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Kernel adaptive filters (KAFs) are powerful tools for online nonlinear system modeling, which are direct extensions of traditional linear adaptive filters in kernel space, with growing linear-in-the-parameters (LIP) structure. However, like most other nonlinear adaptive filters, the KAFs are “black box” models where no prior information about the unknown nonlinear system is utilized. If some prior...
Adaptive filters for Volterra system identification must deal with two difficulties: large filter length M (resulting in high computational complexity and low convergence rate) and high correlation in the input sequence. The second problem is minimized by using the recursive least-squares algorithm (RLS), however, its large computation complexity (O(M2)) might be prohibitive in some applications....
In this paper, we propose a novel realization of sub-band adaptive Volterra filter, which consists of input signal transformation block and only one adaptive Volterra filter. The proposed realization can focus on major frequency band, in which a target nonlinear system has dominant components, by changing the number of taps in each sub-band in order to simultaneously realize high computational efficiency...
Traditional active noise control (ANC) systems, which uses a fixed tap length adaptive filter as the controller may lead to non optimal noise mitigation. In addition, the conventional filtered-x least mean square algorithm based ANC schemes fail to effectively perform noise cancellation in the presence of nonlinearities in the ANC environment. In order to overcome these limitations of traditional...
The Generalized Hammerstein model has been successfully used during last few years in many physical applications to describe the behavior of a nonlinear system under test. The main advantage of such a nonlinear model is its capability to model efficiently nonlinear systems while keeping the computational cost low. On the other hand, this model can not predict complicated nonlinear behaviors such as...
This paper presents the inception and the basic concepts of a hybrid classification algorithm called Genetic-AIRS [1]. Genetic-AIRS, is a combination of the Artificial Immune Resource System (AIRS) algorithm witch uses evolutionary computation techniques. An analysis is presented to determine the final algorithm architecture and parameters. The paper also includes an experimental evaluation on various...
We still have very little knowledge about how our brains decouple different sound sources, which is known as solving the cocktail party problem. Several approaches; including ERP, time-frequency analysis and, more recently, regression and stimulus reconstruction approaches; have been suggested for solving this problem. In this work, we study the problem of correlating of EEG signals to different sets...
A novel method for defining an index based on multi-level clustering of 40-Hz auditory steady state response is presented in this paper. The index is a measure of depth of anaesthesia which can help monitoring depth of anaesthesia more closely and accurately. Multi-level expectation maximization (EM) is used for clustering the recorded 40-Hz auditory steady state response signals recorded from human...
Massive Multiple Input Multiple Output (MIMO) systems can significantly improve the system performance and capacity by using a large number of antenna elements at the base station (BS). To reduce the system complexity and hardware cost, low complexity antenna selection techniques can be used to choose the best antenna subset while keeping the system performance at a certain required level. In this...
In this paper we consider the problem of learning the genetic-interaction-map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double knockout (DK) data. Based on a set of well established biological interaction models we detect and classify the interactions between genes. Furthermore, we propose a novel linear integer optimization framework called Genetic-Interactions-Detector...
This paper presents an efficient approach for collecting data in mobile wireless sensor networks which is specifically designed to gather real-time information of bikers in a bike race. The approach employs the recent HIKOB sensors for tracking the GPS position of each bike and the problem herein addressed is to transmit this information to a collector for visualization or other processing. Our approach...
In compressed sensing (CS) framework, a signal is sampled below Nyquist rate, and the acquired samples are generally random in nature. Thus, for efficient estimation of the actual signal, the sensing matrix must preserve the relative distances among the underlying sparse vectors. Provided this condition is fulfilled, we show that CS samples will also preserve the envelope of the actual signal. Exploiting...
Sparse, non-negative signals occur in many applications. To recover such signals, estimation posed as non-negative least squares problems have proven to be fruitful. Efficient algorithms with high accuracy have been proposed, but many of them assume either perfect knowledge of the dictionary generating the signal, or attempts to explain deviations from this dictionary by attributing them to components...
Multipath propagation is a common phenomenon in wireless communication. Knowledge of propagation path parameters such as complex channel gain, propagation delay or angle-of-arrival provides valuable information on the user position and facilitates channel response estimation. A major challenge in channel parameter estimation lies in its multidimensional nature, which leads to large-scale estimation...
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