Pub Date : 2017-10-01DOI: 10.1109/UEMCON.2017.8249086
J. Finkelstein, Sinan Zhu
We separated all malpractice records for US dentists into two groups according to the total number of malpractice records (0: less than 5 records, 1: more than 4 records), extracted the first malpractice record of all dental practitioners' and used malpractice allegation group, payment and years between graduation and year of the first record in logistic regression to identify crucial factors for predicting dentists who made more than four malpractice records. Bivariate statistics, cross-correlation and principal component analysis were used to identify predictive features. Resulting model allowed prediction of dentists with frequent malpractice records based on the following characteristics of the first malpractice record: allegation type, payment amount and number of years from graduation to the first malpractice claim. Time between provider graduation year and the first malpractice record as well higher malpractice payment for the first claim were negatively correlated with the total number of malpractice records in individual providers.
{"title":"Data mining approaches to identify predictors of frequent malpractice claims against dentists","authors":"J. Finkelstein, Sinan Zhu","doi":"10.1109/UEMCON.2017.8249086","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249086","url":null,"abstract":"We separated all malpractice records for US dentists into two groups according to the total number of malpractice records (0: less than 5 records, 1: more than 4 records), extracted the first malpractice record of all dental practitioners' and used malpractice allegation group, payment and years between graduation and year of the first record in logistic regression to identify crucial factors for predicting dentists who made more than four malpractice records. Bivariate statistics, cross-correlation and principal component analysis were used to identify predictive features. Resulting model allowed prediction of dentists with frequent malpractice records based on the following characteristics of the first malpractice record: allegation type, payment amount and number of years from graduation to the first malpractice claim. Time between provider graduation year and the first malpractice record as well higher malpractice payment for the first claim were negatively correlated with the total number of malpractice records in individual providers.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122492327","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8248978
J. Golbeck
Cable companies and Internet Service Providers have begun to offer public wifi hotspots run through customers' in-home wireless routers. This greatly expands the number of hotspots a company can offer, but it is often done with out the consent of the customers and sometimes without informing them that it is happening. This has led to a range of privacy and security concerns among the customers whose routers are used. In this paper, we analyze 501 online comments posted to news stories about this practice to develop a taxonomy of user concerns and identify their frequency. We found only about 19% of comments were unconcerned about the practice. Of those concerned, over 40% believed the practice was a violation of autonomy. Worries about quality of service impacts were similarly common. Issues of trust, legal liability, deceptive practices, and power were also quite common. We present these results and offer a discussion of the implications.
{"title":"User concerns with personal routers used as public Wi-fi hotspots","authors":"J. Golbeck","doi":"10.1109/UEMCON.2017.8248978","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8248978","url":null,"abstract":"Cable companies and Internet Service Providers have begun to offer public wifi hotspots run through customers' in-home wireless routers. This greatly expands the number of hotspots a company can offer, but it is often done with out the consent of the customers and sometimes without informing them that it is happening. This has led to a range of privacy and security concerns among the customers whose routers are used. In this paper, we analyze 501 online comments posted to news stories about this practice to develop a taxonomy of user concerns and identify their frequency. We found only about 19% of comments were unconcerned about the practice. Of those concerned, over 40% believed the practice was a violation of autonomy. Worries about quality of service impacts were similarly common. Issues of trust, legal liability, deceptive practices, and power were also quite common. We present these results and offer a discussion of the implications.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122805428","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8249112
P. Sharma, Shaswata Banerjee, S. Bose, Biswajit Jana, Omkar Goswami, Surya Sen, Pradipan Dey, A. Bhunia, P. Modak, Arindam Shyam, Rajdip Mitra, A. Chandra, Rajib Sarkar, Supratim Sengupta, D. Raj, R. Kumar, Shiva Ashish, P. Mondal, Kamalesh Sarkar, Naushad Ahmed, Sougata Banerjee
Wireless Power Transfer to long distance objects has been a much awaited task for researchers and scientists but old technologies like magnetic resonance coupling method were failed to provide any satisfactory result for long distance power transfer though it was good for small or very near distance power transfer. Far distant moving objects like space stations, lunar modules, rovers, unmanned aerial vehicles or drones largely suffer from lack of power providing mechanism resulting in short flight time of these vehicles. Major limitation is poor endurance for helicopters and quad-copters due to their very less efficient design and nature of vertical take-off and landing in comparison to those vehicle having fixed wings. Unlimited flight endurance can be achieved by using laser power beaming to recharge any target having optical tracking system done by beam riding method. Here wireless power transmission using a high intensity laser beaming (HILB) system by illuminating vertical multi-junction solar cell (VMJSC) has been discussed and its performance is studied and analyzed. Many of the HILB systems have been used to examine the performance of VMJSC under different types of conditions. Here many of the methods like optimal receiver geometry, parallel cell back-feeding, laser wavelength, thermal effects and non-uniform illumination at high intensities are studied and analyzed. Suggestions are made to further improve the system and to achieve sufficient power densities. Study shows that VMJSC mechanism can be used for high intensity laser beaming to power transfer without being damaged and retaining its efficiency.
{"title":"Performance of vertical multi junction solar cell for long distance wireless power transfer using high intensity laser beam","authors":"P. Sharma, Shaswata Banerjee, S. Bose, Biswajit Jana, Omkar Goswami, Surya Sen, Pradipan Dey, A. Bhunia, P. Modak, Arindam Shyam, Rajdip Mitra, A. Chandra, Rajib Sarkar, Supratim Sengupta, D. Raj, R. Kumar, Shiva Ashish, P. Mondal, Kamalesh Sarkar, Naushad Ahmed, Sougata Banerjee","doi":"10.1109/UEMCON.2017.8249112","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249112","url":null,"abstract":"Wireless Power Transfer to long distance objects has been a much awaited task for researchers and scientists but old technologies like magnetic resonance coupling method were failed to provide any satisfactory result for long distance power transfer though it was good for small or very near distance power transfer. Far distant moving objects like space stations, lunar modules, rovers, unmanned aerial vehicles or drones largely suffer from lack of power providing mechanism resulting in short flight time of these vehicles. Major limitation is poor endurance for helicopters and quad-copters due to their very less efficient design and nature of vertical take-off and landing in comparison to those vehicle having fixed wings. Unlimited flight endurance can be achieved by using laser power beaming to recharge any target having optical tracking system done by beam riding method. Here wireless power transmission using a high intensity laser beaming (HILB) system by illuminating vertical multi-junction solar cell (VMJSC) has been discussed and its performance is studied and analyzed. Many of the HILB systems have been used to examine the performance of VMJSC under different types of conditions. Here many of the methods like optimal receiver geometry, parallel cell back-feeding, laser wavelength, thermal effects and non-uniform illumination at high intensities are studied and analyzed. Suggestions are made to further improve the system and to achieve sufficient power densities. Study shows that VMJSC mechanism can be used for high intensity laser beaming to power transfer without being damaged and retaining its efficiency.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125708207","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8249004
M. Hasan, N. Sakib, Richard R. Love, Sheikh Iqbal Ahamed
The demand for medical image processing is ever growing, especially for medical device manufacturers, researchers, and innovators. In this article, we present the image processing of a fingertip video to investigate the relationship between image pixel information and different hemoglobin (Hb) levels. We use the smartphone camera to record the fingertip videos of different sickle cell patients. We also collect their clinical Hb records. We extract the red, green and blue (RGB) pixel of the video image and make the histogram of selected frames for each video. The averaged histogram values of those selected frames are used as an input feature matrix in the regression analysis. Linear regression as well as the partial least squares (PLS) algorithm is applied to the input feature matrix. We consider five sickle cell patients who received the blood transfusion. We analyze the thirty fingertip videos from five patients where each patient gave three videos at the same time. Fifteen fingertip videos are recorded before blood transfusion, and rest of the videos are captured after two weeks of their blood transfusion. Matlab tool is used for the data analysis and visual image presentation of the RGB image histogram values, masked RGB image, and the confusion matrix of this paper. The result generated from linear regression and the goodness of fit of PLS model shows the reliable performance of this research work.
{"title":"RGB pixel analysis of fingertip video image captured from sickle cell patient with low and high level of hemoglobin","authors":"M. Hasan, N. Sakib, Richard R. Love, Sheikh Iqbal Ahamed","doi":"10.1109/UEMCON.2017.8249004","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249004","url":null,"abstract":"The demand for medical image processing is ever growing, especially for medical device manufacturers, researchers, and innovators. In this article, we present the image processing of a fingertip video to investigate the relationship between image pixel information and different hemoglobin (Hb) levels. We use the smartphone camera to record the fingertip videos of different sickle cell patients. We also collect their clinical Hb records. We extract the red, green and blue (RGB) pixel of the video image and make the histogram of selected frames for each video. The averaged histogram values of those selected frames are used as an input feature matrix in the regression analysis. Linear regression as well as the partial least squares (PLS) algorithm is applied to the input feature matrix. We consider five sickle cell patients who received the blood transfusion. We analyze the thirty fingertip videos from five patients where each patient gave three videos at the same time. Fifteen fingertip videos are recorded before blood transfusion, and rest of the videos are captured after two weeks of their blood transfusion. Matlab tool is used for the data analysis and visual image presentation of the RGB image histogram values, masked RGB image, and the confusion matrix of this paper. The result generated from linear regression and the goodness of fit of PLS model shows the reliable performance of this research work.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126025631","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8249031
Suzanne J. Matthews, A. S. Leger
Smart Grid Technology is becoming an integral part of ensuring reliable and resilient operation of the power grid. The high sample rate and time synchronization of Phasor Measurement Units (PMUs) can provide enhanced situational awareness and more detailed information on power system dynamics as compared to traditional SCADA systems. A smart grid system must be able to detect alarm events (such as sudden voltage fluctuations or drops in current) in close to real-time. However, the communication network and bandwidth requirements to transfer large amounts of PMU data for realtime analysis is problematic. In this paper, we propose the use of a decentralized architecture for rapidly analyzing PMU data using single board computers to provide energy efficient monitoring locally in the power grid. This approach reduces communication requirements and enables real-time analysis. We present a novel anomaly detection scheme and test our approach on a real dataset of 1.4 million measurements derived from 8 PMUs from a 1000:1 scale emulation of a working power grid. Our results show that a single Raspberry Pi is sufficient to analyze data from multiple PMUs at a rate suitable for real-time analysis.
{"title":"Leveraging single board computers for anomaly detection in the smart grid","authors":"Suzanne J. Matthews, A. S. Leger","doi":"10.1109/UEMCON.2017.8249031","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249031","url":null,"abstract":"Smart Grid Technology is becoming an integral part of ensuring reliable and resilient operation of the power grid. The high sample rate and time synchronization of Phasor Measurement Units (PMUs) can provide enhanced situational awareness and more detailed information on power system dynamics as compared to traditional SCADA systems. A smart grid system must be able to detect alarm events (such as sudden voltage fluctuations or drops in current) in close to real-time. However, the communication network and bandwidth requirements to transfer large amounts of PMU data for realtime analysis is problematic. In this paper, we propose the use of a decentralized architecture for rapidly analyzing PMU data using single board computers to provide energy efficient monitoring locally in the power grid. This approach reduces communication requirements and enables real-time analysis. We present a novel anomaly detection scheme and test our approach on a real dataset of 1.4 million measurements derived from 8 PMUs from a 1000:1 scale emulation of a working power grid. Our results show that a single Raspberry Pi is sufficient to analyze data from multiple PMUs at a rate suitable for real-time analysis.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117352327","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8249062
Daniel Perez Ibanez, Debrup Banerjee, C. Kwan, Minh Dao, Yuzhong Shen, Kris Koperski, G. Marchisio, Jiang Li
This paper presents a new deep learning based approach for soil detection using high resolution multispectral satellite images with a resolution of 0.31 m. In particular, a deep convolutional neural network (CNN) is proposed for soil detection to identify potential tunnel digging activities. Spatial and spectral information in the multispectral image cube has been incorporated into the CNN. We also propose a novel method to handle imbalance learning in the context of deep CNN model training. Experimental results on Worldview-2 (WV-2) multispectral satellite images captured at the border between USA and Mexico showed that the proposed CNN model can effectively detect soil in the remote sensed images, and the proposed imbalance learning technique improved the detection performance significantly.
{"title":"Deep learning for effective detection of excavated soil related to illegal tunnel activities","authors":"Daniel Perez Ibanez, Debrup Banerjee, C. Kwan, Minh Dao, Yuzhong Shen, Kris Koperski, G. Marchisio, Jiang Li","doi":"10.1109/UEMCON.2017.8249062","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249062","url":null,"abstract":"This paper presents a new deep learning based approach for soil detection using high resolution multispectral satellite images with a resolution of 0.31 m. In particular, a deep convolutional neural network (CNN) is proposed for soil detection to identify potential tunnel digging activities. Spatial and spectral information in the multispectral image cube has been incorporated into the CNN. We also propose a novel method to handle imbalance learning in the context of deep CNN model training. Experimental results on Worldview-2 (WV-2) multispectral satellite images captured at the border between USA and Mexico showed that the proposed CNN model can effectively detect soil in the remote sensed images, and the proposed imbalance learning technique improved the detection performance significantly.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122685495","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8249018
Zakareya Lasefr, Sai Shiva V. N. R. Ayyalasomayajula, K. Elleithy
Epilepsy is a neurological disease that is referred to as a disorder of the central nervous system characterized by the loss of consciousness and convulsions. Epileptic patients are subject to epileptic seizures caused by abnormal electrical discharges that lead to uncountable movements, convulsions and the loss of consciousness. Approximately 50 million people around the world are diagnosed with epilepsy, children and adults in the age range of 65–70 years old are effected the most. Although the main cause of this disease is unknown, however, most of the symptoms of the epilepsy seizure can be medically treated. Epileptic patients are subject to seizures that cause uncontrollable movements and loss of consciousness which can lead to serious injuries, and sometimes death. As a result, computerized seizure detection techniques are vital solutions for epileptic patients to protect them from dangers at the time of a seizure. In this paper, we propose an epilepsy seizures detecting method that can be implemented in a hardware device to help epileptic patients. The Electroencephalogram (EEG) is widely recognized for diagnosing and assessing brain activities and disorder. Our study utilized an EEG datasets that is used in various research regarding epilepsy detection. We processed the EEG signal in both time and frequency domains and applied a Chebyschev filter for preprocessing the signal, then, by using Wavelet Analysis, we decomposed the filtered signal into five sub-bands in both time and frequency domain. However, we only used the Delta sub-band for further processing. Discrete Wavelet Transform was used for feature extraction, then thresholding was implemented in order to determine the noisy part of the signal. Moreover, we applied some widely used classifiers for epilepsy seizure detection, and compared our results with other approches.
{"title":"Epilepsy seizure detection using EEG signals","authors":"Zakareya Lasefr, Sai Shiva V. N. R. Ayyalasomayajula, K. Elleithy","doi":"10.1109/UEMCON.2017.8249018","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249018","url":null,"abstract":"Epilepsy is a neurological disease that is referred to as a disorder of the central nervous system characterized by the loss of consciousness and convulsions. Epileptic patients are subject to epileptic seizures caused by abnormal electrical discharges that lead to uncountable movements, convulsions and the loss of consciousness. Approximately 50 million people around the world are diagnosed with epilepsy, children and adults in the age range of 65–70 years old are effected the most. Although the main cause of this disease is unknown, however, most of the symptoms of the epilepsy seizure can be medically treated. Epileptic patients are subject to seizures that cause uncontrollable movements and loss of consciousness which can lead to serious injuries, and sometimes death. As a result, computerized seizure detection techniques are vital solutions for epileptic patients to protect them from dangers at the time of a seizure. In this paper, we propose an epilepsy seizures detecting method that can be implemented in a hardware device to help epileptic patients. The Electroencephalogram (EEG) is widely recognized for diagnosing and assessing brain activities and disorder. Our study utilized an EEG datasets that is used in various research regarding epilepsy detection. We processed the EEG signal in both time and frequency domains and applied a Chebyschev filter for preprocessing the signal, then, by using Wavelet Analysis, we decomposed the filtered signal into five sub-bands in both time and frequency domain. However, we only used the Delta sub-band for further processing. Discrete Wavelet Transform was used for feature extraction, then thresholding was implemented in order to determine the noisy part of the signal. Moreover, we applied some widely used classifiers for epilepsy seizure detection, and compared our results with other approches.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130394585","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8248984
Louis B. Rosenberg, N. Pescetelli, G. Willcox
Across the natural world, many species have evolved methods for amplifying their decision-making accuracy by thinking together in real-time closed-loop systems. Known as Swarm Intelligence (SI) in the field of biology, the process has been deeply studied in schools of fish, flocks of bird, and swarms of bees. The present research looks at human groups and tests their ability to make financial predictions by forming online systems modeled after natural swarms. Specifically, groups of financial traders were tasked with predicting the weekly trends of four common market indices (SPX, GLD, GDX, and Crude Oil) over a period of 14 consecutive weeks. Results showed that individual participants, who averaged 61% accuracy when predicting weekly trends on their own, amplified their accuracy to 77% when predicting together as real-time swarms. These results reflect a 26% increase in financial prediction accuracy and show high statistical significance (p=0.001). This suggests that enabling groups of traders to form real-time systems online, governed by swarm intelligence algorithms, has the potential to significantly increase the accuracy of financial forecasts.
{"title":"Artificial Swarm Intelligence amplifies accuracy when predicting financial markets","authors":"Louis B. Rosenberg, N. Pescetelli, G. Willcox","doi":"10.1109/UEMCON.2017.8248984","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8248984","url":null,"abstract":"Across the natural world, many species have evolved methods for amplifying their decision-making accuracy by thinking together in real-time closed-loop systems. Known as Swarm Intelligence (SI) in the field of biology, the process has been deeply studied in schools of fish, flocks of bird, and swarms of bees. The present research looks at human groups and tests their ability to make financial predictions by forming online systems modeled after natural swarms. Specifically, groups of financial traders were tasked with predicting the weekly trends of four common market indices (SPX, GLD, GDX, and Crude Oil) over a period of 14 consecutive weeks. Results showed that individual participants, who averaged 61% accuracy when predicting weekly trends on their own, amplified their accuracy to 77% when predicting together as real-time swarms. These results reflect a 26% increase in financial prediction accuracy and show high statistical significance (p=0.001). This suggests that enabling groups of traders to form real-time systems online, governed by swarm intelligence algorithms, has the potential to significantly increase the accuracy of financial forecasts.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"429 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132108281","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8249002
S. S. Salwe, Krishna Naik Karamtot
Homogeneous wireless network function is based on the horizontal handover between similar wireless technology. Heterogeneous network is formed amongst differing wireless technologies by using the vertical handover scenario. Coexistence between the different wireless standard are based on the interference reduction, access transfer and packet rate arbitration. The reduction in interference technique allows the coexistence of two diverse wireless standards, based on the deterministic and adaptive interference reduction mechanism. In our paper we implemented Normalized Least Mean Square (NLMS) adaptive filter algorithm for excision of the unwanted wideband Wi-Fi signal from the narrowband Bluetooth signal.
{"title":"Heterogeneous network formation by non-collaborative coexistence mechanism","authors":"S. S. Salwe, Krishna Naik Karamtot","doi":"10.1109/UEMCON.2017.8249002","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249002","url":null,"abstract":"Homogeneous wireless network function is based on the horizontal handover between similar wireless technology. Heterogeneous network is formed amongst differing wireless technologies by using the vertical handover scenario. Coexistence between the different wireless standard are based on the interference reduction, access transfer and packet rate arbitration. The reduction in interference technique allows the coexistence of two diverse wireless standards, based on the deterministic and adaptive interference reduction mechanism. In our paper we implemented Normalized Least Mean Square (NLMS) adaptive filter algorithm for excision of the unwanted wideband Wi-Fi signal from the narrowband Bluetooth signal.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131628657","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 : 2017-10-01DOI: 10.1109/UEMCON.2017.8249048
R. Marapareddy, A. Pothuraju
Recent advances in the field of remote sensing technology have opened new prospects and impelled the way of analyzing images from remote sensing satellites to detect or identify an object, or a place which is selected as area of interest. The detection of airport becomes a motivating topic recently because of its applications and importance in military and civil aviation fields. This paper presents an approach for airport detection using remote sensing images by implementing conventional K-means unsupervised classification and implementing unsupervised classification based on decomposed polarimetric features that includes Entropy (H), Anisotropy (A), and Alpha angle (α). The obtained preliminary results reveal that classification based on decomposed polarimetric features provided better results than the conventional unsupervised classification for the detection of target. In this work, the effectiveness of the algorithms was demonstrated using quadpolarimetric L-band Polarimetric Synthetic Aperture Radar (polSAR) imagery from the NASA Jet Propulsion Laboratory's (JPL's) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is Louis Armstrong New Orleans International Airport, LA, USA.
{"title":"Runway detection using unsupervised classification","authors":"R. Marapareddy, A. Pothuraju","doi":"10.1109/UEMCON.2017.8249048","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249048","url":null,"abstract":"Recent advances in the field of remote sensing technology have opened new prospects and impelled the way of analyzing images from remote sensing satellites to detect or identify an object, or a place which is selected as area of interest. The detection of airport becomes a motivating topic recently because of its applications and importance in military and civil aviation fields. This paper presents an approach for airport detection using remote sensing images by implementing conventional K-means unsupervised classification and implementing unsupervised classification based on decomposed polarimetric features that includes Entropy (H), Anisotropy (A), and Alpha angle (α). The obtained preliminary results reveal that classification based on decomposed polarimetric features provided better results than the conventional unsupervised classification for the detection of target. In this work, the effectiveness of the algorithms was demonstrated using quadpolarimetric L-band Polarimetric Synthetic Aperture Radar (polSAR) imagery from the NASA Jet Propulsion Laboratory's (JPL's) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is Louis Armstrong New Orleans International Airport, LA, USA.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124582149","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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