Search Results for author:
Found 61 papers, 4 papers with code
Double-Side Polarization and Beamforming Alignment in Polarization Reconfigurable MISO System with Deep Neural Networks
Polarization reconfigurable (PR) antennas enhance spectrum and energy efficiency between next-generation node B(gNB) and user equipment (UE).
Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks
However, as the number of CubeSats and HALE-UAVs, increases, the scheduling dimension of each ground station (GS) increases.
Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications
Spurred by consistent advances and innovation in deep learning, object detection applications have become prevalent, particularly in autonomous driving that leverages various visual data.
Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation
To tackle these problems, a quantum MARL (QMARL) algorithm based on the concept of actor-critic network is proposed, which is beneficial in terms of scalability, to deal with the limitations in the noisy intermediate-scale quantum (NISQ) era.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes
This paper presents the deep learning-based recent achievements to resolve the problem of autonomous mobility control and efficient resource management of autonomous vehicles and UAVs, i. e., (i) multi-agent reinforcement learning (MARL), and (ii) neural Myerson auction.
Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare
The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scrutinize medical records, clinical notes, and other text-based health information.
Entropy-Aware Similarity for Balanced Clustering: A Case Study with Melanoma Detection
Clustering data is an unsupervised learning approach that aims to divide a set of data points into multiple groups.
Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems
This paper proposes a novel algorithm, named quantum multi-agent actor-critic networks (QMACN) for autonomously constructing a robust mobile access system employing 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.
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.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+1
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
+3
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.
Quantum Split Neural Network Learning using Cross-Channel Pooling
In recent years, the field of quantum science has attracted significant interest across various disciplines, including quantum machine learning, quantum communication, and quantum computing.
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.
Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classification
In recent years, quantum machine learning (QML) has been actively used for various tasks, e. g., classification, reinforcement learning, and adversarial learning.
Learning Location from Shared Elevation Profiles in Fitness Apps: A Privacy Perspective
To preserve the privacy of users while allowing sharing, several of those platforms may allow users to disclose partial information, such as the elevation profile for an activity, which supposedly would not leak the location of the users.
3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications
Moreover, a quantum convolutional neural network (QCNN) is the quantum-version of CNN because it can process high-dimensional vector inputs in contrast to QNN.
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.
Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications
In addition, we demonstrate the self-configurable stabilized detection with YOLOv3-tiny and FlowNet2-S, which are the real-time object detection network and an optical flow estimation network, respectively.
Scalable Quantum Convolutional Neural Networks
With the beginning of the noisy intermediate-scale quantum (NISQ) era, quantum neural network (QNN) has recently emerged as a solution for the problems that classical neural networks cannot solve.
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.
Quantum Multi-Agent Meta Reinforcement Learning
Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing.
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).
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+2
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.
A Reliable, Self-Adaptive Face Identification Framework via Lyapunov Optimization
Realtime face identification (FID) from a video feed is highly computation-intensive, and may exhaust computation resources if performed on a device with a limited amount of resources (e. g., a mobile device).
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.
Joint Pilot Design and Channel Estimation using Deep Residual Learning for Multi-Cell Massive MIMO under Hardware Impairments
After that, a deep learning based pilot design is proposed to minimize the mean square error (MSE) of LMMSE channel estimation, which is utilized to the joint pilot design and channel estimator for transfer learning approach.
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.
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.
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.
Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles
To overcome this, we need to characterize a new type of drones, so-called charging drones, which can deliver energy to MBS drones.
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.
Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games
In real-time strategy (RTS) game artificial intelligence research, various multi-agent deep reinforcement learning (MADRL) algorithms are widely and actively used nowadays.
On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study
Generative Adversarial Network (GAN) is a useful type of Neural Networks in various types of applications including generative models and feature extraction.
A Tutorial on Quantum Convolutional Neural Networks (QCNN)
The second study introduces a method to improve the model's performance by adding a layer using quantum computing to the CNN learning model used in the existing computer vision.
Quantum Physics
Adversarial Imitation Learning via Random Search
As a result, research on imitation learning, which learns policy from a demonstration of experts, has begun to attract attention.
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond.
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL).
Quantum Approximation for Multi-Scale Scheduling
Then, for the given MWIS, the proposed QAOS designs the Hamiltonian of the problem.
Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms
This paper proposes a distributed deep learning framework for privacy-preserving medical data training.
Multi-Agent Deep Reinforcement Learning for Cooperative Connected Vehicles
Millimeter-wave (mmWave) base station can offer abundant high capacity channel resources toward connected vehicles so that quality-of-service (QoS) of them in terms of downlink throughput can be highly improved.
Deep ensemble network with explicit complementary model for accuracy-balanced classification
The average accuracy is one of major evaluation metrics for classification systems, while the accuracy deviation is another important performance metric used to evaluate various deep neural networks.
Randomized Adversarial Imitation Learning for Autonomous Driving
With the evolution of various advanced driver assistance system (ADAS) platforms, the design of autonomous driving system is becoming more complex and safety-critical.
A Personalized Preference Learning Framework for Caching in Mobile Networks
Under our model, we consider a single-hop-based device-to-device (D2D) content delivery protocol and characterize the average hit ratio for the following two file preference cases: the personalized file preferences and the common file preferences.
