This paper presents a comparative overview of the most important approaches presented to address the behavioral modeling of microwave and wireless power amplifiers (PAs). Starting from a theoretical framework of recursive and nonrecursive nonlinear filters, it proposes a classification of the various PA behavioral models, discussing their abilities to represent the different effects observed in practical circuits. Using that formal procedure, one explains how it was possible to integrate a wide range of behavioral modeling activities and to show that some of them, which at first glance seemed to be quite different, are, indeed, identical in their modeling capabilities.

A comparative overview of microwave and wireless power-amplifier behavioral modeling approaches

The emerging machine learning technique called support vector machines is proposed as a method for performing nonlinear equalization in communication systems. The support vector machine has the advantage that a smaller number of parameters for the model can be identified in a manner that does not require the extent of prior information or heuristic assumptions that some previous techniques require. Furthermore, the optimization method of a support vector machine is quadratic programming, which is a well-studied and understood mathematical programming technique. Support vector machine simulations are carried out on nonlinear problems previously studied by other researchers using neural networks. This allows initial comparison against other techniques to determine the feasibility of using the proposed method for nonlinear detection. Results show that support vector machines perform as well as neural networks on the nonlinear problems investigated. A method is then proposed to introduce decision feedback processing to support vector machines to address the fact that intersymbol interference (ISI) data generates input vectors having temporal correlation, whereas a standard support vector machine assumes independent input vectors. Presenting the problem from the viewpoint of the pattern space illustrates the utility of a bank of support vector machines. This approach yields a nonlinear processing method that is somewhat different than the nonlinear decision feedback method whereby the linear feedback filter of the decision feedback equalizer is replaced by a Volterra filter. A simulation using a linear system shows that the proposed method performs equally to a conventional decision feedback equalizer for this problem.

/pdf/support-vector-machine-techniques-for-nonlinear-equalization-j9xy9jfwen.pdf

Support vector machine techniques for nonlinear equalization

The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.

/pdf/recent-advances-on-active-noise-control-open-issues-and-1wg2ydx57i.pdf

Recent Advances on Active Noise Control: Open Issues and Innovative Applications

Fourier Acoustics Sound Radiation And Nearfield Acoustical Holography

https://www.dtc.umn.edu/s/resources/gg-se.pdf

A bibliography on nonlinear system identification

In this paper, we propose an extension of the well-known FLANN filter using trigonometric expansions to include suitable cross-terms, i.e., products of input samples with different time shifts. It is shown that in some applications of nonlinear active noise control, the generalized FLANN filter can offer performance as good as that of high-order Volterra filters with a reduced complexity.

A Generalized FLANN Filter for Nonlinear Active Noise Control

Fourier nonlinear filters

—Monitoring of road surface conditions is a criticalactivity in transport infrastructure management. Many researchsolutions have been proposed in order to automatically controland check the quality of road surfaces. Most of them makeuse of expensive sensors embedded in vehicles or mainly focuson detection of speciﬁc anomalies during monitoring activity.Inthis paper, we describe the design of a system for collaborativemonitoring of road surface quality. The overall architectureencompasses the integration of a custom mobile application, ageoreferenced database system and a visualization front-end.Road surface condition is summarized through a roughnessparameter computed using signal processing algorithms runningon mobile devices. The roughness values computed are sub-sequently transmitted and stored into a back-end geographicinformation system enabling processing of aggregated tracesand visualization of road conditions. The proposed approachintroduces a thoroughly integrated system suitable for monitoringapplications in a scalable, crowdsourcing collaborative setting.Keywords–Roughness; Accelerometer; Smartphone; Monitor-ing; Cloud.

/pdf/smartroadsense-collaborative-road-surface-condition-cmrdq94r62.pdf

SmartRoadSense: Collaborative Road Surface Condition Monitoring

The paper introduces two improvements in the feedforward active noise control system with online secondary path modeling developed by Akhtar, Abe, and Kawamata: (1) optimal variable step-size parameters are derived for the adaptation algorithms of the secondary path modeling filter and of the control filter and (2) a self-tuning power scheduling for the auxiliary noise is introduced. The proposed power scheduling is chosen so that in every operating condition a specific ratio between the powers at the error microphone of the auxiliary noise and of the residual noise is achieved. It is shown that for the same auxiliary noise conditions the adaptation algorithms equipped with the optimal variable step-size parameters improve the convergence speed of the system and the estimation accuracy of the secondary path and of the optimal control filter. It is also shown that, compared with a fixed power auxiliary noise, the power scheduling of the auxiliary noise is capable to better meet the conflicting requirements of fast convergence speed of the secondary path modeling filter and of low residual noise in steady state conditions.

Optimal Variable Step-Size NLMS Algorithms With Auxiliary Noise Power Scheduling for Feedforward Active Noise Control

In this paper, a bounded-input bounded-output (BIBO) stability condition for the recursive functional link artificial neural network (FLANN) filter, based on trigonometric expansions, is derived. This filter is considered as a member of the class of causal shift-invariant recursive nonlinear filters whose output depends linearly on the filter coefficients. As for all recursive filters, its stability should be granted or, at least, tested. The relevant conclusion we derive from the stability condition is that the recursive FLANN filter is not affected by instabilities whenever the recursive linear part of the filter is stable. This fact is in contrast with the case of recursive polynomial filters where, in general, specific limitations on the input range are required. The recursive FLANN filter is then studied in the framework of a feedforward scheme for nonlinear active noise control. The novelty of our study is due to the simultaneous consideration of a nonlinear secondary path and an acoustical feedback between the loudspeaker and the reference microphone. An output error nonlinearly Filtered-U normalized LMS adaptation algorithm, derived for the elements of the above-mentioned class of nonlinear filters, is then applied to the recursive FLANN filter. Computer simulations show that the recursive FLANN filter, in contrast to other filters, is able to simultaneously deal with the acoustical feedback and the nonlinearity in the secondary path.

Alberto Carini

Papers

A Generalized FLANN Filter for Nonlinear Active Noise Control

Fourier nonlinear filters

SmartRoadSense: Collaborative Road Surface Condition Monitoring

Optimal Variable Step-Size NLMS Algorithms With Auxiliary Noise Power Scheduling for Feedforward Active Noise Control

On the BIBO Stability Condition of Adaptive Recursive FLANN Filters With Application to Nonlinear Active Noise Control