Percorrer por autor "Lima, Miguel"
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- Dynamical analysis of compositionsPublication . Costa, António Cardoso; Machado, J. A. Tenreiro; Lima, MiguelThis paper analyzes musical opus from the point of view of two mathematical tools, namely the entropy and the multidimensional scaling (MDS). The Fourier analysis reveals a fractional dynamics, but the time rhythm variations are diluted along the spectrum. The combination of time-window entropy and MDS copes with the time characteristics and is well suited to treat a large volume of data. The experiments focus on a large number of compositions classified along three sets of musical styles, namely “Classical”, “Jazz”, and “Pop & Rock” compositions. Without lack of generality, the present study describes the application of the tools and the sets of musical compositions in a methodology leading to clear conclusions, but extensions to other possibilities are straightforward. The results reveal significant differences in the musical styles, demonstrating the feasibility of the proposed strategy and motivating further developments toward a dynamical analysis of musical compositions.
- Experimental backlash study in mechanical manipulatorsPublication . Lima, Miguel; Machado, J. A. Tenreiro; Crisóstomo, ManuelThe behavior of mechanical manipulators with backlash is analyzed. In order to acquire and study the signals an experimental setup is implemented. The signal processing capabilities of the wavelets are used for de-noising the experimental signals and the energy of the obtained components is analyzed. To evaluate the backlash effect upon the robotic system, it is proposed an index based on the pseudo phase plane representation. Several tests are developed that demonstrate the coherence of the results.
- Filtering method in backlash phenomena analysisPublication . Lima, Miguel; Machado, J. A. Tenreiro; Crisóstomo, ManuelThe behavior of robotic manipulators with backlash is analyzed. Based on the pseudo-phase plane two indices are proposed to evaluate the backlash effect upon the robotic system: the root mean square error and the fractal dimension. For the dynamical analysis the noisy signals captured from the system are filtered through wavelets. Several tests are developed that demonstrate the coherence of the results.
- Fractional dynamics in liquid manipulationPublication . Lima, Miguel; Machado, J. A. TenreiroThis paper presents a fractional calculus perspective in the study of signals captured during the movement of a mechanical manipulator carrying a liquid container. In order to study the signals an experimental setup is implemented. The system acquires data from the sensors, in real time, and, in a second phase, processes them through an analysis package. The analysis package runs off-line and handles the recorded data. The results show that the Fourier spectrum of several signals presents a fractional behavior. The experimental study provides useful information that can assist in the design of a control system and the trajectory planning to be used in reducing or eliminating the effect of vibrations.
- Fractional Dynamics Representation in the Pseudo Phase PlanePublication . Lima, Miguel; Machado, J. A. TenreiroThis paper analyses the spectral of several robotic signals that are approximated by trendlines. The fractional or integer dynamics behavior is determined. For the pseudo phase plane reconstruction of each signal, the time delays are calculated through the fractal dimension, as an alternative to the mutual information that is often used. In order to obtain a smooth pseudo phase plane, the noisy signals are filtered through wavelets. The slopes of the trendlines spectra reveal a relationship with the fractal dimension of the pseudo phase plane and the corresponding time lag.
- A multidimensional scaling analysis of musical sounds based on pseudo phase planePublication . Lima, Miguel; Costa, António Cardoso; Machado, J. A. TenreiroThis paper studies musical opus from the point of view of three mathematical tools: entropy, pseudo phase plane (PPP), and multidimensional scaling (MDS). The experiments analyze ten sets of different musical styles. First, for each musical composition, the PPP is produced using the time series lags captured by the average mutual information. Second, to unravel hidden relationships between the musical styles the MDS technique is used. The MDS is calculated based on two alternative metrics obtained from the PPP, namely, the average mutual information and the fractal dimension. The results reveal significant differences in the musical styles, demonstrating the feasibility of the proposed strategy and motivating further developments towards a dynamical analysis of musical sounds.
- A multidimensional scaling classification of robotic sensorsPublication . Lima, Miguel; Machado, J. A. TenreiroThis chapter analyzes the signals captured during impacts and vibrations of a mechanical manipulator. Eighteen signals are captured and several metrics are calculated between them, such as the correlation, the mutual information and the entropy. A sensor classification scheme based on the multidimensional scaling technique is presented.
- On the Sensor Classification Scheme of Robotic ManipulatorsPublication . Lima, Miguel; Machado, J. A. Tenreiro; Crisóstomo, Manuel; Ferrolho, AntónioThis paper analyzes the signals captured during impacts and vibrations of a mechanical manipulator. The Fourier Transform of eighteen different signals are calculated and approximated by trendlines based on a power law formula. A sensor classification scheme based on the frequency spectrum behavior is presented.
- Robotic manipulators with vibrations: short time fourier transform of fractional spectraPublication . Lima, Miguel; Machado, J. A. Tenreiro; Crisóstomo, ManuelThis paper analyzes the signals captured during impacts and vibrations of a mechanical manipulator. In order to acquire and study the signals an experimental setup is implemented. The signals are treated through signal processing tools such as the fast Fourier transform and the short time Fourier transform. The results show that the Fourier spectrum of several signals presents a non integer behavior. The experimental study provides valuable results that can assist in the design of a control system to deal with the unwanted effects of vibrations.
- A sensor classification strategy for robotic manipulatorsPublication . Lima, Miguel; Machado, J. A. Tenreiro; Ferrolho, AntónioIn practice the robotic manipulators present some degree of unwanted vibrations. The advent of lightweight arm manipulators, mainly in the aerospace industry, where weight is an important issue, leads to the problem of intense vibrations. On the other hand, robots interacting with the environment often generate impacts that propagate through the mechanical structure and produce also vibrations. In order to analyze these phenomena a robot signal acquisition system was developed. The manipulator motion produces vibrations, either from the structural modes or from endeffector impacts. The instrumentation system acquires signals from several sensors that capture the joint positions, mass accelerations, forces and moments, and electrical currents in the motors. Afterwards, an analysis package, running off-line, reads the data recorded by the acquisition system and extracts the signal characteristics. Due to the multiplicity of sensors, the data obtained can be redundant because the same type of information may be seen by two or more sensors. Because of the price of the sensors, this aspect can be considered in order to reduce the cost of the system. On the other hand, the placement of the sensors is an important issue in order to obtain the suitable signals of the vibration phenomenon. Moreover, the study of these issues can help in the design optimization of the acquisition system. In this line of thought a sensor classification scheme is presented. Several authors have addressed the subject of the sensor classification scheme. White (White, 1987) presents a flexible and comprehensive categorizing scheme that is useful for describing and comparing sensors. The author organizes the sensors according to several aspects: measurands, technological aspects, detection means, conversion phenomena, sensor materials and fields of application. Michahelles and Schiele (Michahelles & Schiele, 2003) systematize the use of sensor technology. They identified several dimensions of sensing that represent the sensing goals for physical interaction. A conceptual framework is introduced that allows categorizing existing sensors and evaluates their utility in various applications. This framework not only guides application designers for choosing meaningful sensor subsets, but also can inspire new systems and leads to the evaluation of existing applications. Today’s technology offers a wide variety of sensors. In order to use all the data from the diversity of sensors a framework of integration is needed. Sensor fusion, fuzzy logic, and neural networks are often mentioned when dealing with problem of combing information from several sensors to get a more general picture of a given situation. The study of data fusion has been receiving considerable attention (Esteban et al., 2005; Luo & Kay, 1990). A survey of the state of the art in sensor fusion for robotics can be found in (Hackett & Shah, 1990). Henderson and Shilcrat (Henderson & Shilcrat, 1984) introduced the concept of logic sensor that defines an abstract specification of the sensors to integrate in a multisensor system. The recent developments of micro electro mechanical sensors (MEMS) with unwired communication capabilities allow a sensor network with interesting capacity. This technology was applied in several applications (Arampatzis & Manesis, 2005), including robotics. Cheekiralla and Engels (Cheekiralla & Engels, 2005) propose a classification of the unwired sensor networks according to its functionalities and properties. This paper presents a development of a sensor classification scheme based on the frequency spectrum of the signals and on a statistical metrics. Bearing these ideas in mind, this paper is organized as follows. Section 2 describes briefly the robotic system enhanced with the instrumentation setup. Section 3 presents the experimental results. Finally, section 4 draws the main conclusions and points out future work.