Pub Date : 2017-07-01DOI: 10.1109/ISSCS.2017.8034931
R. Coșniță, Cristian Caba, I. Nafornita
A wireless network is analyzed to provide signal transport between two points defined by their geographical coordinates which are not in direct visibility. Using a radio planning software, RADIO-Mobile, some important parameters were analyzed and a few predictions were made for optimization of radio networks in white areas according to the Ro-NET plan.
{"title":"Radio network planning using a design-build-operate model","authors":"R. Coșniță, Cristian Caba, I. Nafornita","doi":"10.1109/ISSCS.2017.8034931","DOIUrl":"https://doi.org/10.1109/ISSCS.2017.8034931","url":null,"abstract":"A wireless network is analyzed to provide signal transport between two points defined by their geographical coordinates which are not in direct visibility. Using a radio planning software, RADIO-Mobile, some important parameters were analyzed and a few predictions were made for optimization of radio networks in white areas according to the Ro-NET plan.","PeriodicalId":338255,"journal":{"name":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115966846","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 : 2017-07-01DOI: 10.1109/ISSCS.2017.8034945
C. Rusu, J. Astola
The iterative methods are well-known approaches to solve the one-dimensional phase retrieval problem. Amongst them, the error-reduction algorithm is often used since it can easily implement support constraints. Unfortunately this method often stagnates. Recently we have formulated the extended form of the one-dimensional discrete phase retrieval problem and we have assumed that the stagnation can be avoided by oversampling. Simulations have indicated that the conjecture is true. In this work we prove the convergence of the error-reduction algorithm in the proposed extended one-dimensional discrete phase retrieval framework.
{"title":"Convergence analysis of error-reduction algorithm for solving of the extended one-dimensional discrete phase retrieval problem","authors":"C. Rusu, J. Astola","doi":"10.1109/ISSCS.2017.8034945","DOIUrl":"https://doi.org/10.1109/ISSCS.2017.8034945","url":null,"abstract":"The iterative methods are well-known approaches to solve the one-dimensional phase retrieval problem. Amongst them, the error-reduction algorithm is often used since it can easily implement support constraints. Unfortunately this method often stagnates. Recently we have formulated the extended form of the one-dimensional discrete phase retrieval problem and we have assumed that the stagnation can be avoided by oversampling. Simulations have indicated that the conjecture is true. In this work we prove the convergence of the error-reduction algorithm in the proposed extended one-dimensional discrete phase retrieval framework.","PeriodicalId":338255,"journal":{"name":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128089575","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 : 2017-07-01DOI: 10.1109/ISSCS.2017.8034895
L. Țigăeru
A current-mode low power similarity/dissimilarity CMOS circuit is described. The proposed circuit computes a measure of the similarity/dissimilarity level between two variables represented by electrical currents which can be accurately controlled, feature that can be exploited in pattern classification/clustering applications.
{"title":"Programmable current-mode similarity/dissimilarity CMOS circuit","authors":"L. Țigăeru","doi":"10.1109/ISSCS.2017.8034895","DOIUrl":"https://doi.org/10.1109/ISSCS.2017.8034895","url":null,"abstract":"A current-mode low power similarity/dissimilarity CMOS circuit is described. The proposed circuit computes a measure of the similarity/dissimilarity level between two variables represented by electrical currents which can be accurately controlled, feature that can be exploited in pattern classification/clustering applications.","PeriodicalId":338255,"journal":{"name":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134142759","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 : 2017-07-01DOI: 10.1109/ISSCS.2017.8034886
M. Petrovici, C. Damian, D. Coltuc
Maximum Entropy (MaxEnt) and Compressive Sensing (CS) are two paradigms that allow good image reconstruction from a low number of measurements. MaxEnt is based on the maximization of entropy while CS uses the minimization of l1 norm of image sparse representation. In this paper, MaxEnt and CS are tested in conditions simulating the acquisition by Single Pixel Camera. The set of measurements is obtained by non-uniform sampling (NUS) of the image. Before sampling, the images are blurred with a Gaussian kernel in order to simulate the camera Point Spread Function (PSF). The results show that both CS and MaxEnt reconstruct above the quality of blurred image and that, generally, CS performs better than MaxEnt. The impact of sparsity and camera PSF are discussed. The sparsity has higher influence in CS than in MaxEnt while for the PSF, it is the opposite: CS does not seem to be sensitive to the PSF size. The number of measured samples is also discussed. For more than 50% measured pixels, MaxEnt improves only a few the image quality while CS increases constantly the image PSNR.
{"title":"Image reconstruction from incomplete measurements: Maximum Entropy versus L1 norm optimization","authors":"M. Petrovici, C. Damian, D. Coltuc","doi":"10.1109/ISSCS.2017.8034886","DOIUrl":"https://doi.org/10.1109/ISSCS.2017.8034886","url":null,"abstract":"Maximum Entropy (MaxEnt) and Compressive Sensing (CS) are two paradigms that allow good image reconstruction from a low number of measurements. MaxEnt is based on the maximization of entropy while CS uses the minimization of l1 norm of image sparse representation. In this paper, MaxEnt and CS are tested in conditions simulating the acquisition by Single Pixel Camera. The set of measurements is obtained by non-uniform sampling (NUS) of the image. Before sampling, the images are blurred with a Gaussian kernel in order to simulate the camera Point Spread Function (PSF). The results show that both CS and MaxEnt reconstruct above the quality of blurred image and that, generally, CS performs better than MaxEnt. The impact of sparsity and camera PSF are discussed. The sparsity has higher influence in CS than in MaxEnt while for the PSF, it is the opposite: CS does not seem to be sensitive to the PSF size. The number of measured samples is also discussed. For more than 50% measured pixels, MaxEnt improves only a few the image quality while CS increases constantly the image PSNR.","PeriodicalId":338255,"journal":{"name":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132460153","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 : 2017-07-01DOI: 10.1109/ISSCS.2017.8034932
Radu Tănase, C. Cazacu, D. Faur, D. Sacaleanu, M. Datcu
The overall goal of this paper is to assess the potential of fully polarimetric SAR data for land-use mapping in the field of ecosystem monitoring. State of the art ecosystems' studies aim at integrating in-situ measurements acquired by smart sensor networks with Earth observation data analysis to ensure data quality services to multiple applications. The near real time observation using optical and SAR data contributes to evaluation of biodiversity threats in a very dynamic environment, Danube floodplain and inland delta. This paper addresses the need for consolidated approaches to extract biodiversity features from polarimetric SAR data. Two overlapping PolSAR images (L-band PALSAR and C-band RadarSAT 2) were classified in an unsupervised manner using the EntropyAnisotropyAlpha-Wishart algorithm in order to differentiate between various types of vegetation in the region of Braila Island, Romania, and then the results were assessed in a scientific manner, as a function of the acquisition sensors characteristics.
{"title":"Potential of polarimetric SAR data use for ecosystems monitoring","authors":"Radu Tănase, C. Cazacu, D. Faur, D. Sacaleanu, M. Datcu","doi":"10.1109/ISSCS.2017.8034932","DOIUrl":"https://doi.org/10.1109/ISSCS.2017.8034932","url":null,"abstract":"The overall goal of this paper is to assess the potential of fully polarimetric SAR data for land-use mapping in the field of ecosystem monitoring. State of the art ecosystems' studies aim at integrating in-situ measurements acquired by smart sensor networks with Earth observation data analysis to ensure data quality services to multiple applications. The near real time observation using optical and SAR data contributes to evaluation of biodiversity threats in a very dynamic environment, Danube floodplain and inland delta. This paper addresses the need for consolidated approaches to extract biodiversity features from polarimetric SAR data. Two overlapping PolSAR images (L-band PALSAR and C-band RadarSAT 2) were classified in an unsupervised manner using the EntropyAnisotropyAlpha-Wishart algorithm in order to differentiate between various types of vegetation in the region of Braila Island, Romania, and then the results were assessed in a scientific manner, as a function of the acquisition sensors characteristics.","PeriodicalId":338255,"journal":{"name":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129802191","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 : 2017-07-01DOI: 10.1109/ISSCS.2017.8034938
Vasilica-Daniela Andries, L. Goras, Andi Buzo, G. Pelz
The paper presents an approach based on gain scheduling technique for improving the transient performances of a digitally controlled DC-DC Buck Converter working over a large area of operating conditions. In order to determine optimal settings for the control parameters under different test scenarios, an adaptive mechanism based on machine learning algorithms is used. The experimental results, obtained after using this approach are presented as well. An improvement of 20% is observed in the case of using this gain scheduling controller instead of a controller with constant values for the parameters.
{"title":"Automatic tuning for a DC-DC Buck Converter with adaptive controller","authors":"Vasilica-Daniela Andries, L. Goras, Andi Buzo, G. Pelz","doi":"10.1109/ISSCS.2017.8034938","DOIUrl":"https://doi.org/10.1109/ISSCS.2017.8034938","url":null,"abstract":"The paper presents an approach based on gain scheduling technique for improving the transient performances of a digitally controlled DC-DC Buck Converter working over a large area of operating conditions. In order to determine optimal settings for the control parameters under different test scenarios, an adaptive mechanism based on machine learning algorithms is used. The experimental results, obtained after using this approach are presented as well. An improvement of 20% is observed in the case of using this gain scheduling controller instead of a controller with constant values for the parameters.","PeriodicalId":338255,"journal":{"name":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130023671","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 : 2017-07-01DOI: 10.1109/ISSCS.2017.8034884
I. Pavaloi, C. Nita
In this paper we have proposed a color indexing scheme for image classification and retrieval using color features. Experiments were made on the Corel 1000 database, for three different color spaces, LAB, HSV and RGB. In our tests, for image classification, two discriminative classifiers, k-NN (k - Nearest Neighborhood) and SVM (Support Vector Machine) were used. Two new distances were defined and used in k-NN experiments and the results were compared with results obtained using k-NN with three well known distances, Canberra, Euclidian and Manhattan. For image retrieval, the performance of the proposed method, measured in terms of average precision and average recall were compared with performance obtained with methods using color, texture and shape features. Our approach retrieves the highest number of relevant images compared to other more computationally expensive techniques. This color indexing method can improve the robustness of finding images with a similar color composition and can be used as a simple, fast and computationally simple filter, whose output can be then processed by other methods.
本文提出了一种利用颜色特征进行图像分类和检索的颜色索引方案。实验在Corel 1000数据库上进行，针对三种不同的色彩空间，LAB, HSV和RGB。在我们的测试中，对于图像分类，使用了两个判别分类器，k- nn (k -最近邻)和SVM(支持向量机)。定义了两种新的距离并将其应用于k-NN实验中，并与堪培拉距离、欧几里得距离和曼哈顿距离的k-NN实验结果进行了比较。在图像检索方面，将该方法的平均精度和平均召回率与使用颜色、纹理和形状特征的方法的性能进行了比较。与其他计算成本更高的技术相比，我们的方法检索的相关图像数量最多。这种颜色索引方法可以提高寻找具有相似颜色组成的图像的鲁棒性，并且可以作为一种简单、快速、计算简单的滤波器，其输出可用于其他方法的处理。
{"title":"Experiments on image classification and retrieval using statistics on pixels position","authors":"I. Pavaloi, C. Nita","doi":"10.1109/ISSCS.2017.8034884","DOIUrl":"https://doi.org/10.1109/ISSCS.2017.8034884","url":null,"abstract":"In this paper we have proposed a color indexing scheme for image classification and retrieval using color features. Experiments were made on the Corel 1000 database, for three different color spaces, LAB, HSV and RGB. In our tests, for image classification, two discriminative classifiers, k-NN (k - Nearest Neighborhood) and SVM (Support Vector Machine) were used. Two new distances were defined and used in k-NN experiments and the results were compared with results obtained using k-NN with three well known distances, Canberra, Euclidian and Manhattan. For image retrieval, the performance of the proposed method, measured in terms of average precision and average recall were compared with performance obtained with methods using color, texture and shape features. Our approach retrieves the highest number of relevant images compared to other more computationally expensive techniques. This color indexing method can improve the robustness of finding images with a similar color composition and can be used as a simple, fast and computationally simple filter, whose output can be then processed by other methods.","PeriodicalId":338255,"journal":{"name":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128912258","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 : 2017-07-01DOI: 10.1109/ISSCS.2017.8034919
S. Mischie, Andrada Muntean
This paper presents an implementation of a stereo system using image processing techniques in order to determine the distance to an object from a pair of images. Starting from this, the algorithm calculates the disparity image. The distance to an object is determined by using filters, making a histogram of the disparity image and interpolating. The entire algorithm has been implemented on the BeagleBoneBlack (BBB) and RaspberryPi platforms. OpenCV and V4L2 libraries were used to implement the code. The aim of this paper is to try to implement at a lower scale a device used in automotive industry, because the modern cars are today equipped with stereo cameras that helps the driver avoid the impact with pedestrians or any other undesired obstacles.
{"title":"Distance estimation through stereoscopy using BeagleBoneBlack and RaspberryPi","authors":"S. Mischie, Andrada Muntean","doi":"10.1109/ISSCS.2017.8034919","DOIUrl":"https://doi.org/10.1109/ISSCS.2017.8034919","url":null,"abstract":"This paper presents an implementation of a stereo system using image processing techniques in order to determine the distance to an object from a pair of images. Starting from this, the algorithm calculates the disparity image. The distance to an object is determined by using filters, making a histogram of the disparity image and interpolating. The entire algorithm has been implemented on the BeagleBoneBlack (BBB) and RaspberryPi platforms. OpenCV and V4L2 libraries were used to implement the code. The aim of this paper is to try to implement at a lower scale a device used in automotive industry, because the modern cars are today equipped with stereo cameras that helps the driver avoid the impact with pedestrians or any other undesired obstacles.","PeriodicalId":338255,"journal":{"name":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128941594","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 : 2017-07-01DOI: 10.1109/ISSCS.2017.8034903
S. Bejinariu, H. Costin, F. Rotaru, R. Luca, C. Nita
In optimization problems, nature inspired algorithms are able to generate near optimal solutions faster than other optimization algorithms. Based on nature intelligence, these algorithms are preferable especially when the function to be optimized is computationally intensive. In this paper it is proposed an image registration procedure based on the Artificial Bee Colony algorithm. First, its performances are compared to those of Particle Swarming and Cuckoo Search algorithms by optimizing some benchmark functions and then by determining the parameters of geometric transforms in image registration procedures.
{"title":"Performance analysis of Artificial Bee Colony optimization algorithm","authors":"S. Bejinariu, H. Costin, F. Rotaru, R. Luca, C. Nita","doi":"10.1109/ISSCS.2017.8034903","DOIUrl":"https://doi.org/10.1109/ISSCS.2017.8034903","url":null,"abstract":"In optimization problems, nature inspired algorithms are able to generate near optimal solutions faster than other optimization algorithms. Based on nature intelligence, these algorithms are preferable especially when the function to be optimized is computationally intensive. In this paper it is proposed an image registration procedure based on the Artificial Bee Colony algorithm. First, its performances are compared to those of Particle Swarming and Cuckoo Search algorithms by optimizing some benchmark functions and then by determining the parameters of geometric transforms in image registration procedures.","PeriodicalId":338255,"journal":{"name":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122479161","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 : 2017-07-01DOI: 10.1109/ISSCS.2017.8034890
F. Diaconu, L. Scripcariu, P. Matasaru
We design a multilevel data encoder for digital communication systems. Time constraints are imposed for a lot of services provided on portable devices. Hardware solutions are preferred more than software algorithms when the processing time and the power consumption are critical. Powerful error-correction coders processing multi-bit symbols can be implemented as hardware solutions in order to reduce the running-time and the CPU resource consumption. We design a battery powered encoder and investigate the minimum bit period value allowed at its inputs, and the minimum voltage from power supply required for operation.
{"title":"Hardware design of an RS (7, 5) data coding circuit used by digital communication systems","authors":"F. Diaconu, L. Scripcariu, P. Matasaru","doi":"10.1109/ISSCS.2017.8034890","DOIUrl":"https://doi.org/10.1109/ISSCS.2017.8034890","url":null,"abstract":"We design a multilevel data encoder for digital communication systems. Time constraints are imposed for a lot of services provided on portable devices. Hardware solutions are preferred more than software algorithms when the processing time and the power consumption are critical. Powerful error-correction coders processing multi-bit symbols can be implemented as hardware solutions in order to reduce the running-time and the CPU resource consumption. We design a battery powered encoder and investigate the minimum bit period value allowed at its inputs, and the minimum voltage from power supply required for operation.","PeriodicalId":338255,"journal":{"name":"2017 International Symposium on Signals, Circuits and Systems (ISSCS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124066733","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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