Relevant bibliographies by topics / Signal processing. Time-series analysis

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Academic literature on the topic 'Signal processing. Time-series analysis'

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Published: 4 June 2021

Last updated: 16 February 2022

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Journal articles on the topic "Signal processing. Time-series analysis"

Manolakis, Dimitris, Nicholas Bosowski, and Vinay K. Ingle. "Count Time-Series Analysis: A Signal Processing Perspective." IEEE Signal Processing Magazine 36, no. 3 (2019): 64–81. http://dx.doi.org/10.1109/msp.2018.2885853.

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Wang, H. "Recursive estimation and time-series analysis." IEEE Transactions on Acoustics, Speech, and Signal Processing 34, no. 6 (1986): 1678. http://dx.doi.org/10.1109/tassp.1986.1164982.

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Lai, Ying-Cheng, and Nong Ye. "Recent Developments in Chaotic Time Series Analysis." International Journal of Bifurcation and Chaos 13, no. 06 (2003): 1383–422. http://dx.doi.org/10.1142/s0218127403007308.

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In this paper, two issues are addressed: (1) the applicability of the delay-coordinate embedding method to transient chaotic time series analysis, and (2) the Hilbert transform methodology for chaotic signal processing.A common practice in chaotic time series analysis has been to reconstruct the phase space by utilizing the delay-coordinate embedding technique, and then to compute dynamical invariant quantities of interest such as unstable periodic orbits, the fractal dimension of the underlying chaotic set, and its Lyapunov spectrum. As a large body of literature exists on applying the technique to time series from chaotic attractors, a relatively unexplored issue is its applicability to dynamical systems that exhibit transient chaos. Our focus will be on the analysis of transient chaotic time series. We will argue and provide numerical support that the current delay-coordinate embedding techniques for extracting unstable periodic orbits, for estimating the fractal dimension, and for computing the Lyapunov exponents can be readily adapted to transient chaotic time series.A technique that is gaining an increasing attention is the Hilbert transform method for signal processing in nonlinear systems. The general goal of the Hilbert method is to assess the spectrum of the instantaneous frequency associated with the underlying dynamical process. To obtain physically meaningful results, it is necessary for the signal to possess a proper rotational structure in the complex plane of the analytic signal constructed by the original signal and its Hilbert transform. We will describe a recent decomposition procedure for this task and apply the technique to chaotic signals. We will also provide an example to demonstrate that the methodology can be useful for addressing some fundamental problems in chaotic dynamics.

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Broersen, Piet M. T. "Spectral Analysis of Irregularly Sampled Data with Time Series Models." Open Signal Processing Journal 1, no. 1 (2009): 7–14. http://dx.doi.org/10.2174/1876825300801010007.

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Xian-Da Zhang and H. Takeda. "An approach to time series analysis and ARMA spectral estimation." IEEE Transactions on Acoustics, Speech, and Signal Processing 35, no. 9 (1987): 1303–13. http://dx.doi.org/10.1109/tassp.1987.1165272.

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Singh, Omkar. "Physiological Time Series Processing via Empirical Wavelet Transform." Advanced Science, Engineering and Medicine 12, no. 5 (2020): 582–87. http://dx.doi.org/10.1166/asem.2020.2557.

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This paper presents the efficacy of empirical wavelet transform (EWT) for physiological time series processing. At first, EWT is applied to multivariate heterogeneous physiological time series. Secondly, EWT is used for the removal of fast temporal scales in multiscale entropy analysis. Empirical mode decomposition is an adaptive data analysis method in the sense that it does not require prior information about the signal statistics and tend to decompose a signal into various constituent modes. The utility of Standard EMD algorithm is however limited to single channel data as it suffers from the problems of mode alignment and mode mixing when applied channel wise for multivariate data. The standard EMD algorithm was extended to multivariate Empirical mode decomposition (MEMD) that can be used analyze a multivariate data. The MEMD can only be applied to multivariate data in which all the channels have equal data length. EWT is another adaptive technique for mode extraction in a signal using empirical scaling and wavelet functions. The multiscale entropy (MSE) algorithm is generally used to quantify the complexity of a time series. The original MSE approach utilizes a coarse-graining process for the removal of fast temporal scales in a time series which is equivalent to applying a finite impulse response (FIR) moving average filter. In Refined Multiscale entropy (RMSE), the FIR filter was replaced with a low pass Butterworth filter which exhibits a better frequency response than that of a FIR filter. In this paper we have presented a new approach for the removal of fast temporal scales based on empirical wavelet transform. The empirical wavelet transform is also used as an innovative filtering approach in multiscale entropy analysis.

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Fedjajevs, Andrejs, Willemijn Groenendaal, Carlos Agell, and Evelien Hermeling. "Platform for Analysis and Labeling of Medical Time Series." Sensors 20, no. 24 (2020): 7302. http://dx.doi.org/10.3390/s20247302.

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Reliable and diverse labeled reference data are essential for the development of high-quality processing algorithms for medical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Here, we present the Platform for Analysis and Labeling of Medical time Series (PALMS) designed in Python. Its graphical user interface (GUI) facilitates three main types of manual annotations—(1) fiducials, e.g., R-peaks of ECG; (2) events with an adjustable duration, e.g., arrhythmic episodes; and (3) signal quality, e.g., data parts corrupted by motion artifacts. All annotations can be attributed to the same signal simultaneously in an ergonomic and user-friendly manner. Configuration for different data and annotation types is straightforward and flexible in order to use a wide range of data sources and to address many different use cases. Above all, configuration of PALMS allows plugging-in existing algorithms to display outcomes of automated processing, such as automatic R-peak detection, and to manually correct them where needed. This enables fast annotation and can be used to further improve algorithms. The GUI is currently complemented by ECG and PPG algorithms that detect characteristic points with high accuracy. The ECG algorithm reached 99% on the MIT/BIH arrhythmia database. The PPG algorithm was validated on two public databases with an F1-score above 98%. The GUI and optional algorithms result in an advanced software tool that allows the creation of diverse reference sets for existing datasets.

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Liang, S. Y., and D. A. Dornfeld. "Tool Wear Detection Using Time Series Analysis of Acoustic Emission." Journal of Engineering for Industry 111, no. 3 (1989): 199–205. http://dx.doi.org/10.1115/1.3188750.

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This paper discusses the monitoring of cutting tool wear based on time series analysis of acoustic emission signals. In cutting operations, acoustic emission provides useful information concerning the tool wear condition because of the fundamental differences between its source mechanisms in the rubbing friction on the wear land and the dislocation action in the shear zones. In this study, a signal processing scheme is developed which uses an autoregressive time-series to model the acoustic emission generated during cutting. The modeling scheme is implemented with a stochastic gradient algorithm to update the model parameters adoptively and is thus a suitable candidate for in-process sensing applications. This technique encodes the acoustic emission signal features into a time varying model parameter vector. Experiments indicate that the parameter vector ignores the change of cutting parameters, but shows a strong sensitivity to the progress of cutting tool wear. This result suggests that tool wear detection can be achieved by monitoring the evolution of the model parameter vector during machining processes.

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Shah, Nauman, and Stephen J. Roberts. "Dynamically Measuring Statistical Dependencies in Multivariate Financial Time Series Using Independent Component Analysis." ISRN Signal Processing 2013 (June 2, 2013): 1–14. http://dx.doi.org/10.1155/2013/434832.

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We present a computationally tractable approach to dynamically measure statistical dependencies in multivariate non-Gaussian signals. The approach makes use of extensions of independent component analysis to calculate information coupling, as a proxy measure for mutual information, between multiple signals and can be used to estimate uncertainty associated with the information coupling measure in a straightforward way. We empirically validate relative accuracy of the information coupling measure using a set of synthetic data examples and showcase practical utility of using the measure when analysing multivariate financial time series.

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ZHANG, ZHIHUA, JOHN C. MOORE, and ASLAK GRINSTED. "HAAR WAVELET ANALYSIS OF CLIMATIC TIME SERIES." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 02 (2014): 1450020. http://dx.doi.org/10.1142/s0219691314500209.

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In order to extract the intrinsic information of climatic time series from background red noise, in this paper, we will first give an analytic formula on the distribution of Haar wavelet power spectra of red noise in a rigorous statistical framework. After that, by comparing the difference of wavelet power spectra of real climatic time series and red noise, we can extract intrinsic features of climatic time series. Finally, we use our method to analyze Arctic Oscillation (AO) which is a key aspect of climate variability in the Northern Hemisphere, and discover a great change in fundamental properties of the AO, commonly called a regime shift or tripping point.

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Dissertations / Theses on the topic "Signal processing. Time-series analysis"

Morrill, Jeffrey P., and Jonathan Delatizky. "REAL-TIME RECOGNITION OF TIME-SERIES PATTERNS." International Foundation for Telemetering, 1993. http://hdl.handle.net/10150/608854.

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International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada This paper describes a real-time implementation of the pattern recognition technology originally developed by BBN [Delatizky et al] for post-processing of time-sampled telemetry data. This makes it possible to monitor a data stream for a characteristic shape, such as an arrhythmic heartbeat or a step-response whose overshoot is unacceptably large. Once programmed to recognize patterns of interest, it generates a symbolic description of a time-series signal in intuitive, object-oriented terms. The basic technique is to decompose the signal into a hierarchy of simpler components using rules of grammar, analogous to the process of decomposing a sentence into phrases and words. This paper describes the basic technique used for pattern recognition of time-series signals and the problems that must be solved to apply the techniques in real time. We present experimental results for an unoptimized prototype demonstrating that 4000 samples per second can be handled easily on conventional hardware.

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Jiang, Wei. "Signal processing strategies for ground-penetrating radar." Thesis, University of Bath, 2011. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538111.

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Interpretation of ground penetrating radar (GPR) signals can be a key point in the overall operability of a GPR system. In stepped-frequency and Frequency-Modulated Continuous-Wave (FMCW)GPR systems in particular, the target or object of interest is often located by analysis of Fast Fourier Transform (FFT) derived data. Increasing the GPR system bandwidth can improve resolution, but at the cost of reduced penetrating depth. The challenge is to develop high-resolution signal processing strategies for GPR.A number of Fourier based methods are investigated. However, the main response over a target's position can make it difficult to recognise closely spaced targets. The Least-Suare method is found to be the best autoregression-based estimator. However the method requires high Signal-to-Noise ratio to achieve high- resolution. Furthermore a number of subspace-based methods are investigated. Although the MUItiple Signal Classification (MUSIC) method can theoretically offer infinite resolution, they must be seeded with the number of targets actually present. A superimposed MUSIC technique is proposed to suppress false targets. A novel windowed MUSIC (W-MUSIC) algorithm is developed, and it offers high resolution while still able to minimise spurious responses. Since the performance of any FMCW GPR is critically linked to the linearity of the sweep frequency, the non-linearity in the target range estimation is studied. A Novel Short-Time MUSIC method is proposed and higher time and frequency resolution is achieved than the conventional Short-Time Fourier Transform method. In addition a modified Adaptive Sampling method is proposed to solve the non-linear problem by utilising a reference channel in a GPR system.

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Terwilleger, Erin. "Multidimensional time-frequency analysis /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052223.

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Yerramothu, Madhu Kishore. "Stochastic Gaussian and non-Gaussian signal modeling." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Santos, Rui Pedro Silvestre dos. "Time series morphological analysis applied to biomedical signals events detection." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/10227.

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Dissertation submitted in the fufillment of the requirements for the Degree of Master in Biomedical Engineering Automated techniques for biosignal data acquisition and analysis have become increasingly powerful, particularly at the Biomedical Engineering research field. Nevertheless, it is verified the need to improve tools for signal pattern recognition and classification systems, in which the detection of specific events and the automatic signal segmentation are preliminary processing steps. The present dissertation introduces a signal-independent algorithm, which detects significant events in a biosignal. From a time series morphological analysis, the algorithm computes the instants when the most significant standard deviation discontinuities occur, segmenting the signal. An iterative optimization step is then applied. This assures that a minimal error is achieved when modeling these segments with polynomial regressions. The adjustment of a scale factor gives different detail levels of events detection. An accurate and objective algorithm performance evaluation procedure was designed. When applied on a set of synthetic signals, with known and quantitatively predefined events, an overall mean error of 20 samples between the detected and the actual events showed the high accuracy of the proposed algorithm. Its ability to perform the detection of signal activation onsets and transient waveshapes was also assessed, resulting in higher reliability than signal-specific standard methods. Some case studies, with signal processing requirements for which the developed algorithm can be suitably applied, were approached. The algorithm implementation in real-time, as part of an application developed during this research work, is also reported. The proposed algorithm detects significant signal events with accuracy and significant noise immunity. Its versatile design allows the application in different signals without previous knowledge on their statistical properties or specific preprocessing steps. It also brings added objectivity when compared with the exhaustive and time-consuming examiner analysis. The tool introduced in this dissertation represents a relevant contribution in events detection, a particularly important issue within the wide digital biosignal processing research field.

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Pentaris, Fragkiskos. "Digital signal processing for structural health monitoring of buildings." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/10560.

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Structural health monitoring (SHM) systems is a relatively new discipline, studying the structural condition of buildings and other constructions. Current SHM systems are either wired or wireless, with a relatively high cost and low accuracy. This thesis exploits a blend of digital signal processing methodologies, for structural health monitoring (SHM) and develops a wireless SHM system in order to provide a low cost implementation yet reliable and robust. Existing technologies of wired and wireless sensor network platforms with high sensitivity accelerometers are combined, in order to create a system for monitoring the structural characteristics of buildings very economically and functionally, so that it can be easily implemented at low cost in buildings. Well-known and established statistical time series methods are applied to SHM data collected from real concrete structures subjected to earthquake excitation and their strong and weak points are investigated. The necessity to combine parametric and non-parametric approaches is justified and to this direction novel and improved digital signal processing techniques and indexes are applied to vibration data recordings, in order to eliminate noise and reveal structural properties and characteristics of the buildings under study, that deteriorate due to environmental, seismic or anthropogenic impact. A characteristic and potential harming specific case study is presented, where consequences to structures due to a strong earthquake of magnitude 6.4 M are investigated. Furthermore, is introduced a seismic influence profile of the buildings under study related to the seismic sources that exist in the broad region of study.

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wang, xiaoni. "A STUDY OF EQUATORIAL IONOPSHERIC VARIABILITY USING SIGNAL PROCESSING TECHNIQUES." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2415.

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The dependence of equatorial ionosphere on solar irradiances and geomagnetic activity are studied in this dissertation using signal processing techniques. The statistical time series, digital signal processing and wavelet methods are applied to study the ionospheric variations. The ionospheric data used are the Total Electron Content (TEC) and the critical frequency of the F2 layer (foF2). Solar irradiance data are from recent satellites, the Student Nitric Oxide Explorer (SNOE) satellite and the Thermosphere Ionosphere Mesosphere Energetics Dynamics (TIMED) satellite. The Disturbance Storm-Time (Dst) index is used as a proxy of geomagnetic activity in the equatorial region. The results are summarized as follows. (1) In the short-term variations < 27-days, the previous three days solar irradiances have significant correlation with the present day ionospheric data using TEC, which may contribute 18% of the total variations in the TEC. The 3-day delay between solar irradiances and TEC suggests the effects of neutral densities on the ionosphere. The correlations between solar irradiances and TEC are significantly higher than those using the F10.7 flux, a conventional proxy for short wavelength band of solar irradiances. (2) For variations < 27 days, solar soft X-rays show similar or higher correlations with the ionosphere electron densities than the Extreme Ultraviolet (EUV). The correlations between solar irradiances and foF2 decrease from morning (0.5) to the afternoon (0.1). (3) Geomagnetic activity plays an important role in the ionosphere in short-term variations < 10 days. The average correlation between TEC and Dst is 0.4 at 2-3, 3-5, 5-9 and 9-11 day scales, which is higher than those between foF2 and Dst. The correlations between TEC and Dst increase from morning to afternoon. The moderate/quiet geomagnetic activity plays a distinct role in these short-term variations of the ionosphere (~0.3 correlation). Ph.D. School of Electrical Engineering and Computer Science Engineering and Computer Science Electrical Engineering PhD

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Kwong, Siu-shing. "Detection of determinism of nonlinear time series with application to epileptic electroencephalogram analysis." View the Table of Contents & Abstract, 2005. http://sunzi.lib.hku.hk/hkuto/record/B35512222.

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Kirsch, Matthew Robert. "Signal Processing Algorithms for Analysis of Categorical and Numerical Time Series: Application to Sleep Study Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1278606480.

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Jayaraman, Vinoth, Sivakumaran Sivalingam, and Sangeetha Munian. "Analysis of Real Time EEG Signals." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-34164.

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The recent evolution in multidisciplinary fields of Engineering, neuroscience, microelectronics, bioengineering and neurophysiology have reduced the gap between human and machine intelligence. Many methods and algorithms have been developed for analysis and classification of bio signals, 1 or 2-dimensional, in time or frequency distribution. The integration of signal processing with the electronic devices serves as a major root for the development of various biomedical applications. There are many ongoing research in this area to constantly improvise and build an efficient human- robotic system. Electroencephalography (EEG) technology is an efficient way of recording electrical activity of the brain. The advancement of EEG technology in biomedical application helps in diagnosing various brain disorders as tumors, seizures, Alzheimer’s disease, epilepsy and other malfunctions in human brain. The main objective of our thesis deals with acquiring and pre-processing of real time EEG signals using a single dry electrode placed on the forehead. The raw EEG signals are transmitted in a wireless mode (Bluetooth) to the local acquisition server and stored in the computer. Various machine learning techniques are preferred to classify EEG signals precisely. Different algorithms are built for analysing various signal processing techniques to process the signals. These results can be further used for the development of better Brain-computer interface systems.

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Books on the topic "Signal processing. Time-series analysis"

Foundations of digital signal processing and data analysis. Macmillan, 1987.

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Cohen, Leon. Time-frequency analysis. Prentice Hall PTR, 1995.

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A handbook of time-series analysis, signal processing and dynamics. Academic, 1999.

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Cédric, Demeure, ed. Statistical signal processing: Detection, estimation, and time series analysis. Addison-Wesley Pub. Co., 1991.

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P, Petropulu Athina, ed. Higher-order spectra analysis: A nonlinear signal processing framework. PTR Prentice Hall, 1993.

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Nikias, Chrysostomos L. Higher-order spectral analysis: A nonlinear signal processing framework. Prentice Hall, 1993.

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Qian, Shie. Joint time-frequency analysis: Methods and applications. PTR Prentice Hall, 1996.

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IEEE-SP, International Symposium on Time-Frequency and Time-Scale Analysis (1992 Victoria B. C. ). Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, October 4-6, 1992, Victoria, BC, Canada. Institute of Electrical and Electronics Engineers, 1992.

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IEEE-SP, International Symposium on Time-Frequency and Time-Scale Analysis (1994 Philadelphia Pa ). Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, October 25-28, 1994, Philadelphia, Pennsylvania, USA. Institute of Electrical and Electronics Engineers, 1994.

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Miloš, Daković, and Thayaparan Thayannathan, eds. Time-frequency signal analysis with applications. Artech House, 2013.

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Book chapters on the topic "Signal processing. Time-series analysis"

Ouyang, Gaoxiang, and Xiaoli Li. "Order Time Series Analysis of Neural Signals." In Signal Processing in Neuroscience. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1822-0_6.

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Signorini, Maria Gabriella, and Manuela Ferrario. "Nonlinear Analysis of Experimental Time Series." In Advanced Methods of Biomedical Signal Processing. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118007747.ch14.

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Hlawatsch, Franz, Georg Tauböck, and Teresa Twaroch. "Covariant Time-Frequency Analysis." In Wavelets and Signal Processing. Birkhäuser Boston, 2003. http://dx.doi.org/10.1007/978-1-4612-0025-3_7.

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Cohen Tenoudji, Frédéric. "Time–Frequency Analysis." In Modern Acoustics and Signal Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42382-1_12.

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Feng, Yining, Baoqing Ding, Harry Graber, and Ivan Selesnick. "Transient Artifacts Suppression in Time Series via Convex Analysis." In Signal Processing in Medicine and Biology. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36844-9_4.

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Klevecz, Robert R., Caroline M. Li, and James L. Bolen. "Signal Processing and the Design of Microarray Time-Series Experiments." In Microarray Data Analysis. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-390-5_4.

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Sundararajan, D. "Fourier Series." In Fourier Analysis—A Signal Processing Approach. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1693-7_7.

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Cohen, Leon. "The Wavelet Transform and Time-Frequency Analysis." In Wavelets and Signal Processing. Birkhäuser Boston, 2003. http://dx.doi.org/10.1007/978-1-4612-0025-3_1.

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Wegman, Edward J., and Chris Shull. "A Graphical Tool for Distribution and Correlation Analysis of Multiple Time Series." In Topics in Non-Gaussian Signal Processing. Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-8859-3_5.

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Giron-Sierra, Jose Maria. "Time-Frequency Analysis." In Digital Signal Processing with Matlab Examples, Volume 1. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2534-1_7.

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Conference papers on the topic "Signal processing. Time-series analysis"

Kumar, S. A. Pavan, and P. K. Bora. "Time series analysis and signal processing." In 2012 2nd National Conference on Computational Intelligence and Signal Processing (CISP). IEEE, 2012. http://dx.doi.org/10.1109/nccisp.2012.6189672.

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Rodrigues, Pedro Luiz Coelho, Marco Congedo, and Christian Jutten. "Multivariate Time-Series Analysis Via Manifold Learning." In 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2018. http://dx.doi.org/10.1109/ssp.2018.8450771.

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Zhang, Xiao-yong, and Lai-yuan Luo. "Self-similarity analysis of time series." In 2012 11th International Conference on Signal Processing (ICSP 2012). IEEE, 2012. http://dx.doi.org/10.1109/icosp.2012.6491987.

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Ray, Priyadip, and Lawrence Carin. "Nonparametric Bayesian factor analysis of multiple time series." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967742.

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Jánosi, Imre M., and T. Tél. "Time series analysis of transient chaos: Theory and experiment." In Chaotic, fractal, and nonlinear signal processing. AIP, 1996. http://dx.doi.org/10.1063/1.51027.

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Downes, P. "Application of chaotic time series analysis to signal characterization." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150118.

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Nouira, Kaouther, and Abdelwahed Trabelsi. "Time Series Analysis and Outlier Detection in Intensive Care Data." In 2006 8th international Conference on Signal Processing. IEEE, 2006. http://dx.doi.org/10.1109/icosp.2006.345946.

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Passalis, N., M. Kirtas, G. Mourgias-Alexandris, G. Dabos, N. Pleros, and A. Tefas. "Training Noise-Resilient Recurrent Photonic Networks for Financial Time Series Analysis." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287649.

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Sokmen, Zerrin, Volkan Atalay, and Rengul Cetin Atalay. "Short time series microarray data analysis and biological annotation." In 2008 IEEE 16th Signal Processing, Communication and Applications Conference (SIU). IEEE, 2008. http://dx.doi.org/10.1109/siu.2008.4632669.

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Abbas, H. M. "Time series analysis for ECG data compression." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.756278.

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Reports on the topic "Signal processing. Time-series analysis"

Casey, Stephen D. New Techniques in Time-Frequency Analysis: Adaptive Band, Ultra-Wide Band and Multi-Rate Signal Processing. Defense Technical Information Center, 2016. http://dx.doi.org/10.21236/ad1005007.

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