42,814 research outputs found
Single board system for fuzzy inference
The very large scale integration (VLSI) implementation of a fuzzy logic inference mechanism allows the use of rule-based control and decision making in demanding real-time applications. Researchers designed a full custom VLSI inference engine. The chip was fabricated using CMOS technology. The chip consists of 688,000 transistors of which 476,000 are used for RAM memory. The fuzzy logic inference engine board system incorporates the custom designed integrated circuit into a standard VMEbus environment. The Fuzzy Logic system uses Transistor-Transistor Logic (TTL) parts to provide the interface between the Fuzzy chip and a standard, double height VMEbus backplane, allowing the chip to perform application process control through the VMEbus host. High level C language functions hide details of the hardware system interface from the applications level programmer. The first version of the board was installed on a robot at Oak Ridge National Laboratory in January of 1990
Electron screening in the liquid-gas mixed phases of nuclear matter
Screening effects of electrons on inhomogeneous nuclear matter, which
includes spherical, slablike, and rodlike nuclei as well as spherical and
rodlike nuclear bubbles, are investigated in view of possible application to
cold neutron star matter and supernova matter at subnuclear densities. Using a
compressible liquid-drop model incorporating uncertainties in the surface
tension, we find that the energy change due to the screening effects broadens
the density region in which bubbles and nonspherical nuclei appear in the phase
diagram delineating the energetically favorable shape of inhomogeneous nuclear
matter. This conclusion is considered to be general since it stems from a
model-independent feature that the electron screening acts to decrease the
density at which spherical nuclei become unstable against fission and to
increase the density at which uniform matter becomes unstable against proton
clustering.Comment: 12 pages, 8 figures, accepted for publication in Physical Review
Deep Long Short-Term Memory Adaptive Beamforming Networks For Multichannel Robust Speech Recognition
Far-field speech recognition in noisy and reverberant conditions remains a
challenging problem despite recent deep learning breakthroughs. This problem is
commonly addressed by acquiring a speech signal from multiple microphones and
performing beamforming over them. In this paper, we propose to use a recurrent
neural network with long short-term memory (LSTM) architecture to adaptively
estimate real-time beamforming filter coefficients to cope with non-stationary
environmental noise and dynamic nature of source and microphones positions
which results in a set of timevarying room impulse responses. The LSTM adaptive
beamformer is jointly trained with a deep LSTM acoustic model to predict senone
labels. Further, we use hidden units in the deep LSTM acoustic model to assist
in predicting the beamforming filter coefficients. The proposed system achieves
7.97% absolute gain over baseline systems with no beamforming on CHiME-3 real
evaluation set.Comment: in 2017 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP
Deep clustering: Discriminative embeddings for segmentation and separation
We address the problem of acoustic source separation in a deep learning
framework we call "deep clustering." Rather than directly estimating signals or
masking functions, we train a deep network to produce spectrogram embeddings
that are discriminative for partition labels given in training data. Previous
deep network approaches provide great advantages in terms of learning power and
speed, but previously it has been unclear how to use them to separate signals
in a class-independent way. In contrast, spectral clustering approaches are
flexible with respect to the classes and number of items to be segmented, but
it has been unclear how to leverage the learning power and speed of deep
networks. To obtain the best of both worlds, we use an objective function that
to train embeddings that yield a low-rank approximation to an ideal pairwise
affinity matrix, in a class-independent way. This avoids the high cost of
spectral factorization and instead produces compact clusters that are amenable
to simple clustering methods. The segmentations are therefore implicitly
encoded in the embeddings, and can be "decoded" by clustering. Preliminary
experiments show that the proposed method can separate speech: when trained on
spectrogram features containing mixtures of two speakers, and tested on
mixtures of a held-out set of speakers, it can infer masking functions that
improve signal quality by around 6dB. We show that the model can generalize to
three-speaker mixtures despite training only on two-speaker mixtures. The
framework can be used without class labels, and therefore has the potential to
be trained on a diverse set of sound types, and to generalize to novel sources.
We hope that future work will lead to segmentation of arbitrary sounds, with
extensions to microphone array methods as well as image segmentation and other
domains.Comment: Originally submitted on June 5, 201
Apparatus for time‐resolved measurements of acoustic birefringence in particle dispersions
An apparatus for time‐resolved measurements of the birefringence induced in a particle suspension by an acoustic wave pulse is described. Efficient acoustic coupling is obtained by operating near the transducer resonant frequency and by matching the acoustic impedances of the cell constituents. An almost‐overdamped acoustic configuration can alternatively be employed whenever a faster response is needed. Careful design of the optical setup and of the detection unit minimize diffraction and stress‐birefringence parasitic effects and yields a good responsivity at fairly low acoustic intensities. A test of the apparatus on a colloidal suspension of PTFE rodlike particles is presented and discussed
Photoionization yield and absorption coeffi- cient of xenon in the region 860-1022 deg angstrom
Photoionization yield and absorption coefficient of xenon gas measured by photoelectric method
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