Drones, with their ability to vertically take off and land with their stable hovering performance, are becoming favorable in both civilian and military domains. However, this introduces risks of its misuse, which may include security threats to airports, institutes of national importance, VIP security, drug trafficking, privacy breaches, etc. To address these issues, automated drone detection systems are essential for preventing unauthorized drone activities. Real-time detection requires high-performance devices such as GPUs. For our experiments, we utilized the NVIDIA Jetson Nano to support YOLOv9-based drone detection. The performance evaluation of YOLOv9 to detect drones is based on metrics like mean average precision (mAP), frames per second (FPS), precision, recall, and F1-score. Experimental data revealed significant improvements over previous models, with a mAP of 95.7%, a precision of 0.946, a recall of 0.864, and an F1-score of 0.903, marking a 4.6% enhancement over YOLOv8. This paper utilizes YOLOv9, optimized with pre-trained weights and transfer learning, achieving significant accuracy in real-time drone detection. Integrated with the NVIDIA Jetson Nano, the system effectively identifies drones at altitudes ranging from 15 feet to 110 feet while adapting to various environmental conditions. The model’s precision and adaptability make it particularly suitable for deployment in security-sensitive areas, where quick and accurate detection is crucial. This research establishes a solid foundation for future counter-drone applications and shows great promise for enhancing situational awareness in critical, high-risk environments.

Autonomous unmanned aerial vehicles work seamlessly within the GPS signal range, but their performance deteriorates in GPS-denied regions. This paper presents a unique collaborative computer vision-based approach for target tracking as per the image’s specific location of interest. The proposed method tracks any object without considering its properties like shape, color, size, or pattern. It is required to keep the target visible and line of sight during the tracking. The method gives freedom of selection to a user to track any target from the image and form a formation around it. We calculate the parameters like distance and angle from the image center to the object for the individual drones. Among all the drones, the one with a significant GPS signal strength or nearer to the target is chosen as the master drone to calculate the relative angle and distance between an object and other drones considering approximate Geo-location. Compared to actual measurements, the results of tests done on a quadrotor UAV frame achieve $99\%$ location accuracy in a robust environment inside the exact GPS longitude and latitude block as GPS-only navigation methods. The individual drones communicate to the ground station through a telemetry link. The master drone calculates the parameters using data collected at ground stations. Various formation flying methods help escort other drones to meet the desired objective with a single high-resolution first-person view (FPV) camera. The proposed method is tested for Airborne Object Target Tracking (AOT) aerial vehicle model and achieves higher tracking accuracy.

Past research has evidently proved that public key cryptosystems are usually slower than symmetric key cryptosystems due to the reason that they use one additional cryptographic key and different methods for encryption and decryption. RSA is one of the most common asymmetric key cryptography algorithms. Recent research has focused on speeding up RSA using various techniques. With the introduction of distributed computing, parallelization of algorithms enables them to run on multiple cores concurrently at a time. RSA consists of two resource intensive operations namely Modular Exponentiation of up to 1024-bit exponents and repeated calculation of Greatest common divisor. Thus, RSA lays the perfect base for application of Montgomery Reduction algorithm to optimize the Repeated Modular multiplication in exponentiation. In this paper we proposed a parallel scheme for RSA using a new parallel data structure known as Concurrent Indexed List of character blocks. The aim of our research was to improve the speed of RSA encryption and decryption using parallelism and also make it compatible with leading industry cryptography standards. We have simulated four different approaches namely both parallel and sequential with and without Montgomery. We have also integrated our parallel paradigm with renowned C++ Crypto library and achieved a speed-up of upto four times than sequential approach. Unlike any other previous approaches, our implementation got easily integrated with any external library and thus can be adopted by any other algorithmic scheme.

A robotic navigation system operates flawlessly under an adequate GPS signal range, whereas indoor navigation systems use the simultaneous localization and mapping system or other vision-based localization systems. The sensor used in indoor navigation systems is not suitable for low power and small scale robotic systems. The wireless area network transmitting devices have fixed transmission power, and the receivers get the different values of signal strength based on their surrounding environments. In the proposed method, the received signal strength index (RSSI) values of three fixed transmitter units are measured every 1.6 m in mesh format and analyzed by the classifiers, and robot position can be mapped in the indoor area. After navigation, the robot analyzes objects and detects and recognize human faces with the help of object recognition and facial recognition-based classification methods respectively. This robot detects the intruder with the current position in an indoor environment.

High-altitude unmanned aerial vehicles (UAVs) operate in extreme environmental conditions that impose significant constraints on design, stability, and performance. This paper presents a structured review of the major challenges associated with the development of fixed-wing, multirotor, and hybrid Vertical take-off Landing (VTOL) UAVs, with emphasis on their suitability for high-altitude, long-endurance missions. UAVs are classified based on design, weight, aerodynamics, and operational role to provide clarity in application-specific development. Key design elements such as materials, propulsion systems, airfoils, and structural layout are examined alongside aerodynamic considerations including lift, drag, wing loading, and stability. Special attention is given to VTOL systems, focusing on transition dynamics, energy efficiency, and control. The paper also highlights current research gaps, particularly in power systems, thermal regulation, and structural optimization, guiding future innovations in high-altitude UAV technology.