Renato Candido

• Ph.D.
• PostDoc Position at University of São Paulo

In this paper, we perform the bias-variance decomposition of the mean-square deviation of the least-mean-squares algorithm during both the transient and steady-state phases. Although this solution has been extensively studied, to the best of our knowledge, this type of analysis has not been done before explicitly in this manner. We analyze a wide r...

In this paper, we analyze the effects of random sampling on adaptive diffusion networks. These networks consist in a collection of nodes that can measure and process data, and that can communicate with each other to pursue a common goal of estimating an unknown system. In particular, we consider in our theoretical analysis the diffusion least-mean-...

Diffusion kernel algorithms are interesting tools for distributed non-linear estimation. However, for the sake of feasibility, it is essential in practice to restrict their computational cost and the number of communications. In this paper, we propose a censoring algorithm for adaptive kernel diffusion networks based on random Fourier features that...

Recently, we proposed a sampling algorithm for diffusion networks that locally adapts the number of nodes sampled according to the estimation error. Thus, it reduces the computational cost associated with the learning task when the error is low in magnitude, e.g., during steady state, and maintains the sampling of the nodes otherwise, which enables...

We propose a normalized least mean squares algorithm with variable step size. Unlike other solutions, it has low computational cost, only three parameters that are simple to choose, and its steady-state performance can be easily predicted. Simulations show a competitive performance in comparison with other solutions, and validate our theoretical an...

Recently, we proposed a sampling algorithm for diffusion networks, which adapts locally the number of sampled nodes based on the estimation error. It reduces energy consumption and the computational cost. Its performance depends on the choice of the parameter that penalizes sampling, which is a function of the noise power. Inadequate choices of thi...

In this work, we study the effect of dividing the electrocardiogram signal records of different patients between the training and test sets for automatic diagnosis of cardiac arrhythmias. We use convolutional neural network to classify the heartbeats of the signal. The results indicate that using the heartbeats of the same patient in both sets lead...

In this work, we use multilayer neural networks (MLP), recurrent neural networks, linear discriminant analysis (LDA) and combinations of these methods for automatic classification of cardiac arrhythmias. In order to obtain clinically realistic results for the diagnosis, patients records used during the training phase were not used in the test set....

Adaptive diffusion networks have attracted attention in the scientific community as an efficient solution for distributed estimation of signals. They also have been employed in nonlinear estimation problems with distributed kernel adaptive algorithms. These solutions present a high computational cost due to the dictionary of kernel algorithms. In t...

In this paper, we propose a sampling mechanism for adaptive diffusion networks that adaptively changes the amount of sampled nodes based on mean-squared error in the neighborhood of each node. It presents fast convergence during transient and a significant reduction in the number of sampled nodes in steady state. Besides reducing the computational...

Kernel adaptive filters are used to solve nonlinear problems, such as the equalization and decoding of chaos-based communication systems (CBCS). This use was investigated in previous works. However, only with CBCS that use bidimensional maps as chaotic signal generators (CSG), which have higher computational cost. In this paper, it is proposed the...

Distributed signal processing has attracted widespread attention in the scientific community due to its several advantages over centralized approaches. Recently, graph signal processing has risen to prominence, and adaptive distributed solutions have also been proposed in the area. Both in the classical framework and in graph signal processing, sam...

Distributed signal processing has attracted widespread attention in the scientific community due to its several advantages over centralized approaches. Recently, graph signal processing has risen to prominence, and adaptive distributed solutions have also been proposed in the area. Both in the classical framework and in graph signal processing, sam...

In this paper, we propose a sampling mechanism for adaptive diffusion networks that adaptively changes the amount of sampled nodes based on mean-squared error in the neighborhood of each node. It presents fast convergence during transient and a significant reduction in the number of sampled nodes in steady state. Besides reducing the computational...

Graph signal processing has attracted attention in the signal processing community, since it is an effective tool to deal with large quantities of interrelated data. Recently, a diffusion algorithm for graph adaptive filtering was proposed. However, it suffers from high computational cost since all nodes in the graph are sampled even in steady stat...

Graph signal processing has attracted attention in the signal processing community, since it is an effective tool to deal with great quantities of interrelated data. Recently, a diffusion algorithm for adaptively learning from streaming graphs signals was proposed. However, it suffers from high computational cost since all nodes in the graph are sa...

Chaos-based communication systems have attracted attention of researchers in academy and industry in the last decades. A particular family of such systems has as basic idea to use the transmitted message to modify a known nonlinear chaotic signal generator (CSG). In the receiver, the knowledge of the employed nonlinear CSG in conjunction with chaot...

In the last decades many possible applications of nonlinear dynamics in communication systems and signal processing have been reported. Conversely, techniques usually employed by the signal processing and communication systems communities, as correlation, power spectral density analysis, and linear filters, among others have been used to characteri...

Image restoration seeks to eliminate distortions caused in the acquisition process. In this paper, we use an MLP (Multilayer Perceptron) to restore images containing four gray levels that have been degraded by a Gaussian point spread function.

Graph signal processing has been gaining widespread attention within the scientific community, due to the fact that it is an effective tool to deal with great quantities of interrelated data. Applications include communication networks, image analysis, temperature estimation, etc. Recently, two adaptive solutions based on the LMS (least-mean-square...

Cooperative strategies can be exploited in nonlinear adaptive filtering to improve the performance of most available schemes and to facilitate the selection of their parameters.

In this paper, we propose a scheme to simplify the selection of kernel adaptive filters in a multikernel structure. By multiplying the output of each kernel filter by an adaptive biasing factor between zero and one, the degrading effects of poorly adjusted kernel filters can be minimized, increasing the robustness of the multikernel scheme. This ap...

Many communication systems based on the synchronization of chaotic systems have been proposed in the literature. However, due to the lack of robustness of chaos synchronization, in most cases even minor channel imperfections are enough to hinder communication. In this paper, we propose the use of kernel adaptive filters to decode the message and si...

Many communication systems based on the synchronization of chaotic systems have been proposed as an alternative spread spectrum modulation that improves the level of privacy in data transmission. However, due to the lack of robustness of complete chaotic synchronization, even minor channel impairments are enough to hinder communication. In this pap...

Many communication systems based on the synchronization of chaotic systems have been proposed as an alternative spread spectrum modulation that improves the level of privacy in data transmission. However, due to the lack of robustness of complete chaotic synchronization, even minor channel impairments are enough to hinder communication. In this pap...

Neste artigo, é proposta uma função de codificação para sistemas de comunicação baseados em caos, que assegura a geração de sinais caóticos. Com base nessa função, é apresentado um esquema de equalização adaptativa. Por meio de resultados de simulação, é mostrado que esse sistema apresenta maior imunidade à interferência intersimbólica e ruı́do.

Many communication systems based on the synchronism of chaotic systems have been proposed as an alternative spread spectrum modulation that improves the level of privacy in data transmission. However, depending on the map and on the encoding function, the transmitted signal may cease to be chaotic. Therefore, the sensitive dependence on initial con...

Many communication systems applying synchronism of chaotic systems have been proposed as an alternative spread spectrum modulation that improves the level of privacy in data transmission. However, due to the lack of robustness of chaos synchronization, even minor channel imperfections are enough to hinder communication. In this paper, we propose an...

In this chapter, we propose an adaptive equalization scheme for a chaos-based digital communication system. The studied system is a discrete-time version of the one proposed by Wu and Chua that has recently been considered for optical applications. Due to the nonlinear characteristics of the transmitters and receivers and the lack of robustness of...

Many communication systems based on the synchronism of chaotic systems have been proposed in the literature. However, due to the lack of robustness of chaos synchronization, in most cases even minor channel imperfections are enough to hinder communication. In this paper, we propose an adaptive equalization scheme to recover a binary sequence modula...

Many communication systems based on the synchronism of chaotic systems have been proposed as an alternative spread spectrum modulation that improves the level of privacy in data transmission. However, due to the lack of robustness of chaos synchronization, even minor channel imperfections are enough to hinder communication. In this paper, we propos...

In this paper, we propose an approach to the transient and steady-state analysis of the affine combination of one fast and one slow adaptive filters. The theoretical models are based on expressions for the excess mean-square error (EMSE) and cross-EMSE of the component filters, which allows their application to different combinations of algorithms,...

Blind equalization algorithms with good convergence and tracking properties and numerical robustness are desired to ensure the good performance of communications systems. In this paper, we present transient and steady-state analyses for the dual-mode constant modulus algorithm (DM-CMA), a version of CMA that avoids its well-known divergence problem...

Combination schemes are gaining attention as an interesting way to improve adaptive filter performance. In this paper we pay attention to a particular convex combination scheme with nonlinear adaptation that has recently been shown to be universal -i.e., to perform at least as the best component filter- in steady-state; however, no theoretical mode...

We extend the affine combination of one fast and one slow least mean- square (LMS) filter to blind equalization, considering the combination of two constant modulus algorithms (CMA). We analyze the proposed combination in stationary and nonstationary environments verifying that there are situations where the absence of the restriction on the mix- i...

We extend the analysis presented in for the affine combination of two least mean-square (LMS) filters to allow for colored inputs and nonstationary environments. Our theoretical model deals, in a unified way, with any combinations based on the following algorithms: LMS, normalized LMS (NLMS), and recursive-least squares (RLS). Through the analysis,...

alise em regime. Abstract— Recently, an affine combination of two least mean- square (LMS) adaptive filters was proposed and its transient performance analyzed. This method combines linearly the out- puts of two LMS filters operating in parallel with different step- sizes. The purpose of the combination is to obtain an LMS adaptive filter with fast...