Kuldeep Singh

This work presents a modified domain integral equation approach for the forward problem of TE scattering, employing a modified definition of dielectric contrast and discretizing the electric field density using Rao-Wilton-Glisson (RWG) basis functions. The proposed formulation mitigates the numerical challenges introduced by the gradient-divergence operator in traditional electric field-based vector formulations. The use of RWG basis functions over triangular meshes enhances geometric conformity, ensures tangential continuity across dielectric interfaces, and facilitates the application of well known singularity extraction techniques for numerical accuracy. Validation through numerical experiments on a two-layered dielectric cylinder demonstrates excellent agreement between computed and analytical scattered fields. Convergence studies confirm improving solution accuracy with mesh refinement indicating robustness with respect to discretization without increasing the iterations.

Online education has gained significant popularity during the COVID-19 pandemic. The availability of massive open online courses has further strengthened the online learning environment. One of the considerable challenges in online learning environments is to measure the learner’s engagement to meet the educational objectives. This paper addresses the challenge and proposes a convolutional neural network-based architecture that extracts discriminative features from the face (Affective) and the upper body (Behavioral) of the learner and classifies the engagement. The performance of the proposed architecture is evaluated on the publicly available Dataset of Affective States In E-Environments (DAiSEE) and the learning-centered affective state dataset curated from open-source datasets. The experimental results demonstrate that the proposed methodology with affective and behavioral features improves the engagement measurement results. The proposed method outperforms the state-of-the-art in terms of Unweighted Average Recall (UAR), Unweighted Average Precision (UAP), and Unweighted Average F1 (UAF1) with 79.14%, 49.62%, and 55.29% values, respectively. It is threefold more computationally efficient than the previous state-of-the-art method. The proposed method also improves the accuracy of the curated dataset by 2.07% of the prior method.

In this paper, we develop a novel mobility-aware transformer-driven tiered structure (MASSFormer) based cooperative spectrum sensing method that effectively models the spatio-temporal dynamics of user movements. Unlike existing methods, our method considers a dynamic scenario involving mobile primary users (PUs) and secondary users (SUs)and addresses the complexities introduced by user mobility. The transformer architecture utilizes an attention mechanism, enabling the proposed method to adeptly model the temporal dynamics of user mobility by effectively capturing long-range dependencies within the input data. The proposed method first computes tokens from the sequence of covariance matrices (CMs) for each SU and processes them in parallel using the SUtransformer network to learn the spatio-temporal features at SUlevel. Subsequently, the collaborative transformer network learns the group-level PU state from all SU-level feature representations. The attention-based sequence pooling method followed by the transformer encoder adjusts the contributions of all tokens. The main goal of predicting the PU states at each SU-level and group-level is to improve detection performance even more. We conducted a sufficient amount of simulations and compared the detection performance of different SS methods. The proposed method is tested under imperfect reporting channel scenarios to show robustness. The efficacy of our method is validated with the simulation results demonstrating its higher performance compared with existing methods in terms of detection probability, sensing error, and classification accuracy.

In this letter, we design a hierarchical cooperative long short-term memory (LSTM) network-based cooperative spectrum sensing (CSS) method which utilizes convolutional neural network (CNN) and LSTM network. The CNN extracts spatial features from the input covariance matrices (CMs) which are generated by sensing data of each secondary user (SU) and the sequence of spatial features corresponding to multiple sensing periods are fed into secondary user LSTM (SU-LSTM) so that the PU activity pattern at SU level can be learned. The cooperative LSTM learns the group-level PU activity pattern from all SU-level temporal feature representations. The aim of learning the PU activity pattern at SU-level and group-level is to improve the detection performance further. To demonstrate the robustness of the proposed model, the scenario of an imperfect reporting channel is taken into account. With a sufficient amount of simulations, the effectiveness of the proposed method is proven and simulation results demonstrate that the proposed method outperforms the state-of-the-art in terms of detection probability and classification accuracy.