Journal Papers by Ghulam Mubashar Hassan
Engineering Applications of Artificial Intelligence, 2019
Remote deformation monitoring with high accuracy is a challenging task. The technique of Digital ... more Remote deformation monitoring with high accuracy is a challenging task. The technique of Digital Image Correction (DIC) is commonly used to measure remote deformation using images and provides high subpixel accuracy. However, DIC fails if deformation is non-continuous, which is a regularly occurring scenario in deformable solids due to the presence of discontinuities such as cracks and crevices. To overcome the limitation posed by DIC, a novel Discontinuous and Pattern Matching (DPM) algorithm is proposed in this study. Initially DPM algorithm demarcates the area where DIC fails by using the results of DIC. Later, DPM algorithm utilizes the features of pattern matching and embeds discontinuity in DIC to measure deformation in the demarcated areas where DIC failed. The performance of the proposed DPM algorithm is evaluated using two different experiments involving different types of discontinuities. The accuracy achieved in evaluation is higher than the normally required one-tenth of a pixel and the average absolute errors remained in the range of 0.02 to 0.07 pixels. The results are compared with another state-of-the-art DIC and pattern matching based technique and comparative analysis show that the proposed DPM algorithm improved the accuracy of deformation measurement in the range of 0.02 to 0.1 pixels depending on different scenarios.
Optics and Lasers in Engineering, 2019
Deformation measurement is normally achieved by using Digital Image Correlation (DIC) technique w... more Deformation measurement is normally achieved by using Digital Image Correlation (DIC) technique when deformation is not discontinuous. However, the presence of discontinuities makes the deformation process very challenging and DIC fails. An innovative technique is proposed in this study which splits the subset (segment) of an image into multiple parts and use segmented subset of the image for correlation process. The performance of the proposed technique is evaluated using different experiments where different types of discontinuities are introduced in the deformation process at different angles and having different discontinuity opening sizes. The obtained results are compared with the recently proposed Discontinuous Digital Image Correlation (DDIC) technique. The results show that the proposed technique is more reliable and having high accuracy which reaches upto 1/100th of a pixel under favorable circumstances.
Reconstruction of strain and displacement fields from surface images of materials and structures ... more Reconstruction of strain and displacement fields from surface images of materials and structures in the presence of discontinuities is a challenging task. Digital Image Correlation (DIC) – a commonly used technique to reconstruct displacement and strain fields when deformation is continuous – fails in the presence of discontinuities, including cracks and crevices. This paper presents a novel and entirely automated technique, Discontinuous Digital Image Correlation (DDIC), to reconstruct displacement and strain fields with high accuracy from images when deformation is either continuous or discontinuous. The technique is based on introducing additional parameters that characterize the discon-tinuity: the direction of the tangent to the discontinuity line and the corresponding Burgers vectors which express the difference in displacements at the opposite sides of the discon-tinuity line. The proposed technique is validated using synthetic images as well as images obtained from laboratory experiments. The results show that DDIC is able to reconstruct the displacement fields around discontinuities with a subpixel accuracy close to 1=100th of a pixel with a suitable surface pattern. It is also able to recover the size and angle of the discontinuity.
Problem statement: The most dangerous insect for the existence of palm trees in entire world is R... more Problem statement: The most dangerous insect for the existence of palm trees in entire world is Red Palm Weevil (scientifically named as Rynchophorus Ferrugineous, Oliveir). The proposed research is conducted to develop an identification system for Automated Wireless Red Palm Weevil Detection and exterminated. The core idea of the proposed research is to develop software that can utilize image processing and Artificial neural network techniques to identify Red Palm Weevil and distinguishes it from other insects found in palm trees habitat. Approach: Images are taken and processed with image processing techniques. Afterwards, Artificial neural network is used to recognize the presence of Red Palm Weevil in an image. Two different feed-forward supervised learning algorithms of Artificial neural network are used i.e., scaled conjugate gradient and Conjugate Gradient with Powell/Beale Restarts Algorithms. Different Artificial neural network sizes are tested using both algorithms and are compared to find an optimal algorithm and network. The training, verification and testing of the Artificial neural network is accomplished by using a database of 319 images of Red Palm Weevil and 93 images of other insects which are usually found around palm trees. Images are randomly selected from database for training, verification and testing with a fixed percentage of 80, 10 and 10 respectively. Training for every selected set of configuration is repeated 10 times. Results: The best results for scaled conjugate gradient Algorithm is obtained by three layers ANN consuming 221 sec and 167 Epochs while its average success in identification of Red Palm Weevil and other insect is 99 and 93% respectively. On the other hand, best performance of Conjugate Gradient with Powell/Beale Restarts Algorithm is observed by using three layers ANN which consumed 183 sec and 109 Epochs for training while its average success in identification of Red Palm Weevil and other insect is 99.5 and 93.5% respectively. Conclusion: It is gleaned out that 3-layers Artificial neural network using Conjugate Gradient with Powell/Beale Restarts Algorithm for feed-forward supervised learning is optimal for identification of Red Palm Weevil.
Problem statement: Red palm weevil (Rynchophorus Ferrugineous, Oliveir) is an insect which threat... more Problem statement: Red palm weevil (Rynchophorus Ferrugineous, Oliveir) is an insect which threatens the existence of palm trees. The proposed research is to develop a RPW identification system using Support Vector Machine method. The problem is to extract image features from an image and using SVM to find out the existence of RPW in an image. Approach: Images are snapped and image processing techniques of Regional Properties and Zernike Moments are used to extract different features of an image. The obtained features are fed into the SVM based system individually as well as in combination. The database used to train and test the system includes 326 RPW and 93 other insect images. The input data from database is selected randomly and fed into the system in three steps i.e., 25, 50 and 75% while remaining database is used for testing purpose. In SVM, polynomial kernel function and Radial Basis Function are used for training. Each experiment is repeated 10 times and the average results are used for analysis. Results: The optimal results are obtained by using Radial Basis Function in SVM at lower values of sigma ‘σ’ while Polynomial kernel function is not successful in returning adequate results. Further detailed analysis of results for ‘σ’ value of 10 and 15 revealed that proposed system works well with large training data and with inputs obtained by Regional Properties. The optimal value of ‘σ’ for proposed system is found to be 10 when training data ratio is 50%. The training time for proposed system depends on size of database and is found to be 0.025 sec per image while time consumed by proposed system for identification of RPW in an image is found to be 15 milli sec. The proposed system’s success in identification of RPW and other insect is found to be 97 and 93% respectively. Conclusion: It is concluded that SVM based system using Radial Basis Function having ‘σ’ value of 10 is optimal in identifying RPW from an image. The optimal input data for the proposed system needs to be obtained by Regional Properties only.
American Journal of Agricultural and Biological Sciences, 2011
ABSTRACT Problem statement: Red palm weevil is the most destructive insect for palm trees all ove... more ABSTRACT Problem statement: Red palm weevil is the most destructive insect for palm trees all over the world. This research is part of developing an automated wireless red palm weevil detection and control system. The focus for this study was to develop red palm Weevil recognition system which can detect RPW in an image and can be used in wireless image sensor network which will be part of entire proposed system. Approach: Template based recognition techniques were used. Two general recognition methods i.e., Zernike and Regional Properties and an algorithm combining them were used. Besides that, a novel technique for detecting Rostrum of RPW named as Rostrum Analysis was proposed and used for recognition, a conclusive algorithm based on all three techniques was also proposed, 319 test images of RPW and 93 images of other insects which found in RPW habitat were used. Results: It was found that both general techniques i.e., Regional Properties and Zernike Moments methods perform reasonably in recognizing RPW. The algorithm based on both these methods performs better than individual methods. The Rostrum Analysis outperforms better than both the earlier methods and proposed algorithm using all three analytical techniques gives best results among all discussed techniques in recognizing RPW as well as other insects. Conclusion: The most balanced and efficient recognition technique is to use the proposed conclusive algorithm which is combination of Regional Properties, Zernike Moments and Rostrum Analysis techniques. The maximum time for processing an image is 0.47 sec and the results obtained in recognizing the RPW and other insects are 97 and 88% respectively.
American Journal of Agricultural and Biological Sciences, 2012
Problem statement: Red palm weevil (Rynchophorus Ferrugineous, Oliveir) is an insect which threat... more Problem statement: Red palm weevil (Rynchophorus Ferrugineous, Oliveir) is an insect which threatens the existence of palm trees. The proposed research is to develop a RPW identification system using Support Vector Machine method. The problem is to extract image features from an image and using SVM to find out the existence of RPW in an image. Approach: Images are snapped and image processing techniques of Regional Properties and Zernike Moments are used to extract different features of an image. The obtained features are fed into the SVM based system individually as well as in combination. The database used to train and test the system includes 326 RPW and 93 other insect images. The input data from database is selected randomly and fed into the system in three steps i.e., 25, 50 and 75% while remaining database is used for testing purpose. In SVM, polynomial kernel function and Radial Basis Function are used for training. Each experiment is repeated 10 times and the average results are used for analysis. Results: The optimal results are obtained by using Radial Basis Function in SVM at lower values of sigma 'σ' while Polynomial kernel function is not successful in returning adequate results. Further detailed analysis of results for 'σ' value of 10 and 15 revealed that proposed system works well with large training data and with inputs obtained by Regional Properties. The optimal value of 'σ' for proposed system is found to be 10 when training data ratio is 50%. The training time for proposed system depends on size of database and is found to be 0.025 sec per image while time consumed by proposed system for identification of RPW in an image is found to be 15 milli sec. The proposed system's success in identification of RPW and other insect is found to be 97 and 93% respectively. Conclusion: It is concluded that SVM based system using Radial Basis Function having 'σ' value of 10 is optimal in identifying RPW from an image. The optimal input data for the proposed system needs to be obtained by Regional Properties only.
Reconstruction and monitoring of displacement and strain fields is an important problem in engine... more Reconstruction and monitoring of displacement and strain fields is an important problem in engineering. We analyze the remote and nonobtrusive methods of strain measurement based on photogrammetry and Digital Image Correlation (DIC). The method is based on covering the photographed surface with a pattern of speckles and comparing the images taken before and after the deformation. In this study, a comprehensive literature review and comparative analysis of photogrammetric solutions is presented. The analysis is based on a specially developed Digital Image Synthesizer To Reconstruct Strain in Solids (DISTRESS) Simulator to generate synthetic images of displacement and stress fields in order to investigate the intrinsic accuracy of the existing variants of DIC. We investigated the Basic DIC and a commercial software VIC 2D, both based on displacement field reconstruction with post processing strain determination based on numerical differentiation. We also investigated what we call the Extended DIC where the strain field is determined independently of the displacement field. While the Basic DIC and VIC 2D are faster, the Extended DIC delivers the best accuracy of strain reconstruction. The speckle pattern is found to be playing a critical role in achieving high accuracy for DIC. Increase in subset size for DIC does not significantly improves the accuracy, while the smallest subset size depends on the speckle pattern and speckle size. Increase in the overall image size provides more details but does not play significant role in improving the accuracy, while significantly increasing the computation cost.
—Digital image correlation (DIC) is a contactless full-field displacement and strain reconstructi... more —Digital image correlation (DIC) is a contactless full-field displacement and strain reconstruction technique commonly used in the field of experimental mechanics. Comparing with physical measuring devices, such as strain gauges, which only provide very restricted coverage and are expensive to deploy widely, the DIC technique provides the result with full-field coverage and relative high accuracy using an inexpensive and simple experimental setup. It is very important to study the natural patterns effect on the DIC technique because the preparation of the artificial patterns is time consuming and hectic process. The objective of this research is to study the effect of using images having natural pattern on the performance of DIC. A systematical simulation method is used to build simulated deformed images used in DIC. A parameter (subset size) used in DIC can have an effect on the processing and accuracy of DIC and even cause DIC to failure. Regarding to the picture parameters (correlation coefficient), the higher similarity of two subset can lead the DIC process to fail and make the result more inaccurate. The pictures with good and bad quality for DIC methods have been presented and more importantly, it is a systematic way to evaluate the quality of the picture with natural patterns before they install the measurement devices.
The quality of the surface pattern and selection of subset size play a critical role in achieving... more The quality of the surface pattern and selection of subset size play a critical role in achieving high accuracy in Digital Image Correlation (DIC). The subset size in DIC is normally selected by testing different subset sizes across the entire image, which is a laborious procedure. This also leads to the problem that the worst region of the surface pattern influences the performance of DIC across the entire image. In order to avoid these limitations, a Dynamic Subset Selection (DSS) algorithm is proposed in this paper to optimize the subset size for each point in an image before optimizing the correlation parameters. The proposed DSS algorithm uses the local pattern around the point of interest to calculate a parameter called the Intensity Variation Ratio (Λ), which is used to optimize the subset size. The performance of the DSS algorithm is analyzed using numerically generated images and is compared with the results of traditional DIC. Images obtained from laboratory experiments are also used to demonstrate the utility of the DSS algorithm. Results illustrate that the DSS algorithm provides a better alternative to subset size " guessing " and finds an appropriate subset size for each point of interest according to the local pattern.
Reconstruction and monitoring of displacement and strain fields is an important problem in engine... more Reconstruction and monitoring of displacement and strain fields is an important problem in engineering. We analyze the remote and non-obtrusive method of Digital Image Correlation (DIC) in 2D based on photogrammetry. The method involves covering the photographed surface with a pattern of speckles and comparing the images taken before and after the deformation. The analysis is based on a specially developed Digital Image Synthesizer To Reconstruct Strain in Solids (DISTRESS) Simulator to generate synthetic images of displacement and stress fields in two dimensions in order to investigate the intrinsic accuracy of the existing variants of DIC. We investigated the Basic DIC and a commercial software VIC 2d, both based on displacement field reconstruction with post processing strain determination based on numerical differentiation. We also investigated what we call the Extended DIC where the strain field is determined independently of the displacement field. While the Basic DIC is faster, the Extended DIC delivers the best accuracy. The speckle pattern is found to be playing a critical role in achieving high accuracy for DIC. Increase in the subset size for DIC does not significantly improves the accuracy, while the smallest subset size depends on the speckle pattern and speckle size. Increase in the overall image size provides more details but does not play significant role in improving the accuracy, while significantly increasing the computation cost. We observed that it is not reliable to measure very small strains using grayscale images in DIC. Thus, we propose Color DIC using color images and found that it improves the accuracy in measuring small strains.
Shear band formation and evolution is a predominant mechanism of deformation patterning in granul... more Shear band formation and evolution is a predominant mechanism of deformation patterning in granular materials. Independent rotations of separate particles can affect the pattern formation by adding the effect of rotational degrees of freedom to the mechanism of instability. We conducted 2D physical modelling where the particles are represented by smooth steel discs. We use the digital image correlation in order to recover both displacement and independent rotation fields in the model. We performed model calibration and determine the values of mechanical parameters needed for a DEM numerical modelling. Both mono- and polydisperse particle assemblies are used. During the loading, the deformation pattern undergoes stages of shear band formation followed by its dissolution due to recompaction and particle rearrangement with the subsequent formation of multiple shear bands merging into a single one and the final dissolution. We show that while the average (over the assembly) values of the angles of disc rotations are insignificantly different from zero, the particle rotations exhibit clustering at the mesoscale (sizes larger than the particles but smaller than the whole assembly): monodisperse assemblies produce vertical columns of particles rotating the same direction; polydisperse assemblies 2D form clusters of particles with alternating rotations. Thus, particle rotations produce a structure on their own, a structure different form the ones formed by particle displacements and force chains. This can give a rise to moment chains. These emerging mesoscopic structures – not observable at the macroscale – indicate hidden aspects of ‘Cosserat behaviour’ of the particles.
Problem statement: Red palm weevil (Rynchophorus Ferrugineous, Oliveir) is an insect which threat... more Problem statement: Red palm weevil (Rynchophorus Ferrugineous, Oliveir) is an insect which threatens the existence of palm trees. The proposed research is to develop a RPW identification system using Support Vector Machine method. The problem is to extract image features from an image and using SVM to find out the existence of RPW in an image. Approach: Images are snapped and image processing techniques of Regional Properties and Zernike Moments are used to extract different features of an image. The obtained features are fed into the SVM based system individually as well as in combination. The database used to train and test the system includes 326 RPW and 93 other insect images. The input data from database is selected randomly and fed into the system in three steps i.e., 25, 50 and 75% while remaining database is used for testing purpose. In SVM, polynomial kernel function and Radial Basis Function are used for training. Each experiment is repeated 10 times and the average results are used for analysis. Results: The optimal results are obtained by using Radial Basis Function in SVM at lower values of sigma ‘σ’ while Polynomial kernel function is not successful in returning adequate results. Further detailed analysis of results for ‘σ’ value of 10 and 15 revealed that proposed system works well with large training data and with inputs obtained by Regional Properties. The optimal value of ‘σ’ for proposed system is found to be 10 when training data ratio is 50%. The training time for proposed system depends on size of database and is found to be 0.025 sec per image while time consumed by proposed system for identification of RPW in an image is found to be 15 milli sec. The proposed system’s success in identification of RPW and other insect is found to be 97 and 93% respectively. Conclusion: It is concluded that SVM based system using Radial Basis Function having ‘σ’ value of 10 is optimal in identifying RPW from an image. The optimal input data for the proposed system needs to be obtained by Regional Properties only.
Problem statement: The Pecan weevil was considered as the most dangerous pest of Pecan fruits. Th... more Problem statement: The Pecan weevil was considered as the most dangerous pest of Pecan fruits. The aim of this research is to evaluate Support Vector Machine method (SVM) for identifying Pecan Weevil among other insects. Eventually, this recognition system will serve in a wireless imaging network for monitoring Pecan Weevils. Approach: SVM has been evaluated using two different kernel functions i.e., Polynomial Function and Radial Basis Function. Database of 205 Pecan Weevils and 75 other insects which typically exist in pecan habitat has been used. Three sets of input data for SVM have been generated by two standard region-based recognition methods. These sets are comprised of output obtained by Zernike Moments, Regional Properties and combination of these two methods. For each kernel function, the system had been trained by 25, 50 and 75% of data and remaining ratio in each case has been used for testing. Each experiment is repeated ten times and average results are considered for comparisons and analysis. Results: The optimum recognition rate had been found when system is trained by 75% of data. The results are approximately similar when the input data is obtained by Regional Properties and combination of Regional Properties and Zernike Moments methods. The optimum results are obtained when input data has been obtained by Zernike Moments alone for lower values of sigma ‘σ’. The proposed system is able to successfully recognize 99% of Pecan Weevil and 97% of the other insects using the radial basis function. The proposed system took approximately 31 sec for processing 75% of the data which include the time for training. The testing time is found to be 0.15 sec. Conclusion: Promising results can be obtained when input data is obtained by Zernike Moments and SVM is trained by RBF and 75% of data.
Problem statement: The most dangerous insect for the existence of palm trees in entire world is R... more Problem statement: The most dangerous insect for the existence of palm trees in entire world is Red Palm Weevil (scientifically named as Rynchophorus Ferrugineous, Oliveir). The proposed research is conducted to develop an identification system for Automated Wireless Red Palm Weevil Detection and exterminated. The core idea of the proposed research is to develop software that can utilize image processing and Artificial neural network techniques to identify Red Palm Weevil and distinguishes it from other insects found in palm trees habitat. Approach: Images are taken and processed with image processing techniques. Afterwards, Artificial neural network is used to recognize the presence of Red Palm Weevil in an image. Two different feed-forward supervised learning algorithms of Artificial neural network are used i.e., scaled conjugate gradient and Conjugate Gradient with Powell/Beale Restarts Algorithms. Different Artificial neural network sizes are tested using both algorithms and are compared to find an optimal algorithm and network. The training, verification and testing of the Artificial neural network is accomplished by using a database of 319 images of Red Palm Weevil and 93 images of other insects which are usually found around palm trees. Images are randomly selected from database for training, verification and testing with a fixed percentage of 80, 10 and 10 respectively. Training for every selected set of configuration is repeated 10 times. Results: The best results for scaled conjugate gradient Algorithm is obtained by three layers ANN consuming 221 sec and 167 Epochs while its average success in identification of Red Palm Weevil and other insect is 99 and 93% respectively. On the other hand, best performance of Conjugate Gradient with Powell/Beale Restarts Algorithm is observed by using three layers ANN which consumed 183 sec and 109 Epochs for training while its average success in identification of Red Palm Weevil and other insect is 99.5 and 93.5% respectively. Conclusion: It is gleaned out that 3-layers Artificial neural network using Conjugate Gradient with Powell/Beale Restarts Algorithm for feed-forward supervised learning is optimal for identification of Red Palm Weevil.
Problem statement: Red palm weevil is the most destructive insect for palm trees all over the wor... more Problem statement: Red palm weevil is the most destructive insect for palm trees all over the world. This research is part of developing an automated wireless red palm weevil detection and control system. The focus for this study was to develop red palm Weevil recognition system which can detect RPW in an image and can be used in wireless image sensor network which will be part of entire proposed system. Approach: Template based recognition techniques were used. Two general recognition methods i.e., Zernike and Regional Properties and an algorithm combining them were used. Besides that, a novel technique for detecting Rostrum of RPW named as ‘Rostrum Analysis’ was proposed and used for recognition, a conclusive algorithm based on all three techniques was also proposed, 319 test images of RPW and 93 images of other insects which found in RPW habitat were used. Results: It was found that both general techniques i.e., Regional Properties and Zernike Moments methods perform reasonably in recognizing RPW. The algorithm based on both these methods performs better than individual methods. The Rostrum Analysis outperforms better than both the earlier methods and proposed algorithm using all three analytical techniques gives best results among all discussed techniques in recognizing RPW as well as other insects. Conclusion: The most balanced and efficient recognition technique is to use the proposed conclusive algorithm which is combination of Regional Properties, Zernike Moments and Rostrum Analysis techniques. The maximum time for processing an image is 0.47 sec and the results obtained in recognizing the RPW and other insects are 97 and 88% respectively.
Problem statement: The aim of this research was to optimize the performance of solenoid valve use... more Problem statement: The aim of this research was to optimize the performance of solenoid valve used in Variable Rate Application System (VRA) in term of time response. The overall time response is usually divided into four parts i.e., plunger opening time, pressure opening time, plunger closing time and pressure closing time. Approach: The performance and design of the a solenoid valve used in VRA was analyzed methematically and experimentally. Voltage, current, pressure, spring constant, flow rate and mass of the plunger were found to be the main parameters affecting the performance of solenoid valve. Based on the analyses, some modifications were introduced in the design of the solenoid valve to enhance its performance. The newly designed solenoid valve was tested by varying the main parameters and its performance was compared in terms of time response. Results: The time respnose of the modified valve showed improvement. The plunger closing time for the modified valve improved by 79%. Depending on the types of nozzle, the pressure opening and closing time responses were reduced by 37-53% and 55-73% respectively. It was also observed time response was improved by 34% when springs with lower spring constants are used. Conclusion: After thorough testing of both the original and proposed valves, it was observed that proposed valve average performance is faster than the original valve by 22 msec or 56%. However, it was also found that it is mandatory to increase the operating voltage of propsed valve for the better performance.
Conference Papers by Ghulam Mubashar Hassan
IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2018
We have developed a flexible and low-cost hardware testbed for autonomous vehicle research and ed... more We have developed a flexible and low-cost hardware testbed for autonomous vehicle research and education. The testbed provides the ability to autonomously control multiple small vehicles within a 1200x900 mm environment and is entirely open source with regard to both hardware and software, making it easily reproducible. An agent-based approach is used for vehicle control, meaning that different agent models can be applied to each vehicle as desired for simulation. GPS-like position information is provided to the controller in real-time using a global computer vision system. Using our figure-of-eight test environment, we have demonstrated that our system can support six cars driving smoothly around the track whilst avoiding collisions with each other.
Australasian Association of Engineering Educators , 2018
STRUCTURED ABSTRACT CONTEXT Safety in design is an important topic in engineering education for w... more STRUCTURED ABSTRACT CONTEXT Safety in design is an important topic in engineering education for which practical experiences are likely to be beneficial but logistically difficult, and high risk. Virtual reality (VR) offers the possibility for students to learn from an interactive experience without the inconveniences and safety hazards in real site visits. However, one of the challenges of using VR is providing learning experiences to large classes of students. This study investigated the efficacy of VR for teaching safety in design, and an approach to accommodate VR with large numbers of students. Students learned about safety in design in workshops, using a VR environment. They worked in groups in which only one member wore the VR headset and others observed. PURPOSE The research question addressed by this study is 'How can VR be used for teaching large cohorts?' APPROACH The second author developed a VR environment in which students operate a vehicle loading crane, based on a design that had been associated with fatalities. Workshops were held in two 5th year engineering design units (one electrical stream and one mechanical stream) taken by 280 students in total. Students completed a standard construction hazard analysis implementation review (CHAIR). In each group of three to eight students, one student used the VR and others observed that student and their VR headset view displayed on a screen. Each group then extended their CHAIR taking account of learning from the VR activity. The completed CHAIR templates, participants' demographics and evaluations were collected from consenting students and teaching team members, and the researchers recorded notes during the workshops. RESULTS On average students agreed that they identified additional risks after the VR experience regardless of whether they wore the headset. Teaching team members reported that usually quiet students, who were often international students, participated more actively in the group discussions than in their usual weekly group meetings. Analysis of the completed CHAIR templates will be reported elsewhere. CONCLUSIONS It is feasible to use VR with large cohorts by offering the immersive experience to a sample of students. The other students can learn by observing both the student wearing the headset and that student's VR projection.
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Journal Papers by Ghulam Mubashar Hassan
Conference Papers by Ghulam Mubashar Hassan