The Gayatri Vidya Parishad College of Engineering (Autonomous) or GVPCE is a private college established in the year 1996. The educational trust, Gayatri Vidya Parishad (GVP) is formed, managed and promoted by academicians and technocrats in Visakhapatnam, India. The college offers instruction to 1200 undergraduate students in seven branches of Engineering: Chemical, Civil, Computer science and Engineering, Electronics and communication engineering, Electrical engineering, Mechanical engineering and Information Technology. The institution also offers Master of Computer Applications program affiliated to Jawaharlal Nehru Technological University, Kakinada.
Effective and low-power data processing is important in modern Convolutional Neural Network (CNN) accelerators, where the key computational activity is multiple operations. Approximate computing presents an option by effectively reduces hardware complexity while maintaining sufficient accuracy. This study proposes two novel Recursive Leading One-bit-Based Approximate (RLOBA) multiplier architectures that significantly improve both the performance and accuracy metrics. The proposed design incorporates an Exact Multiplier (EM) to compute higher-order n/2-bit products, identify the Leading One-Bit (LOB) positions of both n/2-bit n segments, and produce the result through relatively simple addition, subtraction, and shift operations. All proposed and existing Approximate Multipliers (AMs) are implemented using Verilog HDL for 8–32 bit operand sizes, simulated in Vivado and MATLAB, and synthesized with the Cadence RTL Compiler. From the simulation results, the average improvements for the proposed RLOBA multiplier designs demonstrate significant reductions in delays, area, power, PDP, and EDP by 59.3
Present industrial practices are essential to be directed towards sustainable manufacturing in order to minimize carbon emissions with improved energy efficiency. One promising approach towards machining sector could be attained by engineering ecofriendly lubricants by incorporating nanoparticles to reduce friction and thermal induced energy losses. In this study, sesameoil MoS2 based nano lubricants were developed to improve the machinability of Al-TiC in-situ metal matrix composites (MMC). These MMCs are typically difficult to machine due to the presence of hard reinforcing phases. The effect of nanoparticle solid volume fraction on surface energy interactions was investigated experimentally by measuring the contact angle of the nanolubricants using the sessile drop method. Surface thermodynamic analysis was then applied to estimate the surface energy components of the nanolubricants at different volume fractions. To account for the influence of surface roughness on wettability, Wenzel's relation was used to determine the intrinsic contact angle. Additionally, for different solid volume fraction of nano lubricants, the machining tests were performed on Al-TiC MMC to analyze the impact of surface energy on a set of machining parameters. The tests were conducted under the conditions: dry machining, pure sesame oil, and different concentration of nano lubricants based on minimum quantity lubrication (MQL). The results showed that sesame oil containing 0.4 vol% MoS2 nanoparticles produced the most promising performance based on surface roughness, cutting temperature, and tool life.
Ethephon an organophosphorus compound is widely used as a plant growth regulator in agriculture for early ripening of fruits and vegetables. The excessive use of artificial ripeners has raised serious concerns, as residual chemicals left on fruits enter the environment through household wastewater and runoff from agricultural fields. In this work, an effective and selective mixed micellar cloud point extraction has been developed for the extraction and preconcentration of ethephon residues from contaminated water using surfactants Triton X-114 (TX-114) and cetyl trimethyl ammonium bromide (CTAB). The influence of analytical parameters like pH, surfactant concentration (CTAB and TX-114), concentration of salting out agent (Na2SO4) equilibrium time and temperature were studied. Linearity was obeyed in the range of 0.164-3.294 ng mL–1. The developed method was successfully applied to water samples collected from tomato cultivation fields near Tuni, Andhra Pradesh.
Abstract The tandem photovoltaics design can be used as a viable way of overcoming the Shockley–Queisser limit of photovoltaic cells via optimization of the light absorption spectrum and minimizing thermalization losses. This work presents the numerical analysis of a novel design of an alkali-based double perovskite tandem solar cell (ADPTSC), comprising lead-free absorbers, K 2 ScCuCl 6 and Cs 2 ScCuCl 6 , utilizing the SCAPS-1D software in standard AM 1.5G illumination conditions (1000 W m − 2 ). The ADPTSC structure is built of the FTO/ZnSe electron transport layer, alkali-based double perovskite absorbing layers, and SrCu 2 O 2 hole transport layer. Series-connected (2 T) and parallel-connected (4 T equivalent electrical configuration) tandem structures are considered in order to assess the effect of electrical configuration on the photovoltaic performance of this novel device. Besides the assessment of current density versus voltage characteristics of both series and parallel connected devices, other properties such as band alignment, quantum efficiency, defect density, temperature stability, impedance spectroscopy, and metals’ work function effect are also studied to determine the main limiting factors to performance enhancement. The series-connected structure showed promising performance parameters of an open-circuit voltage of 1.82 V, short-circuit current density of 21.65 mA cm − 2 , fill factor of 85.86%, and power conversion efficiency of 39.43%. Parallel-connected structure provided slightly lower open-circuit voltage, 0.79 V. However, it exhibited significantly higher short-circuit current density of 50.39 mA cm − 2 , leading to a fill factor of 80.55% and improved efficiency of 40.00%. It was found that parallel connection allows to reduce current mismatch problems and series connection enables voltage addition advantage.
In vehicle-to-vehicle (V2V) wireless communication systems, reliable data transmission is a challenging task due to the dynamic nature of wireless channels. Adaptive modulation and coding (AMC) has emerged as a promising solution to mitigate this problem. We propose a convolutional neural network (CNN)-based AMC framework in V2V wireless communication system to maintain reliable signal transmission. A pre-trained CNN model, MobileNetV2 was used to classify modulation and coding schemes (MCSs). The proposed model achieved an accuracy of 98.04%. The model was built using a dataset with eight MCSs based on the IEEE 802.11p Dedicated Short-Range Communications (DSRC) V2V physical layer. For each signalto-noise ratio (SNR) ranging from 0-30 dB, multiple simulations were conducted, and the top 1,012 samples satisfying $\text{PER} \leq 2 \%$ and achieving maximum throughput were selected, resulting in a dataset comprising 250,976 data points across eight MCSs. The developed CNN model is integrated into the proposed AMC model to select the most suitable MCS. Furthermore, the relationship between PER and throughput across varying SNR levels was analyzed to evaluate the performance of the proposed AMC model.
