A relative localisation system of a mobile robot
This paper describes a localisation system applied to vehicle displacements on irregular grounds and at moderate speed (about 1 m/s). It is composed of a gyrometer and a Doppler sensor, which give, by integration, the attitude and position of the vehicle supporting them, without contact with the ground. The precision of the obtained localisation is about 2% for ranges of about a hundred meters.
An experimental system for processing movement information of vehicle
This paper presents a proposed experimental system for processing movement information of vehicle. Using self-contained on-line computing processor which computes position and heading angle (azimuth), trajectory of location of vehicle has been successfully displayed by an experimental system implemented. Principles of self-contained computing system are as follows: accurate digital moving pick-ups are set on both sides of vehicle wheels, accumulation of both sides data which are picked periodically is for distance measuring and subtraction of both sides data is for heading computation, self-contained processor computes the location and heading angle, and also presents location data on X-Y recorder and digital displays. Coordinate location and heading computations are performed by electronic hard-ware mechanization. Methods for reduction of induced errors are discussed and experimentally demonstrated.
Application of SVM on satellite images to detect hotspots in Jharia coal field region of India
The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.