This invited contribution discusses recent applications in breast imaging and hysterectomy guidance from the Photoacoustic & Ultrasonic Systems engineering (PULSE) Lab at Johns Hopkins University. Challenges with ...
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Effective sensing capabilities are crucial for the safe and reliable operation of Connected and autonomous vehicles (CAVs). While traditional approaches focus on enhancing onboard sensors, the integration of road sens...
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Effective sensing capabilities are crucial for the safe and reliable operation of Connected and autonomous vehicles (CAVs). While traditional approaches focus on enhancing onboard sensors, the integration of road sensor networks (RSNs) into the CAV ecosystem presents a promising solution to improve sensing performance, but also introduces significant challenges, including heterogeneous sensing requirements, inconsistencies in multisource sensor data, and efficient resource utilization. To address these challenges, this article proposes a novel cooperative sensing framework that leverages multisource and multilevel sensing information from RSNs to optimize CAV sensing performance in resource-constrained scenarios. We develop an improved decision transformer (DT)-based approach that dynamically adapts to diverse driving conditions and efficiently fuses sensor data at various abstraction levels. To tackle the issue of long-delayed rewards, we introduce a reshaped reward function and a bi-level optimization framework that enables effective propagation of rewards along decision sequences. An advanced gradient approximation technique is employed to efficiently solve the optimization problem. Extensive simulations demonstrate the superior performance of our improved DT approach compared to state-of-the-art reinforcement learning (RL) methods in terms of sensing accuracy, coverage, and data efficiency under various traffic conditions.
The potential of applying diffusion models (DMs) for multiple antenna communications is discussed. A unified framework of applying DM for multiple antenna tasks is first proposed. Then, the tasks are innovatively divi...
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Due to the complex nature of medical image acquisition and annotation, medical datasets inevitably contain noise. This adversely affects the robustness and generalization of deep neural networks. Previous noise learni...
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The Red Palm Weevil (RPW) is one of the most harmful pests to palms globally. Various strategies have been used to killing this pest which includes Phyto sanitation, chemical pesticides, pheromone traps, and biologica...
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ISBN:
(数字)9798331518424
ISBN:
(纸本)9798331518431
The Red Palm Weevil (RPW) is one of the most harmful pests to palms globally. Various strategies have been used to killing this pest which includes Phyto sanitation, chemical pesticides, pheromone traps, and biological management, however the use of microwave energy for heat disinfestation appears to be a potential alternative. This article describes the method to develop a microwave antenna system that uses a circular array of Log-Periodic Dipole Antennas (LPDAs) to disinfect date palms. The simulation results are performed in FDTD solver. The proposed antenna covers a wide frequency range spanning 1.3 GHz − 3 GHz. The reflection coefficient is well below to −10 dB over the whole resonant frequency band, having a peak gain of 9.5 dBi for a single antenna. A circular LPDA antenna array is proposed to perform the thermal analysis for particular area of the palm tree to mitigate the RPW.
Earables (ear wearables) are rapidly emerging as a new platform encompassing a diverse of personal applications, prompting the development of authentication schemes to protect user privacy. Existing earable authentica...
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Hyperspectral image (HSI) open-set classification is critical for HSI classification models deployed in real-world environments, where classifiers must simultaneously classify known classes and reject unknown classes....
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A potential way to improve wireless communication security in the face of increasingly complex cyberthreats is to combine deep learning techniques with hybrid quantum computing. By combining the processing benefits of...
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ISBN:
(数字)9798331522100
ISBN:
(纸本)9798331522117
A potential way to improve wireless communication security in the face of increasingly complex cyberthreats is to combine deep learning techniques with hybrid quantum computing. By combining the processing benefits of quantum algorithms with the pattern recognition powers of deep learning, this article tackles the crucial problems of protecting wireless networks, which are vulnerable to eavesdropping, spoofing, and data breaches. The research uses a hybrid framework in which deep learning models like convolutional neural networks (CNNs) identify and stop possible invasions in real time, while quantum key distribution (QKD) guarantees safe data transfer. Simulations and experimental validation in a 5G network environment show that the proposed system has a 40% reduction in latency and a 35% increase in intrusion detection accuracy compared to traditional security methods, like standard encryption techniques and rule-based intrusion detection systems that lack quantum cryptographic mechanisms and advanced AI-driven pattern recognition. The findings demonstrate that integrating quantum and AI-driven methods to build a more robust wireless communication infrastructure is feasible. The study concludes by highlighting the potential of hybrid quantum-AI systems as a critical first step in protecting next-generation networks.
Neural enhancement through super-resolution (SR) deep neural networks (DNNs) opens up new possibilities for ultra-high-definition (UHD) live streaming. Yet, the heavy SR DNN inference overhead leads to severe deployme...
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