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data Driven Control and Learning Systems (DDCLS)

作者： Zhen Shao Fanrong Kong Zhaoxia Duan Zixu Liu College of Artificial Intelligence and Automation Hohai University Changzhou P. R. China College of Energy and Electrical Engineering Hohai University Nanjing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper focuses on tracking control for a specific class of discrete-time single-input single-output (SISO) nonlinear repetitive systems that encounter data quantization and dropouts during transmission. the output of the system requires quantization before transmission to the controller, and dropouts may occur. To address this issue, we introduce the encoding -decoding model-free adaptive iterative learning control (E-DMFAILC) algorithm. this algorithm leverages an encoding-decoding mechanism and intermittent update principle to manage data quantization and dropouts, reducing the communication load while preserving accurate tracking performance. Our research results show that the proposed algorithm achieves zero convergence of tracking error by using only a small amount of input and output (I/O) data. Finally, we verify the effectiveness of the algorithm through simulation experiments, which further confirm the superiority of E-DMFAILC.

关键词： Adaptation models Quantization (signal) Adaptive systems Trajectory tracking SISO communication data models Nonlinear systems

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Pulsation artifact removal from intra-operatively recorded local field potentials using sparse signal processing and data-specific dictionary*

Pulsation artifact removal from intra-operatively recorded l...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Chandra Prakash Swamy Behrang Fazli Besheli Luciano R. F. Branco Nicole R. Provenza Sameer A. Sheth Wayne K. Goodman Ashwin Viswanathan Nuri Firat Ince Department of Biomedical Engineering University of Houston TX Department of Neurosurgery Baylor College of Medicine Houston TX Menninger Department of Psychiatry and Behavioral Sciences Baylor College of Medicine Houston TX

Neural recordings frequently get contaminated by ECG or pulsation artifacts. these large amplitude components can mask the neural patterns of interest and make the visual inspection process difficult. the current study describes a sparse signal representation strategy that targets to denoise pulsation artifacts in local field potentials (LFPs) recorded intraoperatively. To estimate the morphology of the artifact, we first detect the QRS-peaks from the simultaneously recorded ECG trace as an anchor point. After the LFP data has been epoched with respect to each beat, a pool of raw data segments of a specific length is generated. Using the K-singular value decomposition (K-SVD) algorithm, we constructed a data-specific dictionary to represent each contaminated LFP epoch in a sparse fashion. Since LFP is aligned to each QRS complex and the background neural activity is uncorrelated to the anchor points, we assumed that constructed dictionary will be formed to mainly represent the pulsation artifact. In this scheme, we performed an orthogonal matching pursuit to represent each LFP epoch as a linear combination of the dictionary atoms. the denoised LFP data is thus obtained by calculating the residual between the raw LFP and its approximation. We discuss and demonstrate the improvements in denoised data and compare the results with respect to principal component analysis (PCA). We noted that there is a comparable change in the signal for visual inspection to observe various oscillating patterns in the alpha and beta bands. We also see a noticeable compression of signal strength in the lower frequency band (<13 Hz), which was masked by the pulsation artifact, and a strong increase in the signal-to-noise ratio (SNR) in the denoised *** Relevance— Pulsation artifact can mask relevant neural activity patterns and make their visual inspection difficult. Using sparse signal representation, we established a new approach to reconstruct the quasiperiodic pulsation templ

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Faint Moving small Target Detection based on Optical Flow Method 7

Faint Moving Small Target Detection based on Optical Flow Me...

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7th International conference on Intelligent Computing and signal processing, ICSP 2022

作者： Dong, Yunfei Jieshou Zhongtong Instruments Co. Ltd. Fuyang236500 China

ISBN: (纸本)9781665478571

Moving target detection algorithm plays a vital role in computer vision research. Moving object detection mainly processes video images to identify moving objects differently from the background. Moving target detection algorithm has an excellent application role, such as: used for security and forbidden area security. this paper presents an effective method for detecting moving targets. the authors combine the corner detection method with LK optical flow method. Afterimage preprocessing, image corner detection, finally, we use LK optical flow method to detect the movement of the moving object, and we can judge the movement direction of the moving object only by two frames of pictures. this method can judge the direction of moving objects only by two pictures frames and has an excellent performance in speed detection. In particular, in detecting small moving targets, the results of this method are noticeable. © 2022 IEEE.

关键词： Computer vision

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Foreword

ICETE 2020 - Proceedings of the 17th International Joint Con...

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ICETE 2020 - Proceedings of the 17th International Joint conference on e-Business and Telecommunications 2020年 XI-XII页

作者： Callegari, Christian Ng, Soon Xin Sarigiannidis, Panagiotis Battiato, Sebastiano de León, Ángel Serrano Sánchez Ksentini, Adlen Lorenz, Pascal Obaidat, Mohammad Ben-Othman, Jalel Italy University of Southampton United Kingdom University of Western Macedonia Greece University of Catania Italy Universidad Rey Juan Carlos Spain Eurecom France University of Haute Alsace France College of Computing and Informatics University of Sharjah United Arab Emirates University of Jordan Jordan and University of Science and Technology Beijing China University of Paris 13 France

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Computationally effective spectral MUSIC algorithm for monostatic MIMO radar using real polynomial rooting

Computationally effective spectral MUSIC algorithm for monos...

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IEEE conference on Industrial Electronics and Applications

作者： XU Liqin LI Yong School of Electronics and Information Northwestern Polytechnical University Xi'an China

A computationally effective real-valued variant of multiple signal classification (MUSIC) algorithm for monostatic multiple-input multiple-output (MIMO) radar is presented. Reduced-dimension transformation is utilized to reduce the dimension of the received data matrix at first, and then the unitary transformation is employed to transform the complex covariance matrix of the received data into a real-valued one. To further reduce the computational complexity, a real polynomial rooting technique is presented to determine the local maxima of the MUSIC spectrum that corresponding to the DOAs of the targets instead of the computationally-expensive spectrum search. Simulations results demonstrate that the presented algorithm can greatly reduce the computational complexity without sacrificing the estimation accuracy.

关键词： Multiple input multiple output (MIMO) radar Direction of arrival (DOA) estimation Multiple signal classification (MUSIC)

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Application of Fast Learning Neural-Networks to Identification of Mixed Anuran Vocalizations

Application of Fast Learning Neural-Networks to Identificati...

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2011 13th IEEE Joint International Computer Science and Information Technology conference(JICSIT 2011)

作者： Chenn-Jung Huang Po-An Hsu Yih-Jhe Lien Department of Computer Science and Information Engineering National Dong Hwa University Hualien Institute of Electronics Engineering National Dong Hwa University Hualien

the proposed identification system for mixed anuran vocalizations is to provide the public to easily consult online. the raw mixed anuran vocalization samples are first filtered by noise removal, high frequency compensation, and discrete wavelet transform techniques in order. An adaptive end-point detection segmentation algorithm is proposed to effectively separate the individual syllables from the noise. Six features, including spectral centroid, signal bandwidth, spectral roll-off, threshold-crossing rate, spectral flatness, and average energy, are extracted and serve as the input parameters of wrapper feature selection method. Meanwhile, a decision tree is constructed based on several parameters obtained during sample collection in order to narrow the scope of identification targets. then fast learning neural-networks are employed to classify the anuran species. Experimental results exhibit that the recognition rate of the proposed identification system can achieve up to 93.3%. the effectiveness of the proposed identification system for anuran vocalizations is thus verified.

关键词： anuran vocalizations fast learning neural- networks data mining clustering analysis decision trees feature extraction

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Clinical, neuroradiological, and molecular characterization of mitochondrial threonyl-tRNA- synthetase (TARS2)-related disorder

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GENETICS IN MEDICINE 2023年第11期25卷 100938页

作者： Accogli, Andrea Lin, Sheng-Jia Severino, Mariasavina Kim, Sung-Hoon Huang, Kevin Rocca, Clarissa Landsverk, Megan Zaki, Maha S. Al-Maawali, Almundher Srinivasan, Varunvenkat M. Al-thihli, Khalid Schaefer, G. Bradly Davis, Monica Tonduti, Davide Doneda, Chiara Marten, Lara M. Muehlhausen, Chris Gomez, Maria Lamantea, Eleonora Mena, Rafael Nizon, Mathilde Procaccio, Vincent Begtrup, Amber Telegra, Aida Cui, Hong Schulz, Heidi L. Mohr, Julia Biskup, Saskia Loos, Mariana Amina Araoz, Hilda Veronica Salpietro, Vincenzo Keppen, Laura Davis Chitre, Manali Petree, Cassidy Raymond, Lucy Vogt, Julie Sawyer, Lindsey B. Basinger, Alice A. Pedersen, Signe Vandal Pearson, Toni S. Grange, Dorothy K. Lingappa, Lokesh Mcdunnah, Paige Horvath, Rita Cogne, Benjamin Isidor, Bertrand Hahn, Andreas Gripp, Karen W. Jafarnejad, Seyed Mehdi Stergaard, Elsebet Prada, Carlos E. Ghezzi, Daniele Gowda, Vykuntaraju K. Taylor, Robert W. Sonenberg, Nahum Houlden, Henry Sissler, Marie Varshney, Gaurav K. Maroofian, Reza Oklahoma Med Res Fdn 825 NE 13th St Oklahoma City OK 73104 USA UCL Inst Neurol Queen Sq London WC1N 3BG England McGill Univ Hlth Ctr MUHC Montreal Childrens Hosp Dept Specialized Med Div Med Genet Montreal PQ Canada McGill Univ Dept Human Genet Montreal PQ Canada Oklahoma Med Res Fdn Genes & Human Dis Res Program Oklahoma City OK USA IRCCS Ist Giannina Gaslini Neuroradiol Unit Genoa Italy McGill Univ Goodman Canc Inst Montreal PQ Canada McGill Univ Dept Biochem Montreal PQ Canada UCL Queen Sq Inst Neurol Dept Neuromuscular Dis London England Univ South Dakota Sanford Sch Med Sioux Falls SD USA Sanford Res Pediat & Rare Dis Grp Sioux Falls SD USA Natl Res Ctr Human Genet & Genome Res Inst Clin Genet Dept Cairo Egypt Sultan Qaboos Univ Coll Med & Hlth Sci Dept Genet Muscat Oman Sultan Qaboos Univ Hosp Genet & Dev Med Clin Muscat Oman Indira Gandhi Inst Child Hlth Bangalore India Univ Arkansas Med Sci Dept Pediat Little Rock AR USA V Buzzi Childrens Hosp COALA Ctr Diag & Treatment Leukodystrophies Unit Pediat Neurol Milan Italy Univ Milan Dept Biomed & Clin Sci Milan Italy Childrens Hosp Vittore Buzzi Pediat Radiol & Neuroradiol Dept Milan Italy Univ Med Ctr Gottingen Dept Pediat & Adolescent Med Gottingen Germany Ctr Obsetricia & Ginecol Santo Domingo Dominican Rep Fdn IRCCS Ist Neurol Carlo Besta Unit Med Genet & Neurogenet Milan Italy Cincinnati Childrens Hosp Med Ctr Div Neonatol Cincinnati OH USA Ctr Obsetricia & Ginecol Santo Domingo Dominican Rep Nantes Univ CHU Nantes Serv Genet Medicale Nantes France Nantes Univ Inst Thorax CNRS INSERM Nantes France Univ Angers MitoLab Team UMR CNRS 6015 INSERM U1083Unite MitoVascSFR ICAT Angers France CHU Angers Dept Genet Angers France GeneDx Gaithersburg MD USA Human Genet Ctr Tubingen Baden Wurttemberg Germany CeGaT GmbH Tubingen Germany Hosp Pediat Juan P Garrahan Dept Neurol Buenos Aires Argentina Hosp Pediat Juan P Garrahan Genom Lab Buenos Aires Argentina Univ

Purpose: Biallelic variants in TARS2, encoding the mitochondrial threonyl-tRNA-synthetase, have been reported in a small group of individuals displaying a neurodevelopmental phenotype but with limited neuroradiological data and insufficient evidence for causality of the ***: Exome or genome sequencing was carried out in 15 families. Clinical and neurora-diological evaluation was performed for all affected individuals, including review of 10 previously reported individuals. the pathogenicity of TARS2 variants was evaluated using in vitro assays and a zebrafish model. Results: We report 18 new individuals harboring biallelic TARS2 variants. Phenotypically, these individuals show developmental delay/intellectual disability, regression, cerebellar and cerebral atrophy, basal ganglia signal alterations, hypotonia, cerebellar signs, and increased blood lactate. In vitro studies showed that variants within the TARS2301-381 region had decreased binding to Rag GTPases, likely impairing mTORC1 activity. the zebrafish model recapitulated key features of the human phenotype and unraveled dysregulation of downstream targets of mTORC1 signaling. Functional testing of the variants confirmed the pathogenicity in a zebrafish ***: We define the clinico-radiological spectrum of TARS2-related mitochondrial disease, unveil the likely involvement of the mTORC1 signaling pathway as a distinct molecular mechanism, and establish a TARS2 zebrafish model as an important tool to study variant pathogenicity.& COPY;2023 the Authors. Published by Elsevier Inc. on behalf of American College of Medical Genetics and Genomics. this is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Cerebellar atrophy Mitochondrial dysfunction Mitochondrial threonyl-tRNA-synthetase mTORC1 signaling TARS2

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