Cloud and mobile computing domains have become quite popular these days. Besides, the ubiquitous smartphones are increasingly becoming a dominant platform for collaborative sensing, so we built an android application SmartMobiCam, which aids in forming the robust and scalable Smart city video surveillance system which is critical for upgrading the urban areas. The application obtains video feed, images and sound clips from the users and then uses cloud services for video enhancement and restoration of the content and provides it to all subscribers including police investigators. The application is explained with detailed architectural and technological choices. Performance analysis shows that the proposed application performs well and outperforms the traditional surveillance system.

Research interest in ingestible Wireless Capsule Endoscopy (WCE) studies in humans showed better results than conventional invasive probe endoscopy methods. Because of the structure and the position of the small intestine, proper scanning cannot be done in the area using traditional endoscopic methods. For patient comfort, continuous developments have been suggested in capsule endoscopy designs in terms of the quality of images transmitted, capsule orientation, positioning of the capsule from outside the body, link budget analysis, impedance matching and capsule miniaturization. To improve the image quality, transmission efficiency of the antenna has to be improved. This has led to the development of many antenna structures in an ingestible capsule system. Literatures have identified Med Radio and ISM (Industrial, Scientific and Medical) band as operation bands for the WCE systems. This review aims to highlight: (1) design considerations for various antenna types, (2) miniaturization techniques, (3) operating bands, specifications and various design challenges and (4) research gap, advanced design technologies and targets of ingestible antenna system. The main aim of this paper is to tutorial the up-to-date information on the recent antenna designing techniques and challenges for ingestible system.

The computer-aided diagnosis helps medical professionals detect and classify lung diseases from chest X-rays by leveraging medical image processing and central server-based machine learning models. These technologies provide real-time assistance to analyze the input and help efficiently detect the abnormalities at the earliest. However, traditional learning models are not suitable for live scenarios that require privacy, data diversity, and decentralized processing. The Federated learning-based model facilitates the protection of medical data privacy while processing a large volume of medical images, aiming to improve the overall efficiency of the model. This paper proposes a Federated Learning based Ensemble Model (FLEM) framework for an efficient diagnosis of lung diseases. The FLEM utilizes explainable AI techniques, including SHAP, Grad-CAM, and Differential Privacy, to provide transparency and interpretability of predictions while maintaining the privacy and security of medical data. We applied InceptionV3, Conv2D, VGG16, and ResNet-50 models on the COVID-19, TB, and pneumonia datasets and analysed the performance of the models in FLEM and Central Server-based Learning Model (CSLM). The performance analysis shows that the FLEM model outperformed the traditional CSLM model in terms of accuracy, training time, and bandwidth consumption. CSLM witnesses a quicker convergence time than FLEM. Although the CSLM model converged after a considerable number of epochs, it resulted in a 5, 8, 9, and 10% accuracy reduction compared to the FLEM-based training of InceptionV3, Conv2D, VGG16, and ResNet50 that achieved accuracies of 91.8, 88, 92.5, and 95.5%, respectively.