Gianni Vercelli

Bio: Gianni Vercelli is an academic researcher from University of Genoa. The author has contributed to research in topics: Mobile robot & Semantic Web. The author has an hindex of 11, co-authored 90 publications receiving 428 citations.

Tor traffic analysis and detection via machine learning techniques

Abstract: Tor is an anonymous Internet communication system based on the second generation of onion routing network protocol. Using Tor is really difficult to trace the users Internet activity: this is the reason why the usage of Tor is intended in order to protect the privacy of users, their freedom and the ability to conduct confidential communications without being monitored. Tor is even more used by cyber-criminals in order to cover their illegal activities: the Tor community has observed, for instance an alarming increase in the number of malware that abuse of the popular anonymizing network to hide their command and control infrastructures. In this paper we present a technique able to identify whether an host is generating Tor-related traffic. We resort to well-known machine learning algorithms in order to evaluate the effectiveness of the proposed feature set in a real world environment. In addition we demonstrate that the proposed method is able to recognize the kind of activity (e.g., email or P2P applications) the user under analysis is doing on the Tor network.

Shape analysis and hand preshaping for grasping

Abstract: Observations of human grasping show two phases. During the reaching phase of grasping, the hand preshapes in order to prepare for the second phases which is shape-matching with the object. Planning of grasping with dextrous robot hands cannot be summarized to these two phases. It is necessary to split the grasping process into several phases (frequently overlapped), and to consider problems such as object recognition, planning accessibility, task planning, initial touch and grab phase, and stable grasp phase. These are consciously or unconsciously generated by a human being. A major issue is to integrate a part of these components in preshaping, and in particular in automatic planning accessibility and preshaping of objects to be grasped. The system is based on 2D analysis of slices extracted from an octree representation of objects. >

Achieving dextrous grasping by integrating planning and vision-based sensing

Abstract: This article deals with the automation of dextrous grasping in a partly known environment using a stereo vision system and a multifingered hand mounted on a robot arm. Effective grasping requires a combination of sensing and planning capabilities: sensing to construct a well-adapted model of the situation and to guide the execution of the task, and planning to determine an appropriate grasping strategy and to generate safe, feasible manipulator motions. We propose an integrated approach that combines computer vision, path planning, and manipulator control in three complementary activities: the reconstruction of task-oriented models of the workspace, the determination of appropriate grasping configurations from computed preshapes of the hand, and the automatic generation and execution of hand/arm motions using a hybrid geometric path planner and a hybrid control system. This article outlines the architecture of our system, discusses the new models and techniques we have developed, and finishes with a brief description of work-in-progress on the implementation and some preliminary experimental results.

A Cognitive Hybrid Model for Autonomous Navigation

Abstract: Action representation and planning is one among the most important research fields in which it has been experienced the failure of single paradigms in isolation to solve real, complex problems The goal of this paper is to present a system for action representation and reasoning in complex, real-world, and real time scenarios, characterised by the integration of different representation paradigms: symbolic, diagrammatic, and procedural In this sense the system is called “hybrid” The paper focuses on the cognitive model and on the representation and reasoning system A realistic navigation system for the guidance and control of autonomous mobile robots is used as an example to describe the potentiality of the system in solving real complex problems and it is currently being tested in an indoor environment

Hand-eye calibration

Abstract: Whenever a sensor is mounted on a robot hand, it is important to know the relationship between the sensor and the hand. The problem of determining this relationship is referred to as the hand-eye calibration problem. Hand-eye calibration is impor tant in at least two types of tasks: (1) map sensor centered measurements into the robot workspace frame and (2) tasks allowing the robot to precisely move the sensor. In the past some solutions were proposed, particularly in the case of the sensor being a television camera. With almost no exception, all existing solutions attempt to solve a homogeneous matrix equation of the form AX = X B. This article has the following main contributions. First we show that there are two possible formulations of the hand-eye calibration problem. One formu lation is the classic one just mentioned. A second formulation takes the form of the following homogeneous matrix equation: MY = M'YB. The advantage of the latter formulation is that the extrinsic and intrinsic parameters of the camera need not be made explicit. Indeed, this formulation directly uses the 3 x4 perspective matrices ( M and M' ) associated with two positions of the camera with respect to the calibration frame. Moreover, this formulation together with the classic one covers a wider range of camera-based sensors to be calibrated with respect to the robot hand: single scan-line cameras, stereo heads, range finders, etc. Second, we develop a common mathematical framework to solve for the hand-eye calibration problem using either of the two formulations. We represent rotation by a unit quaternion and present two methods: (1) a closed-form solution for solving for rotation using unit quaternions and then solving for translation and (2) a nonlinear technique for simultane ously solving for rotation and translation. Third, we perform a stability analysis both for our two methods and for the lin ear method developed by Tsai and Lenz (1989). This analysis allows the comparison of the three methods. In light of this comparison, the nonlinear optimization method, which solves for rotation and translation simultaneously, seems to be the most robust one with respect to noise and measurement errors.

Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum

Abstract: The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps most notably, it has been applied extensively within fields ranging from engineering sciences to medicine, biology, and economics. We have collected a comprehensive list of 5384 scientific papers that use the algorithms, have benefited from them, or contain analyses of them. The list is intended to serve as a source for literature surveys. The present addendum contains 2092 new articles, mainly from the years 1998-2002. We have provided a keyword index to help finding articles of interest, and additionally a modern automatically constructed variant of a thematic index: a WEBSOM interface to the whole article collection of years 1981-2000. The SOM of SOMs is available at http://websom.hut.fi/websom/somref/search.cgi for browsing and searching the collection.

Dextrous hand grasping force optimization

Abstract: A key goal in dextrous robotic hand grasping is to balance external forces and at the same time achieve grasp stability and minimum grasping energy by choosing an appropriate set of internal grasping forces. Since it appears that there is no direct algebraic optimization approach, a recursive optimization, which is adaptive for application in a dynamic environment, is required. One key observation in this paper is that friction force limit constraints and force balancing constraints are equivalent to the positive definiteness of a certain matrix subject to linear constraints. Based on this observation, we formulate the task of grasping force optimization as an optimization problem on the smooth manifold of linearly constrained positive definite matrices for which there are known globally exponentially convergent solutions via gradient flows. There are a number of versions depending on the Riemannian metric chosen, each with its advantages, Schemes involving second derivative information for quadratic convergence are also studied. Several forms of constrained gradient flows are developed for point contact and soft-finger contact friction models. The physical meaning of the cost index used for the gradient flows is discussed in the context of grasping force optimization. A discretized version for real-time applicability is presented. Numerical examples demonstrate the simplicity, the good numerical properties, and optimality of the approach.

Toward automatic robot instruction from perception-mapping human grasps to manipulator grasps

Abstract: Our approach of programming a robot is by direct human demonstration. The system observes a human performing the task, recognizes the human grasp, and maps it onto the manipulator. This paper describes how an observed human grasp can be mapped to that of a given general-purpose manipulator for task replication. Planning the manipulator grasp based upon the observed human grasp is done at two levels: the functional and physical levels. Initially, at the functional level, grasp mapping is achieved at the virtual finger level; the virtual finger is a group of fingers acting against an object surface in a similar manner. Subsequently, at the physical level, the geometric properties of the object and manipulator are considered in fine-tuning the manipulator grasp. Our work concentrates on power or enveloping grasps and the fingertip precision grasps. We conclude by showing an example of an entire programming cycle from human demonstration to robot execution.