Although handwritten signature verification has been extensively researched, it has not achieved optimum accuracy rate. Therefore, efficient and accurate signature verification techniques are required since signatures are still widely used as a means of personal verification. This research work presents efficient distance-based classification techniques as an alternative to supervised learning classification techniques (SLTs). Two different feature extraction techniques were used, namely the Enhanced Modified Direction Feature (EMDF) and the Local Directional Pattern feature (LDP). These were used to analyze the effect of using several different distance-based classification techniques. Among the classification techniques used, are the cosine similarity measure, Mahalanobis, Canberra, Manhattan, Euclidean, weighted Euclidean and fractional distances. Additionally, the novel weighted fractional distances, as well as locally optimized resampling of feature vector sizes were tested. The best accuracy was achieved through applying a combination of the weighted fractional distances and locally optimized resampling classification techniques to the Local Directional Pattern feature extraction. This combination of multiple distance-based classification techniques achieved accuracy rate of 89.2% when using the EMDF feature extraction technique, and 90.8% when using the LDP feature extraction technique. These results are comparable to those in literature, where the same feature extraction techniques were classified with SLTs. The best of the distance-based classification techniques were found to produce greater accuracy than the SLTs. / Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2012.

This monograph addresses control and filtering problems for systems with sudden changes in their behavior and whose changes are detected and estimated by an imperfect detector. More precisely it considers continuous-timeMarkov Jump Linear Systems (MJLS) where the current mode of operation is estimated by a detector. This detector is assumed to be imperfect in the sense that it is possible that the detected mode of operation diverges from the real mode of operation. Furthermore the probabilities for these detections are considered to be known. It is assumed that the detector has its own dynamic, which means that the detected mode of information can change independently from the real mode of operation. The novelty of this approach lies in how uncertainties are modeled. A Hidden Markov Model (HMM) is used to model the uncertainties introduced by the detector. For these systems the following problems are addressed: i) Stochastic Stabilizability in mean-square sense, ii) H2 control, iii) H? control and iv) the H? filtering problem. Solutions based on Linear Matrix Inequalities (LMI) are developed for each of these problems. In case of the H2 control problem, the solutionminimizes an upper bound for the H2 norm of the closed-loop control system. For the H? control problem a solution is presented that minimizes an upper bound for the H? norm of the closed-loop control system. In the case of the H? filtering, the solution presented minimizes the H? norm of a system representing the estimation error. The solutions for the control problems are illustrated using a numerical example modeling a simple two-tank process. / Esta monografia aborda problemas de controle e filtragem em sistemas com saltos espontâneos que alteram seu comportamento e cujas mudanças são detectadas e estimadas por um detector imperfeito. Mais precisamente, consideramos sistemas lineares cujos saltos podem ser modelados usando um processo markoviano (Markov Jump Linear Systems) e cujo modo de operação corrente é estimado por um detector. O detector é considerado imperfeito tendo em vista a possibilidade de divergência entre o modo real de operação e o modo de operação detectado. Ademais, as probabilidades das deteccões são consideradas conhecidas. Assumimos que o detector possui uma dinâmica própria, o que significa que o modo de operação detectado pode mudar independentemente do modo real de operação. A novidade dessa abordagem está na modelagem das incertezas. Um processo oculto de Markov (HMM) é usado para modelar as incertezas introduzidas pelo detector. Para esses sistemas, os seguintes problemas são abordados: i) estabilidade quadrática ii) controle H2, iii) controle H? e iv) o problema da filtragem H?. Soluções baseadas em Desigualdades de Matriciais Lineares (LMI) são desenvolvidas para cada um desses problemas. No caso do problema de controle H2, a solução minimiza um limite superior para a norma H2 do sistema de controle em malha fechada. Para o problema H? -controle é apresentada uma solução que minimiza um limite superior para a norma H? do sistema de controle em malha fechada. No caso da filtragem H?, a solução apresentada minimiza a norma H? de um sistema que representa o erro de estimativa. As soluções para os problemas de controle são ilustradas usando um exemplo numérico que modela um processo simples de dois tanques.

A dissertation submitted in partial ful lment of the degree of Master of Science in Engineering (Electrical) (50/50) in the Faculty of Engineering and the Built Environment Department of Electrical and Information Engineering May 2017 / Numerous applications in a wide variety of elds has resulted in a rich history of research into optimisation for scheduling. Although it is a fundamental form of the problem, the single machine scheduling problem with two or more objectives is known to be NP-hard. For this reason we consider the single machine problem a good test bed for solution algorithms. While there is a plethora of research into various aspects of scheduling problems, little has been done in evaluating the performance of the Simulated Annealing algorithm for the fundamental problem, or using it in combination with other techniques. Speci cally, this has not been done for minimising total weighted earliness and tardiness, which is the optimisation objective of this work. If we consider a mere ten jobs for scheduling, this results in over 3.6 million possible solution schedules. It is thus of de nite practical necessity to reduce the search space in order to nd an optimal or acceptable suboptimal solution in a shorter time, especially when scaling up the problem size. This is of particular importance in the application area of packet scheduling in wireless communications networks where the tolerance for computational delays is very low. The main contribution of this work is to investigate the hypothesis that inserting a step of pre-sampling by Markov Chain Monte Carlo methods before running the Simulated Annealing algorithm on the pruned search space can result in overall reduced running times. The search space is divided into a number of sections and Metropolis-Hastings Markov Chain Monte Carlo is performed over the sections in order to reduce the search space for Simulated Annealing by a factor of 20 to 100. Trade-o s are found between the run time and number of sections of the pre-sampling algorithm, and the run time of Simulated Annealing for minimising the percentage deviation of the nal result from the optimal solution cost. Algorithm performance is determined both by computational complexity and the quality of the solution (i.e. the percentage deviation from the optimal). We nd that the running time can be reduced by a factor of 4.5 to ensure a 2% deviation from the optimal, as compared to the basic Simulated Annealing algorithm on the full search space. More importantly, we are able to reduce the complexity of nding the optimal from O(n:n!) for a complete search to O(nNS) for Simulated Annealing to O(n(NMr +NS)+m) for the input variables n jobs, NS SA iterations, NM Metropolis- Hastings iterations, r inner samples and m sections. / MT 2017

The function of wireless sensor networks is to monitor events or gather information and report the information to a sink node, a central location or a base station. It is a requirement that the information is transmitted through the network efficiently. Wireless communication is the main activity that consumes energy in wireless sensor networks through idle listening, overhearing, interference and collision. It becomes essential to limit energy usage while maintaining communication between the sensor nodes and the sink node as the nodes die after the battery has been exhausted. Thus, conserving energy in a wireless sensor network is of utmost importance. Numerous methods to decrease energy expenditure and extend the lifetime of the network have been proposed. Researchers have devised methods to efficiently utilise the limited energy available for wireless sensor networks by optimising the design parameters and protocols. Cross-layer optimisation is an approach that has been employed to improve wireless communication. The essence of cross-layer scheme is to optimise the exchange and control of data between two or more layers to improve efficiency. The number of transmissions is therefore a vital element in evaluating overall energy usage. In this dissertation, a Markov Chain model was employed to analyse the tuning of two layers of the protocol stack, namely the Physical Layer (PHY) and Media Access Control layer (MAC), to find possible energy gains. The study was conducted utilising the IEEE 802.11 channel, SensorMAC (SMAC) and Slotted-Aloha (S-Aloha) medium access protocols in a star topology Wireless Temperature Sensor Network (WTSN). The research explored the prospective energy gains that could be realised through optimizing the Forward Error Correction (FEC) rate. Different Reed Solomon codes were analysed to explore the effect of protocol tuning on energy efficiency, namely transmission power, modulation method, and channel access. The case where no FEC code was used and analysed as the control condition. A MATLAB simulation model was used to identify the statistics of collisions, overall packets transmitted, as well as the total number of slots used during the transmission phase. The bit error probability results computed analytically were utilised in the simulation model to measure the probability of successful transmitting data in the physical layer. The analytical values and the simulation results were compared to corroborate the correctness of the models. The results indicate that energy gains can be accomplished by the suggested layer tuning approach. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

SRSF1 is a widely expressed mammalian protein with multiple functions in the regulation of gene expression through processes including transcription, mRNA splicing, and translation. Although much is known of SRSF1 role in alternative splicing of specific genes little is known about its functions as a transcription factor and its global effect on cellular gene expression. We utilized a RNA sequencing (RNA-¬‐Seq) approach to determine the impact of SRSF1 in on cellular gene expression and analyzed both the short term (12 hours) and long term (48 hours) effects of SRSF1 expression in a human cell line. Furthermore, we analyzed and compared the effect of the expression of a naturally occurring deletion mutant of SRSF1 (RRM12) to the full-¬‐length protein. Our analysis reveals that shortly after SRSF1 is over-¬‐expressed the transcription of several histone coding genes is down-¬‐regulated, allowing for a more relaxed chromatin state and efficient transcription by RNA Polymerase II. This effect is reversed at 48 hours. At the same time key genes for the immune pathways are activated, more notably Tumor Necrosis Factor-¬‐Alpha (TNF-¬‐α), suggesting a role for SRSF1 in T cell functions. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

by Hok-hoi Fung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 58-59). / LIST OF TABLES --- p.ii / LIST OF FIGURES --- p.iii / CHAPTER / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 2. --- LITERATURE REVIEW --- p.3 / Chapter 3. --- METHODOLOGY --- p.6 / Formulation of the TVTP Model --- p.6 / Filtered and Smoothed Probabilities --- p.9 / Maximization of the Expected Log-likelihood --- p.13 / Chapter 4. --- EMPIRICAL RESULTS --- p.15 / The Simple 2-state Markov Switching Model --- p.15 / The TVTP Model --- p.17 / The 3-state Markov Switching Model --- p.26 / Chapter 5. --- OUT - OF- SAMPLE FORECASTING --- p.34 / Chapter 6. --- CONCLUSION --- p.40 / APPENDICES --- p.42 / BIBLIOGRAPHY --- p.58

Lau Wai = 連續粤語語音辨識裏的音調提取和音調特性 / 劉偉. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references. / Text in English; abstracts in English and Chinese. / Lau Wai = Lian xu yue yu yu yin bian shi li de yin diao ti qu he yin diao te xing / Liu Wei. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Speech Recognition of Chinese --- p.3 / Chapter 1.2 --- Tone Recognition --- p.3 / Chapter 1.3 --- Use of Tone Information in Speech Recognition --- p.4 / Chapter 1.4 --- Thesis Objectives --- p.6 / Chapter 1.5 --- Organization of the Thesis --- p.6 / Reference --- p.8 / Chapter 2 --- Properties of Tones in Cantonese --- p.12 / Chapter 2.1 --- The Cantonese Dialect --- p.12 / Chapter 2.1.1 --- "INITIAL, FINAL & TONE" --- p.13 / Chapter 2.1.2 --- Phonological Constraints --- p.16 / Chapter 2.2 --- Tones in Cantonese --- p.17 / Chapter 2.2.1 --- Linguistic Significance --- p.17 / Chapter 2.2.2 --- Acoustic properties --- p.18 / Chapter 2.2.3 --- Discriminative Features of the Cantonese Tones --- p.20 / Chapter 2.3 --- Summary --- p.21 / Reference --- p.22 / Chapter 3 --- Extraction of Tone Features --- p.23 / Chapter 3.1 --- Feature Parameters for Tone Recognition --- p.23 / Chapter 3.1.1 --- F0 Features --- p.23 / Chapter 3.1.2 --- Energy Features --- p.24 / Chapter 3.1.3 --- Log Scale vs. Linear Scale --- p.25 / Chapter 3.2 --- Detection of Voiced Speech --- p.26 / Chapter 3.3 --- Robust Algorithm for Pitch Tracking --- p.27 / Chapter 3.3.1 --- Generation of Period Candidates --- p.27 / Chapter 3.3.2 --- Post-processing --- p.28 / Chapter 3.4 --- Normalization of Fundamental Frequency --- p.29 / Chapter 3.4.1 --- Derivation of the normalization factor --- p.31 / Chapter 3.4.2 --- Moving-Window Normalization --- p.32 / Chapter 3.4.3 --- Energy Normalization --- p.35 / Chapter 3.5 --- FO Smoothing --- p.36 / Chapter 3.6 --- Generation of Tone Feature Vectors --- p.37 / Chapter 3.7 --- Summary --- p.39 / Reference --- p.40 / Chapter 4 --- Tone Recognition using Hidden Markov Models --- p.43 / Chapter 4.1 --- Two Methods of Tone Modeling --- p.43 / Chapter 4.2 --- Hidden Markov Models for Speech Recognition --- p.44 / Chapter 4.3 --- Tone Modeling by HMM --- p.47 / Chapter 4.4 --- Context-Dependent Tone Models --- p.48 / Chapter 4.5 --- Baseline Experiments --- p.49 / Chapter 4.5.1 --- The Speech Database - CUSENT´ёØ --- p.49 / Chapter 4.5.2 --- Data Pre-Processing --- p.50 / Chapter 4.5.3 --- Performance of Context-Independent Models --- p.51 / Chapter 4.5.4 --- Context-Dependent Tone Modeling --- p.52 / Chapter 4.6 --- Experiments on Moving-window FO Normalization --- p.54 / Chapter 4.6.1 --- Symmetric window --- p.54 / Chapter 4.6.2 --- Asymmetric window --- p.55 / Chapter 4.6.3 --- Energy normalization --- p.58 / Chapter 4.7 --- Incorporation of Statistical Tone Information --- p.58 / Chapter 4.8 --- Discussions --- p.59 / Chapter 4.9 --- Summary --- p.60 / Reference --- p.61 / Chapter 5 --- Integration of Tone Informaton into LVCSR for Cantonese --- p.63 / Chapter 5.1 --- The Goal --- p.63 / Chapter 5.2 --- N-best Based Integration --- p.64 / Chapter 5.2.1 --- Base Syllable Recognition --- p.65 / Chapter 5.2.2 --- Tone Recognition --- p.66 / Chapter 5.2.3 --- Language Models --- p.66 / Chapter 5.2.4 --- Integration and N-best Re-scoring --- p.66 / Chapter 5.2.5 --- Experimental Results --- p.67 / Chapter 5.2.6 --- Integration with Perfect Tone Information --- p.68 / Chapter 5.3 --- Broad Tone Classes --- p.68 / Chapter 5.3.1 --- Experimental Results --- p.70 / Chapter 5.3.2 --- Error analyses and Discussions --- p.71 / Chapter 5.4 --- Lattice Based Integration --- p.73 / Chapter 5.4.1 --- Lattice Expansion --- p.74 / Chapter 5.4.2 --- Experiments on Lattice Based Integration --- p.76 / Chapter 5.5 --- Discussions --- p.78 / Chapter 5.6 --- Summary --- p.79 / Reference --- p.80 / Chapter 6 --- Conclusions and Future Work --- p.81 / Chapter 6.1 --- Conclusions --- p.81 / Chapter 6.2 --- Suggestions for Future Work --- p.84 / Reference --- p.85

by Choy Chi Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 111-115). / Text in English; abstract also in Chinese. / by Choy Chi Yan. / ABSTRACT --- p.I / ACKNOWLEDGMENTS --- p.III / TABLE OF CONTENTS --- p.IV / LIST OF FIGURES --- p.VII / LIST OF TABLES --- p.VIII / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 1.1 --- Speech Recognition --- p.1 / Chapter 1.2 --- Development of Speech Recognisers --- p.4 / Chapter 1.3 --- Speech Recognition for Chinese Language --- p.5 / Chapter 1.4 --- Objectives of the thesis --- p.6 / Chapter 1.5 --- Thesis Structure --- p.7 / Chapter 2. --- PHONOLOGICAL AND ACOUSTICAL PROPERTIES OF MANDARIN CHINESE --- p.9 / Chapter 2.1 --- Characteristics of Mandarin Chinese --- p.9 / Chapter 2.1.1 --- Syllabic Structures --- p.10 / Chapter 2.1.2 --- Lexical Tones --- p.11 / Chapter 2.2 --- Basic Phonetic Units for Mandarin Chinese --- p.14 / Chapter 2.2.1 --- Tonal Syllables and Base Syllables --- p.14 / Chapter 2.2.2 --- Initial-Finals --- p.14 / Chapter 2.2.3 --- Phones --- p.16 / Chapter 2.2.4 --- Preme-Core-Finals and Preme-Tonemes --- p.17 / Chapter 2.2.5 --- Summary-The phonological hierarchy of Mandarin Syllables --- p.19 / Chapter 3. --- HIDDEN MARKOV MODELS --- p.20 / Chapter 3.1 --- Introduction --- p.20 / Chapter 3.1.1 --- Speech Data --- p.20 / Chapter 3.1.2 --- Fundamental of HMMs --- p.21 / Chapter 3.2 --- Using Hidden Markov Models for Speech Recognition --- p.22 / Chapter 3.2.1 --- Likelihood of the state sequence of speech observations --- p.22 / Chapter 3.2.2 --- The Recognition Problem --- p.24 / Chapter 3.3 --- Output Probability Distributions --- p.25 / Chapter 3.4 --- Model Training --- p.26 / Chapter 3.4.1 --- State Sequence Estimation --- p.26 / Chapter 3.4.2 --- Gaussian Mixture Models --- p.29 / Chapter 3.4.3 --- Parameter Estimation --- p.30 / Chapter 3.5 --- Speech Recognition and Viterbi Decoding --- p.31 / Chapter 3.6 --- Summary --- p.32 / Chapter 4. --- LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION FOR MANDARIN CHINESE --- p.33 / Chapter 4.1 --- Introduction --- p.33 / Chapter 4.2 --- Large Vocabulary Mandarin Chinese Recognition System --- p.34 / Chapter 4.2.1 --- Overall Architecture for the Speech Recogniser --- p.34 / Chapter 4.2.2 --- Signal Representation and Features --- p.36 / Chapter 4.2.3 --- Subword Unit Models Based on HMMs --- p.39 / Chapter 4.2.4 --- Training of Subword Units --- p.42 / Chapter 4.2.5 --- Language Model (LM) --- p.43 / Chapter 4.2.6 --- "Transcriptions, Word Networks and Dictionaries for LVCSR System" --- p.44 / Chapter 4.2.7 --- Viterbi Decoding --- p.47 / Chapter 4.2.8 --- Performance Analysis --- p.48 / Chapter 4.3 --- Experiments --- p.48 / Chapter 4.3.1 --- Tasks --- p.48 / Chapter 4.3.2 --- Speech Database --- p.49 / Chapter 4.3.3 --- Baseline Experimental Results --- p.51 / Chapter 4.4 --- Context Dependency in Speech --- p.52 / Chapter 4.4.1 --- Introduction --- p.52 / Chapter 4.4.2 --- Context Dependent Phonetic Models --- p.53 / Chapter 4.4.3 --- Word Boundaries and Word network for context-dependent HMMs --- p.54 / Chapter 4.4.4 --- Recognition Results Using Cross-Syllable Context-Dependent Units --- p.56 / Chapter 4.5 --- Tree-Based Clustering --- p.58 / Chapter 4.5.1 --- Introduction --- p.58 / Chapter 4.5.2 --- Decision Tree Based Clustering --- p.59 / Chapter 4.5.3 --- The Question Sets --- p.61 / Chapter 4.5.4 --- Convergence Condition --- p.61 / Chapter 4.4.5 --- The Final Results --- p.63 / Chapter 4.6 --- Conclusions --- p.65 / Chapter 5. --- APPLICATION1 ISOLATED WORD RECOGNITION FOR MANDARIN CHINESE --- p.67 / Chapter 5.1 --- Introduction --- p.67 / Chapter 5.2 --- Isolated Word Recogniser --- p.68 / Chapter 5.2.1 --- System Description --- p.68 / Chapter 5.2.2 --- Experimental Results --- p.70 / Chapter 5.3 --- Discussions and Conclusions --- p.71 / Chapter 6. --- APPLICATION2 SUBWORD UNITS FOR A MANDARIN KEYWORD SPOTTING SYSTEM --- p.74 / Chapter 6.1 --- INTRODUCTION --- p.74 / Chapter 6.2 --- RECOGNITION SYSTEM DESCRIPTION --- p.75 / Chapter 6.2.1 --- Overall Architecture and Recognition Network for the keyword Spotters --- p.75 / Chapter 6.2.2 --- Signal Representation and Features --- p.76 / Chapter 6.2.3 --- Keyword Models --- p.76 / Chapter 6.2.4 --- Filler Models --- p.77 / Chapter 6.2.5 --- Language Modeling and Search --- p.78 / Chapter 6.3 --- EXPERIMENTS --- p.78 / Chapter 6.3.1 --- Tasks --- p.78 / Chapter 6.3.2 --- Speech Database --- p.79 / Chapter 6.3.3 --- Performance Measures --- p.80 / Chapter 6.3.4 --- Details of Different Word-spotters --- p.80 / Chapter 6.3.5 --- General Filler Models --- p.81 / Chapter 6.4 --- EXPERIMENTAL RESULTS --- p.83 / Chapter 6.5 --- CONCLUSIONS --- p.84 / Chapter 7. --- CONCLUSIONS --- p.87 / Chapter 7.1 --- Review of the Work --- p.87 / Chapter 7.1.1 --- Large Vocabulary Continuous Speech Recognition for Mandarin Chinese --- p.87 / Chapter 7.1.2 --- Isolated Word Recognition for a Stock Inquiry Application --- p.88 / Chapter 7.1.3 --- Keyword Spotting for Mandarin Chinese --- p.89 / Chapter 7.2 --- Suggestions for Further Work --- p.89 / Chapter 7.3 --- Conclusion --- p.91 / APPENDIX --- p.92 / BIBLIOGRAPHY --- p.111

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaves 139-142. / by Michael Gene Hluchyj. / Ph.D.

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING / Includes bibliographical references. / by Joseph A. Amram. / B.S.