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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ghosh, Anubhab Eldar, Yonina C. Chatterjee, Saikat Digital Futures Centre School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Faculty of Mathematics and Computer Science The Weizmann Institute of Science Israel

ISBN: (纸本)9798350368741

We consider the problem of causal filtering of a model-free process from (noisy) nonlinear measurements. The 'model-free process' means that we do not have a state-space model (SSM) of the process dynamics, limiting the use of traditional model-driven filters, such as unscented Kalman filter (UKF) and particle filter (PF). To address the problem we propose a particle-based data-driven nonlinear state estimation (pDANSE) method. In pDANSE, a recurrent neural network (RNN) provides the statistical parameters of a Gaussian prior of the underlying state, and particles are then drawn from the prior to compute the posterior moments. pDANSE is typically trained in a semi-supervised fashion. For our experiments we study the use of half-wave rectification as a nonlinear transformation of measurements. We first show that an unsupervised learning-based method under-performs, and subsequently the semi-supervised learning-based pDANSE performs satisfactorily. Using Lorenz-63 system as benchmark, pDANSE is found to be competitive against a model-driven PF that knows the exact SSM. © 2025 IEEE.

关键词： deep neural network Kalman filter particle filter semi-supervised learning State estimation

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Long-term Water Quality Prediction Based on Multimodal Fusion and Temporal 2D-variation 21

Long-term Water Quality Prediction Based on Multimodal Fusio...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wu, Xiangxi Wang, Ziqi Li, Yibo Bi, Jing Yuan, Haitao College of Computer Science Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798350365221

The global water environment confronts numerous challenges, e.g., water pollution, overexploitation, and ecological degradation. Comprehensive protection and management are imperative for sustainable water resource utilization. Water quality predictions provide timely warning of future water quality problems and enable early action to avoid deterioration. As science and technology are increasingly applied in comprehensive water environment management, a diverse array of multimodal data is gathered from various sources, including remote sensing images and hydrological time series. However, current water quality prediction methods, e.g., statistical, machine learning, and deep learning methods fail to utilize multimodal data to enhance their accuracy of water quality prediction. To solve the above problem, this work proposes a multi-factor and long-term water quality prediction model based on multimodal data fusion named Low-rank Multimodal Fusion TimesNet (LMF-TimesN et), It first extracts features from hydrological time series and remote sensing images, respectively. Then, they are fused with the low-rank multimodal fusion network to extract diverse information. Finally, TimesNet is adopted to integrate fused multimodal water environment information for water quality prediction. Experimental results on a real-world dataset show that LMF - TimesNet achieves higher prediction accuracy and generalization ability than its state-of-the-art peers. © 2024 IEEE.

关键词： Data quality

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On Vertex-Disjoint Chorded Cycles and Degree Sum Conditions

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Journal of Combinatorial Mathematics and Combinatorial Computing 2024年 120卷 75-90页

作者： Gould, Ronald J. Hirohata, Kazuhide Rorabaugh, Ariel Keller Department of Mathematics Emory University AtlantaGA30322 United States Department of Industrial Engineering Computer Science National Institute of Technology Ibaraki College Ibaraki Hitachinaka312-8508 Japan Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States

In this paper, we consider a degree sum condition sufficient to imply the existence of k vertex-disjoint chorded cycles in a graph G. Let σ4(G) be the minimum degree sum of four independent vertices of G. We prove that if G is a graph of order at least 11k + 7 and σ4(G) ≥ 12k − 3 with k ≥ 1, then G contains k vertex-disjoint chorded cycles. We also show that the degree sum condition on σ4(G) is sharp. © 2024 the Author(s), licensee Combinatorial Press.

关键词： Degree sequence Minimum degree sum Vertex-disjoint chorded cycles

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Design of a Magneto-Electric Monopole Antenna

Design of a Magneto-Electric Monopole Antenna

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Nakamura, Shunta Sano, Makoto Kuse, Ryuji Fukusako, Takeshi Department of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

ISBN: (纸本)9798350364774

This paper presents a compact magneto-electric (ME) monopole antenna. Based on the image theory, the lateral size of the conventional ME dipole antenna is reduced by half, by placing a vertical ground plane. The ME monopole antenna was designed at 2.5 GHz. High front-to-back ratio of 20 dB and low cross polarization are observed. © 2024 IEEE.

关键词： Monopole antennas

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Transportation Card Data Analysis for Ride-Hailing Demand Estimation in Urban Area

Transportation Card Data Analysis for Ride-Hailing Demand Es...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Kim, Dongju Hwang, Euiseok Choi, Jaehyuk Kim, Hyunsup Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Gwangju Korea Republic of Hyundai Motors Company Korea Republic of

ISBN: (纸本)9798350362480

Ride-hailing has become a vital component of urban transportation due to its flexibility and efficiency in meeting diverse passenger needs. Accurate demand estimation, particularly in identifying origin-destination (OD) pairs, is crucial for optimizing services for ride-hailing providers and drivers and ensuring efficient resource allocation. This study proposes an alternative approach to demand estimation using public transportation card data, addressing privacy concerns associated with directly collecting OD data from ride-hailing services. By analyzing hourly recorded departure and arrival data between administrative districts, the proposed statistical model estimates ride-hailing demand patterns, capturing both spatial and temporal dynamics across urban areas. Transportation data from selected urban districts in Seoul, Korea, was analyzed to validate this approach. Results demonstrate significant daily and hourly variations in demand, with distinct weekday commuting patterns and more balanced weekend flows, reflecting shifts between work-related and leisure activities. These findings highlight the model's potential for accurately estimating demand without direct OD data, providing valuable insights for improving ride-hailing operations and supporting urban transportation planning. This approach can assist service providers in optimizing driver allocation and enhancing operational efficiency, contributing to the broader objectives of urban mobility management. © 2024 IEEE.

关键词： Urban transportation

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Soft-Touch or Hard-Touch: Quantifying Touches through A New System Focusing on A Pool Touchpad for Measuring Timing of Swimmers 23

Soft-Touch or Hard-Touch: Quantifying Touches through A New ...

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10th International Conference on Networking Systems and Security, NSysS 2023

作者： Quaium, Adnan Islam, Md. Ashraful Rahman, M. Sohel Islam, A. B. M. Alim Al Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh and Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798400708787

The swimming turn or finishing presents an integral part of competitive swimming, and the time it takes by a swimmer to push off the wall is a significant factor in their ratings. Therefore, it is essential to have a reliable way to measure the timing of touch, i.e., wall-contact, over the finishing or turning points. To do so, swimming pool touchpads are widely used in swimming competitions to overcome human error in measurements by enabling touch-sensing systems at the finishing or turning points. The touch-sensing devices use their sensitive touch sensors to record time with millisecond accuracy, providing athletes with the most precise measurements possible. However, these devices might need a minimal force applied from the swimmers to sense the touch, and thus, to record the timing of the touch. As such, insufficient force applied on the touchpad during swimming could cause skipping the time log. As a result, it is critical to have a trustworthy method for measuring the extent of touch or wall-contact exerted over the touch-sensing systems. This study, for the first time in the literature to the best of our knowledge, proposes such a method for measuring the extent of touch or wall-contact for swimming pool touchpads. In the process of proposing the method, we design and develop a bi-modal sensing module to distinguish the soft-touch and hard-touch exerted over a real pool touchpad used in a swimming pool organizing national and international competitions. Our experiments show that the pool touchpad under experimentation is not always accurate in registering touches. It sometimes fails to register low-force touches while generally registering touches with high force. © 2023 ACM.

关键词： Sports

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Finite-time synchronization control for a class of delayed neural networks:an improved two-step control method

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science China(Information sciences) 2025年

作者： Yue CHEN Song ZHU Mouquan SHEN Xiaoyang LIU Shiping WEN School of Mathematics China University of Mining and Technology College of Electrical Engineering and Control Science Nanjing Technology University School of Computer Science and Technology Jiangsu Normal University Faculty of Engineering and Information Technology University of Technology Sydney

In this paper,a two-step control method is proposed,leveraging the generalized Halanay inequality and existing finite-time stability theorems,to achieve finite-time synchronization for a class of neural networks with bounded time-varying *** the first step,the system state is attenuated from V （t0） to γV （t0） using the generalized Halanay inequality,where0<γ 1 is a free *** the second step,by applying existing finite-time stability theorems,the system state further decays from γV （t0） to *** on the above ideas,two novel finite-time stability lemmas for the error system are presented,and the convergence rate as well as the settling time is ***,the value of γ that results in the shortest settling time for the error system is also *** the help of the derived lemmas,several sufficient algebraic criteria are established to achieve finite-time synchronization between the considered delayed neural *** results of this paper not only improve the existing two-step control method but also overcome the limitations of certain one-step finite-time control ***,the validity and practical applicability of the obtained theoretical results are demonstrated through two numerical examples and an image protection experiment.

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Exploring Deep Learning Architectures for Enhanced Cyber Threat Detection: A Survey

Exploring Deep Learning Architectures for Enhanced Cyber Thr...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Oyinloye, Toluwase Sunday Arowolo, Micheal Olaolu Ogundoku, Roseline Oluwaseun Prasad, Rajesh African University of Science and Technology Department of Computer Science Abuja Nigeria University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO65201 United States Landmark University Department of Computer Science Omu-Aran Nigeria

ISBN: (纸本)9798350358155

As new organizational innovations and ideal model implementations take place;current methods of interruption detection and protection are becoming outdated. Due to the next flexible innovation, which will transmit incredibly rapid data volumes, new problems will arise for network safety frameworks. Apps on mobile devices are gradually displacing more conventional methods of managing data and information that depend on the web. As a result, there are several threats that could compromise or steal these data and information. A complete system shutdown is possible in the event of one of these attacks. This study introduces a deep learning-based cyber-attack detection system for wireless sensor networks (WSN). This method considers both the actions of the WSN nodes, and the data transmissions made possible by the MQTT protocol. By switching from a traditional machine learning model to a deep learning one, this strategy achieves much better detection accuracy. Using deep learning models that combine real information gathered from network streams, cyberthreats can be identified as digital risks in the network. © 2024 IEEE.

关键词： Cyber attacks

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Investigation of DNA discontinuity for detecting tuberculosis

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1149-1163页

作者： Nimmy, Sonia Farhana Sarowar, Md. Golam Dey, Nilanjan Ashour, Amira S. Santosh, K.C. Department of Computer Science and Engineering Notre Dame University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Information Technology Techno India College of Technology Kolkata India Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta University Tanta Egypt Vermillion United States

Discontinuity in long Deoxyribonucleic Acid (DNA) sequences creates harmful diseases. Changes in the DNA structure refers to changes in the human immunity system. Tuberculosis is a critical disease that causes coughing, fatigue, unintentional weight loss and fever on aged people due to the disorder in the DNA. Breaks or mutations over long DNA sequences are the pivotal reasons for this fatal disease. This study developed an automated machine learning technique to assess the total number of such breaks in the long DNA sequences. Data cleansing and deep neural network techniques are applied to handle this big data. The National Center for Biotechnology Information (NCBI) database has been used to extract the amino acid sequences for Tuberculosis disease from the big DNA datasets. Results reveal that the proposed automated approach is significantly effective for the determination of DNA sequence breaks for the tuberculosis diseases due to the high sensitivity of Markov chain as well as the effective normalization techniques. This approach fixed the size of the training datasets and recursively divide the whole dataset into certain length. The study also adopts multiple predictions approaches, such as the hidden Markov chain, Box-Cox transformation and linear transformation to forecast about the breaks for any long positions of the training and testing datasets. The results demonstrated that hidden the Markov chain model provided faster analysis with more accurate and reliable results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： DNA

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A Deep Learning-Based Method for Crowd Counting Using Shunting Inhibition Mechanism

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5733-5745页

作者： Tivive, Fok Hing Chi Bouzerdoum, Abdesselam Phung, Son Lam Le, Hoang Thanh Baali, Hamza University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Hamad Bin Khalifa University Information and Computing Technology Division College of Science and Engineering Doha Qatar

Image-based crowd counting has gained significant attention due to its widespread applications in security and surveillance. Recent advancements in deep learning have led to the development of numerous methods that have achieved remarkable success in accurately counting crowds. However, many of the existing deep learning methods, which have large model sizes, are unsuitable for deployment on edge devices. This article introduces a novel network architecture and processing element designed to create an efficient and compact deep learning model for crowd counting. The processing element, referred to as the shunting inhibitory neuron, generates complex decision boundaries, making it more powerful than the traditional perceptron. It is employed in both the encoder and decoder modules of the proposed model for feature extraction. Furthermore, the decoder includes alternating convolutional and transformer layers, which provide local receptive fields and global self-attention, respectively. This design captures rich contextual information that is used for generating accurate segmentation and density maps. The self-attention mechanism is implemented using convolution modulation instead of matrix multiplication to reduce computational costs. Experiments conducted on three challenging crowd counting datasets demonstrate that the proposed deep learning network, which comprises a small model size, achieves crowd counting performance comparable to that of state-of-the-art techniques. Codes are available at https://***/ftivive/SINet. © 2024 IEEE.

关键词： Deep learning

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