Back Propagation Technique for Image Reconstruction of Microwave Tomography

Effendi, Mohammad Ridwan; Willyantho, Ricky; Munir, Achmad

doi:10.1145/3326172.3326227

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…Maximum trunk size (mm) Minimum size of detected void (mm) [28] Microwave imaging (single frequency) 300 50 [29] Microwave imaging (single frequency) 300 50 [31] Microwave imaging (single frequency) 230 60 […”

Section: References Methodsmentioning

confidence: 99%

“…The backpropagation technique was performed to reconstruct the image of a hollow in a wooden trunk. However, the obtained constructed images showed that the hollow could not be detected clearly, and the images were blurry (their detected shapes were not the same) due to the noises and the single frequency measurement [28]. A wooden trunk was characterized combining the qualitative and quantitative microwave imaging techniques.…”

Section: Introductionmentioning

confidence: 99%

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Microwave Imaging of Voids in Oil Palm Trunk Applying UWB Antenna and Robust Time-Reversal Algorithm

et al. 2020

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The oil palm trees in Southeast Asia face a great challenge due to voids within tree trunks. Besides, both plantations and the palm oil industry suffered considerable losses due to ineffective inspection of defective trees. Several techniques such as ultrasound, X-ray, capacitive volume, gamma computed tomography, and microwave tomography were applied to detect and control the decay of a trunk. However, all the techniques showed substantial drawbacks (such as limited resolution, tedious processing time, and use of bulky equipment with limited mobility) except for microwave tomography which overtook the other techniques by using a method that distinguishes the dielectric properties of healthy and diseased trunks. This work proposes ultra-wide-band (UWB) signal transmission and reception using antenna sensor arrays that record reflections from affected regions in the trunk. Various factors have been considered, such as different cylindrical arrays of 4, 8, 12, and 16 antennas; different positions of hollows; a heterogeneous trunk; multiple targets; and larger trunk samples (16–30 cm). To validate the system’s capabilities, two cylindrical wood samples with different diameters of 100 mm and 140 mm and with one hollow and three hollows within are 3D-printed, investigated, and then measured. The authors recommended a robust time-reversal algorithm (RTR) to reconstruct 2D images that successfully identified and localized cavities with the smallest diameter of 3.5 mm. Furthermore, reconstructed images of measured data verified a practical and reliable oil palm trunk imaging and sensing system with a high structural similarity index and resolution.

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Section: References Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microwave Imaging of Voids in Oil Palm Trunk Applying UWB Antenna and Robust Time-Reversal Algorithm

et al. 2020

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“…The backpropagation method was carried out to create images of a hole in a trunk made of wood. Nevertheless, the achieved composed images indicated that the void could not be identified, and the images were dim because of the interferences at only a specific measured frequency . The MWI structures used in these studies are complex.…”

Section: Introductionmentioning

confidence: 93%

“…Nevertheless, the achieved composed images indicated that the void could not be identified, and the images were dim because of the interferences at only a specific measured frequency. 25 The MWI structures used in these studies are complex. Also, the antennas employed in the system are enormous, where a 30-element array was used.…”

Section: Introductionmentioning

confidence: 99%

Health Control of Tree Trunk Utilizing Microwave Imaging and Reverse Problem Algorithms

et al. 2023

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The voids in their trunk significantly affect tropical trees' health. Both the wood and timber industries may face substantial financial losses because of the lack of an effective technique to inspect the defected trees through deep zonal monitoring. Microwave imaging offers the advantages of mobility, processing time, compactness, and resolution over alternative imaging methods. An ultra-wide band (UWB) imaging system consisting of UWB antennas and a reverse problem algorithm is proposed. Several conditions, such as the size of trunk samples (16−30 cm), number of targets, size of voids, heterogeneity of media, and number of layers, are considered in experimental studies. Based on these studies, cylindrical wooden models with 100 and 140 mm diameters, one void at the center, and three voids in different locations were 3D printed. After proving the system's ability through simulation and measurements on 3D models, a rubber-wood trunk with a length of 75 cm was cut into smaller pieces. The images created utilizing the measured data showed that the system could detect voids in the rubber trunk. Furthermore, the system indicated a high percentage of reliability and repeatability.

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“…For the identification of defects, some works use artificial intelligence, specifically the artificial neural network [14], [15]. The reconstruction of the internal characteristics of wood using AI is also related in [16], [17]. However, in these works other types of inspection techniques are considered, like: CT and X-ray.…”

Section: A Related Workmentioning

confidence: 99%

Classification Techniques Applied in Wood Logs Tomographic Image

Pereira Junior,

Carvalho

2023

Anais Estendidos Da XXXVI Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2023)

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The forestry assessment field uses the Non-Destructive Tests (NDTs) to analyze woods logs. To assist the identification of anomalies inside the trunks, the ultrasonic tomography can be used as an alternative. With this technique is possible to evaluate the internal conditions of wooden trunks, through wave propagation applied at specific points. To help the identification and analysis of defects in the tomographic image, this work uses resources from the area of Machine Learning approach in order to identify tomographic images with anomalies. In this study is considered three different classifiers: K-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The experiments performed were compared using metrics: Accuracy, Precision and Recall. To carry out the experiments, a dataset with 5000 ultrasonic tomography images was build using the Data Augmentation process. In the first experiment, the metrics are calculated based on texture descriptors. The best accuracy results obtained for the CNN, SVM and k-NN models were respectively 89.00%, 80.70% and 79.81%. In the second experiment, the anomaly segmentation was performed with Otsu’s segmentation, then it was tested in the SVM classification model. The results found that the SVM model has superior results of demarcation when compared to the CNN model.

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Back Propagation Technique for Image Reconstruction of Microwave Tomography

Cited by 9 publications

References 9 publications

Microwave Imaging of Voids in Oil Palm Trunk Applying UWB Antenna and Robust Time-Reversal Algorithm

Microwave Imaging of Voids in Oil Palm Trunk Applying UWB Antenna and Robust Time-Reversal Algorithm

Health Control of Tree Trunk Utilizing Microwave Imaging and Reverse Problem Algorithms

Classification Techniques Applied in Wood Logs Tomographic Image

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