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Academic literature on the topic 'SQUARE FILTER'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "SQUARE FILTER"

1

Nerger, Lars, Tijana Janjić, Jens Schröter, and Wolfgang Hiller. "A Unification of Ensemble Square Root Kalman Filters." Monthly Weather Review 140, no. 7 (July 1, 2012): 2335–45. http://dx.doi.org/10.1175/mwr-d-11-00102.1.

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Abstract In recent years, several ensemble-based Kalman filter algorithms have been developed that have been classified as ensemble square root Kalman filters. Parallel to this development, the singular “evolutive” interpolated Kalman (SEIK) filter has been introduced and applied in several studies. Some publications note that the SEIK filter is an ensemble Kalman filter or even an ensemble square root Kalman filter. This study examines the relation of the SEIK filter to ensemble square root filters in detail. It shows that the SEIK filter is indeed an ensemble square root Kalman filter. Furthermore, a variant of the SEIK filter, the error subspace transform Kalman filter (ESTKF), is presented that results in identical ensemble transformations to those of the ensemble transform Kalman filter (ETKF), while having a slightly lower computational cost. Numerical experiments are conducted to compare the performance of three filters (SEIK, ETKF, and ESTKF) using deterministic and random ensemble transformations. The results show better performance for the ETKF and ESTKF methods over the SEIK filter as long as this filter is not applied with a symmetric square root. The findings unify the separate developments that have been performed for the SEIK filter and the other ensemble square root Kalman filters.

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Matei, Radu. "Design of Adjustable Square-Shaped 2D IIR Filters." ISRN Signal Processing 2013 (September 11, 2013): 1–10. http://dx.doi.org/10.1155/2013/796830.

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This paper proposes an analytical design method for two-dimensional square-shaped IIR filters. The designed 2D filters are adjustable since their bandwidth and orientation are specified by parameters appearing explicitly in the filter matrices. The design relies on a zero-phase low-pass 1D prototype filter. To this filter a frequency transformation is next applied, which yields a 2D filter with the desired square shape in the frequency plane. The proposed method combines the analytical approach with numerical approximations. Since the prototype transfer function is factorized into partial functions, the 2D filter also will be described by a factorized transfer function, which is an advantage in implementation.

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Tengger, Billy Arifa, and Ropiudin Ropiudin. "Pemanfaatan Metode Kalman Filter Diskrit untuk Menduga Suhu Udara." Square : Journal of Mathematics and Mathematics Education 1, no. 2 (December 31, 2019): 127. http://dx.doi.org/10.21580/square.2019.1.2.4202.

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Suhu adalah variabel fisik yang perlu diketahui secara tepat untuk penggunaan tertentu. Suhu adalah derajat panas dan dingin suatu benda. Derajat panas dan dingin ini dipengaruhi oleh enthalpi (energi) yang dimilki suatu zat. Pendugaan suhu udara dapat memberikan manfaat untuk aktivitas pada beberapa bidang, seperti: pertanian, peternakan, perikanan, kesehatan, dan kegiatan-kegiatan lainnya. Metode Kalman Filter Diskrit merupakan salah satu metode matematika yang dapat digunakan untuk menduga data di masa yang akan datang. Kalman Filter Diskrit memiliki keunggulan mampu menduga suatu kondisi berdasarkan data terbatas. Data pengukuran terbaru menjadi suatu bagian terpenting dalam algoritma Kalman Filter, karena data tersebut akan mengkoreksi data hasil pendugaan, sehingga hasil pendugaan selalu mendekati kondisi sesungguhnya. Berdasarkan hasil kajian yang telah dilakukan menggunakan metode Kalman Filter Diskrit untuk menduga suhu udara diperoleh ketepatan pendugaan nilai suhu udara di atas 95%. Pedugaan dilakukan secara harian dan bulanan. Dengan demikian Kalman Filter Diskrit mampu dan handal untuk pendugaan suhu udara secara harian, bulanan, bahkan tahunan.

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Huang, Yulong, Yonggang Zhang, Ning Li, and Lin Zhao. "Improved square-root cubature information filter." Transactions of the Institute of Measurement and Control 39, no. 4 (July 22, 2016): 579–88. http://dx.doi.org/10.1177/0142331215608428.

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In this paper, a theoretical comparison between existing the sigma-point information filter (SPIF) framework and the unscented information filter (UIF) framework is presented. It is shown that the SPIF framework is identical to the sigma-point Kalman filter (SPKF). However, the UIF framework is not identical to the classical SPKF due to the neglect of one-step prediction errors of measurements in the calculation of state estimation error covariance matrix. Thus SPIF framework is more reasonable as compared with UIF framework. According to the theoretical comparison, an improved cubature information filter (CIF) is derived based on the superior SPIF framework. Square-root CIF (SRCIF) is also developed to improve the numerical accuracy and stability of the proposed CIF. The proposed SRCIF is applied to a target tracking problem with large sampling interval and high turn rate, and its performance is compared with the existing SRCIF. The results show that the proposed SRCIF is more reliable and stable as compared with the existing SRCIF. Note that it is impractical for information filters in large-scale applications due to the enormous computational complexity of large-scale matrix inversion, and advanced techniques need to be further considered.

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Yang, X., and T. DelSole. "The diffuse ensemble filter." Nonlinear Processes in Geophysics 16, no. 4 (July 16, 2009): 475–86. http://dx.doi.org/10.5194/npg-16-475-2009.

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Abstract. A new class of ensemble filters, called the Diffuse Ensemble Filter (DEnF), is proposed in this paper. The DEnF assumes that the forecast errors orthogonal to the first guess ensemble are uncorrelated with the latter ensemble and have infinite variance. The assumption of infinite variance corresponds to the limit of "complete lack of knowledge" and differs dramatically from the implicit assumption made in most other ensemble filters, which is that the forecast errors orthogonal to the first guess ensemble have vanishing errors. The DEnF is independent of the detailed covariances assumed in the space orthogonal to the ensemble space, and reduces to conventional ensemble square root filters when the number of ensembles exceeds the model dimension. The DEnF is well defined only in data rich regimes and involves the inversion of relatively large matrices, although this barrier might be circumvented by variational methods. Two algorithms for solving the DEnF, namely the Diffuse Ensemble Kalman Filter (DEnKF) and the Diffuse Ensemble Transform Kalman Filter (DETKF), are proposed and found to give comparable results. These filters generally converge to the traditional EnKF and ETKF, respectively, when the ensemble size exceeds the model dimension. Numerical experiments demonstrate that the DEnF eliminates filter collapse, which occurs in ensemble Kalman filters for small ensemble sizes. Also, the use of the DEnF to initialize a conventional square root filter dramatically accelerates the spin-up time for convergence. However, in a perfect model scenario, the DEnF produces larger errors than ensemble square root filters that have covariance localization and inflation. For imperfect forecast models, the DEnF produces smaller errors than the ensemble square root filter with inflation. These experiments suggest that the DEnF has some advantages relative to the ensemble square root filters in the regime of small ensemble size, imperfect model, and copious observations.

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Psiaki, Mark L. "The Square Root Information Increment Ensemble Filter." Monthly Weather Review 144, no. 12 (November 9, 2016): 4667–86. http://dx.doi.org/10.1175/mwr-d-15-0295.1.

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Abstract A new type of ensemble filter is developed, one that stores and updates its state information in an efficient square root information filter form. It addresses two shortcomings of conventional ensemble Kalman filters: the coarse characterization of random forecast model error effects and the overly optimistic approximation of the estimation error statistics. The new filter uses an assumed a priori covariance approximation that is full rank but sparse, possibly with a dense low-rank increment. This matrix can be used to develop a nominal square root information equation for the system state and uncertainty. The measurements are used to develop an additional low-rank square root information equation. New algorithms provide forecasts and analyses of these increments at a computational cost comparable to that of existing ensemble Kalman filters. Model error effects are implicit in the a priori covariance time history, thereby obviating one of the reasons for including an inflation operation. The use of an a priori full-rank covariance allows the analysis operations to improve the state estimate without the need for a localization adjustment. This new filter exhibited worse performance than a typical covariance square root ensemble Kalman filter when operating on the Lorenz-96 problem in a chaotic regime. It excelled on a version of the Lorenz-96 problem where nonlinearities in the forecast model were weak, where the state vector uncertainty lay predominantly in a small subspace, and where the observations were spatially sparse. Such a problem might be representative of ionospheric space weather data assimilation where forcing variability can dominate the state uncertainty and where remote sensing data coverage can be sparse.

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Altaf, M. U., T. Butler, T. Mayo, X. Luo, C. Dawson, A. W. Heemink, and I. Hoteit. "A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation." Monthly Weather Review 142, no. 8 (August 1, 2014): 2899–914. http://dx.doi.org/10.1175/mwr-d-13-00266.1.

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Abstract This study evaluates and compares the performances of several variants of the popular ensemble Kalman filter for the assimilation of storm surge data with the advanced circulation (ADCIRC) model. Using meteorological data from Hurricane Ike to force the ADCIRC model on a domain including the Gulf of Mexico coastline, the authors implement and compare the standard stochastic ensemble Kalman filter (EnKF) and three deterministic square root EnKFs: the singular evolutive interpolated Kalman (SEIK) filter, the ensemble transform Kalman filter (ETKF), and the ensemble adjustment Kalman filter (EAKF). Covariance inflation and localization are implemented in all of these filters. The results from twin experiments suggest that the square root ensemble filters could lead to very comparable performances with appropriate tuning of inflation and localization, suggesting that practical implementation details are at least as important as the choice of the square root ensemble filter itself. These filters also perform reasonably well with a relatively small ensemble size, whereas the stochastic EnKF requires larger ensemble sizes to provide similar accuracy for forecasts of storm surge.

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Mu, Jing, Yuan Li Cai, and Jun Min Zhang. "Square Root Cubature Particle Filter." Advanced Materials Research 219-220 (March 2011): 727–31. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.727.

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The square root cubature particle filter (SRCPF) uses the square root cubature Kalman filter (SRCKF) for generating the proposal distribution. The SRCPF algorithm is easy to be implemented and has numerical stability. Moreover, the SRCKF based proposal distribution approximates the optimal importance distribution by incorporating the current measurement. Simulation results demonstrate that the SRCPF algorithm has the better performance for state estimation than the generic particle filter (GPF), extended particle filter (EPF) and unscented particle filter (UPF), and its calculation cost decreases largely.

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Tödter, Julian, and Bodo Ahrens. "A Second-Order Exact Ensemble Square Root Filter for Nonlinear Data Assimilation." Monthly Weather Review 143, no. 4 (March 31, 2015): 1347–67. http://dx.doi.org/10.1175/mwr-d-14-00108.1.

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Abstract The ensemble Kalman filter (EnKF) and its deterministic variants, mostly square root filters such as the ensemble transform Kalman filter (ETKF), represent a popular alternative to variational data assimilation schemes and are applied in a wide range of operational and research activities. Their forecast step employs an ensemble integration that fully respects the nonlinear nature of the analyzed system. In the analysis step, they implicitly assume the prior state and observation errors to be Gaussian. Consequently, in nonlinear systems, the analysis mean and covariance are biased, and these filters remain suboptimal. In contrast, the fully nonlinear, non-Gaussian particle filter (PF) only relies on Bayes’s theorem, which guarantees an exact asymptotic behavior, but because of the so-called curse of dimensionality it is exposed to weight collapse. Here, it is shown how to obtain a new analysis ensemble whose mean and covariance exactly match the Bayesian estimates. This is achieved by a deterministic matrix square root transformation of the forecast ensemble, and subsequently a suitable random rotation that significantly contributes to filter stability while preserving the required second-order statistics. The properties and performance of the proposed algorithm are further investigated via a set of experiments. They indicate that such a filter formulation can increase the analysis quality, even for relatively small ensemble sizes, compared to other ensemble filters in nonlinear, non-Gaussian scenarios. Localization enhances the potential applicability of this PF-inspired scheme in larger-dimensional systems. The proposed algorithm, which is fairly easy to implement and computationally efficient, is referred to as the nonlinear ensemble transform filter (NETF).

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Bastioli, Simone, Luca Marcaccioli, and Roberto Sorrentino. "Compact dual-mode rectangular waveguide filters using square ridge resonators." International Journal of Microwave and Wireless Technologies 1, no. 4 (June 22, 2009): 241–47. http://dx.doi.org/10.1017/s1759078709990286.

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A new class of compact rectangular waveguide filters is proposed in this paper. The key element is a square ridge resonator realized within a rectangular waveguide supporting the fundamental TE10 mode. The square ridge can support two modes resonating along orthogonal directions in the gap between the ridge and the opposite waveguide wall. This structure enables dual-mode degeneracy without the need of oversized cavities, thus allowing for considerable size reduction with respect to the conventional dual-mode implementations. Different filter structures employing one or more ridges within short waveguide sections are proposed as attractive solutions for pseudo-elliptic band-pass or dual-band filters. The experimental results of a manufactured fourth-order pseudo-elliptic pass-band filter validate the feasibility of the proposed class of filters.

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Dissertations / Theses on the topic "SQUARE FILTER"

1

Ng, Chee We 1975. "Design of a power-scalable digital least-means-square adaptive filter." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/87168.

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2

Josephson, Chad. "ON THE DESIGN OF A SQUARE-ROOT NYQUIST PULSE SHAPING FILTER FOR AERONAUTICAL TELEMETRY." International Foundation for Telemetering, 2017. http://hdl.handle.net/10150/626963.

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Bandwidth efﬁcient linear modulations require a pulse shape with ﬁnite support in the time domain while simultaneously achieving good spectral containment in the frequency domain. The square-root Nyquist pulse achieves zero intersymbol interference (ISI) at its matched-ﬁlter output but does so with inﬁnite support in the time domain. This paper investigates three different methods for generating an FIR approximation of a square-root Nyquist pulse.

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Vergés, Fortià Vila. "Finite dimensional optimal linear mean square filter for continuos time Markovian jump linear systems." Laboratório Nacional de Computação Científica, 2017. https://tede.lncc.br/handle/tede/277.

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Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)
Stochastic differential equations with Markovian jump parameters constitute one of the most important class of hybrid dynamical systems, which has been extensively used for the modeling of dynamical systems which are subject to abrupt changes in their structure. The abrupt changes can be due, for instance, to abrupt environmental disturbances, component failure, volatility in economic systems, changes in subsystems interconnections, abrupt changes in the operation of a nonlinear plant, etc. This can be found, for instance, in aircraft control systems, robot systems, large flexible structure for space station, etc. We shall be particularly interested in the linear class which is dubbed in the literature as the class of Markov jump linear systems (MJLS). The jump mechanism is modeled by a Markov process, which is also known in the literature as the operation mode. The dissertation address the filtering problem of the operation mode for the class of MJLS. Previous result in the literature on this problem has been obtained by Wonham, which has shown the existence of an optimal nonlinear filter for this problem. The main hindrance with Wonham’s result, in the context of the control problem with partial observation of operation mode, is that it introduces a great deal of nonlinearity in the Hamilton-Jacobi- Belman equation, which makes it difficult to get an explicit closed solution for the control problem. Motivated by this, the main contribution of this dissertation is to devise an optimal linear filter for the mode operation, which we believe could be more favorable in the solution of the control problem with partial observations. In addition, relying on Murayama’s stochastic numerical method and the results of Yuan and Mao, we carry out simulation of Wonham’s filter, and the one devised in the dissertation, in order to compare their performances.
As equações diferenciais estocáticas com salto Markoviano constituem uma das clases de sistemas dinâmicos híbridos mais importantes, e tem sido muito usados para modelar sistemas sujeitos a mudanças abruptas na sua estructura. Essas mudanças podem ser devido a, por exemplo, perturbações ambientais, falhas em componentes, volatilidade em sistemas econômicos, mudanças em interconexões de subsistemas, mudanças abruptas em operações de plantas não lineares, etc. Estas falhas podem ser encontradas em sistemas de controle para aeronaves, sistemas robóticos, estructuras grandes e flexíveis em estações espaciais, etc. Nós estamos especialmente interessados na clase de sistemas lineares que é referenciada na literatura como sistemas lineares com salto Markoviano (SLSM). O mecanismo de salto é modelado por um processo de Markov, que é conhecido na literatura como modo de operação do sistema. Essa dissertação visa o problema de filtragem para o modo de operação do sistema linear com salto. Na literatura pode-se encontrar resultados já obtidos para esse problema como é o caso do filtro ótimo não linear deduzido por Wonham. Mas no contexto de controle ótimo com observações parciais do modo de operação, o filtro de Wonham introduz não linearidades na equação de Hamilton-Jacobi-Belman, fazendo com que seja muito complexo obter uma solução fechada para o problema de controle. A principal motivação desta dissertação é deduzir o filtro ótimo linear para o modo de operação, já que esta pode ser uma solução mais favorável para o problema de controle ótimo. Finalmente, usando o método numérico para equações diferenciais estocásticas de Euler-Murayama e o resultado de Yuan e Mao, realizamos a simulação do filtro de Wonham tal como o filtro deduzido neste trabalho, com o objetivo de comparar as respectivas performances.

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Paulsen, Trevor H. "Low cost/high precision flight dynamics estimation using the square-root unscented Kalman filter /." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3181.pdf.

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Paulsen, Trevor H. "Low Cost/ High Precision Flight Dynamics Estimation Using the Square-Root Unscented Kalman Filter." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1922.

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For over a decade, Brigham Young University's Microwave Earth Remote Sensing (MERS) team has been developing SAR systems and SAR processing algorithms. In order to create the most accurate image reconstruction algorithms, detailed aircraft motion data is essential. In 2008, the MERS team purchased a costly inertial measurement unit (IMU) coupled with a high precision global positioning system (GPS) from NovAtel, Inc. In order to lower the cost of obtaining detailed motion measurements, the MERS group decided to build a system that mimics the capability the NovAtel system as closely as possible for a much lower cost. As a first step, the same sensors and a simplified set of flight dynamics are used. This thesis presents a standalone motion sensor recording system (MOTRON), and outlines a method of utilizing the square-root Unscented Kalman filter (SR-UKF) to estimate aircraft flight dynamics, based on recorded flight data, as an alternative to the extended Kalman filter. While the results of the SR-UKF are not as precise as the NovAtel results, they approach the accuracy of the NovAtel system despite the simplified dynamics model.

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Yamamoto, Kazuhiro, and Koji Tsuneyoshi. "Experimental study of hexagonal and square diesel particulate filters under controlled and uncontrolled catalyzed regeneration." Elsevier, 2013. http://hdl.handle.net/2237/20050.

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Lu, Ting Shan. "A microstrip square-loop dual-mode balun-bandpass filter with simultaneous size reduction and spurious response suppression." Thesis, University of Macau, 2009. http://umaclib3.umac.mo/record=b2129882.

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Vavruška, Marek. "Realised stochastic volatility in practice." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-165381.

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Realised Stochastic Volatility model of Koopman and Scharth (2011) is applied to the five stocks listed on NYSE in this thesis. Aim of this thesis is to investigate the effect of speeding up the trade data processing by skipping the cleaning rule requiring the quote data. The framework of the Realised Stochastic Volatility model allows the realised measures to be biased estimates of the integrated volatility, which further supports this approach. The number of errors in recorded trades has decreased significantly during the past years. Different sample lengths were used to construct one day-ahead forecasts of realised measures to examine the forecast precision sensitivity to the rolling window length. Use of the longest window length does not lead to the lowest mean square error. The dominance of the Realised Stochastic Volatility model in terms of the lowest mean square errors of one day-ahead out-of-sample forecasts has been confirmed.

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Schmidt, Jason Knudsen. "Analysis of Square-Root Kalman Filters for Angles-Only Orbital Navigation and the Effects of Sensor Accuracy on State Observability." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/627.

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Angles-only navigation is simple, robust, and well proven in many applications. However, it is sometimes ill-conditioned for orbital rendezvous and proximity operations because, without a direct range measurement, the distance to approaching satellites must be estimated by firing thrusters and observing the change in the target's bearing. Nevertheless, the simplicity of angles-only navigation gives it great appeal. The viability of this technique for relative navigation is examined by building a high-fidelity simulation and evaluating the sensitivity of the system to sensor errors. The relative performances of square-root filtering methods, including Potter, Carlson, and UD factorization filters, are compared to the conventional and Joseph formulations. Filter performance is evaluated during closed-loop "station keeping" operations in simulation.

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Kim, Taeho, and Monika Ivantysynova. "Active Vibration Control of Axial Piston Machine using Higher Harmonic Least Mean Square Control of Swash Plate." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-199412.

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Noise emission is a major drawback of the positive displacement machine. The noise source can be divided into structure borne noise source (SBNS) and fluid borne noise source (FBNS). Passive techniques such as valve plate optimization have been used for noise reduction of axial piston machines. However, passive techniques are only effective for limited operating conditions or at least need compromises in design. In this paper, active vibration control of swash plate is investigated for vibration and noise reduction over a wide range of operating conditions as an additional method to passive noise reduction techniques. A 75cc pump has been modified for implementation of active vibration control using the swash plate. One tri-axial acceleration sensor and one angle sensor are installed on the swash plate and a high speed servovalve is used for the swash plate actuation. The multi-frequency two-weight least mean square (LMS) filter synthesizes the servovalve input signal to generate a destructive interference force which minimizes the swash plate vibration. An experimental test setup has been realized using Labview field-programmable gate array (FPGA) via cRIO. Simulation and experimental studies are conducted to investigate the possibility of active vibration control.

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Books on the topic "SQUARE FILTER"

1

United States. National Aeronautics and Space Administration., ed. Computer simulation results and analysis for a root-raised cosine filter design using canonical signed digits. [Washington, DC]: National Aeronautics and Space Administration, 1996.

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Gribaudo, Michael Louis. Development of a system model and least mean square (LMS) filter for the Naval Postgraduate School (NPS) Infrared Search and Target Designation (IRSTD) system. Monterey, Calif: Naval Postgraduate School, 1989.

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Haykin, Simon, and Bernard Widrow, eds. Least-Mean-Square Adaptive Filters. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2003. http://dx.doi.org/10.1002/0471461288.

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name, No. Least-mean-square adaptive filters. Hoboken, NJ: Wiley-Interscience, 2003.

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1929-, Widrow Bernard, and Haykin Simon S. 1931-, eds. Least-mean-square adaptive filters. Hoboken, NJ: John Wiley, 2003.

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Sincarsin, G. B. Geostationary orbital estimation using a Kalman filter. Downsview, Ont: Institute for Aerospace Studies, 1986.

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J, Merhav Shmuel, and Ames Research Center, eds. Performance characteristics of an adaptive controller based on least-mean-square filters. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1987.

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Gould, N. I. M. A multidimensional filter algorithm for nonlinear equation and nonlinear least squares. Chilton: Rutherford Appleton Laboratory, 2003.

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Chang, Long Wee. Effects of video bandwidth on the performance of a square law detector with Gaussian IF and video filters. Monterey, Calif: Naval Postgraduate School, 1991.

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Adaptive filtering: Fundamentals of least mean squares with MATLAB. Boca Raton: CRC Press/Taylor & Francis Group, 2014.

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Book chapters on the topic "SQUARE FILTER"

1

Zhao, Meng, Xue-lian Yu, Ming-lei Cui, Xue-gang Wang, and Jing Wu. "Square Root Unscented Kalman Filter Based on Strong Tracking." In Lecture Notes in Electrical Engineering, 797–804. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08991-1_83.

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Kulikova, Maria V., and Innokenti V. Semoushin. "Score Evaluation Within the Extended Square-Root Information Filter." In Computational Science – ICCS 2006, 473–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758501_65.

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Cardona-Morales, Oscar, and German Castellanos-Dominguez. "Order Tracking by Square-Root Cubature Kalman Filter with Constraints." In Lecture Notes in Computer Science, 104–14. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39393-3_11.

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Bekuma, Yohannes, Rituraj Jain, Balachandra Pattanaik, and Adugna Edosa. "Design of FIR Filter-Based on Least Square Error Method." In Algorithms for Intelligent Systems, 413–26. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3311-0_35.

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Osman, P., P. V. Sridevi, and K. V. S. N. Raju. "Plasmonic Square Ring Resonator Based Band-Stop Filter Using MIM Waveguide." In Advances in Intelligent Systems and Computing, 71–76. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5400-1_8.

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Havlík, Jindřich, Ondřej Straka, Jindřich Duník, and Jiří Ajgl. "Stochastic Integration Filter with Improved State Estimate Mean-Square Error Computation." In Informatics in Control, Automation and Robotics, 423–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55011-4_21.

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Bachute, Mrinal, and R. D. Kharadkar. "Analysis of Least Mean Square and Recursive Least Squared Adaptive Filter Algorithm for Speech Enhancement Application." In Communications in Computer and Information Science, 590–604. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8657-1_45.

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Das, Tarun Kumar, and Sayan Chatterjee. "Phase Velocity Compensation in Compact Coupled-Line Bandpass Filter with Square Corrugations." In Social Transformation – Digital Way, 29–41. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1343-1_4.

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Wang, Sisi, and Lijun Wang. "Maneuvering Target Tracking Based on Adaptive Square Root Cubature Kalman Filter Algorithm." In Lecture Notes in Electrical Engineering, 901–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34528-9_95.

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Bitra, Surendra Kumar, and M. Sridhar. "Design of Nanoscale Square Ring Resonator Band-Pass Filter Using Metal–Insulator–Metal." In Lecture Notes in Electrical Engineering, 659–64. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3828-5_68.

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Conference papers on the topic "SQUARE FILTER"

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Xiao, J. K., and H. F. Huang. "Square patch resonator bandstop filter." In 2010 12th IEEE International Conference on Communication Technology (ICCT). IEEE, 2010. http://dx.doi.org/10.1109/icct.2010.5689204.

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Oshman, Y., and I. Y. Bar-itzhack. "New V-A square root filter." In 1985 24th IEEE Conference on Decision and Control. IEEE, 1985. http://dx.doi.org/10.1109/cdc.1985.268794.

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Ramos, J. Humberto, Kevin M. Brink, and John E. Hurtado. "Square Root Partial-Update Kalman Filter." In 2019 22th International Conference on Information Fusion (FUSION). IEEE, 2019. http://dx.doi.org/10.23919/fusion43075.2019.9011285.

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Bierman, Gerald, and Mitchell Belzer. "A decentralized square root information filter/smoother." In 1985 24th IEEE Conference on Decision and Control. IEEE, 1985. http://dx.doi.org/10.1109/cdc.1985.268912.

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Arslanalp, Remzi, and Abdullah T. Tola. "Electronically Tunable Square-Root Domain Filter Circuit." In 2007 15th IEEE Signal Processing and Communications Applications. IEEE, 2007. http://dx.doi.org/10.1109/siu.2007.4298609.

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Closas, Pau, and Carles Fernandez-Prades. "The marginalized square-root Quadrature Kalman Filter." In 2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2010). IEEE, 2010. http://dx.doi.org/10.1109/spawc.2010.5670900.

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Guo, Wenyan, and Hualei Ling. "Square root spherical simplex radial information filter." In 2017 4th International Conference on Systems and Informatics (ICSAI). IEEE, 2017. http://dx.doi.org/10.1109/icsai.2017.8248392.

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Strobach, P. "The square-root Schur RLS adaptive filter." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150713.

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Clark, John, Minfan Pai, Barry Wissman, Guohong He, and Wan-thai Hsu. "Parallel-Coupled Square-Resonator Micromechanical Filter Arrays." In 2006 IEEE International Frequency Control Symposium and Exposition. IEEE, 2006. http://dx.doi.org/10.1109/freq.2006.275434.

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Fernandez-Vazquez, Alfonso, and Gordana Jovanovic Dolecek. "Design of Least-Square GCF compensation filter." In 2009 52nd IEEE International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2009. http://dx.doi.org/10.1109/mwscas.2009.5236028.

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Reports on the topic "SQUARE FILTER"

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Stoffer, David S. Bootstrapping Nonlinear Least Squares Estimates in the Kalman Filter Model. Fort Belvoir, VA: Defense Technical Information Center, January 1986. http://dx.doi.org/10.21236/ada167099.

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Sun, Winston Y. Linear adaptive noise-reduction filters for tomographic imaging: Optimizing for minimum mean square error. Office of Scientific and Technical Information (OSTI), April 1993. http://dx.doi.org/10.2172/10148667.

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Dan, Gan, Yan Feng, Cheng Rui, and Zhou Xuhong. CYCLIC TESTS OF CONCRETE-FILLED U-SHAPED STEEL BEAM TO CONCRETE-FILLED SQUARE STEEL TUBE COLUMN JOINTS. The Hong Kong Institute of Steel Construction, December 2018. http://dx.doi.org/10.18057/icass2018.p.031.

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Collins, Clarence O., and Tyler J. Hesser. altWIZ : A System for Satellite Radar Altimeter Evaluation of Modeled Wave Heights. Engineer Research and Development Center (U.S.), February 2021. http://dx.doi.org/10.21079/11681/39699.

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This Coastal and Hydraulics Engineering Technical Note (CHETN) describes the design and implementation of a wave model evaluation system, altWIZ, which uses wave height observations from operational satellite radar altimeters. The altWIZ system utilizes two recently released altimeter databases: Ribal and Young (2019) and European Space Agency Sea State Climate Change Initiative v.1.1 level 2 (Dodet et al. 2020). The system facilitates model evaluation against 1 Hz1 altimeter data or a product created by averaging altimeter data in space and time around model grid points. The system allows, for the first time, quantitative analysis of spatial model errors within the U.S. Army Corps of Engineers (USACE) Wave Information Study (WIS) 30+ year hindcast for coastal United States. The system is demonstrated on the WIS 2017 Atlantic hindcast, using a 1/2° basin scale grid and a 1/4° regional grid of the East Coast. Consistent spatial patterns of increased bias and root-mean-square-error are exposed. Seasonal strengthening and weakening of these spatial patterns are found, related to the seasonal variation of wave energy. Some model errors correspond to areas known for high currents, and thus wave-current interaction. In conjunction with the model comparison, additional functions for pairing altimeter measurements with buoy data and storm tracks have been built. Appendices give information on the code access (Appendix I), organization and files (Appendix II), example usage (Appendix III), and demonstrating options (Appendix IV).

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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.

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Caritat, P. de, and U. Troitzsch. Towards a regolith mineralogy map of the Australian continent: a feasibility study in the Darling-Curnamona-Delamerian region. Geoscience Australia, 2021. http://dx.doi.org/10.11636/record.2021.035.

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Bulk quantitative mineralogy of regolith is a useful indicator of lithological precursor (protolith), degree of weathering, and soil properties affecting various potential landuse decisions. To date, no national-scale maps of regolith mineralogy are available in Australia. Catchment outlet sediments collected over 80% of the continent as part of the National Geochemical Survey of Australia (NGSA) afford a unique opportunity to rapidly and cost-effectively determine regolith mineralogy using the archived sample material. This report releases mineralogical data and metadata obtained as part of a feasibility study in a selected pilot area for such a national regolith mineralogy database and atlas. The area chosen for this study is within the Darling-Curnamona-Delamerian (DCD) region of southeastern Australia. The DCD region was selected as a ‘deep-dive’ data acquisition and analysis by the Exploration for the Future (2020-2024) federal government initiative managed at Geoscience Australia. One hundred NGSA sites from the DCD region were prepared for X-Ray Diffraction (XRD) analysis, which consisted of qualitative mineral identification of the bulk samples (i.e., ‘major’ minerals), qualitative clay mineral identification of the <2 µm grain-size fraction, and quantitative analysis of both ‘major’ and clay minerals of the bulk sample. The identified mineral phases were quartz, plagioclase, K-feldspar, calcite, dolomite, gypsum, halite, hematite, goethite, rutile, zeolite, amphibole, talc, kaolinite, illite (including muscovite and biotite), palygorskite (including interstratified illite-smectite and vermiculite), smectite (including interstratified illite-smectite), vermiculite, and chlorite. Poorly diffracting material (PDM) was also quantified and reported as ‘amorphous’. Mineral identification relied on the EVA® software, whilst quantification was performed using Siroquant®. Resulting mineral abundances are reported with a Chi-squared goodness-of-fit between the actual diffractogram and a modelled diffractogram for each sample, as well as an estimated standard error (esd) measurement of uncertainty for each mineral phase quantified. Sensitivity down to 0.1 wt% (weight percent) was achieved, with any mineral detection below that threshold reported as ‘trace’. Although detailed interpretation of the mineralogical data is outside the remit of the present data release, preliminary observations of mineral abundance patterns suggest a strong link to geology, including proximity to fresh bedrock, weathering during sediment transport, and robust relationships between mineralogy and geochemistry. The mineralogical data generated by this study are presented in Appendix A of this report and are downloadable as a .csv file. Mineral abundance or presence/absence maps are shown in Appendices B and C to document regional mineralogical patterns.

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ANALYSIS AND DESIGN OF AXIALLY LOADED SQUARE CFST COLUMN TO RC BEAM JOINTS STIFFENED BY DIAGONAL RIBS. The Hong Kong Institute of Steel Construction, March 2023. http://dx.doi.org/10.18057/ijasc.2023.19.1.6.

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This work proposes a partially through-beam joint system to connect square concrete-filled steel tubular (CFST) columns and reinforced concrete (RC) beams. In the system, the holes in the steel tube allow longitudinal beam reinforcements to be continuous through the joint zone to achieve direct load-transfer of the beam, and the square steel tube with holes is strengthened by welding diagonal ribs located at the corners of the steel tube. Finite element (FE) analyses on joints with RC beams were carried out based on verified models. The analysis results showed that diagonal ribs welded to the joint tube confined the concrete in the joint zone efficiently and made up for the reduction in axial load capacity caused by the holes of the steel tube, so the joint system can meet the requirements of strong-joint weak-component under axial compression with ease. Finally, mechanics-based models and axial strength equations of joints were proposed, and the predicted results agreed well with the FE results. These results proved that the square CFST column to RC beam joints stiffened by diagonal ribs were feasible and can be applied in engineering practice based on reasonable design.

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FINITE ELEMENT MODELING OF CONCRETE-FILLED STAINLESS-CLAD BIMETALLIC STEEL SQUARE TUBES UNDER AXIAL COMPRESSION. The Hong Kong Institute of Steel Construction, August 2022. http://dx.doi.org/10.18057/icass2020.p.276.

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As an advanced high-performance bimetallic steel, metallurgically bonded stainless-clad (SC) bimetallic steel has been increasingly used in engineering structures in recent years. Its combination use with concrete in composite structures is beneficial to not only the corrosion resistance and economy, but also to the loading capacity. However, at present, the numerical analysis model suitable for the SC bimetallic steel is still in the lack stage. In order to study the deformation and mechanical properties of concrete-filled stainless-clad bimetallic steel square tubular (CFBSST) stub columns under axial compression, two finite element (FE) models are established based on the FE software ABAQUS. The simulation results are compared and validated according to the experimental results. Finally, the advantages, disadvantages and application scope of each model are summarized to provide reference for subsequent research.

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AXIAL COMPRESSION BEHAVIOR OF SQUARE THIN-WALLED CFST COLUMN TO RC BEAM JOINTS. The Hong Kong Institute of Steel Construction, August 2022. http://dx.doi.org/10.18057/icass2020.p.288.

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To investigate the influence of eccentricity ratio and slenderness ratio on the mechanical properties of eccentric compressed concrete filled steel tubular (CFST) lattice column, the ultimate bearing capacity tests of 20 K shape arrangement lacing strip of four-tube CFST columns were conducted. Based on the stress-strain relationship of CFST and the influence of shear deformation, the equilibrium equation of the mid-section is established and a numerical method for the ultimate bearing capacity of CFST lattice column is proposed. The slenderness reduction coefficient calculation model and equivalent slenderness ratio formula of CFST lattice column are established. Combined with the numerical results and the slenderness ratio reduction coefficient calculation model, the formula of slenderness ratio reduction coefficient is put forward. The comparison between theoretical analysis and experimental results shows that the calculation method of elastic-plastic ultimate bearing capacity of CFST lattice column proposed in this paper is quite accurate. The research outcomes can provide a reference for the application of CFST lattice column and revision of current specifications.

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SEISMIC PERFORMANCE OF HIGH STRENGTH CONCRETE FILLED HIGH STRENGTH SQUARE STEEL TUBES UNDER CYCLIC PURE BENDING. The Hong Kong Institute of Steel Construction, June 2020. http://dx.doi.org/10.18057/ijasc.2020.16.2.3.

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