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In this paper, we present an efficient technique of electronic tongue (ET) signal processing using the sparse decomposition method for the prediction of tea quality. Sparse model coefficients of an under complete dictionary are considered as characteristic attributes of the ET signals obtained for characterization of tea samples. The effectiveness of the proposed method is established by performing experiment on three different types of pulse voltammetry. The ability of the sparse coefficients, as features of ET signal, is tested by employing three different classifiers. High classification accuracy of all the classifiers on three types of voltammetric measurement data validates the usefulness of this technique. The present model-based the tea quality prediction method is very fast, simple, and straightforward. This tea quality prediction method is able to address the problem more efficiently in comparison to other techniques found in the literature.

Liquor characteristics of black cut, tear, and curl tea mostly depend on two biochemical components like theaflavin (TF) and thearubigin (TR). Evaluation of tea quality can be done efficiently by estimating the concentration of TF and TR without using biochemical tests as it takes much time, which requires laborious effort for sample preparation, storage, and measurement. Moreover, the required instruments for this test are very costly. In this paper, we have proposed an efficient method of TF and TR prediction in a given tea sample using electronic tongue (ET) signal. Combinations of transformed features, like discrete cosine transform, Stockwell transform (ST), and singular value decomposition, of ET signals are fused to develop regression models to predict the contents of TF, TR, and TR/TF. Three different regression models such as artificial neural network, vector-valued regularized kernel function approximation, and support vector regression are used to evaluate the performance of the proposed method. High prediction accuracy using fusion of features ensures the effectiveness of the proposed method for prediction of TF and TR using ET signals.

Biochemical means of tea quality evaluation is quite accurate, but it requires very costly instruments and takes a long time to conduct the experiment. Researches show that thearubigin (TR) and theaflavin (TF) are the two most important biochemical compounds present in tea liquor on which liquor characteristics of black CTC tea depends. Consequently, this fact may be the basis of determining tea quality by assessment of TR and TF via electronic tongue (ET) response. This technique is free from subjective factors. In literature, there are only two research works using this technique so more research work is required to address the problem. This chapter proposes a modeling technique of ET response using sparse decomposition technique to estimate TR and TF content in a given tea sample. For each tea sample, sparse model coefficients obtained from the ET response is considered as a characteristic attribute of it. Experimental results using dissimilar regression models show high prediction accuracies which justify the efficacy of the proposed method.

In this paper, a novel method to model the responses of electronic tongue (ET) sensors using autoregressive (AR) and AR moving average techniques is presented. The transient response of each electrode present in the sensor array of an ET is characterized with tea samples of different qualities. Models coefficients are used as the characteristics features of the ET response corresponding to the tea samples. Three different classifiers, namely, artificial neural network, vector valued regularized kernel function approximation, and one-versus-one support vector machine, are employed to evaluate the performance of these features to discriminate the quality of black tea. Experimental results on three types of voltammetric measurement data show that the proposed method may be very useful for prediction of tea quality. The present model-based classification method is very straightforward and provides better or similar performance compared with some other methods proposed in the literature for ET signal classification.

To develop any autonomous computerized algorithm for abnormality detection based on medical image analysis essentially requires a robust segmentation algorithm. To achieve this goal of accurate segmentation of medical image it requires an effective edge detection algorithm. Generalized edge detection algorithms like Sobel, Canny, though effective in normal images but often fails in case of medical images. Medical images obtained from devices such as Ultrasonography, X-Ray, CT and MRI exhibit diverse image characteristics but are essentially collection of intensity variations from which specific abnormalities are needed to be isolated. Homogeneity enhancement, followed by edge detection and resulting segmentation is essential prerequisite for all medical image analysis system. This paper proposes a robust medical image enhancement and edge detection algorithm that is equally effective with most medical images that are popular now-a-days yielding excellent segmentation results in all cases.

Objective: Autism has a strong genetic basis, although the genetics of autism are complex and it is unclear. This descriptive type of cross sectional study was conducted to explore the knowledge on care of autistic children among the mother’s attending Protibondhi Foundation, Dhaka.Methods: A total of 385 samples were selected purposively, but due to time and budget constrict it was 150 finally. Data were collected by using an anonymous, pre-tested, semi structured and self-administered questionnaire.Results: The study revealed that more than 50% of the respondent’s were in the age group 16-30 years, majority of the respondent’s were Muslim. Almost 84% of the respondents were SSC-Graduate and majority of them (nearly 50%) were housewife .The monthly family income was ranged between Taka 25,001-50,000 and its percentage was 53.3%. The knowledge about type of autism is very low, the data showed that only two types of autism they knew and the respondent’s knowledge about the risk factor was also very low; only 24% of the respondents had knowledge about complications during pregnancy and delivery related risk factors. About 28% needed physiotherapy, 30% needed occupational therapy, 34% needed speech therapy & 44.7% needed psychotherapy. Here significant association was found between age, education and knowledge about autism (p

Electronic tongue (ET) system is under extensive development for automatic analysis and prediction of quality of different industrial end products. Each sensor in an ET system generates a specific electronic response in presence of different organic or inorganic compounds in the sample. The vital part of the ET system is the discrimination of the complex pattern generated by the sensor array. In this paper, a novel technique of black tea quality estimation is using the ET signals. A moving window is used to extract discrete wavelet transform coefficients from the transient response of ET. The energy in different frequency bands are used as the features of the ET signal for different positions of the window. The prediction of a new sample is performed by the highest score obtained by a particular class by testing all the patterns generated by windowing ET signal. The performance of the proposed technique is verified to estimate black tea quality using two kernel classifiers, namely support vector machine and recently proposed vector valued regularized kernel function approximation method. High prediction accuracy of both the classifiers confirms the effectiveness of the proposed technique of tea quality estimation using ET signals.

For the first time in Bangladesh, a bench scale membrane bioreactor (MBR) unit was tested in treating a textile wastewater in the industry premises of EOS Textile Mills LTD, Dhaka for three months. The performance of the unit was compared with that of the conventional activated sludge treatment plant, which is in operation in the same premises. The COD and BOD removal efficiency of the MBR unit reached to around 90% and 80% respectively in 20 days whereas the removal efficiency of the conventional treatment plant was as low as 40-50% and 38-40% respectively. The outlet COD and the BOD level for the MBR unit remained stable in spite of the fluctuation in the feed value, while the conventional effluent treatment plant (ETP) failed to keep any stabilized level. The performance of the MBR unit was much superior to that of the functional ETP and the water treated by the MBR system can meet disposal standard. © 2014 Techno-Press, Ltd.

A bacterial isolate Pseudomonas sp. was isolated from the solid waste slump collected from a local duping site of Sylhet district and was efficiently utilized for the removal of dye (Orange 3R) from simulated synthetic waste water in a lab scale Stirred Tank Sequencing Batch Bioreactor (STSBBR) in batch mode. A reactor with 2 L capacity (working volume 0.5 L) equipped with suitable control means and stirring mechanism was operated at room temperature and pH 6.6 ± 1 in fill-react-settling-draw mode with different initial dye concentrations (50, 100, 150, 200 & 300 ppm) where the hydraulic retention time was maintained for 12-72 hours depending on the adaptation of waste water by the bacterial strain. The efficiency of the reactor was analyzed with respect to three strands and found to be negative correlation with the concentration of the dye. Overall color, COD, and BOD in the Stirred tank bioreactor system (STSBBR) were removed by 49.67, 37.45 and 33.89%, respectively with 50 ppm dye concentration and HRT of 24 h. The efficiency of the reactor was found to be in negative correlation with the concentration of the dye. This STSBBR system was found very effective for efficient biological treatment of such dyeing industry waste water by the bacterial strain Pseudomonas sp. DOI: http://dx.doi.org/10.3329/jce.v27i2.17808 Journal of Chemical Engineering, IEB Vol. ChE. 27, No. 2, December 2012: 77-82

The paper archives the proceedings of an expert panel discussion on the issues and future direction of thermal-hydraulic research and development in nuclear power reactors held at the NURETH-14 conference in Toronto, Canada, in September 2011. Thermal-hydraulic issues related to both operating and advanced reactors are presented. Advances in thermal-hydraulics have significantly improved the performance of operating reactors. Further thermal-hydraulics research and development is continuing in both experimental and computational areas for operating reactors, reactors under construction or ready for near-term deployment, and advanced Generation-IV reactors. As the computing power increases, the fine-scale multi-physics computational models, coupled with the systems analysis code, are expected to provide answers to many challenging problems in both operating and advanced reactor designs.

The electronic tongue (ET) system is basically a multi-electrode system where the response of each electrode in presence of tea samples are multi dimensional combinations of different chemical compounds and represented by large number of measured points. It is expected that an ET system will examine and identify these signals precisely. Relevant feature extraction with appropriate signal processing of the responses generated by the electrode array may help to achieve this task of ET. In this work, a feature extraction method using singular value decomposition (SVD) method has been used to represent the ET signal before sending them to appropriate pattern classifier. The efficiency of the proposed method is verified on three types of ET data sets using support vector machine (SVM) classifiers. More than 98% of accuracy is obtained in all the three data sets which prove the efficacy of the proposed method.

This report provides the broad historical review of EM Pump development and details of MATRIX development under this project. This report summarizes the efforts made to modernize the legacy performance models used in previous EM Pump designs and the improvements made to the analysis tools. This report provides information on Tasks 1, 3, and 4 of the entire project. The research for Task 4 builds upon Task 1: Update EM Pump Databank and Task 3: Modernize the Existing EM Pump Analysis Model, which are summarized within this report. Where research for Task 2: Insulation Materials Development and Evaluation identified parameters applicable to the analysis model with Task 4, the analysis code was updated, and analyses were made for additional materials. The important design variables for the manufacture and operation of an EM Pump that the model improvement can evaluate are: space constraints; voltage capability of insulation system; maximum flux density through iron; flow rate and outlet pressure; efficiency and manufacturability. The development of the next-generation EM Pump analysis tools during this two-year program provides information in three broad areas: Status of analysis model development; Improvements made to older simulations; and Comparison to experimental data.

The electronic tongue (ET) system is a multi-electrode system where each electrode generates a specific electronic response in presence of different chemical substances in the sample. The efficiency of an ET system mostly depends on the discriminating power of the electronic signature generated by the electrode array. In this work, a sliding window approach is used to extract discrete cosine transform (DCT) coefficients from the ET response and the corresponding energy for different position of window are used as the features of the ET response. The efficacy of the proposed method is verified on three types of ET data sets to predict four different quality of black tea using two kernel classifiers, namely support vector machine (SVM) and vector valued regularized kernel function approximation (VVRKFA).

Electronic nose (e-nose) is an artificial olfaction system that is being widely used in many industries. E-noses detect smells with the help of electronic signals produced by a number of sensors. The important part of an efficient e-nose system is to recognize these electronic signals accurately by some pattern classification algorithm. Recently developed nonparallel plane proximal classifier (NPPC) has shown its effectiveness in pattern classification task using kernel trick. In general the performance of such classifier depends on the values of optimal parameter set as well as the feature set. In this research work we have studied the effect of simultaneous parameter and feature weight selection on the accuracy of black tea quality estimation employing multiclass one vs. one NPPC. In order to choose the model parameters we have used genetic algorithm (GA). Experimental results show that GA-based tuning and feature weighting scheme increases the performance of NPPC by ∼ 2% in the problem of black tea quality prediction.

Electronic nose (e-nose) is a machine olfaction system that has shown significant possibilities as an improved alternative of human taster as olfactory perceptions vary from person to person. In contrast, electronic noses also detect smells with their sensors, but in addition describe those using electronic signals. An efficient e-nose system should analyze and recognize these electronic signals accurately. For this it requires a robust pattern classifier that can perform well on unseen data. This research work shows the efficient prediction of black tea quality by means of modern kernel classifiers using the e-nose signatures. As kernel classifiers, this work investigates the potential of state of the art support vector machine (SVM) classifier and very recently developed nonparallel plane proximal classifier (NPPC) and vector-valued regularized kernel function approximation (VVRKFA) technique of multiclass data classification to build taster-specific computational models. Experimental results show that VVRKFA and one-versus-rest (OVR) SVM models offer high accuracies to predict the considerable variation in tea quality.

Benchmark-3 is designed to predict strains, punch load and deformed profile after spring-back during single tool incremental sheet forming. AA 7075-O material has been selected. A corn shape is formed to 45 mm depth with an angle of 45°. Problem description, material properties, and simulation reports with experimental data are summarized.

Electronic nose (e-nose) is a machine olfaction system and the sensor array is an essential part of the electronic olfaction process. A pattern recognition unit is necessary in electronic nose system to efficiently decide about the output of the test using the responses of all the sensors in the array. The output of a pattern recognition algorithm depends on the quality of the feature set used for training and testing. Relevant and independent feature set improves the performance of a pattern classification algorithm. In some applications of electronic nose, the responses of few sensors are highly corrupted with noise and are either irrelevant or are redundant to the process. These sensors should be identified and eliminated from the sensor system for better accuracy. This paper addresses the selection of sensors in an e-nose system by different feature selection methods and then integrates them to achieve improved classification performance. We have used three types of feature selection methods namely, t-statistics, Fisher's criterion and minimum redundancy maximum relevance (MRMR) technique to select the most informative features. We have tested the proposed method on data obtained from the major aroma producing chemicals of black tea. Multi-class support vector machine (SVM) has been used as a pattern classifier in an electronic nose with black tea samples. The experimental results show that the performance of the e-nose system increased by 6–10% with the use of the proposed combinational feature selection technique.

Electronic nose (e-nose) is a machine olfaction system that has shown significant possibilities as an improved alternative of human taster as olfactory perceptions vary from person to person. In contrast, electronic noses also detect smells with their sensors, but in addition describe those using electronic signals. An efficient e-nose system should analyze and recognize these electronic signals accurately. For this it requires a robust pattern classifier that can perform well on unseen data. This research work shows the efficient prediction of black tea quality using machine learning algorithm with e-nose. This paper investigates the potential of three different types of multi-class support vector machine (SVM) to build taster-specific computational models. Experimental results show that all the three models offer more than 97% accuracies to predict the considerable variation in tea quality.

This project is organized under four major tasks (each of which has two or more subtasks) with contributions among the three collaborating organizations (MIT, INEEL and ANL-West): Task A: Core Physics and Fuel Cycle; Task B: Core Thermal Hydraulics; Task C: Plant Design; Task D: Fuel Design The lead PI, Michael J. Driscoll, has consolidated and summarized the technical progress submissions provided by the contributing investigators from all sites, under the above principal task headings.

The “Whole Assembly Seed and Blanket” (WASB) design, which utilizes mostly thorium in the blanket, consists of 84 seed and 109 blanket assemblies which may be backfitted into existing Pressurized Water Reactors (PWRs). Since the seed assemblies produce significantly more power than the blanket assemblies, a preliminary safety analysis of this design has been performed. Three accidents/transients (Large Break Loss of Coolant Accident (LBLOCA), Complete Loss of Primary Flow (LOPF) and Loss of Off-site Power (LOSP)), have been analyzed for both the WASB design and a typical all UO2 design for a typical 4-Loop Westinghouse PWR plant. LBLOCA results show that the peak cladding temperature (PCT) for the WASB design is approximately 260 K higher than that for a typical PWR design. However, this higher PCT for the WASB design is still about 200 K lower than the present regulatory safety limit. The response of the WASB and all UO2 core for LOPF and LOSP transients are very similar, and no post-DNB type rapid cladding temperature rise was observed in either of the two calculations.Copyright © 2004 by ASME