806 research outputs found
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
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
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
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
Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment
Chance-constrained optimization (CCO) has been widely used for uncertainty
management in power system operation. With the prevalence of wind energy, it
becomes possible to consider the wind curtailment as a dispatch variable in
CCO. However, the wind curtailment will cause impulse for the uncertainty
distribution, yielding challenges for the chance constraints modeling. To deal
with that, a data-driven framework is developed. By modeling the wind
curtailment as a cap enforced on the wind power output, the proposed framework
constructs a Gaussian process (GP) surrogate to describe the relationship
between wind curtailment and the chance constraints. This allows us to
reformulate the CCO with wind curtailment as a mixed-integer second-order cone
programming (MI-SOCP) problem. An error correction strategy is developed by
solving a convex linear programming (LP) to improve the modeling accuracy. Case
studies performed on the PJM 5-bus and IEEE 118-bus systems demonstrate that
the proposed method is capable of accurately accounting the influence of wind
curtailment dispatch in CCO
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
Approximate MIMO Iterative Processing with Adjustable Complexity Requirements
Targeting always the best achievable bit error rate (BER) performance in
iterative receivers operating over multiple-input multiple-output (MIMO)
channels may result in significant waste of resources, especially when the
achievable BER is orders of magnitude better than the target performance (e.g.,
under good channel conditions and at high signal-to-noise ratio (SNR)). In
contrast to the typical iterative schemes, a practical iterative decoding
framework that approximates the soft-information exchange is proposed which
allows reduced complexity sphere and channel decoding, adjustable to the
transmission conditions and the required bit error rate. With the proposed
approximate soft information exchange the performance of the exact soft
information can still be reached with significant complexity gains.Comment: The final version of this paper appears in IEEE Transactions on
Vehicular Technolog
Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications
Reconfigurable Intelligent Surfaces (RISs) have been recently considered as
an energy-efficient solution for future wireless networks due to their fast and
low-power configuration, which has increased potential in enabling massive
connectivity and low-latency communications. Accurate and low-overhead channel
estimation in RIS-based systems is one of the most critical challenges due to
the usually large number of RIS unit elements and their distinctive hardware
constraints. In this paper, we focus on the downlink of a RIS-empowered
multi-user Multiple Input Single Output (MISO) downlink communication systems
and propose a channel estimation framework based on the PARAllel FACtor
(PARAFAC) decomposition to unfold the resulting cascaded channel model. We
present two iterative estimation algorithms for the channels between the base
station and RIS, as well as the channels between RIS and users. One is based on
alternating least squares (ALS), while the other uses vector approximate
message passing to iteratively reconstruct two unknown channels from the
estimated vectors. To theoretically assess the performance of the ALS-based
algorithm, we derived its estimation Cram\'er-Rao Bound (CRB). We also discuss
the achievable sum-rate computation with estimated channels and different
precoding schemes for the base station. Our extensive simulation results show
that our algorithms outperform benchmark schemes and that the ALS technique
achieve the CRB. It is also demonstrated that the sum rate using the estimated
channels reached that of perfect channel estimation under various settings,
thus, verifying the effectiveness and robustness of the proposed estimation
algorithms
Reconfigurable Intelligent Surface Aided NOMA Networks
Reconfigurable intelligent surfaces (RISs) constitute a promising performance
enhancement for next-generation (NG) wireless networks in terms of enhancing
both their spectrum efficiency (SE) and energy efficiency (EE). We conceive a
system for serving paired power-domain non-orthogonal multiple access (NOMA)
users by designing the passive beamforming weights at the RISs. In an effort to
evaluate the network performance, we first derive the best-case and worst-case
of new channel statistics for characterizing the effective channel gains. Then,
we derive the best-case and worst-case of our closed-form expressions derived
both for the outage probability and for the ergodic rate of the prioritized
user. For gleaning further insights, we investigate both the diversity orders
of the outage probability and the high-signal-to-noise (SNR) slopes of the
ergodic rate. We also derive both the SE and EE of the proposed network. Our
analytical results demonstrate that the base station (BS)-user links have
almost no impact on the diversity orders attained when the number of RISs is
high enough. Numerical results are provided for confirming that: i) the
high-SNR slope of the RIS-aided network is one; ii) the proposed RIS-aided NOMA
network has superior network performance compared to its orthogonal
counterpart.Comment: arXiv admin note: text overlap with arXiv:1910.0095
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