Facial expressions, usually has an adverse effect on the performance of a face recognition system. In this investigation, expression invariant face recognition algorithm is presented that converts input face image with an arbitrary expression into its corresponding neutral facial image. In the present study, deep learning algorithm is used to train classifiers for reference key-points, where key-points are located and deep neural network is trained to make the system able to locate the landmarks in test image. Create an intermediate triangular mesh from the test and reference image and then warp it using affine transform and take the average of the normalized faces. To extract the features presented in the result image shift invariant feature extraction technique is used. Finally, results are compared and the recognition accuracy is determined for different expressions. The present work is tested on three different databases: JAFFE, Cohn-Kanade (CK) and Yale database. Experimental results show that the expression invariant face recognition method is very robust to variety of expressions and recognition accuracy is found to be 97.8 %, 96.8% and 95.7% for CK, JAFFE and Yale databases respectively.

Pose variation is the one of the main difficulty faced by present automatic face recognition system. Due to the pose variations, feature vectors of the same person may vary more than inter person identity. This paper aims to generate virtual frontal view from its corresponding non frontal face image. The approach presented in this paper is based on the assumption of existence of an approximate mapping between the non frontal posed image and its corresponding frontal view. By calculating the mapping between frontal and posed image, the problem of estimating the frontal view will become the regression problem. In the present approach, non linear mapping, kernel extreme learning machine (KELM) regression is used to generate virtual frontal face image from its non frontal counterpart. Kernel ELM regression is used to compensate for the non linear shape of the face. The studies are performed on GTAV database with 5 posed images and compared with linear regression approach.

Engineering education plays a pivotal role in the development of technologies, society, nation, economy and employment. It is well evident that in the information age, technology is developing very fast and correspondingly the demand for highly skilled and qualified professionals is also increasing. One of the most critical issues in engineering education is handling placement of young technocrats from an industrial perspective to prepare the required workforce in the 21st century.
The trajectory of development of Electronics Engineering (EE) has intersected every walk of human life. The last decade has also witnessed an assorted increase in Electronics Engineering Education in India. Infact, most of the engineering institutes imparting EE education countrywide focus only on domain knowledge and a mere 25% of the engineering professionals are actually employable. Along with domain knowledge, there are non-technical skills and competencies which play a significant part in contributing to an individual's effective and successful participation in the workplace.
This article throws light on career opportunities that will go a large way towards ensuring successful career planning and handling placement prospects. The paper addresses the career prospects in a broader domain of electronics engineering and other allied fields. The study will also focus on ongoing activities and initiatives at BPSMV State University in Haryana.

Since the last two decades, much attention has been paid to Electronics Engineering (EE) education in India. Various commercial software tools and simulator such as mathematical application packages, electronics CAD tools, learning management systems, multimedia tools and general purpose tools supports EE education. Traditionally, proprietary software packages such as OrCAD Pspice, MATLAB, LabView, Multisim and others too are widely practiced and well integrated in electronics engineering curricula of various Universities nationwide. In today’s market, there are plenty of software tools and packages; however, most of them are expensive. The high cost of standard commercial packages is often hardship for students and faculties. Selecting various appropriate tools for academic use is considered as one of the key challenges in EE education. Over recent years, an alternative trend based on Free Open Source Software (FOSS) has gained momentum in higher education due to several benefits such as cost, license management flexibility, access to source code, security and stability etc. Presently, a plethora of open source software tools and resource related to EE can be deployed in teaching and learning process of EE education. In this communication, a short exposition of Free Open Source Software useful in electronics engineering education and research is presented. A brief outlook of FOSS packages, their web addresses and major highlights are also presented. The overall aim of this paper is to create awareness among instructors and students to impart active and self learning using FOSS.

A large number of diverse engineering applications are frequently modeled using different approaches, viz., a differential equation or by a transfer function and state space. All these descriptions provide a great deal of information about the system, such as stability of the system, its step or impulse response, and its frequency response.
The present paper addresses different approaches used to derive mathematical models of first and second order system, developing MATLAB script implementation and building a corresponding Simulink model. The dynamic analysis of electric circuit and system using MATLAB/Simulink has been investigated using different approaches for chosen system parameters.