10 resultados para Docker,ARM,Raspberry PI,single board computer,QEMU,Sabayon Linux,Gentoo Linux
em Cambridge University Engineering Department Publications Database
Resumo:
Dasher is an information-efficient text-entry interface, which can be driven by natural continuous pointing gestures or by pressing buttons. Dasher is a competitive text-entry system wherever a full-size keyboard cannot be used - for example, when operating a computer one-handed, by joystick, touchscreen, trackball, or mouse; when operating a computer with zero hands (i.e., by head-mouse or by eyetracker); on a palmtop computer; on a wearable computer. The gazetracking version of Dasher allows an experienced user to write text as fast as normal handwriting - 29 words per minute; using a mouse, experienced users can write at 39 words per minute. Dasher can be used to write efficiently in any language. Dasher is free software (distributed under the GPL) and is available for many computer platforms, including linux, windows, and android.
Resumo:
Increasing the field of view of a holographic display while maintaining adequate image size is a difficult task. To address this problem, we designed a system that tessellates several sub-holograms into one large hologram at the output. The sub-holograms we generate is similar to a kinoform but without the paraxial approximation during computation. The sub-holograms are loaded onto a single spatial light modulator consecutively and relayed to the appropriate position at the output through a combination of optics and scanning reconstruction light. We will review the method of computer generated hologram and describe the working principles of our system. Results from our proof-of-concept system are shown to have an improved field of view and reconstructed image size. ©2009 IEEE.
Resumo:
We propose a new solid state implementation of a quantum computer (quputer) using ballistic single electrons as flying qubits in 1D nanowires. We use a single electron pump (SEP) to prepare the initial state and a single electron transistor (SET) to measure the final state. Single qubit gates are implemented using quantum dots as phase shifters and electron waveguide couplers as beam splitters. A Coulomb coupler acts as a 2-qubit gate, using a mutual phase modulation effect. Since the electron phase coherence length in GaAs/AlGaAs heterostructures is of the order of 30$\mu$m, several gates (tens) can be implemented before the system decoheres.
Resumo:
In this paper, we aim to reconstruct free-from 3D models from a single view by learning the prior knowledge of a specific class of objects. Instead of heuristically proposing specific regularities and defining parametric models as previous research, our shape prior is learned directly from existing 3D models under a framework based on the Gaussian Process Latent Variable Model (GPLVM). The major contributions of the paper include: 1) a probabilistic framework for prior-based reconstruction we propose, which requires no heuristic of the object, and can be easily generalized to handle various categories of 3D objects, and 2) an attempt at automatic reconstruction of more complex 3D shapes, like human bodies, from 2D silhouettes only. Qualitative and quantitative experimental results on both synthetic and real data demonstrate the efficacy of our new approach. ©2009 IEEE.
Resumo:
The technique presented in this paper enables a simple, accurate and unbiased measurement of hand stiffness during human arm movements. Using a computer-controlled mechanical interface, the hand is shifted relative to a prediction of the undisturbed trajectory. Stiffness is then computed as the restoring force divided by the position amplitude of the perturbation. A precise prediction algorithm insures the measurement quality. We used this technique to measure stiffness in free movements and after adaptation to a linear velocity dependent force field. The subjects compensated for the external force by co-contracting muscles selectively. The stiffness geometry changed with learning and stiffness tended to increase in the direction of the external force.
Resumo:
Localization of chess-board vertices is a common task in computer vision, underpinning many applications, but relatively little work focusses on designing a specific feature detector that is fast, accurate and robust. In this paper the `Chess-board Extraction by Subtraction and Summation' (ChESS) feature detector, designed to exclusively respond to chess-board vertices, is presented. The method proposed is robust against noise, poor lighting and poor contrast, requires no prior knowledge of the extent of the chess-board pattern, is computationally very efficient, and provides a strength measure of detected features. Such a detector has significant application both in the key field of camera calibration, as well as in Structured Light 3D reconstruction. Evidence is presented showing its robustness, accuracy, and efficiency in comparison to other commonly used detectors both under simulation and in experimental 3D reconstruction of flat plate and cylindrical objects
Resumo:
Localization of chess-board vertices is a common task in computer vision, underpinning many applications, but relatively little work focusses on designing a specific feature detector that is fast, accurate and robust. In this paper the 'Chess-board Extraction by Subtraction and Summation' (ChESS) feature detector, designed to exclusively respond to chess-board vertices, is presented. The method proposed is robust against noise, poor lighting and poor contrast, requires no prior knowledge of the extent of the chess-board pattern, is computationally very efficient, and provides a strength measure of detected features. Such a detector has significant application both in the key field of camera calibration, as well as in structured light 3D reconstruction. Evidence is presented showing its superior robustness, accuracy, and efficiency in comparison to other commonly used detectors, including Harris & Stephens and SUSAN, both under simulation and in experimental 3D reconstruction of flat plate and cylindrical objects. © 2013 Elsevier Inc. All rights reserved.