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Dr. Xin Wang
School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway 47500, Malaysia

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0 laser pulse
0 lasers
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Gated Imaging
laser pulse
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finite element
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lasers
surface reconstruction
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Journal article
Published: 24 June 2021 in IEEE Access
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The rise of model-based and machine learning methods have created increasingly realistic opportunities to implement personalized, patient-specific mechanical ventilation (MV) in the ICU. These methods require monitoring of real-time patient ventilation waveform data (VWD) during MV treatment. However, there are relatively few non-invasive and/or non-proprietary systems to monitor and record patient-specific lung condition in real-time. In this paper, we present a CARE network data acquisition and monitoring system (CARENet) to automate data collection and to remotely monitor patient-specific lung condition and ventilation parameters. The automated system acquires VWD from a mechanical ventilator using a data acquisition device (DAQ), stores data in network-attached storage (NAS), and processes VWDs via a data management platform (DMP) web application. The web application enables real-time and retrospective model-based monitoring and analysis of clinical MV data. In particular, CARENet provides detailed breath-by-breath patient-specific respiratory mechanics, as well as the overall trends assessing changes in patient condition. Validation testing with a retrospective data set illustrated how breath-to-breath and time-varying patient-ventilator interaction during MV can be monitored, and, in turn, can be used to guide MV treatment. The network data acquisition system design presented is low-cost, open, and enables continuous, automated, scalable, real-time collection and analysis of waveform data, to help improve decision making, care, and outcomes in MV.

ACS Style

Qing Arn Ng; Yeong Shiong Chiew; Xin Wang; Chee Pin Tan; Mohd Basri Mat Nor; Nor Salwa Damanhuri; J. Geoffrey Chase. Network Data Acquisition and Monitoring System for Intensive Care Mechanical Ventilation Treatment. IEEE Access 2021, 9, 1 -1.

AMA Style

Qing Arn Ng, Yeong Shiong Chiew, Xin Wang, Chee Pin Tan, Mohd Basri Mat Nor, Nor Salwa Damanhuri, J. Geoffrey Chase. Network Data Acquisition and Monitoring System for Intensive Care Mechanical Ventilation Treatment. IEEE Access. 2021; 9 ():1-1.

Chicago/Turabian Style

Qing Arn Ng; Yeong Shiong Chiew; Xin Wang; Chee Pin Tan; Mohd Basri Mat Nor; Nor Salwa Damanhuri; J. Geoffrey Chase. 2021. "Network Data Acquisition and Monitoring System for Intensive Care Mechanical Ventilation Treatment." IEEE Access 9, no. : 1-1.

Short communication
Published: 08 April 2021 in Case Studies in Thermal Engineering
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Graphite-oxide (GO) nanofluids with enhanced thermal conductivity are successfully prepared using pulsed laser ablation in liquid (PLAL) of a graphite target in deionized water. The effects of laser frequencies are investigated on the variations of GO nanoparticles. The morphology, structure and composition of the nanoparticles are characterized using various spectroscopic techniques. During the PLAL process, graphite is oxidized to GO which is inherently hydrophilic, no surfactant is required in the preparation of nanofluid. The laser frequency significantly affects the size and morphology of the GO nanoparticles during laser ablation, leading to a profound variation in the thermophysical properties of the GO nanofluids. At the laser frequency of 10 Hz, the maximum thermal conductivity enhancement of 82% is achieved at a temperature of 50 °C while the maximum viscosity increment is recorded at a temperature of 30 °C. This study shows the great potential of the PLAL method in synthesizing GO nanofluid with anomalously enhanced thermal conductivity.

ACS Style

Wai Kit Woo; Yew Mun Hung; Xin Wang. Anomalously enhanced thermal conductivity of graphite-oxide nanofluids synthesized via liquid-phase pulsed laser ablation. Case Studies in Thermal Engineering 2021, 25, 100993 .

AMA Style

Wai Kit Woo, Yew Mun Hung, Xin Wang. Anomalously enhanced thermal conductivity of graphite-oxide nanofluids synthesized via liquid-phase pulsed laser ablation. Case Studies in Thermal Engineering. 2021; 25 ():100993.

Chicago/Turabian Style

Wai Kit Woo; Yew Mun Hung; Xin Wang. 2021. "Anomalously enhanced thermal conductivity of graphite-oxide nanofluids synthesized via liquid-phase pulsed laser ablation." Case Studies in Thermal Engineering 25, no. : 100993.

Journal article
Published: 03 March 2021 in International Journal of Pavement Engineering
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Cracking is a common fault in asphalt and concrete pavements, which causes water damage and further defects if not repaired in timely fashion. Conventional pavement crack sealing methods based on machines and manual operations are generally subjective, labour-intensive, risky, and inefficient. A laboratory prototype of an automatic pavement crack sealing platform is proposed in this paper, which uses a modified three-dimensional (3D) printer and computer vision. A modified 3D printer based on fused deposition modelling (FDM) was combined with an image capturing platform, an image processing algorithm and a path planning method to form the automated pavement crack sealing platform, which can automatically detect pavement cracks and seal them with bitumen emulsion sealant. Specimens of concrete pavement slabs with cracks were produced in the laboratory to test the proposed method, and the cracks were then detected and sealed by the proposed platform. The results show that 3D printing is an effective method for automated pavement crack sealing, which is recommended in the field of automatic road maintenance and repair.

ACS Style

Jingwei Liu; Xu Yang; Xin Wang; Jian Wei Yam. A laboratory prototype of automatic pavement crack sealing based on a modified 3D printer. International Journal of Pavement Engineering 2021, 1 -12.

AMA Style

Jingwei Liu, Xu Yang, Xin Wang, Jian Wei Yam. A laboratory prototype of automatic pavement crack sealing based on a modified 3D printer. International Journal of Pavement Engineering. 2021; ():1-12.

Chicago/Turabian Style

Jingwei Liu; Xu Yang; Xin Wang; Jian Wei Yam. 2021. "A laboratory prototype of automatic pavement crack sealing based on a modified 3D printer." International Journal of Pavement Engineering , no. : 1-12.

Full paper
Published: 27 January 2021 in Small
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In this paper, 2D borophene is synthesized through a liquid‐phase exfoliation. The morphology and structure of as‐prepared borophene are systemically analyzed, and the Z‐scan is used to measure the nonlinear optical properties. It is found that the saturable absorber (SA) properties of borophene make it serve as an excellent broadband optical switch, which is strongly used for mode‐locking in near‐ and mid‐infrared laser systems. Ultrastable pulses with durations as short as 792 and 693 fs are successfully delivered at the central wavelengths of 1063 and 1560 nm, respectively. Furthermore, stable pulses at a wavelength of 1878 nm are demonstrated from a thulium mode‐locked fiber laser based on the same borophene SA. This research reveals a significant potential for borophene used in lasers helping extending the frontiers of photonic technologies.

ACS Style

Chunyang Ma; Peng Yin; Karim Khan; Ayesha Khan Tareen; Rui Huang; Juan Du; Ye Zhang; Zhe Shi; Rui Cao; Songrui Wei; Xin Wang; Yanqi Ge; Yufeng Song; Lingfeng Gao. Broadband Nonlinear Photonics in Few‐Layer Borophene. Small 2021, 17, 2006891 .

AMA Style

Chunyang Ma, Peng Yin, Karim Khan, Ayesha Khan Tareen, Rui Huang, Juan Du, Ye Zhang, Zhe Shi, Rui Cao, Songrui Wei, Xin Wang, Yanqi Ge, Yufeng Song, Lingfeng Gao. Broadband Nonlinear Photonics in Few‐Layer Borophene. Small. 2021; 17 (7):2006891.

Chicago/Turabian Style

Chunyang Ma; Peng Yin; Karim Khan; Ayesha Khan Tareen; Rui Huang; Juan Du; Ye Zhang; Zhe Shi; Rui Cao; Songrui Wei; Xin Wang; Yanqi Ge; Yufeng Song; Lingfeng Gao. 2021. "Broadband Nonlinear Photonics in Few‐Layer Borophene." Small 17, no. 7: 2006891.

Journal article
Published: 21 January 2021 in Laser Physics Letters
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ACS Style

Mengdi Li; Anumol Mathai; Xiping Xu; Xin Wang; Yue Pan; Xuefeng Gao. Non-line-of-sight transparent object detection and reconstruction based on passive single-pixel imaging. Laser Physics Letters 2021, 18, 025204 .

AMA Style

Mengdi Li, Anumol Mathai, Xiping Xu, Xin Wang, Yue Pan, Xuefeng Gao. Non-line-of-sight transparent object detection and reconstruction based on passive single-pixel imaging. Laser Physics Letters. 2021; 18 (2):025204.

Chicago/Turabian Style

Mengdi Li; Anumol Mathai; Xiping Xu; Xin Wang; Yue Pan; Xuefeng Gao. 2021. "Non-line-of-sight transparent object detection and reconstruction based on passive single-pixel imaging." Laser Physics Letters 18, no. 2: 025204.

Short communication
Published: 20 January 2021 in Case Studies in Thermal Engineering
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Laser has become a well-accepted technique for surgical procedures. To fit the physically irradiated area to the clinically planned target area, a multifibre laser system can be used. This paper theoretically investigated the temperature distribution in a three-dimensional biological tissue when irradiated by multifibre lasers with the aids of the dual-phase-lag (DPL) bio-heat conduction model. First, four laser beams located on the corners of a square are used as the heat sources. The method of separation of variables is adopted to obtain the analytical expression of temperature. The characteristics of DPL bio-heat transfer model and the difference with Pennes model were checked graphically. It is found that the effect of multiple laser beams on temperature response differs from those of single laser beam distinctly. A second heating phenomenon occurs and the contour of a square with curved corners are obtained. Then the laser beams are located to form a triangle and a trapezoid and the corresponding temperature responses are studied. Different irradiated zone can be obtained and accomplished by changing the spot size, the arrangement layout and the interval distance of the laser beams.

ACS Style

Qiao Zhang; Yuxin Sun; Jialing Yang; Ai Kah Soh; Xin Wang. Theoretical analysis of thermal response in biological skin tissue subjected to multiple laser beams. Case Studies in Thermal Engineering 2021, 24, 100853 .

AMA Style

Qiao Zhang, Yuxin Sun, Jialing Yang, Ai Kah Soh, Xin Wang. Theoretical analysis of thermal response in biological skin tissue subjected to multiple laser beams. Case Studies in Thermal Engineering. 2021; 24 ():100853.

Chicago/Turabian Style

Qiao Zhang; Yuxin Sun; Jialing Yang; Ai Kah Soh; Xin Wang. 2021. "Theoretical analysis of thermal response in biological skin tissue subjected to multiple laser beams." Case Studies in Thermal Engineering 24, no. : 100853.

Journal article
Published: 12 January 2021 in Journal of Electronic Imaging
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ACS Style

Zhenyong Shin; Horng Sheng Lin; Tong-Yuen Chai; Xin Wang; Sing Yee Chua. Programmable spatially variant single-pixel imaging based on compressive sensing. Journal of Electronic Imaging 2021, 30, 021004 .

AMA Style

Zhenyong Shin, Horng Sheng Lin, Tong-Yuen Chai, Xin Wang, Sing Yee Chua. Programmable spatially variant single-pixel imaging based on compressive sensing. Journal of Electronic Imaging. 2021; 30 (2):021004.

Chicago/Turabian Style

Zhenyong Shin; Horng Sheng Lin; Tong-Yuen Chai; Xin Wang; Sing Yee Chua. 2021. "Programmable spatially variant single-pixel imaging based on compressive sensing." Journal of Electronic Imaging 30, no. 2: 021004.

Journal article
Published: 05 January 2021 in Sensors
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Due to medium scattering, absorption, and complex light interactions, capturing objects from the underwater environment has always been a difficult task. Single-pixel imaging (SPI) is an efficient imaging approach that can obtain spatial object information under low-light conditions. In this paper, we propose a single-pixel object inspection system for the underwater environment based on compressive sensing super-resolution convolutional neural network (CS-SRCNN). With the CS-SRCNN algorithm, image reconstruction can be achieved with 30% of the total pixels in the image. We also investigate the impact of compression ratios on underwater object SPI reconstruction performance. In addition, we analyzed the effect of peak signal to noise ratio (PSNR) and structural similarity index (SSIM) to determine the image quality of the reconstructed image. Our work is compared to the SPI system and SRCNN method to demonstrate its efficiency in capturing object results from an underwater environment. The PSNR and SSIM of the proposed method have increased to 35.44% and 73.07%, respectively. This work provides new insight into SPI applications and creates a better alternative for underwater optical object imaging to achieve good quality.

ACS Style

Mengdi Li; Anumol Mathai; Stephen Lau; Jian Yam; Xiping Xu; Xin Wang. Underwater Object Detection and Reconstruction Based on Active Single-Pixel Imaging and Super-Resolution Convolutional Neural Network. Sensors 2021, 21, 313 .

AMA Style

Mengdi Li, Anumol Mathai, Stephen Lau, Jian Yam, Xiping Xu, Xin Wang. Underwater Object Detection and Reconstruction Based on Active Single-Pixel Imaging and Super-Resolution Convolutional Neural Network. Sensors. 2021; 21 (1):313.

Chicago/Turabian Style

Mengdi Li; Anumol Mathai; Stephen Lau; Jian Yam; Xiping Xu; Xin Wang. 2021. "Underwater Object Detection and Reconstruction Based on Active Single-Pixel Imaging and Super-Resolution Convolutional Neural Network." Sensors 21, no. 1: 313.

Journal article
Published: 28 September 2020 in Energy
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We conduct experiments to study the effects of incorporation of carbon nanotubes (CNTs) coatings on the thermal performance of micro heat pipe (MHP) arrays. The microchannels of MHP are fully coated with CNTs which are functionalized through a thermal curing process. The cured CNTs coating manifests a superhydrophilic characteristic and fast water permeation property. The rapid water permeation through CNTs nanostructure enhances the evaporation at the evaporator section and the fluid circulation synergically in the MHP. For evaporation, the superhydrophilic highly permeable porous CNTs nanostructures increase the nucleation sites and promote film-wise evaporation which is more efficient than the bulk evaporation. For circulation of working fluid, an intricately interconnected CNTs networks facilitate the fluid transport with enhanced capillary pressure. The effective thermal conductivity, which denotes the overall performance of a micro heat pipe manifests a maximum enhancement of 202%; and the evaporator heat transfer coefficient which represents the evaporation strength is enhanced up to 61%. Computationally, molecular dynamics simulations are performed to investigate the fast water permeation property of CNTs nanostructure which leads to the anomalous thermal performance enhancement. This study provides interesting insight into the viability of incorporating CNTs nanostructures into an MHP for microscale cooling applications.

ACS Style

Edmund Chong Jie Ng; Tze Cheng Kueh; Xin Wang; Ai Kah Soh; Yew Mun Hung. Anomalously enhanced thermal performance of carbon-nanotubes coated micro heat pipes. Energy 2020, 214, 118909 .

AMA Style

Edmund Chong Jie Ng, Tze Cheng Kueh, Xin Wang, Ai Kah Soh, Yew Mun Hung. Anomalously enhanced thermal performance of carbon-nanotubes coated micro heat pipes. Energy. 2020; 214 ():118909.

Chicago/Turabian Style

Edmund Chong Jie Ng; Tze Cheng Kueh; Xin Wang; Ai Kah Soh; Yew Mun Hung. 2020. "Anomalously enhanced thermal performance of carbon-nanotubes coated micro heat pipes." Energy 214, no. : 118909.

Journal article
Published: 28 September 2020 in Measurement
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Early detection of tool defects enables proactive prevention of disruption, thus increasing productivity, maintaining quality and agility that brings significant competitive value to the organization. Hence, an effective tool wear monitoring system is vital for intelligent machining process. With the aim to develop an on-machine universal offline monitoring system, a novel quantitative image-based tool wear measurement system based on cross correlation analysis, is proposed to measure tool wear directly from the machining workbench. The sensitivity and accuracy of the proposed technique were further improved through cross-covariance analysis of original and worn tool images. Analyses on various wear conditions of drill bit, end mill, taper tap and carbide insert demonstrated the high effectiveness of the developed measurement system, reflected in the cross-correlation graphs pattern with wear measurement at a microscale down to 100µm. The cross-correlation based measurement enables optimization of the machining productivity through just-in-time tool change through effective monitoring technique.

ACS Style

Ka Mun Fong; Xin Wang; Shahrul Kamaruddin; Mohd-Zulhilmi Ismadi. Investigation on universal tool wear measurement technique using image-based cross-correlation analysis. Measurement 2020, 169, 108489 .

AMA Style

Ka Mun Fong, Xin Wang, Shahrul Kamaruddin, Mohd-Zulhilmi Ismadi. Investigation on universal tool wear measurement technique using image-based cross-correlation analysis. Measurement. 2020; 169 ():108489.

Chicago/Turabian Style

Ka Mun Fong; Xin Wang; Shahrul Kamaruddin; Mohd-Zulhilmi Ismadi. 2020. "Investigation on universal tool wear measurement technique using image-based cross-correlation analysis." Measurement 169, no. : 108489.

Industrial application
Published: 17 September 2020 in Computer-Aided Civil and Infrastructure Engineering
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Cracking is a common pavement distress that would cause further severe problems if not repaired timely, which means that it is important to accurately extract the information of pavement cracks through detection and segmentation. Automated pavement crack detection and segmentation using deep learning are more efficient and accurate than conventional methods, which could be further improved. While many existing studies have utilized deep learning in pavement crack segmentation, which segments cracks from non‐crack regions, few studies have taken the exact pavement crack detection into account, which identifies cracks in the images from other objects. A two‐step pavement crack detection and segmentation method based on convolutional neural network was proposed in this paper. An automated pavement crack detection algorithm was developed using the modified You Only Look Once 3rd version in the first step. The proposed crack segmentation method in the second step was based on the modified U‐Net, whose encoder was replaced with a pre‐trained ResNet‐34 and the up‐sample part was added with spatial and channel squeeze and excitation (SCSE) modules. Proposed method combines pavement crack detection and segmentation together, so that the detected cracks from the first step are segmented in the second step to improve the accuracy. A dataset of pavement crack images in different circumstances were also built for the study. The F1 score of proposed crack detection and segmentation methods are 90.58% and 95.75%, respectively, which are higher than other state‐of‐the‐art methods. Compared with existing one‐step pavement crack detection or segmentation methods, proposed two‐step method showed advantages of accuracy.

ACS Style

Jingwei Liu; Xu Yang; Stephen Lau; Xin Wang; Sang Luo; Vincent Cheng‐Siong Lee; Ling Ding. Automated pavement crack detection and segmentation based on two‐step convolutional neural network. Computer-Aided Civil and Infrastructure Engineering 2020, 35, 1291 -1305.

AMA Style

Jingwei Liu, Xu Yang, Stephen Lau, Xin Wang, Sang Luo, Vincent Cheng‐Siong Lee, Ling Ding. Automated pavement crack detection and segmentation based on two‐step convolutional neural network. Computer-Aided Civil and Infrastructure Engineering. 2020; 35 (11):1291-1305.

Chicago/Turabian Style

Jingwei Liu; Xu Yang; Stephen Lau; Xin Wang; Sang Luo; Vincent Cheng‐Siong Lee; Ling Ding. 2020. "Automated pavement crack detection and segmentation based on two‐step convolutional neural network." Computer-Aided Civil and Infrastructure Engineering 35, no. 11: 1291-1305.

Journal article
Published: 18 August 2020 in Journal of Quantitative Spectroscopy and Radiative Transfer
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Laser-based object detection has been recognized as the most reliable technique for applications such as night vision, 3D (3-Dimensional) imaging, and especially underwater object detection. The key information is extracted from the reflected laser pulse after interacting with the target which surface directly affects the system performance. Due to the variety of the target types, it is necessary to investigate the surface characteristics and their effects on the performance of an underwater pulse laser ranging system. In this paper, the influence of target surface characteristics namely the type of materials, colors, and roughness on the reflectance and system performance are investigated through theoretical analysis using the Bidirectional Reflection Distribution Function (BRDF) and Laser Detection and Ranging (LADAR) model. An underwater peak detection pulse laser ranging system is developed to validate the results of a theoretical study. Both experimental and theoretical results clearly show that the system performance depends on the reflectance caused by the three characteristics of the target surface. This comprehensive research provides a handy reference with regards to the surface material, colors, and roughness for future improvement or correction in this domain.

ACS Style

Qi Chen; Jian Wei Yam; Sing Yee Chua; Ningqun Guo; Xin Wang. Characterizing the performance impacts of target surface on underwater pulse laser ranging system. Journal of Quantitative Spectroscopy and Radiative Transfer 2020, 255, 107267 .

AMA Style

Qi Chen, Jian Wei Yam, Sing Yee Chua, Ningqun Guo, Xin Wang. Characterizing the performance impacts of target surface on underwater pulse laser ranging system. Journal of Quantitative Spectroscopy and Radiative Transfer. 2020; 255 ():107267.

Chicago/Turabian Style

Qi Chen; Jian Wei Yam; Sing Yee Chua; Ningqun Guo; Xin Wang. 2020. "Characterizing the performance impacts of target surface on underwater pulse laser ranging system." Journal of Quantitative Spectroscopy and Radiative Transfer 255, no. : 107267.

Review
Published: 10 August 2020 in Laser Physics
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ACS Style

Mengdi Li; Anumol Mathai; Li Yandi; Qi Chen; Xin Wang; Xiping Xu. A brief review on 2D and 3D image reconstruction using single-pixel imaging. Laser Physics 2020, 30, 095204 .

AMA Style

Mengdi Li, Anumol Mathai, Li Yandi, Qi Chen, Xin Wang, Xiping Xu. A brief review on 2D and 3D image reconstruction using single-pixel imaging. Laser Physics. 2020; 30 (9):095204.

Chicago/Turabian Style

Mengdi Li; Anumol Mathai; Li Yandi; Qi Chen; Xin Wang; Xiping Xu. 2020. "A brief review on 2D and 3D image reconstruction using single-pixel imaging." Laser Physics 30, no. 9: 095204.

Journal article
Published: 29 July 2020 in Sensors
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Transparent object detection and reconstruction are significant, due to their practical applications. The appearance and characteristics of light in these objects make reconstruction methods tailored for Lambertian surfaces fail disgracefully. In this paper, we introduce a fixed multi-viewpoint approach to ascertain the shape of transparent objects, thereby avoiding the rotation or movement of the object during imaging. In addition, a simple and cost-effective experimental setup is presented, which employs two single-pixel detectors and a digital micromirror device, for imaging transparent objects by projecting binary patterns. In the system setup, a dark framework is implemented around the object, to create shades at the boundaries of the object. By triangulating the light path from the object, the surface shape is recovered, neither considering the reflections nor the number of refractions. It can, therefore, handle transparent objects with a relatively complex shape with the unknown refractive index. The implementation of compressive sensing in this technique further simplifies the acquisition process, by reducing the number of measurements. The experimental results show that 2D images obtained from the single-pixel detectors are better in quality with a resolution of 32×32. Additionally, the obtained disparity and error map indicate the feasibility and accuracy of the proposed method. This work provides a new insight into 3D transparent object detection and reconstruction, based on single-pixel imaging at an affordable cost, with the implementation of a few numbers of detectors.

ACS Style

Anumol Mathai; Ningqun Guo; Dong Liu; Xin Wang. 3D Transparent Object Detection and Reconstruction Based on Passive Mode Single-Pixel Imaging. Sensors 2020, 20, 4211 .

AMA Style

Anumol Mathai, Ningqun Guo, Dong Liu, Xin Wang. 3D Transparent Object Detection and Reconstruction Based on Passive Mode Single-Pixel Imaging. Sensors. 2020; 20 (15):4211.

Chicago/Turabian Style

Anumol Mathai; Ningqun Guo; Dong Liu; Xin Wang. 2020. "3D Transparent Object Detection and Reconstruction Based on Passive Mode Single-Pixel Imaging." Sensors 20, no. 15: 4211.

Journal article
Published: 19 June 2020 in IEEE Access
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Automated pavement crack image segmentation is challenging because of inherent irregular patterns, lighting conditions, and noise in images. Conventional approaches require a substantial amount of feature engineering to differentiate crack regions from non-affected regions. In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images. Our approach requires minimal feature engineering compared to other machine learning techniques. We propose a U-Net-based network architecture in which we replace the encoder with a pretrained ResNet-34 neural network. We use a “one-cycle” training schedule based on cyclical learning rates to speed up the convergence. Our method achieves an F1 score of 96% on the CFD dataset and 73% on the Crack500 dataset, outperforming other algorithms tested on these datasets. We perform ablation studies on various techniques that helped us get marginal performance boosts, i.e., the addition of spatial and channel squeeze and excitation (SCSE) modules, training with gradually increasing image sizes, and training various neural network layers with different learning rates.

ACS Style

Stephen L. H. Lau; Edwin K. P. Chong; Xu Yang; Xin Wang. Automated Pavement Crack Segmentation Using U-Net-Based Convolutional Neural Network. IEEE Access 2020, 8, 114892 -114899.

AMA Style

Stephen L. H. Lau, Edwin K. P. Chong, Xu Yang, Xin Wang. Automated Pavement Crack Segmentation Using U-Net-Based Convolutional Neural Network. IEEE Access. 2020; 8 (99):114892-114899.

Chicago/Turabian Style

Stephen L. H. Lau; Edwin K. P. Chong; Xu Yang; Xin Wang. 2020. "Automated Pavement Crack Segmentation Using U-Net-Based Convolutional Neural Network." IEEE Access 8, no. 99: 114892-114899.

Journal article
Published: 27 November 2019 in Photonics
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Underwater detection has always been a challenge due to the limitations caused by scattering and absorption in the underwater environment. Because of their great penetration abilities, lasers have become the most suitable technology for underwater detection. In all underwater laser applications, the reflected laser pulse which contains the key information for most of the system is highly degraded along the laser’s propagation path and during reflection. This has a direct impact on the system’s performance, especially for single-pixel imaging (SPI) which is very dependent on light-intensity information. Due to the complications in the underwater environment, it is necessary to study the influential factors and their impacts on underwater SPI. In this study, we investigated the influence of the angle of incidence, target distance, and medium attenuation. A systematic investigation of the influential factors on the reflectance and ranging accuracy was performed theoretically and experimentally. The theoretical analysis was demonstrated based on the bidirectional reflection distribution function (BRDF) and laser detection and ranging (LADAR) model. Moreover, 2D single-pixel imaging (SPI) systems were setup for experimental investigation. The experimental results agree well with the theoretical results, which show the system’s dependency on the reflection intensity caused by the angle of incidence, target distance, and medium attenuation. The findings should be a reference for works looking to improve the performance of an underwater SPI system.

ACS Style

Qi Chen; Anumol Mathai; Xiping Xu; Xin Wang. A Study into the Effects of Factors Influencing an Underwater, Single-Pixel Imaging System’s Performance. Photonics 2019, 6, 123 .

AMA Style

Qi Chen, Anumol Mathai, Xiping Xu, Xin Wang. A Study into the Effects of Factors Influencing an Underwater, Single-Pixel Imaging System’s Performance. Photonics. 2019; 6 (4):123.

Chicago/Turabian Style

Qi Chen; Anumol Mathai; Xiping Xu; Xin Wang. 2019. "A Study into the Effects of Factors Influencing an Underwater, Single-Pixel Imaging System’s Performance." Photonics 6, no. 4: 123.

Journal article
Published: 01 November 2019 in IEEE Access
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Underwater imaging has always been a challenge due to limitations imposed by scattering and absorption nature of the underwater environment. The light would be highly degraded after reflection and propagation in the water medium. Being an advanced imaging technique, Single-pixel Imaging (SPI) is applicable to acquire object spatial information in low light, severe backscattering, and high absorption conditions. Combination of Compressive Sensing (CS) and SPI can overcome the limitation of SPI algorithms such as long data-acquisition time, low reconstruction efficiency and poor reconstruction quality. In the current research, an underwater SPI system based on CS is established to reconstruct our two-dimensional (2D) transparent object. We have systematically investigated the influence of water turbid degree, measurement pattern types and number of measurements on image reconstruction performance. The proposed system is capable to reconstruct the object even when the turbidity reaches up to 80 Nephelometric Turbidity Unit (NTU), where the conventional imaging systems are unusable. Proposed reconstruction method in our research can save more than 70% data acquisition time, compared to SPI algorithm. Our experimental setup has been compared to a conventional imaging system and an underwater ghost imaging system to show its efficiency in obtaining accurate results from turbid water conditions. Furthermore, various algorithm comparison and imaging enhancement studies demonstrates that our algorithm is superior in bringing highly convex optimization at a faster rate with a smaller number of measurements. This work creates new insight into the SPI application and generates a guideline for researchers to improve their applications.

ACS Style

Qi Chen; Sandeep Kumar Chamoli; Peng Yin; Xin Wang; Xiping Xu. Active Mode Single Pixel Imaging in the Highly Turbid Water Environment Using Compressive Sensing. IEEE Access 2019, 7, 159390 -159401.

AMA Style

Qi Chen, Sandeep Kumar Chamoli, Peng Yin, Xin Wang, Xiping Xu. Active Mode Single Pixel Imaging in the Highly Turbid Water Environment Using Compressive Sensing. IEEE Access. 2019; 7 (99):159390-159401.

Chicago/Turabian Style

Qi Chen; Sandeep Kumar Chamoli; Peng Yin; Xin Wang; Xiping Xu. 2019. "Active Mode Single Pixel Imaging in the Highly Turbid Water Environment Using Compressive Sensing." IEEE Access 7, no. 99: 159390-159401.

Journal article
Published: 09 November 2018 in International Journal of Structural Stability and Dynamics
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Thermoelastic damping (TED) can lead to energy loss in microscale resonators, which is an intrinsic mechanism. To minimize the energy loss, it is required to determine the TED of resonators. Laminated plate resonators are commonly used in practice. However, existing researches on TED of the laminated resonators use mainly the one-dimensional (1D) heat conduction model, as the 3D governing equation is complicated, which cannot show the influences of boundary conditions along the supporting edges. In this paper, the governing equation of thermoelastic problems with 3D heat conduction was established for the out-of-plane vibration of the laminated rectangular plate. The analytical expression of the TED was derived using its physical meaning, namely, the ratio of the energy dissipated to the total elastic strain energy stored per cycle of vibration. It was found that the size and shape of the plate affect crucially the TED. The values of TED for higher-order vibration modes were also evaluated. Most importantly, the influences of supporting conditions and heat conduction conditions along the four edges were studied, which is the first report for laminated plates. The present approach can provide guidance for the design of high-quality bilayered resonators.

ACS Style

Shoubin Liu; Jingxuan Ma; Xianfeng Yang; Yuxin Sun; Jialing Yang; Xin Wang. Theoretical 3D Model of Thermoelastic Damping in Laminated Rectangular Plate Resonators. International Journal of Structural Stability and Dynamics 2018, 18, 1 .

AMA Style

Shoubin Liu, Jingxuan Ma, Xianfeng Yang, Yuxin Sun, Jialing Yang, Xin Wang. Theoretical 3D Model of Thermoelastic Damping in Laminated Rectangular Plate Resonators. International Journal of Structural Stability and Dynamics. 2018; 18 (12):1.

Chicago/Turabian Style

Shoubin Liu; Jingxuan Ma; Xianfeng Yang; Yuxin Sun; Jialing Yang; Xin Wang. 2018. "Theoretical 3D Model of Thermoelastic Damping in Laminated Rectangular Plate Resonators." International Journal of Structural Stability and Dynamics 18, no. 12: 1.

Regular
Published: 27 October 2018 in Photonic Sensors
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Compressed sensing leverages the sparsity of signals to reduce the amount of measurements required for its reconstruction. The Shack-Hartmann wavefront sensor meanwhile is a flexible sensor where its sensitivity and dynamic range can be adjusted based on applications. An investigation is done by using compressed sensing in surface measurements with the Shack-Hartmann wavefront sensor. The results show that compressed sensing paired with the Shack-Hartmann wavefront sensor can reliably measure surfaces accurately. The performance of compressed sensing is compared with those of the iterative modal-based wavefront reconstruction and Fourier demodulation of Shack-Hartmann spot images. Compressed sensing performs comparably to the modal based iterative wavefront reconstruction in both simulation and experiment while performing better than the Fourier demodulation in simulation.

ACS Style

Eddy Mun Tik Chow; Ningqun Guo; Edwin Chong; Xin Wang. Surface Measurement Using Compressed Wavefront Sensing. Photonic Sensors 2018, 9, 115 -125.

AMA Style

Eddy Mun Tik Chow, Ningqun Guo, Edwin Chong, Xin Wang. Surface Measurement Using Compressed Wavefront Sensing. Photonic Sensors. 2018; 9 (2):115-125.

Chicago/Turabian Style

Eddy Mun Tik Chow; Ningqun Guo; Edwin Chong; Xin Wang. 2018. "Surface Measurement Using Compressed Wavefront Sensing." Photonic Sensors 9, no. 2: 115-125.

Letter
Published: 23 October 2018 in Laser Physics Letters
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Hidden object imaging has always been challenging for obtaining satisfiable imaging because of the limitations caused by the reflections from the surrounding environment. The light is highly degraded after propagation and reflection from the hidden object. Single-pixel imaging (SPI) is an advanced imaging approach becoming more remarkable; applicable for acquiring spatial information in low light, high absorption and backscattering conditions. Combination of SPI and compressed sensing (CS) can efficiently tackle the key drawbacks of SPI, such as long data-acquisition time and low reconstruction resolution. In the present study, a CS based hidden object SPI system is designed. This is able to reconstruct an image without the influence of diffuse reflection from a two-dimensional (2D) target, which is placed in a corner practically concealing the objects over 10 × 10 cm of hidden space. The reconstruction obtained by our method is desirable and can save more than half of the data-acquisition time compared to the SPI algorithm. Our contribution presents a new insight for the application of SPI and provides a guideline for researchers to improve their applications.

ACS Style

Qi Chen; Sandeep Kumar Chamoli; Peng Yin; Xin Wang; Xiping Xu. Imaging of hidden object using passive mode single pixel imaging with compressive sensing. Laser Physics Letters 2018, 15, 126201 .

AMA Style

Qi Chen, Sandeep Kumar Chamoli, Peng Yin, Xin Wang, Xiping Xu. Imaging of hidden object using passive mode single pixel imaging with compressive sensing. Laser Physics Letters. 2018; 15 (12):126201.

Chicago/Turabian Style

Qi Chen; Sandeep Kumar Chamoli; Peng Yin; Xin Wang; Xiping Xu. 2018. "Imaging of hidden object using passive mode single pixel imaging with compressive sensing." Laser Physics Letters 15, no. 12: 126201.