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IEEE Sensors Letters 2024年第9期8卷

作者： Singh, Kanishk Chen, Chao-Hung Tai, Li-Chia Huang, Wei-Chen Pan, Tung-Ming Chang Gung University Department of Electronics Engineering Taoyuan33302 Taiwan National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu30093 Taiwan National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu30093 Taiwan Chang Gung Memorial Hospital Division of Urology Taoyuan33305 Taiwan

In this research work, we present a flexible iridium oxide (IrOx) extended-gate field-effect transistor (EGFET) biosensor for label-free detection of the epidermal growth factor receptor (EGFR) biomarker. IrOx was employed as the sensing membrane material due to its excellent electrochemical properties, high conductivity, stability, biocompatibility, and ability to facilitate redox reactions. The IrOx film was deposited on a flexible polyimide (PI) substrate through a sol-gel process and characterized using atomic force microscopy and X-ray photoelectron spectroscopy. As a pH sensor, the IrOx EGFET exhibited a super-Nernstian sensitivity of 68.54 mV/pH with high linearity, a low hysteresis of ∼ 6 mV, and a drift rate of 0.50 mV/h at pH 7 over 12 h. For EGFR detection, the IrOx surface was functionalized with 3-aminopropyltriethoxysilane and glutaraldehyde to immobilize anti-EGFR antibodies. The EGFR sensor demonstrated a wide linear detection range from 1 to 1000 ng/mL with a sensitivity of 11.78 mV/log(ng/mL) and a linearity of 99.8%, making it suitable for clinical EGFR monitoring in cancer patients. The flexible IrOx EGFET shows promise for reliable label-free detection of EGFR. © 2017 IEEE.

关键词： Substrates

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Resilient Routing Table Computation Based on Connectivity Preserving Graph Sequences

Resilient Routing Table Computation Based on Connectivity Pr...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： János Tapolcai Péter Babarczi Pin-Han Ho Lajos Rónyai Department of Telecommunications and Media Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Deptartment of Electrical and Computer Engineering University of Waterloo Canada Eötvös Loránd Research Network and Department of Algebra Institute for Computer Science and Control Institute of Mathematics Budapest University of Technology and Economics Budapest Hungary

Fast reroute (FRR) mechanisms that can instantly handle network failures in the data plane are gaining attention in packet-switched networks. In FRR no notification messages are required as the nodes adjacent to the failure are prepared with a routing table such that the packets are re-routed only based on local information. However, designing the routing algorithm for FRR is challenging because the number of possible sets of failed network links and nodes can be extremely high, while the algorithm should keep track of which nodes are aware of the failure. In this paper, we propose a generic algorithmic framework that combines the benefits of Integer Linear Programming (ILP) and an effective approach from graph theory related to constructive graph characterization of k-connected graphs, i.e., edge splitting-off. We illustrate these benefits through arborescence design for FRR and show that (i) due to the ILP we have great flexibility in defining the routing problem, while (ii) the problem can still be solved very fast. We demonstrate through simulations that our framework outperforms state-of-the-art FRR mechanisms and provides better resilience with shorter paths in the arborescences.

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Distributed Finite-Time k-means Clustering with Quantized Communucation and Transmission Stopping

Distributed Finite-Time k-means Clustering with Quantized Co...

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IEEE Conference on Decision and control

作者： Apostolos I. Rikos Gabriele Oliva Christoforos N. Hadjicostis Karl H. Johansson Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Engineering Unit of Automatic Control Università Campus Bio-Medico di Roma Rome Italy Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this paper, we present a distributed version of the k-means algorithm for multi-agent systems with directed communication links. The goal of k-means is to partition the network’s agents in mutually exclusive sets (groups) such that agents in the same set have (and possibly share) similar information and are able to calculate a representative value for their group. Our distributed algorithm allows each node to transmit quantized values in an event-driven fashion, and exhibits distributed stopping capabilities. Transmitting quantized values leads to more efficient usage of the available bandwidth and reduces the communication bottleneck, whereas distributed stopping preserves available resources. We characterize the properties of the proposed distributed algorithm and show that its execution (on any static and strongly connected digraph) will partition all agents in mutually exclusive clusters in finite time. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithm.

关键词： Clustering algorithms Bandwidth Partitioning algorithms Distributed algorithms Multi-agent systems

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An Efficient Post-Quantum Multi-Signature Scheme for the Internet of Vehicles

An Efficient Post-Quantum Multi-Signature Scheme for the Int...

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IEEE International Conference on Communications (ICC)

作者： Qianyi Zhang Shuai Yuan Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power University Beijing China Department of Finance Operations and Information Systems (FOIS) Brock University CANADA Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

Multi-signature scheme is a unique type of digital signature where a group of participants are capable of producing a signature interactively on a shared message, thus significantly reducing the signature size. This is especially important for Internet of Vehicles (IoV) systems where higher efficiency and lower costs are required during the communication. Most approaches so far, however, are developed by traditional methods such as the integer factoring assumption, which result in potential vulnerability to quantum computing attacks. Although a few lattice-based multi-signature candidates have been proposed, they either rely on hash-and-sign process with higher costs or may be compromised by larger size of public key and signature. Motivated by the Bimodal Lattice Signature Scheme (BLISS) model [1], we propose a new lattice-based multi-signature scheme (Multi-BLISS, MB) in this paper. Our scheme can also be transformed into an aggregate signature scheme (Aggregate MB, AMB) with similar level of performance. We evaluate both schemes by setting security levels of 128, 160 and 192 bits in the experiments, and the results demonstrate significant improvement on security and efficiency comparing to existing lattice-based multi-signature schemes.

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IEEE Transactions on Instrumentation and Measurement 2024年 1-1页

作者： Wu, Jin Wang, Chaoqun Li, Chong Jiang, Yi Zhang, Chengxi Huang, Yulong Qi, Yuhua Xue, Bohuan Jiao, Jianhao Fan, Rui Zhang, Wei Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China School of Control Science and Engineering Shandong University Jinan China Department of Automation and Measurement Ocean University of China Qingdao China Department of Electrical Engineering City University of Hong Kong Hong Kong China School of Internet of Things Jiangnan University Wuxi China Department of Automation Harbin Engineering University Harbin China School of Systems Science and Engineering Sun Yat-Sen University China College of Electronics and Information Engineering Tongji University Shanghai China

Similarity transformation problems are important in robotic instrumentation and computer vision based measurements since in many cases the information of visually observed scene scale is unknown and must be restored for accurate 3-dimensional reconstruction. In existing solvers, the scale is often considered as a scalar, i.e., isotropic, which may be invalid for anisotropic-scale setups. This paper exploits some mathematical coincidences that will lead to efficient solution to these problems. Possible further applications also include hand-eye calibration and structure-from-motion. We revisit pose estimation problems within the framework of similarity transformation, the one that considers scale-stretching, rotation and translation simultaneously. Two major problems are taken into account, i.e., the scale-stretching point-cloud registration and perspective-n-points (PnP). It has been found out that these two problems are quite similar. Moreover, we solve the anisotropic-scale registration problem that is important and is a remaining unsolved one in previous literatures. To compute the globally optimal solution of these non-convex problems, algebraic solution is obtained to compute all local minima using computationally efficient methods. The designed algorithm is deployed for robotic-arm pose estimation. We also extend the algorithm for solving the problem of robust magnetometer calibration. Visual pose experiments verify the superiority of the proposed method compared with representatives, including P3P, Lambda-Twist P3P and EPnP, which can be reproduced by repository in https://***/zarathustr/APnP. IEEE

关键词： Calibration

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Data Link Fault Location Model Based on Machine Learning 7

Data Link Fault Location Model Based on Machine Learning

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7th Annual International Conference on Network and Information Systems for computers, ICNISC 2021

作者： Cui, Shuo Yin, Jiangbo Wang, Jun Xu, Peixin Big Data Center of State Grid Corporation of China Beijing China Beijing China-Power Information Technology Co Ltd Beijing China North China Electric Power University Faculty of Control and Computer Engineering Department of Computer Technology Beijing China

ISBN: (纸本)9781665402323

As the scale of national grid data continues to grow, data link fault location becomes more and more important. The traditional fault location method has many shortcomings due to its cognitive and technical limitations. With the rapid development of artificial intelligence technology today, using machine learning technology for data link fault location has become an effective method. Therefore, this paper proposes an autoencoder-BP neural network model, which uses the autoencoder to extract data features, and then uses the BP neural network for classification. Finally, it is proved through experiments that the combination of the two deep learning algorithms can effectively improve Accuracy of fault location. © 2021 IEEE.

关键词： Location

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Supervised Feature Compression based on Counterfactual Analysis

arXiv

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arXiv 2022年

作者： Piccialli, Veronica Morales, Dolores Romero Salvatore, Cecilia Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Rome Italy Department of Economics Copenhagen Business School Frederiksberg Denmark Department of Civil Engineering and Computer Science University of Rome Tor Vergata Rome Italy

Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work aims to leverage Counterfactual Explanations to detect the important decision boundaries of a pre-trained black-box model. This information is used to build a supervised discretization of the features in the dataset with a tunable granularity. Using the discretized dataset, an optimal Decision Tree can be trained that resembles the black-box model, but that is interpretable and compact. Numerical results on real-world datasets show the effectiveness of the approach in terms of accuracy and sparsity. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision trees

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controller and Trajectory Optimization for a Quadrotor UAV with Parametric Uncertainty

Controller and Trajectory Optimization for a Quadrotor UAV w...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Ali Srour Antonio Franchi Paolo Robuffo Giordano CNRS Univ Rennes Inria IRISA Campus de Beaulieu 35042 Rennes Cedex France Robotics and Mechatronics Department Electrical Engineering Mathematics Computer Science (EEMCS) Faculty University of Twente Enschede The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy LAAS-CNRS Université de Toulouse Toulouse France

In this work, we exploit the recent notion of closed-loop state sensitivity to critically compare three typical controllers for a quadrotor UAV with the goal of evaluating the impact of controller choice, gain tuning and shape of the reference trajectory in minimizing the sensitivity of the closed-loop system against uncertainties in the model parameters. To this end, we propose a novel optimization problem that takes into account both the shape of the reference trajectory and the controller gains. We then run a large statistical campaign for comparing the performance of the three controllers which provides some interesting insight for the goal of increasing closed-loop robustness against parametric uncertainties.

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Learning Sequential Descriptors for Sequence-based Visual Place Recognition

arXiv

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arXiv 2022年

作者： Mereu, Riccardo Trivigno, Gabriele Berton, Gabriele Masone, Carlo Caputo, Barbara The Visual and Multimodal Applied Learning Lab Department of Control and Computer Engineering Politecnico di Torino Torino10138 Italy Roma00185 Italy

In robotics, Visual Place Recognition is a continuous process that receives as input a video stream to produce a hypothesis of the robot's current position within a map of known places. This task requires robust, scalable, and efficient techniques for real applications. This work proposes a detailed taxonomy of techniques using sequential descriptors, highlighting different mechanism to fuse the information from the individual images. This categorization is supported by a complete benchmark of experimental results that provides evidence on the strengths and weaknesses of these different architectural choices. In comparison to existing sequential descriptors methods, we further investigate the viability of Transformers instead of CNN backbones, and we propose a new ad-hoc sequence-level aggregator called SeqVLAD, which outperforms prior state of the art on different datasets. The code is available at https://***/vandal-vpr/vg-transformers. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Multimodal MR Brain Image Segmentation using Hybrid Optimization Techniques for Earlier Prognosis of Tumor

Multimodal MR Brain Image Segmentation using Hybrid Optimiza...

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Advanced Computing and Communication Technologies for High Performance Applications

作者： Baiju Karun T. Arun Prasath M. Pallikonda Rajasekaran M Rakhee Department of Instrumentation and Control Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Biomedical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Computer Science & Engineering School of Engineering Cochin University of Science and Technology Kerala India

Segmentation of medical images is the process of dividing an image into regions that have some characteristics that are homogeneous and consistent. Medical image segmentation for brain images is an important process in the early diagnosis of brain tumors, brain abnormalities, and their treatment planning. When performed by medical specialists, segmentation of Magnetic Resonance Imaging (MRI) takes a long time. This paper needs to propose some new hybrid approaches for image segmentation and classification for early detection and treatment of brain tumors. The conventional fuzzy c-means clustering (FCM) method is susceptible to artifacts; the local spatial information is frequently presented to an objective function to enhance the FCM method's reliability for segmenting the images. To overcome this limitation, the authors proposed the Fast and Robust FCM (FRFCM), an optimized FCM technique that utilizes morphological restoration (MR) and membership filtration which is considerably quicker and much more reliable than classical FCM. Following the clustering procedure, the new Social Spider Optimization (SSO) swarm algorithm relies on the co - operative features of the social spider is employed to obtain the best outcome. The novel integration of FRFCM and SSO has been affirmed through evaluating on numerous different BraTS datasets, yielding improved performance metric values.

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