P. K. Gupta

Work place: Department of Computer Science and Engineering & IT, Jaypee University of Information Technology, Waknaghat, Solan-173 234, India

E-mail: pradeep1976@yahoo.com


Research Interests: Neural Networks, Computer Networks, Computing Platform


P. K. Gupta graduated in Informatics and Computer Engineering from Vladimir State University, Vladimir, Russia, in 1999 and received his M.E. degree in Informatics & Computer Engineering in 2001 from the same university. He has been associated with academics more than ten years in different institutions like BIT M.Nagar, RKGIT Ghaziabad. In India and Currently he is working as Senior Lecturer with the Department of Computer Science and Engineering & IT, Jaypee University of Information Technology, Waknaghat, Solan(HP), India. He is also pursuing his Ph.D. from JUIT Solan. He has supervised a number of B.Tech/M.Tech/M.Phil. theses from various universities of India. His research interests include Storage Networks, Green Computing, Software Testing and Cloud Computing. P.K. Gupta is a Member of IEEE, Life Member of CSI and Life member of Indian Science Congress Association.

Author Articles
Performance Comparison of Various Diversity Techniques using Matlab Simulation

By Sanjiv Kumar P. K. Gupta G. Singh D. S. Chauhan

DOI: https://doi.org/10.5815/ijitcs.2013.11.06, Pub. Date: 8 Oct. 2013

Due to the time-varying nature of the wireless channel and presence of limited resources for the signal transmission, which faces various detrimental effects such as path-loss, delay spread, Doppler spread, shadowing and interference make it very difficult to achieve sufficient data rates. To combat the effect of multi-path fading in the wireless communication system, the diversity combining mechanism has been introduced. In this paper, we have developed an algorithm for the performance evaluation of various spatial diversity combing techniques by using Matlab simulation. The developed new algorithm combine all three diversity techniques such as maximal ratio combining (MRC), selection combining (SC) and equal gain combing (EGC) techniques The combined diversity techniques algorithm computes and compares the MRC, SC, and EGC combing techniques theoretically by using the Matlab simulation.

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Performance Analysis of Rayleigh and Rician Fading Channel Models using Matlab Simulation

By Sanjiv Kumar P. K. Gupta G. Singh D. S. Chauhan

DOI: https://doi.org/10.5815/ijisa.2013.09.11, Pub. Date: 8 Aug. 2013

An effort has been made to illustrate the performance comparison of the Rayleigh and Rician fading channel models by using MATLAB simulation in terms of source velocity and outage probability. We have developed algorithms for the Rayleigh and Rician fading channels, which computes the envelop and outage probability. The parameters such as source velocity and outage probability play very important role in the performance analysis and design of the digital communication systems over the multipath fading environment.

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Energy-Sustainable Framework and Performance Analysis of Power Scheme for Operating Systems: A Tool

By P. K. Gupta G. Singh

DOI: https://doi.org/10.5815/ijisa.2013.01.01, Pub. Date: 8 Dec. 2012

Recently, an Information and Communications Technology (ICT) devices has become more user-friendly, which raised the problem of power dissipation across the globe and computer systems are one among them. This emerging issue of power dissipation has imposed a very significant issue on the system and software design. The concept of ‘green computing’ gaining popularity and is being considered as one of the most promising technology by the designers of Information Technology (IT) industry, which demonstrate the environmentally responsible way to reduce the power consumption and maximize the energy efficiency. In this paper, we have proposed an energy sustainable framework of the power schemes for operating systems to reduce the power consumption by computer systems and presented a Green Power tool (GP tool). This tool is designed using JAVA technology, which requires least configuration to make a decision for reducing the power consumption and proposed Swift mode algorithm, allows users to input the working time of their choice then after the end of time algorithm starts detection of human activity on the computer system. We also compared the Swift mode algorithm with existing power scheme in the operating system that provides up to 66% of the power saving. Finally, we have profiled the proposed framework to analyze the memory and Central Processing Unit (CPU) performance, which demonstrated that there is no memory leakage or CPU degradation problem and framework’s behavior remain constant under various overhead scenarios of the memory as well as CPU. The proposed framework requires 3–7 MB memory space during its execution.

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Minimizing Power Consumption by Personal Computers: A Technical Survey

By P. K. Gupta G. Singh

DOI: https://doi.org/10.5815/ijitcs.2012.10.07, Pub. Date: 8 Sep. 2012

Recently, the demand of “Green Computing”, which represents an environmentally responsible way of reducing power consumption, and involves various environmental issues such as waste management and greenhouse gases is increasing explosively. We have laid great emphasis on the need to minimize power consumption and heat dissipation by computer systems, as well as the requirement for changing the current power scheme options in their operating systems (OS). In this paper, we have provided a comprehensive technical review of the existing, though challenging, work on minimizing power consumption by computer systems, by utilizing various approaches, and emphasized on the software approach by making use of dynamic power management as it is used by most of the OSs in their power scheme configurations, seeking a better understanding of the power management schemes and current issues, and future directions in this field. Herein, we review the various approaches and techniques, including hardware, software, the central processing unit (CPU) usage and algorithmic approaches for power economy. On the basis of analysis and observations, we found that this area still requires a lot of work, and needs to be focused towards some new intelligent approaches so that human inactivity periods for computer systems could be reduced intelligently.

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