Renu Vig

Work place: UIET, Panjab University Chandigarh, India



Research Interests: Signal Processing


Renu Vig is currently working as Director UIET University Institute of Engineering & Technology – a premier engineering institute of Panjab University at Chandigarh, India. She has served at various faculty positions as Professor Assistant Professor and Lecturer, during her long professional carrier of 24 years (including 4 years in research organizations). Graduated from Panjab Engineering College, Chandigarh, she has attained her masters and PhD degree. She has guided the research works at masters and PhD levels and has more than 40 national and international research papers to her credit.

Author Articles
An Experimental and Statistical Analysis to Assess impact of Regional Accent on Distress Non-linguistic Scream of Young Women

By Disha Handa Renu Vig Mukesh Kumar Namarta Vij

DOI:, Pub. Date: 8 Aug. 2023

Scream is recognized as constant and ear-splitting non-linguistic verbal communication that has no phonological structure. This research is based on the study to assess the effect of regional accent on distress screams of women of a very specific age group. The primary goal of this research is to identify the components of non-speech sound so that the region of origin of the speaker can be determined. Furthermore, this research can aid in the development of security techniques based on emotions to prevent and report criminal activities where victims used to yell for help. For the time being, we have limited the study to women because women are the primary victims of all types of criminal’s activities. The Non-Speech corpus has been used to explore different parameters of scream samples collected from three different regions by using high-reliability audio recordings. The detailed investigation is based on the vocal characteristics of female speakers. Further, the investigations have been verified with bi-variate, partial correlation and one-way ANOVA to find out the impact of region-based accent non-speech distress signal. Results from the correlation techniques indicate that out of four attributes only jitter varies with respect to the specific region. Whereas ANOVA depicts that there is no significant regional impact on distress non-speech signals.

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Integration of Clustering, Optimization and Partial Differential Equation Method for Improved Image Segmentation

By Jaskirat Kaur Sunil Agrawal Renu Vig

DOI:, Pub. Date: 8 Oct. 2012

Image segmentation generally refers to the process that partitions an image into mutually exclusive regions that cover the image. Among the various image segmentation techniques, traditional image segmentation methods like edge detection, region based, watershed transformation etc. are widely used but have certain drawbacks, which cannot be used for the accurate result. In this paper clustering based techniques is employed on images which results into segmentation of images. The performance of Fuzzy C-means (FCM) integrated with the Particle Swarm optimization (PSO) technique and its variations are analyzed in different application fields. To analyze and grade the performance, computational and time complexity of techniques in different fields several metrics are used namely global consistency error, probabilistic rand index and variation of information are used. This experimental performance analysis shows that FCM along with fractional order Darwinian PSO give better performance in terms of classification accuracy, as compared to other variation of other techniques used. The integrated algorithm tested on images proves to give better results visually as well as objectively. Finally, it is concluded that fractional order Darwinian PSO along with neighborhood Fuzzy C-means and partial differential equation based level set method is an effective image segmentation technique to study the intricate contours provided the time complexity should be as small as possible to make it more real time compatible.

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