The technological process integration will influence directly on the energy efficient conversion with vital role on system productivity. In this work, an attempt was made to investigate on the performance of a compound parabolic concentrator-concentric tubular solar still (CPC-CTSS) coupled with a single slope solar still. A set of 2 m long concentric tubes with rectangular basins of the same length was fabricated (2 m2 area) and the entire experimental setup was operated with cold water flow over the inner tubes of the concentric arrangement. This pre-heated water was fed to a single slope solar still. The area of the single slope solar still was 0.25 m2 and the glass had an angle of 11° from the horizontal. It was clearly observed that the yield strongly depends on the evaporative heat transfer coefficient. It was concluded that, to increase the distillate augmentation to overnight, phase change material was additionally incorporated in the single slope solar still.
The objective of the current work is to develop an automatic tool to identify microbiological data types using computer vision and pattern recognition. Current systems rely on the subjective reading of profiles by a human expert. This process is time-consuming and prone to errors. Bacteriophage (phage) typing & Fluorescent imaging methods are used to extract representative feature profiles and identify the bacterial types. For feature selection of Bacterial identification system, the most successful method seems to be the appearance-based approach, which generally operates directly on images or appearances of bacterial objects. The image segmentation, Linear Discriminant Analysis (LDA), Direct Fractional LDA (DFLDA) and Principal Component Analysis (PCA) are the powerful tools used for feature extraction. Then the principal components are analyzed by DFLDA and simple Nearest Neighbor Classifier technique is used to identify the type of bacteria. The effectiveness of the proposed method has been verified through experimentation using fifty popular bacterial image databases.
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