394,643 research outputs found
A Mini-History of Computing
This book was produced by George K. Thiruvathukal for the American Institute of Physics to promote interest in the interdisciplinary publication, Computing in Science and Engineering. It accompanied a limited edition set of playing cards that is no longer available (except in PDF).
This book features a set of 54 significant computers by era/category, including ancient calculating instruments, pre-electronic mechanical calculators and computers, electronic era computers, and modern computing (minicomputers, maniframes, personal computers, devices, and gaming consoles)
Graphene oxide based synaptic memristor device for neuromorphic computing
Brain-inspired neuromorphic computing which consist neurons and synapses,
with an ability to perform complex information processing has unfolded a new
paradigm of computing to overcome the von Neumann bottleneck. Electronic
synaptic memristor devices which can compete with the biological synapses are
indeed significant for neuromorphic computing. In this work, we demonstrate our
efforts to develop and realize the graphene oxide (GO) based memristor device
as a synaptic device, which mimic as a biological synapse. Indeed, this device
exhibits the essential synaptic learning behavior including analog memory
characteristics, potentiation and depression. Furthermore,
spike-timing-dependent-plasticity learning rule is mimicked by engineering the
pre- and post-synaptic spikes. In addition, non-volatile properties such as
endurance, retentivity, multilevel switching of the device are explored. These
results suggest that Ag/GO/FTO memristor device would indeed be a potential
candidate for future neuromorphic computing applications.
Keywords: RRAM, Graphene oxide, neuromorphic computing, synaptic device,
potentiation, depressionComment: Nanotechnology (accepted) (IOP publishing
Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier
We present a new approach for recognition of complex graphic symbols in
technical documents. Graphic symbol recognition is a well known challenge in
the field of document image analysis and is at heart of most graphic
recognition systems. Our method uses structural approach for symbol
representation and statistical classifier for symbol recognition. In our system
we represent symbols by their graph based signatures: a graphic symbol is
vectorized and is converted to an attributed relational graph, which is used
for computing a feature vector for the symbol. This signature corresponds to
geometry and topology of the symbol. We learn a Bayesian network to encode
joint probability distribution of symbol signatures and use it in a supervised
learning scenario for graphic symbol recognition. We have evaluated our method
on synthetically deformed and degraded images of pre-segmented 2D architectural
and electronic symbols from GREC databases and have obtained encouraging
recognition rates.Comment: 5 pages, 8 figures, Tenth International Conference on Document
Analysis and Recognition (ICDAR), IEEE Computer Society, 2009, volume 10,
1325-132
Multi-Level Pre-Correlation RFI Flagging for Real-Time Implementation on UniBoard
Because of the denser active use of the spectrum, and because of radio
telescopes higher sensitivity, radio frequency interference (RFI) mitigation
has become a sensitive topic for current and future radio telescope designs.
Even if quite sophisticated approaches have been proposed in the recent years,
the majority of RFI mitigation operational procedures are based on
post-correlation corrupted data flagging. Moreover, given the huge amount of
data delivered by current and next generation radio telescopes, all these RFI
detection procedures have to be at least automatic and, if possible, real-time.
In this paper, the implementation of a real-time pre-correlation RFI
detection and flagging procedure into generic high-performance computing
platforms based on Field Programmable Gate Arrays (FPGA) is described,
simulated and tested. One of these boards, UniBoard, developed under a Joint
Research Activity in the RadioNet FP7 European programme is based on eight
FPGAs interconnected by a high speed transceiver mesh. It provides up to ~4
TMACs with Altera Stratix IV FPGA and 160 Gbps data rate for the input data
stream.
Considering the high in-out data rate in the pre-correlation stages, only
real-time and go-through detectors (i.e. no iterative processing) can be
implemented. In this paper, a real-time and adaptive detection scheme is
described.
An ongoing case study has been set up with the Electronic Multi-Beam Radio
Astronomy Concept (EMBRACE) radio telescope facility at Nan\c{c}ay Observatory.
The objective is to evaluate the performances of this concept in term of
hardware complexity, detection efficiency and additional RFI metadata rate
cost. The UniBoard implementation scheme is described.Comment: 16 pages, 13 figure
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