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CONTENTS
Volume 8, Number 1, July 2011
 

Abstract
Cerebral aneurysm is common lesion among adult population. Current methods for treating the disease have several limitations. Inspired by fern leaves, we have developed a new stent, called leaf stent,which can provide a tailored coverage at the neck of an aneurysm and thus prevent the blood from entering the aneurysm. It alone can be used to treat the cerebral aneurysm and therefore overcomes problems existing in current treating methods. The paper focuses on the numerical simulation of the leaf stents. The mechanical behaviour of the stent in various designs has been investigated using the finite element method. It has been found that certain designs provide adequate radial force and have excellent longitudinal flexibility. The performance of certain leaf stents is comparable and even superior to those of the commercially available cerebral stents such as the Neuroform stent and the Enterprise stent, commonly used for stent assisted coiling, while at the same time, providing sufficient coverage to isolate the aneurysm without using coils.

Key Words
cerebral aneurysm; leaf stent; nitinol; FEA (or finite element analysis); radial force; longitudinal flexibility; stent-artery interaction.

Address
Xiang Zhou and Zhong You: 1Department of Engineering Science, University of Oxford, UK, OX3 7DQ
James Byrne, M.D.: Department of Neuroradiology, Nuffield Department of Surgery, University of Oxford, UK, OX3 9DU

Abstract
The microcantilever (MCL) sensor is one of the most promising platforms for next-generation label-free biosensing applications. It outperforms conventional label-free detection methods in terms of portability and parallelization. In this paper, an overview of recent advances in our understanding of the coupling between biomolecular interactions and MCL responses is given. A dual compact optical MCL sensing platform was built to enable biosensing experiments both in gas-phase environments and in solutions. The thermal bimorph effect was found to be an effective nanomanipulator for the MCL platform calibration. The study of the alkanethiol self-assembly monolayer (SAM) chain length effect revealed that 1-octanethiol (C8H17SH) induced a larger deflection than that from 1-dodecanethiol (C12H25SH) in solutions. Using the clinically relevant biomarker Creactive protein (CRP), we revealed that the analytical sensitivity of the MCL reached a diagnostic level of 1~500

Key Words
microcantilever (MCL); label-free; biomolecular interaction; biosensing; self-assembly; antigen; microfluidics; x-ray photoelectron spectroscopy (XPS); multiscale modeling.

Address
Chuin-Shan Chen, Tzu-Hsuan Chang, Chia-Ching Chou and
Shu-Wei Chang: Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan
Shu Kuan and Long-Sun Huang: Institute of Applied Mechanics, National Taiwan University, Taipei 10617, Taiwan

Abstract
We are developing a biomimetic robot based on the Sea Lamprey. The robot consists of a ylindrical electronics bay propelled by an undulatory body axis. Shape memory alloy (SMA) actuators enerate propagating flexion waves in five undulatory segments of a polyurethane strip. The behavior of the robot is controlled by an electronic nervous system (ENS) composed of networks of discrete-time map-based neurons and synapses that execute on a digital signal processing chip. Motor neuron action potentials gate power transistors that apply current to the SMA actuators. The ENS consists of a set of segmental centralpattern generators (CPGs), modulated by layered command and coordinating neuron networks, that integrate input from exteroceptive sensors including a compass, accelerometers, inclinometers and a short baseline sonar array (SBA). The CPGs instantiate the 3-element hemi-segmental network model established from physiological studies. Anterior and posterior propagating pathways between CPGs mediate intersegmental coordination to generate flexion waves for forward and backward swimming. The command network mediates layered exteroceptive reflexes for homing, primary orientation, and impediment compensation. The SBA allows homing on a sonar beacon by indicating deviations in azimuth and inclination. Inclinometers actuate a bending segment between the hull and undulator to allow climb and dive. Accelerometers can distinguish collisions from impediment to allow compensatory reflexes. Modulatory commands mediate speed control and turning. A SBA communications interface is being developed to allow supervised reactive autonomy.

Key Words
central pattern generator; biomimetic; autonomy; electronic neurons; robotics.

Address
A Westphal and J Ayers:Department of Biology and Marine Science Center, Northeastern University, East Point,
Nahant, MA 01908, USA
N.F. Rulkov: Information Systems Laboratories, Inc., 10070 Barnes Canyon Road, San Diego CA 92121, USA, Institute for Nonlinear Science, UCSD, 9500 Gilman Dr., La Jolla, CA 92093, USA
D Brady: Department of Electrical and Computer Engineering, Northeastern University, 360 Huntington Ave.Boston, MA 02115, USA
M Hunt: Ariel Inc. Santa Ysabel, CA 92070, USA

Abstract
Human state in human-machine systems highly affects the overall system performance, and should be detected and monitored. Physiological cues are essential indicators of human state and useful for the purpose of monitoring. The study presented in this paper was focused on developing a bio-inspired sensing system, i.e., Nano-Skin, to non-intrusively measure physiological cues on human-machine contact surfaces to detect human state. The paper is presented in three parts. The first part is to analyze the relationship between human state and physiological cues, and to introduce the conceptual design of Nano-Skin. Generally, heart rate, skin conductance, skin temperature, operating force, blood alcohol concentration, sweat rate, and electromyography are closely related with human state. They can be measured through human-machine contact surfaces using Nano-Skin. The second part is to discuss the technologies for skin temperature measurement. The third part is to introduce the design and manufacture of the Nano-Skin for skin temperature measurement. Experiments were performed to verify the performance of the Nano-Skin in temperature measurement. Overall, the study concludes that Nano-Skin is a promising product for measuring physiological cues on human-machine contact surfaces to detect human state.

Key Words
human-machine contact; human state detection; physiological cues; sensors; MEMS; NEMS.

Address
Hongjie Leng and Yingzi Lin: Department of Mechanical & Industrial Engineering, Northeastern University, Boston, USA

Abstract
This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immunenetwork-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

Key Words
emergent pattern recognition; immune network theory; hierarchical clustering; artificial immune systems.

Address
Bo Chen: Department of Mechanical Engineering - Engineering Mechanics, Michigan Technological University, 815 R.L. Smith Building, 1400 Townsend Drive, Houghton, MI 49931, USA, Department of Electrical and Computer Engineering, Michigan Technological University, USA
Chuanzhi Zang: Department of Mechanical Engineering - Engineering Mechanics, Michigan Technological University,
815 R.L. Smith Building, 1400 Townsend Drive, Houghton, MI 49931, USA, Shenyang Institute of Automation, Chinese Academy of Science, Nanta Street 114, Shenyang, Liaoning, P.R. China, 110016

Abstract
This paper presents a flexible low-profile antenna sensor for fatigue crack detection and monitoring. The sensor was inspired by the sense of pain in bio-systems as a protection mechanism. Because the antenna sensor does not need wiring for power supply or data transmission, it is an ideal candidate as sensing elements for the implementation of engineering sensor skins with a dense sensor distribution. Based on the principle of microstrip patch antenna, the antenna sensor is essentially an electromagnetic cavity that radiates at certain resonant frequencies. By implementing a metallic structure as the ground plane of the antenna sensor, crack development in the metallic structure due to fatigue loading can be detected from the resonant frequency shift of the antenna sensor. A monostatic microwave radar system was developed to interrogate the antenna sensor remotely. Fabrication and characterization of the antenna sensor for crack monitoring as well as the implementation of the remote interrogation system are presented.

Key Words
sensor skin; antenna sensor; wireless sensor; passive sensor; sensor; microwave radar.

Address
Srikar Deshmukh, Xiang Xu, Irshad Mohammad and Haiying Huang: Department of Mechanical and Aerospace Engineering, University of Texas at Arlington, USA

Abstract
Biological sensory systems continuously monitor and analyze changes in real-world environments that are relevant to an animal

Key Words
adaptive filtering; bio-inspired; multi-scale; neurobiology; sensory system.

Address
Mark E. Nelson: Department of Molecular & Integrative Physiology, Neuroscience Program, Beckman Institute,
University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

Abstract
A bio-inspired two-mode structural health monitoring (SHM) system based on the Naive Bayes(NB) classification method is discussed in this paper. To implement the molecular biology based Deoxyribonucleic acid (DNA) array concept in structural health monitoring, which has been demonstrated to be superior in disease detection, two types of array expression data have been proposed for the development of the SHM algorithm. For the micro-vibration mode, a two-tier auto-regression with exogenous (AR-ARX) process is used to extract the expression array from the recorded structural time history while an ARX process is applied for the analysis of the earthquake mode. The health condition of the structure is then determined using the NB classification method. In addition, the union concept in probability is used to improve the accuracy of the system. To verify the performance and reliability of the SHM algorithm, a downscaled eight-storey steel building located at the shaking table of the National Center for Research on Earthquake Engineering (NCREE) was used as the benchmark structure. The structural response from different damage levels and locations was collected and incorporated in the database to aid the structural health monitoring process. Preliminary verification has demonstrated that the structure health condition can be precisely detected by the proposed algorithm. To implement the developed SHM system in a practical application, a SHM prototype consisting of the input sensing module, the transmission module, and the SHM platform was developed. The vibration data were first measured by the deployed sensor, and subsequently the SHM mode corresponding to the desired excitation is chosen automatically to quickly evaluate the health condition of the structure. Test results from the ambient vibration and shaking table test showed that the condition and location of the benchmark structure damage can be successfully detected by the proposed SHM prototype system, and the information is instantaneously transmitted to a remote server to facilitate real-time monitoring. Implementing the bio-inspired two-mode SHM practically has been successfully demonstrated.

Key Words
structural health monitoring; naive bayes; bio-inspired; prototype system.

Address
Tzu-Kang Lin: National Center for Research on Earthquake Engineering, Taiwan
Li-Chen Yu, Chang-Hung Ku and Kuo-Chun Chang: Department of Civil Engineering, National Taiwan University, Taiwan
Anne Kiremidjian: Department of Civil and Environmental Engineering, Stanford University, USA

Abstract
Polymeric thin-film assemblies whose bulk electrical conductivity and mechanical performance have been enhanced by single-walled carbon nanotubes are proposed for measuring strain and corrosion activity in metallic structural systems. Similar to the dermatological system found in animals, the proposed self-sensing thin-film assembly supports spatial strain and pH sensing via localized changes in electrical conductivity. Specifically, electrical impedance tomography (EIT) is used to create detailed mappings of film conductivity over its complete surface area using electrical measurements taken at the film boundary. While EIT is a powerful means of mapping the sensing skin

Key Words
structural health monitoring; sensing skin; wireless sensor; carbon nanotube; bio-inspired sensing; impedance tomography.

Address
Sukhoon Pyo: Dept. of Civil & Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Kenneth J. Loh: Dept. of Civil & Environmental Engineering, University of California, Davis, CA 95616, USA
Tsung-Chin Hou: Dept. of Civil Engineering, National Cheng Kung University(NCKU), Tainan, Taiwan
Erik Jarva: Dept. of Electrical Engineering & Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
Jerome P. Lynch: Dept. of Civil & Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA, Dept. of Electrical Engineering & Computer Science, University of Michigan, Ann Arbor, MI 48109, USA


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