stem-cell-based-engineered-immunity-against-hiv-infection-humanized

Stem-cell Based Engineered Immunity Against HIV Infection in the Humanized Mouse Model

With the rapid development of stem cell-based gene therapies against HIV, there is pressing requirement for an animal model to study the hematopoietic ...

University of California, Los Angeles

We present a general framework for the modeling and optimization of scalable multi-projector displays. Based on this framework, we derive algorithms that can robustly optimize the visual quality of an arbitrary combination of projectors without manual adjustment. When the projectors are tiled, we show that our framework automatically produces blending maps that outperform state-of-the-art projector blending methods. When all the projectors are superimposed, the framework can produce high-resolution images beyond the Nyquist resolution limits of component projectors. When a combination of tiled and superimposed projectors are deployed, the same framework harnesses the best features of both tiled and superimposed multi-projector projection paradigms. The framework creates for the first time a new unified paradigm that is agnostic to a particular configuration of projectors yet robustly optimizes for the brightness, contrast, and resolution of that configuration. In addition, we demonstrate that our algorithms support high resolution video at real-time interactive frame rates achieved on commodity graphics platforms. This work allows for inexpensive, compelling, flexible, and robust large scale visualization systems to be built and deployed very efficiently.

A unified paradigm for scalable multi-projector displays.

Visual feature extraction with scale invariant feature transform (SIFT) is widely used for object recognition. However, its real-time implementation suffers from long latency, heavy computation, and high memory storage because of its frame level computation with iterated Gaussian blur operations. Thus, this paper proposes a layer parallel SIFT (LPSIFT) with integral image, and its parallel hardware design with an on-the-fly feature extraction flow for real-time application needs. Compared with the original SIFT algorithm, the proposed approach reduces the computational amount by 90% and memory usage by 95%. The final implementation uses 580-K gate count with 90-nm CMOS technology, and offers 6000 feature points/frame for VGA images at 30 frames/s and ∼ 2000 feature points/frame for 1920 × 1080 images at 30 frames/s at the clock rate of 100 MHz.

Nelson Chang

David Geffen School of Medicine

University of California, Los Angeles

Stem-cell Based Engineered Immunity Against HIV Infection in the Humanized Mouse Model

Nelson Chang

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}David Geffen School of Medicine

University of California, Los Angeles

Stem-cell Based Engineered Immunity Against HIV Infection in the Humanized Mouse Model

David Geffen School of Medicine