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Cognitive Robotics 2025年 5卷 126-139页

作者： Zhang, Zeyu Li, Hongran Todo, Yuki Division of Electrical Engineering and Computer Science Kanazawa University Ishikawa Kanazawa9201192 Japan School of Computer Engineering Jiangsu Ocean University Jiangsu Lianyungang222005 China Faculty of Electrical Information and Communication Engineering Kanazawa University Ishikawa Kanazawa9201192 Japan

This paper considers the security of non-minimum phase systems, a typical kind of cyber-physical systems. Non-minimum phase systems are characterized by unstable zeros in their transfer functions, making them particularly susceptible to disturbances and attacks. The non-minimum phase systems are more vulnerable to zero-dynamics attack (ZDA) than minimum phase systems. ZDA is a stealthy attack strategy that exploits the internal dynamics of a system, remaining undetectable while causing gradual system destabilization. Recent cyber incidents have demonstrated the increasing risk of such hidden attacks in critical infrastructures, such as power grids and transportation systems. This paper first demonstrates that the existing ZDA has the limitation of falling into local convergence, and then proposes an enhanced zero-dynamics attack (EZDA), which overcomes local convergence by diverging system data. Furthermore, this paper presents an autoregressive model which can build the data association between the original data and the forged data. By observing the fluctuations in state values, the presented model can detect not only ZDA, but also EZDA. Finally, numerical simulations and an application example are provided to verify the theoretical results. © 2025

关键词： Cyber attacks

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Near-field Source Localization in 3-D Using Two Parallel Centrally Symmetric Unfold Coprime Array

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4738-4749页

作者： Chen, Hua Li, Junjie Yang, Songjie Liu, Wei Eldar, Yonina C. Yuen, Chau Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu610054 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Kowloon Hong Kong Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel Nanyang Technological University School of Electrical and Electronics Engineering Singapore639798 Singapore

Most near-field (NF) localization algorithms cannot deal with the underdetermined case, while those which can are computationally expensive due to employment of fourth-order cumulants. In this work, a low-complexity solution is provided for underdetermined three-dimensional (3-D) NF localization, by employing second-order statistics with a tailored array configuration named two parallel centrally symmetric unfold coprime (TPSC) array. Its implementation can be divided into three stages. Firstly, the proposed algorithm constructs two cross-correlation matrices based on the received array data, which eliminates the non-linear range-related information of NF signals. Secondly, covariance and vectorization operations are applied to these two cross-correlation matrices to form a virtual array with extended aperture. Finally, the two-dimensional (2-D) angle parameters are estimated by the sparse and parametric approach (SPA) and a phase retrieval operation, and then the one-dimensional (1-D) range parameter is achieved by the multiple signal classification (MUSIC) algorithm. One specific feature is that the estimated angle and range parameters are matched automatically. An analysis of the properties of the TPSC array is provided, and an optimal parameter configuration is derived, given that the total number of array elements is fixed. Simulation results demonstrate that the designed TPSC array can achieve underdetermined 3-D NF localization, and deliver enhanced estimation capabilities, surpassing those of established algorithms. © 2002-2012 IEEE.

关键词： Multiple signal classification

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A Sub-nanosecond Delay Floating-voltage Level Shifter for SiC MOSFETs Gate Driver Application 6

A Sub-nanosecond Delay Floating-voltage Level Shifter for Si...

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6th International Conference on Energy Systems and electrical Power, ICESEP 2024

作者： Wan, Yue Xia, Yinshui Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350353563

In order to meet the requirements for the design of SiC gate driver in terms of speed and dv/dt immunity, a sub-nanosecond delay level shifter based on a high-voltage (HV) digital level shifter circuit is proposed. It can decrease the response time compared with the existing level-shifting circuitry, enabling its application in high-voltage scenarios such as closed-loop drive circuits for silicon carbide. The edge detection circuit and simple pull-up branches are utilized to reduce the transmission delay and power consumption. The circuit is simulated at a frequency of 10MHz in a 018um BCD process with Virtuoso, and the results demonstrate that the average transmission delay is about 380ps and the charging response time of the slow path is reduced to 3.3ns. © 2024 IEEE.

关键词： Delay circuits

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Real-Time Spectrum Sensing for TDD-LTE Signals Using Deep Learning 16

Real-Time Spectrum Sensing for TDD-LTE Signals Using Deep Le...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Xu, Xiang Jin, Ming Fang, Xiangqian Jiang, Tao Yu, Yanghang Li, Yaming Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331506322

Existing spectrum sensing techniques for Time Division Duplex-Long Term Evolution (TDD-LTE) signals primarily focus on large time-scale signals. However, the spectrum holes that exist in the small time-scale special subframes of TDD-LTE are often ignored. This paper proposes a method for spectrum sensing within TDD-LTE special subframes by applying Parallel Convolutional Neural Networks (ParallelCNN). The effective feature extraction capability of ParallelCNN enhances the spectrum sensing performance. Simulation results demonstrate superior sensing performance under various signal-to-noise ratio (SNR) conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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An Efficient Closed-Form Method For Underwater Source Localization With Isogradient Sound Speed Profile 16

An Efficient Closed-Form Method For Underwater Source Locali...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Chen, Guanxu Wang, Gang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331506322

In this paper, we address the underwater localization problem with isogradient sound speed profile (SSP) using time-of-arrival (TOA) measurements. The sound speed is related to the unknown source depth in the isogradient SSP model, which is highly nonlinear. Owing to this nature of the isogradient SSP model, the maximum likelihood (ML) problem for estimating the source position is non-convex such that local convergence may occur without sufficiently good initialization. To address this difficulty, we first approximate the isogradient SSP model, and then propose a closed-form two-step weighted least squares (TSWLS) method for estimating the source position based on the approximate model. The solution method is accurate since the approximate model takes the source depth into consideration, and can be used to solve the ML problem as an initialization. Moreover, the proposed TSWLS solution is simple and computationally attractive since it is in closed form. Simulation results show that the proposed TSWLS solution is sufficiently accurate and can be used as the initial estimate for solving the non-convex ML problem, such that the mean square error (MSE) of the ML solution can reach the Cramér-Rao lower bound (CRLB). Index Terms-Underwater localization, time-of-arrival, isogradient sound speed profile, two-step weighted least squares. © 2024 IEEE.

关键词： Mean square error

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Millimeter-Wave Radar-Based Vital Sign Signal Extraction Using Multiple Distance Bin

Millimeter-Wave Radar-Based Vital Sign Signal Extraction Usi...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Zheng, Zhiwei Huo, Jianjian Wang, Jian Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350355581

This paper introduces a multi-distance non-contact vital sign signal reconstruction and extraction algorithm based on frequency modulated continuous wave (FMCW) millimeter wave radar. The technology of measuring vital signs with millimeter wave radar has significant medical value. However, issues such as environmental noise, unconscious micro-movements of the human body, and the extremely subtle vibrations of the thoracic cavity caused by heartbeats greatly reduce the signal-to-noise ratio of the vital sign signals. To address these issues, considering the characteristic that the human body is perceived as a point cloud target under millimeter wave radar, this paper proposes a technique to enhance the signal-to-noise ratio of weak heartbeat signals by utilizing multi-distance vital sign signals. Furthermore, this paper employs the variational mode decomposition (VMD) algorithm for the reconstruction of vital sign signals, achieving high-precision extraction of the human heart rate signal. © 2024 IEEE.

关键词： Signal to noise ratio

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Speech Quality Assessment and Emotions - Effect on the PESQ Metric 15

Speech Quality Assessment and Emotions - Effect on the PESQ ...

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15th International Conference Elektro, ELEKTRO 2024

作者： Zgank, Andrej Donaj, Gregor Vlaj, Damjan University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia

ISBN: (纸本)9798350372359

This paper analyzes how emotional speech influ-ences the quality assessment process. The objective metrics for speech quality assessment are usually evaluated with neutral speech recordings, but human interaction frequently involves emotions. Those can modify speech signals' acoustic and phonetic characteristics largely, which are essential from the objective speech quality assessment perspective. The proposed analysis was carried out with the English and Slovenian Interface emotional speech database, which includes two neutral and six emotional speaking styles. Speex codec was used to simulate the degradations, which were then assessed with the lTV - T P.862 PESQ objective metric. The results' analysis showed that in both languages surprise, in English fear, and in Slovenian disgust influenc the speech quality assessment significantly. © 2024 IEEE.

关键词： Quality control

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Deep Reinforcement Learning based Adaptive Discontinuous Reception Method for Cognitive Radios 16

Deep Reinforcement Learning based Adaptive Discontinuous Rec...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Sun, Weijie Jin, Ming Jiang, Tao Li, Zhaoteng Chen, Guo Li, Wenjuan Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (纸本)9798331506322

Discontinuous reception (DRX) is one of the important power-saving mechanisms for 5G/B5G cognitive radio systems. Conventional DRX methods improve the power-saving performance at the cost of data transmission delay. To address this issue, a deep reinforcement learning based DRX method is proposed. An adaptive time-slot DRX structure is propsed and the DRX mechanism is modeled as a Markov decision process. The state set, action space and reward expressions are jointly designed for the DRX mechanism. A DRX decision strategy is obtained by deep reinforcement learning, which adaptively adjusts the window length of wake-up period. Simulation results are provided to demonstrate that the proposed DRX method improves the power-saving performance while the average transmission delay of data packages closes to the lower bound. © 2024 IEEE.

关键词： Deep reinforcement learning

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Robust Covert and Secure Transmission for ISAC System With Transceiver Hardware Impairments 16

Robust Covert and Secure Transmission for ISAC System With T...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Li, Yaming Jin, Ming Jiang, Tao Chen, Chaodi Yin, Lei Zhang, Huifang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331506322

In integrated sensing and communication (ISAC) systems, communication signals can easily be wiretapped by targets (potential eavesdroppers) or detected by wardens. Different from previous works that address a single security problem, we investigate a covert and secure beamforming design in the ISAC system to address this issue. Meanwhile, we also consider the hardware impairments (HWI) of transceiver, the angle and distance estimation errors of the eavesdroppers, and the imperfect warden channel. We formulate a secure beamforming optimization problem under sensing integrated sidelobe to mainlobe ratio (ISMR), communication rate, and covert communication constraints. To solve the non-convex optimization problem, we adopt semidefinite relaxation (SDR) and S-procedure techniques to transform it into a convex problem and solve it. The simulation results demonstrate the effectiveness of the designed scheme and reveal a trade-off between the communication and sensing performance of the system. © 2024 IEEE.

关键词： Convex optimization

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Few-Shot Aviation Materials Surface Defection Classification: From Non-Standardized Image Acquisitions 4

Few-Shot Aviation Materials Surface Defection Classification...

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4th International Symposium on Intelligent Robotics and Systems, ISoIRS 2024

作者： Wu, Zewei Peng, Chengbin Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350364798

Defect classification in the aviation industry plays a crucial role in manufacturing. However, in non-standardized captured scenes, the object to be inspected and the background are often complex and variable, e.g., lighting, angle, occlusion, etc., which can affect the core features of the image data. Therefore, achieving accurate classification using a limited number of defect samples from products photographed in non-standardized environments has emerged as a challenging yet promising research focus. To address this issue, this study proposes a learning approach that can be trained using standardized industrial defect datasets to detect defects in few-shot, non-standardized industrial defect production. Specifically, this approach initially utilizes the knowledge acquired from a standardized pre-training dataset to crop the data within the query set. Subsequently, an embedding extraction is employed to obtain feature embedding and classification results. Our practice demonstrates a performance improvement of roughly 5% on non-standard datasets compared to few-shot classification methods. © 2024 IEEE.

关键词： Zero-shot learning

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