Automatic Data Processes

Well, the data process is created by the need to process the data more quickly and efficiently since the manual process that was carried out no longer met the expectations of people and companies, for this reason the data process arises automatic. In an automatic data process, the computer has different physical elements that allow it to carry out these same processes. That is, practically the speed of operation of the system is limited by human control. The entry by electromechanical type machines, as well as the exit, have a higher operating speed than the conventional one (until then this was achieved by tachy-typing). We can define data processing as the technique of converting data into information by any means, whether manual or automatic. Manual data processing is the technique of converting data into information using tools such as pencil, pens, typewriters, etc. The automatic data process within its objectives is the technique of converting data into information using appropriate methods, procedures and equipment for this purpose; such as interviews, surveys, computer. Automatic data processing came to renew the world, creating a special science for its study, such as computer science.

Intrinsic f0 and Sound Change: Evidence from Australian Languages

In this paper, a study is presented investigating two types of intrinsic f0 effects in sixteen Australian languages. Vowels are known to vary systematically in their mean f0 as a function of vowel height as well as voicing of the previous consonant, a property known as intrinsic f0. Vowel height has been known to have a positive correlation with f0, in which high vowels have a higher intrinsic f0 than low vowels on average. Differences in intrinsic f0 also vary systematically based on the voicing of a preceding stop, with voiceless stops correlating with higher intrinsic f0 and voiced stops with lower. Using automatic data processing methods on archival audio data, this study shows wide variation in the presence and robustness of intrinsic f0 effects in the Australian langauges investigated.

OBTAINING AND AUTOMATIC DATA PROCESSING FROM ELECTROMAGNETIC SCANNING OF ROAD PAVEMENTS WITH SUBSEQUENT VISUALIZATION

Рассмотрены вопросы получения и автоматизации обработки данных электромагнитного сканирования дорожной одежды с последующей визуализацией. В качестве инженерного оборудования для георадарного обследования использован георадар «ОКО». В результате сбора и анализа данных электромагнитного сканирования выявлены деструктивные участки дорожного покрытия. На основании изучения методов обработки, интерпретации визуализации данных георадарного сканирования принято решение о необходимости разработки специализированных средств автоматизации анализа таких данных при сканировании дорожных конструкций. Произведен анализ методов и средств, используемых для обработки данных радарограмм, рассмотрены структура файла хранения данных электромагнитного сканирования и доступные в программе «GeoScan32» средства обработки и конвертации данных. Предложена последовательность действий для реализации процедуры поиска трасс, характеризующихся недопустимым уровнем отклонения характеристик сигнала от средних значений The article discusses the issues of obtaining and automatic data processing from electromagnetic scanning of road pavements with subsequent visualization. Georadar "OKO" was used as engineering equipment for georadar survey. As a result of collecting and analyzing the electromagnetic scanning data, destructive sections of the road surface were identified. Based on the study of processing methods, interpretation and visualization of GPR scanning data, we decided that it is necessary to develop specialized tools for automating the analysis of such data when scanning road structures. The analysis of the methods and means used for processing the radarogram data was carried out, the structure of the file for storing the data of electromagnetic scanning and the means of processing and converting data available in the GeoScan32 program were considered. A sequence of actions was proposed to implement the procedure for searching for traces characterized by an unacceptable level of deviation of signal characteristics from the average values

DEVELOPMENT OF AUTOMATIC DATA PROCESSING FOR BATAN’S HRPD AND FCD/TD USING PYTHON CODE

High Resolution Powder Diffractometer (HRPD) and Four Circle Diffractometer/Texture Diffractometer (FCD/TD) are two BATAN-owned neutron diffractometers which have been fully operational since 1992. These are used to investigate structure and texture of crystalline materials, respectively. Before analyzing, the acquired raw neutron diffraction data should first be processed in a specific way to achieve the suitable data format required by the analysis software. This data processing step is a repetitive task for every single experiment which is previously done manually and very time-consuming. The purpose of this development project was to optimize this step to be fully automatic and executable by a code. This work was performed by means of Python code utilizing the array manipulation in re-arranging and re-formatting the raw data. The resulted Python codes were named as hrpd.py and fcdtd.py. These have been successfully done and validated, making data processing step easier, simpler, and significantly faster with only 20 seconds or less required.Keywords: HRPD, FCD/TD, Automatic Data Processing, Neutron Diffraction, Python

Aquarium: an automatic data-processing and experiment information management system for biological macromolecular crystallography beamlines

With the popularity of hybrid pixel array detectors, hundreds of diffraction data sets are collected at a biological macromolecular crystallography (MX) beamline every day. Therefore, the manual processing and recording procedure will be a very time-consuming and error-prone task. Aquarium is an automatic data processing and experiment information management system designed for synchrotron radiation source MX beamlines. It is composed of a data processing module, a daemon module and a web site module. Before experiments, the sample information can be registered into a database. The daemon module will submit data processing jobs to a high-performance cluster as soon as the data set collection is completed. The data processing module will automatically process data sets from data reduction to model building if the anomalous signal is available. The web site module can be used to monitor and inspect the data processing results.