42,683 research outputs found
Network Coding Tree Algorithm for Multiple Access System
Network coding is famous for significantly improving the throughput of
networks. The successful decoding of the network coded data relies on some side
information of the original data. In that framework, independent data flows are
usually first decoded and then network coded by relay nodes. If appropriate
signal design is adopted, physical layer network coding is a natural way in
wireless networks. In this work, a network coding tree algorithm which enhances
the efficiency of the multiple access system (MAS) is presented. For MAS,
existing works tried to avoid the collisions while collisions happen frequently
under heavy load. By introducing network coding to MAS, our proposed algorithm
achieves a better performance of throughput and delay. When multiple users
transmit signal in a time slot, the mexed signals are saved and used to jointly
decode the collided frames after some component frames of the network coded
frame are received. Splitting tree structure is extended to the new algorithm
for collision solving. The throughput of the system and average delay of frames
are presented in a recursive way. Besides, extensive simulations show that
network coding tree algorithm enhances the system throughput and decreases the
average frame delay compared with other algorithms. Hence, it improves the
system performance
Multi-Label Zero-Shot Human Action Recognition via Joint Latent Ranking Embedding
Human action recognition refers to automatic recognizing human actions from a
video clip. In reality, there often exist multiple human actions in a video
stream. Such a video stream is often weakly-annotated with a set of relevant
human action labels at a global level rather than assigning each label to a
specific video episode corresponding to a single action, which leads to a
multi-label learning problem. Furthermore, there are many meaningful human
actions in reality but it would be extremely difficult to collect/annotate
video clips regarding all of various human actions, which leads to a zero-shot
learning scenario. To the best of our knowledge, there is no work that has
addressed all the above issues together in human action recognition. In this
paper, we formulate a real-world human action recognition task as a multi-label
zero-shot learning problem and propose a framework to tackle this problem in a
holistic way. Our framework holistically tackles the issue of unknown temporal
boundaries between different actions for multi-label learning and exploits the
side information regarding the semantic relationship between different human
actions for knowledge transfer. Consequently, our framework leads to a joint
latent ranking embedding for multi-label zero-shot human action recognition. A
novel neural architecture of two component models and an alternate learning
algorithm are proposed to carry out the joint latent ranking embedding
learning. Thus, multi-label zero-shot recognition is done by measuring
relatedness scores of action labels to a test video clip in the joint latent
visual and semantic embedding spaces. We evaluate our framework with different
settings, including a novel data split scheme designed especially for
evaluating multi-label zero-shot learning, on two datasets: Breakfast and
Charades. The experimental results demonstrate the effectiveness of our
framework.Comment: 27 pages, 10 figures and 7 tables. Technical report submitted to a
journal. More experimental results/references were added and typos were
correcte
Transferable Positive/Negative Speech Emotion Recognition via Class-wise Adversarial Domain Adaptation
Speech emotion recognition plays an important role in building more
intelligent and human-like agents. Due to the difficulty of collecting speech
emotional data, an increasingly popular solution is leveraging a related and
rich source corpus to help address the target corpus. However, domain shift
between the corpora poses a serious challenge, making domain shift adaptation
difficult to function even on the recognition of positive/negative emotions. In
this work, we propose class-wise adversarial domain adaptation to address this
challenge by reducing the shift for all classes between different corpora.
Experiments on the well-known corpora EMODB and Aibo demonstrate that our
method is effective even when only a very limited number of target labeled
examples are provided.Comment: 5 pages, 3 figures, accepted to ICASSP 201
A Total Fractional-Order Variation Model for Image Restoration with Non-homogeneous Boundary Conditions and its Numerical Solution
To overcome the weakness of a total variation based model for image
restoration, various high order (typically second order) regularization models
have been proposed and studied recently. In this paper we analyze and test a
fractional-order derivative based total -order variation model, which
can outperform the currently popular high order regularization models. There
exist several previous works using total -order variations for image
restoration; however first no analysis is done yet and second all tested
formulations, differing from each other, utilize the zero Dirichlet boundary
conditions which are not realistic (while non-zero boundary conditions violate
definitions of fractional-order derivatives). This paper first reviews some
results of fractional-order derivatives and then analyzes the theoretical
properties of the proposed total -order variational model rigorously.
It then develops four algorithms for solving the variational problem, one based
on the variational Split-Bregman idea and three based on direct solution of the
discretise-optimization problem. Numerical experiments show that, in terms of
restoration quality and solution efficiency, the proposed model can produce
highly competitive results, for smooth images, to two established high order
models: the mean curvature and the total generalized variation.Comment: 26 page
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