The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia ***-quality images require well-tuned communication *** Single C...
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The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia ***-quality images require well-tuned communication *** Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and *** data need a space away fromunauthorized access,modification,or *** requirements are to be fulfilled by digital image watermarking and *** paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication *** introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver *** proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD *** addition,image encryption is implemented based on chaos and DNA *** encrypted watermarked images are then transmitted through the wireless SC-FDMA *** linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted *** subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this *** study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked *** addition,different variants of SC-FDMA bas
For a set of red and blue points in the plane, a minimum bichromatic spanning tree (MinBST) is a shortest spanning tree of the points such that every edge has a red and a blue endpoint. A MinBST can be computed in O(n...
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Various crowdsourced logistics platforms are forming rapidly along with the booming mobile Internet. Motivated by modern crowdsourced truck logistics platforms, we introduce the online crowdsourced truck delivery (OCT...
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Various crowdsourced logistics platforms are forming rapidly along with the booming mobile Internet. Motivated by modern crowdsourced truck logistics platforms, we introduce the online crowdsourced truck delivery (OCTD) problem and reformulate it as the online bipartite hyper-matching problem. We then explore the possibility of accommodating historical information to design efficient online algorithms to serve online orders. To the best of our knowledge, it is the first work on incorporating historical information to solve the online bipartite hyper-matching problem. Depending on whether orders can be partially served, we investigate two practical situations, i.e. separable and inseparable cases. For the inseparable case, we propose a randomized online algorithm, named HYPER-MATCHING , whose competitive ratio is a non-decreasing function of the amount of historical information. For the separable case, we modify HYPER-MATCHING to present another randomized online algorithm, named SEPARABLE-HYPER-MATCHING . It is worth noting that the competitive ratios of HYPER-MATCHING and SEPARABLE-HYPER-MATCHING either beat or match the current best online algorithms when no historical information is considered. We then present four computationally efficient heuristic algorithms, including a greedy variant and a batch processing variant for each of the inseparable and separable cases. We perform a sequence of experiments using synthetic and real-world datasets, with an emphasis on the influence that historical information has on algorithm performance. The experiment results demonstrate the effectiveness of our algorithms and particularly the positive influence of historical information on our algorithms.(c) 2021 Elsevier B.V. All rights reserved.
Real-time individuals' destination prediction is of great significance for real-time user tracking, service recommendation and other related applications. Traditional technology mainly used statistical methods bas...
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Trustworthy Graph Neural Networks (GNNs) for EEG emotion recognition should identify emotions accurately and elucidate corresponding rationales. Current GNNs have achieved notable performance by dynamically modeling e...
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Image Captioning is a traditional vision-and-language task that aims to generate the language description of an image. Recent studies focus on scaling up the model size and the number of training data, which significa...
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Heuristics are widely used for dealing with complex search and optimization problems. However, manual design of heuristics can be often very labour extensive and requires rich working experience and knowledge. This pa...
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Heuristics are widely used for dealing with complex search and optimization problems. However, manual design of heuristics can be often very labour extensive and requires rich working experience and knowledge. This paper proposes Evolution of Heuristic (EoH), a novel evolutionary paradigm that leverages both Large Language Models (LLMs) and Evolutionary Computation (EC) methods for Automatic Heuristic Design (AHD). EoH represents the ideas of heuristics in natural language, termed thoughts. They are then translated into executable codes by LLMs. The evolution of both thoughts and codes in an evolutionary search framework makes it very effective and efficient for generating high-performance heuristics. Experiments on three widely studied combinatorial optimization benchmark problems demonstrate that EoH outperforms commonly used handcrafted heuristics and other recent AHD methods including FunSearch. Particularly, the heuristic produced by EoH with a low computational budget (in terms of the number of queries to LLMs) significantly outperforms widely-used human hand-crafted baseline algorithms for the online bin packing problem. Copyright 2024 by the author(s)
Data is generated over time by each device in the Internet of Things (IoT) ecosphere. Recent years have seen a resurgence in interest in the IoT due to its positive impact on society. However, due to the automatic man...
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Current research on text-conditional image generation shows parallel performance with ordinary painters but still has much room for improvement when compared to that of artist-ability paintings,which usually represent...
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Current research on text-conditional image generation shows parallel performance with ordinary painters but still has much room for improvement when compared to that of artist-ability paintings,which usually represent multilevel semantics by gathering features ofmultiple objects into one *** a preliminary experiment,we confirm this and then seek the opinions of three groups of individuals with varying levels of art appreciation ability to determine the distinctions that exist between painters and *** then use these opinions to improve an artificial intelligence(AI)painting system from painter-level image generation toward artistic-level image generation.
The technologies and sensors developed for standard traffic streams often fail to accurately measure the heterogeneous traffic with no lane discipline. This research proposes an efficient framework to measure traffic ...
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