Pub Date : 2013-11-11DOI: 10.1109/CEEC.2013.6659436
Sen Wang, Ling Chen, Huosheng Hu, K. Mcdonald-Maier
Traditionally, it is difficult for elderly and disabled people to control a wheelchair passing a narrow doorway manually. This paper presents a dynamic trajectory planning algorithm for wheelchairs to pass a door smoothly and automatically. It is a sensor-based approach in which two laser rangefinders are deployed in the wheelchair for real-time door detection. To generate smooth trajectories that enable a wheelchair to pass a door perpendicularly, Bézier curve based trajectories are calculated repeatedly during the whole course of door passing. The proposed approach is tested on a real wheelchair and the experimental results are presented to show the good performance and effectiveness of our proposed automatic door passing strategy.
{"title":"Sensor-based dynamic trajectory planning for smooth door passing of intelligent wheelchairs","authors":"Sen Wang, Ling Chen, Huosheng Hu, K. Mcdonald-Maier","doi":"10.1109/CEEC.2013.6659436","DOIUrl":"https://doi.org/10.1109/CEEC.2013.6659436","url":null,"abstract":"Traditionally, it is difficult for elderly and disabled people to control a wheelchair passing a narrow doorway manually. This paper presents a dynamic trajectory planning algorithm for wheelchairs to pass a door smoothly and automatically. It is a sensor-based approach in which two laser rangefinders are deployed in the wheelchair for real-time door detection. To generate smooth trajectories that enable a wheelchair to pass a door perpendicularly, Bézier curve based trajectories are calculated repeatedly during the whole course of door passing. The proposed approach is tested on a real wheelchair and the experimental results are presented to show the good performance and effectiveness of our proposed automatic door passing strategy.","PeriodicalId":309053,"journal":{"name":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125574246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-11-11DOI: 10.1109/CEEC.2013.6659460
Rasher D. Rashid, S. Jassim, H. Sellahewa
In this paper, a high invisibility face biometric data transfer technique is proposed. The proposed method decomposes a face image into multiple frequency bands using wavelet transform. Each sub-band in the wavelet domain is divided into non-overlapping blocks. Then, local binary pattern histograms (LBPHs) are extracted from each block in each subband using only 4 neighbours to extract LBP code. Then, all of the LBPHs are concatenated into a single feature histogram to effectively represent the face image. Finally, the extracted face features are embedded in an image using one of the robust steganography techniques in order for them to be ready for transmission. PSNR between original and stego-image is calculated to measure invisibility of the system, while recognition rate of the system is calculated using Euclidean distance followed by a nearest neighbour classifier. The recognition is performed on the receiver side after extracting the embedded face features. The above strategy was tested on two publicly available face databases (Yale and ORL) using different scenarios and different combinations of sub-bands. Results obtained show that embedding LBPH features using our method will give higher invisibility whilst maintaining the recognition rate at the same level or better when compared with the original uniform LBP.
{"title":"Covert exchange of face biometric data using steganography","authors":"Rasher D. Rashid, S. Jassim, H. Sellahewa","doi":"10.1109/CEEC.2013.6659460","DOIUrl":"https://doi.org/10.1109/CEEC.2013.6659460","url":null,"abstract":"In this paper, a high invisibility face biometric data transfer technique is proposed. The proposed method decomposes a face image into multiple frequency bands using wavelet transform. Each sub-band in the wavelet domain is divided into non-overlapping blocks. Then, local binary pattern histograms (LBPHs) are extracted from each block in each subband using only 4 neighbours to extract LBP code. Then, all of the LBPHs are concatenated into a single feature histogram to effectively represent the face image. Finally, the extracted face features are embedded in an image using one of the robust steganography techniques in order for them to be ready for transmission. PSNR between original and stego-image is calculated to measure invisibility of the system, while recognition rate of the system is calculated using Euclidean distance followed by a nearest neighbour classifier. The recognition is performed on the receiver side after extracting the embedded face features. The above strategy was tested on two publicly available face databases (Yale and ORL) using different scenarios and different combinations of sub-bands. Results obtained show that embedding LBPH features using our method will give higher invisibility whilst maintaining the recognition rate at the same level or better when compared with the original uniform LBP.","PeriodicalId":309053,"journal":{"name":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124380629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-11-11DOI: 10.1109/CEEC.2013.6659441
L. P. Damuut, Felix Ngobigha, Dongbing Gu
The challenges involved in the application of fuzzy logic in wireless sensors networks often stem from the limitation in processing and storage capabilities of the nodes. This anomaly can be overcome by using a centralized data sink, equipped with more storage and processing capabilities and which can also serve as the decider on the occurrence or otherwise of the event of interest based on selected readings of a subset of the deployed nodes. It is known that selecting a finite subset of a universal set can be intractable especially with relatively large size of the problem space. In this paper, we propose the application of T-norm Fuzzy Logic(TFL) to address the sensor selection problem and compare its performance to that of a standard Genetic Algorithm (GA). Extensive simulation results reveal the usefulness of this approach and how it is closely related to the GA technique.
{"title":"Applying T-norm fuzzy logic to the sensor selection problem in WSNs","authors":"L. P. Damuut, Felix Ngobigha, Dongbing Gu","doi":"10.1109/CEEC.2013.6659441","DOIUrl":"https://doi.org/10.1109/CEEC.2013.6659441","url":null,"abstract":"The challenges involved in the application of fuzzy logic in wireless sensors networks often stem from the limitation in processing and storage capabilities of the nodes. This anomaly can be overcome by using a centralized data sink, equipped with more storage and processing capabilities and which can also serve as the decider on the occurrence or otherwise of the event of interest based on selected readings of a subset of the deployed nodes. It is known that selecting a finite subset of a universal set can be intractable especially with relatively large size of the problem space. In this paper, we propose the application of T-norm Fuzzy Logic(TFL) to address the sensor selection problem and compare its performance to that of a standard Genetic Algorithm (GA). Extensive simulation results reveal the usefulness of this approach and how it is closely related to the GA technique.","PeriodicalId":309053,"journal":{"name":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","volume":"21 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132118546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-11-11DOI: 10.1109/CEEC.2013.6659456
A. Wahid, J. Schormans
Network Sampling is vital for estimating network parameters. Recent seminal work has proposed that there are theoretically derived upper limits to sampling rate. In this paper we explore the maximum sampling rate to use for estimating packet loss probability given a fixed number of samples, by simulation. Our results show that there is a strong relationship between the sampling rate and the accuracy of measurement. We find the maximum sampling rate for optimal measurement of PLP for specific network scenarios given a fixed number of samples. An important area of application of this work is certain Measurement Based Admission Control scenarios, where faster measurement is important.
{"title":"Bounding the maximum sampling rate when measuring PLP in a packet buffer","authors":"A. Wahid, J. Schormans","doi":"10.1109/CEEC.2013.6659456","DOIUrl":"https://doi.org/10.1109/CEEC.2013.6659456","url":null,"abstract":"Network Sampling is vital for estimating network parameters. Recent seminal work has proposed that there are theoretically derived upper limits to sampling rate. In this paper we explore the maximum sampling rate to use for estimating packet loss probability given a fixed number of samples, by simulation. Our results show that there is a strong relationship between the sampling rate and the accuracy of measurement. We find the maximum sampling rate for optimal measurement of PLP for specific network scenarios given a fixed number of samples. An important area of application of this work is certain Measurement Based Admission Control scenarios, where faster measurement is important.","PeriodicalId":309053,"journal":{"name":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123543243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-11-11DOI: 10.1109/CEEC.2013.6659455
Ahmad Alhindi, Qingfu Zhang
Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation into a number of single-objective problem and optimises them in a collaborative manner. This paper investigates how to use the Guided Local Search (GLS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the GLS applies to these subproblems to escape local Pareto optimal solutions. The experimental studies have shown that MOEA/D with GLS outperforms the classical MOEA/D on a bi-objective travelling salesman problem.
{"title":"MOEA/D with guided local search: Some preliminary experimental results","authors":"Ahmad Alhindi, Qingfu Zhang","doi":"10.1109/CEEC.2013.6659455","DOIUrl":"https://doi.org/10.1109/CEEC.2013.6659455","url":null,"abstract":"Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation into a number of single-objective problem and optimises them in a collaborative manner. This paper investigates how to use the Guided Local Search (GLS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the GLS applies to these subproblems to escape local Pareto optimal solutions. The experimental studies have shown that MOEA/D with GLS outperforms the classical MOEA/D on a bi-objective travelling salesman problem.","PeriodicalId":309053,"journal":{"name":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116152966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-11-11DOI: 10.1109/CEEC.2013.6659446
Mahvish Nazir, D. Pycock
Frequency modulated continuous wave (FMCW) vehicle radar presents severe range-velocity ambiguity due to the velocities and ranges encountered. This is exacerbated in wide angle radar systems where there are likely to be 100 or more responses. Existing methods of interpretation reject low amplitude responses that can be from a close target of low radar cross section. This could result in a critical target not being detected. These are most likely to be responses from a bicycle or a pushchair but could be the only response from an electrical vehicle with a small number of metal components with a small radar cross section at the front of the vehicle. We present a method in which confidence estimates are used to preserve all radar responses and apply constraints to radar responses from a set of radar chirp waveforms, each from a set of radar sensors and reduce the level of ambiguity to a manageable level.
{"title":"A novel strategy for interpreting multiple responses in vehicle radar: A novel consideration of the ambiguity function","authors":"Mahvish Nazir, D. Pycock","doi":"10.1109/CEEC.2013.6659446","DOIUrl":"https://doi.org/10.1109/CEEC.2013.6659446","url":null,"abstract":"Frequency modulated continuous wave (FMCW) vehicle radar presents severe range-velocity ambiguity due to the velocities and ranges encountered. This is exacerbated in wide angle radar systems where there are likely to be 100 or more responses. Existing methods of interpretation reject low amplitude responses that can be from a close target of low radar cross section. This could result in a critical target not being detected. These are most likely to be responses from a bicycle or a pushchair but could be the only response from an electrical vehicle with a small number of metal components with a small radar cross section at the front of the vehicle. We present a method in which confidence estimates are used to preserve all radar responses and apply constraints to radar responses from a set of radar chirp waveforms, each from a set of radar sensors and reduce the level of ambiguity to a manageable level.","PeriodicalId":309053,"journal":{"name":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126919373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-11-11DOI: 10.1109/CEEC.2013.6659453
Mohamed Al-Momin, J. Cosmas, S. Amin
A novel idea to enhance the ACO method for solving the problem of RWA in WDM optical networks has been proposed in this paper. This enhancement has been achieved by suggesting the idea of re-sorting the requests that are needed to be connected in order of their distances to the destinations before serving them. The enhanced ACO version has been tested through this paper on both static and dynamic RWA types with different wavelength conversion scenarios. Results clearly show the feasibility of our enhanced ACO algorithm.
{"title":"Enhanced ACO based RWA on WDM optical networks using requests accumulation and re-sorting method","authors":"Mohamed Al-Momin, J. Cosmas, S. Amin","doi":"10.1109/CEEC.2013.6659453","DOIUrl":"https://doi.org/10.1109/CEEC.2013.6659453","url":null,"abstract":"A novel idea to enhance the ACO method for solving the problem of RWA in WDM optical networks has been proposed in this paper. This enhancement has been achieved by suggesting the idea of re-sorting the requests that are needed to be connected in order of their distances to the destinations before serving them. The enhanced ACO version has been tested through this paper on both static and dynamic RWA types with different wavelength conversion scenarios. Results clearly show the feasibility of our enhanced ACO algorithm.","PeriodicalId":309053,"journal":{"name":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129809272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-11-11DOI: 10.1109/CEEC.2013.6659449
X. Shang, T. Ji, Mengshi Li, P. Wu, Qinghua Wu
This paper focuses on implementing a reinforcement learning algorithm for solving parameter optimization problems of Proportional Integral Derivative (PID) controllers. Function Optimization by Reinforcement Learning (FORL) remarkably outperforms a number of population-based intelligent algorithms when executed on benchmark functions in high-dimension circumstances. Therefore, this paper aims at examining the performance of FORL when optimizing parameters of PID controllers in a low-dimension space. According to the experiment studies in this paper, FORL is able to optimize the PID parameters with advantage over GA and PSO in terms of convergence speed.
{"title":"Parameter optimization of PID controllers by reinforcement learning","authors":"X. Shang, T. Ji, Mengshi Li, P. Wu, Qinghua Wu","doi":"10.1109/CEEC.2013.6659449","DOIUrl":"https://doi.org/10.1109/CEEC.2013.6659449","url":null,"abstract":"This paper focuses on implementing a reinforcement learning algorithm for solving parameter optimization problems of Proportional Integral Derivative (PID) controllers. Function Optimization by Reinforcement Learning (FORL) remarkably outperforms a number of population-based intelligent algorithms when executed on benchmark functions in high-dimension circumstances. Therefore, this paper aims at examining the performance of FORL when optimizing parameters of PID controllers in a low-dimension space. According to the experiment studies in this paper, FORL is able to optimize the PID parameters with advantage over GA and PSO in terms of convergence speed.","PeriodicalId":309053,"journal":{"name":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128697619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-11-11DOI: 10.1109/CEEC.2013.6659457
Taban F. Majeed, Naseer Al-Jawad, H. Sellahewa
In this paper, we propose a new method for breast border extraction, artifact removal and removal of annotations typically found in the background of mammograms. The proposed method uses adaptive local thresholding to create an initial binary mask for an image. This is followed by the use of morphological operations to remove background artifacts. Then an adaptive algorithm is proposed to automatically detect and remove the pectoral muscle depending on the gray-level intensity values. Preliminary results of experiments conducted on the Mini-MIAS database (Mammographic Image Analysis Society, London, U.K.) show that the proposed method achieves a near 100% success rate for breast contour extraction and the proposed method for pectoral muscle removal achieves nearly 89% accuracy. More importantly, the proposed pre-processing techniques improved the mammogram classification results when compared to using previous pre-processing methods.
{"title":"Breast border extraction and pectoral muscle removal in MLO mammogram images","authors":"Taban F. Majeed, Naseer Al-Jawad, H. Sellahewa","doi":"10.1109/CEEC.2013.6659457","DOIUrl":"https://doi.org/10.1109/CEEC.2013.6659457","url":null,"abstract":"In this paper, we propose a new method for breast border extraction, artifact removal and removal of annotations typically found in the background of mammograms. The proposed method uses adaptive local thresholding to create an initial binary mask for an image. This is followed by the use of morphological operations to remove background artifacts. Then an adaptive algorithm is proposed to automatically detect and remove the pectoral muscle depending on the gray-level intensity values. Preliminary results of experiments conducted on the Mini-MIAS database (Mammographic Image Analysis Society, London, U.K.) show that the proposed method achieves a near 100% success rate for breast contour extraction and the proposed method for pectoral muscle removal achieves nearly 89% accuracy. More importantly, the proposed pre-processing techniques improved the mammogram classification results when compared to using previous pre-processing methods.","PeriodicalId":309053,"journal":{"name":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133193138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-11-11DOI: 10.1109/CEEC.2013.6659450
P. Wu, M. S. Li, T. Ji, Q. Wu, X. Shang
This paper proposes a novel stochastic algorithm, which aims to describe the random distributions of experimentally acquired data. Generally, such data can be satisfactorily modeled through the use of a Gaussian distribution. However, it is not always the case, instances can arise in which the distributions of measured data are not strictly Gaussian in their nature. The present work adopts Bacterial Swarm Optimizer (BSO), which has been inspired from bacterial foraging behavior and quorum sensing, to optimize the Probability Density Function (PDF) for describing a particle identification spectrum constructed from data collected in an experiment undertaken at Gesellschaft fur Schwerionenforschung (GSI), Darmstadt, Germany. Our studies indicates that the PDF proposed in the present paper is more accurate than that of several convention methods.
{"title":"A novel fitting algorithm based on Bacterial Swarm Optimizer for stochastic data","authors":"P. Wu, M. S. Li, T. Ji, Q. Wu, X. Shang","doi":"10.1109/CEEC.2013.6659450","DOIUrl":"https://doi.org/10.1109/CEEC.2013.6659450","url":null,"abstract":"This paper proposes a novel stochastic algorithm, which aims to describe the random distributions of experimentally acquired data. Generally, such data can be satisfactorily modeled through the use of a Gaussian distribution. However, it is not always the case, instances can arise in which the distributions of measured data are not strictly Gaussian in their nature. The present work adopts Bacterial Swarm Optimizer (BSO), which has been inspired from bacterial foraging behavior and quorum sensing, to optimize the Probability Density Function (PDF) for describing a particle identification spectrum constructed from data collected in an experiment undertaken at Gesellschaft fur Schwerionenforschung (GSI), Darmstadt, Germany. Our studies indicates that the PDF proposed in the present paper is more accurate than that of several convention methods.","PeriodicalId":309053,"journal":{"name":"2013 5th Computer Science and Electronic Engineering Conference (CEEC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134371048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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