ICAPS 2020
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/21780
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Item Estimating separability of magnetisation signals by fast implementation of Bloch equation simulations across multiple tissues and distance correlation function(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Iddagoda, M.; Jayaweera, H.H.E.; Wansapura, J.Magnetic Resonance Fingerprinting (MRF) is an emerging field in Magnetic Resonance Imaging (MRI) where tissues to be identified are subjected to a series of magnetic pulses. The resulting magnetisation signal is governed by both the tissue properties as well as the chosen pulse acquisition parameters. By employing a suitable classifier, the tissue properties are recovered from the magnetisation signal in MRF. Depending on the chosen pulse acquisition parameters, the resulting magnetisation signals must be unique for different tissue properties for MRF to be effective. But the acquisition parameters of magnetic pulses in MRF are traditionally chosen in random. Hence, it is possible that the magnetic signals for tissues of concern may not be sufficiently distinguishable for efficient classification. Therefore, to explore the possibility of optimising the level of separability of magnetisation signals of different tissue types, optimal values of acquisition parameters of the pulse sequence must be carefully engineered. This task requires means of estimating the level of separability of magnetisation signals for different tissues. In this study, a fast simulation mechanism is implemented that estimates the level of separability of magnetisation signals generated in MRF for a chosen set of tissue properties and pulse acquisition parameters. An in-house built Bloch equations simulator was used to model nuclear magnetisation of atoms for both Balanced Steady State Free Precession (BSSFP) and Echo Planar Imaging (EPI) pulse sequences with variable pulse acquisition parameters. For the two pulse sequences chosen, calculating the magnetisation signal for a single tissue is sequential by nature. However, the calculations are parallel when repeated across multiple tissues. Therefore, the simulator was implemented on a Graphical Processing Unit (GPU) to exploit the parallel nature of the problem and to shorten execution time. To determine the level classification of magnetisation signals, distance correlation Function, which measures both linear and non-linear association between two signals was chosen. Since for N number of tissues, there are NC2 number of correlation computations, the computational demand will be prohibitively expensive with higher numbers of tissues. Therefore, the distance correlation which, given the parallel nature of calculations, was reformulated as a series of array operations to be able to execute in the GPU. It was observed that as compared to a CPU only implementation, GPU execution of Bloch equation calculations sped up significantly. Through reformulation as array operations, calculation of distance correlation, which computationally is more expensive than Bloch equation simulations, sped up roughly by a factor of 10,000 times. With the fast execution time through GPU, the implementation provides practical means of evaluating a vast number of tissues to indicate the level of separability for a chosen set of pulse acquisition parameters within a few seconds. Therefore, the system developed facilitates a designer to carefully engineer the optimal pulse sequence parameters to ensure that the magnetisation signals generated are efficiently classifiable prior to carrying out physical scans for MRF using the MRI machine.Item Calibration of the rolling angle of a Quadrant Photo Detector mounted in the image plane of a dark-field passive LIDAR system(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Abeywickrama, S.S.; Perera, H.E.; Jayaweera, H.H.E.Passive Light Detection and Ranging (LIDAR) has successfully been used for observing insects and their activities. It was reported that such techniques are more efficient compared to traditional approaches. Quadrant Photo Detectors (QPDs) are widely used at the image plane with the use of a modified eye-piece to detect both wing-beats and heading angles of insects. In these systems, knowing the exact orientation of the QPD in the image plane is an imperative task. This study was carried out to propose a method to calibrate the rolling angle of a QPD mounted in the image plane of a Newtonian telescope in a dark-field passive LIDAR system using a Hamamatsu S4349 Silicon QPD. Each segment of the QPD was connected to a data acquisition card through four Trans-impedance amplifiers and programmable gain amplifiers. A white coloured inverted pendulum oscillated across the Field of View (FOV) of the QPD at a known distance was used for calibration. Intensities registered at the individual segments of the QPD were recorded at a rate of 10 kHz while the pendulum swept the FOV. Thirty-six of such measurements were obtained by changing the rolling angles by 10-degree at a time. The four filtered and normalized signals were used to calculate the activation times (full width at 10%) and four unique sinusoidal functions were fitted to the whole range of angles. These coefficients can be used to estimate the rolling angle of the QPD using a test oscillation. It was found that the accuracy of the estimate was ± 6.04 degrees. A ray tracing-based simulation was conducted to simulate this activity and findings from the activity agrees with the theoretical simulation results. It was noted that the highest performance can be obtained when the pendulum oscillates in a plane normal to the optical axis.Item Implementation of a wireless distributed node-based system for monitoring, controlling and data logging of a Parabolic Trough Concentrator(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Viraj, M.P.S.; Perera, H.E.; Kumara, P.D.C.; Jayaweera, H.H.E.Solar thermal energy harnessing through a Parabolic Trough Concentrator (PTC) type plant is the most efficient and cheapest technique in the field of renewable energy harnessing. Near real time performance monitoring and frequent maintenance of such plants should be done in order to maintain a consistent thermal output from the system. Typically, the temperature of a wellfocused Heat Collecting Element (HCE) of a PTC exceeds 300 ℃ during peak operation. It is necessary to have an unmanned data acquisition system due to physical limitations in accessing the HCE and measuring the HCE temperatures. This also reduces the downtime and increases the efficiency of the monitoring and management process. The objective of this study was to develop a wireless distributed node-based controlling and monitoring system to monitor the status of a medium scale PTC, based on Wi-Fi enabled IoT devices. The system was designed as distributed nodes and a custom firmware was developed in order to handle data transmission using Message Queuing Telemetry Transport (MQTT). For long-term storage and redundancy, the collected data was uploaded to a cloud storage. Automated error and status reporting features were also implemented. The system was built using five low power wireless nodes. The temperature node was specially designed to measure the temperature profile across the focal plane to optimize the performance of the PTC. Twenty K-type calibrated thermocouples were used as the sensor. The trough angle was also measured using a MPU 6050 accelerometer. The tracking node was developed to use the current trough angle to move the trough according to the calculated solar angle using the Sun Position Algorithm developed by the National Renewable Energy Laboratory, USA. Ambient temperature, relative humidity and solar irradiance measurement were logged along with the temperature measurements. The average response time of the temperature, weather and trough-angle nodes were observed to be 7.10s, 150ms and 30ms respectively. The slow response of the temperature monitoring node was due to the switching of 20 thermocouples. The average power consumption of a node was found to be 0.42 W during the data transmission and 0.14 W when the system is idling. This system can be upscaled and adapted to similar data acquisition tasks involving spatially distributed applications.Item Use of Google Earth Engine to monitor surface water: A case study in water tanks located in the dry zone of Sri Lanka(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Perera, H.E.; Jayaweera, H.H.E.Water is a valuable and limited resource that needs to be managed properly. The amount of surface water changes over time due to a variety of reasons including rainfall, temperature, wind patterns and agricultural usage. Large scale surface water level monitoring is one of the most labourintensive tasks in managing water resources. Satellite based remote sensing is a commonly used technique in such scenarios, where earth orbiting satellites are used to monitor the changes on the earth's surface using different types of sensors. A large amount of remote sensed data sets has been made available by different agencies. However, analysis of such data sets requires specialized computing systems with large storage, memory and processing power. With the public release of Landsat data in 2008, Google archived all the data sets and linked them to a cloud computing engine named, Google Earth Engine (GEE) providing a free and open source platform which handles all low-level data handling, allowing users to manipulate the data set at a much higher level. In the present study, GEE was used to evaluate the feasibility of surface water monitoring in water tanks located in the dry zone of Sri Lanka from January 2017 to December 2019. Sentinel-1 (S1), Synthetic-Aperture Radar (SAR) data and Sentinel-2 (S2) Multi-spectral Instruments were used to identify the surface water body coverage area. Normalized water index (NDWI) was calculated based on the B3 and B8 bands of S2 images. Due to significant local cloud coverage within the region of interest, most of the available data points had to be discarded. It was noted that NDWI based water level estimation was not suitable for analyzing temporal dynamics. S1-SAR Ground Range Detected (GRD) data was processed by segmenting the area using a K-means clustering algorithm. Image dilation and erosion operations were used to reduce the effect of speckle noise. The water level was estimated for the considered time period based on individually segmented images. Ground data was obtained, which corresponds to the satellite passes that were published online by the Department of Irrigation, Sri Lanka. The estimated water surface area for Kaudulla, Senanayaka Samudraya and Lunugamwehera tanks showed a good linear relationship against the reported water volume with coefficient of determinants of 0.73, 0.94 and 0.67 respectively. SAR-GRD measures backscatter and it depends on the surface flatness. Therefore, water quality or cloud cover has no effect on the detected water surface area estimation. Hence, SAR-GRD image-based classification is better suited to detect short time scale changes in water level in selected tanks even under uncooperative weather.