GRAPH BASESD WORD SENSE DISAMBIGUATION FOR CLINICAL ABBREVIATIONS USING APACHE SPARK

Identification of the correct sense for an ambiguous word is one of the major challenges for language processing in all domains. Word Sense Disambiguation is the task of identifying the correct sense of an ambiguous word by referencing the surrounding context of the word. Similar to the narrative documents, clinical documents suffer from ambiguity issues that impact automatic extraction of correct sense from the document. In this project, we propose a graph-based solution based on an algorithm originally implemented by Osmar R. Zaine et al. for word sense disambiguation specifically focusing on clinical text. The algorithm makes use of proposed UMLS Metathesaurus as its source of knowledge. As an enhancement to the existing implementation of the algorithm, this project uses Apache Spark - A Big Data Technology for cluster based distributed processing and performance optimization

An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel

Computing the distinct features from input data, before the classification, is a part of complexity to the methods of Automatic Modulation Classification (AMC) which deals with modulation classification was a pattern recognition problem. Although the algorithms that focus on MultiLevel Quadrature Amplitude Modulation (M-QAM) which underneath different channel scenarios was well detailed. A search of the literature revealed indicates that few studies were done on the classification of high order M-QAM modulation schemes like128-QAM, 256-QAM, 512-QAM and1024-QAM. This work is focusing on the investigation of the powerful capability of the natural logarithmic properties and the possibility of extracting Higher-Order Cumulant's (HOC) features from input data received raw. The HOC signals were extracted under Additive White Gaussian Noise (AWGN) channel with four effective parameters which were defined to distinguished the types of modulation from the set; 4-QAM~1024-QAM. This approach makes the recognizer more intelligent and improves the success rate of classification. From simulation results, which was achieved under statistical models for noisy channels, manifest that recognized algorithm executes was recognizing in M-QAM, furthermore, most results were promising and showed that the logarithmic classifier works well over both AWGN and different fading channels, as well as it can achieve a reliable recognition rate even at a lower signal-to-noise ratio (less than zero), it can be considered as an Integrated Automatic Modulation Classification (AMC) system in order to identify high order of M-QAM signals that applied a unique logarithmic classifier, to represents higher versatility, hence it has a superior performance via all previous works in automatic modulation identification systemComment: 18 page

An Algorithm for On-Line Digital Image Processing for Assisting Automatic Focusing and Astigmatism Correction in Electron Microscopy

An algorithm for processing a diffractogram of an electron image input by an on-line system has been improved, with the aid of projection theory, so as to achieve real time operation. This algorithm can, therefore, automatically focus in scanning electron microscope, and correct for the astigmatic aberration of both transmission and scanning electron microscopes by analyzing the spectrum computed from the diffractogram. Experimental results show that this algorithm is effective for detecting the amount of defocus and astigmatism in both transmission and scanning electron microscopes

Design and control of laser micromachining workstation

The production process of miniature devices and microsystems requires the utilization of non-conventional micromachining techniques. In the past few decades laser micromachining has became micro-manufacturing technique of choice for many industrial and research applications. This paper discusses the design of motion control system for a laser micromachining workstation with particulars about automatic focusing and control of work platform used in the workstation. The automatic focusing is solved in a sliding mode optimization framework and preview controller is used to control the motion platform. Experimental results of both motion control and actual laser micromachining are presented

Automatic Lesser Kestrel’s Gender Identification using Video Processing

Traditionally, animal surveillance is a common task for biologists. However, this task is often accompanied by the inspection of huge amounts of video. In this sense, this paper proposes an automatic video processing algorithm to identify the gender of a kestrel species. It is based on optical flow and texture analysis. This algorithm makes it possible to identify the important information and therefore, minimizing the analysis time for biologists. Finally, to validate this algorithm, it has been tested against a set of videos, getting good classification results.Junta de Andalucía P10-TIC-570