Electronics Corporation of India

Rapid response of Emergency Medical Services (EMS) is critically affected by urban traffic congestion, where conventional siren-based and manual traffic control mechanisms often fail to ensure timely right-of-way for ambulances. To address this limitation, this paper presents a real-time, vision-based ambulance detection system optimized for deployment on resource-constrained edge devices. The proposed system employs a fine-tuned YOLOv5s object detection model trained on a custom ambulance dataset and optimized using the Open Neural Network Exchange (ONNX) framework for efficient CPUbased inference. The optimized model is deployed on a Raspberry Pi (64-bit) platform using ONNX Runtime and integrated with a live IP camera stream for continuous detection. Experimental results demonstrate a Mean Average Precision (mAP@0.5) of 91.3% and a real-time inference speed of 2–5 FPS on the edge device. A comparative evaluation shows that ONNX Runtime significantly outperforms native PyTorch inference on the same hardware. The results demonstrate the practical feasibility of deploying a vision-based ambulance detection system on CPU-only edge devices without hardware accelerators, making the solution suitable for cost-sensitive Intelligent Transportation System deployments. The work primarily validates edge-level detection feasibility, with traffic control components evaluated via simulation. This work is positioned as an applied feasibility and deployment-oriented study rather than a novel algorithmic contribution. The study primarily evaluates deployment feasibility and runtime performance on CPU-only edge hardware.

Mining is one of the major contributor for the creation of wasteland in the country. In India, around 0.8 million hectares of land is under mining affecting the local environment drastically. Mining specially opencast, drastically destroys the land ecosystem, structure, and microbial community. Several microbial processes such as nitrogen and carbon cycling, humification and soil aggregation are practically nonfunctional and hence do not support biomass development. It is also responsible for contamination of water bodies, stream, groundwater and adjoining lands leading to severe environmental problems. Revegetation and reclamation of mine land offers a great challenge as more often they do not have suitable surface soil to provide bedding layer to encourage and support the biomass development, while in other cases they do not support plant growth due to presence of toxic metals. Gradual increase in such landscapes due to extensive mining activities may endanger not only the agro-forest productivity of the country but also the ecosystem and ecological balance. Hence, R&D is urgently warranted for the reclamation of degraded land in open cast mines through plantation with a view of restoring its productivity and fertility as also the ecological cycles in the rhizosphere with identification of appropriate plant species for plantation on such land.

This paper presents the design and performance analysis of a compact and efficient pressure sensor using a two-dimensional photonic crystal (2D-PC) structure integrated with a hexagonal ring resonator. The proposed sensor uses two quasi-waveguides coupled to a hexagonal ring cavity to detect pressure variations via refractive-index modulation. The optical behavior is modeled and simulated using the Finite-Difference Time-Domain (FDTD) method. Key performance metrics resonant wavelength shift, quality factor (Q), normalized output power, and sensitivity are analyzed. Simulation results show a resonant wavelength near 1.5708 µm with a full width at half maximum (FWHM) ≈ 0.06 nm, resulting in a high Q ≈ 2.6×10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup>. The sensor exhibits a sensitivity of 1.35 nm/MPa over the evaluated pressure range (0–6 MPa) with an approximately linear wavelength shift, confirming the device’s reliability for pressure measurement. Compact size, high spectral resolution, and compatibility with integrated photonic circuits make this design suitable for biomedical, industrial, and environmental monitoring applications.

Signal enhancement of biomedical signals is an important area of real-time biomedical monitoring, particularly in resource-constrained embedded systems. Conventional filtering methods tend to confuse noise and critical signal changes whereas pure neural methods require a lot of computation which is inapplicable to low-power wearable devices. This paper presents Neuro-ElectroFusion which is an intelligent embedded system that provides a lightweight neural processing with rule-based signal correction that would provide adaptive improvement of multi-modal biomedical signals, including EEG, ECG, and EMG. The system is based on a quantized 1D-CNN-BiLSTM architecture and a fuzzy logic unit that dynamically denoises and reconstructs signals, depending on the context and physiological state. An entropy-based event-driven sampling scheme consumes considerably less energy and latency and has no fidelity trade-offs. PhysioNet and MIT-BIH performance On PhysioNet and MIT-BIH data sets, the average SNR improvement of 13.7 dB, the reduction in RMSE of 73.4% and the real-time latency of the inference of 16.3 ms per signal window were obtained. The proposed system recorded a high accuracy of 97.68% in various noise levels, which was higher than the traditional DSP-based and TinyML models. Moreover, context-conscious Q-learning was capable of providing adaptive signal prioritization and it was noteworthy that over the baseline systems, context-aware Q-learning improved energy efficiency by 17.6%.

Viscosity modifiers (VMs) are widely applied in lubricants. Advanced VMs can modulate viscosity in response to various stimuli, such as pH, light, salinity, and temperature. This article explores comb-shaped macromolecules that exhibit thermoresponsive behavior, with potential applications as oil additives for use in transportation. Copolymers were prepared by free-radical polymerization of up to four methacrylate-based monomers with different polarities into sparsely branched comb-shaped macromolecules. The side chains of the comb comprised hydrogenated poly(butadiene), whereas the backbone contained lauryl, benzyl, and butyl methacrylates. Rheological measurements of concentrated solutions in a Yubase oil revealed an initial increase followed by a decrease in viscosity as temperature was increased from 25 to 105 °C. The magnitude of this effect was dependent on the concentration, between 10 and 20 wt % polymer, and the fraction of benzyl methacrylate versus butyl methacrylate in the copolymers. Cryogenic transmission electron microscopy (cryo-TEM) and small-angle X-ray scattering (SAXS) revealed polymer aggregates at low temperatures, which first swelled and then dissolved as individual solvated molecules on heating. This aggregation-expansion-dissolution phenomenon qualitatively accounts for the changes in viscosity.

The dc fast charging stations operate at megawatt-scale power levels, drawing energy from the medium voltage ac (MVAC) grid to reduce transmission losses. It also requires local energy storage and the capability for renewable integration to reduce the load on the grid. Conventional solutions use a bulky line frequency transformer (LFT) to step down MVAC to low voltage ac (LVAC), followed by multiple ac–dc and isolated dc–dc converters, leading to lower efficiency especially during power flow between local storage and electric vehicles. Existing multiport MVAC-to-LVDC converters often rely on modular multilevel converters (MMCs), which has higher component count, or cascaded H-bridge (CHB) solutions with additional battery storage and additional bridges for power exchange. This article proposes a novel CHB-based multiport topology that eliminates the need for additional battery storage, additional semiconductor devices, and additional MV-insulated transformer winding compared to an MVAC to single port LVDC converter. The proposed modulation and control strategy ensures the independent operation of each LVDC port, and unity power factor (UPF) operation at the grid interface. A step-by-step design for an 11 kV two-port converter is presented, and experimental validation is performed using a 1.2 kW laboratory-scale prototype with an efficiency of 95.3% at rated load.