Support Vector Regression Machines to Evaluate Resonant Frequency of Elliptic Substrate Integrate Waveguide Resonators

Angiulli, Giovanni; Carlo, Domenico De; Amendola, Giandomenico; Arnieri, Emilio; Costanzo, Sandra

doi:10.2528/pier08041803

Cited by 18 publications

(7 citation statements)

References 21 publications

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“…It is argued that when NNSC model is learnt from image data, the learnt basis components have the properties of the spatial receptive fields of simple cells in the primary visual cortex [26]. So, the NNSC technique provides a principled method for using training data to determine the significant parts of an image, which has been successfully applied in image denoising and pattern recognition.…”

Section: Non-negative Sparse Codingmentioning

confidence: 99%

HRR Profiles Time-Frequency Non-Negative Sparse Coding for Sar Target Classification

Zhang¹,

Wu²,

Liu³

et al. 2014

PIER B

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Abstract-A new approach to classify synthetic aperture radar (SAR) targets is presented based on high range resolution (HRR) profiles time-frequency matrix non-negative sparse coding (NNSC). Firstly, SAR target images have been converted into HRR profiles. And the non-negative time-frequency matrix for each of the profiles is obtained by using an adaptive Gaussian representation (AGR). Secondly, NNSC is applied to learn target time-frequency basis of the training set. Feature vectors are constructed by projecting each HRR profile time-frequency matrix to low dimensional time-frequency basis space. Finally, the target classification decision is found with support vector machine and nearest neighbor algorithm respectively. To demonstrate the performance of the proposed approach, experiments are performed with Moving and Stationary Target Acquisition and Recognition (MSTAR) public release SAR database. The experimental results support the effectiveness of the proposed technique for SAR target classification.

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Section: Non-negative Sparse Codingmentioning

confidence: 99%

HRR Profiles Time-Frequency Non-Negative Sparse Coding for Sar Target Classification

Zhang¹,

Wu²,

Liu³

et al. 2014

PIER B

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“…SVM based approaches have been shown to outperform conventional templatebased approaches, providing better generalization capabilities. For classification tasks, SVM has advantages of elegant mathematical tractability and working with a relatively small number of training samples [17][18][19][20]. But SVM cannot allow the number of supports to be adapted to the specific signal being characterized.…”

Section: Introductionmentioning

confidence: 99%

Sar Target Classification Using Bayesian Compressive Sensing With Scattering Centers Features

Zhang¹,

Qin²,

Li³

2013

PIER

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Abstract-The emerging field of compressed sensing provides sparse reconstruction, which has demonstrated promising results in the areas of signal processing and pattern recognition. In this paper, a new approach for synthetic aperture radar (SAR) target classification is proposed based on Bayesian compressive sensing (BCS) with scattering centers features. Scattering centers features is extracted as a l 1 -norm sparse problem on the basis of SAR observation physical model, which can improve discrimination ability compared with original SAR image. Using an overcomplete dictionary constructed by training samples, BCS is utilized to design targets classifier. For target classification performance evaluation, the proposed method is compared with several state-of-art methods through experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) public release database. Experiment results illustrate the effectiveness and robustness of the proposed approach.

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“…Both of these learning machines are powerful, efficient, and robust in solving these problems. They are able to be trained from learning set and to generalize the target characterize accurately [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Support Vector Machine for Identification of Monitors Based on Their Unintended Electromagnetic Emanation

Mo¹,

Jin-ling³

et al. 2013

PIER M

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Abstract-Electrical equipments usually radiate unintended emission which carries characteristic information when running, such as emanation from computers monitors, keyboards and other components, this emanation can be possibly used to reconstruct the source information. Most of the experiments related to this area are carried out inside a semi-anechoic chamber, and measurement out of it may not be considered to be optimal, because the data captured are usually not sufficient. Yet in this study, we take LCD monitors as typical examples and find that characteristics significantly differ between products, parameters such as the magnitude and spectrum were measured under normal environment. We take the PCB traces as antennas and acquire the raw signal directly near the antenna and extract the parameters to use as input to support vector machine (SVM) which was trained to identify the emanating source (LCD monitors). In this study, the method was tested using the emission captured from one Samsung (SyncMaster E1920) and two LG (L1753s) monitors, and a laptop (ACER Aspire 5542). The SVM was able to classify the source of signals with 98.9510% accuracy while using emission that captured from the running monitors.

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Support Vector Regression Machines to Evaluate Resonant Frequency of Elliptic Substrate Integrate Waveguide Resonators

Cited by 18 publications

References 21 publications

HRR Profiles Time-Frequency Non-Negative Sparse Coding for Sar Target Classification

HRR Profiles Time-Frequency Non-Negative Sparse Coding for Sar Target Classification

Sar Target Classification Using Bayesian Compressive Sensing With Scattering Centers Features

A Support Vector Machine for Identification of Monitors Based on Their Unintended Electromagnetic Emanation

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