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

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