Use of Metals and Anion Species with Chemometrics Tools for Classification of Unprocessed and Processed Coconut Waters

Coconut water is a natural isotonic, nutritive, and low-caloric drink. Preservation process is necessary to increase its shelf life outside the fruit and to improve commercialization. However, the influence of the conservation processes, antioxidant addition, maturation time, and soil where coconut is cultivated on the chemical composition of coconut water has had few arguments and studies. For these reasons, an evaluation of coconut waters (unprocessed and processed) was carried out using Ca, Cu, Fe, K, Mg, Mn, Na, Zn, chloride, sulfate, phosphate, malate, and ascorbate concentrations and chemometric tools. The quantitative determinations were performed by electrothermal atomic absorption spectrometry, inductively coupled plasma optical emission spectrometry, and capillary electrophoresis. The results showed that Ca, K, and Zn concentrations did not present significant alterations between the samples. The ranges of Cu, Fe, Mg, Mn, PO (4) (3-) , and SO (4) (2-) concentrations were as follows: Cu (3.1-120 A mu g L(-1)), Fe (60-330 A mu g L(-1)), Mg (48-123 mg L(-1)), Mn (0.4-4.0 mg L(-1)), PO (4) (3-) (55-212 mg L(-1)), and SO (4) (2-) (19-136 mg L(-1)). The principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to differentiate unprocessed and processed samples. Multivariated analysis (PCA and HCA) were compared through one-way analysis of variance with Tukey-Kramer multiple comparisons test, and p values less than 0.05 were considered to be significant.

Automotive Display outside Royal Buick, INC. Service & Parts

A representative from Buick and what is likely a representative from the New York Trade School show a part in front of the Royal Buick Showroom located at 1356 2nd Avenue near 65th Street. Black and white photograph.

Machine vision for the automatic classification of images acquired from Non-destructive tests

This project is based on Artificial Intelligence (A.I) and Digital Image processing (I.P) for automatic condition monitoring of sleepers in the railway track. Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway track infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required.Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the railway track searching for sleeper anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Images of wooden sleepers have been used as data for my project. The aim of this project is to present a vision based technique for inspecting railway sleepers (wooden planks under the railway track) by automatic interpretation of Non Destructive Test (NDT) data using A.I. techniques in determining the results of inspection.

Färgperception och grafisk profilering av Hedemora Assistansservie

An inquiring study of literature has been conducted, about the human colour perception (theimpression of colours). The colour has been examined, both as conscious and subconscious signal,and reasons for it’s influence have been exammed.The practical parts of the degree project have been carried out in active collaboration with thecustomer, The Association Hedemora Assistansservice (HASS), which offers handicapped persons astimulating spare time by personal assistance. A graphical profile-programme and an informationfolderhave been produced, easy received by both handicapped (with defective vision) and normallysighted persons. The graphical profile-programme was made in collaboration with the customer.Concerning the information-folder HASS took the main responsibility for the choice of photographswhile layout, text writing, colour-reproduction, original-production and connecting printing workswere made independently. The customer has shown engagement and interest and had a lot of opinionsabout the degree project, of which have been paid attention.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease

The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

Effects of central bank independence reforms on inflation in different parts of the world

The purpose of this study is to analyze the effect of CBI-reforms on inflation in different parts of the world from a theoretical and empirical perspective. Compared to previous studies, this study focuses on whether CBI-reforms have different effects on reducing inflation in different parts of the world. The study is based on a 132 country data-set from 1980 to 2005 compiled by Daunfeldt et al. (2008). The result indicates that the reduction in inflation due to the CBI-reforms varies between 2.2 and 12.32 percentage points in Asia, Europe, South America and Oceania, supporting the claim that implementing CBI-reforms can be successful in reducing inflation in most of the parts of the world.

Vocational English in policy and practice

The aim of this licentiate thesis is to examine how, and in what ways, vocational English is a part of English language teaching in the Building and Construc­tion Programme in Sweden, and what the influences are for such pedagogy. The main research question is how policy documents relate to the views of teachers and their educational practice regarding vocational English. The study consists of two parts: a textual policy analysis of the three latest upper secondary school reforms in Sweden (Lgy 70, Lpf 94, and Gy 2011), and semi-structured interviews with practicing English teachers in the Building and Construction Programme. The interviews are categorised by using Spradley’s (1979) semantic relationships and taxonomies. Balls’ (Ball, 1993) and Ozga’s (1990; 2000) concept of policy enactment is used in the analysis as well as Bernstein’s (1990; 2000) theoretical framework of classification, framing, and horizontal and vertical discourse. The results show that five of the six teachers in the interviews work with vocational English in some way. The study also shows that there is a distinct gap between policy and practice. Several of the teachers have the notion that they are supposed to work with vocational English and that it must be written down in policy somewhere. The greatest influence on the teaching for these teachers are their students, either indirectly or directly. Further, the study shows that different frame factors such as time poverty hinders the teachers from reading policy texts and cooperating with the vocational teachers in the Building and Construction Programme.

Benchmark Evaluation of HOG Descriptors as Features for Classification of Traffic Signs

The purpose of this paper is to analyze the performance of the Histograms of Oriented Gradients (HOG) as descriptors for traffic signs recognition. The test dataset consists of speed limit traffic signs because of their high inter-class similarities.   HOG features of speed limit signs, which were extracted from different traffic scenes, were computed and a Gentle AdaBoost classifier was invoked to evaluate the different features. The performance of HOG was tested with a dataset consisting of 1727 Swedish speed signs images. Different numbers of HOG features per descriptor, ranging from 36 features up 396 features, were computed for each traffic sign in the benchmark testing. The results show that HOG features perform high classification rate as the Gentle AdaBoost classification rate was 99.42%, and they are suitable to real time traffic sign recognition. However, it is found that changing the number of orientation bins has insignificant effect on the classification rate. In addition to this, HOG descriptors are not robust with respect to sign orientation.

Steel Sheet Applications and Integrated Heat Management

Increasing energy use has caused many environmental problems including global warming. Energy use is growing rapidly in developing countries and surprisingly a remarkable portion of it is associated with consumed energy to keep the temperature comfortable inside the buildings. Therefore, identifying renewable technologies for cooling and heating is essential. This study introduced applications of steel sheets integrated into the buildings to save energy based on existing technologies. In addition, the proposed application was found to have a considerable chance of market success. Also, satisfying energy needs for space heating and cooling in a single room by using one of the selected applications in different Köppen climate classes was investigated to estimate which climates have a proper potential for benefiting from the application. This study included three independent parts and the results related to each part have been used in the next part. The first part recognizes six different technologies through literature review including Cool Roof, Solar Chimney, Steel Cladding of Building, Night Radiative Cooling, Elastomer Metal Absorber, and Solar Distillation. The second part evaluated the application of different technologies by gathering the experts’ ideas via performing a Delphi method. The results showed that the Solar Chimney has a proper chance for the market. The third part simulated both a solar chimney and a solar chimney with evaporation which were connected to a single well insulated room with a considerable thermal mass. The combination was simulated as a system to estimate the possibility of satisfying cooling needs and heating needs in different climate classes. A Trombe-wall was selected as a sample design for the Solar Chimney and was simulated in different climates. The results implied that the solar chimney had the capability of reducing the cooling needs more than 25% in all of the studied locations and 100% in some locations with dry or temperate climate such as Mashhad, Madrid, and Istanbul. It was also observed that the heating needs were satisfied more than 50% in all of the studied locations, even for the continental climate such as Stockholm and 100% in most locations with a dry climate. Therefore, the Solar Chimney reduces energy use, saves environment resources, and it is a cost effective application. Furthermore, it saves the equipment costs in many locations. All the results mentioned above make the solar chimney a very practical and attractive tool for a wide range of climates.