Microblogs have increasingly been used by the crowd to post their thoughts and speeches about everything. Thus, one of the themes is the stock market that is exploited by many researchers. Although obtaining indicators of stock market dynamics through online social networking has been gaining the attention of academia and the business world, there are many questions to be analyzed about their effectiveness. This work presents the development of a simulator for buying and selling stocks based on microblog data. Therefore, we collected tweets about the Brazilian stock exchange market, produced indicators using sentiment analysis and performed a set of heuristics for decision making. The first technique is the composition of the decision-making strategy for buying and selling stocks composed of pure logic, tweets volume thresholds, profit objective and technical analysis in the stock exchange. The contribution of this work is a decision-making architecture using Twitter's data as an index of future expectation about the social mood that may change the market behavior. As a result, it has pointed to attractive profits for Brazilian market actions and many issues that can be analyzed and improved. Our study showed that it is possible to obtain market dynamics information on the Twitter social network and this information could be used to compose stock buying and selling strategies.

This paper presents a new strategy for task space control in the cooperative manipulation framework. We extend the cooperative dual task-space (CDTS) - which uses dual quaternions to represent the bimanual manipulation-to explicitly regard self-motion dynamics that arise from redundant kinematics. In this sense, we propose a flexible task execution criterion that enriches the Jacobian null space with additional degrees of freedom by relaxing control requirements upon specific geometric task objectives. The criterion is satisfied with a hysteresis-based switching strategy that ensures stability and convergence upon traditional and relaxed constraints. Simulation results highlight the importance and effectiveness of the proposed technique.

This paper addresses the H∞robust control problem for robot manipulators using unit dual quaternion representation, which allows an utter description of the end-effector transformation without decoupling rotational and translational dynamics. We propose three different H∞control criteria that ensure asymptotic convergence, whereas reducing the influence of disturbances upon the system stability. Also, with a new metric of dual quaternion error in SE(3) we prove independence from robot coordinate changes. Simulation results highlight the importance and effectiveness of the proposed approach in terms of performance, robustness, and energy efficiency.

This paper presents a semi-automatic system for robotically assisted 2D needle steering that uses duty-cycling to perform insertions with arcs of adjustable curvature radius. It combines image feedback manually provided by an operator with an adaptive path planning strategy to compensate for system uncertainties and changes in the workspace during the procedure. Experimental results are presented to validate the proposed platform.

This paper presents an adaptive approach for 2D motion planning of steerable needles. It combines duty-cycled rotation of the needle with the classic Rapidly-Exploring Random Tree (RRT) algorithm to obtain fast calculation of feasible trajectories. The motion planning is used intraoperatively at each cycle to compensate for system uncertainties and perturbations. Simulation results demonstrate the performance of the proposed motion planner on a workspace based on ultrasound images.

This work addresses the problem of stochastic data fusion for systems liable to heavy disturbances, which denote environmental perturbations strong enough to modify the system's internal structure, including signal interference, sensor faults, physical structure modification, and many other sources of disturbance. In these such cases, traditional filtering methods usually fail to provide reliable estimates because of the highly corrupted sensor measurements. This work proposes to model the data fusion problem for heavily disturbed systems through a hybrid systems modeling framework and presents an online hybrid stochastic filter capable of tracking the system's state in unfavorable operating conditions. Simulated and experimental results compare the proposed filter's performance with the traditionally used Extended Kalman Filter (EKF) and show its usefulness as a robust localization filter for an Unmanned Aerial Vehicle (UAV) designed for aerial power lines inspection.

This paper presents a novel two-step approach for modeling forward and backward network delays in networked control systems (NCS). The first step is to build a colored Petri net (CPN) structural model for the simulation of Ethernet based networked control systems. The modular model captures the most important features of industrial networked control systems, such as client/server input/output scanning and cyclical execution of the control algorithm on a programmable logic controller (PLC). CPN tools software is used to build and simulate the model with different parameter sets. This first easy to build model is used in a second step to identify a compact phenomenological model from an extensive set of simulation results. In this second step a finite state Markov chain delay model (FSMD) is used to capture the delay patterns generated with the CPN model. The resulting model is more compact than the CPN model and due to its mathematical form it can be integrated in existing design and analysis methods for NCS.

This paper presents a new path planning algorithm that uses adaptive random walks to incrementally construct a roadmap in the robot's free configuration space. This algorithm, named Incremental Adaptive Random Walks (iARW), uses a modified version of the ARW algorithm proposed by Carpin and Pillonetto for exploring the configuration space and storing the discovered path in a roadmap. Thus, the main idea is to use bidirectional adaptive random walks to explore the configuration space but also to use and expand the roadmap whenever possible.With this approach it is possible to construct a roadmap that captures the connectivity of the free configuration space without a preprocessing phase. A comparison of our approach with other state of the art path planners illustrates the good performance of the proposed method.

This paper presents a new application in the field of rehabilitation robotics. It is part of a research project which consists of the development of a robotic leg prosthesis for above knee amputees. This application aims at providing real-time estimates of the prosthesis foot orientation with respect to ground. An Extended Kalman Filter (EKF) is used in order to estimate the foot orientation based on gyroscope and infrared measurements. Moreover a constraint equation is also included in the EKF to guarantee the constraints in the estimates. Preliminary tests were performed in a platform built for the prosthesis prototype. The results discussed in this paper reveal the feasibility of such technique.

In this paper, the kinematic model of a quadruped robot is derived. The model is equivalent to that of a parallel manipulator, in that each leg can be seen as a manipulator. However, the model is extended to consider that in one gait cycle some legs are in contact with the ground and others are not. In order to obtain the inverse kinematics model, this paper presents as contribution the use of the extended Kalman filter as optimizer in two different situations of the leg motion: unconstrained case, for the swing leg(s), and constrained case, for the leg(s) in contact with the ground. This method was evaluated for locomotion in plain and inclined surfaces. The results obtained with the kinematics model were satisfactory when implemented in a point-to-point trajectory in simulation, and also in an experiment with a four-legged platform with three degrees of freedom in each leg.

This letter proposes algorithms for the automatic identification of direct (Pg) and head (Pn) wave groups from first break readings of a deep seismic refraction profile. The process employs a split-and-merge fuzzy iterative prototype clustering technique to extract line segments from first arrivals. A constant velocity value is used as a threshold to identify Pg and Pn groups. Synthetic and real data were used to evaluate the algorithm with satisfactory identification results.

This work is focused on the design of a rotor speed regulator for small-scale helicopters. A nominal model identification procedure for the main rotor system is performed using an iterative hybrid procedure. Such a model has both linear and nonlinear components and models not only engine throttle but also the effect of collective pitch variation. Collective pitch is the main disturbance source. The identified model is used in the design of different control strategies: polynomial, optimal linear quadratic, both with and without integral action, and H∞. Experimental evaluations were conducted to verify the controllers performance under different circumstances.

This paper presents a reconfigurable matrix VoC that can be applied to stereo vision computation. VoC accelerates block pixel matching by providing a highly parallel implementation of the sum of absolute differences metric. Reconfigurability allows VoC to deal with different block sizes, ranging from a single 7 /spl times/ 7 SAD computation to 9 simultaneous 5 /spl times/ 5 block computations. The pipelined version mapped to Xilinx FPGA could be simulated at 158 MHz, producing 1.42 billion matching per second.

This paper introduces a decoupled approach of concurrent mapping and localization for mobile robots. Its theoretical aspects rely on recent techniques for correct uncertainty handling using stochastic models: covariance intersection and unscented transform. Further, stochastic constraints are considered as a way to minimize map incoherence with respect to the real environment. Experimental results obtained from multisensory data acquired in a large real environment illustrate the performance of the proposed method.

We propose a weighted least-squares (WLS) algorithm for optimal pose estimation of mobile robots using geometrical maps as environment models. Pose estimation is achieved from feature correspondences in a nonlinear framework without linearization. The proposed WLS approach yields optimal estimates in the least-squares sense, is applicable to heterogeneous geometrical features decomposed in points and lines, and has an O(N) computation time.

Theoretical solutions based on the matching of 2D range measurements with a map of the environment have been proposed to solve the robot localization problem. However most of them have not been experimented with in real conditions: the robot was stopped or it moved slowly during range data acquisition, and the environment was supposed to be static. We propose and evaluate a dynamic localization method based on feature matching. Experiments carried out in real cluttered indoor environments including people and unknown obstacles show the good performance of the proposed algorithm against the classical solution based on Kalman filtering.

This paper presents a segmentation method for line extraction in 2D range images. It uses a prototype-based fuzzy clustering algorithm in a split-and-merge framework. The split-and-merge structure allows one to use the fuzzy clustering algorithm without any previous knowledge on the number of prototypes. This algorithm aims to be used in mobile robots navigation systems for dynamic map building. Simulation results show its good performance compared to some classical approaches.

This article describes an iterative algorithm for relative motion estimation from 2D range images. The matching is achieved in the geometric feature domain represented by straight lines and ellipsoidal clusters. Using infinite length straight lines instead of line segments as features, the algorithm attempts to achieve robustness to partial occlusions. The motion estimates and the feature correspondence measures are determined in order to minimize a cost function. The performance of this algorithm was evaluated with experiments carried out in real cluttered indoor environments.