Uni-Cycle Genetic Algorithm as an Adaptation Engine for Wireless Channel Equalizers

A recently developed adaptive channel equalizer driven by a so-called Uni-Cycle Genetic Algorithm (UCGA) is examined in the paper. The authors consider different initialization strategies of the iterative process and compare UCGA against the reference Recursive Least Squares (RLS) algorithm in terms of Bit Error Rate (BER) vs. Signal to Noise Ratio (SNR) performance and convergence rate of an adaptive channel equalizer. The results display a reasonable performance gain of UCGA over RLS for most of wireless channel models studied in the paper. Additionally, UCGA is capable of boosting the equalizer convergence. Thus, it can be considered a promising candidate for the future adaptive wireless channel equalizer.

Multi-Zone Active Noise Control Strategy for the Scattered Sound Control of an Infinite Rigid Cylinder

Active noise control can be used to reduce the scattered sound of a reflecting object to make it invisible to incident acoustic waves. For the multi-zone active noise control of scattered sound from an infinite rigid cylinder, an active control strategy is proposed that combines the least absolute shrinkage and selection operator (LASSO) algorithm with constraint points and regularized least squares (RLS) algorithm. The proposed control strategy is used to promote control performance through optimizing the secondary loudspeaker placement of an active noise control system. Compared with the RLS algorithm employing the uniformly placed loudspeakers and the traditional LASSO algorithm, the proposed strategy has better reduction performance both in the forward-scattered and backward-scattered sound target areas, and there is less sound amplification in the far field. From 400 Hz–1100 Hz, the proposed strategy provides a 5 dB–16 dB reduction performance advantage in the target area compared to the RLS algorithm employing uniformly placed loudspeakers.

Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms

Modern solutions for system identification problems employ multilinear forms, which are based on multiple-order tensor decomposition (of rank one). Recently, such a solution was introduced based on the recursive least-squares (RLS) algorithm. Despite their potential for adaptive systems, the classical RLS methods require a prohibitive amount of arithmetic resources and are sometimes prone to numerical stability issues. This paper proposes a new algorithm for multiple-input/single-output (MISO) system identification based on the combination between the exponentially weighted RLS algorithm and the dichotomous descent iterations in order to implement a low-complexity stable solution with performance similar to the classical RLS methods.

A Novel Method for Online Extraction of Small-Angle Scattering Pulse Signals from Particles Based on Variable Forgetting Factor RLS Algorithm

The small-angle optical particle counter (OPC) can detect particles with strong light absorption. At the same time, it can ignore the properties of the detected particles and detect the particle size singly and more accurately. Reasonably improving the resolution of the low pulse signal of fine particles is key to improving the detection accuracy of the small-angle OPC. In this paper, a new adaptive filtering method for the small-angle scattering signals of particles is proposed based on the recursive least squares (RLS) algorithm. By analyzing the characteristics of the small-angle scattering signals, a variable forgetting factor (VFF) strategy is introduced to optimize the forgetting factor in the traditional RLS algorithm. It can distinguish the scattering signal from the stray light signal and dynamically adapt to the change in pulse amplitude according to different light absorptions and different particle sizes. To verify the filtering effect, small-angle scattering pulse extraction experiments were carried out in a simulated smoke box with different particle properties. The experiments show that the proposed VFF-RLS algorithm can effectively suppress system stray light and background noise. When the particle detection signal appears, the algorithm has fast convergence and tracking speed and highlights the particle pulse signal well. Compared with that of the traditional scattering pulse extraction method, the resolution of the processed scattering pulse signal of particles is greatly improved, and the extraction of weak particle scattering pulses at a small angle has a greater advantage. Finally, the effect of filter order in the algorithm on the results of extracting scattering pulses is discussed.

A continuous switching contact model for virtual environment based teleoperation

Virtual environment (VE), as the proxy of slave contact environment, is the most promising technology to solve the time-delay problems in teleoperation. The accuracy of the predicted force depends not only on the reliability of the contact model but also on the estimation algorithm’s adaptability. A new contact model is proposed to be applicable in various materials, which includes both the Kelvin–Voigt model (KVM) and Hunt–Crossley model (HCM). An extra parameter is set in the model to express the capacity of continuous switching between KVM and HCM, whose rationality is proved based on the energy loss. The energy loss is proportional to a power of impact velocity, and the exponent is bounded at [2,3], which exactly lies between KVM and HCM. Furthermore, to estimate the parameters with a single-stage method, the nonlinear model is linearized approximatively with logarithm function and polynomials. Then, the recursive least squares (RLS) algorithm combining forgetting factor and self-perturbing action is designed to identify the four parameters online. Finally, the model’s continuous switching is verified with ideal simulation, and the model parameters are continuously changed without jumpy switch error. In the experiment, sponge, foam, and human hand represent the complex contact materials of the slave environment where the predicted force is shown to follow the real contact force with enough accuracy. Therefore, the virtual model can be considered the substitution of slave contact environment so that the feedback force in master can be calculated in real-time.

A Modified Recursive Regularization Factor Calculation for Sparse RLS Algorithm with l1-Norm

In this paper, we propose a new calculation method for the regularization factor in sparse recursive least squares (SRLS) with l1-norm penalty. The proposed regularization factor requires no prior knowledge of the actual system impulse response, and it also reduces computational complexity by about half. In the simulation, we use Mean Square Deviation (MSD) to evaluate the performance of SRLS, using the proposed regularization factor. The simulation results demonstrate that SRLS using the proposed regularization factor calculation shows a difference of less than 2 dB in MSD from SRLS, using the conventional regularization factor with a true system impulse response. Therefore, it is confirmed that the performance of the proposed method is very similar to that of the existing method, even with half the computational complexity.

Adaptive Echo Cancellation Analysis

Echo cancellation is a classic problem in DSP and digital communication. Adaptive echo cancellation is an application of adaptive filtering to the attenuation of undesired echo in the telecommunication network. This is accomplished by modeling the echo path using an adpative filter and subtracting the estimated echo from the echo-path output. In this project, the concept of echo cancellation and echo cancellation systems are studied, simulated, and implemented in Matlab and TI TMS3206C6711 DSK. The LMS, NLMS. Fast block LMS and RLS algorithm and investigated for echo canceller and two double talk detection algorithm are presented and combined with NLMS adaptive algorithm against double talk. Finally the adaptive echo cancellation system successfully developled by the NLMS and normalized cross-correlation DTD algorithms meet the general ITU G. 168 requirements and show excellent robustness against double talk.