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Author: search.filters.author.Joshi, Manjunath V

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Now showing 1 - 10 of 29
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    Spectrum Sensing for Cognitive Radio : Fundamentals and Applications
    (CRC Press, Boca Raton, 2021-12-31) Captain, Kamal M; Joshi, Manjunath V
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    Multi-resolution Image Fusion in Remote Sensing
    (Cambridge University Press, Cambridge, 2019-03-01) Joshi, Manjunath V; Upla, Kishor p
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    Digital heritage reconstruction using super-resolution and inpainting
    (Morgan & Claypool, California, 2017-01-01) Padalkar, Milind G; Joshi, Manjunath V; Khatri, Nilay L
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    Motion-Free Super-Resolution
    (Springer, New York, 2005-07-06) Chaudhuri, Subhasis; Joshi, Manjunath V
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    Hybrid and parallel GAN architecture with non-IID noise input
    (Springer, 10-06-2025) Gohel, Prashant; Joshi, Manjunath V; DA-IICT, Gandhinagar
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    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, Gandhinagar
    Inductively 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.
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    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, Gandhinagar
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    Matching parameter estimation for high power Inductively coupled plasma sources using Machine learning techniques
    (Elsevier) Tyagi, Himanshu; Joshi, Manjunath V; Bandyopadhyay, Mainak; Singh, MJ; Pandya, Kaushal; Chakraborty, Arun; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar
    Inductively coupled plasma�or ICP sources form a basis for multiple applications ranging from�semiconductor fabrication�to reliable heating systems for�tokamak�machines. To meet the functional requirements, ICP sources need efficient plasma formation utilizing the various input parameters. Operation of ICP sources is a complex and challenging task since it involves scanning a wide multi-dimensional parameter space involving filament bias, radio frequency (RF) power, gas pressure, matching parameters, and other system configurations. The foremost challenge is to maximize the coupling of RF power in the ion source for efficient plasma formation. Standard ICP sources use a matching network that consists of variable capacitors to compensate for plasma inductance to enable maximum power coupling. Identification of an accurate set of matching parameters for high power sources is a complex task and is generally driven by operator experience which is established after years of operations. Due to these challenges, recent developments in the area of machine learning can be utilized for identifying the underlying model function to make accurate predictions and explore an alternative approach to the existing Physics-electrical models developed for the estimation of matching parameters for�plasma sources. The present work attempts to perform a data-driven model discovery for the identification of appropriate matching parameters utilizing�machine learning algorithms. In this work, ROBIN, a high-power ICP source that operates with a 1MHz, 100 kW RF generator is considered which has been operational since 2011 and has generated a considerable database. This database can be utilized for training/developing data-driven models for the estimation of matching parameters for ensuring better power coupling. The paper describes the development of two data-driven regression models for predicting the coupling efficiency in terms of power factor (denoted by Cos) and the capacitor values based on input parameters utilizing well known algorithms such as�support vector machine, random forest and�neural networks. Emphasis has been laid on developing the models using parameters that are tuneable externally. Also, the effect of system configurations on parameter prediction is investigated. The developed machine learning-based models have achieved test accuracy scores of 0.93 and 0.91 for predicting Cos�and capacitor values respectively. The paper presents the training and optimization process for various machine and�deep learning algorithms�in detail.
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    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.
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    Design of Complex Adaptive Multiresolution Directional Filter Bank and Application to Pansharpening
    (Springer, 01-02-2017) Gajbhar, Shrishail S; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar; Gajbhar, Shrishail S (201121016)
    This paper proposes a new 2-D transform design, namely�complex adaptive multiresolution directional filter bank, to represent the spatial orientation features of an input image�adaptively. The proposed design is completely�shift invariant�and represents the input image by one low-pass and multiscale�N�directional band-pass subbands. Here,�N�represents estimated number of dominant directions present in the input image. Our design consists of two main filter bank stages. A fix partitioned complex-valued directional filter bank (CDFB) is at the core of the design followed by a novel partition filter bank stage. Fine partitioning of the CDFB subbands is used to get the adaptive nature of the proposed transform. The partitioning decision is made based on the directional significance of range of CDFB subband angle selectivity in the input image. Partition filter bank stage which�nonuniformly�partitions the CDFB subbands provides total�N�dominant direction selective subbands. Local orientation map of the input image is used to determine the dominant directions and hence�N. For better sparsity properties, we design the multiresolution stage with filters having high�vanishing moments�and better frequency selectivity. Applicability of the proposed adaptive design is shown for pansharpening of multispectral images. Our proposed pansharpening approach is evaluated on images captured using QuickBird and IKONOS-2 satellites. Results obtained using the proposed approach on these datasets show considerable improvements in qualitative as well as quantitative evaluations when compared to state-of-the-art pansharpening approaches including transform-based methods.