Evaluating the Performances of the Agoraphilic Navigation Algorithm under Dead-Lock Situations
- Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: 29th IEEE International Symposium on Industrial Electronics, ISIE 2020 Vol. 2020-June, p. 536-542
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- Description: This paper presents a summary of the research which was conducted in developing a new free-space based (Agoraphilic) navigation algorithm. This new methodology is capable of maneuvering robots in static as well as dynamically cluttered unknown environments. The new algorithm uses only one force to drive the robot. This force is always an attractive force created by the freespace. This force is focused towards the goal by a force shaping module. Consequently, the robot is motivated to follow free-space directing towards the goal. As this method only based on the attractive forces, the robot always moves towards the goal as long as there is free-space . This method has eradicated many drawbacks of the traditional APF method. Several experimental tests were conducted using Turtlebot3 research platform. These tests were focused on testing the behavior of the new algorithm under dead-lock (local minima) situations for APF method. The test results proved that the proposed algorithm has successfully eliminated the local minima problem of APF method. © 2020 IEEE.
Agoraphilic navigation algorithm in dynamic environment with and without prediction of moving objects location
- Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 Vol. 2019-October, p. 5179-5185
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- Description: This paper presents a summary of research conducted in performance improvement of Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE). The ANADE is an optimistic navigation algorithm which is capable of navigating robots in static as well as in unknown dynamic environments. ANADE has been successfully extended the capacity of original Agoraphilic algorithm for static environment. However, it could identify that ANADE takes costly decisions when it is used in complex dynamic environments. The proposed algorithm in this paper has been successfully enhanced the performance of ANADE in terms of safe travel, speed variation, path length and travel time. The proposed algorithm uses a prediction methodology to estimate future growing free space passages which can be used for safe navigation of the robot. With motion prediction of moving objects, new set of future driving forces were developed. These forces has been combined with present driving force for safe and efficient navigation. Furthermore, the performances of proposed algorithm (Agoraphilic algorithm with prediction) was compared and benched-marked with ANADE (Without predication) under similar environment conditions. From the investigation results, it was observed that the proposed algorithm extends the effective decision making ability in a complex navigation environment. Moreover, the proposed algorithm navigated the robot in a shorter and quicker path with smooth speed variations. © 2019 IEEE.
- Description: E1
Measuring soil strain using fibre optic sensors
- Authors: Costa, Susanga , Kahandawa, Gayan , Chen, Jian , Xue, Jianfeng
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 8th International Congress on Environmental Geotechnics, ICEG 2018; Hangzhou, China; 28th October-1st November 2018; part of the Environmental Science and Engineering book series p. 43-50
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- Description: Monitoring subsurface soil movement is important in many geotechnical engineering applications such as stability of slopes, road embankments and settlement in foundations. Soil displacement measurement is also helpful in understanding the formation of shrinkage cracks. Clay soils undergo shrinkage during drying and experience substantial stresses and strains, which results in shrinkage cracks. This paper presents a novel approach to measure soil strain using Fibre Bragg grating (FBG) sensors. In the experiments described, FBG sensors have been used to investigate the strain development in clay during drying. FBG sensors are fabricated in the core region of specially fabricated single mode low-loss germanium doped silicate optical fibres. The grating is the laser-inscribed region with a periodically varying refractive index, which reflects a specific light wavelength. Due to the applied strain, ε, there is a change in the wavelength which can be measured and is directly proposal to strain. Kaolin clay, mixed with water close to the liquid limit, was allowed to dry under room temperature. The specimens were prepared in thin, long linear shrinkage moulds. FBG sensors were placed inside soil at the centre of the specimen. The strain development during drying underwent four phases moving from compression to tension. An oscillating nature of strain was also observed throughout the drying process. Results obtained are useful to develop analytical solutions to describe stress-strain behavior of drying soil. © Springer Nature Singapore Pte Ltd. 2019.
Passive detection of splicing and copy-move attacks in image forgery
- Authors: Islam, Mohammad , Kamruzzaman, Joarder , Karmakar, Gour , Murshed, Manzur , Kahandawa, Gayan
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 25th International Conference on Neural Information Processing, ICONIP 2018; Siem Reap, Cambodia; 13th-16th December 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11304 LNCS, p. 555-567
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- Description: Internet of Things (IoT) image sensors for surveillance and monitoring, digital cameras, smart phones and social media generate huge volume of digital images every day. Image splicing and copy-move attacks are the most common types of image forgery that can be done very easily using modern photo editing software. Recently, digital forensics has drawn much attention to detect such tampering on images. In this paper, we introduce a novel feature extraction technique, namely Sum of Relevant Inter-Cell Values (SRIV) using which we propose a passive (blind) image forgery detection method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP). First, the input image is divided into non-overlapping blocks and 2D block DCT is applied to capture the changes of a tampered image in the frequency domain. Then LBP operator is applied to enhance the local changes among the neighbouring DCT coefficients, magnifying the changes in high frequency components resulting from splicing and copy-move attacks. The resulting LBP image is again divided into non-overlapping blocks. Finally, SRIV is applied on the LBP image blocks to extract features which are then fed into a Support Vector Machine (SVM) classifier to identify forged images from authentic ones. Extensive experiment on four well-known benchmark datasets of tampered images reveal the superiority of our method over recent state-of-the-art methods.
Industry-led mechatronics degree development in regional Australia
- Authors: Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer , Mazid, Abdul Md
- Date: 2017
- Type: Text , Conference proceedings , Conference paper
- Relation: 2017 IEEE International Conference on Mechatronics, ICM 2017; Gippsland, Australia; 13th-15th February 2017 p. 419-424
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- Description: This paper presents a technique that was used in the recent development of a new Mechatronics degree in Australia. This technique addressed the local industry needs and the available resources for a well-balanced Mechatronics degree program. The degree development was based on project-based learning and industry engagement. The development of the new Mechatronics degree was made possible via a State Government grant of AU$2.4 Million which was matched by industry contribution of AU$10 Million in cash and in-kind. Since industry was a major stake holder in this degree, a specific industry survey was conducted to check the desired graduates attributes, from industry point of view. The results of this survey is also included in this papers. In addition, the program also addressed the regional industry's challenge of retaining qualified engineers via a clear pathway program for students knowledge and skills development. This paper presents industry's anticipated outputs of the academic Mechatronics program. In addition the paper also discusses the mechanisms adopted for the development of this new degree. The developed fully integrated Mechatronics program was founded on the realisation that if a person undertook a mechanical degree followed by an electronics degree followed by a computer science degree, that person is, still, NOT a Mechatronics engineer. © 2017 IEEE.
- Description: Proceedings - 2017 IEEE International Conference on Mechatronics, ICM 2017
Novel tire inflating system using extreme learning machine algorithm for efficient tire identification
- Authors: Choudhury, Tanveer , Kahandawa, Gayan , Ibrahim, Yousef , Dzitac, Pavel , Mazid, Abdul Md , Man, Zhihong
- Date: 2017
- Type: Text , Conference proceedings , Conference paper
- Relation: 2017 IEEE International Conference on Mechatronics, ICM 2017; Gippsland, Victoria; 13th-15th February 2017 p. 404-409
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- Description: Tire inflators are widely used all around the word and the efficient and accurate operation is essential. The main difficulty in improving the inflation cycle of a tire inflator is the identification of the tire connected for inflation. A robust single hidden layer feed forward neural network (SLFN) is, thus, used in this study to model and predict the correct tire size. The tire size is directly related to the tire inflation cycle. Once the tire size is identified, the inflation process can be optimized to improve performance, speed and accuracy of the inflation system. Properly inflated tire and tire condition is critical to vehicle safety, stability and controllability. The training times of traditional back propagation algorithms, mostly used to model such tire identification processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. It is found that networks trained with ELM have relatively good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The result represents robustness of the trained networks and enhance reliability of the mode. Together with short training time, the algorithm has valuable application in tire identification process. © 2017 IEEE.
- Description: Proceedings - 2017 IEEE International Conference on Mechatronics, ICM 2017