Publications
Permanent URI for this collectionhttps://ir.daiict.ac.in/handle/123456789/32
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Publication Metadata only Spectrum Sensing for Cognitive Radio : Fundamentals and Applications(CRC Press, Boca Raton, 2021-12-31) Captain, Kamal M; Joshi, Manjunath VPublication Metadata only Hybrid and parallel GAN architecture with non-IID noise input(Springer, 10-06-2025) Gohel, Prashant; Joshi, Manjunath V; DA-IICT, GandhinagarPublication Metadata only Data-Driven Soft Sensor for Optical Intensity Estimation in High-Power Plasma Source(IEEE, 15-07-2025) Tyagi, Himanshu; Joshi, Manjunath V; Bandyopadhyay, Mainak; Singh, M J; DA-IICT, GandhinagarInductively coupled plasma (ICP) sources are used in multiple industrial and research applications varying from material reactors, semiconductor fabrication, and nuclear fusion-based reactors. Experiments using ICP source are prone to noise due to the presence of high-power radio frequency (RF) radiation as well as the high voltage (HV). In order to operate such plasma sources, we need rich set of diagnostics that supports the control system as well as the operators in order to derive the important plasma parameters. Some of these sensors are prone to degradation and need constant maintenance and testing that creates challenges during operations. In order to monitor the performance and also mitigate the risks associated with the sensor failure, soft sensors can offer an alternative low-cost approach in estimating the physical parameters. Although soft sensors have found applications in industrial environments, limited implementation has been seen in experimental systems. Hence, in this article, we propose a soft sensor model for an optical (light) sensor which is one of the critical sensors used in ICP and other plasma sources. The proposed model is developed using data-driven machine-learning (ML) and deep-learning (DL) algorithms for a plasma-based system operating under noisy environments. After a thorough exploration, a comparatively better performance was observed using the artificial neural network (ANN)-based models. The ANN model was trained with various hyper parameters in order to obtain a test R2 score of 0.91 that was able to model the transient behavior of the sensor. After the model development, it was used for performing fault identification in the signal by comparing the predicted signal with actual signal. The article presents the steps followed in developing the data-driven models for the plasma light sensor and its application for sensor fault identification along with associated challenges.Publication Metadata only Robust Adversarial Defense: An Analysis on Use of Auto-Inpainting(Springer, 01-01-2025) Sharma, Shivam; Joshi, Rohan; Bhilare, Shruti; Joshi, Manjunath V; DA-IICT, GandhinagarPublication Metadata only Identifying the Source of Water on Plant Using the Leaf Wetness Sensor and via Deep Learning-Based Ensemble Method(IEEE, 01-01-2024) Saini, Riya; Garg, Pooja; Kumar, Naveen Chaudhary; Joshi, Manjunath V; Palaparthy, Vinay; Kumar, Ahlad; DA-IICT, Gandhinagar; Garg, Pooja (202021011)Plant disease detection and management is one of the pivotal areas in the agriculture sector, which needs attention to abate crop loss. The recent trends in machine learning and deep learning have played a significant role in reducing crop loss with the help of early plant disease detection. For plant disease detection prior information on soil moisture, ambient temperature, relative humidity, leaf wetness sensor (LWS), rainfall are crucial parameters. In this work, the objective is to identify the source of leaf wetness on the leaf canopy, which can arise due to irrigation, rainfall, or dew. To identify the source of wetness on the leaf canopy, either rainfall or humidity/mist sensors are used, which substantially increases the cost of the system. For this purpose, we have used the LWS, which is deployed in the field and various patterns for the irrigation, rainfall, or dew has been analyzed by using the in-house developed the Internet of Things (IoT)-enabled sensor system. The data collected from the field is used as a learning dataset for the proposed ensemble neural network (NN) developed to identify the source of leaf wetness. Short-time Fourier transform (STFT) has been employed to enhance data representation by transforming numerical data from the LWS into informative images. The provided ensemble model incorporates convolutional NN (CNN) and multilayer perceptron (MLP), which process image and numerical data (ambient temperature, relative humidity, leaf wetness duration, and maximum magnitude of frequency of images) as input. Their outputs combined in an artificial neural network (ANN) sub-model for precise leaf wetness event detection (dew, rainfall, or irrigation). The proposed model achieved an accuracy of 96.13% with average precision, recall, and F1 score for the leaf wetness events is about 84%, 85%, and 83%, respectively.