Percepções sociais sobre o futuro da humanidade no planeta: um estudo na cidade de Piracicaba, SP

A pesquisa sobre as percepções de residentes da região de Piracicaba em relação às questões ambientais e ao futuro da humanidade no planeta foi desenvolvida com base em um questionário semi-estruturado. As questões buscaram levantar dados autoavaliativos sobre perfil, comportamentos, estado de humor, qualidade de vida, condição econômica e hábitos de consumo, práticas para destinação de resíduos, iniciativas de exercício de cidadania em prol da sustentabilidade socioambiental, para enfim, indagar sobre percepções de futuro e avaliações sobre o contexto socioambiental dos participantes da pesquisa. A aplicação dos questionários foi feita de forma aleatória estratificada nos bairros das cinco regiões da cidade de Piracicaba: Norte, Sul, Leste, Oeste e Centro. Por meio dessas aplicações, foram obtidos 655 questionários, que foram sistematizados, tabulados e analisados estatisticamente, utilizando-se gráficos de frequência, o Teste de Kruskal Wallis, os Testes de Correlação de Spearman e Kendall, o Teste de Qui-Quadrado e o Teste Exato de Fisher. Foram também criadas nuvens de palavras, desenvolvidas no software online \"Wordle\" (FEINBERG, 2014). Os resultados obtidos com essa pesquisa e as análises desenvolvidas indicam que 227 pessoas, isto é, aproximadamente 35% dos respondentes, possui uma percepção pessimista sobre o futuro da humanidade no planeta. Porém, 493 pessoas, o equivalente a aproximadamente 75% do total de respondentes, considerou que, dentre as alternativas apresentadas no questionário (Desenvolvimento de tecnologias; Controle de natalidade; Educação e mudanças culturais; Cobrança de impostos com base nos impactos ambientais; Intervenção do Estado), a educação e mudanças culturais são fundamentais no processo de transformação social e de superação das problemáticas ambientais. Observou-se também que a crise hídrica vivenciada na época em que os questionários foram aplicados também influenciou na percepção social dos respondentes, uma vez que a palavra \"água\" foi citada 380 vezes. Por meio do trabalho, foi também possível analisar o comportamento ambiental dos pesquisados, notando-se que ainda há a necessidade de promoção de atividades educacionais e comunicacionais que possam estimular a adoção de hábitos e comportamentos mais comprometidos com ideias de sustentabilidade e que levem a mudanças mais efetivas nos padrões de relacionamento entre sociedade e meio ambiente.

A strongly magnetized pulsar within the grasp of the milky way’s supermassive black hole

The center of our Galaxy hosts a supermassive black hole, Sagittarius (Sgr) A∗. Young, massive stars within 0.5 pc of Sgr A∗ are evidence of an episode of intense star formation near the black hole a few million years ago, which might have left behind a young neutron star traveling deep into Sgr A∗’s gravitational potential. On 2013 April 25, a short X-ray burst was observed from the direction of the Galactic center. With a series of observations with the Chandra and the Swift satellites, we pinpoint the associated magnetar at an angular distance of 2.4±0.3 arcsec from Sgr A∗, and refine the source spin period and its derivative (P = 3.7635537(2) s and ˙ P = 6.61(4) × 10−12 s s−1), confirmed by quasi simultaneous radio observations performed with the Green Bank Telescope and Parkes Radio Telescope, which also constrain a dispersion measure of DM = 1750 ± 50 pc cm−3, the highest ever observed for a radio pulsar. We have found that this X-ray source is a young magnetar at ≈0.07–2 pc from Sgr A∗. Simulations of its possible motion around Sgr A∗ show that it is likely (∼90% probability) in a bound orbit around the black hole. The radiation front produced by the past activity from the magnetar passing through the molecular clouds surrounding the Galactic center region might be responsible for a large fraction of the light echoes observed in the Fe fluorescence features.

An obscured cluster associated with the H II region RCW173

Context. The discovery of several clusters of red supergiants towards l = 24°−30° has triggered interest in this area of the Galactic plane, where lines of sight are very complex and previous explorations of the stellar content were very preliminary. Aims. We attempt to characterise the stellar population associated with the H ii region RCW 173 (=Sh2-60), located at, as previous studies have suggested that this population could be beyond the Sagittarius arm. Methods. We obtained UBV photometry of a stellar field to the south of the brightest part of RCW 173, as well as spectroscopy of about twenty stars in the area. We combined our new data with archival 2MASS near-infrared photometry and Spitzer/GLIMPSE imaging and photometry, to achieve a more accurate characterisation of the stellar sources and the associated cloud. Results. We find a significant population of early-type stars located at d = 3.0 kpc, in good agreement with the “near” dynamical distance to the H ii region. This population should be located at the near intersection of the Scutum-Crux arm. A luminous O7 II star is likely to be the main source of ionisation. Many stars are concentrated around the bright nebulosity, where GLIMPSE images in the mid infrared show the presence of a bubble of excited material surrounding a cavity that coincides spatially with a number of B0-1 V stars. We interpret this as an emerging cluster, perhaps triggered by the nearby O7 II star. We also find a number of B-type giants. Some of them are located at approximately the same distance, and may be part of an older population in the same area, characterised by much lower reddening. A few have shorter distance moduli and are likely to be located in the Sagittarius arm. Conclusions. The line of sight in this direction is very complex. Optically visible tracers delineate two spiral arms, but seem to be absent beyond d ≈ 3 kpc. Several H ii regions in this area suggest that the Scutum-Crux arm contains thick clouds actively forming stars. All these populations are projected on top of the major stellar complex signposted by the clusters of red supergiants.

Portable 3D laser-camera calibration system with color fusion for SLAM

Nowadays, the use of RGB-D sensors have focused a lot of research in computer vision and robotics. These kinds of sensors, like Kinect, allow to obtain 3D data together with color information. However, their working range is limited to less than 10 meters, making them useless in some robotics applications, like outdoor mapping. In these environments, 3D lasers, working in ranges of 20-80 meters, are better. But 3D lasers do not usually provide color information. A simple 2D camera can be used to provide color information to the point cloud, but a calibration process between camera and laser must be done. In this paper we present a portable calibration system to calibrate any traditional camera with a 3D laser in order to assign color information to the 3D points obtained. Thus, we can use laser precision and simultaneously make use of color information. Unlike other techniques that make use of a three-dimensional body of known dimensions in the calibration process, this system is highly portable because it makes use of small catadioptrics that can be placed in a simple manner in the environment. We use our calibration system in a 3D mapping system, including Simultaneous Location and Mapping (SLAM), in order to get a 3D colored map which can be used in different tasks. We show that an additional problem arises: 2D cameras information is different when lighting conditions change. So when we merge 3D point clouds from two different views, several points in a given neighborhood could have different color information. A new method for color fusion is presented, obtaining correct colored maps. The system will be tested by applying it to 3D reconstruction.

Portable 3D laser-camera calibration system with color fusion for SLAM

Paper submitted to the 43rd International Symposium on Robotics (ISR2012), Taipei, Taiwan, Aug. 29-31, 2012.

3D visual sensing of human hand for remote operation of a robotic hand

New low cost sensors and open free libraries for 3D image processing are making important advances in robot vision applications possible, such as three-dimensional object recognition, semantic mapping, navigation and localization of robots, human detection and/or gesture recognition for human-machine interaction. In this paper, a novel method for recognizing and tracking the fingers of a human hand is presented. This method is based on point clouds from range images captured by a RGBD sensor. It works in real time and it does not require visual marks, camera calibration or previous knowledge of the environment. Moreover, it works successfully even when multiple objects appear in the scene or when the ambient light is changed. Furthermore, this method was designed to develop a human interface to control domestic or industrial devices, remotely. In this paper, the method was tested by operating a robotic hand. Firstly, the human hand was recognized and the fingers were detected. Secondly, the movement of the fingers was analysed and mapped to be imitated by a robotic hand.

RGBD Human-Hand recognition for the Interaction with Robot-Hand

New low cost sensors and the new open free libraries for 3D image processing are permitting to achieve important advances for robot vision applications such as tridimensional object recognition, semantic mapping, navigation and localization of robots, human detection and/or gesture recognition for human-machine interaction. In this paper, a method to recognize the human hand and to track the fingers is proposed. This new method is based on point clouds from range images, RGBD. It does not require visual marks, camera calibration, environment knowledge and complex expensive acquisition systems. Furthermore, this method has been implemented to create a human interface in order to move a robot hand. The human hand is recognized and the movement of the fingers is analyzed. Afterwards, it is imitated from a Barret hand, using communication events programmed from ROS.

Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC

Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods.

Magnetars: Properties, Origin and Evolution

Magnetars are neutron stars in which a strong magnetic field is the main energy source. About two dozens of magnetars, plus several candidates, are currently known in our Galaxy and in the Magellanic Clouds. They appear as highly variable X-ray sources and, in some cases, also as radio and/or optical pulsars. Their spin periods (2–12 s) and spin-down rates (∼10−13–10−10 s s−1) indicate external dipole fields of ∼1013−15 G, and there is evidence that even stronger magnetic fields are present inside the star and in non-dipolar magnetospheric components. Here we review the observed properties of the persistent emission from magnetars, discuss the main models proposed to explain the origin of their magnetic field and present recent developments in the study of their evolution and connection with other classes of neutron stars.

3D model reconstruction using neural gas accelerated on GPU

In this work, we propose the use of the neural gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other neural methods like growing neural gas and Kohonen maps or classical methods like Voxel Grid. We also reconstructed models acquired by low cost sensors that can be used in virtual and augmented reality environments for redesign or manipulation purposes. Since the NG algorithm has a strong computational cost we propose its acceleration. We have redesigned and implemented the NG learning algorithm to fit it onto Graphics Processing Units using CUDA. A speed-up of 180× faster is obtained compared to the sequential CPU version.

3DCOMET: 3D compression methods test dataset

The use of 3D data in mobile robotics applications provides valuable information about the robot’s environment. However usually the huge amount of 3D information is difficult to manage due to the fact that the robot storage system and computing capabilities are insufficient. Therefore, a data compression method is necessary to store and process this information while preserving as much information as possible. A few methods have been proposed to compress 3D information. Nevertheless, there does not exist a consistent public benchmark for comparing the results (compression level, distance reconstructed error, etc.) obtained with different methods. In this paper, we propose a dataset composed of a set of 3D point clouds with different structure and texture variability to evaluate the results obtained from 3D data compression methods. We also provide useful tools for comparing compression methods, using as a baseline the results obtained by existing relevant compression methods.

SMR analysis using 3DPC datasets

Datasets and results of the paper: Characterization of rock slopes through slope mass rating using 3D point clouds, Riquelme et al 2016, IJRMMS.

Evaluation of sampling method effects in 3D non-rigid registration

Since the beginning of 3D computer vision problems, the use of techniques to reduce the data to make it treatable preserving the important aspects of the scene has been necessary. Currently, with the new low-cost RGB-D sensors, which provide a stream of color and 3D data of approximately 30 frames per second, this is getting more relevance. Many applications make use of these sensors and need a preprocessing to downsample the data in order to either reduce the processing time or improve the data (e.g., reducing noise or enhancing the important features). In this paper, we present a comparison of different downsampling techniques which are based on different principles. Concretely, five different downsampling methods are included: a bilinear-based method, a normal-based, a color-based, a combination of the normal and color-based samplings, and a growing neural gas (GNG)-based approach. For the comparison, two different models have been used acquired with the Blensor software. Moreover, to evaluate the effect of the downsampling in a real application, a 3D non-rigid registration is performed with the data sampled. From the experimentation we can conclude that depending on the purpose of the application some kernels of the sampling methods can improve drastically the results. Bilinear- and GNG-based methods provide homogeneous point clouds, but color-based and normal-based provide datasets with higher density of points in areas with specific features. In the non-rigid application, if a color-based sampled point cloud is used, it is possible to properly register two datasets for cases where intensity data are relevant in the model and outperform the results if only a homogeneous sampling is used.

Notebook of John Allen, 1772

Paper-covered notebook containing handwritten poems and verse by Harvard graduate John Allen. Some of the poems refer to Allen’s illnesses in October 1772. The notebook also contains a short list titled “The Gentleman that I wrote diplomas for," with a list of sixteen individuals who received degrees from Harvard. The inside cover includes the inscription: “John Allen – November 4, 1772. Poetic Composition.” “Dr. T. C. Gilman” is stamped on cover.

Stress Inversion Using LiDAR Tunnel Deformation Measurements

Far-field stresses are those present in a volume of rock prior to excavations being created. Estimates of the orientation and magnitude of far-field stresses, often used in mine design, are generally obtained by single-point measurements of stress, or large-scale, regional trends. Point measurements can be a poor representation of far-field stresses as a result of excavation-induced stresses and geological structures. For these reasons, far-field stress estimates can be associated with high levels of uncertainty. The purpose of this thesis is to investigate the practical feasibility, applications, and limitations of calibrating far-field stress estimates through tunnel deformation measurements captured using LiDAR imaging. A method that estimates the orientation and magnitude of excavation-induced principal stress changes through back-analysis of deformation measurements from LiDAR imaged tunnels was developed and tested using synthetic data. If excavation-induced stress change orientations and magnitudes can be accurately estimated, they can be used in the calibration of far-field stress input to numerical models. LiDAR point clouds have been proven to have a number of underground applications, thus it is desired to explore their use in numerical model calibration. The back-analysis method is founded on the superposition of stresses and requires a two-dimensional numerical model of the deforming tunnel. Principal stress changes of known orientation and magnitude are applied to the model to create calibration curves. Estimation can then be performed by minimizing squared differences between the measured tunnel and sets of calibration curve deformations. In addition to the back-analysis estimation method, a procedure consisting of previously existing techniques to measure tunnel deformation using LiDAR imaging was documented. Under ideal conditions, the back-analysis method estimated principal stress change orientations within ±5° and magnitudes within ±2 MPa. Results were comparable for four different tunnel profile shapes. Preliminary testing using plastic deformation, a rough tunnel profile, and profile occlusions suggests that the method can work under more realistic conditions. The results from this thesis set the groundwork for the continued development of a new, inexpensive, and efficient far-field stress estimate calibration method.