Model Identifikasi Pass Lintas Batas (PLB) Smart Card Dengan Pengenalan Pola Wajah Pelintas Batas Antar Indonesia-Malaysia Berbatas Wilayah Di Kalimantan Barat

-Technology to pass the immigration authorities that serve cross-border (PLB) for ease in knowing every passer identity and reduce violations that occur as LocalBorderLinePass abuse at the border. The technology will be useful to assess the immigration authorities in the past the border to make it easier and safer in comparison to manually security, then the method of analysis with a specific purpose to be very necessary. One of the technology is to use limit LocalBorderLinePass identity identification in the form of a smart card with a face pattern recognition in this study using eigenface methods in the face passer, On the problem of this research is based on studies or previous research in general use manual methods which only make use of manual records the identity of citizens, but in this study will try to apply the facial pattern recognition methods on cross-border pass safer and efesiens. The process of smart cards in this study will use the data in the new citizens take dar in 2012 and 2013, where the data will be process further by using a smart card and facial pattern recognition. If the result of the introduction of identity have found it to be determined pattern of trend data that can answer the problem to pass traffic in an efficient strategy

A CASE STUDY ON SUPPORT VECTOR MACHINES VERSUS ARTIFICIAL NEURAL NETWORKS

The capability of artificial neural networks for pattern recognition of real world problems is well known. In recent years, the support vector machine has been advocated for its structure risk minimization leading to tolerance margins of decision boundaries. Structures and performances of these pattern classifiers depend on the feature dimension and training data size. The objective of this research is to compare these pattern recognition systems based on a case study. The particular case considered is on classification of hypertensive and normotensive right ventricle (RV) shapes obtained from Magnetic Resonance Image (MRI) sequences. In this case, the feature dimension is reasonable, but the available training data set is small, however, the decision surface is highly nonlinear.For diagnosis of congenital heart defects, especially those associated with pressure and volume overload problems, a reliable pattern classifier for determining right ventricle function is needed. RV¡¦s global and regional surface to volume ratios are assessed from an individual¡¦s MRI heart images. These are used as features for pattern classifiers. We considered first two linear classification methods: the Fisher linear discriminant and the linear classifier trained by the Ho-Kayshap algorithm. When the data are not linearly separable, artificial neural networks with back-propagation training and radial basis function networks were then considered, providing nonlinear decision surfaces. Thirdly, a support vector machine was trained which gives tolerance margins on both sides of the decision surface. We have found in this case study that the back-propagation training of an artificial neural network depends heavily on the selection of initial weights, even though randomized. The support vector machine where radial basis function kernels are used is easily trained and provides decision tolerance margins, in spite of only small margins

Pattern recognition of satellite cloud imagery for improved weather prediction

The major accomplishment was the successful development of a method for extracting time derivative information from geostationary meteorological satellite imagery. This research is a proof-of-concept study which demonstrates the feasibility of using pattern recognition techniques and a statistical cloud classification method to estimate time rate of change of large-scale meteorological fields from remote sensing data. The cloud classification methodology is based on typical shape function analysis of parameter sets characterizing the cloud fields. The three specific technical objectives, all of which were successfully achieved, are as follows: develop and test a cloud classification technique based on pattern recognition methods, suitable for the analysis of visible and infrared geostationary satellite VISSR imagery; develop and test a methodology for intercomparing successive images using the cloud classification technique, so as to obtain estimates of the time rate of change of meteorological fields; and implement this technique in a testbed system incorporating an interactive graphics terminal to determine the feasibility of extracting time derivative information suitable for comparison with numerical weather prediction products

Semi-wildlife gait patterns classification using Statistical Methods and Artificial Neural Networks

Several studies have focused on classifying behavioral patterns in wildlife and captive species to monitor their activities and so to understanding the interactions of animals and control their welfare, for biological research or commercial purposes. The use of pattern recognition techniques, statistical methods and Overall Dynamic Body Acceleration (ODBA) are well known for animal behavior recognition tasks. The reconfigurability and scalability of these methods are not trivial, since a new study has to be done when changing any of the configuration parameters. In recent years, the use of Artificial Neural Networks (ANN) has increased for this purpose due to the fact that they can be easily adapted when new animals or patterns are required. In this context, a comparative study between a theoretical research is presented, where statistical and spectral analyses were performed and an embedded implementation of an ANN on a smart collar device was placed on semi-wild animals. This system is part of a project whose main aim is to monitor wildlife in real time using a wireless sensor network infrastructure. Different classifiers were tested and compared for three different horse gaits. Experimental results in a real time scenario achieved an accuracy of up to 90.7%, proving the efficiency of the embedded ANN implementation.Junta de Andalucía P12-TIC-1300Ministerio de Economía y Competitividad TEC2016-77785-

The feasibility of using pattern recognition software to measure the influence of computer use on the consultation

BACKGROUND: A key feature of a good general practice consultation is that it is patient-centred. A number of verbal and non-verbal behaviours have been identified as important to establish a good relationship with the patient. However, the use of the computer detracts the doctor's attention away from the patient, compromising these essential elements of the consultation. Current methods to assess the consultation and the influence of the computer on them are time consuming and subjective. If it were possible to measure these quantitatively, it could provide the basis for the first truly objective way of studying the influence of the computer on the consultation. The aim was to assess whether pattern recognition software could be used to measure the influence and pattern of computer use in the consultation. If this proved possible it would provide, for the first time, an objective quantitative measure of computer use and a measure of the attention and responsiveness of the general practitioner towards the patient. METHODS: A feasibility study using pattern recognition software to analyse a consultation was conducted. A web camera, linked to a data-gathering node was used to film a simulated consultation in a standard office. Members of the research team enacted the role of the doctor and the patient, using pattern recognition software to try and capture patient-centred, non-verbal behaviour. As this was a feasibility study detailed results of the analysis are not presented. RESULTS: It was revealed that pattern recognition software could be used to analyse certain aspects of a simulated consultation. For example, trigger lines enabled the number of times the clinician's hand covered the keyboard to be counted and wrapping recorded the number of times the clinician nodded his head. It was also possible to measure time sequences and whether the movement was brief or lingering. CONCLUSION: Pattern recognition software enables movements associated with patient-centredness to be recorded. Pattern recognition software has the potential to provide an objective, quantitative measure of the influence of the computer on the consultation

Some Studies on Mass Spectrometry

Chapter One is a short historical introduction to the subject of Mass Spectrometry, Current research in this field is briefly reviewed to place the work of this thesis in perspective. Chapter Two is the main body of the work and is entitled "An Application of Pattern Recognition to Mass Spectral Data," The particular problemn being considered, the detection and identification of highly toxic atmospheric pollutants, is discussed and the applicability of pattern recognition techniques is explored. The general ideas and methods of pattern recognition are mentioned. The particular unsupervised method of pattern recognition, "Cluster Analysis", is described in greater detail and an outline of the mathematics involved in employing the method is given. Four separate studies on cluster analysis of mass spectral data are described and discussed. The first study is on ninety compounds of various types containing only carbon, hydrogen and sulphur. They are taken from the classes non-cyclic thioethers, cyclic thioethers, thiophenes and thiols. The second study is on sixty compounds which are recognised as organic atmospheric pollutants. The third study is on twenty-two different pyrazines. The forth study is on the compounds found as volatile metabolites in normal subjects (forty-two compounds) and subjects with diabetes mellitus (sixty compounds). These studies show that the pattern recognition approach to the analysis of large quantities of mass spectral data is of considerable potential, especially for data reduction and compound identification. Chapter Three contains the mass spectra of thirty-five various benzo-2,1,3-thiadiazoles, many of which have been shown to have considerable herbicidal and fungicidal activity. The mass spectra of all the compounds are discussed