Pub Date : 2013-12-01DOI: 10.1109/ICENCO.2013.6736476
A. Tharwat, A. M. Ghanem, A. Hassanien
The exact age estimation is often treated as a classification problem; while it can be formulated as a regression problem. In this article, three different classifiers based on KNN classifier's concept for facial age estimation were designed and developed to achieve high efficiency calculation of facial age estimation. In the first classifier, we adopt KNN-distance approach to calculate minimum distance between test face image and all instances belong to the class that has the highest number of nearest samples. Additionally, in the second classifier a modified-KNN version was proposed and the classifier scoring results interpolated to calculate the exact age estimation. Furthermore, KNN-regression classifier as third classifier that used to combine the classification and regression approaches to improve the accuracy of the age estimation system. Moreover, we compared age estimation errors under two situations: case 1, age estimation is performed without discrimination between males and females (gender unknown); and case 2, age estimation is performed for males and females separately (gender known). Results of experiments conducted on well know benchmark FG-NET Database show the effectiveness of the proposed approach.
{"title":"Three different classifiers for facial age estimation based on K-nearest neighbor","authors":"A. Tharwat, A. M. Ghanem, A. Hassanien","doi":"10.1109/ICENCO.2013.6736476","DOIUrl":"https://doi.org/10.1109/ICENCO.2013.6736476","url":null,"abstract":"The exact age estimation is often treated as a classification problem; while it can be formulated as a regression problem. In this article, three different classifiers based on KNN classifier's concept for facial age estimation were designed and developed to achieve high efficiency calculation of facial age estimation. In the first classifier, we adopt KNN-distance approach to calculate minimum distance between test face image and all instances belong to the class that has the highest number of nearest samples. Additionally, in the second classifier a modified-KNN version was proposed and the classifier scoring results interpolated to calculate the exact age estimation. Furthermore, KNN-regression classifier as third classifier that used to combine the classification and regression approaches to improve the accuracy of the age estimation system. Moreover, we compared age estimation errors under two situations: case 1, age estimation is performed without discrimination between males and females (gender unknown); and case 2, age estimation is performed for males and females separately (gender known). Results of experiments conducted on well know benchmark FG-NET Database show the effectiveness of the proposed approach.","PeriodicalId":256564,"journal":{"name":"2013 9th International Computer Engineering Conference (ICENCO)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128109869","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 : 2013-12-01DOI: 10.1109/ICENCO.2013.6736486
Walaa Saber, R. Rizk, H. Harb
Matrix factorization (MF) based Network coordinate (NC) systems solve the triangle inequality violations (TIVs) that is the main problem of Euclidean distances. However, these systems suffer from low prediction accuracy. In this paper, Conditional Clustered Network Coordinate (CCNC) System is proposed. It divides the space into a number of clusters in a balanced, dynamic, and decentralized way. Clustering the whole space is based on two thresholds in order to guarantee a balanced clustered operation. The performance of CCNC system is evaluated with King data set and PlanetLab data set to be compared against two well known NC systems: Phoenix and Pancake. The simulation results show that CCNC outperforms Phoenix and Pancake significantly in terms of estimation accuracy, expected time to construct the clusters, and the communication overhead.
{"title":"Improving prediction accuracy of Matrix Factorization based Network coordinate systems","authors":"Walaa Saber, R. Rizk, H. Harb","doi":"10.1109/ICENCO.2013.6736486","DOIUrl":"https://doi.org/10.1109/ICENCO.2013.6736486","url":null,"abstract":"Matrix factorization (MF) based Network coordinate (NC) systems solve the triangle inequality violations (TIVs) that is the main problem of Euclidean distances. However, these systems suffer from low prediction accuracy. In this paper, Conditional Clustered Network Coordinate (CCNC) System is proposed. It divides the space into a number of clusters in a balanced, dynamic, and decentralized way. Clustering the whole space is based on two thresholds in order to guarantee a balanced clustered operation. The performance of CCNC system is evaluated with King data set and PlanetLab data set to be compared against two well known NC systems: Phoenix and Pancake. The simulation results show that CCNC outperforms Phoenix and Pancake significantly in terms of estimation accuracy, expected time to construct the clusters, and the communication overhead.","PeriodicalId":256564,"journal":{"name":"2013 9th International Computer Engineering Conference (ICENCO)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134374745","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 : 2013-12-01DOI: 10.1109/ICENCO.2013.6736485
Yasmin Alkady, Mohmed I. Habib, R. Rizk
A group of sensor nodes deployed in particular environment constitutes a wireless sensor network (WSN). At times the WSN can be even deployed in a very sensitive area where the security becomes a great problem. The present asymmetric encryption methods and symmetric encryption methods can offer the security levels but with many limitations. For instance key maintenance is a great problem faced in symmetric encryption methods and less security level is the problem of asymmetric encryption methods even though key maintenance is easy. A hybrid encryption method that combines both symmetric and asymmetric key can provide high security with minimized key maintenance. In this paper, a new security protocol using combination of both symmetric and asymmetric cryptographic techniques is proposed. This protocol provides three cryptographic primitives, integrity, confidentiality and authentication. It is a hybrid encryption method where elliptical curve cryptography (ECC) and advanced encryption (AES) are combined to provide node encryption. XOR-DUAL RSA algorithm is considered for authentication and (MD5) for integrity. The results show that the proposed hybrid cryptographic algorithm gives better performance in terms of computation time and the size of cipher text.
{"title":"A new security protocol using hybrid cryptography algorithms","authors":"Yasmin Alkady, Mohmed I. Habib, R. Rizk","doi":"10.1109/ICENCO.2013.6736485","DOIUrl":"https://doi.org/10.1109/ICENCO.2013.6736485","url":null,"abstract":"A group of sensor nodes deployed in particular environment constitutes a wireless sensor network (WSN). At times the WSN can be even deployed in a very sensitive area where the security becomes a great problem. The present asymmetric encryption methods and symmetric encryption methods can offer the security levels but with many limitations. For instance key maintenance is a great problem faced in symmetric encryption methods and less security level is the problem of asymmetric encryption methods even though key maintenance is easy. A hybrid encryption method that combines both symmetric and asymmetric key can provide high security with minimized key maintenance. In this paper, a new security protocol using combination of both symmetric and asymmetric cryptographic techniques is proposed. This protocol provides three cryptographic primitives, integrity, confidentiality and authentication. It is a hybrid encryption method where elliptical curve cryptography (ECC) and advanced encryption (AES) are combined to provide node encryption. XOR-DUAL RSA algorithm is considered for authentication and (MD5) for integrity. The results show that the proposed hybrid cryptographic algorithm gives better performance in terms of computation time and the size of cipher text.","PeriodicalId":256564,"journal":{"name":"2013 9th International Computer Engineering Conference (ICENCO)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128832181","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 : 2013-12-01DOI: 10.1109/ICENCO.2013.6736468
M. Wafy, Hashem Ibrahim, E. Kamel
The problem of plant seed identification is important for agricultural sector, such as maintaining seed quality and to prevent the spreading of weed species. Seed identification is currently performed by a human seed analyst; human must often search through many seed images before finding the desired seed. This process of manual identification is slow and posses a degree of subjectivity which is hard to be quantified. Therefore, it is highly recommended economically to introduce an automatic system for seed identification. Modern techniques in different computer science fields such as image analysis, pattern recognition and computer vision can be applied in this system. In this paper, we use Scale-Invariant Feature Transform (SIFT) algorithm to identification three types of weed seeds (Coronopus didymus (L.) Sm., Lolium multiflorum Lam. and Chenopodium ambrosioides L.) that mixed with wheat grains samples. The accuracies of weed seeds detection were 90.5%, 89.2 and 95.3 for the three species respectively. SIFT algorithm discriminated well between wheat grains and weed seeds.
{"title":"Identification of weed seeds species in mixed sample with wheat grains using SIFT algorithm","authors":"M. Wafy, Hashem Ibrahim, E. Kamel","doi":"10.1109/ICENCO.2013.6736468","DOIUrl":"https://doi.org/10.1109/ICENCO.2013.6736468","url":null,"abstract":"The problem of plant seed identification is important for agricultural sector, such as maintaining seed quality and to prevent the spreading of weed species. Seed identification is currently performed by a human seed analyst; human must often search through many seed images before finding the desired seed. This process of manual identification is slow and posses a degree of subjectivity which is hard to be quantified. Therefore, it is highly recommended economically to introduce an automatic system for seed identification. Modern techniques in different computer science fields such as image analysis, pattern recognition and computer vision can be applied in this system. In this paper, we use Scale-Invariant Feature Transform (SIFT) algorithm to identification three types of weed seeds (Coronopus didymus (L.) Sm., Lolium multiflorum Lam. and Chenopodium ambrosioides L.) that mixed with wheat grains samples. The accuracies of weed seeds detection were 90.5%, 89.2 and 95.3 for the three species respectively. SIFT algorithm discriminated well between wheat grains and weed seeds.","PeriodicalId":256564,"journal":{"name":"2013 9th International Computer Engineering Conference (ICENCO)","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131460603","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 : 2013-12-01DOI: 10.1109/ICENCO.2013.6736466
K. Salah
Hybrid Memory Cube (HMC) is a revolutionary standard in DRAM architecture based on 3D integration. It provides marvelous concurrency and reduced latency. HMC uses CRC32 for data integrity, but conventional Serial CRC calculation is very slow and has long latencies, here we propose three methods to implement parallel CRC to be very fast. The first method uses symbolic toolbox in MATLAB to generate the final equations of the CRC, and then these equations are exported to VERILOG so that we are able to calculate it in only one clock cycle. The second method is depending on using an existing tool that can generate parallel CRC but this tool has a limitation on the input data width as it is less than the maximum allowed data width in HMC which is 1152 bits, so we were able to find a work around method that enable us to calculate CRC32 for large data widthwith this tool. The third method is based on using the polynomial mathematics for CRC, as the CRC can be calculated using long division method. Method 1 latency is one clock cycle, Method 2 latency is 2 clock cycles, and method 3 latency is 37 clock cycles compared to serial CRC which latency is 1152 clock cycles.
{"title":"An online parallel CRC32 realization for Hybrid Memory Cube protocol","authors":"K. Salah","doi":"10.1109/ICENCO.2013.6736466","DOIUrl":"https://doi.org/10.1109/ICENCO.2013.6736466","url":null,"abstract":"Hybrid Memory Cube (HMC) is a revolutionary standard in DRAM architecture based on 3D integration. It provides marvelous concurrency and reduced latency. HMC uses CRC32 for data integrity, but conventional Serial CRC calculation is very slow and has long latencies, here we propose three methods to implement parallel CRC to be very fast. The first method uses symbolic toolbox in MATLAB to generate the final equations of the CRC, and then these equations are exported to VERILOG so that we are able to calculate it in only one clock cycle. The second method is depending on using an existing tool that can generate parallel CRC but this tool has a limitation on the input data width as it is less than the maximum allowed data width in HMC which is 1152 bits, so we were able to find a work around method that enable us to calculate CRC32 for large data widthwith this tool. The third method is based on using the polynomial mathematics for CRC, as the CRC can be calculated using long division method. Method 1 latency is one clock cycle, Method 2 latency is 2 clock cycles, and method 3 latency is 37 clock cycles compared to serial CRC which latency is 1152 clock cycles.","PeriodicalId":256564,"journal":{"name":"2013 9th International Computer Engineering Conference (ICENCO)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124974552","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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