Dr. N. Rudraiah : a biobibliometric study

Dr. Rudraiah has worked in various fields in applied mathenlatics like fluid mechanics, magnetohydrodynamics, electrodynamics and smart materals of nanostructures. In his 43 pears of productive life, he has collaborated with 102 colleagues and students and has published 271 papers during 1962-2004. The collaboration co-efficient is 0.54. Highest collaborations were with M. Venkatachalappa (31), and B.C. Chandrasekhara (21). The core journals publishing his papers were: Indian Journal of Pure and Applied Mathematics, Current Science, International Journal of Heat and Mass Transfer, Acta Mechanica, Journal of Fluid Mechanics, Proc. Royal Cambridge Society of London and Physics of Fluid

Exploring Alternate Cache Indexing Techniques

Cache memory is a bridging component which covers the increasing gap between the speed of a processor and main memory. An excellent performance of the cache is crucial to improve system performance. Conflict misses are one of the critical reasons that limit the cache performance by mapping blocks to the same set which results in the eviction of many blocks. However, many blocks in the cache sets are not mapped, and thus the available space is not efficiently utilized. A direct way to reduce conflict misses is to increase associativity, but this comes with the cost of an increase in the hit time. Another way to reduce conflict misses is to change the cache-indexing scheme and distribute the accesses across all sets. This thesis focuses on the second way mentioned above and aims to evaluate the impact of the matrix-based indexing scheme on cache performance against the traditional modulus-based indexing scheme. A correlation between the proposed indexing scheme and different cache replacement policies is also observed. The matrix-based indexing scheme yields a geometric mean speedup of 1.2% for SPEC CPU 2017 benchmarks for single core simulations when applied for direct-mapped last level cache. In this case, an improvement of 1.5% and 4% is observed for at least eighteen and seven of SPEC CPU2017 applications respectively. Also, it yields 2% of performance improvement over sixteen SPEC CPU2006 benchmarks. The new indexing scheme correlates well with multiperspective reuse prediction. It is observed that LRU benefits machine learning benchmark by a performance of 5.1%. For multicore simulations, the new indexing scheme does not improve performance significantly. However, this scheme also does not impact the application’s performance negatively

The Dynamic Simulation of the Three-Phase Brushless Permanent Magnet AC Motor Drives Using Lab View

In this paper, a mathematical model of the three-phase brushless permanent magnet AC motor drives in abc reference frame is described. A computer simulation of the motor drive is provided which utilized Lab VIEW software. The simulation can be conveniently used to study the dynamic as well as the Steady-state performance of the three-phase permanent magnet AC motor drives; with either trapezoidal or sinusoidal back emfs, under various operating conditions. The simulation results have been given in this paper

Multi-Model Network Intrusion Detection System Using Distributed Feature Extraction and Supervised Learning

Intrusion Detection Systems (IDSs) monitor network traffic and system activities to identify any unauthorized or malicious behaviors. These systems usually leverage the principles of data science and machine learning to detect any deviations from normalcy by learning from the data associated with normal and abnormal patterns. The IDSs continue to suffer from issues like distributed high-dimensional data, inadequate robustness, slow detection, and high false-positive rates (FPRs). We investigate these challenges, determine suitable strategies, and propose relevant solutions based on the appropriate mathematical and computational concepts. To handle high-dimensional data in a distributed network, we optimize the feature space in a distributed manner using the PCA-based feature extraction method. The experimental results display that the classifiers built upon the features so extracted perform well by giving a similar level of accuracy as given by the ones that use the centrally extracted features. This method also significantly reduces the cumulative time needed for extraction. By utilizing the extracted features, we construct a distributed probabilistic classifier based on Naïve Bayes. Each node counts the local frequencies and passes those on to the central coordinator. The central coordinator accumulates the local frequencies and computes the global frequencies, which are used by the nodes to compute the required prior probabilities to perform classifications. Each node, being evenly trained, is capable of detecting intrusions individually to improve the overall robustness of the system. We also propose a similarity measure-based classification (SMC) technique that works by computing the cosine similarities between the class-specific frequential weights of the values in an observed instance and the average frequency-based data centroid. An instance is classified into the class whose weights for the values in it share the highest level of similarity with the centroid. SMC contributes alongside Naïve Bayes in a multi-model classification approach, which we introduce to reduce the FPR and improve the detection accuracy. This approach utilizes the similarities associated with each class label determined by SMC and the probabilities associated with each class label determined by Naïve Bayes. The similarities and probabilities are aggregated, separately, to form new features that are used to train and validate a tertiary classifier. We demonstrate that such a multi-model approach can attain a higher level of accuracy compared with the single-model classification techniques. The contributions made by this dissertation to enhance the scalability, robustness, and accuracy can help improve the efficacy of IDSs

Structural and Functional Analysis of Grapefruit Flavonol-Specific-3-O-GT Mutant P145T

This research is focused on the study of the effect of mutating proline 145 to threonine on the substrate and regiospecificity of flavonol specific 3-O-glucosyltransferase (Cp3GT). While the mutant P145T enzyme did not glucosylate anthocyanidins, it did glucosylate flavanones and flavones in addition to retaining activity with flavonols. HPLC was used for product identification and showed mutant P145T glucosylated naringenin at the 7-OH position forming naringenin-7-O-glucoside and flavonols at the 3-OH position. Homology modeling and docking was done to predict the acceptor substrate recognition pattern and models were validated by experimental results. In other related work, a thrombin cleavage site was inserted into wild type Cp3GT and recombinant P145T enzyme between the enzyme and the C-myc tags in order to be able to cleave off tags. This provides the tool needed for future efforts to crystallize these proteins for structural determination

Impact of Small-Scale Aquaculture on Rural Livelihood. A study on how an aquaculture project affects the livelihood choices of poor people

Nepal is a poor country where the problems of poverty and food insecurity is very much evident. Aquaculture has been identified as one of the major sectors that could bring about the changes in the lives of poor communities through employment, food security and income. Currently, the Government of Nepal has identified and recognized the contribution of aquaculture towards poverty alleviation and food security, and the development in the fisheries and aquaculture has been emphasized, providing special attention to better productivity and production enhancement. This research is conducted to find out the impact of small-scale aquaculture project on the livelihood choices and how the poor people maximize their resources in the long run

Homeomorphisms of the Sierpinski Carpet

The Sierpinski carpet is a fractal formed by dividing the unit square into nine congruent squares, removing the center one, and repeating the process for each of the eight remaining squares, continuing infinitely many times. It is a well-known fractal with many fascinating topological properties that appears in a variety of different contexts, including as rational Julia sets. In this project, we study self-homeomorphisms of the Sierpinski carpet. We investigate the structure of the homeomorphism group, identify its finite subgroups, and attempt to define a transducer homeomorphism of the carpet. In particular, we find that the symmetry groups of platonic solids and D_n x Z_2 for positive integers n are all subgroups of the homeomorphism group of the carpet, using the theorem of Whyburn that any two S-curves are homeomorphic