Presents the requirements gathering and software architecture derivation approach developed by the University of Texas at Austin and leveraged by the National Cancer Institute (NCI) in their efforts to automate the cr...
详细信息
ISBN:
(纸本)0769510868
Presents the requirements gathering and software architecture derivation approach developed by the University of Texas at Austin and leveraged by the National Cancer Institute (NCI) in their efforts to automate the creation, management and evaluation of clinical trials. NCI must face the complexity of managing clinical trials and of coordinating large numbers and varied types of stakeholders. SEPA (systemsengineeringproc.ss Activities), from the University of Texas at Austin, is well-suited to address domain modeling and software development at NCI due to a strong emphasis on explicit traceability from a derived architecture to individual knowledge acquisition sessions, as well as facilitated resolution among conflicting stakeholder contributions. Specifically, this paper focuses on the SEPA Domain Reference Architecture (DRA), a software architecture designed to capture domain requirements (i.e. domain data, functionality and timing).
A real-time, low-power video encoder design for pyramid vector quantization (PVQ) has been presented. The quantizer is estimated to dissipate only 2.1 mW for real-time video compression of images of 256 × 256 pix...
A real-time, low-power video encoder design for pyramid vector quantization (PVQ) has been presented. The quantizer is estimated to dissipate only 2.1 mW for real-time video compression of images of 256 × 256 pixels at 30 frames per second in standard 0.8-micron CMOS technology with a 1.5 V supply. Applying this quantizer to subband decomposed images, the quantizer performs better than JPEG on average. We achieve this high level of power efficiency with image quality exceeding that of variable rate codes through algorithmic and architectural reformulation. The PVQ encoder is well-suited for wireless, portable communication applications.
The authors present a self-organizing artificial neural network (ANN) that exhibits deterministically reliable behavior to noise interference when the noise does not exceed a specified level of tolerance. The complexi...
详细信息
The authors present a self-organizing artificial neural network (ANN) that exhibits deterministically reliable behavior to noise interference when the noise does not exceed a specified level of tolerance. The complexity of the proposed ANN, called DIGNET, in terms of neuron requirements versus stored patterns, increases linearly with the number of stored patterns and their dimensionality. The self-organization of DIGNET is based on the idea of competitive generation and elimination of attraction wells in the pattern space. The same neural network can be used for both pattern recognition and classification.< >
暂无评论