Efficient Adaptive Nonlinear Filters for Nonlinear Active Noise Control

In this paper, we treat nonlinear active noise control (NANC) with a linear secondary path (LSP) and with a nonlinear secondary path (NSP) in a unified structure by introducing a new virtual secondary path filter concept and using a general function expansion nonlinear filter. We discover that using the filtered-error structure results in greatly reducing the computational complexity of NANC. As a result, we extend the available filtered-error-based algorithms to solve NANC/LSP problems and, furthermore, develop our adjoint filtered-error-based algorithms for NANC/NSP. This family of algorithms is computationally efficient and possesses a simple structure. We also find that the computational complexity of NANC/NSP can be reduced even more using block-oriented nonlinear models, such as the Wiener, Hammerstein, or linear-nonlinear-linear (LNL) models for the NSP. Finally, we use the statistical properties of the virtual secondary path and the robustness of our proposed methods to further reduce the computational complexity and simplify the implementation structure of NANC/NSP when the NSP satisfies certain conditions. Computational complexity and simulation results are given to confirm the efficiency and effectiveness of all of our proposed methods     

Hybrid filtered error LMS algorithm: another alternative to filtered-x LMS

The filtered-error LMS (FELMS) algorithms are widely used in multi-input and multi-output control (MIMO) active noise control (ANC) systems as an alternative to the filtered-x LMS (FXLMS) algorithms to reduce the computational complexity and memory requirements. However, the available FELMS algorithms introduce significant delays in updating the adaptive filter coefficients that slow the convergence rate. In this paper, we introduce a novel algorithm called the hybrid filtered-error LMS algorithm (HFELMS) which, while still a form of the FELMS algorithm, allows users to have some freedom to construct the error filter that guarantees its convergence with a sufficiently small step size. Without increasing the computational complexity, the proposed algorithm can improve the control system performance in one of several ways: 1) increasing the convergence rate without extra computation cost; 2) reducing the remaining noise mean square error (MSE); or 3) shaping the excess noise power. Simulation results show the effectiveness of the proposed method.     

The telecomputing laboratory: a multipurpose laboratory

The authors' telecomputing laboratory is a multiuse state-of-the-art undergraduate teaching facility designed to be both flexible and powerful. The controlled workstation and computer communications facilities allow undergraduate students an opportunity to apply and integrate classroom knowledge in communications, signal processing, computer architecture and algorithm and software development via a set of practical experiments. Students are exposed to state-of-the-art computer communications systems, on which they can implement everything from modern digital communication and signal processing algorithms to parallel algorithms and real-time databases. State-of-the-art computer software provides the undergraduate students modern models of quality software, as well as the tools necessary to design and build sophisticated software/hardware systems. This laboratory significantly impacts many undergraduate students who will take positions with organizations in the telecommunications/computer industry, where the skills gained from laboratory experiences will be greatly valued     

Fire-Resistant Hydraulic Fluids for Aerospace Vehicles

This paper reports on the research to develop a fire-resistant hydraulic fluid for aerospace vehicles. Model compounds containing halogen, phosphorus, and nitrogen in five chemical classes are characterized and compared to currently used materials with emphasis on rheological properties and fire resistance as measured by autogenous ignition, 1300 F molten metal test, flash and fire points. The chemical classes covered were: (1) partially fluorinated aliphatics (hydrocarbons, silicates, esters, amines), (2) halogenated ω,ω′-bis(aryldimethyl-silyl)alkanes, (3) fluoroaromatic ethers, (4) halogenated ω,ω′-diphenylalkanes, and (5) phosphorus esters.     

Lapped multiple bases algorithms for still image compressionwithout blocking effect

We describe a system for still image compression that uses several transform sets in a multiple bases realization algorithm. Our algorithms reduce the number of encoded transform coefficients 20% beyond DCT-only compression. We extend these algorithms to use several newly developed lapped orthogonal transform (LOT) bases, resulting in useful algorithms for low bit rate (high compression) operation without blocking effect.     

Computational analysis of rotational search methods for adaptivePisarenko harmonic retrieval

The rotational search method (RSM), which reduces the computational burden of the Pisarenko method for identifying undamped sinusoids in additive noise, is discussed. A brief review of previous adaptive algorithms for implementing the method is presented. Two simulations are presented which show the inherent sensitivity of the RSM algorithm. The sensitivity is shown to depend upon the Rayleigh-Ritz eigenvector estimate and the value of the minimum eigenvalue estimate. The errors in their tracking result in a loss of search direction orthogonality to the estimated minimum eigenvector. A higher precision for the enforced search direction orthogonality which minimizes the added computation is proposed, resulting in the altered RSM (ARSM)     

Multiple fully adaptive notch filter design based on allpasssections

We develop a canonical, adaptive cascade-structure IIR notch filter to detect and track multiple time-varying frequencies in additive white Gaussian noise. The algorithm uses allpass frequency transformation filters and a truncated gradient. Simulations indicate that our algorithm is computationally simple, converges rapidly, and has good frequency resolution     

Using parameter sensitivity and interdependence to predict model scope and falsifiability.

One important criterion for a model's utility is its scope, the ability to predict a wide range of results. Scope is often difficult to ascertain without extensive data fitting. For example J. E. Cutting, N. Bruno, N. P. Brady, and C. Moore (see record 1993-00237-001) compared 2 models of perceived visual depth by fitting many data sets that were arbitrarily generated from underlying functions. They then defined scope as the number of functions a model could account for. We present an alternative technique for scope evaluation that is based on analysis of the behavior of a model's parameters and does not require extensive data fitting. The technique examines the ratio between the overall interdependence among model parameters and their sensitivity, which we show to be inversely related to a model's scope. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Novel Adaptive Nonlinear Predistorters Based on the Direct Learning Algorithm

The adaptive nonlinear predistorter is an effective technique to compensate for the nonlinear distortion existing in digital communication and control systems. However, available adaptive nonlinear predistorters using indirect learning are sensitive to measurement noise and do not perform optimally. Other available types are either slow to converge, complicated in structure and computationally expensive, or do not consider the memory effects in nonlinear systems such as a high power amplifier (HPA). In this paper, we first propose several novel adaptive nonlinear predistorters based on direct learning algorithms - the nonlinear filtered-x RLS (NFXRLS) algorithm, the nonlinear adjoint LMS (NALMS) algorithm, and the nonlinear adjoint RLS (NARLS) algorithm. Using these new learning algorithms, we design adaptive nonlinear predistorters for an HPA with memory effects or for an HPA following a linear system. Because of the direct learning algorithm, these novel adaptive predistorters outperform nonlinear predistorters that are based on the indirect learning method in the sense of normalized mean square error (NMSE), bit error rate (BER), and spectral regrowth. Moreover, our developed adaptive nonlinear predistorters are computationally efficient and/or converge rapidly when compared to other adaptive nonlinear predistorters that use direct learning, and furthermore can be easily implemented. We further simplify our proposed algorithms by exploring the robustness of our proposed algorithm as well as by examining the statistical properties of what we call the "instantaneous equivalent linear" (IEL) filter. Simulation results confirm the effectiveness of our proposed algorithms