This thesis describes the implementation of a speaker dependent connected-digit recognizer using continuous Hidden Markov Modeling (HMM). The speech recognition system was implemented using MATLAB and on the ADSP-2181, a digital signal processor manufactured by Analog Devices. Linear predictive coding (LPC) analysis was first performed on a speech signal to model the characteristics of the vocal tract filter. A 7 state continuous HMM with 4 mixture density components was used to model each digit. The Viterbi reestimation method was primarily used in the training phase to obtain the parameters of the HMM. Viterbi decoding was used for the recognition phase. The system was first implemented as an isolated word recognizer. Recognition rates exceeding 99% were obtained on both the MATLAB and the ADSP-2181 implementations. For continuous word recognition, several algorithms were implemented and compared. Using MATLAB, recognition rates exceeding 90% were obtained. In addition, the algorithms were implemented on the ADSP-2181 yielding recognition rates comparable to the MATLAB implementation. / Master of Science

Cognitive radio is a growing field in communications which allows a radio to automatically configure its transmission or reception properties in order to reduce interference, provide better quality of service, or allow for more users in a given spectrum. Such processes require several complex features that are currently being utilized in cognitive radio. Two such features, spectrum sensing and identification, have been implemented in numerous ways, however, they generally suffer from high computational complexity. Additionally, Hidden Markov Models (HMMs) are a widely used mathematical modeling tool used in various fields of engineering and sciences. In electrical and computer engineering, it is used in several areas, including speech recognition, handwriting recognition, artificial intelligence, queuing theory, and are used to model fading in communication channels. The research presented in this thesis proposes a new approach to spectrum identification using a parallel implementation of Hidden Markov Models. Algorithms involving HMMs are usually implemented in the traditional serial manner, which have prohibitively long runtimes. In this work, we study their use in parallel implementations and compare our approach to traditional serial implementations. Timing and power measurements are taken and used to show that the parallel implementation can achieve well over 100Ã speedup in certain situations. To demonstrate the utility of this new parallel algorithm using graphics processing units (GPUs), a new method for signal identification is proposed for both serial and parallel implementations using HMMs. The method achieved high recognition at -10 dB Eb/N0. HMMs can benefit from parallel implementation in certain circumstances, specifically, in models that have many states or when multiple models are used in conjunction. / Master of Science

The Interactive Video Data System (IVDS) project began with an initial abstract concept of achieving interactive television by transmitting hidden digital information in the audio of commercials. Over the course of three years such a communication method was successfully developed, the hardware systems to realize the application were designed and built, and several full-scale field tests were conducted. The novel coding scheme satisfies all of the design constraints imposed by the project sponsors. By taking advantage of psychoacoustic properties, the hidden digital signature is inaudible to most human observers yet is detectable by the hardware decoder. The communication method is also robust against most extraneous room noise as well as the wow and flutter of videotape machines. The hardware systems designed for the application have been tested and work as intended. A triple-stage audio amplifier buffers the input signal, eliminates low frequency interference such as human voices, and boosts the filtered result to an appropriate level. A codec samples the filtered and amplified audio, and feeds it into the digital signal processor. The DSP, after applying a pre-emphasis and compensation filter, performs the data extraction by calculating FFTs, compensating for frequency shifts, estimating the digital signature, and verifying the result via a cyclic redundancy check. It then takes action appropriate for the command specified in the digital signature. If necessary it will verbally prompt and provide information to the user, and will decode infrared signals from a remote control. The results of interactions are transmitted by radio frequency spread spectrum to a cell cite, where they are then forwarded to the host computer. / Master of Science

Specific emitter identification is a technology used to uniquely identify a class of wireless devices, and in some cases a single device. Minute differences in the implementation of a wireless communication standard from one device manufacturer to another make it possi- ble to extract a wireless "fingerprint" from the transmitted signal. These differences may stem from imperfect radio frequency (RF) components such as filters and power amplifiers. However, the problem of identifying a wireless device through analysis of these key signal characteristics presents several difficulties from an algorithmic perspective. Given that the differences in these features can be extremely subtle, in general a high signal to noise ratio (SNR) is necessary for a sufficient probability of correct detection. If a sufficiently high SNR is not guaranteed, then some from of identification algorithm which operates well in low SNR conditions must be used. Cyclostationary analysis offers a method of specific emitter iden- tification through analysis of second order spectral correlation features which can perform well at relatively low SNRs. The eigenvector/eigenvalue decomposition (EVD) is capable of separating principal components from uncorrelated gaussian noise. This work proposes a technique of specific emitter identification which utilizes the principal components of the EVD of the spectral correlation function which has been arranged into a square matrix. An analysis of this EVD-based SEI technique is presented herein, and some limitations are identified. Analysis is constrained to orthogonal frequency division multiplexing (OFDM) using the IEEE 802.16 specification (used for WiMAX) as a guideline for a variety of pilot arrangements. / Master of Science

Sedan flera hundra år tillbaka har människor immigrerat till Sverige. Efterhand har denna invandring mer och mer reglerats genom olika lagar och samarbeten. I Sverige har det så uppstått en grupp av personer som inte är asylsökande och som av olika skäl lever i landet utan uppehållstillstånd. En del av dessa har tidigare sökt asyl men fått avslag, medan andra aldrig sökt asyl. Dessa personer kan inte åtnjuta hälso- och sjukvård på samma villkor som den svenska befolkningen eller ens på de villkor som staten beslutat att asylsökande ska ha rätt till. Den vård gömda flyktingar har rätt till är den vård som klassas som omedelbar. Många gömda flyktingar är dessutom rädda för att söka vård och deras vårdbehov täcks till stor del av ideella organisationers insatser. Som sjuksköterska kan man komma att möta denna patientgrupp i stort sett var man än arbetar och det är då av största vikt att man funderat över sina etiska ställningstaganden och satt sig in i de lagar som är aktuella. I denna uppsats granskas och sammanställes den litteratur som finns att tillgå kring gömda flyktingar och sjukvård. Det sammanställda materialet diskuteras sedan ur en vårdvetenskaplig synvinkel och med hjälp av begreppen livsvärld, hälsa och lidande. Man kan i det valda materialet konstatera att det saknas litteratur skriven med vårdvetenskaplig ansats. I de åtta texterna som granskats har tre fokus hittats; Barns situation, Att leva som gömd flykting samt Hinder och möjligheter för tillgång till vård. Slutligen diskuteras vikten av vårdvetenskaplig forskning på området med mål att underlätta för kliniskt verksamma sjuksköterskor i mötet med denna patientgrupp. / <p>Program: Sjuksköterskeutbildning</p><p>Uppsatsnivå: C</p>

Orchards demand large areas of land, thus they are often situated far from major population centres. As a result it is often difficult to obtain the necessary personnel, limiting both growth and productivity. However, if autonomous robots could be integrated into the operation of the orchard, the manpower demand could be reduced. A key problem for any autonomous robot is localisation; how does the robot know where it is? In agriculture robots, the most common approach is to use GPS positioning. However, in an orchard environment, the dense and tall vegetation restricts the usage to large robots that reach above the surroundings. In order to enable the use of smaller robots, it is instead necessary to use a GPS independent system. However, due to the similarity of the environment and the lack of strong recognisable features, it appears unlikely that typical non-GPS solutions will prove successful. Therefore we present a GPS independent localisation system, specifically aimed for orchards, that utilises the inherent structure of the surroundings. Furthermore, we examine and individually evaluate three related sub-problems. The proposed system utilises a 3D point cloud created from a 2D LIDAR and the robot’s movement. First, we show how the data can be segmented into individual trees using a Hidden Semi-Markov Model. Second, we introduce a set of descriptors for describing the geometric characteristics of the individual trees. Third, we present a robust localisation method based on Hidden Markov Models. Finally, we propose a method for detecting segmentation errors when associating new tree measurements with previously measured trees. Evaluation shows that the proposed segmentation method is accurate and yields very few segmentation errors. Furthermore, the introduced descriptors are determined to be consistent and informative enough to allow localisation. Third, we show that the presented localisation method is robust both to noise and segmentation errors. Finally it is shown that a significant majority of all segmentation errors can be detected without falsely labeling correct segmentations as incorrect. / Eftersom fruktodlingar kräver stora markområden är de ofta belägna långt från större befolkningscentra. Detta gör det svårt att finna tillräckligt med arbetskraft och begränsar expansionsmöjligheterna. Genom att integrera autonoma robotar i drivandet av odlingarna skulle arbetet kunna effektiviseras och behovet av arbetskraft minska. Ett nyckelproblem för alla autonoma robotar är lokalisering; hur vet roboten var den är? I jordbruksrobotar är standardlösningen att använda GPS-positionering. Detta är dock problematiskt i fruktodlingar, då den höga och täta vegetationen begränsar användandet till större robotar som når ovanför omgivningen. För att möjliggöra användandet av mindre robotar är det istället nödvändigt att använda ett GPS-oberoende lokaliseringssystem. Detta problematiseras dock av den likartade omgivningen och bristen på distinkta riktpunkter, varför det framstår som osannolikt att existerande standardlösningar kommer fungera i denna omgivning. Därför presenterar vi ett GPS-oberoende lokaliseringssystem, speciellt riktat mot fruktodlingar, som utnyttjar den naturliga strukturen hos omgivningen.Därutöver undersöker vi och utvärderar tre relaterade delproblem. Det föreslagna systemet använder ett 3D-punktmoln skapat av en 2D-LIDAR och robotens rörelse. Först visas hur en dold semi-markovmodell kan användas för att segmentera datasetet i enskilda träd. Därefter introducerar vi ett antal deskriptorer för att beskriva trädens geometriska form. Vi visar därefter hur detta kan kombineras med en dold markovmodell för att skapa ett robust lokaliseringssystem.Slutligen föreslår vi en metod för att detektera segmenteringsfel när nya mätningar av träd associeras med tidigare uppmätta träd. De föreslagna metoderna utvärderas individuellt och visar på goda resultat. Den föreslagna segmenteringsmetoden visas vara noggrann och ge upphov till få segmenteringsfel. Därutöver visas att de introducerade deskriptorerna är tillräckligt konsistenta och informativa för att möjliggöra lokalisering. Ytterligare visas att den presenterade lokaliseringsmetoden är robust både mot brus och segmenteringsfel. Slutligen visas att en signifikant majoritet av alla segmenteringsfel kan detekteras utan att felaktigt beteckna korrekta segmenteringar som inkorrekta.

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Estimation of the number of hidden states is challenging in hidden Markov models. Motivated by the analysis of a specific type of cell adhesion experiments, a new frame-work based on hidden Markov model and double penalized order selection is proposed. The order selection procedure is shown to be consistent in estimating the number of states. A modified Expectation-Maximization algorithm is introduced to efficiently estimate parameters in the model. Simulations show that the proposed framework outperforms existing methods. Applications of the proposed methodology to real data demonstrate the accuracy of estimating receptor-ligand bond lifetimes and waiting times which are essential in kinetic parameter estimation. The second part of the thesis is concerned with prediction of a deterministic response function y at some untried sites given values of y at a chosen set of design sites. The intended application is to computer experiments in which y is the output from a computer simulation and each design site represents a particular configuration of the input variables. A Bayesian version of the cubic spline method commonly used in numerical analysis is proposed, in which the random function that represents prior uncertainty about y is taken to be a specific stationary Gaussian process. An MCMC procedure is given for updating the prior given the observed y values. Simulation examples and a real data application are given to compare the performance of the Bayesian cubic spline with that of two existing methods.

One of the objectives of this study is to develop a methodology, together with a set of software programs that evaluate, in a power system, the risks of catastrophic failures caused by hidden failures in the hardware or software components of the protection system. The disturbance propagation mechanism is revealed by the analysis of the 1977 New York Blackout. The step-by-step process of estimating the relay hidden failure probability is presented. A Dynamic Event Tree for the risk-based analysis of system catastrophic failures is proposed. A reduced 179-bus WSCC sample system is studied and the simulation results obtained from California sub-system are analyzed. System weak links are identified in the case study. The issues relating to the load and generation uncertainties for the risk assessment of system vulnerabilities are addressed. A prototype system - the Hidden Failure Monitoring and Control System (HFMCS) - is proposed to mitigate the risk of power system catastrophic failures. Three main functional modules - Hidden Failure Monitoring, Hidden Failure Control and Misoperation Tracking Database - and their designs are presented. Hidden Failure Monitoring provides the basis that allows further control actions to be initiated. Hidden Failure Control is realized by using Adaptive Dependability/Security Protection, which can effectively stop possible relay involvement from triggering or propagating disturbance under stressed system conditions. As an integrated part of the HFMCS, a Misoperation Tracking Database is proposed to track the performance of automatic station equipment, hence providing automatic management of misoperation records for hidden failure analysis. / Ph. D.

The performance of automatic speech recognition systems often degrades in adverse conditions where there is a mismatch between training and testing conditions. This is true for most modern systems which employ Hidden Markov Models (HMMs) to decode speech utterances. One strategy is to map the distorted features back to clean speech features that correspond well to the features used for training of HMMs. This can be achieved by treating the noisy speech as the distorted version of the clean speech of interest. Under this framework, we can track and consequently extract the underlying clean speech from the noisy signal and use this derived signal to perform utterance recognition. Particle filter is a versatile tracking technique that can be used where often conventional techniques such as Kalman filter fall short. We propose a particle filters based algorithm to compensate the corrupted features according to an additive noise model incorporating both the statistics from clean speech HMMs and observed background noise to map noisy features back to clean speech features. Instead of using specific knowledge at the model and state levels from HMMs which is hard to estimate, we pool model states into clusters as side information. Since each cluster encompasses more statistics when compared to the original HMM states, there is a higher possibility that the newly formed probability density function at the cluster level can cover the underlying speech variation to generate appropriate particle filter samples for feature compensation. Additionally, a dynamic joint tracking framework to monitor the clean speech signal and noise simultaneously is also introduced to obtain good noise statistics. In this approach, the information available from clean speech tracking can be effectively used for noise estimation. The availability of dynamic noise information can enhance the robustness of the algorithm in case of large fluctuations in noise parameters within an utterance. Testing the proposed PF-based compensation scheme on the Aurora 2 connected digit recognition task, we achieve an error reduction of 12.15% from the best multi-condition trained models using this integrated PF-HMM framework to estimate the cluster-based HMM state sequence information. Finally, we extended the PFC framework and evaluated it on a large-vocabulary recognition task, and showed that PFC works well for large-vocabulary systems also.