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VSS : a VHDL synthesis system
This report describes a register transfer synthesis system that allows a designer to interact with the design process. The designer can modify the compiled design by changing the input description, selecting optimization and mapping strategies, or graphically changing the generated design schematic. The VHDL language is used for input and output descriptions. An intermediate representation which incorporates signal typing and component attributes simplifies compilation and facilitates design optimization. The compilation process consists of two phases. First, a design composed of generic components is synthesized from the input description. Second, this design is translated into components from a particular library by a mapper and optimized by a logic optimizer. Redesign to new technologies can be accomplished by changing only the component library
Speech Processing in Computer Vision Applications
Deep learning has been recently proven to be a viable asset in determining features in the field of Speech Analysis. Deep learning methods like Convolutional Neural Networks facilitate the expansion of specific feature information in waveforms, allowing networks to create more feature dense representations of data. Our work attempts to address the problem of re-creating a face given a speaker\u27s voice and speaker identification using deep learning methods. In this work, we first review the fundamental background in speech processing and its related applications. Then we introduce novel deep learning-based methods to speech feature analysis. Finally, we will present our deep learning approaches to speaker identification and speech to face synthesis. The presented method can convert a speaker audio sample to an image of their predicted face. This framework is composed of several chained together networks, each with an essential step in the conversion process. These include Audio embedding, encoding, and face generation networks, respectively. Our experiments show that certain features can map to the face and that with a speaker\u27s voice, DNNs can create their face and that a GUI could be used in conjunction to display a speaker recognition network\u27s data
Improved methods for detecting gravitational waves associated with short gamma-ray bursts
In the era of second generation ground-based gravitational wave detectors,
short gamma-ray bursts (GRBs) will be among the most promising astrophysical
events for joint electromagnetic and gravitational wave observation. A targeted
search for gravitational wave compact binary merger signals in coincidence with
short GRBs was developed and used to analyze data from the first generation
LIGO and Virgo instruments. In this paper, we present improvements to this
search that enhance our ability to detect gravitational wave counterparts to
short GRBs. Specifically, we introduce an improved method for estimating the
gravitational wave background to obtain the event significance required to make
detections; implement a method of tiling extended sky regions, as required when
searching for signals associated to poorly localized GRBs from Fermi Gamma-ray
Burst Monitor or the InterPlanetary Network; and incorporate astrophysical
knowledge about the beaming of GRB emission to restrict the search parameter
space. We describe the implementation of these enhancements and demonstrate how
they improve the ability to observe binary merger gravitational wave signals
associated with short GRBs.Comment: 13 pages, 6 figure
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