College of Engineering, Pune

About: College of Engineering, Pune is a based out in . It is known for research contribution in the topics: Sliding mode control & Control theory. The organization has 4264 authors who have published 3492 publications receiving 19371 citations. The organization is also known as: COEP.

Employee Engagement; Tool of Talent Retention: Study of a Pharmaceutical Company

Abstract: Research article is a study of employee engagement at a pharma company located in Hyderabad. The main focus of this study is to understand the factors which influence employee engagement and to find the level of employees engaged at this pharma company located in Hyderabad. Research indicates that companies whose level of engaged employees are higher have less attrition rate than other companies and also is marked higher in productivity and customer satisfaction, so this proves that result of employee engagement are impacting the result in bottom line. For the purpose of the study a questionnaire was prepared based on understanding various aspects of company, relationship with the manager and the job. The questionnaire was checked for validation and reliability and responses were collected from employees who are working at a division on this pharma company in Hyderabad. The data analysis of the responses has resulted in some findings on gender & flexible policies, experienced employees & interaction, experienced employees & appreciation and level of engaged employees. If it is not taken care on time such issues can lead to high attrition rate in company. Happy employees keep customers happy. Pharmaceutical sector being a sector of critical talent, the retention of talent is a huge responsibility for HR. To improve the engagement among the employees, based on findings some of the engagement activities like opportunities to share feedback on manager, acknowledge employee contribution on real time basis, paternity leave policy were suggested in the study to retain employees.

Optimization of DSR routing protocol in MANET using passive clustering

Abstract: Routing algorithms in Mobile Ad hoc Networks like DSR, AODV use flooding as the primary mechanism for discovery of routes in the network. In flooding, each node forwards flood packets to its neighbors so as to ensure that the packets reach the intended destination. Forwarding of redundant flood packets create extra overhead on network bandwidth. However, only a subset of nodes need to forward the packet to make it reach its destination. Clustering has been proven as an efficient mechanism to reduce flooding overhead in MANETs. Active Clustering algorithms use explicit control packets to create and maintain clusters whereas passive clustering piggybacks the control information in the outgoing data packets thus significantly reducing control overhead. The proposed algorithm integrates Passive Clustering with DSR routing protocol. Route Discovery phase of DSR floods the Route Request packets in the entire network. However, the proposed algorithm creates the clusters in the network in the initial phase of Route Discovery process and once the clusters are formed only a subset of nodes will forward the Route Request packets. This approach reduces the overhead on bandwidth by curtailing the flooding of packets and thereby increasing the packet delivery ratio. The simulation results show that optimized DSR algorithm enhances the packet delivery ratio by minimum of 20% and reduces the RREQ flooding by a minimum of 30%.

Role of features and classifiers on accuracy of identification of musical instruments

Abstract: Selection of effective feature set and proper classifier is a challenging task in problems where machine learning techniques are used. In automatic identification of musical instruments also it is very crucial to find the right set of features and accurate classifier. In this paper, we have discussed the role of various features with different classifiers on automatic identification of musical instruments. Piano, flute, trumpet, guitar, xylophone and violin are identified using various features and classifiers. Spectral features like spectral centroid, spectral slope, spectral spread, spectral kurtosis, spectral skewness and spectral roll-off are used along with zero crossing rate, autocorrelation and Mel Frequency Cepstral Coefficients (MFCC) for this purpose. The dependence of instrument identification accuracy on these features is studied for different classifiers. Decision trees, Naive Bayes classifier, k nearest neighbour classifier, multilayer perceptron, Sequential Minimal Optimization Algorithm (SMO) and multi class classifier (metaclassifier) are used. We have obtained improved accuracy by proper selection of these features and classifier. The analysis also confirms the selection of features and classifiers as the results are better.

Higher-order nonlinear electromechanical effects in wurtzite GaN/AlN quantum dots

Abstract: As we demonstrated earlier, conventional mathematical models based on linear approximations may be inadequate in the analysis of properties of low-dimensional nanostructures and band structure calculations. In this work, a general three-dimensional axisymmetric coupled electromechanical model accounting for lattice mismatch, spontaneous polarization and higher-order nonlinear electrostriction effects has been applied to analyze properties of GaN/AlN quantum dots coupled with wetting layer. The generalized model that accounts for five independent electrostriction coefficients has been solved numerically via a finite-element implementation. The results, exemplified for truncated conical GaN/AlN quantum dots, demonstrate that the effect of nonlinear electrostriction in GaN/AlN nanoheterostructure quantum dots could be significant. In particular, the influence of nonlinear electromechanical effects on optoelectronic properties is highlighted by the results on band structure calculations based on a multiband effective mass theory.