n. In this paper, a redundancy problem of the Gold-washing data compression algorithm is considered. It is demonstrated that for any memoryless source with finite alphabet A and generic distribution p and for any R>0, the redundancy of the Gold-washing data compression algorithm with dimension n (defined as the difference between the average performance of the algorithm and the distortion-rate function D(p,R) of p) is upper-bounded by |/sub /spl delta/R///sup /spl delta//D(p,R)|((|A|+2/spl xi/+4 log n)/2n)+/spl sigma/(logn/n) where /sub /spl delta/R///sup /spl delta//D(p,R) is the partial derivative of D(p,R) with respect to R, |A| is the cardinality of A, and /spl xi/>0 is a parameter used to control the threshold in the Gold-washing algorithm. In connection with the results of Zhang, Yang, and Wei (see ibid., vol.43, no.1, p.71-91, 1997) on the redundancy of lossy source coding, this shows that the Gold-washing algorithm has the optimal convergence rate among all adaptive finite-state vector quantizers.
32960689
On Maximal Shadows of Members in Left-compressed Sets
Whereas Stanley, Proctor and others studied Sperner properties of the poset L(k,m) we consider shadows of left-compressed sets and derive asymptotic bounds on their sizes.
6153081
Zero-Error Capacity for Models with Memory and the Enlightened Dictator Channel
Summary form only given. We present a general class of "combinatorial" 0-error capacity problems with the "memory" covering known and many new cases. This class can be incorporated into a model of channels with memory. W analyse a seemingly basic channel in this class, which we call the "dictator channel". Clearly, a dictator always follows his own opinion. However, we speak of an enlightened dictator, if he responds to the unanimous vote of the people against his opinion to the degree, in this case he reaches a decision by coin tossing.
43476378
Partial converse for a relay channel
A special case of the general discrete memoryless relay channel is studied. This channel consists of an input x, a relay output z, a channel output y, and a noiseless channel of capacity C/sub r/ allowing the relay sender to send additional information to the decoder. The channel is described by a collection of distributions (p(y, z mod x)). The decoder uses the channel output y and a function f(z) of the relay output at a rate no greater than C/sub r/ to recover the message. Some results on the capacity of the channel are obtained. >
32850524
Linear inequalities for covering codes: Part I: Pair covering inequalities
The lower bounds on K(n,R), minimum number of codewords of any binary code of length n, and covering radius R are improved. A new technique combining the Hamming association scheme and the results of a classic problem of covering pairs by k-tuples is introduced. The new lower bounds are obtained by studying various linear inequalities satisfied by covering codes, and such an inequality is derived. The lower bounds for K(n,R) are studied for some special values of n and R, which serve as examples showing how the methods developed are applied to concrete cases. >
15006226
Bounds on the sizes of constant weight covering codes
Motivated by applications in universal data compression algorithms we study the problem of bounds on the sizes of constant weight covering codes. We are concerned with the minimal sizes of codes of lengthn and constant weightu such that every word of lengthn and weightv is within Hamming distanced from a codeword. In addition to a brief summary of part of the relevant literature, we also give new results on upper and lower bounds to these sizes. We pay particular attention to the asymptotic covering density of these codes. We include tables of the bounds on the sizes of these codes both for small values ofn and for the asymptotic behavior. A comparison with techniques for attaining bounds for packing codes is also given. Some new combinatorial questions are also arising from the techniques.
5700504
Localized algorithm for aggregate fairness in wireless sensor networks
For data-collection applications in sensor networks, it is important to ensure all data sources have equal (or weighted) access to network bandwidth so that the base stations receive a complete picture about the monitored area. We point out the fairness problem in the current design of sensor networks, which may cause extremely biased bandwidth allocations. It is a challenge to design a fully distributed fairness solution due to the lack of global knowledge about the distribution of data sources and their routing paths. This paper proposes a new aggregate fairness model and a localized algorithm (called AFA) that implements the model. AFA is designed to work with any routing protocol. In particular, it allows the packets from a data source to follow an arbitrary set of forwarding paths to the base stations. This flexibility makes it considerably harder to allocate bandwidth fairly among different data sources. AFA solves the problem with only localized operations at the sensors. It is easy to implement, which is an attractive property for sensor networks. Moreover, the algorithm automatically adjusts a sensor's forwarding rate to avoid packet drops due to downstream congestion, which helps improve energy efficiency. We perform extensive simulations, demonstrating that the proposed algorithm can effectively improve end-to-end fairness.
29303828
Parameter estimation techniques: a tutorial with application to conic fitting
Almost all problems in computer vision are related in one form or another to the problem of estimating parameters from noisy data. In this tutorial, we present what is probably the most commonly used techniques for parameter estimation. These include linear least-squares (pseudo-inverse and eigen analysis); orthogonal least-squares; gradient-weighted least-squares; bias-corrected renormalization; Kalman filtering; and robust techniques (clustering, regression diagnostics, M-estimators, least median of squares). Particular attention has been devoted to discussions about the choice of appropriate minimization criteria and the robustness of the different techniques. Their application to conic fitting is described.
81034
Co-clustering by block value decomposition
Dyadic data matrices, such as co-occurrence matrix, rating matrix, and proximity matrix, arise frequently in various important applications. A fundamental problem in dyadic data analysis is to find the hidden block structure of the data matrix. In this paper, we present a new co-clustering framework, block value decomposition(BVD), for dyadic data, which factorizes the dyadic data matrix into three components, the row-coefficient matrix R, the block value matrix B, and the column-coefficient matrix C. Under this framework, we focus on a special yet very popular case -- non-negative dyadic data, and propose a specific novel co-clustering algorithm that iteratively computes the three decomposition matrices based on the multiplicative updating rules. Extensive experimental evaluations also demonstrate the effectiveness and potential of this framework as well as the specific algorithms for co-clustering, and in particular, for discovering the hidden block structure in the dyadic data.
5218256
Instruction set extension with shadow registers for configurable processors
Configurable processors are becoming increasingly popular for modern embedded systems (especially for the field-programmable system-on-a-chip). While steady progress has been made in the tools and methodologies of automatic instruction set extension for configurable processors, the limited data bandwidth available in the core processor (e.g., the number of simultaneous accesses to the register file) becomes a potential performance bottleneck. In this paper we first present a quantitative analysis of the data bandwidth limitation in configurable processors, and then propose a novel low-cost architectural extension and associated compilation techniques to address the problem. The application of our approach results in a promising performance improvement.
5573915
Head pose determination from one image using a generic model
We present a new method for determining the pose of a human head from its 2D image. It does not use any artificial markers put on a face. The basic idea is to use a generic model of a human head, which accounts for variation in shape and facial expression. Particularly, a set of 3D curves are used to model the contours of eyes, lips and eyebrows. A technique called iterative closest curve matching (ICC) is proposed, which aims at recovering the pose by iteratively minimizing the distances between the projected model curves and their closest image curves. Because curves contain richer information (such as curvature and length) than points, ICC is both more robust and more efficient than the well-known iterative closest point matching techniques (ICP). Furthermore, the image can be taken by a camera with unknown internal parameters, which can be recovered by our technique thanks to the 3D model. Preliminary experiments show that the proposed technique is promising and that an accurate pose estimate can be obtained from just one image with a generic head model.