Fault-Tolerant Recursive Least-Squares Computations on a Mesh-Connected Parallel Processor

The recursive least square (RLS) algorithm is insensitive to the dispersions of the correlation matrix. Consequently, the usage of the RLS algorithm to update the adaptive recursive filter coefficients are attractive for the system modeling and identification fields. In this paper, an error function RLS (EFRLS) algorithm is derived for the adaptive recursive filter. Furthermore, a scaled version of the EFRLS algorithm is proposed where the computation complexity is significantly reduced. The scaled EFRLS algorithm can be realized in real time using the available digital signal processors. The convergence to the optimal solution of both the EFRLS algorithm and its scaled version is ensured while the general RLS algorithm fails to converge to the optimal solution for the multimodal performance criterion.

A new voiced/unvoiced segmentation based on RLS (recursive Least Square) in the foundation of traditional short-time analysis (i.e. Zero-Energy decision), with the adaptive tracking capacity to the non-steady pronunciation signal, has been present. The algorithm can rapidly realize precise voiced/unvoiced segmentation, without parameter-adjustment by samples training. In voiced section and unvoiced each one is relatively steady, the non-stability of pronunciation mainly manifests in the intersection point of voiced section and unvoiced section. In voiced/unvoiced section and the intersection point, RLS will present a different prediction track capacity inevitably. The adaptability establishes on a single pronunciation sample, and decides voiced/unvoiced segmentation according to the different tracking capacity of RLS in voiced/unvoiced section and the intersection point. It is different from the methods based on threshold, the algorithm based on recognizing the extreme value point of RLS prediction error would avoid the drawback of various adaptive learning algorithms in generalization, which better adapt for various varying factors of different sampling rate, speaker, volume, background noise.

Piezo actuators are widely use in positioning and control applications. Piezo actuators are nonlinear and possess time varying hysteresis nature. For controlling piezo actuators mostly inverse hysteresis model or adaptive inverse control model are utilize. To implement adaptive inverse control, adaptive model of piezo actuator is to be determined. There are various adaptive methods viz. Recursive Least Square (RLS), Kalman Filter (KF), Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) by which the model can be determined. This paper discusses the system of piezo actuator and different methods of finding adaptive model for piezo actuator system. Further a comparative study of adaptive model identification is carried out and suitable model for this system is identified and discussed. A novel approach of identifying a suitable model is discussed in this paper. The experimentation is carried out using electro pneumatic positioner, which utilizes the piezo valve for positioning. LabVIEW 2014 is used along with data acquisition hardware for modeling of real time system. The results of experimentation shows that, RLS adaptive model is more suitable as compared to other adaptive algorithms.

Energy consumption in Digital Signal Processing (DSP) application is a vital parameter of consideration. Mostly DSP applications deplete power. Battery constraint is important in many mobiles, processors, and sensor-related devices. The major critical operations in DSP architecture are multiplication and addition. Multiplication process is performed using repetitive addition. Hence, adder is the basic component used in digital signal processor. DSP architecture is proposed with the energy-efficient goal. Traditional carry select adder contains ripple carry adder block is replaced by parallel prefix adder. By replacing that block and introducing folded tree, we can reduce power used by DSP processors. This architecture can be used in all power constrained DSP applications. In the existing system, folded tree is used in architecture to reduce the number of processing elements, and they used carry look-ahead adder to perform the internal processing element operations. In the proposed system, carry look-ahead adder is replaced by LFA and PA to harness the energy depleted in an application (e.g., DSP application). Power consumption is reduced by 12–15% as compared with the existing algorithms using Cyclone III (EP3C16F484C6).

Sparse adaptive filtering algorithms are utilized to exploit potential sparse structure information as well as to mitigate noises in many unknown sparse systems. Sparse recursive least square (RLS) algorithms have been attracted intensely attentions due to their low-complexity and easy- implementation. Basically, these algorithms are constructed by standard RLS algorithm and sparse penalty functions (e.g., l_1-norm). However, existing sparse RLS algorithms do not exploit the sparsity efficiently. In this paper, an improved adaptive filtering algorithm is proposed by incorporating a novel correntropy induced metric (CIM) constraint into RLS, which is termed as RLS- CIM algorithm. Specifically, we adopt a well-known Gaussian kernel in CIM and further devise a novel variable kernel width to control the sparse penalty in different transient-error scenarios. Numerical simulation results are given to corroborate the proposed algorithm via mean square deviation (MSD).

Finite Impulse Response (FIR) Filter occupied a significant position in most of Digital Signal Processing (DSP) Applications. The behaviour of FIR filters can be realized in software for DSP applications and executed on Digital Signal Processor architectures. These kinds of digital filters consider digital components, digital input and perform mathematical computations to produce digital output. In modern DSP applications demanding area efficient architecture for DSP algorithms while optimizing throughput. This can be achieved by design of hardware architectures for DSP algorithms and apply design optimization techniques to accelerate the throughput and minimize area. This article attempts to develop hardware architectures for FIR Filter and apply the design optimization techniques present in literature. In this article, High Level Synthesis concepts were adapted to develop hardware architecture for FIR filter and optimization techniques have been applied to optimize design attributes like area, throughput and power. Finally, design attributes are summarized after applying optimization techniques and published significant architectural attributes suitable for real life DSP applications.

A new adaptive algorithm is proposed by introducing some modifications to the recursive least squares (RLS) algorithm. Except for the noise variance, the proposed algorithm does not require any statistics or knowledge of the desired signal, thus, it is suitable for adaptive filtering for channel estimation in code division multiple access (CDMA) systems in cases where the standard RLS approach cannot be used. A theoretical analysis demonstrates the convergence of the proposed algorithm, and simulation results for CDMA channel estimation show that the proposed algorithm outperforms existing channel estimation schemes.

There is an evidence that channel estimation in communication systems plays a crucial issue in recovering the transmitted data. In recent years, there has been an increasing interest to solve problems due to channel estimation and equalization especially when the channel impulse response is fast time varying Rician fading distribution that means channel impulse response change rapidly. Therefore, there must be an optimal channel estimation and equalization to recover transmitted data. However. this paper attempt to compare epsilon normalized least mean square (ε-NLMS) and recursive least squares (RLS) algorithms by computing their performance ability to track multiple fast time varying Rician fading channel with different values of Doppler frequency, as well as mean square deviation (MSD) has simulated to measure the difference between original channel and what is estimated. The simulation results of this study showed that (ε-NLMS) tend to perform fast time varying Rician fading channel better than (RLS) adaptive filter.

The rapid increase in digital signal processing (DSP) applications has generated a strong demand for electrical engineers with DSP backgrounds; however, the gap between industry needs and university curricula still exists. To answer this challenge, a sequence of innovative DSP courses that emphasize hands-on experiments and practical applications were developed for continuing education in electrical and computer engineering. These courses are taught in the evening for night-school students having at least three years of work experience. These courses enable students to experiment with sophisticated DSP applications to augment the theoretical, conceptual, and analytical materials provided in traditional DSP courses. The inclusion of both software and hardware developments allows students to undertake a wide range of DSP projects for real-world applications. Assessment data concludes that the digital signal processor fundamentals course can increase learning interest and overcome the prerequisite problem of DSP laboratory experiments. This paper also briefly introduces representative examples of some challenging DSP applications.

This paper analyzes the performance of well known Least-mean-square (LMS) and Recursive-least-squares (RLS) adaptive algorithms in terms of bit error rate (BER) verses signal to noise ratio (SNR) performances on the space-time coded multicarrier CDMA (STC-MC-CDMA) systems in frequency selective environments. For system we consider Alamouti's space-time coding scheme that involves two transmit antennas and provide transmit diversity at transmitting end. We compare the performance of RLS and LMS detector with help of computer simulation. It is found that BER performance of RLS detector is always considerably better than that of LMS detector. Thus, RLS detector outperforms the LMS detector. In this paper, we propose an application of the RLS algorithm for the detection of space-time coded MC-CDMA system. Simulation results presented in this paper confirms the performance analysis.

Any finite impulse response (FIR) adaptive algorithm has an inherent recursive structure, since the error at time n is fed back into the algorithm to provide the next tap vector. The authors explicitly provide this recursive structure and show that a least mean square (LMS) adaptive algorithm can be expressed in terms of a recursive filter, the coefficients of this recurrence being the correlation coefficients of the input signal. Corresponding structures are proposed for the recursive least squares (RLS) algorithm, the constant modulus algorithm (CMA), and the decision-directed (DD) algorithm. These schemes provide block algorithms that are equivalent to the corresponding sample-by-sample algorithm, while requiring fewer computations. The corresponding structures are easily used for studying equilibrium states of the adaptive algorithms.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Linear modeling of nonlinear systems like equalizers, loudspeaker etc is not acceptable in practical situations. In this paper, the nonlinear state space model of the loudspeaker is considered as an unknown nonlinear system and is identified by adaptive second order Volterra filters (ASVF). The adaptations were done by using normalized least mean square (NLMS) and recursive least squares (RLS) algorithms. While cascading two SVFs, better approximation of the original system is obtained i.e. three harmonics of the input frequency are obtained at the output as compared to one harmonic with a single SVF. Experimental verification of the simulation results is done by identifying unknown non-linear system comprising of a loud speaker using adaptive second order Volterra filter. During the identification process, it is observed that the eigen value spread of the input correlation matrix and the bulk delay of 6.2 msec, introduced by the nonlinear system had a significant impact on the convergence of the adaptation algorithms. It is found that RLS algorithm converged after 200 iterations as compared to NLMS algorithm which took 500 iterations converge.

Signal processing has always been a critical aspect of interferometric spectroscopy. The main value of Fourier Transform Spectroscopy (FTS) is to provide the maximum information per unit time. This means that careful signal processing is required to maximize the signal to noise ratio of the result. Digital signal processing (DSP) is not new in FTS. DSP for apodization, Fourier transform, phase correction, and some digital filtering have been used for many years. The first difficulty encountered in using DSP is the required transformation of the continuous time signal to a sequence of finite precision numbers, that is a discrete time, discrete magnitude representation of the continuous signal. Today, sampling and digitization with conventional techniques are reasonably straightforward, with digitization noise of the order of 2−16. Advances in several aspects of FTS technology, including step scanning, have created the need to lower the digitization noise. The processing of signals from multiple modulation FTS experiments, specially step-scan signals, has presented serious limitations until recently, due primarily to the performance restrictions of the available hardware, and the fundamental limitations of the analog systems used. The application of DSP techniques to reduce the sampling and digitization errors are reviewed, and practical solutions that can provide digitization noise lower than 2−24 with low cost systems are described. Some of the causes and effects of sampling error are reviewed, to clarify common misunderstandings. DSP applications in the detection of multiple modulation step-scan experiments and photoacoustic spectroscopy are described in detail. These applications allow the measurement of the signal during the entire step time, eliminate interference of secondary effects of phase modulation, and integrate the entire experiment in the data system, eliminating all the analog adjustments (knobs) needed when lock-in amplifiers are used.

Channel estimation is employed to get the current knowledge of channel states for an optimum detection in fading environments. In this paper, a new recursive multiple input multiple output (MIMO) channel estimation is proposed which is based on the recursive least square solution. The proposed recursive algorithm utilizes short training sequence on one hand and requires low computational complexity on the other hand. The algorithm is evaluated on a MIMO communication system through simulations. It is realized that the proposed algorithm provides fast convergence as compared to recursive least square (RLS) and robust variable forgetting factor RLS (RVFF-RLS) adaptive algorithms while utilizing lesser computational cost and provides independency on forgetting factor.

Adaptive signal processing algorithms derived from LS (least squares) cost functions are known to converge extremely fast and have excellent capabilities to "track" an unknown parameter vector. This paper treats analytically and experimentally the steady-state operation of RLS (recursive least squares) adaptive filters with exponential windows for stationary and nonstationary inputs. A new formula for the "estimation-noise" has been derived involving second- and fourth-order statistics of the filter input as well as the exponential windowing factor and filter length. Furthermore, it is shown that the adaptation process associated with "lag effects" depends solely on the exponential weighting parameter λ. In addition, the calculation of the excess mean square error due to the lag for an assumed Markov channel provides the necessary information about tradeoffs between speed of adaptation and steady-state error. It is also the basis for comparison to the simple LMS algorithm, in a simple case of channel identification, it is shown that the LMS and RLS adaptive filters have the same tracking behavior. Finally, in the last part, we present new RLS restart procedures applied to transversal structures for mitigating the disastrous results of the third source of noise, namely, finite precision arithmetic.