系統識別號 U0026-1011201822103700
論文名稱(中文) 智慧物聯網應用框架之設計與實作
論文名稱(英文) Design and Implementation of an Intelligent Application Framework on Internet of Things
校院名稱 成功大學
系所名稱(中) 電腦與通信工程研究所
系所名稱(英) Institute of Computer & Communication
學年度 107
學期 1
出版年 107
研究生(中文) 李仕雄
研究生(英文) Shih-Hsiung Lee
學號 Q38044024
學位類別 博士
語文別 英文
論文頁數 123頁
口試委員 口試委員-黃能富
中文關鍵字 物聯網  基於權杖授權服務  心跳偵測  盲訊號分離  物件偵測  影像前處理及辨識 
英文關鍵字 Internet of Things  Token-based Authorization Service  Heart beats detection  Blind Source Separation  Object detection  Image processing and recognition 
中文摘要 在本論文中,主要探討智慧物聯網的應用框架設計及實作。物聯網是表示數十億個互聯的設備,設備上通常搭載著感知器和通訊裝置。如何使其成為一個智慧終端是重要的議題。而物聯網裝置一般來說是具備低功耗、有限儲存空間和低處理能力的特點,關於可靠性,運算性能,安全性及保密性來說都相當有限。因此本論文探討如何整合雲端平台技術及如何實現邊緣運算,來達到智慧終端的實現。而物聯網的架構從宏觀角度來看,分為三個階層:感知層、網路層及應用服務層。本論文研究的範圍,從感知層擷取感知器的資料,進而分析資料。透過網路層的通訊能力將資料傳送至雲端,實現多個應用情境。並且針對資料提出有效的解決演算法,如盲訊號分離來處理多重感知器問題,影像前處理和及時物件計數及辨識演算法。
在應用情境及框架面上,本論文設計及實作包含以下幾個領域: 智慧醫療照護、智慧工業及智慧綠能等主題。在智慧醫療照護應用情境中,我們實作了二種應用情境。(1) 心率偵測應用在孕婦照護及登山者。(2) 影像偵測應用在指甲、頭髮及頭皮的照護上。在智慧工業上,我們採用了工業用相機實作一個及時高效率的工業檢測情境。 在智慧綠能上,我們實作一個智慧插座,有效監控電壓電流的資訊,並且透過此資訊訓練一個模型達到智能的控制效果。
而當資料傳送至雲端平台時,一般智慧終端裝置會藉由RESTful API存取雲端上的服務。而雲端伺服器如何驗證及授權數十億裝置的權限是一大挑戰。除此之外,個人使用智慧終端裝置時,伴隨著隱私性資料或安全性的問題。因此在智慧物聯網應用框架中,我們提出一個基於權杖的授權服務架構。透過權杖,裝置可以更安全的存取雲端服務。而權杖是經由第三方驗證中心頒布,可以提高其可靠度及安全性。權杖具備高度隱私性的特色,能使敏感性資料不輕易地被揭露。權杖僅在一段時間內有效或當權杖計數超過系統定義的閥值會失去其作用,藉此降低裝置被駭的風險。本論文所提出的基於權杖的授權服務架構會應用在本論文的情境中,尤其對於在醫療穿戴式裝置上更顯得重要。在各章節會分別介紹各個應用情境、應用框架及實驗結果。
英文摘要 In this thesis, we mainly discuss the application framework and implementation of the intelligent Internet of Things. The Internet of Things represents billions of interconnected devices. The devices are usually equipped with sensors and communication modules. How to make it become a smart terminal device is an important topic. IoT devices are generally characterized by low power consumption, limited storage space, and low processing capacity. They are quite limited in terms of reliability, computing performance, security, and confidentiality. Therefore, this work explores how to integrate cloud platform technology and how to implement edge computing to achieve the realization of smart terminals. The architecture of the Internet of Things is divided into three levels from a macro perspective: the perception layer, the network layer, and the application service layer. The scope of this work is to extract the data of the sensors from the perceptual layer and analyze the data. Furthermore, it can achieve multiple application scenarios by transmitting data to the cloud through the communication capabilities of the network layer. Add to this, we propose some effective intelligent algorithms for the data, such as blind source separation algorithm for handling with multiple sensors, image preprocessing algorithm and real time object counting and recognition algorithm.
On the application scenario and framework side, this thesis discusses and implements the following areas: smart health care, smart industry and smart green energy. In the context of smart health care applications, we have implemented two application scenarios. (1) Heart rate detection is applied to pregnant women and mountain climbers. (2) Image detection is applied to the care of nails, hair and scalp. In the smart industry, we have used industrial cameras to implement a real time and efficient industrial inspection scenario. In Smart green energy, we implement a smart plug that effectively monitors voltage and current information and we train a model to achieve an intelligent control through this information.
In the course of the research, it was found that to achieve a better intelligent terminal, it is necessary to rely on the edge computing device. This thesis uses an embedded device with a graphics processing core as the edge computing device, which can return the operation result to the terminal timely than the cloud platform. In addition, for the algorithm of data processing, we also propose some novel algorithms that can be effectively applied in the intelligent Internet of Things framework.
Add to this, when the data is transmitted to the cloud platform, the smart terminal device accesses the service on the cloud through the RESTful API generally. How cloud servers verify and authorize the permissions of billions of devices is a challenge. In addition, personal use of smart terminal devices is accompanied by privacy issues or security issues. Therefore, in the intelligent IoT application framework, we propose a token-based authorization service architecture. Through the token, the device can access the cloud service more securely. The token is released by a third-party verification center to improve its reliability and security. The token has a highly private feature that makes sensitive data not easily revealed. The token is only valid for a period of time or when the count of token does not exceed the system-defined threshold. Thereby, the token-based authorization service can reduce the risk of the device being hacked. The token-based authorization service architecture proposed in this thesis will be applied in the context of this work, especially for medical wearable devices. It is important obviously. Each application scenario, application framework, and experimental results are presented in each chapter.
論文目次 Table of Contents
摘要 i
Abstract ii
Acknowledgements iv
Table of Contents v
List of Tables viii
List of Figures ix
Chapter 1. Introduction 1
1.1. Background 1
1.2. Motivation and Aim of the Research 2
1.3. Organization of the Thesis 4
Chapter 2. TBAS:Token-based Authorization Service 5
2.1. Introduction 5
2.2. Related Works 6
2.2.1. OpenID 6
2.2.2. OAuth 7
2.3. TBAS 9
2.3.1. Authorization Check Layer 10
2.3.2. Generating a Token 10
2.3.3. Granting a Token 11
2.3.4. Mobile Device Binding 11
2.4. Application Scenario of Pregnant Women 13
2.4.1. Platform Deployment 13
2.4.2. Token Request and Generation of Wearable Medical Device 14
2.4.3. Token Security and Data Privacy Protection 15
2.4.4. Differences among Token, OpenID and Oauth 16
2.4.5. Features of Management Platform 16
2.5. Application Scenario of mountaineers 19
2.5.1. An Intelligent Emergency Rescue Assistance System 20
2.5.2. Design of Wearable Device 21
2.5.3. Altitude Sickness and Hypothermia Detection 22
2.5.4. Climber’s Posture Recognition 23
2.5.5. Event Reminding 23
2.5.6. Beacon Signal for Search and Rescue 24
2.5.7. Experimental Results 24
Chapter 3. Intelligent Algorithms for Heart Rate Detection 27
3.1. Intelligent Heart Rate Monitoring Algorithms 27
3.1.1. Introduction 27
3.1.2. Problem Definition 27
3.1.3. Proposed Algorithm 28
3.1.4. Preprocessing Steps 28
3.1.5. Dynamic Time Wrapping 29
3.1.6. Empirical Algorithm 31
3.1.7. Experimental Results 34
3.2. GPSO-ICA: Independent Component Analysis based on Gravitational Particle Swarm Optimization 38
3.2.1. Problem Definition 38
3.2.2. Background 39
3.2.3. Independent Component Analysis 41
3.2.4. Particle Swarm Optimization 43
3.2.5. Gravitation Searching Algorithm 44
3.2.6. Particle Swarm Optimization based ICA 46
3.2.7. Proposed Algorithm 48
3.2.8. Experimental Results 48
Chapter 4. Smart Camera Sensors in Internet of Things 57
4.1. A Real Time Object Recognition and Counting System 57
4.1.1. Problem Definition 57
4.1.2. Background 57
4.1.3. System Overview 60
4.1.4. Preloading 61
4.1.5. Motion Estimation for Dropping Frames 62
4.1.6. Object Recognition 63
4.1.7. Matching Template 64
4.1.8. Counting 66
4.1.9. Experimental Results 66
4.2. Fingernails Analysis Management System 70
4.2.1. Problem Definition 70
4.2.2. Background 71
4.2.3. The Proposed System 72
4.2.4. Fingernails Image Preprocessing 73
4.2.5. Biometric Authentication Using Fingernails 76
4.2.6. Experimental Results 76
4.3. An Intelligent Hair and Scalp Analysis System 80
4.3.1. Problem Definition 80
4.3.2. Background 81
4.3.3. Proposed Methods 83
4.3.4. Preprocessing 84
4.3.5. Detection of Baldness Status 85
4.3.6. Detection of Scalp Status 87
4.3.7. Parameter Adjustment based on Different Lighting Conditions 88
4.3.8. Experimental Results 89
Chapter 5. Smart Plug in Internet of Things 95
5.1. Problem Definition 95
5.2. Background 95
5.2.1. Smart Plug 96
5.2.2. Communication Protocol on Smart Plug 96
5.2.3. Energy Management System 97
5.2.4. Data Analysis of Smart Plugs 98
5.3. Proposed Power Management Framework System 98
5.3.1. System overview 98
5.3.2. The Design of Smart Plug 99
5.3.3. The Design of Smart Gateway 100
5.3.4. Network Communication between Smart Plug and Gateway 101
5.3.5. The Protocol Design of Management Platform 102
5.3.6. Continuously Learning Power Management System 103
5.4. Experimental Results 105
Chapter 6. Conclusions and Future Works 109
6.1. Conclusions 109
6.2. Future Works 111
References 113
參考文獻 [1] Embedded World 2016. https://www.embedded-world.de/en.
[2] Ridi A, Gisler C, and Hennebert J. Processing smart plug signals using machine learn- ing. IEEE Wireless Communications and Networking Conference Workshops (WC- NCW), 2015.
[3] Tsuda A, Kato H, Yoneyama A, and Hangai S. Nail art simulation by hue difference feature with light. Proc. of the 2009 IEICE General Conference, 2009.
[4] ACS712. https://www.sparkfun.com/datasheets/breakoutboards/0712.pdf.
[5] I. Ahn and C. Kim. Face and hair region labeling using semi-supervised spec- tral clustering-based multiple segmentations. IEEE Transactions on Multimedia, 18(7):1414–1421, 2016.
[6] A. Alahi, R. Ortiz, and P. Vandergheynst. Freak: Fast retina keypoint. Computer Vision and Pattern Recognition (CVPR), pages 510–517, 2012.
[7] A. Anand, S. S. Tripathy, and R. S. Kumar. An improved edge detection using mor- phological laplacian of gaussian operator. Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on, pages 532–536, 2015.
[8] Arthur and Vassilvitskii S. k-means++: the advantages of careful seeding. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 2007.
[9] Arthur and S. Vassilvitskii. k-means++: the advantages of careful seeding. Proceed- ings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 2007.
[10] Barbosa B, Theoharis I, Abdallah, and Ali E. On the use of fingernail images as transient biometric identifiers. Machine Vision and Applications, 27(1):65–76, 2016.
[11] H. Bay, T. Tuytelaars, and L. V. Gool. Surf: Speeded up robust features. European Conference on Computer Vision, pages 404–417, 2006.
[12] Belkin. http://www.belkin.com/us/f7c027-belkin/p/p-f7c027/.
[13] D Benslimane, S Dustdar, and Sheth A. Services mashups: The new generation of web applications. IEEE Internet Computing, 2008.
[14] L. Wang B.Liu and Y.-H. Jin. An effective pso-based memetic algorithm for flow shop scheduling. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 37:18–27, 2007.
[15] F Bonomi, R Milito, J Zhu, and Addepalli S. Fog computing and its role in the internet of things. Cloud Computing, pages 13–16, 2012.
[16] A Botta, DW de, V Persico, and A Pescape. On the integration of cloud computing and internet of things. Future Internet of Things and Cloud (FiCloud), 2014 International Conference on, 2014.
[17] J Bughin and Manyika M. How businesses are using web 2.0: A mckinsey global survey. McKinsey Quarterly Web Exclusive. McKinsey and Company, 2007.
[18] Basler camera. http://www.baslerweb.com/en/products/cameras/area-scan- cameras/ace/aca1920-155um.
[19] Racing car toys. https://www.amazon.com/artin-scale-ultimate-express-racing/.
[20] C. C. Chang and C. J. Lin. Libsvm - a library for support vector machines.
[21] G. N. Chaple, R. D. Daruwala, and M. S. Gofane. Comparisions of robert, prewitt, sobel operator based edge detectionmethods for real time uses on fpga. Technologies for Sustainable Development (ICTSD), 2015 International Conference on, 2015.
[22] H. Chen and S.-C. Zhu. A generative sketch model for human hair analysis and syn- thesis. IEEE Trans. Pattern Anal. Mach. Intell., 28(7):1025–1040, 2006.
[23] W.-N. Chen, J. Zhang, H.S.-H. Chung, W.-L. Zhong, W. Gang Wu, and Y.H. Shi. A novel set-based particle swarm optimization method for discrete optimization prob- lems, evolutionary computation. IEEE Transactions on, 14(2):278–300, 2010.
[24] J.T. Chien and B. C. Chen. A new independent component analysis for speech recog- nition and separation. IEEE Trans. Audio, Speech Lang. Process, 14(4):1245–1254, 2006.
[25] S. Choi and Z. Jiang. A novel wearable sensor device with conductive fabric and pvdf film for monitoring cardiorespiratory signals. Sensors and Actuators A-Physical, 128:317–326, 2006.
[26] S Cirani, M Picone, P Gonizzi, L Veltri, and G Ferrari. Iot-oas: An oauth-based authorization service architecture for secure services in iot scenarios. IEEE Sensors Journal, 15(2).
[27] CM01-B. http://www.meas-spec.com/downloads/contact_microphone.pdf. Sensors and Actuators A-Physical.
[28] P. Comon. Independent component analysis, a new concept? Signal Processing, 36:287–314, 1994.
[29] Benslimane D, Dustdar S, and Sheth A. Services mashups: The new generation of web applications. IEEE Internet Computing, 12:13–15, 2008.
[30] Hardt D. The oauth 2.0 authorization framework. RFC 6749, Internet Engineering Task Force, 2012.
[31] DH11. http://www.micropik.com/pdf/dht11.pdf.
[32] T Dierks and E Rescorla. The transport layer security (tls) protocol version 1.2. RFC 5246, Internet Engineering Task Force, 2008.
[33] DLink. http://us.dlink.com/products/connected-home/wi-fi-smart-plug/.
[34] MC Domenech, E Comunello, and Wangham MS. Security analysis of the saml single sign-on browser/artifact profile. Identity management in e-Health: A case study of web of things application using OpenID connect, 2014.
[35] G. Duan, Y. W. Chen, and T. Sakekawa. Automatic optical inspection of micro drill bit in printed circuit board manufacturing based on pattern classification. IEEE In- strumentation and Measurement Technology Conference Proceedings (IMTC) 2008, pages 279–283, 2008.
[36] R. O. Duda and R. E. Hart. Use of the hough transformation to detect lines and curves in pictures. Commun. ACM, 15(1):11–15, 1972.
[37] Hammer-Lahav E. The oauth 1.0 protocol. RFC 5849, Internet Engineering Task Force, 2010.
[38] Mocanu E, Nguyen PH, Gibescu M, and Kling LW. Deep learning to estimate building energy demands in the smart grid context. SNN Symposium Intelligent Machines, 2015.
[39] ESP8266. https://espressif.com/en/products/hardware/esp8266ex/overview.
[40] J. R. Evans and T. Arslan. Implementation of a robust image registration algorithm on an arm system-on-chip platform. Circuits and Systems (ISCAS), IEEE International Symposium on, pages 269–272, 2002.
[41] Bonomi F, Milito R, Zhu J, and Addepalli S. Fog computing and its role in the internet of things. Cloud Computing, pages 13–16, 2012.
[42] T.J.R. Francis. Immersion hypothermia. Journal of the South Pacific Underwater Medicine Society, 1998.
[43] G7-PMS7003. http://aqicn.org/air/view/sensor/spec/pms7003.pdf.
[44] Y. Gao and S. Xie. A blind source separation algorithm using particle swarm opti- mization. In Proceedings of the Circuits and Systems Symposium on Emerging Tech- nologies: Frontiers of Mobile and Wireless Communication, 1, 2004.
[45] M. Guarrera, P. Cardo, P. Arrigo, and A. Rebora. Reliability of hamilton-norwood classification. International Journal of Trichology, 1(2):120–122, 2009.
[46] M. Gupta and V. Mysore. Classifications of patterned hair loss: A review. Journal of cutaneous and aesthetic surgery, 9(1):3–12, 2016.
[47] Morsali H, Shekarabi MS, Ardekani K, Khayami H, Fereidunian A, Ghassemian M, and Lesani H. Smart plugs for building energy management systems. IEEE Transac-
tions on Energy Conversion, page 1–5, 2012.
[48] Shajahan HA and Anand A. Data acquisition and control using arduino-android plat- form: Smart plug. IEEE Transactions on Energy Conversion, pages 241–244, 2013.
[49] HC-SR501. https://www.mpja.com/download/31227sc.pdf.
[50] HEXIWEAR. http://www.hexiwear.com/.
[51] R. Hoffmann. Trichoscan. a new instrument for digital hair analysis. Hautarzt, 53(12):798–804, 2002.
[52] Kim HS, Park MK, Kim HY, and Park SH. Capillary dimension measured by computer-based digitalized image correlated with plasma endothelin-1 levels in pa- tients with systemic sclerosis. Clin Rheumatol., 29(3):247–254, 2010.
[53] A. Huang, S.-Y. Kwan, W.-Y. Chang, M.-Y. Liu, M.-H. Chi, and G.-S. Chen. A robust hair segmentation and removal approach for clinical images of skin lesions. in Proc.
35th Annu. Int. Conf. IEEE EMBS, page 3315–3318, 2013.
[54] C.-L. Huang, W.-C. Huang, H.-Y. Chang, Y.-C. Yeh, and T. C.-Y Tsai. Hybridization strategies for continuous ant colony optimization and particle swarm optimization ap- plied to data clustering. Applied Soft Computing, 13(9):3864–3872, 2013.
[55] A. Hyvarinen. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 1(3):626–634, 1999.
[56] A. Hyvarinen. Survey on independent component analysis. Neural Comp. Surveys, 2:94–128, 1999.
[57] A. Hyvarinen and E. Oja. Independent component analysis: algorithms and applica- tions. Neural Netw., 13:411–430, 2000.
[58] Goodfellow I, Bengio Y, and Courville A. Deep learning (adaption computation and machine learning series). MIT Press, 2016.
[59] Goodfellow I, Bengio Y, and Courville A. Deep learning (adaption computation and machine learning series). MIT Press, 2016.
[60] Horvat I, Lukac N, and Pavlovic R. Smart plug solution based on bluetooth low energy.
Consumer Electronics - Berlin (ICCE-Berlin), 2015 IEEE 5th International Confer- ence on, 2015.
[61] Korhonen I., Parkka J., and M. Van Gils. Health monitoring in the home of the future.
IEEE Eng. Med. Biol., 22:66–73, 2003.
[62] Barros IB, Theoharis T, Schellewald C, and Athwal C. Transient biometrics using finger nails. Biometrics: Theory, Applications and Systems (BTAS), 2013 IEEE Sixth International Conference on, 2013.
[63] iHome. http://ihomeaudiointl.com/discover/smart_plug/.
[64] Abushnaf J, Rassau A, and Górnisiewicz W. Impact of dynamic energy pricing schemes on a novel multi-user home energy management system. Electric Power Systems Research, 125:124–132, 2015.
[65] Kennedy J. and Eberhart R. Particle swarm optimization. In IEEE International Con- ferenceon Neural Networks, 4:1942–1948, 1995.
[66] S. Joseph and J. R. Panicker. Skin lesion analysis system for melanoma detection with an effective hair segmentation method. Information Science (ICIS), International Conference on, 2017.
[67] P. Julian, C. Dehais, F. Lauze, V. Charvillat, A. Bartoli, and A. Choukroun. Automatic hair detection in the wild. in Proc. 20th IEEE ICPR, page 4617–4620, 2010.
[68] Cho K, Kim S, Kang B, Jang SM, and Park S. Intelligent office energy management system by analysis in hyper-connected-iot environments. 2017 IEEE International Conference on Consumer Electronics(ICCE), 2017.
[69] Hild K., Erdogmus D., and J. Principe. Blind source separation using renyi’s mutual information. IEEE Signal Processing Letters, 8(6):174–176, 2001.
[70] Kiviluoto K. and Oja E. Independent component analysis for parallel financial time series. In Proceedings of the International Conference on Neural information Pro- cessing, 2:895–898, 1998.
[71] N. Kanopoulos, N. Vasanthavada, and R.L. Baker. Design of an image edge detection filter using the sobel operator. IEEE Journal of Solid-State Circuits, 23:358–367, 1998.
[72] H. Kim, W. Kim, J. Rew, S. Rho, J. Park, and E. Hwang. Evaluation of hair and scalp condition based on microscopy image analysis. Platform Technology and Service (PlatCon), 2017 International Conference on, 2017.
[73] Jui-Le Chen Ko-Wei Huang, Chu-Sing Yang, and Chun-Wei Tsai. Psgo: Particle swarm gravitation optimization algorithm. Journal of Intelligent and Fuzzy Systems, 28(6):2655–2665, 2015.
[74] Breiman L. Random forests. Machine learning, 45(1):5–32, 1979.
[75] T. W. Lee. Independent component analysis-theory and application. Norwell, MA: Kuwer, 1998.
[76] Hu CN Lee SH and Yang CS. Token-oriented based for internet of things and cloud computing service. International Conference of things and Cloud Computing, 2016.
[77] S. Leutenegger, M. Chli, and R. Y. Siegwart. Brisk: Binary robust invariant scalable keypoints.
[78] J.J. Liang, A.K. Qin, P.N. Suganthan, and S. Baskar. Comprehensive learning parti- cle swarm optimizer for global optimization of multimodal functions. Evolutionary Computation, IEEE Transactions on, 10(3):281–295, 2006.
[79] C. F. Liew and T. Yairi. Generalized brief: A novel fast feature extraction method for robust hand detection. Pattern Recognition (ICPR), 2014 22nd International Confer- ence on, pages 3014–3019, 2014.
[80] Y. C. Lin and K. Wang. Position determination of a ball grid array by automated optical inspection method. Nano/Micro Engineered and Molecular Systems (NEMS), 2014 9th IEEE International Conference on, pages 97–101, 2014.
[81] R. Lionnie and M. Alaydrus. An analysis of haar wavelet transformation for andro- genic hair pattern recognition. Informatics and Computing (ICIC), International Con- ference on, 2017.
[82] LM35. http://prodtech.biz/sensor/lm35.pdf.
[83] Choi M, Park WK, and Lee I. Smart office energy-saving service using bluetooth low energy beacons and smart plugs. Data Science and Data Intensive Systems (DSDIS),
2015 IEEE International Conference on, page 11–13, 2015.
[84] Li M and Lin HJ. Design and implementation of smart home control systems based on wireless sensor networks and power line communications. Industrial Electronics, IEEE Transactions on, 62(7):4430–4442, 2015.
[85] Bell M. A. J. and T. J. Sejnowski. An information maximization approach to blind separation and blind deconvolution. Neural Computation, 8:1129–1159, 1995.
[86] Hou MC, Huang SC, and Wang HM. A computerized system of nail-fold capil- laroscopy for dry eye disease diagnosis. Springer plus, Multidim Syst Sign Process, pages 515–524, 2012.
[87] S. Mirjalili and S. Z. M.Hashim. A new hybrid psogsa algorithm for function opti- mization. International Conference on Computer and Information Application, 2010.
[88] J. M. Morel and G. Yu. A sift.·a new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2:438–469, 2009.
[89] Devi MR and Banu TV. Study of nail unit using image processing methods. 2015 In- ternational Conference on Computer Communication and Informatics(ICCCI-2015), 2015.
[90] Dalal N and Triggs B. Histograms of oriented gradients for human detection. in Computer Vision and Pattern Recognition(CVPR).IEEE Computer Society Conference on, 1:886–893, 2005.
[91] Fujishima N and Hoshino K. Virtual nail art system. 2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE), 2014.
[92] Otsu N. A threshold selection method from gray-level histograms. IEEE Trans. Sys., Man., Cyber., 9(1):62–66, 1979.
[93] N. H. Nguyen, T. K. Lee, and M. S. Atkins. Segmentation of light and dark hair in dermoscopic images: A hybrid approach using a universal kernel. Proc. SPIE, Med. Imag., 76(23), 2010.
[94] F. Nian, W. Li, X. Sun, and M. Li. An improved particle swarm optimization appli- cation to independent component analysis. Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on, 2009.
[95] A. Nickabadi, M. M. Ebadzadeh, and R. Safabakhsh. Dnpso: A dynamic niching par- ticle swarm optimizer for multi-modal optimization. In Proceedings of IEEE Congress on Evolutionary Computation, pages 26–32, 2008.
[96] Kumuda NS and Dinesh MS. Human fingernail segmentation. 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), 2015.
[97] NXP. https://cache.freescale.com/files/sensors/doc/app_note/an4248.pdf.
[98] NXP. http://www.nxp.com/assets/documents/data/en/data-sheets/fxas21002.pdf.
[99] Elma O, Selamogullari, and Ugur S. A home energy management algorithm with smart plug for maximized customer comfort. IEEE Transactions on Energy Conversion, pages 1–4, 2015.
[100] OpenID. http://openid.net/specs/openid-authentication-2_0.html. [101] A. V. Oppenheim and R. W. Schafer. Digital signal processing. 1975. [102] Orvibo. http://www.orvibo.com/en/product/productlist.html.
[103] N. Otsu. A threshold selection method from gray-level histograms. IEEE Trans. Syst.
Man Cybern., 9(1):62–66, 1979.
[104] N. Otsu. A threshold selection method from gray-level histograms. IEEE Trans. Syst., Man, Cybern., 9(1):62–66, 1979.
[105] Chaikan P, Songkhla, and Karnjanadecha M. The use of top-view finger image for personal identification image and signal processing and analysis. ISPA 2007. 5th In- ternational Symposium on 2007, pages 343–346, 2007.
[106] Dolezalova P, Young SP, Bacon PA, and Southwood TR. Nailfold capillary microscopy in healthy children and in childhood rheumatic diseases: a prospective single blind observational study. Ann Rheum Dis., 62:444–449, 2003.
[107] Pattanasethanon P and Attachoo B. A unified histogram and laplacian based for image sharpening. 2009 9th International Symposium on Communications and Information Technology, 2009.
[108] Panasonic. http://www.panasonic.com/nz/consumer/home-monitoring/home- monitoring-accessories/kx-hna101.html.
[109] H. Proença and J. C. Neves. Soft biometrics globally coherent solutions for hair seg- mentation and style recognition based on hierarchical mrfs. IEEE Transactions on Information Forensics and Security, 12(7):1637–1645, 2017.
[110] Housley R. A 224-bit one-way hash function: Sha224. RFC 3874, Internet Engineer- ing Task Force, 2004.
[111] N. Rajagopal, S. Chayapathy, B. Sinopoli, and A. Rowe. Beacon placement for range- based indoor localization. Indoor Positioning and Indoor Navigation (IPIN), 2016 International Conference on., 2016.
[112] S. Rajala and J. Lekkala. Film-type sensor materials pvdf and emfi in measurement of cardiorespiratory signals-a review. IEEE Sensor Journal, pages 439–446, 2012.
[113] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi. Gsa: A gravitational search algo- rithm. Information Sciences, 179(13):2232–2248, 2009.
[114] Gonzalez RC and Woods RE. Digital image processing. Pearson, 3, 2007.
[115] Gisler C Ridi A and Hennebert J. Acs-f2 - a new database of appliance consumption analysis. Proceedings of the International Conference on Soft Computing and Pattern Recognition (SocPar 2014), 2014.
[116] R.Poli, J.Kennedy, and T.Blackwell. Particle swarm optimization: An overview.
Swarm Intelligence, 1(1):33–57, 2007.
[117] E RUBLEE, V RABAUD, and K KONOLIGE. Orb:an efficient alternative to sift or surf. IEEE International Conference on Computer Vision, pages 564–2571, 2011.
[118] J Rui and S. Danpeng. Architecture design of the internet of things based on cloud computing. Measuring Technology and Mechatronics Automation (ICMTMA), 2015 Seventh International Conference on, 2015.
[119] Easwaramoorthy S, Sophia F, and Prathik A. Biometric authentication using finger nails. 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), 2016.
[120] Lambova S and Müller-Ladner U. Capillaroscopic pattern inparaneoplastic raynaud’s phenomenon. Rheumatol Int., 33(6):1597–1599, 2013.
[121] Makeig S., Bell A., Jung T., and Sejnowski T. Independent component analysis of electroencephalographic. Advances in Neural Information Processing System, 8:145– 151, 1996.
[122] Lee S. H. and C. S. Yang. Pso ica with brm for image enhancement. Computer, Consumer and Control (IS3C), 2016 International Symposium on, 2016.
[123] Hsieh S. T., Sun T. Y., Lin C. L., and C. C. Liu. Effective learning rate adjustment of blind source separation based on an improved particle swarm optimizer. Evolutionary Computation, IEEE Transactions on, 12:242 –151, 2008.
[124] Mulay SA, Devale PR, and Garje GV. Image classification system using support vector machine and decision tree. International Journal of Computer Applications, 3:172– 179, 2010.
[125] P. Schmid-Saugeon, J. Guillod, and J.-P. Thiran. Towards a computeraided diagnosis system for pigmented skin lesions. Comput. Med. Imag. Graph., 27(1):65–78, 2003.
[126] Alexa Voice Service. https://developer.amazon.com/alexa-voice-service.
[127] Z Shelby, K Hartke, and Bormann C. The constrained application protocol (coap).
RFC 7252, Internet Engineering Task Force, 2014.
[128] H.-C. Shih. An unsupervised hair segmentation and counting system in microscopy images. IEEE Sensors Journal, 15(6):3565–3572, 2015.
[129] D. Smith, J. Lukasiak, and I. S. Burnett. An analysis of the limitations of blind signal separation application with speech. Signal Processing, 86:353–359, 2006.
[130] C. Soell, L. Shi, J. Roeber, M. Reichenbach, R. Weigel, and A. Hagelauer. Low-power analog smart camera sensor for edge detection. 2016 IEEE International Conference on Image Processing (ICIP), pages 4408–4412, 2016.
[131] SSDP. https://tools.ietf.org/html/draft-cai-ssdp-v1-03.
[132] Sultana, Madeena, Paul PP, and Gavrilova M. A concept of social behavioral bio- metrics: Motivation, current developments, and future trends. In Cyberworlds (CW), 2014 International Conference on, pages 271–278, 2014.
[133] M. Sund-Levander, Forsberg C., and L.K. Wahren. Normal oral, rectal, tympanic and axillary body temperature in adult men and women: a systematic literature review. Scand J Caring Sci., 2002.
[134] M. Svanera, U. R. Muhammad, R. Leonardi, and S. Benini. Figaro, hair detection and segmentation in the wild. Image Processing (ICIP), 2016 IEEE International Conference on, 2016.
[135] M. J. Swain and D. H. Ballard. Color indexing. International Journal of Computer Vision, 7(1):11–32, 1991.
[136] Ahonen T, Hadid A, and Pietik M. Face description with local binary patterns: ap- plication to face recognition. IEEE transactions on pattern analysis and machine intelligence, 28(12):2037–2041, 2006.
[137] Gross T. Security analysis of the saml single sign-on browser/artifact profile. Com- puter Security Applications Conference 2003. Proceedings. 19th Annual, 2003.
[138] Ojala T, Pietik M, and Harwood D. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 21(9):51–59, 1996.
[139] Ojala T, Pietikainen M, and Maenpaa T. Multiresolution gray-scale and rotation in- variant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):971–987, 2002.
[140] Ince TA, Elma O, Selamoğulları SU, Partal PH, and Vural B. Data reliability and la- tency test for zigbee-based smart home energy management systems. 7th International Ege Energy Symposium and Exhibition, 2014.
[141] E. G Talbi. A taxonomy of hybrid metaheuristic. Journal of Heuristics, 8(5):541–546, 2002.
[142] TIDUAY. http://www.ti.com/lit/ug/tiduay7/tiduay7.pdf.
[143] NVidia Jetson TK1. http://www.nvidia.com/object/jetson-tk1-dev-kit.html.
[144] NVidia Jetson TK1. http://www.nvidia.com/object/jetson-tk1-embedded-dev- kit.html.
[145] TP-Link. http://www.tp-link.com/us/products/details/cat-5516_hs100.html.
[146] Sakazawa S Tsuda A, Ueno S and Hangai S. A method for extraction of nail area under varying luminance for the 3d nail art system on mobile phones. IEICE Technical Committee. PRMU, 110(381):59–64, 2011.
[147] NVidia Jetson TX1. http://www.nvidia.com/object/jetson-tx1-dev-kit.html.
[148] A. Unler and A. Murat. A discrete particle swarm optimization method for feature se- lection in binary classification problems. European Journal of Operational Research, 206(3):528–539, 2010.
[149] Arduino UNO. https://www.arduino.cc/en/main/arduinoboarduno. [150] UPnP. https://openconnectivity.org/resources/specifications/upnp.
[151] URI. https://www.w3.org/tr/uri-clarification/.
[152] Machine vision lense. http://www.tokina.co.jp/en/security/machine-vision- lenses/tc2514-3mp.html.
[153] D. Wang, S. Shan, H. Zhang, W. Zeng, and X. Chen. Isomorphic manifold inference for hair segmentation. in Proc. 10th IEEE Automat. Face Gesture Recognit, pages 1–6, 2013.
[154] H. Wang, H. Sun, C. Li, S. Rahnamayan, and J.S. Pan. Diversity enhanced particle swarm optimization with neighborhood search. Information Sciences, 223(0):119– 135, 2013.
[155] L. Wang, K. Zhou Y. Yu, and B. Guo. Example-based hair geometry synthesis. in Proc. ACM SIGGRAPH, 2009.
[156] Y. C. Wang, J. C. Lin, and S. F. Chiu. The automatic image inspection system for measuring dimensionalparameters of a saw blade. Control and Automation (ICCA), 2010 8th IEEE International Conference on, pages 1557–1561, 2010.
[157] C. Xiansheng. An edge detection new algorithm based on laplacian operator. Com- munication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on, pages 202–206, 2011.
[158] Iwafune Y, Ikegami T, Fonseca JrSDGJ, Oozeki T, and Ogimoto K. Cooperative home energy management using batteries for a photovoltaic system considering the diversity of households. Energy Conversion and Management, 96:322–329, 2015.
[159] Lecun Y, Bottou L, Bengio Y, and Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 1998.
[160] Thongkhao Y and Pora W. A low-cost wi-fi smart plug with on-off and energy me- tering functions. Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2016 13th International Conference on, 2016.
[161] M. Yaron, R.D. Paterson, and C.B. Davis. High-altitude medicine. Rosen’s Emergency Medicine: Concepts and Clinical Practice. 8th ed., 2014.
[162] C.-K. Yeh, P. K. Jayaraman, X. Liu, C.-W. Fu, and T.-Y. Lee. 2.5d cartoon hair mod- eling and manipulation. IEEE Transactions on Visualization and Computer Graphics, 21(3):304–314, 2015.
[163] J. Yu and L. Li. A vivid visual emotion synthesis system: From face to hair. Signal Processing (ICSP), 2016 IEEE 13th International Conference on, 2016.
[164] Z.-H. Zhan, J. Zhang, Y. Li, and S.Y. Hui. Orthogonal learning particle swarm opti- mization. IEEE Transactions on Evolutionary Computation, 15(6):832–847, 2011.
[165] Sanchez ZS, Fernandez-Canti MR, and Lazaro AJ. Monitoring and remote control of energy consumption by wifi networks. Systems, Signals and Devices (SSD), 2014 11th International Multi-Conference on, 2014.
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