301 research outputs found
Beamforming and Power Splitting Designs for AN-aided Secure Multi-user MIMO SWIPT Systems
In this paper, an energy harvesting scheme for a multi-user
multiple-input-multiple-output (MIMO) secrecy channel with artificial noise
(AN) transmission is investigated. Joint optimization of the transmit
beamforming matrix, the AN covariance matrix, and the power splitting ratio is
conducted to minimize the transmit power under the target secrecy rate, the
total transmit power, and the harvested energy constraints. The original
problem is shown to be non-convex, which is tackled by a two-layer
decomposition approach. The inner layer problem is solved through semi-definite
relaxation, and the outer problem, on the other hand, is shown to be a single-
variable optimization that can be solved by one-dimensional (1- D) line search.
To reduce computational complexity, a sequential parametric convex
approximation (SPCA) method is proposed to find a near-optimal solution. The
work is then extended to the imperfect channel state information case with
norm-bounded channel errors. Furthermore, tightness of the relaxation for the
proposed schemes are validated by showing that the optimal solution of the
relaxed problem is rank-one. Simulation results demonstrate that the proposed
SPCA method achieves the same performance as the scheme based on 1-D but with
much lower complexity.Comment: 12 pages, 6 figures, submitted for possible publicatio
Resolution Improvement for OpticalCoherence Tomography based on Sparse Continuous Deconvolution
We propose an image resolution improvement method for optical coherence
tomography (OCT) based on sparse continuous deconvolution. Traditional
deconvolution techniques such as Lucy-Richardson deconvolution suffers from the
artifact convergence problem after a small number of iterations, which brings
limitation to practical applications. In this work, we take advantage of the
prior knowledge about the sample sparsity and continuity to constrain the
deconvolution iteration. Sparsity is used to achieve the resolution improvement
through the resolution preserving regularization term. And the continuity based
on the correlation of the grayscale values in different directions is
introduced to mitigate excessive image sparsity and noise reduction through the
continuity regularization term. The Bregman splitting technique is then used to
solve the resulting optimization problem. Both the numerical simulation study
and experimental study on phantoms and biological samples show that our method
can suppress artefacts of traditional deconvolution techniques effectively.
Meanwhile, clear resolution improvement is demonstrated. It achieved nearly
twofold resolution improvement for phantom beads image that can be
quantitatively evaluate
SwG-former: Sliding-window Graph Convolutional Network Integrated with Conformer for Sound Event Localization and Detection
Sound event localization and detection (SELD) is a joint task of sound event
detection (SED) and direction of arrival (DoA) estimation. SED mainly relies on
temporal dependencies to distinguish different sound classes, while DoA
estimation depends on spatial correlations to estimate source directions. To
jointly optimize two subtasks, the SELD system should extract spatial
correlations and model temporal dependencies simultaneously. However, numerous
models mainly extract spatial correlations and model temporal dependencies
separately. In this paper, the interdependence of spatial-temporal information
in audio signals is exploited for simultaneous extraction to enhance the model
performance. In response, a novel graph representation leveraging graph
convolutional network (GCN) in non-Euclidean space is developed to extract
spatial-temporal information concurrently. A sliding-window graph (SwG) module
is designed based on the graph representation. It exploits sliding-windows with
different sizes to learn temporal context information and dynamically
constructs graph vertices in the frequency-channel (F-C) domain to capture
spatial correlations. Furthermore, as the cornerstone of message passing, a
robust Conv2dAgg function is proposed and embedded into the SwG module to
aggregate the features of neighbor vertices. To improve the performance of SELD
in a natural spatial acoustic environment, a general and efficient SwG-former
model is proposed by integrating the SwG module with the Conformer. It exhibits
superior performance in comparison to recent advanced SELD models. To further
validate the generality and efficiency of the SwG-former, it is seamlessly
integrated into the event-independent network version 2 (EINV2) called
SwG-EINV2. The SwG-EINV2 surpasses the state-of-the-art (SOTA) methods under
the same acoustic environment
Deep recurrent spiking neural networks capture both static and dynamic representations of the visual cortex under movie stimuli
In the real world, visual stimuli received by the biological visual system
are predominantly dynamic rather than static. A better understanding of how the
visual cortex represents movie stimuli could provide deeper insight into the
information processing mechanisms of the visual system. Although some progress
has been made in modeling neural responses to natural movies with deep neural
networks, the visual representations of static and dynamic information under
such time-series visual stimuli remain to be further explored. In this work,
considering abundant recurrent connections in the mouse visual system, we
design a recurrent module based on the hierarchy of the mouse cortex and add it
into Deep Spiking Neural Networks, which have been demonstrated to be a more
compelling computational model for the visual cortex. Using Time-Series
Representational Similarity Analysis, we measure the representational
similarity between networks and mouse cortical regions under natural movie
stimuli. Subsequently, we conduct a comparison of the representational
similarity across recurrent/feedforward networks and image/video training
tasks. Trained on the video action recognition task, recurrent SNN achieves the
highest representational similarity and significantly outperforms feedforward
SNN trained on the same task by 15% and the recurrent SNN trained on the image
classification task by 8%. We investigate how static and dynamic
representations of SNNs influence the similarity, as a way to explain the
importance of these two forms of representations in biological neural coding.
Taken together, our work is the first to apply deep recurrent SNNs to model the
mouse visual cortex under movie stimuli and we establish that these networks
are competent to capture both static and dynamic representations and make
contributions to understanding the movie information processing mechanisms of
the visual cortex
Sampling via Gradient Flows in the Space of Probability Measures
Sampling a target probability distribution with an unknown normalization
constant is a fundamental challenge in computational science and engineering.
Recent work shows that algorithms derived by considering gradient flows in the
space of probability measures open up new avenues for algorithm development.
This paper makes three contributions to this sampling approach by scrutinizing
the design components of such gradient flows. Any instantiation of a gradient
flow for sampling needs an energy functional and a metric to determine the
flow, as well as numerical approximations of the flow to derive algorithms. Our
first contribution is to show that the Kullback-Leibler divergence, as an
energy functional, has the unique property (among all f-divergences) that
gradient flows resulting from it do not depend on the normalization constant of
the target distribution. Our second contribution is to study the choice of
metric from the perspective of invariance. The Fisher-Rao metric is known as
the unique choice (up to scaling) that is diffeomorphism invariant. As a
computationally tractable alternative, we introduce a relaxed, affine
invariance property for the metrics and gradient flows. In particular, we
construct various affine invariant Wasserstein and Stein gradient flows. Affine
invariant gradient flows are shown to behave more favorably than their
non-affine-invariant counterparts when sampling highly anisotropic
distributions, in theory and by using particle methods. Our third contribution
is to study, and develop efficient algorithms based on Gaussian approximations
of the gradient flows; this leads to an alternative to particle methods. We
establish connections between various Gaussian approximate gradient flows,
discuss their relation to gradient methods arising from parametric variational
inference, and study their convergence properties both theoretically and
numerically.Comment: Related and text overlap with arXiv:2302.1102
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