Qualification: 
MCA, M.Tech
vv_sajithvariyar@cb.amrita.edu

Sajith Variyar V. V.  currently serves as Research Associate at Amrita Center for Computational Engineering and Networking (CEN), Coimbatore Campus. He pursued his M.Tech in Computational Engineering and Networking (CEN).

Invited Talks 

  1. An invited talk on ED RASP PI V1.0 at Computer Science and Engineering, Royal College of Engineering and Technology from July-31 to August 1, 2015. 
  2. An invited talk on IIIC Programme on “Arduino and Raspberry PI” at the Department of Information Technology, Government  Engineering College ,Sreekrishnapuram  from March 13-14, 2015.
  3. An invited talk on “Pi Your Day” at the two-day Workshop on Raspberry Pi by Department of Electronics and Communication Engineering  Rajagiri School of Engineering & Technology, Kochi.

Guided M. Tech. Dissertations

YEAR TOPIC
2017 Design and fabrication of robotic systems : converting a conventional Car to a driverless car
2017 Vision based closed loop PID controller design and implementation for autonomous car
2017 Obstacle classification and detection for vision based navigation for autonomous driving
2017 Steering angle estimation for autonomous vehicle
2017 Effective utilization and analysis of ROS on embedded platform for implementing autonomous car vision and navigation modules
2017 GPS based navigation system for autonomous car
2018 Obstacle detection and distance estimation for autonomous electric vehicle using stereo Vision and DNN

Workshop Conducted 

  1. Two day workshop on Embedded system and IOT  in the month of June 2015 at Amrita Vishwa Vidyapeetham, Ettimadai.

Poster Presented

  1. Paper Title: Lessons Learned from Imaging Targets on Top of Tree Canopies
    Sajith Variyar, V.V., Sowmya, V., Gopalakrishnan, E.A., and Soman K.P., Amrita Vishwa Vidyapeetham, Coimbatore, India; Ramesh Sivanpillai and Gregory Brown, University of Wyoming, WY

Publications

Publication Type: Conference Paper

Year of Publication Title

2019

V. V. Sajith Variyar, Dr. E. A. Gopalakrishnan, Sowmya V., and Dr. Soman K. P., “A complex network approach for plant growth analysis using images”, in Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, Melmaruvathur; India, 2019.[Abstract]


The process of plant growth monitoring and analysis changed its perspective from the way it was. The recent farming practices demand vision based sensors for monitoring and analyses of the plant growth characteristics from images acquired by satellites and Unmanned Aerial Vehicles (UAV's). The advanced plant phenotyping systems are equipped with digital cameras to report the plant growth on a daily basis. The time determined images from plant monitoring system require a better computational representation to understand and study the plant life cycle, plant to plant interaction and correlations between plants with-in the community. This paper presents a new and yet simple approach towards plant growth analysis and its correlations in community by applying the theory of complex network on visible images from a plant phenotyping system. The method is highly promising in the area of precision agriculture when we have large area to monitor. © 2019 IEEE.

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2019

S. Emani, Dr. Soman K. P., Variyar, V. V. Sajith, and Adarsh, S., “Obstacle Detection and Distance Estimation for Autonomous Electric Vehicle Using Stereo Vision and DNN”, in ICSCSP , 2019.[Abstract]


Automation—replacement of humans with technology—is everywhere. It is going to become far more widespread, as industries are continuing to adapt to new technologies and are trying to find novel ways to save time, money, and effort. Automation in automobiles aims at replacing human intervention during the run time of vehicle by perceiving the environment around automobile in real time. This can be achieved in multitude of ways such as using passive sensors like camera and applying vision algorithms on their data or using active sensors like RADAR, LIDAR, time of flight (TOF). Active sensors are costly and not suitable for use in academic and research purposes. Since we have advanced computational platforms and optimized vision algorithms, we can make use of low-cost vision sensors to capture images in real time and map the surroundings of an automobile. In this paper, we tried to implement stereo vision on autonomous electric vehicle for obstacle detection and distance estimation.

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2018

Vijay Krishna Menon, Variyar, V. V. Sajith, Dr. Soman K. P., Gopalakrishnan, E. A., Kottayil, S. K., Almas, M. Shoaib, and Nordström, L., “A Spark™ Based Client for Synchrophasor Data Stream Processing”, in 2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE), Phuket, Thailand, 2018.[Abstract]


The SCADA based monitoring systems, having a very low sampling of one reading per 2-4 seconds is known to produce roughly 4.3 Tera Bytes (TiBs) of data annually. With synchrophasor technology, this will go up at least 100 times more as the rate of streaming is as high as 50/100 (60/120) Hz. Phasor data concentrators (PDCs) transmit byte streams encapsulating a comprehensive list of power system parameter including multiple phasor measurements, instantaneous frequency estimates, rate of change of frequency and several analog and digital quantities; this high volume and velocity of data makes it truly ‘Big Data’. This helps in making the power grid a lot more observable, enabling real-time monitoring of crucial grid events such as voltage stability, grid stress and transient oscillations. Synchrophasor technology uses the IEEE C37.118.2-2011™ Phasor Measurement Unit (PMU) / PDC communication protocol for data exchange which has no direct interface with any contemporary big data stream APIs or protocols. In this paper we propose a streaming interface in Apache Spark™, a popular big data platform, using Scala programming language, implementing a complete IEEE C37.118.2-2011™ client inside a stream receiver so that we can effortlessly receive synchrophasor data directly to Spark™ applications for real-time processing and archiving.

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2017

C. K. Chandni, Variyar, V. V. Sajith, and Guruvayurappan, K., “Vision Based Closed Loop PID Controller Design and Implementation for Autonomous Car”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017.[Abstract]


Autonomous cars are one of the fascinating technological trends in the present automotive industry. These cars enticed substantial attention in industry as well as in academia. It aims at safety regulations along with the emerging customary needs, traffic safety and transportation efficiency. In general, autonomous car integrates environmental perception, control and automatic driving modules for path tracking and effective navigation. The automatic driving module maneuvers the corresponding actuators according to the requisite of planning module, and ensure that the vehicle moves at the desired speed profile and path trajectory. This paper propounds closed loop PID control architecture for accurate steering, acceleration and braking control of an autonomous self-driving car designed from scratch. The control signals are triggered, when the perception module detects the lane and obstacle inputs. Then the actuators of the vehicle are controlled. Steering control subsystem constitutes a PWM motor, driving a steering linkage that eventually steers the front axle of the car. The steering subsystem receives the PWM input from the PID controller and outputs the current wheel angle. The closed loop PID controlling enables accurate steering control and hence efficient path tracking.

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2017

V. S. Dev, Variyar, V. V. Sajith, and Dr. Soman K. P., “Steering Angle Estimation for Autonomous Vehicle”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017.[Abstract]


The modern era has witnessed huge spike in the number of road accidents and fatalities. The common origin of traffic accidents is driver error. This is not going to change anytime soon thanks to the immense number of cell-phone users, in-car entertainment audio and video systems, and finally abundant traffic. There's one death every four minutes due to road accident in India. Such severe situation asks for a better scope that can propitiate the burden on the human drivers in certain critical instants. This demands a computationally efficient and fast responsive decision-making methods. But the course of autonomous vehicles is still in its early stages with companies still booming to build a fully autonomous vehicle. Volvo, Google, Tesla, BMW and more are still in the autonomous car research area. The mechanization and industrial science related to this field will mature over time, it is only a matter of time where the capability of driverless cars will outpace the outlook of any vehicle in the city. This paper exemplifies a novel approach to figure out the steering angle required to control the vehicle in the center of the road.

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2017

A. G. Babu, Guruvayoorappan, K., Variyar, V. V. Sajith, and Soman, K. P., “Design and Fabrication of Robotic Systems: Converting a Conventional Car to a Driverless Car”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017.[Abstract]


Drastic changes in the robotics and intelligent controls brought a radical change in Automotive engineering sector that leads to the driverless vehicles in this new era. For these vehicles to safely run in today's traffic and in harsh environments, a number of problems in vision, navigation, and control have to be solved. Driverless cars use sensors to detect the environment, computers to process the data and actuators for mechanical systems. To adopt the self driving car technology to current academic and research , we need a cost efficient and affordable mechanisms. In this scenario we need a system that can incorporate existing vehicles and convert that driverless cars which will reach to academicians and research fields. Considering the possibilities and implementation of driverless vehicles in Indian scenario we need an agile mechanical design to be included on existing vehicles. This paper proposes a portable mechanical design that can be fabricated and fit into existing vehicles and can be used as a platform to develop an autonomous car. Conventional cars can be altered to be a driverless car by using different actuators. Popularly motors are used as actuators in the automation of the vehicle. A pneumatic system is designed to automate the intended platform apart from the motors. The mechanical structure is an essential part of an autonomous car and is to be altered and designed in such a way that it is dynamically unwavering. Further improvements will make the system capable of being commercially produced.

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2017

S. Jose, Variyar, V. V. Sajith, and Soman, K. P., “Effective Utilization and Analysis of Ros on Embedded Platform for Implementing Autonomous Car Vision and Navigation Modules”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017.[Abstract]


In the last few decades, the mobile robotic field has witnessed an incredible progress in the field of development. It finds application in different areas like space exploration, military, intelligent transportation, medical areas, agriculture and in the field of entertainment. Hence, the need for a practical integration tool is necessary in the field of robotics research. Nowadays, the main problems relating to different engineering applications are the high computational time requirement and the power related issues. To use the robots in real-Time applications, the output should obtain within a fraction of seconds. We need real-Time robots having the capability to respond within a limited amount of time. An autonomous car, which is also called a driver-less car is a vehicle, that is skilled of sensing its surroundings and can navigate without any human input. Self-driving cars have become a cutting-edge research topic in the robotics automation domain. The complexity associated with modelling, computation, implementation of various functionality hindered the development of self-driving cars in academia and research. The ROS gave an easy platform, where we can integrate and test various modules of robotics and automation. This paper deals with the effective utilization of ROS in an embedded platform to implement self-driving cars tasks like vision and navigation.

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2017

N. Deepika and Variyar, V. V. Sajith, “Obstacle Classification and Detection for Vision Based Navigation for Autonomous Driving”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017.[Abstract]


With the rising trend in research and development of autonomous vehicles, it is important to keep in mind the cost effectiveness of the system. The cost of high-end sensor technologies being astronomically expensive, the research opportunities are restricted to a select few of high-tech companies and research laboratories such as Google, Tesla, Ford, and the likes of it. Hence our main focus is to develop an autonomous system suitable for academic and research purposes as well. This can be achieved by using available sensors such as the monocular cameras. The existing computer vision techniques along with the deep learning tools like Convolutional Neural Network (CNN) can together be used for developing a robust vision based autonomous driving system. The proposed method uses the SegNet encoder-decoder architecture for pixel-wise semantic segmentation of the video frame followed by an obstacle detection algorithm. The entire algorithm was implemented and tested on a mobile embedded platform of NVIDIA's Jetson TK1.

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2017

P. G. Parakkal and Variyar, V. V. Sajith, “GPS Based Navigation System for Autonomous Car”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017.[Abstract]


Navigation is an indispensable part of a mobile robot, especially the ability to self-navigate is of immense importance to an autonomous car. For this purpose, it is necessary for the car to know its current position as well as an area of a map it has to navigate. GPS provides the required position data and a compass gives the current heading. This data in conjunction with a list of way-points obtained from the map is used to calculate the required correction in heading. This is then used to obtain the steering angle to navigate the vehicle.

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Publication Type: Journal Article

Year of Publication Title

2016

S. Chandran, Variyar, V. V. Sajith, Prabhakar, T. V. Nidhin, and Dr. Soman K. P., “Aerial image classification using regularized least squares classifier”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 889-895, 2016.[Abstract]


The land cover classification and urban analysis of remotely sensed images has become a challenging problem, hence efficient classifiers are required in order to combat the problem of classifying the huge remote sensing aerial datasets. In this paper we have proposed the use of Random Kitchen Sink (RKS) algorithm and Regularized Least Squares (RLS) classifier for the classification of aerial image. The new machine learning algorithm RKS, primarily engages in mapping the feature data to a higher dimensional space and thereby generates random features. These randomized data are then adopted by RLS classifier for the classification task. It is observed that the randomization of the data reduces the computation time needed for training. The experiment is performed on five classes of the UC Merced Land Use Aerial Imagery Dataset. The efficiency of the proposed method is estimated by comparing the accuracy results with the conventional classifier namely, Support Vector Machine (SVM). Experimental result shows that the proposed method produces a high degree of classification accuracy i.e. 94.4%, when RBF kernel with LOO (Leave One Out) cross-validation was used, when compared to SVM. In this paper, statistical features show better precision and accuracy in classifying different set of classes, compared to textural features in both the classification approaches. Hence, better accuracies could be attained for multi class classification when compared to other classification technique like, SVM since, the random features reduces computation time and enhance the performance of kernel machines.

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2016

V. V. Sajith Variyar, Haridas, N., Aswathy, C., and Soman, K. P., “Pi doctor: A low cost aquaponics plant health monitoring system using infragram technology and raspberry Pi”, Advances in Intelligent Systems and Computing, vol. 397, pp. 909-917, 2016.[Abstract]


The technological and scientific advancement in the field of agriculture has opened a new era for design and development of modern devices for plant health monitoring. The analysis of various parameters, which affects the plant health such as soil temperature, moisture level and pH are easier with the use of advanced devices like Raspberry Pi and Arduino integrated with different types of sensors. The development of infragram technology has created new possibilities to capture infragram images, where both infrared and visible reflectance are obtained in a single image. The rationale of this paper is to monitor the health of a small scale aquaponics vegetation using Infragram technology and Raspberry Pi. The proposed experimental setup captures infragram images using a low cost modified web camera containing infra-blue filter. These images are post-processed to calculate normalized difference vegetation index (NDVI), which is a good indicator of photosynthetic activity in plants. The study also assesses and monitors the influence of various parameters in the aquaponics system such as nitrogen usage by plants and pH change in the system under different illumination conditions. The study shows that the change in pH and health condition of the plant due to the variation in photosynthesis are the major factors that affects the balance of nitrogen cycle in the aquaponics system. © Springer India 2016.

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