Sri Ramakrishna Engineering College

About: Sri Ramakrishna Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Control theory. The organization has 1030 authors who have published 843 publications receiving 3822 citations.

Hardware/Software Partitioning for Embedded Systems

Abstract: Current methods for designing embedded systems require specifying and designing hardware and software separately. Hardware/software partitioning is concerned with deciding which function is to be implemented in Hardware (HW) and Software (SW). This type of partitioning process is decided a priori to the design process and is adhered to as much as possible because any changes in this partition may necessitate extensive redesign. As partitioning is an NP hard problem, application of the exact methods tends to be quite slow for bigger dimensions of the problem. Heuristic/evolutionary methods have been proposed for partitioning problems. This chapter deals with multi-objective optimization of minimizing two objectives area and the execution time of the partition. To validate the efficiency of the algorithms, performance metrics are calculated. Experimental results for the HW/SW partition for mediabench and DSP benchmarks are tabulated and analyzed.

Genetic Grey Wolf Optimizer Based Channel Estimation in Wireless Communication System

Abstract: Various methods are available for channel estimation in the orthogonal frequency division multiplexing and orthogonal frequency and code division multiplexing (OFCDM) based wireless communication schemes. Along with this, the most utilized techniques are namely the minimum mean square error (MMSE) and least square (LS). The process of LS channel estimation method is simple but it occupies a very high mean square error. On the other hand, the performance of MMSE is better than LS in terms of SNR, though it shows high computational complexity. Compared to MMSE and LS based techniques, the combination of MMSE and LS techniques using evolutionary programming reduces the error significantly to receive exact signal. In this study, we propose a hybrid method namely GGWO that includes grey wolf optimization (GWO) and genetic algorithms (GA) for estimate the channel in MIMO–OFCDM schemes. At first, the best channel is estimated using GWO and afterwards, the MMSE and LS are hybridized through GA for calculating the best channel to decrease error. Overall, the GWO and GA contribute in fine tuning the obtained channel scheme so that the channel model is derived further to correlate with the ideal scheme. Our results demonstrate that the proposed scheme is superior to conventional MMSE and LS in terms of BER and SNR.

Soil Nutrient Analysis using Colorimetry Method

Abstract: In most parts of the world, agriculture is an important source of livelihood. This entails hard work, but it contributes to the nation’s food safety and health. The harvest majorly depends on soil fertility, moisture level and also the level of fertilizers used. Time and distance are the major factors that limits farmers in performing the laboratory analysis of nutrients. Due to this inconvenience caused, nutrient testing is not performed regularly, which results the soil to lose its fertility. Using the concept of Internet of Things (IoT) a system is proposed which adopts precision agriculture in order to overcome such drawbacks and to remotely monitor the soil characteristics like soil moisture and its nutrients. Awareness about the quantity of nutrients present in the soil is essential to ensure optimum usage of fertilizers and constant monitoring of irrigation is also required for better productivity. The sensors are integrated with arduino microcontroller and the values obtained from the sensors are transmitted to the cloud and displayed in a mobile application. The proposed IoT based software system has the intelligence to measure the moisture level and quantity of the nutrients using colorimetry which improves the quality of the soil and ensures optimum growth of the crop.

Design and analysis of SRAM cells for power reduction using low power techniques

Abstract: SRAM memory cell consists of many input signals like precharge, write enable, sense amplifier enable, read enable and row and column encoders. To develop a novel SRAM design, different transistor circuits are available. Normally SRAM cell uses conventional 4 transistor circuit in low power applications. In this thesis, instead of conventional circuit, 8 transistor (8T) and ten transistor(10T) designs are tried to improve the power efficiency under various temperature conditions. Initially 8T and 10T SRAM circuits are designed with write driver logic and the power is calculated for both static and dynamic conditions. Then, charge recycling logic is tried along with precharge logic with various temperature ranges. From the design, total power, static power, dynamic power, Transient time, transient delay and static current in 8T SRAM and 10T SRAM cell are calculated and compared. The 8T SRAM has the least transistor count and least area efficient, but speed of operation is somewhat reduced. Further, increase in the transistor count in 10T SRAM cell, however, makes area and delay large in room temperature. When temperature increases from a particular value, the 10T SRAM cell performs better than the 8T SRAM cell. This justifies the use of 10T SRAM cell for low power applications with varying temperature conditions. The proposed SRAM memory design can be implemented in any digital circuit.