Search Results for author:
Found 31 papers, 2 papers with code
Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems
This paper proposes a novel quantum multi-agent actor-critic networks (QMACN) algorithm for autonomously constructing a robust mobile access system using multiple unmanned aerial vehicles (UAVs).
Situation-Aware Deep Reinforcement Learning for Autonomous Nonlinear Mobility Control in Cyber-Physical Loitering Munition Systems
Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios.
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding
While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study.
Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning
Quantum machine learning (QML) has received a lot of attention according to its light training parameter numbers and speeds; and the advances of QML lead to active research on quantum multi-agent reinforcement learning (QMARL).
Multi-agent Reinforcement Learning
Quantum Machine Learning
+2
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.
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.
Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control
The reliable mobile access services can be achieved in following two ways, i. e., (i) energy-efficient UAV operation and (ii) reliable wireless communication services.
Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networks
One of the key topics in network security research is the autonomous COA (Couse-of-Action) attack search method.
Slimmable Quantum Federated Learning
Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL).
Search Space Adaptation for Differentiable Neural Architecture Search in Image Classification
As deep neural networks achieve unprecedented performance in various tasks, neural architecture search (NAS), a research field for designing neural network architectures with automated processes, is actively underway.
Tutorial on Course-of-Action (COA) Attack Search Methods in Computer Networks
In the literature of modern network security research, deriving effective and efficient course-of-action (COA) attach search methods are of interests in industry and academia.
SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks
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.
Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design
This paper extends and demonstrates the QRL to quantum multi-agent RL (QMARL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Feasibility Study of Multi-Site Split Learning for Privacy-Preserving Medical Systems under Data Imbalance Constraints in COVID-19, X-Ray, and Cholesterol Dataset
It seems as though progressively more people are in the race to upload content, data, and information online; and hospitals haven't neglected this trend either.
Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications
Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational power dependency.
Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification
In many deep neural network (DNN) applications, the difficulty of gathering high-quality data in the industry field hinders the practical use of DNN.
Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control
This paper creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services.
Neural Myerson Auction for Truthful and Energy-Efficient Autonomous Aerial Data Delivery
In this paper, multiple delivery drones compete to offer data transfer to a single fixed-location surveillance drone.
Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles
The RAIL is a novel derivative-free imitation learning method for autonomous driving with various ADAS functions coordination; and thus it imitates the operation of decision maker that controls autonomous driving with various ADAS functions.
Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding
To address the heterogeneous communication throughput problem, each full-width (1. 0x) SNN model and its half-width ($0. 5$x) model are superposition-coded before transmission, and successively decoded after reception as the 0. 5x or $1. 0$x model depending on the channel quality.
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks
By applying SC, SlimFL exchanges the superposition of multiple width configurations that are decoded as many as possible for a given communication throughput.
Spatio-Temporal Split Learning for Autonomous Aerial Surveillance using Urban Air Mobility (UAM) Networks
This paper utilizes surveillance UAVs for the purpose of detecting the presence of a fire in the streets.
Stable Marriage Matching for Traffic-Aware Space-Air-Ground Integrated Networks: A Gale-Shapley Algorithmic Approach
In keeping with the rapid development of communication technology, a new communication structure is required in a next-generation communication system.
Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies with COVID-19 CT, X-Ray, and Cholesterol Data
Since the centralized server does not need to access the training data and trains the deep neural network with parameters received from the privacy-preserving layer, privacy of original data is guaranteed.
Trends in Neural Architecture Search: Towards the Acceleration of Search
In modern deep learning research, finding optimal (or near optimal) neural network models is one of major research directions and it is widely studied in many applications.
Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation
The emergence of quantum computing enables for researchers to apply quantum circuit on many existing studies.
Spatio-Temporal Split Learning
This paper proposes a novel split learning framework with multiple end-systems in order to realize privacypreserving deep neural network computation.
Quantum Neural Networks: Concepts, Applications, and Challenges
Quantum deep learning is a research field for the use of quantum computing techniques for training deep neural networks.
Quantum Scheduling for Millimeter-Wave Observation Satellite Constellation
In beyond 5G and 6G network scenarios, the use of satellites has been actively discussed for extending target monitoring areas, even for extreme circumstances, where the monitoring functionalities can be realized due to the usage of millimeter-wave wireless links.
Distributed and Autonomous Aerial Data Collection in Smart City Surveillance Applications
The data those are produced from surveillance cameras in aerial devices such as unmanned aerial networks (UAVs) are needed to be transferred to ground stations for secure data analysis.
Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication
In this article, we study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user.
