Linescan Projector and Camera Offset From Each Other Around Display for Creating Image that Appears to be Captured from Middle of Display

A linescan projector and camera can be offset from each other around a display, such as the linescan projector being at the top of the display and the camera being at the side of the display. The linescan projector can sweep vertical lines horizontally, and the camera can capture horizontal images at different vertical locations. A processor can construct an image of a user’s face that appears to have been taken from a middle of the screen, creating the appearance that the user is looking directly into the camera

Cluster randomisation or randomised consent as an appropriate methodology for trials in palliative care: a feasibility study [ISRCTN60243484]

BACKGROUND: Although guidelines for the care of the dying patient exist the evidence base to support the guidelines is poor. Some of the factors contributing to this include failure to recruit to trials, protective healthcare professionals and subsequent attrition from trials due to the death of the patients. Recent studies report favourably on the use of cluster randomisation as an appropriate methodology for use in this patient group. METHODS/DESIGN: A feasibility study, exploring two types of randomisation as appropriate methodology for trials involving dying patients. Cluster randomisation and randomised consent will be utilised following a crossover design at two sites, one oncology ward and one Macmillan unit within the Northwest Wales NHS Trust. All patients commencing on the Integrated Care Pathway (ICP) for the Last Days of Life will be eligible for inclusion in the study. Using the hypothesis that it is not necessary to prescribe an anti-emetic medication when setting up a syringe driver for the dying patient, the study will evaluate different models of research methodology. DISCUSSION: The identification of the most appropriate methodology for use in studies concerning this patient group will inform the development of future clinical studies. Furthermore, the outcomes of this feasibility study will inform the development, of a proposal seeking funding for Wales-wide trials in palliative care. The identification of an appropriate methodology will provide a starting point for the establishment of a robust evidence base for the care of the dying patient

Virtual Reality Video Compression

Uncompressed light field files tend to be big, and may take gigabytes of space for storing a small portion of a scene. However, there can be redundancy in the data. Therefore, compressing the data can result in significant reduction of data size for transmission and/or storage. Further, interpolating uncaptured light-fields can be based on two blurry images generated from a frame of streaming video

Human-swarm interaction via e-ink displays

This paper proposes the use of e-ink displays to enhance human-swarm interaction research, and presents example hardware for the e-puck robot. We outline potential applications, including the display of a robot's internal status, as well as the use of e-ink displays to create dynamic fiducial markers

Regular and irregular signal resampling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 107-110).In this thesis, we consider three main resampling problems. The first is the sampling rate conversion problem in which the input and output grids are both regularly spaced. It is known that the output signal is obtained by applying a time-varying filter to the input signal. The existing methods for finding the coefficients of this filter inherently tradeoff computational and memory requirements. Instead, we present a recursive scheme for which the computational and memory requirements are both low. In the second problem which we consider, we are given the instantaneous samples of a continuous-time (CT) signal taken on an irregular grid from which we wish to obtain samples on a regular grid. This is referred to as the nonuniform sampling problem. We present a noniterative algorithm for solving this problem, which, in contrast to the known iterative algorithms, can easily be implemented in real time. We show that each output point may be calculated by using only a finite number of input points, with an error which falls exponentially in the number of points used. Finally we look at the nonuniform lowpass reconstruction problem. In this case, we are given regular samples of a CT signal from which we wish to obtain amplitudes for a sequence of irregularly spaced impulses. These amplitudes are chosen so that the original CT signal may be recovered by lowpass filtering this sequence of impulses. We present a general solution which exhibits the same exponential localization obtained for the nonuniform sampling problem. We also consider a special case in which the irregular grid is obtained by deleting a single point from an otherwise regular grid. We refer(cont.) to this as the missing pixel problem, since it may be used to model cases in which a single defective element is present in a regularly spaced array such as the pixel arrays used in flat-panel video displays. We present an optimal solution which minimizes the energy of the reconstruction error, subject to the constraint that only a given number of pixels may be adjusted.by Andrew I. Russell.Ph.D

Synthesis of nasal consonants : a theoretically based approach

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 62-63).by Andrew Ian Russell.M.Eng

Assessment of the learning curve in health technologies: a systematic review

Objective: We reviewed and appraised the methods by which the issue of the learning curve has been addressed during health technology assessment in the past. Method: We performed a systematic review of papers in clinical databases (BIOSIS, CINAHL, Cochrane Library, EMBASE, HealthSTAR, MEDLINE, Science Citation Index, and Social Science Citation Index) using the search term "learning curve:" Results: The clinical search retrieved 4,571 abstracts for assessment, of which 559 (12%) published articles were eligible for review. Of these, 272 were judged to have formally assessed a learning curve. The procedures assessed were minimal access (51%), other surgical (41%), and diagnostic (8%). The majority of the studies were case series (95%). Some 47% of studies addressed only individual operator performance and 52% addressed institutional performance. The data were collected prospectively in 40%, retrospectively in 26%, and the method was unclear for 31%. The statistical methods used were simple graphs (44%), splitting the data chronologically and performing a t test or chi-squared test (60%), curve fitting (12%), and other model fitting (5%). Conclusions: Learning curves are rarely considered formally in health technology assessment. Where they are, the reporting of the studies and the statistical methods used are weak. As a minimum, reporting of learning should include the number and experience of the operators and a detailed description of data collection. Improved statistical methods would enhance the assessment of health technologies that require learning

Assessing the learning curve effect in health technologies: Lessons from the non-clinical literature

Introduction: Many health technologies exhibit some form of learning effect, and this represents a barrier to rigorous assessment. It has been shown that the statistical methods used are relatively crude. Methods to describe learning curves in fields outside medicine, for example, psychology and engineering, may be better. Methods: To systematically search non–health technology assessment literature (for example, PsycLit and Econlit databases) to identify novel statistical techniques applied to learning curves. Results: The search retrieved 9,431 abstracts for assessment, of which 18 used a statistical technique for analyzing learning effects that had not previously been identified in the clinical literature. The newly identified methods were combined with those previously used in health technology assessment, and categorized into four groups of increasing complexity: a) exploratory data analysis; b) simple data analysis; c) complex data analysis; and d) generic methods. All the complex structured data techniques for analyzing learning effects were identified in the nonclinical literature, and these emphasized the importance of estimating intra- and interindividual learning effects. Conclusion: A good dividend of more sophisticated methods was obtained by searching in nonclinical fields. These methods now require formal testing on health technology data sets