The analysis of the health condition in Rheumatoid Arthritis (RA) remains a qualitative process dependent on visual inspection by a clinician. Fully automatic techniques that can accurately classify the health of the muscle have yet to be developed. The intended purpose of this work is to develop a novel spatio-temporal technique to assist in a rehabilitation program framework, by identifying motion features inherited in the muscles in order to classify them as either healthy or diseased. Experiments are based on ultrasound image sequences during which the muscles were undergoing contraction. The proposed system uses an optical flow technique to estimate the velocity of contraction. Analyzing and manipulating the velocity vectors reveal valuable information which encourages the extraction of motion features to discriminate the healthy against the sick. Experimental results for classification prove helpful in essential developments of therapy processes and the performance of the system has been validated by the cross-validation technique “leave-one-out”. The method leads to an analytical description of both the global and local muscle’s features in a way which enables the derivation of an appropriate strategy for classification. To our knowledge this is the first reported spatio-temporal method developed and evaluated for RA assessment. In addition, the progress of physical therapy to improve strength of muscles in RA patients has also been evaluated by the features used for classification.

As a technique to solve new problems based on previous successful cases, CBR represents significant prospects for improving the accuracy and effectiveness of unstructured decision-making problems. Similar problems have similar solutions is the main assumption. Utility oriented similarity modeling is gradually becoming an important direction for Case-based reasoning research. In this thesis, we propose a new way to represent the utility of case by using fuzzy rules. Our method could be considered as a new way to estimate case utility based on fuzzy rule based reasoning. We use modified WANG’s algorithm to generate a fuzzy if-then rule from a case pair instead of a single case. The fuzzy if-then rules have been identified as a powerful means to capture domain information for case utility approximation than traditional similarity measures based on feature weighting. The reason why we choose the WANG algorithm as the foundation is that it is a simpler and faster algorithm to generate if-then rules from examples. The generated fuzzy rules are utilized as a case matching mechanism to estimate the utility of the cases for a given problem. The given problem will be formed with each case in the case library into pairs which are treated as the inputs of fuzzy rules to determine whether or to which extent a known case is useful to the problem. One case has an estimated utility score to the given problem to help our system to make decision. The experiments on several data sets have showed the superiority of our method over traditional schemes, as well as the feasibility of learning fuzzy if-then rules from a small number of cases while still having good performances.

The increasing need for more natural human machine interfaces has generated intensive research work directed toward designing and implementing natural speech enabled systems. The Spectrum of speech recognition applications ranges from understanding simple commands to getting all the information in the speech signal such as words, meaning and emotional state of the user. Because it is very hard to constrain a speaker when expressing a voice-based request, speech recognition systems have to be able to handle (by filtering out) out of vocabulary words in the users speech utterance, and only extract the necessary information (keywords) related to the application to deal correctly with the user query. In this thesis, we investigate an approach that can be deployed in keyword spotting systems. We propose a confidence measure feedback module that provides confidence values to be compared against existing Automatic Speech Recognizer word confidences. The feedback module mainly consists of a soft computing tool-based system using fuzzy Gaussian mixture models to identify all English phonemes. Testing has been carried out on the JULIUS system and the preliminary results show that our feedback module outperforms JULIUS confidence measures for both the correct spotted words and the falsely mapped ones. The results obtained could be refined even further using other type of confidence measure and the whole system could be used for a Natural Language Understanding based module for speech understanding applications.

Part of the problem with implementing a rangeland insurance program is that the acreage of different pasture types, which is required in order to determine an indemnity payment, is difficult to measure on the ground over large areas. Remote sensing techniques provide a potential solution to this problem. This study applied single-date SPOT (Satellite Pour IObservation de la Terre) imagery, field collected data, and geographic information system (GIS) data to study the classification of land cover and vegetation at species level. Two topographic correction models, Minnaert model and C-correction, and two classifying algorithms, maximum likelihood classifier (MLC) and artificial neural network (ANN), were evaluated. The feasibility of discriminating invasive crested wheatgrass from natives was investigated, and an exponential normalized difference vegetation index (ExpNDMI) was developed to increase the separability between crested wheatgrass and natives. Spectral separability index (SSI) was used to select proper bands and vegetation indices for classification. The results show that topographic corrections can be effective to reduce intra-class rediometric variation caused by topographic effect in the study area and improve the classification. An overall accuracy of 90.5% was obtained by MLC using Minnaert model corrected reflectance, and MLC obtained higher classification accuracy (~5%) than back-propagation based ANN. Topographic correction can reduce intra-class variation and improve classification accuracy at about 4% comparing to the original reflectance. The crested wheatgrass was over-estimated in this study, and the result indicated that single-date SPOT 5 image could not classify crested wheatgrass with satisfactory accuracy. However, the proposed ExpNDMI can reduce intra-class variation and enlarge inter-class variation, further, improve the ability to discriminate invasive crested wheatgrass from natives at 4% of overall accuracy. This study revealed that single-date SPOT image may perform an effective classification on land cover, and will provide a useful tool to update the land cover information in order to implement a rangeland insurance program.

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The construction industry is known for its high accident rate which leads to numerous fatalities every year. Currently, the Occupational Safety and Health Administration (OSHA) requires injury/illness recording forms to be completed only for injuries requiring medical treatment and fatalities. The assertion of this paper is that underlying problems can be best determined through the causes of first-aid injuries that have the potential to prevent serious injuries in the future. Therefore, by classifying and recording first-aid cases on project sites, the common trend type of injury can be followed and appropriate measures can be taken to eliminate hazards. The main objective of this research is to establish a comprehensive standardized database to record first-aid injury cases, injuries requiring medical treatment and fatalities all in one. The recording format described in this research will facilitate the analysis of the data in a more effective manner which can subsequently be used to develop pre-emptive measures to eliminate common causes for construction injuries. In order, to create the Form, 900 sets of injury data were obtained from an industrial construction firm and analyzed. These data provided a good indication of the classification system adopted by industries today. The proposed Injury and Illness Database/Form (I

In this thesis, an estimation method adapted from MUSIC algorithm is presented for estimation of signal arrival time for impulse radio UWB systems. An accurate estimate of signal arrival time is considered essential in time-based wireless and indoor location systems. Since most wireless communications systems used for indoor position location may suffer from dense multipath situation, the accuracy of determining signal arrival time become an important issue for the time-based location systems. The fine resolution of UWB signals provides potentially accurate ranging for indoor location applications. However, the ambiguity caused by the unresolved first arrival path may still yield an error in determining the true signal arrival time. The presented method uses improved MUSIC techniques in time domains to estimate the shortest and the real signal arrival time for UWB radio link. For a two-multipath case, analysis and simulation results of multipath resolvability and the variance of estimation errors of signal arrival time are discussed.

With the improvement of computer technologies and the wide use of the Internet, network security becomes more and more significant. According to the relevant statistics, malicious codes such as virus, worms, backdoors, and Trojans launch a lot of attacks. Backdoors are especially critical. Not only can it cross firewalls and antivirus software but also will steal confidential information and misuse network resources and launch attacks such as DDoS¡]Distributed Denial of Service¡^. In this research, we analyze the properties and categories of backdoors and the application of data mining and support vector machines in intrusion detection. This research will focus on detecting the behavior of backdoor connection, and we propose a detecting architecture. The architecture is based on SVM, which is a machine learning method based on statistic theory and proposed by Vapnik to solve the problems in Neural Network techniques. In system modules, this research chooses IPAudit as our network monitor and libsvm as a SVM classifier. The packets captured by IPAudit will be classified into interactive or non-interactive flow by libsvm, and the result will be compared with legal service lists to determine whether a connection is a backdoor connection. We compare the accuracy of SVM, C4.5, and Na

The purpose of this study is to assess the instructional use of mathematics picture books and teaching activities in the teaching of two second-grade mathematical units: ¡§Multiples¡¨ and ¡§Classification¡¨. In order to achieve this purpose, the researcher created four mathematics picture books. Furthermore, she explored the mathematical learning effects of second-grade elementary school students through mathematics picture book teaching. The stages of this study were three: creation of 4 picture books; implementation of using picture books in mathematics class; and, data analyzes on the collection of pre-test and post-test data; checklists of picture books follow-up activities; mathematics diaries; and, notes on students interview, classroom observations by teacher and investigator. The findings are three: 1) the creation of mathematics picture books; 2) the implementation of mathematics picture books in mathematics teaching; and, 3) the effects of using picture books in second-grade elementary school children's learning of mathematics. The creation of mathematics picture books. While ¡§Multi-colored Ice-cream¡¨ and ¡§Grandpa¡¦s Magic Forest¡¨ were designed in accordance to the unit of ¡§Multiples¡¨, two other books, named ¡§My Swimsuit is Lost¡¨ and ¡§Lala is Sick¡¨ were designed to integrate instruction in ¡§Classification¡¨. In these four stories, the plots were close to real life of students. These vivid presentations of illustrations and compositions not only drew students¡¦ interests and kept their high concentration, but also made them feel happy in exploring mathematical concepts implied in these books. The implementation of mathematics picture books. During the process of picture book teaching activities, students could keep high degrees of participation. Moreover, showing picture books through TV screen was applicable to whole class; using mathematics activities that were highly connected with contents of these picture books could motivate students¡¦ mathematics learning; applying these extensive activities flexibly could limit these activities which were infinite. The effects of using mathematics picture books. There was not only an advance in the cognition and understanding on ¡§Multiples¡¨ and ¡§Classification¡¨, but also positive attitude towards mathematics, mathematics class and mathematics learning. Besides, most students expressed their high expectations in mathematics picture book related teaching activities.

The El Nino event causes the changes of the ocean and atmosphere system that induces the rainfall unusual increasing or reduction in some areas and then cause local lives and economical losses. Previous studies have found that the El Nino actually applies impact on the rainfall, however most of the studies focus on the impact of separated stations but little on regional variation. The study on the other hand focus on the variation of the rainfall based on the climatic classification primarily and the physiographic region position auxiliary during the El Nino event and La Nina events. The main method of this research is the correlation analysis, when the correlation coefficient draws close to +1, it mean that the rainfall is positive relative with the parameter of the El Nino, and when the correlation coefficient draws close to -1, it mean that the rainfall is negativity relative with the parameter of the El Nino event. The analysis parameters of the El Nino event index include the sea water temperature and anomaly of every area in Pacific Ocean, sea water surface temperature difference of two areas opposite, Southern Oscillation index and Multivariate ENSO Index. It is found in the study that the best parameter of the El Nino event is the sea water temperature difference of (Nino1¡Ï2- Nino34). The result showed the most climatic classifications have good relation with the parameter of the El Nino, especially winter-dry climatic classifications is the best. Because the result of the research influence on the season variation, it is not to conclude the relation with the El Nino event. It is need to study deeply for calculating the rainfall of the areas where influenced by the El Nino event.