Assessing task understanding in remote ultrasound diagnosis via gesture analysis

Pattern Analysis and Applications - This work presents a gesture-based approach to estimate task understanding and performance during remote ultrasound tasks. Our approach is comprised of...     

Tensor Clustering for Rendering Many‐Light Animations

Rendering animations of scenes with deformable objects, camera motion, and complex illumination, including indirect lighting and arbitrary shading, is a long‐standing challenge. Prior work has shown that complex lighting can be accurately approximated by a large collection of point lights. In this formulation, rendering of animation sequences becomes the problem of efficiently shading many surface samples from many lights across several frames. This paper presents a tensor formulation of the animated many‐light problem, where each element of the tensor expresses the contribution of one light to one pixel in one frame. We sparsely sample rows and columns of the tensor, and introduce a clustering algorithm to select a small number of representative lights to efficiently approximate the animation. Our algorithm achieves efficiency by reusing representatives across frames, while minimizing temporal flicker. We demonstrate our algorithm in a variety of scenes that include deformable objects, complex illumination and arbitrary shading and show that a surprisingly small number of representative lights is sufficient for high quality rendering. We believe out algorithm will find practical use in applications that require fast previews of complex animation.     

Improvement of differential optical absorption spectroscopy with a multichannel scanning technique

Differential optical absorption spectroscopy (DOAS) of atmospheric trace gases requires the detection of optical densities below 0.1%. Photodiode arrays are used more and more as detectors for DOAS because they allow one to record larger spectral intervals simultaneously. This type of optical multichannel analyzer (OMA), however, shows sensitivity differences among the individual photodiodes (pixels), which are of the order of 1%. To correct for this a sensitivity reference spectrum is usually recorded separately from the trace-gas measurements. Because of atmospheric turbulence the illumination of the detector while an atmospheric absorption spectrum is being recorded is different from the conditions during the reference measurement. As a result the sensitivity patterns do not exactly match, and the corrected spectra still show a residual structure that is due to the sensitivity difference. This effect usually limits the detection of optical densities to approximately 3 × 10(-4). A new method for the removal of the sensitivity pattern is presented in this paper: Scanning the spectrometer by small wavelength increments after each readout of the OMA allows one to separate the OMA-fixed pattern and the wavelength-fixed structures (absorption lines). The properties of the new method and its applicability are demonstrated with simulated spectra. Finally, first atmospheric measurements with a laser long-path instrument demonstrate a detection limit of 3 × 10(-5) of a DOAS experiment.     

Technologies and logistics for phosphorus recovery from livestock waste

Phosphorus (P) runoff from livestock waste can trigger algal blooms that adversely affect aquatic life and human health. One strategy to mitigate this problem is to install nutrient recovery technologies that concentrate and mobilize nutrients from nutrient-rich regions to nutrient-deficient ones. We present supply chain design formulations to identify optimal types and locations for P recovery technologies. The formulations capture trade-offs in transportation costs, technology efficiency, investment/operational costs, revenue collected from different recovered products (struvite and nutrient cakes), and environmental impact. Our computational framework is used to analyze the impact of different scenarios for market prices of recovered products, recovery yields, and remediation costs. We find that transportation of waste alone (without any processing) can achieve significant reductions in environmental impact at low cost, but cannot achieve economic sustainability in the long run due to the lack of direct revenue streams. Mechanical separation technologies that recover P in the form of nutrient cakes are low-cost solutions that can achieve high environmental benefits and reduced transportation costs, but revenues are also limited due to low values of the cakes. Struvite crystallization in fluidized beds is found to be a highly attractive option under current struvite prices, but economic sustainability is strongly dependent on yield recoveries (which are currently highly uncertain).     

Using Selective Electron Beam Melting to Enhance the High-Temperature Strength and Creep Resistance of NiAl–28Cr–6Mo In Situ Composites

By increasing the density of interfaces in NiAl–CrMo in situ composites, the mechanical properties can be significantly improved compared to conventionally cast material. The refined microstructure is achieved by manufacturing through electron beam powder bed fusion (PBF-EB). By varying the process parameters, an equiaxed or columnar cell morphology can be obtained, exhibiting a plate-like or an interconnected network of the (Cr,Mo) reinforcement phase which is embedded in a NiAl matrix. The microstructure of the different cell morphologies is investigated in detail using scanning electron microscope, transmission electron microscopy, and atom probe tomography. For both morphologies, the mechanical properties at elevated temperatures are analyzed by compression and creep experiments parallel and perpendicular to the building direction. In comparison to cast NiAl and NiAl–(Cr, Mo), the yield strength of the PBF-EB fabricated specimens is significantly improved at temperatures up to 1,027 °C. While the columnar morphology exhibits the best improved mechanical properties at high temperatures, the equiaxial morphology shows nearly ideal isotropic mechanical behavior, which is a substantial advantage over directionally solidified material.     

An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.     

Industrial implementation of controller performance analysis technology

In this paper, a controller performance monitoring technology is introduced with a focus on its industrial implementation and applications. The technology to be introduced in this paper is known as Performance Analysis Toolbox and Solutions (PATS). The main components are discussed, which include data collection, closed-loop systems identification, control valve stiction monitoring, univariate control performance monitoring, multivariate control performance monitoring, model predictive control performance monitoring, etc. It is shown, through a thorough industry case study with various components of PATS, that the integration of the technology provides more convincing diagnosis results and facilitates identification of control improvement opportunities. Some components of the developed technology can be downloaded from the website http://www.ualberta.ca/～bhuang/research/research.htm.     

Nano-texturing of magnetic recording sliders via laser ablation

To increase the storage density of hard disk drives, the flying height of the slider needs to be reduced to <10 nm. This requires super-smooth surfaces of the disk and slider. As the roughness decreases, stiction and adhesion are found to increase substantially leading to failures of the head/disk interface. Texturing the slider surface is a well-known approach to this issue. In this study we investigated laser ablation as a potential process for texturing magnetic recording sliders. It was found that straight laser machining caused unwanted re-deposition of material. These deposits could be significantly reduced by using a chemical etching enhanced laser process.     

State estimation in high-mix semiconductor manufacturing

The traditional way of state estimation in semiconductor manufacturing, known as “threaded” state estimation, segregates the process data into different bins and uses the ones that match the current event of the specific context information (such as tools, layers, products) to update the process state. The limitation of threaded state estimation is that a narrowly defined process stream can result in too many different threads and insufficient data for each thread. This limitation becomes more severe in high-mix manufacturing, where there can be many products and many tools. Hence there is great interest in estimation methods that utilize all available data in the analysis. In this work, the characteristics inherent in state estimation of high-mix semiconductor manufacturing processes are analyzed, and a general framework is introduced for the non-threaded state estimation methods, i.e., state estimation without segregating the process data into different bins. The framework is based on the best linear unbiased estimate (BLUE) of a simplified stationary singular Gauss–Markov process, and non-threaded state estimation methods based on the Kalman filter, least squares and recursive least squares (RLS) are analyzed using the general framework. Simulation examples are presented to illustrate the equivalence between different algorithms. As real processes are rarely stationary, modifications to the Kalman filter and RLS are discussed. We show that in non-threaded state estimation, how to regulate the estimate covariance plays a significant role in estimation performance. To handle nonstationary disturbances that often occur in semiconductor processes, Bayesian-enhanced adaptive versions of the Kalman filter and RLS are proposed. Both simulated and industrial nonstationary processes are used to demonstrate the effectiveness of the proposed adaptive methods.     

Seeing size and feeling weight: the size-weight illusion in natural and virtual reality

OBJECTIVE: We experimentally tested the degree that the size-weight illusion depends on perceptual conditions allowing the observer to assume that both the visual and the kinesthetic stimuli of a weight seen and lifted emanate from the same object. We expected that the degree of the illusion depended on the "realism" provided by different kinds of virtual reality (VR) used when the weights are seen in virtual reality and at the same time lifted in natural reality. BACKGROUND: Welch and Warren (1980) reported that an intermodal influence can be expected only if perceptual information of different modalities is compellingly related to only one object. METHOD: Objects of different sizes and weights were presented to 50 participants in natural reality or in four virtual realities: two immersive head-mounted display VRs (with or without head tracking) and two nonimmersive desktop VRs (with or without screening from input of the natural environment using a visor). The objects' heaviness was scaled using the magnitude estimation method. RESULTS: Data show that the degree of the illusion is largest in immersive and lowest in nonimmersive virtual realities. CONCLUSION: The higher the degree of the illusion is, the more compelling the situation is perceived and the more the observed data are in correspondence with the data predicted for the illusion in natural reality. This shows that the kind of mediating technology used strongly influences the presence experienced. APPLICATION: The size-weight illusion's sensitivity to conditions that affect the sense of presence makes it a promising objective presence measure.