Dissertations / Theses: 'Signal processing applications' – Grafiati (2024)

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Spectroscopic techniques allow for studies of materials and organisms on the atomic and molecular level. Examples of such techniques are nuclear magnetic resonance (NMR) spectroscopy—one of the principal techniques to obtain physical, chemical, electronic and structural information about molecules—and magnetic resonance imaging (MRI)—an important medical imaging technique for, e.g., visualization of the internal structure of the human body. The less well-known spectroscopic technique of nuclear quadrupole resonance (NQR) is related to NMR and MRI but with the difference that no external magnetic field is needed. NQR has found applications in, e.g., detection of explosives and narcotics. The first part of this thesis is focused on detection and identification of solid and liquid explosives using both NQR and NMR data. Methods allowing for uncertainties in the assumed signal amplitudes are proposed, as well as methods for estimation of model parameters that allow for non-uniform sampling of the data. The second part treats two medical applications. Firstly, new, fast methods for parameter estimation in MRI data are presented. MRI can be used for, e.g., the diagnosis of anomalies in the skin or in the brain. The presented methods allow for a significant decrease in computational complexity without loss in performance. Secondly, the estimation of blood flow velo-city using medical ultrasound scanners is addressed. Information about anomalies in the blood flow dynamics is an important tool for the diagnosis of, for example, stenosis and atherosclerosis. The presented methods make no assumption on the sampling schemes, allowing for duplex mode transmissions where B-mode images are interleaved with the Doppler emissions.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Electrical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Many signal processing, data analysis and graphical user interface algorithms are computationallyintensive. This paper investigates a method of off-loading some of the calculations to remotely locatedprocessors. Inexpensive, commercial off the shelf processors are used to perform operations such as fastFourier transforms and other numerically intensive algorithms. The data is passed to the processors, andresults collected, using conventional network interfaces such as TCP/IP. This allows the processors to belocated at any location, and also allows potentially large caches of processors to be shared between multipleapplications.

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In this dissertation some advanced methods for extracting sources from single and multichannel data are developed and utilized in biomedical applications. It is assumed that the sources of interest have periodic structure and therefore, the periodicity is exploited in various forms. The proposed methods can even be used for the cases where the signals have hidden periodicities, i.e., the periodic behaviour is not detectable from their time representation or even Fourier transform of the signal. For the case of single channel recordings a method based on singular spectrum anal ysis (SSA) of the signal is proposed. The proposed method is utilized in localizing heart sounds in respiratory signals, which is an essential pre-processing step in most of the heart sound cancellation methods. Artificially mixed and real respiratory signals are used for evaluating the method. It is shown that the performance of the proposed method is superior to those of the other methods in terms of false detection. More over, the execution time is significantly lower than that of the method ranked second in performance. For multichannel data, the problem is tackled using two approaches. First, it is assumed that the sources are periodic and the statistical characteristics of periodic sources are exploited in developing a method to effectively choose the appropriate delays in which the diagonalization takes place. In the second approach it is assumed that the sources of interest are cyclostationary. Necessary and sufficient conditions for extractability of the sources are mathematically proved and the extraction algorithms are proposed. Ballistocardiogram (BCG) artifact is considered as the sum of a number of independent cyclostationary components having the same cycle frequency. The proposed method, called cyclostationary source extraction (CSE), is able to extract these components without much destructive effect on the background electroencephalogram (EEG)

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This thesis focuses on the concept of Single Well Imaging (SWI) in which a seismic source and receivers are deployed in a borehole to investigate the surrounding geology. The Uniwell project (1997-1999) was the first attempt to develop the SWI method; it used a fluid-coupled downhole source, which unfortunately generated high amplitude guided waves in the borehole which obscured all other useful information. Initial research work detailed in this thesis focused on removing the high amplitude guided waves, known as tube waves. Two-step source signature deconvolution using first the recorded source signature, and then the tube-wave reflected from the bottom of the well, succeeded in compressing the tube wave. The results were not consistent across all receivers, but there is enough correlation to identify a P-wave. Further work concentrates on using a new technique called Empirical Mode Decomposition to separate the tube-wave mode from the data. This identifies three dominant modes and a possible body wave arrival, but the results are ambiguous due to the inability of the decomposition to focus on the narrow bandwidth of interest. The source signature deconvolution technique can also be used to process real-time vertical seismic profiling (VSP) data down-hole, during pauses in drilling, in what is referred to as a Seismic-While-Drilling (SWD) setup. Results show that the technique is versatile and robust, giving 1 ms precision on first-break picking even in very noisy data. I also apply the technique to normal VSP data to improve both the resolution and the signal-to-noise ratio. A major effort in this thesis is to consider the effect of a clamped downhole source to overcome the tube-wave problem, using a magnetostrictive source. Earlier work established that the use of a reaction mass tended to excite resonances in the tool which caused the transducer to break. A new design for the source was developed in cooperation with colleagues which utilises a hydraulic amplifier design and a low power coded waveform driving method exploiting the time-bandwidth product to extract the signal from the noise. My results show that as the run time increases the resolution improves. With a run length of 80s it is possible to resolve the signal transmitted 50 cm through a granite formation. This analysis led to a revised design of the source to improve its efficiency. I have used finite difference modelling, with a variable grid technique, to compare an ideal explosive source with an ideal clamped source. The fluid-coupled source emits high amplitude tube waves which virtually obscure the body wave, whereas the clamped source emits a clearly identifiable P-wave along with lower amplitude tube waves. This clearly illustrates the advantage of an ideal clamped source. To model the source more accurately the idealwavelet is replaced by the respective recorded source signatures, and the data is then processed by cross correlation with the appropriate signature. The results show that the coded waveform approaches the resolution of the ideal wavelet very well, with all major events being visible. However, the fluid-coupled source performs very poorly with only the highest amplitude tube-wave visible. This work illustrates that by replacing a fluid-coupled source by a clamped source driven by a coded waveform, and by processing the data using cross correlation or signature deconvolution, it is possible to minimise or eliminate tube-wave noise from a SWI survey. It is hoped that the results outlined here will provide the basis for a new SWI method than can be used to prolong the supply of North Sea oil.

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Dans cette thèse, nous considérons le problème de la localisation de source dans les grands réseaux de capteurs, quand le nombre d'antennes du réseau et le nombre d'échantillons du signal observé sont grands et du même ordre de grandeur. Nous considérons le cas où les signaux source émis sont déterministes, et nous développons un algorithme de localisation amélioré, basé sur la méthode MUSIC. Pour ce faire, nous montrons de nouveaux résultats concernant la localisation des valeurs propres des grandes matrices aléatoires gaussiennes complexes de type information plus bruit
In this thesis, we consider the problem of source localization in large sensor networks, when the number of antennas of the network and the number of samples of the observed signal are large and of the same order of magnitude. We also consider the case where the source signals are deterministic, and we develop an improved algorithm for source localization, based on the MUSIC method. For this, we fist show new results concerning the position of the eigen values of large information plus noise complex gaussian random matrices

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Because the voltage amplitude of a heart beat is small compared to the amplitude of exponential noise, pacemakers have difficulty registering the responding heart beat immediately after a pacing pulse. This thesis investigates use of digital filters, an inverse filter and a lowpass filter, to eliminate the effects of exponential noise following a pace pulse. The goal was to create a filter which makes recognition of a haversine wave less dependent on natural subsidence of exponential noise. Research included the design of heart system, pacemaker, pulse generation, and D sensor system simulations. The simulation model includes the following components: \ • Signal source, A MA TLAB generated combination of a haversine signal, exponential noise, and myopotential noise. The haversine signal is a test signal used to simulate the QRS complex which is normally recorded on an ECG trace as a representa tion of heart function. The amplitude is approximately 10 mV. Simulated myopotential noise represents a uniformly distributed random noise which is generated by skeletal muscle tissue. The myopotential noise has a frequency spectrum extending from 70 to 1000Hz. The amplitude varies from 2 to 5 mV. Simulated exponential noise represents the depolarization effects of a pacing pulse as seen at the active cardiac lead. The amplitude is about -1 volt, large in comparison with the haversine signal. • AID converter, A combination of sample & hold and quantizer functions translate the analog signal into a digital signal. Additionally, random noise is created during quantization. • Digital filters, An inverse filter removes the exponential noise, and a lowpass filter removes myopotential noise. • Threshold level detector, A function which detects the strength and amplitude of the output signal was created for robustness and as a data sampling device. The simulation program is written for operation in a DOS environment. The program generates a haversine signal, myopotential noise (random noise), and exponential noise. The signals are amplified and sent to an AID converter stage. The resultant digital signal is sent to a series of digital filters, where exponential noise is removed by an inverse digital filter, and myopotential noise is removed by the Chebyshev type I lowpass digital filter. The output signal is "detected" if its waveform exceeds the noise threshold level. To determine what kind of digital filter would remove exponential noise, the spectrum of exponential noise relative to a haversine signal was examined. The spectrum of the exponential noise is continuous because the pace pulse is considered a non-periodic signal (assuming the haversine signal occurs immediately after a pace pulse). The spectrum of the haversine is also continuous, existing at every value of frequency co. The spectrum of the haversine is overlapped by the spectrum of and amplitude of the exponential, which is several orders of magnitude larger. The exponential cannot be removed by conventional filters. Therefore, an inverse filter approach is used to remove exponential noise. The transfer function of the inverse filter of the model has only zeros. This type of filter is called FIR, all-zero, non recursive, or moving average. Tests were run using the model to investigate the behavior of the inverse filter. It was found that the haversine signal could be clearly detected within a 5% change in the time constant of the exponential noise. Between 5% and 15% of change in the time constant, the filtered exponential amplitude swamps the haversine signal. The sensitivity of the inverse filter was also studied: when using a fixed exponential time constant but changing the location of the transfer function, the effect of the exponential noise on the haversine is minimal when zeros are located between 0.75 and 0.85 of the unit circle. After the source signal passes the inverse filter, the signal consists only of the haversine signal, myopotential noise, and some random noise introduced during quantization. To remove these noises, a Chebyshev type I lowpass filter is used.

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The combination of the well-known Hilbert spectral analysis (HAS) and the recently developed Empirical Mode Decomposition (EMD) designated as the Hilbert-Huang Transform (HHT) by Huang in 1998, represents a paradigm shift of data analysis methodology. The HHT is designed specifically for analyzing nonlinear and nonstationary data. The key part of HHT is EMD with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMFs). For two dimension, bidimensional IMFs (BIMFs) is decomposed by use of bidimensional EMD (BEMD). However, the HHT has some limitations in signal processing and image processing. This thesis addresses the problems of using HHT for signal and image processing. To reduce end effect in EMD, we propose a boundary extend method for EMD. A linear prediction based method combined with boundary extrema points information is employed to extend the signal, which reduces the end effect in EMD sifting process. It is a simple and effective method. In the EMD decomposition, interpolation method is another key point to get ideal components. The envelope mean in EMD is computed from the upper and lower envelopes by cubic spline interpolation, which has overshooting problem and is time-consuming. Based on the linear interpolation (straight line) method, we propose using the extrema points information to get the mean envelope, which is Extrema Mean Empirical Mode Decomposition (EMEMD). The mean envelope taken by EMEMD is smoother than EMD and the undershooting and overshooting problems in cubic spline are reduced compared with EMD. EMEMD also reduces the computation complex. Experimental results show the IMFs of EMEMD present more and clearer time-frequency information than EMD. Hilbert spectral of EMEMD is also clearer and more meaningful than EMD. Furthermore, based on the procedure of EMEMD method, a fast method to detect the frequency change location information of the piecewise stationary signal is also proposed, which is Extrema Points Empirical Mode Decomposition (EPEMD). Later, two applications based on the improved EMD/BEMD methods are proposed. One application is texture classification in image processing. A saddle points added BEMD is developed to supply multi-scale components (BIMFs) and Riesz transform is used to get the frequency domain characters of these BIMFs. Based on local descriptor Local Binary Pattern (LBP), two new features (based on BIMFs and based on Monogenic-BIMFs signals) are developed. In these new multi-scale components and frequency domain components, the LBP descriptor can achieve better performance than in original image. Experimental results show the texture images recognition rate based on our methods are better than other texture features methods. Another application is signal forecasting in one dimensional time series. EMEMD combined with Local Linear Wavelet Neural Network (LLWNN) for signal forecasting is proposed. The architecture is a decomposition-trend detection-forecasting-ensemble methodology. The EMEMD based decomposition forecasting method decomposed the time series into its basic components, and more accurate forecasts are obtained. In short, the main contributions of this thesis are summarized as following: 1. A boundary extension method is developed for one dimensional EMD. This extension method is based on linear prediction and end points adjusting. This extension method can reduce the end effect in EMD. 2. A saddle points added BEMD is developed to analysis and classify the texture images. This new BEMD detected more high oscillation in BIMFs and contributed for texture analysis. 3. A new texture analysis and classification method is proposed, which is based on BEMD (no/with saddle points), LBP and Riesz transform. The texture features based on BIMFs and BIMFs’ frequency domain 2D monogenic phase are developed. The performances and comparisons on the Brodatz, KTH-TIPS2a, CURet and Outex databases are reported. 4. An improved EMD method, EMEMD, is proposed to overcome the shortcoming in interpolation. EMEMD can provide more meaningful IMFs and it is also a fast decomposition method. The decomposition result and analysis in simulation temperature signal compare with Fourier transform, Wavelet transform are reported. 5. A forecasting methodology based on EMEMD and LLWNN is proposed. The architecture is a decomposition-trend detection-forecasting-ensemble methodology. The predicted results of Hong Kong Hang Seng Index and Global Land-Ocean Temperature Index are reported.

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This thesis details the simulation and experimental validation of several all-optical signal-processing configurations that utilise competitive optics principles. Competitive optics refers to any number of optical systems where different optical modes or wavelengths compete for a limited system resource, such as the optical gain of some common gain medium, in order to receive amplification. The gain medium can utilise different materials including photorefractive materials (e.g. BaTO3 crystals) and saturated gain material (e.g. semiconductor optical amplifiers). Competitive optics configurations are capable of sophisticated all-optical signal processing functions ranging from all-optical wavelength conversion to optical logic and storage.This thesis will present a series of simulated competitive optics configurations that utilise a semiconductor ring laser a basic competitive optics structure. These simulations will prove the viability and validity of competitive optics configurations that utilise saturated gain material, specifically the Semiconductor Optical Amplifier. The thesis will demonstrate the application of the Lotka-Volterra mathematical model of competitive interactions to the modelling of some of aforementioned configurations. Finally, experimental investigations of different semiconductor ring lasers configurations are presented and analysed from a competitive optics point of view.

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The dissertation focuses on the applicability of Support Vector Regression (SVR) in signal processing contexts. This is shown to be particularly well-suited to filtering in alpha-stable noise environments, and a further slight modification is proposed to this end. The main work in this dissertation on SVR is on the application to audio filtering based on perceptual criteria. This appears an ideal solution to the problem due to the fact that the loss function typically used by perceptual audio filtering practitioners incorporates a region of zero loss, as does SVR. SVR is extended to the problem of complex-valued regression, for application in the audio filtering problem to the frequency domain. This is with regions of zero loss that are both square and circular, and the circular case is extended to the problem of vector-valued regression. Three experiments are detailed with a mix of both good and poor results, and further refinements are proposed. Polychotomous, or multi-category classification is then studied. Many previous attempts are reviewed, and compared. A new approach is proposed, based on a geometrical structure. This is shown to overcome many of the problems identified with previous methods in addition to being very flexible and efficient in its implementation. This architecture is also derived, for just the three-class case, using a complex-valued kernel function. The general architecture is used experimentally in three separate implementations to give a demonstration of the overall approach. The methodology is shown to achieve results comparable to those of many other methods, and to include many of them as special cases. Further possible refinements are proposed which should drastically reduce optimisation times for so-called 'all-together' methods.

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This dissertation presents practical issues in Ultrawideband – Ultrawidebeam (UWB) Synthetic Aperture Radar (SAR) signal processing and crucial applications developed on UWB SAR. In the context of this dissertation, UWB SAR refers to the SAR systems utilizing large fractional bandwidth signals and synthesizing long apertures associated with wide antenna beamwidths. On one hand, such specific systems give us opportunities to develop unique applications. One the other hand, signal processing for data collected by these systems is very challenging and therefore requires much effort due to their characteristics. In the signal processing part, the tools supporting the UWB SAR system design and evaluation are introduced. They include an Impulse Response Function in UWB SAR imaging (IRF-SAR), azimuth and range resolution equations for UWB SAR, and a definition of UWB SAR quality measurements. Pre-processing, processing and post-processing for UWB SAR are also topics that will be examined in the signal processing part. The processing is here defined by SAR algorithms. With this definition, the pre-processing refers to RFI suppression approaches whereas the post-processing implies apodization or sidelobe control methods. In the application part, Ground Moving Target Indication (GMTI) is selected for study due to its interest to both military and civilian end-users. GMTI developed on UWB SAR relates to the moving target detection by focusing technique which can be combined with the space-time processing such as Displaced Phase Center Antenna (DPCA) and Space-Time Adaptive Processing (STAP).

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Oxygen is a critical component in living organisms and its concentration in tissue is an important parameter indicative of tissue metabolism, level of activity and health condition. As a result, measuring oxygen concentration in the tissue is essential in many clinical and research applications. Near Infrared Spectroscopy (NIRS)is a non invasive method of measuring tissue oxygenation using diffusion of light in the tissue. NIRS as a safe, non invasive and low cost monitoring technology has been used in a wide range of applications including monitoring muscle andbrain oxygenation, brain computer interface and rehabilitation. The motivation for this thesis has been to develop new signal processing methods and to investigate potential new applications for NIRS.One major characteristic of NIRS is its sensitivity to movement of the target tissue during the measurement. The effects of movements, known as motion artifacts, have limited clinical applications of NIRS in ambulant patients as well as experimental applications of NIRS monitoring in areas such as exercise science and sports medicine. In this thesis, we present a new method of reducing the effect of motion artifacts on NIRS signal using Discrete Wavelet Transform (DWT).One of the areas of application which can significantly benefit from reduction of motion artifacts is NIRS-based wearable sensors. In particular, a potential and unexplored application of NIRS is providing a monitoring method for people with bladder control problems, which occurs in a variety of conditions including spinal cord injury and stroke. We investigate the application of NIRS for detection of bladder filling to capacity using a wearable wireless monitoring sensor which canbe used to warn the subject once the bladder content reaches a predefined percentageof the full capacity. NIRS can be used as a functional neuroimaging method to identify brain activations during practice of a motor/cognitive task. One important question in this field is how the activated brain areas are interconnected. We thus investigate the use of phase information in NIRS channels to identify cortical connections and in particular, show the applicability of this approach in identifying language networkin human infants.

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The Hilbert transform is a widely used transform in signal processing. In this thesis we explore its use for three different applications: electrocardiography, the Hilbert-Huang transform and modulation. For electrocardiography, we examine how and why the Hilbert transform can be used for QRS complex detection. Also, what are the advantages and limitations of this method? The Hilbert-Huang transform is a very popular method for spectral analysis for nonlinear and/or nonstationary processes. We examine its connection with the Hilbert transform and show limitations of the method. Lastly, the connection between the Hilbert transform and single-sideband modulation is investigated.

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When a speaker is addressing an audience, a PA system consisting of a microphone and a loudspeaker is often used. If the microphone picks up too much of the loudspeaker energy, acoustic feedback in the form of an unwanted characteristic howling can occur. Limes Audio is a software company that specializes in improving sound quality in digital communications, mainly conference telephony, and has developed a reference product, the Magneto mixer, to demonstrate the capability of their software TrueVoice. The company now wishes to expand the field of usage for the Magneto mixer to enable it to work as a microphone mixer in PA scenarios, and for this, a feedback suppression feature is needed. This master’s thesis aims at surveying the market and the literature in the field and specifying the requirements for a feedback suppression feature. Three methods for suppressing howling feedback are evaluated through simulations and compared in terms of maximum stable gain (MSG) and subjective listening experience. The method that performed the best based on these criteria was acoustic feedback cancellation with a 5 Hz frequency shift on the loudspeaker signal. This method makes use of an adaptive filter to model the acoustic feedback path and to remove the feedback component from the microphone signal. In the simulations, the method was able to increase the stable gain by approximately 10 dB while maintaining a good sound quality.
När en talare talar för en publik används ofta ett PA system bestående av en mikrofon och en högtalare. Om mikrofonen tar upp för mycket av ljudet från högtalaren finns en överhängande risk för akustisk rundgång i form av ett karaktäristiskt oönskat tjut. Limes Audio är ett företag som utvecklar mjukvara för att förbättra ljudkvaliten i digital kommunikation, främst inom konferenstelefoni. De har utvecklat en demonstrationsprodukt, Magnetomixern, som kan användas som en konferenstelefon för att demonstrera deras programvara TrueVoice. Företaget önskar nu utveckla Magnetomixern till att även fungera som en ljudmixer för PA-scenarion, eller konferenstelefoni där intern ljudförstärkning i rummet behövs, och för detta behövs en funktion för att ta bort eventuell rundgång. Detta examensarbete har som mål att lägga grunden för en sådan funktion i Magnetomixern genom att undersöka marknaden och litteraturen på området. Tre metoder för att eliminera rundgång utvärderas i simuleringar och jämförs beträffande maximal stabil förstärkning (MSG) och subjektiv ljudkvalitet. Metoden ”Acoustic feedback cancellation” tillsammans med ett 5 Hz frekvensskifte på högtalarsignalen gav högst MSG och bäst ljudkvalitet. Metoden använder ett adaptivt filter för att approximera den akustiska återkopplingsvägen mellan högtalare och mikrofon samt tar bort rundgångskomponenter från mikrofonsignalen. I simuleringarna kunde metoden öka den maximala stabila förstärkningen med upp till 10 dB medan en god ljudkvalitet på talet bibehölls.

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There are lots of communication links and standards currently being employed to build systems today. These methods are in many way standardised, but far from everyone of them are. The trick is to select the communication method that best suit your needs. Also there is currently a trend that things have to be cheaper and have shorter time to market. That leads to more Component Off The Shelf (COTS) systems being build using commodity components. As one part of this work, Gigabit Ethernet is evaluated as a COTS-solution to building large, high-end systems. The computers used are running Windows and the protocol used over Ethernet will be both TCP and UDP. In this work an attempt is also made to evaluate one of the non-standard protocols, the Link Port protocol for the TigerSHARC 20X-series, which is a narrow-bus, doubledata- rate protocol, able to provide multi-gigabit-per-second performance. The studies have shown lots of interesting things, e.g. that using a standard desktop computer and network card, the theoretical throughput of TCP over Gigabit Ethernet can almost be met, reaching well over 900 Mbps. UDP performance gives on the other hand birth to a series of new questions about how to achieve good performance in a Windows environment, since it is constantly outperformed by the TCP connections. For the Link Port assessment a custom built IP block is made that is able to support the protocol in full speed, using a Xilinx Virtex 6 FPGA. The IP block is verified through simulation against a model of the Link Port protocol. It is also shown that the transmitter of the IP block is able to send successfully to the receiver IP block. The IP block that is created, is evaluated against some competing multi-gigabit protocols to show it in comparison, and it is a rather small IP block, capable of handling all transactions on the bus as long as data is provided by its host.
I nuläget finns många olika sorters kommunikationslänkar, både standardiserade och inte. Dessutom har krav på kortare tid till marknad i många fall gett upphov till att fler och fler system byggs med färdiga komponenter som kopplas ihop till hela system. Som ett led i detta används ofta väl beprövade tekniker som man vet fungerar. Som en del i det här arbetet kommer prestandan hos Gigabit Ethernet att utvärderas för vanliga persondatorer som kör Windows genom att använda TCP och UDPprotokollen. Dessa är utrustade med standardnätverkskort med låg kostnad och undersökningen går ut på att ta reda på om dessa kort och datorer kan användas till att bygga system med hög prestanda. Dessutom kommer ett ickestandardiserat protokoll, Länkportsprotokollet för TigerSHARC 20X-serien, som är ett protokoll som stödjer flera Gbps, att utvärderas för prestanda. Studien av TCP och UDP ledde till mycket intressanta resultat. Bland annat så har studien visat att man kan få TCP-kommunikation mellan två persondatorer att vara bara enstaka Mbps från det teoretiska maximala värdet, och kommunikationshastigheter långt över 900 Mbps har uppmätts för TCP. UDP i sin tur, väckte mer frågor än det nåddes svar, och den hade genomgående sämre prestanda än TCP-testerna. Det tyder på att man, när man gör program för vanliga persondatorer, inte tjänar något på att använda UDP utan snarare tvärt om. För studien av Länkportar så skapades ett IP-block som kan sända och ta emot data i samma hastighet som specificeras som den högsta i protokollbeskrivningen, fyra gigabit per sekund. Blocket verifierades genom simulering och genom att låta sändaren sända data som mottagaren lyckades ta emot. slu*tligen jämfördes Länkporten mot andra protokoll med liknande karakteristik, och jämförelsen framställer det skapade IP-blocket som ett gott alternativ till andra protokoll, mycket på grund av sin enkelhet.

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This dissertation is directed towards improving the state of art cardiac monitoring methods and automatic diagnosis of cardiac anomalies through modern engineering approaches such as adaptive signal processing, and machine learning methods. The dissertation will describe the invention and associated methods of a cardiac rhythm monitor dubbed the Integrated Vectorcardiogram (iVCG). In addition, novel machine learning approaches are discussed to improve diagnoses and prediction accuracy of cardiac diseases.It is estimated that around 17 million people in the world die from cardiac related events each year. It has also been shown that many of such deaths can be averted with long-term continuous monitoring and actuation. Hence, there is a growing need for better cardiac monitoring solutions. Leveraging the improvements in computational power, communication bandwidth, energy efficiency and electronic chip size in recent years, the Integrated Vectorcardiogram (iVCG) was invented as an answer to this problem. The iVCG is a miniaturized, integrated version of the Vectorcardiogram that was invented in the 1930s. The Vectorcardiogram provides full diagnostic quality cardiac information equivalent to that of the gold standard, 12-lead ECG, which is restricted to in-office use due to its bulky, obtrusive form. With the iVCG, it is possible to provide continuous, long-term, full diagnostic quality information, while being portable and unobtrusive to the patient. Moreover, it is possible to leverage this ‘Big Data’ and create machine learning algorithms to deliver better patient outcomes in the form of patient specific machine diagnosis and timely alerts.First, we present a proof-of-concept investigation for a miniaturized vectorcardiogram, the iVCG system for ambulatory on-body applications that continuously monitors the electrical activity of the heart in three dimensions. We investigate the minimum distance between a pair of leads in the X, Y and Z axes such that the signals are distinguishable from the noise. The target dimensions for our prototype iVCG are 3x3x2 cm and based on our experimental results we show that it is possible to achieve these dimensions.Following this, we present a solution to the problem of transforming the three VCG component signals to the familiar 12-lead ECG for the convenience of cardiologists. The least squares (LS) method is employed on the VCG signals and the reference (training) 12-lead ECG to obtain a 12x3 transformation matrix to generate the real-time ECG signals from the VCG signals.The iVCG is portable and worn on the chest of the patient and although a physician or trained technician will initially install it in the appropriate position, it is prone to subsequent rotation and displacement errors introduced by the patient placement of the device. We characterize these errors and present a software solution to correct the effect of the errors on the iVCG signals.We also describe the design of machine learning methods to improve automatic diagnosis and prediction of various heart conditions. Methods very similar to the ones described in this dissertation can be used on the long term, full diagnostic quality ‘Big Data’ such that the iVCG will be able to provide further insights into the health of patients.The iVCG system is potentially breakthrough and disruptive technology allowing long term and continuous remote monitoring of patient’s electrical heart activity. The implications are profound and include 1) providing a less expensive device compared to the 12-lead ECG system (the “gold standard”); 2) providing continuous, remote tele-monitoring of patients; 3) the replacement of current Holter shortterm monitoring system; 4) Improved and economic ICU cardiac monitoring; 5) The ability for patients to be sent home earlier from a hospital since physicians will have continuous remote monitoring of the patients.

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This thesis covers the investigation and application of continuous spatial models for multiple antenna signal processing. The use of antenna arrays for advanced sensing and communications systems has been facilitated by the rapid increase in the capabilities of digital signal processing systems. The wireless communications channel will vary across space as different signal paths from the same source combine and interfere. This creates a level of spatial diversity that can be exploited to improve the robustness and overall capacity of the wireless channel. Conventional approaches to using spatial diversity have centered on smart, adaptive antennas and spatial beam forming. Recently, the more general theory of multiple input, multiple output (MIMO) systems has been developed to utilise the independent spatial communication modes offered in a scattering environment.¶Underlying any multiple antenna system is the basic physics of electromagnetic wave propagation. Whilst a MIMO system may present a set of discrete inputs and outputs, each antenna element must interact with the underlying continuous spatial field. Since an electromagnetic disturbance will propagate through space, the field at different positions in the space will be interrelated. In this way, each position in the field cannot assume an arbitrary independent value and the nature of wave propagation places a constraint on the allowable complexity of a wave-field over space. To take advantage of this underlying physical constraint, it is necessary to have a model that incorporates the continuous nature of the spatial wave-field. ¶This thesis investigates continuous spatial models for the wave-field. The wave equation constraint is introduced by considering a natural basis expansion for the space of physically valid wave-fields. This approach demonstrates that a wave-field over a finite spatial region has an effective finite dimensionality. The optimal basis for representing such a field is dependent on the shape of the region of interest and the angular power distribution of the incident field. By applying the continuous spatial model to the problem of direction of arrival estimation, it is shown that the spatial region occupied by the receiver places a fundamental limit on the number and accuracy with which sources can be resolved. Continuous spatial models also provide a parsimonious representation for modelling the spatial communications channel independent of specific antenna array configurations. The continuous spatial model is also applied to consider limits to the problem of wireless source direction and range localisation.

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Since their discovery in 1978 by Hill et al. and the development of the transverse holographic technique for their fabrication by Meltz et al. in 1989, fiber Bragg gratings (FBG) have become an important device for applications in optical communications, optical signal processing and fiber-optical sensors.A superstructured fiber Bragg grating (SFBG), also called a sampled fiber Bragg grating, is a special FBG that consists of a several small FBGs placed in close proximity to one another. SFBGs have attracted much attention in recent years with the discovery of techniques allowing the creation of equivalent chirp or equivalent phase shifts. The biggest advantage of an SFBG with equivalent chirp or equivalent phase shifts is the possibility to design and fabricate gratings with greatly varying phase and amplitude responses by adjusting the spatial profile of the superstructure. The realization of SFBGs with equivalent chirp or equivalent phase shifts requires only sub-millimeter precision. This is a relief from the sub-micron precision required by traditional approaches.In this thesis, the mathematical modeling of FBGs and SFBGs is reviewed. The use of SFBGs for various applications in photonic microwave signal processing is considered. Four main topics are presented in this thesis. The first topic is the use of SFBG as a photonic true-time delay (TTD) beamformer for phased array antennas (PAAs). The second topic addresses non-linearities in the group delay response of an SFBG with equivalent chirp in its sampling period. An SFBG with an equivalent chirp using only a linear chirp coefficient may yield a group delay response that deviates from the linear response required by a TTD beamformer. In the thesis, a technique to improve the linearity of the group delay response is proposed and an adaptive algorithm to find the optimal linear and non-linear chirp coefficients to produce the best linear group delay response is described. Since no closed-form solution exists to represent the amplitude and phase responses of an SFBG, we rely on a Fourier transform analogy under a weak grating approximation as a starting point in the design of an SFBG. Simulations are then used to refine the response of the SFBG. The algorithm proposed provides an optimal set of chirp coefficients that minimizes the error in the group delay response. Four gratings are fabricated using the optimized chirp coefficients and their application in a TTD PAA system is discussed.The third topic discusses the use of an SFBG with equivalent phase shifts in its sampling period as a means to realize optical single sideband (SSB) modulation. SSB modulation eliminates the power penalty caused by chromatic dispersion experienced by an optical signal traveling through a long length of optical fiber. By introducing two π phase shifts through equivalent sampling to the SFBG, two ultra-narrow transmission bands are created in the grating stop band of the +/- 1st spectral orders. In the proposed system, a double-sideband plus carrier (DSB+C) modulated optical signal is sent to the input of an optical SSB filter based on the equivalent phase-shift SFBG in order to select the optical carrier and a single sideband, effectively blocking one sideband from propagating.Finally, the fourth topic focuses on the implementation of a photonic microwave bandpass filter based on an SFBG with equivalent chirp. Photonic microwave filters are used to process microwave signals in the optical domain. By using a technique called phase-modulation to intensity-modulation (PM-IM) conversion, a two-tap delay line filter is created with one negative tap. A single SFBG with a chirp in its sampling period is used as a means to achieve the PM-IM conversion for the two taps. Two phase modulated optical carriers are used to generate the two taps, each entering a different port of the SFBG and thus experiencing an opposite dispersion value. The two optical signals are then recombined before being sent to a photodetector (PD) where the filtered microwave signal is recovered.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1989.
Includes bibliographical references.
Partly funded by US Air Force Office of Scientific Research. AFOSR-86-0164 Partly funded by Draper Laboratories.
by William S. Song.
Ph.D.

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This thesis details theoretical and empirical work that draws from two main subject areas: Machine Learning (ML) and Digital Signal Processing (DSP). A unified general framework is given for the application of sparse machine learning methods to multivariate signal processing. In particular, methods that enforce sparsity will be employed for reasons of computational efficiency, regularisation, and compressibility. The methods presented can be seen as modular building blocks that can be applied to a variety of applications. Application specific prior knowledge can be used in various ways, resulting in a flexible and powerful set of tools. The motivation for the methods is to be able to learn and generalise from a set of multivariate signals. In addition to testing on benchmark datasets, a series of empirical evaluations on real world datasets were carried out. These included: the classification of musical genre from polyphonic audio files; a study of how the sampling rate in a digital radar can be reduced through the use of Compressed Sensing (CS); analysis of human perception of different modulations of musical key from Electroencephalography (EEG) recordings; classification of genre of musical pieces to which a listener is attending from Magnetoencephalography (MEG) brain recordings. These applications demonstrate the efficacy of the framework and highlight interesting directions of future research.

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Asynchronous circuits, in comparison with synchronous circuits, have the potential to offer power and speed advantages combined with improved design reuse and composition. Continual improvements in fabrication technology increase die sizes and decrease device sizes, increasing the difficulty of clock distribution and timing validation in synchronous designs. As a consequence there has been a resurgence of interest in asynchronous circuits and design methods. This work examines aspects of asynchronous micropipeline controller design, verification and application. A micropipeline controller circuit is presented and compared with other controller circuits. A method for modelling asynchronous circuits using process algebra at an individual gate level is examined and used to verify the controller circuit. Two applications in the context of the discrete cosine transform (DCT) are then explored. The first application is an area and power efficient circuit for bit serialisation and matrix transposition. This can be used either to embed a synchronous bit-serial processing core into a bit-parallel environment or to perform matrix transposition as part of a DCT. Key elements are modelled using process algebra. The second application is an initial attempt at an asynchronous application specific processor which is used to implement the DCT, and is intended to be extendible to other signal transforms. The presented micropipeline controller was found to be superior to other controllers for linear micropipelines, which are key parts in the applications studied. The modelling method used has been found suitable for the verification of manually designed gate-level circuits. Finally the applications have illustrated that the use of asynchronous methods makes new or simpler architectures possible.

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The increases in power dissipation and transient fault rates experienced in applications based on state-of-the-art integrated processor technologies now lead to bottlenecks in energy consumption, processing-throughput scaling, and system reliability. Therefore, application and system designers are now beginning to accept the notion of error tolerance across the system stack, i.e., new methods that detect and tolerate (or mitigate) faults in exchange for resource efficiency at the application, runtime, compiler, architecture and hardware layers are now under intensive investigation. Within this context, this thesis proposes and develops novel approaches to data packing and numerical entanglement for accelerated, error-tolerant, signal processing applications. In particular, different data packing strategies suitable for integer and floating point inputs are studied, and they are subsequently used to create the new concept of numerical entanglement, which is proposed for highly-reliable numerical processing of integer data streams. The results of this thesis demonstrate that up to seven-fold decrease of processing time or energy consumption can be obtained if the signal processing application can operate under increased margins in the mean-squared error or signal-to-noise ratio of linear or sesquilinear processing kernels. In addition, for methods that scale processing throughput or energy consumption at the expense of reliability, our proposal for highly-reliable numerical processing of integer data streams is shown to detect all possible system-induced errors in one out of M input streams (M=3) with minimal overhead. Therefore, the thesis proposals can be used in software or hardware systems for resource scaling of error-tolerant signal processing applications.

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This thesis describes some advances of Mathematical Morphology (MM), in order to improve the performance of MM filters in I-D signal processing, . especially in the application to power system protection. MM methodologies are founded on set-theoretic concepts and nonlinear superpositions of signals and images. The morphological operations possess outstanding geometrical properties which make it undoubted that they are efficient image processing methods. However in I-D signal processing, MM filters are not widely employed. To explore the applications of MM for I-D signal processing, our contributions in this area can be summarized in the following two aspects. Firstly, the fram.ework of the traditional signal processing methods is based on the frequency domain representation of the signal and the analysis of the operators' transfer function in the frequency domain. But to the morphological operations, their representations in the frequency domain are uncertain. In order to tackle this problem, this thesis presents our attempt to describe the weighted morphological dilation in the frequency domain. Under certain restrictions to the signal and the structuring element, weighted dilation is transformed to a mathematical expression in the frequency domain. Secondly, although the frequency domain analysis plays an important role in signal processing, the geometrical properties of a signal such as the shape of the signal cannot be ignored. MM is an effective method in dealing with such problems. In this thesis, based on the theory of Morphological Wavelet (MW), three multi-resolution signal decomposition schemes are presented. They are Multiresolution Morphological Top-Hat scheme (MMTH), Multi-resolution Morphov logical Gradient scheme (MMG) and Multi-resolution Noise Tolerant Morphological Gradient scheme (MNTMG). The MMTH scheme shows its significant effect in distinguishing symmetrical features from asymmetrical features on the waveform, which owes to its signal analysis operator: morphological Top-Hat transformation, an effective morphological technique. In this thesis, the MMTH scheme is employed in the identification of transformer magnetizing inrush curr~nt from internal fault. Decomposing the signal by MMTH, the asymmetrical features of the inrush waveform are exposed, and the other irrelevant components are attenuated. The MMG scheme adopts morphological gradient, a commonly used operator for edge detection in image and signal processing, as its signal analysis / operator. The MMG scheme bears significant property in characterizing and recognizing the sudden changes with sharp peaks and valleys on the waveform. Furthermore, to the MMG scheme, by decomposing the signal into different levels, the higher the level is processed, the more details of the sudden changes are revealed. In this thesis, the MMG scheme is applied for the design of fault locator of power transmission lines, by extracting the transient features directly from fault-generated transient signals. The MNTMG decomposition scheme can effectively reduce the noise and extract transient features at the same time. In this thesis, the MNTMG scheme is applied to extract the fault generated transient wavefronts from noise imposed signals in the application of fault location of power transmission lines. The proposed contributions focus on the effect of weighted dilation in the frequency domain, constructions of morphological multi-resolution decomposition schemes and their applications in power systems.

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This thesis presents two novel algorithms for converting a normalised binary floating point number into a binary logarithmic number with the single-precision of a floating point number. The thesis highlights the importance of logarithmic number systems in real-time DSP applications. A real-time cross-correlation application where logarithmic signal processing is used to simplify the complex computation is presented. The first algorithm presented in this thesis comprises two stages. A piecewise linear approximation to the original logarithmic curve is performed in the first stage and a scaled-down normalised error curve is stored in the second stage. The algorithm requires less than 20 kbits of ROM and a maximum of three small multipliers. The architecture is implemented on Xilinx's Spartan3 and Spartan6 FPGA family. Synthesis results confirm that the algorithm operates at a frequency of 42.3 MHz on a Spartan3 device and 127.8 MHz on a Spartan6. Both solutions have a pipeline latency of two clocks. The operating speed increases to 71.4 MHz and 160 MHz respectively when the pipeline latencies increase to eight clocks. The proposed algorithm is further improved by using a PWL (Piece-Wise Linear) approximation of the transform curve combined with a PWL approximation of a scaled version of the normalized segment error. A hardware approach for reducing the memory with additional XOR gates in the second stage is also presented. The architecture presented uses just one 18k bit Block RAM (BRAM) and synthesis results indicate operating frequencies of 93 and 110 MHz when implemented on the Xilinx Spartan3 and Spartan6 devices respectively. Finally a novel prototype of an FPGA-based four channel correlation velocimetry system is presented. The system operates at a higher sampling frquency than previous published work and outputs the new result after every new sample it receives. The system works at a sampling frequency of 195.31 kHz and a sample resolution of 12 bits. The prototype system calculates a delay in a range of 0 to 2.6 ms with a resolution of 5.12 us.

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Cette étude présente des contributions en traitement statistique du signal avec des applications biomédicales. La thèse est divisée en deux parties. La première partie traite de la détection des hotspots à l'interface des protéines. Les hotspots sont les résidus dont les contributions énergétiques sont les plus importantes dans l'interaction entre protéines. Les forêts aléatoires (Random Forests) sont utilisées pour la classification. Une nouvelle famille de descripteurs de hotspot est également introduite. Ces descripteurs sont basés seulement sur la séquence primaire unidimensionnelle d'acides aminés constituant la protéine. Aucune information sur la structure tridimensionnelle de la protéine ou le complexe n'est nécessaire. Ces descripteurs, capitalisant les caractéristiques fréquentielle des protéines, nous permettent de savoir la façon dont la séquence primaire d'une protéine peut déterminer sa structure tridimensionnelle et sa fonction. Dans la deuxième partie, le RDT (Random Distortion Testing), un test robuste d'hypothèse, est considéré. Son application en détection du signal a montré que le RDT peut résister aux imperfections du modèle d'observation. Nous avons également proposé une extension séquentielle du RDT. Cette extension s'appelle le RDT Séquentiel. Trois problèmes classiques de détection d'écart/distorsion du signal sont reformulés et résolus dans le cadre du RDT. En utilisant le RDT et le RDT Séquentiel, nous étudions la détection d'AutoPEEP (auto-Positive End Expiratory Pressure), une anomalie fréquente en ventilation mécanique. C'est la première étude de ce type dans la littérature. L'extension à la détection d'autres types d'asynchronie est également étudiée et discutée. Ces détecteurs d'AutoPEEP et d'asynchronies sont les éléments principaux de la plateforme de suivi de manière automatique et continue l'interface patient-ventilateur en ventilation mécanique
This PhD thesis presents some contributions to Statistical Signal Processing with applications in biomedical engineering. The thesis is separated into two parts. In the first part, the detection of protein interface hotspots ¿ the residues that play the most important role in protein interaction ¿ is considered in the Machine Learning framework. The Random Forests is used as the classifier. A new family of protein hotspot descriptors is also introduced. These descriptors are based exclusively on the primary one-dimensional amino acid sequence. No information on the three dimensional structure of the protein or the complex is required. These descriptors, capturing the protein frequency characteristics, make it possible to get an insight into how the protein primary sequence can determine its higher structure and its function. In the second part, the RDT (Random Distortion Testing) robust hypothesis testing is considered. Its application to signal detection is shown to be resilient to model mismatch. We propose an extension of RDT in the sequential decision framework, namely Sequential RDT. Three classical signal deviation/distortion detection problems are reformulated and cast into the RDT framework. Using RDT and Sequential RDT, we investigate the detection of AutoPEEP (auto-Positive End Expiratory Pressure), a common ventilatory abnormality during mechanical ventilation. This is the first work of that kind in the state-of-the-art. Extension to the detection of other types of asynchrony is also studied and discussed. These early detectors of AutoPEEP and asynchrony are key elements of an automatic and continuous patient-ventilator interface monitoring framework

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Chaos, an unstable steady-state phenomenon, arises in apparently random optical sequences from semiconductor lasers subjected to reflection. This condition, referred to as cacophony, might provide a new pseudo-random source for use in coherent fibre optic systems. Coherent optical signal processing is expected to find substantially increased application, especially in local data networks. An optical spread-spectrum source may suit two apparent needs of these networks: 1) a high resolution optical time-domain reflectometer, using correlation of sequences, which can identify the closely spaced features found in these systems; and 2) data security through optical frequency-hopping encryption, especially in broadcast data networks. The link between cacophony and chaotic processes suggests that, although noise like, the spectral evolution of cacophony is deterministic. This implied reproducibility, akin the binary pseudo-random sequences, would be advantageous in spread-spectrum applications. Experimental examination of reflection effects on lasers has explored various lasing and external reflection conditions. Computer simulation of cacophonous generators supplement the experimental work with quick trials of experiments under typical, hypothetical, or even unrealisable conditions. A new in-phase and quadrature equivalent circuit models optical magnitude with phase information, and with modest computing requirements. Cacophony has been generated experimentally and in the computer model, and reproducible sequences up to 10ns long have been demonstrated. Modelling shows that reproducibility may be improved if conditions, especially at the start of lasing, are better controlled. It is concluded that, in order to reach the kind of optical sequence reproducibility that is called for in the applications described above, it is probably necessary to introduce quantisation into the generator. The work has attempted to characterize optical cacophony, and has perhaps added some knowledge to the general problems of coherent optical signal processing.

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The overall purpose with this thesis is to investigate and provide computationally efficient methods for estimation and detection. The focus is on airborne applications, and we seek estimation and detection methods which are accurate and reliable yet effective with respect to computational load. In particular, the methods shall be optimized for terrain-aided navigation andcollision avoidance respectively. The estimation part focuses on particle filtering and the in general much more efficient marginalized particle filter. The detection part focuses on finding efficient methods for evaluating the probability of extreme values. This is achieved by considering the, in general, much easier task to compute the probability of level-crossings. The concept of aircraft navigation using terrain height information is attractive because of the independence of external information sources. Typicallyterrain-aided navigation consists of an inertial navigation unit supported by position estimates from a terrain-aided positioning (TAP) system. TAP integrated with an inertial navigation system is challenging due to its highly nonlinear nature. Today, the particle filter is an accepted method for estimation of more or less nonlinear systems. At least when the requirements on computational load are not rigorous. In many on-line processing applications the requirements are such that they prevent the use of theparticle filter. We need more efficient estimation methods to overcome this issue, and the marginalized particle filter constitutes a possible solution. The basic principle for the marginalized particle filter is to utilize linear and discrete substructures within the overall nonlinear system. These substructures are used for efficient estimation by applying optimal filters such as the Kalman filter. The computationally demanding particle filter can then be concentrated on a smaller part of the estimation problem. The concept of an aircraft collision avoidance system is to assist or ultimately replace the pilot in order to to minimize the resulting collision risk. Detection is needed in aircraft collision avoidance because of the stochastic nature of thesensor readings, here we use information from video cameras. Conflict is declared if the minimum distance between two aircraft is less than a level. The level is given by the radius of a safety sphere surrounding the aircraft.We use the fact that the probability of conflict, for the process studied here, is identical to the probability for a down-crossing of the surface of the sphere. In general, it is easier to compute the probability of down-crossings compared to extremes. The Monte Carlo method provides a way forward to compute the probability of conflict.However, to provide a computationally tractable solution we approximate the crossing of the safety sphere with the crossing of a circular disc. The approximate method yields a result which is as accurate as the Monte Carlo method but the computational load is decreased significantly.

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Brain Computer Interface (BCI) is a direct communication channel between brain and computer. It allows the users to control the environment without the need to control muscle activity [1-2]. P300-Speller is a well known and widely used BCI system that was developed by Farwell and Donchin in 1988 [3]. The accuracy level of the P300-BCI Speller as measured by the percent of communicated characters correctly identified by the system depends on the ability to detect the P300 event related potential (ERP) component among the ongoing electroencephalography (EEG) signal. Different techniques have been tested to reduce the number of trials needed to be averaged together to allow the reliable detection of the P300 response. Some of them have achieved high accuracies in multiple-trial P300 response detection. However the accuracy of single trial P300 response detection still needs to be improved. In this research, two single trial P300 response classification methods were designed. One is based on independent component analysis (ICA) with blind tracking and the other is based on variance analysis. The purpose of both methods is to detect a chosen character in real-time in the P300-BCI speller. The experimental results demonstrate that the proposed methods dramatically reduce the signal processing time, improve the data communication rate, and achieve overall accuracy of 79.1% for ICA based method and 84.8% for variance analysis based method in single trial P300 response classification task. Both methods showed better performance than that of the single trial stepwise linear discriminant analysis (SWLDA), which has been considered as the most accurate and practical technique working with P300-BCI Speller.