Authors - Vandana R. Babrekar, Sandeep V. Gaikwad Abstract - This paper presents a literature review of current state of knowledge and innovation related to precision agriculture (PA) technologies leveraging Internet of Things (IoT), Artificial intelligence (AI), Computer vision (CV), Machine learning (ML), and Deep learning (DL). The review provides a thorough understanding of the various developments made in the above-mentioned technologies in precision farming. It also states several challenges after reviewing research publications, and it also recommends thrust areas for future studies.
Authors - Killol Pandya, Trushit Upadhyaya, Aneri Pandya, Upesh Patel, Poonam Thanki, Kanwarpreet Kaur, Jinesh Varma Abstract - The 2x2 Ultra Wide Band(UWB) MIMO antenna model with 31 x 26 x 1.56 mm3 are designed and presented. The proposed antenna system consists of two primary radiators over the surface layer and positioned with sufficient space between each other to receive adequate response. The conducting patches include U shaped and inverse U shaped slots to increase the current traveling surface. The bottom corners of the patches are having fractional geometry and supporting arms associated with microstrip line to offer resonances at desired frequencies. The Ultra Wide band characteristics are reported due to partial ground plane geometry. The additional strip with slots are deployed with ground plane to receive the satisfactory isolation as the radiators create mutual effect on the other ones which turns into performance degradation. For UWB, the structure exhibits gain of around 2 dB and around a bandwidth of 85% which shows the applicability of discussed research. The other diversity parameters such as Mean Effective Gain(MEG), Gain diversity, Channel Capacity Loss and Envelope Correlation parameter are analyzed and presented. The proposed antenna is well appropriate for WiMAX, WLAN and C band applications.
Authors - V V N Sai Rajeshwar, Gangalam Sumanth Abstract - Attributes like size, shape, color, quantity, quality, etc. are very hard to find in an image of a particular object. There are various ways to process an image and derive attributes namely – Faster R – CNN, SSD (Single Shot Detector), SPP (Spatial Pyramid Pooling) - Net. Many modern technologies use YOLOv8, the 8th version of YOLO (You Only Look Once), which is quite accurate and detects real - time objects. To enhance the proficiency and scalability of the model Deep Convolutional Neural Networks (DCNN) were used. This paper highlights a very unique strategy to enhance the abilities of YOLO with the help of dynamic masking and also advances the searching and findings of attributes in a unique way. The use of Compute Unified Device Architecture (CUDA) which uses GPUs rather than CPU, made it convenient to run on a higher loads of data. After masking an image or stream of images, detecting colors is easier, and expressing dominant colors is the motive of this research. Availing Coordinates of specified images in a particular image or stream of images is very helpful in various surveillance-related actions like military, and navy, and also useful in domestic purposes.
Authors - Priya Deokar, Sandhya Arora Abstract - Multiparty Computation (MPC) allows users to use their private inputs to compute a function without revealing anything about the inputs. This feature is useful in computing the aggregation of smart meter readings. The smart grid provides bi-directional communication between the smart meter and the utility supplier. Periodic sharing of fine-grained information by a smart meter presents a greater danger to privacy. Secure multiparty computing is one of the most efficient ways to preserve privacy among users. This paper briefly overviews MPC techniques and their usage in Smart Grid communication.
Authors - Tamanna Kalariya, Bimal Patel, Martin Parmer, Mrugendra Rahevar Abstract - The Agile model has demonstrated clear superiority over traditional software process models by offering enhanced flexibility and adaptability to evolving project requirements. Unlike rigid, linear methodologies, Agile promotes iterative development, allowing teams to quickly incorporate feedback and adapt to changing needs. This iterative approach is particularly advantageous for software testers, enabling continuous testing throughout the development lifecycle, which improves code quality and reduces defects. GitHub, as a collaborative development platform, amplifies these Agile benefits by providing robust version control and seamless integration with Agile workflows. For testers, GitHub facilitates real-time collaboration, enabling immediate feedback and shared responsibility for quality assurance. Features such as pull requests, issue tracking, and continuous integration streamline the testing process and enhance communication between developers and testers. Additionally, GitHub’s Agile-driven workflow provides a competitive advantage over other code-sharing tools by optimizing development cycles and fostering a strong collaborative culture. The platform's rich ecosystem of tools, combined with extensive community support, further boosts productivity and innovation. This paper examines how Agile methodologies within GitHub enhance both the development and testing processes, positioning GitHub as a leader in the collaborative development landscape and driving greater efficiency in modern software engineering practices.
Authors - Nishit Patel, Bimal Patel, Mansi Patel, Parth Shah, Nishat Shaikh Abstract - Agile model has demonstrated clear superiority over traditional software process models by offering enhanced flexibility and responsiveness to changing requirements, particularly essential in the fast-paced automotive industry. Prior to adopting Agile, Tesla encountered significant challenges, including prolonged development cycles and difficulties in scaling manufacturing processes to meet increasing demand. These constraints limited Tesla’s ability to innovate rapidly and deliver advanced features such as Full Self-Driving (FSD) capabilities. By implementing Agile methodologies, Tesla transformed its software development and manufacturing ramp-ups, enabling iterative development with continuous testing and integration. This shift significantly accelerated FSD development, allowing for faster adaptation to technological changes. The adoption of Agile led to measurable improvements, including reduced time-to-market for new features, improved cross-functional collaboration, and enhanced product quality. Compared to other automotive giants, Tesla’s Agile integration has provided a distinct competitive advantage in terms of operational efficiency and innovation. While competitors often rely on established, rigid processes, Tesla’s Agile approach enables rapid adaptation to market demands and continuous consumer feedback. This paper highlights the transformative impact of Agile methodologies on Tesla’s product development and operational strategies, demonstrating how Agile has positioned Tesla as a leader in automotive innovation, driving significant advancements in technology and customer satisfaction.
