EXPERIMENTAL INVESTIGATION AND PROPERTIES OF REACTIVE POWDER CONCRETE

P.B.SIVA SANKAR ^a, B.VENKATESAN ^b

^a Assistant professor, Department of Civil Engineering, Vickram college of Engineering, Madurai, India

^b Assistant professor, Department of Civil Engineering, University College of Engineering, Ramanathapuram, India

     Reactive powder concrete (RPC) is the term has been used to describe a fibre reinforced , super plasticized, silica fume- cement mixture with reduced water-cement ratio, characterized by the presence of very fine quartz sand instead of ordinary aggregate. In this investigation the mechanical property performance of RPC has been investigated by varying the water dosage, silica fume dosage, and with addition of 1%, 2%, 3%, 4%and 5% crimped steel fibres.RPC have also been investigated under different combinations of normal water curing, hot water curing and high temperature curing, after this 7 days of curing regimes, the specimens were cured in water until the testing. Test results indicate that compressive strength of RPC increases significantly after heat treating compared to the standard water curing. Temperature, Rate of heating and Rate of curing also influenced the mechanical performance of RPC considerably.

View full abstract...

A REVIEW ON SCALABLE DIVIDEND SERVICE INTEGRITY FOR SOFTWARE-AS-A-SERVICE CLOUDS

P.Keerthiga, S.Rajeswari

¹PG Student STET Women’s college, mannargudi

²Professor of cs department, STET Women’s college, mannargudi

Software-as-a-service (SaaS) cloud systems enable application service providers to deliver their applications via massive cloud computing infrastructures. However, due to their sharing nature, SaaS clouds are vulnerable to malicious attacks. In this paper, we present IntTest, a scalable and effective service integrity attestation framework for SaaS clouds. IntTest provides a novel integrated attestation graph analysis scheme that can provide stronger attacker pinpointing power than previous schemes. Moreover, IntTest can automatically enhance result quality by replacing bad results produced by malicious attackers with good results produced by benign service providers. We have implemented a prototype of the IntTest system and tested it on a production cloud computing infrastructure using IBM System S stream processing applications. Our experimental results show that IntTest can achieve higher attacker pinpointing accuracy than existing approaches. IntTest does not require any special hardware or secure kernel support and imposes little performance impact to the application, which makes it practical for large-scale cloud systems.

View full abstract...

REDUCTION OF SEMANTIC GAP USING LOW LEVEL VISUAL FEATURES

¹ A.ATHIRAJA, ² K.BALA MURALI, ³ Dr. A. ASKARUNISA, ⁴ S.UMAMAHESWARAN
¹ Asst Professor CSE, ² PG student CSE, ³ Professor and Head of CSE, ⁴ Asst Professor CSE
Department of Computer Science and engineering, Vickram College Of Engineering, Enathi, Sivagangai, India.

     Digital image libraries and other multimedia databases have been dramatically expanded in recent years. In order to effectively and precisely retrieve the desired images from a large image database, the development of a content-based image retrieval (CBIR) system has become an important research issue. Most of the existing approaches lack the capability to effectively incorporate human intuition and emotion into retrieving images. In order to reduce the semantic gap the proposed approaches emphasize on finding the best representation for different image features. Furthermore, very few of the representative works will consider the user’s subjectivity and preferences in the retrieval process. In this project, a user-oriented mechanism for CBIR method based on low level visual features like color texture, histogram and correlation are used. Color attributes like the mean value, the standard deviation were used. The entropy based on the gray level co-occurrence matrix of an image is considered as the texture feature. Further the histogram values are used for effective image retrieval process; finally the images are compared using correlation values at both the ends. The efficiency of proposed technique is evaluated and the experimental result indicates that it outperforms other existing systems. In this project based on reduction of I have used corel datasets is used which consist of 1000 images of varying 10 semantic contents, out of which 100 images were used.

View full abstract...

COMPUTATION OF IMAGE DISTANCES FOR HUMAN IDENTIFICATION IN DENTAL RADIOGRAPHS

¹ PG Student, ² Assistant Professor, ³ Professor & Head, ⁴ Assistant Professor
Department of Computer Science and engineering, Vickram College Of Engineering, Enathi, Sivagangai, India.

     Dental radiographs are used for human identification in dental biometrics. The dental radiograph gives us various information such as tooth contours, relative positions of neighbouring teeth, and shapes of the dental work (e.g., crowns, fillings, and bridges). The proposed system has 2 stages namely (1) Feature Extraction and (2) Matching. In feature extraction, active contour model is used to extract the contour. The matching stage has 2 steps viz. Computation of Image distances and Subject identification. In tooth level matching tooth contours are matched using “Shape registration Method” and depending upon the overlapping areas the dental works are matched. Then the values of the distance between the tooth contours and dental works are combined using posterior probabilities. Tooth correspondence between query radiograph and database radiograph are established. Distance between the teeth are used to calculate the similarity between the two radiographs. Finally the distance between the radiographs provide the details about the subject associated with these radiographs. The dataset contains 10 normal images and 55 OPG images which was collected from Madura Dental Hospital. The accuracy of the algorithm is measured by the ratio of Correct Detection images to Total No of images. The experimental results show that this proposed algorithm is accurate about 72%.

View full abstract...