Diego Haddad

Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (CEFET/RJ) · Engenharia de Computação

Person reidentification, i.e., retrieving a person of interest across several non-overlapping cameras, is a task that is far from trivial. Despite its great commercial value and wide range of applications (e.g., surveillance, intelligent environments, forensics, service robotics, marketing), it remains unsolved, even when the individuals do not cha...

Person reidentification, i.e., retrieving a person of interest across several non-overlapping cameras, is a task that is far from trivial. Despite its great commercial value and wide range of applications (e.g., surveillance, intelligent environments, forensics, service robotics, marketing), it remains unsolved, even when the individuals do not cha...

Unmanned Aerial Vehicles are a suitable solution to automate inspections on large structures that require periodic monitoring once this process could be complex and highly dynamic. In this sense, 3D path planners are crucial in aerial tasks. However, a drawback is the computational time dependency on the environment’s complexity and scale. This wor...

In most applications of wireless sensor networks the specification of the corresponding topology can be useful for the optimization of some important features, such as: node energy consumption, connectivity and coverage area. This is known as the Sensor Allocation Problem (SAP). Our work proposes an approach based on memetic algorithm concepts to f...

The coefficient reuse strategy is able to improve the steady-state performance of adaptive filter algorithms, especially in very challenging low signal-to-noise scenarios. This paper advances deterministic and stochastic models that predict various learning characteristics of the LMS algorithm with coefficient reuse. First-order and second-order an...

The novel concept of Digital Twins has become ubiquitous in manufacturing operations. It allows the creation of a virtual environment, in which all devices (or even the system itself) have digital pairs (twins) at the cloud. Doing so, one can instantiate these digital pairs in order to perform predictions, for example. Moreover, the real and digita...

In the offshore oil and gas industry, the most productive fields are located in increasingly distant waters, so the oil pipelines as a solution to transport the production become less attractive to build and operate. Therefore, the offshore loading operations with shuttle tanker (ST) vessels take the lead in transporting oil production. These opera...

Linear active noise control (ANC) systems have been used in the past to effectively suppress Gaussian noise. A practical ANC system must consider nonlinearities in the secondary path with a non-minimum phase. For an ANC system to effectively operate in a modern-day acoustic environment with multiple electrical/electronic systems operating in the vi...

The recent development of new offshore projects in pre-salt deepwater fields has placed offshore loading operations as the main production outflow alternative, increasing the operational complexity and risks. Numerous dangerous situations are associated with oil offloading, such as the messenger line transfer during the mooring stage. Nowadays, thi...

Rotating machines are frequently subject to a wide range of rough conditions, resulting in mechanical failures and performance degradation. Thus, it is important to apply proper failure detection and recognition techniques, such as machine learning algorithms, to prevent these issues early. In industrial environments, little data exists regarding f...

Optical image sensors are the most common remote sensing data acquisition devices present in Unmanned Aerial Systems (UAS). In this context, assigning a location in a geographic frame of reference to the acquired image is a necessary task in the majority of the applications. This process is denominated direct georeferencing when ground control poin...

One of the main challenges of maneuvering an Unmanned Aerial Vehicle (UAV) to keep a stabilized flight is dealing with its fast and highly coupled nonlinear dynamics. There are several solutions in the literature, but most of them require fine-tuning of the parameters. In order to avoid the exhaustive tuning procedures, this work employs a Fuzzy Lo...

The capabilities of signal processing tools to extract high-level features of interest from fiber sensors measurements have not been properly explored for interrogation purposes. In this context, this paper advances an interrogation system for long-period grating sensors by means of a novel low-cost method that combines an optical time-domain refle...

The constrained least mean square (CLMS) algorithm is one of the most popular online linear-equality-constrained beamforming algorithms. This paper demonstrates for the first time that it solves a deterministic minimum-disturbance optimization problem in an exact manner. Such a framework is employed to insert the coefficient reusing technique into...

Among the existing energy storage devices, supercapacitors have attracted an increasing attention in recent years. In this work, two different transition metal (M = Cu, Co) oxides grafted on graphitic carbon nitride (GCN) nanosheets were successfully synthesized through single-step pyrolysis assisted route. The as-prepared nanocomposites exhibit hi...

Sparse component analysis techniques have been successfully applied to the separation of speech sources. This paper presents an efficient algorithm based on the matching pursuit approach to deal with multichannel records. The proposed algorithm explicitly employs spatial constraints among different channels to express mixed signals as linear combin...

A popular computer puzzle, the game of Minesweeper requires its human players to have a mix of both luck and strategy to succeed. Analyzing these aspects more formally, in our research, we assessed the feasibility of a novel methodology based on reinforcement learning as an adequate approach to tackle the problem presented by this game. For this...

Sensitive information being shared on the internet is growing. Becauseof this, it is increasingly necessary to take security measureswhilst this information travels in the network. Digital steganographyallows one to send sensitive information in a hidden manner.Although there is a plethora of techniques for such a goal, findingan appropriate one is...

Failure detection from mechanical vibration analysis is crucial in industry machinery, with early discovery allowing for preventive action to be performed. This paper introduces a prototype of an IoT system capable of (i) identifying combined failures of a rotating machine and (ii) predicting failures, in a non-invasive manner. An embedded solution...

Compared to standard solutions, soft robotics presents enhanced adaptability to unpredictable and unstructured environments, encompassing advances in fabrication, modeling, and control. The absence of a general theory for the latter is one of the biggest challenges in the field, which constrains these robots’ employment in real-world applications....

The adjustable weights of adaptive filtering algorithms are usually assumed to obey a Gaussian distribution. This is somewhat natural under maximal-entropy considerations, since most analyses in the open literature only take into account first-and second-order statistics. This work investigates the third-order statistical feature known as skewness...

Adaptive filtering algorithms implement an estimation of a set of parameters. Frequently, the system to be identified is sparse, in the sense that most of its energy is concentrated among a few coefficients. Adaptive algorithms, such as the \(\ell _0\)-LMS, can incorporate this property in order to increase the convergence rate. In this work, a sto...

Adaptive filtering algorithms, which adopt the set-membership strategy, are able to attain good steady-state performance with low computational burden. In general, such advantages are obtained by defining a bounded-error. This specification translates into a time-variant step size, chosen in each iteration according to a nonlinear function of the i...

Abstract Speech enhancement and acoustic noise reduction are two important tasks where adaptive filtering algorithms emerge as a competitive solution. Unfortunately, in such applications the convergence rate of the system identification is hampered when the excitation data is highly correlated. Subband adaptive algorithms have been developed to add...

The current COVID-19 pandemic is affecting different countries in different ways. The assortment of reporting techniques alongside other issues, such as underreporting and budgetary constraints, makes predicting the spread and lethality of the virus a challenging task. This work attempts to gain a better understanding of how COVID-19 will affect on...

In most supervised adaptive filtering settings, only the additive noise of the reference signal is taken into account. However, in many practical situations the excitation data is also immersed in noise, which leads to a bias in the estimation procedure. In order to mitigate such issue, adaptive algorithms with bias compensation schemes have been p...

Abstract Adaptive filtering algorithms are widespread today owing to their flexibility and simplicity. Due to environments in which they are normally immersed, their robustness against noise has been a topic of interest. Traditionally, in the literature it is assumed that noise is mainly active in the reference signal. Since this hypothesis is ofte...

Dye-sensitized solar cells, termed as DSSC's have been gaining interest from researchers and industry in the last few years. Such an interest derives from their low-cost manufacturing and easy processing to replace conventional silicon cells. A DSSC operating system consists of three steps: a photoelectrode (anode), an electrolyte solution and a co...

Considering the increasingly wide application of optical fiber sensors, this article aims to present an alternative form of interrogation without the use of an optical spectrum analyzer or any other high‐cost devices. The sensor studied here is a long‐period grating being used to measure temperature. The interrogator is composed of optical filters...

Convolutive mixtures of signals generated by more than one source and acquired by a set of microphones are commonly found in acoustic signal processing applications. Subband methods have been proposed to reduce computational complexity and improve the convergence rate of adaptive algorithms developed for blind source separation of these mixtures, w...

Active noise control is an expanding field that requires a suitable synthesis of secondary perturbations. Unfortunately, most schemes for noise cancelling do not take into account that the input signal that drives the adaptive filter can be noisy. In this paper, it is theoretically shown that noise perturbations in the excitation data degrade the p...

Subband adaptive filtering algorithms can increase the convergence rate of system identification tasks when the input signal is colored. Recently, a new normalized subband adaptive filtering algorithm with sparse subfilters (NSAF-SF) has been proposed, whose main advantage is the lower computational complexity when compared to state-of-the-art subb...

Nonstationary environments are ubiquitous in communications and acoustic systems. The ability to track their dynamics is one of the most desirable features of adaptive processing algorithms. The designers of these algorithms employ guidelines derived from stochastic analyses to adjust user-defined parameters to maximize performance or avoid stabili...

It is known that adaptive filtering algorithms may tackle relevant communication tasks. In order to reduce the adaptation rate, the least mean squares algorithm and its normalized version may be implemented in a block manner, so that the filter coefficients are adjusted once per each output block. This paper advances a stochastic model that is able...

Wireless Sensor Networks (WSN) are of significant importance with increasingly diverse and viable applications. They gained even more traction after the IEEE 802.15.4 standard was defined. Distributed adaptive filtering algorithms have added statistical inference to WSN applications, employing techniques that extract data from distributed devices....

In the last decades, current trends in autonomous navigation have demonstrated an increased use of computational vision over traditional techniques. This relies on the fact that most of the spaces are designed for human navigation. As a result, they are filled with visual cues. In this sense, visual recognition is an essential ability to avoid obst...

Brazilian society suffers constant financial losses when higher education students disassociate from universities without completing the degree program in which they were enrolled. This is especially true when the institutions are funded through public resources. In order to minimize evasion losses, socioeconomic policies and programs were created...

One important strength of adaptive filtering algorithms is their ability to learn from their interactions with the environment. Such a flexibility comes with a price, since their performance strongly depends on adjustable parameters. In order to provide to the designer guidelines and guaranties for both performance and stability, the open literatur...

In this paper, a new family of adaptive filtering algorithms is presented, which aims to combine the small misalignment resulting from the reuse of past weight vectors with the fast convergence arising from the proportionate adaptation and logarithmic cost functions. This family of algorithms is obtained as a solution to a deterministic constra...

The current COVID-19 pandemic is affecting different countries in different ways. The assortment of reporting techniques alongside other issues, such as underreporting and budgetary constraints, makes predicting the spread and lethality of the virus a challenging task. This work attempts to gain a better understanding of how COVID-19 will affect on...

The current COVID-19 pandemic is affecting different countries in different ways. The assortment of reporting techniques alongside other issues, such as underreporting and budgetary constraints, makes predicting the spread and lethality of the virus a challenging task. This work attempts to gain a better understanding of how COVID-19 will affect on...

Due to the inherent feedback feature of adaptive filtering algorithms, a comprehensive theoretical understanding of their learning process is still challenging. In order to make the mathematics tractable, most stochastic analyses adopt simplifications. One of these is the independence assumption, which presumes statistical independence between adap...

Normalized subband adaptive filtering algorithms have attracted attention due to their ability to present faster convergence in the case of colored excitation data. The NSAF-SF scheme is an example of a state-of-the-art subband adaptive algorithm that demands a low computational burden. This paper proposes a deterministic local optimization approac...

In many practical applications, systems and signals show energy concentration in a few coefficients. This prior knowledge can often be incorporated into algorithms designed for tasks such as compressive sensing and system identification. This paper proposes a new LMS-based algorithm that exploits the hidden sparsity of the system that the adaptive...

In many practical applications, systems and signals show energy concentration in a few coefficients. This prior knowledge can often be incorporated into algorithms designed for tasks such as compressive sensing and system identification. This paper proposes a new LMS based algorithm that exploits the hidden sparsity of the system that the adaptive...

In recent years, the search to make cities smart often has been a strategy designed to mitigate problems generated by urban population growth. To improve the population's quality of life and optimize the use of resources and infrastructure, applications in various fields have been developed. Public bus services are widely deployed in cities around...

Dropout is one of the main challenges of educational institutions. In this sense, this paper presents the implementation of a Data Warehouse for data analysis and decision making in a higher education institution in Brazil. The presented Data Warehouse allows integrated views that assist in analysis such as: 1) distribution of students' performance...

Most adaptive filtering schemes employ the tapped-delay line. In part, such a fact can be explained by the assumption that the plant they intend to estimate is linear. Although such a hypothesis can be reasonable if the input signal is constrained to a certain range, sometimes it may not be valid. In this last case, the performance and stability gu...

An adaptive filtering algorithm should present fast convergence and good steady-state behaviour. Both metrics can be enhanced if one makes use of the sparsity (energy concentration in few coefficients) of the involved (unknown) transfer function. Unfortunately, such an approach can suffer from steady-state performance degradation when the signal-to...

The absence of a general theory of how to control soft structures is the biggest challenge in soft robotics. In this work the application of Scheduling by Multiple Edge Reversal (SMER) as an artificial Central Pattern Generator (CPG) is proposed for the activation of soft quadrupedal robots. In order to evaluate the proposed application, a soft dev...

Unmanned Aerial Vehicles (UAVs) have recently been used in a wide variety of applications due to their versatility, reduced cost, rapid deployment, among other advantages. Search and Rescue (SAR) is one of the most prominent areas for the employment of UAVs in place of a manned mission, especially because of its limitations on the costs, human reso...

The Volterra series consists of a powerful method for the identification of non-linear relationships. However, the identification of the series active basis sets requires intense research in order to reduce the computational burden of such a procedure. This is a result of a large number of measurements being required in order to produce an adequate...

Asset-Liability Management (ALM) is a technique used to optimize investment portfolios, considering a future flow of liabilities. Its stochastic nature and multi-period decision structure favors its modeling as a Markov Decision Process (MDP). Reinforcement Learning is a state-of-the-art group of algorithms for MDP solving, and with its recent perf...

Stochastic models that predict adaptive filtering algorithms performance usually employ several assumptions in order to simplify the analysis. Although these simplifications facilitate the recursive update of the statistical quantities of interest, they by themselves may hamper the modeling accuracy. This paper simultaneously avoids for the first t...

Noise is an ubiquitous phenomenon that hampers adaptive filtering-based system identification procedures. Recently, the coefficient reuse strategy has been proposed to address the challenging case where the signal-to-noise ratio is low. In this paper, a new derivation approach that incorporates both coefficient reusing (which reduces the oscillatio...

Sparse component analysis techniques have been successfully applied to the separation of speech sources. This article advances an efficient algorithm based on the matching pursuit approach to deal with multichannel records. The proposed algorithm explicitly employs spatial constraints among different channels with the purpose of expressing mixed si...

Stochastic models that predict adaptive filtering algorithms performance usually employ several assumptions in order to simplify the analysis. Although these simplifications facilitate the recursive update of the statistical quantities of interest, they by themselves may hamper the modeling accuracy. This paper simultaneously avoids for the first t...

In adaptive filtering, there is usually a trade-off between the speed of convergence and the accuracy of the learning procedure. Recently, variable step-size algorithms and coefficient vector reusing schemes were proposed to solve this trade-off. This paper presents a new adaptive filtering algorithm that combines both strategies to achieve fast co...

Educational Data Mining (EDM) may be an instrumental technique as much to understand student behavior as to plan and manage government investments in education. EDM helps to analyzes and to expose the hidden information of educational data. Notably, an essential application of EDM is to predict or analyze the students' dropout. This problem affects...

The choice of a fixed step size in adaptive filtering algorithms implies a conflict between the convergence rate and the steady-state performance. In order to address this trade-off more effectively, variable step-size schemes have been proposed. The efficiency evaluation of such techniques requires comparisons of the resulting step size values wit...