Self-driving shuttle services frequently provide a less comfortable experience for passengers than human drivers. This observation emphasizes the importance of motion planning and control methods. This paper proposes ...
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Background: Cancer patients with metastasis face a much lower survival rate and a higher risk of recurrence than those without metastasis. So far, several learning methods have been proposed to predict cancer metastas...
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DolphinAttacks (i.e., inaudible voice commands) modulate audible voices over ultrasounds to inject malicious commands silently into voice assistants and manipulate controlled systems (e.g., doors or smart speakers). E...
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DolphinAttacks (i.e., inaudible voice commands) modulate audible voices over ultrasounds to inject malicious commands silently into voice assistants and manipulate controlled systems (e.g., doors or smart speakers). Eliminating DolphinAttacks is challenging if ever possible since it requires to modify the microphone hardware. In this paper, we design EarArray, a lightweight method that can not only detect such attacks but also identify the direction of attackers without requiring any extra hardware or hardware modification. Essentially, inaudible voice commands are modulated on ultrasounds that inherently attenuate faster than the one of audible sounds. By inspecting the command sound signals via the built-in multiple microphones on smart devices, EarArray is able to estimate the attenuation rate and thus detect the attacks. We propose a model of the propagation of audible sounds and ultrasounds from the sound source to a voice assistant, e.g., a smart speaker, and illustrate the underlying principle and its feasibility. We implemented EarArray using two specially-designed microphone arrays and our experiments show that EarArray can detect inaudible voice commands with an accuracy of above 99% and recognize the direction of the attackers with an accuracy of 97.89% and can also detect the laser-based attack with an accuracy of 100%. IEEE
Traffic light recognition in autonomous driving is an essential but very challenging task because its performance is affected by unpredictable environmental conditions. Moreover, the shapes and installations of traffi...
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The performance of camera-based place recognition has significantly improved with the rapid advancement of deep learning. However, RGB cameras still face challenges in handling variations in lighting conditions due to...
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In multilevel inverters, unused energies are created due to the asynchronous use of the input DC-sources. This means that when the input DC-sources are replaced by renewable systems such as photovoltaic arrays, some o...
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One of the most popular techniques for changing the purpose of an image or resizing a digital image with content awareness is the seam-carving method. The performance of image resizing algorithms based on seam machini...
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Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) have been actively employed for tasks that are challenging for humans, such as scenarios like battlefield and disaster. Swarm unmanned systems, compr...
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The integration of renewable energy sources (RESs) and energy storage (ES) systems into power grids has introduced significant challenges, particularly in terms of economic and environmental dispatch. The intermittent...
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The integration of renewable energy sources (RESs) and energy storage (ES) systems into power grids has introduced significant challenges, particularly in terms of economic and environmental dispatch. The intermittent nature of RESs and uncertainties related to their output make it difficult for traditional dynamic economic/emission dispatch (DEED) methods to ensure optimal power generation. The primary goal of DEED problems is to efficiently manage the production of electrical energy while satisfying various operational and system constraints, such as ramp rate (RR) limitations and the valve point effect (VPE). This optimization is achieved by minimizing both the overall fuel cost and emissions of power generation units. To address these challenges, this paper proposes a novel hybrid optimization approach that incorporates demand response programs (DRP) alongside ES and RES to enhance system flexibility. By leveraging DRP, consumers' participation in shifting or reducing their demand is considered, leading to a more balanced and cost-effective dispatch strategy. The proposed optimization framework employs a hybrid method that combines particle swarm optimization (PSO) and modified shuffled frog leaping algorithm (MSFLA) to effectively solve the DEED problem. Additionally, a fuzzy-based approach is utilized to achieve a trade-off between economic and environmental objectives, ensuring an optimal balance between fuel cost reduction and emission minimization. The effectiveness of the proposed methodology is validated on a test system with 10 generating units over a 24-h period. The numerical results indicate that, compared to conventional techniques such as grasshopper optimization (GO), PSO, shuffled frog leaping algorithm (SFLA), and other methods reported in the literature, the proposed strategy achieves significantly improved trade-off solutions. The incorporation of DRP further enhances the adaptability of the system by reducing peak demand and improving cost eff
In recent times, AI and UAV have progressed significantly in several applications. This article analyzes applications of UAV with modern green computing in various sectors. It addresses cutting-edge technologies such ...
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