248 research outputs found
An optimized QoS scheme for IMS-NEMO in heterogeneous networks
The network mobility (NEMO) is proposed to support the mobility management when users move as a whole. In IP Multimedia Subsystem (IMS), the individual Quality of Service (QoS) control for NEMO results in excessive signaling cost. On the other hand, current QoS schemes have two drawbacks: unawareness of the heterogeneous wireless environment and inefficient utilization of the reserved bandwidth. To solve these problems, we present a novel heterogeneous bandwidth sharing (HBS) scheme for QoS provision under IMS-based NEMO (IMS-NEMO). The HBS scheme selects the most suitable access network for each session and enables the new coming non-real-time sessions to share bandwidth with the Variable Bit Rate (VBR) coded media flows. The modeling and simulation results demonstrate that the HBS can satisfy users' QoS requirement and obtain a more efficient use of the scarce wireless bandwidth
Gr\"obner-Shirshov bases and linear bases for free multi-operated algebras over algebras with applications to differential Rota-Baxter algebras and integro-differential algebras
Quite much recent studies has been attracted to the operated algebra since it
unifies various notions such as the differential algebra and the Rota-Baxter
algebra. An -operated algebra is a an (associative) algebra equipped
with a set of linear operators which might satisfy certain operator
identities such as the Leibniz rule. A free -operated algebra can
be generated on an algebra similar to a free algebra generated on a set. If
has a Gr\"{o}bner-Shirshov basis and if the linear operators
satisfy a set of operator identities, it is natural to ask when the
union is a Gr\"{o}bner-Shirshov basis of . A previous work
answers this question affirmatively under a mild condition, and thereby obtains
a canonical linear basis of .
In this paper, we answer this question in the general case of multiple linear
operators. As applications we get operated Gr\"{o}bner-Shirshov bases for free
differential Rota-Baxter algebras and free integro-differential algebras over
algebras as well as their linear bases. One of the key technical difficulties
is to introduce new monomial orders for the case of two operators, which might
be of independent interest.Comment: 27 page
Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion
RGB-guided depth completion aims at predicting dense depth maps from sparse
depth measurements and corresponding RGB images, where how to effectively and
efficiently exploit the multi-modal information is a key issue. Guided dynamic
filters, which generate spatially-variant depth-wise separable convolutional
filters from RGB features to guide depth features, have been proven to be
effective in this task. However, the dynamically generated filters require
massive model parameters, computational costs and memory footprints when the
number of feature channels is large. In this paper, we propose to decompose the
guided dynamic filters into a spatially-shared component multiplied by
content-adaptive adaptors at each spatial location. Based on the proposed idea,
we introduce two decomposition schemes A and B, which decompose the filters by
splitting the filter structure and using spatial-wise attention, respectively.
The decomposed filters not only maintain the favorable properties of guided
dynamic filters as being content-dependent and spatially-variant, but also
reduce model parameters and hardware costs, as the learned adaptors are
decoupled with the number of feature channels. Extensive experimental results
demonstrate that the methods using our schemes outperform state-of-the-art
methods on the KITTI dataset, and rank 1st and 2nd on the KITTI benchmark at
the time of submission. Meanwhile, they also achieve comparable performance on
the NYUv2 dataset. In addition, our proposed methods are general and could be
employed as plug-and-play feature fusion blocks in other multi-modal fusion
tasks such as RGB-D salient object detection
Recurrent Contour-based Instance Segmentation with Progressive Learning
Contour-based instance segmentation has been actively studied, thanks to its
flexibility and elegance in processing visual objects within complex
backgrounds. In this work, we propose a novel deep network architecture, i.e.,
PolySnake, for contour-based instance segmentation. Motivated by the classic
Snake algorithm, the proposed PolySnake achieves superior and robust
segmentation performance with an iterative and progressive contour refinement
strategy. Technically, PolySnake introduces a recurrent update operator to
estimate the object contour iteratively. It maintains a single estimate of the
contour that is progressively deformed toward the object boundary. At each
iteration, PolySnake builds a semantic-rich representation for the current
contour and feeds it to the recurrent operator for further contour adjustment.
Through the iterative refinements, the contour finally progressively converges
to a stable status that tightly encloses the object instance. Moreover, with a
compact design of the recurrent architecture, we ensure the running efficiency
under multiple iterations. Extensive experiments are conducted to validate the
merits of our method, and the results demonstrate that the proposed PolySnake
outperforms the existing contour-based instance segmentation methods on several
prevalent instance segmentation benchmarks. The codes and models are available
at https://github.com/fh2019ustc/PolySnake
LRRU: Long-short Range Recurrent Updating Networks for Depth Completion
Existing deep learning-based depth completion methods generally employ
massive stacked layers to predict the dense depth map from sparse input data.
Although such approaches greatly advance this task, their accompanied huge
computational complexity hinders their practical applications. To accomplish
depth completion more efficiently, we propose a novel lightweight deep network
framework, the Long-short Range Recurrent Updating (LRRU) network. Without
learning complex feature representations, LRRU first roughly fills the sparse
input to obtain an initial dense depth map, and then iteratively updates it
through learned spatially-variant kernels. Our iterative update process is
content-adaptive and highly flexible, where the kernel weights are learned by
jointly considering the guidance RGB images and the depth map to be updated,
and large-to-small kernel scopes are dynamically adjusted to capture
long-to-short range dependencies. Our initial depth map has coarse but complete
scene depth information, which helps relieve the burden of directly regressing
the dense depth from sparse ones, while our proposed method can effectively
refine it to an accurate depth map with less learnable parameters and inference
time. Experimental results demonstrate that our proposed LRRU variants achieve
state-of-the-art performance across different parameter regimes. In particular,
the LRRU-Base model outperforms competing approaches on the NYUv2 dataset, and
ranks 1st on the KITTI depth completion benchmark at the time of submission.
Project page: https://npucvr.github.io/LRRU/.Comment: Published in ICCV 202
A linear chained approach for service invocation in IP multimedia subsystem.
IP Multimedia Subsystem (IMS) is considered to provide multimedia services to users through an IP-based control plane. The current IMS service invocation mechanism, however, requires the Serving-Call Session Control Function (S-CSCF) invokes each Application Server (AS) sequentially to perform service subscription pro?le, which results in the heavy load of the S-CSCF and the long session set-up delay. To solve this issue, this paper proposes a linear chained service invocation mechanism to invoke each AS consecutively. By checking all the initial Filter Criteria (iFC) one-time and adding the addresses of all involved ASs to the ?Route? header, this new approach enables multiple services to be invoked as a linear chain during a session. We model the service invocation mechanisms through Jackson networks, which are validated through simulations. The analytic results verify that the linear chained service invocation mechanism can effectively reduce session set-up delay of the service layer and decrease the load level of the S-CSC
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