Rambabu Chunchu

EDUCATION

B.Tech, M.Tech., and Ph.D

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering, Control and Systems Engineering, Process Chemistry and Technology

Scopus Publications

Design of NULLMAC Protocol for Mobile Ad Hoc Network Using Adaptive Antenna Array
Mahendrakumar Subramaniam, Vanitha Krishnan, Chunchu Rambabu, Gokul Chandrasekaran, Neelam Sanjeev Kumar
Mobile Information Systems, 2023
Usually, omnidirectional radiation pattern antenna is used in the mobile ad hoc network (MANET) which causes neighbor node interference, consumes more power, and supports only limited range of transmission. To overcome these problems, smart antennas are used. A lot of medium access control (MAC) protocols are proposed using smart antennas. Existing works addressed various problems such as hidden terminal problem, hidden beam problem, deafness of nodes, and head of line blocking problem. However, certain factors including determination of weight vector and conveying it to the neighbor nodes for distortion-free transmission are not considered. In this study, nullifying MAC (NULLMAC) framework is proposed using an adaptive antenna array (AAA) for improving network performance in MANET. NULLMAC framework uses channel information for achieving high throughput and spatial reuse through integrated physical and MAC layer. Before the transfer of data packets, the receiver initially determines its weight vector and conveys it to the transmitter through control packets. Then, the transmitter computes its weight to nullify the dynamic receivers present in the neighborhood region to find the desired receiver. Beamformer weights are determined through channel coefficients between a transmitter-receiver pair to establish distortion-free transmission. Extensive simulations are performed using OPNET integrated with MATLAB. NULLMAC framework achieves 27.22% more throughput and 40.46% increase in signal-to-noise ratio.
A Traffic Density-Based Congestion Control Method for VANETs
Mahendrakumar Subramaniam, Chunchu Rambabu, Gokul Chandrasekaran, Neelam Sanjeev Kumar
Wireless Communications and Mobile Computing, 2022
This research presents a vehicle ID-based congestion aware message (CAM) for beacon signals on the vehicle environment. At the MAC protocol of the vehicle environment, enhanced vehicle ID-based analysis model is given first. With the automobile ID embedded in their separate CAMs, the model weights the randomized back-off numbers chosen by cars engaging in the back-off procedure. This leads to identifying a car ID-based randomized back-off code, which reduces the likelihood of a collision due to the identical back-off number. A traffic density based-congestion control algorithm (TDCCA) is suggested in this research. The revised mathematical approach surpasses previous work’s overall packet latency because just one-fourth of the congestion window is employed during the experiment. The research includes a congestion management method that adjusts the rate of CAM transmitted over the host controller to improve the efficiency of the model parameters. The method considers various circumstances, from nonsaturated to substantially saturated networks (in terms of congestion probability) and sparsely dispersed and teemed networks (in the form of vehicular intensity). The technique is run across various automobile ID-based back-off values for high-standard results analysis. The simulation outcomes in terms of packet delivery ratio, energy consumption, delay, success rate, and collision ensure the effectiveness of the TDCCA method. Even at high traffic densities, the automobile ID-based CAM following information method outperforms the typical fixed CAM frequency IEEE 802.11p, according to simulation findings for all back-off figures.
Future Energy Source for Remote IoT Systems using MEMS-based Piezoelectric Energy Harvesting Devices.
F. Fareeza, S. Krishna Veni, Chunchu Rambabu, Tigabu Zewude Yanore, P. Rajkumar
Journal of Physics Conference Series, 2021
Piezoelectric energy harvesting (PEH) device is an energy converter that will convert mechanical vibration energy into electrical energy. The energy converter is implemented using Micro-Electronic Mechanical System (MEMS). The vibration is extracted from the surroundings, and the extracted vibration is converted into electrical energy using PEH for low power sensors used in the IoT environment. PHE device will generate the maximum power when the vibration of the surrounding is exactly matched with the resonant frequency of the device. This paper presents two different PHE MEMS devices which will convert the vibration into electrical energy. The proposed device has two design materials; T shape resonant model is designed by arranging beams in multilayer and an ultra-violet resin seismic mass. There are four-layer formed together; the substrate first layer is built using polyethylene terephthalate (PET). The third layer is formed by using piezo-electric material; the second and fourth layers are build using aluminium and platinum electrode. In the model, two different types of piezoelectric materials are used to build the PEH device. Two types of material used in the devices are ZnO and PZT-5A. Rayleigh-Ritz and Macaulay methods are used to model the system for analysing the mechanical behaviour of the model and structural analysis for the better energy extraction using FEM. The proposed PHE device using ZnO and PZT-5A is generating power at the rate of 1.8 W and 1.35 W with a voltage rating of 545 and 45 mV, respectively. The Proposed PHE device is built for remote location low power IoT devices.
Automation of DMPS manufacturing by Using LabView and PLC
Fareeza F, Chunchu Rambabu, S. Krishnaveni, Abel Chernet Kabiso
International Journal of Electrical and Computer Engineering, 2018
<p>This Paper is to enable the Siemens (Programmable Logic Control) CPU 313-5A to communicate with the Lab VIEW and to control the process accuracy by image processing. The communication between CPU 313-5A and Lab VIEW is via OPC (OLE for Process Control).Process Accuracy is achieved with the use of Labview Image Processing and Gray Scale matching Pattern. Accuracy in the gray scale matching will purely depend on the calibration of the camera with respect to the corresponding image. The digital output from the labview is communicated to PLC via Ethernet Protocol for the industrial process control. With the use of Labview the dead time while using the normal image vision module in PLC can be minimized. Labview uses the gray scale matching technique which is more accurate than the normal image vision module used in PLC.</p>
Gesture-based wheelchair control for the physically challenged
International Journal of Applied Engineering Research, 2015
System identification techinques for non linear level process
International Journal of Applied Engineering Research, 2015
Image based approach for cognitive classification using eeg signals
Arpn Journal of Engineering and Applied Sciences, 2015
Reconfigurable filter based self paced artifacts removal scheme for neurologically extracted features
European Journal of Scientific Research, 2012