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1400 JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 the similar research about land ecology in the areas where the land ecology may suffer damage should be done. ACKNOWLEDGMENT The author wishes to thank Hangzhou municipal bureau of land and resources for providing the origin data. This work was supported in part by a grant from National Natural Science Foundation of China (No.70973047). REFERENCES [1] K. S. Rogers, “Ecological security and multinational corporations,” Environmental Change and Security Project (ECSP) Report, 1997. [2] X. Wang, Z. Li, C. Cai, Z. Shi, Q. Xu, Z. Fu, and Z. Guo, “Effects of rock fragment cover on hydrological response and soilloss from Regosols in a semi-humid environment in South-West China,” Geomorphology, pp. 234–242, vol. 151–152, May 2012. [3] R. Tan, F. Qu, N. Heerink, and E. Mettepenningen “Rural to Urban Land Conversion in China-How Large is the Over-conversion and What are its Welfare Implications,” China Economic Review, vol.22, pp. 474–484, December 2011. [4] J. Sorvari, R. Antikainen, M. Kosola, P. Hokkanen, and T. Haavisto, “Eco-efficiency in contaminated land management in Finland – Barriers and development needs”, Journal of Environmental Management, vol.90, pp. 1715–172, April 2009. [5] J. S. Brandt, T. Kuemmerle, H. Li, G. Ren, J. Zhu, and V. C. Radeloff, “Using Landsat imagery to map forest change in southwest China in response to the national logging ban and ecotourism development,” Remote Sensing of Environment, vol. 121, , pp. 358–369, June 2012. [6] S. Su, X. Chen, S. D. DeGloria, and J. Wu, “Integrative fuzzy set pair model for land ecological security assessment: a case study of Xiaolangdi Reservoir Region, China,” Stochastic Environment Research and Risk Assessment, vol. 24, pp. 639–647, May 2010. [7] J. J. Ewel, “Natural systems as models for the design of sustainable systems of land use,” Agroforestry Systems, vol. 45, pp.1-21, January 1999. [8] C. Walter and H. Stützel, “A new method for assessing the sustainability of land-use systems (II): Evaluating impact indicators,” Ecological Indicators, Ecological Economics, vol. 68, pp. 1288–1300, March 2009. [9] Y. Y. YIN; and J. T. PIERCE, “Integrated resource assessment and sustainable land-use,” Environmental Management, vol. 17, pp. 319–327, May-Jun 1993. [10] V. H. D. Zuazo, C. R. R. Pleguezuelo, D. Flanagan, I. G. Tejero, and J. L. M. Fernández, “Sustainable land use and agricultural soil,” Alternative Farming Systems, Biotechnology, Drought Stress and Ecological Fertilisation Sustainable Agriculture Reviews, vol. 6, pp.107-192, 2011. [11] A. Cooper, T. Shine, T. McCann, and D.A. Tidane, “An ecological basis for sustainable land use of Eastern Mauritanian wetlands,” Journal of Arid Environments, vol. 67, pp. 116–141 October 2006. [12] S. Su, D. Li, X. Yu, Z. Zhang, Q. Zhang, R. Xiao, J. Zhi, and J. Wu, “Assessing land ecological security in Shanghai (China) based on catastrophe theory,” Stochastic Environment Research and Risk Assessment, vol. 25, pp. 737–746, June 2011. [13] D. Svozil, V. Kvasnicka, and J. Pospichal, “Introduction to multi-layer feed-forward neural networks,” Chemometrics and Intelligent Laboratory Systems, vol. 39, pp.43–62, November 1997. [14] J. He; Z.He, ; D.Zou, and Y. Xia, “A BP neural network method for RNA secondary structure prediction based on ENSSEL labels,” Journal of Computers, vol. 6, pp. 569-576, April 2009. [15] F. Zhang, P. Li, Z. Hou, Z. Lu, Y. Chen, Q. Li, and M. Tan a “sEMG-based continuous estimation of joint angles of human legs by using BP neural network,” Neurocomputing, vol. 78, pp.139–148, February 2012. [16] W. Xiang, Y. Gu, and D. Ge, “Testing of rounded corner for micro-drill on hybrid of BP neural network and adaptive particle swarm optimization,” Journal of Computers, vol. 7, pp. 1116-1121, May 2012. [17] OECD, Environmental indicators. OECD core set, Organization for Economic Co-operation and Development, Paris, 1994. [18] B. Wolfslehner and H. Vacik, “Evaluating sustainable forest management strategies with the Analytic Network Process in a Pressure-State-Response framework,” Journal of Environmental Management, vol. 88, pp.1–10, July 2008. [19] A. J. J. Lynch, “The usefulness of a threat and disturbance categorization developed for Queensland Wetlands to environmental management, monitoring, and evaluation,” Environmental Management, Vol. 47, pp.40-55, January 2011. [20] D. Howard and M. Beale, Neural Network Toolbox for Use with MATLAB, User’s Guide, version 4. The Math Works, Inc., Natick, MA, 2000. [21] H. Demuth and M. Beale, Neural Network Toolbox: For Use with Matlab. Mathworks, Inc., Natick, MA ,2003. Heyuan You, he was born in Wenzhou City, Zhejiang Province of China in 1983. He received the PhD degree in land resource management from Institute of Land Science and Property Management, Zhejiang University in 2012. He is currently working as a lecturer in the College of Business Administration, Zhejiang University of Finance and Economics, Zhejiang, China. His research area centers on land use simulation and land ecology management. © 2013 ACADEMY PUBLISHER
JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 1401 Magellan: Technical Description of a New System for Robot-Assisted Nerve Blocks Joshua Morse ∗ , Mohamad Wehbe ‡ , Riccardo Taddei † , Shantale Cyr § , and Thomas M. Hemmerling § ∗ Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada email: joshua.morse@mail.mcgill.ca ‡ Department of Experimental Surgery, McGill University, Montreal, QC, Canada † Department of Anesthesiology, University of Pisa, Pisa, Italy § Department of Anesthesia, McGill University, Montreal, QC, Canada Institute of Biomedical Engineering, University of Montreal, Montreal, QC, Canada email: thomas.hemmerling@mcgill.ca 1 Abstract—Nerve blocks are common procedures used to remove sensation from a specific region of the body via injection of local anesthetic. Ultrasound-guided nerve blocks are common-place in anesthesia, but require specialized training and advanced bi-manual dexterity. This paper describes a system designed to robotically assist in ultrasoundguided nerve blocks. Robot-assisted nerve blocks could allow for more precise needle placement, and therefore a higher efficacy of blocks. This system is the first step in developing a completely automated nerve block system, which would also require the incorporation of ultrasound image recognition of nerves and other physiological markers. Index Terms—Regional anesthesia, nerve blocks, robotic anesthesia. I. INTRODUCTION NERVE blocks are a procedure of regional anesthesia used to remove the sensitivity from an area of the body via the injection of an anesthetic drug into the nerve innervating the target area. Nerve blocks were first used in surgery in 1885 [1] and are now a common procedure performed routinely around the world. Performing regional nerve blocks requires special training. Anesthesiologists performing regional nerve blocks only on an occasional basis have a significant failure rate, as high as 45% [2]. Most regional blocks are performed using ultrasound guidance; this necessitates careful bimanual operation of the ultrasound probe and the nerve block needle. Precise movement of the needle is important for successful blocks. One centimeter movement in any direction can make the difference between a failed and a successful block. Mechanical robots have been used in surgery for more than 10 years, the da Vinci Surgical System (Intuitive Surgical, Inc., Sunnyvale, CA) being the latest. These mechanical robots are shown to increase precision of movements and improve outcome [3]. Recently, Tighe et al. have used the da Vinci Surgical System to perform successful nerve blocks in an ultrasound phantom [4]. We present the first robotic system, called Magellan, designed specifically to perform routine nerve blocks. II. MATERIALS AND METHODS The Magellan system is designed to perform robotassisted, ultrasound-guided nerve blocks. The system has 4 primary components: a standard nerve block needle and syringe mounted via a custom clamp to a robotic arm (JACO robotic arm, Kinova, Montreal, QC, Canada), an ultrasound machine, a joystick (ThrustMaster T.Flight Hotas X, Guillemot Inc., New York, NY, USA), and a software control system. The system is designed to work with any ultrasound machine with a video output. The ultrasound video signal is captured via a USB video capture device (Dazzle DVC100, Pinnacle Systems, Mountain View, CA, USA). The software system is designed on a client/server model so that nerve blocks can be performed remotely. Both the client and server programs were written in C# and communicate using UDP/IP. The client software interfaces with the ultrasound machine, robotic arm, and a webcam (Lifecam HD, Microsoft Corporation, Redmond, WA, USA). The ultrasound and webcam video feeds are streamed from the client to the server, where they are displayed in a graphical user interface (GUI) created in LabView (National Instruments, Austin, TX, USA). The webcam is positioned in order to provide a direct view of the target nerve insertion area and the ultrasound probe. The server software interfaces with the joystick and transmits the joystick commands to the client over the IP network. The client and server, as well as their software subsystems, are detailed in Fig. 1. Further explanation of the individual subsystems of both applications are presented below. A. Software Control System 1) Server Application: The Controller Subsystem implements an interface that decouples the precise controller’s driver from the system, allowing for the controller to be easily changed. This subsystem reads the state of the controller and provides it to the Server Networking Subsystem. The Server Networking Subsystem is responsible for © 2013 ACADEMY PUBLISHER doi:10.4304/jcp.8.6.1401-1405
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Call for Papers and Special Issues
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(Contents Continued from Back Cover
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