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Satellite remote-sensing data are essential for monitoring and quantifying water properties in the Great Lakes, providing useful monitoring and management tools for understanding water optical, biological, and ecological processes and phenomena. However, during the winter season, large parts of the Great Lakes are often covered by ice, which can cause significant uncertainties in satellite-measured water quality products. Although some developed radiance-based ice-detection algorithms for satellite ocean color data processing can eliminate most of the ice pixels in a region, there are still some significant errors due to misidentification of ice-contaminated pixels, particularly for the thin ice-covered regions. Therefore, it is necessary to improve the ice-detection methods for satellite ocean color data processing in the Great Lakes. In this paper, impacts of ice contamination on satellite-derived ocean color products in the Great Lakes are investigated, and a refined regional ice-detection algorithm which is based on the radiance spectra and normalized water-leaving radiance at the wavelength of 551 nm, nLw(551), is developed and assessed for satellite ocean color data processing in the Great Lakes. Results show that this proposed ice-detection method can reasonably identify ice-contaminated pixels, including those in very thin ice-covered regions, and provide accurate satellite ocean color products for the winter season in the Great Lakes. [ABSTRACT FROM PUBLISHER]

This paper combines a digital signal processor (DSP) and a field programmable gate array (FPGA) to realize the online empirical mode decomposition (EMD)-based signal processing system. The EMD algorithm is a novel signal analysis technique, decomposing signals into a series of intrinsic mode functions. First, the EMD algorithm is implemented in the DSP, named the EMD processor, which has the ability to eliminate noise from the original signal. Next, in order to process the online sequential signal, this paper proposes and implements pipeline and data transfer controllers in the FPGA, called the data processing flow processor. Then, the data processing flow processor coordinates the EMD processor, analog-to-digital converter, and digital-to-analog converter module boards. Finally, this paper presents a prototype of the online EMD-based electrocardiogram denoise system to verify the features of the proposed architecture. The emulations and experimental results demonstrate the effectiveness of the presented system as expected. [ABSTRACT FROM AUTHOR]

The ATLAS Hadronic Tile Calorimeter detector (TileCal) is an iron-scintillating tiles sampling calorimeter de- signed to operate at the Large Hadron Collider accelerator at CERN. The central element of the back-end system of the TileCal detector is a 9U VME Read-Out Driver (ROD) board. The operation of the TileCal calorimeter requires a total of 32 ROD boards. This paper summarizes the tests performed during the ROD production and the results obtained. Data processing is performed in the ROD by digital signal processors, whose operation is based on the use of online algorithms such as the optimal filtering algorithm for the signal amplitude, pedestal and time reconstruction and the online Muon tagging algorithm which identifies low transverse momentum muons. The initial performance of both algorithms run during commissioning is also presented in this paper. [ABSTRACT FROM AUTHOR]

The presence of radio frequency interferences (RFIs) in the data collected by the Soil Moisture and Ocean Salinity (SMOS) mission led by the European Space Agency is larger than what was anticipated, and it has a strong impact on the retrieval of these geophysical parameters. The first efforts to deal with this problem were proposed by DEIMOS Engenharia in the Level 1 Prototype Processor released in November 2010, and they are also a part of its latest versions. DEIMOS focused on three key aspects to detect the RFIs, to mitigate these sources, and to flag the data in SMOS products affected by RFIs. The detection algorithm is based on a statistic approach that computes the most probable position of the RFI source based on multiangular observations made by SMOS. The mitigation algorithm has been designed taking as reference the existing technique to remove the sun effect. The mechanism to flag the data has been developed in cooperation with the European Space Agency and consists in flagging geolocated pixels according to the strength of the RFI. In this paper, three algorithms will be presented, as well as their results. For completion, a high-level description of the data processing for SMOS is also a part of this text. [ABSTRACT FROM AUTHOR]

The range-Doppler algorithm and the chirp-scaling algorithm (CSA) process synthetic aperture radar (SAR) data with approximations to ideal SAR processing. These approximations are invalid for data from systems with wide beamwidths, large bandwidths, and/or low center frequencies. While simple efficient, these frequency-domain methods are thus limited by SAR parameters. This paper explores these limits and proposes a generalized chirp-scaling approach for extending the utility of frequency-domain processing. We demonstrate how different order approximations of the SAR signal in the 2-D frequency domain affect image focusing for varying SAR parameters. From these results, a guideline is set forth, which suggests the required order of approximation terms for proper focusing. A proposed generalized frequency-domain processing approach is derived. This method is an efficient arbitrary-order CSA that processes data using the appropriate number of approximation terms. The new method is demonstrated using simulated data. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of reconstruction of the optical absorption spectra on the basis of low-resolution data provided by a spectrophotometric transducer. It is shown that the performance of the algorithms of (generalized) deconvolution with a built-in positivity constraint, which is frequently used for this purpose, deteriorates for data containing a considerable baseline (background). A new method for overcoming that limitation is proposed. It consists of the subtraction of a rough estimate of the baseline (called pseudo-baseline) from the data, reconstruction of the spectrum on the basis of the data modified in this way, and restoration of the pseudo-baseline in the final result of spectrum reconstruction. The proposed amelioration is illustrated by means of an algorithm of spectrum reconstruction based on Jansson's method of deconvolution. [ABSTRACT FROM AUTHOR]

The Tropospheric Emission Spectrometer (TES) is one of four instruments onboard the Earth Observing System Aura satellite. The TES Science Investigator-led Processing System (SIPS) Facility performs production processing of all TES science data. When the TES project was proposed in 1988, its science algorithms were still evolving, but it was already understood to be at least two orders of magnitude more complex than its NASA predecessor, the Atmospheric Trace Molecule Spectroscopy (ATMOS) instrument. In addition, the expected data volume of the TES instrument would be more than 1000 times greater than that of ATMOS. Development of the TES SIPS faced a number of technical challenges. It also would have been impractical and prohibitively costly to develop the TES SIPS facility without carefully deploying the computing technologies that have recently become available. This paper describes how the challenges were met in the development of the facility by making use of evolving hardware technology and software refinement. The process revealed that the architecture of the hardware implemented was highly dependent upon the processing algorithm, and a stable algorithm was needed early in the hardware development process for performance analysis and benchmarking. [ABSTRACT FROM AUTHOR]

The Earth Observing System (EOS) Microwave Limb Sounder (MLS) is an atmospheric remote sensing experiment led by the Jet Propulsion Laboratory of the California Institute of Technology. The objectives of the EOS MLS are to learn more about stratospheric chemistry and causes of ozone changes, processes affecting climate variability, and pollution in the upper troposphere. The EOS MLS is one of four instruments on the National Aeronautics and Space Administration (NASA) EOS Aura spacecraft launched on July 15, 2004, with an operational period extending at least 5 years after launch. This paper describes the architecture and capabilities of the Science Data Processing System (SDPS) for the EOS MLS. The SDPS consists of two major component—the Science Computing Facility and the Science Investigator-led Processing System. The Science Computing Facility provides the facilities for the EOS MLS Science Team to perform the functions of scientific algorithm development, processing software development, quality control of data products, and scientific analyses. The Science Investigator-led Processing System processes and reprocesses the science data for the entire mission and delivers the data products to the Science Computing Facility and to the Goddard Space Flight Center Earth Science Distributed Active Archive Center, which archives and distributes the standard science products. The Science Investigator-led Processing System is developed and operated by Raytheon Information Technology and Scientific Services of Pasadena under contract with Jet Propulsion Laboratory. [ABSTRACT FROM AUTHOR]

The Event Filter (EF) selection stage is a fundamental component of the ATLAS Trigger and Data Acquisition architecture. Its primary function is the reduction of data flow and rate to values acceptable by the mass storage operations and by the subsequent offline data reconstruction and analysis steps. The computing instrument of the EF is organized as a set of independent subfarms, each connected to one output of the Event Builder (EB) switch fabric. Each subfarm comprises a number of processors analyzing several complete events in parallel. This paper describes the design of the ATLAS EF system, its deployment in the 2004 ATLAS combined test beam together with some examples of integrating selection and monitoring algorithms. Since the processing algorithms are not explicitly designed for EF but are adapted from the offline ones, special emphasis is reserved to system reliability and data security, in particular for the case of failures in the processing algorithms. Other key design elements have been system modularity and scalability. The EF shall be able to follow technology evolution and should allow for using additional processing resources possibly remotely located [ABSTRACT FROM PUBLISHER]

An on-line segmentation algorithm is presented in this paper. It is developed to preprocess data describing the patient's state, sampled at high frequencies In intensive care units, with a further purpose of alarm filtering. The algorithm splits the signal monitored into line segments -- continuous or discontinouus -- of various lengths and determines on-line when a new segment must be calculated. The delay of detection of a new line segment depends on the importance of the change: the more important the change, the quicker the detection. The linear segments are a correct approximation of the structure of the signal. They emphasise steady-states, level changes and trends occurring on the data. The information returned by the algorithm, with is the time at which the segment begins, its ordinate and its slope, is sufficient to completely reconstruct the filtered signal. This makes the algorithm an interesting tool to provide a processed time history record of the monitored variable. it can also be used to extract on-line information on the signal, such as its trend, In the short or long term. [ABSTRACT FROM AUTHOR]