22 research outputs found
Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception
In modern on-driving computing environments, many sensors are used for
context-aware applications. This paper utilizes two deep learning models, U-Net
and EfficientNet, which consist of a convolutional neural network (CNN), to
detect hand gestures and remove noise in the Range Doppler Map image that was
measured through a millimeter-wave (mmWave) radar. To improve the performance
of classification, accurate pre-processing algorithms are essential. Therefore,
a novel pre-processing approach to denoise images before entering the first
deep learning model stage increases the accuracy of classification. Thus, this
paper proposes a deep neural network based high-performance nonlinear
pre-processing method.Comment: 4 pages, 7 figure
Age-of-Information Aware Contents Caching and Distribution for Connected Vehicles
To support rapid and accurate autonomous driving services, road environment
information, which is difficult to obtain through vehicle sensors themselves,
is collected and utilized through communication with surrounding infrastructure
in connected vehicle networks. For this reason, we consider a scenario that
utilizes infrastructure such as road side units (RSUs) and macro base station
(MBS) in situations where caching of road environment information is required.
Due to the rapidly changed road environment, a concept which represents a
freshness of the road content, age of information (AoI), is important. Based on
the AoI value, in the connected vehicle system, it is essential to keep
appropriate content in the RSUs in advance, update it before the content is
expired, and send the content to the vehicles which want to use it. However,
too frequent content transmission for the minimum AoI leads to indiscriminate
use of network resources. Furthermore, a transmission control, that content AoI
and service delay are not properly considered adversely, affects user service.
Therefore, it is important to find an appropriate compromise. For these
reasons, the objective of this paper is about to reduce the system cost used
for content delivery through the proposed system while minimizing the content
AoI presented in MBS, RSUs and UVs. The transmission process, which is able to
be divided into two states, i.e., content caching and service, is approached
using Markov decision process (MDP) and Lyapunov optimization framework,
respectively, which guarantee optimal solutions, as verified via data-intensive
performance evaluation
Workload-Aware Scheduling using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance Networks
In modern networking research, infrastructure-assisted unmanned autonomous
vehicles (UAVs) are actively considered for real-time learning-based
surveillance and aerial data-delivery under unexpected 3D free mobility and
coordination. In this system model, it is essential to consider the power
limitation in UAVs and autonomous object recognition (for abnormal behavior
detection) deep learning performance in infrastructure/towers. To overcome the
power limitation of UAVs, this paper proposes a novel aerial scheduling
algorithm between multi-UAVs and multi-towers where the towers conduct wireless
power transfer toward UAVs. In addition, to take care of the high-performance
learning model training in towers, we also propose a data delivery scheme which
makes UAVs deliver the training data to the towers fairly to prevent problems
due to data imbalance (e.g., huge computation overhead caused by larger data
delivery or overfitting from less data delivery). Therefore, this paper
proposes a novel workload-aware scheduling algorithm between multi-towers and
multi-UAVs for joint power-charging from towers to their associated UAVs and
training data delivery from UAVs to their associated towers. To compute the
workload-aware optimal scheduling decisions in each unit time, our solution
approach for the given scheduling problem is designed based on Markov decision
process (MDP) to deal with (i) time-varying low-complexity computation and (ii)
pseudo-polynomial optimality. As shown in performance evaluation results, our
proposed algorithm ensures (i) sufficient times for resource exchanges between
towers and UAVs, (ii) the most even and uniform data collection during the
processes compared to the other algorithms, and (iii) the performance of all
towers convergence to optimal levels.Comment: 15 pages, 10 figure
Visual Simulation Software Demonstration for Quantum Multi-Drone Reinforcement Learning
Quantum computing (QC) has received a lot of attention according to its light
training parameter numbers and computational speeds by qubits. Moreover,
various researchers have tried to enable quantum machine learning (QML) using
QC, where there are also multifarious efforts to use QC to implement quantum
multi-agent reinforcement learning (QMARL). Existing classical multi-agent
reinforcement learning (MARL) using neural network features non-stationarity
and uncertain properties due to its large number of parameters. Therefore, this
paper presents a visual simulation software framework for a novel QMARL
algorithm to control autonomous multi-drone systems to take advantage of QC.
Our proposed QMARL framework accomplishes reasonable reward convergence and
service quality performance with fewer trainable parameters than the classical
MARL. Furthermore, QMARL shows more stable training results than existing MARL
algorithms. Lastly, our proposed visual simulation software allows us to
analyze the agents' training process and results.Comment: 5 pages, 4 figure
Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications
This paper proposes a novel centralized training and distributed execution
(CTDE)-based multi-agent deep reinforcement learning (MADRL) method for
multiple unmanned aerial vehicles (UAVs) control in autonomous mobile access
applications. For the purpose, a single neural network is utilized in
centralized training for cooperation among multiple agents while maximizing the
total quality of service (QoS) in mobile access applications.Comment: 2 pages, 4 figure
Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility
It has been considered that urban air mobility (UAM), also known as
drone-taxi or electrical vertical takeoff and landing (eVTOL), will play a key
role in future transportation. By putting UAM into practical future
transportation, several benefits can be realized, i.e., (i) the total travel
time of passengers can be reduced compared to traditional transportation and
(ii) there is no environmental pollution and no special labor costs to operate
the system because electric batteries will be used in UAM system. However,
there are various dynamic and uncertain factors in the flight environment,
i.e., passenger sudden service requests, battery discharge, and collision among
UAMs. Therefore, this paper proposes a novel cooperative MADRL algorithm based
on centralized training and distributed execution (CTDE) concepts for reliable
and efficient passenger delivery in UAM networks. According to the performance
evaluation results, we confirm that the proposed algorithm outperforms other
existing algorithms in terms of the number of serviced passengers increase
(30%) and the waiting time per serviced passenger decrease (26%).Comment: 6 pages, 5 figure
SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks
Federated learning (FL) is a key enabler for efficient communication and
computing, leveraging devices' distributed computing capabilities. However,
applying FL in practice is challenging due to the local devices' heterogeneous
energy, wireless channel conditions, and non-independently and identically
distributed (non-IID) data distributions. To cope with these issues, this paper
proposes a novel learning framework by integrating FL and width-adjustable
slimmable neural networks (SNN). Integrating FL with SNNs is challenging due to
time-varying channel conditions and data distributions. In addition, existing
multi-width SNN training algorithms are sensitive to the data distributions
across devices, which makes SNN ill-suited for FL. Motivated by this, we
propose a communication and energy-efficient SNN-based FL (named SlimFL) that
jointly utilizes superposition coding (SC) for global model aggregation and
superposition training (ST) for updating local models. By applying SC, SlimFL
exchanges the superposition of multiple-width configurations decoded as many
times as possible for a given communication throughput. Leveraging ST, SlimFL
aligns the forward propagation of different width configurations while avoiding
inter-width interference during backpropagation. We formally prove the
convergence of SlimFL. The result reveals that SlimFL is not only
communication-efficient but also deals with non-IID data distributions and poor
channel conditions, which is also corroborated by data-intensive simulations
TmToll-7 Plays a Crucial Role in Innate Immune Responses Against Gram-Negative Bacteria by Regulating 5 AMP Genes in Tenebrio molitor
Although it is known that the Drosophila Toll-7 receptor plays a critical role in antiviral autophagy, its function in other insects has not yet been reported. Here, we have identified a Toll-like receptor 7 gene, TmToll-7, in the coleopteran insect T. molitor and examined its potential role in antibacterial and antifungal immunity. We showed that TmToll-7 expression was significantly induced in larvae 6 h after infection with Escherichia coli and Staphylococcus aureus and 9 h after infection with Candida albicans. However, even though TmToll-7 was induced by all three pathogens, we found that TmToll-7 knockdown significantly reduced larval survival to E. coli, but not to S. aureus, and C. albicans infections. To understand the reasons for this difference, we examined the effects of TmToll-7 knockdown on antimicrobial peptide (AMP) gene expression and found a significant reduction of E. coli-induced expression of AMP genes such as TmTenecin-1, TmDefensin-1, TmDefensin-2, TmColeoptericin-1, and TmAttacin-2. Furthermore, TmToll-7 knockdown larvae infected with E. coli showed significantly higher bacterial growth in the hemolymph compared to control larvae treated with Vermilion dsRNA. Taken together, our results suggest that TmToll-7 plays an important role in regulating the immune response of T. molitor to E. coli
Truthful electric vehicle charging via neural-architectural Myerson auction
The electric vehicle (EV) market increases due to the benefits of reducing greenhouse gas emissions using renewable energy resources. In this context, the charging scheme of electric vehicles in charging stations (CSs) is also important. Electronic devices’ charging between EV and multiple CS should consider EV’s short battery capacity, long charging time, residual energy in each CS, and time of use (ToU) for charging. In this paper, multiple CSs compete to offer electricity charging to a single EV. Based on this need, this paper proposes a deep learning-based auction which increases the charging amounts using Myerson auction while preserving truthfulness