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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Huy Phan Elisabeth Heremans Oliver Y. Chén Philipp Koch Alfred Mertins Maarten De Vos Amazon Alexa Cambridge MA USA Department of Electrical Engineering KU Leuven Belgium School of Economics Finance and Management University of Bristol UK Institute for Signal Processing University of Lübeck Germany German Research Center for Artificial Intelligence (DFKI) Germany

We propose a regularization method, so-called temporal smoothness regularization, for training deep neural networks for automatic sleep staging in small data settings. In intuition, we constrain the cross-entropy losses of any two adjacent epochs in the sequential input to be as close to each other as possible. The regularization closely reflects the slow transition nature of sleep process which implies small information changes between two consecutive sleep epochs. Via the regularization, we essentially discourage the network from overfitting to these small changes. Our experiments show that training the SeqSleepNet base network with the proposed regularization leads to performance improvement over the baseline without the regularization applied. Furthermore, our developed method achieves the performance on par with the state-of-the-art performance while outperforming other existing methods.

关键词： Training Deep learning Neural networks signal processing Acoustics Speech processing

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Learning Fair Representations through Uniformly Distributed Sensitive Attributes

Learning Fair Representations through Uniformly Distributed ...

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Secure and Trustworthy Machine Learning (SaTML), IEEE Conference on

作者： Patrik Joslin Kenfack Adín Ramírez Rivera Adil Mehmood Khan Manuel Mazzara Machine Learning and Knowledge Representation Lab Innopolis University Innopolis Russia Department of Informatics Digital Signal Processing and Image Analysis (DSB) group University of Oslo Oslo Norway School of Computer Science University of Hull Hull UK Institute of Software Development and Engineering Innopolis University Innopolis Russia

Machine Learning (ML) models trained on biased data can reproduce and even amplify these biases. Since such models are deployed to make decisions that can affect people's lives, ensuring their fairness is critical. One approach to mitigate possible unfairness of ML models is to map the input data into a less-biased new space by means of training the model on fair representations. Several methods based on adversarial learning have been proposed to learn fair representation by fooling an adversary in predicting the sensitive attribute (e.g., gender or race). However, adversarial-based learning can be too difficult to optimize in practice; besides, it penalizes the utility of the representation. Hence, in this research effort we train bias-free representations from the input data by inducing a uniform distribution over the sensitive attributes in the latent space. In particular, we propose a probabilistic framework that learns these representations by enforcing the correct reconstruction of the original data, plus the prediction of the attributes of interest while eliminating the possibility of predicting the sensitive ones. Our method leverages the inability of Deep Neural Networks (DNNs) to generalize when trained on a noisy label space to regularize the latent space. We use a network head that predicts a noisy version of the sensitive attributes in order to increase the uncertainty of their predictions at test time. Our experiments in two datasets demonstrated that the proposed model significantly improves fairness while maintaining the prediction accuracy of downstream tasks.

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CrackCLF: Automatic Pavement Crack Detection based on Closed-Loop Feedback

arXiv

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arXiv 2023年

作者： Li, Chong Fan, Zhun Chen, Ying Lin, Huibiao Moretti, Laura Loprencipe, Giuseppe Sheng, Weihua Wang, Kelvin C.P. The Key Lab of Digital Signal and Image Processing of Guangdong Province College of Engineering Shantou University Shantou515063 China The Department of Civil Construction and Environmental Engineering Sapienza University of Rome Rome00184 Italy The School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States The School of Civil and Environmental Engineering Oklahoma State University StillwaterOK74078 United States

Automatic pavement crack detection is an important task to ensure the functional performances of pavements during their service life. Inspired by deep learning (DL), the encoder-decoder framework is a powerful tool for crack detection. However, these models are usually open-loop (OL) systems that tend to treat thin cracks as the background. Meanwhile, these models can not automatically correct errors in the prediction, nor can it adapt to the changes of the environment to automatically extract and detect thin cracks. To tackle this problem, we embed closed-loop feedback (CLF) into the neural network so that the model could learn to correct errors on its own, based on generative adversarial networks (GAN). The resulting model is called CrackCLF and includes the front and back ends, i.e. segmentation and adversarial network. The front end with U-shape framework is employed to generate crack maps, and the back end with a multi-scale loss function is used to correct higher-order inconsistencies between labels and crack maps (generated by the front end) to address open-loop system issues. Empirical results show that the proposed CrackCLF outperforms others methods on three public datasets. Moreover, the proposed CLF can be defined as a plug and play module, which can be embedded into different neural network models to improve their performances. Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Methodological Contribution to Efficient Dynamic Assessment of Reliability Using Satisfiability Approach

SSRN

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SSRN 2023年

作者： Fotsa-Mbogne, David Jaurès Ftatsi-Mbetmi, Guy-De-Patience Ndje, Martial Tadie-Silatchom, Markert Benjaulys Department of Mathematics and Computer Science ENSAI The University of Ngaoundere Cameroon Department of Mechanical Engineering UIT The University of Ngaoundere Cameroon Department of Electrical Engineering UIT The University of Dschang Cameroon Department of Mechanical Engineering ENSAI The University of Ngaoundere Cameroon Laboratory of Experimental Mathematics ENSAI The University of Ngaoundere Cameroon Laboratory of Electrical Signal Image et Automatics ENSAI The University of Ngaoundere Cameroon Laboratory of Simulation and Testing The University Institute of Technology The University of Ngaoundere Cameroon

This work is concerned with the problem of computing of the reliability for multi-component systems given their dynamic fault trees. The recent literature provided satisfiability methods using the conjunctive normal form (CNF) of the structure function which is usually initially given under its disjunctive normal form (DNF) before using existing satisfiability (SAT) solvers. The conversion of the DNF of the structure function to its CNF is actually very costly. The main contribution of this paper is to propose an efficient method to compute the reliability polynomial using directly the DNF which is itself obtained efficiently from the dynamic fault tree. This involves the representation of logic functions by sets of three-level numeric tuples and the definition of logic operations adapted on these sets. The proposed method ensures polynomial complexity with respect to the number of nodes in the fault tree, the maximum number of inputs of the different logic gates being fixed. © 2023, The Authors. All rights reserved.

关键词： Reliability

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Toward Non-contact Muscle Activity Estimation using FMCW Radar

arXiv

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arXiv 2023年

作者： Tsengwa, Kukhokuhle Paine, Stephen Nicolls, Fred Albertus, Yumna Patel, Amir The University of Cape Town Cape Town South Africa The Oxford Robotics Institute Department of Engineering Science University of Oxford Oxford United Kingdom Department of Electrical Engineering University of Cape Town Cape Town South Africa The Digital Image Processing Laboratory Department of Electrical Engineering University of Cape Town Cape Town South Africa Research Centre Department of Human Biology University of Cape Town Cape Town South Africa The Department of Electrical Engineering University of Cape Town Cape Town South Africa

Surface electromyography (sEMG) is a widely used muscle activity monitoring technique. sEMG measures muscle activity through monopolar and bipolar, multi-electrode electrodes. The surface electrodes are placed on the surface of the skin above the target muscle and the received signal can be used to infer the state of the muscle — active, inactive or fatigued — which serves as vital information during neurological and orthopaedic rehabilitation. Additionally, the sEMG signal can also be used for the control of prostheses. sEMG requires contact with the participant’s skin and s thus a potentially uncomfortable method for the measurement of muscle activity. Moreover, the setup procedure has been termed time-consuming by sEMG experts and is listed as one of the main barriers to the clinical employment of the technique. Previous studies have shown that architectural changes, particularly muscle deformation, can provide information about the activity of the muscle, providing an alternative to sEMG. In all these studies, the muscle deformation signal is acquired using ultrasound imaging, an approach known as sonomyography (SMG). Despite its advantages, such as improved spatial resolution, SMG is still a contact based approach. In this paper, we propose a non-contact muscle activity monitoring approach that measures the muscle deformation signal using a Frequency Modulated Continuous Wave (FMCW) mmWave radar which we call radiomyography (RMG). In future, this system will enable muscle activation to be measured in an unconstrained and less cumbersome manner for both the person conducting the test and the individual being tested. Copyright © 2023, The Authors. All rights reserved.

关键词： Muscle

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Enhancing emission performance of red TADF emitters via the introduction of electronically inert pendant

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Organic Electronics 2024年 126卷

作者： Tang, Yukun He, Jian-Li Zhang, Kai Zhao, Yue Lin, Yu-Ching Chen, Chia-Hsun Chiu, Tien-Lung Lee, Jiun-Haw Wang, Chuan-Kui Fan, Jian Wu, Chung-Chih Department of Electrical Engineering Graduate Institute of Electronics Engineering and Graduate Institute of Photonics and Optoelectronics National Taiwan University Taipei 10617 Taiwan Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices Soochow University Jiangsu Suzhou215123 China Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University Jinan250014 China Coordination Chemistry Institute State Key Laboratory of Coordination Chemistry School of Chemistry and Chemical Engineering Nanjing National Laboratory of Microstructures Nanjing University Nanjing210023 China Department of Chemistry National Taiwan University Taipei10617 Taiwan Department of Electrical Engineering Yuan Ze University Taoyuan32003 Taiwan Graduate Institute of Photonics and Optoelectronics and Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan State Key Laboratory of Structural Chemistry Fujian Institute of Research on Structure of Matter Chinese Academy of Sciences Fujian Fuzhou35002 China

In addition to emission efficiency and colors, emission characteristics such as preferentially horizontal emitting dipole orientation and resistance against concentration quenching are also highly desired for high-performance thermally activated delayed fluorescence (TADF) emitters. In this work, we report a simple and yet effective strategy for enhancing such emission performance of the pure red-to-near infrared (NIR) TADF emitter by introducing bulky aryl derivatives (m-xylene) to the efficient emission core. Incorporating m-xylene groups as the electronically inert pendants to the acceptor unit of the TADF emitter TPA–CN–N4-CH3 successfully retains favorable emission properties (e.g., pure red color chromaticity, nearly unitary photoluminescence quantum yield etc.) of the parent compound TPA–CN–N4 and yet also renders significantly higher horizontal emitting dipole ratio Θ// of ∼85 % with the more extended molecular configuration and reduced concentration quenching at very high concentrations in thin films. As a result, OLEDs adopting TPA–CN–N4-CH3 can demonstrate rather high EQEs of 29.8–32.1 % for pure red emission (625–650 nm) at low doping concentrations and decent EQEs of 18.9–27.5 % in the deep red to NIR range (650–700 nm) at higher doping concentrations. The results of this work shall provide a useful reference for development of efficient TADF emitters. © 2024 Elsevier B.V.

关键词： Fluorescence

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Cellular Metaverse: Enhancing Real-Time Communications in Virtual World

Cellular Metaverse: Enhancing Real-Time Communications in Vi...

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International Technical Conference o Circuits/Systems, Computers and Communications (ITC-CSCC)

作者： Aisha Munir Muhammad Zain Siddiqi Siwanart Jeravongtakul Shashi Shah Ambar Bajpai Piya Kovintavewat Lunchakorn Wittisittikulkij Department of Electrical Engineering Wireless Communication Ecosystem Research Unit Chulalongkorn University Bangkok Thailand Atria Institute of Technology Bangalore India Faculty of Science and Technology Advanced signal processing for disruptive innovation research center Nakhon Pathom Rajabhat University Nakhon Pathom Thailand

In the next few years, various technologies including artificial intelligence, the Internet of Things, virtual reality, and augmented reality will coexist to realize the objectives of 6G wireless systems. In this paper, we propose a digital twin (DT) prototype of conventional wireless communication systems in a metaverse/virtual environment. We develop the modelling scenario of a cellular communications set-up in the Unity platform at first. Then, we establish the real-time communication between BS/s and user/s' in a metaverse environment after introducing some features such as cell coverage area, operating frequency, and global positioning system (GPS). With the aid of GPS, we continuously monitor the user/s' position in the metaverse environment. Moreover, we use the C# scripting tool in the metaverse environment to make all the modelling units functional and interconnected. The experiment results show that the user/s' location can be localized in the digital world, creating the DT of the real cellular communications set-up.

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Dynamic Analysis of a Hyperchaotic Hyperjerk Circuit, FPGA Implementation and Its Application in RNG for Medical images Encryption

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Transactions of the Indian National Academy of engineering 2022年第3期7卷 753-773页

作者： Feudjio, E. R. Tamba, V. Kamdoum Tagne, F. Kapche Tchendjeu, A. E. Tchahou Djatche, G. P. Biamou, A. L. Mbanda Fotsin, H. B. Research Unit of Automation and Applied Computer (URAIA) Department of Electrical Engineering IUT-FV Bandjoun University of Dschang Bandjoun Cameroon Research Unit of Condensed Matter Electronics and Signal Processing (URMACETS) Department of Physics University Of Dschang Dschang Cameroon Department of Computer Engineering National Polytechnic Institute University of Bamenda Bambili Cameroon

Nonlinear systems with high dimension have received considerable attention recently due to their possibility to exhibit complex dynamics including chaos, hyperchaos, pattern formation, self-organization, and other related dynamics. In this work, a novel four dimensional autonomous hyperchaotic hyperjerk circuit with hyperbolic sine and quintic nonlinearities is proposed and its corresponding dynamics is analyzed theoretically, numerically and experimentally. Using Kirchhoff’s laws, the state equations of the proposed hyperchaotic hyperjerk circuit are described. Some fundamental properties of the model including symmetry, dissipation, equilibrium points and their stability are examined. Bifurcation diagrams, Lyapunov exponent spectrum and phase portraits are plotted to point out the complex dynamical behaviors exhibited by the system. The numerical results are validated using PSpice-based circuit simulations and Field-Programmable Gate Array (FPGA) implementations. In addition, to use the proposed hyperchaotic hyperjerk circuit for medical images encryption, a random number generator (RNG) is designed. The resulting random number generator (RNG) successfully passes the National institute of Standards and Technology (NIST-800-22) statistical tests. This confirms that the proposed hyperchaotic hyperjerk circuit is effective in showing randomness. Finally, a medical images encryption algorithm is developed based on the generated random bits. The security performances analyses are performed and the results show that the proposed algorithm can effectively encrypt and decrypt medical images with better security performances.

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Exploration of Optimizing FPGA-based Qubit Controller for Experiments on Superconducting Quantum Computing Hardware

Exploration of Optimizing FPGA-based Qubit Controller for Ex...

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IEEE International Conference on Electro-Information Technology

作者： Hans Johnson Silvia Zorzetti Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A. Superconducting Quantum Materials and Systems Center (SQMS) Fermi National Accelerator Laboratory Batavia IL U.S.A.

This work explores avenues and target areas for optimizing FPGA-based control hardware for experiments conducted on superconducting quantum computing systems and serves as an introduction to some of the current research at the intersection of classical and quantum computing hardware. With the promise of building larger-scale error-corrected quantum computers based on superconducting qubit architecture, innovations to room-temperature control electronics are needed to bring these quantum realizations to fruition. The QICK (Quantum Instrumentation Control Kit) is one leading experimental FPGA-based implementations. However, its integration into other experimental quantum computing architectures, especially those using superconducting radiofrequency (SRF) cavities, is largely unexplored. We identify some key target areas for optimizing control electronics for superconducting qubit architectures and provide some preliminary results to the resolution of a control pulse waveform. With optimizations targeted at 3D superconducting qubit setups, we hope to bring to light some of the requirements in classical computational methodologies to bring out the full potential of this quantum computing architecture, and to convey the excitement of progress in this research.

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Iterative Random Training Sampling Convolutional Neural Network for Hyperspectral image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2023年 61卷

作者： Chang, Chein-I Liang, Chia-Chen Hu, Peter Fuming University of Maryland Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan Providence University Department of Computer Science and Information Management Taichung02912 Taiwan University of Maryland School of Medicine R. A. Cowley Shock Trauma Center Shock Trauma Anesthesia Organized Research Center Department of Anesthesia BaltimoreMD21201 United States

Convolutional neural network (CNN) has received considerable interest in hyperspectral image classification (HSIC) lately due to its excellent spectral-spatial feature extraction capability. To improve CNN, many approaches have been directed at exploring the infrastructure of its network by introducing different paradigms. This article takes a rather different approach by developing an iterative CNN that extends a CNN by including a feedback system to repeatedly process the same CNN in an iterative manner. Its idea is to take advantage of a recently developed iterative training sampling spectral-spatial classification (IRTS-SSC) that allows CNN to update its spatial information of classification maps through a feedback spatial fil- tering system via IRTS. The resulting CNN is called iterative random training sampling CNN (IRTS-CNN) with several unique features. First, IRTS-CNN combines CNN and IRTS-SSC into one paradigm, an architecture that has never been investigated in the past. Second, it implements a series of spatial filters to capture spatial information of classified data samples and further feeds this information back via an iterative process to expand the current input data cube for the next iteration. Third, it utilizes the expanded data cube to randomly reselect training samples and then to reimplement CNN iteratively. Last but not least, IRTS-CNN provides a general framework that can implement any arbitrary CNN as an initial classifier to improve its performance through an iterative process. Extensive experiments are conducted to demonstrate that IRTS-CNN indeed significantly improves CNN, specifically when only a small size of limited training samples is used. © 1980-2012 IEEE.

关键词： Iterative methods

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