Distributed Adaptive Networks: A Graphical Evolutionary Game-Theoretic View

Distributed adaptive filtering has been considered as an effective approach for data processing and estimation over distributed networks. Most existing distributed adaptive filtering algorithms focus on designing different information diffusion rules, regardless of the nature evolutionary characteristic of a distributed network. In this paper, we study the adaptive network from the game theoretic perspective and formulate the distributed adaptive filtering problem as a graphical evolutionary game. With the proposed formulation, the nodes in the network are regarded as players and the local combiner of estimation information from different neighbors is regarded as different strategies selection. We show that this graphical evolutionary game framework is very general and can unify the existing adaptive network algorithms. Based on this framework, as examples, we further propose two error-aware adaptive filtering algorithms. Moreover, we use graphical evolutionary game theory to analyze the information diffusion process over the adaptive networks and evolutionarily stable strategy of the system. Finally, simulation results are shown to verify the effectiveness of our analysis and proposed methods.Comment: Accepted by IEEE Transactions on Signal Processin

H∞ Adaptive Filtering

H∞ optimal estimators guarantee the smallest possible estimation error energy over all possible disturbances of fixed energy, and are therefore robust with respect to model uncertainties and lack of statistical information on the exogenous signals. We have shown that if the prediction error is considered, then the celebrated LMS adaptive filtering algorithm is H∞ optimal. We consider prediction of the filter weight vector itself, and for the purpose of coping with time-variations, exponentially weighted, finite-memory and time-varying adaptive filtering. This results in some new adaptive filtering algorithms that may be useful in uncertain and non-stationary environments. Simulation results are given to demonstrate the feasibility of the algorithms and to compare them with well-known H^2 (or least-squares based) adaptive filters

Concepts of Adaptive Information Filtering

This paper was written for the project study “Adaptive Information Filtering” at the Department of Computer Science, Leiden University, The Netherlands. The assignment was to write an introduction to Adaptive Information Filtering (AIF), based on the author’s ideas for his M.Sc. thesis, and with as large an audience as possible in mind. In addition to a simple introduction to AIF, this paper should also provide easy introductions to clustering algorithms, evolutionary computation, and n-gram analysis. (Preface, page 2

Complexity Reduction and Improvement in Convergence Characteristics of Fir Filter Using Adaptive Algorithm

Abstract: Linear filtering is required in a variety of application. A filter will be optimal only if it is designed with some knowledge about the input data. If this information is not known, adaptive filters are used. These filters are adaptable to the changing environment. Adaptive filters finds its application in various fields like adaptive noise cancelling, line enhancing, frequency tracking, channel equalisation etc. Adaptive filtering involves two basic operations filtering and adaptation algorithms. First is filtering process in which output signal is generated from input signal using digital filter. Second is adaptation process which consists of adaptive algorithm which adjusts the coefficient of filter to minimize a desired cost function. There are two basic adaptive algorithms which are used in adaptive filtering least mean square algorithm and normalised least mean square algorithm. Least-mean-square (LMS) adaptive algorithm the most popular and widely used. Another most popular mean of FIR filtering technique is to utilize NLMS algorithm but as the length of the filter increases, number of filter coefficient increases so design of filter become complex in NLMS design but by using MMax -NLMS algorithms design of filter become easy but convergence characteristics occur at later stage take too long time for computation for processing of signal. In this work proposal of improving the convergence characteristics is made which doesn't affect the performance of design without compromising the tap-selection process of the MMax-NLMS algorithms

AIDI: An adaptive image denoising FPGA-based IP-core for real-time applications

The presence of noise in images can significantly impact the performances of digital image processing and computer vision algorithms. Thus, it should be removed to improve the robustness of the entire processing flow. The noise estimation in an image is also a key factor, since, to be more effective, algorithms and denoising filters should be tuned to the actual level of noise. Moreover, the complexity of these algorithms brings a new challenge in real-time image processing applications, requiring high computing capacity. In this context, hardware acceleration is crucial, and Field Programmable Gate Arrays (FPGAs) best fit the growing demand of computational capabilities. This paper presents an Adaptive Image Denoising IP-core (AIDI) for real-time applications. The core first estimates the level of noise in the input image, then applies an adaptive Gaussian smoothing filter to remove the estimated noise. The filtering parameters are computed on-the-fly, adapting them to the level of noise in the image, and pixel by pixel, to preserve image information (e.g., edges or corners). The FPGA-based architecture is presented, highlighting its improvements w.r.t. a standard static filtering approac

Automated detection of extended sources in radio maps: progress from the SCORPIO survey

Automated source extraction and parameterization represents a crucial challenge for the next-generation radio interferometer surveys, such as those performed with the Square Kilometre Array (SKA) and its precursors. In this paper we present a new algorithm, dubbed CAESAR (Compact And Extended Source Automated Recognition), to detect and parametrize extended sources in radio interferometric maps. It is based on a pre-filtering stage, allowing image denoising, compact source suppression and enhancement of diffuse emission, followed by an adaptive superpixel clustering stage for final source segmentation. A parameterization stage provides source flux information and a wide range of morphology estimators for post-processing analysis. We developed CAESAR in a modular software library, including also different methods for local background estimation and image filtering, along with alternative algorithms for both compact and diffuse source extraction. The method was applied to real radio continuum data collected at the Australian Telescope Compact Array (ATCA) within the SCORPIO project, a pathfinder of the ASKAP-EMU survey. The source reconstruction capabilities were studied over different test fields in the presence of compact sources, imaging artefacts and diffuse emission from the Galactic plane and compared with existing algorithms. When compared to a human-driven analysis, the designed algorithm was found capable of detecting known target sources and regions of diffuse emission, outperforming alternative approaches over the considered fields.Comment: 15 pages, 9 figure

Noise Cancellation In Speech Signal Processing Using Adaptive Algorithm

Speech has always been one of the most important carriers of information for people it becomes a challenge to maintain its high quality. In many application of noise cancellation, the changes in signal characteristics could be quite fast. This requ ires the utilization of adaptive algorithms, which converge rapidly. Least Mean Squares (LMS) and Normalized Least Mean Squares (NLMS) adaptive filters have been used in a wide range of signal processing application because of its simplicity in computation and implementation. The Recursive Least Squares (RLS) algorithm has established itself as the "ultimate" adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Unfortunately, practical implementations o f the algorithm are often associated with high computational complexity and/or poor numerical properties. Recently adaptive filteri ng was presented, have a nice tradeoff between complexity and the convergence speed. This paper describes a new approach for n oise cancellation in speech signal using the new adaptive filtering algorithm named affine projection algorithm for attenuating no ise in speech signals. The simulation results demonstrate the good performance of the new algorithm in attenuating the noise