Seemal Asif

The proliferation of deepfake technologies has created an urgent need for robust, real-time detection systems capable of verifying image authenticity, particularly in mobile environments. This paper presents a scalable deepfake image detection framework integrated into the AI-Guard mobile application. Our approach leverages fine-tuned, computationally efficient convolutional neural network (CNN) architectures, including VGG19, InceptionV3, Xception, EfficientNetB0, ResNet50, and MobileNetV3Large. Trained on a large-scale, balanced dataset of over 450,000 real and fake images sourced from six publicly available datasets, the models incorporate advanced preprocessing, adversarial data augmentation, and optimized training pipelines. Among these, VGG19 achieved the highest generalization performance with 98.9% validation accuracy and 93.2% accuracy on previously unseen real-world data. The system supports real-time inference via a REST API, enabling practical mobile deployment with low latency. To support transparency and reproducibility, the curated training dataset has been made publicly available through our institutional repository. Our results demonstrate that AI-Guard offers an effective, deployable solution for forensic image verification, contributing to countering misinformation and enhancing trust in digital media.

In large-scale precision manufacturing, achieving real-time high-precision alignment between industrial robots and metrology systems remains challenging. This paper proposes a cloud-integrated communication architecture that enables real-time, bidirectional data exchange between industrial robots and laser trackers. The system leverages the MQTT publish/subscribe protocol within the AMRC Factory+ cloud environment to orchestrate metrology data flows. Robot controllers publish motion commands while laser trackers provide real-time position feedback, forming a closed-loop calibration process. Measurement data is processed through dedicated adapter layers and calibration modules, then disseminated by an MQTT broker to a central control system which computes alignment corrections and updates robot motion. The architecture employs edge computing and cloud services for modularity, scalability, and low latency, and integrates with existing metrology software (e.g., Spatial Analyzer, PolyWorks). These findings underscore the efficacy of cloud-based robot-tracker coordination for dynamic, highprecision calibration and control. The proposed approach provides a robust foundation for future Industrial IoT (IIoT) deployments in metrology-driven manufacturing systems.

The rapid advancement and expanding application of robotics in various sectors necessitate the development of versatile and efficient gripping systems. Robotic grippers, serving as the primary interface between robots and the physical world, are pivotal for the success of robotic applications ranging from industrial automation to delicate tasks in healthcare. This paper delves into the diverse landscape of robotic grippers, examining their design principles, material choices, and the intricate balance between flexibility and strength required to cater to a broad spectrum of operational demands. It further explores the spectrum of actuation mechanisms that empower these grippers, from traditional electric and pneumatic systems to cutting-edge soft robotics technologies that offer unprecedented adaptability and safety. The integration of sensors within grippers, enhancing their intelligence and autonomy, is also scrutinized, alongside a critical evaluation of control strategies that range from conventional PID controllers to sophisticated machine learning algorithms. In addition, this paper emphasizes the significance of real-time control and adaptation in ensuring robots’ ability to respond promptly to environmental changes, leveraging fast data processing and feedback mechanisms for improved performance. The aim of this comprehensive review is not only to provide a foundational understanding of the current state of robotic gripper technology but also to highlight key considerations and emerging trends that will shape the future of robotic manipulation. Through this exploration, the paper seeks to equip designers, engineers, and researchers with the insights needed to innovate and advance the field of robotic gripping systems.