Based on the principle of backscattering of laser radiation from tissues, a non-invasive PC-AT based reflectance imaging technique is developed. The laser beam from a semiconductor laser operating at 670 nm is guided to the tissue site by an optical fibre. The backscattered radiation is collected by another fibre placed in the same probe, and is detected by a photodiode-amplifier assembly. This probe is moved manually over the organs under observation, and the data after the ADC, interpolation and median filtering are displayed in the form of reflectance image of the organ along with grey scale. By this technique images of the human hands and forearms are obtained, which depend on the variations in their colour, composition and blood flow. A comparison is made with perfusion images, obtained by a Periflux laser Doppler flowmeter. These show that the reflectance images provide greater details of the tissue structure than the perfusion images.
This paper analyzes a lost sales (s, S) type perishable inventory system with varying ordering quantity under renewal demands. The items have constant hazard rates and the mean rate of replenishment is dependent on the order size. The techniques of semiregenerative processes are applied to obtain the various operating characteristics. Cost optimization, which includes sensitivity analysis of various system parameters, is also discussed with numerical illustrations.
Agriculture has been an important research area in the field of image processing for the last five years. Diseases affect the quality and quantity of fruits, thereby disrupting the economy of a country. Many computerized techniques have been introduced for detecting and recognizing fruit diseases. However, some issues remain to be addressed, such as irrelevant features and the dimensionality of feature vectors, which increase the computational time of the system. Herein, we propose an integrated deep learning framework for classifying fruit diseases. We consider seven types of fruits, i.e., apple, cherry, blueberry, grapes, peach, citrus, and strawberry. The proposed method comprises several important steps. Initially, data increase is applied, and then two different types of features are extracted. In the first feature type, texture and color features, i.e., classical features, are extracted. In the second type, deep learning characteristics are extracted using a pretrained model. The pretrained model is reused through transfer learning. Subsequently, both types of features are merged using the maximum mean value of the serial approach. Next, the resulting fused vector is optimized using a harmonic threshold-based genetic algorithm. Finally, the selected features are classified using multiple classifiers. An evaluation is performed on the PlantVillage dataset, and an accuracy of 99% is achieved. A comparison with recent techniques indicate the superiority of the proposed method.
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