Msa Altair 4x Spec Sheet

[Hermila Farquhar]

Thisoperator can be used to load data from Microsoft Excel spreadsheets. This operator is able to read data from Excel 95, 97, 2000, XP, and 2003. The user has to define which of the spreadsheets in the workbook should be used as data table. The table must have a format such that each row is an example and each column represents an attribute. Please note that the first row of the Excel sheet might be used for attribute names which can be indicated by a parameter. The data table can be placed anywhere on the sheet and can contain arbitrary formatting instructions, empty rows and empty columns. Missing data values in Excel should be indicated by empty cells or by cells containing only "?".

For complete understanding of this operator read the parameters section. The easiest and shortest way to import an Excel file is to use the import configuration wizard from the Parameters panel. The best way, which may require some extra effort, is to first set all the parameters in the Parameters panel and then use the wizard. Please make sure that the Excel file is read correctly before building a process using it.

Sets the index of the row that should be used for Attribute names.The Attributes names are determined by the content of this row. As a consequence, data will only be read below this row.The parameter data set meta data information overwrites the data from the header row if it is set.

This is an expert parameter. A list of time zones is provided; users can select any of them.Dates (from Excel date columns or text converted dates, see below) are read with the time zone specified in this parameter.

In this Example Process we first store the 'Golf' data set under '%tempdir/golf.xlsx'. The data set was copied on sheet 1 of the Excel file thus the sheet number parameter is given value 1. The first cell of the sheet is A1 and last required cell is E15, thus the imported cell range parameter is provided value 'A1:E15'. As the first row of the sheet contains names of attributes, the first row as names parameter is checked. The remaining parameters were used with default values. Run the process, you will see almost the same results as you would have gotten from using the Retrieve operator to retrieve the 'Golf' data set from the Repository. You will see a difference in the meta data though, for example here the types and roles of attributes are different from those in the 'Golf' data set. You can change the role and type of attributes using the data set meta data information parameter. It is always good to make sure that all attributes are of desired role and type. In this example one important change that you would like to make is to change the role of the Play attribute. Its role should be changed to label if you want to use any classification operators on this data set.

The detection, location, identification and recognition are very important activities for the air forces. Imaging systems are tools used for those functions, so it is mandatory to characterize those systems to really know their actual operational limits. This paper presents a set of measurements for spectral, radiometric and spatial camera characterization to be applied to imaging systems operating in the thermal infrared. A SC5600 camera manufactured by FLIR Systems was used and assembled with lenses of 27 or 54 mm equivalent focal length. The camera spectral characterization was done by comparison to a calibrated system composed by thermal source, monochromator and a broadband reference detector. The radiometric characterization was performed using an extensive blackbody (CI Systems) for temperatures between 10 and 55 C to evaluate the camera accuracy and obtain the calibration curves. The spatial characterization was carried out using the same extensive blackbody and 2 standard USAF 1951 machined targets, one made of steel and other of aluminum, serving as masks for the blackbody. Using recycled material, a homemade extended blackbody for outdoor use was built. The results obtained using the 2 blackbodies in laboratory were similar.

An imaging system is generally composed by a collector (usually lenses and/or mirrors), filters, detector, processor, a system for data storage and an output unit. The output can be a monitor that provides an image of the collected electromagnetic radiation. The operational wavelength range of the imaging systems is usually between 0.35 and 15 m (Lillesand and Kiefer 1994Lillesand TM, Kiefer RM (1994) Remote sensing and image interpretation. 3rd edition. New York: Wiley.). In addition, every electro-optical system has its unique characteristics and some internal parameters to be settled, which directly implies on the quality of the images to be acquired. This quality may or may not be suitable for target location, identification and/or recognition (Johnson 1958Johnson J (1958) Analysis of image forming systems. Proceedings of the Image Intensifier Symposium; Fort Belvoir, USA.), under certain environmental conditions (atmospheric, altitude etc.) or the scenario in which the target was encompassed (Pace and Sutherland 2001Pace PW, Sutherland J (2001) Detection, recognition, identification, and tracking of military vehicles using biomimetic intelligence. Proceedings of the 11th Automatic Target Recognition. Vol. 4379; Orlando, USA.). This paper presents results for a camera with spectral response between 2.5 and 5.1 m. The measurement procedure can be applied to cameras having different spectral responses, if the respective sources and masks for characterization are available.

The complete characterization of these imaging systems is necessary for knowing the operational limits of the equipment. For example, in the case of using a thermal camera for operational purposes, as search and rescue or maritime patrol, one must previously know: what is the electromagnetic spectrum range at which the camera operates and what is its relative sensitivity (spectral characterization)? What is the accuracy on the electromagnetic radiation measurement (radiometric characterization)? What is the actual field of view of each detector element, or how detailed is the target at a certain distance/altitude (spatial characterization)?

Once this characterization methodology was applied to a thermal camera, the performance characteristics would be known and this would allow choosing the best camera settings as well as planning the search flight to maximize the mission success.

The characterization of a thermal or visible camera comprises 3 steps: spectral, radiometric and spatial. The camera evaluated in this study was a SC5600 by FLIR Systems. It is a high-performance thermal imaging camera for industrial and educational use. It has an array of 640 512 pixels, with frequency between 5 and 100 Hz. The detectors are chilled indium antimonide (InSb), with spectral response between 2.5 and 5.1 m (FLIR Systems 2017FLIR Systems (2017) SC5600-M. Large format infrared cameras for R&D and thermography applications; [accessed 2017 Jul 6]. -systems-sc5600-cctv-camera/co-2752-ga/AU_SC5600-M_Leaflet_APAC.pdf

). The SC5600 camera used in this study had 2 optical assemblies, one with an equivalent focus of 27 mm and the other, of 54 mm, both by FLIR Systems. The optical assemblies had an integrated autofocus system.

According to the data sheet, the SC5600 has high sensitivity, precision and speed, keeping an excellent linearity. The smallest observed temperature difference is described as 20 mK (0.020 C). The SC5600 camera can perform temperature measurements from 5 to 300 C, and the integration time can be adjusted by increments of s.

An indirect method was chosen to determine the spectral response. It uses a broadband calibrated detector as reference (Schumaker 1996Schumaker DL (1996) The infrared and electro-optical systems handbook. Vol. 5. Ann Arbor: ERIM; Bellingham: SPIE.). The schematic diagram of the experimental arrangement is shown in Fig. 1a and basically includes the following equipment:

The results obtained by the Judson detector were used as standard for the camera. Figure 1a shows the schematic diagram and Fig. 1b, the camera assembled for measurements, where: 1 is the camera; 2 is the Judson detector (not assembled); 3 is the monochromator; and 4 is the data acquisition computer. The Judson detector is at the center of Fig. 1b, but it was not assembled for measurement at that moment. Three sets of measurements were registered for the Judson detector and also for the camera: no filter, filter 1 (long pass 4.5 m) and filter 2 (long pass 2.5 m).

The determination of the spectral response was done in a roundabout way. It used a source, monochromator and a calibrated detector. The camera response was compared to the detector one. The measurements were made in the same ambient as well as geometric and lighting conditions (Zolin Neto 2012Zolin Neto A (2012) Estudo de caracterizao espectral de cmera termogrfica (Term paper). So Jos dos Campos: Instituto Tecnolgico de Aeronutica.).

The experimental arrangement (Fig. 2) was used to perform the radiometric characterization, where 1 represents the camera and 2, the blackbody. In this arrangement, in addition to the camera, it is also used a blackbody SR800 (CI Systems 2004). The camera was placed perpendicularly to the blackbody surface, at 1.55 m. This distance was chosen so the complete Schott's Contrast Transfer Function (CTF) could be obtained for the spatial characterization. For all measurements, the largest thermalization time was awaited. According to the camera manufacturer, this time is 2 h. An integration time of 1.6 ms was chosen, as recommended by FLIR Systems, for temperatures between 10 and 55 C. The sample rate was 60 Hz. For each established temperature in the blackbody, the data provided by the camera were the medium value of digital levels. For radiance and target temperature measurements, the surface emissivity was considered equal to 1 for the blackbody and 0.97 (aluminum) for the target.

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