Дисертації з теми "Timer signals"
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Case, David Robert. "Real-time signal processing of multi-path video signals." Thesis, University of Salford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334170.
Повний текст джерелаPalekar, Trishul Ajit. "Signal optimization at isolated intersections using pre-signals." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4279.
Повний текст джерелаValančius, Valdas. "Garsinio signalo apdorojimo realiame laike įtaisas." Bachelor's thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20120702_130041-11722.
Повний текст джерелаMost modern desktop computers are equipped with audio hardware. This hardware allows audio to be recorded as digital information for storage and later playback. This digital information can be manipulated to change how the audio sounds when played back. But if we don’t have a computer, or just need to get some sound effects quickly without recording audio? Maybe You are a student who are learning sound processing hardware and need some examples of audio synthesis? Do you like an “Arduino” and want to see what it can? This device is for You! This small device based on “Arduino” controller makes audio processing in real time, producing some audio effects and also it can play some synthetic sound. In this device is integrated LCD screen, where you can see some information about what the device is doing, when you have pressed one of some buttons on it. Also it is possible to manage the device over the internet. You need just plug in an Ethernet cable, open an internet browser on the computer in local network and add an IP address of this device. You will get small web page where you will found few buttons. By clicking with mouse on these buttons you can listen to a synthetic sound, which system can produce. In this work You will be introduced to the sound signal processing. You will also find graphs and flowchart detailing the sequence of event between the user and system, the exchange of data inside and the static structure of the system in the architectural specification. In... [to full text]
Ristic, Branko. "Some aspects of signal dependent and higher-order time-frequency and time-scale analysis of non-stationary signals." Thesis, Queensland University of Technology, 1995.
Знайти повний текст джерелаFirla, Marcin. "Automatic signal processing for wind turbine condition monitoring. Time-frequency cropping, kinematic association, and all-sideband demodulation." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT006/document.
Повний текст джерелаThis thesis proposes a three signal-processing methods oriented towards the condition monitoring and diagnosis. In particular the proposed techniques are suited for vibration-based condition monitoring of rotating machinery which works under highly non-stationary operational condition as wind turbines, but it is not limited to such a usage. All the proposed methods are automatic and data-driven algorithms.The first proposed technique enables a selection of the most stationary part of signal by cropping time-frequency representation of the signal.The second method is an algorithm for association of spectral patterns, harmonics and sidebands series, with characteristic frequencies arising from kinematic of a system under inspection. This method features in a unique approach dedicated for rolling-element bearing which enables to overcome difficulties caused by a slippage phenomenon.The third technique is an all-sideband demodulation algorithm. It features in a multi-rate filter and proposes health indicators to facilitate an evaluation of the condition of the investigated system.In this thesis the proposed methods are validated on both, simulated and real-world signals. The presented results show good performance of all the methods
Nguyen, Linh Trung. "Estimation and separation of linear frequency- modulated signals in wireless communications using time - frequency signal processing." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15984/.
Повний текст джерелаNguyen, Linh-Trung. "Estimation and separation of linear frequency- modulated signals in wireless communications using time - frequency signal processing." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/15984/1/Nguyen_Linh-Trung_Thesis.pdf.
Повний текст джерелаAlderson, Robert. "ADHD AND STOP-SIGNAL BEHAVIORAL INHIBITION: IS MEAN REACTION TIME CONTAMINATED BY EXPOSURE TO INTERMITTENT STOP-SIGNALS?" Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2464.
Повний текст джерелаPh.D.
Department of Psychology
Sciences
Psychology PhD
Alderson, Robert Matthew. "ADHD and stop-signal behavioral inhibition is mean reaction time contaminated by exposure to intermittent stop-signals? /." Orlando, Fla. : University of Central Florida, 2008. http://purl.fcla.edu/fcla/etd/CFE0002218.
Повний текст джерелаT, N. Santhosh Kumar, K. Abdul Samad A, and M. Sarojini K. "DSP BASED SIGNAL PROCESSING UNIT FOR REAL TIME PROCESSING OF VIBRATION AND ACOUSTIC SIGNALS OF SATELLITE LAUNCH VEHICLES." International Foundation for Telemetering, 1995. http://hdl.handle.net/10150/608530.
Повний текст джерелаMeasurement of vibration and acoustic signals at various locations in the launch vehicle is important to establish the vibration and acoustic environment encountered by the launch vehicle during flight. The vibration and acoustic signals are wideband and require very large telemetry bandwidth if directly transmitted to ground. The DSP based Signal Processing Unit is designed to measure and analyse acoustic and vibration signals onboard the launch vehicle and transmit the computed spectrum to ground through centralised baseband telemetry system. The analysis techniques employed are power spectral density (PSD) computations using Fast Fourier Transform (FFT) and 1/3rd octave analysis using digital Infinite Impulse Response (IIR) filters. The programmability of all analysis parameters is achieved using EEPROM. This paper discusses the details of measurement and analysis techniques, design philosophy, tools used and implementation schemes. The paper also presents the performance results of flight models.
Kollin, Lori. "Comparing the Effects of Static and Dynamic Signals during Multiple Schedules." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6882.
Повний текст джерелаJayaraman, 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.
Повний текст джерелаBartůšek, Jan. "Time Frequency Analysis of ERP Signals." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2007. http://www.nusl.cz/ntk/nusl-412769.
Повний текст джерелаYeung, W. K. "Microwave imaging with impulsive signals." Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382334.
Повний текст джерелаRowsell, Timothy Dominic. "Real-time analysis of fetal phonography signals." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308245.
Повний текст джерелаBikdash, Marwan. "Analysis and filtering of time-varying signals." Thesis, Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/80015.
Повний текст джерелаMaster of Science
Sridharan, Kousik Sarathy. "Real-time acquisition and analysis ofElectro-oculography signals." Thesis, Linköpings universitet, Biomedicinsk instrumentteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-76734.
Повний текст джерелаGranados, Omar D. "Space-Time Coding for Polynomial Phase Modulated Signals." FIU Digital Commons, 2011. http://digitalcommons.fiu.edu/etd/378.
Повний текст джерелаAHMAD, FAHEEM, and PRAMOD KAKKERLA. "FOCUSING OF UWB RADAR SIGNALS USING TIME REVERSAL." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-15500.
Повний текст джерелаVelasco, Solano Carlos Hernando. "ARMA modeling of signals in the time domain." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23820.
Повний текст джерелаThis thesis develops an iterative algorithm for the design of ARMA models of signals in the time domain. The algorithm is based on optimization techniques, particularly a gradient technique known as the restricted step method is used. The new algorithm is called the iterative Prony method, and the results obtained using this new method are compared to those obtained using the iterative prefiltering algorithm. The thesis shows that the performance of the iterative Prony method is in most of the cases comparable or superior to that of the iterative prefiltering algorithm.
Okamura, Shuhei. "The Short Time Fourier Transform and Local Signals." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/58.
Повний текст джерелаIves, David Timothy. "Audibility of time-shifted signals using auditory modelling." Thesis, University of Brighton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323526.
Повний текст джерелаAl-Jajjoka, Sam Nooh K. "Time domain threshold crossing for signals in noise." Thesis, University of Hull, 1995. http://hydra.hull.ac.uk/resources/hull:11535.
Повний текст джерелаZhang, Liyang. "Real-time Coding for Kinesthetic and Tactile Signals." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-196557.
Повний текст джерелаBertoncini, Crystal Ann. "Applications of pattern classification to time-domain signals." W&M ScholarWorks, 2010. https://scholarworks.wm.edu/etd/1539623559.
Повний текст джерелаAbdelfatah, Akmal Saad. "Time-dependent signal control and system optimal traffic assignment in congested vehicular traffic networks /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.
Повний текст джерелаYang, Zhenghong. "Joint time frequency analysis of Global Positioning System (GPS) multipath signals." Ohio : Ohio University, 1998. http://www.ohiolink.edu/etd/view.cgi?ohiou1176234303.
Повний текст джерелаBousfield, Bruce M. "Real time aero engine signal analysis." Thesis, Loughborough University, 1988. https://dspace.lboro.ac.uk/2134/10425.
Повний текст джерелаMessaoud, Safa. "Translating Discrete Time SIMULINK to SIGNAL." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/49299.
Повний текст джерелаMaster of Science
Saghafi, Abolfazl. "Real-time Classification of Biomedical Signals, Parkinson’s Analytical Model". Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6946.
Повний текст джерелаThomson, Ty M., and Drew Endy. "Rapid Characterization of Cellular Pathways Using Time-Varying Signals." International Conference on Systems Biology, 2005. http://hdl.handle.net/1721.1/29803.
Повний текст джерелаPoster presented at the 2005 ICSB meeting, held at Harvard Medical School in Boston, MA.
Niethammer, Marc. "Application of time frequency representations to characterize ultrasonic signals." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/19005.
Повний текст джерелаOlhese, Sofia Charlotta. "Analysis via time, frequency and scale of nonstationary signals." Thesis, Imperial College London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401707.
Повний текст джерелаNguyen, Yen Thi Hong. "Time-frequency distributions : approaches for incomplete non-stationary signals." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/19681/.
Повний текст джерелаJonas, Eric Michael. "Real-time analog acquisition of electrophysiological signals with Soma." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/61306.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (p. 71-72).
Soma is a high-density recording system for real-time acquisition and analysis of extracellular electrophysiological signals. Here I describe the design, implementation, and evaluation of the Soma Acquisition Board, an 8-channel low-latency amplifier for amplification and digitization of these signals. Design trade-offs are discussed, and the resulting analog performance is quantified.
by Eric Michael Jonas.
M.Eng.
Nunes, Neuza Filipa Martins. "Algorithms for time series clustering applied to biomedical signals." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/5666.
Повний текст джерелаThe increasing number of biomedical systems and applications for human body understanding creates a need for information extraction tools to use in biosignals. It’s important to comprehend the changes in the biosignal’s morphology over time, as they often contain critical information on the condition of the subject or the status of the experiment. The creation of tools that automatically analyze and extract relevant attributes from biosignals, providing important information to the user, has a significant value in the biosignal’s processing field. The present dissertation introduces new algorithms for time series clustering, where we are able to separate and organize unlabeled data into different groups whose signals are similar to each other. Signal processing algorithms were developed for the detection of a meanwave, which represents the signal’s morphology and behavior. The algorithm designed computes the meanwave by separating and averaging all cycles of a cyclic continuous signal. To increase the quality of information given by the meanwave, a set of wave-alignment techniques was also developed and its relevance was evaluated in a real database. To evaluate our algorithm’s applicability in time series clustering, a distance metric created with the information of the automatic meanwave was designed and its measurements were given as input to a K-Means clustering algorithm. With that purpose, we collected a series of data with two different modes in it. The produced algorithm successfully separates two modes in the collected data with 99.3% of efficiency. The results of this clustering procedure were compared to a mechanism widely used in this area, which models the data and uses the distance between its cepstral coefficients to measure the similarity between the time series.The algorithms were also validated in different study projects. These projects show the variety of contexts in which our algorithms have high applicability and are suitable answers to overcome the problems of exhaustive signal analysis and expert intervention. The algorithms produced are signal-independent, and therefore can be applied to any type of signal providing it is a cyclic signal. The fact that this approach doesn’t require any prior information and the preliminary good performance make these algorithms powerful tools for biosignals analysis and classification.
Fan, Lingke. "Spectral and time-frequency analysis of ultrasonic Doppler signals." Thesis, University of Leicester, 1994. http://hdl.handle.net/2381/34342.
Повний текст джерелаSivaramakrishnan, Kamakshi. "Universal schemes for denoising discrete-time continuous-amplitude signals /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
Повний текст джерелаBrandman, Onn. "Feedback loops shape cellular signals in space and time /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
Повний текст джерелаXi-Hua, Li. "A Real-time Counting-measuring Method for PPM(PPK) Signals." International Foundation for Telemetering, 1991. http://hdl.handle.net/10150/612915.
Повний текст джерелаOn the disscussion of custom real-time counting-measuring method, this paper presents a new method suitable for the working condition of non-man duty, which possesses the feature of 100ns counting-measuring accuracy and high fidelity. In addition, the concept of “Signal-time/digit converter” is proposed for the first time and the principle and working procedure of this method are introduced in brief.
Cho, Hanseon. "Preemption strategy for traffic signals at intersections near highway-railroad grade crossings." Texas A&M University, 2003. http://hdl.handle.net/1969.1/406.
Повний текст джерелаAlmutairi, Omar Eid. "Effect of Traffic Signal Countdown Timers and Speed and Red-Light Cameras on Operation and Safety." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543840523279126.
Повний текст джерелаMcLean, Roger, Niles Walker, and William Slivkoff. "HIGH PERFORMANCE SATELLITE RANGING TECHNIQUE UTILIZING A FLEXIBLE RANGING SIGNAL WAVEFORM." International Foundation for Telemetering, 2000. http://hdl.handle.net/10150/607705.
Повний текст джерелаRange to an orbiting satellite from a ground reference point (ground station) can be determined by measuring the round trip time for a waveform transmitted to the satellite and returned to the ground station (Turnaround Ranging) and more recently by using the Global Positioning System (GPS). This paper first summarizes and compares the two approaches. The paper then describes and analyzes a new turn-around ranging system which uses a flexible ranging waveform that provides spectral compatibility with existing Military, NASA, and Commercial satellite uplink/downlink signals.
Meynard, Adrien. "Stationnarités brisées : approches à l'analyse et à la synthèse." Thesis, Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0475.
Повний текст джерелаNonstationarity characterizes transient physical phenomena. For example, it may be caused by a speed variation of an accelerating engine. Similarly, because of the Doppler effect, a stationary sound emitted by a moving source is perceived as being nonstationary by a motionless observer. These examples lead us to consider a class of nonstationary signals formed from stationary signals whose stationarity has been broken by a physically relevant deformation operator. After describing the considered deformation models (chapter 1), we present different methods that extend the spectral analysis and synthesis to such signals. The spectral estimation amounts to determining simultaneously the spectrum of the underlying stationary process and the deformation breaking its stationarity. To this end, we consider representations of the signal in which this deformation is characterized by a simple operation. Thus, in chapter 2, we are interested in the analysis of locally deformed signals. The deformation describing these signals is simply expressed as a displacement of the wavelet coefficients in the time-scale domain. We take advantage of this property to develop a method for the estimation of these displacements. Then, we propose an instantaneous spectrum estimation algorithm, named JEFAS. In chapter 3, we extend this spectral analysis to multi-sensor signals where the deformation operator takes a matrix form. This is a doubly nonstationary blind source separation problem. In chapter 4, we propose a synthesis approach to study locally deformed signals. Finally, in chapter 5, we construct a time-frequency representation adapted to the description of locally harmonic signals
Kouteas, Stefanos D. "Time delay estimation for underwater signals and application to localization." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA393232.
Повний текст джерелаThesis advisors, Charles W. Therrien, Kevin B. Smith. Includes bibliographical references (p. 161-163). Also Available online.
Zhao, Rui. "A public key-based encryptiondecryption technique for real-time signals." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27944.
Повний текст джерелаMarkle, Blake L. "A comparative study of time-stretching algorithms for audio signals /." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=31119.
Повний текст джерелаArroyo, Palacios Jorge. "Real-time emotion recognition, adaptation and interaction using physiological signals." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.548646.
Повний текст джерелаWanfang, Zhang. "THE TIME DIVISION MULTIPLEX MEASURING SYSTEM FOR SINGLE-TRANSIENT SIGNALS." International Foundation for Telemetering, 1993. http://hdl.handle.net/10150/608868.
Повний текст джерелаIn order to reduce the measuring channels for the single-transient signals, the author propose the time division multiplex technique and introduce the method of SAW delay line in this paper. That used method of SAW tap-delay line in this system is different from previous methods consists in making traditional method, which is one-path signal input different delayed multi- path signals output, alter new method, which is simultaneous multi-path signal inputs that are respectively delayed and one-path signal serial output.
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.
Повний текст джерелаAutomated 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.