4,063 research outputs found
New modelling technique for aperiodic-sampling linear systems
A general input-output modelling technique for aperiodic-sampling linear
systems has been developed. The procedure describes the dynamics of the system
and includes the sequence of sampling periods among the variables to be
handled. Some restrictive conditions on the sampling sequence are imposed in
order to guarantee the validity of the model. The particularization to the
periodic case represents an alternative to the classic methods of
discretization of continuous systems without using the Z-transform. This kind
of representation can be used largely for identification and control purposes.Comment: 19 pages, 0 figure
A Joint Criterion for Reachability and Observability of Nonuniformly Sampled Discrete Systems
A joint characterization of reachability (controllability) and observability
(constructibility) for linear SISO nonuniformly sampled discrete systems is
presented. The work generalizes to the nonuniform sampling the criterion known
for the uniform sampling. Emphasis is on the nonuniform sampling sequence,
which is believed to be an additional element for analysis and handling of
discrete systems.Comment: 8 pages, 1 figur
Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection
Background: Voice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively. Disordered sustained vowels exhibit wide-ranging phenomena, from nearly periodic to highly complex, aperiodic vibrations, and increased "breathiness". Modelling and surrogate data studies have shown significant nonlinear and non-Gaussian random properties in these sounds. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for this inherent biophysical nonlinearity and non-Gaussian randomness, often using linear signal processing methods insensitive to these properties. They do not directly measure the two main biophysical symptoms of disorder: complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Often these tools cannot be applied to more severe disordered voices, limiting their clinical usefulness.

Methods: This paper introduces two new tools to speech analysis: recurrence and fractal scaling, which overcome the range limitations of existing tools by addressing directly these two symptoms of disorder, together reproducing a "hoarseness" diagram. A simple bootstrapped classifier then uses these two features to distinguish normal from disordered voices.

Results: On a large database of subjects with a wide variety of voice disorders, these new techniques can distinguish normal from disordered cases, using quadratic discriminant analysis, to overall correct classification performance of 91.8% plus or minus 2.0%. The true positive classification performance is 95.4% plus or minus 3.2%, and the true negative performance is 91.5% plus or minus 2.3% (95% confidence). This is shown to outperform all combinations of the most popular classical tools.

Conclusions: Given the very large number of arbitrary parameters and computational complexity of existing techniques, these new techniques are far simpler and yet achieve clinically useful classification performance using only a basic classification technique. They do so by exploiting the inherent nonlinearity and turbulent randomness in disordered voice signals. They are widely applicable to the whole range of disordered voice phenomena by design. These new measures could therefore be used for a variety of practical clinical purposes.

An Analytical Solution for Probabilistic Guarantees of Reservation Based Soft Real-Time Systems
We show a methodology for the computation of the probability of deadline miss
for a periodic real-time task scheduled by a resource reservation algorithm. We
propose a modelling technique for the system that reduces the computation of
such a probability to that of the steady state probability of an infinite state
Discrete Time Markov Chain with a periodic structure. This structure is
exploited to develop an efficient numeric solution where different
accuracy/computation time trade-offs can be obtained by operating on the
granularity of the model. More importantly we offer a closed form conservative
bound for the probability of a deadline miss. Our experiments reveal that the
bound remains reasonably close to the experimental probability in one real-time
application of practical interest. When this bound is used for the optimisation
of the overall Quality of Service for a set of tasks sharing the CPU, it
produces a good sub-optimal solution in a small amount of time.Comment: IEEE Transactions on Parallel and Distributed Systems, Volume:27,
Issue: 3, March 201
On human motion prediction using recurrent neural networks
Human motion modelling is a classical problem at the intersection of graphics
and computer vision, with applications spanning human-computer interaction,
motion synthesis, and motion prediction for virtual and augmented reality.
Following the success of deep learning methods in several computer vision
tasks, recent work has focused on using deep recurrent neural networks (RNNs)
to model human motion, with the goal of learning time-dependent representations
that perform tasks such as short-term motion prediction and long-term human
motion synthesis. We examine recent work, with a focus on the evaluation
methodologies commonly used in the literature, and show that, surprisingly,
state-of-the-art performance can be achieved by a simple baseline that does not
attempt to model motion at all. We investigate this result, and analyze recent
RNN methods by looking at the architectures, loss functions, and training
procedures used in state-of-the-art approaches. We propose three changes to the
standard RNN models typically used for human motion, which result in a simple
and scalable RNN architecture that obtains state-of-the-art performance on
human motion prediction.Comment: Accepted at CVPR 1
Auralization of Air Vehicle Noise for Community Noise Assessment
This paper serves as an introduction to air vehicle noise auralization and documents the current state-of-the-art. Auralization of flyover noise considers the source, path, and receiver as part of a time marching simulation. Two approaches are offered; a time domain approach performs synthesis followed by propagation, while a frequency domain approach performs propagation followed by synthesis. Source noise description methods are offered for isolated and installed propulsion system and airframe noise sources for a wide range of air vehicles. Methods for synthesis of broadband, discrete tones, steady and unsteady periodic, and a periodic sources are presented, and propagation methods and receiver considerations are discussed. Auralizations applied to vehicles ranging from large transport aircraft to small unmanned aerial systems demonstrate current capabilities
Long-term Periodicities in the Flux from Low Mass X-ray Binaries
Using data from the All Sky Monitor (ASM) on the Rossi X-ray Timing Explorer
(RXTE) we have searched for long term periodicities in the X-ray flux of GX
1+4, Sco X-2 (GX 349+2), and GX 339-4. For GX 1+4 we also used data from BATSE
and Galactic Centre scans performed by RXTE. We find no evidence for X-ray
modulations at the suggested ~304 d orbital period of GX 1+4. However, we find
tentative evidence for a periodicity at 420 d to 460 d. An upper limit of 15%
peak-to-peak is set on any sinusoidal modulation in the 1.5 - 3.0 keV flux of
Sco X-2 for periods in the 30 to 100 d range. For GX 339-4 we confirm the Low
State modulation and report the detection of significant low-frequency
modulations in both the High State and Very High State. We fail to detect this
modulation in the Off State. We show that if the reported orbital period of GX
339-4 lies in the range 0.5 - 1.7 d, then it is not present in the RXTE ASM
light curve.Comment: 7 pages, 8 figures. Accepted for publication in Advanced in Space
Research, 16th of March 200
Fourier Policy Gradients
We propose a new way of deriving policy gradient updates for reinforcement
learning. Our technique, based on Fourier analysis, recasts integrals that
arise with expected policy gradients as convolutions and turns them into
multiplications. The obtained analytical solutions allow us to capture the low
variance benefits of EPG in a broad range of settings. For the critic, we treat
trigonometric and radial basis functions, two function families with the
universal approximation property. The choice of policy can be almost arbitrary,
including mixtures or hybrid continuous-discrete probability distributions.
Moreover, we derive a general family of sample-based estimators for stochastic
policy gradients, which unifies existing results on sample-based approximation.
We believe that this technique has the potential to shape the next generation
of policy gradient approaches, powered by analytical results
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