Wisconsin Alumni Research Foundation

About: Wisconsin Alumni Research Foundation is a nonprofit organization based out in Madison, Wisconsin, United States. It is known for research contribution in the topics: Signal & Layer (electronics). The organization has 4209 authors who have published 3523 publications receiving 61017 citations.

Tannin-chitosan composites

Abstract: The invention provides a composition comprising a matrix of chitosan and a tannin wherein the chitosan is electrostatically bonded to the tannin to form a chitosan-tannin composite material. The chitosan can be partially or fully deacetylated, and the tannin can be a monomeric or an oligomeric proanthocyanidin or a hydrolysable tannin. The chitosan-tannin composite material can be a nanoparticle, a hydrogel film, a bio-foam, or a biogel, or the chitosan-tannin composite material can coat a liposome. The composite materials can be used for drug delivery, for antibacterial and/or antifungal applications, for tissue engineering applications, for wound healing applications, or they can be used as adjuvants for vaccination, including oral vaccinations. The invention also provides methods of preparing the composite materials and their various forms.

Image transfer protocol in progressively increasing resolution

Abstract: A method of transferring digital image data over a communication link transforms and orders the data so that, as data is received by a receiving station, a low detail version of the image is immediately generated with later transmissions of data providing progressively greater detail in this image. User instructions are accepted, limiting the ultimate resolution of the image or suspending enhancement of the image except in certain user defined regions. When a low detail image is requested followed by a request for a high detailed version of the same image, the originally transmitted data of the low resolution image is not discarded or retransmitted but used with later data to improve the originally transmitted image. Only a single copy of the transformed image need be retained by the transmitting device in order to satisfy requests for different amounts of image detail.

Magnetic resonance imaging with fat suppression

Abstract: A three-dimensional projection reconstruction pulse sequence acquires two half echoes in a steady state free precession (SSFP) scan. A method for combining the two echoes to suppress either fat or water in the reconstructed image is described includes shifting the phase of one echo and combining them in a regridding process used to transform the radial data to a Cartesian grid prior to image reconstruction.

High power laterally antiguided semiconductor light source with reduced transverse optical confinement

Abstract: A semiconductor structure for use as a laser or amplifier has a multilayer structure including a substrate, an active region, optical confinement and cladding layers on each side of the active region to surround the active region. The structure includes at least one core element at which light emission occurs and interelement regions laterally adjacent to the core element with the effective refractive index of the interelement regions higher than that of the core element to provide antiguiding of light emitted in the core element. The optical confinement and cladding layers on opposite sides of the active region have different indexes of refraction to provide an optical waveguiding structure in the transverse direction in the core element which is asymmetrical and which favors lasing only in the fundamental transverse mode. The structure allows larger core elements to be utilized than otherwise possible and results in a significantly increased light emission spot size enabling much higher emission power levels for lasers and higher saturation power levels for amplifiers.

Efficient Large-Scale Approximate Nearest Neighbor Search on OpenCL FPGA

Abstract: We present a new method for Product Quantization (PQ) based approximated nearest neighbor search (ANN) in high dimensional spaces. Specifically, we first propose a quantization scheme for the codebook of coarse quantizer, product quantizer, and rotation matrix, to reduce the cost of accessing these codebooks. Our approach also combines a highly parallel k-selection method, which can be fused with the distance calculation to reduce the memory overhead. We implement the proposed method on Intel HARPv2 platform using OpenCL-FPGA. The proposed method significantly outperforms state-of-the-art methods on CPU and GPU for high dimensional nearest neighbor queries on billion-scale datasets in terms of query time and accuracy regardless of the batch size. To our best knowledge, this is the first work to demonstrate FPGA performance superior to CPU and GPU on high-dimensional, large-scale ANN datasets.