Optimal fractional delay-IIR filter design using cuckoo search algorithm

Enhanced chimp optimization algorithm for high level synthesis of digital filters

The HLS of digital filters is a complex optimization task in electronic design automation that increases the level of abstraction for designing and scheming digital circuits. The complexity of this issue attracting the interest of the researcher and solution of this issue is a big challenge for the researcher. The scientists are trying to present the various most powerful methods for this issue, but keep in mind these methods could be trapped in the complex space of this problem due to own weaknesses. Due to shortcomings of these methods, we are trying to design a new framework with the mixture of the phases of the powerful approaches for high level synthesis of digital filters in this work. This modification has been done by merging the chimp optimizer with sine cosine functions. The sine cosine phases helped in enhancing the exploitation phase of the chimp optimizer and also ignored the local optima in the search area during the searching of new shortest paths. The algorithms have been applied on 23-standard test suites and 14-digital filters for verifying the performance of the algorithms. Experimental results of single and multi-objective functions have been compared in terms of best score, best maxima, average, standard deviation, execution time, occupied area and speed respectively. Furthermore, by analyzing the effectiveness of the proposed algorithm with the recent algorithms for the HLS digital filters design, this can be concluded that the proposed method dominates the other two methods in HLS digital filters design. Another prominent feature of the proposed system in addition to the stated enhancement, is its rapid runtime, lowest delay, occupied area and lowest power in achieving an appropriate response. This could greatly reduce the cost of systems with broad dimensions while increasing the design speed.

Moving data window-based partially-coupled estimation approach for modeling a dynamical system involving unmeasurable states

The simultaneous parameter and state estimation for a multi-input multi-output (MIMO) state space system from a set of measurement data is taken into account in this paper. Firstly, in line with the number of the system outputs, the considered MIMO system is transformed to some subsystems, which lessens the dimensions and the number of the parameters to be estimated. Secondly, by designing the moving data window that contains the latest batch of collected data, we develop a moving data window-based partially-coupled average extended stochastic gradient algorithm for parameter estimation. Thirdly, once the parameter estimates are obtained, a new state filter is designed to produce the estimates of the unmeasurable states by means of the Kalman filtering principle. Then we propose a combined state filtering and moving data window-based partially-coupled average extended stochastic gradient (CSF-MDW-PC-A-ESG) algorithm to produce the estimates of the parameters and states simultaneously. To reveal the superiority of the CSF-MDW-PC-A-ESG algorithm, a combined state filtering and partially-coupled average extended stochastic gradient (CSF-PC-A-ESG) algorithm is given to make a comparison. Finally, the effectiveness and superiority of the proposed CSF-MDW-PC-A-ESG algorithm are proved in a simulation example. The results from the illustrative example show that the CSF-MDW-PC-A-ESG algorithm is effective to produce the estimates of the parameters and states and that the CSF-MDW-PC-A-ESG algorithm has the higher efficient data utilization, the more accurate parameter estimation capability and the better model fitting ability than the CSF-PC-A-ESG algorithm.

Event-triggering robust fusion estimation for a class of multi-rate systems subject to censored observations

This novel is concerned with the event-triggering robust fusion estimation problem for multi-rate systems (MRSs) subject to stochastic nonlinearities (SNs) and censored observations (COs). The considered multi-rate system includes several sensor nodes, and each sensor is with different sampling rate. To reflect the dead-zone-like censoring phenomenon, a Tobit-1 regression model with prescribed left-censoring threshold is introduced, and the stochastic nonlinearities characterized by statistical means are considered in the MRSs. In order to save the limited resource, the event-triggering mechanism (ETM) has been introduced to determine whether the specified sensor node should transmit the information to the corresponding local filter. For the addressed MRSs, we aim to design a local Tobit Kalman filtering (TKF) algorithm for each sensor node firstly in the sense of the upper bound on each local filtering error covariance being minimal. Then, such a minimized upper bound is derived by designing the filter gain properly at each iteration. In the sequel, the fusion centre manipulates the local estimates by the CI scheme. Moreover, we discuss the issue of consistency for the proposed multi-rate fusion estimation (MRFE) approach. At last, experimental simulation are exploited to demonstrate the validation of the designed MRFE algorithm.

An improved low-frequency noise reduction method in shock wave pressure measurement based on mode classification and recursion extraction

The ill-posed problem of shock wave pressure (SWP) measurement comes from the influence of low-frequency noise components to measured responses and leads to an inaccurate result. To address this problem, an improved method, referred to as recursive empirical mode decomposition (REMD), is proposed for filtering low-frequency noises from the SWP measurement signals. By means of empirical mode decomposition (EMD), the measurement signal is adaptively decomposed into several intrinsic mode functions (IMFs) without any prior information. A mode classification scheme is firstly developed to select two mode indexes for separating the useful, mixed and noisy IMFs based on energy gradient and ringing amplitude ratio. Then, an adaptive diminishing white noise-assisted technology is presented to iteratively extract the remaining useful components from the mixed IMFs based on the damping ratio of SWP measurement signal. The final denoised result is achieved by a partial reconstruction with the useful IMFs and the useful components obtained from each extraction process. The effectiveness of the proposed method is verified through a series of simulated and real SWP measurement signals. Simulated results show that the REMD method always produces the largest SNR and the smallest RMSE and PRD in both single and mixed noise situations. Furthermore, the denoised results from real SWP measurement experiments with PR and PE sensors under different pressure conditions also demonstrate the superiority of the proposed method over the existing approaches in both denoising ability and signal integrity.

A novel approach for optimal design of digital FIR filter using grasshopper optimization algorithm

The idea behind designing digital filters is to compute the optimal filter coefficients such that the magnitude response of the designed matches the ideal frequency response using optimization algorithms. The proposed work employed a recently proposed swarm-based optimization technique, namely, a grasshopper optimization algorithm (GOA) to design a linear phase finite impulse response (FIR) low pass, high pass, band pass , and band stop filters. This proposed algorithm models the behaviour of grasshoppers while seeking food sources to solve optimization problems. For the designing of the FIR filter, an absolute error difference fitness function is used, which is minimized using GOA to obtain optimal filter coefficients. The performance comparison of the proposed work is done with already existing algorithms such as cuckoo search, particle swarm optimization, artificial bee colony to prove its superiority and consistency. It is found that GOA based filter meets the objective efficiently with reduced ripples in pass band and higher attenuation in stop band with least execution time.

A new approach for the design of fractional delay by an FIR filter

Fractional delay filters modeling non-integer delays are digital filters which ideally have flat group delays. In this letter, a new, simple, accurate and efficient FIR filter design to implement the digital ideal fractional delay is presented. The design technique is based on the MacLaurin series expansion formula which is applied to a given function to obtain a closed form FIR digital filter approximation of the digital ideal fractional delay operator z^-α. The obtained FIR filter approximation formula of the digital ideal fractional delay operator z^-α is similar to the well known Grünwald-Letnikov FIR digital filter approximation formula of the ideal analog fractional differentiator s^α. Some examples have been presented to illustrate the performance and the effectiveness of this new design method. Some comparisons results show that the proposed design yields better performances than the existing designs in the literature.

Design of wavelet transform based electrocardiogram monitoring system

The new age advancements in information technology due to materials and integrated circuit (IC) technologies and their applications in biomedical sciences have made the healthcare facilities more compact and affordable for the aging population. Market trends in healthcare and related devices indicate a sharp rise in their demand. Hence the researchers have converged the efforts on designing more smart and advanced medical devices using IC technology. Among these devices, cardiac pacemakers have become a recurrent biomedical device which is engrafted in the human body to detect and monitor a person's heart beating rate. The data thus generated is processed for various medical usages and devices via wireless methods. Cardiovascular diseases (CVDs) or diseases related to the heart are due to abnormalities or disorders of the heart and blood vessels. Till date, limited literature is available which focuses on a single technique that can perform all of the ECG signal denoising, ECG detection, lossless data compression and wireless transmission. In this work, a joint approach for denoising, detection, compression, and wireless transmission of ECG signal is proposed. The modified biorthogonal wavelet transform is used for denoising, detection and lossless compression of ECG signal. To reduce the circuit complexity, biorthogonal wavelet transform is realized using linear phase structure. Further, it is found in this work that the usage of modified biorthogonal wavelet transform increases the detection accuracy and CR of the proposed design. Also, in this work, the Wi-Fi-based wireless protocol is used for compressed data transmission. The proposed ECG detector achieves the highest sensitivity and positive predictivity of 99.95% and 99.92%, respectively, with the MIT-BIH arrhythmia database. The use of modified biorthogonal 3.1 wavelet transform and run-length encoding (RLE) for the compression of ECG data achieves a higher compression ratio (CR) of 6.271. To justify the effectiveness of the proposed algorithm, which uses modified biorthogonal wavelet 3.1transform, the results are compared with the existing methods, namely, Huffman coding/simple predictor, Huffman coding/adaptive, and slope predictor/fixed length packaging.

Heart rate monitoring and therapeutic devices: A wavelet transform based approach for the modeling and classification of congestive heart failure

Heart rate monitoring and therapeutic devices include real-time sensing capabilities reflecting the state of the heart. Current circuitry can be interpreted as a cardiac electrical signal compression algorithm representing the time signal information into a single event description of the cardiac activity. It is observed that some detection techniques developed for ECG signal detection like artificial neural network, genetic algorithm, Hilbert transform, hidden Markov model are some sophisticated algorithms which provide suitable results but their implementation on a silicon chip is very complicated. Due to less complexity and high performance, wavelet transform based approaches are widely used. In this paper, after a thorough analysis of various wavelet transforms, it is found that Biorthogonal wavelet transform is best suited to detect ECG signal's QRS complex. The main steps involved in ECG detection process consist of de-noising and locating different ECG peaks using adaptive slope prediction thresholding. Furthermore, the significant challenges involved in the wireless transmission of ECG data are data conversion and power consumption. As medical regulatory boards demand a lossless compression technique, lossless compression technique with a high bit compression ratio is highly required. Furthermore, in this work, LZMA based ECG data compression technique is proposed. The proposed methodology achieves the highest signal to noise ratio, and lowest root mean square error. Also, the proposed ECG detection technique is capable of distinguishing accurately between healthy, myocardial infarction, congestive heart failure and coronary artery disease patients with a detection accuracy, sensitivity, specificity, and error of 99.92%, 99.94%, 99.92% and 0.0013, respectively. The use of LZMA data compression of ECG data achieves a high compression ratio of 18.84. The advantages and effectiveness of the proposed algorithm are verified by comparing with the existing methods.

A simple tuning method of fractional order PIλ-PDμ controllers for time delay systems

In this paper, a practical tuning technique is presented to obtain all stabilizing fractional order PI^λ-PD^μ controller parameters ensuring stability for processes with time delay using the stability boundary locus and the weighted geometrical center (WGC) methods. The method is based on obtaining of stability regions plotted by using the stability boundary locus in the (k_d,k_f)-plane and (k_p,k_i)-plane, and then computing the weighted geometrical centers of these regions. After obtaining PD^μ controller parameters using the WGC method from the stability region, desired PI^λ controller parameters are computed by the same procedure. This paper provides a simple and efficient tuning method to obtain stabilizing parameters of PI^λ-PD^μ controller for time delay systems. The important advantages of the method are both calculating of controller parameters without using any complex solution methods and ensuring the stability of closed loop system. Illustrative examples are given to demonstrate the benefits and the simplicity of the proposed method.

Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method.

Design of minimum multiplier fractional order differentiator based on lattice wave digital filter

In this paper, a novel design of fractional order differentiator (FOD) based on lattice wave digital filter (LWDF) is proposed which requires minimum number of multiplier for its structural realization. Firstly, the FOD design problem is formulated as an optimization problem using the transfer function of lattice wave digital filter. Then, three optimization algorithms, namely, genetic algorithm (GA), particle swarm optimization (PSO) and cuckoo search algorithm (CSA) are applied to determine the optimal LWDF coefficients. The realization of FOD using LWD structure increases the design accuracy, as only N number of coefficients are to be optimized for Nth order FOD. Finally, two design examples of 3rd and 5th order lattice wave digital fractional order differentiator (LWDFOD) are demonstrated to justify the design accuracy. The performance analysis of the proposed design is carried out based on magnitude response, absolute magnitude error (dB), root mean square (RMS) magnitude error, arithmetic complexity, convergence profile and computation time. Simulation results are attained to show the comparison of the proposed LWDFOD with the published works and it is observed that an improvement of 29% is obtained in the proposed design. The proposed LWDFOD approximates the ideal FOD and surpasses the existing ones reasonably well in mid and high frequency range, thereby making the proposed LWDFOD a promising technique for the design of digital FODs.

An efficient closed-form design method for nearly perfect reconstruction of non-uniform filter bank

In this paper, an efficient closed form method for the design of multi-channel nearly perfect reconstruction of non-uniform filter bank with the prescribed stopband attenuation and channel overlapping is presented. In this method, the design problem of multi-channel non-uniform filter bank (NUFB) is considered as the design of a prototype filter whose magnitude response at quadrature frequency is 0.707, which is exploited for finding the optimum passband edge frequency through empirical formula instead of using single or multivariable optimization technique. Two main attributes used in assessing the performance of filter bank are peak reconstruction error (PRE) and computational time (CPU time). As compared to existing methods, this method is very simple and easy to implement for NUFBs. To implement this algorithm, a Matlab program has been developed, and several examples are presented to illustrate the performance of proposed method.