Generalized Filtering Decomposition

This paper introduces a new preconditioning technique that is suitable for matrices arising from the discretization of a system of PDEs on unstructured grids. The preconditioner satisfies a so-called filtering property, which ensures that the input matrix is identical with the preconditioner on a given filtering vector. This vector is chosen to alleviate the effect of low frequency modes on convergence and so decrease or eliminate the plateau which is often observed in the convergence of iterative methods. In particular, the paper presents a general approach that allows to ensure that the filtering condition is satisfied in a matrix decomposition. The input matrix can have an arbitrary sparse structure. Hence, it can be reordered using nested dissection, to allow a parallel computation of the preconditioner and of the iterative process

Parallel-Interference-Cancellation-Assisted Decision-Directed Channel Estimation for OFDM Systems using Multiple Transmit Antennas

The number of transmit antennas that can be employed in the context of least-squares (LS) channel estimation contrived for orthogonal frequency division multiplexing (OFDM) systems employing multiple transmit antennas is limited by the ratio of the number of subcarriers and the number of significant channel impulse response (CIR)-related taps. In order to allow for more complex scenarios in terms of the number of transmit antennas and users supported, CIR-related tap prediction-filtering-based parallel interference cancellation (PIC)-assisted decision-directed channel estimation (DDCE) is investigated. New explicit expressions are derived for the estimator’s mean-square error (MSE), and a new iterative procedure is devised for the offline optimization of the CIR-related tap predictor coefficients. These new expressions are capable of accounting for the estimator’s novel recursive structure. In the context of our performance results, it is demonstrated, for example, that the estimator is capable of supporting L = 16 transmit antennas, when assuming K = 512 subcarriers and K0 = 64 significant CIR taps, while LS-optimized DDCE would be limited to employing L = 8 transmit antennas. Index Terms—Decision-directed channel estimation (DDCE), multiple transmit antennas, orthogonal frequency division multiplexing (OFDM), parallel interference cancellation (PIC)

High-loop-delay sixth-order bandpass continuous-time sigma-delta modulators

International audienceThis study focuses on the design of high-loop-delay modulators for parallel sigma-delta conversion. Parallel converters, allowing a global low oversampling ratio, consist of several bandpass modulators with adjacent central frequencies. To ensure the global performance, the noise transfer function (NTF) of each modulator must be adjusted regarding its central frequency. In this thematic a new topology of sixth-order modulators based on weighted-feedforward techniques is developed. This topology offers an adequate control of the NTF at each central frequency by simple means. Additive signal paths are moreover proposed to obtain an auto-filtering signal transfer function. An optimisation method is also developed to calculate the optimised coefficients of the modulators at different central frequencies. The main concerns are improving the stability and reducing the sensitivity of the continuous-time circuit to analogue imperfections. This is essential for parallel conversion since, in each channel, the modulator works at a central frequency which differs from the fourth of the sampling frequency. The performance of the optimised modulator is compared with its discrete-time counterpart with good argument

Thermally-robust spatiotemporal parallel reservoir computing by frequency filtering in frustrated magnets

Physical reservoir computing is a framework for brain-inspired information processing that utilizes nonlinear and high-dimensional dynamics in non-von-Neumann systems. In recent years, spintronic devices have been proposed for use as physical reservoirs, but their practical application remains a major challenge, mainly because thermal noise prevents them from retaining short-term memory, the essence of neuromorphic computing. Here, we propose a framework for spintronic physical reservoirs that exploits frequency domain dynamics in interacting spins. Through the effective use of frequency filters, we demonstrate, for a model of frustrated magnets, both robustness to thermal fluctuations and feasibility of frequency division multiplexing. This scheme can be coupled with parallelization in spatial domain even down to the level of a single spin, yielding a vast number of spatiotemporal computational units. Furthermore, the nonlinearity via the exchange interaction allows information processing among different frequency threads. Our findings establish a design principle for high-performance spintronic reservoirs with the potential for highly integrated devices

Comparative study of parallel hybrid filters in resonance damping

The resonance damping of parallel hybrid filters is analyzed. Active filters can be made to behave as variable resistances and/or inductances when they are connected in power systems; and the operating condition of the complete system can be adjusted dynamically to damp resonance. The effect of the hybrid filter configuration and the control strategy has been evaluated on the resonance damping, as well as harmonic filtering. The frequency characteristics of three parallel hybrid filters topologies are discussed. The principles are validated by simulation and key time-domain results are presented

Neural grey-box guitar amplifier modelling with limited data

This paper combines recurrent neural networks (RNNs) with the discretised Kirchhoff nodal analysis (DK-method) to create a grey-box guitar amplifier model. Both the objective and subjective results suggest that the proposed model is able to outperform a baseline black-box RNN model in the task of modelling a guitar amplifier, including realistically recreating the behaviour of the amplifier equaliser circuit, whilst requiring significantly less training data. Furthermore, we adapt the linear part of the DK-method in a deep learning scenario to derive multiple state-space filters simultaneously. We frequency sample the filter transfer functions in parallel and perform frequency domain filtering to considerably reduce the required training times compared to recursive state-space filtering. This study shows that it is a powerful idea to separately model the linear and nonlinear parts of a guitar amplifier using supervised learning