History, culture and economy constitute the foundation of language. Mandarin Chinese is our native language, spoken by over 1.2 billion people. Its population is ranked number one in the world. In the recent years, the emerging China not only possesses market and labor forces, but also develops the Chinese culture circle in Asia. British history and American politics make English the most influential language in the 20th century. Vietnam has been under the profound influence of Chinese culture. The reformed and opened economy in the past decade brought her tremendous foreign investments, including those from Taiwan. It is our objective to establish a trilingual system for travel, living and speech learning. This thesis investigates the design and implementation strategies for a trilingual speech recognition system of Chinese, English and Vietnamese. It utilizes the speech features of 404 Chinese, 925 English and 154 Vietnamese mono-syllables as the major training and recognition methodology. Mel-frequency cepstral coefficients, linear predicted cepstral coefficients, and hidden Markov model are used as the two syllable feature models and the recognition model respectively. Under the AMD XP 2800+ personal computer and Ubuntu 9.04 operating system environment, the correct rates of 88.16%, 82.74% and 87.45% can be reached using phonotactical rules for the 82,000 Chinese, 30,795 English and 3,300 Vietnamese phrase database respectively. The computation for each system can be completed within 2 seconds. Furthermore, a trilingual language-speech recognition system for 300 common words, composed of 100 words from each language, is developed. A 98% correct language-phrase recognition rate can be obtained with the computation time less than 2 seconds.

China, Italy and Iran are seemingly quite different in language, history, culture and economy. However, there have been existed mutual interactions among these three countries during the past age. In the fourth century, the Chinese Northern Wei Dynasty established close relation with the Persian Empire, located in Iran today. Persian language is also called Farsi in her native name. The unearthed silver bowls from China in the recent years showed similar appearance and material with the Sassanid-Persian silverware of Iran. Archaeologists found that ancient China and Iran used to be close international trading partners. In the thirteenth century, Marco-Polo, an Italian travel adventurer and merchant, visited Chinese Yuan Dynasty, and wrote a marvelous book ¡§The Travels of Marco-Polo¡¨. Fantastic experiences in China were depicted in this journal, and these initiated the Sino-Italian relation in the early days. Armani suits and Ferrari super racers become the oriental passion to the Italy in the Modern China, and this may represent the achievement of Asian-European culture exchange. Therefore, it is our objective to design a trilingual speech recognition system to help us to learn Chinese, Italian and Farsi languages. Linear predicted cepstral coefficients, Mel-frequency cepstral coefficients, hidden Markov model and phonotactics are used in this system as the two syllable feature models and the recognition model respectively. For the Chinese system, a 2,699 two-syllable words database is used as the training corpus. For the Italian and Farsi systems, a database of 10 utterances per mono-syllable is established by applying their pronunciation rules. These 10 utterances are collected through reading 5 rounds of the same mono-syllables twice with tone 1 and tone 4. The correct recognition rates of 87.54%, 87.48%, and 90.33% can be reached for the 82,000 Chinese, 27,900 Italian, and 4,000 Farsi phrase databases respectively. The computation time for each system is within 1.5 seconds. Furthermore, a trilingual language-speech recognition system for 300 common words, composed of 100 words from each language, is developed. A 98.67 % correct language-phrase recognition rate can be obtained with the computation time about 2 seconds.

The BRICS, Brazil, Russia, India, China and South Africa, have been making a significant amount of contribution to the global economy growth in the past few years. China possesses not only the largest population, but also the most splendid history in the world. During the recent years, the rapid development on all respects, including the enhanced economic trade with Taiwan, has made China in the line of the Super Powers. Brazil is the largest Portuguese speaking country in the world, where the world class manufacturer Foxconn Technology decided to build Apple iPad/iPhone factory in 2011. India has been flourishing in software, tele-communications and aviation industries since last decade. Offshore outsourcing consulting is so popular due to cost-down policy of the Western companies. Chinese, Portuguese and Hindi speaking population are over 1.573 billion, and account for over 22% of the world population. Therefore, it is our objective to establish a trilingual speech recognition system to help verbal communication and cultural understanding among languages. This thesis investigates the design and implementation strategies for a trilingual speech recognition system for Chinese, Portuguese and Hindi. Based on their pronunciation rules, the 404 Chinese, 515 Portuguese and 244 Hindi common mono-syllables are selected and utilized as the major speech training and recognition methodology. Mel-frequency cepstral coefficients, linear predicted cepstral coefficients, and hidden Markov model are used as the two syllable feature models and the recognition model respectively. Under the AMD 2.2 GHz Athlon XP 2800+ personal computer and Ubuntu 9.04 operating system environment, the correct phrase recognition rates of 87.69%, 85.14% and 86.74% can be reached using phonotactical rules for the 82,000 Chinese, 30,000 Portuguese and 3,900 Hindi phrase database respectively. Furthermore, a trilingual language-speech recognition system for 300 common words, composed of 100 words from each language, is developed. A 98% correct language-phrase recognition rate can be reached. The average computation time for each system is within 2 seconds.

Chinese as well as Arabic is one of the six official languages in the United Nations. The population of Chinese is over 1.2 billion, ranked number one in the world. Arabic, a language used in the Arab World, has a more than 2,800 year history. Her religion, culture and oil economy have been making far-reaching effects around the globe. The worldwide energy supply greatly relies on the petroleum from the Arab World. Netherland, whose official language is Dutch, has been an international trading power since ancient time. She has become an industrial giant today. Recently, European-study-abroad is getting more popular, many famous Netherland universities offer opportunities for foreign students. Therefore, it is our objective to design a trilingual speech recognition system to help us learn Chinese, Arabic and Dutch, as well as appreciate their profound history and beautiful culture. This thesis investigates the design and implementation strategies for a Chinese, Arabic and Dutch speech recognition system. A 2,699 two-syllable recorded words database is utilized as the Chinese training corpus. For the Arabic and Dutch systems, 396 and 205 common mono-syllables are selected respectively as the major training and recognition methodology. Each mono-syllable is uttered twice with tone 1 and tone 4, and ten training patterns are used for system implementation. Mel-frequency cepstral coefficients, linear predicted cepstral coefficients, hidden Markov model and phonotactics are applied as the two syllable feature models and the recognition model respectively. The correct recognition rates of 90.17%, 84.65%, and 86.69% can be reached for the 82,000 Chinese, 31,000 Arabic, and 3,600 Dutch phrase databases respectively. Furthermore, a trilingual language-speech recognition system for 300 common words, composed of 100 words from each language, is developed. A 98.67 % correct language-phrase recognition rate can be obtained. The computation time for each system is about 2 seconds.

According to the statistics of Summer Institute of Linguistics, USA, there are about 7,000 languages in the world. Chinese, Hakka and Swedish are all the first 100 most popular languages. Chinese is spoken in Taiwan, Mainland China, Hong Kong and Macau. Hakka is the second popular dialect in Taiwan. The population is only less than that of Taiwanese. The ancestors of Hakka are from the Han people in Honan, China. Hakka culture has been cultivated by enormous migrations since the fourth century, and transformed to represent the tradition. Taiwan and Sweden are developed, free and democratic countries, with similar level of living standard. The ancestors of Sweden are from the Germanic peoples in Northern Europe. Swedish has been also evolved and transformed by massive migrations since the ninth century, sharing the analogous evolution route with Chinese and Hakka. Therefore, it is our objective to establish a trilingual speech recognition system to help verbal communication among languages in the global economic arena. This thesis investigates the design and implementation strategies for a trilingual speech recognition system for Chinese, Hakka and Swedish. Based on their pronunciation rules, the 404 Chinese, 204 Hakka and 369 Swedish common mono-syllables are selected as the major speech training and recognition methodology. A 2,699 two-syllable words database is recorded as the Chinese training corpus. The five rounds with four tones and six rounds with two tones training strategies are used for Hakka and Swedish respectively. Correct rates of 92.29%, 90.70% and 89.09% can be reached for the 82,000 Chinese, 3,900 Hakka and 3,750 Swedish phrase database respectively. Besides, a trilingual language-speech recognition system for 300 common words, composed of 100 words from each language, is developed. A 98.67% correct language-phrase recognition rate can be obtained. The average computation time for each system is within 2 seconds.

In this thesis, both Turkish and Tamil, a language spoken in southern India and Sri Lanka, are studied in addition to Mandarin Chinese. It is hoped that the history, culture, and economy behind each language can be acquainted, tasted and appreciated during the learning process. In the ancient Chinese Han and Tang Dynasties, the ¡§Silk Road¡¨ played the most magnificent role to connect among the Oriental China, the Western Turkey and the Southern India as the international trading corridor. In this modern era, Turkey and India are both the most important cotton exporting countries. Moreover, China, Turkey and India have been showing their potential to the newly emerging markets in the world. Therefore, a trilingual speech recognition system is developed and implemented to help us to learn Chinese, Turkish and Tamil, as well as to enhance our understanding to their history and culture. In this trilingual system, linear predicted cepstral coefficients, Mel-frequency cepstral coefficients, hidden Markov model and phonotactics are used as the two syllable feature models and the recognition model respectively. For the Chinese system, a 2,699 two-syllable words database is used as the training corpus. For the Turkish and Tamil systems, a database of 10 utterances per mono-syllable is established by applying their pronunciation rules. These 10 utterances are collected through reading 5 rounds of the same mono-syllables twice with tone 1 and tone 4. The correct rates of 88.30%, 84.21%, and 88.74% can be reached for the 82,000 Chinese, 30,795 Turkish, and 3,500 Tamil phrase databases respectively. The computation time for each system is within 1.5 seconds. Furthermore, a trilingual language-speech recognition system for 300 common words, composed of 100 words from each language, is developed. A 98% correct language-phrase recognition rate can be reached with the computation time less than 2 seconds.

This thesis investigates the design and implementation strategies for a Japanese speech recognition system. It utilizes the speech features of the 188 common Japanese mono-syllables as the major training and recognition methodology. A training database of 10 utterances per mono-syllable is established by applying Japanese pronunciation rules. These 10 utterances are collected through reading 5 rounds of 188 mono-syllables, where every mono-syllable is consecutively read twice in each round. Mel-frequency cepstrum coefficients, linear predicted cepstrum coefficients, and hidden Markov model are used as the two feature models and the recognition model respectively. Under the Pentium 2.4 GHz personal computer and Ubuntu 8.04 operating system environment, a correct phrase recognition rate of 87% can be reached for a 34,000 Japanese phrase database. The average computation time for each phrase is about 1.5 seconds.

This thesis investigates the design and implementation strategies for a English speech recognition system. Two speech inputting methods, the spelling inputting and the reading inputting, are implemented for English word recognition and query. Mel-frequency cepstrum coefficients, linear predicted cepstrum coefficients, and hidden Markov model are used as the two feature models and the recognition model respectively. Under the Pentium 1.6 GHz personal computer and Ubuntu 8.04 operating system environment, a 95% correct recognition rate can be obtained for a 110 thousand English word database by the spelling inputting method; and a 93% correct recognition rate can be achieved for a 1,500 English word database by the reading inputting method. The average computation time for each word using either inputting method is about 1.5 seconds.

Using the method of matched asymptotic expansions the reflection and transmission coefficients are calculated for scattering of oblique water waves by a vertical barrier. Here an assumption is made that the barrier is small compared to the wavelength and the depth of water. A number of sloshing problems are considered. The eigenfrequencies are calculated when a body is placed in a rectangular tank. Here the bodies considered are a vertical surface-piercing or bottom-mounted barrier, and circular and elliptic cylinders. When the body is a vertical barrier, the eigenfunction expansion method is applied. When the body is either a circular or elliptic cylinder, and the motion is two-dimensional, the boundary element method is applied to calculate the eigenfrequencies. For comparison, two approximations, "a wide-spacing", and "a small-body" are used for a vertical barrier and circular cylinder. In the wide-spacing approximation, the assumption is made that the wavelength is small compared with the distance between the body and walls. The small-body approximation means that a typical dimension of the body is much larger than the cross-sectional length scale of the fluid motion. For an elliptic cylinder, the method of matched asymptotic expansions is used and compared with the result of the boundary- element method. Also a higher-order solution is obtained using the method of matched asymptotic expansions, and it is compared with the exact solution for a surface-piercing barrier. Again the assumption is made that the length scale of the motion is much larger than a typical body dimension. Finally, the drift force on multiple bodies is considered the ratio of horizontal drift force in the direction of wave advance on two cylinders to that on an isolated cylinder is calculated. The method of matched asymptotic expansions is used under the assumption that the wavelength is much greater than the cylinder spacing.

La maîtrise du comportement en vol d’un projectile est, en partie, conditionnée par la connaissance des coefficients associés à chaque effort aérodynamique. Différents outils sont utilisés dans l’industrie, tels que les codes numériques aérodynamiques ou les essais en soufflerie, afin d’obtenir une première estimation des coefficients en phase d’avant-projet. Il est ensuite nécessaire de vérifier la valeur des coefficients et de valider le comportement du projectile en vol au moyen de tirs instrumentés. Un outil automatisé est donc proposé afin d’identifier les coefficients aérodynamiques d’un projectile à partir des mesures issues d’un vol. La technique d’identification est pensée pour être applicable à une gamme la plus large de projectiles. Elle introduit un problème d’optimisation non linéaire en dimension finie. La fonctionnelle du problème contient deux termes : un terme d’écart entre les paramètres d’état et les mesures, de sorte à s’approcher au mieux des mesures et à les relaxer, et un terme de pénalisation prenant en compte les équations de la mécanique du vol. L’outil proposé est testé, pour un projectile de type flèche, avec des données simulées ou avec des données issues de tirs. Il permet l’identification des coefficients aérodynamiques recherchés. L’algorithme est robuste face au bruit et permet également la reconstruction d’une trajectoire débruitée. / The control of the flight behaviour of a projectile partly depends on the knowledge of the coefficients associated with each aerodynamic loading. Different tools are used in the industry, such as numerical aerodynamic codes or wind tests in order to obtain a first estimate of the coefficients during the stage of pilot study. It is then necessary to verify the value of the coefficients and to validate the behaviour of the projectile thanks to scored fires. An automated tool is consequently proposed in order to identify the aerodynamic coefficients of a projectile from flight data. The identification technique is designed so as to be applicable to the widest range of projectiles. It presents a nonlinear optimization problem in finite dimension. The functional of the problem contains two terms : the first one is a gap between the state parameters and the measurements, in order to approach the measurements at best and to relax them, and the second one is a penalization term which takes the flight mechanics equations into account. The proposed tool is tested, for a Kinectic Energy projectile, with simulated data or real flight data. It enables the identification of the searched out aerodynamic coefficients. The algorithm is robust in a noisy environment and also enables the reconstruction of a denoised trajectory.