Dissertations / Theses on the topic 'Timer signals'
To see the other types of publications on this topic, follow the link: Timer signals.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Timer signals.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
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.
Full text
2
Palekar, Trishul Ajit. "Signal optimization at isolated intersections using pre-signals." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4279.
Full textAbstract:
This research proposes a new signal operation strategy aimed at efficient utilization of
green time by cutting down on the start up and response loss times. The idea is to have a
"pre-signal" on each main approach a few hundred feet upstream of the intersection in
addition to the main intersection signal, which is coordinated with the pre-signal. The
offset between the main and pre-signal ensures that the majority of start up losses does
not occur at the main signal. The benefits of the system under various traffic conditions
were evaluated based on analysis of the queue discharge process and Corridor
Simulation (CORSIM) study. The proposed measure should reduce the travel time and
total control delay for the signalized network.
To attain the objective the following two studies were undertaken:
1. Development of a queue discharge model to investigate the expected benefits of
the system.
2. Simulation of the system: In the second part of the research, the proposed
strategy was tested using CORSIM to evaluate its performance vis-ÃÂ -vis the
baseline case.
The queue discharge model (QDM) was found to be linear in nature in contrast to prior
expectations. The model was used to quantify the benefits obtained from the pre-signal
system. The result of this analysis indicated that the proposed strategy would yield
significant travel time savings and reductions in total control delay. In addition to the QDM analysis, CORSIM simulations were used to code various
hypothetical scenarios to test the concept under various constraints and limitations. As
per expectations, it was found that the system was beneficial for high demand levels and
longer offsets. The upper limit on offsets was determined by visual observation of
platoon dispersion and therefore the maximum offset distance was restricted to 450 feet.
For scenarios where split phasing was used, the break even point in terms of demand
level was found to be 2500 vph on a three lane approach, whereas that for a lag-lag type
of phasing strategy was found to be 1800 vph, also on a three lane approach.
3
Valančius, 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.
Full textAbstract:
Šio darbo tikslas suprojektuoti ir sukurti nesudėtingą garsinio signalo apdorojimo realiu laiku įtaisą, populiaraus „Arduino“ mikrovaldiklio pagrindu, kuriuo būtų lengva naudotis ir būtų galimybė pagrindines funkcijas valdyti naudojantis TC/IP protokolu. „Arduino“ mikrovaldiklis pasirinktas dėl jo populiarumo, prieinamos kainos ir dėl gausios informacijos, apie jo panaudojimo galimybes. Be abejo yra daug ir kitų platformų skirtų įvairių prietaisų kūrimui su pakankamai išsamia informacija, bet „Arduino“ platformai yra sukurta nemokama atviro kodo programinė įranga, „Arduino“ kontrolerio programavimui tiesiogiai iš personalinio kompiuterio, nenaudojant papildomų priemonių, dirbanti su Windows ir Unix operacinėmis sistemomis.Most 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]
4
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.
Find full text
5
Firla, 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.
Full textAbstract:
Cette thèse propose trois méthodes de traitement du signal orientées vers la surveillance d’état et le diagnostic. Les techniques proposées sont surtout adaptées pour la surveillance d’état, effectuée à la base de vibrations, des machines tournantes qui fonctionnent dans des conditions d’opération non-stationnaires comme par exemple les éoliennes mais elles ne sont pas limitées à un tel usage. Toutes les méthodes proposées sont des algorithmes automatiques et gérés par les données.La première technique proposée permet de sélectionner la partie la plus stationnaire d’un signal en cadrant la représentation temps-fréquence d’un signal.La deuxième méthode est un algorithme pour l’association des dispositions spectrales, des séries harmoniques et des séries à bandes latérales avec des fréquences caractéristiques provennant du cinématique d'un système analysé. Cette méthode propose une approche unique dédiée à l’élément roulant du roulement qui permet de surmonter les difficultés causées par le phénomène de glissement.La troisième technique est un algorithme de démodulation de bande latérale entière. Elle fonctionne à la base d’un filtre multiple et propose des indicateurs de santé pour faciliter une évaluation d'état du système sous l’analyse.Dans cette thèse, les méthodes proposées sont validées sur les signaux simulés et réels. Les résultats présentés montrent une bonne performance de toutes les méthodesThis 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
6
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/.
Full textAbstract:
Signal processing has been playing a key role in providing solutions to key problems encountered in communications, in general, and in wireless communications, in particular. Time-Frequency Signal Processing (TFSP) provides eective tools for analyzing nonstationary signals where the frequency content of signals varies in time as well as for analyzing linear time-varying systems. This research aimed at exploiting the advantages of TFSP, in dealing with nonstationary signals, into the fundamental issues of signal processing, namely the signal estimation and signal separation. In particular, it has investigated the problems of (i) the Instantaneous Frequency (IF) estimation of Linear Frequency-Modulated (LFM) signals corrupted in complex-valued zero-mean Multiplicative Noise (MN), and (ii) the Underdetermined Blind Source Separation (UBSS) of LFM signals, while focusing onto the fast-growing area of Wireless Communications (WCom). A common problem in the issue of signal estimation is the estimation of the frequency of Frequency-Modulated signals which are seen in many engineering and real-life applications. Accurate frequency estimation leads to accurate recovery of the true information. In some applications, the random amplitude modulation shows up when the medium is dispersive and/or when the assumption of point target is not valid; the original signal is considered to be corrupted by an MN process thus seriously aecting the recovery of the information-bearing frequency. The IF estimation of nonstationary signals corrupted by complex-valued zero-mean MN was investigated in this research. We have proposed a Second-Order Statistics approach, rather than a Higher-Order Statistics approach, for IF estimation using Time-Frequency Distributions (TFDs). The main assumption was that the autocorrelation function of the MN is real-valued but not necessarily positive (i.e. the spectrum of the MN is symmetric but does not necessary has the highest peak at zero frequency). The estimation performance was analyzed in terms of bias and variance, and compared between four dierent TFDs: Wigner-Ville Distribution, Spectrogram, Choi-Williams Distribution and Modified B Distribution. To further improve the estimation, we proposed to use the Multiple Signal Classification algorithm and showed its better performance. It was shown that the Modified B Distribution performance was the best for Signal-to-Noise Ratio less than 10dB. In the issue of signal separation, a new research direction called Blind Source Separation (BSS) has emerged over the last decade. BSS is a fundamental technique in array signal processing aiming at recovering unobserved signals or sources from observed mixtures exploiting only the assumption of mutual independence between the signals. The term "blind" indicates that neither the structure of the mixtures nor the source signals are known to the receivers. Applications of BSS are seen in, for example, radar and sonar, communications, speech processing, biomedical signal processing. In the case of nonstationary signals, a TF structure forcing approach was introduced by Belouchrani and Amin by defining the Spatial Time- Frequency Distribution (STFD), which combines both TF diversity and spatial diversity. The benefit of STFD in an environment of nonstationary signals is the direct exploitation of the information brought by the nonstationarity of the signals. A drawback of most BSS algorithms is that they fail to separate sources in situations where there are more sources than sensors, referred to as UBSS. The UBSS of nonstationary signals was investigated in this research. We have presented a new approach for blind separation of nonstationary sources using their TFDs. The separation algorithm is based on a vector clustering procedure that estimates the source TFDs by grouping together the TF points corresponding to "closely spaced" spatial directions. Simulations illustrate the performances of the proposed method for the underdetermined blind separation of FM signals. The method developed in this research represents a new research direction for solving the UBSS problem. The successful results obtained in the research development of the above two problems has led to a conclusion that TFSP is useful for WCom. Future research directions were also proposed.
7
Nguyen, 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.
Full textAbstract:
Signal processing has been playing a key role in providing solutions to key problems encountered in communications, in general, and in wireless communications, in particular. Time-Frequency Signal Processing (TFSP) provides eective tools
for analyzing nonstationary signals where the frequency content of signals varies in time as well as for analyzing linear time-varying systems. This research aimed at exploiting the advantages of TFSP, in dealing with nonstationary signals, into the fundamental issues of signal processing, namely the signal estimation and signal separation. In particular, it has investigated the problems of (i) the Instantaneous Frequency (IF) estimation of Linear Frequency-Modulated (LFM) signals corrupted in complex-valued zero-mean Multiplicative Noise (MN), and (ii) the Underdetermined Blind Source Separation (UBSS) of LFM signals, while focusing onto the fast-growing area of Wireless Communications (WCom).
A common problem in the issue of signal estimation is the estimation of the frequency of Frequency-Modulated signals which are seen in many engineering and real-life applications. Accurate frequency estimation leads to accurate recovery of the true information. In some applications, the random amplitude modulation
shows up when the medium is dispersive and/or when the assumption of point target is not valid; the original signal is considered to be corrupted by an MN process thus seriously aecting the recovery of the information-bearing frequency. The IF estimation of nonstationary signals corrupted by complex-valued zero-mean MN
was investigated in this research. We have proposed a Second-Order Statistics approach,
rather than a Higher-Order Statistics approach, for IF estimation using Time-Frequency Distributions (TFDs). The main assumption was that the autocorrelation function of the MN is real-valued but not necessarily positive (i.e. the spectrum of the MN is symmetric but does not necessary has the highest peak at zero frequency). The estimation performance was analyzed in terms of bias and variance, and compared between four dierent TFDs: Wigner-Ville Distribution, Spectrogram, Choi-Williams Distribution and Modified B Distribution. To further improve the estimation, we proposed to use the Multiple Signal Classification algorithm
and showed its better performance. It was shown that the Modified B Distribution performance was the best for Signal-to-Noise Ratio less than 10dB.
In the issue of signal separation, a new research direction called Blind Source Separation (BSS) has emerged over the last decade. BSS is a fundamental technique in array signal processing aiming at recovering unobserved signals or sources from observed mixtures exploiting only the assumption of mutual independence between the signals. The term "blind" indicates that neither the structure of the mixtures nor the source signals are known to the receivers. Applications of BSS are seen in, for example, radar and sonar, communications, speech processing, biomedical signal processing. In the case of nonstationary signals, a TF structure forcing
approach was introduced by Belouchrani and Amin by defining the Spatial Time- Frequency Distribution (STFD), which combines both TF diversity and spatial diversity. The benefit of STFD in an environment of nonstationary signals is the direct exploitation of the information brought by the nonstationarity of the signals.
A drawback of most BSS algorithms is that they fail to separate sources in situations where there are more sources than sensors, referred to as UBSS. The UBSS of nonstationary signals was investigated in this research. We have presented a new approach for blind separation of nonstationary sources using their TFDs. The
separation algorithm is based on a vector clustering procedure that estimates the
source TFDs by grouping together the TF points corresponding to "closely spaced"
spatial directions. Simulations illustrate the performances of the proposed method for the underdetermined blind separation of FM signals. The method developed in this research represents a new research direction for solving the UBSS problem.
The successful results obtained in the research development of the above two problems has led to a conclusion that TFSP is useful for WCom. Future research directions were also proposed.
8
Alderson, 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.
Full textAbstract:
The current study investigates two recently identified threats to the construct validity of behavioral inhibition as a core deficit of attention-deficit/hyperactivity disorder (ADHD) based on the Stop-signal task: calculation of mean reaction time from go-trials presented adjacent to intermittent stop-trials, and non-reporting of the stop-signal delay metric. Children with ADHD (n=12) and typically developing children (TD) (n=11) were administered the standard stop-signal task and three variant stop-signal conditions. These included a No-Tone condition administered without the presentation of an auditory tone; an Ignore-Tone condition that presented a neutral (i.e., not associated with stopping) auditory tone; and a second Ignore-Tone condition that presented a neutral auditory tone after the tone had been previously paired with stopping. Children with ADHD exhibited significantly slower and more variable reaction times to go-stimuli, and slower stop-signal reaction times (SSRT) relative to TD controls. Stop-signal delay (SSD) was not significantly different between groups, and both groups' go-trial reaction times slowed following meaningful tones. Collectively, these findings corroborate recent meta-analyses and indicate that previous findings of stop-signal performance deficits in ADHD reflect slower and more variable responding to visually presented stimuli and concurrent processing of a second stimulus, rather than deficits of motor behavioral inhibition.Ph.D.
Department of Psychology
Sciences
Psychology PhD
9
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.
Full text
10
T, 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.
Full textAbstract:
International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, NevadaMeasurement 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.
11
Kollin, Lori. "Comparing the Effects of Static and Dynamic Signals during Multiple Schedules." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6882.
Full textAbstract:
Functional communication training (FCT) is an effective procedure to teach a functional communication response (FCR) and decrease problem behavior. However, there are limitations to FCT. These limitations include excessive manding (e.g., requesting items at a high rate) and manding at inappropriate times (e.g., requesting attention when caregiver is driving). Multiple schedules using static signals (e.g., colored cards) have been used to decrease these limitations while maintaining appropriate levels of the FCRs and low levels of problem behavior. Moreover, dynamic signals have been used to maintain low levels of problem behavior and appropriate levels of alternative responses outside of a multiple schedule format. Presently, no research has examined the comparison of static and dynamic signals to address the limitations of FCT. Therefore, the purpose of the current study is to compare the effects of static and dynamic signals (i.e., Time Timer®) during multiple schedules consisting of reinforcement and extinction components following FCT.
Key words: functional communication training, multiple schedules, static signals, Time Timers®
12
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.
Full textAbstract:
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.
13
Bartůšek, 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.
Full textAbstract:
Tato práce se zabývá vylepšením algoritmu pro sdružování (clustering) ERP signálů pomocí analýzy časových a prostorových vlastností pseudo-signálů získaných za pomocí metody analýzy nezávislých komponent (Independent Component Analysis). Naším zájmem je nalezení nových vlastností, které by zlepšily stávající výsledky. Tato práce se zabývá použitím Fourierovy transformace (Fourier Transform), FIR filtru a krátkodobé Fourierovy transformace ke zkvalitnění informace pro sdružovací algoritmy. Princip a použitelnost metody jsou popsány a demonstrovány ukázkovým algoritmem. Výsledky ukázaly, že pomocí dané metody je možné získat ze vstupních dat zajímavé informace, které mohou být úspěšně použity ke zlepšení výsledků.
14
Yeung, W. K. "Microwave imaging with impulsive signals." Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382334.
Full text
15
Rowsell, 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.
Full text
16
Bikdash, Marwan. "Analysis and filtering of time-varying signals." Thesis, Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/80015.
Full textAbstract:
The characterization, analysis and filtering of a slowly time-varying (STV) deterministic signal are considered. A STV signal is characterized as a sophisticated signal whose windowed sections are elementary signals. Mixed time-frequency representations (MTFRs) such as the Wigner distribution (WD), the Pseudo-Wigner distribution (PWD), the Short-time Fourier transform (STFT) and the optimally smoothed Wigner distribution (OSWD) used in analyzing STV signals are analyzed and compared. The OSWD is shown to perform satisfactorily even if the signals are amplitude modulated. The OSWD is shown to yield the exact instantaneous frequency for STV signals having quadratic phase: and to have a minimal and meaningful Bandwidth (BW) that does not depend on the slope of the instantaneous frequency curve in the time-frequency plane, unlike the BW of the spectrogram. We also present some contributions to the ongoing debate addressing the issue of choosing the MTFR that is best suited to the analysis of STV signals. Using analytical and experimental results, the performances of the different MTFRs are compared, and the conditions under which a given MTFR performs better are considered.
The filtering of a signal from a noise-corrupted measurement, and the decomposition of a STV signal into its components in the presence of noise, are considered. These two related problems have been solved through masking the MTFRs of the measured signal. This approach has been successfully used in the case of the WD, PWD and the STFT. We propose extending the use of this approach to the OSWD. An equivalent time-domain implementation based on linear shift-variant (LSV) filters is derived and fully analyzed. It is based on the concept of local nonstationarity cancellation. The proposed filter is shown to have a superior performance when compared to the filter based on masking the STFT. The sensitivity of the filter is studied. The filter ability to suppress white noise and to decompose a STV signal into its components is analyzed and illustrated.Master of Science
17
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.
Full textAbstract:
Electro-oculography signals are corneo-retinal potentials that carry informationpertaining to eye movements. This information can be used to estimate drowsinesslevel of the subject which could provide interesting insights into research of acci-dent prevention. Of all features present, blink duration has been proved to be aneffective measure of drowsiness. The aim of this thesis work is to build a portablesystem to acquire and analyze electro-oculographic (EOG) signals in real-time.The system contains two sub-systems; a hardware sub-system that consists of thefilters, amplifiers, data acquisition card and isolation and the software sub-systemthat contains the program to acquire and analyze the signal and present the resultsto the observer. The filters were designed starting with simulation, implementa-tion on the prototype board, culminating in the design of a printed circuit board(PCB) and packaging. The complete software was written in PythonTMusing sev-eral relevant libraries for data processing. A text-based user interface was createdto enable easy user interaction. The results are graphically displayed in real-time. Ex-situ tests were done with two volunteers while in-situ test was done onone subject. The data from the in-situ tests showed "good signal quality" in a"noisy" environment concurring with the design specifications. To motivate theimportance of calibration, two calibration paradigms were used during ex-situtests, where one paradigm records only normal blinks while the other records longblinks and the results showed differences in detection and error rates. The obser-vations made from performance tests at various levels gave "satisfactory results"and proved the usefulness of the system for experimental purposes in-situ.
18
Granados, Omar D. "Space-Time Coding for Polynomial Phase Modulated Signals." FIU Digital Commons, 2011. http://digitalcommons.fiu.edu/etd/378.
Full textAbstract:
Polynomial phase modulated (PPM) signals have been shown to provide improved error rate performance with respect to conventional modulation formats under additive white Gaussian noise and fading channels in single-input single-output (SISO) communication systems. In this dissertation, systems with two and four transmit antennas using PPM signals were presented. In both cases we employed full-rate space-time block codes in order to take advantage of the multipath channel. For two transmit antennas, we used the orthogonal space-time block code (OSTBC) proposed by Alamouti and performed symbol-wise decoding by estimating the phase coefficients of the PPM signal using three different methods: maximum-likelihood (ML), sub-optimal ML (S-ML) and the high-order ambiguity function (HAF). In the case of four transmit antennas, we used the full-rate quasi-OSTBC (QOSTBC) proposed by Jafarkhani. However, in order to ensure the best error rate performance, PPM signals were selected such as to maximize the QOSTBC’s minimum coding gain distance (CGD). Since this method does not always provide a unique solution, an additional criterion known as maximum channel interference coefficient (CIC) was proposed. Through Monte Carlo simulations it was shown that by using QOSTBCs along with the properly selected PPM constellations based on the CGD and CIC criteria, full diversity in flat fading channels and thus, low BER at high signal-to-noise ratios (SNR) can be ensured. Lastly, the performance of symbol-wise decoding for QOSTBCs was evaluated. In this case a quasi zero-forcing method was used to decouple the received signal and it was shown that although this technique reduces the decoding complexity of the system, there is a penalty to be paid in terms of error rate performance at high SNRs.
19
AHMAD, 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.
Full textAbstract:
Focusing techniques and detection of targets is usually associated to defense and military use. However in recent past things have moved ahead. Now target detection using UWB radars is being done in many industries and corporations. Radarbolaget AB is one of them; one of their projects uses UWB radars to detect steel strips inside a furnace. This research solves a potential problem of detecting middle steel strip out of total three strip edges which can be seen by radar placed on the front. For better understanding of the reader, existing system and introductory UWB radar principles are discussed. As there can be many solutions to focusing of targets here (steel strip edge detection). Available focusing techniques have been discussed in detail along with the possible physical and simulation setups. Later in the document, detection methods have been proposed. UWB time reversed signal detection is a fairly new method and a very limited research has been done so far. PRBS sequence has been focused on in detection mechanism. Results section show that the pulse of the PRBS works better and produces more promising results rather than a repetitive signal. Time reversal methods for locating the target have been used to find the approximate location of the target. Manual distance calculations from target to the transmitter and receiver have been done. Comparison of actual distance from target to the transmitter is compared with simulation results. Different model simulation setups and their results have proved that using UWB Time reversed signals; a still or moving target can be detected with centimeter window precision.
20
Velasco, Solano Carlos Hernando. "ARMA modeling of signals in the time domain." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23820.
Full textAbstract:
Approved for public release; distribution is unlimitedThis 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.
21
Okamura, Shuhei. "The Short Time Fourier Transform and Local Signals." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/58.
Full textAbstract:
In this thesis, I examine the theoretical properties of the short time discrete Fourier transform (STFT). The STFT is obtained by applying the Fourier transform by a fixed-sized, moving window to input series. We move the window by one time point at a time, so we have overlapping windows. I present several theoretical properties of the STFT, applied to various types of complex-valued, univariate time series inputs, and their outputs in closed forms. In particular, just like the discrete Fourier transform, the STFT’s modulus time series takes large positive values when the input is a periodic signal. One main point is that a white noise time series input results in the STFT output being a complex-valued stationary time series and we can derive the time and time-frequency dependency structure such as the cross- covariance functions. Our primary focus is the detection of local periodic signals. I present a method to detect local signals by computing the probability that the squared modulus STFT time series has consecutive large values exceeding some threshold after one exceeding observation following one observation less than the threshold. We discuss a method to reduce the computation of such probabilities by the Box-Cox transformation and the delta method, and show that it works well in comparison to the Monte Carlo simulation method.
22
Ives, 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.
Full text
23
Al-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.
Full textAbstract:
This work investigates the discrimination of times between threshold crossings for deterministic periodic signals with added band-limited noise. The methods include very low signal to noise ratio (one or less). Investigation has concentrated on the theory of double threshold crossings, with especial care taken in the effects of correlations in the noise, and their effects on the probability of detection of double crossings. A computer program has been written to evaluate these probabilities for a wide range of signal to noise ratiOS, a wide range of signal to bandwidth ratios, and a range of times between crossings of up to two signal periods. Correlations due to the extreme cases of a Brickwall filter and a second order Butterworth filter have been included; other filters can easily be included in the program. The method is simulated and demonstrated by implementing on a digital signal processor (DSP) using a TMS32020. Results from the DSP technique are in agreement with the theoretical evaluations. Probability results could be used to determine optimum time thresholds and windows for signal detection and frequency discrimination, to determine the signal length for adequate discrimination, and to evaluate channel capacities. The ability to treat high noise, including exact effects of time correlations, promises new applications in electronic signal detection, communications, and pulse discrimination neural networks.
24
Zhang, 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.
Full textAbstract:
The Tactile Internet is at the core of the 5G era, when the world will experience paradigm shift from content-delivery networks to service/labour-delivery ones. Systems that enable wireless communications of haptic data feature bi-directionality, high packet rate and resolution, large degrees of freedom, and above all, strict latency requirements in many applications, aggravating the shortage of wireless resources. Thus, more efficient haptic data reduction techniques are continuously summoned for. Previous studies on haptic compression mostly resort to DPCM/ADPCM plus entropy coding and perception-based down-sampling for real-time scenarios, and model-based techniques such as DCT and LP for the rest. However, with few exceptions they always segregate tactile signals from kinaesthetic signals, employing only kinaesthetic feedbacks in real-time compression experiments. In addition, these techniques are not optimized for efficient performance at scale. This thesis project proposes a novel multi-channel real-time haptic compression system aimed at teleoperation applications with both kinaesthetic and tactile feedbacks. It consists of a lossy compression layer featuring predictive coding and a lossless layer featuring channel reshuffle and group transmission. By using different quantizer designs in the lossy layer, it abates the need for entropy coding, and leave room for future perception-based data compression modules. The lossless layer exploits inter-channel sparsity for further data reduction. The system is evaluated on a tactile texture database published by University of Pennsylvania in MATLAB. The performance measurements are in both time and frequency domain, mostly objective, but include subjective considerations as well.
25
Bertoncini, Crystal Ann. "Applications of pattern classification to time-domain signals." W&M ScholarWorks, 2010. https://scholarworks.wm.edu/etd/1539623559.
Full textAbstract:
Many different kinds of physics are used in sensors that produce time-domain signals, such as ultrasonics, acoustics, seismology, and electromagnetics. The waveforms generated by these sensors are used to measure events or detect flaws in applications ranging from industrial to medical and defense-related domains. Interpreting the signals is challenging because of the complicated physics of the interaction of the fields with the materials and structures under study. often the method of interpreting the signal varies by the application, but automatic detection of events in signals is always useful in order to attain results quickly with less human error. One method of automatic interpretation of data is pattern classification, which is a statistical method that assigns predicted labels to raw data associated with known categories. In this work, we use pattern classification techniques to aid automatic detection of events in signals using features extracted by a particular application of the wavelet transform, the Dynamic Wavelet Fingerprint (DWFP), as well as features selected through physical interpretation of the individual applications. The wavelet feature extraction method is general for any time-domain signal, and the classification results can be improved by features drawn for the particular domain. The success of this technique is demonstrated through four applications: the development of an ultrasonographic periodontal probe, the identification of flaw type in Lamb wave tomographic scans of an aluminum pipe, prediction of roof falls in a limestone mine, and automatic identification of individual Radio Frequency Identification (RFID) tags regardless of its programmed code. The method has been shown to achieve high accuracy, sometimes as high as 98%.
26
Abdelfatah, 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.
Full text
27
Yang, Zhenghong. "Joint time frequency analysis of Global Positioning System (GPS) multipath signals." Ohio : Ohio University, 1998. http://www.ohiolink.edu/etd/view.cgi?ohiou1176234303.
Full text
28
Bousfield, Bruce M. "Real time aero engine signal analysis." Thesis, Loughborough University, 1988. https://dspace.lboro.ac.uk/2134/10425.
Full textAbstract:
For many years the analysis of dynamic signals obtained from aero engine mounted transducers has been performed either by using real time spectrum analysers within the test facility, or by making high quality tape recordings during engine tests and analysing the data via an off-line main frame computer. Although real time analysers produce the information where it is most needed, they provide no history of events and the results are operator dependent. Analysis from tape recordings enables information extraction algorithms to be performed and tables and graphs of notable events to be printed. However much of this information could be more effectively utilised if produced within the test facility and in real time. This thesis describes the design and development of a real time data acquisition, signal processing and information extraction system ideally suited for engine health and performance monitoring within test facilities. The thesis begins with a detailed description of the problems encountered in dynamic signal analysis in the field of aero engine performance testing, and with an overview of digital signal processing and the latest technology signal processing micro processors that have made this project possible. It then describes the problems encountered and the subsequent solutions found during the design and development of the hardware and software needed for the·high bandwidth data acquisition and fast signal processing algorithms. The fast Fourier transform has been used for very many years in the field of spectrum analysis, ... however this technique has limitations which are overcome by some of the more modern spectrum estimation techniques. This thesis makes an assessment of some of these techniques, noting particularly their performance on aero engine type signals. The results of these tests are recorded and the possible use of the techniques in aero engine analysis is discussed.
29
Messaoud, Safa. "Translating Discrete Time SIMULINK to SIGNAL." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/49299.
Full textAbstract:
As Cyber Physical Systems (CPS) are getting more complex and safety critical, Model Based
Design (MBD), which consists of building formal models of a system in order to be used in
verification and correct-by-construction code generation, is becoming a promising methodology
for the development of the embedded software of such systems. This design paradigm
significantly reduces the development cost and time while guaranteeing better robustness,
capability and correctness with respect to the original specifications, when compared with
the traditional ad-hoc design methods. SIMULINK has been the most popular tool for embedded
control design in research as well as in industry, for the last decades. As SIMULINK
does not have formal semantics, the application of the model based design methodology and
tools to its models is very limited. In this thesis, we present a semantic translator that
transform discrete time SIMULINK models into SIGNAL programs. The choice of SIGNAL
is motivated by its polychronous formalism that enhances synchronous programming with
asynchronous concurrency, as well as, by the ability of its compiler of generating deterministic
multi thread code. Our translation involves three major steps: clock inference, type
inference and hierarchical top-down translation. We validate the semantic preservation of
our prototype tool by testing it on different SIMULINK models.Master of Science
30
Saghafi, Abolfazl. "Real-time Classification of Biomedical Signals, Parkinson’s Analytical Model." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6946.
Full textAbstract:
The reach of technological innovation continues to grow, changing all industries as it evolves. In healthcare, technology is increasingly playing a role in almost all processes, from patient registration to data monitoring, from lab tests to self-care tools. The increase in the amount and diversity of generated clinical data requires development of new technologies and procedures capable of integrating and analyzing the BIG generated information as well as providing support in their interpretation.
To that extent, this dissertation focuses on the analysis and processing of biomedical signals, specifically brain and heart signals, using advanced machine learning techniques. That is, the design and implementation of automatic biomedical signal pre-processing and monitoring algorithms, the design of novel feature extraction methods, and the design of classification techniques for specific decision making processes.
In the first part of this dissertation Electroencephalogram (EEG) signals that are recorded in 14 different locations on the scalp are utilized to detect random eye state change in real-time. In summary, cross channel maximum and minimum is used to monitor real-time EEG signals in 14 channels. Upon detection of a possible change, Multivariate Empirical Mode Decomposes the last two seconds of the signal into narrow-band Intrinsic Mode Functions. Common Spatial Pattern is then employed to create discriminating features for classification purpose. Logistic Regression, Artificial Neural Network, and Support Vector Machine classifiers all could detect the eye state change with 83.4% accuracy in less than two seconds. We could increase the detection accuracy to 88.2% by extracting relevant features from Intrinsic Mode Functions and directly feeding it to the classification algorithms.
Our approach takes less than 2 seconds to detect an eye state change which provides a significant improvement and promising real-life applications when compared to slow and computationally intensive instance based classification algorithms proposed in literatures. Increasing the training examples could even improve the accuracy of our analytic algorithms. We employ our proposed analytic method in detecting the three different dance moves that honey bees perform to communicate the location of a food source. The results are significantly better than other alternative methods in the literature in terms of both accuracy and run time.
The last chapter of the dissertation brings out a collaborative research on Parkinson's disease. As a Parkinson’s Progression Markers Initiative (PPMI) investigator, I had access to the vast database of The Michael J. Fox Foundation for Parkinson's Research. We utilized available data to study the heredity factors leading to Parkinson's disease by using Maximum Likelihood and Bayesian approach. Through sophisticated modeling, we incorporated information from healthy individuals and those diagnosed with Parkinson's disease (PD) to available historical data on their grandparents' family to draw Bayesian estimations for the chances of developing PD in five types of families. That is, families with negative history of PD (type 1) and families with positive history in which estimations provided for the prevalence of developing PD when none of the parents (type 2), one of the parents (type 3 and 4), or both of the parents (type 5) carried the disease.
The results in the provided data shows that for the families with negative history of PD the prevalence is estimated to be 20% meaning that a child in this family has 20% chance of developing Parkinson. If there is positive history of PD in the family the chance increases to 33% when none of the parents had PD and to 44% when both of the parents had the disease. The chance of developing PD in a family whose solely mother is diagnosed with the disease is estimated to be 26% in comparison to 31% when only father is diagnosed with Parkinson's.
31
Thomson, 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.
Full textAbstract:
The use of traditional tools for the discovery and characterization of biological systems has resulted in a wealth of biological knowledge. Unfortunately, only a small portion of the biological world is well-understood to date, and the study of the rest remains a daunting task. This work involves using time-varying stimuli in order to more rapidly interrogate and characterize signaling pathways. The time-dependent stimulation of a signaling pathway can be used in conjunction with a model of the pathway to efficiently evaluate and test hypotheses. We are developing this technology using the yeast pheromone signal transduction pathway as a model system. The time-varying stimuli will be applied to the yeast cells via a novel microfluidic device, and the pathway output will be measured via various fluorescent reporters. The output of the pathway can then be compared to the output from a computational model of the pathway in order to test hypotheses and constrain our knowledge of the pathway. Initial work shows that a computational model can be used to identify stimuli time-courses that increase the parameter sensitivity, meaning that corresponding experiments could potentially be much more informative.Poster presented at the 2005 ICSB meeting, held at Harvard Medical School in Boston, MA.
32
Niethammer, Marc. "Application of time frequency representations to characterize ultrasonic signals." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/19005.
Full text
33
Olhese, 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.
Full text
34
Nguyen, Yen Thi Hong. "Time-frequency distributions : approaches for incomplete non-stationary signals." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/19681/.
Full textAbstract:
There are many sources of waveforms or signals existing around us. They can be natural phenomena such as sound, light and invisible like electromagnetic fields, voltage, etc. Getting an insight into these waveforms helps explain the mysteries surrounding our world and the signal spectral analysis (i.e. the Fourier transform) is one of the most significant approaches to analyze a signal. Nevertheless, Fourier analysis cannot provide a time-dependent spectrum description for spectrum-varying signals-non-stationary signal. In these cases, time-frequency distribu- tions are employed instead of the traditional Fourier transform. There have been a variety of methods proposed to obtain the time-frequency representations (TFRs) such as the spectrogram or the Wigner-Ville distribution. The time-frequency distributions (TFDs), indeed, offer us a better signal interpretation in a two-dimensional time-frequency plane, which the Fourier transform fails to give. Nevertheless, in the case of incomplete data, the time-frequency displays are obscured by artifacts, and become highly noisy. Therefore, signal time-frequency features are hardly extracted, and cannot be used for further data processing. In this thesis, we propose two methods to deal with compressed observations. The first one applies compressive sensing with a novel chirp dictionary. This method assumes any windowed signal can be approximated by a sum of chirps, and then performs sparse reconstruction from windowed data in the time domain. A few improvements in computational complexity are also included. In the second method, fixed kernel as well as adaptive optimal kernels are used. This work is also based on the assumption that any windowed signal can be approximately represented by a sum of chirps. Since any chirp's auto-terms only occupy a certain area in the ambiguity domain, the kernel can be designed in a way to remove the other regions where auto-terms do not reside. In this manner, not only cross-terms but also missing samples’ artifact are mitigated significantly. The two proposed approaches bring about a better performance in the time-frequency signature estimations of the signals, which are sim- ulated with both synthetic and real signals. Notice that in this thesis, we only consider the non-stationary signals with frequency changing slowly with time. It is because the signals with rapidly varying frequency are not sparse in time-frequency domain and then the compressive sensing techniques or sparse reconstructions could not be applied. Also, the data with random missing samples are obtained by randomly choosing the samples’ positions and replacing these samples with zeros.
35
Jonas, Eric Michael. "Real-time analog acquisition of electrophysiological signals with Soma." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/61306.
Full textAbstract:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged 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.
36
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.
Full textAbstract:
Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical EngineeringThe 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.
37
Fan, Lingke. "Spectral and time-frequency analysis of ultrasonic Doppler signals." Thesis, University of Leicester, 1994. http://hdl.handle.net/2381/34342.
Full textAbstract:
Spectral analysis of Doppler signals plays a very important role in non-invasive measurements of blood velocity distributions. Among the various spectral analysis methods available, the fast Fourier transform (FFT) is regarded as a "traditional" spectral analysis tool and is widely used in commercial, clinical, experimental and research equipment. Some drawbacks of this method, however, have imposed limitations on its use in some clinical cases. A numbers of spectral and time-frequency analysis methods have been studied in this dissertation. These include the traditional FFT, the autoregressive (AR) method, the Wigner-Ville distribution (WVD), and the Choi-Williams distribution (CWD). The advantages and disadvantages of each method have been studied and summarised. Efforts have been made to improve the temporal and frequency resolution of the results. New analysis methods such as the WVD and CWD have been interpreted physically, and some of their new properties have been explored. The results have suggested that the heights of the peaks in the AR spectra of narrow-band signals are not necessarily proportional to signal power, and should be used with caution in the context of Doppler signal processing. The results have also shown that it is appropriate to use the WVD or CWD to analyse signals when high temporal resolution is required. In practice, it is easier for the operator to handle the WVD, which usually produces reasonably good results. The above methods have been applied in practice. Considerable software and hardware development has been carried out, and a number of analysers have been implemented for use under different practical conditions. These analysers were also used to compare experimentally the analysis methods mentioned above, and to confirm the results of theoretical analyses. Some of these analysers have found applications in clinical practice.
38
Sivaramakrishnan, Kamakshi. "Universal schemes for denoising discrete-time continuous-amplitude signals /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
Full text
39
Brandman, Onn. "Feedback loops shape cellular signals in space and time /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
Full text
40
Xi-Hua, Li. "A Real-time Counting-measuring Method for PPM(PPK) Signals." International Foundation for Telemetering, 1991. http://hdl.handle.net/10150/612915.
Full textAbstract:
International Telemetering Conference Proceedings / November 04-07, 1991 / Riviera Hotel and Convention Center, Las Vegas, NevadaOn 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.
41
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.
Full textAbstract:
Because the operational characteristics of signalized intersections near highway-railroad grade crossings (IHRGCs) are different from those of signalized intersections located elsewhere in the traffic system, standard operational strategies do not apply. This is because safe operation at IHRGCs takes precedence over all other objectives.
Because the prime objective of the current preemption methods is to clear the crossing, secondary objectives such as safe pedestrian crossing time and minimized delay are given less consideration or ignored completely. Consequently, state-of-the-practice strategies may cause serious pedestrian safety and efficiency problems at IHRGCs. Therefore, there is a definite need for research on how to improve traffic signal preemption strategies.
An important element of preemption strategy is detection of trains and prediction of arrival times. However, because of the limitations of current detection technologies, estimation algorithms, etc., there is a wide range in these warning times. In this dissertation, a new train-arrival prediction algorithm was developed using detection equipment located farther upstream from the HRGC.
The state-of-the-art transition preemption strategy (TPS) was developed to ensure that as preemption is initiated by approaching trains, the signal display does not change in a manner that endangers either pedestrians or drivers. However, because it does not account for the variability of predicted train arrival times, there is still a possibility of failure.
Therefore, a new transition preemption algorithm that is specifically designed to improve intersection performance while maintaining or improving the current level of safety is developed. This dissertation developed a preemption strategy (TPS3) that uses better train arrival time estimates to improve the safety and efficiency of IHRGCs. The approach was simulated on a test bed in College Station, Texas, and it was concluded that the new TPS improves the safety and operation of intersections near highway-railroad grade crossings.
42
Almutairi, 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.
Full text
43
McLean, 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.
Full textAbstract:
International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, CaliforniaRange 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.
44
Meynard, Adrien. "Stationnarités brisées : approches à l'analyse et à la synthèse." Thesis, Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0475.
Full textAbstract:
La non-stationnarité est caractéristique des phénomènes physiques transitoires. Par exemple, elle peut être engendrée par la variation de vitesse d'un moteur lors d'une accélération. De même, du fait de l'effet Doppler, un son stationnaire émis par une source en mouvement sera perçu comme étant non stationnaire par un observateur fixe. Ces exemples nous conduisent à considérer une classe de non-stationnarité formée des signaux stationnaires dont la stationnarité a été brisée par une opérateur de déformation physiquement pertinent. Après avoir décrit les modèles de déformation considérés (chapitre 1), nous présentons différentes méthodes permettant d'étendre l'analyse et la synthèse spectrale à de tels signaux. L'estimation spectrale des signaux revient à déterminer le spectre du processus stationnaire sous-jacent et la déformation ayant brisé sa stationnarité. Ainsi, dans le chapitre 2, nous nous intéressons à l'analyse de signaux localement déformés pour lesquels la déformation subie s'exprime simplement comme un déplacement des coefficients d'ondelettes dans le plan temps-échelle. Nous tirons profit de cet propriété pour proposer l'algorithme d'estimation du spectre instantané JEFAS. Dans le chapitre 3, nous étendons cette analyse spectrale aux signaux multi-capteurs pour lesquels l'opérateur de déformation prend une forme matricielle. Il s'agit d'un problème de séparation de sources doublement non stationnaire. Dans le chapitre 4, nous proposons un approche à la synthèse pour étudier des signaux localement déformés. Enfin, dans le chapitre 5, nous construisons une représentation temps-fréquence adaptée à l'étude des signaux localement harmoniquesNonstationarity 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
45
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.
Full textAbstract:
Thesis (M.S. in Engineering Acoustics and M.S. in Electrical Engineering) Naval Postgraduate School, June 2001.Thesis advisors, Charles W. Therrien, Kevin B. Smith. Includes bibliographical references (p. 161-163). Also Available online.
46
Zhao, Rui. "A public key-based encryptiondecryption technique for real-time signals." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27944.
Full textAbstract:
Pay-TV Satellite Broadcast Service is very common today. However, protecting signals from being pirated is a principal issue. That is done using a conditional access system. In this thesis, we introduce and analyze different existing conditional access system design methods. Modification of the current method is proposed, and significant performance improvement is obtained. Satellite communication and cryptology are studied theoretically, which enables us to design public key-based encryption/decryption techniques for satellite broadcast that can supply higher secret solutions. Our simulation results show that the new design system is superior and more practical.
47
Markle, 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.
Full textAbstract:
Algorithms exist which will perform independent transformations on frequency or duration of a digital audio signal. These processes have different results different types of audio signals. A comparative study of granular and phase vocoder algorithms, implementation, and their respective effects on audio signals was made to determine which algorithm is best suited to a particular type of audio signal.
48
Arroyo, 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.
Full text
49
Wanfang, Zhang. "THE TIME DIVISION MULTIPLEX MEASURING SYSTEM FOR SINGLE-TRANSIENT SIGNALS." International Foundation for Telemetering, 1993. http://hdl.handle.net/10150/608868.
Full textAbstract:
International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, NevadaIn 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.
50
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.
Full textAbstract:
Dissertation submitted in the fufillment of the requirements for the Degree of Master in Biomedical EngineeringAutomated 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.