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Hypercomplex signal processing in Digital Twin of the Ocean: Theory and application [Hypercomplex signal and image processing]

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IEEE signal processing MAGAZINE 2024年第3期41卷 33-48页

作者： Yu, Zhaoyuan Li, Dongshuang Du, Pei Luo, Wen Kou, Kit Ian Bhatti, Uzair Aslam Benger, Werner Lv, Guonian Yuan, Linwang Nanjing Normal Univ Geog Dept Nanjing 210023 Peoples R China Nanjing Normal Univ Nanjing 210023 Peoples R China Univ Macau Fac Sci & Technol Dept Math Macau Peoples R China Hainan Univ Hainan 570228 Peoples R China Airborne HydroMapping GmbH A-6020 Innsbruck Austria Minist Educ Key Lab Virtual Geog Environm Nanjing 210023 Peoples R China Nanjing Normal Univ Sch geog & Sci Nanjing 210023 Peoples R China

The digital twin of the ocean (DTO) is a groundbreaking concept that uses interactive simulations to improve decision-making and promote sustainability in earth science. The DTO effectively combines ocean observations, artificial intelligence (AI), advanced modeling, and high-performance computing to unite digital replicas, forecasting, and what-if scenario simulations of the ocean systems. However, there are several challenges to overcome in achieving the DTO's objectives, including the integration of heterogeneous data with multiple coordinate systems, multidimensional data analysis, feature extraction, high-fidelity scene modeling, and interactive virtual-real feedback. Hypercomplex signal processing offers a promising solution to these challenges, and this study provides a comprehensive overview of its application in DTO development. We investigate a range of techniques, including geometric algebra, quaternion signal processing, Clifford signal processing, and hypercomplex machine learning, as the theoretical foundation for hypercomplex signal processing in the DTO. We also review the various application aspects of the DTO that can benefit from hypercomplex signal processing, such as data representation and information fusion, feature extraction and pattern recognition, and intelligent process simulation and forecasting, as well as visualization and interactive virtual-real feedback. Our research demonstrates that hypercomplex signal processing provides innovative solutions for DTO advancement and resolving scientific challenges in oceanography and broader earth science.

关键词： Analytical models Computational modeling Data visualization Geoscience signal processing Predictive models Hypercomplex Digital twins Oceanography Artificial intelligence High performance computing

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Hybrid modeling and simulation for Through-the-Wall Human Detection Using IR-UWB Distributed Radar systems

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IEEE SENSORS JOURNAL 2024年第23期24卷 39137-39148页

作者： Lee, Soyeong Yoon, Daeyeong Hwang, Kyuhwan An, Min Park, Kyung-Hwan Park, Yong Bae Ajou Univ Dept Artificial Intelligence AI Convergence Netwo Suwon 16499 Gyeonggi South Korea Ajou Univ Dept Elect & Comp Engn Suwon 16499 Gyeonggi South Korea Elect & Telecommun Res Inst Daejeon 34129 South Korea

We propose a hybrid modeling technique for human signal detection through the walls using distributed radar. The high resolution and excellent penetration performance of the impulse radio ultrawideband (IR-UWB) radar systems can be used in important applications such as survivor detection in disaster scenarios. The radar channel transfer function between the transmitting and receiving radars obtained from the electromagnetic (EM) simulator is used for signal processing on MATLAB, followed by coherent synthesis. We perform a hybrid simulation of human detection through the walls using a single radar and verify it through experiments. In addition, we model vital signs behind the walls in various scenarios using the distributed radar and present the coherent synthesis results. The proposed hybrid modeling technique allows us to analyze various distributed radar scenarios in advance. This can reduce the time-consuming and resource-intensive real experiments. It is expected to contribute to radar system design optimization, signal processing algorithm development, and the identification of potential errors before practical implementation. The proposed method is expected to play an important role in various applications of IR-UWB radar technology, including disaster relief operations, noncontact biosignal monitoring, security, and surveillance systems.

关键词： Coherent distributed radar hybrid distributed radar modeling impulse radio ultrawideband (IR-UWB) radar through-the-wall radar

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GANInSAR: Deep Generative modeling for Large-Scale InSAR signal simulation

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 5303-5316页

作者： Zhou, Zhongrun Sun, Xinyao Yang, Fei Wang, Zheng Goldsbury, Ryan Cheng, Irene Univ Alberta Comp Sci Edmonton AB T6G 2R3 Canada 3V Geomat Inc RnD Vancouver BC V5Y 0M6 Canada

Interferometric synthetic aperture radar (InSAR) technology is widely used to create digital elevation models and measure dynamics on the Earth's surface, including monitoring ground displacements. The lack of or limited-collected ground-truth data, however, often poses a bottleneck in validating the research outcome, particularly at high precision and resolution levels. To mitigate the gap, we introduce a new deep generative model (DGM) for the simulation of linear deformation rate maps. We demonstrate that our adversarial DGM architecture with carefully designed preprocessing and postprocessing modules performs well for InSAR deformation signal synthesis, even when limited data are available. We also introduce a dimensionality reduction method, based on the distance between the real-world and generated image feature vectors, to address the lack of quantitative evaluation for data simulation. Furthermore, we introduce a hybrid evaluation metric integrating quantitative and qualitative measures, which is more intuitive than the existing methods and makes it easier for domain experts to participate in the evaluation. We compare the results of our model with established methods. The comparison result illustrates the superior performance of our proposed method.

关键词： Deformation Generative adversarial networks Atmospheric modeling Deformable models Training Data models Generators Deep generative modeling generative adversarial network (GAN) interferometric synthetic aperture radar (InSAR) radar signal processing remote sensing images simulation

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A Comprehensive Deep Learning Framework for Microwave Stroke Classification: Combining signal Analysis, Clinical Variables, and Antenna System Measurements

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IEEE ACCESS 2025年 13卷 39935-39949页

作者： Yuan, Wei Thammasorn, Phawis Wang, Lingxiao Mo, Shih Chengdu Pidu Dist Peoples Hosp Chengdu 611730 Peoples R China Tiposi Milpitas CA 95035 USA

Microwave signal-based binary classification for detecting the presence of stroke presents a promising avenue for cost-effective and portable diagnosis. However, implementing this technology in real-world settings remains challenging due to difficulties in accurately modeling wave scattering, which often leads to suboptimal performance of classification systems. Our proposed deep learning framework addresses this issue by bypassing complex modeling tasks and directly extracting stroke signatures from wavelet-filtered microwave signals, combined with clinical variables and antenna measurements. Experiments conducted with total of 431 real-world stroke patients demonstrate that our system significantly outperforms recent classification approaches achieving 76.19% accuracy. Furthermore, our approach shows comparable performance to magnetic resonance imaging (MRI) based deep learning classification, in scenarios with limited imaging data. These results not only highlight the potential of microwave imaging (MWI) as a critical diagnostic tool but also suggest that MWI devices could become a viable option for clinical stroke diagnosis in the near future.

关键词： Microwave imaging Stroke (medical condition) Microwave antennas Deep learning Antenna measurements Head Scattering parameters Microwave measurement performance evaluation Microwave filters microwave signal processing stroke classification health care medical image analysis

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LSHIM: Low-Power and Small-Area Inexact Multiplier for High-Speed Error-Resilient Applications

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND systems 2025年第1期15卷 94-104页

作者： Izadi, Azin Jamshidi, Vahid Shahid Bahonar Univ Kerman Dept Comp Engn Kerman *** Iran

Numerical computations in various applications can often tolerate a small degree of error. In fields such as data mining, encoding algorithms, image processing, machine learning, and signal processing where error resilience is crucial approximate computing can effectively replace precise computing to minimize circuit delay and power consumption. In these contexts, a certain level of error is permissible. Multiplication, a fundamental arithmetic operation in computer systems, often leads to increased circuit delay, power usage, and area occupation when performed accurately by multipliers, which are key components in these applications. Thus, developing an optimal multiplier represents a significant advantage for inexact computing systems. In this paper, we introduce a novel approximate multiplier based on the Mitchell algorithm. The proposed design has been implemented using the Cadence software environment with the TSMC 45nm standard-cell library and a supply voltage of 1.1V. simulation results demonstrate an average reduction of 31.7% in area, 46.8% in power consumption, and 36.1% in circuit delay compared to previous works. The mean relative error distance (MRED) for the proposed method is recorded at 2.6%.

关键词： Approximation algorithms signal processing algorithms Circuits Accuracy Delays performance evaluation Power demand Arithmetic Adders Energy consumption Approximate multiplier approximate computing Mitchell's multiplication algorithm energy-consumption area delay

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Unlocking signal processing With image Detection: A Frequency Hopping Detection Scheme for Complex EMI Environments Using STFT and CenterNet

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IEEE ACCESS 2023年 11卷 46004-46014页

作者： Chen, Ziyi Shi, Yaowu Wang, Yingwei Li, Xinbo Yu, Xiaohui Shi, Yiran Jilin Univ Coll Commun Engn Changchun 130022 Peoples R China

Accurate detection and parameter estimation of frequency hopping (FH) signals remain challenging in FH signal-based transmission systems. This study proposes a scheme combining time-frequency analysis (TFA) and deep learning (DL)-based image processing algorithms to alleviate the degradation of detection accuracy and estimation performance caused by complex electromagnetic interference (EMI). A short-time Fourier transform (STFT) was used to obtain the signal spectrogram, which reflects the signal energy in a concentration-dependent manner. Then, a CenterNet-based deep network was employed to identify each FH hop's shape and position, reducing the computational burden via a lightweight neural network while maintaining high recognition accuracy. Inverse mapping from the coordinates to the spectrogram was used to perform parameter estimation in the time-frequency (TF) domain. The estimation error was reduced by precisely locating the centroid of the signal energy using CenterNet. The simulation results demonstrate that the proposed scheme can accurately estimate the FH signal at a low signal-to-noise ratio (SNR) with complex EMI. Furthermore, appropriately determining the optimal parameters of CenterNet to ensure the estimator performance provides a novel approach for integrating DL into signal detection and estimation in complex EMI environments.

关键词： Spectrogram Electromagnetic interference Feature extraction Time-frequency analysis signal detection Computational modeling Time-domain analysis FH signal TFA CenterNet complex EMI image detection

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Electromagnetic Compatibility Test signal Prediction Method Based on LSTM Network

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JOURNAL OF signal processing systems FOR signal image AND VIDEO TECHNOLOGY 2024年第12期96卷 837-848页

作者： Li, Dongyue Chen, Hongxi Zhou, Shuanghong Zhang, Yuting Harbin Engn Univ Harbin Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Dalian Univ Technol Dalian Peoples R China Beijing Inst Spacecraft Syst Engn Beijing Peoples R China

Ensuring electromagnetic compatibility (EMC) represents a critical requirement for maintaining reliable operation of electronic systems in electromagnetic interference (EMI) environments. Current EMC testing methodologies, while essential for evaluating device resilience, often fail to encompass the full spectrum of operational scenarios due to limitations in captured frequency ranges and temporal durations. This constraint necessitates predictive modeling of unobserved electromagnetic phenomena to enable comprehensive EMC performance evaluation. Our study presents an innovative framework employing Long Short-Term Memory (LSTM) networks enhanced by Bayesian optimization (BO) for accurate prediction of EMC test signals. Empirical validation demonstrates that our optimized LSTM architecture achieves superior prediction accuracy and generalization capabilities compared to conventional neural network approaches, producing reliable outcomes when applied to laboratory-controlled EMC test datasets. These findings substantiate the method's potential for enhancing EMC testing protocols through advanced signal forecasting.

关键词： Electromagnetic compatibility signal prediction LSTM Bayesian optimization

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HTML5-based graphic image processing and collaborative drawing technology

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systems AND SOFT COMPUTING 2024年 6卷

作者： Liang, Yinghua Guangdong Vocat Inst Sport Basic Teaching Dept Guangzhou 510663 Peoples R China

HTML5 technology has great technical advantages and influence on the graphical image processing function of mobile devices. The purpose of this study is to evaluate the performance of HTML5 graphics and image processing and co-rendering technologies and to verify the effectiveness and usefulness of these technologies, including the measurement of user experience and efficiency. The simulation analysis is carried out on MATLAB, and different image compression algorithms are used for comparison. The evaluation indexes include peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Comparing the two methods of Joint image Expert Group (JPEG) and fractal image Compressed file (IFS), the results showed that the proposed algorithm provided better PSNR and SSIM values than IFS, and provided higher PSNR and lower SSIM values than JPEG under the condition of increasing compression ratio, showing excellent image processing and compression efficiency. Then, the correlation loss, brightness distortion, contrast distortion, and color distortion of image processing are analyzed for the four real scenes of blur, illumination, cloud, and strong light. The results showed that compared with JPEG and IFS, the correlation loss of the proposed algorithm was 15.46, 16.25, 16.78, and 17.45, respectively. It had lower correlation loss, brightness distortion, contrast distortion, and color distortion, and showed better image processing effects. In general, the study found that the graphics and image processing and co-rendering technology based on HTML5 has high efficiency and effect in image processing and compression, especially in complex environments and conditions, such as fuzzy conditions, lighting conditions, cloudy weather, and strong light conditions, showing excellent performance and adaptability. This discovery provides important evidence for its practical application in many fields such as computer vision, medical imaging, and remote sensing image analysis, and helps

关键词： HTML5 image processing Collaborative drawing Haar Wavelet

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Nonlinear multi-order coupled stochastic resonance modeling under extremely low signal-to-noise ratios

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MECHANICAL systems AND signal processing 2025年 224卷

作者： Song, Jinhui Shi, Xingxing Wu, Jiu Hui Zheng, Tengyue Song, Zhiwei Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

Based on the noise-enhanced signal characteristics of stochastic resonance (SR), this paper proposes a nonlinear multi-order coupled SR model, aiming to make full use of nonlinear zero-order vibrational coupling and first-order damping coupling to enhance the detected signal-to-noise ratio (SNR) and increase the detection distance of the underwater passive sonar. First, the influence of nonlinear vibration coupling on the system potential function and output SNR is analyzed to obtain the optimal nonlinear coupling parameters under the selected potential function structure. Further for the coupling damping, the output SNR is used as the evaluation metric to explore the influence of self-coupling and mutual-coupling damping on the system performance. It is found that under the optimal vibration coupling parameters, when the two damping parameters are matched, the energy concentration effect occurs in the system. This effect maximizes noise energy transfer to the signal, concentrating spectral energy at the feature frequency. Furthermore, based on this energy concentration effect, the Renyi entropy is used as a metric to propose an adaptive feature frequency detection model without prior knowledge, and the simulation results show that this model can accurately extract the feature frequency even at an extremely low SNR of -40 dB. Finally, this model is demonstrated by detecting and extracting of weak feature signals in ocean background noise from a compact hydrophone at the Vigo harbor.

关键词： Stochastic resonance Nonlinear coupling Multi-order Method without prior knowledge Underwater acoustic detection

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Designing of an 8 x 8 Multiplier with New Inexact 4:2 Compressors for image processing Applications

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CIRCUITS systems AND signal processing 2023年第11期42卷 6749-6779页

作者： Rahmani, Mitra Babaeinik, Majid Ghods, Vahid Khalesi, Hassan Islamic Azad Univ Dept Elect & Comp Engn Semnan Branch Semnan Iran Islamic Azad Univ Dept Elect & Comp Engn Garmsar Branch Garmsar Iran

Inexact computing brings benefits to error-tolerant applications, including multimedia and signal processing. Although inexact computing reduces precision, it provides meaningful and faster results yet, with lower energy consumption and lower system architecture complexity. We introduce two 4:2 inexact compressors into the unsigned 8 x 8 multiplier circuit by truncating and combining the presented compressors, which in terms of dynamic power shows 25 and 57.14%, respectively, improvement in comparison with the exact multiplier and the best value of similar models. The proposed multiplier with the truncating and combining method and the second proposed compressor shows 7.78 and 67.97% improvement in the MED and PDUEP, respectively, improvement in comparison with the best value of similar models. In design of the transistor level, the proposed models improve the power up to 0.007 mu W. In the image processing application, the proposed multipliers with the truncating and combining method, improve the PSNR up to13.23 and 16.17%, respectively, and improve the SSIM up to 0.90 and 0.94%.The routing and mapping of the proposed circuits are conducted using the hybrid method to improve the evaluation criteria for image processing applications. The validity of the performance of the proposed multipliers are examined with simulations of FPGA circuits (Virtex-6 family) and ASIC circuits (RF_CMOS 0.18 mu m), and simulation of applications in image processing is carried out in MATLAB software.

关键词： Multiplier Inexact compressor Dynamic power consumption image processing FPGA ASIC

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