220 research outputs found
Aiming in Harsh Environments: A New Framework for Flexible and Adaptive Resource Management
The harsh environment imposes a unique set of challenges on networking
strategies. In such circumstances, the environmental impact on network
resources and long-time unattended maintenance has not been well investigated
yet. To address these challenges, we propose a flexible and adaptive resource
management framework that incorporates the environment awareness functionality.
In particular, we propose a new network architecture and introduce the new
functionalities against the traditional network components. The novelties of
the proposed architecture include a deep-learning-based environment resource
prediction module and a self-organized service management module. Specifically,
the available network resource under various environmental conditions is
predicted by using the prediction module. Then based on the prediction, an
environment-oriented resource allocation method is developed to optimize the
system utility. To demonstrate the effectiveness and efficiency of the proposed
new functionalities, we examine the method via an experiment in a case study.
Finally, we introduce several promising directions of resource management in
harsh environments that can be extended from this paper.Comment: 8 pages, 4 figures, to appear in IEEE Network Magazine, 202
Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation
The divergence between labeled training data and unlabeled testing data is a
significant challenge for recent deep learning models. Unsupervised domain
adaptation (UDA) attempts to solve such problem. Recent works show that
self-training is a powerful approach to UDA. However, existing methods have
difficulty in balancing the scalability and performance. In this paper, we
propose a hard-aware instance adaptive self-training framework for UDA on the
task of semantic segmentation. To effectively improve the quality and diversity
of pseudo-labels, we develop a novel pseudo-label generation strategy with an
instance adaptive selector. We further enrich the hard class pseudo-labels with
inter-image information through a skillfully designed hard-aware pseudo-label
augmentation. Besides, we propose the region-adaptive regularization to smooth
the pseudo-label region and sharpen the non-pseudo-label region. For the
non-pseudo-label region, consistency constraint is also constructed to
introduce stronger supervision signals during model optimization. Our method is
so concise and efficient that it is easy to be generalized to other UDA
methods. Experiments on GTA5 to Cityscapes, SYNTHIA to Cityscapes, and
Cityscapes to Oxford RobotCar demonstrate the superior performance of our
approach compared with the state-of-the-art methods.Comment: arXiv admin note: text overlap with arXiv:2008.1219
Domain Adaptation based Enhanced Detection for Autonomous Driving in Foggy and Rainy Weather
Typically, object detection methods for autonomous driving that rely on
supervised learning make the assumption of a consistent feature distribution
between the training and testing data, however such assumption may fail in
different weather conditions. Due to the domain gap, a detection model trained
under clear weather may not perform well in foggy and rainy conditions.
Overcoming detection bottlenecks in foggy and rainy weather is a real challenge
for autonomous vehicles deployed in the wild. To bridge the domain gap and
improve the performance of object detectionin foggy and rainy weather, this
paper presents a novel framework for domain-adaptive object detection. The
adaptations at both the image-level and object-level are intended to minimize
the differences in image style and object appearance between domains.
Furthermore, in order to improve the model's performance on challenging
examples, we introduce a novel adversarial gradient reversal layer that
conducts adversarial mining on difficult instances in addition to domain
adaptation. Additionally, we suggest generating an auxiliary domain through
data augmentation to enforce a new domain-level metric regularization.
Experimental findings on public V2V benchmark exhibit a substantial enhancement
in object detection specifically for foggy and rainy driving scenarios.Comment: only change the title of this pape
Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather
Most object detection methods for autonomous driving usually assume a
consistent feature distribution between training and testing data, which is not
always the case when weathers differ significantly. The object detection model
trained under clear weather might not be effective enough in foggy weather
because of the domain gap. This paper proposes a novel domain adaptive object
detection framework for autonomous driving under foggy weather. Our method
leverages both image-level and object-level adaptation to diminish the domain
discrepancy in image style and object appearance. To further enhance the
model's capabilities under challenging samples, we also come up with a new
adversarial gradient reversal layer to perform adversarial mining for the hard
examples together with domain adaptation. Moreover, we propose to generate an
auxiliary domain by data augmentation to enforce a new domain-level metric
regularization. Experimental results on public benchmarks show the
effectiveness and accuracy of the proposed method. The code is available at
https://github.com/jinlong17/DA-Detect.Comment: Accepted by WACV2023. Code is available at
https://github.com/jinlong17/DA-Detec
Energy-Efficient Beamforming Design for Integrated Sensing and Communications Systems
In this paper, we investigate the design of energy-efficient beamforming for
an ISAC system, where the transmitted waveform is optimized for joint
multi-user communication and target estimation simultaneously. We aim to
maximize the system energy efficiency (EE), taking into account the constraints
of a maximum transmit power budget, a minimum required
signal-to-interference-plus-noise ratio (SINR) for communication, and a maximum
tolerable Cramer-Rao bound (CRB) for target estimation. We first consider
communication-centric EE maximization. To handle the non-convex fractional
objective function, we propose an iterative quadratic-transform-Dinkelbach
method, where Schur complement and semi-definite relaxation (SDR) techniques
are leveraged to solve the subproblem in each iteration. For the scenarios
where sensing is critical, we propose a novel performance metric for
characterizing the sensing-centric EE and optimize the metric adopted in the
scenario of sensing a point-like target and an extended target. To handle the
nonconvexity, we employ the successive convex approximation (SCA) technique to
develop an efficient algorithm for approximating the nonconvex problem as a
sequence of convex ones. Furthermore, we adopt a Pareto optimization mechanism
to articulate the tradeoff between the communication-centric EE and
sensing-centric EE. We formulate the search of the Pareto boundary as a
constrained optimization problem and propose a computationally efficient
algorithm to handle it. Numerical results validate the effectiveness of our
proposed algorithms compared with the baseline schemes and the obtained
approximate Pareto boundary shows that there is a non-trivial tradeoff between
communication-centric EE and sensing-centric EE, where the number of
communication users and EE requirements have serious effects on the achievable
tradeoff
Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Ly alpha Systems
We have updated and applied a convolutional neural network (CNN) machine-learning model to discover and characterize damped Lyα systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the training process and constructed a CNN model that yields a DLA classification accuracy above 99% for spectra that have signal-to-noise ratios (S/N) above 5 per pixel. The classification accuracy is the rate of correct classifications. This accuracy remains above 97% for lower S/N ≈1 spectra. This CNN model provides estimations for redshift and H i column density with standard deviations of 0.002 and 0.17 dex for spectra with S/N above 3 pixel-1. Also, this DLA finder is able to identify overlapping DLAs and sub-DLAs. Further, the impact of different DLA catalogs on the measurement of baryon acoustic oscillations (BAO) is investigated. The cosmological fitting parameter result for BAO has less than 0.61% difference compared to analysis of the mock results with perfect knowledge of DLAs. This difference is lower than the statistical error for the first year estimated from the mock spectra: above 1.7%. We also compared the performances of the CNN and Gaussian Process (GP) models. Our improved CNN model has moderately 14% higher purity and 7% higher completeness than an older version of the GP code, for S/N > 3. Both codes provide good DLA redshift estimates, but the GP produces a better column density estimate by 24% less standard deviation. A credible DLA catalog for the DESI main survey can be provided by combining these two algorithms
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