1,720,992 research outputs found
The HydroNet ASV, a Small-Sized Autonomous Catamaran for Real-Time Monitoring of Water Quality: From Design to Missions at Sea
No full textIn this paper, we describe the design, the development, and the sea trials of a novel small-sized autonomous surface vehicle (ASV) designed for monitoring the coastal water quality. The vehicle is characterized by the capability of measuring hydrocarbon and heavy metal concentrations directly onboard by means of custom-made miniaturized sensors. This capability, novel for an ASV, is combined with a winch-based sampling system specifically designed for small-sized vehicles. The sampling system can collect water samples up to 50 m in depth and measure the physical/water quality parameters of the water column. With these two features, the HydroNet ASV provides an autonomous, practical, real-time monitoring system, conceived to complement the current water monitoring practices in which samples are collected by a dedicated boat and analyzed in specialized laboratories at a later stage. The design process had the aim of realizing a vehicle capable of hosting the sampling system and the custom-made sensors that represent a unique payload in the world of small-sized ASVs. A twofold objective was pursued: realizing an ASV suited for monitoring missions in realistic scenarios (e.g., attention was paid to avoid water sample contamination), at the same time limiting the size for the ease of transportability and deployment. Severe constraints rose from these considerations and were addressed during the realization of the robot such as reduced length/weight (that limit the available space for the sensor payload) and low draft and protected propellers to allow the ASV to navigate in shallow waters with likely floating obstacles such as plastic bags. We report the design process aiming at a tradeoff between ease of transportability (small vehicle), available payload, and navigation performance in terms of achievable speed, endurance, and resistance to environmental disturbances (favored by larger ASV dimensions). We present sea trials of the realized vehicle validating the design choices. In particular, a long-range mission is discussed in which the robot executed a monitoring survey covering autonomously 12.5 km in front of Livorno, Italy, coast
Enhancing Activity Recognition of Self-Localized Robot Through Depth Camera and Wearable Sensors
Full text linkRobots will become part of our everyday life as helpers and companions, sharing the environment with us. Thus, robots should become social and able to naturally interact with the users. Recognizing human activities and behaviors will enhance the capabilities of the robot to plan an appropriate action and tailor the approach according to what the user is doing. Therefore, this paper addresses the problem of providing mobile robots with the ability to recognize common daily activities. The fusion of heterogeneous data gathered by multiple sensing strategies, namely wearable inertial sensors, depth camera, and location features, is proposed to improve the recognition of human activity. In particular, the proposed work aims to recognize 10 activities using data from a depth camera mounted on a mobile robot able to self-localize in the environment and from customized sensors worn on the hand. Twenty users were asked to perform the selected activities in two different relative positions between them and the robot while the robot was moving. The analysis was carried out considering different combinations of sensors to evaluate how the fusion of the different technologies improves the recognition abilities. The results show an improvement of 13% in the F-measure when different sensors are considered with respect to the use of the sensors of the robot. In particular, the system is able to recognize not only the performed activity, but also the relative position, enhancing the robot capabilities to interact with the users
A neural network approach to human posture classification and fall detection using RGB-D camera
No full textIn this paper, we describe a human posture classification and a falling detector module suitable for smart homes and assisted living solutions. The system uses a neural network that processes the human joints produced by a skeleton tracker using the depth streams of an RGB-D sensor. The neural network is able to recognize standing, sitting and lying postures. Using only the depth maps from the sensor, the system can work in poor light conditions and guarantees the privacy of the person. The neural network is trained with a dataset produced with the Kinect tracker, but it is also tested with a different human tracker (NiTE). In particular, the aim of this work is to analyse the behaviour of the neural network even when the position of the extracted joints is not reliable and the provided skeleton is confused. Real-time tests have been carried out covering the whole operative range of the sensor (up to 3.5 m). Experimental results have shown an overall accuracy of 98.3% using the NiTE tracker for the falling tests, with the worst accuracy of 97.5%
A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data
No full textHuman activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO). The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60) and the Telecommunication Systems Team (TST) Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames) and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
A systematic method for dynamic modeling and identification of a small-sized autonomous surface vehicle using simulated annealing techniques
No full textThis paper presents and validates a method for the dynamic modeling and identification of an Autonomous Surface Vehicle (ASV) taking into account the performance of the sensors usually installed in relatively low-cost surface vehicles. For the estimate of the parameters of the adopted model we propose an onboard sensor-based, off-line identification procedure based on Simulated Annealing. The method is systematic and was successfully applied to produce a nonlinear model of a robotic catamaran belonging to the HydroNet ASV class [1]. The used sensors consisted of a compass measuring the heading angle and a paddle wheel speed sensor to measure the robot surge speed: GPS data were not used during the identification to loose the need of a relatively expensive GPS receiver to produce an accurate model. Extended experiments at sea showed our approach is effective in producing a dynamic model of the ASV. It uses data produced by inexpensive sensors in sea trials without needing costly facilities such as tow-tanks or planar motion mechanisms. The resulting model proved sufficiently accurate to be a valid support to simulation and control law design
Two-person Activity Recognition using Skeleton Data
Full text linkHuman activity recognition is an important and active field of research
having a wide range of application in numerous fields, including ambient
assisted living. Although most of the researches are focused on the single
user, the ability to recognize two-person interactions is perhaps more
important for its social implications.
This paper presents a two-person activity recognition system that uses
skeleton data extracted from a depth camera. The human actions are encoded
using a set of a few basic postures obtained with an unsupervised
clustering approach. Multiclass Support Vector Machines (SVMs) are
used to build models on the training set, while the X-means algorithm
is employed to dynamically find the optimal number of clusters for each
sample during the classification phase. The system is evaluated on the
ISR-UoL and SBU datasets, reaching an overall accuracy of 0.87 and 0.88
respectively. Although the results show that the performances of the system
are comparable with the state-of-the-art, recognition improvements
are obtained with the activities related to health-care environments, showing
promise for applications in the assisted-living realm
- …
