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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Kai Liang, Wei Cao, Peng Yang, Jinzhu Li, Weiping Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China School of Software and Microelectronics Peking University Beijing China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466731

Multi-label learning has received much attention due to its wide range of application domains. Multi-label data often has high-dimensional features, which brings more challenges to classification algorithms. Feature selection based on sparse learning is one of the most important approaches, which can select discriminative features to alleviate the curse of dimensionality. However, most of these methods do not consider feature redundancy and label correlation simultaneously. In this work, we propose a multi-label feature selection method that takes feature redundancy and label correlation into account. Specifically, the proposed method first divides features into groups according to feature correlation and then encourages competition within each group but relaxes competition between groups to eliminate redundant and irrelevant features. Moreover, we propose an approach to capture label correlation and exploit it to improve the feature selection. Finally, an iterative optimization algorithm is designed to obtain the feature weights for multi-label feature selection. Extensive experiments on various multi-label datasets demonstrate the superiority of the proposed method compared with some state-of-the-art multi-label feature selection methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature Selection

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Design and Characterization of the Output Network of a High-Current Buck Converter

Design and Characterization of the Output Network of a High-...

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Proceedings of the International Convention MIPRO

作者： Krešimir Šolaja Raul Blečić Andro Broznić Adrijan Barić Department of Electronics Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Rimac Technology d.o.o./Embedded Hardware Sveta Nedelja Croatia

A 10-A automotive buck converter is designed and realized on a four layer printed circuit board. The output network is designed in the electromagnetic solver to account for the trace and package parasitic elements. The output network of the converter is simulated in the SPICE solver. The converter is characterized in the time domain by the load transient measurements and in the frequency domain by the shunt measurement method. Three configurations of the output network are compared. The results in the frequency domain and the results of the SPICE simulations are correlated.

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Confidential Computing as an Attempt to Secure Service Provider's Confidential Client Data in a Multi-Tenant Cloud Environment

Confidential Computing as an Attempt to Secure Service Provi...

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Proceedings of the International Convention MIPRO

作者： Bojan Novković Anita Božić Marin Golub Stjepan Groš Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing Zagreb Croatia

Cloud-oriented infrastructure posed itself as a predominant deployment paradigm in the recent decade due to its ease of provisioning and relatively low cost. However, entrusting a third party with sensitive data in a multi-tenant environment brings about increased data breach risks. The aim of this paper is to give an insight into challenges and threats encountered in mitigating data breach and repudiation risks for service providers utilizing cloud-based environments. Through constructing and studying a possible cloud-based service, we form a corresponding threat model with a special focus on risks originating from internal actors. We explore confidential computing as a possible solution for data confidentiality and mitigation of internal data breach risks for Software as a Service (SaaS) providers.

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Simplicial Convolutional Filters

arXiv

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arXiv 2022年

作者： Yang, Maosheng Isufi, Elvin Schaub, Michael T. Leus, Geert Dept. of Intelligent Systems Delft University of Technology Netherlands Dept. of Computer Science RWTH Aachen University Germany Dept. of Microelectronics Delft University of Technology Netherlands

We study linear filters for processing signals supported on abstract topological spaces modeled as simplicial complexes, which may be interpreted as generalizations of graphs that account for nodes, edges, triangular faces, etc. To process such signals, we develop simplicial convolutional filters defined as matrix polynomials of the lower and upper Hodge Laplacians. First, we study the properties of these filters and show that they are linear and shift-invariant, as well as permutation and orientation equivariant. These filters can also be implemented in a distributed fashion with a low computational complexity, as they involve only (multiple rounds of) simplicial shifting between upper and lower adjacent simplices. Second, focusing on edge-flows, we study the frequency responses of these filters and examine how we can use the Hodge-decomposition to delineate gradient, curl and harmonic frequencies. We discuss how these frequencies correspond to the lower- and the upper-adjacent couplings and the kernel of the Hodge Laplacian, respectively, and can be tuned independently by our filter designs. Third, we study different procedures for designing simplicial convolutional filters and discuss their relative advantages. Finally, we corroborate our simplicial filters in several applications: to extract different frequency components of a simplicial signal, to denoise edge flows, and to analyze financial markets and traffic networks. © 2022, CC BY-SA.

关键词： Chebyshev polynomials

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Improving Urban Canyons Based on a Graph Transformer

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Navigation, Journal of the Institute of Navigation 2024年第4期71卷

作者： Zheng, Shaolong Zeng, Kungan Li, Zhenni Wang, Qianming Xie, Kan Liu, Ming Xie, Shengli School of Automation Guangdong University of Technology Guangzhou China Guangdong–HongKong–Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China 111 Center for Intelligent Batch Manufacturing Based on Internet of Things Technology Guangzhou China Techtotop Microelectronics Technology Co. Ltd Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology China Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou China

Signals from global navigation satellite systems (GNSSs) in urban areas suffer from serious multipath errors caused by building blockages and reflections. The use of deep neural networks offers great potential for predicting and eliminating com-plex multipath/non-line-of-sight (NLOS) errors. However, existing methods for predicting the original signals face two remaining challenges. The first challenge is an inability to effectively exploit irregular GNSS measurement data caused by an inconsistent number of visible satellites in different epochs. The second challenge is degradation in the generalization performance of the multipath/NLOS prediction model when using data collected from different locations and periods. To address these challenges, this paper proposes a novel graph transformer neural network (GTNN) for predicting satellite visibility that effectively learns environment representations from irregular GNSS measurements to both alleviate multipath interference and improve the generalization performance of the multipath prediction model. To learn from irregular GNSS measurements, a sky satellite graph is constructed as input to a graph neural network by using satellites captured in the same epoch, which can represent the spatial relationships between satellites and enable the model to learn satellite-related features sufficiently well. To improve the generalization ability of our multipath prediction model, a multihead attention mechanism is introduced to aggregate satellite node information by computing the correlation between satellites to extract the environment representation around the receiver. Based on the constructed sky satellite graph and the multihead attention mechanism, our novel GTNN for predicting satellite visibility can not only handle irregular GNSS measurements but can also learn an environment representation via graph attention. Comparative experiments were conducted on real-world GNSS measurement data in urban areas, demonstrating that

关键词： Graph neural networks

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Geometric Parameterized Macromodel of Uniaxial Capacitance Accelerometer Based on Modal Superposition

Geometric Parameterized Macromodel of Uniaxial Capacitance A...

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International Conference on Actual Problems of Electronic Instrument Engineering (APEIE)

作者： Aleksandr V. Gridchin Aleksei N. Koleda Evgenii S. Barbin Tamara G. Nesterenko Vladimir A. Kolchuzhin Department of Semiconductor Devices and Microelectronics Novosibirsk State Technical University Novosibirsk Russia Department of Electronic Engineering National Research Tomsk Polytechnic University Tomsk Russia Laboratory of Intelligent Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Advanced Cellular Group Qorvo Munich GmbH Munich Germany

The paper presents a piecewise geometric parameterization method and a parametric analysis of uniaxial capacitance accelerometer using a reduced model formulated on the modal superposition method. The extraction of parameters for the macromodel such as modal frequencies, modal masses, modal stiffnesses, strain energy, and capacitances are described. The reduced model consists of the two modes: torsion mode, and transversal mode, three electrodes in the electrostatic domain, and eight master nodes in the geometrical domain located at the edges of the plate. The generation of the discrete macromodels is executed using commercial software. The thickness of the plate has been selected as a design parameter. Linear piecewise interpolation is used for macromodel parameterization which have been done inside VHDL-AMS code. The accelerometer’s dynamic response simulated using of parametrized macromodel considering the mechanical contact phenomena and the nonlinear electrostatic interaction are presented and discussed. The developed approach can be applied for fast system level simulations in view of the variation of the geometric parameters.

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On the potential of lateral BJTs and SiGe HBTs in advanced CMOS technologies

On the potential of lateral BJTs and SiGe HBTs in advanced C...

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Pacific Rim Meeting on Electrochemical and Solid State Science 2020, PRiME 200

作者： Suligoj, Tomislav Žilak, Josip Osrečki, Željko Koričić, Marko Micro and Nano Electronics Laboratory Department of Electronics Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing University of Zagreb Croatia

ISBN: (纸本)9781607689003

The potential of lateral bipolar transistors, based on Horizontal Current Bipolar Transistor (HCBT), in scaled CMOS technologies are examined by TCAD simulations. Thorough and consistent simulations show that SiGe HCBT can achieve fT/fmax of 358/490 GHz outperforming standard vertical-current HBT with fT/fmax of 348/423 GHz, assuming the same intrinsic transistor doping profile. By optimizing the extrinsic base shape and width, the characteristics of SiGe HCBT can be further improved. SiGe HCBTs in bulk silicon substrates result in better performance comparing to the transistors in SOI substrates. © The Electrochemical Society

关键词： Si-Ge alloys

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Efficient Graph Neural Network Driven Recurrent Reinforcement Learning for GNSS Position Correction 36

Efficient Graph Neural Network Driven Recurrent Reinforcemen...

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36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Zhao, Haoli Tang, Jianhao Li, Zhenni Wu, Zhuoyu Xie, Shengli Wu, Zhaofeng Liu, Ming Kumara, Banage T.G.S. School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd. Guangzhou510000 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (纸本)9780936406350

With the wide applications of the Global Navigation Satellite System (GNSS) in autonomous driving scenarios, the demand for high-precision positioning of navigation systems has increased dramatically in complex multipath environments. Conventional model-based methods are constrained by strict assumptions about noise models and can hardly model complex environment errors. In contrast, approaches based on artificial intelligent learning have become an important direction to solving the problem of high-precision positioning because learning-based approaches only require simple assumptions. However, current learning-based approaches are facing the following issues. The existing Graph Neural Network-based (GNN) method could hardly adapt to dynamically changing driving environment scenarios since it considers positioning discretely. On the other hand, existing Reinforcement Learning-based (RL) approaches ignore the relationship between multi-constellation satellites, resulting in an inadequate description of the driving correction environment observations. In this paper, we construct a GNN-driven recurrent reinforcement learning method to consider the GNSS measurement of multi-constellation satellites and to learn real-time correction strategy in the dynamic driving environment. To establish a comprehensive positioning correction environment, we construct a multi-constellation graph observation, based on the feature vector concerning GNSS measurement of multi-constellation satellites and edges for satellites in and between constellations. To make more effective use of GNSS measurements, we employ the graph embedding module to deal with the multi-constellation graph inputs, to extract hidden topological features to form the brief states about relationships between multi-constellation satellites for the RL environment. Finally, we construct a recurrent actor-critic structured RL model with cumulative reward and continuous action space to exploit historical information and a

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Tapered CPW Transmission Line for Enlarging Dynamic Range of Transmission Spectroscopy

Tapered CPW Transmission Line for Enlarging Dynamic Range of...

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European Conference on Microwave

作者： Marie Mertens Raphaël Trouillon Ke Wu Bart Nauwelaers Tomislav Markovic Dominique Schreurs Department of Electrical Engineering Division WaveCoRE KU Leuven Belgium Department of Electrical Engineering Poly-Grames Research Center Polytechnique Montreal Canada Department of Electrical Engineering Polytechnique Montreal Canada TransMedTech Research Institute Canada Department of Electronics Microelectronics Computer and Intelligent Systems University of Zagreb Croatia

We propose a new coplanar waveguide (CPW) technique to enhance transmission efficiency and field interaction through a biological sample in two-port transmission line measurements. The aim is to overcome low dynamic range and enlarge parametric sensitivity in dielectric characterization. The new design consists of a CPW line with adapted dimensions in its sensing region to obtain a loaded characteristic impedance of 50 $\Omega$. Glucose measurements show improved distinguishability in concentration. Furthermore, the transmission shows lower ripple at low frequencies (1-4 GHz) and improvement of 4 to 7 dB at higher frequencies (5-10 GHz) for our board loaded with PC12 cell suspensions. In addition, lower reflection is observed.

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Finite impulse response filters for simplicial complexes

arXiv

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arXiv 2021年

作者： Yang, Maosheng Isufi, Elvin Schaub, Michael T. Leus, Geert Dept. of Intelligent Systems Delft University of Technology Delft Netherlands Dept. of Computer Science RWTH Aachen University Aachen Germany Dept. of Microelectronics Delft University of Technology Delft Netherlands

In this paper, we study linear filters to process signals defined on simplicial complexes, i.e., signals defined on nodes, edges, triangles, etc. of a simplicial complex, thereby generalizing filtering operations for graph signals. We propose a finite impulse response filter based on the Hodge Laplacian, and demonstrate how this filter can be designed to amplify or attenuate certain spectral components of simplicial signals. Specifically, we discuss how, unlike in the case of node signals, the Fourier transform in the context of edge signals can be understood in terms of two orthogonal subspaces corresponding to the gradient-flow signals and curl-flow signals arising from the Hodge decomposition. By assigning different filter coefficients to the associated terms of the Hodge Laplacian, we develop a subspace-varying filter which enables more nuanced control over these signal types. Numerical experiments are conducted to show the potential of simplicial filters for sub-component extraction, denoising and model approximation. Copyright © 2021, The Authors. All rights reserved.

关键词： Laplace transforms

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