Basaveshvara Engineering College

The detection of nitroaromatic compounds (NACs) is critically important due to their extensive use in explosive materials and the associated environmental and security risks. In this work, three structurally tailored coumarin derivatives 6-chloro-4-(4-methoxyphenoxymethyl)-chromen-2-one (S1), 1-(4-methoxyphenoxymethyl)-benzo[f]chromen-3-one (S2), and 6-methoxy-4-(4-methoxyphenoxymethyl)-chromen-2-one (S3) were explored as fluorescent probes for NAC sensing in Dimethyl sulfoxide (DMSO). Steady-state absorption, emission, and time-resolved spectroscopic investigations revealed significant fluorescence quenching upon gradual addition of nitrobenzene (NB), 2-nitrotoluene (2NT), 4-nitrotoluene (4NT), and 2,4,6-trinitrophenol (TNP). Stern–Volmer (S-V) analysis displayed positive deviations for NB, 2NT and 4NT, suggesting a combined dynamic and static quenching mechanism, whereas negative deviations observed for TNP indicates the major contribution from dynamic quenching mechanism. Fluorescence lifetime measurements confirmed dynamic quenching as the dominant pathway. Thermodynamic analysis revealed negative free energy change (ΔGPET) of the photoinduced electron transfer (PET) values for all coumarin–NAC systems, establishing the favourable nature of the PET process. Among the probes, S2 exhibited superior sensing performance owing to its extended π-conjugation and enhanced donor characteristics, resulting in the highest Stern–Volmer constants and quenching efficiencies. Both solution mode and contact mode studies demonstrated practical applicability, with S2 achieving the lowest detection limit for TNP (2.55 × 10⁻⁶ M). Overall, these findings highlight the potential of rationally designed coumarin derivatives as efficient, cost-effective, and selective fluorescent sensors for nitroaromatic detection, particularly for TNP.

In this paper, development of experimental setup to identify the wind velocity deficit due to wake effect of Vertical-Axis Wind Turbine (VAWT) on Horizontal-Axis Wind Turbine (HAWT) in Renewable Energy Research Laboratory Basaveshwar Engineering College, Bagalkote. Downstream turbines have reduced incident energy and momentum. A VAWT causes a wake behind it to extend linearly when a uniform wind strikes it. The free wind will experience a partial reduction in speed from Vup to Vdown. The wake effect in which the incoming wind with speed Vup hits the blades of turbines and creates a wake cone. Downstream wind turbines inside the wake cone experience the velocity deficit in wind speed and extracts lesser power. Energy wake losses typically 5–20

Nowadays safety and security are significant challenges for emergency users. In emergency situations such as acid burning, kidnapping, robbery, theft, and so on, there is no assurance for the safety of women, old aged people, and children in a real-world environment. Such kinds of situations have created an intense fear and anxiety among women, old-aged people, and children. In this work, agent-based framework for providing real time services to users using machine learning approach for emergency contexts is proposed. The proposed system monitors diverse emergency situations and also providing emergency services. In this paper, the proposed system for emergency users would work constantly and replace human patrols. Finally, it evaluates the performance parameters in terms of data processing time, throughput, and computation overhead.

Advanced Driver Assistance Systems (ADAS) remain prohibitively expensive for two-wheeler markets, particularly in developing economies. This paper presents a cost-effective, vision-based ADAS tailored for two-wheelers. The proposed system uses a monocular camera setup processed by YOLOv8 algorithms, targeting the NVIDIA Jetson Nano for edge deployment. The system provides Forward Collision, Lane Departure, and Blind Spot warnings. Leveraging Inverse Perspective Mapping for distance estimation and a Kalman Filter for tracking, the system ensures robust performance. The architecture is validated using Hardware-in-the-Loop simulation using an NVIDIA RTX 3070 to emulate edge constraints. Results show a mean Average Precision of 84% and inference speeds exceeding 160 FPS using TensorRT. These findings confirm that low-cost computer vision can provide essential safety features for the mass two-wheeler market.

Edge computing has emerged as a critical paradigm for enabling low-latency, bandwidth-efficient, and scalable data processing in distributed IoT environments. However, its effectiveness fundamentally depends on how data is cached, stored, aggregated, and fused across heterogeneous and resource-constrained edge nodes. To address this, the present survey conducts a comprehensive and methodologically rigorous examination of data-management techniques in edge computing. An initial corpus of 150 publications was collected from major scientific databases and processed through the PRISMA framework, resulting in 25 high-quality surveys that revealed data management as the most fragmented and underdeveloped component of the edge ecosystem. Building on these insights, we performed an in-depth analysis of 75 state-of-the-art research papers published between 2018 and 2025, covering four core data-management pillars: data caching, data storage, data aggregation, data validation and data fusion. For each area, we synthesize current design strategies, highlight measurable performance outcomes, and critically evaluate architectural, algorithmic, and system-level limitations. A unified cross-technique analysis further reveals unresolved challenges in scalable data placement, coded storage, privacy-preserving aggregation, multi-modal fusion, and the absence of integrated data pipelines. The survey concludes by outlining open research directions and proposing a consolidated roadmap toward intelligent, interoperable, and workload-aware data-management frameworks for next-generation edge computing systems.