Pub Date : 2018-05-03DOI: 10.1109/EIT.2018.8500304
Zakariya Alaseel, D. Debnath
This paper intends to look into the advent of a technological systems that recently emerged in telemedicine and healthcare domain. Specifically, the paper proposes a Vital Signs Monitoring System VSMS that can be used to control and monitor vital signs of patients such as blood pressure, body temperature, and heart rate pulse. The main purpose of this proposed system is to keep all patients under 24 hour monitoring and be able to alert the staff in case of any abnormalities. The system is built on distributed control system (DCS) architecture. The paper covers three main areas. First, it proposes the system architecture and its subsystems in detail along with all functions. Second, it discusses data historians, which is how to store and handle the aggregated “Big Data” that resulted from continuous monitoring. Finally, how and why this system is handled and built on cloud-based computing environment.
{"title":"Vital Signs Monitoring System in Cloud Environment","authors":"Zakariya Alaseel, D. Debnath","doi":"10.1109/EIT.2018.8500304","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500304","url":null,"abstract":"This paper intends to look into the advent of a technological systems that recently emerged in telemedicine and healthcare domain. Specifically, the paper proposes a Vital Signs Monitoring System VSMS that can be used to control and monitor vital signs of patients such as blood pressure, body temperature, and heart rate pulse. The main purpose of this proposed system is to keep all patients under 24 hour monitoring and be able to alert the staff in case of any abnormalities. The system is built on distributed control system (DCS) architecture. The paper covers three main areas. First, it proposes the system architecture and its subsystems in detail along with all functions. Second, it discusses data historians, which is how to store and handle the aggregated “Big Data” that resulted from continuous monitoring. Finally, how and why this system is handled and built on cloud-based computing environment.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115049425","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 : 2018-05-03DOI: 10.1109/EIT.2018.8500283
Qing Wu, Wenbing Zhao, Tessadori Jacopo
In this paper, we propose a novel framework to objectively evaluate the quality of movie trailers by fusing two sensing modalities: (1) Human Electroencephalogram (EEG), and (2) computer-vision based facial expression recognition. The EEG sensing data are acquired via a cap instrumented with a set of 4-channel EEG sensors from the OpenBCI Ganglion board. The facial expressions are captured while a user is watching a movie trailer using a regular webcam to help establish the context for EEG analysis. On their own, facial expressions reveal how engaged a user is while watching a movie trailer. Additionally, facial expression data help us identify situations where noises caused by muscle movement in EEG data. Using a shallow neural network, we classify facial expressions into two categories: positive and negative emotions. A quarter-central decision making strategy model is used to analyze EEG signals with a low pass filter activated by time stamp when large human movements are detected. A small human subject test showed that the adaptive analysis method can achieve higher accuracy than that obtained via EEG alone. Besides for movie trailer evaluation, this framework can be utilized in the future towards remote training evaluation, wearable device personalization, and assisting paralyzed people to communicate with others.
{"title":"Towards Objective Assessment of Movie Trailer Quality Using Human Electroencephalogram and Facial Recognition","authors":"Qing Wu, Wenbing Zhao, Tessadori Jacopo","doi":"10.1109/EIT.2018.8500283","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500283","url":null,"abstract":"In this paper, we propose a novel framework to objectively evaluate the quality of movie trailers by fusing two sensing modalities: (1) Human Electroencephalogram (EEG), and (2) computer-vision based facial expression recognition. The EEG sensing data are acquired via a cap instrumented with a set of 4-channel EEG sensors from the OpenBCI Ganglion board. The facial expressions are captured while a user is watching a movie trailer using a regular webcam to help establish the context for EEG analysis. On their own, facial expressions reveal how engaged a user is while watching a movie trailer. Additionally, facial expression data help us identify situations where noises caused by muscle movement in EEG data. Using a shallow neural network, we classify facial expressions into two categories: positive and negative emotions. A quarter-central decision making strategy model is used to analyze EEG signals with a low pass filter activated by time stamp when large human movements are detected. A small human subject test showed that the adaptive analysis method can achieve higher accuracy than that obtained via EEG alone. Besides for movie trailer evaluation, this framework can be utilized in the future towards remote training evaluation, wearable device personalization, and assisting paralyzed people to communicate with others.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134429789","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 : 2018-05-03DOI: 10.1109/EIT.2018.8500232
Boyang Wang, J. Saniie
Fetal Electrocardiography (FECG) signal contains valuable and meaningful information that would help doctors to make decisions during pregnancy and labor. It is also an important indicator of the fetal status. However, extracting FECG from non-invasive sensors is not easy since the FECG signal is weak compared to the Maternal ECG (MECG) signal. In conventional signal processing methods, it requires an adaptive filter with the MECG signal and the mixture of Electrocardiography (ECG) signal to reveal the FECG signal. This procedure requires significant computation power and multiple sensors applied on the pregnant women. As machine learning algorithms become more and more popular, applying neural network to signal processing is widely adapted in all types of applications. This paper presents a method based on neural network to recognize the FECG signal from the abdominal ECG signal acquired by non-invasive sensors. Training and evaluation procedure are achieved in TensorFlow on a heterogeneous platform. This algorithm can precisely identify both MECG and FECG signal from the maternal abdominal ECG signal.
{"title":"Fetal Electrocardiogram Recognition Using Multilayer Perceptron Neural Network","authors":"Boyang Wang, J. Saniie","doi":"10.1109/EIT.2018.8500232","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500232","url":null,"abstract":"Fetal Electrocardiography (FECG) signal contains valuable and meaningful information that would help doctors to make decisions during pregnancy and labor. It is also an important indicator of the fetal status. However, extracting FECG from non-invasive sensors is not easy since the FECG signal is weak compared to the Maternal ECG (MECG) signal. In conventional signal processing methods, it requires an adaptive filter with the MECG signal and the mixture of Electrocardiography (ECG) signal to reveal the FECG signal. This procedure requires significant computation power and multiple sensors applied on the pregnant women. As machine learning algorithms become more and more popular, applying neural network to signal processing is widely adapted in all types of applications. This paper presents a method based on neural network to recognize the FECG signal from the abdominal ECG signal acquired by non-invasive sensors. Training and evaluation procedure are achieved in TensorFlow on a heterogeneous platform. This algorithm can precisely identify both MECG and FECG signal from the maternal abdominal ECG signal.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122140213","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 : 2018-05-03DOI: 10.1109/EIT.2018.8500311
Raed Alharthi, Abdelnasser Banihani, Abdulrahman Alzahrani, A. Alshehri, Hani Alshahrani, Huirong Fu, Anyi Liu, Ye Zhu
Spatial crowdsourcing has appealed attention in collecting and processing social, environmental, and other spatio-temporal data by the contribution of individuals, communities and groups of workers in the physical world. The objective of spatial crowdsourcing is to outsource a set of spatio-temporal tasks to a set of workers, which requires the workers to be physically traveling to the tasks' locations in order to perform them, i.e., taking photos or collecting real time weather information at prespecified location. However, the crowd workers privacy could be compromised by disclosing their locations to untrusted parties. This paper aims to provide a brief description of spatial crowdsourcing and highlight its privacy concerns. Thereafter, it demonstrates the common attacks in the location privacy of spatial crowdsourcing.
{"title":"Location Privacy Challenges in Spatial Crowdsourcing","authors":"Raed Alharthi, Abdelnasser Banihani, Abdulrahman Alzahrani, A. Alshehri, Hani Alshahrani, Huirong Fu, Anyi Liu, Ye Zhu","doi":"10.1109/EIT.2018.8500311","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500311","url":null,"abstract":"Spatial crowdsourcing has appealed attention in collecting and processing social, environmental, and other spatio-temporal data by the contribution of individuals, communities and groups of workers in the physical world. The objective of spatial crowdsourcing is to outsource a set of spatio-temporal tasks to a set of workers, which requires the workers to be physically traveling to the tasks' locations in order to perform them, i.e., taking photos or collecting real time weather information at prespecified location. However, the crowd workers privacy could be compromised by disclosing their locations to untrusted parties. This paper aims to provide a brief description of spatial crowdsourcing and highlight its privacy concerns. Thereafter, it demonstrates the common attacks in the location privacy of spatial crowdsourcing.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125783704","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 : 2018-05-03DOI: 10.1109/EIT.2018.8500083
Ghassan A. Bilal, Haider Hashim, P. Gómez, I. Abdel-Qader, A. Al-Bayati
Arc flash accidents are one of the leading causes of fatal and non-fatal injuries in both construction and general industries. This paper describes the causes of arc flash and the underlying concepts associated with short-circuit fault analysis. These concepts are applied to model and simulate arc flash scenarios based on the NFPA 70E Standard - 2015 Edition. The case studies considered in this project correspond to two public services facilities located in the City of Kalamazoo, Michigan. These studies are simulated using EasyPower software for short circuit and incident energy analyses. The final result of this work was the successful placement of arc-flash labels according to NFPA safety standard for all main system components.
{"title":"Arc Flash Assessment: Two-Case Study of Public Service Facilities in Kalamazoo, Michigan","authors":"Ghassan A. Bilal, Haider Hashim, P. Gómez, I. Abdel-Qader, A. Al-Bayati","doi":"10.1109/EIT.2018.8500083","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500083","url":null,"abstract":"Arc flash accidents are one of the leading causes of fatal and non-fatal injuries in both construction and general industries. This paper describes the causes of arc flash and the underlying concepts associated with short-circuit fault analysis. These concepts are applied to model and simulate arc flash scenarios based on the NFPA 70E Standard - 2015 Edition. The case studies considered in this project correspond to two public services facilities located in the City of Kalamazoo, Michigan. These studies are simulated using EasyPower software for short circuit and incident energy analyses. The final result of this work was the successful placement of arc-flash labels according to NFPA safety standard for all main system components.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125889394","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 : 2018-05-03DOI: 10.1109/EIT.2018.8500153
Miguel Saez, Steven Lengieza, F. Maturana, K. Barton, D. Tilbury
The manufacturing industry is constantly seeking novel solutions to improve productivity and gain a competitive advantage. Considering the large amount of data that manufacturing operations generate, the capability to make a smart decision is tied to the ability to process plant floor data gaining insight into machine and system level performance. This work aims to bridge the gap between the plant floor operation and “Big Data” analysis solutions to help improve manufacturing productivity, quality, and sustainability. The proposed framework incorporates three main elements: data sourcing, analysis, and visualization. The combination of these aspects lays the groundwork for processing large amounts of data on a multi-layer infrastructure that leverages both edge and cloud computing. The data processing framework was tested using a manufacturing testbed with with machines, robots, conveyors, and different types of sensors to replicate the diverse data sources in a manufacturing plant. The data processing infrastructure was used to monitor machine health, detect anomalies, and evaluate throughput.
{"title":"A Data Transformation Adapter for Smart Manufacturing Systems with Edge and Cloud Computing Capabilities","authors":"Miguel Saez, Steven Lengieza, F. Maturana, K. Barton, D. Tilbury","doi":"10.1109/EIT.2018.8500153","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500153","url":null,"abstract":"The manufacturing industry is constantly seeking novel solutions to improve productivity and gain a competitive advantage. Considering the large amount of data that manufacturing operations generate, the capability to make a smart decision is tied to the ability to process plant floor data gaining insight into machine and system level performance. This work aims to bridge the gap between the plant floor operation and “Big Data” analysis solutions to help improve manufacturing productivity, quality, and sustainability. The proposed framework incorporates three main elements: data sourcing, analysis, and visualization. The combination of these aspects lays the groundwork for processing large amounts of data on a multi-layer infrastructure that leverages both edge and cloud computing. The data processing framework was tested using a manufacturing testbed with with machines, robots, conveyors, and different types of sensors to replicate the diverse data sources in a manufacturing plant. The data processing infrastructure was used to monitor machine health, detect anomalies, and evaluate throughput.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"342 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120981308","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 : 2018-05-03DOI: 10.1109/EIT.2018.8500235
Mina Etehadi Abari
The existing pedestrian detection methods are still challenging under abrupt illumination, different human shape, and cluttered backgrounds. In this contribution, we suggest a novel method to handle the above detection failures. On account of the fact that the potential of features are different and a single feature cannot extract the comprehensive information and human appearance can be better acquired by combinations of efficacious features, we combine HOG, LBP, and Haar-like features. Thus, the proposed method contains the edge, texture information, and local shape information. It should be mentioned that there has not been a method based on combination of these three features yet. After feature combination, linear SVM classifier is used to detect pedestrian images from nonpedestrian. In experiments, INRIA dataset, Daimler dataset, and ETH dataset are adopted as the training and testing sets. Each dataset was recorded in various environments, resolution, and background occlusion. As a result, employing three various datasets can help not only further enrich our data but also scrutinize the robustness and precision of the proposed method in more depth. The substantial experimental result indicated that the proposed scheme outperformed the state of the art methods in terms of the accuracy with comparable computational time.
{"title":"A Novel Pedestrian Detection Method Based on Combination of LBP, HOG, and Haar-Like Features","authors":"Mina Etehadi Abari","doi":"10.1109/EIT.2018.8500235","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500235","url":null,"abstract":"The existing pedestrian detection methods are still challenging under abrupt illumination, different human shape, and cluttered backgrounds. In this contribution, we suggest a novel method to handle the above detection failures. On account of the fact that the potential of features are different and a single feature cannot extract the comprehensive information and human appearance can be better acquired by combinations of efficacious features, we combine HOG, LBP, and Haar-like features. Thus, the proposed method contains the edge, texture information, and local shape information. It should be mentioned that there has not been a method based on combination of these three features yet. After feature combination, linear SVM classifier is used to detect pedestrian images from nonpedestrian. In experiments, INRIA dataset, Daimler dataset, and ETH dataset are adopted as the training and testing sets. Each dataset was recorded in various environments, resolution, and background occlusion. As a result, employing three various datasets can help not only further enrich our data but also scrutinize the robustness and precision of the proposed method in more depth. The substantial experimental result indicated that the proposed scheme outperformed the state of the art methods in terms of the accuracy with comparable computational time.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114298621","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 : 2018-05-03DOI: 10.1109/EIT.2018.8500290
R. Marsh, M. N. Amin, C. Crandall, Raymond Davis
This work was intended to direct the choice of an image interpolation/zoom algorithm for use in UND's Open Prototype for Educational Nanosats (OPEN) satellite program. Whether intended for a space-borne platform or a balloon-borne platform, we expect to use a low cost camera (Raspberry Pi) and expect to have very limited bandwidth for image transmission. However, the technique developed could be used for any imaging application. The approach developed analyzes overlapping $3times 3$ blocks of pixels looking for “L” patterns that suggest the center pixel should be changed such that a triangle pattern results. We compare this approach against different types of single-frame image interpolation algorithms, such as zero-order-hold (ZOH), bilinear, bicubic, and the directional cubic convolution interpolation (DCCI) approach. We use the peak signal-to-noise ratio (PSNR) and mean squared error (MSE) as the primary means of comparison. In all but one of the test cases the proposed method resulted in a lower MSE and higher PSNR than the other methods. Meaning this method results in a more accurate image after zooming than the other methods.
{"title":"Image Zooming Using Corner Matching","authors":"R. Marsh, M. N. Amin, C. Crandall, Raymond Davis","doi":"10.1109/EIT.2018.8500290","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500290","url":null,"abstract":"This work was intended to direct the choice of an image interpolation/zoom algorithm for use in UND's Open Prototype for Educational Nanosats (OPEN) satellite program. Whether intended for a space-borne platform or a balloon-borne platform, we expect to use a low cost camera (Raspberry Pi) and expect to have very limited bandwidth for image transmission. However, the technique developed could be used for any imaging application. The approach developed analyzes overlapping $3times 3$ blocks of pixels looking for “L” patterns that suggest the center pixel should be changed such that a triangle pattern results. We compare this approach against different types of single-frame image interpolation algorithms, such as zero-order-hold (ZOH), bilinear, bicubic, and the directional cubic convolution interpolation (DCCI) approach. We use the peak signal-to-noise ratio (PSNR) and mean squared error (MSE) as the primary means of comparison. In all but one of the test cases the proposed method resulted in a lower MSE and higher PSNR than the other methods. Meaning this method results in a more accurate image after zooming than the other methods.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114249541","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 : 2018-05-03DOI: 10.1109/EIT.2018.8500256
Tazwar Muttaqi, S. Mousavinezhad, S. Mahamud
User identification proof framework is essential for securing data from illicit access. To build a robust user identification system using voice, a new system is proposed to identify users using Mel-Scale Frequency Cepstral Coefficients (MFCC) and Dynamic Time Warping (DTW) along with a package of digital signal processing. Human voice is a sign of boundless data. Precise voice recognition requires computerized processing. Proposed method extracts unique features from a voice signal by MFCC and DTW to compare the components between two signals with the aid of some efficient signal processing such as filtering, signal alignment, removing unvoiced part, amplitude normalization and zero-part removal. All these steps work perfectly for accurate voice signal recognition. Based on the similarity between voice signals, it distinguishes different users and grant access to the secured area for multiple users which could be substantial for internal security for any classified organization or nation.
用户身份证明框架对于防止非法访问数据至关重要。为了构建鲁棒的语音用户识别系统，提出了一种基于Mel-Scale Frequency Cepstral Coefficients (MFCC)和Dynamic Time Warping (DTW)的语音用户识别系统。人的声音是无限数据的标志。精确的声音识别需要计算机处理。该方法通过对语音信号进行滤波、信号对准、去浊音部分、幅度归一化和去零部分等有效的信号处理，通过MFCC和DTW提取语音信号的独特特征，比较两种信号的分量。所有这些步骤都完美地实现了准确的语音信号识别。基于语音信号之间的相似性，它可以区分不同的用户，并为多个用户授予访问安全区域的权限，这对于任何机密组织或国家的内部安全都是至关重要的。
{"title":"User Identification System Using Biometrics Speaker Recognition by MFCC and DTW Along with Signal Processing Package","authors":"Tazwar Muttaqi, S. Mousavinezhad, S. Mahamud","doi":"10.1109/EIT.2018.8500256","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500256","url":null,"abstract":"User identification proof framework is essential for securing data from illicit access. To build a robust user identification system using voice, a new system is proposed to identify users using Mel-Scale Frequency Cepstral Coefficients (MFCC) and Dynamic Time Warping (DTW) along with a package of digital signal processing. Human voice is a sign of boundless data. Precise voice recognition requires computerized processing. Proposed method extracts unique features from a voice signal by MFCC and DTW to compare the components between two signals with the aid of some efficient signal processing such as filtering, signal alignment, removing unvoiced part, amplitude normalization and zero-part removal. All these steps work perfectly for accurate voice signal recognition. Based on the similarity between voice signals, it distinguishes different users and grant access to the secured area for multiple users which could be substantial for internal security for any classified organization or nation.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123029797","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 : 2018-05-03DOI: 10.1109/EIT.2018.8500220
Jean Jiang, R. Brewer, Ryan Jakubowski, Li Tan
In this paper, a piano frequency detecting system is developed utilizing the Goertzel Algorithm. The system is capable of detecting piano key frequencies ranging from “G3” to “B6” in a low background noise environment. Frequency detection is made possible by applying the Goertzel algorithm on a digital signal processing board, i.e. TMS320C6713 DSK. Then a microcontroller, the Arduino Due, is used to encode and decode the detected key using a frequency-based encoding scheme similar to binary. The decoded key information is finally output to a liquid crystal display (LCD) to display the detected piano key.
{"title":"Development of a Piano Frequency Detecting System Using the Goertzel Algorithm","authors":"Jean Jiang, R. Brewer, Ryan Jakubowski, Li Tan","doi":"10.1109/EIT.2018.8500220","DOIUrl":"https://doi.org/10.1109/EIT.2018.8500220","url":null,"abstract":"In this paper, a piano frequency detecting system is developed utilizing the Goertzel Algorithm. The system is capable of detecting piano key frequencies ranging from “G3” to “B6” in a low background noise environment. Frequency detection is made possible by applying the Goertzel algorithm on a digital signal processing board, i.e. TMS320C6713 DSK. Then a microcontroller, the Arduino Due, is used to encode and decode the detected key using a frequency-based encoding scheme similar to binary. The decoded key information is finally output to a liquid crystal display (LCD) to display the detected piano key.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129658629","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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