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Upsampling

About: Upsampling is a research topic. Over the lifetime, 2426 publications have been published within this topic receiving 57613 citations.


Papers
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Proceedings ArticleDOI
06 Mar 2010
TL;DR: TD-MRFIR (Thread Decomposition MRFIR), an alternative representation and implementation technique, to decompose MRFIR into output computational threads, in contrast to a structural decomposition of the original filter as done in the polyphase decomposition.
Abstract: Multirate (decimation/interpolation) filters are among the essential signal processing components in space-borne instruments where Finite Impulse Response (FIR) filters are often used to minimize nonlinear group delay and finite-precision effects. Cascaded (multi-stage) designs of Multi-Rate FIR (MRFIR) filters are further used for large rate change ratio, in order to lower the required throughput while simultaneously achieving comparable or better performance than single-stage designs. Traditional representation and implementation of MRFIR employ polyphase decomposition of the original filter structure, whose main purpose is to compute only the needed output at the lowest possible sampling rate. In this paper, an alternative representation and implementation technique, called TD-MRFIR (Thread Decomposition MRFIR), is presented. The basic idea is to decompose MRFIR into output computational threads, in contrast to a structural decomposition of the original filter as done in the polyphase decomposition. Each thread represents an instance of the finite convolution required to produce a single output of the MRFIR. The filter is thus viewed as a finite collection of concurrent threads. The technical details of TD-MRFIR will be explained, first showing its applicability to the implementation of downsampling, upsampling, and resampling FIR filters, and then describing a general strategy to optimally allocate the number of filter taps. A particular FPGA design of multi-stage TD-MRFIR for the L-band radar of NASA's SMAP (Soil Moisture Active Passive) instrument is demonstrated; and its implementation results in several targeted FPGA devices are summarized in terms of the functional (bit width, fixed-point error) and performance (time closure, resource usage, and power estimation) parameters.

10 citations

Journal ArticleDOI
TL;DR: This article proposes an iterative SSBI cancellation scheme, which can be effectively applied without digital upsampling at low CSPR values, and experimentally demonstrated a 30 Gbaud 128 QAM SSB direct detection transmission over 80 km with a lowCSPR of 5 dB, showing 4.6 dB performance improvement compared to the Kramers–Kronig scheme operated without digitalupsampling.
Abstract: Distributed data-center networks rely on power and cost-efficient high-speed fiber optical connections over distances up to 80 km which can be densely wavelength-division multiplexed (WDM) in the C-band. Recently, single-sideband (SSB) direct detection (DD) has been considered as an attractive transmission scheme for achieving data rates beyond 100 Gb/s per channel over 80 km. The advantages of SSB DD transmission include its simple transceiver architecture and its capability of electronic dispersion compensation. However, SSB transmissions require a carrier-to-signal power ratio (CSPR) of around 10 dB, even when signal-signal beat interference (SSBI) cancellations are applied. This high CSPR value reduces the power efficiency of the system and limits the total number of WDM channels and accordingly the total system capacity due to power limitation of optical amplifiers. In this article, we propose an iterative SSBI cancellation scheme, which can be effectively applied without digital upsampling at low CSPR values. Using this technique, we have experimentally demonstrated a 30 Gbaud 128 QAM SSB direct detection transmission over 80 km with a low CSPR of 5 dB, showing 4.6 dB performance improvement compared to the Kramers–Kronig scheme operated without digital upsampling.

10 citations

Journal ArticleDOI
TL;DR: The experimental results indicate that the error of BFS extracted by the proposed algorithm is the same as that of the other schemes when no hotspot is presented and the error is smaller in cross-correlation based schemes than that in Lorentzian fitting scheme around hotspot.
Abstract: A fast interpolation algorithm based on cross-correlation is proposed to estimate the BFS in BOTDA systems The performances of the proposal are investigated through simulation and experiment in this paper The simulation results demonstrate that the computational cost of the proposed algorithm is reduced to less than 1/22, 1/36, 1/2 and 1/5 of the Lorentzian fitting, upsampling cross-correlation, partial quadratic fitting and partial cross-correlation algorithm respectively The experimental results indicate that the error of BFS extracted by our proposal is the same as that of the other schemes when no hotspot is presented and the error of BFS is smaller in cross-correlation based schemes than that in Lorentzian fitting scheme around hotspot

10 citations

Journal ArticleDOI
TL;DR: The final results show that the root mean square error (RMSE) of the proposed approach, when the training and testing images are selected from remote sensing images, is about 3.5 m, which is better than the other methods in the literature and demonstrates the promising performance of the proposal.
Abstract: A convolutional neural network (CNN) architecture has been proposed for estimating the digital surface model (DSM) from a single airborne or spaceborne image, which is inherently an ambiguous and illposed problem. Deriving the three-dimensional information and reconstructing the geometry of a surface from a monocular image require a deep network that has the ability to extract the local and global characteristics of the surface. In order to address this challenging issue, a deep CNN with residual blocks is employed as a downsampling part of the network, and an upsampling procedure is presented for improving the output accuracy. Moreover, an approach is proposed for connecting the estimated DSM patches and generating a seamless continuous surface. In order to assess the proposed methodologies, scenarios are designed and implemented in various datasets. The final results show that the root mean square error (RMSE) of the proposed approach, when the training and testing images are selected from remote sensing images, is about 3.5 m. In addition, evaluating the capability of the proposed approach for depth estimation using the terrestrial images of outdoor scenes reports about 4 m for RMSE, which is better than the other methods in the literature and demonstrates the promising performance of the proposed approach.

10 citations

Journal ArticleDOI
01 Jan 2022-Fuel
TL;DR: In this paper , an upscaling method taking advantage of convolutional neural networks (CNNs) and downsampling techniques was proposed to predict the upscaled properties of low-resolution samples.

10 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023469
2022859
2021330
2020322
2019298
2018236