Fault detection and isolation

Erroneous measurements in multisensor navigation systems must be detected and isolated. A recursive estimator can find fast growing errors; a least squares batch estimator can find slow growing errors. This process is called fault detection. A protection radius can be calculated as a function of time for a given location. This protection radius can be used to guarantee the integrity of the navigation data. Fault isolation can be accomplished using either a snapshot method or by examining the history of the fault detection statistics

Small carbon chains in circumstellar envelopes

Observations were made for a number of carbon-rich circumstellar envelopes using the Phoenix spectrograph on the Gemini South telescope to determine the abundance of small carbon chain molecules. Vibration-rotation lines of the $\nu_{3}$ antisymmetric stretch of C$_{3}$ near 2040 cm$^{-1}$ (4.902 $\mu$m) have been used to determine the column density for four carbon-rich circumstellar envelopes: CRL 865, CRL 1922, CRL 2023 and IRC +10216. We additionally calculate the column density of C$_{5}$ for IRC +10216, and provide an upper limit for 5 more objects. An upper limit estimate for the C$_{7}$ column density is also provided for IRC+10216. A comparison of these column densities suggest a revision to current circumstellar chemical models may be needed

Computationally Efficient Target Classification in Multispectral Image Data with Deep Neural Networks

Detecting and classifying targets in video streams from surveillance cameras is a cumbersome, error-prone and expensive task. Often, the incurred costs are prohibitive for real-time monitoring. This leads to data being stored locally or transmitted to a central storage site for post-incident examination. The required communication links and archiving of the video data are still expensive and this setup excludes preemptive actions to respond to imminent threats. An effective way to overcome these limitations is to build a smart camera that transmits alerts when relevant video sequences are detected. Deep neural networks (DNNs) have come to outperform humans in visual classifications tasks. The concept of DNNs and Convolutional Networks (ConvNets) can easily be extended to make use of higher-dimensional input data such as multispectral data. We explore this opportunity in terms of achievable accuracy and required computational effort. To analyze the precision of DNNs for scene labeling in an urban surveillance scenario we have created a dataset with 8 classes obtained in a field experiment. We combine an RGB camera with a 25-channel VIS-NIR snapshot sensor to assess the potential of multispectral image data for target classification. We evaluate several new DNNs, showing that the spectral information fused together with the RGB frames can be used to improve the accuracy of the system or to achieve similar accuracy with a 3x smaller computation effort. We achieve a very high per-pixel accuracy of 99.1%. Even for scarcely occurring, but particularly interesting classes, such as cars, 75% of the pixels are labeled correctly with errors occurring only around the border of the objects. This high accuracy was obtained with a training set of only 30 labeled images, paving the way for fast adaptation to various application scenarios.Comment: Presented at SPIE Security + Defence 2016 Proc. SPIE 9997, Target and Background Signatures I

Experimental energy levels of the water molecule

Experimentally derived energy levels are presented for 12 248 vibration–rotation states of the H2 16O isotopomer of water, more than doubling the number in previous, disparate, compilations. For each level an error and reference to source data is given. The levels have been checked using energy levels derived from sophisticated variational calculations. These levels span 107 vibrational states including members of all polyads up to and including 8v. Band origins, in some cases estimates, are presented for 101 vibrational modes

Das Fernstudienzentrum der Carl von Ossietzky Universität Oldenburg - Kosten- und Leistungsaspekte ausgewählter Aufgabenbereiche: Beitrag im Rahmen der FiBS-Konferenz 2003 "eLearning an Hochschulen - Marktpotenziale und Geschäftsmodelle" (30. Juni und 1. Juli 2003, Köln)

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Emission spectrum of hot HDO in the 380-2190 cm(-1) region

Fourier transform emission spectra were recorded using a mixture of H2O and D2O at a temperature of 1500 degreesC. The spectra were recorded in three overlapping sections and cover the wavenumber range 380-2190 cm(-1). A total of 22106 lines were measured, of which 60% are thought to belong to HDO. A total of 6430 FIDO transition,, are assigned, including the first transitions to the (040) vibrational state, with a term value of 5420.042 cm(-1). A total of 1536 new energy levels of HDO belonging to the (000), (010) (020), (030), and (040) stated are presented, significantly extending the degree of rotational excitation compared to previous studies. (C) 2001 Elsevier Science

Hot methane line lists for exoplanet and brown dwarf atmospheres

We present comprehensive experimental line lists of methane (CH4) at high temperatures obtained by recording Fourier transform infrared emission spectra. Calibrated line lists are presented for the temperatures 300 - 1400 degC at twelve 100 degC intervals spanning the 960 - 5000 cm-1 (2.0 - 10.4 microns) region of the infrared. This range encompasses the dyad, pentad and octad regions, i.e., all fundamental vibrational modes along with a number of combination, overtone and hot bands. Using our CH4 spectra, we have estimated empirical lower state energies (Elow in cm-1) and our values have been incorporated into the line lists along with line positions (cm-1) and calibrated line intensities (S' in cm molecule-1). We expect our hot CH4 line lists to find direct application in the modeling of planetary atmospheres and brown dwarfs.Comment: Supplementary material is provided via the Astrophysical Journal referenc