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IEEE 99th Vehicular Technology Conference (VTC-Spring)

作者： Huan, Mingsai Guo, Baoxi Tu, Yu Liang, Junli Ma, Yugang Zeng, Yonghong Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China ASTAR Inst Infocomm Res I2R Singapore Singapore

ISBN: (纸本)9798350387414

Antenna position errors caused by manufacturing accuracy limits lead to a great difference between the real and ideal models of antenna arrays in extremely high frequency (EHF) band MIMO radar with tiny physical elements and spacings. As the real array is a non-uniform array, the conventional window function fails when applied to non-uniform sampling data, which causes angular ambiguities and seriously degrades the performance of DOA estimation. Based on an advanced analysis of spectral distortion in non-uniform sampling, this paper introduces a method to design a specialized window function for non-uniform MIMO arrays, aiming to minimize the equivalent noise bandwidth (ENBW) while sufficiently suppressing the side-lobe-to-peak ratio of the window function's spectrum. The simulation results show the effectiveness of the optimized window function, which reduces angular ambiguities and thereby improves the performance of DOA estimation for non-uniform MIMO arrays.

关键词： Non-uniform Sampling Non-uniform Array window function DOA estimation MIMO Radar

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Design of an asymmetric window function in single grid-based dark-field X-ray imaging for ensuring improved image quality

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2025年 1070卷

作者： Lee, Jonghyeok Lim, Hyunwoo Lee, Hunwoo Jeon, Duhee Cho, Hyosung Yonsei Univ Dept Radiat Convergence Engn Wonju 26493 South Korea

Single grid-based dark-field X-ray imaging (SG-DFXI) is a single-exposure, non-interferometric technique used to obtain dark-field images using a microfocus X-ray source and a common X-ray grid. It requires minimal exposure and minimal system setup for potential medical and industrial applications. However, dark-field images are often distorted by the image artifacts caused by spectral overlap of adjacent harmonic peaks in the Fourier spectrum when an X-ray grid is used. This can degrade the SG-DFXI quality. In this study, we designed a new type of rectangular window function that was applied asymmetrically to the first-order harmonic peak to extract its corresponding harmonic image, avoiding spectral-overlap artifacts and improving image resolution. An experiment was conducted on test samples of a cookie (attached to a piece of wooden chopstick) and a resolution pattern using a tabletop setup to demonstrate the efficacy of the proposed asymmetric window function. Our experimental results indicate that the asymmetric window function effectively eliminated spectral-overlap artifacts and improved the spatial resolution of dark-field images. The contrast-to-noise ratio of the dark-field image obtained using the asymmetric window function with a partial Fourier factor of PF = 0.55 was approximately 12.0, which represents a 4.3-fold improvement over the image using the symmetric window function, demonstrating the efficacy of the proposed window function.

关键词： Dark-field X-ray imaging X-ray grid Spatial harmonic imaging Spectral-overlap artifacts window function

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Structural modal parameter identification with the Power-Exponential window function

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 222卷

作者： Hou, Jilin Xu, Dengzheng Jankowski, Lukasz Dalian Univ Technol Sch Civil Engn Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China Polish Acad Sci Inst Fundamental Technol Res Warsaw Poland

In view of the demand for accurate modal identification, and based on the characteristics of free vibration response, this paper introduces a new window function for Fourier Transform called the Power-Exponential window. The Power-Exponential window addresses the characteristics of free vibration response. It significantly enhances the accuracy of modal identification by improving the spectral properties of structural response. The proposed window function consists of exponential and power terms. This study focuses on the additional damping and frequency-domain differentiation introduced by the Power-Exponential window function. The exponential term weakens the boundary effect related to the time-domain truncation and suppresses the spectral leakage. Moreover, it can be interpreted in clear physical terms as providing additional damping to the signal. The power term in the window function corresponds to frequency domain differentiation, and it alleviates the spectral broadening that arises due to the additional damping. Furthermore, the analytical expression for the response spectrum confirms that the Power- -Exponential window not only aligns the peak response frequency with the damped natural frequency but also establishes an explicit linear relationship between the actual structural damping ratio and the identification result from the half power bandwidth method. Both contribute to an improved accuracy and usability of certain frequency-domain modal identification methods. The influence of the Power-Exponential window parameters on modal parameter identification is analyzed, and the optimal selection principle and suggested parameter values are proposed. Finally, numerical simulations and an experimental frame model test are conducted to verify the accuracy and validity of modal parameter identification based on the Power- -Exponential window.

关键词： Modal identification window function Frequency domain Spectrum leakage Fourier Transform (FT)

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Enhanced data preprocessing with novel window function in Raman spectroscopy: Leveraging feature selection and machine learning for raspberry origin identification

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024年 323卷 124913页

作者： Zhao, Yaju Lv, Wei Zhang, Yinsheng Tang, Minmin Wang, Haiyan Zhejiang Gongshang Univ Zhejiang Engn Res Inst Food & Drug Qual & Safety Hangzhou 310018 Peoples R China Jiangsu Prov Prod Qual Supervis & Inspection Inst Nanjing 210007 Peoples R China

In this study, a simple and accurate approach is proposed for enhancing the origin identification of raspberry samples using a combination of innovative Raman spectral preprocessing techniques, feature selection, and machine learning algorithms. window function was creatively introduced and combined with baseline removal technique to preprocess the Raman spectral data, reducing the dimensionality of the raw data and ensuring the quality of the processed data. An optimization process was conducted to determine the optimal parameter for the window function, resulting in a binning window width of 5 that yielded the highest accuracy. After applying three feature selection techniques, it was found that the information gain model had the best performance in extracting discriminative spectral features. Finally, ten different machine learning algorithms were employed to construct predictive models, and the optimal models were selected. Linear Support Vector Classifier (LinearSVC), Multi-Layer Perceptron Classifier (MLPClassifier), and Linear Discriminant Analysis (LDA) achieve accuracy, precision, recall, and F1 values above 0.96, while the Random Vector functional Link Network Classifier (RVFLClassifier) surpasses 0.93 for these performance metrics. These results demonstrate the effectiveness of the proposed approach in identifying the origin of raspberry samples with high accuracy and robustness, providing a valuable tool for agricultural product authentication and quality control.

关键词： Raman spectroscopy window function Feature selection Machine learning Origin identification

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Parameter Estimation of Sub-/Super-synchronous Oscillation Based on Interpolated All-phase Fast Fourier Transform with Optimized window function

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Journal of Modern Power Systems and Clean Energy 2024年第4期12卷 1031-1041页

作者： Bo Sun Xi Wu Xi Chen Zixiao Zou Qiang Li Bixing Ren School of Electrical Engineering Southeast UniversityNanjingChina State Grid Jiangsu Electric Power Company Limited Research Institute NanjingChina

In recent years,with increasing amounts of renewable energy sources connecting to power grids,sub-/super-synchronous oscillations(SSOs)occurred more *** to the time-variant nature of SsO magnitudes and frequencies,as well as the mutual interferences among SsO modes with close frequencies,the accurate parameter estimation of SsO has become a particularly challenging *** solve this issue,this paper proposes an improved spectrum analysis method by improving the window function and a spectrum correction method to achieve higher ***,by aiming at the sidelobe characteristics of the window function as evaluation criteria,a combined cosine function is optimized using a genetic algorithm(GA).Furthermore,the obtained window function is self-convolved to extend its excellent characteristics,which have better performance in reducing mutual interference from other SSO ***,a new form of interpolated all-phase fast Fourier transform(IpApFFT)using the optimized window function is proposed to estimate the parameters of *** method allows for phase-unbiased estimation while maintaining algorithmic simplicity and *** performance of the pro-posed method is demonstrated under various conditions,com-pared with other estimation *** results validate the effectiveness and superiority of the proposed method.

关键词： Sub-/Super-synchronous oscillation(SSO) window function parameter estimation interpolated all-phase fast Fourier transform(ApFFT)

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A multispectral transmission image cluster analysis method based on "Terrace compression Method" and window function

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024年 306卷 123547页

作者： Lin, Ling Song, Yue Guo, Wenli Yu, Tao Fan, Meilin Win, Nan Su Su Li, Gang Tianjin Univ State Key Lab Precis Measuring Technol & Instrumen Tianjin 300072 Peoples R China Tianjin Univ Tianjin Key Lab Biomed Detecting Tech Instruments Tianjin Peoples R China China Med Univ Shengjing Hosp Shengjing Peoples R China

Multispectral transmission imaging has a great potential for the early screening of breast cancer due to the low cost, safety and ease in operation. Accurate detection of heterogeneity is important for the diagnosis of breast disease. The low contrast and unclear heterogeneity boundaries of transmission images can lead to difficulties in recognition and segmentation. Therefore, we propose a clustering segmentation method of multispectral transmission images based on "terrace compression method" and window transformation. The images are preprocessed by the frame accumulation to improve the signal-to-noise ratio. After that, the "terrace compression method" is used to compress the images nonlinearly, to reduce data redundancy and then to improve the edge information of heterogeneities. Afterwards, the window function is used to eliminate the redundant information of the background and to reduce the influence of background noise on clustering. Finally, the processed images at each wavelength are transformed into multidimensional data for cluster analysis. The multispectral transmission images of breast phantom are acquired for experimental validation. Then, compared the method with common clustering segmentation methods (including K-means, K-means++, Mean-shift, Gaussian Mixture). The results showed that this processing method can effectively segment and classify the three heterogeneities in the breast phantom. Among these methods, the method proposed in the paper has the best segmentation and classification results for the three types of heterogeneities in the breast phantom. The Dice coefficients of all the heterogeneities segmentation reached more than 0.84 and increased by a maximum of 1.08 times as compared to the common clustering methods. The applications of the terrace compression method and the grayscale window transformation improved the effect of image clustering segmentation.

关键词： Terrace Compression Method Multispectral transmission image window function Multidimensional clustering

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Application of a Hybrid Particle Image Velocimetry Method Based on window function in the Field of Turbulence 20th

Application of a Hybrid Particle Image Velocimetry Method Ba...

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20th International Conference on Intelligent Computing (ICIC)

作者： Guo, Shuqiang Gao, Ming Xiao, Bin Xie, Zhicheng Ping, Wenzhi Northeast Elect Power Univ Sch Comp Jilin 132000 Jilin Peoples R China Northeast Elect Power Univ Sch Energy & Power Engn Jilin 132000 Jilin Peoples R China

ISBN: (纸本)9789819756025;9789819756032

Due to the complex flow characteristics such as strong nonlinearity and strong non-equilibrium of turbulence, there are significant errors in using the initial velocity field generated based on cross-correlation algorithm in mixed particle image velocimetry methods. This can result in inaccurate offset images generated, leading to errors in generating the final fine velocity field using optical flow method. This article adds a window function to weight the pixels in the window before operating based on the cross-correlation algorithm, and generates a velocity field through the cross-correlation algorithm. It is then compared and analyzed with the cross-correlation algorithm without adding a window function and the mixed particle image velocity measurement method before and after adding a window function. Subsequently, uniform flow field particle images are used for testing to verify the effectiveness of the method proposed in this paper. The results confirm that the method proposed in this article has a significant effect on turbulent particle images, and is always more accurate in generating the initial velocity field of turbulence than based on cross-correlation algorithms. It can provide a more accurate initial velocity field in mixed particle image velocity measurement methods.

关键词： particle image velocimetry window function turbulence velocity field

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Multiresolution Controller Based on window function Networks for a Quanser Helicopter 22nd

Multiresolution Controller Based on Window Function Networks...

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22nd Mexican International Conference on Artificial Intelligence (MICAI)

作者： Federico Garcia-Castro, Oscar Enrique Ramos-Velasco, Luis Garcia-Rodriguez, Rodolfo Vega-Navarrete, Mario A. Escamilla-Hernandez, Enrique Metropolitan Polytech Univ Hidalgo Postgrad Program Aerosp Engn Tolcayuca Mexico Natl Polytech Inst ESIME Culhuacan Mexico City DF Mexico

ISBN: (纸本)9783031477645;9783031477652

To improve neural network (NN) performance, new activation functions, such as ReLU, GELU, and SELU, to name a few, have been proposed. windows-based functions, such as flat-top or atomic functions, used in processing signals have begun to be used in NNs for dynamical systems. Although wavelet functions are the most popular, some additional functions with similar properties must be evaluated. This paper presents a window function neural network (WFNN) for identification tasks. Using the identification by WFNN, the self-tuning gains of the proportional multi-resolution (PMR) controller are carried out. To show the performance of the proposed approach for different window functions, numerical simulations of the Quanser helicopter of 2 degrees of freedom are presented.

关键词： Multiresolution analysis Intelligent control window function Quanser helicopter Neural network

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A Novel window function Enables Memristor Model With High Efficiency Spiking Neural Network Applications

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IEEE TRANSACTIONS ON ELECTRON DEVICES 2022年第7期69卷 3667-3674页

作者： Dai, Yuehua Feng, Zhe Wu, Zuheng Anhui Univ Sch Integrated Circuits Hefei 230601 Anhui Peoples R China

Memristor, a nanoscale device with the advantages of simple structure, excellent scalability, and complementary metal-oxide-semiconductor (CMOS) process compatibility, has drawn extensive research attention for various applications. An appropriate memristor model is essential for researcher to explore potential applications of memristor-based systems. Hewlett-Packard (HP) memristor model with the simple computation complexity is a favorable choice for simulation works. Incorporating a window function into the HP memristor model is essential for emulating the fealty nonlinearity response of practical device. However, a window function, with the ability of controllable resistance evolution speed and range for memristor model, has not been reported. In this work, we introduced two parameters (G and S) into window function for controlling the evolution speed and range of memristor model, respectively. The results indicate that the memristor model based on this window function exhibits high flexibility in resistance evolution speed and range. Furthermore, the memristor model based on this window function is used to implement the spiking neural network (SNN) simulation. The results demonstrated that memristor model based on this novel window function shows higher computation efficiency for SNN than previous window functions.

关键词： Memristors Evolution (biology) Resistance Computational modeling Adaptation models Semiconductor device modeling Biological system modeling Memristor model spiking neural networks (SNNs) synaptic characteristics window function

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Appropriate window function and window Length in Multifrequency Velocity Estimator for Rapid Motion and Locality of Layered Myocardium

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2022年第4期69卷 1353-1369页

作者： Obara, Yu Mori, Shohei Arakawa, Mototaka Kanai, Hiroshi Tohoku Univ Grad Sch Biomed Engn Sendai Miyagi 9808579 Japan Tohoku Univ Grad Sch Engn Sendai Miyagi 9808579 Japan

The heart wall has a multilayered structure and moves rapidly during ejection and rapid filling periods. Local strain rate (SR) measurements of each myocardial layer can contribute to accurate and sensitive evaluations of myocardial function. However, ultrasound-based velocity estimators using a single-frequency phase difference cannot realize these measurements owing to insufficient maximum detectable velocity, which is limited by a quadrature frequency. We previously proposed a velocity estimator using multifrequency phase differences to improve the maximum detectable velocity. However, the improvement is affected by a spatial discrete Fourier transform (DFT) window length that represents the locality of the velocity estimation. In this article, we theoretically describe that shortening the window increases the interference between different frequency components and decreases the maximum detectable velocity. The tradeoff between the maximum detectable velocity and the window length was confirmed through simulations and a water-tank experiment. Under the tradeoff, the Hanning window, which was used in previous studies, is not always appropriate for the local measurement of the velocity, which sometimes exceeds 100 mm/s depending on the subject, direction of the ultrasound beam to the heart wall, and cardiac periods. In the in vivo measurement with the short window, the Tukey window with a large flat part that has a high-frequency resolution and ameliorates the discontinuity at both edges of the windowed signal was appropriate to measure the maximum velocity. This study offers the potential for local measurements of each myocardial layer using the multifrequency velocity estimator with the appropriate window function and window length.

关键词： Aliasing discrete Fourier transform (DFT) multifrequency phase differences velocity measurement window function window length

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