The Discovery of Agriculture is the first big step towards civilized life, advancement of agricultural tools is the basic trend of agricultural improvement. Now the qualitative approach of this project is to develop a system which minimizes the working cost and also reduces the time for digging operation and seed sowing operation by utilizing solar energy to run the agribot. In this machine, solar panel is used to capture solar energy and then it is converted into electrical energy which is used to charge battery, which then gives the necessary power to a shunt wound DC motor. Ultrasonic Sensor and Digital Compass Sensor are used with the help of Wi-Fi interface operated on Android Application to manoeuvre robot in the field. This brings down labour dependency. Seed sowing and digging robot will move on various ground contours and performs digging, sowing the seed and covers the ground by closing it. The paper spells out the complete installation of the agribot including hardware and software facet.

Automated seed sowing agribot using arduino

World Report on Road Traffic Injury Prevention: a critique

BACKGROUND Commercially-made low-cost electroencephalography (EEG) devices have become increasingly available over the last decade. One of these devices, Emotiv EPOC, is currently used in a wide variety of settings, including brain-computer interface (BCI) and cognitive neuroscience research. PURPOSE The aim of this study was to chart peer-reviewed reports of Emotiv EPOC projects to provide an informed summary on the use of this device for scientific purposes. METHODS We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following electronic databases: PsychINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, clinical, signal processing, experimental research, and validation) and location of use (as indexed by the first author’s address). RESULTS We identified 382 relevant studies. The top five publishing countries were the United States (n = 35), India (n = 25), China (n = 20), Poland (n = 17), and Pakistan (n = 17). The top five publishing cities were Islamabad (n = 11), Singapore (n = 10), Cairo, Sydney, and Bandung (n = 7 each). Most of these studies used Emotiv EPOC for BCI purposes (n = 277), followed by experimental research (n = 51). Thirty-one studies were aimed at validating EPOC as an EEG device and a handful of studies used EPOC for improving EEG signal processing (n = 12) or for clinical purposes (n = 11). CONCLUSIONS In its first 10 years, Emotiv EPOC has been used around the world in diverse applications, from control of robotic limbs and wheelchairs to user authentication in security systems to identification of emotional states. Given the widespread use and breadth of applications, it is clear that researchers are embracing this technology.

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10 years of EPOC: A scoping review of Emotiv’s portable EEG device

A relative simple approach based on the computation of the area of a parametric curve produced by the 2D space representation of a set of parametric experimental functions defined by the signals of only two active EEG electrodes of a low cost neuroheadset (Emotiv EPOC) is proposed on this paper for the fast recognition of eye winks activity as control commands. This approach together with the use of the signals from the gyroscope available in the EPOC device, allowed the development of a Head-Computer Interface that enables complete interaction with a robotic arm. The results obtained indicate that the proposed approach is effective for detecting the eye-wink commands with a good rate of accuracy (over 93%).

Low complexity approach for controlling a robotic arm using the Emotiv EPOC headset

The entire floor cleaning robot is divided into several parts, namely consisting of an Ultrasonic Sensor, Motor Shield L298, Arduino Uno microcontroller, Servo, and Dc Motor. This tool works when the Arduino Uno microcontroller processes the ultrasonic sensor as a distance detector and a DC motor as a robot driver, then the DC motor is driven by the Motor Shield L298. When an ultrasonic sensor detects a barrier in front of it, the robot will automatically look for a direction that is not a barrier to the floor cleaning robot. The distance value on the sensor has been determined, that is, when the distance read by the ultrasonic sensor is below 15 cm. The results of testing the value of the ultrasonic sensor distance found different conditions that occur. In a distance of> 15 cm, the condition of the prototype cleaning robot for the road floor cleaning is obtained, while the distance <15 cm, the condition for the prototype of the street floor cleaning robot has stopped.

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Automatic Floor Cleaning Robot Using Arduino and Ultrasonic Sensor

The ability to acquire Electroencephalogram (EEG) signals from the brain has led to the development of Brain Computer Interfaces (BCI), which capture signals generated by the physical processes in the brain and use them to control external devices. In this paper, we establish an application to control a robot on the Arduino platform by the use of a BCI system, which does not require training for individual users. We present the design and development of a BCI processing pipeline built on open-source platforms using the Emotiv EEG headset. Our system achieves around 96% accuracy using computationally inexpensive feature extraction and classification techniques, namely, band power and Support Vector Machines (SVM). We are also able to guide a robot's movement efficiently using multiple intents.

Controlling an arduino robot using Brain Computer Interface

In urban areas, local commuting is mostly done using automobiles comprising of cars and motor cycles. With complex road networks, it becomes essential to use a device (smart phone or tablet) for viewing routes on the Global Position System (GPS) to reach the destination. However repeated looking at the device while driving becomes risky and cumbersome for safety reasons. Encountering this problem, this project deals with simplifying directions to the destination along with notifications such as missed calls, text messages and weather updates on a small part of the screen of a car's wind shield with the use of a mini inexpensive projector. This paper discusses how it was achieved through acquiring images of navigation, applying digital image processing using Matrix Laboratory (MATLAB) and increasing its speed with the help of Compute Unified Device Architecture (CUDA).

Special projection interfacing device for enhanced routing- S.P.I.D.E.R.

A Brain Computer Interface (BCI) is developed to navigate a micro-controller based robot using Emotiv sensors. The BCI system has a pipeline of 5 stages- signal acquisition, pre-processing, feature extraction, classification and CUDA inter- facing. It shall aid in serving a prototype for physical movement of neurological patients who are unable to control or operate on their muscular movements. All stages of the pipeline are designed to process bodily actions like eye blinks to command navigation of the robot. This prototype works on features learning and classification centric techniques using support vector machine. The suggested pipeline, ensures successful navigation of a robot in four directions in real time with accuracy of 93 percent.

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Parth Gargava

Papers

Controlling an arduino robot using Brain Computer Interface

Special projection interfacing device for enhanced routing- S.P.I.D.E.R.

Brain Computer Interface for Micro-controller Driven Robot Based on Emotiv Sensors