World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering最新文献
Pub Date : 2019-01-01DOI: 10.17706/IJCCE.2019.8.1.18-31
Sittichai Sukreep, Khalid Elgazzar, Henry Chu, P. Mongkolnam, Chakarida Nukoolkit
Recent reports show that the average life expectancy is increasing worldwide, posing significant overhead on healthcare systems and increasing demands on long-term care facilities. One of the grand challenges directly related to growing ageing societies is the implications of falling. Many elderly people live alone, especially those in Western countries who cannot afford living in a senior house or retirement facility. In such cases, not only falling is a major concern, but also daily activities must be continuously monitored and analyzed to provide immediate support when needed. Vital signs and environment context are also crucial conditions for preand post-event assessments. Thanks to technology advancements and widespread adoption of the Internet of Things which enables us to provide smart and ubiquitous healthcare services. In this paper, we propose iWatch, a smart and flexible system for fall detection and activity recognition using common smart devices, a smartwatch and a smartphone. Machine learning techniques are used to build efficient and highly accurate activity recognition classifiers. iWatch also provides health risk analysis using threshold-based models and leverages visualization tools to better communicate with the user. iWatch is a promising technology that provides a small step in a giant leap to revolutionize healthcare services, especially for those who needs extra care.
{"title":"iWatch: A Fall and Activity Recognition System Using Smart Devices","authors":"Sittichai Sukreep, Khalid Elgazzar, Henry Chu, P. Mongkolnam, Chakarida Nukoolkit","doi":"10.17706/IJCCE.2019.8.1.18-31","DOIUrl":"https://doi.org/10.17706/IJCCE.2019.8.1.18-31","url":null,"abstract":"Recent reports show that the average life expectancy is increasing worldwide, posing significant overhead on healthcare systems and increasing demands on long-term care facilities. One of the grand challenges directly related to growing ageing societies is the implications of falling. Many elderly people live alone, especially those in Western countries who cannot afford living in a senior house or retirement facility. In such cases, not only falling is a major concern, but also daily activities must be continuously monitored and analyzed to provide immediate support when needed. Vital signs and environment context are also crucial conditions for preand post-event assessments. Thanks to technology advancements and widespread adoption of the Internet of Things which enables us to provide smart and ubiquitous healthcare services. In this paper, we propose iWatch, a smart and flexible system for fall detection and activity recognition using common smart devices, a smartwatch and a smartphone. Machine learning techniques are used to build efficient and highly accurate activity recognition classifiers. iWatch also provides health risk analysis using threshold-based models and leverages visualization tools to better communicate with the user. iWatch is a promising technology that provides a small step in a giant leap to revolutionize healthcare services, especially for those who needs extra care.","PeriodicalId":23787,"journal":{"name":"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81662236","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 : 2019-01-01DOI: 10.17706/ijcce.2019.8.4.169-177
Mingduan Zhou, Beijing China Architecture, Qian-lin Wang, Hong-jie Tan, Miao Wang
A novel anti-collision monitoring method is proposed based on GNSS single-epoch positioning technology via high-precision carrier phase observations, which is applied to intelligent anti-collision monitoring for construction tower crane group. GNSS-based anti-collision monitoring principles are given in detail. A set of GNSS-based anti-collision monitoring auxiliary system named as GNSS_ACS for construction tower crane group is designed and developed. It can realize three kinds of alarm monitoring function consist of C-level, B-level and A-level respectively. The monitoring accuracy in the GNSS_ACS system ,for 600 consecutive epochs of the rover station such as rover_1727, the min-error of N-RMS is 0.007m, the max-error of N-RMS is 0.012m and the avg-error of N-RMS is 0.010m; the minerror of E-RMS is 0.005m, the max-error of E-RMS is 0.008m and the avg-error of E-RMS is 0.011m; the min-error of U-RMS is 0.015m, the max-error of U-RMS is 0.029m and the avg-error of U-RMS is 0.022m, is obtained in cm-level which verifies the effectiveness and feasibility of the proposed solutions in the experimental results. It can provide a new solution for intelligent anti-collision monitoring of construction tower crane group.
{"title":"Research on Intelligent Anti-collision Monitoring for Construction Tower Crane Group Based on GNSS Sensors","authors":"Mingduan Zhou, Beijing China Architecture, Qian-lin Wang, Hong-jie Tan, Miao Wang","doi":"10.17706/ijcce.2019.8.4.169-177","DOIUrl":"https://doi.org/10.17706/ijcce.2019.8.4.169-177","url":null,"abstract":"A novel anti-collision monitoring method is proposed based on GNSS single-epoch positioning technology via high-precision carrier phase observations, which is applied to intelligent anti-collision monitoring for construction tower crane group. GNSS-based anti-collision monitoring principles are given in detail. A set of GNSS-based anti-collision monitoring auxiliary system named as GNSS_ACS for construction tower crane group is designed and developed. It can realize three kinds of alarm monitoring function consist of C-level, B-level and A-level respectively. The monitoring accuracy in the GNSS_ACS system ,for 600 consecutive epochs of the rover station such as rover_1727, the min-error of N-RMS is 0.007m, the max-error of N-RMS is 0.012m and the avg-error of N-RMS is 0.010m; the minerror of E-RMS is 0.005m, the max-error of E-RMS is 0.008m and the avg-error of E-RMS is 0.011m; the min-error of U-RMS is 0.015m, the max-error of U-RMS is 0.029m and the avg-error of U-RMS is 0.022m, is obtained in cm-level which verifies the effectiveness and feasibility of the proposed solutions in the experimental results. It can provide a new solution for intelligent anti-collision monitoring of construction tower crane group.","PeriodicalId":23787,"journal":{"name":"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85779668","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 : 2019-01-01DOI: 10.17706/ijcce.2019.8.2.73-82
Krissada Asavaskulkiet
In this paper, the semi-orthogonal multi-linear principal component analysis (MPCA) method has been proposed for color face recognition. Recently, MPCA seems to be an appropriate scheme for dimensionality reduction and feature extraction from color images, handling the color channels in a natural, “holistic" manner. However, it is difficult to develop an effective MPCA method with the orthogonality constraint. Then, the semi-orthogonal MPCA results in more captured variance and more learned features than full orthogonality. In addition, this method can obtain correlation information among different color channels. In these experiments, the facial images in FERET database are used to test for a proposed method. The experimental results also indicate that the proposed method achieve better recognition rates than the well-known methods and it can be suitable for other color models such as HSV, YCbCr and CIELAB. Finally, the proposed recognition method can reduce the computational complexity in the color face recognition process.
{"title":"A Novel Color Face Recognition with Semi-orthogonal MPCA Method","authors":"Krissada Asavaskulkiet","doi":"10.17706/ijcce.2019.8.2.73-82","DOIUrl":"https://doi.org/10.17706/ijcce.2019.8.2.73-82","url":null,"abstract":"In this paper, the semi-orthogonal multi-linear principal component analysis (MPCA) method has been proposed for color face recognition. Recently, MPCA seems to be an appropriate scheme for dimensionality reduction and feature extraction from color images, handling the color channels in a natural, “holistic\" manner. However, it is difficult to develop an effective MPCA method with the orthogonality constraint. Then, the semi-orthogonal MPCA results in more captured variance and more learned features than full orthogonality. In addition, this method can obtain correlation information among different color channels. In these experiments, the facial images in FERET database are used to test for a proposed method. The experimental results also indicate that the proposed method achieve better recognition rates than the well-known methods and it can be suitable for other color models such as HSV, YCbCr and CIELAB. Finally, the proposed recognition method can reduce the computational complexity in the color face recognition process.","PeriodicalId":23787,"journal":{"name":"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78219197","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 : 2019-01-01DOI: 10.17706/ijcce.2019.8.2.60-72
S. Rajalakshmi, K. V. Madhav, R. Abhishek, Yedavalli Venkata Raghava Rao
: In IEEE 802.15.4 Wireless Body Area Networks, the existing remote patient monitoring rarely address the joint issues of power consumption, reliability and mobility. Generally, there is a tradeoff between reliability and power consumption since, increasing the reliability may result in increased power consumption. Moreover, when the patient moves from one location to another, it may affect the accuracy of results and leads to increased delay, due to poor channel conditions. To solve the identified problems, in this paper, we propose a Collaborative Remote Patient Monitoring System using IEEE 802.15.4 Wireless Body Area Networks. The proposed architecture consists of clusters of local sensors situated on various parts of the body. Each cluster head communicates with a wireless local gateway (WLG) which lies within the patient’s premises. The WLG in turn communicates with a remote hospital gateway (HG) such that the collected data from WLG is transmitted to the corresponding destination in the HG. The HG applies fuzzy logic decision model based on the input variables patient age, heartbeat, body temperature, percentage of the blood oxygen saturation and blood pressure and determines the criticality condition of patient. By simulation results, we show that the proposed module provides accurate estimation of patient condition .
{"title":"Collaborative Remote Patient Monitoring System Using IEEE 802.15.4 Wireless Body Area Networks","authors":"S. Rajalakshmi, K. V. Madhav, R. Abhishek, Yedavalli Venkata Raghava Rao","doi":"10.17706/ijcce.2019.8.2.60-72","DOIUrl":"https://doi.org/10.17706/ijcce.2019.8.2.60-72","url":null,"abstract":": In IEEE 802.15.4 Wireless Body Area Networks, the existing remote patient monitoring rarely address the joint issues of power consumption, reliability and mobility. Generally, there is a tradeoff between reliability and power consumption since, increasing the reliability may result in increased power consumption. Moreover, when the patient moves from one location to another, it may affect the accuracy of results and leads to increased delay, due to poor channel conditions. To solve the identified problems, in this paper, we propose a Collaborative Remote Patient Monitoring System using IEEE 802.15.4 Wireless Body Area Networks. The proposed architecture consists of clusters of local sensors situated on various parts of the body. Each cluster head communicates with a wireless local gateway (WLG) which lies within the patient’s premises. The WLG in turn communicates with a remote hospital gateway (HG) such that the collected data from WLG is transmitted to the corresponding destination in the HG. The HG applies fuzzy logic decision model based on the input variables patient age, heartbeat, body temperature, percentage of the blood oxygen saturation and blood pressure and determines the criticality condition of patient. By simulation results, we show that the proposed module provides accurate estimation of patient condition .","PeriodicalId":23787,"journal":{"name":"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84264914","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 : 2019-01-01DOI: 10.17706/ijcce.2019.8.2.83-92
S. M. A. Maraikkayar, K. Rajakumar, R. Tamilselvi, M. Beham, A. Afroze
Medical Information transmitted through the internet and secured against any attacks is an international challenging fear. In the present scenario, a fabulous relation emerges between chaos and cryptography. Various features of chaotic systems such as initial state sensitivity, ergodicity, mixing properties and complexity in the structure produce deterministic pseudo randomness in the input data. Chaotic Random Bit Generator (CRBG) makes the bit sequence unpredictable by an intruder, in the field of medical research. In current years, mixture of chaos-based cryptosystems have been projected. To be used in medical field, a CRBG may require in meeting stronger desires than for any other applications. Motivated by all these issues, a novel chaotic random bit generator is proposed based on two different chaotic based logistic maps in parallel and with preliminary self-determining initial conditions. The random bit sequence which is chaotic in character is created by predicting the outputs of both the chaotic logistic maps. Also it is projected to put forward dissimilar tests by stressing some of its alluring arithmetic features, which make it an ideal preference for the expected random bit generation. Lastly, the results of all the statistical tests generated bit sequences, is tested under all the most powerful NIST suit tests for the prediction of randomness: The tests validate the exact expected uniqueness expected of real random sequences.
{"title":"Performance and Statistical Analysis of Chaotic Random Bit Generator","authors":"S. M. A. Maraikkayar, K. Rajakumar, R. Tamilselvi, M. Beham, A. Afroze","doi":"10.17706/ijcce.2019.8.2.83-92","DOIUrl":"https://doi.org/10.17706/ijcce.2019.8.2.83-92","url":null,"abstract":"Medical Information transmitted through the internet and secured against any attacks is an international challenging fear. In the present scenario, a fabulous relation emerges between chaos and cryptography. Various features of chaotic systems such as initial state sensitivity, ergodicity, mixing properties and complexity in the structure produce deterministic pseudo randomness in the input data. Chaotic Random Bit Generator (CRBG) makes the bit sequence unpredictable by an intruder, in the field of medical research. In current years, mixture of chaos-based cryptosystems have been projected. To be used in medical field, a CRBG may require in meeting stronger desires than for any other applications. Motivated by all these issues, a novel chaotic random bit generator is proposed based on two different chaotic based logistic maps in parallel and with preliminary self-determining initial conditions. The random bit sequence which is chaotic in character is created by predicting the outputs of both the chaotic logistic maps. Also it is projected to put forward dissimilar tests by stressing some of its alluring arithmetic features, which make it an ideal preference for the expected random bit generation. Lastly, the results of all the statistical tests generated bit sequences, is tested under all the most powerful NIST suit tests for the prediction of randomness: The tests validate the exact expected uniqueness expected of real random sequences.","PeriodicalId":23787,"journal":{"name":"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82740539","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 : 2019-01-01DOI: 10.17706/ijcce.2019.8.4.138-154
M. Salhani
In Ultra-Dense Networks (UDNs), the load across the small cells is not equally distributed due to the random deployment of small cells, the mobility of user equipments (UEs) and the preference of small cells during the selection and reselection. This results in performance degradation concerning the throughput and successful handovers. To address this problem, this paper proposes proactive algorithms for balancing the load across the small-cell clusters and compares their balancing results to the previous reactive algorithms. The proactive algorithms distribute the new UEs, one by one, to the small cells, while the reactive algorithms are only triggered when the load of the chosen cluster reaches a predefined threshold. In addition, this paper employs the design structure matrix (DSM) method in order to balance the load across the small cells and to reduce the inter-communications between the access points (APs) as well. The numerical analysis indicates that the load distribution and the balance efficiency using the proactive algorithm with user rejection are better than those in the reactive algorithms by 34.97% and 9.09%, respectively. Moreover, the proactive algorithm without user rejection with the DSM method achieves the best balance efficiency and reduces the inter-communications between the APs in some cases by 60.60%.
{"title":"Employing the Proactive Algorithms and the Design Structure Matrix Method for Load Balancing in UND Networks","authors":"M. Salhani","doi":"10.17706/ijcce.2019.8.4.138-154","DOIUrl":"https://doi.org/10.17706/ijcce.2019.8.4.138-154","url":null,"abstract":"In Ultra-Dense Networks (UDNs), the load across the small cells is not equally distributed due to the random deployment of small cells, the mobility of user equipments (UEs) and the preference of small cells during the selection and reselection. This results in performance degradation concerning the throughput and successful handovers. To address this problem, this paper proposes proactive algorithms for balancing the load across the small-cell clusters and compares their balancing results to the previous reactive algorithms. The proactive algorithms distribute the new UEs, one by one, to the small cells, while the reactive algorithms are only triggered when the load of the chosen cluster reaches a predefined threshold. In addition, this paper employs the design structure matrix (DSM) method in order to balance the load across the small cells and to reduce the inter-communications between the access points (APs) as well. The numerical analysis indicates that the load distribution and the balance efficiency using the proactive algorithm with user rejection are better than those in the reactive algorithms by 34.97% and 9.09%, respectively. Moreover, the proactive algorithm without user rejection with the DSM method achieves the best balance efficiency and reduces the inter-communications between the APs in some cases by 60.60%.","PeriodicalId":23787,"journal":{"name":"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89215423","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 : 2019-01-01DOI: 10.17706/ijcce.2019.8.2.39-49
Poornima Eshwara, H. Shahnasser
: ECG is the most commonly performed cardiology test that provides vital information to understand the state of person’s heart condition. It essentially trances the electrical activity of the heart as it pumps blood to rest of the body and is very useful for determining the state of the heart and any symptoms. Conventionally patient visits the clinic for the ECG to be taken and is given report in a day or two. The whole process is both time consuming and tedious for the patient. With the latest development in Internet of Things, Cloud technology and reliable and faster data transmission, this process can be made lot more convenient. This paper proposes a cost-effective, remote system to read such ECG data of a patient via a handy sensor-Shimmer sensing device and send it to an android device via Bluetooth. The android device in turn sends this data to the cloud for storage and analysis and is then transmitted to a doctor’s android device for observation. Unique feature of the proposed unit lies in the fact that it provides a singular platform wherein the patient is directly connected to his/her healthcare provider for transmitting the ECG data with little or no delay. Apart from ensuring secure transmission of ECG data from patient to doctor, this channel between the patient and the doctor lets them communicate with each other. It is used to receive any valuable feedback or guidance from the doctor and gives opportunity to constantly monitor the effects or symptoms and responses to medicines that the patient is undergoing. The unit makes use of several technological advancements in cloud such as data processing, real-time data streaming, security, user account sync while making all these available remotely through the android and the sensor device.
{"title":"Mobile Application-IoT Based EKG Monitoring System","authors":"Poornima Eshwara, H. Shahnasser","doi":"10.17706/ijcce.2019.8.2.39-49","DOIUrl":"https://doi.org/10.17706/ijcce.2019.8.2.39-49","url":null,"abstract":": ECG is the most commonly performed cardiology test that provides vital information to understand the state of person’s heart condition. It essentially trances the electrical activity of the heart as it pumps blood to rest of the body and is very useful for determining the state of the heart and any symptoms. Conventionally patient visits the clinic for the ECG to be taken and is given report in a day or two. The whole process is both time consuming and tedious for the patient. With the latest development in Internet of Things, Cloud technology and reliable and faster data transmission, this process can be made lot more convenient. This paper proposes a cost-effective, remote system to read such ECG data of a patient via a handy sensor-Shimmer sensing device and send it to an android device via Bluetooth. The android device in turn sends this data to the cloud for storage and analysis and is then transmitted to a doctor’s android device for observation. Unique feature of the proposed unit lies in the fact that it provides a singular platform wherein the patient is directly connected to his/her healthcare provider for transmitting the ECG data with little or no delay. Apart from ensuring secure transmission of ECG data from patient to doctor, this channel between the patient and the doctor lets them communicate with each other. It is used to receive any valuable feedback or guidance from the doctor and gives opportunity to constantly monitor the effects or symptoms and responses to medicines that the patient is undergoing. The unit makes use of several technological advancements in cloud such as data processing, real-time data streaming, security, user account sync while making all these available remotely through the android and the sensor device.","PeriodicalId":23787,"journal":{"name":"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76349891","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 : 2019-01-01DOI: 10.17706/ijcce.2019.8.4.178-183
S. J. H. Pirzada, A. Murtaza, Jianwei Liu, Tongge Xu
Recently, the increase in the use of fast and reliable communication systems has increased the significance and utilization of satellite-based communication systems. The communication systems used in the space environment is more reliable and robust as compared to communication systems used on earth. Because unlike ground communication, the communication systems in space have to bear harsh space environment and its effects like radiations, pressure, and vacuum, which causes anomalies in communication systems. These effects are known as Single Event Effects (SEE), which results in loss of data or sometimes even damage to the equipment. Like ground systems, in satellite, the Advanced Encryption Standard (AES) is a widely used encryption algorithm which is not only used to provide data confidentiality but also used in data authentication & integrity algorithms (e.g. CMAC) as well as in authenticated encryption (AE) algorithm (e.g., AES-GCM). The Substitution Box (S-Box) is a main component of the AES algorithm, which is generally implemented on memory blocks. The memory blocks in space are vulnerable to radiations and mostly affected by SEE; hence, protection techniques against SEE are proposed by researchers. Two methods for implementation of the S-Box algorithm are by a look-up table or by an algorithm. In this work, analysis of using these two methods of the S-Box implementation for SEE is performed. The implementation of both methods is performed on FPGA, and results show that the algorithm implementation is more reliable in the space environment as compared to table-based implementation.
{"title":"Single Event Effects Tolerant AES-CTR Implementation for Authentication of Satellite Communication","authors":"S. J. H. Pirzada, A. Murtaza, Jianwei Liu, Tongge Xu","doi":"10.17706/ijcce.2019.8.4.178-183","DOIUrl":"https://doi.org/10.17706/ijcce.2019.8.4.178-183","url":null,"abstract":"Recently, the increase in the use of fast and reliable communication systems has increased the significance and utilization of satellite-based communication systems. The communication systems used in the space environment is more reliable and robust as compared to communication systems used on earth. Because unlike ground communication, the communication systems in space have to bear harsh space environment and its effects like radiations, pressure, and vacuum, which causes anomalies in communication systems. These effects are known as Single Event Effects (SEE), which results in loss of data or sometimes even damage to the equipment. Like ground systems, in satellite, the Advanced Encryption Standard (AES) is a widely used encryption algorithm which is not only used to provide data confidentiality but also used in data authentication & integrity algorithms (e.g. CMAC) as well as in authenticated encryption (AE) algorithm (e.g., AES-GCM). The Substitution Box (S-Box) is a main component of the AES algorithm, which is generally implemented on memory blocks. The memory blocks in space are vulnerable to radiations and mostly affected by SEE; hence, protection techniques against SEE are proposed by researchers. Two methods for implementation of the S-Box algorithm are by a look-up table or by an algorithm. In this work, analysis of using these two methods of the S-Box implementation for SEE is performed. The implementation of both methods is performed on FPGA, and results show that the algorithm implementation is more reliable in the space environment as compared to table-based implementation.","PeriodicalId":23787,"journal":{"name":"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84400352","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 : 2019-01-01DOI: 10.17706/ijcce.2019.8.4.155-168
Li Yue, Richard Nuetey Nortey, Michael Adjeisah, P. Agbedanu, Xinyi Lui
Currently, storing sensitive data related to patient’s medical healthcare into Electronic Health Records (EHRs) has developed rapidly. Specifically, the distribution of healthcare records has brought convenience to hospitals and other different third parties in accessing this sensitive medical health information of patients for various purposes, thus leading to the generation of big data. In the field of healthcare, big data plays a significant role as it can be employed in predicting outcomes of diseases, preventing co-morbidities fatality and saving the cost spent on medical treatment. However, it is most likely to lead to both security breaches and privacy violations in the process of data collection. In this paper, a platform employing the blockchain technology for privacy preservation during the process of collecting, managing and distributing EHR data is proposed. This paper aims to ensure the total privacy, integrity and access control of distributed electronic health records possessed by the data owners in the process of it being distributed on the blockchain. Simulated results demonstrate that the system proposed by us, which is totally transparent, is able to ensure perfect privacy within the distributed network of sharing EHRs in the medical setting by employing the blockchain.
目前，将与患者医疗保健相关的敏感数据存储到电子健康记录(Electronic Health Records, EHRs)中发展迅速。具体来说，医疗记录的分布为医院和其他不同的第三方出于各种目的访问患者的敏感医疗健康信息带来了便利，从而导致了大数据的产生。在医疗保健领域，大数据在预测疾病结局、预防合并症和死亡率以及节省医疗费用方面发挥着重要作用。然而，在数据收集过程中，这很可能导致安全漏洞和隐私侵犯。本文提出了一个利用区块链技术实现电子病历数据采集、管理和分发过程中的隐私保护的平台。本文旨在确保数据所有者所拥有的分布式电子健康记录在区块链上分发过程中的全部隐私性、完整性和访问控制。仿真结果表明，我们提出的系统是完全透明的，能够利用区块链在医疗环境中共享电子病历的分布式网络中保证完美的隐私。
{"title":"Blockchain Enabled Privacy Security Module for Sharing Electronic Health Records (EHRs)","authors":"Li Yue, Richard Nuetey Nortey, Michael Adjeisah, P. Agbedanu, Xinyi Lui","doi":"10.17706/ijcce.2019.8.4.155-168","DOIUrl":"https://doi.org/10.17706/ijcce.2019.8.4.155-168","url":null,"abstract":"Currently, storing sensitive data related to patient’s medical healthcare into Electronic Health Records (EHRs) has developed rapidly. Specifically, the distribution of healthcare records has brought convenience to hospitals and other different third parties in accessing this sensitive medical health information of patients for various purposes, thus leading to the generation of big data. In the field of healthcare, big data plays a significant role as it can be employed in predicting outcomes of diseases, preventing co-morbidities fatality and saving the cost spent on medical treatment. However, it is most likely to lead to both security breaches and privacy violations in the process of data collection. In this paper, a platform employing the blockchain technology for privacy preservation during the process of collecting, managing and distributing EHR data is proposed. This paper aims to ensure the total privacy, integrity and access control of distributed electronic health records possessed by the data owners in the process of it being distributed on the blockchain. Simulated results demonstrate that the system proposed by us, which is totally transparent, is able to ensure perfect privacy within the distributed network of sharing EHRs in the medical setting by employing the blockchain.","PeriodicalId":23787,"journal":{"name":"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78613392","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 : 2019-01-01DOI: 10.17706/IJCCE.2019.8.1.1-17
Maha Al-Ghalibi, Adil Al-Azzawi, K. Lawonn
In many of today’s big data analytics applications, it might need to analyze social media feeds as well as to visualize users’ opinions. This will provide a viable alternative source to establish new metrics in our digital life. Social interaction with people in Twitter is open-ended, making media analysis in Twitter easier in comparison with other social media. That is because the interaction in those media is often different since most of them are private. This work is therefore devoted to focus merely on design and implementation a Deep model for Twitter opinion (Mood) visualization based Deep Learning network. It is concerned with Natural Language Processing (NLP)-based sentiment analysis and Deep Learning framework for Twitter’s opinion mining visualization and classification. The utilized methodology is based on applying sentiment analysis NLP on a large number of tweets in order to visualize the predicted mood scoring of the tweet and thus to exploit public tweeting for knowledge discovery. This will moreover serve for fake news detection. The pertinent mechanism involves several consecutive steps, namely: dataset collection stage, the pre-processing stage, NLP stage, sentiment analysis stage, and prediction and classification stage using Deep Learning Model. The U.S. Airlines Sentiment Analysis Twitter dataset has been utilized which is already provided with Data for Everyone. The presented system is monitoring Twitter streams from both the media and the public. It is capable to visualize and extract meaningful data from tweets in real-time and store them into a Deep model for analysis. It is convenient for a wide application spectrum involving: big data analytics solutions, predicting e-commerce customer’s behavior, improving marketing strategy, getting market competitive advantages, besides visualization in various data mining applications.
{"title":"Deep Tweets Analyzer Model for Twitter Mood Visualization and Prediction Based Deep Learning Approach","authors":"Maha Al-Ghalibi, Adil Al-Azzawi, K. Lawonn","doi":"10.17706/IJCCE.2019.8.1.1-17","DOIUrl":"https://doi.org/10.17706/IJCCE.2019.8.1.1-17","url":null,"abstract":"In many of today’s big data analytics applications, it might need to analyze social media feeds as well as to visualize users’ opinions. This will provide a viable alternative source to establish new metrics in our digital life. Social interaction with people in Twitter is open-ended, making media analysis in Twitter easier in comparison with other social media. That is because the interaction in those media is often different since most of them are private. This work is therefore devoted to focus merely on design and implementation a Deep model for Twitter opinion (Mood) visualization based Deep Learning network. It is concerned with Natural Language Processing (NLP)-based sentiment analysis and Deep Learning framework for Twitter’s opinion mining visualization and classification. The utilized methodology is based on applying sentiment analysis NLP on a large number of tweets in order to visualize the predicted mood scoring of the tweet and thus to exploit public tweeting for knowledge discovery. This will moreover serve for fake news detection. The pertinent mechanism involves several consecutive steps, namely: dataset collection stage, the pre-processing stage, NLP stage, sentiment analysis stage, and prediction and classification stage using Deep Learning Model. The U.S. Airlines Sentiment Analysis Twitter dataset has been utilized which is already provided with Data for Everyone. The presented system is monitoring Twitter streams from both the media and the public. It is capable to visualize and extract meaningful data from tweets in real-time and store them into a Deep model for analysis. It is convenient for a wide application spectrum involving: big data analytics solutions, predicting e-commerce customer’s behavior, improving marketing strategy, getting market competitive advantages, besides visualization in various data mining applications.","PeriodicalId":23787,"journal":{"name":"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85838969","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}
World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering
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