F/NAS/ Pressure Temperature Retrieval Techniques

The purpose of this investigation is to study methods and ways for rapid inversion programs involving the correlated k-method, and to modify the existing programs so that the rapid analysis of data can be accomplished. The earth's atmosphere as well as those around the planets consist of gases which emit radiation in the infrared spectral region, providing wealth of information about chemical and physical processes in the atmosphere. The atmospheric molecular constituents absorb and radiate by vibrational and rotational transitions, and the observed spectra exhibit characteristic spectral features in the region of the electromagnetic spectrum. The observed absorption or thermal emission spectra may be obtained with space-borne high resolution infrared spectrometers the 50-1000 micrometers spectral region. A detailed analysis of the observed spectra leads to information about the atmospheric thermal structure, composition, and the physical and chemical processes. The analytic techniques involve the development of radiative transfer models for the calculation of the observed radiance and transmittances for realistic atmospheric conditions and observational geometries, and the development of inversion methods for retrieval of atmospheric parameters from the observations

FNAS/Rapid Spectral Inversion Methods

The purpose of this investigation was to study methods and ways for rapid inversion programs involving the correlated k-method, and to study the infrared observations of Saturn from the Cassini orbiter

On classes of non-Gaussian asymptotic minimizers in entropic uncertainty principles

In this paper we revisit the Bialynicki-Birula & Mycielski uncertainty principle and its cases of equality. This Shannon entropic version of the well-known Heisenberg uncertainty principle can be used when dealing with variables that admit no variance. In this paper, we extend this uncertainty principle to Renyi entropies. We recall that in both Shannon and Renyi cases, and for a given dimension n, the only case of equality occurs for Gaussian random vectors. We show that as n grows, however, the bound is also asymptotically attained in the cases of n-dimensional Student-t and Student-r distributions. A complete analytical study is performed in a special case of a Student-t distribution. We also show numerically that this effect exists for the particular case of a n-dimensional Cauchy variable, whatever the Renyi entropy considered, extending the results of Abe and illustrating the analytical asymptotic study of the student-t case. In the Student-r case, we show numerically that the same behavior occurs for uniformly distributed vectors. These particular cases and other ones investigated in this paper are interesting since they show that this asymptotic behavior cannot be considered as a "Gaussianization" of the vector when the dimension increases

Digital Chunk Processing with Orthogonal GFDM Doubles Wireless Channel Capacity

A novel physical layer (PHY) transmission technique for increasing the channel capacity of transmission, termed as Orthogonal Generalized Frequency Division Multiplexing (OGFDM), has been proposed, investigated and evaluated in this paper. A combination of the Digital Hilbert Filter (DHF) with Generalized Frequency Division Multiplexing (GFDM) has been shown to double wireless channel capacity for each transmitted frequency sub-carrier at acceptable Bit Error Rate (BER) limits. By making use of the great properties of Hilbert transforms, orthogonality is achieved between the traditionally non-orthogonal GDFM subcarriers improving the BER and wireless channel capacity of the transmission. The OGFDM seems to combine the attributes of GFDM and Orthogonal Frequency Division Multiplexing (OFDM) in one sustainable system. The proposed solution achieves orthogonality between the filters of adjacent frequencies of subcarriers instead of between the frequencies of subcarriers themselves. Also, an OGFDM system model is presented, based on which, the relation between the main filter parameters and the system BER and channel capacity performance is specified in a wireless electrical back-to-back transmission system. Finally, by means of simulations, the impact of applying the proposed advanced filters on the aggregated system performance of the BER and channel capacity is shown in an Additive White Gaussian Noise (AWGN) wireless channel

Hartley transform and the use of the Whitened Hartley spectrum as a tool for phase spectral processing

The Hartley transform is a mathematical transformation which is closely related to the better known Fourier transform. The properties that differentiate the Hartley Transform from its Fourier counterpart are that the forward and the inverse transforms are identical and also that the Hartley transform of a real signal is a real function of frequency. The Whitened Hartley spectrum, which stems from the Hartley transform, is a bounded function that encapsulates the phase content of a signal. The Whitened Hartley spectrum, unlike the Fourier phase spectrum, is a function that does not suffer from discontinuities or wrapping ambiguities. An overview on how the Whitened Hartley spectrum encapsulates the phase content of a signal more efficiently compared with its Fourier counterpart as well as the reason that phase unwrapping is not necessary for the Whitened Hartley spectrum, are provided in this study. Moreover, in this study, the product–convolution relationship, the time-shift property and the power spectral density function of the Hartley transform are presented. Finally, a short-time analysis of the Whitened Hartley spectrum as well as the considerations related to the estimation of the phase spectral content of a signal via the Hartley transform, are elaborated

Residential end-uses disaggregation and demand response evaluation using integral transforms

[EN] Demand response is a basic tool used to develop modern power systems and electricity markets. Residential and commercial segments account for 40%-50% of the overall electricity demand. These segments need to overcome major obstacles before they can be included in a demand response portfolio. The objective of this paper is to tackle some of the technical barriers and explain how the potential of enabling technology (smart meters) can be harnessed, to evaluate the potential of customers for demand response (end-uses and their behaviors) and, moreover, to validate customers' effective response to market prices or system events by means of non-intrusive methods. A tool based on the Hilbert transform is improved herein to identify and characterize the most suitable loads for the aforesaid purpose, whereby important characteristics such as cycling frequency, power level and pulse width are identified. The proposed methodology allows the filtering of aggregated load according to the amplitudes of elemental loads, independently of the frequency of their behaviors that could be altered by internal or external inputs such as weather or demand response. In this way, the assessment and verification of customer response can be improved by solving the problem of load aggregation with the help of integral transforms.This work has been supported by Spanish Government (Ministerio de Economia, Industria y Competitividad) and EU FEDER fund (No. ENE2013-48574-C2-2-P&1-P, No. ENE2015-70032-REDT).Gabaldón Marín, A.; Molina, R.; Marin-Parra, A.; Valero, S.; Álvarez, C. (2017). Residential end-uses disaggregation and demand response evaluation using integral transforms. Journal of Modern Power Systems and Clean Energy. 5(1):91-104. https://doi.org/10.1007/s40565-016-0258-8S9110451Chardon A, Almén O, Lewis PE (2009) Demand response: a decisive breakthrough for Europe. Capgemini, Enerdata. https://www.capgemini.com/resources/demand_response_a_decisive_breakthrough_for_europeFaruqui A, Harris D, Hledick R (2010) Unlocking the €53 billion savings from smart meters in the EU: how increasing the adoption of dynamic tariffs could make or break the EU’s smart grid investment. Energy Policy 38(10):6222–6231Federal Energy Regulatory Commission (FERC) (2015) Assessment of demand response and advanced metering, staff report. http://www.ferc.gov/legal/staff-reports/2015/demand-response.pdfFERC Order 745 (December 2011) & 719 (December 2009). http://www.ferc.gov/legal/maj-ord-reg.aspEuropean Commission (2011) Energy efficiency plan 2011, COM (2011) 109 final. http://eur-lex.europa.eu/LexUriServJoitn Research Center (JRC), European Commission (2010) Energy Service Companies market in Europe, status report 2010. http://publications.jrc.ec.europa.eu/repository/handle/111111111/15108European Commission (2011) Impact assessment accompanying document “Energy Efficiency Plan 2011”, SEC (2011) 277 final. http://www.eurosfaire.prd.fr/7pc/bibliotheque/consulter.php?id=2357European Environment Agency (EEA) (2012) Final energy consumption by sector and fuel. http://www.eea.europa.eu/data-and-maps/indicators/final-energy-consumption-by-sector-8/assessment-2Piette MA, Watson D, Motegi N et al (2007) Automated critical peak pricing field tests: 2006 pilot program description and results. California Energy Commission, PIER Energy Systems Integration Research Program: CEC-500-03-026Gomatom K, Holmes C, Kresta D (2013) Non-intrusive load monitoring. https://www.e3tnw.orgDimetrosky S, Parkinson K, Lieb N (2013) Residential lighting evaluation protocol, Report NREl/SR-7A30-53827. National Renewable Energy LaboratoryZeifman M, Roth K (2011) Nonintrusive appliance load monitoring: review and outlook. IEEE Trans Consum Electron 57(1):76–84Liang J, Simon K, Kendall G et al (2010) Load signature study part I: basic concept, structure and methodology. IEEE Trans Power Deliv 25(2):551–560Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891Powers J, Margossian B, Smith B (1991) Using a rule-based algorithm to disaggregate end-use load profiles from premise-level data. IEEE Comput Appl Power 4(2):42–47Farinaccio L, Zmeureanu R (1999) Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy Build 30(3):245–259Marceau ML, Zmeureanu R (2000) Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses in residential buildings. Energy Convers Manag 41(13):1389–1403Baransju M, Voss J (2004) Genetic algorithm for pattern detection in NIALM systems. In: IEEE international conference on systems, man and cybernetics, 10–13 Oct, pp 3462–3468Baranski H, Voss J (2004) Detecting patterns of appliances from total load data using a dynamic programming approach. In: Fourth IEEE international conference on data mining (ICDM’04), 1–4 Nov, Brighton, UK, pp 327–330Sankara A (2015) Energy disaggregation in NIALM using hidden Markov models. Masters Theses, Missouri University of Science and Technology, Paper 7414Kolter J, Jaakkola T (2012) Approximate inference in additive factorial HMMs with application to energy disaggregation. J Mach Learn 22:1472–1482Leeb SB, Shaw SR, Kirtley SL (1995) Transient event detection in spectral envelope estimates for nonintrusive load monitoring. IEEE Trans Power Deliv 10(3):1200–1210Patel SN, Robertson T, Kientz JA et al (2007) At the flick of a switch: detecting and classifying unique electrical events on the residential power line. In: Conference on ubiquitous computing, pp 271–288Bonglifi R, Squartini S, Fagiani M et al (2015) Unsupervised algorithms for non-intrusive load monitoring: an up-to-date overview. In: EEEIC conference. doi: 10.1109/EEEIC.2015.7165334Browne TJ, Vittal V, Heydt GT et al (2008) A comparative assessment of two techniques for modal identification from power system measurements. 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Understanding digital signal processing with MATLAB and solutions

xv, 455 p. : ill. ; 26 cm

Transforms and Applications Handbook

Highlighting the use of transforms and their properties, this title offers an introduction to signals and systems, including properties of the delta function and some classical orthogonal functions. It then details different transforms, including lapped, Mellin, wavelet, and Hartley varietie