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Azhar Zam - One of the best experts on this subject based on the ideXlab platform.
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miniaturized optoacoustic Feedback Sensor for smart laser osteotome fiber coupled fabry perot etalon Sensor
Sensors and Actuators A-physical, 2021Co-Authors: Herve Nguendon Kenhagho, Ferda Canbaz, Raphael Guzman, Philippe C Cattin, Azhar ZamAbstract:Abstract We report on a custom-made, fiber-coupled Fabry-Perot etalon Sensor to measure the acoustic shock waves (ASW) generated during laser ablation. A frequency-doubled Nd:YAG laser (532 nm) with a 5 ns pulse duration was used to produce craters on the surfaces of five different tissues —hard and soft bone, muscle, fat and skin— from five fresh porcine proximal and distal femur specimens. After collecting the ASW signals generated during laser ablation, we split the Fourier spectrum of the measured ASW into six equal bands and used each as an input for principal component analysis (PCA). We used PCA to reduce the dimensionality of each band and fed the PCA scores to an Artificial Neural Network (ANN) for classification. The most accurate tissue differentiation occurred at a band of 1.67–2.08 MHz. In total 18000 data points were collected from the femur samples and split into training (10800), validation (3600, and testing (3600) data. From a confusion matrix and the receiver operating characteristic (ROC), we observed that the experimental-based scores of hard and soft bone, fat, muscle and skin yielded average classification accuracies (with leave-one-out cross-validation) of 100 %, 99.55 %, 88.89%, 99.33%, and 100%, respectively. The area under the ROC curve (AUC) was more than 98.61 %, for all tissue types. The proposed method has the potential to provide real-time Feedback during laser osteotomy, to prevent the cutting of vital tissues.
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a first approach to miniaturized optoacoustic Feedback Sensor for smart laser osteotome fiber coupled fabry perot etalon Sensor
IEEE Sensors, 2019Co-Authors: Herve Nguendon Kenhagho, Ferda Canbaz, Raphael Guzman, Philippe C Cattin, Georg Rauter, Azhar ZamAbstract:We used a custom-made, fiber-coupled Fabry-Perot etalon Sensor to measure the acoustic shock waves (ASW) generated during laser ablation. Based on the ASW signal measured, we could differentiate hard bone, muscle, and fat tissues with an average classification error of 6.39 %. A frequency-doubled Nd:YAG laser (532 nm) with a 5 ns pulse duration, was used to produce craters on the surface of tissues derived from an extracted fresh porcine proximal femur. After recording the ASW signals generated during laser ablation, we split the Fourier spectrum of measured ASWs into six equal bands and each used as an input for Principal Component Analysis (PCA). We used PCA to reduce the dimensionality of each band, and the Mahalanobis distance measure to classify tissue types based on the PC-scores. The most accurate differentiation was possible in the band of 1.25–1.67 MHz. The first 840 data points measured were used as "training data", while the last 360 were considered "testing data". Based on a confusion matrix, the ASW-based scores yielded classification errors of 5 % (hard bone), 6.94 % (muscle) and 7.22 % (fat), respectively. The proposed method has the potential for real-time Feedback during laser osteotomy.
Raphael Guzman - One of the best experts on this subject based on the ideXlab platform.
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miniaturized optoacoustic Feedback Sensor for smart laser osteotome fiber coupled fabry perot etalon Sensor
Sensors and Actuators A-physical, 2021Co-Authors: Herve Nguendon Kenhagho, Ferda Canbaz, Raphael Guzman, Philippe C Cattin, Azhar ZamAbstract:Abstract We report on a custom-made, fiber-coupled Fabry-Perot etalon Sensor to measure the acoustic shock waves (ASW) generated during laser ablation. A frequency-doubled Nd:YAG laser (532 nm) with a 5 ns pulse duration was used to produce craters on the surfaces of five different tissues —hard and soft bone, muscle, fat and skin— from five fresh porcine proximal and distal femur specimens. After collecting the ASW signals generated during laser ablation, we split the Fourier spectrum of the measured ASW into six equal bands and used each as an input for principal component analysis (PCA). We used PCA to reduce the dimensionality of each band and fed the PCA scores to an Artificial Neural Network (ANN) for classification. The most accurate tissue differentiation occurred at a band of 1.67–2.08 MHz. In total 18000 data points were collected from the femur samples and split into training (10800), validation (3600, and testing (3600) data. From a confusion matrix and the receiver operating characteristic (ROC), we observed that the experimental-based scores of hard and soft bone, fat, muscle and skin yielded average classification accuracies (with leave-one-out cross-validation) of 100 %, 99.55 %, 88.89%, 99.33%, and 100%, respectively. The area under the ROC curve (AUC) was more than 98.61 %, for all tissue types. The proposed method has the potential to provide real-time Feedback during laser osteotomy, to prevent the cutting of vital tissues.
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a first approach to miniaturized optoacoustic Feedback Sensor for smart laser osteotome fiber coupled fabry perot etalon Sensor
IEEE Sensors, 2019Co-Authors: Herve Nguendon Kenhagho, Ferda Canbaz, Raphael Guzman, Philippe C Cattin, Georg Rauter, Azhar ZamAbstract:We used a custom-made, fiber-coupled Fabry-Perot etalon Sensor to measure the acoustic shock waves (ASW) generated during laser ablation. Based on the ASW signal measured, we could differentiate hard bone, muscle, and fat tissues with an average classification error of 6.39 %. A frequency-doubled Nd:YAG laser (532 nm) with a 5 ns pulse duration, was used to produce craters on the surface of tissues derived from an extracted fresh porcine proximal femur. After recording the ASW signals generated during laser ablation, we split the Fourier spectrum of measured ASWs into six equal bands and each used as an input for Principal Component Analysis (PCA). We used PCA to reduce the dimensionality of each band, and the Mahalanobis distance measure to classify tissue types based on the PC-scores. The most accurate differentiation was possible in the band of 1.25–1.67 MHz. The first 840 data points measured were used as "training data", while the last 360 were considered "testing data". Based on a confusion matrix, the ASW-based scores yielded classification errors of 5 % (hard bone), 6.94 % (muscle) and 7.22 % (fat), respectively. The proposed method has the potential for real-time Feedback during laser osteotomy.
Herve Nguendon Kenhagho - One of the best experts on this subject based on the ideXlab platform.
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miniaturized optoacoustic Feedback Sensor for smart laser osteotome fiber coupled fabry perot etalon Sensor
Sensors and Actuators A-physical, 2021Co-Authors: Herve Nguendon Kenhagho, Ferda Canbaz, Raphael Guzman, Philippe C Cattin, Azhar ZamAbstract:Abstract We report on a custom-made, fiber-coupled Fabry-Perot etalon Sensor to measure the acoustic shock waves (ASW) generated during laser ablation. A frequency-doubled Nd:YAG laser (532 nm) with a 5 ns pulse duration was used to produce craters on the surfaces of five different tissues —hard and soft bone, muscle, fat and skin— from five fresh porcine proximal and distal femur specimens. After collecting the ASW signals generated during laser ablation, we split the Fourier spectrum of the measured ASW into six equal bands and used each as an input for principal component analysis (PCA). We used PCA to reduce the dimensionality of each band and fed the PCA scores to an Artificial Neural Network (ANN) for classification. The most accurate tissue differentiation occurred at a band of 1.67–2.08 MHz. In total 18000 data points were collected from the femur samples and split into training (10800), validation (3600, and testing (3600) data. From a confusion matrix and the receiver operating characteristic (ROC), we observed that the experimental-based scores of hard and soft bone, fat, muscle and skin yielded average classification accuracies (with leave-one-out cross-validation) of 100 %, 99.55 %, 88.89%, 99.33%, and 100%, respectively. The area under the ROC curve (AUC) was more than 98.61 %, for all tissue types. The proposed method has the potential to provide real-time Feedback during laser osteotomy, to prevent the cutting of vital tissues.
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a first approach to miniaturized optoacoustic Feedback Sensor for smart laser osteotome fiber coupled fabry perot etalon Sensor
IEEE Sensors, 2019Co-Authors: Herve Nguendon Kenhagho, Ferda Canbaz, Raphael Guzman, Philippe C Cattin, Georg Rauter, Azhar ZamAbstract:We used a custom-made, fiber-coupled Fabry-Perot etalon Sensor to measure the acoustic shock waves (ASW) generated during laser ablation. Based on the ASW signal measured, we could differentiate hard bone, muscle, and fat tissues with an average classification error of 6.39 %. A frequency-doubled Nd:YAG laser (532 nm) with a 5 ns pulse duration, was used to produce craters on the surface of tissues derived from an extracted fresh porcine proximal femur. After recording the ASW signals generated during laser ablation, we split the Fourier spectrum of measured ASWs into six equal bands and each used as an input for Principal Component Analysis (PCA). We used PCA to reduce the dimensionality of each band, and the Mahalanobis distance measure to classify tissue types based on the PC-scores. The most accurate differentiation was possible in the band of 1.25–1.67 MHz. The first 840 data points measured were used as "training data", while the last 360 were considered "testing data". Based on a confusion matrix, the ASW-based scores yielded classification errors of 5 % (hard bone), 6.94 % (muscle) and 7.22 % (fat), respectively. The proposed method has the potential for real-time Feedback during laser osteotomy.
Shaohui Foong - One of the best experts on this subject based on the ideXlab platform.
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lateral optical Sensor with slip detection for locating live products on moving conveyor
IEEE Transactions on Automation Science and Engineering, 2010Co-Authors: Kokmeng Lee, Shaohui FoongAbstract:This paper presents a method to determine the 2-D profile and motion of a live product (such as chicken for poultry meat processing) on a moving conveyor from a lateral optical Sensor that consists of an orthogonal pair of line array (LA) scanners. Unlike most line array (LA) scanners designed to provide a 2-D image of a static object, the lateral optical Sensor presented here offers a practical means to detect object slippage on the conveyor in real time. Three examples are given to illustrate the effectiveness of this sensing method. The first simulates the 2-D boundary of a geometrically well-defined object on an accelerating conveyor, which offers intuitive insights on the effects of conveyor dynamics and object slippage on the accuracy of the 2-D boundary measurement. The second experimentally demonstrates the extendibility of LA Sensors to detect both engineering and natural objects. The final example illustrates the application of the lateral optical Sensor as a real time Feedback Sensor for active singulation of natural objects.
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lateral optical Sensor with slip detection of natural objects on moving conveyor
International Conference on Robotics and Automation, 2008Co-Authors: Kokmeng Lee, Shaohui FoongAbstract:This paper presents a method to determine the 2D profile and velocity of an object on a moving conveyor from a lateral optical Sensor that consists of an orthogonal pair of line array (LA) scanners. Unlike most LA scanners which are designed to provide a 2D image of a static object, the lateral optical Sensor presented here offers an additional and practical means to detect object slippage on the conveyor in real time. We illustrate numerically the effectiveness of this sensing method with two illustrative examples. The first simulates the 2D boundary of a geometrically well-defined object on an accelerating conveyor, which offers intuitive insights on the effects of conveyor dynamics and object slippage on the accuracy of the 2D boundary measurement. The second demonstrates the application of the lateral optical Sensor as a real time Feedback Sensor for active singulation of natural objects.
Ferda Canbaz - One of the best experts on this subject based on the ideXlab platform.
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miniaturized optoacoustic Feedback Sensor for smart laser osteotome fiber coupled fabry perot etalon Sensor
Sensors and Actuators A-physical, 2021Co-Authors: Herve Nguendon Kenhagho, Ferda Canbaz, Raphael Guzman, Philippe C Cattin, Azhar ZamAbstract:Abstract We report on a custom-made, fiber-coupled Fabry-Perot etalon Sensor to measure the acoustic shock waves (ASW) generated during laser ablation. A frequency-doubled Nd:YAG laser (532 nm) with a 5 ns pulse duration was used to produce craters on the surfaces of five different tissues —hard and soft bone, muscle, fat and skin— from five fresh porcine proximal and distal femur specimens. After collecting the ASW signals generated during laser ablation, we split the Fourier spectrum of the measured ASW into six equal bands and used each as an input for principal component analysis (PCA). We used PCA to reduce the dimensionality of each band and fed the PCA scores to an Artificial Neural Network (ANN) for classification. The most accurate tissue differentiation occurred at a band of 1.67–2.08 MHz. In total 18000 data points were collected from the femur samples and split into training (10800), validation (3600, and testing (3600) data. From a confusion matrix and the receiver operating characteristic (ROC), we observed that the experimental-based scores of hard and soft bone, fat, muscle and skin yielded average classification accuracies (with leave-one-out cross-validation) of 100 %, 99.55 %, 88.89%, 99.33%, and 100%, respectively. The area under the ROC curve (AUC) was more than 98.61 %, for all tissue types. The proposed method has the potential to provide real-time Feedback during laser osteotomy, to prevent the cutting of vital tissues.
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a first approach to miniaturized optoacoustic Feedback Sensor for smart laser osteotome fiber coupled fabry perot etalon Sensor
IEEE Sensors, 2019Co-Authors: Herve Nguendon Kenhagho, Ferda Canbaz, Raphael Guzman, Philippe C Cattin, Georg Rauter, Azhar ZamAbstract:We used a custom-made, fiber-coupled Fabry-Perot etalon Sensor to measure the acoustic shock waves (ASW) generated during laser ablation. Based on the ASW signal measured, we could differentiate hard bone, muscle, and fat tissues with an average classification error of 6.39 %. A frequency-doubled Nd:YAG laser (532 nm) with a 5 ns pulse duration, was used to produce craters on the surface of tissues derived from an extracted fresh porcine proximal femur. After recording the ASW signals generated during laser ablation, we split the Fourier spectrum of measured ASWs into six equal bands and each used as an input for Principal Component Analysis (PCA). We used PCA to reduce the dimensionality of each band, and the Mahalanobis distance measure to classify tissue types based on the PC-scores. The most accurate differentiation was possible in the band of 1.25–1.67 MHz. The first 840 data points measured were used as "training data", while the last 360 were considered "testing data". Based on a confusion matrix, the ASW-based scores yielded classification errors of 5 % (hard bone), 6.94 % (muscle) and 7.22 % (fat), respectively. The proposed method has the potential for real-time Feedback during laser osteotomy.
