On October 8-10,2009, an interdisciplinary group met in Beverley, Massachusetts, to evaluate the state of the art in the computational modeling of narrative. Three important findings emerged: (1) current work in compu...
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This paper presents a soft information fusion framework for creating a propositional graph from natural language messages with an emphasis on producing these graphs for fusion with other messages. The framework utiliz...
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ISBN:
(纸本)9780982443811
This paper presents a soft information fusion framework for creating a propositional graph from natural language messages with an emphasis on producing these graphs for fusion with other messages. The framework utilizes artificial intelligence techniques from natural language understanding, knowledge representation, and information retrieval.
Node failures in Wireless Sensor Networks composed by static sensor nodes are common due to the nature of the sensor devices and the usually harsh environments in which they are deployed. Node failures can diminish th...
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Node failures in Wireless Sensor Networks composed by static sensor nodes are common due to the nature of the sensor devices and the usually harsh environments in which they are deployed. Node failures can diminish the performance of the network as a whole, thus affecting its functionality in delivering the desired services. For instance, significant regions can become uncovered due to failure of several nearby nodes. This paper reports a study about the use of mobile sensor nodes acting in cooperation with static ones in order to fill gaps created by faulty static nodes. The proposed fault handling mechanism presents alternative policies with pros and cons, depending on the user priorities imposed to the system and the occurrence of failures. A discussion about this topic is presented based on results obtained by simulation of the proposed mechanisms.
We used a spiking neural network (SNN) to decode neural data recorded from a 96-electrode array in premotor/motor cortex while a rhesus monkey performed a point-to-point reaching arm movement task. We mapped a Kalman-...
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We used a spiking neural network (SNN) to decode neural data recorded from a 96-electrode array in premotor/motor cortex while a rhesus monkey performed a point-to-point reaching arm movement task. We mapped a Kalman-filter neural prosthetic decode algorithm developed to predict the arm's velocity on to the SNN using the Neural engineering Framework and simulated it using Nengo, a freely available software package. A 20,000-neuron network matched the standard decoder's prediction to within 0.03% (normalized by maximum arm velocity). A 1,600-neuron version of this network was within 0.27%, and run in real-time on a 3GHz PC. These results demonstrate that a SNN can implement a statistical signal processing algorithm widely used as the decoder in high-performance neural prostheses (Kalman filter), and achieve similar results with just a few thousand neurons. Hardware SNN implementations - neuromorphic chips - may offer power savings, essential for realizing fully-implantable cortically controlled prostheses.
We study three types of learning with Bayesian agent-based modeling. First, we show that previous results obtained from learning chains can be generalized to a more realistic lattice world involving multiple social in...
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We briefly introduce noise-based logic. After describing the main motivations we outline classical, instantaneous (squeezed and non-squeezed), continuum, spike and random-telegraph-signal based schemes with applicatio...
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We briefly introduce noise-based logic. After describing the main motivations we outline classical, instantaneous (squeezed and non-squeezed), continuum, spike and random-telegraph-signal based schemes with applications such as circuits that emulate the brain functioning and string verification via a slow communication channel.
Of the common commutative binary logical connectives, only and and or may be used as operators that take arbitrary numbers of arguments with order and multiplicity being irrelevant, that is, as connectives that take s...
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Feature extraction is an important issue in graphics retrieval. Local feature based descriptors are currently the predominate method used in image retrieval and object recognition. Inspired by the success of Haar feat...
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Classifiers in a high dimensional space based on the signals of multiple electrodes in EEG-based BCIs suffer from the curse of dimensionality due to the limited training dataset. In order to tackle this problem, we de...
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Step-graded InGaP_ySb_(1-y) and In_xGa_(1-x)As metamorphic buffer layer (MBL) structures are grown on GaAs substrates by metal-organic chemical vapor deposition (MOCVD). AFM analysis indicates that graded group V InGa...
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ISBN:
(纸本)9781457717536
Step-graded InGaP_ySb_(1-y) and In_xGa_(1-x)As metamorphic buffer layer (MBL) structures are grown on GaAs substrates by metal-organic chemical vapor deposition (MOCVD). AFM analysis indicates that graded group V InGaP_ySb_(1-y) MBLs exhibit significantly lower surface roughness (～4.7nm) compared with more conventional graded group III In_xGa_(1-x)As MBLs, which typically have rms surface roughness in the range of 7-14nm. To reduce the surface roughness of the In_xGa_(1-x)As MBL, a post growth Chemical-Mechanical Polishing (CMP) procedure is implemented. AFM image analysis indicates the CMP process is effective in reducing the step-graded In_xGa_(1-x)As MBL surface roughness from ～7.3 nm (as-grown) to 2.3 nm post CMP. Preliminary studies indicate that bulk InGaAs layers regrown on top of the MBL subjected to CMP exhibit improved static and transient PL characteristics compared with those deposited on as-grown MBLs.
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