Visual Reconstruction and Feature Analysis of the Three-Dimensional Surface of Earthworm

This paper demonstrates a method for visual reconstruction and feature analysis of the three-dimensional surface of earthworm in CATIA (Computer Aided Three Dimensional Interactive Application) and IDL (Interactive Data Language). The earthworm, with a relatively simple surface morphology and good capability in reducing soil adhesion and resistance, was selected to study the feasible methods in the visual reconstruction and feature analysis of the three-dimensional surface of living things. The digital measurements of surfaces of the earthworm were carried out using a three-dimensional laser scanner. Point clouds, the scanning digital data of the surface of the earthworm, were processed by screening unwanted data, reconstructing surface and analysing feature in CATIA. In order to get more detail information about the point clouds, IDL, which integrates a powerful, array-oriented language with numerous mathematical analysis and graphical display techniques, was adopted for the visual reconstruction and feature analysis of three- dimensional surface of the earthworm. Importing of point clouds and reconstruction of the surface of earthworm were conducted in CATIA. Analysis feature of the scanning data and reconstructing surface were carried out in IDL, which provides a high level of flexibility to access, analyse and visualize the data using different methods. Polynomial regression equation of the surface of earthworm in the longitudinal plane was derived. In addition, point clouds were more easily displayed and analysed by resizing, rotating and zooming in IDL. Methods and results presented in this paper prove to be potentially useful for analyzing the feature of biological prototype, optimizing the mathematical model and affording deformable physical model to bionic engineering, those works would have great implications to the research of biological coupling theory and technological creation in bionic engineering. Keywords: Visual Reconstruction; Feature Analysis; Three-Dimensional Surface; Earthworm; CATIA; ID

Estimating Sparse Signals Using Integrated Wideband Dictionaries

In this paper, we introduce a wideband dictionary framework for estimating sparse signals. By formulating integrated dictionary elements spanning bands of the considered parameter space, one may efficiently find and discard large parts of the parameter space not active in the signal. After each iteration, the zero-valued parts of the dictionary may be discarded to allow a refined dictionary to be formed around the active elements, resulting in a zoomed dictionary to be used in the following iterations. Implementing this scheme allows for more accurate estimates, at a much lower computational cost, as compared to directly forming a larger dictionary spanning the whole parameter space or performing a zooming procedure using standard dictionary elements. Different from traditional dictionaries, the wideband dictionary allows for the use of dictionaries with fewer elements than the number of available samples without loss of resolution. The technique may be used on both one- and multi-dimensional signals, and may be exploited to refine several traditional sparse estimators, here illustrated with the LASSO and the SPICE estimators. Numerical examples illustrate the improved performance

Intention recognition for gaze controlled robotic minimally invasive laser ablation

Eye tracking technology has shown promising results for allowing hands-free control of robotically-mounted cameras and tools. However existing systems present only limited capabilities in allowing the full range of camera motions in a safe, intuitive manner. This paper introduces a framework for the recognition of surgeon intention, allowing activation and control of the camera through natural gaze behaviour. The system is resistant to noise such as blinking, while allowing the surgeon to look away safely at any time. Furthermore, this paper presents a novel approach to control the translation of the camera along its optical axis using a combination of eye tracking and stereo reconstruction. Combining eye tracking and stereo reconstruction allows the system to determine which point in 3D space the user is fixating, enabling a translation of the camera to achieve the optimal viewing distance. In addition, the eye tracking information is used to perform automatic laser targeting for laser ablation. The desired target point of the laser, mounted on a separate robotic arm, is determined with the eye tracking thus removing the need to manually adjust the laser's target point before starting each new ablation. The calibration methodology used to obtain millimetre precision for the laser targeting without the aid of visual servoing is described. Finally, a user study validating the system is presented, showing clear improvement with median task times under half of those of a manually controlled robotic system

Streaming visualisation of quantitative mass spectrometry data based on a novel raw signal decomposition method

As data rates rise, there is a danger that informatics for high-throughput LC-MS becomes more opaque and inaccessible to practitioners. It is therefore critical that efficient visualisation tools are available to facilitate quality control, verification, validation, interpretation, and sharing of raw MS data and the results of MS analyses. Currently, MS data is stored as contiguous spectra. Recall of individual spectra is quick but panoramas, zooming and panning across whole datasets necessitates processing/memory overheads impractical for interactive use. Moreover, visualisation is challenging if significant quantification data is missing due to data-dependent acquisition of MS/MS spectra. In order to tackle these issues, we leverage our seaMass technique for novel signal decomposition. LC-MS data is modelled as a 2D surface through selection of a sparse set of weighted B-spline basis functions from an over-complete dictionary. By ordering and spatially partitioning the weights with an R-tree data model, efficient streaming visualisations are achieved. In this paper, we describe the core MS1 visualisation engine and overlay of MS/MS annotations. This enables the mass spectrometrist to quickly inspect whole runs for ionisation/chromatographic issues, MS/MS precursors for coverage problems, or putative biomarkers for interferences, for example. The open-source software is available from http://seamass.net/viz/

A Software Tool for Parameter Estimation from Flight Test Data

A software package called FIDA is developed and implemented in PC MATLAB for estimating aircraft stability and control derivatives from flight test data using different system identification techniques. FIDA also contains data pre-processing tools to remove wild points and high frequency noise components from measured flight data. FIDA is a menu driven and user interactive software which is useful to scientists/flight test engineers/pilots who are engaged in experimental flights and analysis of flight test data. Also it has an educational value for students and practising engineers who are new to the field of aircraft parameter estimation