Traditional agriculture is transforming into smart agriculture due to the prominence of the Internet of Things (IoT). Low-cost and low-power are the key factors to make any IoT network useful and acceptable to the farmers. In this paper, we have proposed a low-power, low-cost IoT network for smart agriculture. For monitoring the soil moisture content, we have used an in-house developed sensor. In the proposed network, the IITH mote is used as a sink and sensor node which provides low-power communication. We have evaluated our network with state of the art networks, proposed for agriculture monitoring. Power and cost are the two metrics used for evaluation of these networks. Results show that the proposed network consumes less power and has on average 83% prolonged lifetime at a lower cost compared to previously proposed network in the agriculture field.

The advent of 6G marks a transformative phase in wireless communication, ushering in a hyperconnected experience. This paper explores optimizing sidelink technologies in Vehicle-to-Everything (V2X) communication through integrating 6G capabilities. Emphasizing challenges in sidelink resource allocation, the study introduces mathematical solutions. The survey further investigates the evolution of Cellular-V2X (C-V2X) and sidelink standardization within the 6G V2X Sidelink context, highlighting key features and applications. Additionally, it examines inter-UE coordination, resource re-evaluation, and pre-emption operations in 5G-V2X Sidelink, addressing associated challenges and mathematical formulations. The paper focuses on power-based resource allocation in 5G-V2X, addressing challenges and proposing solutions for the 6G V2X Sidelink landscape. Encompassing direct communication, collision issues, spectrum compliance, resource fairness, uncertainty, and interference management, the survey comprehensively explores challenges and solutions in current sidelink resource allocation. It evaluates traditional and emerging techniques, such as cognitive radio, cooperative communication, power control, dynamic spectrum access, ML-aided allocation, blockchain-enabled allocation, and edge computing-driven allocation. The resource allocation requirements for diverse V2X services in 6G V2X Sidelink are outlined, explicitly focusing on Vehicular to Vehicular (V2V), Vehicular to Infrastructure (V2I), Vehicular to Pedestrian (V2P), and other V2X services, addressing their specific needs.

Multispectral images (MSIs) are valuable for precision agriculture due to the extra spectral information acquired compared to natural color RGB (ncRGB) images. In this paper, we thus aim to generate high spatial MSIs through a robust, deep-learning-based reconstruction method using ncRGB images. Using the data from the agronomic research trial for maize and breeding research trial for rice, we first reproduced ncRGB images from MSIs through a rendering model, Model-True to natural color image (Model-TN), which was built using a benchmark hyperspectral image dataset. Subsequently, an MSI reconstruction model, Model-Natural color to Multispectral image (Model-NM), was trained based on prepared ncRGB (ncRGB-Con) images and MSI pairs, ensuring the model can use widely available ncRGB images as input. The integrated loss function of mean relative absolute error (MRAE_loss) and spectral information divergence (SID_loss) were most effective during the building of both models, while models using the MRAE_loss function were more robust towards variability between growing seasons and species. The reliability of the reconstructed MSIs was demonstrated by high coefficients of determination compared to ground truth values, using the Normalized Difference Vegetation Index (NDVI) as an example. The advantages of using “reconstructed” NDVI over Triangular Greenness Index (TGI), as calculated directly from RGB images, were illustrated by their higher capabilities in differentiating three levels of irrigation treatments on maize plants. This study emphasizes that the performance of MSI reconstruction models could benefit from an optimized loss function and the intermediate step of ncRGB image preparation. The ability of the developed models to reconstruct high-quality MSIs from low-cost ncRGB images will, in particular, promote the application for plant phenotyping in precision agriculture.

Classification of medical data is one of the most challenging pattern recognition problems. As stated in literature a single classifier is unable to solve all medical image classification problems due to high sensitivity to noise and other imperfections like data imbalance. So, several individual classifiers have been studied to solve the different types of classification problems arising in medical datasets but all have proven to be useful on some specific datasets. Hence, in this paper, we propose a generic multi-level classification approach for medical datasets using sparsity based dictionary learning and support vector machine approaches. The proposed technique demonstrates the following advantages: 1) gives better performance of classification accuracy over all datasets 2) solves imbalanced data problems 3) needs no fusion and ensemble methods in multi-level classification. The results presented on the 5 standard UCI medical datasets demonstrate that the efficacy of the proposed multi-level classification technique.

Fatty Liver Disease (FLD), if left untreated can progress into fatal chronic diseases (Eg. fibrosis, cirrhosis, liver cancer, etc.) leading to permanent liver failure. Doctors usually use ultrasound scanning as the primary modality for quantifying the amount of fat deposition in the liver tissues, to categorize the FLD into normal and abnormal. However, this quantification or diagnostic accuracy depends on the expertise and skill of the radiologist. With the advent of Health 4.0 and the Computer Aided Diagnosis (CAD) techniques, the accuracy in detection of FLD using the ultrasound by the sonographers and clinicians can be improved. Along with an accurate diagnosis, the CAD techniques will help radiologists to diagnose more patients in less time. Hence, to improve the classification accuracy of FLD using ultrasound images, we propose a novel CAD framework using convolution neural networks and transfer learning (pre-trained VGG-16 model). Performance analysis shows that the proposed framework offers an FLD classification accuracy of 90.6% in classifying normal and fatty liver images.