In vivo studies of an implantable energy convertor for skeletal muscle powered cardiac assist

A device that harnesses the mechanical energy of skeletal muscle contracting in a linear configuration has been implanted in goats. This energy convertor transforms muscle work to hydraulic energy that could drive a variety of cardiac assist devices. The device is mounted with a rib clamp and plate affixed to the sternum by cortical bone screws. A transcutaneous hydraulic line carries a silicon based working fluid to an external system that controls the muscle load. In 60 to 70 kg goats, the latissimus dorsi insertion was reattached to the energy convertor. A Telectronics myostimulator with intramuscular electrodes stimulated the latissimus dorsi. In acute implants, hydraulic pressures in excess of 150 psi were obtained. Chronic implantation of the device allowed system evaluation in the conscious unanesthetized animal. Two weeks after implant, hydraulic pressures in excess of 200 psi were obtained and energy transferred to the external loading system exceeded 1 J per contraction. Six weeks after implant, the device continued to cycle freely. These initial results are very promising and suggest an implantable energy convertor is feasible. Development of an energy convertor is an important step toward tether-free skeletal muscle powered cardiac assist devices.     

High-resolution venography of the brain using magnetic resonance imaging

The purpose of this study was to evaluate a non-flow related magnetic resonance imaging method to visualize small veins independent of arteries in the human brain. A long TE, high-resolution 3D gradient echo MR acquisition was used to highlight venous information. The method is based on the paramagnetic property of deoxyhemoglobin and the resulting phase difference between veins and brain parenchyma at long echo times. The MR magnitude images were masked with a phase mask filter to enhance small structure visibility.. Venous information down to sub-pixel vessel diameters of several hundred microns is visible. Venous data are displayed in an angiographic manner using a minimum intensity projection algorithm. Both superficial veins and deep white matter veins are visible. The method has been successfully applied in volunteers. Preliminary results in patients with cerebral arteriovenous malformations indicate its potential in clinical applications. The proposed method is easy to implement and does not require administration of a contrast agent or application of specially designed rf pulses to highlight the veins. Rather it exploits the intrinsic magnetic properties (BOLD-effect) and the prolonged T ^_2* of venous blood. The method may be of diagnostic potential in the assessment of arteriovenous malformations or other vascular venous lesions. © 1998 Elsevier Science B.V. All rights reserved.     

[2.2]Paracyclophane as a Target of the Organic Solid State: Emergent Properties via Supramolecular Construction

In this account, we review the synthesis of [2.2]paracyclophanes in the organic solid state. Reactions in crystalline solids provide a means to obtain molecules with high degrees of stereocontrol that can also be unattainable in solution. We show that [2.2]paracyclophanes form in the solid state stereospecifically and in quantitative yield via intermolecular [2+2] double photodimerization reactions. The double cycloaddition that affords a paracyclophane in the solid state does not readily occur in solution. Small molecules in the form of hydrogen-bond-donor templates can provide access to [2.2]paracyclophanes in a solid by design. A [2.2]paracyclophane obtained using a hydrogen-bond template is shown to exhibit attractive optical properties and has been employed as a building block of a metal-organic framework (MOF).     

IPAS: a design framework for analysis,synthesis and optimization of image processing applications for heterogenous computing architectures

Recent trends in the image processing field have led to the use of more heterogeneous hardware architectures. The reason for this increase is that specialized cores, compared to standard CPUs, offer a more efficient way of achieving image processing applications. Specialized cores have less power, resource, and area consumption. On the other hand, designing such a heterogenous system with specialized cores is a challenging, error-prone and time-consuming task. Therefore, new frameworks are necessary for bringing an image processing application onto a given target platform by means of a tool chain. Some frameworks exist, but they do not address each need of a heterogeneous image processing application. Common weaknesses are (1) the low utilization of the image processing domain, (2) the inflexibility of the programming paradigms for different hardware architectures. Therefore, we define our own domain-specific design language called IPOL. To automate the derivation and optimization process, a synthesis tool named Image Processing Architecture Synthesis was created. This tool will be the focus of this work.     

Visual Target Localization with the Spincopter

The goal of the Spincopter project was to design an energy efficient aircraft capable of autorotation glide that could be used for both indoor and outdoor surveillance. This paper demonstrates how to utilize the self-rotating capability of the Spincopter in order to acquire a 3D image of its environment and localize predefined object(s). The omnidirectional capabilities of such an UAV extends the otherwise very limited field of view of conventional cameras usually used for aerial photography and surveillance. The paper presents the theoretical background and the actual implementation of such a system. It presents the resulting images and offers a brief survey of the image quality.     

Prediction of diesel fuel cold properties using artificial neural networks

In this paper, two neural networks, multilayer perceptron and networks with radial-basis function, were used to predict important cold properties of commercial diesel fuels, namely cloud point and cold filter plugging point. The developed models predict the named properties using cetane number, density, viscosity, contents of total aromatics, and distillation temperatures at 10, 50, and 90 vol. % recovery as input data. The training algorithms, number of hidden layer neurons, and number of training data points were optimized in order to obtain a model with optimal predictive ability. The results indicated better prediction of cloud and cold filter plugging points in the case of multilayer perceptron networks. The obtained absolute error mean for the optimal neural network models (0.58°C for the cloud point and 1.46°C for the cold filter plugging point) are within the range of repeatability of standard cold properties determination methods.