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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

Sablatash, M. "Designs and architectures of filter bank trees for spectrally efficient multi-user communications: review, modifications and extensions of wavelet packet filter bank trees." Signal, Image and Video Processing 2, no. 1 (September 18, 2007): 9–37. http://dx.doi.org/10.1007/s11760-007-0033-4.

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2

Gopinath, R. A., and C. S. Burrus. "Factorization approach to unitary time-varying filter bank trees and wavelets." IEEE Transactions on Signal Processing 43, no. 3 (March 1995): 666–80. http://dx.doi.org/10.1109/78.370621.

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3

Bąk, Artur, Jakub Segen, Kamil Wereszczyński, Pawel Mielnik, Marcin Fojcik, and Marek Kulbacki. "Detection of linear features including bone and skin areas in ultrasound images of joints." PeerJ 6 (March 15, 2018): e4411. http://dx.doi.org/10.7717/peerj.4411.

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Identifying the separate parts in ultrasound images such as bone and skin plays a crucial role in the synovitis detection task. This paper presents a detector of bone and skin regions in the form of a classifier which is trained on a set of annotated images. Selected regions have labels: skin or bone or none. Feature vectors used by the classifier are assigned to image pixels as a result of passing the image through the bank of linear and nonlinear filters. The filters include Gaussian blurring filter, its first and second order derivatives, Laplacian as well as positive and negative threshold operations applied to the filtered images. We compared multiple supervised learning classifiers including Naive Bayes, k-Nearest Neighbour, Decision Trees, Random Forest, AdaBoost and Support Vector Machines (SVM) with various kernels, using four classification performance scores and computation time. The Random Forest classifier was selected for the final use, as it gives the best overall evaluation results.

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4

Wieben, O., V. X. Afonso, and W. J. Tompkins. "Classification of premature ventricular complexes using filter bank features, induction of decision trees and a fuzzy rule-based system." Medical & Biological Engineering & Computing 37, no. 5 (September 1999): 560–65. http://dx.doi.org/10.1007/bf02513349.

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5

Dou, Hanjie, Changyuan Zhai, Liping Chen, Xiu Wang, and Wei Zou. "Comparison of Orchard Target-Oriented Spraying Systems Using Photoelectric or Ultrasonic Sensors." Agriculture 11, no. 8 (August 8, 2021): 753. http://dx.doi.org/10.3390/agriculture11080753.

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Orchard pesticide off-target deposition and drift cause substantial soil and water pollution, and other environmental pollution. Orchard target-oriented spraying technologies have been used to reduce the deposition and drift caused by off-target spraying and control environmental pollution to within an acceptable range. Two target-oriented spraying systems based on photoelectric sensors or ultrasonic sensors were developed. Three spraying treatments of young cherry trees and adult apple trees were conducted using a commercial sprayer with a photoelectric-based target-oriented spraying system, an ultrasonic-based target-oriented spraying system or no target-oriented spraying system. A rhodamine tracer was used instead of pesticide. Filter papers were fixed in the trees and on the ground. The tracer on the filter papers was washed off to calculate the deposition distribution in the trees and on the ground. The deposition data were used to evaluate the systems and pesticide off-target deposition achieved with orchard target-oriented sprayers. The results showed that the two target-oriented spraying systems greatly reduced the ground deposition compared to that caused by off-target spraying. Compared with that from off-target spraying, the ground deposition from photoelectric-based (trunk-based) and ultrasonic-based (canopy-based) target-oriented spraying decreased by 50.63% and 38.74%, respectively, for the young fruit trees and by 21.66% and 29.87%, respectively, for the adult fruit trees. The trunk-based target-oriented detection method can be considered more suitable for young trees, whereas the canopy-based target-oriented detection method can be considered more suitable for adult trees. The maximum ground deposition occurred 1.5 m from the tree trunk at the back of the tree canopy and was caused by the high airflow at the air outlet of the sprayer. A suitable air speed and air volume at the air outlet of the sprayer can reduce pesticide deposition on the ground.

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6

ARIVAZHAGAN, S., D. GNANADURAI, J. R. ANTONY VANCE, K. M. SAROJINI, and L. GANESAN. "IMPLEMENTATION OF ZERO TREE WAVELET CODERS IN DSP PROCESSOR." International Journal of Wavelets, Multiresolution and Information Processing 02, no. 01 (March 2004): 75–86. http://dx.doi.org/10.1142/s0219691304000366.

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With the fast evolution of Multimedia systems, Image compression algorithms are very much needed to achieve effective transmission and compact storage by removing the redundant information of the image data. Wavelet transforms have received significant attention, recently, due to their suitability for a number of important signal and image compression applications and the lapped nature of this transform and the computational simplicity, which comes in the form of filter bank implementations. In this paper, the implementation of image compression algorithms based on discrete wavelet transform such as embedded zero tree wavelet (EZW) coder, set partitioning in hierarchical trees coder without lists (SPIHT — No List) and packetizable zero tree wavelet (PZW) coder in DSP processor is dealt in detail and their performance analysis is carried out in terms of different compression ratios, execution timing and for different packet losses. PSNR is used as the criteria for the measurement of reconstructed image quality.

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7

Sharma, Manish, Jainendra Tiwari, Virendra Patel, and U. Rajendra Acharya. "Automated Identification of Sleep Disorder Types Using Triplet Half-Band Filter and Ensemble Machine Learning Techniques with EEG Signals." Electronics 10, no. 13 (June 25, 2021): 1531. http://dx.doi.org/10.3390/electronics10131531.

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A sleep disorder is a medical condition that affects an individual’s regular sleeping pattern and routine, hence negatively affecting the individual’s health. The traditional procedures of identifying sleep disorders by clinicians involve questionnaires and polysomnography (PSG), which are subjective, time-consuming, and inconvenient. Hence, an automated sleep disorder identification is required to overcome these limitations. In the proposed study, we have proposed a method using electroencephalogram (EEG) signals for the automated identification of six sleep disorders, namely insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, rapid eye movement behavior disorder (RBD), periodic leg movement disorder (PLM), and sleep-disordered breathing (SDB). To the best of our belief, this is one of the first studies ever undertaken to identify sleep disorders using EEG signals employing cyclic alternating pattern (CAP) sleep database. After sleep-scoring EEG epochs, we have created eight different data subsets of EEG epochs to develop the proposed model. A novel optimal triplet half-band filter bank (THFB) is used to obtain the subbands of EEG signals. We have extracted Hjorth parameters from subbands of EEG epochs. The selected features are fed to various supervised machine learning algorithms for the automated classification of sleep disorders. Our proposed system has obtained the highest accuracy of 99.2%, 98.2%, 96.2%, 98.3%, 98.8%, and 98.8% for insomnia, narcolepsy, NFLE, PLM, RBD, and SDB classes against normal healthy subjects, respectively, applying ensemble boosted trees classifier. As a result, we have attained the highest accuracy of 91.3% to identify the type of sleep disorder. The proposed method is simple, fast, efficient, and may reduce the challenges faced by medical practitioners during the diagnosis of various sleep disorders accurately in less time at sleep clinics and homes.

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8

Edwar, E., and Achmad Munir. "Prototype of Dual DDS FMCW Transmitter for L-Band Synthetic Aperture Radar." Journal of Measurements, Electronics, Communications, and Systems 4, no. 1 (June 30, 2018): 8. http://dx.doi.org/10.25124/jmecs.v4i1.1694.

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One of the popular remote sensing technique is remote sensing using radar technology such as Synthetic Aperture Radar (SAR). In this paper, a compact SAR prototype transmitter was designed so it could be installed in small platform. This SAR transmitter was designed to generate a frequency modulated continuous wave (FMCW) using direct digital synthesizer (DDS) integrated with RF front end modules such as analog filter and power amplifier. The bandwidth of the radar spectrum is 10 MHz, and the carrier frequency used is 1.27 GHz. The L-Band has been chosen as the carrier signal in order to detect the target (trees). The payload was designed for aerial vehicle, hence the choice of components should be as small as possible. The FMCW-SAR transmitter was implemented by using DDS module AD9850 and integrated with another RF component. The FMCW-SAR transmitter produces the transmit power about -17.67 dBm.

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9

Zheng, Xianwei, Yuan Yan Tang, Jiantao Zhou, Jianjia Pan, Shouzhi Yang, Youfa Li, and Patrick S. P. Wang. "Multi-Level Downsampling of Graph Signals via Improved Maximum Spanning Trees." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 03 (February 19, 2019): 1958005. http://dx.doi.org/10.1142/s0218001419580059.

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Graph signal processing (GSP) is an emerging field in the signal processing community. Novel GSP-based transforms, such as graph Fourier transform and graph wavelet filter banks, have been successfully utilized in image processing and pattern recognition. As a rapidly developing research area, graph signal processing aims to extend classical signal processing techniques to signals with irregular underlying structures. One of the hot topics in GSP is to develop multi-scale transforms such that novel GSP-based techniques can be applied in image processing or other related areas. For designing graph signal multi-scale frameworks, downsampling operations that ensuring multi-level downsampling should be specifically constructed. Among the existing downsampling methods in graph signal processing, the state-of-the-art method was constructed based on the maximum spanning tree (MST). However, when using this method for multi-level downsampling of graph signals defined on unweighted densely connected graphs, such as social network data, the sampling rates are not close to [Formula: see text]. This phenomenon is summarized as a new problem and called downsampling unbalance problem in this paper. Due to the unbalance, MST-based downsampling method cannot be applied to construct graph signal multi-scale transforms. In this paper, we propose a novel and efficient method to detect and reduce the downsampling unbalance generated by the MST-based method. For any given graph signal, we apply the graph density to construct a measurement of the downsampling unbalance generated by the MST-based method. If a graph signal has large unbalance possibility, the multi-level downsampling is conducted after the MST is improved. The experimental results on synthetic and real-world social network data show that downsampling unbalance can be efficiently detected and then reduced by our method.

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10

WANG, CHENG-YOU, ZHENG-XIN HOU, and AI-PING YANG. "BINARY TREE IMAGE CODING ALGORITHM BASED ON NON-SEPARABLE WAVELET TRANSFORM VIA LIFTING SCHEME." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 05 (September 2008): 761–75. http://dx.doi.org/10.1142/s0219691308002641.

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In recent years, image coding based on wavelet transform has made rapid progress. In this paper, quincunx lifting scheme in wavelet transform is introduced and all phase interpolation filter banks which can be used in the lifting scheme for prediction and update are designed. Based on the basic idea of set partitioning in hierarchical trees (SPIHT) algorithm, the binary tree image coding algorithm is proposed. Just like SPIHT, the encoding algorithms can be stopped at any compressed file size or let run until the compressed file is a representation of a nearly lossless image. The experimental results on test images show that compared with SPIHT algorithm, the PSNRs of the proposed algorithm are superior by about 0.5 dB at the same bit rates and the subjective quality of reconstructed images is also better.

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11

Izquierdo-Villalba, David, Raul E. Angulo, Alvaro Orsi, Guillaume Hurier, Gonzalo Vilella-Rojo, Silvia Bonoli, Carlos López-Sanjuan, et al. "J-PLUS: Synthetic galaxy catalogues with emission lines for photometric surveys." Astronomy & Astrophysics 631 (October 24, 2019): A82. http://dx.doi.org/10.1051/0004-6361/201936232.

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We present a synthetic galaxy lightcone specially designed for narrow-band optical photometric surveys. To reduce time-discreteness effects, unlike previous works, we directly include the lightcone construction in the L-Galaxies semi-analytic model applied to the subhalo merger trees of the Millennium simulation. Additionally, we add a model for the nebular emission in star-forming regions, which is crucial for correctly predicting the narrow- and medium-band photometry of galaxies. Specifically, we consider, individually for each galaxy, the contribution of 9 different lines: Lyα (1216 Å), Hβ (4861 Å), Hα (6563 Å), [O II] (3727 Å, 3729 Å), [O III] (4959 Å, 5007 Å), [Ne III] (3870 Å), [O I] (6300 Å), [N II] (6548 Å, 6583 Å), and [S II] (6717 Å, 6731 Å). We validate our lightcone by comparing galaxy number counts, angular clustering, and Hα, Hβ, [O II], and [O III]5007 luminosity functions to a compilation of observations. As an application of our mock lightcones, we generated catalogues tailored for J-PLUS, a large optical galaxy survey featuring five broad-band and seven medium-band filters. We study the ability of the survey to correctly identify, with a simple three-filter method, a population of emission-line galaxies at various redshifts. We show that the 4000 Å break in the spectral energy distribution of galaxies can be misidentified as line emission. However, all significant excess (> 0.4 mag) can be correctly and unambiguously attributed to emission-line galaxies. Our catalogues are publicly released to facilitate their use in interpreting narrow-band surveys and in quantifying the impact of line emission in broad-band photometry.

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12

Stutte, Gary W. "CLASSIFICATION OF STRESS-INDUCED SPATIAL VARIABILITY IN NEAR-INFRARED CANOPY REFLECTANCE." HortScience 25, no. 9 (September 1990): 1136b—1136. http://dx.doi.org/10.21273/hortsci.25.9.1136b.

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A digital video camera (Panasonic Industrial Co., Secaucus, NJ) with a 1.7 cm charged coupled device detector (574 (h) × 499 (v) pixel elements) was modified with a custom made FRF-700 band pass filter to visualize canopy reflectance in the near-infrared (NIR) from 700 to 1100 nm. Images of canopy reflectance under a range of environmental stresses were obtained from peach and apple trees under greenhouse and field conditions. Individual video frames were digitized with Image Capture and Analysis System (Agri Imaging Systems, Inc., Fayetteville, AR). Image contrast was increased with digital equalization and filtering before classification into one of five stress levels. There was a high correlation (r2 > 0.8) between leaf stress and canopy reflectance in both apple and peach at distances < 5 meters. Spatial variability in stress-induced NIR reflectance could be detected and classified at vertical distances from 150 to 500 M. Analysis of vertical imagery revealed sections of the orchard which were most susceptible to environmental stress.

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13

V G, Rajendran, Jayalalitha S, Adalarasu K, and Usha G. "Analysis and Classification of Stress Among Students Using EEG as Biomarker." ECS Transactions 107, no. 1 (April 24, 2022): 1857–65. http://dx.doi.org/10.1149/10701.1857ecst.

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As per a report from National Crime Records Bureau (NCRB), in India a total of 10,159 students committed suicide during 2018, which implies that average of 28 students reported suicide every day. This increase in the number that leads to an impact on study and analysis of stress among college students. An experimental research carried out by acquiring EEG data from six students under two different conditions. Before going to write their examination EEG data acquired for 3 minutes with recollection of topics for the examination and after examination 3 minutes of data collected with psychological test carried out for all the eight students using eight channel EEG device. Preprocessing of raw EEG with using band pass filter with corner frequencies of 0.1Hz to 40Hz to eliminate low as well as high frequency signal. EEG features extracted by using discrete wavelet transformation technique with six level of decomposition using db4 as mother wavelet. The main EEG features, such as theta, alpha, and beta relative sub band wave energies, were computed using MATLAB. Four different machine learning algorithms, such as decision trees, support vector machine (SVM), k-Nearest Neighbour (k-NN), and ensemble classifier were used to classify stress or non-stress state. Among four ML classifier algorithm ensemble classifier obtained highest classification accuracy of 87%.

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14

Jaber, Nael, Naseem Daher, and Daniel Asmar. "Deep-Pest-Detector: Automated Detection and Localization of Processionary Moth Nests on Pine Trees via Aerial Drones and Deep Neural Networks." Applied Engineering in Agriculture 38, no. 2 (2022): 253–72. http://dx.doi.org/10.13031/aea.14715.

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HighlightsA deep learning architecture, which relies on multi-modal imagery from the RGB and thermal spectra, is proposed to detect PPM nests via aerial drones.A GPS-based computation method is implemented to geo-tag the position of the detected nests in the drone’s perimeter.A Kalman filter is utilized as a nest tracker to avoid reporting back the position of the same nest several times.The system is tested in a pine forest and exhibits accurate detection and localization, as evidenced by reporting back the positions of the detected PPM nests.Abstract.The pine processionary moth (PPM) is considered the main defoliator of pine trees and is a menacing threat to various other perennial species including oak and cedar. Given their negative secondary effects, spraying of pesticides has been banned as a means for the eradication of PPM; instead, an individualized approach is to be adopted, in which each nest is localized and destroyed. Detection of PPM nests using optical sensing is challenging because of the changing outdoor lighting conditions and the camouflaged appearance of moth in the underlying foliage. This article proposes a promising solution for nest detection by fusing sensory data from an RGB camera on one hand, and a thermal camera on the other, both mounted on an aerial drone. The proposed detection system is built on a two-channeled deep convolutional neural network, one for each spectrum of the collected sensory data. Experiments performed in a pine forest demonstrate successful detection rates with an average accuracy of 97% in various experiments and settings. Geo-localization is performed to report the position of the detected nests within a scanned forest map by means of an estimation scheme that is particularly designed for this purpose achieving centimeter accuracy (<20 cm). Keywords: Aerial robotics, Agriculture, Detection, Machine learning, Multi-stream CNN, Pest control.

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15

Fang, Lei, Ellen Crocker, Jian Yang, Yan Yan, Yuanzheng Yang, and Zhihua Liu. "Competition and Burn Severity Determine Post-Fire Sapling Recovery in a Nationally Protected Boreal Forest of China: An Analysis from Very High-Resolution Satellite Imagery." Remote Sensing 11, no. 6 (March 13, 2019): 603. http://dx.doi.org/10.3390/rs11060603.

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Anticipating how boreal forest landscapes will change in response to changing fire regime requires disentangling the effects of various spatial controls on the recovery process of tree saplings. Spatially explicit monitoring of post-fire vegetation recovery through moderate resolution Landsat imagery is a popular technique but is filled with ambiguous information due to mixed pixel effects. On the other hand, very-high resolution (VHR) satellite imagery accurately measures crown size of tree saplings but has gained little attention and its utility for estimating leaf area index (LAI, m2/m2) and tree sapling abundance (TSA, seedlings/ha) in post-fire landscape remains untested. We compared the explanatory power of 30 m Landsat satellite imagery with 0.5-m WorldView-2 VHR imagery for LAI and TSA based on field sampling data, and subsequently mapped the distribution of LAI and TSA based on the most predictive relationships. A random forest (RF) model was applied to assess the relative importance and causal mechanisms of spatial controls on tree sapling recovery. The results showed that pixel percentage of canopy trees (PPCT) derived from VHR imagery outperform all Landsat-derived spectral indices for explaining variance of LAI (R2VHR = 0.676 vs. R2Landsat = 0.427) and TSA (R2VHR = 0.508 vs. R2Landsat = 0.499). The RF model explained an average of 55.5% (SD = 3.0%, MSE = 0.382, N = 50) of the variation of estimated LAI. Understory vegetation coverage (competition) and post-fire surviving mature trees (seed sources) were the most important spatial controls for LAI recovery, followed by burn severity (legacy effect), topographic factors (environmental filter) and nearest distance to unburned area (edge effect). These analyses allow us to conclude that in our study area, mitigating wildfire severity and size may increase forest resilience to wildfire damage. Given the easily-damaged seed banks and relatively short seed dispersal distance of coniferous trees, reasonable human help to natural recovery of coniferous forests is necessary for severe burns with a large patch size, particularly in certain areas. Our research shows the VHR WorldView-2 imagery better resolves key characteristics of forest landscapes like LAI and TSA than Landsat imagery, providing a valuable tool for land managers and researchers alike.

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16

Dong, Tianyang, Yuqi Shen, Jian Zhang, Yang Ye, and Jing Fan. "Progressive Cascaded Convolutional Neural Networks for Single Tree Detection with Google Earth Imagery." Remote Sensing 11, no. 15 (July 30, 2019): 1786. http://dx.doi.org/10.3390/rs11151786.

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High-resolution remote sensing images can not only help forestry administrative departments achieve high-precision forest resource surveys, wood yield estimations and forest mapping but also provide decision-making support for urban greening projects. Many scholars have studied ways to detect single trees from remote sensing images and proposed many detection methods. However, the existing single tree detection methods have many errors of commission and omission in complex scenes, close values on the digital data of the image for background and trees, unclear canopy contour and abnormal shape caused by illumination shadows. To solve these problems, this paper presents progressive cascaded convolutional neural networks for single tree detection with Google Earth imagery and adopts three progressive classification branches to train and detect tree samples with different classification difficulties. In this method, the feature extraction modules of three CNN networks are progressively cascaded, and the network layer in the branches determined whether to filter the samples and feed back to the feature extraction module to improve the precision of single tree detection. In addition, the mechanism of two-phase training is used to improve the efficiency of model training. To verify the validity and practicability of our method, three forest plots located in Hangzhou City, China, Phang Nga Province, Thailand and Florida, USA were selected as test areas, and the tree detection results of different methods, including the region-growing, template-matching, convolutional neural network and our progressive cascaded convolutional neural network, are presented. The results indicate that our method has the best detection performance. Our method not only has higher precision and recall but also has good robustness to forest scenes with different complexity levels. The F1 measure analysis in the three plots was 81.0%, which is improved by 14.5%, 18.9% and 5.0%, respectively, compared with other existing methods.

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17

Jitsufuchi, Tetsuya. "Development of an Optical Multispectral Remote Sensing System for Measuring Volcanic Surface Phenomena – Promotion Project for Next Generation Volcano Research B2 (Subtopic 2-2)." Journal of Disaster Research 14, no. 5 (August 1, 2019): 728–43. http://dx.doi.org/10.20965/jdr.2019.p0728.

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In 2016, we launched the “Promotion Project for Next Generation Volcano Research B2 (Theme B: Development of Cutting-edge Volcano Observation Technology, subtheme 2: Development of Remote Sensing Techniques for Volcano Observation), subtopic 2-2 (Development of Remote Sensing Techniques for Surface Phenomena of Volcano)” under the “Integrated Program for Next Generation Volcano Research and Human Resources Development” [1], aiming at the development of an optical multispectral remote sensing system for measuring volcanic surface phenomena. With subtopic 2-2, we are planning to develop a new observation device called a surface phenomena imaging camera (SPIC), which is technically superior to current remote sensing techniques, i.e., optical remote observation techniques used to observe volcanic surface phenomena from aircrafts or ground. We are also aiming at applying the developed observation system to quantify volcanic activities and determine volcanic eruption potentials (degrees of urgency) or branching of event trees for volcanic crises with high accuracy, contributing to better predictions of volcanic eruption transitions. To achieve the above-mentioned aims, we started the development of the SPIC by equipping it with camera-type sensors, based on preliminary analyses of the experimental observations made with the airborne spectral imaging system ARTS-SE, which consists of a pushbroom scanner and a camera system, developed by the National Research Institute for Earth Science and Disaster Resilience in FY 2015. We have already developed its components, such as the prototype filter-type multiband cameras SPIC-UC, a prototype uncooled infrared camera, SPIC-C, a cooled camera, and SPIC-SS, a visible-light camera. The SPIC-UC is a two-band camera with the function of visualizing temperature and SO2 gas concentration distributions. The SPIC-C has the function of measuring temperatures between 2 and 1075◦C with high accuracy (noise equivalent temperature difference, NETD: 16 mK); it is equipped with a sensor and a filter wheel that work in the middle wave infrared region (MWIR). The SPIC-SS is a six-lens multiband camera system that estimates the measured images from multiband spectra (6 bands) to hyper spectra (300 bands). Further, we studied a method to estimate digital surface model with a ∼30-m error. As our plan has progressed as scheduled, we intend to complete the prototype SPIC by 2020.

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18

Lasko, Kristofer. "Gap Filling Cloudy Sentinel-2 NDVI and NDWI Pixels with Multi-Frequency Denoised C-Band and L-Band Synthetic Aperture Radar (SAR), Texture, and Shallow Learning Techniques." Remote Sensing 14, no. 17 (August 27, 2022): 4221. http://dx.doi.org/10.3390/rs14174221.

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Multispectral imagery provides unprecedented information on Earth system processes: however, data gaps due to clouds and shadows are a major limitation. Normalized-Difference Vegetation Index (NDVI) and Normalized-Difference Water Index (NDWI) are two spectral indexes employed for monitoring vegetation phenology, land-cover change and more. Synthetic Aperture Radar (SAR) with its cloud-penetrating abilities can fill data gaps using coincident imagery. In this study, we evaluated C-band Sentinel-1, L-band Uninhabited Aerial Vehicle SAR (UAVSAR) and texture for gap filling using efficient machine learning regression algorithms across three seasons. Multiple models were evaluated including Support Vector Machine, Random Forest, Gradient Boosted Trees and an ensemble of models. The Gap filling ability of SAR was evaluated with Sentinel-2 imagery from the same date, 3 days and 8 days later than both SAR sensors in September. Sentinel-1 and Sentinel-2 imagery from winter and spring seasons were also evaluated. Because SAR imagery contains noise, we compared two robust de-noising methods and evaluated performance against a refined lee speckle filter. Mean Absolute Error (MAE) rates of the cloud gap-filling model were assessed across different dataset combinations and land covers. The results indicated de-noised Sentinel-1 SAR and UAVSAR with GLCM texture provided the highest predictive abilities with random forest R2 = 0.91 (±0.014), MAE = 0.078 (±0.003) (NDWI) and R2 = 0.868 (±0.015), MAE = 0.094 (±0.003) (NDVI) during September. The highest errors were observed across bare ground and forest, while the lowest errors were on herbaceous and woody wetland. Results on January and June imagery without UAVSAR were less strong at R2 = 0.60 (±0.036), MAE = 0.211 (±0.005) (NDVI), R2 = 0.61 (±0.043), MAE = 0.209 (±0.005) (NDWI) for January and R2 = 0.72 (±0.018), MAE = 0.142 (±0.004) (NDVI), R2 = 0.77 (±0.022), MAE = 0.125 (±0.004) (NDWI) for June. Ultimately, the results suggest de-noised C-band SAR with texture metrics can accurately predict NDVI and NDWI for gap-filling clouds during most seasons. These shallow machine learning models are rapidly trained and applied faster than intensive deep learning or time series methods.

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Truc, Phan T. H., Md A. U. Khan, Young-Koo Lee, Sungyoung Lee, and Tae-Seong Kim. "Vessel enhancement filter using directional filter bank." Computer Vision and Image Understanding 113, no. 1 (January 2009): 101–12. http://dx.doi.org/10.1016/j.cviu.2008.07.009.

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20

Lim, Y. C., and B. Farhang-Boroujeny. "Fast filter bank (FFB)." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 39, no. 5 (May 1992): 316–18. http://dx.doi.org/10.1109/82.142033.

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21

Chebira, Amina, Matthew Fickus, and Dustin G. Mixon. "Filter Bank Fusion Frames." IEEE Transactions on Signal Processing 59, no. 3 (March 2011): 953–63. http://dx.doi.org/10.1109/tsp.2010.2097255.

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22

Mohammed, Mahmood Jasim, and Abd Al Kareem Ali Mohammed. "Prototype filter design for filter bank multicarrier modulation." Journal of Physics: Conference Series 1973, no. 1 (August 1, 2021): 012098. http://dx.doi.org/10.1088/1742-6596/1973/1/012098.

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23

Ikehara, Masaaki, and Shinichi Takahashi. "Uniform filter bank containing N-path digital filter." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 72, no. 5 (1989): 62–70. http://dx.doi.org/10.1002/ecjc.4430720508.

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24

Pfister, Luke, and Yoram Bresler. "Learning Filter Bank Sparsifying Transforms." IEEE Transactions on Signal Processing 67, no. 2 (January 15, 2019): 504–19. http://dx.doi.org/10.1109/tsp.2018.2883021.

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25

Alhava, J., and M. Renfors. "Biorthogonal exponentially modulated filter bank." Signal Processing 97 (April 2014): 195–206. http://dx.doi.org/10.1016/j.sigpro.2013.10.016.

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26

Farhang-Boroujeny, Behrouz. "OFDM Versus Filter Bank Multicarrier." IEEE Signal Processing Magazine 28, no. 3 (May 2011): 92–112. http://dx.doi.org/10.1109/msp.2011.940267.

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27

Hribšek, M. F. "Electromechanical silicon beam filter bank." Microelectronics Journal 27, no. 6 (September 1996): 525–30. http://dx.doi.org/10.1016/0026-2692(95)00119-0.

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28

Nalbalwar, S. L., and S. D. Joshi. "Equivalence between multirate filter bank and circular lattice filter and its application in statistically matched multirate filter bank." International Journal of Speech Technology 22, no. 2 (March 25, 2019): 351–70. http://dx.doi.org/10.1007/s10772-019-09609-6.

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29

Parameshappa, G., and D. Jayadevapp. "Efficient uniform digital filter bank with linear phase and FRM technique for hearing aids." International Journal of Engineering & Technology 7, no. 1.9 (March 1, 2018): 69. http://dx.doi.org/10.14419/ijet.v7i1.9.9738.

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Анотація:

This paper attempts to present an uniform digital filter bank based on linear phase FIR and IIR filters applied for Frequency Response Masking (FRM) technique in hearing aid applications.In the proposed filter bank, nine uniformly spaced sub-bands are formed with the help of half band filters and masking filters. These nine channel FIR filter bank is realized using an interpolated half band linear phase FIR filter and an appropriate number of masking FIR filters. The nine channel IIR filter bank is realized using an interpolated half band approximately linear phase IIR filter and an appropriate number of masking filters. The proposed approximately linear phase IIR half band filter bank is compared with filter bank based on linear phase FIR half band filters in terms of area, power, memory and number of gates needed for implementation. The experiment was carried on various hearing loss cases and the results obtained from these tests proves that, the proposed filter bank achieved the required matching between audiograms and magnitude response of the filter bank at very reasonable range with less computational complexity.

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30

Nishi, Kazuki, and Shigeru Ando. "Optimal Harmonics Extraction Filter with Auditory Filter Bank Structure." IEEJ Transactions on Sensors and Micromachines 122, no. 6 (2002): 338–44. http://dx.doi.org/10.1541/ieejsmas.122.338.

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31

Park, Kyusik, and Jaehyun Park. "Optimum filter design for quantized multidimensional subband filter bank." Digital Signal Processing 15, no. 6 (November 2005): 582–603. http://dx.doi.org/10.1016/j.dsp.2005.01.007.

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32

Ghosh, Debalina. "A Comparative Study of Performance of Fpga Based Mel Filter Bank & Bark Filter Bank." International Journal of Artificial Intelligence & Applications 3, no. 3 (May 31, 2012): 37–54. http://dx.doi.org/10.5121/ijaia.2012.3304.

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33

Sanko, N. S., and M. I. Vashkevich. "Research of application of DFT-modulated filter bank in systems with significant spectral component amplification." Doklady BGUIR 19, no. 6 (October 1, 2021): 14–22. http://dx.doi.org/10.35596/1729-7648-2021-19-6-14-22.

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Анотація:

The purpose of this article is to investigate the application of DFT-modulated filter bank in systems with significant spectral component amplification like hearing aid. There is a description of analysis / synthesis method based on short-time Fourier transform (STFT), which is used in most systems of speech information processing. It is shown that DFT-modulated filter bank is a generalization of STFT-method. In analysis / synthesis system based on DFT-modulated filter bank, the input signal is divided into subbands, passing through the analysis filter bank then each subband is amplified and the last step is to reconstruct the signal with synthesis filter bank. However, in digital systems with significant spectral component amplification, the resulting signal is distorted after reconstruction because of amplification factor difference in each subband. The article provides expressions for the distortion and the aliasing functions, allowing to estimate the distortion value, which appears in analysis / synthesis system of DFT-modulated filter bank. Efficient algorithms for calculating the distortion and the aliasing functions are also offered. In future it is planning to develop a procedure for optimizing the DFT-modulated filter bank based on the proposed efficient algorithms for calculating distortion and spectral aliasing in the filter bank.

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34

Miraki, Amir, Hamid Saeedi-Sourck, Nicola Marchetti, and Arman Farhang. "Spectral Domain Spline Graph Filter Bank." IEEE Signal Processing Letters 28 (2021): 469–73. http://dx.doi.org/10.1109/lsp.2021.3059203.

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35

Jason, Gradiyanto, Theresia Ghozali, and Kumala Indriati. "FILTER BANK MULTICARRIER (FBMC) UNTUK 5G." Jurnal Elektro 13, no. 2 (February 17, 2021): 135–42. http://dx.doi.org/10.25170/jurnalelektro.v13i2.1977.

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Анотація:

Saat ini, teknologi pada bidang telekomunikasi berkembang dengan pesat. Pengguna telekomunikasi membutuhkan teknologi komunikasi yang cepat dengan bandwidth yang lebih lebar. Oleh karena itu dikembangkanlah Filter Bank Multi Carrier/Offset Quadrature Amplitude Modulation yang merupakan teknologi kandidat modulasi yang akan digunakan pada 5G. Filter Bank Multi Carrier/Offset Quadrature Amplitude Modulation (FMBC/OQAM) merupakan perkembangan dari Orthogonal Frequency Division Multiplexing (OFDM) yang dimodifikasi dengan menggunakan filter untuk mengurangi noise. OFDM menggunakan teknik multiplexing yang membagi bandwith menjadi beberapa frekuensi sub-carrier. Tetapi OFDM memiliki kelemahan yaitu memerlukan Cyclic Prefix (CP) untuk mengatasi Intersymbol Interference (ISI) serta Intercarrier Interference (ICI). Dengan menggunakan Filter berdasarkan Lembaga Physical layer for dynamic spectrum access and cognitive radio (PHYDYAS) ,lebar bandwith yang bertambah akibat Cyclic prefix akan dapat dikurangi Hasil yang dicapai dalam penelitian ini adalah FBMC berhasil diterapkan dan data yang dikirim sama dengan data yang diterima.

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36

Anand, S., T. Thivya, and S. Jeeva. "Edge Detection using Directional Filter Bank." International Journal of Applied Information Systems 1, no. 4 (February 18, 2012): 21–27. http://dx.doi.org/10.5120/ijais12-450162.

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37

Kovacevic, J., P. L. Dragotti, and V. K. Goyal. "Filter bank frame expansions with erasures." IEEE Transactions on Information Theory 48, no. 6 (June 2002): 1439–50. http://dx.doi.org/10.1109/tit.2002.1003832.

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38

Yu-Min Cheng and Bor-Sen Chen. "Nonuniform filter bank design with noises." IEEE Transactions on Signal Processing 46, no. 9 (1998): 2326–44. http://dx.doi.org/10.1109/78.709515.

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39

Waldén, Johan. "Filter Bank Methods for Hyperbolic PDEs." SIAM Journal on Numerical Analysis 36, no. 4 (January 1999): 1183–233. http://dx.doi.org/10.1137/s0036142996313130.

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40

Hoang Duong Tuan, T. T. Son, P. Apkarian, and T. Q. Nguyen. "Low-order IIR filter bank design." IEEE Transactions on Circuits and Systems I: Regular Papers 52, no. 8 (August 2005): 1673–83. http://dx.doi.org/10.1109/tcsi.2005.851673.

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41

Gera, Zoltán. "Filter Bank Design for Melody Recognition." Acta Cybernetica 19, no. 1 (2009): 159–75. http://dx.doi.org/10.14232/actacyb.19.1.2009.11.

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42

Sangnier, M., J. Gauthier, and A. Rakotomamonjy. "Filter bank learning for signal classification." Signal Processing 113 (August 2015): 124–37. http://dx.doi.org/10.1016/j.sigpro.2014.12.028.

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43

Rajgopal, K., J. Dinesh Babu, and S. Venkataraman. "Generalized adaptive IFIR filter bank structures." Signal Processing 87, no. 7 (July 2007): 1575–96. http://dx.doi.org/10.1016/j.sigpro.2006.12.019.

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44

Waldén, Johan. "Filter bank subdivisions of bounded domains." Applied Numerical Mathematics 32, no. 3 (March 2000): 331–57. http://dx.doi.org/10.1016/s0168-9274(99)00025-2.

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45

Ling, Bingo Wing-Kuen, Charlotte Yuk-Fan Ho, and Peter Kwong-Shun Tam. "Chaotic filter bank for computer cryptography." Chaos, Solitons & Fractals 34, no. 3 (November 2007): 817–24. http://dx.doi.org/10.1016/j.chaos.2006.03.105.

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46

Alfsmann, Daniel, and Heinz Goeckler. "FILTER BANK SYSTEM FOR HEARING AIDS." Journal of the Acoustical Society of America 131, no. 6 (2012): 4866. http://dx.doi.org/10.1121/1.4728391.

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47

Yao, Susu. "Signal-adapted wavelet filter bank design." Electronics Letters 34, no. 5 (1998): 434. http://dx.doi.org/10.1049/el:19980342.

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48

Milić, L., U. Kams^ek, and A. Jocić. "Analysis of filter bank transfer functions." IEE Proceedings G (Electronic Circuits and Systems) 134, no. 1 (1987): 23. http://dx.doi.org/10.1049/ip-g-1.1987.0004.

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49

Karagözoglu, B. "A novel filter bank for biotelemetry." Australasian Physics & Engineering Sciences in Medicine 24, no. 1 (March 2001): 49–56. http://dx.doi.org/10.1007/bf03178285.

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50

Hung Gia Hoang, Hoang Duong Tuan, and T. Q. Nguyen. "Frequency-Selective KYP Lemma, IIR Filter, and Filter Bank Design." IEEE Transactions on Signal Processing 57, no. 3 (March 2009): 956–65. http://dx.doi.org/10.1109/tsp.2008.2009012.

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