Pub Date : 2019-07-01DOI: 10.1109/TSP.2019.8769039
Anam Fatima, Ritesh Maurya, M. Dutta, Radim Burget, J. Masek
Android platform due to open source characteristic and Google backing has the largest global market share. Being the world’s most popular operating system, it has drawn the attention of cyber criminals operating particularly through wide distribution of malicious applications. This paper proposes an effectual machine-learning based approach for Android Malware Detection making use of evolutionary Genetic algorithm for discriminatory feature selection. Selected features from Genetic algorithm are used to train machine learning classifiers and their capability in identification of Malware before and after feature selection is compared. The experimentation results validate that Genetic algorithm gives most optimized feature subset helping in reduction of feature dimension to less than half of the original feature-set. Classification accuracy of more than 94% is maintained post feature selection for the machine learning based classifiers, while working on much reduced feature dimension, thereby, having a positive impact on computational complexity of learning classifiers.
{"title":"Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and Machine Learning","authors":"Anam Fatima, Ritesh Maurya, M. Dutta, Radim Burget, J. Masek","doi":"10.1109/TSP.2019.8769039","DOIUrl":"https://doi.org/10.1109/TSP.2019.8769039","url":null,"abstract":"Android platform due to open source characteristic and Google backing has the largest global market share. Being the world’s most popular operating system, it has drawn the attention of cyber criminals operating particularly through wide distribution of malicious applications. This paper proposes an effectual machine-learning based approach for Android Malware Detection making use of evolutionary Genetic algorithm for discriminatory feature selection. Selected features from Genetic algorithm are used to train machine learning classifiers and their capability in identification of Malware before and after feature selection is compared. The experimentation results validate that Genetic algorithm gives most optimized feature subset helping in reduction of feature dimension to less than half of the original feature-set. Classification accuracy of more than 94% is maintained post feature selection for the machine learning based classifiers, while working on much reduced feature dimension, thereby, having a positive impact on computational complexity of learning classifiers.","PeriodicalId":399087,"journal":{"name":"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134424419","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 : 2019-07-01DOI: 10.1109/TSP.2019.8769065
E. Minguez, M. Faúndez-Zanuy
This paper presents a first indoor prototype of a fall detection device based on a Cortex M4 microcontroller. The main features of this device are its low cost, the communication capabilities that permit to send alarms and that it does not require the users to bring any device in their body.
{"title":"Low Cost Fall Detection Based on Cortex M4","authors":"E. Minguez, M. Faúndez-Zanuy","doi":"10.1109/TSP.2019.8769065","DOIUrl":"https://doi.org/10.1109/TSP.2019.8769065","url":null,"abstract":"This paper presents a first indoor prototype of a fall detection device based on a Cortex M4 microcontroller. The main features of this device are its low cost, the communication capabilities that permit to send alarms and that it does not require the users to bring any device in their body.","PeriodicalId":399087,"journal":{"name":"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132959362","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 : 2019-07-01DOI: 10.1109/TSP.2019.8769024
György Kalmár
Loudspeakers are transducers that convert electrical signals to sounds. However, it is well-known that in reverse mode, they can convert sounds to electrical signals. The paper studies the feasibility of designing an embedded system that detects suspicious events like gunshots or screaming by utilizing the reverse mode of loudspeakers. A proof-of-concept system was introduced, which can record the induced reverse mode signal in the inactive states of a speaker while providing the direct mode functionality in the active states. To examine the impact of the reverse mode’s distorting effects on the event detection accuracy, traditional audio event datasets were transformed into forms as they would have been recorded by speakers. Three different signal processing scenarios were introduced and evaluated by different methods and classifiers. The results suggested that in all three scenarios the reverse mode speakers could be used for event detection.
{"title":"Smart Speaker: Suspicious Event Detection with Reverse Mode Speakers","authors":"György Kalmár","doi":"10.1109/TSP.2019.8769024","DOIUrl":"https://doi.org/10.1109/TSP.2019.8769024","url":null,"abstract":"Loudspeakers are transducers that convert electrical signals to sounds. However, it is well-known that in reverse mode, they can convert sounds to electrical signals. The paper studies the feasibility of designing an embedded system that detects suspicious events like gunshots or screaming by utilizing the reverse mode of loudspeakers. A proof-of-concept system was introduced, which can record the induced reverse mode signal in the inactive states of a speaker while providing the direct mode functionality in the active states. To examine the impact of the reverse mode’s distorting effects on the event detection accuracy, traditional audio event datasets were transformed into forms as they would have been recorded by speakers. Three different signal processing scenarios were introduced and evaluated by different methods and classifiers. The results suggested that in all three scenarios the reverse mode speakers could be used for event detection.","PeriodicalId":399087,"journal":{"name":"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114066783","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 : 2019-07-01DOI: 10.1109/TSP.2019.8769093
S. Jamal, G. Bilgin
This study aims to increase the segmentation accuracy by using spatial information in biomedical histopathological images. The first step in the study is to provide pre-segmentation of H & E stained images using supervised learning methods, which are k-nearest neighbors algorithm, support vector machine and random forest. In order to build necessary classifier models, several training sets are created from intracellular and extra-cellular image patches extracted from histopathological images. As a two-class classification approach, supervised learning based segmentation are applied to test images in the evaluations. Spatial information should be used to improve the segmentation accuracy of output image obtained in the classification step. In the second step of the study, Markov and conditional random fields methods are utilized to exploit spatial information in histopathological images as a post processing approach. Comparative results prove that the use of spatial information via Markov and conditional random fields can be used to improve the segmentation accuracy of histopathological images.
{"title":"Use of Spatial Information via Markov and Conditional Random Fields in Histopathological Images","authors":"S. Jamal, G. Bilgin","doi":"10.1109/TSP.2019.8769093","DOIUrl":"https://doi.org/10.1109/TSP.2019.8769093","url":null,"abstract":"This study aims to increase the segmentation accuracy by using spatial information in biomedical histopathological images. The first step in the study is to provide pre-segmentation of H & E stained images using supervised learning methods, which are k-nearest neighbors algorithm, support vector machine and random forest. In order to build necessary classifier models, several training sets are created from intracellular and extra-cellular image patches extracted from histopathological images. As a two-class classification approach, supervised learning based segmentation are applied to test images in the evaluations. Spatial information should be used to improve the segmentation accuracy of output image obtained in the classification step. In the second step of the study, Markov and conditional random fields methods are utilized to exploit spatial information in histopathological images as a post processing approach. Comparative results prove that the use of spatial information via Markov and conditional random fields can be used to improve the segmentation accuracy of histopathological images.","PeriodicalId":399087,"journal":{"name":"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124078077","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 : 2019-07-01DOI: 10.1109/TSP.2019.8769063
T. Holynski
The paper presents construction of a highly robust estimator for block-error ratio in the binomial transmission model under heavy additional noise or disturbances. The estimator is based on the empirical probability generating function computed at single point in the transform domain. Such construction leads to explicit expressions for influence function and asymptotic variance. The influence analysis explains why the estimator is notably useful when estimating small error probabilities and how to tune its performance in presence of gross outliers. While robustness comes often at the expense of increased bias, variance and/or computational effort, the proposed estimator is nearly unbiased, possibly very efficient, and easy to compute without processing the data or any optimization procedure. The last feature makes it attractive for automated real-time and online applications. The asymptotic arguments are validated in simulations for small and moderate sample sizes. Advantages over the sample median, the maximum likelihood estimator and the minimum Hellinger distance estimator in context of this application are discussed.
{"title":"Robust Estimation of Block-Error Ratio under Excessive Noise Based on Empirical Probability Generating Function","authors":"T. Holynski","doi":"10.1109/TSP.2019.8769063","DOIUrl":"https://doi.org/10.1109/TSP.2019.8769063","url":null,"abstract":"The paper presents construction of a highly robust estimator for block-error ratio in the binomial transmission model under heavy additional noise or disturbances. The estimator is based on the empirical probability generating function computed at single point in the transform domain. Such construction leads to explicit expressions for influence function and asymptotic variance. The influence analysis explains why the estimator is notably useful when estimating small error probabilities and how to tune its performance in presence of gross outliers. While robustness comes often at the expense of increased bias, variance and/or computational effort, the proposed estimator is nearly unbiased, possibly very efficient, and easy to compute without processing the data or any optimization procedure. The last feature makes it attractive for automated real-time and online applications. The asymptotic arguments are validated in simulations for small and moderate sample sizes. Advantages over the sample median, the maximum likelihood estimator and the minimum Hellinger distance estimator in context of this application are discussed.","PeriodicalId":399087,"journal":{"name":"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124592309","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 : 2019-07-01DOI: 10.1109/TSP.2019.8768837
Alexandros Bantaloukas-Arjmand, C. T. Angelis, A. Tzallas, M. Tsipouras, E. Glavas, R. Forlano, P. Manousou, N. Giannakeas
Nonalcoholic fatty liver disease (NAFLD) presents a wide range of pathological conditions, varying from nonalcoholic steatohepatitis (NASH) to cirrhosis and hepatocellular carcinoma (HCC). Their prevalence is characterized by increased fat accumulation and hepatocellular ballooning. They have become a cause of concern among physicians and engineers, as significant implications tend to occur regarding their accurate diagnosis and treatment. Although magnetic resonance, ultrasonography and other noninvasive methods can reveal the presence of NAFLD, image quantitative interpretation through histology has become the gold standard in clinical examinations. The proposed work introduces a fully automated diagnostic tool, taking into account the high discrimination capability of histological findings in liver biopsy images. The developed methodology is based on deep supervised learning and image analysis techniques, with the determination of an efficient convolutional neural network (CNN) architecture, performing eventually a classification accuracy of 95%.
{"title":"Deep Learning in Liver Biopsies using Convolutional Neural Networks","authors":"Alexandros Bantaloukas-Arjmand, C. T. Angelis, A. Tzallas, M. Tsipouras, E. Glavas, R. Forlano, P. Manousou, N. Giannakeas","doi":"10.1109/TSP.2019.8768837","DOIUrl":"https://doi.org/10.1109/TSP.2019.8768837","url":null,"abstract":"Nonalcoholic fatty liver disease (NAFLD) presents a wide range of pathological conditions, varying from nonalcoholic steatohepatitis (NASH) to cirrhosis and hepatocellular carcinoma (HCC). Their prevalence is characterized by increased fat accumulation and hepatocellular ballooning. They have become a cause of concern among physicians and engineers, as significant implications tend to occur regarding their accurate diagnosis and treatment. Although magnetic resonance, ultrasonography and other noninvasive methods can reveal the presence of NAFLD, image quantitative interpretation through histology has become the gold standard in clinical examinations. The proposed work introduces a fully automated diagnostic tool, taking into account the high discrimination capability of histological findings in liver biopsy images. The developed methodology is based on deep supervised learning and image analysis techniques, with the determination of an efficient convolutional neural network (CNN) architecture, performing eventually a classification accuracy of 95%.","PeriodicalId":399087,"journal":{"name":"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)","volume":"2008 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128849028","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 : 2019-07-01DOI: 10.1109/TSP.2019.8768819
J. Hirunporm, M. Siripruchyanun
This paper presents a new voltage-mode (VM) proportional-integral-derivative (PID) controller based on voltage differencing gain amplifier (VDGA). The developed PID controller has minimum component, consisting of two VDGAs and two capacitors. The proposed circuit offers advantage of fully/electronically independent tunability of PID parameters. The PSpice simulation results demonstrating the performances of the proposed PID controller are given. Additionally, a closed-loop control system with second-order low pass filter as an example is introduced. The total power consumption in the closed-loop control system obtained approximately 2. SSmW, at ±1.5V supply voltage.
{"title":"A Fully/Independently Tunable Voltage-mode PID Controller Using Voltage Differencing Gain Amplifiers with Electronic Method","authors":"J. Hirunporm, M. Siripruchyanun","doi":"10.1109/TSP.2019.8768819","DOIUrl":"https://doi.org/10.1109/TSP.2019.8768819","url":null,"abstract":"This paper presents a new voltage-mode (VM) proportional-integral-derivative (PID) controller based on voltage differencing gain amplifier (VDGA). The developed PID controller has minimum component, consisting of two VDGAs and two capacitors. The proposed circuit offers advantage of fully/electronically independent tunability of PID parameters. The PSpice simulation results demonstrating the performances of the proposed PID controller are given. Additionally, a closed-loop control system with second-order low pass filter as an example is introduced. The total power consumption in the closed-loop control system obtained approximately 2. SSmW, at ±1.5V supply voltage.","PeriodicalId":399087,"journal":{"name":"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)","volume":"277 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115854623","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 : 2019-07-01DOI: 10.1109/tsp.2019.8769110
{"title":"TSP 2019 TOC","authors":"","doi":"10.1109/tsp.2019.8769110","DOIUrl":"https://doi.org/10.1109/tsp.2019.8769110","url":null,"abstract":"","PeriodicalId":399087,"journal":{"name":"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116381717","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 : 2019-07-01DOI: 10.1109/TSP.2019.8768888
Elie F. Kfoury, J. Crichigno, E. Bou-Harb, David J. Khoury, Gautam Srivastava
Previous studies have observed that TCP pacing evenly spacing out packets-minimizes traffic burstiness, reduces packet losses, and increases throughput. However, the main drawback of pacing is that the number of flows and the bottleneck link capacity must be known in advance. With this information, pacing is achieved by manually tuning sender nodes to send at rates that aggregate to the bottleneck capacity. This paper proposes a scheme based on programmable switches by which rates are dynamically adjusted. These switches store the network’s state in the data plane and notify sender nodes to update their pacing rates when the network’s state changes, e.g., a new flow joins or leaves the network. The scheme uses a custom protocol that is encapsulated inside the IP Options header field and thus is compatible with legacy switches (i.e., the scheme does not require all switches to be programmable). Furthermore, the processing overhead at programmable switches is minimal, as custom packets are only generated when a flow joins or leaves the network. Simulation results conducted in Mininet demonstrate that the proposed scheme is capable of dynamically notifying hosts to adapt the pacing rate with a minimum delay, increasing throughput, mitigating the TCP sawtooth behavior, and achieving better fairness among concurrent flows. The proposed scheme and preliminary results are particularly attractive to applications such as Science DMZ, where typically a small number of large flows must share the bandwidth capacity.
{"title":"Enabling TCP Pacing using Programmable Data Plane Switches","authors":"Elie F. Kfoury, J. Crichigno, E. Bou-Harb, David J. Khoury, Gautam Srivastava","doi":"10.1109/TSP.2019.8768888","DOIUrl":"https://doi.org/10.1109/TSP.2019.8768888","url":null,"abstract":"Previous studies have observed that TCP pacing evenly spacing out packets-minimizes traffic burstiness, reduces packet losses, and increases throughput. However, the main drawback of pacing is that the number of flows and the bottleneck link capacity must be known in advance. With this information, pacing is achieved by manually tuning sender nodes to send at rates that aggregate to the bottleneck capacity. This paper proposes a scheme based on programmable switches by which rates are dynamically adjusted. These switches store the network’s state in the data plane and notify sender nodes to update their pacing rates when the network’s state changes, e.g., a new flow joins or leaves the network. The scheme uses a custom protocol that is encapsulated inside the IP Options header field and thus is compatible with legacy switches (i.e., the scheme does not require all switches to be programmable). Furthermore, the processing overhead at programmable switches is minimal, as custom packets are only generated when a flow joins or leaves the network. Simulation results conducted in Mininet demonstrate that the proposed scheme is capable of dynamically notifying hosts to adapt the pacing rate with a minimum delay, increasing throughput, mitigating the TCP sawtooth behavior, and achieving better fairness among concurrent flows. The proposed scheme and preliminary results are particularly attractive to applications such as Science DMZ, where typically a small number of large flows must share the bandwidth capacity.","PeriodicalId":399087,"journal":{"name":"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115441028","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 : 2019-07-01DOI: 10.1109/TSP.2019.8769075
R. Tuduce, Mircea Sorin Rusu, H. Cucu, C. Burileanu
Timely addressing baby cries is always a challenge for new parents. Our project aims to develop a baby cry recognition system, capable of distinguishing between different kinds of baby cries, in real-world conditions. This will inform parents of their specific baby need, while they learn to make the distinction for themselves. In this study, we describe a series of experiments designed to establish the accuracy of popular machine learning algorithms on the categorization of 7 types of baby cries. We tested the algorithms on our own baby cry database, SPLANN[1], containing over 13K baby cries, recorded in a neonatal hospital. We extract acoustic features, perform best feature selection and report increased classification accuracies, from a coin-toss rate of 14.2%.
{"title":"Automated Baby Cry Classification on a Hospital-acquired Baby Cry Database","authors":"R. Tuduce, Mircea Sorin Rusu, H. Cucu, C. Burileanu","doi":"10.1109/TSP.2019.8769075","DOIUrl":"https://doi.org/10.1109/TSP.2019.8769075","url":null,"abstract":"Timely addressing baby cries is always a challenge for new parents. Our project aims to develop a baby cry recognition system, capable of distinguishing between different kinds of baby cries, in real-world conditions. This will inform parents of their specific baby need, while they learn to make the distinction for themselves. In this study, we describe a series of experiments designed to establish the accuracy of popular machine learning algorithms on the categorization of 7 types of baby cries. We tested the algorithms on our own baby cry database, SPLANN[1], containing over 13K baby cries, recorded in a neonatal hospital. We extract acoustic features, perform best feature selection and report increased classification accuracies, from a coin-toss rate of 14.2%.","PeriodicalId":399087,"journal":{"name":"2019 42nd International Conference on Telecommunications and Signal Processing (TSP)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114321922","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：