In the 5G Advanced and 6G era, wireless communication systems face security challenges, notably adversarial interference from unknown jammers in multi-user scenarios. Reconfigurable intelligent Surfaces (RIS) present ...
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ISBN:
(纸本)9798350361261;9798350361278
In the 5G Advanced and 6G era, wireless communication systems face security challenges, notably adversarial interference from unknown jammers in multi-user scenarios. Reconfigurable intelligent Surfaces (RIS) present a cost-effective solution due to their low power consumption and easy deployment. Existing RIS techniques typically address simple jamming scenarios with a single static jammer, focusing on a single objective. This study introduces a multi-objective optimization approach deploying UAV-mounted RIS to counter jamming threats in wireless communications within a densely populated smart city environment. The proposed solution aims to safeguard essential services from potential disruptions caused by malicious jamming attacks during public events. We employ Proximal Policy Optimization (PPO), a lightweight Deep Reinforcement Learning (DRL) technique, to concurrently optimize the trajectory of UAV and RIS passive beamforming to address computational complexity. The objectives include maximizing the average sum rate and minimizing energy consumption. Our experiments highlight the efficacy of the PPO-based strategy, demonstrating significant improvements in average sum rates and energy efficiency amid numerous mobile devices and moving jammers. Importantly, our proposed system model outperforms a baseline from related works in maximizing the sum rate and minimizing overall energy consumption.
In this paper, we propose an optimization method for face infrared image recognition model based on convolutional neural network for the challenge of limited computational resources of edge computing devices. The meth...
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This article sheds light on legal implications and challenges surrounding emotion data processing within the EU's legal framework. Despite the sensitive nature of emotion data, the GDPR does not categorize it as s...
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ISBN:
(纸本)9798350327434
This article sheds light on legal implications and challenges surrounding emotion data processing within the EU's legal framework. Despite the sensitive nature of emotion data, the GDPR does not categorize it as special data, resulting in a lack of comprehensive protection. The article also discusses the nuances of different approaches to affective computing and their relevance to the processing of special data under the GDPR. Moreover, it points to potential tensions with data protection principles, such as fairness and accuracy. Our article also highlights some of the consequences, including harm, that processing of emotion data may have for individuals concerned. Additionally, we discuss how the AI Act proposal intends to regulate affective computing. Finally, the article outlines the new obligations and transparency requirements introduced by the DSA for online platforms utilizing emotion data. Our article aims at raising awareness among the affective computing community about the applicable legal requirements when developing AC systems intended for the EU market, or when working with study participants located in the EU. We also stress the importance of protecting the fundamental rights of individuals even when the law struggles to keep up with technological developments that capture sensitive emotion data.
The widespread spread of the epidemic has caused great damage to human beings. In order to coordinate the layout and utilization of medical resources, the focus is to do a good job in the medical treatment and managem...
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Many people around the world enjoy traveling as a popular leisure activity. The process of trip planning can be time-consuming, requiring travelers to choose between various options and make decisions based on their p...
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ISBN:
(纸本)9798350364309;9798350364293
Many people around the world enjoy traveling as a popular leisure activity. The process of trip planning can be time-consuming, requiring travelers to choose between various options and make decisions based on their preferences and interests. This paper addresses the tourist trip design problem by proposing a Travel Planning Optimization System (TPOS), which includes an architecture that employs genetic algorithms to streamline travel planning. The paper's novelty lies in considering numerous travel factors, such as duration of visits, travel time, preferred types of locations, operating hours, destination popularity and the scheduling of places to eat in the daily program, into a single metric. This approach holds great promise in transforming travel planning by providing customized experiences that closely match the unique preferences and interests of each traveler, while using real location-related information in determining an efficient multiple-day itinerary.
Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-c...
ISBN:
(纸本)9798350377712;9798350377705
Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance.
The need for environmental protection and energy conservation in the twenty-first century has highlighted the urgency of advancing Electric Vehicle (EV) technology. This research study contributes to this trajectory b...
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In urban areas, traffic congestion is a major problem causing increased travel times, higher consumption of fuel, and environmental pollution. The traditional traffic controlsystems cannot adapt to dynamic traffic co...
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With the development of novel power systems, emerging scenarios such as real-Time power trading, and real-Time control of virtual power plants demand higher standards for the timeliness of power information. We consid...
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The application of artificial intelligence (AI) and robotics in extreme environments, is crucial for addressing complex challenges and performing high risk tasks. We highlight the importance of multi-modal sensor redu...
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ISBN:
(纸本)9798350364200;9798350364194
The application of artificial intelligence (AI) and robotics in extreme environments, is crucial for addressing complex challenges and performing high risk tasks. We highlight the importance of multi-modal sensor redundancy to ensure system reliability and accuracy despite sensor failures caused by harsh environmental conditions. We propose design considerations for sensors in extreme environments, emphasizing both the hardware and software design. One method is the non-contact heart rate and temperature monitoring using RGB visible and infrared cameras. This method addresses the limitations of traditional visible light sensors under complex illumination conditions, enhancing data reliability through advanced data fusion techniques. Furthermore, we propose a panoramic sensor lens design with a 270-degree view for comprehensive environmental perception, reducing mechanical vulnerabilities. These designs demonstrate the effectiveness of combining infrared and visible light sensors for improved environmental perception and physiological monitoring.
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