Showing papers by "SDM College of Engineering and Technology published in 2015"

Influence of zinc oxide nanoparticles on structural and electrical properties of polyvinyl alcohol films

Abstract: This study has been conducted with the objective of investigating the effects of zinc oxide nanoparticles on the structural and electrical properties of polyvinyl alcohol films. The fabricated nanocomposites were characterised by Fourier transform infrared spectroscopy, UV–visible (vis) spectra, X-ray diffraction and SEM techniques. UV–vis spectra showed that the addition of ZnO nanoparticles did not affect the absorbance in the visible region of nanocomposites. The SEM image showed that ZnO nanoparticles were homogeneously dispersed throughout the entire film’s polymeric matrix. The dielectric properties were found to be strongly dependent on frequency and nanofiller content. AC conductivity σac of polyvinyl alcohol/ZnO nanocomposites increased with increasing frequency. The dissipation factor tan δ also increased with nanoparticle addition and decreased with frequency. At low nanofiller concentrations, nanocomposites exhibited low dielectric values at higher frequency, thus behaving like a lossl...

Adaptive self-tuning techniques for performance tuning of database systems: a fuzzy-based approach with tuning moderation

Abstract: Self-tuning of database management systems (DBMS) offers important advantages such as improved performance, reduced total cost of ownership, eliminates the need for an expert database administrator (DBA), and improves business prospects. Several techniques have been proposed by researchers and the database vendors to self-tune the DBMS. However, the research focus was confined to physical tuning techniques, and the algorithms used for self-tuning the shared memory of DBMS have high computational overheads as they use large statistical data. As a result, these approaches are not only computationally expensive but also do not adapt well to highly unpredictable workload types and user-load patterns. Hence, in this paper an important soft-computing method, namely, fuzzy-based self-tuning approach has been proposed wherein, three inputs namely, buffer-hit-ratio, number of users and database size are extracted from the database management system as sensor inputs that indicate degradation in performance, and key tuning parameters called the effectors are altered (Burlson and Donald 2010) according to the fuzzy rules. The fuzzy rules are framed after a detailed study of impact of each tuning parameter on the response-time of user queries. The proposed self-tuning architecture is based on modified Monitor, Analyze, Plan and Execute (MAPE) feedback control loop framework termed Monitor, Estimate and Execute (MEE). The self-tuning approach using this method has been tested under various workload types. The results have been validated by comparing the performance of the proposed self-tuning system with the workload-analysis-based self-tuning feature of the commercial database system, Oracle 10g. The results show significant improvement in performance under two workload types, namely, TPC-C and TPC-E and user-load variations in the range 2---100. The system is also tested under TPC-D workload for the user-load 1---10. This improved self-tuning helps in simplifying the job of the DBA, and results in cost saving and betters the business prospectus of the enterprise. A novel tuning moderation technique is also presented in this paper, that provides the necessary stability to the system while the tuning parameters are dynamically altered.

Hyperspectral imaging technique for plant leaf identification

Abstract: Hyperspectral imaging is the procedure to gather and handle information across the electromagnetic spectrum. The fundamental objective of hyperspectral imaging is to achieve the spectrum for every pixel in the picture. The spectrum helps in computer vision, i.e., locating items, material detection or process discovery. This approach is constantly developing in the field of remote sensing applications. The wide spectrum range, offers a high spectral resolution, thus permitting the detection and understanding of the exterior and material components of the viewed image. Numerous elements, for example, defective imaging optics, climatic disturbances, optical brightening impacts and sensor noise cause the corruption of the procured image quality, making spatial determination amongst the costliest and hardest to improve in imaging frameworks. Blended pixels, i.e., pixels containing a blend of diverse materials, are normal in hyperspectral pictures due to such a requirement. In this work, we propose the use of the rich spectral data of hyperspectral images to resolve classification issues.

Decomposition-based shape template matching for CBIR system

Abstract: Content Based Image Retrieval is a process to get a desired image from a substantial database. We propose a template for shape based hierarchical feature matching approach for content based image retrieval system. It utilizes a combination of global feature for shape based templates. In this work a new learning method is put forth which is based on the hierarchal decomposition of the data. The proposed method establishes learning algorithm where the feature extraction process is executed to detect edge, orientations and shape of the dataset images. Thus extracted shape based features are used for matching the template to improve the retrieval accuracy. The proposed model is tested for the Wang dataset. The classification of dataset is taken care by the support vector machine algorithm with the accuracy of 99.09 % The retrieval results of proposed model is illustrated in terms of precision and recall, the improved efficiency of retrieval is compared to other existing models.

Solar Load Voltage Tracking for Water Pumping: An Algorithm

Abstract: Maximum power is to be harnessed from solar photovoltaic (PV) panel to minimize the effective cost of solar energy. This is accomplished by maximum power point tracking (MPPT). There are different methods to realise MPPT. This paper proposes a simple algorithm to implement MPPT lv method in a closed loop environment for centrifugal pump driven by brushed PMDC motor. Simulation testing of the algorithm is done and the results are found to be encouraging and supportive of the proposed method MPPT lv .

Open Assessment Method for Better Understanding of Student’s Learnability to Create Personalised Recommendations

Abstract: It is well accepted that learners even at academic institutions come with different abilities and flavours. Personalising the education is one of the 16 grand challenges as per the National Academy of Engineering (NAE). Measuring the outcomes of an education, progress of learning, etc., plays an important role in generating personalised feedbacks and recommendations in personalised learning environments facilitated by either an intelligent tutoring system (ITS) or an expert academician in conventional institutions. Any assessment would require a medium of expression to measure the achievements/outcomes of learning. Most common among them are writing a set of reasoning statements, writing algorithms, diagrams/sketches, occasional viva voce, etc.. The intention of this paper is to share one of our assessment practices during 2011–2013 where the medium of expression is as chosen by the students; we call this method of assessment as “open assessment”. We also share the techniques of recording assessment data, policies to ensure student‘s participation in assessments and grading philosophy for such assessment methods. Finally, we present a short analysis to highlight advantages and disadvantages of this method when practiced in institutions with academic autonomy in India.

A content based image retrieval system using shape analysis

Abstract: Image retrieval is a process of retrieving the similar images from a huge dataset. The retrieving of images is done based on the query image and the matched feature with the dataset images. In this paper we have proposed a novel method for efficient retrieval using shape analysis of the query and dataset images. The key points of the work are detection of shape and extraction of the features based on the shape. The extracted features are trained by using the support vector machine classification method. The simulation results show the accuracy of the proposed system as 96.69%.

Analysis of New Data Sources in Modern Teaching and Learning Processes in the Perspective of Personalized Recommendation

Abstract: The increased variety of learning resources have substantially affected learning styles of students, like e-books with modern collaborative tools, video lectures of different teachers across the world, lively discussion boards etc Having accepted such forms of learning materials, teaching and learning processes in conventional set up do not have a way to capture the data generated out of students’ learning activities involving such resources and use them effectively This paper analyses data generated by the student’s activities in Compiler of Resources in Engineering and Technology to Aid Learning (CRETAL ) restricted to video resources and asserts that they are indeed critically helpful data for teachers/tutoring systems in generating personalised recommendations which are possible only because of said data CRETAL is the modern learning station, an intelligent system, being developed at author’s institution to facilitate variety of learning resources created and adapted by the faculty and the teachers worldwide to students

Rank and Weight Based Protocol for Cluster Head Selection for WSN

Abstract: With the evolution of wireless sensor network, the interests in their application have increased considerably. The architecture of the system differs with the application requirement and characteristics. Now days there are number of applications in which hierarchal based networks are highly in demand and key concept of such network is clustering. Some of the most well-known hierarchical routing protocols like LEACH, SEP, TEEN, APTEEN and HEED are discussed in brief. These different conventional protocols have diverse strategies to select their cluster head but still have some limitations. Based on the limitations of these conventional models, a new approach has been proposed on the basis of ranks and weights assignment based protocol known as RWBP. This approach considers not only residual energy but also node’s degree and distance of nodes with base station. The node which has higher weight will be chosen as a cluster head. The objective of this approach is to have balance distribution of clusters, enhance lifetime and better efficiency than traditional protocols. The same approach is also applied for multi hop clustering i.e. multi hop RWBP in which the sensing field is divided into more number of areas and the area which lie farther from the base station is sending indirectly via intermediate cluster heads to the base station. The simulations are done in MATLAB with the network size 100x100 meters. The results of the proposed approach are resulting in better lifetime and stability region as compared to LEACH and SEP.

Parallelized genomic sequencing model: A big data approach for bioinformatics application

Abstract: Genomic sequence alignment is one of the most significant applications in bioinformatics. In future gene sequencing technologies are expected to produce terabyte of genomic data. Cloud Computing and MapReduce framework play an important role in bioinformatics intensive application in achieving parallelization since it provides a consistent performance over time and it provides good fault tolerant mechanism. The existing sequencing methodologies are based on Hadoop MapReduce Framework which adopts a serial execution strategy which is an area of concern. This work introduces a Smith-Waterman Alignment on the Parallel Azure Map Reduce (SW-PAMR) Cloud platform for bioinformatics sequence alignment. This work adopts a widely accepted and accurate Smith-Waterman algorithm for sequence alignment and parallelization methodology of Map and Reduce framework. A customised MapReduce based on Azure Cloud platform is developed to overcome the issue in Hadoop MapReduce framework. The experimental study presented in this work proves that the SW-PAMR can accurately and effectively align bioinformatics genomic sequences.