420 research outputs found
Kinematic Analysis and Quantitative Evaluation for Reach Movements in Stroke Rehabilitation
No full textabstract: In this thesis, quantitative evaluation of quality of movement during stroke rehabilitation will be discussed. Previous research on stroke rehabilitation in hospital has been shown to be effective. In this thesis, we study various issues that arise when creating a home-based system that can be deployed in a patient's home. Limitation of motion capture due to reduced number of sensors leads to problems with design of kinematic features for quantitative evaluation. Also, the hierarchical three-level tasks of rehabilitation requires new design of kinematic features. In this thesis, the design of kinematic features for a home based stroke rehabilitation system will be presented. Results of the most challenging classifier are shown and proves the effectiveness of the design. Comparison between modern classification techniques and low computational cost threshold based classification with same features will also be shown.Dissertation/ThesisM.S. Electrical Engineering 201
Coordinate coding on the riemannian manifold of symmetric positive-definite matrices for image classification
No full textOver the years, coding—in its broadest definition—has proven a crucial step in visual recognition systems [4, 7]. Many techniques have been investigated, such as bag of words [1, 9, 16, 18, 19, 31], sparse coding [21, 34], and locality-based coding[33, 35]. All these techniques follow a similar flow: Given a dictionary of code words, a query is associated to one or multiple dictionary elements with different weights (i.e. let@tokeneonedot, binary or real). These weights, or codes, act as the new representation for the query and serve as input to a classifier (i.e., support vector machine (SVM)) after an optional pooling step
Elastic shape analysis of surfaces and images
No full textWe describe two Riemannian frameworks for statistical shape analysis of parameterized surfaces. These methods provide tools for registration, comparison, deformation, averaging, statistical modeling, and random sampling of surface shapes. A crucial property of both of these frameworks is that they are invariant to reparameterizations of surfaces. Thus, they result in natural shape comparisons and statistics. The first method we describe is based on a special representation of surfaces termed square-root functions (SRFs). The pullback of the L2 metric from the SRF space results in the Riemannian metric on the space of surfaces. The second method is based on the elastic surface metric. We show that a restriction of this metric, which we call the partial elastic metric, becomes the standard L2 metric under the square-root normal field (SRNF) representation. We show the advantages of these methods by computing geodesic paths between highly articulated surfaces and shape statistics of manually generated surfaces. We also describe applications of this framework to image registration and medical diagnosis
Digital Media and Knowledge Production Within Social Movements: Insights From the Transition Movement in Italy
No full textsponsorship: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This article grounds in research activites carried out in the period 2016-2017 within the framework of the project "MAKERS- Movements as knowledge producers and learning spaces in the digital age" funded by the Scuola Normale Superiore. (Scuola Normale Superiore)status: Publishe
Statistical and Geometric Modeling of Spatio-Temporal Patterns for Video Understanding
No full textSpatio-temporal patterns abound in the real world, and understanding them computationally holds the promise of enabling a large class of applications such as video surveillance, biometrics, computer graphics and animation. In this dissertation, we study models and algorithms to describe complex spatio-temporal patterns in videos for a wide range of applications.
The spatio-temporal pattern recognition problem involves recognizing an input video as an instance of a known class. For this problem, we show that a first order Gauss-Markov process is an appropriate model to describe the space of primitives. We then show that the space of primitives is not a Euclidean space but a Riemannian manifold. We use the geometric properties of this manifold to define distances and statistics. This then paves the way to model temporal variations of the primitives. We then show applications of these techniques in the problem of activity recognition and pattern discovery from long videos.
The pattern discovery problem on the other hand, requires uncovering patterns from large datasets in an unsupervised manner for applications such as automatic indexing and tagging. Most state-of-the-art techniques index videos according to the global content in the scene such as color, texture and brightness. In this dissertation, we discuss the problem of activity based indexing of videos. We examine the various issues involved in such an effort and describe a general framework to address the problem. We then design a cascade of dynamical systems model for clustering videos based on their dynamics. We augment the traditional dynamical systems model in two ways. Firstly, we describe activities
as a cascade of dynamical systems. This significantly enhances the expressive power of the model while retaining many of the computational advantages of using dynamical models. Secondly, we also derive methods to incorporate view and rate-invariance into these models so that similar actions are clustered together irrespective of the viewpoint or
the rate of execution of the activity. We also derive algorithms to learn the model parameters from a video stream and demonstrate how a given video sequence may be segmented into different clusters where each cluster represents an activity.
Finally, we show the broader impact of the algorithms and tools developed in this dissertation for several image-based recognition problems that involve statistical inference over non-Euclidean spaces. We demonstrate how an understanding of the geometry of the
underlying space leads to methods that are more accurate than traditional approaches. We present examples in shape analysis, object recognition, video-based face recognition, and age-estimation from facial features to demonstrate these ideas
Timbral Learning for Musical Robots
No full textabstract: The tradition of building musical robots and automata is thousands of years old. Despite this rich history, even today musical robots do not play with as much nuance and subtlety as human musicians. In particular, most instruments allow the player to manipulate timbre while playing; if a violinist is told to sustain an E, they will select which string to play it on, how much bow pressure and velocity to use, whether to use the entire bow or only the portion near the tip or the frog, how close to the bridge or fingerboard to contact the string, whether or not to use a mute, and so forth. Each one of these choices affects the resulting timbre, and navigating this timbre space is part of the art of playing the instrument. Nonetheless, this type of timbral nuance has been largely ignored in the design of musical robots. Therefore, this dissertation introduces a suite of techniques that deal with timbral nuance in musical robots. Chapter 1 provides the motivating ideas and introduces Kiki, a robot designed by the author to explore timbral nuance. Chapter 2 provides a long history of musical robots, establishing the under-researched nature of timbral nuance. Chapter 3 is a comprehensive treatment of dynamic timbre production in percussion robots and, using Kiki as a case-study, provides a variety of techniques for designing striking mechanisms that produce a range of timbres similar to those produced by human players. Chapter 4 introduces a machine-learning algorithm for recognizing timbres, so that a robot can transcribe timbres played by a human during live performance. Chapter 5 introduces a technique that allows a robot to learn how to produce isolated instances of particular timbres by listening to a human play an examples of those timbres. The 6th and final chapter introduces a method that allows a robot to learn the musical context of different timbres; this is done in realtime during interactive improvisation between a human and robot, wherein the robot builds a statistical model of which timbres the human plays in which contexts, and uses this to inform its own playing.Dissertation/ThesisDoctoral Dissertation Media Arts and Sciences 201
Esiste in Italia un diritto al figlio sano? (Riflessioni a margine della causa Costa et Pavan vs Italia)
No full textThe author, starting from the case Costa et Pavan vs Italy decided by the European Court of Human Rights, poses the question about the existence in Italy of the right to a healthy son. In the light of the data both legislative and jurisprudential, she concludes to deny the existence of such a right in italian law and casts doubt on the power of the Strasbourg Court to take basic policy decisions on "new rights", for which there are no specific constitutional requirements and there is a tragic conflict about them within society and societies
In Dowland’s Own Words: Poetry and Rhetoric in ‘Flow My Tears’ and ‘Lachrimae’ Pavan
No full textJohn Dowland (1563–1626) was among the finest lute players of his time and is widely recognized as the greatest English composer of lute music and lute song. Despite there being nearly 100 sources containing Dowland’s music, only 10 per cent of these can be directly connected to Dowland, and only his single-author songbooks can be considered authoritative texts. As a result, modern scholar-performers are required to look beyond the tablature to identify Dowland’s personal performance style, seeking justification for interpretive decisions in other historical sources. While treatises and organology dominate historical performance research, Dowland’s contrafacts—pieces existing as both songs and instrumental dances—offer equally valuable insights. This article focuses on the most famous example of this musical interrelationship: the instrumental solo ‘Lachrimae’ pavan and the corresponding lute song ‘Flow My Tears.’ The published lute song provides an important opportunity to directly examine an authoritative Dowland composition, with particular focus on his treatment of rhetorical devices, word stress, articulations, and punctuation. Although the links to the original instrumental pavan are not always immediately clear or easy to identify, once established, they provide robust opportunities to learn from the vocal version when interpreting the related solo piece
Linguistic Representations of Motion Do Not Depend on the Visual Motion System
No full textEmbodied semantics proposes that constructing the meaning of motion verb phrases relies on representations of motion in sensory cortex. However, the data reported by earlier studies as evidence for this claim are also explained by a symbolic-semantics view proposing interactions between dissociable systems. In the experiments reported here, participants were visually adapted to real and implied leftward or rightward motion, which produced a motion aftereffect opposite to the direction of the adapting stimulus. Participants then decided whether a directionally ambiguous or a leftward- or rightward-directional verb phrase implied leftward or rightward motion. Because the visual system is engaged in the motion aftereffect, embodied semantics predicts that responses in the motion-aftereffect direction (opposite to the direction of the adapting stimulus) are facilitated, whereas symbolic semantics predicts response facilitation in the direction of the adapting stimulus (opposite to the direction of the motion aftereffect). We found response facilitation in the direction of real- and implied-motion adapting stimuli in ambiguous and directional verb phrases. These results suggest that visual and linguistic representations of motion can be dissociated. © The Author(s) 2012
Estimation of Subspace Occupancy
No full textabstract: The ability to identify unoccupied resources in the radio spectrum is a key capability for opportunistic users in a cognitive radio environment. This paper draws upon and extends geometrically based ideas in statistical signal processing to develop estimators for the rank and the occupied subspace in a multi-user environment from multiple temporal samples of the signal received at a single antenna. These estimators enable identification of resources, such as the orthogonal complement of the occupied subspace, that may be exploitable by an opportunistic user. This concept is supported by simulations showing the estimation of the number of users in a simple CDMA system using a maximum a posteriori (MAP) estimate for the rank. It was found that with suitable parameters, such as high SNR, sufficient number of time epochs and codes of appropriate length, the number of users could be correctly estimated using the MAP estimator even when the noise variance is unknown. Additionally, the process of identifying the maximum likelihood estimate of the orthogonal projector onto the unoccupied subspace is discussed.Dissertation/ThesisMasters Thesis Electrical Engineering 201
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