Automated solar radio burst detection on radio spectrum: a review of techniques in image processing

The information of solar atmosphere was obtained after investigating the recording radiation of the space mission. With technology growing recently, a lot of solar radio receiver was introduced to monitor the solar radio activity on the ground with high efficiency. It is recorded in every second for 24 hours per day. A massive of solar radio spectra data produced every day that makes it impossible to identify, whether the data contain burst or not. By doing manual detection, human effort and error become the issues when the solar astronomer needs the fast and accurate result. Recently, the success of various techniques in image processing to identify solar radio burst automatically was presented. This paper reviews previous technique in image processing. This discussion will help the solar astronomer to find the best technique in pre-processing before moving into the next stage for detection of solar radio burst.Keywords: monitoring solar activity; automated solar radio burst detection; image processing; techniqu

Fast, scalable, Bayesian spike identification for multi-electrode arrays

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human intervention is minimized and streamlined via a graphical interface. We illustrate our method on data from a mammalian retina preparation and document its performance on simulated data consisting of spikes added to experimentally measured background noise. The algorithm is highly accurate

A Machine Vision Algorithm Combining Adaptive Segmentation and Shape Analysis for Orange Fruit Detection

 Over the last several years there has been a renewed interest in the automation of harvesting of fruits and vegetables. The two major challenges in the automation of harvesting are the recognition of the fruit and its detachment from the tree. This paper deals with fruit recognition and it presents the development of a machine vision algorithm for the recognition of orange fruits. The algorithm consists of segmentation, region labeling, size filtering, perimeter extraction and perimeter-based detection. In the segmentation of the fruit, the orange was enhanced by using the red chromaticity coefficient which enabled adaptive segmentation under variable outdoor illumination. The algorithm also included detection of fruits which are in clusters by using shape analysis techniques. Evaluation of the algorithm included images taken inside the canopy (varying lighting condition) and on the canopy surface. Results showed that more than 90% of the fruits visually recognized in the images were detected in the 110 images tested with a false detection rate of 4%. The proposed segmentation was able to deal with varying lighting condition and the perimeter-based detection method proved to be effective in detecting fruits in clusters. The development of this algorithm with its capability of detecting fruits in varying lighting condition and occlusion would enhance the overall performance of robotic fruit harvesting