Roll angular rate extraction based on modified spline-kernelled chirplet transform

Zhao, Hui; Su, Zhong; Li, Qing; Liu, Fu-chao; Liu, Ning

doi:10.1016/j.dt.2021.04.012

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…Let us consider the narrow band that distributes the interesting frequency of signals, the MSCT 24 is used in this paper. The FFT operator is replaced by the MSCT in SCT from the Chip Z‐transform (CZT).…”

Section: Proposed Methodologymentioning

confidence: 99%

An optimal self adaptive deep neural network and spine‐kernelled chirplet transform for image registration

Pandi¹,

Senthilselvi

Maragatharajan

et al. 2022

Concurrency and Computation

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Image registration is one of the image processing techniques that align more than two images of a similar scene captured under different perspectives at different intervals of time. In demographical research, the image registration process assists to study differences in the structure of brain tissue. Due to enhanced technological advancements, numerous image registration methods have been established. On the other hand, these traditional techniques face few real-time challenges while processing huge input data. In addition to this, uncertainty analysis becomes a crucial step in medical applications which is utilized to judge whether the registration result is valuable or not.The high percentage of uncertainty than the threshold makes the registration result abnormal. Therefore, to conquer such circumstances, this research work proposed a modified spine-kernelled chirplet transform (MCST) based optimal Self-Adaptive Deep Neural Network (SADNN) which focuses mainly on enhancing registration accuracy by reducing the uncertainties of registration results. The experimental analysis is conducted and from the evaluation results, the proposed MCST-based optimal SADNN technique outperforms existing techniques in terms of accuracy, specificity, sensitivity, F-measure, and DICE values. Moreover, the proposed method achieves 97.2% accuracy for accurate image registration.

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Section: Proposed Methodologymentioning

confidence: 99%

An optimal self adaptive deep neural network and spine‐kernelled chirplet transform for image registration

Pandi¹,

Senthilselvi

Maragatharajan

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…This section focuses on the utilization of the MSCT for feature extraction. The MSCT is introduced in this paper [29] as a solution for signals with interested frequencies concentrated in a narrow band. The main idea of the MSCT is to use the CZT operator instead of the FFT operator in the 'spline-kernelled chirplet transform'.…”

Section: Feature Extraction Using Modified Spline-kernelled Chirplet ...mentioning

confidence: 99%

“…In the design of the LOA, it is postulated that the lyrebird will haphazardly seek refuge in one of the designated safe areas. Utilizing the lyrebird displacement modelling during this stage, a fresh position is computed for every LOA member using Equation (29). Subsequently, if the objective function value shows improvement, the previous position of the corresponding member is substituted with this new position as per Equation (30).…”

Section: Step 3: Fitness Functionmentioning

confidence: 99%

Music Teaching Mode of Colleges and Universities Based On Hierarchically Gated Recurrent Neural Network (HGRNN) and Lyrebird Optimization Algorithm (LOA)

Kegang Lu

2024

jes

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Colleges and universities play a crucial role in nurturing talent and providing highly skilled individuals for various sectors of society. Through modifications over time, the model of music education at colleges and universities has advanced. However, there are still numerous issues that demand careful consideration. This manuscript proposes a hierarchically gated recurrent neural network (HGRNN) optimized with the lyrebird Optimization Algorithm (LOA) for predicting music teaching mode of colleges and universities (MTM-HGRNN-LOA). Initially, the data is collected via real time basis. Afterward, the data is fed to an unscented trainable kalman filter (UTKF) based pre-processing process. In the pre-processing segment, it enhances training rate and eliminates the of batch size dependency. The pre-processing output is given to modified spline-kernelled chirp let transform (MSKCT). The input signal undergoes feature extraction to derive the primary features, which are subsequently combined to yield more comprehensive features in an efficient manner. After that, the extracted features are given to a hierarchically gated recurrent neural network and lyrebird optimization algorithm for effectively classifying the music teaching mode for best, good, normal, satisfactory and poor. The weight parameters of hierarchically gated recurrent neural network are optimized using the lyrebird optimization algorithm. The proposed method is implemented in python and evaluated their performance with existing methods. The performance metrics, like precision, F1-score, accuracy, specificity, sensitivity, and ROC is analysed to the proposed method's performance. The proposed MTM-HGRNN-LOA methods of accuracy are provide 97% best, 98% good, 95% normal, 98% satisfactory and 97% poor music teaching mode. The existing methods MTM-CNN, MTM-BPNN and MTM-GNN, the specificity becomes 90%, 70%, 79% best, 77%, 75%, 65% good, 66%, 85%, 84% normal, 59%, 58%, 70% satisfactory, 61%, 79%, 81% poor music teaching mode. The results show that the proposed MTM-HGRNN-LOA method outperforms other existing techniques, such as online vocal music teaching quality using Back Propagation neural network and convolutional neural network based College-Level Music Teaching Quality Evaluation.

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Synchronous spline-kernelled Chirplet squeezing transform and its application for seismic data analysis

Chen,

Zhang,

Chen

et al. 2024

Digital Signal Processing

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Roll angular rate extraction based on modified spline-kernelled chirplet transform

Cited by 9 publications

References 25 publications

An optimal self adaptive deep neural network and spine‐kernelled chirplet transform for image registration

An optimal self adaptive deep neural network and spine‐kernelled chirplet transform for image registration

Music Teaching Mode of Colleges and Universities Based On Hierarchically Gated Recurrent Neural Network (HGRNN) and Lyrebird Optimization Algorithm (LOA)

Synchronous spline-kernelled Chirplet squeezing transform and its application for seismic data analysis

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