Pub Date : 2017-04-04DOI: 10.1109/IACS.2017.7921981
Hazim Shakhatreh, Abdallah Khreishah, A. Alsarhan, Issa M. Khalil, Ahmad H. Sawalmeh, Noor Shamsiah Othman
Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider an Air-to-Ground path loss model, which assumes that the users are outdoor and they are located on a 2D plane. In this paper, we propose using a single UAV to provide wireless coverage for indoor users inside a high-rise building under disaster situations (such as earthquakes or floods), when cellular networks are down. We assume that the locations of indoor users are uniformly distributed in each floor and we propose a particle swarm optimization algorithm to find an efficient 3D placement of a UAV that minimizes the total transmit power required to cover the indoor users.
{"title":"Efficient 3D placement of a UAV using particle swarm optimization","authors":"Hazim Shakhatreh, Abdallah Khreishah, A. Alsarhan, Issa M. Khalil, Ahmad H. Sawalmeh, Noor Shamsiah Othman","doi":"10.1109/IACS.2017.7921981","DOIUrl":"https://doi.org/10.1109/IACS.2017.7921981","url":null,"abstract":"Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks go down. Prior studies on UAV-based wireless coverage typically consider an Air-to-Ground path loss model, which assumes that the users are outdoor and they are located on a 2D plane. In this paper, we propose using a single UAV to provide wireless coverage for indoor users inside a high-rise building under disaster situations (such as earthquakes or floods), when cellular networks are down. We assume that the locations of indoor users are uniformly distributed in each floor and we propose a particle swarm optimization algorithm to find an efficient 3D placement of a UAV that minimizes the total transmit power required to cover the indoor users.","PeriodicalId":180504,"journal":{"name":"2017 8th International Conference on Information and Communication Systems (ICICS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114768036","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 : 2017-04-04DOI: 10.1109/IACS.2017.7921950
S. Nair, Khadija Al Farei
Memorizing and recollecting the contents of long text that has been read is a real challenge. Carrying out research, organizing and associating research ideas are equally challenging. The well-known saying that “A picture is worth a thousand words” is utilized in this teaching and learning process to empower research-minded practice among students. To accomplish this task, the concept of mind mapping is exploited. Researchers point out that visual models such as mind maps are very effective to organize one's thoughts, ideas and findings. Mind map is a graphical representation that mimics how the information flows through the neuronal cells in a way how human brain works. In short, mind map is a brain friendly tool. An attempt has been made to inspect how the concept of mind mapping can assist in expediting the learning experience of students in completing research based assessments successfully, using a mind mapping tool named Xmind.
{"title":"A brain friendly tool to facilitate research-teaching nexus: Mind maps","authors":"S. Nair, Khadija Al Farei","doi":"10.1109/IACS.2017.7921950","DOIUrl":"https://doi.org/10.1109/IACS.2017.7921950","url":null,"abstract":"Memorizing and recollecting the contents of long text that has been read is a real challenge. Carrying out research, organizing and associating research ideas are equally challenging. The well-known saying that “A picture is worth a thousand words” is utilized in this teaching and learning process to empower research-minded practice among students. To accomplish this task, the concept of mind mapping is exploited. Researchers point out that visual models such as mind maps are very effective to organize one's thoughts, ideas and findings. Mind map is a graphical representation that mimics how the information flows through the neuronal cells in a way how human brain works. In short, mind map is a brain friendly tool. An attempt has been made to inspect how the concept of mind mapping can assist in expediting the learning experience of students in completing research based assessments successfully, using a mind mapping tool named Xmind.","PeriodicalId":180504,"journal":{"name":"2017 8th International Conference on Information and Communication Systems (ICICS)","volume":" 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120833435","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 : 2017-04-04DOI: 10.1109/IACS.2017.7921956
Yaser Saleh, E. Edirisinghe
Although face recognition is considered a popular area of research and study, it still has few unresolved challenges, and with the appearance of devices such as the Microsoft Kinect, new possibilities for researchers were uncovered. With the goal of enhancing face recognition techniques, this paper presents a novel way to reconstruct face images in different angles, through the use of the data of one front image captured by the Kinect, using faster techniques than ever before, also, this paper utilizes a deep learning network called Overfeat, where it functioned as a feature extractor that was used on normal images and on the new 3D created images, which introduced a new application for the network. To check the capabilities of the new created images, they were used as a testing set in three main experiments. Finally, results of the experiments are presented to prove the ability of the created images to function as new data sets for face recognition; also, proving the capability of the Overfeat network, working with computer generated face images.
{"title":"3D face reconstruction and recognition using the overfeat network","authors":"Yaser Saleh, E. Edirisinghe","doi":"10.1109/IACS.2017.7921956","DOIUrl":"https://doi.org/10.1109/IACS.2017.7921956","url":null,"abstract":"Although face recognition is considered a popular area of research and study, it still has few unresolved challenges, and with the appearance of devices such as the Microsoft Kinect, new possibilities for researchers were uncovered. With the goal of enhancing face recognition techniques, this paper presents a novel way to reconstruct face images in different angles, through the use of the data of one front image captured by the Kinect, using faster techniques than ever before, also, this paper utilizes a deep learning network called Overfeat, where it functioned as a feature extractor that was used on normal images and on the new 3D created images, which introduced a new application for the network. To check the capabilities of the new created images, they were used as a testing set in three main experiments. Finally, results of the experiments are presented to prove the ability of the created images to function as new data sets for face recognition; also, proving the capability of the Overfeat network, working with computer generated face images.","PeriodicalId":180504,"journal":{"name":"2017 8th International Conference on Information and Communication Systems (ICICS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123144143","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 : 2017-04-04DOI: 10.1109/IACS.2017.7921949
Saif A. Ahmad Alrababah, K. H. Gan, T. Tan
The extracted product aspects (like “battery life”, “zoom”) from online customer reviews are dissimilar in their significances, some of these aspects have a great influence on the potential customer's decision likewise on the businesses' strategies for product enhancements. Supporting the probable customers with a list of the most representative product aspects will assist their purchasing decision and facilitate the comparative process among the presented products. For the firms, identifying critical product aspects creates a new perspective of product manufacturing and marketing strategies to be competitive and innovative. However the manual identification of the most representative product aspects from the huge amounts of the extracted product aspects in online reviews is a tedious and time-consuming task. Thus, ranking the extracted aspects becomes a necessity to identify the important product aspects mentioned in the customer reviews. The purpose of this study is to formulate the product aspect ranking problem as a decision making process using Multi-Criteria Decision Making (MCDM). In response, a comparative analysis between two different MCDM ranking approaches, namely; TOPSIS and VIKOR has been conducted to investigate their performances in prioritizing the most important product aspects in customer reviews. The experimental results on different product reviews demonstrate the effectiveness of these two methods in prioritizing the genuine product aspects in customer feedback.
{"title":"Comparative analysis of MCDM methods for product aspect ranking: TOPSIS and VIKOR","authors":"Saif A. Ahmad Alrababah, K. H. Gan, T. Tan","doi":"10.1109/IACS.2017.7921949","DOIUrl":"https://doi.org/10.1109/IACS.2017.7921949","url":null,"abstract":"The extracted product aspects (like “battery life”, “zoom”) from online customer reviews are dissimilar in their significances, some of these aspects have a great influence on the potential customer's decision likewise on the businesses' strategies for product enhancements. Supporting the probable customers with a list of the most representative product aspects will assist their purchasing decision and facilitate the comparative process among the presented products. For the firms, identifying critical product aspects creates a new perspective of product manufacturing and marketing strategies to be competitive and innovative. However the manual identification of the most representative product aspects from the huge amounts of the extracted product aspects in online reviews is a tedious and time-consuming task. Thus, ranking the extracted aspects becomes a necessity to identify the important product aspects mentioned in the customer reviews. The purpose of this study is to formulate the product aspect ranking problem as a decision making process using Multi-Criteria Decision Making (MCDM). In response, a comparative analysis between two different MCDM ranking approaches, namely; TOPSIS and VIKOR has been conducted to investigate their performances in prioritizing the most important product aspects in customer reviews. The experimental results on different product reviews demonstrate the effectiveness of these two methods in prioritizing the genuine product aspects in customer feedback.","PeriodicalId":180504,"journal":{"name":"2017 8th International Conference on Information and Communication Systems (ICICS)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121142268","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 : 2017-04-04DOI: 10.1109/IACS.2017.7921951
Yahya M. Tashtoush, Majd Al-Soud, Manar Fraihat, Walaa Al-Sarayrah, M. Alsmirat
With the emanation of educational data mining field, it is being increasingly connected to a number of research areas such as adaptive and intelligent web-based tutors, intelligent educational applications and other accommodating online educational data mining systems. The applications of educational data mining takes into account the system academic aspects, the academic background, and the learner's classification. This paper proposes a new adaptive e-learning system. The proposed system integrates a well known intelligent web-based English e-learning tutor with data mining techniques. Also, the data minig techniques are used in order to cluster students' learning styles according to Jackson's learning styles. The ultimate goal of the proposed system is to determine the best teaching pattern for each learner. The proposed system can be made available through the web Everywhere as well as Every Time (EWET). It also offers adaptive facilities such as learning videos, adaptive presentations, and quizzes for the students. Moreover, it helps both teachers and students to follow the best learning process and achieve the highest academic rates. The results show that the highest student's achievement pattern is the pattern (Speaking — Reading — Grammar — Writing) with score of at least 87.4%.
{"title":"Adaptive e-learning web-based English tutor using data mining techniques and Jackson's learning styles","authors":"Yahya M. Tashtoush, Majd Al-Soud, Manar Fraihat, Walaa Al-Sarayrah, M. Alsmirat","doi":"10.1109/IACS.2017.7921951","DOIUrl":"https://doi.org/10.1109/IACS.2017.7921951","url":null,"abstract":"With the emanation of educational data mining field, it is being increasingly connected to a number of research areas such as adaptive and intelligent web-based tutors, intelligent educational applications and other accommodating online educational data mining systems. The applications of educational data mining takes into account the system academic aspects, the academic background, and the learner's classification. This paper proposes a new adaptive e-learning system. The proposed system integrates a well known intelligent web-based English e-learning tutor with data mining techniques. Also, the data minig techniques are used in order to cluster students' learning styles according to Jackson's learning styles. The ultimate goal of the proposed system is to determine the best teaching pattern for each learner. The proposed system can be made available through the web Everywhere as well as Every Time (EWET). It also offers adaptive facilities such as learning videos, adaptive presentations, and quizzes for the students. Moreover, it helps both teachers and students to follow the best learning process and achieve the highest academic rates. The results show that the highest student's achievement pattern is the pattern (Speaking — Reading — Grammar — Writing) with score of at least 87.4%.","PeriodicalId":180504,"journal":{"name":"2017 8th International Conference on Information and Communication Systems (ICICS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126103981","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 : 2017-04-04DOI: 10.1109/IACS.2017.7921993
Nabil Djedjig, Djamel Tandjaoui, Faiza Medjek, I. Romdhani
Establishing trust relationships between routing nodes represents a vital security requirement to establish reliable routing processes that exclude infected or selfish nodes. In this paper, we propose a new security scheme for the Internet of things and mainly for the RPL (Routing Protocol for Low-power and Lossy Networks) called: Metric-based RPL Trustworthiness Scheme (MRTS). The primary aim is to enhance RPL security and deal with the trust inference problem. MRTS addresses trust issue during the construction and maintenance of routing paths from each node to the BR (Border Router). To handle this issue, we extend DIO (DODAG Information Object) message by introducing a new trust-based metric ERNT (Extended RPL Node Trustworthiness) and a new Objective Function TOF (Trust Objective Function). In fact, ERNT represents the trust values for each node within the network, and TOF demonstrates how ERNT is mapped to path cost. In MRTS all nodes collaborate to calculate ERNT by taking into account nodes' behavior including selfishness, energy, and honesty components. We implemented our scheme by extending the distributed Bellman-Ford algorithm. Evaluation results demonstrated that the new scheme improves the security of RPL.
{"title":"New trust metric for the RPL routing protocol","authors":"Nabil Djedjig, Djamel Tandjaoui, Faiza Medjek, I. Romdhani","doi":"10.1109/IACS.2017.7921993","DOIUrl":"https://doi.org/10.1109/IACS.2017.7921993","url":null,"abstract":"Establishing trust relationships between routing nodes represents a vital security requirement to establish reliable routing processes that exclude infected or selfish nodes. In this paper, we propose a new security scheme for the Internet of things and mainly for the RPL (Routing Protocol for Low-power and Lossy Networks) called: Metric-based RPL Trustworthiness Scheme (MRTS). The primary aim is to enhance RPL security and deal with the trust inference problem. MRTS addresses trust issue during the construction and maintenance of routing paths from each node to the BR (Border Router). To handle this issue, we extend DIO (DODAG Information Object) message by introducing a new trust-based metric ERNT (Extended RPL Node Trustworthiness) and a new Objective Function TOF (Trust Objective Function). In fact, ERNT represents the trust values for each node within the network, and TOF demonstrates how ERNT is mapped to path cost. In MRTS all nodes collaborate to calculate ERNT by taking into account nodes' behavior including selfishness, energy, and honesty components. We implemented our scheme by extending the distributed Bellman-Ford algorithm. Evaluation results demonstrated that the new scheme improves the security of RPL.","PeriodicalId":180504,"journal":{"name":"2017 8th International Conference on Information and Communication Systems (ICICS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127368582","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 : 2017-04-01DOI: 10.1109/IACS.2017.7921985
F. Awad, Mohammad Al-Refai, Ahmad Al-qerem
Determining the location of a rogue access point is an important research problem due to the security threats it imposes. Rogue access points can be used to carry out different types of attacks such as man-in-the-middle, denial of service, and building a private channel for information theft. The main contribution of this research is a novel efficient approach to locate a rogue access points using Particle Swarm Optimization. In this paper, the received signal strength is used to estimate the distance between the access point transmitter and number of known locations around it. The set of received signal strength samples, along with their corresponding known locations, is used as an input to a customized Particle Swarm Optimization algorithm. The algorithm searches for the optimal location of the access point that matches the given sample set. The proposed approach was evaluated via simulation and was shown to estimate the location of the rogue access point quickly and precisely in different practical scenarios. Comparative analysis demonstrated that the proposed approach can prominently outperform the state-of-the-art techniques.
{"title":"Rogue access point localization using particle swarm optimization","authors":"F. Awad, Mohammad Al-Refai, Ahmad Al-qerem","doi":"10.1109/IACS.2017.7921985","DOIUrl":"https://doi.org/10.1109/IACS.2017.7921985","url":null,"abstract":"Determining the location of a rogue access point is an important research problem due to the security threats it imposes. Rogue access points can be used to carry out different types of attacks such as man-in-the-middle, denial of service, and building a private channel for information theft. The main contribution of this research is a novel efficient approach to locate a rogue access points using Particle Swarm Optimization. In this paper, the received signal strength is used to estimate the distance between the access point transmitter and number of known locations around it. The set of received signal strength samples, along with their corresponding known locations, is used as an input to a customized Particle Swarm Optimization algorithm. The algorithm searches for the optimal location of the access point that matches the given sample set. The proposed approach was evaluated via simulation and was shown to estimate the location of the rogue access point quickly and precisely in different practical scenarios. Comparative analysis demonstrated that the proposed approach can prominently outperform the state-of-the-art techniques.","PeriodicalId":180504,"journal":{"name":"2017 8th International Conference on Information and Communication Systems (ICICS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117021613","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 : 2017-04-01DOI: 10.1109/IACS.2017.7921965
A. Al-Badarneh, Hassan M. Najadat, Salah Rababah
Map Reduce (MP) Is an efficient programming model for processing big data. However, MR has some limitations in performing the join operation. Recent researches have been made to alleviate this problem, such as Bloom join. The idea of the Bloom join lies in constructing a Bloom filter to remove redundant records before performing the join operation. The size of the constructed filter is very critical and it should be chosen in a good manner. In this paper, we evaluate the performance of the Bloom filter size for two Bloom join algorithms, Map-side Bloom join and Reduce-side Bloom join. In our methodology, we constructed multiple Bloom filters with different sizes for two static input datasets. Our experimental results show that it is not always the best solution to construct a small or a large filter size to produce a good performance, it should be constructed based on the size of the input datasets. Also, the results show that tuning the Bloom filter size causes major effects on the join performance. Furthermore, the results show that it is recommended to choose small sizes of the Bloom filter, small enough to produce neglected false positive rate, in the implementation of the two algorithms when there is a concern about the memory. On the other hand, small to medium sizes of the Bloom filter in the Reduce-side join produce smaller elapsed time compared to the Map-side join, while large sizes produce larger elapsed time.
{"title":"Performance evaluation of bloom filter size in map-side and reduce-side bloom joins","authors":"A. Al-Badarneh, Hassan M. Najadat, Salah Rababah","doi":"10.1109/IACS.2017.7921965","DOIUrl":"https://doi.org/10.1109/IACS.2017.7921965","url":null,"abstract":"Map Reduce (MP) Is an efficient programming model for processing big data. However, MR has some limitations in performing the join operation. Recent researches have been made to alleviate this problem, such as Bloom join. The idea of the Bloom join lies in constructing a Bloom filter to remove redundant records before performing the join operation. The size of the constructed filter is very critical and it should be chosen in a good manner. In this paper, we evaluate the performance of the Bloom filter size for two Bloom join algorithms, Map-side Bloom join and Reduce-side Bloom join. In our methodology, we constructed multiple Bloom filters with different sizes for two static input datasets. Our experimental results show that it is not always the best solution to construct a small or a large filter size to produce a good performance, it should be constructed based on the size of the input datasets. Also, the results show that tuning the Bloom filter size causes major effects on the join performance. Furthermore, the results show that it is recommended to choose small sizes of the Bloom filter, small enough to produce neglected false positive rate, in the implementation of the two algorithms when there is a concern about the memory. On the other hand, small to medium sizes of the Bloom filter in the Reduce-side join produce smaller elapsed time compared to the Map-side join, while large sizes produce larger elapsed time.","PeriodicalId":180504,"journal":{"name":"2017 8th International Conference on Information and Communication Systems (ICICS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130572246","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 : 2017-04-01DOI: 10.1109/IACS.2017.7921984
Aisha Al-Sadi, Hana' Al-Theiabat, F. Awad
Mobile device localization and location-based services have become an integral part of our lives, especially after the advent and dramatic widespread use of Smartphones. Hence, people have become very much attached to and dependent on their Smartphone in their daily lives due to the advanced features and technologies equipped within, among which are the Global Positioning System and Wi-Fi. In case of natural or man-made disasters, victims may get stuck under rubble, where the global positioning system or cellular phone signals may be either unreachable or not strong enough to make a call or provide location information with enough accuracy; bearing in mind that, mostly, the infrastructure gets dismantled during catastrophes. In such situations, locating and rescuing victims is a very tough and time-critical task. This paper presents a new algorithm that allows the Smartphones of the rescuers and the victims to seamlessly collaborate in order to estimate the locations of the victims without relying on any infrastructure. The algorithm uses both the Received Signal Strength Indicator of the Wi-Fi signals and the global positioning system location information of the rescuers' phones to estimate the locations of the victims; based on well-known mathematical communication theory models. The performance of the algorithm was evaluated via computer simulation and the results demonstrated that, under reasonable practical conditions, the victim location can be estimated relatively quickly and accurately.
{"title":"Smartphone-assisted location identification algorithm for search and rescue services","authors":"Aisha Al-Sadi, Hana' Al-Theiabat, F. Awad","doi":"10.1109/IACS.2017.7921984","DOIUrl":"https://doi.org/10.1109/IACS.2017.7921984","url":null,"abstract":"Mobile device localization and location-based services have become an integral part of our lives, especially after the advent and dramatic widespread use of Smartphones. Hence, people have become very much attached to and dependent on their Smartphone in their daily lives due to the advanced features and technologies equipped within, among which are the Global Positioning System and Wi-Fi. In case of natural or man-made disasters, victims may get stuck under rubble, where the global positioning system or cellular phone signals may be either unreachable or not strong enough to make a call or provide location information with enough accuracy; bearing in mind that, mostly, the infrastructure gets dismantled during catastrophes. In such situations, locating and rescuing victims is a very tough and time-critical task. This paper presents a new algorithm that allows the Smartphones of the rescuers and the victims to seamlessly collaborate in order to estimate the locations of the victims without relying on any infrastructure. The algorithm uses both the Received Signal Strength Indicator of the Wi-Fi signals and the global positioning system location information of the rescuers' phones to estimate the locations of the victims; based on well-known mathematical communication theory models. The performance of the algorithm was evaluated via computer simulation and the results demonstrated that, under reasonable practical conditions, the victim location can be estimated relatively quickly and accurately.","PeriodicalId":180504,"journal":{"name":"2017 8th International Conference on Information and Communication Systems (ICICS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123972501","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 : 2017-04-01DOI: 10.1109/IACS.2017.7921960
Qanita Bani Baker, Khaled Balhaf
White blood cell (WBC) segmentation is one of the important topics in the medical image processing field. Several researchers used K-means clustering approach to segment WBC from blood smear microscopic images. In this paper, we use the parallelism capabilities of the Graphics Processing Units (GPUs) to accelerate the segmentation of WBC from microscopic images. We implement the K-means algorithm and the preprocess steps for WBC image segmentation in CUDA programming to take the advantages of large number of cores in GPUs. We systematically implement and evaluate the performance of WBC segmentation operations on CPU, GPUs, and CPU-GPU hybrid systems. In this work, we gained about 3X faster performance than sequential implementation achieved without affecting WBC segmentation accuracy.
{"title":"Exploiting GPUs to accelerate white blood cells segmentation in microscopic blood images","authors":"Qanita Bani Baker, Khaled Balhaf","doi":"10.1109/IACS.2017.7921960","DOIUrl":"https://doi.org/10.1109/IACS.2017.7921960","url":null,"abstract":"White blood cell (WBC) segmentation is one of the important topics in the medical image processing field. Several researchers used K-means clustering approach to segment WBC from blood smear microscopic images. In this paper, we use the parallelism capabilities of the Graphics Processing Units (GPUs) to accelerate the segmentation of WBC from microscopic images. We implement the K-means algorithm and the preprocess steps for WBC image segmentation in CUDA programming to take the advantages of large number of cores in GPUs. We systematically implement and evaluate the performance of WBC segmentation operations on CPU, GPUs, and CPU-GPU hybrid systems. In this work, we gained about 3X faster performance than sequential implementation achieved without affecting WBC segmentation accuracy.","PeriodicalId":180504,"journal":{"name":"2017 8th International Conference on Information and Communication Systems (ICICS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128106428","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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