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Journal articles on the topic 'Signal processing- models'

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Author: Grafiati

Published: 6 September 2023

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1

Culver, R. Lee, and H. John Camin. "Sonar signal processing using probabilistic signal and ocean environmental models." Journal of the Acoustical Society of America 124, no. 6 (December 2008): 3619–31. http://dx.doi.org/10.1121/1.3006379.

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2

Pagès-Zamora, Alba, and Miguel A. Lagunas. "Fourier models for non-linear signal processing." Signal Processing 76, no. 1 (July 1999): 1–16. http://dx.doi.org/10.1016/s0165-1684(98)00243-6.

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3

Sottek, Roland, and Klaus Genuit. "Models of signal processing in human hearing." AEU - International Journal of Electronics and Communications 59, no. 3 (June 2005): 157–65. http://dx.doi.org/10.1016/j.aeue.2005.03.016.

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4

Liu, Keying, Rui Li, and Fasong Wang. "Blind Signal Processing models and methods for Foetal Electrocardiogram signals extraction." International Journal of Biomedical Engineering and Technology 7, no. 3 (2011): 225. http://dx.doi.org/10.1504/ijbet.2011.043296.

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5

Rogozinsky, G., M. Chesnokov, and A. Kutlyiarova. "Some New Mathematical Models of Synthesized Sound Signals." Proceedings of Telecommunication Universities 8, no. 2 (June 30, 2022): 76–81. http://dx.doi.org/10.31854/1813-324x-2022-8-2-76-81.

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Modern sound synthesis systems make it possible to implement various signal generation algorithms of higher complexity. The theory of sound synthesis actively uses the mathematical apparatus of analog and digital radio engineering and signal processing, however, it should be noted that the classical signal models used in acoustics are not adequate to real-world synthesized signals, mainly due to the significant complexity of the latter. This article presents some models of synthesized signals typical for practical use.

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6

Willsky, A. S. "Multiresolution Markov models for signal and image processing." Proceedings of the IEEE 90, no. 8 (August 2002): 1396–458. http://dx.doi.org/10.1109/jproc.2002.800717.

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7

Schooley, Larry C. "Charge-coupled device signal processing models and comparisons." Journal of Electronic Imaging 2, no. 2 (April 1, 1993): 100. http://dx.doi.org/10.1117/12.138355.

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8

Nakajima, Jouchi, and Mike West. "Dynamic network signal processing using latent threshold models." Digital Signal Processing 47 (December 2015): 5–16. http://dx.doi.org/10.1016/j.dsp.2015.04.008.

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9

Arik, Sercan O., Joseph M. Kahn, and Keang-Po Ho. "MIMO Signal Processing for Mode-Division Multiplexing: An overview of channel models and signal processing architectures." IEEE Signal Processing Magazine 31, no. 2 (March 2014): 25–34. http://dx.doi.org/10.1109/msp.2013.2290804.

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10

Fisher, B., and N. Bershad. "ALE behavior for two sinusoidal signal models." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 3 (June 1985): 658–65. http://dx.doi.org/10.1109/tassp.1985.1164590.

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11

Crouse, M. S., R. D. Nowak, and R. G. Baraniuk. "Wavelet-based statistical signal processing using hidden Markov models." IEEE Transactions on Signal Processing 46, no. 4 (April 1998): 886–902. http://dx.doi.org/10.1109/78.668544.

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12

Aksenova, Tetiana, and Tatyana V. Ryzhkova. "Oscillatory Models for Biological Signal Processing and Pattern Recognition." EPJ Web of Conferences 224 (2019): 03004. http://dx.doi.org/10.1051/epjconf/201922403004.

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Among biomedical signals, repetitive or quasi-periodic signals are particularly widespread. While the periodic component is still presented these signals are characterized by period variations (fundamental frequency, amplitude, etc.). The lack of synchronization or phase shifts results in variations in similar segments’ durations, nominally identical signals demonstrate a variation at peak retention times, etc. The inverse methods of oscillation theory were proposed recently as a tool to solve the problems of modelling of repetitive signals with phase shift. In the article, the inverse method of oscillation theory is considered as a tool to solve the problems of supervised and non-supervised classification, and filtering of repetitive signals with phase shift. Examples of application are presented.

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13

Sandryhaila, Aliaksei, Jelena Kovacevic, and Markus Puschel. "Algebraic Signal Processing Theory: 1-D Nearest Neighbor Models." IEEE Transactions on Signal Processing 60, no. 5 (May 2012): 2247–59. http://dx.doi.org/10.1109/tsp.2012.2186133.

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14

Mahata, Kaushik, Amit Mitra, and Sharmishtha Mitra. "Consistency of M-estimators of nonlinear signal processing models." Statistical Methodology 28 (January 2016): 18–36. http://dx.doi.org/10.1016/j.stamet.2015.07.004.

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15

Atlas, Les, Pascal Clark, and Ivars Kirsteins. "Alternative signal processing models for broadband underwater propeller sounds." Journal of the Acoustical Society of America 125, no. 4 (April 2009): 2607. http://dx.doi.org/10.1121/1.4783923.

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16

Gide, Milind S., and Linda J. Karam. "Computational Visual Attention Models." Foundations and Trends® in Signal Processing 10, no. 4 (2017): 347–427. http://dx.doi.org/10.1561/2000000055.

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17

Gibson, Jerry. "Entropy Power, Autoregressive Models, and Mutual Information." Entropy 20, no. 10 (September 30, 2018): 750. http://dx.doi.org/10.3390/e20100750.

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Autoregressive processes play a major role in speech processing (linear prediction), seismic signal processing, biological signal processing, and many other applications. We consider the quantity defined by Shannon in 1948, the entropy rate power, and show that the log ratio of entropy powers equals the difference in the differential entropy of the two processes. Furthermore, we use the log ratio of entropy powers to analyze the change in mutual information as the model order is increased for autoregressive processes. We examine when we can substitute the minimum mean squared prediction error for the entropy power in the log ratio of entropy powers, thus greatly simplifying the calculations to obtain the differential entropy and the change in mutual information and therefore increasing the utility of the approach. Applications to speech processing and coding are given and potential applications to seismic signal processing, EEG classification, and ECG classification are described.

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18

Monakov, A. A., and A. A. Tarasenkov. "Comparative Analysis of Mathematical Models of Tracking Radio Altimeters." Journal of the Russian Universities. Radioelectronics 25, no. 4 (September 29, 2022): 72–80. http://dx.doi.org/10.32603/1993-8985-2022-25-4-72-80.

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Introduction. Tracking radio altimeters of low altitudes are widely used in civil aviation. These devises use periodic frequency modulated continuous wave (FMCW) signals, while altitude measurements are based on processing the beat signal processing. For this purpose, a closed automatic control loop is arranged to maintain the frequency of the beat signal at a fixed level by changing parameters of the transmitted signal (the frequency deviation or the modulation period). An alternative approach to arranging the tracking loop for altitude variations is based on the use of a phase locked loop (PLL), which adjusts the reference signal – a copy of the emitted signal – to obtain the maximum cross-correlation of the beat and reference signals. А comparative analysis of short-range radio altimeters with other currently known tracking radio altimeters for various types of frequency modulation of the transmitted signal seems to be a relevant research task.Aim. An analysis of the influence of the type of frequency modulation on the accuracy of altitude estimation in a PLL-based radar altimeter, as well as a comparative analysis of this altimeter with other known tracking altimeters.Materials and methods. Mathematical models of tracking radio altimeters are proposed, and a computer simulation of their performance is carried out for the case of altitude estimation over a smooth flat surface.Results. The conducted comparative analysis of tracking radio altimeters confirmed the effectiveness of the PLL when processing signals of different frequency modulation type (sawtooth, triangular, and harmonic FM). Altitude estimates produced by PLL-based radar altimeters are unbiased, with their standard deviation not exceeding 3 cm for the signalto-noise ratio of greater than 10 dB and under the scenario parameters adopted in the work. The conducted comparison with other tracking altimeters showed that estimation errors of this radar altimeter are an order of magnitude smaller.Conclusion. A PLL-based tracking radar altimeter can be used to estimate the height of the aircraft flight. The quality of altitude estimates produced by this device is higher than those produced by other known tracking radio altimeters. Further research and field tests will investigate the accuracy of altitude estimation when working over a rough surface.

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19

ELLIOTT, ROBERT J., WILLIAM C. HUNTER, and BARBARA M. JAMIESON. "FINANCIAL SIGNAL PROCESSING: A SELF CALIBRATING MODEL." International Journal of Theoretical and Applied Finance 04, no. 04 (August 2001): 567–84. http://dx.doi.org/10.1142/s0219024901001140.

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Previous work on multifactor term structure models has proposed that the short rate process is a function of some unobserved diffusion process. We consider a model in which the short rate process is a function of a Markov chain which represents the "state of the world". This enables us to obtain explicit expressions for the prices of zero-coupon bonds and other securities. Discretizing our model allows the use of signal processing techniques from Hidden Markov Models. This means we can estimate not only the unobserved Markov chain but also the parameters of the model, so the model is self-calibrating. The estimation procedure is tested on a selection of U.S. Treasury bills and bonds.

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20

Carin, Lawrence, Richard Baraniuk, Volkan Cevher, David Dunson, Michael Jordan, Guillermo Sapiro, and Michael Wakin. "Learning Low-Dimensional Signal Models." IEEE Signal Processing Magazine 28, no. 2 (March 2011): 39–51. http://dx.doi.org/10.1109/msp.2010.939733.

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21

Lainiotis, D. G., Paraskevas Papaparaskeva, and Kostas Plataniotis. "Nonlinear filtering for LIDAR signal processing." Mathematical Problems in Engineering 2, no. 5 (1996): 367–92. http://dx.doi.org/10.1155/s1024123x96000397.

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LIDAR (Laser Integrated Radar) is an engineering problem of great practical importance in environmental monitoring sciences. Signal processing for LIDAR applications involves highly nonlinear models and consequently nonlinear filtering. Optimal nonlinear filters, however, are practically unrealizable. In this paper, the Lainiotis's multi-model partitioning methodology and the related approximate but effective nonlinear filtering algorithms are reviewed and applied to LIDAR signal processing. Extensive simulation and performance evaluation of the multi-model partitioning approach and its application to LIDAR signal processing shows that the nonlinear partitioning methods are very effective and significantly superior to the nonlinear extended Kalman filter (EKF), which has been the standard nonlinear filter in past engineering applications.

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22

Hawley, Scott H. "Development tools for deep learning models of acoustical signal processing." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A230. http://dx.doi.org/10.1121/10.0011154.

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We present a survey of available frameworks for developing acoustical signal processing models based on deep neural networks. Given that this is a dynamic space with new frameworks, libraries, and even companies appearing on timescales measured in months, we provide an up-to-date assessment of the strength, popularity, and near-future directions of several tools and platforms available for research and product deployment for deep learning models of audio signal processing. Similarly, those new to these spaces may be unaware of software systems that will allow them to obtain and interrogate results more quickly and easily, while also integrating the nearly state-of-the-art optimization methods. Included tools, packages and platforms include PyTorch, Tensorflow, Keras, JAX, fastai, PyTorch Lightning, Julia, nbdev, HuggingFace, Weights and Biases, and Gradio. Examples will be drawn from the speaker's recent research publications in musical signal processing and computer vision applied to musical acoustics, as well as recent work by others. The goal of the talk is to provide acoustics researchers, educators, students with a set of helpful possibilities for pursuing and improving their understanding, research practices, and communications.

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23

Esch, J. "Prolog to multiresolution Markov models for signal and image processing." Proceedings of the IEEE 90, no. 8 (August 2002): 1395. http://dx.doi.org/10.1109/jproc.2002.800720.

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24

Han, Jae-Joon, Peter C. Doerschuk, Saul B. Gelfand, and Sean J. O'Connor. "Models and Signal Processing for an Implanted Ethanol Bio-Sensor." IEEE Transactions on Biomedical Engineering 55, no. 2 (February 2008): 603–13. http://dx.doi.org/10.1109/tbme.2007.912652.

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25

Hoole, P. R. P., P. H. Tan, and D. Goh. "Isar Image and Signal Processing Using Line Element Scatterer Models." Journal of Electromagnetic Waves and Applications 12, no. 12 (January 1998): 1639–51. http://dx.doi.org/10.1163/156939398x00575.

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26

Konstantinides, K., R. T. Kaneshiro, and J. R. Tani. "Task allocation and scheduling models for multiprocessor digital signal processing." IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 12 (1990): 2151–61. http://dx.doi.org/10.1109/29.61542.

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27

Jian, Xingchao, Feng Ji, and Wee Peng Tay. "Generalizing Graph Signal Processing: High Dimensional Spaces, Models and Structures." Foundations and Trends® in Signal Processing 17, no. 3 (2023): 209–90. http://dx.doi.org/10.1561/2000000119.

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28

Konopel'kin, M. Yu, S. V. Petrov, and D. A. Smirnyagina. "Implementation of stochastic signal processing algorithms in radar CAD." Russian Technological Journal 10, no. 5 (October 21, 2022): 49–59. http://dx.doi.org/10.32362/2500-316x-2022-10-5-49-59.

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Objectives. In 2020, development work on the creation of a Russian computer-assisted design system for radars (radar CAD) was completed. Radar CAD provides extensive opportunities for creating simulation models for developing the hardware-software complex of radar algorithms, which take into account the specific conditions of aerospace environment observation. The purpose of the present work is to review and demonstrate the capabilities of radar CAD in terms of implementing and testing algorithms for processing stochastic signals.Methods. The work is based on the mathematical apparatus of linear algebra. Analysis of algorithms characteristics was carried out using the simulation method.Results. A simulation model of a sector surveillance radar with a digital antenna array was created in the radar CAD visual functional editor. The passive channel included the following algorithms: algorithm for detecting stochastic signals; algorithm for estimating the number of stochastic signals; direction finding algorithm for stochastic signal sources; adaptive spatial filtering algorithm. In the process of simulation, the algorithms for detecting and estimating the number of stochastic signals produced a correct detection sign and an estimate of the number of signals. The direction-finding algorithm estimated the angular position of the sources with an accuracy of fractions of degrees. The adaptive spatial filtering algorithm suppressed interfering signals to a level below the antenna's intrinsic noise power.Conclusions. The processing of various types of signals can be simulated in detail on the basis of the Russian radar CAD system for the development of functional radar models. According to the results of the simulation, coordinates of observing objects were obtained and an assessment of the effectiveness of the algorithms was given. The obtained results are fully consistent with the theoretical prediction. The capabilities of radar CAD systems demonstrated in this work can be used by specialists in the field of radar and signal processing.

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29

Biing-Hwang Juang and L. Rabiner. "Mixture autoregressive hidden Markov models for speech signals." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 6 (December 1985): 1404–13. http://dx.doi.org/10.1109/tassp.1985.1164727.

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30

Anastasio, Thomas J., and David A. Robinson. "Distributed Parallel Processing in the Vestibulo-Oculomotor System." Neural Computation 1, no. 2 (June 1989): 230–41. http://dx.doi.org/10.1162/neco.1989.1.2.230.

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The mechanisms of eye-movement control are among the best understood in motor neurophysiology. Detailed anatomical and physiological data have paved the way for theoretical models that have unified existing knowledge and suggested further experiments. These models have generally taken the form of black-box diagrams (for example, Robinson 1981) representing the flow of hypothetical signals between idealized signal-processing blocks. They approximate overall oculomotor behavior but indicate little about how real eye-movement signals would be carried and processed by real neural networks. Neurons that combine and transmit oculomotor signals, such as those in the vestibular nucleus (VN), actually do so in a diverse, seemingly random way that would be impossible to predict from a block diagram. The purpose of this study is to use a neural-network learning scheme (Rumelhart et al. 1986) to construct parallel, distributed models of the vestibulo-oculomotor system that simulate the diversity of responses recorded experimentally from VN neurons.

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31

Lavanya, S., S. Prabakaran, and N. Ashok Kumar. "A Deep Learning Technique for Detecting High Impedance Faults in Medium Voltage Distribution Networks." Engineering, Technology & Applied Science Research 12, no. 6 (December 1, 2022): 9477–82. http://dx.doi.org/10.48084/etasr.5288.

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Utility companies always struggle with the High Impedance Fault (HIF) in the electrical distribution systems. In this article, the current signal is seen in situations involving 10,400 different samples, with and without HIF, like linear, non-linear load, and capacitance switching. A better method that processes signals very fast and with low sample rates, requiring less memory and computational labor, is demonstrated by Mathematical Morphology (MM). For HIF identification, Deep Convolution Neural Networks (DCNNs) are being developed. This paper presents a novel method for signal processing with low sample rates, high signal processing speed, and low computational and memory requirements. The suggested six-layer DCNN is compared with other models, such as the four-layer and eight-layer DCNN models and the results are discussed.

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32

Kumar, R. Suresh, and P. Manimegalai. "Detection and Separation of Eeg Artifacts Using Wavelet Transform." International Journal of Informatics and Communication Technology (IJ-ICT) 7, no. 3 (December 1, 2018): 149. http://dx.doi.org/10.11591/ijict.v7i3.pp149-156.

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Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.

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33

Табаков, Yu Tabakov, Лавлинский, V. Lavlinskiy, Бибиков, and D. Bibikov. "LF SIGNAL PROCESSING FOR INTELLECTUAL SOFTWARE SIMULATORS WITH USING LINEAR FILTERS AND DISCRETE TIME." Modeling of systems and processes 7, no. 3 (December 1, 2014): 45–47. http://dx.doi.org/10.12737/6681.

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34

Qu, Qiuhui. "Application of MATLAB in signal and system." SHS Web of Conferences 145 (2022): 01029. http://dx.doi.org/10.1051/shsconf/202214501029.

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Signal and system course is an important professional basic course for electronic information and communication majors. Signal and system are abstract concepts, which are described by mathematical models. In daily life, simple signals can be calculated or drawn manually, but complex signals are difficult to be accurately processed. Matlab contains graphics processing and symbol operation functions, which provides us with powerful tools to solve the above problems. This paper will introduce how to use Matlab to express, calculate and process signals, and realize the systematic analysis of signals.

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35

SUOD, MAHMOUD, Alexander Ushkarenka, Abdel Soliman, Mahmoud Zeidan, Abdullah Awwad, and Alaa Quteimat. "Development of Graphical Analytical Models for Digital Signal Processing System Structures." Jordan Journal of Electrical Engineering 6, no. 2 (2020): 140. http://dx.doi.org/10.5455/jjee.204-1581484702.

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36

Le Gall, Yann, Francois-Xavier Socheleau, and Julien Bonnel. "Matched-Field Processing Performance Under the Stochastic and Deterministic Signal Models." IEEE Transactions on Signal Processing 62, no. 22 (November 2014): 5825–38. http://dx.doi.org/10.1109/tsp.2014.2360818.

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37

Gales, Mark, and Steve Young. "The Application of Hidden Markov Models in Speech Recognition." Foundations and Trends® in Signal Processing 1, no. 3 (2007): 195–304. http://dx.doi.org/10.1561/2000000004.

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38

Quivy, Charles-Henri, and Itsuo Kumazawa. "Normalization of Active Appearance Models for Fish Species Identification." ISRN Signal Processing 2011 (June 23, 2011): 1–16. http://dx.doi.org/10.5402/2011/103293.

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In recent years, automatic visual coral reef monitoring has been proposed to solve the demerits of manual monitoring techniques. This paper proposes a novel method to reduce the computational cost of the standard Active Appearance Model (AAM) for automatic fish species identification by using an original multiclass AAM. The main novelty is the normalization of species-specific AAMs using techniques tailored to meet with fish species identification. Shape models associated to species-specific AAMs are automatically normalized by means of linear interpolations and manual correspondences between shapes of different species. It leads to a Unified Active Appearance Model built from species that present characteristic texture patterns. Experiments are carried out on images of fish of four different families. The technique provides correct classification rates up to 92% on 5 species and 84.5% on 12 species and is more than 4 times faster than the standard AAM on 12 species.

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39

Suolan, Liu, Wang Jia, and Sun Changyin. "Targets Association across Multiple Cameras by Learning Transfer Models." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 1 (January 31, 2016): 185–96. http://dx.doi.org/10.14257/ijsip.2016.9.1.17.

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40

Broersen, P. "Selecting the order of autoregressive models from small samples." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 4 (August 1985): 874–79. http://dx.doi.org/10.1109/tassp.1985.1164654.

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41

Lawton, W. "A complete spectral characterization of quarter-plane autoregressive models." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 6 (December 1985): 1617–19. http://dx.doi.org/10.1109/tassp.1985.1164721.

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42

Barnes, C. "State-space realizations of covariance-invariant discrete-time models." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 6 (December 1985): 1603–4. http://dx.doi.org/10.1109/tassp.1985.1164742.

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43

Kopec, G. "Formant tracking using hidden Markov models and vector quantization." IEEE Transactions on Acoustics, Speech, and Signal Processing 34, no. 4 (August 1986): 709–29. http://dx.doi.org/10.1109/tassp.1986.1164908.

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44

Knockaert, L. "An order-recursive algorithm for estimating pole-zero models." IEEE Transactions on Acoustics, Speech, and Signal Processing 35, no. 2 (February 1987): 154–57. http://dx.doi.org/10.1109/tassp.1987.1165112.

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45

Stoica, P., and A. Nehorai. "On stability and root location of linear prediction models." IEEE Transactions on Acoustics, Speech, and Signal Processing 35, no. 4 (April 1987): 582–84. http://dx.doi.org/10.1109/tassp.1987.1165162.

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46

Ali, Walid S. Ibrahim. "Scaling the Evolutionary Models for Signal Processing System Optimization with Applications in Digital Video Processing." Color and Imaging Conference 9, no. 1 (January 1, 2001): 291–97. http://dx.doi.org/10.2352/cic.2001.9.1.art00053.

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47

Ahmad, Zeeshan, and Naimul Khan. "A Survey on Physiological Signal-Based Emotion Recognition." Bioengineering 9, no. 11 (November 14, 2022): 688. http://dx.doi.org/10.3390/bioengineering9110688.

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Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as pre-processing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data pre-processing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally, we discuss key challenges and future directions in this field.

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48

Gaudette, Jason E., and James A. Simmons. "Linear time-invariant (LTI) modeling for aerial and underwater acoustics." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A95. http://dx.doi.org/10.1121/10.0018285.

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Most newcomers to acoustic signal processing understand that linear time-invariant (LTI) filters can remove out-of-band noise from time series signals. What many acoustics researchers may not realize is that LTI models can be applied much more broadly, including to non-linear and time-variant systems. This presentation covers an overview of the autoregressive (AR), moving-average (MA), and autoregressive moving-average (ARMA) family of LTI models and their many useful applications in acoustics. Examples include analytic time-frequency processing of multi-component echolocation signals, fractional-delay filtering for acoustic time series simulations, broadband acoustic array beamforming, adaptive filtering for noise cancelation, and system identification for acoustic equalizers (i.e., flattening the frequency response of a source-receiver pair). This talk serves as a brief tutorial and inspiration for researchers who want to expand their use of signal processing, especially those in the fields of animal bioacoustics, aerial acoustics, and underwater acoustics.

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49

Ashraf A. Ahmad, Mustapha M. Aji, Yusuf Abdulmumin, Ilyasu A. Jae, and Uthman I. Bello-Imokhuede. "Profiling radar signals based of pulse-to-pulse frequency agility." Global Journal of Engineering and Technology Advances 15, no. 2 (May 30, 2023): 141–49. http://dx.doi.org/10.30574/gjeta.2023.15.2.0100.

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It is well known that the application of radar is becoming more and more popular with the development of signal technology progress. Therefore, this paper presents a first-stage process for radar signals analysis involving four different radar signals based on pulse-to-pulse frequency Agility. The radar signals include a normal radar signal (NRS), frequency hopping radar signal (FHRS), 2-frequency shift keying radar signal (2FSKRS), and a combination of frequency hopping radar signal (FHRS) and 2-frequency shift keying radar signal (2FSKRS). The process of modeling and generating the radar signals is presented and thereafter, results on the outcome of this process and their implications are discussed. It is observed from the obtained results of an accurate depiction of key parameters of pulse width (PW) of 1 µs and frequency of 10 MHz of the radar signals among others, that the developed models of the radar signals are feasible for further analysis using robust model signal processing tools such as time-frequency analysis can be used. Hence, these models can be used in practical radar signal analysis such as electronic intelligence (ELINT) and electronic warfare support (ES).

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Tague, John A., and Kerry D. Schutz. "Seismic transient deconvolution with model‐based signal processing." GEOPHYSICS 62, no. 4 (July 1997): 1321–30. http://dx.doi.org/10.1190/1.1444234.

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Abstract:

Short duration seismic disturbances, obscured by earth noise and distorted by the seismometers used to measure them, can be reconstructed using model‐based signal processing. “Model based” means that mathematical models of the seismic transient, earth noise, and seismometer dynamics are infused into the signal processor that estimates the disturbance. The processor imposes no predetermined structure on the transient and the earth noise need not be white. Model‐based processors produce good quality estimates for a broad class of transient waveforms.

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