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Design of a Multiband Antenna Using auxiliary classifier wasserstein generative adversarial network for IoT Applications

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第5期38卷

作者： Bessant, Y. R. Annie St Xaviers Catholic Coll Engn Dept Elect & Commun Engn Nagercoil Tamil Nadu India

The rapid expansion of Internet of Things (IoT) applications presents unprecedented challenges for antenna design, demanding solutions that are versatile, efficient, and capable of operating across multiple frequency bands. This paper addresses these challenges through the development of a design of a multiband antenna using auxiliary classifier wasserstein generative adversarial network for IoT applications (DMA-ACWGAN-IoT). Here, the auxiliary classifier wasserstein generative adversarial network (ACWGAN) is employed to generate synthetic data representing electromagnetic field distributions and antenna characteristics across various frequencies. The proposed metamaterial-based Multiple-Input Multiple-Output (MIMO) antenna contains four discrete elements, each provided by a micro strip feed. The structures overall width and length are 60 and 52 mm. The metamaterial is printed on a patch and dispersed from the fields with the most effective coupling. The proposed structures strong impedance bandwidth works at 8.3 GHz, 10.8 GHz, 12.3 GHz, 13.7 GHz, 16.1 GHz, and 18.1 GHz, fabricating the proposed antenna prototype using a substrate made of FR-4 material. The proposed DMA-ACWGAN-IoT design provides 6.5 dB maximum gain and 25.40%, 21.60%, and 20.05% higher efficiency compared to existing dual band antenna design with resonance frequency prediction under machine learning models (DBA-PRF-ML), machine learning verification depending on a distinctive SWB multiple slotted four-port high isolated MIMO antenna loaded with metasurface for the applications of IoT (SWB-MIMO-IOT-ML), and dual-band miniaturized composite right-left-handed transmission line ZOR antenna along machine learning method for microwave communication (DB-ZORA-MC-ML).

关键词： auxiliary classifier wasserstein generative adversarial network complementary split ring resonator internet of things multiband antenna multiple-input multiple-output

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Lung Cancer Classification based on auxiliary classifier (WGAN) Optimised with HOA from CT Images

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IETE JOURNAL OF RESEARCH 2024年第9期70卷 7504-7512页

作者： Suganthy, Mahamuni Krishnamoorthy, Ranganathan Nagarajaiah, Kavyashree Daya Sagar, Ketaraju Venkata Vel TechMulti Tech Dr Rangarajan Dr Sakunthala Eng Dept Elect & Commun Engn Chennai India Chennai Inst Technol Ctr Adv Wireless Integrated Technol Chennai India Sri Siddhartha Inst Technol Tumakuru Dept Master Comp Applicat Tumakuru 572105 Karnataka India Koneru Lakshmaiah Educ Fdn Deemed Univ Vaddeswaram Dept Elect & Comp Engn Guntur Andhra Pradesh India Kyushu Inst Technol Dept Intelligent & Control Syst Fukuoka Japan

The cancer diagnosis is currently experiencing an alteration of molecular biomarkers using s paradigm for the diagnostic panel. One of the most important genomic datasets showing genetic sequences is the classification of lung cancer. Many research studies have been conducted in this area, but none of them yields satisfied outcomes on account of lower classification accuracy. At first, the data are collected through the lung cancer dataset. Afterward, data are fed into pre-processing. The pre-processing segment removes the noise and enhances the input images utilising Dynamic Context-Sensitive Filtering. The pre-processing output is fed to the feature extraction segment. Here, four statistical features are extracted based on the Force Invariant Improved Feature Extraction Model. The extracting features are fed to the feature segmentation segment. Lung cancer images can segment the ROI region using Adaptive Density-Based Spatial Clustering. After that, the segmentation features are given to ACWGAN for effectively categorizing cancer and non-cancer of the lung cancer Hence, the Hunter-prey optimisation algorithm is employed to optimise the ACWGAN classifier. It classifies the lung cancer images accurately. The LCC-ACWGAN-HPOA-CTI algorithm is activated in MATLAB under the lung cancer database. The performance of the LCC-ACWGAN-HPOA-CTI approach shows higher accuracy and lower computation time than those of the existing methods.

关键词： auxiliary classifier wasserstein generative adversarial network dynamic context-sensitive filtering force-invariant improved feature extraction method hunter-prey optimisation algorithm

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