João Marcos Travassos Romano

A fast least-squares algorithm for linearly constrained adaptive filtering

An extension of the field of fast least-squares techniques is presented. It is shown that the adaptation gain, which is updated with a number of operations proportional to the number of transversal filter coefficients, can be used to update the coefficients of a linearly constrained adaptive filter. An algorithm that is robust to round-off errors is derived. It is general and flexible. It can handle multiple constraints and multichannel signals. Its performance is illustrated by simulations and compared with the classical LMS-based Frost (1972) algorithm.

The Compression of Electric Signal Waveforms for Smart Grids: State of the Art and Future Trends

In this paper, we discuss the compression of waveforms obtained from measurements of power system quantities and analyze the reasons why its importance is growing with the advent of smart grid systems. While generation and transmission networks already use a considerable number of automation and measurement devices, a large number of smart monitors and meters are to be deployed in the distribution network to allow broad observability and real-time monitoring. This situation creates new requirements concerning the communication interface, computational intelligence and the ability to process data or signals and also to share information. Therefore, a considerable increase in data exchange and in storage is likely to occur. In this context, one must achieve an efficient use of channel communication bandwidth and a reduced need of storage space for power system data. Here, we review the main compression techniques devised for electric signal waveforms providing an overview of the achievements obtained in the past decades. Additionally, we envision some smart grid scenarios emphasizing open research issues regarding compression of electric signal waveforms. We expect that this paper will contribute to motivate joint research efforts between electrical power system and signal processing communities in the area of signal waveform compression.

A Novel MDL-based Compression Method for Power Quality Applications

This paper introduces a novel source coding method for voltage and current signals, called fundamental, harmonic and transient coding method (FHTCM), which is a generalization of the enhanced disturbance compression method (EDCM). The proposed method makes use of notch filtering-warped discrete Fourier transform (NF-WDFT) technique for estimating the parameters (amplitude, frequency, and phase) of the fundamental and harmonic components acquired from power lines so that only the transient components are compressed with wavelet transform (WT) coding technique. For the WT-based compression of transient components, we formulate a minimum description length (MDL) criterion, taking into account the selection of wavelet bases in a dictionary, wavelet decomposition structure, and quantization. Computational simulations have verified that the proposed method outperforms the EDCM as well as the traditional WT-based compression techniques

Tensor space-time (TST) coding for MIMO wireless communication systems

In this paper, we propose a tensor space-time (TST) coding for multiple-input multiple-output (MIMO) wireless communication systems. The originality of TST coding is that it allows spreading and multiplexing the transmitted symbols, belonging to R data streams, in both space (antennas) and time (chips and blocks) domains, owing the use of two (stream- and antenna-to-block) allocation matrices. This TST coding is defined in terms of a third-order code tensor admitting transmit antenna, data stream and chip as modes. Assuming flat Rayleigh fading propagation channels, the signals received by K receive antennas during P time blocks, composed of N symbol periods each, with J chips per symbol, form a fourth-order tensor that satisfies a new constrained tensor model, called a PARATUCK-(2,4) model. Conditions for identifiability and uniqueness of this model are established, and a performance analysis of TST coding is made, before presenting a blind receiver for joint channel estimation and symbol recovery. Finally, some simulation results are provided to evaluate the performance of this receiver.

Unsupervised Processing of Geophysical Signals: A Review of Some Key Aspects of Blind Deconvolution and Blind Source Separation

This article reviews some key aspects of two important branches in unsupervised signal processing: blind deconvolution and blind source separation (BSS). It also gives an overview of their potential application in seismic processing, with an emphasis on seismic deconvolution. Finally, it presents illustrative results of the application, on both synthetic and real data, of a method for seismic deconvolution that combines techniques of blind deconvolution and blind source separation. Our implementation of this method contains some improvements overthe original method in the literature described.

An enhanced data compression method for applications in power quality analysis

This paper presents an enhanced method for data compression using a wavelet transform, to be applied in power systems signals for quality evaluation. The proposed approach is based on a previous estimation of the sinusoidal components of the signal under analysis, so that it could be subtracted from the original data in order to generate a transient type signal, which is subsequently applied to the compression techniques. The approach employs the Kalman filter and the adaptive notch filter techniques to provide the estimation of the sinusoidal components. Taking into account the wavelet property of sparse representation makes an improvement in the compression rate and in the signal degradation is attained. Finally, a proposed frame format to store the coded signal is presented.

An improved method for signal processing and compression in power quality evaluation

This paper introduces a new waveform coding technique, based on wavelet transform, for power quality monitoring purposes. The proposed enhanced data compression method (EDCM) presents a complete adaptive signal processing approach to estimate the fundamental sinusoidal component and separate it from the transient ones in the monitored signal. When these nonstationary components are submitted to the compression technique, the sparse representation property of the wavelet transform provides an improvement in the compression ratio. Also, the degradation inserted by the lossy compression process is minimized. Simulation results confirm the effectiveness of the proposed method when compared to the standard solution, characterized by the compression of the whole monitored signal.

Application of Blind Source Separation Methods to Ion-Selective Electrode Arrays in Flow-Injection Analysis

As shown recently, the interference problem typical of ion-selective electrodes can be dealt with via smart arrays adjusted by blind source separation methods. In this letter, we resume this study and show that such an approach can be applied even when faced with a limited number of samples acquired through flow-injection analysis.

Modified Kalman Filters for Channel Estimation in Orthogonal Space-Time Coded Systems

We derive and analyze two modified Kalman channel estimators (KCE) for time-varying, flat, spatially correlated MIMO channels in systems employing orthogonal space-time block codes: the steady-state KCE, which is less complex than the KCE, and the fading memory KCE, which is more robust to model mismatch.

Blind Compensation of Nonlinear Distortions: Application to Source Separation of Post-Nonlinear Mixtures

In this paper, we address the problem of blind compensation of nonlinear distortions. Our approach relies on the assumption that the input signal is bandlimited. We then make use of the classical result that the output of a nonlinearity has a wider spectrum than the one of the input signal. However, differently from previous works, our approach does not assume knowledge of the input signal bandwidth. The proposal is considered in the development of a two-stage method for blind source separation (BSS) in post-nonlinear (PNL) models. Indeed, once the functions present in the nonlinear stage of a PNL model are compensated, one can apply the well-established linear BSS algorithms to complete the task of separating the sources. Numerical experiments performed in different scenarios attest the viability of the proposal. Moreover, the proposed method is tested in a real situation where the data are acquired by smart chemical sensor arrays.