228 research outputs found
Face recognition based on improved Retinex and sparse representation
AbstractIn this paper, we proposed a method based on improved Retinex theory and sparse representation to deal with the difficulties for face recognition under inhomogeneous illumination. In our work, the total variation model was introduced to optimize the parameters of Retinex and the illumination insensitive features were extracted as the dictionary of sparse representation. Finally, the facial images could be recognized by the proposed algorithm. The experimental results on different benchmark face databases indicated that the proposed approach could be more efficient than traditional methods for face images under uncontrolled illumination conditions
Uplink Transceiver Design and Optimization for Transmissive RMS Multi-Antenna Systems
In this paper, a novel uplink communication for the transmissive
reconfigurable metasurface (RMS) multi-antenna system is investigated.
Specifically, a transmissive RMS-based receiver equipped with a single
receiving antenna is first proposed, and a far-near field channel model is also
given. Then, in order to maximize the system sum-rate, we formulate a joint
optimization problem over subcarrier allocation, power allocation and RMS
transmissive coefficient design. Since the coupling of optimization variables,
the problem is non-convex, so it is challenging to solve it directly. In order
to tackle this problem, the alternating optimization (AO) algorithm is used to
decouple the optimization variables and divide the problem into two subproblems
to solve. Numerical results verify that the proposed algorithm has good
convergence performance and can improve system sum-rate compared with other
benchmark algorithms.Comment: arXiv admin note: text overlap with arXiv:2109.0546
On the Performance of RIS-Aided Spatial Scattering Modulation for mmWave Transmission
In this paper, we investigate a state-of-the-art reconfigurable intelligent
surface (RIS)-assisted spatial scattering modulation (SSM) scheme for
millimeter-wave (mmWave) systems, where a more practical scenario that the RIS
is near the transmitter while the receiver is far from RIS is considered. To
this end, the line-of-sight (LoS) and non-LoS links are utilized in the
transmitter-RIS and RIS-receiver channels, respectively. By employing the
maximum likelihood detector at the receiver, the conditional pairwise error
probability (CPEP) expression for the RIS-SSM scheme is derived under the two
scenarios that the received beam demodulation is correct or not. Furthermore,
the union upper bound of average bit error probability (ABEP) is obtained based
on the CPEP expression. Finally, the derivation results are exhaustively
validated by the Monte Carlo simulations.Comment: arXiv admin note: substantial text overlap with arXiv:2307.1466
Throughput Maximization for UAV-enabled Integrated Periodic Sensing and Communication
Unmanned aerial vehicle (UAV) is expected to revolutionize the existing
integrated sensing and communication (ISAC) system and promise a more flexible
joint design. Nevertheless, the existing works on ISAC mainly focus on
exploring the performance of both functionalities simultaneously during the
entire considered period, which may ignore the practical asymmetric sensing and
communication requirements. In particular, always forcing sensing along with
communication may make it is harder to balance between these two
functionalities due to shared spectrum resources and limited transmit power. To
address this issue, we propose a new integrated periodic sensing and
communication mechanism for the UAV-enabled ISAC system to provide a more
flexible trade-off between two integrated functionalities. Specifically, the
system achievable rate is maximized via jointly optimizing UAV trajectory, user
association, target sensing selection, and transmit beamforming, while meeting
the sensing frequency and beam pattern gain requirement for the given targets.
Despite that this problem is highly non-convex and involves closely coupled
integer variables, we derive the closed-form optimal beamforming vector to
dramatically reduce the complexity of beamforming design, and present a tight
lower bound of the achievable rate to facilitate UAV trajectory design. Based
on the above results, we propose a penalty-based algorithm to efficiently solve
the considered problem. The optimal achievable rate and the optimal UAV
location are analyzed under a special case of infinity number of antennas.
Furthermore, we prove the structural symmetry between the optimal solutions in
different ISAC frames without location constraints and propose an efficient
algorithm for solving the problem with location constraints.Comment: 32 pages, This work has been submitted to the IEEE for possible
publicatio
Reconfigurable Intelligent Surface Assisted Free Space Optical Information and Power Transfer
Free space optical (FSO) transmission has emerged as a key candidate
technology for 6G to expand new spectrum and improve network capacity due to
its advantages of large bandwidth, low electromagnetic interference, and high
energy efficiency. Resonant beam operating in the infrared band utilizes
spatially separated laser cavities to enable safe and mobile high-power energy
and high-rate information transmission but is limited by line-of-sight (LOS)
channel. In this paper, we propose a reconfigurable intelligent surface (RIS)
assisted resonant beam simultaneous wireless information and power transfer
(SWIPT) system and establish an optical field propagation model to analyze the
channel state information (CSI), in which LOS obstruction can be detected
sensitively and non-line-of-sight (NLOS) transmission can be realized by
changing the phased of resonant beam in RIS. Numerical results demonstrate
that, apart from the transmission distance, the NLOS performance depends on
both the horizontal and vertical positions of RIS. The maximum NLOS energy
efficiency can achieve 55% within a transfer distance of 10m, a translation
distance of 4mm, and rotation angle of 50{\deg}
Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning
Hierarchical Federated Learning (HFL) is a distributed machine learning
paradigm tailored for multi-tiered computation architectures, which supports
massive access of devices' models simultaneously. To enable efficient HFL, it
is crucial to design suitable incentive mechanisms to ensure that devices
actively participate in local training. However, there are few studies on
incentive mechanism design for HFL. In this paper, we design two-level
incentive mechanisms for the HFL with a two-tiered computing structure to
encourage the participation of entities in each tier in the HFL training. In
the lower-level game, we propose a coalition formation game to joint optimize
the edge association and bandwidth allocation problem, and obtain efficient
coalition partitions by the proposed preference rule, which can be proven to be
stable by exact potential game. In the upper-level game, we design the
Stackelberg game algorithm, which not only determines the optimal number of
edge aggregations for edge servers to maximize their utility, but also optimize
the unit reward provided for the edge aggregation performance to ensure the
interests of cloud servers. Furthermore, numerical results indicate that the
proposed algorithms can achieve better performance than the benchmark schemes
CUTS: Neural Causal Discovery from Irregular Time-Series Data
Causal discovery from time-series data has been a central task in machine
learning. Recently, Granger causality inference is gaining momentum due to its
good explainability and high compatibility with emerging deep neural networks.
However, most existing methods assume structured input data and degenerate
greatly when encountering data with randomly missing entries or non-uniform
sampling frequencies, which hampers their applications in real scenarios. To
address this issue, here we present CUTS, a neural Granger causal discovery
algorithm to jointly impute unobserved data points and build causal graphs, via
plugging in two mutually boosting modules in an iterative framework: (i) Latent
data prediction stage: designs a Delayed Supervision Graph Neural Network
(DSGNN) to hallucinate and register unstructured data which might be of high
dimension and with complex distribution; (ii) Causal graph fitting stage:
builds a causal adjacency matrix with imputed data under sparse penalty.
Experiments show that CUTS effectively infers causal graphs from unstructured
time-series data, with significantly superior performance to existing methods.
Our approach constitutes a promising step towards applying causal discovery to
real applications with non-ideal observations.Comment: https://openreview.net/forum?id=UG8bQcD3Em
- …