Pub Date : 2016-09-01DOI: 10.1109/CEEC.2016.7835900
Shagufta Yasmin, S. Sangwine
A quaternion linear colour edge-sharpening filter is presented. A quaternion convolution mask is created using a genetic algorithm (GA) and a zooming technique. When the new filter is applied to colour images, it produces sharpened colour edges in all directions in regions where colour (but not intensity) edges occur in the image. The methodology of the proposed filter depends on the zooming technique and fitness function. The new filter is tested on different kind of colour images and the experimental results show that the proposed scheme is needed for sharpening colour edges in all directions with only one mask. This proposed filter is a great achievement for developing linear colour vector image filters because it is difficult to design manually/mathematically. This new filter is an example of a linear colour vector image filter developed using genetic algorithm and zooming techniques.
{"title":"Quaternion linear colour edge-sharpening filter using genetic algorithm","authors":"Shagufta Yasmin, S. Sangwine","doi":"10.1109/CEEC.2016.7835900","DOIUrl":"https://doi.org/10.1109/CEEC.2016.7835900","url":null,"abstract":"A quaternion linear colour edge-sharpening filter is presented. A quaternion convolution mask is created using a genetic algorithm (GA) and a zooming technique. When the new filter is applied to colour images, it produces sharpened colour edges in all directions in regions where colour (but not intensity) edges occur in the image. The methodology of the proposed filter depends on the zooming technique and fitness function. The new filter is tested on different kind of colour images and the experimental results show that the proposed scheme is needed for sharpening colour edges in all directions with only one mask. This proposed filter is a great achievement for developing linear colour vector image filters because it is difficult to design manually/mathematically. This new filter is an example of a linear colour vector image filter developed using genetic algorithm and zooming techniques.","PeriodicalId":114518,"journal":{"name":"2016 8th Computer Science and Electronic Engineering (CEEC)","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122993145","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 : 2016-09-01DOI: 10.1109/CEEC.2016.7835907
Faris Al-Baadani, S. Yousef, L. Al-Jobouri, Sourabh Bhart
Clustering in wireless sensor networks (WSN) is a proven technique to avoid redundant transmissions to the sink for better utilization of scarce network resources such as energy. Most of the clustering algorithms proposed in literature involves high number of message exchanges which results in unnecessary energy consumption. In this paper, we propose a standard deviation based weighted cluster head selection algorithm to avoid such message exchanges. The proposed mechanism uses distance and connectivity as two key parameters for optimal cluster head selection. Simulation results show that the proposed algorithm results in low packet drop, delay and control overhead.
{"title":"Standard deviation based weighted clustering algorithm for wireless sensor networks","authors":"Faris Al-Baadani, S. Yousef, L. Al-Jobouri, Sourabh Bhart","doi":"10.1109/CEEC.2016.7835907","DOIUrl":"https://doi.org/10.1109/CEEC.2016.7835907","url":null,"abstract":"Clustering in wireless sensor networks (WSN) is a proven technique to avoid redundant transmissions to the sink for better utilization of scarce network resources such as energy. Most of the clustering algorithms proposed in literature involves high number of message exchanges which results in unnecessary energy consumption. In this paper, we propose a standard deviation based weighted cluster head selection algorithm to avoid such message exchanges. The proposed mechanism uses distance and connectivity as two key parameters for optimal cluster head selection. Simulation results show that the proposed algorithm results in low packet drop, delay and control overhead.","PeriodicalId":114518,"journal":{"name":"2016 8th Computer Science and Electronic Engineering (CEEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129781062","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 : 2016-09-01DOI: 10.1109/CEEC.2016.7835917
Patrick Veenstra, C. Cooper, S. Phelps
Spectral clustering is a technique that uses the spectrum of a similarity graph to cluster data. Part of this procedure involves calculating the similarity between data points and creating a similarity graph from the resulting similarity matrix. This is ordinarily achieved by creating a k-nearest neighbour (kNN) graph. In this paper, we show the benefits of using a different similarity graph, namely the union of the kNN graph and the minimum spanning tree of the negated similarity matrix (kNN-MST). We show that this has some distinct advantages on both synthetic and real datasets. Specifically, the clustering accuracy of kNN-MST is less dependent on the choice of k than kNN is.
{"title":"Spectral clustering using the kNN-MST similarity graph","authors":"Patrick Veenstra, C. Cooper, S. Phelps","doi":"10.1109/CEEC.2016.7835917","DOIUrl":"https://doi.org/10.1109/CEEC.2016.7835917","url":null,"abstract":"Spectral clustering is a technique that uses the spectrum of a similarity graph to cluster data. Part of this procedure involves calculating the similarity between data points and creating a similarity graph from the resulting similarity matrix. This is ordinarily achieved by creating a k-nearest neighbour (kNN) graph. In this paper, we show the benefits of using a different similarity graph, namely the union of the kNN graph and the minimum spanning tree of the negated similarity matrix (kNN-MST). We show that this has some distinct advantages on both synthetic and real datasets. Specifically, the clustering accuracy of kNN-MST is less dependent on the choice of k than kNN is.","PeriodicalId":114518,"journal":{"name":"2016 8th Computer Science and Electronic Engineering (CEEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130742381","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 : 2016-09-01DOI: 10.1109/CEEC.2016.7835902
W. Viant, Jon Purdy, J. Wood
The Incident Commander plays a vital role in the effectiveness of the UK's Fire and Rescue Services, in tackling fires. The reduction in the number of incidents along with budget cuts is placing an increased emphasis on training. In this paper we propose a serious game as a replacement for the tradition training methods for these important command positions, with a discussion of immersion versus more traditional platform.
{"title":"Serious games for Fire and Rescue training","authors":"W. Viant, Jon Purdy, J. Wood","doi":"10.1109/CEEC.2016.7835902","DOIUrl":"https://doi.org/10.1109/CEEC.2016.7835902","url":null,"abstract":"The Incident Commander plays a vital role in the effectiveness of the UK's Fire and Rescue Services, in tackling fires. The reduction in the number of incidents along with budget cuts is placing an increased emphasis on training. In this paper we propose a serious game as a replacement for the tradition training methods for these important command positions, with a discussion of immersion versus more traditional platform.","PeriodicalId":114518,"journal":{"name":"2016 8th Computer Science and Electronic Engineering (CEEC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115922091","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 : 2016-09-01DOI: 10.1109/CEEC.2016.7835914
O. Osanaiye, Kim-Kwang Raymond Choo, M. Dlodlo
Notwithstanding the increased popularity of cloud computing, Distributed Denial of Service (DDoS) remains a threat to its adoption. In this paper, we propose the use of a change-point monitoring algorithm to detect DDoS flooding attacks against cloud services by examining the packet inter-arrival time (IAT). This method leverages on the fact that most DDoS attacks are automated and exhibit similar patterns. These patterns, when closely examined, can be distinguished from normal traffic patterns, and can therefore be tracked using a cumulative sum (CUSUM) algorithm. The proposed solution was validated by conducting a trace-driven simulation and empirical evaluation. The results demonstrated the efficiency and accuracy of this proposed solution.
{"title":"Change-point cloud DDoS detection using packet inter-arrival time","authors":"O. Osanaiye, Kim-Kwang Raymond Choo, M. Dlodlo","doi":"10.1109/CEEC.2016.7835914","DOIUrl":"https://doi.org/10.1109/CEEC.2016.7835914","url":null,"abstract":"Notwithstanding the increased popularity of cloud computing, Distributed Denial of Service (DDoS) remains a threat to its adoption. In this paper, we propose the use of a change-point monitoring algorithm to detect DDoS flooding attacks against cloud services by examining the packet inter-arrival time (IAT). This method leverages on the fact that most DDoS attacks are automated and exhibit similar patterns. These patterns, when closely examined, can be distinguished from normal traffic patterns, and can therefore be tracked using a cumulative sum (CUSUM) algorithm. The proposed solution was validated by conducting a trace-driven simulation and empirical evaluation. The results demonstrated the efficiency and accuracy of this proposed solution.","PeriodicalId":114518,"journal":{"name":"2016 8th Computer Science and Electronic Engineering (CEEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124344782","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 : 2016-09-01DOI: 10.1109/CEEC.2016.7835879
Zina Abu-Almaalie, Zabih Ghassemlooy, Alaa A. S. Al-Rubaie, It Ee Lee, H. L. Minh
Physical layer network coding (PNC) is a promising technique to improve the network throughput in a wireless two-way relay (TWR) channel. The PNC is embraced for TWR with free space optical (FSO) communication link, TWR-FSO, for full utilization of network resources. In this paper, forward error correction (FEC) is employed with TWR-FSO PNC system. The convolutional code (CC) is considered to combat the deleterious effect of FSO turbulence channel to increase the system reliability. The performance of end-to-end (E2E) CC with TWR-FSO PNC scheme is examined in terms of bit error rate (BER) under the influence of turbulence-induced channel fading. The results show that the proposed scheme can achieve a significant BER performance improvement through the introduction of CC joint with PNC mapping, which enables the system to effectively mitigate the impact of channel.
{"title":"Forward error correction with physical layer network coding in two-way relay free space optical links","authors":"Zina Abu-Almaalie, Zabih Ghassemlooy, Alaa A. S. Al-Rubaie, It Ee Lee, H. L. Minh","doi":"10.1109/CEEC.2016.7835879","DOIUrl":"https://doi.org/10.1109/CEEC.2016.7835879","url":null,"abstract":"Physical layer network coding (PNC) is a promising technique to improve the network throughput in a wireless two-way relay (TWR) channel. The PNC is embraced for TWR with free space optical (FSO) communication link, TWR-FSO, for full utilization of network resources. In this paper, forward error correction (FEC) is employed with TWR-FSO PNC system. The convolutional code (CC) is considered to combat the deleterious effect of FSO turbulence channel to increase the system reliability. The performance of end-to-end (E2E) CC with TWR-FSO PNC scheme is examined in terms of bit error rate (BER) under the influence of turbulence-induced channel fading. The results show that the proposed scheme can achieve a significant BER performance improvement through the introduction of CC joint with PNC mapping, which enables the system to effectively mitigate the impact of channel.","PeriodicalId":114518,"journal":{"name":"2016 8th Computer Science and Electronic Engineering (CEEC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129220323","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 : 2016-09-01DOI: 10.1109/CEEC.2016.7835903
T. Jan, H. Zafar, R. A. Khalil, M. Ashraf
Here we present a new algorithm for the separation of convolutive speech observations using recordings from 2 microphones. This method is the union of independent vector analysis (IVA) and ideal binary mask (IBM), in conjunction with a post-filtering process in the cepstral domain. The proposed algorithm comprises of 3 steps. In the first step, an IVA algorithm is applied for the separation of the source signals from 2-microphone recordings. Second step is the estimation of IBM by the comparison of the energy of corresponding time-frequency (T-F) units of the segregated sources that are achieved using the IVA technique. Final step is the reduction of the musical noise by employing cepstral smoothing and such a noise is generated due to T-F masking. The signal to noise ratio (SNR) measurement has been used to evaluate the overall performance of the proposed method by employing the reverberant mixtures that are produced via simulated room model. The evaluation shows that it is more efficient and speech quality has been improved while generating similar segregation performance compared to a state-of-the-art approach.
{"title":"A blind source separation approach based on IVA for convolutive speech mixtures","authors":"T. Jan, H. Zafar, R. A. Khalil, M. Ashraf","doi":"10.1109/CEEC.2016.7835903","DOIUrl":"https://doi.org/10.1109/CEEC.2016.7835903","url":null,"abstract":"Here we present a new algorithm for the separation of convolutive speech observations using recordings from 2 microphones. This method is the union of independent vector analysis (IVA) and ideal binary mask (IBM), in conjunction with a post-filtering process in the cepstral domain. The proposed algorithm comprises of 3 steps. In the first step, an IVA algorithm is applied for the separation of the source signals from 2-microphone recordings. Second step is the estimation of IBM by the comparison of the energy of corresponding time-frequency (T-F) units of the segregated sources that are achieved using the IVA technique. Final step is the reduction of the musical noise by employing cepstral smoothing and such a noise is generated due to T-F masking. The signal to noise ratio (SNR) measurement has been used to evaluate the overall performance of the proposed method by employing the reverberant mixtures that are produced via simulated room model. The evaluation shows that it is more efficient and speech quality has been improved while generating similar segregation performance compared to a state-of-the-art approach.","PeriodicalId":114518,"journal":{"name":"2016 8th Computer Science and Electronic Engineering (CEEC)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129558956","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 : 2016-09-01DOI: 10.1109/CEEC.2016.7835883
Bilal R. Al-Kaseem, H. Al-Raweshidy
Machine-to-Machine (M2M) and Internet-of-Things (IoT) buildup of a large number of devices that are capable of sensing or actuating and provide ubiquitous connectivity and processing to enhance the daily life activities. This paper proposes a wireless Software Defined Networking (SDN) solution for M2M gateway based on the cloud environment. The proposed approach takes the advantage of SDN in separating the control plane from the data plane in network devices and running the software component on centralized M2M gateway connected to the cloud. The proposed approach validated through experimental analysis testbed, the obtained result shows that SD-M2M gateway reduces the end-to-end delay by approximately 23% and 15% compared to M2M gateway without SDN in terms of data gathering and control command sending respectively. The proposed approach provides significant flexibility for network resource management.
{"title":"Enabling wireless Software Defined Networking in cloud based Machine-to-Machine gateway","authors":"Bilal R. Al-Kaseem, H. Al-Raweshidy","doi":"10.1109/CEEC.2016.7835883","DOIUrl":"https://doi.org/10.1109/CEEC.2016.7835883","url":null,"abstract":"Machine-to-Machine (M2M) and Internet-of-Things (IoT) buildup of a large number of devices that are capable of sensing or actuating and provide ubiquitous connectivity and processing to enhance the daily life activities. This paper proposes a wireless Software Defined Networking (SDN) solution for M2M gateway based on the cloud environment. The proposed approach takes the advantage of SDN in separating the control plane from the data plane in network devices and running the software component on centralized M2M gateway connected to the cloud. The proposed approach validated through experimental analysis testbed, the obtained result shows that SD-M2M gateway reduces the end-to-end delay by approximately 23% and 15% compared to M2M gateway without SDN in terms of data gathering and control command sending respectively. The proposed approach provides significant flexibility for network resource management.","PeriodicalId":114518,"journal":{"name":"2016 8th Computer Science and Electronic Engineering (CEEC)","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132960627","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 : 2016-07-06DOI: 10.1109/CEEC.2016.7835909
Jialin Liu, Diego Perez Liebana, S. Lucas
This paper describes a new algorithm for decision making in two-player real-time video games. As with Monte Carlo Tree Search, the algorithm can be used without heuristics and has been developed for use in general video game AI.
{"title":"Rolling Horizon Coevolutionary planning for two-player video games","authors":"Jialin Liu, Diego Perez Liebana, S. Lucas","doi":"10.1109/CEEC.2016.7835909","DOIUrl":"https://doi.org/10.1109/CEEC.2016.7835909","url":null,"abstract":"This paper describes a new algorithm for decision making in two-player real-time video games. As with Monte Carlo Tree Search, the algorithm can be used without heuristics and has been developed for use in general video game AI.","PeriodicalId":114518,"journal":{"name":"2016 8th Computer Science and Electronic Engineering (CEEC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132627319","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 : 1900-01-01DOI: 10.1109/ceec.2016.7835894
David Jardim, Luís Nunes, José Miguel Salles Dias
Human Activity Recognition (HAR) is an interdisciplinary research area that has been attracting interest from several research communities specialized in machine learning, computer vision, medical and gaming research. The potential applications range from surveillance systems, human computer interfaces, sports video analysis, digital shopping assistants, video retrieval, games and health-care. Several and diverse approaches exist to recognize a human action. From computer vision techniques, modeling relations between human motion and objects, marker-based tracking systems and RGB-D cameras. Using a Kinect sensor that provides the position of the main skeleton joints we extract features based solely on the motion of those joints. This paper aims to compare the performance of several supervised classifiers trained with manually labeled data versus the same classifiers trained with data automatically labeled. We propose a framework capable of recognizing human actions using supervised classifiers trained with automatically labeled data.
{"title":"Human Activity Recognition from automatically labeled data in RGB-D videos","authors":"David Jardim, Luís Nunes, José Miguel Salles Dias","doi":"10.1109/ceec.2016.7835894","DOIUrl":"https://doi.org/10.1109/ceec.2016.7835894","url":null,"abstract":"Human Activity Recognition (HAR) is an interdisciplinary research area that has been attracting interest from several research communities specialized in machine learning, computer vision, medical and gaming research. The potential applications range from surveillance systems, human computer interfaces, sports video analysis, digital shopping assistants, video retrieval, games and health-care. Several and diverse approaches exist to recognize a human action. From computer vision techniques, modeling relations between human motion and objects, marker-based tracking systems and RGB-D cameras. Using a Kinect sensor that provides the position of the main skeleton joints we extract features based solely on the motion of those joints. This paper aims to compare the performance of several supervised classifiers trained with manually labeled data versus the same classifiers trained with data automatically labeled. We propose a framework capable of recognizing human actions using supervised classifiers trained with automatically labeled data.","PeriodicalId":114518,"journal":{"name":"2016 8th Computer Science and Electronic Engineering (CEEC)","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134404875","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：