A full -wave CMOS rectifier is presented in this paper which is used for powerlessly power up low voltage biomedical implantable devices. Bootstrapped capacitors are used to decrease the effective threshold voltage of main switch transistor in the rectifier architecture. This architecture has low voltage drop and good overall efficiency even at very low input voltage and over a wide range of frequency. Therefore, rectifier is suitable to use in biomedical implantable devices with low voltage power supplies over RF link. Leakage current through bulk has been reduced due to lowering on resistance, which is reduced because of biasing the bulk of the transistor with Dynamic Bulk Switching (DBS) technique. DBS technique uses highest voltage available in the circuit for biasing the circuit. Performance of the rectifier is verified and compared using standard TSMC 0.18 CMOS model and PTM 32nm CMOS model respectively.

In this work we have studied the criteria for assessing the quality of educational games and their impact on student learning outcomes by designing two experiments that were conducted in a duration of total of 6 years. The first experiment involves students in their final year of high school studies and the second on first year students in higher education. Our main results have shown that in comparison with traditional methods in education, teaching with the use of educational games increases the degree of mastering of educational material. It is also observed to increase the involvement and motivation of students to perform educational tasks by significantly reducing the time that will be required for reinforcement of the passed material. Additionally, it tends to cause less fatigue and holds their concentration much longer. Based on the results of our research, we have also identified the main criteria for evaluating the effectiveness of educational games. Our results suggest that a robust and comprehensive integration of educational games into the learning process will significantly improve the quality of education at all levels.

Brain tumor detection through magnetic resonance imaging (MRI) is a difficult investigation to conduct. Developing a fast and reliable clinical decision making tool is paramount. Modern techniques like deep learning and convolutional neural networks (CNNs) have demonstrated great promise in automating the process of detecting tumor masses from MRI scans. In this study, we train a VGG16-based CNN, merging two publicly available datasets (Figshare and Kaggle), introducing inter-dataset variability simulating real-world diagnostic conditions. We preprocess data, use stratified splitting for training, testing, and validation, and apply data augmentation techniques. Our model achieves a validation accuracy of 84.4% and demonstrates consistent performance across tumor types. Grad-CAM heatmaps highlight tumor regions with reasonable precision, enhancing model transparency and trust. This work highlights the effectiveness of a lightweight, generalizable CNN architecture along with visual interpretability.

Mobile applications have become ubiquitous, but their widespread adoption has increased security concerns, particularly regarding malware infiltration through official app stores. Current detection methods predominantly focus either on static analysis of app permissions or dynamic user-based feedback, limiting the effectiveness of malware detection. This paper proposes a novel hybrid approach integrating Permission Profiling—examining the explicit permissions requested by an app—and User Experience Analysis—leveraging metadata from app stores and user-generated reviews. Using the NATICUSdroid dataset (29,333 applications) for permissions analysis and a curated dataset of app store metadata and reviews, we trained individual models for each approach and combined them through soft voting ensemble techniques. The hybrid model achieved 98.05% accuracy, significantly outperforming individual models (96.98% and 76.00% respectively), with a 70% reduction in false positives. SMS-related permissions emerged as the strongest indicators of malicious intent, while negative sentiment in user reviews provided complementary signals. Our findings demonstrate that integrating technical permission analysis with user experience data creates a more robust malware detection system capable of addressing limitations inherent in each individual approach.

The app 'Picasso' attempts to unify the worlds of robotics and digital creativity by allowing users to engage with a vertical painting robot. This app lets users take an image from their device’s gallery, convert it into contour graphics, and send it via Bluetooth to a robot to render it physically. The app has the added knowledge of advanced AI-generated imagery allowing the user to create unique images from a text prompt using Stability AI’s API. The main objective of this project is to create the easiest, most efficient, and most exciting way for users of all backgrounds to create and send computer-generated art to a robotic system that will then execute that art in reality. In contrast to traditional painting, "Picasso" offers the best of three worlds, combining artificial intelligence, digital design, and robotic execution, to create uniquely detailed and personalized drawings. The app allows users to do more than just transfer images; it encourages users to engage with the creative possibilities of artificial intelligence and complete an entire drawing experience with their hands free and automated. By providing an intuitive interface and powerful resources, Picasso wants to make AI art creation more accessible and integrated into robots' applications. This project is a step towards the future of human-machine collaboration in the field of creativity, making the creation of robotic art easier.