The random phase transducer: a new technique for incoherent processing-experimental results

Ultrasonic speckle is an inherent consequence of the stochastic nature of the reflectivity of biological tissues. Speckle is a source of noise on envelope detected echoes as well as on the power spectra. A technique where the spatial coherence of the transducer is controlled by moving a random phase screen (RPS) through the ultrasonic beam is described. An RPS that is a rough surface generating phase shifts that are randomly distributed between 0 and 2pi is presented. Measurements with unfocused and focused transducers working in broadband pulse-echo mode were performed. Experimental results on the directivity pattern and the ability of the system to obtain uncorrelated speckle patterns are presented. The performance of the RPS in SNR improvements is discussed. Experimental results are in good agreement with theoretical predictions.     

Ring piezoelectric transducers radiating ultrasonic energy into the air

Ring piezoelectric transducers generating ultrasonic energy in air were designed and tested experimentally and numerically. The radiation pattern of the transducer is very narrow (4 degrees ) and its frequency response has a broadband character. Resonant frequencies of the transducers were determined experimentally and verified theoretically. Applications of the proposed ring transducers in a robotic ranging system were examined.     

Using previous models to bias structural learning in the hierarchical BOA

Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. While the primary goal of applying EDAs is to discover the global optimum or at least its accurate approximation, besides this, any EDA provides us with a sequence of probabilistic models, which in most cases hold a great deal of information about the problem. Although using problem-specific knowledge has been shown to significantly improve performance of EDAs and other evolutionary algorithms, this readily available source of problem-specific information has been practically ignored by the EDA community. This paper takes the first step toward the use of probabilistic models obtained by EDAs to speed up the solution of similar problems in the future. More specifically, we propose two approaches to biasing model building in the hierarchical Bayesian optimization algorithm (hBOA) based on knowledge automatically learned from previous hBOA runs on similar problems. We show that the proposed methods lead to substantial speedups and argue that the methods should work well in other applications that require solving a large number of problems with similar structure.     

Flow-based local optimization for image-to-geometry projection

The projection of a photographic data set on a 3D model is a robust and widely applicable way to acquire appearance information of an object. The first step of this procedure is the alignment of the images on the 3D model. While any reconstruction pipeline aims at avoiding misregistration by improving camera calibrations and geometry, in practice a perfect alignment cannot always be reached. Depending on the way multiple camera images are fused on the object surface, remaining misregistrations show up either as ghosting or as discontinuities at transitions from one camera view to another. In this paper we propose a method, based on the computation of Optical Flow between overlapping images, to correct the local misalignment by determining the necessary displacement. The goal is to correct the symptoms of misregistration, instead of searching for a globally consistent mapping, which might not exist. The method scales up well with the size of the data set (both photographic and geometric) and is quite independent of the characteristics of the 3D model (topology cleanliness, parametrization, density). The method is robust and can handle real world cases that have different characteristics: low level geometric details and images that lack enough features for global optimization or manual methods. It can be applied to different mapping strategies, such as texture or per-vertex attribute encoding.     

Model-driven design for the visual analysis of heterogeneous data

As heterogeneous data from different sources are being increasingly linked, it becomes difficult for users to understand how the data are connected, to identify what means are suitable to analyze a given data set, or to find out how to proceed for a given analysis task. We target this challenge with a new model-driven design process that effectively codesigns aspects of data, view, analytics, and tasks. We achieve this by using the workflow of the analysis task as a trajectory through data, interactive views, and analytical processes. The benefits for the analysis session go well beyond the pure selection of appropriate data sets and range from providing orientation or even guidance along a preferred analysis path to a potential overall speedup, allowing data to be fetched ahead of time. We illustrate the design process for a biomedical use case that aims at determining a treatment plan for cancer patients from the visual analysis of a large, heterogeneous clinical data pool. As an example for how to apply the comprehensive design approach, we present Stack'n'flip, a sample implementation which tightly integrates visualizations of the actual data with a map of available data sets, views, and tasks, thus capturing and communicating the analytical workflow through the required data sets.     

A neuron membrane mesh representation for visualization of electrophysiological simulations

We present a process to automatically generate three-dimensional mesh representations of the complex, arborized cell membrane surface of cortical neurons (the principal information processing cells of the brain) from nonuniform morphological measurements. Starting from manually sampled morphological points (3D points and diameters) from neurons in a brain slice preparation, we construct a polygonal mesh representation that realistically represents the continuous membrane surface, closely matching the original experimental data. A mapping between the original morphological points and the newly generated mesh enables simulations of electrophysiolgical activity to be visualized on this new membrane representation. We compare the new mesh representation with the state of the art and present a series of use cases and applications of this technique to visualize simulations of single neurons and networks of multiple neurons.     

A note on octonionic support vector regression

This note presents an analysis of the octonionic form of the division algebraic support vector regressor (SVR) first introduced by Shilton A detailed derivation of the dual form is given, and three conditions under which it is analogous to the quaternionic case are exhibited. It is shown that, in the general case of an octonionic-valued feature map, the usual "kernel trick" breaks down. The cause of this (and its interpretation) is discussed in some detail, along with potential ways of extending kernel methods to take advantage of the distinct features present in the general case. Finally, the octonionic SVR is applied to an example gait analysis problem, and its performance is compared to that of the least squares SVR, the Clifford SVR, and the multidimensional SVR.     

Co-located collaborative visual analytics around a tabletop display

Co-located collaboration can be extremely valuable during complex visual analytics tasks. We present an exploratory study of a system designed to support collaborative visual analysis tasks on a digital tabletop display. Fifteen participant pairs employed Cambiera, a visual analytics system, to solve a problem involving 240 digital documents. Our analysis, supported by observations, system logs, questionnaires, and interview data, explores how pairs approached the problem around the table. We contribute a unique, rich understanding of how users worked together around the table and identify eight types of collaboration styles that can be used to identify how closely people work together while problem solving. We show how the closeness of teams’ collaboration and communication influenced how they performed on the task overall. We further discuss the role of the tabletop for visual analytics tasks and derive design implications for future co-located collaborative tabletop problem solving systems.     

The right view from the wrong location: depth perception in stereoscopic multi-user virtual environments

Stereoscopic depth cues improve depth perception and increase immersion within virtual environments (VEs). However, improper display of these cues can distort perceived distances and directions. Consider a multi-user VE, where all users view identical stereoscopic images regardless of physical location. In this scenario, cues are typically customized for one "leader" equipped with a head-tracking device. This user stands at the center of projection (CoP) and all other users ("followers") view the scene from other locations and receive improper depth cues. This paper examines perceived depth distortion when viewing stereoscopic VEs from follower perspectives and the impact of these distortions on collaborative spatial judgments. Pairs of participants made collaborative depth judgments of virtual shapes viewed from the CoP or after displacement forward or backward. Forward and backward displacement caused perceived depth compression and expansion, respectively, with greater compression than expansion. Furthermore, distortion was less than predicted by a ray-intersection model of stereo geometry. Collaboration times were significantly longer when participants stood at different locations compared to the same location, and increased with greater perceived depth discrepancy between the two viewing locations. These findings advance our understanding of spatial distortions in multi-user VEs, and suggest a strategy for reducing distortion.