Department of Electrical and Electronics Engineering

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    Application of Deep Neural Networks for Weed Detection and Classification
    (IEEE, 2023-06) Bhatt, Upendra Mohan
    Weeds compete for natural resources both in forest areas, harming the development of native vegetation, and in agricultural areas, affecting crop quality. The need then arises to classify these species, so that mechanical or chemical methods can be applied appropriately to contain the pests. This research presents the application and comparison of machine learning techniques, with the aim of automating the classification of images for agricultural challenges, such as the detection of defective seeds, and weeds and the category between these and native vegetation, while finally, the architecture of a convolutional neural network is presented. As a differential, the network's self-learning ability stands out, as images are captured in less than ideal conditions at varying heights and lighting levels in most cases. This research is expected to provide important information on artificial intelligence techniques that can be used in the classification of weed images, a factor that will contribute to the forestry and agricultural sector.
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    Deep Learning Based Super Resolution Network for Channel Estimation
    (Taylor & Francis, 2024-12) Joshi, Sandeep
    This paper proposes and investigates a deep learning-based channel estimation scheme for wireless communication system. In this approach, the channel response in pilot positions is considered a low-resolution image, which is further converted into a high-resolution image using the super-resolution (SR) network. It is observed that the proposed model shows an improvement of 50% and 42.5% as compared to the ChannelNet and super-resolution convolutional neural network, respectively, in the case of 16 pilots. The novelty of the proposed SR model is its low complexity, as it uses one model instead of two for channel estimation. Besides, the proposed SR model uses fewer pilots for channel estimation, making it bandwidth-efficient and fast. Furthermore, the proposed model is compared using extensive simulations for benchmarking.
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    A Graph Convolutional Network for Visual Categorization
    (Springer, 2024-10) Bera, Asish; Hazra, Arnab
    The Convolutional Neural Networks (CNNs) have attained enhanced performance over conventional feature descriptors for image classification. Recently, Graph Convolutional Networks (GCNs) have also been witnessed in achieving improved performances for visual classification in various domains. A typical GCN is pertinent for propagating deep features using graph-based message passing methods. There are several domains such as the disease diagnosis of humans and plants where GCN could be explored for further performance enhancement. Thus, ample research attention is essential for solving different kinds of visual classification problems. In this direction, this work integrates the benefits of CNN and GCN for improving the feature representation by building a spatial relation using a GCN. In this work, a simple deep learning model is proposed that extracts the high-level deep features using a backbone CNN. Then, a GCN is applied for enhancing feature representation capabilities further for image classification. The proposed method has achieved improved performances on seven benchmark public datasets representing dance postures, hand shapes, agriculture, medical imaging, and aerial scene classification. The proposed method is developed using four different CNN backbones. Particularly, the proposed method based on ResNet-50 backbone has attained 89.98% accuracy on Dance-12, 90.34% accuracy on REST hand shape, 94.06% accuracy on Kvasir, and 75.89% accuracy on ISIC skin cancer, 91.73% accuracy on AID aerial scene classification, and 95.24% accuracy on PlantPathology datasets.
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    Detecting UAV Presence Using Convolution Feature Vectors in Light Gradient Boosting Machine
    (IEEE, 2022-12) Alladi, Tejasvi; Chamola, Vinay
    The growing number of Unmanned Aerial Vehicle (UAV) applications brings with it, a rising number of privacy concerns. The high availability of commercial drones is also increasing the need for strict regulations. As far away as we are from establishing such protocols to ensure that the most basic human right to privacy is not exploited, we are further away from enforcing them. Thus, there is a need for a generalised drone detection system to detect different drones operating in a broad range of Radio Frequencies (RF). Previous attempts to tackle this problem have been made using audio, video, radar, WiFi and RF signals. While all these methods have their own benefits and drawbacks, RF has various characteristics which make them suitable for practical applications on a large scale. In this paper, we propose a novel technique called the ConvLGBM model which combines the feature extraction capability of a Convolution Neural Network (CNN) with the high classification accuracy of the Light Gradient Boosting Machine (LightGBM). We develop and evaluate the classifications done by an optimal CNN and the LightGBM model and then compare both models with the ConvLGBM.
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    Upgrading security and protection in ear biometrics
    (IET, 2019-02) Ajmera, Pawan K.
    Biometrics is being widely accepted for user authentication across the globe. Integration of biometrics in the daily life provokes the need to design secure authentication systems. This study proposes the use of outer ear images as a biometric modality. The comparable complexity between the human outer ear and face in terms of its uniqueness and permanence has increased interest in the use of ear as a biometric. However, similar to face recognition, it poses challenges of variation in illumination, contrast, rotation, scale and pose. Owing to the extensive work in the field of computer vision using convolutional neural networks (CNNs), its feasibility in the field of ear biometrics has been presented in this work. The proposed technique uses a CNN as a feature extractor and a support vector machine (SVM) for the classification task. The joint CNN-SVM framework is used for mapping ear images to random base-n codes. The codes are further hashed using the secure hash algorithm SHA-3 to generate secure ear templates. The feasibility of the proposed technique has been evaluated on annotated web ears dataset. This work demonstrates 12.52% average equal error rate without any image pre-processing, which shows that the proposed approach is promising in the field of secure ear biometrics.
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    Audio classification using braided convolutional neural networks
    (IET, 2020-09) Ajmera, Pawan K.
    Convolutional neural networks (CNNs) work surprisingly well and have helped drastically enhance the state-of-the-art techniques in the domain of image classification. The unprecedented success motivated the application of CNNs to the domain of auditory data. Recent publications suggest hidden Markov models and deep neural networks for audio classification. This study aims to achieve audio classification by representing audio as spectrogram images and then use a CNN-based architecture for classification. This study presents an innovative strategy for a CNN-based neural architecture that learns a sparse representation imitating the receptive neurons in the primary auditory cortex in mammals. The feasibility of the proposed CNN-based neural architecture is assessed for audio classification tasks on standard benchmark datasets such as Google Speech Commands datasets (GSCv1 and GSCv2) and the UrbanSound8K dataset (US8K). The proposed CNN architecture, referred to as braided convolutional neural network, achieves 97.15, 95 and 91.9% average recognition accuracy on GSCv1, GSCv2 and US8 K datasets, respectively, outperforming other deep learning architectures.