Abstract Biomass of Saccharomyces cerevisiae wasenhancedin rice polishing by fermentation to increase protein contents of feedfor its use in livestock. Broth culture of Saccharomyces cerevisiae (2.6×108CFU/mL) was prepared from culture obtained by continuous streaking. The isolated culture was identified morphologically by Gram staining and confirmed by biochemical characteristics. Rice polishing was sieved to remove larger particles. Then it was distributed to 4 treatments in triplicates. Treatments were represented as rice polishing (RP), rice polishing plus Saccharomyces culture (RPSC), rice polishing plus ammonium sulphate (RPAS), rice polishing plus Saccharomyces culture plus ammonium sulphate (RPSCAS).Fermentation was provided for 144 hours at 320C,while samples were collected after every 24 hours. Samples were dried, ground and subjected to proximate analysis. It was observed that protein content was increased from 11% to 21.51% and maximum increment was obtained after 144 hours of incubation in RPSC treatment. Ether extract and ash were increased from 14% and 10% to 16.96% and 11.11% in RPSCAS respectively. A significant reduction in neutral detergent fiber was observed after fermentation. It is concluded that Saccharomyces cerevisiae has potential to improve mineral and protein contents of rice polishing by fermentation process with or without addition of nitrogen source.

Face recognition is one of a complex biometrics in the field of pattern recognition due to the constraints imposed by variation in the appearance of facial images. These changes in appearance are affected by variation in illumination, expression or occlusions etc. Illumination can be considered a complex problem in both indoor and outdoor pattern matching. Literature studies have revealed that two problems of textural based illumination handling in face recognition seem to be very common. Firstly, textural values are changed during illumination normalization due to increase in the contrast that changes the original pixels of face. Secondly, it minimizes the distance between interclasses which increases the false acceptance rates. This paper addresses these issues and proposes a robust algorithm that overcomes these limitations. The limitations are resolved through transforming pixels from nonillumination side to illuminated side. It has been revealed that proposed algorithm produced better results as compared to existing related algorithms.

By gaining the place of active and important research area, Content based image retrieval has been proposed in a number of different ways after its inception. In the proposed method, a new angle orientation histogram has been introduced named as Angle Edge Histogram. By applying Pythagorean theory to image, very useful characteristics have been obtained for image matching, search and retrieval. Proposed method has also been compared with existing methods and the results show that it outperforms the existing methods in values of precision and recall and balance of precision and recall. Proposed method receives an average of 94% of precision and 79% of recall rates.

Space research organizations, hospitals and military air surveillance activities, among others, produce a huge amount of data in the form of images hence a large storage space is required to record this information. In hospitals, data produced during medical examination is in the form of a sequence of images and are very much correlated; because these images have great importance, some kind of lossless image compression technique is needed. Moreover, these images are often required to be transmitted over the network. Since the availability of storage and bandwidth is limited, a compression technique is required to reduce the number of bits to store these images and take less time to transmit them over the network. For this purpose, there are many state-of the-art lossless image compression algorithms like CALIC, LOCO-I, JPEG-LS, JPEG20000; Nevertheless, these compression algorithms take only a single file to compress and cannot exploit the correlation among the sequence frames of MRI or CE images. To exploit the correlation, a new algorithm is proposed in this paper. The primary goals of the proposed compression method are to minimize the memory resource during storage of compressed data as well as minimize the bandwidth requirement during transmission of compressed data. For achieving these goals, the proposed compression method combines the single image compression technique called super spatial structure prediction with inter-frame coding to acquire grater compression ratio. An efficient compression method requires elimination of redundancy of data during compression; therefore, for elimination of redundancy of data, initially, the super spatial structure prediction algorithm is applied with the fast block matching approach and later Huffman coding is applied for reducing the number of bits required for transmitting and storing single pixel value. Also, to speed up the block-matching process during motion estimation, the proposed method compares those blocks that have identical sum and leave the others; therefore, the time taken by the block-matching process is reduced by minimizing the unnecessary overhead during the block-matching process. Thus, in the proposed fast lossless compression method for medical image sequences, the two-stage redundant data elimination process ultimately reduces the memory resource required for storing and transmission. The method is tested on the sequences of MRI and CE images and produces an improved compression rate.