8,773 research outputs found
Decision Fusion in Space-Time Spreading aided Distributed MIMO WSNs
In this letter, we propose space-time spreading (STS) of local sensor
decisions before reporting them over a wireless multiple access channel (MAC),
in order to achieve flexible balance between diversity and multiplexing gain as
well as eliminate any chance of intrinsic interference inherent in MAC
scenarios. Spreading of the sensor decisions using dispersion vectors exploits
the benefits of multi-slot decision to improve low-complexity diversity gain
and opportunistic throughput. On the other hand, at the receive side of the
reporting channel, we formulate and compare optimum and sub-optimum fusion
rules for arriving at a reliable conclusion.Simulation results demonstrate gain
in performance with STS aided transmission from a minimum of 3 times to a
maximum of 6 times over performance without STS.Comment: 5 pages, 5 figure
Simultaneous Codeword Optimization (SimCO) for Dictionary Update and Learning
We consider the data-driven dictionary learning problem. The goal is to seek
an over-complete dictionary from which every training signal can be best
approximated by a linear combination of only a few codewords. This task is
often achieved by iteratively executing two operations: sparse coding and
dictionary update. In the literature, there are two benchmark mechanisms to
update a dictionary. The first approach, such as the MOD algorithm, is
characterized by searching for the optimal codewords while fixing the sparse
coefficients. In the second approach, represented by the K-SVD method, one
codeword and the related sparse coefficients are simultaneously updated while
all other codewords and coefficients remain unchanged. We propose a novel
framework that generalizes the aforementioned two methods. The unique feature
of our approach is that one can update an arbitrary set of codewords and the
corresponding sparse coefficients simultaneously: when sparse coefficients are
fixed, the underlying optimization problem is similar to that in the MOD
algorithm; when only one codeword is selected for update, it can be proved that
the proposed algorithm is equivalent to the K-SVD method; and more importantly,
our method allows us to update all codewords and all sparse coefficients
simultaneously, hence the term simultaneous codeword optimization (SimCO).
Under the proposed framework, we design two algorithms, namely, primitive and
regularized SimCO. We implement these two algorithms based on a simple gradient
descent mechanism. Simulations are provided to demonstrate the performance of
the proposed algorithms, as compared with two baseline algorithms MOD and
K-SVD. Results show that regularized SimCO is particularly appealing in terms
of both learning performance and running speed.Comment: 13 page
Objective assessment of region of interest-aware adaptive multimedia streaming quality
Adaptive multimedia streaming relies on controlled
adjustment of content bitrate and consequent video quality variation in order to meet the bandwidth constraints of the communication
link used for content delivery to the end-user. The values of the easy to measure network-related Quality of Service metrics have no direct relationship with the way moving images are
perceived by the human viewer. Consequently variations in the video stream bitrate are not clearly linked to similar variation in the user perceived quality. This is especially true if some human visual system-based adaptation techniques are employed. As research has shown, there are certain image regions in each frame of a video sequence on which the users are more interested than in the others. This paper presents the Region of Interest-based Adaptive Scheme (ROIAS) which adjusts differently the regions within each frame of the streamed multimedia content based on the user interest in them. ROIAS is presented and discussed in terms of the adjustment algorithms employed and their impact on the human perceived video quality. Comparisons with existing approaches, including a constant quality adaptation scheme across the whole frame area, are performed employing two objective metrics which estimate user perceived video quality
Secrecy Throughput Maximization for Full-Duplex Wireless Powered IoT Networks under Fairness Constraints
In this paper, we study the secrecy throughput of a full-duplex wireless
powered communication network (WPCN) for internet of things (IoT). The WPCN
consists of a full-duplex multi-antenna base station (BS) and a number of
sensor nodes. The BS transmits energy all the time, and each node harvests
energy prior to its transmission time slot. The nodes sequentially transmit
their confidential information to the BS, and the other nodes are considered as
potential eavesdroppers. We first formulate the sum secrecy throughput
optimization problem of all the nodes. The optimization variables are the
duration of the time slots and the BS beamforming vectors in different time
slots. The problem is shown to be non-convex. To tackle the problem, we propose
a suboptimal two stage approach, referred to as sum secrecy throughput
maximization (SSTM). In the first stage, the BS focuses its beamforming to
blind the potential eavesdroppers (other nodes) during information transmission
time slots. Then, the optimal beamforming vector in the initial non-information
transmission time slot and the optimal time slots are derived. We then consider
fairness among the nodes and propose max-min fair (MMF) and proportional fair
(PLF) algorithms. The MMF algorithm maximizes the minimum secrecy throughput of
the nodes, while the PLF tries to achieve a good trade-off between the sum
secrecy throughput and fairness among the nodes. Through numerical simulations,
we first demonstrate the superior performance of the SSTM to uniform time
slotting and beamforming in different settings. Then, we show the effectiveness
of the proposed fair algorithms
VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection
Although traffic sign detection has been studied for years and great progress
has been made with the rise of deep learning technique, there are still many
problems remaining to be addressed. For complicated real-world traffic scenes,
there are two main challenges. Firstly, traffic signs are usually small size
objects, which makes it more difficult to detect than large ones; Secondly, it
is hard to distinguish false targets which resemble real traffic signs in
complex street scenes without context information. To handle these problems, we
propose a novel end-to-end deep learning method for traffic sign detection in
complex environments. Our contributions are as follows: 1) We propose a
multi-resolution feature fusion network architecture which exploits densely
connected deconvolution layers with skip connections, and can learn more
effective features for the small size object; 2) We frame the traffic sign
detection as a spatial sequence classification and regression task, and propose
a vertical spatial sequence attention (VSSA) module to gain more context
information for better detection performance. To comprehensively evaluate the
proposed method, we do experiments on several traffic sign datasets as well as
the general object detection dataset and the results have shown the
effectiveness of our proposed method
GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection
Change detection (CD) is an important application of remote sensing, which
provides timely change information about large-scale Earth surface. With the
emergence of hyperspectral imagery, CD technology has been greatly promoted, as
hyperspectral data with the highspectral resolution are capable of detecting
finer changes than using the traditional multispectral imagery. Nevertheless,
the high dimension of hyperspectral data makes it difficult to implement
traditional CD algorithms. Besides, endmember abundance information at subpixel
level is often not fully utilized. In order to better handle high dimension
problem and explore abundance information, this paper presents a General
End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image
change detection (HSI-CD). The main contributions of this work are threefold:
1) Mixed-affinity matrix that integrates subpixel representation is introduced
to mine more cross-channel gradient features and fuse multi-source information;
2) 2-D CNN is designed to learn the discriminative features effectively from
multi-source data at a higher level and enhance the generalization ability of
the proposed CD algorithm; 3) A new HSI-CD data set is designed for the
objective comparison of different methods. Experimental results on real
hyperspectral data sets demonstrate the proposed method outperforms most of the
state-of-the-arts
Turbo NOC: a framework for the design of Network On Chip based turbo decoder architectures
This work proposes a general framework for the design and simulation of
network on chip based turbo decoder architectures. Several parameters in the
design space are investigated, namely the network topology, the parallelism
degree, the rate at which messages are sent by processing nodes over the
network and the routing strategy. The main results of this analysis are: i) the
most suited topologies to achieve high throughput with a limited complexity
overhead are generalized de-Bruijn and generalized Kautz topologies; ii)
depending on the throughput requirements different parallelism degrees, message
injection rates and routing algorithms can be used to minimize the network area
overhead.Comment: submitted to IEEE Trans. on Circuits and Systems I (submission date
27 may 2009
PEA265: Perceptual Assessment of Video Compression Artifacts
The most widely used video encoders share a common hybrid coding framework
that includes block-based motion estimation/compensation and block-based
transform coding. Despite their high coding efficiency, the encoded videos
often exhibit visually annoying artifacts, denoted as Perceivable Encoding
Artifacts (PEAs), which significantly degrade the visual Qualityof- Experience
(QoE) of end users. To monitor and improve visual QoE, it is crucial to develop
subjective and objective measures that can identify and quantify various types
of PEAs. In this work, we make the first attempt to build a large-scale
subjectlabelled database composed of H.265/HEVC compressed videos containing
various PEAs. The database, namely the PEA265 database, includes 4 types of
spatial PEAs (i.e. blurring, blocking, ringing and color bleeding) and 2 types
of temporal PEAs (i.e. flickering and floating). Each containing at least
60,000 image or video patches with positive and negative labels. To objectively
identify these PEAs, we train Convolutional Neural Networks (CNNs) using the
PEA265 database. It appears that state-of-theart ResNeXt is capable of
identifying each type of PEAs with high accuracy. Furthermore, we define PEA
pattern and PEA intensity measures to quantify PEA levels of compressed video
sequence. We believe that the PEA265 database and our findings will benefit the
future development of video quality assessment methods and perceptually
motivated video encoders.Comment: 10 pages,15 figures,4 table
POLYPHONIC PIANO TRANSCRIPTION USING NON-NEGATIVE MATRIX FACTORISATION WITH GROUP SPARSITY
(c)2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Published in: Proc IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), Florence, Italy, 5-9 May 2014. pp.3136-3140
Timed Consistent Network Updates
Network updates such as policy and routing changes occur frequently in
Software Defined Networks (SDN). Updates should be performed consistently,
preventing temporary disruptions, and should require as little overhead as
possible. Scalability is increasingly becoming an essential requirement in SDN.
In this paper we propose to use time-triggered network updates to achieve
consistent updates. Our proposed solution requires lower overhead than existing
update approaches, without compromising the consistency during the update. We
demonstrate that accurate time enables far more scalable consistent updates in
SDN than previously available. In addition, it provides the SDN programmer with
fine-grained control over the tradeoff between consistency and scalability.Comment: This technical report is an extended version of the paper "Timed
Consistent Network Updates", which was accepted to the ACM SIGCOMM Symposium
on SDN Research (SOSR) '15, Santa Clara, CA, US, June 201
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