Velagapudi Ramakrishna Siddhartha Engineering College

About: Velagapudi Ramakrishna Siddhartha Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 1307 authors who have published 1155 publications receiving 6163 citations.

Optimization Of Process Parameters In 3d Printing-Fused Deposition Modeling Using Taguchi Method

Abstract: 3D printing or Rapid Prototyping (RP) or Additive Manufacturing (AM) is an innovative manufacturingmethod that is mostly used for prototypes in various Industries such as Aerospace, Defense, Medical, Automotive, etc. One of the worthy and widely used 3 D printing techniques is Fused Deposition Modeling (FDM), which involves adding melted material layer by layer. FDM has several benefits over other manufacturing methods. While setting the 3 D printing options, we have to take several parameters into account, such as speed, layer thickness and infill density, etc. The present experimental work illustrates the performance of ABS built parts fabricated by AION 500 make FDM machine. The quality of parts produced by FDM process mainly depends on the selection of process parameters. The present research work consists of process parameters such as layer thickness, infill density and speed of deposition, etc. This study aims to find effects of process parameters on different performance parameters i.e., mechanical considerations (impact strength), build time, surface roughness in a systematic manner with less number of experimental runs. Taguchi design of experiment (DOE) approach has been used to save cost and time of experimentation. Statistical significance of process parameters is analyzed using analysis of variance (ANOVA). The significant parameters for optimum results and optimal parameter setting has been suggested using S/N ratio.

ACS-fed wideband mirrored Z- and L-shaped triple band uniplanar antenna for WLAN applications

Abstract: A mirrored Z- and L-shaped printed uniplanar antenna has been proposed and developed in this paper for triple band applications. The developed geometry realizes simple radiating branches with 50 Ω Asymmetric Coplanar Strip (ACS) feedline and rectangular ground plane. It occupies a very compact area of 16×24 mm including the ground plane, the size of which is only 0.18×0.34λ, at the frequency of 2.3 GHz in free space. The simulated results obtained in the environment of CST Microwave Studio package have been compared with the measured results of antenna using the PNA N5222A Vector Network Analyzer, and a good agreement of results has been achieved. For −10 dB reflection coefficient the bandwidth is about 230 MHz in the band of 2.27–2.5 GHz, 200 MHz in the band of 3.65–3.85, and 1700 MHz in the band 5.2–6.9 GHz. The proposed uniplanar antenna is not only compact in size, but also has wide bandwidths that cover the Long Term Evolution (LTE), Wireless Broadband (WiBro), Worldwide Interoperability for Microwave Access (WiMAX), Wireless Local Area Network (WLAN) and Industrial Scientific and Medical radio band (ISM) applications.

Intelligent system for leaf disease detection using capsule networks for horticulture

Abstract: Horticulture crops take a crucial part of the Indian economy by creating employment, supplying raw materials to different food processing industries. Mangoes are one of the major crops in horticulture. General Infections in Mango trees are common by various climatic and fungal infections, which became a cause for reducing the quality and quantity of the mangos. The most common diseases with bacterial infection are anthracnose and Powdery Mildew. In recent years, it has been perceived that different variants of deep learning architectures are proposed for detecting and classifying the problems in the agricultural domain. The Convolutional Neural Network (CNN) based architectures have performed amazingly well for disease detection in plants but at the same time lacks rotational or spatial invariance. A relatively new neural organization called Capsule Network (CapsNet) addresses these limitations of CNN architectures. Hence, in this work, a variant of CapsNet called Multilevel CapsNet is introduced to characterize the mango leaves tainted by the anthracnose and powdery mildew diseases. The proposed architecture of this work is validated on a dataset of mango leaves collected in the natural environment. The dataset comprises both healthy and contaminated leaf pictures. The test results approved the undeniable level of exactness of the proposed framework for the characterization of mango leaf diseases with an accuracy of 98.5%. The outcomes conceive the higher-order precision of the proposed Multi-level CapsNet model when contrasted with the other classification algorithms such as Support Vector Machine (SVM) and CNNs.

Circuit and Architectural Co-design for Reliable Adder Cells with Steep Slope Tunnel Transistors for Energy Efficient Computing

Abstract: Tunnel FETs (TFETs) as steep slope devices have attracted much attention for designing energy efficient digital systems at scaled supply voltages. In this paper, we propose a circuit/architectural co-design approach for designing reliable and energy efficient adder cells for new computing platforms at supply voltages as low as 0.1V. At circuit level, widely used XOR gates such as 6T and 8T designs are explored and at the architectural level, adder cells with static CMOS like design (28T), tarnsmission gate design (24T), XOR based design (22T and 18T), and MUX based design (MBFA-22T) have been considered. The performance of all TFET designs have been benchmarked with 20nm double gate Si Fin FET technology. TFET designs have lower energy and energy delay product (EDP) due to TFET's steep sub threshold slope characteristics at 0.1V. 18T design is more energy efficient with slight trade-off in logic swing (i.e., robustness) and 22T and 28T designs are more robust and optimal energy efficient options.

Comparative analysis of propagation models in LTE networks with spline interpolation

Abstract: This paper deals with the radio propagation models predominantly used for the 4th Generation (4G) of cellular networks generally known as Long Term Evolution (LTE). It is necessary to study the radio wave propagation models at the development level of any wireless communication network or system. A comparative analysis is made among various radio propagation prediction models to assess the appropriate prediction model which can be helpful for LTE networks in a particular environment. In the analysis part; the mean, standard deviation and root mean square value are computed. In the evaluation Free space model predicts the minimum path loss and SUI model predicts the more path loss for the given values of frequency, base station antenna heights, and Mobile equipment antenna heights and transmitted power. Ericsson, winner II, Cost-231 and ECC models are showing better results when compared with the practical data obtained in the NCR region Delhi (INDIA). Among these models Ericsson model is showing least RMSE and standard deviation. From the analysis carried out in this paper, it is observed that the Ericsson path loss model is the best model for NCR region Delhi (INDIA). To acquire more accurate results in the existed Ericsson model some modifications are given using statistical measures.