Статті в журналах: "Filtering response"

1

Elliott, Robert J., Dilip B. Madan, and King Wang. "Filtering Response Directions." SIAM Journal on Financial Mathematics 12, no. 3 (January 2021): 1285–306. http://dx.doi.org/10.1137/20m1339830.

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2

Pan, Wei-Qiang, Jin-Xu Xu, Kai Xu Wang, and Xiao Lan Zhao. "Compact Unequal Power Divider with Filtering Response." International Journal of Antennas and Propagation 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/658102.

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We present a novel unequal power divider with bandpass responses. The proposed power divider consists of five resonators and a resistor. The power division ratio is controlled by altering the coupling strength among the resonators. The output ports have the characteristic impedance of 50 Ω and impedance transformers in classical Wilkinson power dividers are not required in this design. Use of resonators enables the filtering function of the power divider. Two transmission zeros are generated near the passband edges, resulting in quasielliptic bandpass responses. For validation, a 2 : 1 filtering power divider is implemented. The fabricated circuit size is 0.22λg × 0.08λg, featuring compact size for unequal filtering power dividers, which is suitable for the feeding networks of antenna arrays.

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3

Kikuchi, Hiroaki, and Anna Mochizuki. "Privacy-preserving Collaborative Filtering Using Randomized Response." Journal of Information Processing 21, no. 4 (2013): 617–23. http://dx.doi.org/10.2197/ipsjjip.21.617.

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4

Li, Lin, and Gang Liu. "A Differential Microstrip Antenna With Filtering Response." IEEE Antennas and Wireless Propagation Letters 15 (2016): 1983–86. http://dx.doi.org/10.1109/lawp.2016.2547884.

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5

Yang, Haiqin, Guang Ling, Yuxin Su, Michael R. Lyu, and Irwin King. "Boosting Response Aware Model-Based Collaborative Filtering." IEEE Transactions on Knowledge and Data Engineering 27, no. 8 (August 1, 2015): 2064–77. http://dx.doi.org/10.1109/tkde.2015.2405556.

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6

Xu, Jin-Xu, Xiu Yin Zhang, and Yan-Mei Xue. "Compact Filtering Switch With Wide-Stopband Response." IEEE Access 5 (2017): 23803–8. http://dx.doi.org/10.1109/access.2017.2768098.

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7

Maeda, Yoshihiro, Norishige Fukushima, and Hiroshi Matsuo. "Taxonomy of Vectorization Patterns of Programming for FIR Image Filters Using Kernel Subsampling and New One." Applied Sciences 8, no. 8 (July 26, 2018): 1235. http://dx.doi.org/10.3390/app8081235.

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This study examines vectorized programming for finite impulse response image filtering. Finite impulse response image filtering occupies a fundamental place in image processing, and has several approximated acceleration algorithms. However, no sophisticated method of acceleration exists for parameter adaptive filters or any other complex filter. For this case, simple subsampling with code optimization is a unique solution. Under the current Moore’s law, increases in central processing unit frequency have stopped. Moreover, the usage of more and more transistors is becoming insuperably complex due to power and thermal constraints. Most central processing units have multi-core architectures, complicated cache memories, and short vector processing units. This change has complicated vectorized programming. Therefore, we first organize vectorization patterns of vectorized programming to highlight the computing performance of central processing units by revisiting the general finite impulse response filtering. Furthermore, we propose a new vectorization pattern of vectorized programming and term it as loop vectorization. Moreover, these vectorization patterns mesh well with the acceleration method of subsampling of kernels for general finite impulse response filters. Experimental results reveal that the vectorization patterns are appropriate for general finite impulse response filtering. A new vectorization pattern with kernel subsampling is found to be effective for various filters. These include Gaussian range filtering, bilateral filtering, adaptive Gaussian filtering, randomly-kernel-subsampled Gaussian range filtering, randomly-kernel-subsampled bilateral filtering, and randomly-kernel-subsampled adaptive Gaussian filtering.

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8

Dogariu, Laura-Maria, Cristian-Lucian Stanciu, Camelia Elisei-Iliescu, Constantin Paleologu, Jacob Benesty, and Silviu Ciochină. "Tensor-Based Adaptive Filtering Algorithms." Symmetry 13, no. 3 (March 15, 2021): 481. http://dx.doi.org/10.3390/sym13030481.

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Tensor-based signal processing methods are usually employed when dealing with multidimensional data and/or systems with a large parameter space. In this paper, we present a family of tensor-based adaptive filtering algorithms, which are suitable for high-dimension system identification problems. The basic idea is to exploit a decomposition-based approach, such that the global impulse response of the system can be estimated using a combination of shorter adaptive filters. The algorithms are mainly tailored for multiple-input/single-output system identification problems, where the input data and the channels can be grouped in the form of rank-1 tensors. Nevertheless, the approach could be further extended for single-input/single-output system identification scenarios, where the impulse responses (of more general forms) can be modeled as higher-rank tensors. As compared to the conventional adaptive filters, which involve a single (usually long) filter for the estimation of the global impulse response, the tensor-based algorithms achieve faster convergence rate and tracking, while also providing better accuracy of the solution. Simulation results support the theoretical findings and indicate the advantages of the tensor-based algorithms over the conventional ones, in terms of the main performance criteria.

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9

Chen, Chi-Feng, Jhong-Jhen Li, Guo-Yun Wang, Kai-Wei Zhou, and Ruei-Yi Chen. "Design of Compact Filtering 180-Degree Hybrids With Arbitrary Power Division and Filtering Response." IEEE Access 7 (2019): 18521–30. http://dx.doi.org/10.1109/access.2019.2896884.

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10

Koh, Hyeong Seok, Jong Wook Lee, Kiwoon Kwon, and Gil Ho Yoon. "Development of Post Processing for Wave Propagation Problem: Response Filtering Method." Applied Sciences 10, no. 24 (December 17, 2020): 9032. http://dx.doi.org/10.3390/app10249032.

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This study develops a new response filtering approach for recovering dynamic mechanical stresses under impact loading. For structural safety, it is important to consider the propagation of transient mechanical stresses inside structures under impact loads. Commonly, mechanical stress waves can be obtained by solving Newton’s second law using explicit or implicit finite element procedures. Regardless of the numerical approach, large discrepancies called the Gibb’s phenomenon are observed between the numerical solution and the analytical solution. To reduce these discrepancies and enhance the accuracy of the numerical solution, this study develops a response filtering method (RFM). The RFM averages the transient responses within split time domains. By solving several benchmark problems and analyzing the stresses in the frequency domain, it was possible to verify that the RFM can provide an improved solution that converges toward the analytical solution. A mathematical theory is also presented to correlate the relationship between the filtering length and the frequency components of the filtered stress values.

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11

Zhou, Wei, and Ti Jing Cai. "Study on Filtering Methods of Airborne Gravity." Applied Mechanics and Materials 333-335 (July 2013): 516–21. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.516.

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There are a great number of high-frequency and low-frequency noises in the airborne gravity data, so the filtering technology is needed to pick up the weak gravity anomaly. Based on the current filtering methods in airborne gravity data processing: butterworth infinite impulse response(IIR) filtering, window function finite impulse response(FIR) filtering and Kalman smoothing, several methods were compared and studied for the measured airborne gravity data, and the accuracies of extracted gravity anomalies were presented. Results show that the accuracy of Kalman smoothing is better than that of the combined filtering with butterworth IIR and window function FIR.

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12

Song, Kaijun, Te Kong, Fan Zhang, Yu Zhu, and Yong Fan. "Novel high-isolation power divider integrated filtering response." Electromagnetics 38, no. 5 (June 2018): 291–302. http://dx.doi.org/10.1080/02726343.2018.1473142.

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13

&NA;. "Fluorouracil enhances the response to ocular filtering surgery." Inpharma Weekly &NA;, no. 727 (March 1990): 15. http://dx.doi.org/10.2165/00128413-199007270-00029.

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14

Llacer, Jorge, Nzhde Agazaryan, Timothy D. Solberg, and Claus Promberger. "Degeneracy, frequency response and filtering in IMRT optimization." Physics in Medicine and Biology 49, no. 13 (June 18, 2004): 2853–80. http://dx.doi.org/10.1088/0031-9155/49/13/007.

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15

Wu, Tian Li, Yong Mei Pan, and Peng Fei Hu. "Wideband Omnidirectional Slotted Patch Antenna With Filtering Response." IEEE Access 5 (2017): 26015–21. http://dx.doi.org/10.1109/access.2017.2768067.

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16

Zhao, Yan. "Design of Garment Style Recommendation System Based on Interactive Genetic Algorithm." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–8. http://dx.doi.org/10.1155/2022/9132165.

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Recommender systems provide users with product information and suggestions, which has gradually become an important research tool in e-commerce IT technology, which has attracted a lot of attention of researchers. Collaborative filtering recommendation technology has been the most successful recommendation technology so far, but there are two major problems—recommendation quality and scalability. At present, research at home and abroad mainly focuses on recommendation quality, and there is less discussion on scalability. The scalability problem is that as the size of the system increases, the response time of the system increases to a point where users cannot afford it. Existing solutions often result in a significant drop in recommendation quality while reducing recommendation response time. In this paper, the clustering analysis subsystem based on the genetic algorithm is innovatively introduced into the traditional collaborative filtering recommendation system, and its design and implementation are given. In addition, when obtaining the nearest neighbors, only the clustered users of the target user are searched, making it a collaborative filtering recommender system based on genetic clustering. The experimental results show that the response time of the traditional collaborative filtering recommender system increases linearly with the increase in the number of users while the response time of the collaborative filtering recommender system based on genetic clustering remains unchanged with the increase in the number of users. On the other hand, the recommendation quality of the collaborative filtering recommender system based on genetic clustering is basically not degraded compared with that of the traditional collaborative filtering recommender system. Therefore, the collaborative filtering recommender system based on genetic clustering can effectively solve the scalability problem of the collaborative filtering recommender system.

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17

Wills, Robert C. J., David S. Battisti, Kyle C. Armour, Tapio Schneider, and Clara Deser. "Pattern Recognition Methods to Separate Forced Responses from Internal Variability in Climate Model Ensembles and Observations." Journal of Climate 33, no. 20 (October 15, 2020): 8693–719. http://dx.doi.org/10.1175/jcli-d-19-0855.1.

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AbstractEnsembles of climate model simulations are commonly used to separate externally forced climate change from internal variability. However, much of the information gained from running large ensembles is lost in traditional methods of data reduction such as linear trend analysis or large-scale spatial averaging. This paper demonstrates how a pattern recognition method (signal-to-noise-maximizing pattern filtering) extracts patterns of externally forced climate change from large ensembles and identifies the forced climate response with up to 10 times fewer ensemble members than simple ensemble averaging. It is particularly effective at filtering out spatially coherent modes of internal variability (e.g., El Niño, North Atlantic Oscillation), which would otherwise alias into estimates of regional responses to forcing. This method is used to identify forced climate responses within the 40-member Community Earth System Model (CESM) large ensemble, including an El Niño–like response to volcanic eruptions and forced trends in the North Atlantic Oscillation. The ensemble-based estimate of the forced response is used to test statistical methods for isolating the forced response from a single realization (i.e., individual ensemble members). Low-frequency pattern filtering is found to skillfully identify the forced response within individual ensemble members and is applied to the HadCRUT4 reconstruction of observed temperatures, whereby it identifies slow components of observed temperature changes that are consistent with the expected effects of anthropogenic greenhouse gas and aerosol forcing.

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18

Ershov, I. A. "Using effective signal filtering methods to data processing from a fiber-optic temperature sensor." Omsk Scientific Bulletin, no. 177 (2021): 89–94. http://dx.doi.org/10.25206/1813-8225-2021-177-89-94.

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The article deals with signal processing of a fiber-optic temperature sensor using extremal filtering and filtering with Wavelet transforms. The aim of this work is to find a way to reduce the response time in a fiber-optic temperature sensor by using effective signal processing methods. Reducing the response time of systems for monitoring hazardous production facilities is rarely discussed in the literature. The results showed that the use of extreme filtering and filtering with Wavelet transforms can significantly reduce the number of implementations necessary to identify a signal with a low signal-to-noise ratio

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19

Short, David A., John G. Mengel, Thomas J. Crowley, William T. Hyde, and Gerald R. North. "Filtering of Milankovitch Cycles by Earth's Geography." Quaternary Research 35, no. 2 (March 1991): 157–73. http://dx.doi.org/10.1016/0033-5894(91)90064-c.

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AbstractEarth's land-sea distribution modifies the temperature response to orbitally induced perturbations of the seasonal insolation. We examine this modification in the frequency domain by generating 800,000-yr time series of maximum summer temperature in selected regions with a linear, two-dimensional, seasonal energy balance climate model. Previous studies have demonstrated that this model has a sensitivity comparable to general circulation models for the seasonal temperature response to orbital forcing on land. Although the observed response in the geologic record is sometimes significantly different than modeled here (differences attributable to model limitations and feedbacks involving the ocean-atmosphere-cryosphere system), there are several results of significance: (1) in mid-latitude land areas the orbital signal is translated linearly into a large (>10°C) seasonal temperature response; (2) although the modeled seasonal response to orbital forcing on Antarctica is 6°C, the annual mean temperature effect (<2°C) is only about one-fifth that inferred from the Vostok ice core, and primarily restricted to periods near 41,000 yr; (3) equatorial regions have the richest spectrum of temperature response, with a 3000-yr phase shift in the precession response, plus some power near periods of 10,000–12,000 yr, 41,000 yr, 100,000 yr, and 400,000 yr. Peaks at 10,000–12,000 yr and 100,000 and 400,000 yr result from the twice-yearly passage of the sun across the equator. The complex model response in equatorial regions has some resemblance to geologic time series from this region. The amplification of model response over equatorial land masses at the 100,000-yr period may explain some of the observed large variance in this band in geologic records, especially in pre-Pleistocene records from times of little or no global ice volume.

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20

Wiechetek, Katarzyna, and Jacek Piskorowski. "A Concept of the Non-Stationary Filtering Network with Reduced Transient Response." Applied Sciences 9, no. 21 (October 28, 2019): 4570. http://dx.doi.org/10.3390/app9214570.

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This paper presents a concept of the non-stationary filtering network with reduced transient response consisting of the first-order digital elements with time-varying parameters. The digital filter section is based on the analog system. In order to design the filtering network, the analog prototype was subjected to the discretization process. The time constant and the gain factor were then temporarily varied in time in order to suppress the transient response of the designed filtering structure. The optimization method, based on the Particle Swarm Optimization (PSO) algorithm which is aimed at reducing the settling time by a proper parameter manipulation, is presented. Simulation results proving the usefulness of the proposed concept are also shown and discussed.

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21

KINOSHITA, Shigeo. "State Feedback Sensor and Response Compensation using Complementary Filtering." Zisin (Journal of the Seismological Society of Japan. 2nd ser.) 66, no. 4 (2014): 113–25. http://dx.doi.org/10.4294/zisin.66.113.

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22

Tang, Yu, and A. M. Norcia. "Application of adaptive filtering to steady-state evoked response." Medical & Biological Engineering & Computing 33, no. 3 (May 1995): 391–95. http://dx.doi.org/10.1007/bf02510521.

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23

Wu, Xuehua, Qianqian Qian, and Yuqing Bao. "Demand Response Using Disturbance Estimation-Based Kalman Filtering for the Frequency Control." Energies 15, no. 24 (December 11, 2022): 9377. http://dx.doi.org/10.3390/en15249377.

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Demand response (DR) has a great potential for stabilizing the frequency of power systems. However, the performance is limited by the accuracy of the frequency detection, which is affected by measurement disturbances. To overcome this problem, this paper proposes a disturbance estimation-based Kalman filtering method, which is utilized for the frequency control. By using the rate of change of frequency (RoCoF), the Kalman filtering method can estimate the state of the ON/OFF loads well. In this way, the influence of detection error can be reduced, and the DR performance can be improved. Test results show that the proposed disturbance estimation-based Kalman filtering method has a higher accuracy of frequency detection than existing methods (such as the low-pass filter method) and therefore improves the frequency control performance of DR.

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24

Ostalczyk, Piotr, and Piotr Duch. "On a Real-Time First-Order Difference Evaluation." Image Processing & Communications 17, no. 4 (December 1, 2012): 167–71. http://dx.doi.org/10.2478/v10248-012-0043-x.

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Abstract A subject of this paper is a real-time evaluation of the first-order backward difference of a measured signal with noise. It is wellknown that a difference evaluation strongly increase a noise amplitude so the signal prefiltering is necessary. A very short review of practically used filters is given. To perform the pre-filtering operation one mainly apply linear filters characterized by their impulse response shape: finite impulse response (FIR) and infinite impulse response (IIR). To enhance the filtering operation one successfully apply so called adaptive filters and these described by non-linear characteristics. In this paper a simple non-linear filtering algorithm is proposed and examined in the first-order backward difference of a measured signal.

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25

Pitenis, Eleftherios, Elisavet Mamagiannou, Dimitrios A. Natsiopoulos, Georgios S. Vergos, Ilias N. Tziavos, Vassilios N. Grigoriadis, and Michael G. Sideris. "FIR, IIR and Wavelet Algorithms for the Rigorous Filtering of GOCE SGG Data to the GOCE MBW." Remote Sensing 14, no. 13 (June 23, 2022): 3024. http://dx.doi.org/10.3390/rs14133024.

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Gravity field and steady-state Ocean Circulation Explorer (GOCE) data are strongly affected by noise and long-wavelength errors outside the satellite measurement bandwidth (MBW). One of the main goals in utilizing GOCE data for gravity field modeling is the application of filtering techniques that can remove gross errors and reduce low-frequency errors and high-frequency noise while preserving the original signal. This paper aims to present and analyze three filtering strategies used to de-noise the GOCE Level 2 data from long-wavelength correlated errors and noise. These strategies are Finite Impulse Response (FIR), Infinite Impulse Response (IIR), and Wavelet Multi-resolution Analysis (WL), which have been applied to GOCE residual second order derivatives of the gravity potential. Several experiments were performed for each filtering scheme in order to identify the ideal filtering parameters. The outcomes indicate that all the suggested filtering strategies proved to be effective in removing low-frequency errors while preserving the signals in the GOCE MBW, with FIR filtering providing the overall best results.

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26

Linovich, A. Yu, V. S. Litvinova, and M. D. Korolev. "COMB ADAPTIVE FILTERING ALGORITHM." Vestnik of Ryazan State Radio Engineering University 77 (2021): 3–16. http://dx.doi.org/10.21667/1995-4565-2021-77-3-16.

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The problem of multipath channel frequency response equalization in a receiver is considered. The aim is to develop an algorithm of comb adaptive filtering, which makes possible, on the one hand, to provide high rate of multirate receiver system adaptation, and on the other hand, to reduce computational complexities of real-time processing. The robustness analysis of the suggested algorithm is carried out. Two variants of comb adaptive filter are studied. For the second one a fast modification is proposed. On the assumption of multichannel communication system equalizer realization the developed algorithm is able to provide the advantage in rate almost by a factor of ten and at the same time to reduce computational complexities by a factor of three as compared with the least-mean-square algorithm as the experimental results demonstrate.

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27

Lu, Yong, Philip H.-S. Jen, and Min Wu. "GABAergic Disinhibition Affects Responses of Bat Inferior Collicular Neurons to Temporally Patterned Sound Pulses." Journal of Neurophysiology 79, no. 5 (May 1, 1998): 2303–15. http://dx.doi.org/10.1152/jn.1998.79.5.2303.

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Lu, Yong, Philip H.-S. Jen, and Min Wu. GABAergic disinhibition affects responses of bat inferior collicular neurons to temporally patterned sound pulses. J. Neurophysiol. 79: 2303–2315, 1998. Using the big brown bat, Eptesicus fuscus, as a model mammalian auditory system, we studied the effect of GABAergic disinhibition by bicuculline on the responses of inferior collicular (IC) neurons to temporally patterned trains of sound pulses delivered at different pulse repetition rates (PRRs) under free-field stimulation conditions. All 66 neurons isolated from eight bats either discharged one to two impulses (phasic on responders, n = 41, 62%), three to eight impulses (phasic bursters, n = 19, 29%), or many impulses throughout the entire duration of the stimulus (tonicresponders, n = 6, 9%). Whereas 50 neurons responded vigorously to frequency-modulated (FM) pulses, 16 responded poorly ornot at all to FM pulses. Bicuculline application increased the number of impulses of all 66 neurons in response to 4 ms pulses by 15–1,425%. The application also changed most phasic on responders into phasic bursters or tonic responders, resulting in 12 (18%) phasic on responders, 34 (52%) phasic bursters, and 20 (30%) tonic responders. Response latencies of these neurons were either shortened ( n = 25, 38%) by 0.5–6.0 ms, lengthened ( n = 9, 14%) by 0.5–2.5 ms or not changed ( n = 32, 48%) on bicuculline application. Each neuron had a highest response repetition rate beyond which the neuron failed to respond. Bicuculline application increased the highest response repetition rates of 62 (94%) neurons studied. The application also increased the highest 100% pulse-locking repetition rates of 21 (32%) neurons and facilitated 27 (41%) neurons in response to more pulses at the same PRR than predrug conditions. According to average rate-based modulation transfer functions (average rate MTFs), all 66 neurons had low-pass filtering characteristics both before and after bicuculline application. According to total discharge rate-based modulation transfer functions (total rate MTFs), filtering characteristics of these neurons can be described as band-pass ( n = 52, 79%), low-pass ( n = 12, 18%), or high-pass ( n = 2, 3%) before bicuculline application. Bicuculline application changed the filtering characteristics of 14 (21%) neurons. According to synchronization coefficient-based modulation transfer functions, filtering characteristics of these neurons can be described as low-pass ( n = 41, 62%), all-pass ( n = 11, 17%), band-suppression ( n = 7, 10.5%), and band-suppression–band-pass filters ( n = 7, 10.5%). Bicuculline application changed filtering characteristics of 19 (29%) neurons.

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28

Mathis, Gary L. "Smoothing spectral gamma logs: A simple but effective technique." GEOPHYSICS 52, no. 3 (March 1987): 363–67. http://dx.doi.org/10.1190/1.1442310.

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Spectral gamma‐ray logs suffer from unusually poor counting statistics because of the extremely low counting rates used to compute the concentrations of potassium (K), uranium (U), and thorium (T). Filtering is therefore a prerequisite to interpretation. Kalman filtering has been suggested, but this approach is complex and involves uncertain assumptions. Simple weighted averaging, on the other hand, fails to take into account abrupt changes that can occur in geologic response. Effective filtering of real logging data is possible, however, by a simple adaptive filter which uses the total gamma responses of a gamma‐ray tool to compute filter weights based on the error function. This filter is mathematically and computationally simple to implement for real time or postprocessing of spectral gamma logs.

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29

Kotera, Hiroaki. "Inverse-Scaled Lanczos Filtering for Image Sharpening." Color and Imaging Conference 2020, no. 28 (November 4, 2020): 215–20. http://dx.doi.org/10.2352/issn.2169-2629.2020.28.34.

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The edge response in retinal image is the first step for human vision recognizing the outside world. A variety of receptive field models for describing the impulse response have been proposed. Which satisfies the uncertain principle? occupied the interest from a point of minimizing the product (Δx)(Δ w) both in spatial and spectral. Among the typical edge response models, finally Gabor function and 2nd. Gaussian Derivative GD2 remained as strong candidates. While famous D. Marr and R. Young support GD2, many vision researchers prefer Gabor. The retinal edge response model is used for image sharpening.<br/> Different from the conventional image sharpening filters, this paper proposes a novel image sharpening filter by modifying the Lanczos resampling filter. The Lanczos filter is used for image scaling to resize digital images. Usually it works to interpolate the discrete sampled points like as a kind of smoothing filter not as sharpening. The Lanczos kernel is given by the product of sampling Sinc function and the scaled Sinc function. The scaled Sinc function expanded by the scale "s" plays a role of window function. The author noticed that the inverse scaling of Lanczos window can be used not for smoothing but for sharpening filter.<br/> This paper demonstrates how the proposed model works effectively in comparison with Gabor and GD2.

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30

Bannawat, Lakkhana, Akkarat Boonpoonga, Santana Burintramart, and Prayoot Akkaraekthalin. "On the Resolution Improvement of Radar Target Identification with Filtering Antenna Effects." International Journal of Antennas and Propagation 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/3405908.

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An investigation on the improvement of the resolution of a radar target identification system is presented in this paper. Degradation of resolution is mainly due to influence factors associated with antennas, including the strong coupling between transmitting and receiving antennas and the variation in the antenna response. A filtering technique was therefore introduced to mitigate the underlying problem. In the technique, the antenna effects were filtered out of the total response backscattered from the objects in the radar target identification system. The short-time matrix pencil method (STMPM) was then employed to extract the poles from the backscattered response in order to identify the object. Simulation and experimentation examples are illustrated to confirm the improvement of the resolution by filtering the antenna effects. The simulation and experimentation were divided into several categories, that is, different antennas and differently shaped objects, in order to validate the advantage of filtering the antenna effects. They were setup in order to demonstrate that the poles obtained from performing the STMPM without the filtering technique were mainly because of the antenna rather than the object’s characteristic. The results showed that the resolution of the identification was significantly increased when performing pole extraction and filtering the antenna effects.

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31

Grzesiak, M. "Wavelet filtering of chaotic data." Nonlinear Processes in Geophysics 7, no. 1/2 (June 30, 2000): 111–16. http://dx.doi.org/10.5194/npg-7-111-2000.

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Abstract. Satisfactory method of removing noise from experimental chaotic data is still an open problem. Normally it is necessary to assume certain properties of the noise and dynamics, which one wants to extract, from time series. The wavelet based method of denoising of time series originating from low-dimensional dynamical systems and polluted by the Gaussian white noise is considered. Its efficiency is investigated by comparing the correlation dimension of clean and noisy data generated for some well-known dynamical systems. The wavelet method is contrasted with the singular value decomposition (SVD) and finite impulse response (FIR) filter methods.

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32

Hu, Jinhua, Jiuzhou Yu, Xiu-Hong Liu, Jun Zou, Long Zhang, and Jijun Zhao. "Tunable Flat-Top Filtering Response in Cascaded Resonant Waveguide Gratings." IEEE Photonics Journal 13, no. 2 (April 2021): 1–8. http://dx.doi.org/10.1109/jphot.2021.3067032.

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33

Zapata, Luis M., Matthias Patalong, Stefan Teufelhart, Wim Desmet, and Frank Naets. "Combined impulse-response/Kalman filtering (CIRKF) for input/state estimation." Mechanical Systems and Signal Processing 181 (December 2022): 109527. http://dx.doi.org/10.1016/j.ymssp.2022.109527.

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Adrian Niță, Valentin. "On Improving Sine Sweep Impulse Response Measurments through Adaptive Filtering." Saudi Journal of Engineering and Technology 6, no. 1 (January 5, 2021): 9–16. http://dx.doi.org/10.36348/sjet.2021.v06i01.002.

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TONOIKE, MITSUO. "Response Characteristics of Olfactory Evoked Potentials Using Time-Varying Filtering." Annals of the New York Academy of Sciences 510, no. 1 Olfaction and (November 1987): 658–61. http://dx.doi.org/10.1111/j.1749-6632.1987.tb43658.x.

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Brock, James M. R., George L. W. Perry, Tynan Burkhardt, and Bruce R. Burns. "Forest seedling community response to understorey filtering by tree ferns." Journal of Vegetation Science 29, no. 5 (September 2018): 887–97. http://dx.doi.org/10.1111/jvs.12671.

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Andrews, Burton W., Tau-Mu Yi, and Pablo A. Iglesias. "Optimal Noise Filtering in the Chemotactic Response of Escherichia coli." PLoS Computational Biology 2, no. 11 (2006): e154. http://dx.doi.org/10.1371/journal.pcbi.0020154.

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Andrews, Burt W., Tau-Mu Yi, and Pablo A. Iglesias. "Optimal noise filtering in the chemotactic response of E. coli." PLoS Computational Biology preprint, no. 2006 (2005): e154. http://dx.doi.org/10.1371/journal.pcbi.0020154.eor.

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Sinelnikov, Yegor, Andrew Boyden, Richard Lavery, and Russell Calvarese. "Audio and ultrasonic frequency response measurements using regularized inverse filtering." Journal of the Acoustical Society of America 145, no. 3 (March 2019): 1883. http://dx.doi.org/10.1121/1.5101820.

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Stupakov, Oleksandr, and Yevgen Melikhov. "Influence of Magnetizing and Filtering Frequencies on Barkhausen Noise Response." IEEE Transactions on Magnetics 50, no. 4 (April 2014): 1–4. http://dx.doi.org/10.1109/tmag.2013.2291933.

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41

Hu, Peng Fei, Yong Mei Pan, Xiu Yin Zhang, and Bin Jie Hu. "A Compact Quasi-Isotropic Dielectric Resonator Antenna With Filtering Response." IEEE Transactions on Antennas and Propagation 67, no. 2 (February 2019): 1294–99. http://dx.doi.org/10.1109/tap.2018.2883611.

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Wei, Zhaohui, Zhao Zhou, Zhaoyang Tang, Jia Yuan Yin, Jian Ren, and Yingzeng Yin. "Broadband Filtering Magnetoelectronic Dipole Antenna With Quasi-Elliptic Gain Response." IEEE Transactions on Antennas and Propagation 68, no. 4 (April 2020): 3225–30. http://dx.doi.org/10.1109/tap.2019.2944540.

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43

Barua, Susamma. "Finite impulse response-median hybrid filtering techniques for image smoothing." Optical Engineering 30, no. 3 (1991): 271. http://dx.doi.org/10.1117/12.55797.

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Gross, Markus, and Masato Miyoshi. "Adaptive inverse filtering to equalize and linearize a loudspeaker response." Journal of the Acoustical Society of Japan (E) 19, no. 6 (1998): 405–8. http://dx.doi.org/10.1250/ast.19.405.

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Wu, Lin-Sheng, Yong-Xin Guo, and Jun-Fa Mao. "Balanced-to-Balanced Gysel Power Divider With Bandpass Filtering Response." IEEE Transactions on Microwave Theory and Techniques 61, no. 12 (December 2013): 4052–62. http://dx.doi.org/10.1109/tmtt.2013.2287684.

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Robinson, Alan E., Paul S. Hammon, and Virginia R. de Sa. "Explaining brightness illusions using spatial filtering and local response normalization." Vision Research 47, no. 12 (June 2007): 1631–44. http://dx.doi.org/10.1016/j.visres.2007.02.017.

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Saeedi Vahdat, Armin, Ghader Rezazadeh, and Saeid Afrang. "Improving response of a MEMS capacitive microphone filtering shock noise." Microelectronics Journal 42, no. 5 (May 2011): 614–21. http://dx.doi.org/10.1016/j.mejo.2011.03.007.

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48

Hernández, Wilmar. "Improving the response of an accelerometer by using optimal filtering." Sensors and Actuators A: Physical 88, no. 3 (January 2001): 198–208. http://dx.doi.org/10.1016/s0924-4247(00)00520-3.

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Nielsen, Ulrik Dam. "Response-based estimation of sea state parameters—influence of filtering." Ocean Engineering 34, no. 13 (September 2007): 1797–810. http://dx.doi.org/10.1016/j.oceaneng.2007.03.002.

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Oh, Jung Hun, Harry P. Wong, Xiaowei Wang, and Joseph O. Deasy. "A Bioinformatics Filtering Strategy for Identifying Radiation Response Biomarker Candidates." PLoS ONE 7, no. 6 (June 29, 2012): e38870. http://dx.doi.org/10.1371/journal.pone.0038870.

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