A Computationally Efficient Algorithm for Feedforward Active Noise Control Systems

In this paper, a novel algorithm with high computational efficiency is proposed for the filter adaptation in a feedforward active noise control system. The proposed algorithm Zero Forcing Block Adaptive Filter (ZF-BAF) performs filter adaptation on a block-by-block basis in the frequency domain. Filtering is performed in the time domain on a sample-by-sample basis. Working in the frequency domain permits us to get sub-linear complexity, whereas filtering in the time domain minimizes the latency. Furthermore, computational burden is tunable to meet specific requirements about adaptation speed and processing load. No other parameter tuning according to the working condition is required. Computer simulations, performed in different realistic cases against other high-performing time and frequency-domain algorithms, show that achievable performances are comparable, or even better, with those of the algorithms perfectly tuned for each specific case. Robustness exhibited in the tests suggests that performances are expected to be even better in a wide range of real cases where it is impossible to know a priori how to tune the algorithms.

Error bounds of block sparse signal recovery based on q-ratio block constrained minimal singular values

In this paper, we introduce the q-ratio block constrained minimal singular values (BCMSV) as a new measure of measurement matrix in compressive sensing of block sparse/compressive signals and present an algorithm for computing this new measure. Both the mixed ℓ2/ℓq and the mixed ℓ2/ℓ1 norms of the reconstruction errors for stable and robust recovery using block basis pursuit (BBP), the block Dantzig selector (BDS), and the group lasso in terms of the q-ratio BCMSV are investigated. We establish a sufficient condition based on the q-ratio block sparsity for the exact recovery from the noise-free BBP and developed a convex-concave procedure to solve the corresponding non-convex problem in the condition. Furthermore, we prove that for sub-Gaussian random matrices, the q-ratio BCMSV is bounded away from zero with high probability when the number of measurements is reasonably large. Numerical experiments are implemented to illustrate the theoretical results. In addition, we demonstrate that the q-ratio BCMSV-based error bounds are tighter than the block-restricted isotropic constant-based bounds.

Video Block Motion Estimation Based on Walsh-Hadamard Projection Kernels

In modern video coders, motion is estimated using an algorithm that calculates the distance and direction of motion on a block-by-block basis. In this paper, a new motion estimation scheme is proposed. This scheme uses the sum of absolute difference between the Walsh-Hadamard projections of two blocks as measurement. And integral image is used to perform the scheme. Different from other methodologies using WH projections, the method proposed in this paper does not require iteration over every position to effectively calculate the WH projections of a block at any location. And the complexity of this scheme is regardless of the size (2N×2N) of the block. Comparing to the methods (Full Search, Three Step Search and Diamond Search) based on sum of absolute differences (SAD), experiments show that the proposed scheme significantly reduces computational complexity with little increase in the bit-rate.

A Whole Rate-Distortion Optimize Pronounce Based on Wireless Sensor Network

Based on the Joint Video Team (JVT) of the ITU-T Video Coding Experts Group VC EG and the IS O/IEC Moving Picture Experts Group MPEG, an RD optimal Macro Block mode decision scheme for Internet error channel streaming is introduced. The scheme employs the luminance Rate Distortion (RD) optimal mode decision scheme so as to take the effects of video encoding distortion and the channel error propagation to get higher error robustness for error transmission. Based on the Wireless Sensor Network, this paper analyzes the data distortion problem when transmitting H.264 coded video stream over error-prone channel. And the authors also have discussed a widely accepted technique that introduces more intra-coded information on macro block basis. Additionally, this paper introduces a simple loss and multiplication factor estimation method, the rate-distortion optimized assessing strategy over the whole situation.

Using Statistical Texture Analysis for Medical Image Tamper Proofing

This chapter discusses an approach for both authentication of medical images and confidentiality for the related textual data in an online medical application paradigm. The image authentication is achieved in a soft manner through a feature-based digital signature while the confidentiality of the related patient information is achieved through reversible data hiding. The selected features are robust towards geometric transformations, while fragile towards texture alterations that are characteristic of medical images. The processing scheme is done in a block by block basis to permit the localization of tampered image’s regions. The effectiveness of the scheme, proven through experiments on a sample of medical images, enables us to argue that implementing mechanisms lying on this approach will help to maintain personal patient privacy and medical image integrity.