http://localhost:8080/xmlui/handle/123456789/37 2026-03-27T08:18:28Z 2026-03-27T08:18:28Z Kumar, Jitender http://localhost:8080/xmlui/handle/123456789/1203 2026-03-13T09:17:22Z 2014-01-01T00:00:00Z

Title: An Iterative Unsymmetrical Trimmed Midpoint- Median Filter for Removal of High Density Salt and Pepper Noise Authors: Kumar, Jitender

2014-01-01T00:00:00Z Singla, Sanjeev http://localhost:8080/xmlui/handle/123456789/1150 2026-03-06T04:12:23Z 2013-01-01T00:00:00Z

Title: Enhanced Image Compression using Edge Strength Filter and Color Normalization Authors: Singla, Sanjeev

2013-01-01T00:00:00Z Singh, H Sharma, R K Kumar, R et al. http://localhost:8080/xmlui/handle/123456789/782 2023-07-26T03:47:25Z 2020-01-01T00:00:00Z

Title: A benchmark dataset of onlinehandwritten Gurmukhi script words and numerals Authors: Singh, H; Sharma, R K; Kumar, R et al. Abstract: This paper presents an online handwritten benchmark dataset (OHWR-Gurmukhi) for Gurmukhi script. TIET, Patiala released the unconstrained online handwriting databases, OHWR-GNumerals and OHWR-GScript, which contain isolated strokes samples produced by 190 writers. The OHWR-GNumerals covers 10 stroke classes and OHWR-GScript covers 95 stroke classes to represent the Gurmukhi character set. For data collection, two data sets of Gurmukhi words have been finalized after having a consultation with language experts in order to collect the balanced stroke samples. The preprocessing methods used to prepare these datasets include: size normalization, removing duplicate points, interpolating missing points and re-sampling. The purpose of this benchmark is to create a common platform and make the benchmark dataset publically available for research endeavors in the area of online handwriting recognition.

2020-01-01T00:00:00Z Singh, S P Kumar, R Sharma, A et. al. http://localhost:8080/xmlui/handle/123456789/781 2023-07-26T03:53:44Z 2020-01-01T00:00:00Z

Title: Leveraging energy-efficient load balancing algorithms in fog computing Authors: Singh, S P; Kumar, R; Sharma, A et. al. Abstract: Cloud computing and smart gadgets are the need of smart world these days. This often leads to latency and irregular connectivity issues in many situations. In order to overcome this issue, an emerging technique of fog computing is used for cloud and smart devices. A decentralized computing infrastructure in which all the elements, that is, storage, compute, data and the applications in use, are passed in an efficient and logical place between cloud and the data source, is called Fog computing. The cloud computing and services are generally extended by fog computing, which brings the power and advantages of data creation and data analysis at the network edge. Real-time location based services and applications with mobility support are enabled due to the physical proximity of users and high speed internet connection to the cloud. Fog computing is promoted with leveraging load balancing techniques so as to balance the load which is done in two ways, that is, static load balancing and dynamic load balancing. In this paper, different load balancing algorithms are discussed and their comparative analysis has been carried out. Round Robin load balancing is the simplest and easiest load balancing technique to be implemented in fog computing environments. The major problem of Source IP Hash load balancing algorithm is that each change can redirect to anyone with a different server, and thus, is least preferred in fog networks. The mechanisms to make energy efficient load balancing are also considered as the part of this paper.

2020-01-01T00:00:00Z