Subaperture Keystone Transform Matched Filtering Algorithm and Its Application for Air Moving Target Detection in an SBEWR System

Zhan, Muyang; Chun-hua, Zhao; Kun, Qin; Huang, Penghui; Fang, Ming; Zhao, Chunlei

doi:10.1109/jstars.2023.3245295

Cited by 8 publications

(4 citation statements)

References 45 publications

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“…In this section, the acquired data are pre-processed using SAKTMF [30] technique is used to clean the unwanted data. SAKTMF are methods for pre-processing improving data quality.…”

Section: B Pre-processing Using Subaperture Keystone Transform Matche...mentioning

confidence: 99%

Application of Multimodal Data Fusion Attentive Dual Residual Generative Adversarial Network in Sentiment Recognition and Sentiment Analysis

Yongfang Zhang

2024

jes

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Recent advancements in Internet technology have led to increased multi-modal data posting on social media, online shopping portals, and video repositories recognizing significance of inter-modal utterances before combining multiple modes. In this manuscript, Application of Multimodal Data Fusion Attentive Dual Residual Generative Adversarial Network in Sentiment Recognition and Sentiment Analysis (MDF-DRGAN-SR-SA) is proposed. The input data are collected from CMU-MOSI dataset. Initially the input data is preprocessed using Subaperture Keystone Transform Matched Filtering (SAKTMF) is used to clean unwanted data. Then, feature extraction is done by Two-Sided Offset Quaternion Linear Canonical Transform (TSOQLCT) to extract unimodal features likes acoustic, textual, visual. Then the selected features are given to ADRGAN classifying Sentiment Recognition and Sentiment Analysis likes positive, negative, neutral. In general, ADRGAN doesn’t express some adaption of optimization strategies for determining optimal parameters to assure accurate classification of Sentiment Recognition and Sentiment Analysis. Hence, Northern Goshawk Optimization Algorithm (GOA) is proposed to enhance weight parameter of ADRGAN, which precisely classifies the Sentiment Recognition and Sentiment Analysis in positive, negative and neutral. The proposed model is implemented and its efficiency is evaluated utilizing some performance metrics likes accuracy, precision, specificity, sensitivity,F1-score. The MDF-DRGAN-SR-SA method provides 25.85%, 26.79% and 27.63% higher accuracy; 35.66%, 34.97% and 26.57% higher precision; 28.18%, 29.52% and 25.68% higher specificity is compared with existing method such as Two-Level Multimodal Fusion for SA in Public Security (TMDF-SA-PS), Multimodal SA Depend on Adaptive Modality-Specific Weight Fusion Network (MFN-SA-AMW) and Multimodal SA Utilizing Multi-tensor Fusion Network and Cross-modal Modeling(MTFN-SA) respectively.

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“…In this section, the acquired data are pre-processed using SAKTMF [30] technique is used to clean the unwanted data. SAKTMF are methods for pre-processing improving data quality.…”

Section: B Pre-processing Using Subaperture Keystone Transform Matche...mentioning

confidence: 99%

Application of Multimodal Data Fusion Attentive Dual Residual Generative Adversarial Network in Sentiment Recognition and Sentiment Analysis

Yongfang Zhang

2024

jes

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“…In this section, Sub Aperture Keystone Transform Matched Filtering (SAKTMF) is used eliminate the missing data [27]. Missing data and duplicate packets could be present in the input data.…”

Section: B Pre-processing Using Sub Aperture Keystone Transform Match...mentioning

confidence: 99%

Socialized Teaching of English Learning in Mobile Internet Technology

Jing Tang

2024

jes

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Distractions and multitasking can hinder the effectiveness of language learning, requiring innovative strategies to engage and maintain learners focus. Equitable access to mobile internet technology and ensuring accurate and culturally appropriate language learning materials are challenges in the socialized teaching of English learning in mobile internet technology. To overcome this issue, present for Socialized Teaching of English Learning in Mobile Internet Technology (STEL-MIT) is proposed. Initially, the data is collected from English education database. In pre-processing segment; it removes the noise and enhances the input data’s utilizing Sub Aperture Keystone Transform Matched Filtering (SAKTMF).Afterward, the data’s are fed to pre-processing. In pre-processing segment; it eliminates the missing data and enhances the input data’s utilizing Sub Aperture Keystone Transform Matched Filtering (SAKTMF).The outcome from the pre-processing data is transferred to the ITFCZNN. The listening, speaking, reading, writing are successfully classified by using ITFCZNN. The MCOA is used to optimize the weight parameter of ITFCZNN. The proposed method is implemented using MATLAB, and several performance metrics, including accuracy, precision, recall, sensitivity, F1 score, and calculation time, are used to determine the proposed STEL-MIT method's efficiency. Proposed STEL-MIT method attains higher accuracy 16.65%, 18.85% and 16.45%; higher sensitivity 16.34%, 12.23%, and 19.12%; higher precision 14.89%, 16.89% and 20.67%,higher recall 16.34%, 12.23%, and 18.54% analysed to the existing methods, such as Access to Online Learning: Machine Learning Analysis from a Social Justice Perspective (AOL-ML-SJP), Design of Online Intelligent English Teaching Platform Based on Artificial Intelligence Techniques (OIET-AI), and Novel Attack Finding System for Investigating Pedagogical Challenges of Mobile Technology to English Teaching (IPC-MT-ET), respectively.

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“…The data is pre-processed using SAKTMF in this part [27]. The data is cleaned up by data processing.…”

Section: Pre-processing Using Sub Aperture Keystone Transform Matched...mentioning

confidence: 99%

Artwork Traceability and Anti-Counterfeiting Model Based on Block Chain Technology

Rui Yin

2024

jes

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The Chinese art market fostered the advancement of culture and spirituality. Nevertheless, false and counterfeit artwork is not uncommon. The art technology and security introduce the innovative method to determine Artwork Traceability and Anti-Counterfeiting. The aim of the work is to prevent the creation and circulation of fake or unauthorized copies of artworks. Because of the cryptographic security and immutability of block chains, it would be challenging to falsify artwork records or make accurate duplicates. This would foster confidence in the art market and shield collectors and artists from monetary losses and harm to their reputations. The data on mobile Application Data Center (ADC) platform and the cleansing the data using sub-aperture keystone transform matched filtering (SAKTMF) and the preprocessed data is fed into the Hierarchically Gated Recurrent Neural Network (HGRNN) classify the art work and Anti-Counterfeiting. The Bald Eagle Search Optimization (BES) Algorithm is used to optimize the weight parameter of HGRNN. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such as Dual-Branch Multi-Scale Feature Fusion network (DMF-Net), Region Convolutional Neural Network (R-CNN) and practical Byzantine fault tolerance (PBFT) algorithm. The gained results of the proposed HGRNN-BES method attains higher accuracy 97%, 96%, and 99%, higher precision 98%, 99%, and 97%.

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Subaperture Keystone Transform Matched Filtering Algorithm and Its Application for Air Moving Target Detection in an SBEWR System

Cited by 8 publications

References 45 publications

Application of Multimodal Data Fusion Attentive Dual Residual Generative Adversarial Network in Sentiment Recognition and Sentiment Analysis

Application of Multimodal Data Fusion Attentive Dual Residual Generative Adversarial Network in Sentiment Recognition and Sentiment Analysis

Socialized Teaching of English Learning in Mobile Internet Technology

Artwork Traceability and Anti-Counterfeiting Model Based on Block Chain Technology

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