Assam Engineering College

This manuscript presents a dual-band Voltage-Controlled Oscillator (VCO) design with temperature resistance circuit. It employs two Temperature Resistant VCOs (TRVCOs) that can operate from 350 MHz to 2.4 GHz and 2 GHz to 6.29 GHz. A temperature resistant circuit (TRC) is used to convert the temperature variation to frequency which can be used as one of the features for estimation of aging effect of the circuit using machine learning model. Three models -linear regression, polynomial regression (2nd degree) and random forest are considered and trained using synthetic data and random forest outperformed the other two in terms of mean absolute error. The variation of the frequency is controlled up to 260 MHz at 1500 degrees C, making it useful for use even in harsh environment. The design is implemented in cadence 90 nm gpdk CMOS Technology.

This study provides a detailed assessment of an innovative solar drying system developed for Terminalia chebula, evaluating its performance across energy, exergy, economic, and quality dimensions. The dryer incorporates a canned solar air collector (CSAC) integrated with sensible heat storage to maintain stable drying conditions under variable solar radiation. Experimental results showed a 41.51 % reduction in drying time compared to open sun drying (OSD), achieving a final moisture content of 12.5 % (w.b.) within 31 h. The CSAC exhibited average energy and exergy efficiencies of 74.4 f 1.41 % and 26.9 f 2.50 %, respectively, while the dryer demonstrated energy and exergy efficiencies of 21.15 f 2.04 % and 12.77 f 3.01 %, respectively. Exergy-based sustainability indicators were evaluated to assess the environmental impact and thermodynamic performance of the system, confirming its suitability for sustainable operation. A comprehensive techno-economic evaluation highlighted the system's economic viability, with a short payback period of 0.94 years. Quality analysis revealed improved retention of polyphenols, flavonoids, and favorable color attributes compared to OSD. This design, integrating sensible heat storage with solar drying technology, demonstrates substantial improvements in both operational efficiency and product quality, positioning it as a sustainable and practical solution for agricultural drying needs in resource-limited regions.

Abstract This work reports a TCAD-based investigation and comparative analysis of HfO2, SiO2 and hetero-dielectric (HfO2-SiO2) TFET-based photosensor in visible light region of the spectrum. The study examines the effects of dielectric material and demonstrates that superior photosensor parameters are obtained when a combination of HfO2 and SiO2 (hetero-dielectric) is employed as dielectric materials. Therefore, the hetero-dielectric TFET photosensor is considered for further analyses in this work. The photosensor induces two dielectric pockets each at source/channel (vacuum) and channel/drain interface (SiO2) whose impacts on optical performance are analyzed too. The vacuum pocket aids in diminishing trap effect while the SiO2 pocket suppresses ambipolar effect. The TFET-based photosensor is put under test in both illuminated and dark conditions to investigate their optical characteristics. The efficacy of the photosensor is determined by measuring its performance for varying wavelengths (λ) across the visible spectrum. The photosensor operates on the phenomenon of band-to-band tunneling, which, we validate, is able to detect visible light wavelength at a low incident intensity of 0.5 W/cm2. The sensor can obtain a maximum photosensitivity and signal to noise ratio (SNR) of 4.7×102 and 53.5 dB for an intensity of 0.5 W/cm2 and wavelength of 0.4 µm for the proposed photosensor with heterodielectric gate. The performance of the proposed photodetector is also investigated under the effect of uniform and gaussian interface traps. It is observed that Gaussian trap has larger effect on the performance of the photodetectorin respect of drain current, spectral sensitivity and SNR. Moreover, considering Gaussian trap, the impact of acceptor/donor traps are also analysed for optical metrics. Additionally, we investigate the impact of temperature on photosensor’s response. It has been perceived that increased temperature results in enhanced performance parameters indicating the suitability of the proposed photo TFET at elevated temperature. A status map comparing this work with state-of-the-art literature is also presented which indicates the proficiency of the proposed work.

Photovoltaic (PV) system output is critically dependent on the accuracy of sun-alignment control under real outdoor conditions. This paper presents the design and experimental characterization of an instrumented dual-axis solar tracking system for sustainable energy applications. The architecture integrates a differential sensing layer comprising a Light-Dependent Resistor (LDR) matrix with an ESP32-based embedded platform for real-time autonomous orientation control. A key instrumentation contribution is the implementation of a software-defined deadband threshold that filters stochastic environmental noise and suppresses mechanical oscillations during signal acquisition - distinguishing this work from prior LDR-based tracker implementations that address sensing and actuation in isolation. A comparative experimental analysis against a singleaxis configuration under identical clear-sky conditions yielded a peak power of 11.80 W and a gross daily energy yield of 74.91 Wh. After deducting actuator parasitic losses (1.65 Wh), the net daily yield is 73.26 Wh - a 48.0% improvement over the single-axis baseline of 49.48 Wh - with MPP identified at 5.44 V. The results validate that software-defined noise rejection combined with systematic instrumentation provides a scalable, low-cost solution for enhanced solar energy harvesting in sustainable e-mobility charging infrastructures.

The increasing integration of renewable energy sources and advanced communication technologies is transforming conventional power systems into smart grids. However, the stochastic and dynamic nature of energy generation and demand introduces significant challenges in achieving efficient, reliable, and cost-effective energy distribution. Traditional optimization techniques, such as classical Particle Swarm Optimization (PSO), often suffer from premature convergence and limited exploration capability in complex, non-linear environments. This paper proposes a Quantum-Inspired Particle Swarm Optimization (QPSO) approach for smart grid energy distribution. The method leverages probabilistic position updates and mean best guidance to enhance global search capability and avoid local optima. A multi-objective optimization framework is developed to minimize operational cost, reduce transmission losses, and improve load balancing while satisfying system constraints. The proposed model is evaluated on standard test systems and compared with classical PSO. Results demonstrate improved convergence speed, better solution quality, and enhanced robustness under dynamic grid conditions. The findings highlight the effectiveness of QPSO for real-time smart grid optimization and its potential for largescale energy management applications.