Pub Date : 2008-07-20DOI: 10.1109/SSD.2008.4632831
R. Benzid, D. Arar, M. Bentoumi
Presented is a fast technique dedicated to the multilevel image thresholding and quantization based on the Shannonpsilas entropy maximization. The elaborated method uses efficiently the cumulative density function for the rapid determination of the optimal thresholds for segmentation. Some simulation results are reported for the aim of illustration and demonstration of its effectiveness.
{"title":"A fast technique for gray level image thresholding and quantization based on the entropy maximization","authors":"R. Benzid, D. Arar, M. Bentoumi","doi":"10.1109/SSD.2008.4632831","DOIUrl":"https://doi.org/10.1109/SSD.2008.4632831","url":null,"abstract":"Presented is a fast technique dedicated to the multilevel image thresholding and quantization based on the Shannonpsilas entropy maximization. The elaborated method uses efficiently the cumulative density function for the rapid determination of the optimal thresholds for segmentation. Some simulation results are reported for the aim of illustration and demonstration of its effectiveness.","PeriodicalId":267264,"journal":{"name":"2008 5th International Multi-Conference on Systems, Signals and Devices","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115300180","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 : 2008-07-20DOI: 10.1109/SSD.2008.4632836
L. Abdou, F. Soltani
Genetic algorithms (GAs) are algorithms of exploration based on natural selection and on genetic. They are very flexible tools used to optimise very irregular functions, badly conditioned or complexes to calculate. The use of reproduction operators: crossover and mutation, and also the cumulative information prune the search space and generate a set of plausible solutions. Also, other techniques based on the evolutionary strategies (ESs) are proposed in literature as heuristic optimisation techniques. In this work we propose an optimisation of distributed OS-CFAR systems parameters by both a GA and an ES in order to optimise the threshold and also to give a comparison between the two manners to achieve the best performance in detection. The results showed that some improvement had brought by the use of the ES according to the number of sensors in the system, the number of cells in the sensor, the Probability of false alarm (Pfa), and the fusion rule.
{"title":"Improvement of the performance of distributed OS-CFAR system by (μ+λ)-ES optimisation","authors":"L. Abdou, F. Soltani","doi":"10.1109/SSD.2008.4632836","DOIUrl":"https://doi.org/10.1109/SSD.2008.4632836","url":null,"abstract":"Genetic algorithms (GAs) are algorithms of exploration based on natural selection and on genetic. They are very flexible tools used to optimise very irregular functions, badly conditioned or complexes to calculate. The use of reproduction operators: crossover and mutation, and also the cumulative information prune the search space and generate a set of plausible solutions. Also, other techniques based on the evolutionary strategies (ESs) are proposed in literature as heuristic optimisation techniques. In this work we propose an optimisation of distributed OS-CFAR systems parameters by both a GA and an ES in order to optimise the threshold and also to give a comparison between the two manners to achieve the best performance in detection. The results showed that some improvement had brought by the use of the ES according to the number of sensors in the system, the number of cells in the sensor, the Probability of false alarm (Pfa), and the fusion rule.","PeriodicalId":267264,"journal":{"name":"2008 5th International Multi-Conference on Systems, Signals and Devices","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115501910","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 : 2008-07-20DOI: 10.1109/SSD.2008.4632793
D.A. Al Nadi, A.M. Mansour
This paper presents a texture classification algorithm using independent component analysis (ICA). ICA is used for creating basis functions or basis images bank. These basis functions are used in texture classification because they are able to capture the inherent properties of textured images. These properties enable us to use the ICA bank to generate feature vectors for effective texture classification. These feature vectors are used first for training and then for testing the classifier. The experimental setup consists of texture images from the Brodatz Album and a combination of some images therein. Experimental results for both two and multiple class texture have shown that the proposed algorithm which uses ICA has an encouraging performance. With ICA, a large classification improvement was observed. It obtains an average of just 2.85% misclassified pixels compared with 10.26% misclassified pixels by other methods.
{"title":"Independent Component Analysis (ICA) for texture classification","authors":"D.A. Al Nadi, A.M. Mansour","doi":"10.1109/SSD.2008.4632793","DOIUrl":"https://doi.org/10.1109/SSD.2008.4632793","url":null,"abstract":"This paper presents a texture classification algorithm using independent component analysis (ICA). ICA is used for creating basis functions or basis images bank. These basis functions are used in texture classification because they are able to capture the inherent properties of textured images. These properties enable us to use the ICA bank to generate feature vectors for effective texture classification. These feature vectors are used first for training and then for testing the classifier. The experimental setup consists of texture images from the Brodatz Album and a combination of some images therein. Experimental results for both two and multiple class texture have shown that the proposed algorithm which uses ICA has an encouraging performance. With ICA, a large classification improvement was observed. It obtains an average of just 2.85% misclassified pixels compared with 10.26% misclassified pixels by other methods.","PeriodicalId":267264,"journal":{"name":"2008 5th International Multi-Conference on Systems, Signals and Devices","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117351386","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 : 2008-07-20DOI: 10.1109/SSD.2008.4632875
K. Abbes, A. Hentati, M. Masmoudi
This paper presents a built-in self test (BIST) methodology to measure offset error and gain error of SigmaDelta modulator. This structure is made up of a generator of stimulus and an analyzer of response. We propose a digital technique for the test of static characteristics of the modulator. A memory based signal generator is presented which can concurrently produce test stimuli and reference signals. A first order SigmaDelta is evaluated and the simulation results show the static errors effect on the modulator bitstream output.
{"title":"Test and characterization of 1 bit Σ — Δ modulator","authors":"K. Abbes, A. Hentati, M. Masmoudi","doi":"10.1109/SSD.2008.4632875","DOIUrl":"https://doi.org/10.1109/SSD.2008.4632875","url":null,"abstract":"This paper presents a built-in self test (BIST) methodology to measure offset error and gain error of SigmaDelta modulator. This structure is made up of a generator of stimulus and an analyzer of response. We propose a digital technique for the test of static characteristics of the modulator. A memory based signal generator is presented which can concurrently produce test stimuli and reference signals. A first order SigmaDelta is evaluated and the simulation results show the static errors effect on the modulator bitstream output.","PeriodicalId":267264,"journal":{"name":"2008 5th International Multi-Conference on Systems, Signals and Devices","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114942437","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 : 2008-07-20DOI: 10.1109/SSD.2008.4632852
I. Boutana, M. Mekidèche
A numerical method for modeling the deformation and impact that occurs during the electromagnetic forming process is presented. The numerical model employs a strong coupling of the electromagnetic analysis with the plastic structural one. An electromagnetic finite element model is developed to modelise the time varying currents that are discharged through the coil in order to obtain the transient magnetic forces that are imparted to the work piece. The body forces generated by electromagnetic induction are then used as the loading condition to model the plastic deformation of the workpiece using a dynamic finite element modeling. According to the displacement and/or the deformation of the metal sheet, the modeling system is remeshed when a new step begins. Our iterative coupled model accurately predicted the final geometry of the sheet as well as the deformation at each time step.
{"title":"Simulation of aluminum sheet electromagnetic forming with several dies","authors":"I. Boutana, M. Mekidèche","doi":"10.1109/SSD.2008.4632852","DOIUrl":"https://doi.org/10.1109/SSD.2008.4632852","url":null,"abstract":"A numerical method for modeling the deformation and impact that occurs during the electromagnetic forming process is presented. The numerical model employs a strong coupling of the electromagnetic analysis with the plastic structural one. An electromagnetic finite element model is developed to modelise the time varying currents that are discharged through the coil in order to obtain the transient magnetic forces that are imparted to the work piece. The body forces generated by electromagnetic induction are then used as the loading condition to model the plastic deformation of the workpiece using a dynamic finite element modeling. According to the displacement and/or the deformation of the metal sheet, the modeling system is remeshed when a new step begins. Our iterative coupled model accurately predicted the final geometry of the sheet as well as the deformation at each time step.","PeriodicalId":267264,"journal":{"name":"2008 5th International Multi-Conference on Systems, Signals and Devices","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116916656","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 : 2008-07-20DOI: 10.1109/SSD.2008.4632810
W. Al-Jawhar, A.M. Mansour, Z.M. Kuraz
Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area since early 90psilas. A number of current face recognition algorithms using face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. This paper proposed an algorithm that uses PCA on wavelet subband and the optical flow (OF). In comparison with the traditional use of PCA, the proposed method gave a better recognition accuracy of up to (73.24%) on an image database of 157 human faces. Then a new method using the independent component analysis (ICA) was used to improve the recognition rate. The relative performance of PCA and ICA are compared on the same database mentioned before. A recognition accuracy rate of (90.45%) was achieved with the ICA which is much better than the PCA.
{"title":"Multi technique face recognition using PCA/ICA with wavelet and Optical Flow","authors":"W. Al-Jawhar, A.M. Mansour, Z.M. Kuraz","doi":"10.1109/SSD.2008.4632810","DOIUrl":"https://doi.org/10.1109/SSD.2008.4632810","url":null,"abstract":"Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area since early 90psilas. A number of current face recognition algorithms using face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. This paper proposed an algorithm that uses PCA on wavelet subband and the optical flow (OF). In comparison with the traditional use of PCA, the proposed method gave a better recognition accuracy of up to (73.24%) on an image database of 157 human faces. Then a new method using the independent component analysis (ICA) was used to improve the recognition rate. The relative performance of PCA and ICA are compared on the same database mentioned before. A recognition accuracy rate of (90.45%) was achieved with the ICA which is much better than the PCA.","PeriodicalId":267264,"journal":{"name":"2008 5th International Multi-Conference on Systems, Signals and Devices","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116230564","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 : 2008-07-20DOI: 10.1109/SSD.2008.4632848
S. Ghorbel, M. Ben Jmeaa, M. Chtourou
In this paper, a new SVM (Support Vector Machines) synthesis method is presented. This method is based essentially on training criterion optimization of this machine by a set of hierarchical structures of learning automata. This methodology is adopted for the development of off-line isolated handwritten digits recognition system. A comparison is taken between this new approach and that of a standard approach for SVM synthesis. These two methodologies are also compared with a neural network based classification method. The obtained results show the performances of the new suggested method for SVM synthesis.
{"title":"SVM synthesis by hierarchical structures of learning automata application for handwritten digits recognition","authors":"S. Ghorbel, M. Ben Jmeaa, M. Chtourou","doi":"10.1109/SSD.2008.4632848","DOIUrl":"https://doi.org/10.1109/SSD.2008.4632848","url":null,"abstract":"In this paper, a new SVM (Support Vector Machines) synthesis method is presented. This method is based essentially on training criterion optimization of this machine by a set of hierarchical structures of learning automata. This methodology is adopted for the development of off-line isolated handwritten digits recognition system. A comparison is taken between this new approach and that of a standard approach for SVM synthesis. These two methodologies are also compared with a neural network based classification method. The obtained results show the performances of the new suggested method for SVM synthesis.","PeriodicalId":267264,"journal":{"name":"2008 5th International Multi-Conference on Systems, Signals and Devices","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124808053","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 : 2008-07-20DOI: 10.1109/SSD.2008.4632840
Khalil H. Sayidmarie, A. Aboud
Position perturbation of the array elements is used for reducing the sidelobe structure in the radiation pattern of phased arrays. Results of computer simulations showed good improvements in the side lobe structure as compared to the equal size linear array. A quantitative measure of the improvement is postulated. Results are compared with those obtainable from the technique of adding 2 auxiliary elements.
{"title":"Improvement of array radiation pattern by element position perturbation","authors":"Khalil H. Sayidmarie, A. Aboud","doi":"10.1109/SSD.2008.4632840","DOIUrl":"https://doi.org/10.1109/SSD.2008.4632840","url":null,"abstract":"Position perturbation of the array elements is used for reducing the sidelobe structure in the radiation pattern of phased arrays. Results of computer simulations showed good improvements in the side lobe structure as compared to the equal size linear array. A quantitative measure of the improvement is postulated. Results are compared with those obtainable from the technique of adding 2 auxiliary elements.","PeriodicalId":267264,"journal":{"name":"2008 5th International Multi-Conference on Systems, Signals and Devices","volume":"612 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126894152","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 : 2008-07-20DOI: 10.1109/SSD.2008.4632827
A. Mohamed, Ying Weng, Jianmin Jiang, S. Ipson
This paper proposes a robust schema for face detection system via Gaussian mixture model to segment image based on skin color. After skin and non skin face candidatespsila selection, features are extracted directly from discrete cosine transform (DCT) coefficients computed from these candidates. Moreover, the back-propagation neural networks are used to train and classify faces based on DCT feature coefficients in Cb and Cr color spaces. This schema utilizes the skin color information, which is the main feature of face detection. DCT feature values of faces, representing the data set of skin/non-skin face candidates obtained from Gaussian mixture model are fed into the back-propagation neural networks to classify whether the original image includes a face or not. Experimental results shows that the proposed schema is reliable for face detection, and pattern features are detected and classified accurately by the backpropagation neural networks.
{"title":"Face detection based neural networks using robust skin color segmentation","authors":"A. Mohamed, Ying Weng, Jianmin Jiang, S. Ipson","doi":"10.1109/SSD.2008.4632827","DOIUrl":"https://doi.org/10.1109/SSD.2008.4632827","url":null,"abstract":"This paper proposes a robust schema for face detection system via Gaussian mixture model to segment image based on skin color. After skin and non skin face candidatespsila selection, features are extracted directly from discrete cosine transform (DCT) coefficients computed from these candidates. Moreover, the back-propagation neural networks are used to train and classify faces based on DCT feature coefficients in Cb and Cr color spaces. This schema utilizes the skin color information, which is the main feature of face detection. DCT feature values of faces, representing the data set of skin/non-skin face candidates obtained from Gaussian mixture model are fed into the back-propagation neural networks to classify whether the original image includes a face or not. Experimental results shows that the proposed schema is reliable for face detection, and pattern features are detected and classified accurately by the backpropagation neural networks.","PeriodicalId":267264,"journal":{"name":"2008 5th International Multi-Conference on Systems, Signals and Devices","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124919069","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 : 2008-07-20DOI: 10.1109/SSD.2008.4632790
H. Bourdoucen, A. Al-Lawati
A performance analysis of the effect of embedding an FBG sensor in an optical communications system is presented. The simulations considered focus on the effects on both the sensing and the communications systems. The effect of power levels of the interrogating optical source on the performance of the two systems is also investigated under different excitation levels. The results obtained show a good tolerance in terms of quality of transmission for the two systems.
{"title":"Performance analysis of FBG sensors system embedded in an optical communications system","authors":"H. Bourdoucen, A. Al-Lawati","doi":"10.1109/SSD.2008.4632790","DOIUrl":"https://doi.org/10.1109/SSD.2008.4632790","url":null,"abstract":"A performance analysis of the effect of embedding an FBG sensor in an optical communications system is presented. The simulations considered focus on the effects on both the sensing and the communications systems. The effect of power levels of the interrogating optical source on the performance of the two systems is also investigated under different excitation levels. The results obtained show a good tolerance in terms of quality of transmission for the two systems.","PeriodicalId":267264,"journal":{"name":"2008 5th International Multi-Conference on Systems, Signals and Devices","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124802918","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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