http://irgu.unigoa.ac.in/drs/handle/unigoa/26 2026-10-12T06:40:56Z http://irgu.unigoa.ac.in/drs/handle/unigoa/8024 Artificial Intelligence Enabled Instrumentation for Arecanut Segregation Patil, Sameer Manohar 2024-10-01T00:00:00Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7986 Benchmarking Laguerre-Gaussian mode decoding in atmospheric turbulence: phase-only versus ideal complex-field approaches Abhyankar, G.; Gad, R.S.; Gawali, S.B.; Vetrekar, N.; Thomas, A.; Naik, G.M. Orbital Angular Momentum (OAM)- based Free Space Optical (FSO) communication offers a potential solution for achieving high spectral efficiency. OAM multiplexing and demultiplexing are key components of these systems. The core of OAM demultiplexing is OAM mode decoding, especially in the presence of atmospheric turbulence. However, OAM beams are distorted by atmospheric turbulence, severely impairing decoding efficiency and limiting the usefulness of the technology. Existing phase-only decoding approaches, such as spiral phase plate (SPP) decoders, have limited capacity to recover OAM modes in the presence of significant turbulence. In this work, we use a non-unitary ideal complex-field-based method to propose a theoretical decoding benchmark and compare it with a realistic phase-only approach. The OAM spectrum reconstruction and modal purity are analysed in weak-to-strong turbulence regimes. The study quantifies the performance gap between practical phase-only decoding and idealised upper-bound complex-field reconstruction under atmospheric turbulence. Findings show that the SPP-based phase-only decoding exhibits a gradual loss of modal purity of the target mode as turbulence severity increases, whereas the theoretical approach performs better and sets an upper-bound performance benchmark. 2026-01-01T00:00:00Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7984 Gender classification across different age groups: An extensive benchmark evaluation De Ataide, M.; Raut, S.; Vetrekar, N.; Gad, R.S. Gender is a non-intrusive, soft biometric trait which is gaining popularity in the field of biometric authentication systems for making them more robust. Gender classification across different age groups becomes challenging when it is carried out using facial characteristics, which varies due to photometric reflectance properties of human skin. In this paper, we carried out extensive study using the visible facial images, across 11 different state-of the-art feature extraction methods employed in gender classification followed by linear Support Vector Machine (SVM) classifier across different age groups. Experiments are performed independently on visible face database comprising of 313 subjects of which 183 are male and 130 are female from both Adults and Child age groups. The empirical performance of study obtains higher average classification accuracy of 94.843.35 percent for age groups greater than or equal to 20 years i.e Adults using Log Gabor (LG) feature extraction method. While the average classification accuracy for age group less than or equal to 15 years i.e Child was 89.744.57 percent using Binarized Statistical Image Features (BSIF), which indicates the variations in the performance accuracy for two different age group data. 2026-01-01T00:00:00Z http://irgu.unigoa.ac.in/drs/handle/unigoa/7985 Towards exploring RGB color channels of visible face images to band for gender classification Patel, K.; Vetrekar, N.; Gaonkar, A.A.; Gad, R.S. Gender classification across different spectra has consistently been a challenging task due to the spectral gap between the training and testing samples. This work explores the Red (R), Green (G) and Blue (B) channel independently for visible to spectral band gender classification, leveraging photometric normalization methods and Probabilistic Collaborative Representation Classifier (ProCRC). We employ 22 photometric normalization methods to address the spectral gap between training and testing samples. We investigate the experimental evaluation result separately on R, G, and B channels based on 145 subjects which corresponds to visible and multi-spectral face database. We report the average classification accuracy by conducting the experiment over 10 different trials and the highest classification accuracy reported is 95.75 plus or minus 1.87 percent demonstrating the applicability of our approach for gender classification. 2026-01-01T00:00:00Z