Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00123
H. Hexmoor, K. Ahmed
The real world road segments contain negative obstacles and hazards as well as congestions that impede free flowing traffic. In addition, real world vehicles are differently equipped and at various roadworthy states. Vehicle platoons are more efficient when the leader selection accounts for actual road conditions and specific attributes of vehicle involved. We propose real world consideration and attributes that represent vehicles and roads in the real world and how to select the most fit platoon leaders. Communication and leader selection methodology is discussed and preliminary results for negative obstacle detection are offered.
{"title":"Real World Road Platoons and Negative Obstacles","authors":"H. Hexmoor, K. Ahmed","doi":"10.1109/CSCI49370.2019.00123","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00123","url":null,"abstract":"The real world road segments contain negative obstacles and hazards as well as congestions that impede free flowing traffic. In addition, real world vehicles are differently equipped and at various roadworthy states. Vehicle platoons are more efficient when the leader selection accounts for actual road conditions and specific attributes of vehicle involved. We propose real world consideration and attributes that represent vehicles and roads in the real world and how to select the most fit platoon leaders. Communication and leader selection methodology is discussed and preliminary results for negative obstacle detection are offered.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129189476","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00180
Muhammad Wasimuddin, K. Elleithy, Abdel-shakour Abuzneid, M. Faezipour, O. Abuzaghleh
Cardiovascular diseases, listed as the underlying cause of death, accounted for approximately 836,546 deaths in the United States in 2018. Statistics show that almost one of every three deaths in the US is a result of heart disease. Nearly 2,300 Americans die of cardiovascular disease each day, an average of one death every 38 seconds. This is while quick and immediate action at the onset of such heart conditions can save many lives. To this end, ample research has been reported in the literature on Electrocardiogram (ECG) signal analysis to determine arrhythmia and other cardiac conditions. However, more accurate and near real-time techniques are still under investigation. This work introduces a classifier that will detect abnormalities of the ECG signal with its analysis as a 2-D image fed to a Convolutional Neural Network (CNN) classifier. The proposed method classifies the ECG signal as normal or abnormal by transforming the single-lead ECG signal into images and then applying CNN classification. Images are taken from the European ST-T dataset on PhysioNet databank. Our method yields an accuracy of 97.47%.
{"title":"ECG Signal Analysis Using 2-D Image Classification with Convolutional Neural Network","authors":"Muhammad Wasimuddin, K. Elleithy, Abdel-shakour Abuzneid, M. Faezipour, O. Abuzaghleh","doi":"10.1109/CSCI49370.2019.00180","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00180","url":null,"abstract":"Cardiovascular diseases, listed as the underlying cause of death, accounted for approximately 836,546 deaths in the United States in 2018. Statistics show that almost one of every three deaths in the US is a result of heart disease. Nearly 2,300 Americans die of cardiovascular disease each day, an average of one death every 38 seconds. This is while quick and immediate action at the onset of such heart conditions can save many lives. To this end, ample research has been reported in the literature on Electrocardiogram (ECG) signal analysis to determine arrhythmia and other cardiac conditions. However, more accurate and near real-time techniques are still under investigation. This work introduces a classifier that will detect abnormalities of the ECG signal with its analysis as a 2-D image fed to a Convolutional Neural Network (CNN) classifier. The proposed method classifies the ECG signal as normal or abnormal by transforming the single-lead ECG signal into images and then applying CNN classification. Images are taken from the European ST-T dataset on PhysioNet databank. Our method yields an accuracy of 97.47%.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128508444","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00291
K. Shima, Ryo Nakamura, Kazuya Okada, Tomohiro Ishihara, Daisuke Miyamoto, Y. Sekiya
Improperly configured Domain Name System (DNS) servers are sometimes used as packet reflectors as part of a DoS or DDoS attack. Detecting packets created as a result of this activity is logically possible by monitoring the DNS request and response traffic. Any response that does not have a corresponding request can be considered a reflected message; checking and tracking every DNS packet, however, is a non-trivial operation. In this paper, we propose a detection mechanism for DNS servers used as reflectors by using a DNS server feature matrix built from a small number of packets and a machine learning algorithm. The F1 score of bad DNS server detection was over 0.9 when the test and training data are generated within the same day.
{"title":"Classifying DNS Servers Based on Response Message Matrix Using Machine Learning","authors":"K. Shima, Ryo Nakamura, Kazuya Okada, Tomohiro Ishihara, Daisuke Miyamoto, Y. Sekiya","doi":"10.1109/CSCI49370.2019.00291","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00291","url":null,"abstract":"Improperly configured Domain Name System (DNS) servers are sometimes used as packet reflectors as part of a DoS or DDoS attack. Detecting packets created as a result of this activity is logically possible by monitoring the DNS request and response traffic. Any response that does not have a corresponding request can be considered a reflected message; checking and tracking every DNS packet, however, is a non-trivial operation. In this paper, we propose a detection mechanism for DNS servers used as reflectors by using a DNS server feature matrix built from a small number of packets and a machine learning algorithm. The F1 score of bad DNS server detection was over 0.9 when the test and training data are generated within the same day.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129052242","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00191
Daniel Chang, David Chang, M. Pourhomayoun
Monitoring vital signs for Intensive Care Unit (ICU) patients is an absolute necessity to help assess the general physical health. In this research, we use machine learning to make a classification forecast that uses continuous ICU vital signs measurements to predict whether the vital signs of the next hour would reach the critical value or not. With the early warning, nurses and doctors can prevent emergency situations that may cause organ dysfunction or even death before it is too late. In this study, the data includes vital sign measurements, laboratory test results, procedures, medications collected from over 40,000 patients. After data preprocessing, bias data balancing, feature extraction, and feature selection, we have a clean dataset with informative and discriminating features. Then, various machine learning algorithms including Random Forest, XGBoost, Artificial Neural Networks (ANN), and LSTM were developed to predict critical vital signs of ICU patients 1-hour beforehand. We particularly developed predictive models to predict Heart Rate, Blood Oxygen Level (SpO2), Mean Arterial Pressure (MAP), Respiratory Rate (RR), Systolic Blood Pressure (SBP). The results demonstrated the accuracy of the developed methods.
{"title":"Risk Prediction of Critical Vital Signs for ICU Patients Using Recurrent Neural Network","authors":"Daniel Chang, David Chang, M. Pourhomayoun","doi":"10.1109/CSCI49370.2019.00191","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00191","url":null,"abstract":"Monitoring vital signs for Intensive Care Unit (ICU) patients is an absolute necessity to help assess the general physical health. In this research, we use machine learning to make a classification forecast that uses continuous ICU vital signs measurements to predict whether the vital signs of the next hour would reach the critical value or not. With the early warning, nurses and doctors can prevent emergency situations that may cause organ dysfunction or even death before it is too late. In this study, the data includes vital sign measurements, laboratory test results, procedures, medications collected from over 40,000 patients. After data preprocessing, bias data balancing, feature extraction, and feature selection, we have a clean dataset with informative and discriminating features. Then, various machine learning algorithms including Random Forest, XGBoost, Artificial Neural Networks (ANN), and LSTM were developed to predict critical vital signs of ICU patients 1-hour beforehand. We particularly developed predictive models to predict Heart Rate, Blood Oxygen Level (SpO2), Mean Arterial Pressure (MAP), Respiratory Rate (RR), Systolic Blood Pressure (SBP). The results demonstrated the accuracy of the developed methods.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"234 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114139542","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00051
Derrik E. Asher, Michael Garber-Barron, Sebastian S. Rodriguez, Erin G. Zaroukian, Nicholas R. Waytowich
The current work utilized a multi-agent reinforcement learning (MARL) algorithm embedded in a continuous predator-prey pursuit simulation environment to measure and evaluate coordination between cooperating agents. In this simulation environment, it is generally assumed that successful performance for cooperative agents necessarily results in the emergence of coordination, but a clear quantitative demonstration of coordination in this environment still does not exist. The current work focuses on 1) detecting emergent coordination between cooperating agents in a multi-agent predator-prey simulation environment, and 2) showing coordination profiles between cooperating agents extracted from systematic state perturbations. This work introduces a method for detecting and comparing the typically 'black-box' behavioral solutions that result from emergent coordination in multi-agent learning spatial tasks with a shared goal. Comparing coordination profiles can provide insights into overlapping patterns that define how agents learn to interact in cooperative multi-agent environments. Similarly, this approach provides an avenue for measuring and training agents to coordinate with humans. In this way, the present work looks towards understanding and creating artificial team-mates that will strive to coordinate optimally.
{"title":"Multi-Agent Coordination Profiles through State Space Perturbations","authors":"Derrik E. Asher, Michael Garber-Barron, Sebastian S. Rodriguez, Erin G. Zaroukian, Nicholas R. Waytowich","doi":"10.1109/CSCI49370.2019.00051","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00051","url":null,"abstract":"The current work utilized a multi-agent reinforcement learning (MARL) algorithm embedded in a continuous predator-prey pursuit simulation environment to measure and evaluate coordination between cooperating agents. In this simulation environment, it is generally assumed that successful performance for cooperative agents necessarily results in the emergence of coordination, but a clear quantitative demonstration of coordination in this environment still does not exist. The current work focuses on 1) detecting emergent coordination between cooperating agents in a multi-agent predator-prey simulation environment, and 2) showing coordination profiles between cooperating agents extracted from systematic state perturbations. This work introduces a method for detecting and comparing the typically 'black-box' behavioral solutions that result from emergent coordination in multi-agent learning spatial tasks with a shared goal. Comparing coordination profiles can provide insights into overlapping patterns that define how agents learn to interact in cooperative multi-agent environments. Similarly, this approach provides an avenue for measuring and training agents to coordinate with humans. In this way, the present work looks towards understanding and creating artificial team-mates that will strive to coordinate optimally.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114150350","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00140
Benjamin Tan, P. Granieri, T. Taufik
A standard home in the United States has access to the 120V AC power for use with home appliances. However, many home electronics are powered by DC electricity. This introduces energy loss in the conversion process. A residential DC electrical system will avoid such conversion loss by storing energy in batteries and supplying it to DC appliances. Unfortunately, there is no existing voltage standards for DC appliances, which makes it challenging to power DC appliances straight from a DC wall outlet. The Smart DC Wall outlet addresses this by automatically adjusting its output voltage to meet any required DC load voltage. A solution involving low-voltage detection algorithm within a DC-DC converter is presented in this paper. The proposed solution monitors trends in the output current and sets the output voltage accordingly. Simulation tests resulted in identification of the required output voltage of five out of seven test devices. Results also indicate the possibility of generalizing the turn on characteristics of DC devices with more refined algorithm to improve successful voltage identification by the Smart Wall outlet.
{"title":"Smart DC Wall Outlet with Load Voltage Detection","authors":"Benjamin Tan, P. Granieri, T. Taufik","doi":"10.1109/CSCI49370.2019.00140","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00140","url":null,"abstract":"A standard home in the United States has access to the 120V AC power for use with home appliances. However, many home electronics are powered by DC electricity. This introduces energy loss in the conversion process. A residential DC electrical system will avoid such conversion loss by storing energy in batteries and supplying it to DC appliances. Unfortunately, there is no existing voltage standards for DC appliances, which makes it challenging to power DC appliances straight from a DC wall outlet. The Smart DC Wall outlet addresses this by automatically adjusting its output voltage to meet any required DC load voltage. A solution involving low-voltage detection algorithm within a DC-DC converter is presented in this paper. The proposed solution monitors trends in the output current and sets the output voltage accordingly. Simulation tests resulted in identification of the required output voltage of five out of seven test devices. Results also indicate the possibility of generalizing the turn on characteristics of DC devices with more refined algorithm to improve successful voltage identification by the Smart Wall outlet.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114579767","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00022
Neil Balram, G. Hsieh, Christian McFall
Static malware analysis is used to analyze executable files without executing the code to determine whether a file is malicious or not. Data analytic and machine learning techniques have been used increasingly to help process the large number of malware files circulating in the wild and detect new attacks. In this paper, we present the design and implementation of six different machine learning classifiers, and two distinct categories of features statically extracted from the executables: strings and Portable Executable header information. A total of twelve malware detectors were implemented for each of the six classifiers to operate with each of the two feature categories separately. These classifiers and feature extraction algorithms were implemented in Python using the scikit-learn machine learning library. The performances in detection accuracy and required processing time of the twelve malware detectors were compared and analyzed.
{"title":"Static Malware Analysis Using Machine Learning Algorithms on APT1 Dataset with String and PE Header Features","authors":"Neil Balram, G. Hsieh, Christian McFall","doi":"10.1109/CSCI49370.2019.00022","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00022","url":null,"abstract":"Static malware analysis is used to analyze executable files without executing the code to determine whether a file is malicious or not. Data analytic and machine learning techniques have been used increasingly to help process the large number of malware files circulating in the wild and detect new attacks. In this paper, we present the design and implementation of six different machine learning classifiers, and two distinct categories of features statically extracted from the executables: strings and Portable Executable header information. A total of twelve malware detectors were implemented for each of the six classifiers to operate with each of the two feature categories separately. These classifiers and feature extraction algorithms were implemented in Python using the scikit-learn machine learning library. The performances in detection accuracy and required processing time of the twelve malware detectors were compared and analyzed.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116178537","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00089
M. Neilsen, Chendi Cao
WindowsTM Dam Analysis Modules (WinDAM) is a set of modular software components used to analyze the erosion and peak discharges that results from the overtopping or internal erosion in earthen embankment dams. The initial computational modules address routing of floods through the reservoir with dam overtopping and evaluation of the potential for vegetation or riprap to delay or prevent failure of the embankment. Subsequent modules perform dam breach analysis. Current work is underway to include analysis of internal erosion, non-homogeneous, zoned embankments, and the analysis of various other forms of embankment protection. The focus of this paper is on sensitivity analysis of internal erosion models using Sandia National Laboratories' Dakota software suite 6.10 to perform the analysis.
{"title":"Sensitivity Analysis of Internal Erosion Models for Dam Safety","authors":"M. Neilsen, Chendi Cao","doi":"10.1109/CSCI49370.2019.00089","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00089","url":null,"abstract":"WindowsTM Dam Analysis Modules (WinDAM) is a set of modular software components used to analyze the erosion and peak discharges that results from the overtopping or internal erosion in earthen embankment dams. The initial computational modules address routing of floods through the reservoir with dam overtopping and evaluation of the potential for vegetation or riprap to delay or prevent failure of the embankment. Subsequent modules perform dam breach analysis. Current work is underway to include analysis of internal erosion, non-homogeneous, zoned embankments, and the analysis of various other forms of embankment protection. The focus of this paper is on sensitivity analysis of internal erosion models using Sandia National Laboratories' Dakota software suite 6.10 to perform the analysis.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114876734","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00011
Katanosh Morovat, B. Panda
The integrity of files stored on cloud is crucial for many applications, specifically applications that manage online social networks. Now-a-days social networking sites have become primary locations for sharing documents. Due to security lapses at these sites, shared documents could be stolen or changed by cyber attackers. We have developed a model to protect shared data and resources from unauthorized accesses. This method, which is called policy-based attribute access control (PBAAC) [20], enables resource owners to define policies to manage their resources from unmanaged accesses. However, policies are saved as plain text in a file and could be compromised by unauthorized users, violating the integrity of those policies. In this paper, we proposed a method to protect policies saved in text files. We developed an algorithm to extract critical information from each policy and create a hash value, by executing a hash cryptography algorithm.
{"title":"Data Integrity in Policy-Based Attribute Access Control in Social Network Cloud","authors":"Katanosh Morovat, B. Panda","doi":"10.1109/CSCI49370.2019.00011","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00011","url":null,"abstract":"The integrity of files stored on cloud is crucial for many applications, specifically applications that manage online social networks. Now-a-days social networking sites have become primary locations for sharing documents. Due to security lapses at these sites, shared documents could be stolen or changed by cyber attackers. We have developed a model to protect shared data and resources from unauthorized accesses. This method, which is called policy-based attribute access control (PBAAC) [20], enables resource owners to define policies to manage their resources from unmanaged accesses. However, policies are saved as plain text in a file and could be compromised by unauthorized users, violating the integrity of those policies. In this paper, we proposed a method to protect policies saved in text files. We developed an algorithm to extract critical information from each policy and create a hash value, by executing a hash cryptography algorithm.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126592216","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 : 2019-12-01DOI: 10.1109/CSCI49370.2019.00200
S. Jarng, Y. Kwon, Dongsoo Joseph Jarng
This paper is about the development of Digital Radio Mondiale (DRM) App. It describes overall technology and development motivation that may enable us to overcome the limitation of 1:1 Bluetooth technology. It introduces the DRM compression and modulation, background technology of DRM transmitter and receiver, requirements for DRM signal creation, DRM App configuration, additional tools used for field test, the field test results, and future application of DRM.
{"title":"Digital Radio Mondiale App Development","authors":"S. Jarng, Y. Kwon, Dongsoo Joseph Jarng","doi":"10.1109/CSCI49370.2019.00200","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00200","url":null,"abstract":"This paper is about the development of Digital Radio Mondiale (DRM) App. It describes overall technology and development motivation that may enable us to overcome the limitation of 1:1 Bluetooth technology. It introduces the DRM compression and modulation, background technology of DRM transmitter and receiver, requirements for DRM signal creation, DRM App configuration, additional tools used for field test, the field test results, and future application of DRM.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124829592","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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