Showing 3 results for Handa
Sujan Rajbhandari, Zabih Ghassemlooy, Maia Angelova,
Volume 5, Issue 2 (June 2009)
Abstract
Artificial neural network (ANN) has application in communication engineering in diverse areas such as channel equalization, channel modeling, error control code because of its capability of nonlinear processing, adaptability, and parallel processing. On the other hand, wavelet transform (WT) with both the time and the frequency resolution provides the exact representation of signal in both domains. Applying these signal processing tools for channel compensation and noise reduction can provide an enhanced performance compared to the traditional tools. In this paper, the slot error rate (SER) performance of digital pulse interval modulation (DPIM) in diffuse indoor optical wireless (OW) links subjected to the artificial light interference (ALI) is reported with new receiver structure based on the discrete WT (DWT) and ANN. Simulation results show that the DWT-ANN based receiver is very effective in reducing the effect of multipath induced inter-symbol interference (ISI) and ALI.
Gh. Khandar-Shahabad, J. Beiza, J. Pouladi, T. Abedinzadeh,
Volume 18, Issue 3 (September 2022)
Abstract
A new regionalization algorithm is presented to improve wide-area backup protection (WABP) of the power system. This method divides the power system into several protection zones based on the proposed optimal measurement device (MD) placement and electrical distances. The modified binary particle swarm optimization is used to achieve the optimal MD placement in the first step. Next, the power system is divided into small protection zones (SPZ) using the topology matrix of the power system and MD locations. Finally, the SPZs are combined to accomplish the main protection zones and protection centers according to electrical distances, degree of buses, and communication link constraints. The introduced regionalization formulation can help provide a rapid and secure WABP for power systems. This method was applied to several IEEE standard test systems, and the simulation results demonstrated the effectiveness of the proposed scheme.
Priyanka Handa, Balkrishan Jindal ,
Volume 20, Issue 1 (March 2024)
Abstract
The potential adverse effects of maize leaf diseases on agricultural productivity highlight the significance of precise disease diagnosis using effective leaf segmentation techniques. In order to improve maize leaf segmentation, especially for maize leaf disease detection, a hybrid optimization method is proposed in this paper. The proposed method provides better segmentation accuracy and outperforms traditional approaches by combining enhanced Particle Swarm Optimisation (PSO) with Firefly algorithm (FFA). Extensive tests on images of maize leaves taken from the Plant Village dataset are used to show the algorithm's superiority. Experimental results show a considerable decrease in Hausdorff distances, indicating better segmentation accuracy than conventional methods. The proposed method also performs better than expected in terms of Jaccard and Dice coefficients, which measure the overlap and similarity between segmented sections. The proposed hybrid optimization method significantly contributes to agricultural research and indicates that the method may be helpful in real scenarios. The performance of proposed method is compared with existing techniques like K-Mean, OTSU, Canny, FuzzyOTSU, PSO and Firefly. The overall performance of the proposed method is satisfactory.
