Pub Date : 2017-03-30DOI: 10.1109/ICIVPR.2017.7890877
Mohd Salzahrin Mohd Hamzah, Rosminah Md Kasim, A. Yunus, O. M. Rijal, N. Noor
A novel procedure to detect Interstitial Lung Disease (ILD) with High Resolution Computed Tomography (HRCT) images is proposed. Seven texture measures from a selected slice of HRCT lung image of fifteen ILD cases, fifteen non-ILD cases and fifteen healthy individuals were obtained and their pairwise correlation calculated. Two groups of texture measure obtained from standard clustering procedure were then used to create a discrimination procedure using simple linear regression. The texture measure Contrast and Standard Deviation of Energy (STDE) gave the highest detection rate.
{"title":"Detection of Interstitial Lung Disease using correlation and regression methods on texture measure","authors":"Mohd Salzahrin Mohd Hamzah, Rosminah Md Kasim, A. Yunus, O. M. Rijal, N. Noor","doi":"10.1109/ICIVPR.2017.7890877","DOIUrl":"https://doi.org/10.1109/ICIVPR.2017.7890877","url":null,"abstract":"A novel procedure to detect Interstitial Lung Disease (ILD) with High Resolution Computed Tomography (HRCT) images is proposed. Seven texture measures from a selected slice of HRCT lung image of fifteen ILD cases, fifteen non-ILD cases and fifteen healthy individuals were obtained and their pairwise correlation calculated. Two groups of texture measure obtained from standard clustering procedure were then used to create a discrimination procedure using simple linear regression. The texture measure Contrast and Standard Deviation of Energy (STDE) gave the highest detection rate.","PeriodicalId":126745,"journal":{"name":"2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133096053","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 : 2017-03-30DOI: 10.1109/ICIVPR.2017.7890863
Fazly Rabby Akash, Amin Sheikh, Habibur Rahman, M. Ahmad
A great revolution in health science could be done if the disease could be diagnosis at very early stage. The conventional chemically manipulated biological analysis of group cells is not able to illustrate the fundamental properties of a cell such as cell proliferations, cell growths, cell damage and electro-mechanical properties. In this paper, we are representing a method to measure the mass of a single cell using the deformation of a nanofork (which will pick the cell form a line array substrate). We have used Newton's third law related with the deformation angle caused by the moment of inertia (as the fork will bend downward). Silicon is used as a base material of the nanofork. Firstly, the nanofork is inserted into the line array substrate then it picks up the cell to the upwards creating a deformation of the nanofork because of the cell weight. Then deformation angle is calculated form simulation result. For the experimental purpose we have used cell size is 5 µm. We observed the deformation angle 0.4 µm form the simulation result. Which is sufficient to find out the mass of the cell. Using the deformation angle and the related equations we have measured the mass of a single cell 0.16 pg. This result is very consistent with the previously reported single yeast cell mass.
{"title":"Single cell mass measurement from deformation of nanofork","authors":"Fazly Rabby Akash, Amin Sheikh, Habibur Rahman, M. Ahmad","doi":"10.1109/ICIVPR.2017.7890863","DOIUrl":"https://doi.org/10.1109/ICIVPR.2017.7890863","url":null,"abstract":"A great revolution in health science could be done if the disease could be diagnosis at very early stage. The conventional chemically manipulated biological analysis of group cells is not able to illustrate the fundamental properties of a cell such as cell proliferations, cell growths, cell damage and electro-mechanical properties. In this paper, we are representing a method to measure the mass of a single cell using the deformation of a nanofork (which will pick the cell form a line array substrate). We have used Newton's third law related with the deformation angle caused by the moment of inertia (as the fork will bend downward). Silicon is used as a base material of the nanofork. Firstly, the nanofork is inserted into the line array substrate then it picks up the cell to the upwards creating a deformation of the nanofork because of the cell weight. Then deformation angle is calculated form simulation result. For the experimental purpose we have used cell size is 5 µm. We observed the deformation angle 0.4 µm form the simulation result. Which is sufficient to find out the mass of the cell. Using the deformation angle and the related equations we have measured the mass of a single cell 0.16 pg. This result is very consistent with the previously reported single yeast cell mass.","PeriodicalId":126745,"journal":{"name":"2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133320844","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 : 2017-03-30DOI: 10.1109/ICIVPR.2017.7890882
A. Nijholt, A. Minuto
In this paper we draw attention to the emerging field of smart material interfaces. These novel composites, that in some cases are already celebrated as the answer for the 21st century's technological needs, are generally referred to as materials that are capable of sensing the environment and actively responding to environmental changes by changing their physical properties. Smart materials have physical properties that can be changed or controlled by external stimuli such as electric or magnetic fields, light, temperature or stress. Shape, size and color are among the properties that can be changed. Smart material interfaces are physical interfaces that utilize these materials to sense the environment and display responses by changing their physical properties. Common smart materials appear in the form of polymers, ceramics, memory shape alloys or hydro-gels. This paper aims at stimulating research and development in interfaces that make novel use of such smart materials.
{"title":"Smart material interfaces: Playful and artistic applications","authors":"A. Nijholt, A. Minuto","doi":"10.1109/ICIVPR.2017.7890882","DOIUrl":"https://doi.org/10.1109/ICIVPR.2017.7890882","url":null,"abstract":"In this paper we draw attention to the emerging field of smart material interfaces. These novel composites, that in some cases are already celebrated as the answer for the 21st century's technological needs, are generally referred to as materials that are capable of sensing the environment and actively responding to environmental changes by changing their physical properties. Smart materials have physical properties that can be changed or controlled by external stimuli such as electric or magnetic fields, light, temperature or stress. Shape, size and color are among the properties that can be changed. Smart material interfaces are physical interfaces that utilize these materials to sense the environment and display responses by changing their physical properties. Common smart materials appear in the form of polymers, ceramics, memory shape alloys or hydro-gels. This paper aims at stimulating research and development in interfaces that make novel use of such smart materials.","PeriodicalId":126745,"journal":{"name":"2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR)","volume":"22 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125705965","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 : 2017-02-15DOI: 10.1109/ICIVPR.2017.7890866
Akm Ashiquzzaman, A. Tushar
Handwritten character recognition is an active area of research with applications in numerous fields. Past and recent works in this field have concentrated on various languages. Arabic is one language where the scope of research is still widespread, with it being one of the most popular languages in the world and being syntactically different from other major languages. Das et al. [1] has pioneered the research for handwritten digit recognition in Arabic. In this paper, we propose a novel algorithm based on deep learning neural networks using appropriate activation function and regularization layer, which shows significantly improved accuracy compared to the existing Arabic numeral recognition methods. The proposed model gives 97.4 percent accuracy, which is the recorded highest accuracy of the dataset used in the experiment. We also propose a modification of the method described in [1], where our method scores identical accuracy as that of [1], with the value of 93.8 percent.
手写体字符识别是一个活跃的研究领域，在许多领域都有应用。这一领域过去和最近的研究主要集中在各种语言上。阿拉伯语是一种研究范围仍然广泛的语言，它是世界上最流行的语言之一，在语法上与其他主要语言不同。Das et al. b[1]是阿拉伯语手写数字识别研究的先驱。在本文中，我们提出了一种基于深度学习神经网络的新算法，该算法使用适当的激活函数和正则化层，与现有的阿拉伯数字识别方法相比，该算法的准确率显着提高。该模型给出了97.4%的准确率，这是实验中使用的数据集记录的最高准确率。我们还提出了对[1]中描述的方法的修改，其中我们的方法与[1]的精度相同，值为93.8%。
{"title":"Handwritten Arabic numeral recognition using deep learning neural networks","authors":"Akm Ashiquzzaman, A. Tushar","doi":"10.1109/ICIVPR.2017.7890866","DOIUrl":"https://doi.org/10.1109/ICIVPR.2017.7890866","url":null,"abstract":"Handwritten character recognition is an active area of research with applications in numerous fields. Past and recent works in this field have concentrated on various languages. Arabic is one language where the scope of research is still widespread, with it being one of the most popular languages in the world and being syntactically different from other major languages. Das et al. [1] has pioneered the research for handwritten digit recognition in Arabic. In this paper, we propose a novel algorithm based on deep learning neural networks using appropriate activation function and regularization layer, which shows significantly improved accuracy compared to the existing Arabic numeral recognition methods. The proposed model gives 97.4 percent accuracy, which is the recorded highest accuracy of the dataset used in the experiment. We also propose a modification of the method described in [1], where our method scores identical accuracy as that of [1], with the value of 93.8 percent.","PeriodicalId":126745,"journal":{"name":"2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128135421","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 : 2017-02-01DOI: 10.1109/ICIVPR.2017.7890857
Mohammad Asiful Hossain, A. Tushar
Detection of corner is the most essential process in a large number of computer vision and image processing applications. We have mentioned a number of popular contour-based corner detectors in our paper. Among all these detectors chord to triangular arm angle (CTAA) has been demonstrated as the most dominant corner detector in terms of average repeatability. We introduce a new effective method to calculate the value of curvature in this paper. By demonstrating experimental results, our proposed technique outperforms CTAA and other detectors mentioned in this paper. The results exhibit that our proposed method is simple yet efficient at finding out corners more accurately and reliably.
{"title":"Chord Angle Deviation using Tangent (CADT), an efficient and robust contour-based corner detector","authors":"Mohammad Asiful Hossain, A. Tushar","doi":"10.1109/ICIVPR.2017.7890857","DOIUrl":"https://doi.org/10.1109/ICIVPR.2017.7890857","url":null,"abstract":"Detection of corner is the most essential process in a large number of computer vision and image processing applications. We have mentioned a number of popular contour-based corner detectors in our paper. Among all these detectors chord to triangular arm angle (CTAA) has been demonstrated as the most dominant corner detector in terms of average repeatability. We introduce a new effective method to calculate the value of curvature in this paper. By demonstrating experimental results, our proposed technique outperforms CTAA and other detectors mentioned in this paper. The results exhibit that our proposed method is simple yet efficient at finding out corners more accurately and reliably.","PeriodicalId":126745,"journal":{"name":"2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129170792","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 : 2017-02-01DOI: 10.1109/ICIVPR.2017.7890868
Mashruha Raquib Mitashe, A. Habib, Anindita Razzaque, Ismat Ara Tanima, J. Uddin
In this paper, a novel adaptive digital image watermarking model based on modified Fuzzy C-means clustering is proposed. For watermark embedding process, we used Discrete Wavelet Transform (DWT). A segmentation technique XieBeni integrated Fuzzy C-means clustering (XFCM) is used to identify the segments of original image to expose suitable locations for embedding watermark. We also pre-processed the host image using Particle Swarm Optimization (PSO) to lend a hand to the clustering process. The goal is to focus on proper segmentation of the image so that the embedded watermark can withstand common image processing attacks and provide security to digital images. Several attacks were performed on the watermarked images and original watermark was extracted. Performance measures like PSNR, MSE, CC were computed to test the extracted watermarks with and without attacks. Experimental results show that the proposed scheme has performed well in terms of imperceptibility and robustness when compared to other watermarking models.
{"title":"An adaptive digital image watermarking scheme with PSO, DWT and XFCM","authors":"Mashruha Raquib Mitashe, A. Habib, Anindita Razzaque, Ismat Ara Tanima, J. Uddin","doi":"10.1109/ICIVPR.2017.7890868","DOIUrl":"https://doi.org/10.1109/ICIVPR.2017.7890868","url":null,"abstract":"In this paper, a novel adaptive digital image watermarking model based on modified Fuzzy C-means clustering is proposed. For watermark embedding process, we used Discrete Wavelet Transform (DWT). A segmentation technique XieBeni integrated Fuzzy C-means clustering (XFCM) is used to identify the segments of original image to expose suitable locations for embedding watermark. We also pre-processed the host image using Particle Swarm Optimization (PSO) to lend a hand to the clustering process. The goal is to focus on proper segmentation of the image so that the embedded watermark can withstand common image processing attacks and provide security to digital images. Several attacks were performed on the watermarked images and original watermark was extracted. Performance measures like PSNR, MSE, CC were computed to test the extracted watermarks with and without attacks. Experimental results show that the proposed scheme has performed well in terms of imperceptibility and robustness when compared to other watermarking models.","PeriodicalId":126745,"journal":{"name":"2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133716625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ICIVPR.2017.7890871
A. Zaidi, M. Ahmed, A. Bakibillah
Electrocardiogram (ECG) is a test that represents electrical activity of heart and plays an important role in monitoring the condition of the heart. The diagnosis of cardiac condition is greatly dependent upon ECG signals. This paper presents a method of feature extraction and characterization of ECG signals for normal sinus rhythm and three different types of cardiovascular arrhythmia, namely Slow Term Atrial Fibrillation, Paroxysmal Atrial Fibrillation and Supraventricular Tachycardia. The proposed algorithm is implemented using NI LabVIEW Biomedical Workbench to perform signal processing that extracts features of ECG signal such as heart rate, QRS width, PR interval, QT interval and the RR interval which are then used to characterize both cardiovascular arrhythmia and normal sinus rhythms. About Forty-five sets of data of ECG signals are used in this work for analysis and verification and satisfactory result is obtained.
{"title":"Feature extraction and characterization of cardiovascular arrhythmia and normal sinus rhythm from ECG signals using LabVIEW","authors":"A. Zaidi, M. Ahmed, A. Bakibillah","doi":"10.1109/ICIVPR.2017.7890871","DOIUrl":"https://doi.org/10.1109/ICIVPR.2017.7890871","url":null,"abstract":"Electrocardiogram (ECG) is a test that represents electrical activity of heart and plays an important role in monitoring the condition of the heart. The diagnosis of cardiac condition is greatly dependent upon ECG signals. This paper presents a method of feature extraction and characterization of ECG signals for normal sinus rhythm and three different types of cardiovascular arrhythmia, namely Slow Term Atrial Fibrillation, Paroxysmal Atrial Fibrillation and Supraventricular Tachycardia. The proposed algorithm is implemented using NI LabVIEW Biomedical Workbench to perform signal processing that extracts features of ECG signal such as heart rate, QRS width, PR interval, QT interval and the RR interval which are then used to characterize both cardiovascular arrhythmia and normal sinus rhythms. About Forty-five sets of data of ECG signals are used in this work for analysis and verification and satisfactory result is obtained.","PeriodicalId":126745,"journal":{"name":"2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124414296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ICIVPR.2017.7890886
Nuzhat Tabassum, Sujan Chowdhury, M. Hossen, Salah Uddin Mondal
There are many conventional methods for book detection and title recognition from bookshelf images. But most of these methods are worked on single row bookshelf images. Here this paper presents a technique for segmenting books spine and recognizing book title from multi-row bookshelf images. Horizontal edges are detected and extracted from the images as to indicate individual rows. These separated row images are used in the next module where vertical lines are extracted in order to segment the book regions. Later all book spine images are converted into binary images. At the next step, small and unwanted objects are removed using region properties and subsequently and extracts titles from individual book spines. Then the characters of the title are segmented and extracted by using bounding box and connected component region. Separated character images are matched or unmatched with the data set images by applying template matching. As a result, the developed new method recognizes the title. The system design as a whole makes a contribution, but the extraction of the book titles from the multi-cell images makes the main principal of this paper. To test the proposed framework various bookshelf images with a variety of conditions are used and results are presented to prove its effectiveness.
{"title":"An approach to recognize book title from multi-cell bookshelf images","authors":"Nuzhat Tabassum, Sujan Chowdhury, M. Hossen, Salah Uddin Mondal","doi":"10.1109/ICIVPR.2017.7890886","DOIUrl":"https://doi.org/10.1109/ICIVPR.2017.7890886","url":null,"abstract":"There are many conventional methods for book detection and title recognition from bookshelf images. But most of these methods are worked on single row bookshelf images. Here this paper presents a technique for segmenting books spine and recognizing book title from multi-row bookshelf images. Horizontal edges are detected and extracted from the images as to indicate individual rows. These separated row images are used in the next module where vertical lines are extracted in order to segment the book regions. Later all book spine images are converted into binary images. At the next step, small and unwanted objects are removed using region properties and subsequently and extracts titles from individual book spines. Then the characters of the title are segmented and extracted by using bounding box and connected component region. Separated character images are matched or unmatched with the data set images by applying template matching. As a result, the developed new method recognizes the title. The system design as a whole makes a contribution, but the extraction of the book titles from the multi-cell images makes the main principal of this paper. To test the proposed framework various bookshelf images with a variety of conditions are used and results are presented to prove its effectiveness.","PeriodicalId":126745,"journal":{"name":"2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116957761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ICIVPR.2017.7890888
A. Paul, P. C. Shill
For understanding the complex processes of regulation within the system of cellular and every process of life in different developmental and environmental contexts, reconstructing Gene Regulatory Networks(GRNs) is an essential part of Systems Biology. A recently developed maximal information coefficient (MIC) is better to detect all kinds of association than others and it maintains both generality and equitability properties. In this study, we combined MIC as an entropy estimator with gene regulatory network method Backward Elimination based Information-Theoretic Inference and then compare this proposed method with the MI-based algorithm MRNETB by examining SynTReN's datasets. The performance of our proposed MIC based MRNETB (MRNETB-MIC) is given by using both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve and from these, the proposed method shows significantly better performance in reconstructing gene regulatory network.
{"title":"Reconstruction of gene network through Backward Elimination based Information-Theoretic Inference with Maximal Information Coefficient","authors":"A. Paul, P. C. Shill","doi":"10.1109/ICIVPR.2017.7890888","DOIUrl":"https://doi.org/10.1109/ICIVPR.2017.7890888","url":null,"abstract":"For understanding the complex processes of regulation within the system of cellular and every process of life in different developmental and environmental contexts, reconstructing Gene Regulatory Networks(GRNs) is an essential part of Systems Biology. A recently developed maximal information coefficient (MIC) is better to detect all kinds of association than others and it maintains both generality and equitability properties. In this study, we combined MIC as an entropy estimator with gene regulatory network method Backward Elimination based Information-Theoretic Inference and then compare this proposed method with the MI-based algorithm MRNETB by examining SynTReN's datasets. The performance of our proposed MIC based MRNETB (MRNETB-MIC) is given by using both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve and from these, the proposed method shows significantly better performance in reconstructing gene regulatory network.","PeriodicalId":126745,"journal":{"name":"2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132720510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/ICIVPR.2017.7890860
Md.Ibrahim Khalil, M. Hossain, Raisa Mamtaz, Imtiaz Ahmed, Moni Akter
The features of underwater wireless sensor networks are different from those found in the terrestrial ones, while their architecture is penetrable to several issues such as huge propagation delays, limited bandwidth, and multiple messages receptions due to reflections on the sea surface. It also depends on the flexibility of floating sensor nodes. Underwater sensor network has flowed as a powerful technique for aquatic applications. A Sleep Scheduling strategy is a feasible scheme built on tree topology combining TDMA with duty-cycling. Hence TDMA has time slots, so here is no data collision in this network. ROSS, a Receiver Oriented Sleep scheduling strategy use TDMA based on tree topology. But they have not given any solution to the Energy hole problem. They have no data recovery option, if any node has missed data when they die. In this paper we have proposed a mechanism to save transaction time compare to traditional UWSNs MAC protocol.
{"title":"Time Efficient Receiver Oriented Sleep Scheduling for Underwater Sensor Network","authors":"Md.Ibrahim Khalil, M. Hossain, Raisa Mamtaz, Imtiaz Ahmed, Moni Akter","doi":"10.1109/ICIVPR.2017.7890860","DOIUrl":"https://doi.org/10.1109/ICIVPR.2017.7890860","url":null,"abstract":"The features of underwater wireless sensor networks are different from those found in the terrestrial ones, while their architecture is penetrable to several issues such as huge propagation delays, limited bandwidth, and multiple messages receptions due to reflections on the sea surface. It also depends on the flexibility of floating sensor nodes. Underwater sensor network has flowed as a powerful technique for aquatic applications. A Sleep Scheduling strategy is a feasible scheme built on tree topology combining TDMA with duty-cycling. Hence TDMA has time slots, so here is no data collision in this network. ROSS, a Receiver Oriented Sleep scheduling strategy use TDMA based on tree topology. But they have not given any solution to the Energy hole problem. They have no data recovery option, if any node has missed data when they die. In this paper we have proposed a mechanism to save transaction time compare to traditional UWSNs MAC protocol.","PeriodicalId":126745,"journal":{"name":"2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR)","volume":"345 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123400198","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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