João Marcos Travassos Romano

Bio: João Marcos Travassos Romano is an academic researcher from State University of Campinas. The author has contributed to research in topics: Blind signal separation & Adaptive filter. The author has an hindex of 16, co-authored 186 publications receiving 1279 citations. Previous affiliations of João Marcos Travassos Romano include Ericsson & Pantheon-Sorbonne University.

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

Abstract: 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 fast least-squares algorithm for linearly constrained adaptive filtering

Abstract: 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.

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

Abstract: 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.

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

Abstract: 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.

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

Abstract: 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.

Space-Time Block Coding for Wireless Communications

Abstract: II

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Abstract: The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.