Fast Recursive Research Articles

Evolution from ultrafast demagnetization to magnetization reversal at the Fe sites in Gd23Fe67Co10 with laser fluence, observed by the element-selective magneto-optical Kerr effect using an X-ray free electron laser

In the present study, we investigated the magnetization dynamics of a Gd23Fe67Co10 nanofilm. We subjected the nanofilm to a time-resolved experiment to examine its magneto-optical effect using an X-ray free electron laser. By tuning the energy of the photons to the core-level resonance of the Fe absorption edge, we selectively determined the magnetic behavior of the elements at the Fe site in the nanofilm. When triggered by an ultrashort pulse of infrared radiation, the materials underwent demagnetization and magnetic reversal, depending on fluence of the laser. The following relaxation process was found to be associated not only with the fast recursion but also with a long sustention of the reversed magnetization. The experimental observation consistently match with the recent prediction by the magnetic dynamic simulation. The ultrafast dynamics was triggered by the ultrafast thermal effect and it was recovered toward the initial state in the non-thermal manner.

Secure Routing and Reliable Packets Transmission In MANET Using Fast Recursive Transfer Algorithm

Mobile Ad-hoc Network (MANET) autonomous operation can be multi-hop it is the infrastructure-less wireless network. Security is one of the biggest challenges in Mobile Adhoc Network. The MANETs security there are considerations must be so that the routing protocol in order to protect the secure data transmission. In the routing and security that is an important aspect for in a MANETs, existing method routing protocol, however, is not enough to security requirements. The proposed method using Fast Recursive Transmission Algorithm (FRTA) used designed to maximize the data security, routing optimization, minimizing the impact of malicious attack using Collision Detection Avoid Algorithm (CDAA) activity over the MANET and select the best path. Table-driven routing protocols, also known as proactive routing protocols, mandate that every node on the network keep up-to-date routing data. To maintain consistent routing information for network nodes, these protocols propagate frequent updates to the routing table throughout the network, necessitating changes to the network topology. There's a lot of overhead from these upgrades. The suggested FRTA algorithm lowers node data loss rates and increases network energy efficiency. Table-driven routing protocols, also known as proactive routing protocols, mandate that every node on the network keep up-to-date routing data. To maintain consistent routing information for network nodes, these protocols propagate frequent updates to the routing table throughout the network, necessitating changes to the network topology. There's a lot of overhead from these upgrades. The suggested FRTA algorithm lowers node data loss rates and increases network energy efficiency. The proposed method shows high performance than other existing evaluations of the most advanced security and routing energy, end-to-end delay, packet transfer rate, packet loss

High-Precision Position Control of PMLSM Using Fast Recursive Terminal Sliding Mode With Disturbance Rejection Ability

High-Precision Position Control of PMLSM Using Fast Recursive Terminal Sliding Mode With Disturbance Rejection Ability

Singular Signal Measurement Based on Bidirectional Recursive Complex-Valued Wavelet Algorithm

The normal operation fault of the power system is usually caused by a short-circuit fault. At this time, the system changes drastically from one state to another, accompanied by complex transient phenomena. Therefore, the measured signal contains a large number of transient components. How to effectively analyze such signals, extract their characteristics, and develop new protection devices has always been an important research field in power system protection technology. The protection of the power system is to achieve the purpose of correct action and elimination of faults by quickly detecting and locating faults. At present, the power signal analysis tools used in microcomputer protection include FFT, Kalman filter, and finite impulse response filter. They are efficient for the analysis of stationary signals, but have their limitations in analyzing nonstationary signals; especially it is difficult to identify nonlinear faults, such as the detection of high-impedance nonlinear short-circuit faults, which is a long-term unsolved problem in power systems. Based on wavelet transform, this paper selects complex-valued wavelet algorithm, analyzes a real-time recursive wavelet algorithm, and deduces the realization process of the algorithm in detail. The algorithm greatly reduces the computational complexity of the existing two-way recursive algorithm, can be used for real-time detection of fault signals in various fields of power system, and can be extended to realize other fast recursive algorithms of wavelet functions. Based on the sensitivity of complex-valued wavelet transform phase information to singularity, a method for real-time monitoring of power system fault mutation signals using the phase information of complex-valued wavelet fast recursion algorithm to assist amplitude information is proposed. The validity and practicability of this complex-valued wavelet and its real-time recursive algorithm for fault detection are demonstrated by an example.

재생에너지 기반 Hybrid 전력망의 주파수 추정

The frequency is one of the very important factors in power grid. It is essential that the frequency of a power grid should be maintained close to its nominal frequency. Recently, under the leadership of the government, interest in the expansion and distribution of renewable energy sources (RES) is increasing. Therefore, technology for stable operation in preparation for grid connection variability of RES is required. There is growing interest in the rapid measurement and accurate estimation of grid frequency for renewable energy monitoring and stability, using GPS based time-synchronous PMU (Phasor Measurement Unit) for wide area blackout prevention. In this paper, FRDWT (Fast Recursive Discrete Wavelet Transform), GCDFT (Gain Compensator Discrete Fourier Transform) and PAD (Phase Angle Difference) are selected as optimal frequency measurement methods for frequency estimation of hybrid power grid based on renewable energy. This study is to evaluate the performance of the proposed technique using domestic 345kV power grid model data by EMTP-RV S/W.

A Fast Recursive Algorithm For Spectrum Tracking in Power Grid Systems.

A major challenge for future wide area measurement systems is how to efficiently monitor and control the power system, which requires accurate and real-time tracking of the phase, frequency, and Power Factor (PF) of the system. The presence of inter and sub harmonics, especially in a distribution system caused by widely used non-linear power loads, rectifiers, and inverters, will distort estimation of phase/frequency and make it difficult to track the voltage, current and frequency variations. In this paper we present a new approach to identify and track the harmonics, sub-harmonics and inter-harmonics, as well as observe their impact on the power system. We propose a two-stage processing approach that consists of a subspace-based estimation method to detect and identify all harmonic components, followed by a low-complexity fast tracking algorithm to monitor frequency variations of voltage and current signals in real-time with great accuracy. The simulation results show that the proposed approach can provide highly reliable estimation and fast tracking of the harmonic components, while avoiding the impact of time variance.

Fast Recursive Computation of Krawtchouk Polynomials

Krawtchouk polynomials (KPs) and their moments are used widely in the field of signal processing for their superior discriminatory properties. This study proposes a new fast recursive algorithm to compute Krawtchouk polynomial coefficients (KPCs). This algorithm is based on the symmetry property of KPCs along the primary and secondary diagonals of the polynomial array. The $$n-x$$ plane of the KP array is partitioned into four triangles, which are symmetrical across the primary and secondary diagonals. The proposed algorithm computes the KPCs for only one triangle (partition), while the coefficients of the other three triangles (partitions) can be computed using the derived symmetry properties of the KP. Therefore, only N / 4 recursion times are required. The proposed algorithm can also be used to compute polynomial coefficients for different values of the parameter p in interval (0, 1). The performance of the proposed algorithm is compared with that in previous literature in terms of image reconstruction error, polynomial size, and computation cost. Moreover, the proposed algorithm is applied in a face recognition system to determine the impact of parameter p on feature extraction ability. Simulation results show that the proposed algorithm has a remarkable advantage over other existing algorithms for a wide range of parameters p and polynomial size N, especially in reducing the computation time and the number of operations utilized.

Wavelet Based Adaptive Filtering Algorithms for Acoustic Noise Cancellation

This paper prefers Acoustic Noise cancellation (ANC) system using Wavelet based adaptive filtering algorithms. The Acoustic Noise canceller is implemented using adaptive algorithms like LMS (Least Mean Square), NLMS (Normalized Least Mean Square),RLS (Recursive Least Square), and FRLS (Fast Recursive Least Square). The inclusion of wavelet based transformation in ANC reduces the number of samples to be processed and increase the efficiency of the system by minimizing the processing time. The simulation results shows that the wavelet transform based adaptive algorithms produce improvement in SNR (Signal to Noise Ratio) with less execution time compared to conventional adaptive algorithms.

Three Dimensional Minimum Error Threshold Algorithm and Its Fast Recursive Method

针对现有算法无法有效解决混合噪声图像的阈值分割的问题，该文提出3维最小误差阈值法。该方法充分考虑图像像元点的灰度分布信息及像元点之间的灰度相关信息，结合图像灰度、均值和中值信息，构造出3维观测空间。然后基于相对熵定义出3维最佳阈值判别式。为了提高该算法的处理速度，给出相应的快速递推算法，其时间复杂度为O(L3)。实验结果表明，在不同噪声环境及非均匀光照条件下，尤其对混合噪声图像，与现有方法相比，文中算法均取得了更好的分割效果。

A Fast Recursive Algorithm and Architecture for Pruned Bit-reversal Interleavers

In this paper, pruned bit-reversal permutations employed in variable-length interleavers and their associated fast pruning algorithms and architectures are considered. Pruning permutations is mathematically formulated as a counting problem in a set of k integers and any subset of $\alpha $ consecutive integers under some permutation, where integers from this subset that map into indices less than some $\beta are to be counted. A solution to this problem using sums involving integer floors and related functions is proposed. It is shown that these sums can be evaluated recursively using integer operations. Specifically, a mathematical treatment for bit-reversal permutations (BRPs) and their permutation statistics are presented. These permutations have been mainly addressed using numerical techniques in the literature to speed up in-place computations of fast Fourier and related transforms. Closed-form expressions for BRP statistics including inversions, serial correlations, and a new statistic called permutation inliers that characterizes the pruning gap of pruned interleavers, are derived. Using the inliers statistic, a recursive algorithm that computes the minimum number of inliers in a pruned BR interleaver (PBRI) in logarithmic time complexity is presented. This algorithm enables parallelizing a serial PBRI algorithm by any desired parallelism factor by computing the pruning gap in lookahead rather than a serial fashion, resulting in significant reduction in interleaving latency and memory overhead. Extensions to 2-D block and stream interleavers are also presented. Moreover, efficient hardware architectures for the proposed algorithms employing simple logic gates are presented. Simulation results of interleavers employed in modern communication standards demonstrate 3 to 4 orders of magnitude improvement in interleaving time compared to existing approaches.

Digital Portfolios

Digital assets are investments with binary returns: their payoffs are either very large or very small. The author explores the features of optimal portfolios of digital assets, such as venture investments, credit assets, and lotteries. These portfolios comprise correlated assets with joint Bernoulli distributions and have substantial skews and kurtosis. The intuitions of mean–variance analysis do not carry over to these portfolios. Using a simple, standard, fast recursion technique to generate a portfolio’s return distribution, the author derives investor guidelines for constructing digital-asset portfolios. Das finds that digital portfolios perform better when their assets are homogeneous in size but heterogeneous in the assets’ probability of success. <b>TOPICS:</b>Other real assets, portfolio construction, statistical methods

Guest Editors’ Introduction to Special Issue on Advances in DSP System Design

The constant advances in IC technologies have introduced new challenges for implementations and design methodologies; higher integration level allows more complex systems to be implemented but on the other hand implementations often have strict constraints on power consumption. These challenges are present in signal processing systems implying the need to improve design methods and find more efficient algorithm-architecture optimizations. This special issue contains a selection of recent papers on design and implementation of signal processing systems ranging from circuit level architectures to scheduling methods and from application-specific architectures to implementations on many-core systems. In Data Center Switch for Load Balanced Fat-Trees, Lai, and Chiu demonstrate a fault tolerant switch IC operating at the maximum rate of 5.8 Gbps per channel. This work employs a load-balanced fat-tree architecture that does not consume all of its bandwidth even under heavy traffic. When there are broken links or faulty switches in the network even in heavy traffic load situations, available bandwidth remains in every connection pattern and alternative paths are provided to re-route the traffic. Fault tolerance capability evaluations of link or switch faults in the fattree network are given to support the presented idea, and a 4×4 Banyan type switch IC is developed as the commodity switch for building the fault tolerant fat-tree data center networks. Lee and Sung propose a cell-to-cell interference (CCI) cancellation technique for multi-level NAND flash memory in their paper Least Squares Based Coupling Cancellation for MLC NAND Flash Memory with a Small Number of Voltage Sensing Operations. Their two-step algorithm consists of training and then interference removal performed during the page read operation. A least-squares adaptive CCI canceller is developed and optimal quantization schemes are studied. Experimental results show a significant BER improvement despite a low number of voltage sensing operations. In A Fast Recursive Algorithm and Architecture for Pruned Bit-Reversal Interleavers, Mansour describes an algorithm and architecture for implementing interleavers used in communications applications such as errorcorrecting codes (turbo codes) and bit-interleaved coded modulation. A mathematical formulation for developing flexible-length interleavers is developed along with a study of permutation statistics. Practical examples of implementations of parallel interleavers are provided. In Highly Parallelable Bidimensional Median Filter for Modern Parallel Programming Models, Sanchez and Rodriguez present efficient parallel implementation methods for median filtering. The authors implement their previous work on the parallel ccdf-based median filter (PCMF) on a GPU (Graphics Processing Unit), and show that the proposed median filtering algorithm is efficient and can outperform other generic median filters for the GPU. The proposed algorithm is implemented in three parallel programming models: SIMD Intel, multi-core Intel with SIMD, and SIMT (CUDA). Additionally they make use of the salt & pepper noise model to improve the image reconstruction quality with a small performance impact. J. Takala (*) Department of Pervasive Computing, Tampere University of Technology, Tampere, Finland e-mail: jarmo.takala@tut.fi

A Comparative Study on Frequency Estimation Methods

In this paper, a comparative study on the frequency estimation methods using IRDWT (Improved Recursive Discrete Wavelet Transform), FRDWT(Fast Recursive Discrete Wavelet Transform), and GCDFT(Gain Compensator Discrete Fourier Transform) is presented. The 345[kV] power system modeling data of the Republic of Korea by EMTP-RV is used to evaluate the performance of the proposed two kinds of RDWT(IRDWT and FRDWT) and GCDFT. The simulation results show that the frequency estimation technique based on FRDWT could be the optimal frequency measurement method, and thus can be applied to FDR(Fault Disturbance Recorder) for wide-area blackout protection or frequency measurement apparatus.

Fast Array Multichannel 2D-RLS Based OFDM Channel Estimator

In this paper a Fast Array Multichannel Two-Dimensional Recursive Least Square (FAM 2D-RLS) adaptive filter is proposed for estimating an OFDM channel in frequency domain. This filter makes use of the shift structure of the input data vector. Thus the computational cost of the classical RLS filter which is O(M2) is reduced to O(M) for each iteration where M is the order of the filter. In order to ensure numerical stability in finite precision, we make use of array-based methods for implementing FAM 2D-RLS. The adaptive filters illustrated in the standard literature consist of a weight vector and desired data as a scalar. But in our scenario of OFDM channel estimation the weight is a matrix while the desired data are a vector. Hence the algorithm for the matrix form of FAM-2D RLS and its steady state equations are derived. Numerical stability, steady state and convergence performance are verified using MATLAB simulations.

고장 왜란 기록기를 위한 주파수 추정 기법의 비교 연구

Voltage and current phasor estimation has been executed by GPS-based synchronized PMU, which has become an important way of wide-area blackout protection for the prevention of expending faults in a power system. The PMU technique can not easily get the field data and it is impossible to share information, so that there has been used a FNET(Frequency Monitoring Network) method for the wide-area intelligent protection in USA. It consists of FDR(Fault Disturbance Recorder) and IMS (Information Management System). Therefore, FDR must provide an optimal frequency estimation method that is robust to noise and failure. In this paper, we present comparative studies for the frequency estimation method using IRDWT(Improved Recursive Discrete Wavelet Transform), FRDWT(Fast Recursive Discrete Wavelet Transform), and DFT(Discrete Fourier Transform). The Republic of Korea345[kV] power system modeling data by EMTP-RV are used to evaluate the performance of the proposed two kinds of RDWT(Recursive Discrete Wavelet Transform) and DFT. The simulation results show that the proposed frequency estimation technique using FRDWT could be the optimal frequency measurement method, and thus be applied to FDR.

A Fast Recursive Algorithm for the Estimation of Frequency, Amplitude, and Phase of Noisy Sinusoid

This paper presents an adaptive method for tracking the amplitude, phase, and frequency of a time-varying sinusoid in white noise. Although the conventional techniques like adaptive linear elements and discrete or fast Fourier transforms are still widely used in many applications, their accuracy and convergence speed pose serious limitations under sudden supply frequency drift, fundamental amplitude, or phase variations. This paper, therefore, proposes a fast and low-complexity multiobjective Gauss-Newton algorithm for estimating the fundamental phasor and frequency of the power signal instantly and accurately. Further, the learning parameters in the proposed algorithm are tuned iteratively to provide faster convergence and better accuracy. The proposed method can also be extended to include time-varying harmonics and interharmonics mixed with noise of low signal-to-noise ratio with a great degree of accuracy. Numerical and experimental results are presented in support of the effectiveness of the new approach.

Improved nonlinear PCA for process monitoring using support vector data description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.

A Fast Recursive Algorithm for G-STBC

This paper proposes a fast recursive algorithm for Group-wise Space-Time Block Code (G-STBC), which takes full advantage of the Alamouti structure in the equivalent channel matrix to reduce the computational complexity. With respect to the existing efficient algorithms for G-STBC, the proposed algorithm achieves better performance and usually requires less computational complexity.

Removal of Ocular Artifacts from EEG Signals by Fast RLS Algorithm using Wavelet Transform

This paper presents an adaptive filtering method to remove ocular artifacts in the electroencephalogram (EEG) records. The major concern in analyzing EEG signal is the presence of ocular artifacts in EEG records caused due to various factors. It is essential to design specific filters to remove the artifacts in EEG records. Here, we proposed an adaptive filtering method that uses RLS (Recursive Least Square) algorithm and FRLS (Fast Recursive Least Squares) to remove ocular artifacts from EEG recordings through wavelet transform. We compared RLS & FRLS algorithms with wavelet transforms. Elapsed time can be decreased by using the FRLS algorithm compared to other techniques and also we can compare the PSNR and MSE values.

Essential energy space random walks to accelerate molecular dynamics simulations: Convergence improvements via an adaptive-length self-healing strategy

Recently, accelerated molecular dynamics (AMD) technique was generalized to realize essential energy space random walks so that further sampling enhancement and effective localized enhanced sampling could be achieved. This method is especially meaningful when essential coordinates of the target events are not priori known; moreover, the energy space metadynamics method was also introduced so that biasing free energy functions can be robustly generated. Despite the promising features of this method, due to the nonequilibrium nature of the metadynamics recursion, it is challenging to rigorously use the data obtained at the recursion stage to perform equilibrium analysis, such as free energy surface mapping; therefore, a large amount of data ought to be wasted. To resolve such problem so as to further improve simulation convergence, as promised in our original paper, we are reporting an alternate approach: the adaptive-length self-healing (ALSH) strategy for AMD simulations; this development is based on a recent self-healing umbrella sampling method. Here, the unit simulation length for each self-healing recursion is increasingly updated based on the Wang-Landau flattening judgment. When the unit simulation length for each update is long enough, all the following unit simulations naturally run into the equilibrium regime. Thereafter, these unit simulations can serve for the dual purposes of recursion and equilibrium analysis. As demonstrated in our model studies, by applying ALSH, both fast recursion and short nonequilibrium data waste can be compromised. As a result, combining all the data obtained from all the unit simulations that are in the equilibrium regime via the weighted histogram analysis method, efficient convergence can be robustly ensured, especially for the purpose of free energy surface mapping.