We establish the noiseberg conjecture regarding the Han-Kobayashi region of the Gaussian Z-Interference channel with Gaussian signaling. We also provide a refined conjecture for the optimality of the HK inner bound with Gaussian signaling.

In this paper we establish that a maximizer of a non-convex problem in positive semidefinite matrices has a certain property using information-theoretic methods. Further, we propose a Gaussian extremality conjecture, which if true, would imply that Gaussian signaling achieves the capacity region of the Gaussian Z-interference channel. The non-convex problem mentioned above arose naturally in the reduction from the conjecture to the optimality of Gaussian signaling.

This work provides an algebraic framework for source coding with decoder side information and its dual problem, channel coding with encoder side information, showing that nested concatenated codes can achieve the corresponding rate-distortion and capacity-noise bounds. We show that code concatenation preserves the nested properties of codes and that only one of the concatenated codes needs to be nested, which opens up a wide range of possible new code combinations for these side information based problems. In particular, the practically important binary version of these problems can be addressed by concatenating binary inner and non-binary outer linear codes. By observing that list decoding with folded Reed-Solomon codes is asymptotically optimal for encoding IID q-ary sources and that in concatenation with inner binary codes it can asymptotically achieve the rate-distortion bound for a Bernoulli symmetric source, we illustrate our findings with a new algebraic construction which comprises concatenated nested cyclic codes and binary linear block codes.

Hierarchical transforms are widely used in image and video coding to produce multilevel decomposition of signals. After applying these transforms, same level signals are typically uncorrelated. However, there is often still significant cross level information. Traditionally, this cross-level information is exploited at the entropy coding step, but not at the transform step. The main contribution of this work is an energy compaction technique/transform that can also exploit these cross-resolution-level structural similarities. The core idea of the technique is to include in the hierarchical transform a number of adaptive basis functions derived from the lower resolution of the signal. A full image codec was developed in order to measure the performance of the new transform. Results are presented in terms of transform coding gain, energy concentration and distortion versus rate curves compared with standard JPEG, JPEG 2000 and JPEG XR.

This paper presents a tree-based algebraic construction of nested cyclic codes through a tree construction method. These codes can be used to encode different data packets, producing codewords that are added for transmission. Both encoding and decoding are performed by polynomial operations with no need of side information, so the proposed scheme may be useful in applications that require low computational complexity. In cases where the number of information sources is large, it is convenient to design the system in a systematic manner. Hence, the tree construction method may contribute to simplify the code design.

We explore several local and global strategies for adaptive scan ordering of transform coefficients in JPEG XR/HD Photo. This codec applies a global adaptive scan-order heuristic with respect to an effective localized predictor. The global ordering heuristic, although simple, performs as well as localized techniques that are computationally significantly more complex. We conclude that effective localized prediction not only minimizes but also essentially randomizes coefficient residuals, so that a global statistic is sufficient to deliver near-optimal compression performance.

In this paper we consider a scheme of packet scheduling for the forward link of a MIMO cellular system, to increase system capacity by exploiting multiuser diversity based on spatial multiplexing (SM). The proposed scheme exhibits considerable increases in system capacity compared to commonly used schemes, while, at the same time, giving a fair chance of channel access to all users regardless of their channel fading status

This paper describes an efficient run-length encoding of binary sources with unknown statistics. Binary entropy coders are used in many multimedia codec standards, which uses adaptive Golomb-Rice coders. Using a maximum-likelihood approach, an excess rate for the Golomb-like coder when compared to an adaptive Rice coder is up to 4.2% for binary sources with unknown statistics with respect to the source entropy.

We investigate the problem of allocating power to send uncoded information over parallel Gaussian channels. We examine the reason why regular "water-filling" does not work in the uncoded case, and show that a modification of the noise level of each subchannel lets "water-filling" work in this case.

Registering radar imagery is not an easy task since the images may be heavily contaminated with noise. Strong speckle noise can produce artifacts that mimic good control points and may produce low precision or even wrong registration. This article presents an automatic registration method that tries to overcome these problems. The method was originally developed for optical images and adapted for radar images.

In computerized tomography (CT), standard filtered backprojection tomogram reconstruction from two dimensional projection data acquired in a time sequential manner assumes no relative motion between the gantry and the patient's plane of interest during data acquisition. In a realistic situation, this condition may not he satisfied, resulting in motion artifacts in the reconstructed image. the authors propose to perform image reconstruction from the interpolated projection data corresponding to a single time instant, the mid-scan time. They present simulation results to illustrate the obtained reduction of motion artifacts.>

The capacity region of the discrete memoryless interference channel with strong interference is established.

A new coding theorem for the broadcast channel with arbitrarily correlated sources is presented. The result covers all the previously established coding techniques. In particular, it includes Marton's coding theorem as a properly special case.

The determination of the capacity region of the Gaussian interference channel remains an open problem. The channel model is described and cases that have been solved by other researchers are summarized. The optimality is established of two extreme points in the achievable region of the general Gaussian interference channel. It is proved that the class of degraded Gaussian interference channels is equivalent to the class ofZ(one-sided) interference channels.

A strengthened version of Shannon's entropy power inequality for the case where one of the random vectors involved is Gaussian is proved. In particular it is shown that if independent Gaussian noise is added to an arbitrary, multivariate random variable, the entropy power of the resulting random variable is a concave function of the variance (power) of the added noise. The strengthened inequality is shown to hold for the class of stable distributions.

The entropy power inequality states that the effective variance (entropy power) of the sum of two independent random variables is greater than the sum of their effective variances. The Brunn-Minkowski inequality states that the effective radius of the set sum of two sets is greater than the sum of their effective radii. Both these inequalities are recast in a form that enhances their similarity. In spite of this similarity, there is as yet no common proof of the inequalities. Nevertheless, their intriguing similarity suggests that new results relating to entropies from known results in geometry and vice versa may be found. Two applications of this reasoning are presented. First, an isoperimetric inequality for entropy is proved that shows that the spherical normal distribution minimizes the trace of the Fisher information matrix given an entropy constraint--just as a sphere minimizes the surface area given a volume constraint. Second, a theorem involving the effective radii of growing convex sets is proved.

A channel with outputY = X + S + Zis examined, The stateS \sim N(0, QI)and the noiseZ \sim N(0, NI)are multivariate Gaussian random variables (Iis the identity matrix.). The inputX \in R^{n}satisfies the power constraint(l/n) \sum_{i=1}^{n}X_{i}^{2} \leq P. IfSis unknown to both transmitter and receiver then the capacity is\frac{1}{2} \ln (1 + P/( N + Q))nats per channel use. However, if the stateSis known to the encoder, the capacity is shown to beC^{\ast} =\frac{1}{2} \ln (1 + P/N), independent ofQ. This is also the capacity of a standard Gaussian channel with signal-to-noise power ratioP/N. Therefore, the stateSdoes not affect the capacity of the channel, even thoughSis unknown to the receiver. It is shown that the optimal transmitter adapts its signal to the stateSrather than attempting to cancel it.

The capacity region of a class of deterministic discrete memoryless interference channels is established. In this class of channels the outputsY_{1}andY_{2}are (deterministic) functions of the inputsX_{1}andX_{2}such thatH(Y_{1}|X_{1})=H(V_{2})andH(Y_{2}|X_{2})=H(V_{l})for all product probabiliW distributions onX_{1}X_{2}, whereV_{1}is a function ofX_{1}andV_{2}a function ofX_{2}. The capacity, region for the case in whichV_{2} \equiv 0andY_{1}depends randomly onX_{1}is also obtained and illustrated with an example.