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This paper will focus on a new approach, which was derived from the multi-band ANC concept. The proposed structures referred as Fast Multi-Band ANCs (FMB-ANC) are immune to crosstalk, allowing higher robustness to instability and faster convergence than the CrossTalk Resistant ANC approach (CTRANC). The method is efficient for speech enhancement because it removes reasonably the correlation between...
In this paper, we propose a frequency domain active noise control (ANC) system without a secondary path model. The proposed system is based on the frequency domain simultaneous perturbation (FDSP) method we have proposed. In this system, the coefficients of the adaptive filter are updated only by error signals. The conventional ANC system using the filtered-x algorithm becomes unstable due to the...
Reset compensation has been used to overcome limitations of LTI compensation. However, since a reset compensator may destabilize a stable base LTI system, stability needs to be guaranteed for a proper application of reset control. The goal of this work is the study of the stability of a nonlinear/hybrid compensator previously developed by some of the authors, referred to as the PI+CI compensator....
In this paper, we present an exact performances analysis of adaptive filtering without any unrealistic hypothesis. In particular, we demonstarte that convergence rate depends on the high order statistics of the input. Consequently, in some cases, adaptive algorithms with decor-relating properties do not speed up the convergence as expected. In fact, we give example showing that the convergence for...
To avoid an increased noise response under high-gain learning, a Q-filter with varying cut-off frequency is proposed. The Q-filter design is of particular interest in the wafer scanning industry where nano-position accuracy should be achieved under high-speed repetitive motion. In a lifted iterative learning control (ILC) setting, the nonlinear Q-filter is given state-dependent low-pass filter characteristics...
This work addresses data-based (DB) control design; the properties and limitations inherent to DB design are discussed under a common theoretical framework and illustrated through experimental results. Theoretical results concerning the convergence and precision are discussed and specified for a particular class of processes. Two DB methods, representative of this design approach, are used to illustrate...
Based on the adaptive filtering mode, we discussed and analyzed how to improve the quality of microphone array speech enhancement and reduce the complexity of the algorithm. This paper described a new variable step least mean square (LMS) algorithm which could reduce the effect of input noise to step factor, improve the performance of the algorithm. The algorithm smoothed the step factor in time domain...
This paper proposed a hybrid-cascaded framework for image reconstruction. This framework consists of breaking the reconstruction process into two parts viz. primary and secondary. The primary part consists of simple algebraic iterative technique using Simultaneous Algebraic Reconstruction Technique (SART) for image reconstruction. The task of primary reconstruction will be to provide an enhanced image...
In this paper, a bridgeless single stage LLC resonant PFC converter is analyzed in time domain. This converter is simultaneously controlled by frequency and pulse width modulations techniques. Soft switching is greatly achieved thanks to the LLC resonant converter. Consequently, switching losses and EMI noises are significantly decreased and switching frequency is increased to reduce cost and volume,...
This paper compares the performance analysis of our proposed New Time Varying LMS (NTVLMS) algorithm with other well-known adaptive algorithms such as LMS, NLMS, RVSSLMS, NVSSLMS and TVLMS algorithm. These algorithms have been tested for their adaptive noise cancellation capabilities in the context of stationary signal corrupted by additive white Gaussian noise. The parameters Convergence rate, output...
In iris recognition systems, iris localization is a critical step which affects the further results definitively. Most of the traditional localization methods were time consuming and sensitive to noises. To solve the problems, we propose an algorithm which adopts a momentum based level set method to locate the pupil boundary. This method hasan advantage of decreasing the effect of local optima solutions...
When the system model and noise statistical characteristics are known, the conventional Kalman filtering algorithm is suitable. In most cases, the noise statistics are unknown. To improve the alignment precision and convergence speed of strap-down inertial navigation system, an initial alignment method based on Sage-Husa adaptive filter is proposed. Automatic on-line estimation and correction for...
The diffusion least mean squares (LMS) [1] algorithm gives faster convergence than the original LMS in a distributed network. Also, it outperforms other distributed LMS algorithms like spatial LMS and incremental LMS [2]. However, both LMS and diffusion-LMS are not applicable in non-linear environments where data may not be linearly separable [3]. A variant of LMS called kernel-LMS (KLMS) has been...
This paper first reviews an adaptation algorithm named Recursive Least Moduli (RLM) algorithm for complex-domain adaptive filters. The RLM algorithm achieves significant improvement in the filter convergence speed when the filter input is strongly correlated. Stochastic models are presented for two types of impulse noise found in adaptive filtering systems: one in observation noise and another at...
Q-learning and other linear dynamic learning algorithms are subject to Bellman's curse of dimensionality for any realistic learning problem. This paper introduces a framework for satisficing state abstraction -- one that reduces state dimensionality, improving convergence and reducing computational and memory resources -- by eliminating useless state dimensions. Statistical parameters that are dependent...
We consider the problem of online completion of ill-conditioned low-rank matrices. While many matrix completion algorithms have been proposed recently, they often struggle with ill-conditioned matrices and take a long time to converge. In this paper, we present a new algorithm called Polar Incremental Matrix Completion (PIMC) to address this problem. Our method is based on the GROUSE algorithm, and...
A number of intriguing decision scenarios, such as order picking, revolve around partitioning a collection of objects so as to optimize some application specific objective function. In its general form, this problem is referred to as the Object Partitioning Problem (OOP), known to be NP-hard. We here consider a variant of OPP, namely the Stochastic Online Equi-Partitioning Problem (SO-EPP). In SO-EPP,...
Study on water lubricated rubber bearing has important theoretical significance and applied value in military affairs. This paper builds a low noise composite rubber bearing parallel computing model, and drives the difference equations of bearing displacement. According to the step values, the parallel computing algorithm is given, and the convergence time t varied with time step length is obtained.
Modern Electronic Warfare systems need to provide real time location information in a consistent, unambiguous and timely manner. The time difference of arrival (TDOA) techniques used for location determination make use of the method of hyperbolic multilateration. One technique which is widely used for solving non linear hyperbolic equations is the Taylor Series method. This paper discusses the convergence...
Optimization in noisy environments is regard as a favorite application domains of genetic algorithms. Different methods for reducing the influence of noise are presented and discussed. A new fitness evaluation method is proposed that reevaluates all survival individuals each generation. Compared with re-sampling and population sizing, the new evaluation approach shows higher probability of searching...
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