A sensor classification strategy for robotic manipulators

In 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.

Voices in search of a reader: the polyphonic writing of António Lobo Antunes

This paper aims at analysing the writing of the Portuguese author António Lobo Antunes, considered one of the major writers in European Literature with 26 books published, by focusing on the strategies deployed in his texts of creating micro-narratives within the main frame, and conveying the elements of individual and collective memory, past and present, the self and the others, using various voices and silences. Lobo Antunes incorporates in his writing his background as a psychiatrist at a Mental Hospital in Lisbon, until 1985 (when he decided to commit exclusively to writing), his experience as a doctor in the Portuguese Colonial War battlefield, but also the daily routines of the pre and post 25th of April 1974 (Portuguese Revolution) with subtle and ironic details of the life of the middle and upper class of Lisbon‘s society: from the traumas of the war to the simple story of the janitor, or the couple who struggles to keep their marriage functional, everything serves as material to develop and interweave a complex plot, that a lot of readers find too enwrapped and difficult to follow through. Some excerpts taken from his first three novels and books of Chronicles and his later novel – Ontem não te Vi em Babilónia (2006) – will be put forward to exemplify the complexity of the writing and the main difficulties of the reader, lost in a multitude of narrators‘ voices. Recently, Lobo Antunes has commented on his work stating: What I write can be read in the darkness. This paper aims at throwing some light by unfolding some of the strategies employed to defy new borders in the process of reading.

On the development of an automatic voice pleasantness classification and intensity estimation system

In the last few years, the number of systems and devices that use voice based interaction has grown significantly. For a continued use of these systems, the interface must be reliable and pleasant in order to provide an optimal user experience. However there are currently very few studies that try to evaluate how pleasant is a voice from a perceptual point of view when the final application is a speech based interface. In this paper we present an objective definition for voice pleasantness based on the composition of a representative feature subset and a new automatic voice pleasantness classification and intensity estimation system. Our study is based on a database composed by European Portuguese female voices but the methodology can be extended to male voices or to other languages. In the objective performance evaluation the system achieved a 9.1% error rate for voice pleasantness classification and a 15.7% error rate for voice pleasantness intensity estimation.

The role of functional polymorphisms in immune response genes as biomarkers of BCG Immunotherapy outcome in bladder cancer: Establishment of a predictive profile in a Southern Europe population

OBJECTIVE: To evaluate the predictive value of genetic polymorphisms in the context of BCG immunotherapy outcome and create a predictive profile that may allow discriminating the risk of recurrence. MATERIAL AND METHODS: In a dataset of 204 patients treated with BCG, we evaluate 42 genetic polymorphisms in 38 genes involved in the BCG mechanism of action, using Sequenom MassARRAY technology. Stepwise multivariate Cox Regression was used for data mining. RESULTS: In agreement with previous studies we observed that gender, age, tumor multiplicity and treatment scheme were associated with BCG failure. Using stepwise multivariate Cox Regression analysis we propose the first predictive profile of BCG immunotherapy outcome and a risk score based on polymorphisms in immune system molecules (SNPs in TNFA-1031T/C (rs1799964), IL2RA rs2104286 T/C, IL17A-197G/A (rs2275913), IL17RA-809A/G (rs4819554), IL18R1 rs3771171 T/C, ICAM1 K469E (rs5498), FASL-844T/C (rs763110) and TRAILR1-397T/G (rs79037040) in association with clinicopathological variables. This risk score allows the categorization of patients into risk groups: patients within the Low Risk group have a 90% chance of successful treatment, whereas patients in the High Risk group present 75% chance of recurrence after BCG treatment. CONCLUSION: We have established the first predictive score of BCG immunotherapy outcome combining clinicopathological characteristics and a panel of genetic polymorphisms. Further studies using an independent cohort are warranted. Moreover, the inclusion of other biomarkers may help to improve the proposed model.

An automatic voice pleasantness classification system based on prosodic and acoustic patterns of voice preference

In the last few years the number of systems and devices that use voice based interaction has grown significantly. For a continued use of these systems the interface must be reliable and pleasant in order to provide an optimal user experience. However there are currently very few studies that try to evaluate how good is a voice when the application is a speech based interface. In this paper we present a new automatic voice pleasantness classification system based on prosodic and acoustic patterns of voice preference. Our study is based on a multi-language database composed by female voices. In the objective performance evaluation the system achieved a 7.3% error rate.