2,295 research outputs found
ERIGrid Holistic Test Description for Validating Cyber-Physical Energy Systems
Smart energy solutions aim to modify and optimise the operation of existing energy infrastructure. Such cyber-physical technology must be mature before deployment to the actual infrastructure, and competitive solutions will have to be compliant to standards still under development. Achieving this technology readiness and harmonisation requires reproducible experiments and appropriately realistic testing environments. Such testbeds for multi-domain cyber-physical experiments are complex in and of themselves. This work addresses a method for the scoping and design of experiments where both testbed and solution each require detailed expertise. This empirical work first revisited present test description approaches, developed a newdescription method for cyber-physical energy systems testing, and matured it by means of user involvement. The new Holistic Test Description (HTD) method facilitates the conception, deconstruction and reproduction of complex experimental designs in the domains of cyber-physical energy systems. This work develops the background and motivation, offers a guideline and examples to the proposed approach, and summarises experience from three years of its application.This work received funding in the European Community’s Horizon 2020 Program (H2020/2014–2020)
under project “ERIGrid” (Grant Agreement No. 654113)
Stochastic Analysis of the LMS Algorithm for System Identification with Subspace Inputs
This paper studies the behavior of the low rank LMS adaptive algorithm for the general case in which the input transformation may not capture the exact input subspace. It is shown that the Independence Theory and the independent additive noise model are not applicable to this case. A new theoretical model for the weight mean and fluctuation behaviors is developed which incorporates the correlation between successive data vectors (as opposed to the Independence Theory model). The new theory is applied to a network echo cancellation scheme which uses partial-Haar input vector transformations. Comparison of the new model predictions with Monte Carlo simulations shows good-to-excellent agreement, certainly much better than predicted by the Independence Theory based model available in the literature
Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition
Hyperspectral images (HSIs) are often corrupted by a mixture of several types
of noise during the acquisition process, e.g., Gaussian noise, impulse noise,
dead lines, stripes, and many others. Such complex noise could degrade the
quality of the acquired HSIs, limiting the precision of the subsequent
processing. In this paper, we present a novel tensor-based HSI restoration
approach by fully identifying the intrinsic structures of the clean HSI part
and the mixed noise part respectively. Specifically, for the clean HSI part, we
use tensor Tucker decomposition to describe the global correlation among all
bands, and an anisotropic spatial-spectral total variation (SSTV)
regularization to characterize the piecewise smooth structure in both spatial
and spectral domains. For the mixed noise part, we adopt the norm
regularization to detect the sparse noise, including stripes, impulse noise,
and dead pixels. Despite that TV regulariztion has the ability of removing
Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian
noise for some real-world scenarios. Then, we develop an efficient algorithm
for solving the resulting optimization problem by using the augmented Lagrange
multiplier (ALM) method. Finally, extensive experiments on simulated and
real-world noise HSIs are carried out to demonstrate the superiority of the
proposed method over the existing state-of-the-art ones.Comment: 15 pages, 20 figure
Efficient Fast-Convolution-Based Waveform Processing for 5G Physical Layer
This paper investigates the application of fast-convolution (FC) filtering
schemes for flexible and effective waveform generation and processing in the
fifth generation (5G) systems. FC-based filtering is presented as a generic
multimode waveform processing engine while, following the progress of 5G new
radio standardization in the Third-Generation Partnership Project, the main
focus is on efficient generation and processing of subband-filtered cyclic
prefix orthogonal frequency-division multiplexing (CP-OFDM) signals. First, a
matrix model for analyzing FC filter processing responses is presented and used
for designing optimized multiplexing of filtered groups of CP-OFDM physical
resource blocks (PRBs) in a spectrally well-localized manner, i.e., with narrow
guardbands. Subband filtering is able to suppress interference leakage between
adjacent subbands, thus supporting independent waveform parametrization and
different numerologies for different groups of PRBs, as well as asynchronous
multiuser operation in uplink. These are central ingredients in the 5G waveform
developments, particularly at sub-6-GHz bands. The FC filter optimization
criterion is passband error vector magnitude minimization subject to a given
subband band-limitation constraint. Optimized designs with different guardband
widths, PRB group sizes, and essential design parameters are compared in terms
of interference levels and implementation complexity. Finally, extensive coded
5G radio link simulation results are presented to compare the proposed approach
with other subband-filtered CP-OFDM schemes and time-domain windowing methods,
considering cases with different numerologies or asynchronous transmissions in
adjacent subbands. Also the feasibility of using independent transmitter and
receiver processing for CP-OFDM spectrum control is demonstrated
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