Simulation of Laser Triangulation Sensors Scanning for Design and Evaluation Purposes

Cajal, C.; Santolaria, J.; Samper, D.

doi:10.2507/ijsimm14(2)6.296

Cited by 7 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although a number of studies have described the use of advanced technologies in diagnosis and treatment [12] to [14], only a few studies have focused on improving the specificity of AFB [15] to [17]. In one study of the spectra of autofluorescent light, the authors added endoscopic optical spectroscopy to AFB to improve its specificity [17].…”

Section: In Addition To Its Utilization Of Changes In Fluorescence Inmentioning

confidence: 99%

Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy

Finkst

Tasič

Terčelj

et al. 2017

SV-JME

View full text Add to dashboard Cite

This paper presents a novel approach to the classification of bronchial tissue as either malignant or precancerous based on autofluorescence bronchoscopy (AFB) images. The study consisted of 44 images, of which 22 were confirmed as malignant and 22 as nonmalignant precancerous cases. Our approach starts with the detection of a region of interest (ROI). This is followed by an analysis of semi-normal intensity distributions in gray-scale images of red and green components of the previously identified ROI. Based on the results of this analysis, features are computed, which are then used to build an image-classification model. This model classifies the tissue images into malignant/ nonmalignant classes. We utilized several classification algorithms, i.e., naive Bayes, K-nearest-neighbor (K-NN), and support vector machine (SVM) with dot kernel. The criteria used when testing their performance were accuracy, sensitivity, specificity, and the area under the curve. Wilcoxon's signed-rank test was used to confirm the accuracy of the classification method. The proposed method was compared to a similar approach reported by Buountris et al., who analyzed the texture features in a gray-level co-occurrence matrix (GLCM). Using the bestperforming classification algorithm (SVM with dot kernel), the accuracy of the proposed approach (95.8 %) was better than that reported by Bountris et al. (92.1 %).

show abstract

Section: In Addition To Its Utilization Of Changes In Fluorescence Inmentioning

confidence: 99%

Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy

Finkst

Tasič

Terčelj

et al. 2017

SV-JME

View full text Add to dashboard Cite

show abstract

“…Although previous studies have investigated the simulation of laser triangulation sensors for scanning parts, focusing on the geometrical model of the sensor, the impact of the roughness of the scanned material is not usually considered. For example, the work in [5] presents the simulation of scanning laser triangulation sensors for design and evaluation purposes. Based on the CAD files of the parts, the simulation allows for the virtual recreation of the camera acquisition, enabling collision and occlusion detection for complete and effective digitization.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Laser Profilometer Measurements in the Presence of Speckle Using Perlin Noise

Roos-Hoefgeest,

Álvarez

et al. 2023

Sensors

View full text Add to dashboard Cite

In the manufacturing industry, inspection systems play a crucial role in ensuring product quality. High-resolution profilometric sensors have become increasingly popular for inspection due to their ability to provide detailed surface information. However, the development and testing of inspection systems can be costly and time-consuming. This paper presents the development of a simulation of an inspection system using a high-resolution profilometric sensor. A geometrical and noise model is proposed to simulate the readings of any actual profilometric sensor. The model replicates the sensor’s movement on the CAD model of the inspected part. The model incorporates the physical properties of the sensor and combines noise sources from sensor uncertainty and speckle noise induced by the roughness of the material. Our contribution lies in noise modeling. This work proposes a combination of Perlin noise to simulate the speckle noise and Gaussian noise for the uncertainty-related noise. Perlin noise is generated based on the surface roughness parameters of the inspected part. The accuracy of the simulation system is evaluated by comparing the simulated scans with real scans. The results highlight the ability to simulate real scans of different parts, using commercial sensor specifications and the CAD model of the inspected part.

show abstract

“…In many applications, there are usually a variety of monitoring objects in the surveillant area. For example, the sensor nodes need to monitor water temperature and salinity in water environmental monitoring, or the sensor nodes need to surveil a variety of chemical diffusions to provide early warnings about factory pollution [3,9,10,12,30].…”

Section: Introductionmentioning

confidence: 99%

Coverage Hole Recovery Algorithm Based on Molecule Model in Heterogeneous WSNs

Song

Gong

Jin

et al. 2017

INT J COMPUT COMMUN

View full text Add to dashboard Cite

In diverse application fields, the increasing requisitions of Wireless Sensor Networks (WSNs) have more and more research dedicated to the question of sensor nodes’ deployment in recent years. For deployment of sensor nodes, some key points that should be taken into consideration are the coverage area to be monitored, energy consumed of nodes, connectivity, amount of deployed sensors and lifetime of the WSNs. This paper analyzes the wireless sensor network nodes deployment optimization problem. Wireless sensor nodes deployment determines the nodes’ capability and lifetime. For node deployment in heterogeneous sensor networks based on different probability sensing models of heterogeneous nodes, the author refers to the organic small molecule model and proposes a molecule sensing model of heterogeneous nodes in this paper. DSmT is an extension of the classical theory of evidence, which can combine with any type of trust function of an independent source, mainly concentrating on combined uncertainty, high conflict, and inaccurate source of evidence. Referring to the data fusion model, the changes in the network coverage ratio after using the new sensing model and data fusion algorithm are studied. According to the research results, the nodes deployment scheme of heterogeneous sensor networks based on the organic small molecule model is proposed in this paper. The simulation model is established by MATLAB software. The simulation results show that the effectiveness of the algorithm, the network coverage, and detection efficiency of nodes are improved, the lifetime of the network is prolonged, energy consumption and the number of deployment nodes are reduced, and the scope of perceiving is expanded. As a result, the coverage hole recovery algorithm can improve the detection performance of the network in the initial deployment phase and coverage hole recovery phase.

show abstract

Simulation of Laser Triangulation Sensors Scanning for Design and Evaluation Purposes

Cited by 7 publications

References 23 publications

Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy

Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy

Simulation of Laser Profilometer Measurements in the Presence of Speckle Using Perlin Noise

Coverage Hole Recovery Algorithm Based on Molecule Model in Heterogeneous WSNs

Contact Info

Product

Resources

About