Multi-object tracking (MOT) is one of the most important problems in computervision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation...
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Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning...
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Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.
This paper introduces a new hybrid method to address the issue of redundant and irrelevant features selected by filter-based methods for text classification. The method utilizes an enhanced genetic algorithm called &q...
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Support vector machine(SVM)is a binary classifier widely used in machine ***,neglecting the latent data structure in previous SVM can limit the performance of SVM and its *** address this issue,the authors propose a n...
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Support vector machine(SVM)is a binary classifier widely used in machine ***,neglecting the latent data structure in previous SVM can limit the performance of SVM and its *** address this issue,the authors propose a novel SVM with discriminative low-rank embedding(LRSVM)that finds a discriminative latent low-rank subspace more suitable for SVM *** extension models of LRSVM are introduced by imposing different orthogonality constraints to prevent computational inaccuracies.A detailed derivation of the authors’iterative algorithms are given that is essentially for solving the SVM on the low-rank ***,some theorems and properties of the proposed models are presented by the *** is worth mentioning that the subproblems of the proposed algorithms are equivalent to the standard or the weighted linear discriminant analysis(LDA)*** indicates that the projection subspaces obtained by the authors’algorithms are more suitable for SVM classification compared to those from the LDA *** convergence analysis for the authors proposed algorithms are also ***,the authors conduct experiments on various machine learning data sets to evaluate the *** experiment results show that the authors’algorithms perform significantly better than other algorithms,which indicates their superior abilities on classification tasks.
Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate bot...
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Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computervision. In this paper, we presented a traffic sign classification system implemented using a hybrid qu...
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According to WHO reports, cancer is the leading cause of death worldwide. The second most prevalent cause of cancer-related death in both men and women is colorectal cancer (CRC). One potential approach for reducing t...
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The majority of current deepfake detection methods are constrained to identifying one or two specific types of counterfeit images,which limits their ability to keep pace with the rapid advancements in deepfake ***,in ...
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The majority of current deepfake detection methods are constrained to identifying one or two specific types of counterfeit images,which limits their ability to keep pace with the rapid advancements in deepfake ***,in this study,we propose a novel algorithm,StereoMixture Density Network(SMNDNet),which can detect multiple types of deepfake face manipulations using a single network *** is an end-to-end CNNbased network specially designed for detecting various manipulation types of deepfake face ***,we design a Subtle Distinguishable Feature Enhancement Module to emphasize the differentiation between authentic and forged ***,we introduce aMulti-Scale Forged Region AdaptiveModule that dynamically adapts to extract forged features from images of varying synthesis ***,we integrate a Nonlinear Expression Capability Enhancement Module to augment the model’s capacity for capturing intricate nonlinear patterns across various types of ***,these modules empower our model to efficiently extract forgery features fromdiverse manipulation types,ensuring a more satisfactory performance in multiple-types deepfake *** show that the proposed method outperforms alternative approaches in detection accuracy and AUC across all four types of deepfake *** also demonstrates strong generalization on cross-dataset and cross-type detection,along with robust performance against post-processing manipulations.
Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(D...
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Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive *** and DT underpin the evolution of Industry 4.0 by bridging the physical and digital *** survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation *** layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless *** study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these *** in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT *** addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT *** synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and *** findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.
The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the ***,smart healthcare has emerged as a significant application of the IoMT,parti...
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The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the ***,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning *** healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical *** smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance *** security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of ***,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission *** address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos *** results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,*** validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.
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