Rongshan Yu

A Statistical Demand-Price Model With Its Application in Optimal Real-Time Price

In this paper, the price elasticity of electrical demand is studied in a smart grid framework where electricity loads are scheduled by distributed Energy Management Controller (EMC) units. It is shown that different price responsive behaviors of electricity loads are results from interactions between their utilities to customers as a function of time and electricity prices. Based on this observation, a parametric utility model is introduced, from which the price elastic behaviors of aggregated loads are effectively modeled as a set of multi-dimensional demand-price functions. The developed demand elasticity model is further utilized in determining the optimal price signal for Real-Time Pricing (RTP) based Demand Response (DR) programs. Considering price elastic behaviors of customers, it is shown that the optimal real-time prices to induce the desired power consumption behaviors from customers for social welfare maximization can be effectively pre-calculated by utility company using the developed demand elasticity model without the need for excessive information exchange among end users and utility companies. Typical results of the proposed methods are further illustrated through numerical examples from a 6-bus test system.

A fine granular scalable to lossless audio coder

This paper presents Advanced Audio Zip (AAZ), a fine grained scalable to lossless (SLS) audio coder that has recently been adopted as the reference model for MPEG-4 audio SLS work. AAZ integrates the functionalities of high-compression perceptual audio coding, fine granular scalable audio coding, and lossless audio coding in a single framework, and simultaneously provides backward compatibility to MPEG-4 Advanced Audio Coding (AAC). AAZ provides the fine granular bit-rate scalability from lossy to lossless coding, and such a scalability is achieved in a perceptually meaningful way, i.e., better perceptual quality at higher bit-rates. Despite its abundant functionalities, AAZ only introduces negligible overhead in terms of lossless compression performance compared with a nonscalable, lossless only audio coder. As a result, AAZ provides a universal yet efficient solution for digital audio applications such as audio archiving, network audio streaming, portable audio playing, and music downloading which were previously catered for by several different audio coding technologies, and eliminates the need for any transcoding system to facilitate sharing of digital audio contents across these application domains

Bit-plane Golomb coding for sources with Laplacian distributions

This paper presents a bit-plane coding algorithm for Laplacian distributed sources that are commonly encountered in signal compression applications. By exploiting the statistical characteristics of the sources, the proposed algorithm achieves a rate-distortion performance that is essentially comparable to an optimal nonscalable scalar quantizer, while at the same time operates at a complexity level suitable for most practical implementations.

Improving coding efficiency for MPEG-4 Audio Scalable Lossless coding

The recently introduced MPEG standard for lossless audio coding, MPEG-4 Audio Scalable to Lossless (SLS) coding technology, provides a universal audio format that integrates the functionalities of lossy audio coding, lossless audio coding and fine granular scalable audio coding in a single framework. We propose two coding methods that improve the coding efficiency of SLS, namely, a context-based arithmetic code (CBAC) method and a low energy mode code method. These two coding methods work harmonically with the current SLS framework and preserve all its desirable features, such as fine granular scalability, while successfully improving its lossless compression ratio performance.

A low-complexity noise estimation algorithm based on smoothing of noise power estimation and estimation bias correction

This paper presents a low-complexity algorithm for tracking the noise spectral variance of speech contaminated by non-stationary noise sources. The proposed algorithm is based upon a recursive refinement process in which each step of the algorithm expectation of the instantaneous noise power is calculated based on information from the incoming signal and the current estimated distribution parameters, and estimation of the distribution parameter is refined accordingly to incorporate the expectation results. A bias estimation correction method is also introduced in the algorithm to avoid estimation errors that may occur when there is a significant mismatch between the statistics of the input signal and the current estimated distribution parameters. The proposed algorithm is compared to the Minimum Statistics method and it is found that the proposed algorithm achieves similar or better performances for various noise conditions and SNR settings.

A scalable lossy to lossless audio coder for MPEG-4 lossless audio coding

In this paper, we present Advanced Audio Zip (AAZ), a scalable lossless audio coding technology that was recently selected as the reference model for MPEG audio scalable lossless coding (SLS) work. AAZ provides excellent compression performance while delivering fine grain bit-rate scalability from lossy to lossless coding. Moreover, AAZ provides backward compatibility to the MPEG advanced audio coding (AAC) system by embedding an AAC compliant bit-stream into the lossless bit-stream. As a result, AAZ serves as a universal coding solution with functionalities that were previously offered by several distinct audio coding technologies such as lossless audio coding, perceptual audio coding, or scalable audio coding; and maximizes the interchangeability for digital audio contents migrating among these application domains.

A statistics study of the MDCT coefficient distribution for audio

The modified discrete cosine transform (MDCT) has been widely used in many transform audio coding algorithms such as MPEG-1/2 layer III (mp3), MPEG-2/4 AAC, Dolby AC2/AC3, and numerous experimental audio coding algorithms. In this paper, we study the probabilistic distribution properties of the MDCT coefficient for audio signals. It is shown that the generalized Gaussian function with distribution parameter r=0.5 or r=1 (Laplacian) provides a good approximation to the distributions of MDCT coefficients for a variety of audio signals. Results from our study also show that although the distribution of these coefficients is not strictly Laplacian, the divergence between them is in fact very small. Therefore, it leads to only marginal redundancy if these coefficients are simply coded with some low-complexity codes designed for Laplacian sources

Cascaded RLS-LMS Prediction in MPEG-4 Lossless Audio Coding

This paper describes the cascaded recursive least square-least mean square (RLS-LMS) prediction, which is part of the recently published MPEG-4 Audio Lossless Coding international standard. The predictor consists of cascaded stages of simple linear predictors, with the prediction error at the output of one stage passed to the next stage as the input signal. A linear combiner adds up the intermediate estimates at the output of each prediction stage to give a final estimate of the RLS-LMS predictor. In the RLS-LMS predictor, the first prediction stage is a simple first-order predictor with a fixed coefficient value 1. The second prediction stage uses the recursive least square algorithm to adaptively update the predictor coefficients. The subsequent prediction stages use the normalized least mean square algorithm to update the predictor coefficients. The coefficients of the linear combiner are then updated using the sign-sign least mean square algorithm. For stereo audio signals, the RLS-LMS predictor uses both intrachannel prediction and interchannel prediction, which results in a 3% improvement in compression ratio over using only the intrachannel prediction. Through extensive tests, the MPEG-4 Audio Lossless coder using the RLS-LMS predictor has demonstrated a compression ratio that is on par with the best lossless audio coders in the field. In this paper, the structure of the RLS-LMS predictor is described in detail, and the optimal predictor configuration is studied through various experiments.

Integer MDCT with enhanced approximation of the DCT-IV

Integer transform plays an important role in lossless signal compression. In order to achieve high accuracy to its corresponding theoretical transform, the rounding number should be as low as possible. At the same time, the matrix factorization for reversible integer mapping should be handled with care, especially when the processing block length is high (N>16). In this correspondence, a new method for realizing reversible integer discrete cosine transform type IV (DCT-IV) is proposed that is a key component used in audio compression. The proposed method exhibits low rounding number (2.5N) and low complexity O(Nlog/sub 2/N) as well as well as accurately represents its counterpart floating-point DCT-IV transformation.

Perceptually Enhanced Bit-Plane Coding for Scalable Audio

The MPEG-4 scalable to lossless (SLS) audio coding is recently being developed to provide a unified solution for high-compression perceptual audio coding and high-quality lossless audio coding. SLS provides efficient fine granular scalable (FGS) coding from AAC core layer to lossless, and achieves reasonable perceptual quality at its scalable coding range using a sequential bit-plane scanning method, which minimizes the audio distortion according to the spectral shape of the core layer quantization errors. In this paper, it is shown that the perceptual quality performance of SLS at intermediate rates can be further improved by incorporating psycho acoustic model into the bit-plane coding process. In addition, it is also found that such an improvement can be achieved by slightly tweaking the original bit-plane coding process of SLS and hence preserving its nice features such as compatibility to lossless coding and low complexity