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A Robust Indoor Vehicle Localization Strategy based on UWB and INS

IEEE Transactions on Vehicular Technology 2025年第5期74卷 7292-7302页

作者： Cheng, Long Cui, Chen Zhang, Hao Northeastern University Department of Computer and Communication Engineering Qinhuangdao066004 China Northeastern University Hebei Key Laboratory of Marine Perception Network and Data Processing Hebei Qinhuangdao066004 China

High-precision indoor vehicle localization technology has recently been a research hotspot for indoor location services. This paper investigate the indoor combined positioning technology based on Ultra wide band (UWB) and inertial navigation system(INS), aiming to overcome the limitations of UWB in non-line of sight (NLOS) environment and cumulative errors of INS. For dealing with NLOS errors conveniently, we propose a two-layer NLOS error weakening method. Firstly, k-mediods clustering method is used to preprocess the obtained distance measurements, and some of the measurements containing NLOS errors are replaced with the measurements undisturbed by NLOS transmission. The processed result is processed again with gray Kalman filter, which reduces the process noise and NLOS error. A hybrid Gaussian fitting approach is suggested to determine the propagation conditions and simulate the likelihood of propagation through the residual after calculating the difference between the distance that was filtered and the distance that was measured. The two-sided correction technique changed the findings of the fitting method and the improved particle swarm optimization method. The adaptive Kalman filter was then used to combine the INS and UWB location results to get the final location data. Simulation and experimental results show that the proposed algorithm owns higher localization accuracy and dependability. © 1967-2012 IEEE.

关键词： Inertial navigation systems

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A Hybrid EKF/WUFIR Filter for Indoor Localization Integrating INS and UWB data

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IEEE Transactions on network Science and Engineering 2025年第3期12卷 2266-2276页

作者： Cheng, Long Song, Jiahe Zhao, Wenhao Northeastern University Department of Computer and Communication Engineering Qinhuangdao066004 China Northeastern University Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China

Due to the complex and variable indoor environment, ultra-wideband (UWB) signal transmission is often obstructed by walls and obstacles, resulting in non-line-of-sight (NLOS), which reduces localization accuracy. Inertial navigation system (INS) is an autonomous navigation system that does not rely on external information and is not affected by NLOS. Therefore, a hybrid EKF/WUFIR filter indoor localization algorithm that integrates INS and UWB data is proposed. The proposed algorithm is composed of three parts: INS localization, UWB localization and data fusion. In the INS localization part, the motion model is used to determine the state of the target in real time using measurement data obtained from the inertial measurement unit (IMU). In the UWB localization part, a resettable residual weighted particle filter algorithm is proposed to mitigate the effect of NLOS on the localization results. In the data fusion part, a hybrid filtering algorithm combining extended Kalman filter (EKF) and weighted unbiased finite impulse response (WUFIR) filtering is proposed to fuse the INS and UWB localization data. Simulation and experimental results show that the proposed algorithm outperforms other comparative algorithms in terms of robustness and localization accuracy. © 2013 IEEE.

关键词： Inertial navigation systems

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In-Context Multitask Learning for Few-shot Fine-tuning of Large Language Models in Traditional Chinese Medicine Tongue Diagnosis

In-Context Multitask Learning for Few-shot Fine-tuning of La...

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

作者： Fu, Changzeng Fu, Zelin Yan, Shaojun Lyv, Xiaoyong Zhao, Yuliang Northeastern University China Osaka University Japan Hebei Key Laboratory of Marine Perception Network and Data Processing @ NEUQ China

ISBN: (纸本)9798350368741

Tongue diagnosis is integral to Traditional Chinese Medicine (TCM) for evaluating a patient's body constitution. Yet, this field faces challenges such as indirect constitution diagnosis, a dearth of labeled datasets, and the complexities of few-shot learning. Existing studies focus mainly on analyzing tongue color and coating, rather than directly analyzing the body constitution from the patient's tongue. Moreover, the lack of publicly available datasets with constitution labels impedes model development for constitution diagnosis. The resource-intensive process of creating large-scale labeled datasets calls for efficient training methods on small datasets. Addressing these issues, this paper presents an In-Context Multitask Learning approach to improve few-shot fine-tuning accuracy for Large Language Models (LLMs) in constitution diagnosis. We created a dataset for color-coating analysis and a few-shot dataset with constitution labels and a structured prompt-label framework, allowing LLMs to learn from varied datasets. Our method shows improved performance over traditional methods in constitution diagnosis and significantly boosts LLMs' generalization and robustness in TCM's complex diagnostic tasks, providing a viable path for automating tongue diagnosis. © 2025 IEEE.

关键词： Few-shot Fine-Tuning Large Language Models Multitask Learning Tongue Diagnosis

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Research on the detection and analysis of weak signals with low SNR based on the Welch algorithm 24

Research on the detection and analysis of weak signals with ...

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2nd International Conference on Communication networks and Machine Learning, CNML 2024

作者： Wu, Bin Zhang, Wanqing Zhang, Jun Yu, Jing Huangfu, Taoyu Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Northeastern University at Qinhuangdao Hebei Qinhuangdao China Southeast University Jiangsu Nanjing China

ISBN: (纸本)9798400711688

Submarines are vital for military defense and national security in strategic communication systems. Power Spectral Density (PSD) characterizes signal spectrum frequency dependence and is crucial for analyzing broadband random signals. This study proposes a simplified PSD estimation method using the Welch algorithm for detecting weak signals in noisy environments, particularly in submarine communications. MATLAB simulations demonstrate that the Welch algorithm with a Blackman window function and 512-point overlap improves variance performance and resolution, especially in low signal-to-noise ratio conditions. The results underscore the algorithm’s effectiveness in enhancing signal detection in submarines. © 2024 Copyright held by the owner/author(s).

关键词： MATLAB

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Legal judgment prediction via legal knowledge extraction and fusion

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Journal of King Saud University - Computer and Information Sciences 2025年第3期37卷 1-16页

作者： Zhao, Qihui School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao 066004 China Hebei Key Laboratory of Marine Perception Network and Data Processing Northeastern University at Qinhuangdao Qinhuangdao 066004 China

Legal judgment prediction (LJP) task aims to predict relevant law articles, charges and terms of penalty through the textual fact description of legal cases. However, existing methods face the core challenge is external legal knowledge under-exploitation, which hinders the performance of LJP. To overcome this limitation, a legal knowledge enhanced prompt learning based approach (LKEPL) is proposed. Our method first design the legal knowledge extraction which utilizes the model trained on our labeled legal dataset to identify legal entity relation knowledge and obtains law articles knowledge by the attention mechanism. Afterwards, our method employs legal knowledge projector to translate legal knowledge vectors into the transformer space. Then our method incorporates two kinds of knowledge by concatenating those vectors before the key and value matrix of each multi-head attention of BERT. Finally, our method designs the LJP task-specific prompt learning method. Experimental results on the CAIL2018 dataset demonstrate that our method outperforms existing strong baseline models. Furthermore, our method offers interpretability and shows effectiveness under few-shot scenarios. © The Author(s) 2025.

关键词： Legal intelligence Legal judgment prediction Legal knowledge fusion Prompt learning

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Research and Implementation of OFC-Based Multicarrier Sliceable Bandwidth Variable Transponders in Hybrid Laser/Microwave Satellite networks

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IEEE Access 2025年 13卷 93046-93056页

作者： Bin Wu Hao Zhang Qingchun Zhao Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China

In this paper, a multicarrier sliceable bandwidth variable transponder based on optical frequency comb (OFC) is proposed. The structure of the transponder’s internal switching unit is designed, and the principle of generating ultra-flat OFC based on a standalone dual-parallel Mach-Zehnder modulator (DPMZM) is analyzed. A scheme for dynamic allocation of multi-channel frequency slots and flexible resource configuration in hybrid laser/satellite networks is provided, and an experimental system is built to test the data transmission and bandwidth allocation capabilities of laser and microwave links. Experimental results indicate that the muticarrier generation unit can produce a 7-line flat OFC with a flatness of 1.6 dB, and the switching unit can achieve channel switching and frequency slot allocation, with a minimum slice width of 12.5 GHz. After passing through this node, the phase noise of the 2 GHz IF signal with frequency offsets at 10 kHz and 100 kHz are −59.28 dBc/Hz and −87.95 dBc/Hz, respectively, and the bit error rates (BERs) of the recovered baseband data from the laser and microwave links are less than $10^{-9}$ . These results validate the feasibility of the designed transponder to be applied in the key relay nodes of satellite networks. The proposed scheme can solve the problems of low spectrum utilization and the inability to dynamically allocate resources in traditional WDM satellite networks.

关键词： Satellites Transponders Masers Optical switches Frequency modulation Bandwidth Resource management Optical filters Microwave communication Optical fiber communication

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M3ADD: A Novel Benchmark for Physiology Signal-based Automatic Depression Detection with Multimodal Multitask Multievent Framework

M3ADD: A Novel Benchmark for Physiology Signal-based Automat...

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

作者： Changzeng Fu Kaifeng Su Yikai Su Fengkui Qian Yixuan Zhang Chaoran Liu Siyang Song Le Yang Xiaoyong Lv Peng Shan Yuliang Zhao Northeastern University China Osaka University Japan Hebei Key Laboratory of Marine Perception Network and Data Processing @ NEUQ National Institute of Informatics Japan University of Cambridge UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The prevalence of depression is escalating, especially among youth, which has become a critical mental health concern. Current assessment methods, relying heavily on questionnaires, clinical observations, and AI-driven analyses, are limited by their focus on single-event data, failing to encapsulate the nuanced expressions of depressive symptoms. Moreover, a significant oversight in existing research is the underutilization of electromyogram (EMG) alongside electroencephalogram (EEG) data, which could provide a more holistic view of unconscious body behaviors. Additionally, given the high variability of depression among individuals, traditional analysis models are in urgent need of refinement to accommodate the personality of different individuals. To address these limitations, we propose M 3 ADD, a novel benchmark for Automatic Depression Detection that employs a Multimodal, Multitask, and Multievent framework. We collected EEG and EMG data from 97 participants across varied events (interview, reading tasks, walking), coupled with standardized questionnaires assessing depression, wellbeing, and personality, enriching our multitask learning approach. Our benchmark recognition algorithm leverages multitask learning, channel and interactive attention mechanisms to synthesize event-specific and modal-specific features, enhancing adaptability to individual differences and improving data utilization efficiency. M 3 ADD surpasses existing models by achieving 87% accuracy in detecting depression and 95% accuracy in assessing wellbeing, providing a promising avenue for early identification.

关键词： Accuracy Signal processing algorithms Benchmark testing Signal processing Depression Brain modeling Electromyography Electroencephalography Physiology Speech processing

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E-MAEL: Efficient Multi-Cloud data Auditing With Error Localization for IoT Consumer Devices in Metaverse

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IEEE Transactions on Consumer Electronics 2025年

作者： Liu, Mingxi Fan, Kuan Lu, Ning Shi, Wenbo Alenazi, Mohammed J.F Chen, Chien-Ming Kumari, Saru Northeastern University at Qinhuangdao College of Computer Science and Engineering Northeastern University Hebei Key Laboratory of Marine Perception Network and Data Processing Shenyang China Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao China Northeastern University College of Computer Science and Engineering Shenyang China Northeastern University at Qinhuangdao sHebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao China Department of Computer Engineering Riyadh11451 Saudi Arabia Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut India

By integrating multi-cloud (MC) storage with the "edge-to-cloud" architecture of consumer IoT, the metaverse paradigm achieves enhanced efficiency in real-time data collection and interactive virtual feedback. However, data stored across multiple clouds is vulnerable to tampering and theft due to the inherent insecurity of cloud computing, and cloud service providers, driven by economic interests, may conceal data losses. Existing data integrity auditing techniques fail to ensure prompt and reliable verification for distributed data in the MC-enabled metaverse paradigm, limiting their effectiveness in eliminating erroneous data swiftly. To address these challenges, we propose E-MAEL (Efficient Multi-cloud data Auditing with Error Localization) for IoT devices in the metaverse. We design an MC self-auditing framework based on blockchain to eliminate audit delays caused by third-party auditors. A variable-length auditing protocol based on Schnorr is introduced, reducing computation overhead by up to 21% compared to schemes relying on bilinear pairing and RSA assumptions. We develop a single-round error localization strategy based on symmetric unbalanced block design, enabling efficient identification and removal of erroneous data while resisting collusion attacks. A dynamic reputation update strategy using convex functions is proposed, providing robust reputation management for MCs and effectively penalizing audit errors. Theoretical security analyses and experimental results demonstrate the effectiveness of EMAEL in improving computational efficiency and enhancing reliability in MC auditing systems. © 1975-2011 IEEE.

关键词： Cloud storage

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Performance of fiber-based QAM/FSO systems in turbulence with anisotropic tilt angle and random angular jitter

arXiv

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arXiv 2025年

作者： Zhai, Chao Xue, Zhenyuan School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China Hebei Key Laboratory of Marine Perception Network and Data Processing Northeastern University at Qinhuangdao Qinhuangdao066004 China

Nowadays, the subsistent anisotropic non-Kolmogorov (ANK) turbulence models are all established on the supposition that the long axis of turbulence cell ought to be level with the ground. Nevertheless, Beason et al. and Wang et al. have illustrated through their recent experimental results that there is an anisotropic tilt angle in the turbulence cell, i.e., the long axis of turbulence cell is probably not level with the ground but has a particular angle with the ground. For the practical free-space optical (FSO) communication link, bias error and random angular jitter are critical elements which influence the fiber-based FSO communication system performance. In this paper, employing the new ANK turbulence spectrum models in the horizontal link with anisotropic tilt angle, we derive the normalized probability density function (PDF) for Gamma-Gamma distribution with anisotropic tilt angle, and the PDF of fiber-coupling efficiency in the presence of bias error, anisotropic tilt angle, and random angular jitter. And then the average bit error rate (BER) expression of fiber-based J × Q rectangular quadrature amplitude modulation (QAM) FSO systems for a plane wave transmission through the weak ANK horizontal link in the presence of bias error, anisotropic tilt angle, and random angular jitter is developed. © 2025, CC BY-NC-ND.

关键词： Quadrature amplitude modulation

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Efficient Wideband Adaptive Beamforming With Null Broadening Using MHSA-CNN

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and networking 2025年

作者： Liu, Fulai Huang, Hai Liu, Ruxin Yang, Jinwei Suo, Luyao Du, Ruiyan Northeastern University at Qinhuangdao Lab of Electromagnetic Environment Cognition and Control Utilization Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

In the case of interference perturbation, the wideband adaptive beamforming (WAB) weight vector may be mismatched, which leads to the decrease of interference suppression ability. To improve communication quality under the interference position perturbation, this paper presents a multi-head self-attention conventional neural network (MHSA-CNN)-based WAB algorithm with null broadening. In the presented approach, a MHSA-CNN structure is proposed to improve the prediction accuracy of beamforming weight vector in the case of interference perturbation. Specifically, by processing multiple attention heads in parallel to obtain the information of different signal subspaces, MHSA mechanism enables the network to dynamically adjust the attention distribution of signal features and effectively extract the global features of the covariance matrix. Then, based on focused reconstruction and null broadening, an effective neural network training label is used to enhance the ability of suppressing interferences. Finally, the well-trained MHSA-CNN can accurately output the weight vector suitable for WAB with null broadening in real time. Simulation results demonstrate that the proposed algorithm can suppress interferences accurately within the interference perturbation range and enhance the output signal-to-interference-plus-noise ratio while ensuring real-time communication performance. © 2025 IEEE.

关键词： Signal to noise ratio

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