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In active noise control (ANC), online secondary path modeling can be achieved by adding a small auxiliary noise signal. If the secondary path changes are slow, then this signal can be low, but keeping the stability of the ANC system with sudden secondary path changes requires higher values for this signal, so auxiliary noise power scheduling is required. The proposed algorithm deals well with sudden...
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...
The consensus problem for nonlinear stochastic networks with switched topology and noise in measurements is considered. A modification of known local voting protocol with non-decreasing step-size is analyzed for the case when inputs appear nonlinearly in the network model. The analysis is based on introduction of an averaged continuous-time model of the initial discrete-time stochastic system. It...
A problem of frequency identification for a biased sinusoidal signal is considered. An identification algorithm is proposed which ensures exponential convergence for a case of an input signal with no noise and additionally allows to estimate an amplitude and a bias of the input signal. For a case of a noised input signal an adaptive cascade of band-pass filters is proposed coupled with the proposed...
This paper proposes a memory proportionate affine projection sign algorithm (IAF-MP-APSA) by assigning an individual activation factor to each filter coefficient. In this algorithm, each individual activation factor is calculated by past and current values of the corresponding coefficient magnitude. Moreover, taking into account the memory property of the proportionate factors leads to a decrease...
In FDD radios, the problem of Self Interference, where the transmitter's own noise and distortion can desensitize the receiver is traditionally solved by RF filtering. For bands with narrow duplex spacing, this selectivity comes at the cost of bulky filters and insertion loss with an associated power loss in the front end. Moreover, with the drive towards, cheaper, smaller, integrated duplexers and...
The maximum likelihood (ML) estimation method has been extensively applied to identifying the parameters of an aircraft. But it has to derive sensitivity equations in advance and solve sensitivity matrices, thus being complicated for its application and easily reaching locally optimal solutions. The paper proposes an aircraft's parameter identification algorithm, which optimizes the ML function with...
A new approach is presented for the detection of infrared small target in the single-frame cloud image, based on the block prediction of background. The new approach estimates the background of image with block prediction algorithm. Both the rule for reference window selecting and the method for weight matrix updating adopted by the new technique can improve the accuracy of background prediction,...
This paper proposes an adaptive filtering system where a least-square algorithm and a stochastic gradient algorithm are alternately switched depending on its operational mode. The switching mechanism is quite simple and realized with a small amount of computational cost. Several numerical examples show that the resulting filter could achieve both sufficiently fast convergence and enhanced robustness...
The Filtered-Reference LMS, also known as FxLMS algorithm, is one of the most commonly used adaptive algorithms for noise control systems. It is appreciated due to its simplicity, low computational complexity, and performance efficiency. For its convergence, responses of the acousto-electric secondary path and its model should not differ by more than pi/2 for any frequency contributing to the noise...
This paper presents a cost-effective adaptive feedback Active Noise Control (FANC) method for controlling functional Magnetic Resonance Imaging (fMRI) acoustic noise by decomposing it into dominant periodic components and residual random components. Periodicity of fMRI acoustic noise is exploited by using linear prediction (LP) filtering to achieve signal decomposition. A hybrid combination of adaptive...
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