1,819 research outputs found
A high-performance inner-product processor for real and complex numbers.
A novel, high-performance fixed-point inner-product processor based on a redundant binary number system is investigated in this dissertation. This scheme decreases the number of partial products to 50%, while achieving better speed and area performance, as well as providing pipeline extension opportunities. When modified Booth coding is used, partial products are reduced by almost 75%, thereby significantly reducing the multiplier addition depth. The design is applicable for digital signal and image processing applications that require real and/or complex numbers inner-product arithmetic, such as digital filters, correlation and convolution. This design is well suited for VLSI implementation and can also be embedded as an inner-product core inside a general purpose or DSP FPGA-based processor. Dynamic control of the computing structure permits different computations, such as a variety of inner-product real and complex number computations, parallel multiplication for real and complex numbers, and real and complex number division. The same structure can also be controlled to accept redundant binary number inputs for multiplication and inner-product computations. An improved 2's-complement to redundant binary converter is also presented
Conserving Historic Urban Landscape and Beautifying the City by Means of its History
This paper focuses on three issues concerning historic urban landscape conservation under the background of new urbanization: the first one is attaching great importance to cultural heritage conservation in rapid urbanization; the second one is laying great emphasis on historic urban landscape conservation which is part of cultural heritage conservation; the third is Hangzhou’s practice in cultural heritage conservation
Quality Classified Image Analysis with Application to Face Detection and Recognition
Motion blur, out of focus, insufficient spatial resolution, lossy compression
and many other factors can all cause an image to have poor quality. However,
image quality is a largely ignored issue in traditional pattern recognition
literature. In this paper, we use face detection and recognition as case
studies to show that image quality is an essential factor which will affect the
performances of traditional algorithms. We demonstrated that it is not the
image quality itself that is the most important, but rather the quality of the
images in the training set should have similar quality as those in the testing
set. To handle real-world application scenarios where images with different
kinds and severities of degradation can be presented to the system, we have
developed a quality classified image analysis framework to deal with images of
mixed qualities adaptively. We use deep neural networks first to classify
images based on their quality classes and then design a separate face detector
and recognizer for images in each quality class. We will present experimental
results to show that our quality classified framework can accurately classify
images based on the type and severity of image degradations and can
significantly boost the performances of state-of-the-art face detector and
recognizer in dealing with image datasets containing mixed quality images.Comment: 6 page
Some double series for and their -analogues
By applying the partial derivative operator to several summation formulas for
hypergeometric series, we prove several double series for in this paper.
Similarly, we also establish several -analogues of them
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