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检索条件"机构=School of Computer Science and Engineering Xi’an University of Technology"

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Reverse Time Migration of Ultra-Wideband Ground-Penetrating Radar Based on Multiple Time-Gating Algorithm

Reverse Time Migration of Ultra-Wideband Ground-Penetrating ...

引用

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

作者： Wu, Junjie Li, Haixiao Zhou, Yuanguo Cao, Le Wang, Yan Xi'an University of Science and Technology College of Communication and Information Engineering Xi'an China Xi'an University of Science and Technology College of Electrical and Control Engineering Xi'an China Graduate School of Chinese Aeronautical Establishment Yangzhou China Aviation Key Laboratory of Science and Technology on Electromagnetic Environment Effects Shenyang China National Key Laboratory of Electromagnetic Information Control and Effects Shenyang China

ISBN: (纸本)9798350355581

To address the issue of clutter interference in ground-penetrating radar (GPR) imaging using a VNA (Vector Network Analyzer), this paper improves the traditional reverse time migration (RTM) technique for ultra-wideband GPR imaging. The improved method retains the effective signals in the detection wavefield as the valid imaging area and suppresses signals in non-valid areas. Due to variations in the distance between the transmitting and receiving antennas and the number of anomalies in the experiment, the traditional time window cross-correlation is not ideal for this scenario. The improved method automatically applies reasonable windowing to each data channel, avoiding the simultaneous enhancement of clutter interference and effective signals through direct stacking, and preventing the filtering out of effective signals. Experimental results demonstrate that this method, when applied to real measured data and compared with the traditional cross-correlation RTM method, shows advantages in more accurately locating anomalous areas and suppressing clutter interference. This validates that the Multiple Time-Gating Reverse Time Migration (MTG-RTM) method offers better localization and higher imaging accuracy in practical engineering applications. © 2024 IEEE.

关键词： Receiving antennas

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SSD-MIR: Towards Medical Image Restoration With Structured Visual State Space Duality

SSD-MIR: Towards Medical Image Restoration With Structured V...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Binghong Zhang, Zhengqian Wang, Haiyu Yu, Tianshuo Wang, Dong Wang, Jialiang Ren, Junjun Liu, Yongzhuang Jiang, Wei Harbin Institute of Technology School of Mathematics School of Computer Science and Technology Harbin China Harbin Institute of Technology Department of Fundamental Education Harbin China University of Glasgow James Watt School of Engineering Glasgow United Kingdom Harbin Institute of Technology School of Computer Science and Technology Harbin China Harbin Institute of Technology School of Computer Science and Technology School of Mathematics Harbin China

ISBN: (纸本)9798350386226

In the field of Medical image processing, Transformers and CNNs bring filter-based local sensing and attention-based global modelling, which strengthened their dominant position. However, the scales required in different applications vary, and improper usage or selection on models can lead to performance decrease, resulting in more serious consequences in downstream tasks. The recent advanced Structured State Space Model has shown its potential as a baseline model, and is further proved to maintaining implicit duality with the attention mechanism, The corresponding model Mamba2 shows its long-range modelling ability with optimized long-term memory and linear computational *** this paper, we propose SSD-MIR, a novel architecture leveraging the advantages of state space duality mechanism. SSD-MIR includes three parts: input projection and feature representation, deep feature transformation, output projection and feature inversion. With the three components sequentially connected to form our model, it is able to model long-range dependencies and learn the mapping between low-quality input and high-quality output simultaneously. In the main deep feature transformation part, depthwise convolution is used for additional feature mapping as an alternative of 1D causal convolution, and an optimized quad-direction scanning mechanism is implemented to form the overall 2D state space dual module. The corresponding wrapper module is built in MetaFormer style with the usage of residual connection and LayerNorm. We perform extensive experiments on multiple medical image restoration tasks, and the results are superior to the state-of-the-arts, demonstrating the strength and efficacy of our model. © 2024 IEEE.

关键词： Mapping

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A Directional Coupler for W-Band Based on Rectangular Micro-Coaxial technology

A Directional Coupler for W-Band Based on Rectangular Micro-...

引用

2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024

作者： He, xin Dai, Moye Yang, Jinping Li, Bo Tang, Yiming Li, Sheng School of Electronic Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210094 China China Academy of Space Technology Xi'an Branch National Key Laboratory of Science and Technology On Space Microwave Xi'an China

ISBN: (纸本)9798350383317

This paper introduces a design of a branch-line directional coupler based on micro-coaxial technology. Compared to traditional microstrip lines, rectangular coaxial lines have advantages such as high frequency range, low loss, and high isolation performance, making them suitable for operation at higher frequencies. The designed branch-line coupler, with a center frequency of 91 GHz, was simulated, and the results showed that at the center frequency, the coupler's return loss and port isolation were both less than -30 dB, while the total insertion loss was less than 0.35 dB. Additionally, the phase difference between the through port and the coupled port was (90 ±3)°. © 2024 IEEE.

关键词： Directional couplers

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Revolutionizing Point Cloud Classification with Multiview Self-Supervised Learning 17

Revolutionizing Point Cloud Classification with Multiview Se...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Zhou, Wei Liu, Kunlong Jin, Weiwei Wang, Qian She, Yunfeng Yu, Yongxiang Ma, Caiwen Northwest University School of Information Science and Technology Xi'an China East China Jiaotong University School of Electrical and Automation Engineering Nanchang China Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an China

ISBN: (纸本)9798350387384

Self-supervised models are shaping the future of point cloud processing by minimizing reliance on labeled data and addressing the challenges associated with point cloud annotation. Traditional self-supervised point cloud models often employ mask reconstruction, which involves globally dividing patches. This approach can lead to increased computational costs and insufficient consideration of global features. In this paper, we introduce MVMAE, a multiview-based self-supervised model designed for point cloud processing. MVMAE firstly converts point clouds into multiview images through rendering. It then integrates views from various angles as inputs to a mask self-encoder, where each view is treated as a distinct patch. This method provides a more comprehensive perspective for feature learning, enhancing point cloud classification performance. Experiments conducted on datasets like ModelNet40 demonstrate that MVMAE achieves a classification accuracy of 90.1% and offers improved processing speed while managing computational complexity and parameter count. The code for MVMAE is available at https://***/NWUzhouwei/MVMAE. © 2024 IEEE.

关键词： Self-supervised learning

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Infrared-Based Gender Recognition from Facial Images: A Comparative Study Using Deep Learning 2

Infrared-Based Gender Recognition from Facial Images: A Comp...

引用

2nd International Conference on Optical Imaging and Measurement, ICOIM 2023

作者： Cao, Zhicheng Qin, Guoli Zhao, Heng Schmid, Natalia Pang, Liaojun Xidian University School of Life Science and Technology Xi'an China West Virginia University Lane Dept. of Computer Science and Electrical Engineering MorgantownWV United States

ISBN: (纸本)9798350358636

The gender is an important soft biometric trait of a person, upon which many social and economic functionalities depend or rely. Automatic recognition of the gender of individuals is crucial to many real-world applications, such as public video surveillance, gender-specific access and services, human-computer interaction, demographical studies. Up until now, most research works on gender recognition focus on the visible light, leaving the problem of infrared-based gender recognition scarcely studied. Thus, this research work studies IR facial images-based gender recognition, using deep learning models. We design a CNN model on top of ResNet using a multiscale technique (named as IR-GenderNet). Then, we compare the performance of gender recognition between the visible light and IR bands. We also compare the performance of IR-GenderNet against other methods in the literature that are either traditional or deep learning-based. Experimental results demonstrate that gender recognition using IR is not just feasible but even more beneficial than visible light. It is also proven that the proposed model of IR-GenderNet performs better than all other methods for the problem of IR-based gender recognition. Lastly, cross-dataset experiments also justify the generalization ability of IR-based gender recognition using the proposed algorithm. © 2023 IEEE.

关键词： Deep learning

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Network Connectivity Analysis of Coding and Noncoding RNAs in Cancer

Network Connectivity Analysis of Coding and Noncoding RNAs i...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Wang, Bingbo Han, Chao Ma, xiujuan Xidian University School of Computer Science and Technology Xi'an China

ISBN: (纸本)9781665468190

Cancer development is associated with thousands of coding and non-coding genes. Understanding the role of non-coding RNAs (ncRNAs) in cancer neighbourhood is of great significance for interpreting the occurrence and diagnosis of cancer. This paper first constructs a heterogeneous interaction network containing coding and non-coding genes. Then, the performance of network connectivities when applied to cancer-perturbed genes is evaluated by statistical index. We found that ncRNAs considerably improve the connectivity of perturbed genes and mediate more genes into cancer neighbourhood. Additionally, a method connected Perturbed Region (CPR) for mining cancer neighbourhood with the participation of ncRNAs in the network is proposed based on the bridging role of ncRNAs among cancer perturbed genes. Furthermore, we observed the role of ncRNAs in biologically complementing and promoting the formation of cancer-related pathways. Our results topologically promote the pattern of ncRNAs in forming cancer and biologically are of great benefit to reveal a more accurate disease relationship. © 2022 IEEE.

关键词： Genes

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An Application Ontology for Reproducibility of Machine Learning Solutions 44

An Application Ontology for Reproducibility of Machine Learn...

引用

44th International Conference on Information Systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023

作者： Bandara, Madhushi Jiang, Yuchao Gill, Asif Rabhi, Fethi A. Beydoun, Ghassan School of Computer Science University of Technology Sydney Sydney Australia School of Computer Science & Engineering University of New South Wales Sydney Australia

ISBN: (纸本)9781713893622

With Artificial Intelligence and Machine Learning (ML) on the rise, organisations of different scales and nature are looking to utilise ML systems to support their day-to-day operations. Many enterprises find it difficult to adapt existing ML solutions to their organisations without huge investments in solution understanding, customisation, infrastructure enablement and workforce training. Some organisations utilise external service providers to provision their standard analytics services, and this often leads to solutions that either do not fit well with their organisation goals or may lead to the loss of expert knowledge behind the establishment of the AI system. This paper aims to address some of these challenges by proposing an ontology for ensuring the reproducibility of ML models in research as well as their integration within application environments. Our work will ensure that the knowledge about a developed ML system or process is accumulated and recorded within an organisation and can be used in the future, either by new employees or other teams within the organisation. This approach can also be utilised by researchers and developers of ML systems to record and publish metadata of their studies, ensuring that future researchers can reuse their work with minimal effort. Copyright © 2023 [Madhushi Bandara, Yuchao Jiang, Asif Gill, Fethi A. Rabhi, Ghassan Beydoun].

关键词： Ontology

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Global Mask Autoencoder for Streamlined Point Cloud Learning and Voxel Token Integration 17

Global Mask Autoencoder for Streamlined Point Cloud Learning...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Zhou, Wei Jin, Weiwei Wang, Qian Liu, Kunlong She, Yunfeng Yu, Yongxiang Ma, Caiwen Northwest University School of Information Science and Technology Xi'an China East China Jiaotong University School of Electrical and Automation Engineering Nanchang China Chinese Academy of Sciences Xi'an Institute of Optics and Precision Mechanics Xi'an China

ISBN: (纸本)9798350387384

The Masked Autoencoder (MAE)-based approach for self-supervised point cloud learning has demonstrated strong feature extraction capabilities but encounters several challenges. Most MAE methods rely on Farthest Point Sampling (FPS) and K-Nearest Neighbors (KNN) for partitioning point clouds, which is time-consuming and can result in redundant information due to overlapping patches. To address these issues, we introduce the Global Masked Autoencoder (GMAE), which leverages voxel-based global shapes at various resolutions as tokens instead of local patches. This approach mitigates redundancy and reduces computational time associated with local patch partitioning in MAE. Further building on this, we propose VP-GMAE, a self-supervised point cloud model that reconstructs masked voxel features at multiple scales as a pretext task. VP-GMAE incorporates a channel expansion module to standardize the number of channels across different voxel scales, facilitating their simultaneous input into the encoder. We pre-train VP-GMAE on the ShapeNet dataset and fine-tune it on ModelNet40 and ScanObjectNN, evaluating its performance across object classification, few-shot classification, and part segmentation tasks. The code for VP-GMAE will be available at https://***/NWUzhouwei/VP-GMAE. © 2024 IEEE.

关键词： Self-supervised learning

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Simulation of non-inductive vector control of permanent magnet synchronous motor based on sliding mode observer 3

Simulation of non-inductive vector control of permanent magn...

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3rd IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Zhang, Caiyue Liu, Zipin Xu, Bowen Guilin University of Electronic Technology School of Electronic Engineering and Automation Guilin China Xi'An University of Science and Technology School of Electrical and Control Engineering Shaanxi Xi'an China Harbin Institute of Technology Shenzhen School of Mechanical Engineering and Automation Shenzhen China

ISBN: (纸本)9798350398410

Permanent magnet synchronous motors (PMSM) are widely used due to their numerous benefits. It is critical to get rotor position and speed information in order to operate the motor accurately. Sensorless control techniques have emerged as a popular study area both at home and overseas. The sliding mode observer (SMO) may indirectly detect rotor position and has the benefits of easy implementation and efficient algorithms. In this study, a mathematical model for sensorless control of a PMSM is developed using SMO, vector control, and other techniques. With a surface-mounted PMSM as the study object, a mathematical model for sensorless control of PMSM is developed. PMSM's sliding mode observer model is built in the matlab/simulink environment. Experiments demonstrate that the system can track the rotor position and speed of the motor precisely and fulfill the requirements of sensorless vector control of PMSM. © 2023 IEEE.

关键词： Sensorless control

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Differential privacy protection method for trip-oriented shared data

Differential privacy protection method for trip-oriented sha...

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作者： Du, Danlei Luo, Entao Yi, Yang Peng, Tao Li, Xubin Zhang, Shaobo Jiang, Xu Wang, Tian School of Information Engineering Hunan University of Science and Engineering YongZhou China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Department of Computer Science Northeastern Illinois University ChicagoIL United States School of Computer Science and Engineering Guangzhou University Guangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Computer Science and Engineering Nanjing University of Science and Technology NanJing China Zhuhai China Zhuhai China

While location information sharing technology provides convenience for unmanned driving and journey navigation, user journey information sharing has also become a disaster for privacy information leakage. The traditional differential privacy method can only perturb the data entirely and cannot consider the design of data availability. In this paper, the difference privacy algorithm is improved by combining it with the Apriori algorithm, and the relevant perturbation is carried out after mining the associated data of the user's trip. In the face of possible data attacks, the privacy protection of the sensitive information of the user's actual data is ensured while the availability of the data is ensured. By testing 3000 trip data generated by experimental simulation, the results show that the correlation information between the original datasets is destroyed. However good availability is maintained after the Laplace data perturbation of the proposed algorithm for both simultaneous and multi-person trips. © 2022 John Wiley & Sons, Ltd.

关键词： Machine learning

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