405 research outputs found
Unsupervised Feature Learning by Deep Sparse Coding
In this paper, we propose a new unsupervised feature learning framework,
namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer
architecture for visual object recognition tasks. The main innovation of the
framework is that it connects the sparse-encoders from different layers by a
sparse-to-dense module. The sparse-to-dense module is a composition of a local
spatial pooling step and a low-dimensional embedding process, which takes
advantage of the spatial smoothness information in the image. As a result, the
new method is able to learn several levels of sparse representation of the
image which capture features at a variety of abstraction levels and
simultaneously preserve the spatial smoothness between the neighboring image
patches. Combining the feature representations from multiple layers, DeepSC
achieves the state-of-the-art performance on multiple object recognition tasks.Comment: 9 pages, submitted to ICL
A Novel Admission Control Model in Cloud Computing
With the rapid development of Cloud computing technologies and wide adopt of
Cloud services and applications, QoS provisioning in Clouds becomes an
important research topic. In this paper, we propose an admission control
mechanism for Cloud computing. In particular we consider the high volume of
simultaneous requests for Cloud services and develop admission control for
aggregated traffic flows to address this challenge. By employ network calculus,
we determine effective bandwidth for aggregate flow, which is used for making
admission control decision. In order to improve network resource allocation
while achieving Cloud service QoS, we investigate the relationship between
effective bandwidth and equivalent capacity. We have also conducted extensive
experiments to evaluate performance of the proposed admission control
mechanism
THE ROLE OF SELECTIVE AUTOPHAGY AND CELL SIGNALING IN FUNGAL DEVELOPMENT AND PATHOGENESIS
Ph.DDOCTOR OF PHILOSOPH
Integrated Sensing, Computation, and Communication: System Framework and Performance Optimization
Integrated sensing, computation, and communication (ISCC) has been recently
considered as a promising technique for beyond 5G systems. In ISCC systems, the
competition for communication and computation resources between sensing tasks
for ambient intelligence and computation tasks from mobile devices becomes an
increasingly challenging issue. To address it, we first propose an efficient
sensing framework with a novel action detection module. It can reduce the
overhead of computation resource by detecting whether the sensing target is
static. Subsequently, we analyze the sensing performance of the proposed
framework and theoretically prove its effectiveness with the help of the
sampling theorem. Then, we formulate a sensing accuracy maximization problem
while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve
it, we propose an optimal resource allocation strategy, in which the minimal
resource is allocated to computation tasks, and the rest is devoted to sensing
tasks. Besides, a threshold selection policy is derived. Compared with the
conventional schemes, the results further demonstrate the necessity of the
proposed sensing framework. Finally, a real-world test of action recognition
tasks based on USRP B210 is conducted to verify the sensing performance
analysis, and extensive experiments demonstrate the performance improvement of
our proposal by comparing it with some benchmark schemes
Design and Performance Analysis of Wireless Legitimate Surveillance Systems with Radar Function
Integrated sensing and communication (ISAC) has recently been considered as a
promising approach to save spectrum resources and reduce hardware cost.
Meanwhile, as information security becomes increasingly more critical issue,
government agencies urgently need to legitimately monitor suspicious
communications via proactive eavesdropping. Thus, in this paper, we investigate
a wireless legitimate surveillance system with radar function. We seek to
jointly optimize the receive and transmit beamforming vectors to maximize the
eavesdropping success probability which is transformed into the difference of
signal-to-interference-plus-noise ratios (SINRs) subject to the performance
requirements of radar and surveillance. The formulated problem is challenging
to solve. By employing the Rayleigh quotient and fully exploiting the structure
of the problem, we apply the divide-and-conquer principle to divide the
formulated problem into two subproblems for two different cases. For the first
case, we aim at minimizing the total transmit power, and for the second case we
focus on maximizing the jamming power. For both subproblems, with the aid of
orthogonal decomposition, we obtain the optimal solution of the receive and
transmit beamforming vectors in closed-form. Performance analysis and
discussion of some insightful results are also carried out. Finally, extensive
simulation results demonstrate the effectiveness of our proposed algorithm in
terms of eavesdropping success probability
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