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- Bengtsson, Daniel, et al.
(författare)
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Trellis coding in a discrete multitone modulation system
- 1996
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Ingår i: Proceedings of the 1996 SNRV and NUTEK Conference on Radio Sciences and Telecommunications in Luleå and Kiruna June 3-6, 1996. - Luleå : Luleå tekniska universitet. - 9163044552 ; , s. 668-672
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Konferensbidrag (refereegranskat)abstract
- Discrete Multitone (DMT) modulation is a multicarrier technique which makes efficient use of the channel, maximizing the throughput by sending different numbers of bits on different subchannels. The number of bits on each subchannel depends on the Signal-to-Noise Ratio of the subchannel. The performance of a DMT system can be further increased by using powerful coding techniques. This paper investigates an implementation of trellis coded modulation in a DMT system intended for transmission over short copper cables, less than 1000m. We suggested trellis code is Wei's 4-dimensional 16-state coder combined with trellis shaping. A single encoder is used which codes across the tones of each DMT-symbol. At a bit error probability of 10-7, the suggested code gains 4-5 dB over uncoded transmission.
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| 2. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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A machine learning approach for biomass characterization
- 2019
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Ingår i: Energy Procedia. - : Elsevier Ltd. - 1876-6102. ; , s. 1279-1287
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Konferensbidrag (refereegranskat)abstract
- The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SG1) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications. © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy.
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| 3. |
- Barrera, Tony, et al.
(författare)
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Vectorized table driven algorithms for double precision elementary functions using Taylor expansions
- 2009
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Ingår i: APLIMAT 8th international conference. ; 2, s. 171-178
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Konferensbidrag (refereegranskat)abstract
- This paper presents fast implementations of the inverse square root and arcsine, both in double precision. In single precision it is often possible to use a small table and one ordinary Newton-Raphson iteration to compute elementary functions such as the square root. In double precision a substantially larger table is necessary to obtain the desired precision, or, if a smaller table is used, the additional Newton-Raphson iterations required to obtain the precision often requires the evaluation of other expensive elementary functions. Furthermore, large tables use a lot of the cash memory that should have been used for the application code.Obtaining the desired precision using a small table can instead be realised by using a higher order method than the second order Newton-Raphson method. A generalization of Newton's method to higher order is Householder's method, which unfortunately often results in very complicated expressions requiring many multiplications, additions, and even divisions.We show how a high-order method can be used, which only requires a few extra additions and multiplications for each degree of higher order. The method starts from the Taylor expansion of the difference of the value of the elementary function and a starting guess value for each iteration. If the Taylor series is truncated after the second term, ordinary Newton iterations are obtained. In several cases it is possible to algebraically simplify the difference between the true value and the starting guess value. In those cases we show that it is advantageous to use the Taylor series to higher order to obtain the fast convergent method. Moreover, we will show how the coefficients of a Chebyshev polynomial can be fitted to give as little error as possible for the functions close to zero and in the same time reduce the terms in the Taylor expansion.In the paper we benchmark two example implementations of the method on the x86_64 architecture. The first is the inverse square root, where the actual table (to 12 bit precision) is provided by the processor hardware. The inverse square root is important in many application programs, including computer graphics, and explicit particle simulation codes, for instance the Monte Carlo and Molecular Dynamics methods of statistical mechanics. The other example is the arcsine function, which has a slow converging Taylor expansion and where no tables are provided by the hardware. The vectorized versions of the implementations of the inverse square root are 3.5 times faster than compiled code on the Athlon64 and about 5 times faster on the Core 2. The scalar version of the arcsine function is, depending on order and table size, between 2 and 3 times faster than the compiled code, and the vectorized version is between 3 and 4 times faster on the Athlon64, while it is between 4 and 5 times faster than the compiled version on the Core 2.
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| 8. |
- Gharanjik, Ahmad, et al.
(författare)
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Precoding design and user selection for multibeam satellite channels
- 2015
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Ingår i: IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC. - : IEEE conference proceedings. - 9781479919307 ; , s. 420-424
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Konferensbidrag (refereegranskat)abstract
- Precoding for the downlink of a multibeam satellite system has been recently shown, under ideal conditions, to be promising technique towards employing aggressive frequency reuse gainfully. However, time varying phase uncertainties imposed by the components and the channel, combined with delayed feedback perturbs the channel state information at the transmitter (CSIT). In this paper, we consider a power constrained robust formulation of the downlink precoding problem to counter the phase uncertainties. In particular it considers imposing conditions on the average signal to interference plus noise ratio (SINR), to deal with imperfect CSIT. In addition to the robust formulation, the primacy of user selection is highlighted and a new approach exploiting the satellite system design is proposed. Performance of the derived robust precoder in conjunction with the proposed location based user selection is then evaluated and the gains are tabulated.
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| 10. |
- Gunnarsson, Adrian, 1990, et al.
(författare)
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Soot Formation and Radiative Heat Transfer in Oxy-Fuel and Oxygen-Enhanced Propane Flames
- 2018
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This work aims to determine radiation-related properties of various propane flames, where the measurements were conducted in a down-fired and cylindrical 100 kW furnace equipped with a swirl burner. The combustion conditions were varied by altering the composition of the oxidant. For six cases, oxygen-enhanced air was used, step-wise varying the oxygen concentration in the oxidant from 21% to 32%. Also for six cases, the furnace was operated in oxy-fuel mode, recirculating dry flue gas and varying the oxygen concentration from 25% to 42%. All measurements were conducted at an axial distance of 384 mm from the burner. Temperature, gas composition and radiative intensity were measured (by intrusive instruments) along the furnace diameter using probes while the soot volume fraction was quantified using non-intrusive laser induced incandescence (LII). An Nd:YAG laser at wavelength 1064 nm was used for the LII measurements, and a diode laser at wavelength 808 nm was used for extinction measurements for absolute calibration of the LII-signal. Two-dimensional images of the LII-signal were captured using an intensified CCD-camera and radial profiles of the soot volume fraction were achieved. The soot volume fraction increased with increasing oxygen concentration in the feed gas, and, when the oxygen concentration exceeded 30 and 42% for the oxygen-enriched air and oxy-fuel cases, respectively, the soot formation was substantially enhanced with volume fractions more than 10 times higher than for lower oxygen concentrations. The higher oxygen concentration required for the increased soot production in the oxy-fuel combustion cases is mainly due to the higher heat capacity of carbon dioxide that lowers the flame temperatures. The data collected from the measurements was used to model the radiative intensity using a discrete transfer model. In this model, gas properties are calculated using a statistical narrow-band model and particle properties are calculated using Rayleigh theory. Good agreement was achieved between the modeled and measured radiative intensity for most flames and the use of an LII-system to measure the soot volume fraction in this type of furnace was successful.
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