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IEEE Symposium (IUS) Ultrasonics

作者： Alycen Wiacek Eniola Falomo Kelly Myers Ole Marius Hoel Rindal Kelly Fabrega-Foster Susan Harvey Muyinatu A. Lediju Bell Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD Department of Radiology and Radiological Science Johns Hopkins Medicine Baltimore MD Department of Informatics University of Oslo Oslo Norway Department of Computer Science Johns Hopkins University Baltimore MD

Ultrasound imaging is often used in conjunction with mammography, particularly for patients with dense breast tissue, which causes an increased amount of acoustic clutter, obscuring lesions of interest, and contributing to the false positive rate of breast ultrasound. Coherence-based imaging methods, such as short-lag spatial coherence (SLSC) and robust short-lag spatial coherence (R-SLSC), display the coherence of backscattered ultrasound signals instead of their amplitude or brightness information, which offers opportunities to reduce acoustic clutter. This paper focuses on SLSC and R-SLSC beamforming applied to three in vivo masses in the female breast: (1) cyst, (2) fibroadenoma, and (3) ductal carcinoma in situ (DCIS). Contrast is improved by up to 7.8 dB with SLSC imaging and 4.86 dB with R-SLSC imaging in fluid-filled regions. However, contrast is degraded in coherence-based images of solid hypoechoic lesions because coherence is displayed in these regions with pathologically determined solid content. This interesting finding indicates the potential of coherence-based imaging to assist with the differentiation between solid and fluid-filled hypoechoic breast masses. Examples of duplex mode B-mode and R-SLSC images are shown to convey clinical potential.

关键词： Coherence Breast Imaging Solids Ultrasonic imaging Signal to noise ratio Array signal processing

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Collaborative Authentication for 6G Networks: An Edge Intelligence based Autonomous Approach

arXiv

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arXiv 2023年

作者： Fang, He Xiao, Zhenlong Wang, Xianbin Xu, Li Hanzo, Lajos The School of Electronic and Information Engineering Soochow University Soochow215301 China The Department of Informatics and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada The College of Computer and Cyber Security The Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou350117 China School of Electronics and Computer Science University of Southampton SO17 1BJ United Kingdom

The conventional device authentication of wireless networks usually relies on a security server and centralized process, leading to long latency and risk of single-point of failure. While these challenges might be mitigated by collaborative authentication schemes, their performance remains limited by the rigidity of data collection and aggregated result. They also tend to ignore attacker localization in the collaborative authentication process. To overcome these challenges, a novel collaborative authentication scheme is proposed, where multiple edge devices act as cooperative peers to assist the service provider in distributively authenticating its users by estimating their received signal strength indicator (RSSI) and mobility trajectory (TRA). More explicitly, a distributed learning-based collaborative authentication algorithm is conceived, where the cooperative peers update their authentication models locally, thus the network congestion and response time remain low. Moreover, a situation-aware secure group update algorithm is proposed for autonomously refreshing the set of cooperative peers in the dynamic environment. We also develop an algorithm for localizing a malicious user by the cooperative peers once it is identified. The simulation results demonstrate that the proposed scheme is eminently suitable for both indoor and outdoor communication scenarios, and outperforms some existing benchmark schemes. Copyright © 2023, The Authors. All rights reserved.

关键词： Authentication

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Tackling heavy-tailed noise in distributed estimation: Asymptotic performance and tradeoffs

Tackling heavy-tailed noise in distributed estimation: Asymp...

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Telecommunications Forum (TELFOR)

作者： Dragana Bajovic Dusan Jakovetic Soummya Kar Manojlo Vukovic Department of Power Electronic and Communication Engineering Faculty of Technical Sciences University of Novi Sad Department of Mathematics and Informatics Faculty of Sciences University of Novi Sad Department of Electrical and Computer Engineering Carnegie Mellon University Department of Fundamental Sciences Faculty of Technical Sciences University of Novi Sad

ISBN: (数字)9798350391060

ISBN: (纸本)9798350391077

We present an algorithm for distributed estimation of an unknown vector parameter θ ∗ ∈ ℝ M in the presence of heavy-tailed observation and communication noises. Heavy-tailed noises frequently appear, e.g., in densely deployed Internet of Things (IoT) or wireless sensor network systems. The presented algorithm falls within the class of consensus+innovation estimators and combats the effect of the heavy-tailed noises by adding general nonlinearities in the consensus and innovations update parts. We present results on almost sure convergence and asymptotic normality of the estimator. In addition, we provide novel analytical studies that reveal interesting tradeoffs between the system noises and the underlying network topology.

关键词： Technological innovation Wireless sensor networks Heavily-tailed distribution Network topology Noise Estimation Vectors Telecommunications Internet of Things Convergence

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Extended PD+I fuzzy logic controllers with self-tuning capability

Extended PD+I fuzzy logic controllers with self-tuning capab...

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Annual Conference of Industrial Electronics Society

作者： F. C. Teng A. Lotfi A. C. Tsoi Department of Electrical & Computer Engineering Polytechnic University of Puerto Rico San Juan Puerto Rico School of Computing & Informatics Nottingham Trent University Nottingham UK Hong Kong Baptist University Hong Kong China

In this paper, we propose two controllers which extend the original PD+I fuzzy logic controller to treat the plant with time varying nonlinear dynamics. The adaptation ability of the first self tuning PD+I fuzzy logic controller (STPD+I_31) is obtained by adjusting the output scaling factor automatically thereby contributing to significant improvement in performance. Second proposed controller (STPD+I_9) is the simplified version of STPD+I_31 which is designed under the imposed constraint that allows only minimum number of rules in the rule bases. The proposed controllers are compared with two classical nonlinear controllers: the pole placement self tuning PID controller and sliding mode controller. All the controllers are applied to the two-links revolute robot for the tracking control. The tracking performance of STPD+I_31 and STPD+I_9 are much better than the pole placement self tuning PID controller during high speed operations while the performance are comparable at low and medium speed. In addition, STPD+I_31 and STPD+I_9 outperform sliding mode controller under same method of comparison study.

关键词： Fuzzy logic Sliding mode control Automatic control Tuning Robots Fuzzy control State feedback Three-term control PD control Adaptive control

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Implicit Neural Representation in Medical Imaging: A Comparative Survey

arXiv

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arXiv 2023年

作者： Molaei, Amirali Aminimehr, Amirhossein Tavakoli, Armin Kazerouni, Amirhossein Azad, Bobby Azad, Reza Merhof, Dorit School of Computer Engineering Iran University of Science and Technology Tehran Iran School of Electrical Engineering Iran University of Science and Technology Tehran Iran South Dakota State University BrookingsSD United States Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany Institute of Image Analysis and Computer Vision Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functions, INRs offer several benefits. Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging. In medical settings, numerous challenging and ill-posed problems exist, making INRs an attractive solution. The survey explores the application of INRs in various medical imaging tasks, such as image reconstruction, segmentation, registration, novel view synthesis, and compression. It discusses the advantages and limitations of INRs, highlighting their resolution-agnostic nature, memory efficiency, ability to avoid locality biases, and differentiability, enabling adaptation to different tasks. Furthermore, the survey addresses the challenges and considerations specific to medical imaging data, such as data availability, computational complexity, and dynamic clinical scene analysis. It also identifies future research directions and opportunities, including integration with multimodal imaging, real-time and interactive systems, and domain adaptation for clinical decision support. To facilitate further exploration and implementation of INRs in medical image analysis, we have provided a compilation of cited studies along with their available open-source implementations on GitHub. Finally, we aim to consistently incorporate the most recent and relevant papers regularly. © 2023, CC BY.

关键词： Medical imaging

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A new way of generating grid-scroll chaos and its application to biometric authentication

A new way of generating grid-scroll chaos and its applicatio...

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Annual Conference of Industrial Electronics Society

作者： F. Han X. Yu J. Hu Faculty of Informatics and Communication Central Queensland University Rockhampton QLD Australia School of Electrical and Computer Engineering Royal Melbourne Institute of Technology Melbourne VIC Australia School of Computer Science and Information Technology Royal Melbourne Institute of Technology Melbourne VIC Australia

A new circuit design is proposed for generating grid-scroll chaos in the phase plane by using a second-order linear system with a control of double-hysteresis series. The double-hysteresis series is constructed in a systematic way by using the basic double-hysteresis building blocks. With the proposed scheme, the number of scrolls can be arbitrarily assigned, and the multi-scroll chaotic attractors can be placed anywhere and can cover any chosen area in the phase plane. The grid-scroll chaotic attractor is applied to biometric (fingerprint image) authentication. In this application, a two-dimensional (2D) discrete chaotic image is created from these grid-scroll chaotic attractors. Some of the parameters of the 2D chaotic image are extracted from the stable global structure of fingerprint image, and used to derive the encryption key at a terminal. Decryption, minutiae extraction and matching are fulfilled at the central server.

关键词： Mesh generation Chaos Biometrics Authentication Fingerprint recognition Image matching Circuit synthesis Linear systems Control systems Cryptography

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Decomposed and Distributed Modulation to Achieve Secure Transmission

Decomposed and Distributed Modulation to Achieve Secure Tran...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zhao Li Siwei Le Jie Chen Kang G. Shin Riku Jäntti Zheng Yan Jia Liu School of Cyber Engineering Xidian University Xi'an China Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor USA Department of Communications and Networking Aalto University Espoo Finland Center for Strategic Cyber Resilience Research and Development National Institute of Informatics Tokyo Japan

Due to the broadcast nature of wireless transmissions, they are exposed to all surrounding entities and thus vulnerable to eavesdropping. To counter this vulnerability, we propose a new physical-layer secure transmission scheme, called DDM-Sec, based on decomposed and distributed modulation (DDM). DDM-Sec realizes traditional QPSK modulation by using two cooperative transmitters (Txs), each generating a BPSK signal, in a distributed manner. The legitimate receiver (Rx) can decode the desired/intended information from the mixed received signal while preventing the eavesdropper from accessing the legitimate user's information. DDM-Sec can effectively exploit the randomness of wireless channels to encrypt data transmission, enrich the spatial signatures of the legitimate transmission by employing two cooperative Txs. Moreover, DDM-Sec distributes user's information to two transmissions so that none of the decomposed signals alone carry the legitimate user's full information. Our theoretical analysis, hardware experiment, and simulation have shown that DDM-Sec can effectively prevent the eavesdropping, and hence guarantee the secrecy of the legitimate user's data transmission.

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Handwritten and Machine Printed Text Separation in Document Images Using the Bag of Visual Words Paradigm

Handwritten and Machine Printed Text Separation in Document ...

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International Workshop on Frontiers in Handwriting Recognition

作者： Konstantinos Zagoris Ioannis Pratikakis Apostolos Antonacopoulos Basilis Gatos Nikos Papamarkos Pattern Recognition and Image Analysis (PRImA) Research Laboratory School of Computing Science and Engineering University of Salford Greater Manchester UK Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Institute of Informatics and Telecommunications National Center for Scientific Research Demokritos Athens Greece

In a number of types of documents, ranging from forms to archive documents and books with annotations, machine printed and handwritten text may be present in the same document image, giving rise to significant issues within a digitisation and recognition pipeline. It is therefore necessary to separate the two types of text before applying different recognition methodologies to each. In this paper, a new approach is proposed which strives towards identifying and separating handwritten from machine printed text using the Bag of Visual Words paradigm (BoVW). Initially, blocks of interest are detected in the document image. For each block, a descriptor is calculated based on the BoVW. The final characterization of the blocks as Handwritten, Machine Printed or Noise is made by a Support Vector Machine classifier. The promising performance of the proposed approach is shown by using a consistent evaluation methodology which couples meaningful measures along with a new dataset.

关键词： Visualization Noise Image segmentation Hidden Markov models Gabor filters Handwriting recognition Text recognition

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Hybrid machine learning model of extreme learning machine radial basis function for breast cancer detection and diagnosis;A multilayer fuzzy expert system

arXiv

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arXiv 2019年

作者： Mojrian, Sanaz Pinter, Gergo Joloudari, Javad Hassannataj Felde, Imre Nabipour, Narjes Nadai, Laszlo Mosavi, Amir Department of Information Technology Mazandaran Uni. of Science and Technology Babol Iran John von Neumann Faculty of Informatics Obuda University Budapest Hungary Electrical and Computer Engineering Faculty University of Birjand Birjand Iran Kalman Kando Faculty of Electrical Engineering Obuda University Budapest Hungary Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Kalman Kando Faculty of Electrical Engineering Budapest Hungary

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R2, MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM *** Codes 68Q05 Copyright © 2019, The Authors. All rights reserved.

关键词： Radial basis function networks

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An efficient approximate algorithm for delay-cost-constrained QoS routing

An efficient approximate algorithm for delay-cost-constraine...

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International Conference on computer Communications and Networks (ICCCN)

作者： Gang Feng K. Makki N. Pissinou C. Douligeris Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Center for Engineering and Applied science Telecommunication Information technology Florida International University Miami FL USA Florida International University Miami FL USA Department of Informatics University of Piraeus Piraeus Greece

The problem of finding a path with two additive constraints is called a multi-constrained path (MCP) problem in the literature. In this paper, we explore the MCP problem based on the idea of single mixed weight. Given two infeasible paths, it can be theoretically proved that a better path can possibly be found if we choose an appropriate parameter to construct the mixed weight. An approximate algorithm is thus proposed to solve the MCP problem. A large number of experiments on networks of different sizes demonstrate that this algorithm can make a correct judgment whether there is a feasible path or not with a very high probability. On the other hand, the time complexity of this algorithm is very small since it only needs to execute Dijkstra's algorithm a limited number of times even for a network of relatively large size.

关键词： Delay Routing

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