WorldWideScience

Sample records for neural mems applications

  1. MEMS for automotive and aerospace applications

    CERN Document Server

    Kraft, Michael

    2013-01-01

    MEMS for automotive and aerospace applications reviews the use of Micro-Electro-Mechanical-Systems (MEMS) in developing solutions to the unique challenges presented by the automotive and aerospace industries.Part one explores MEMS for a variety of automotive applications. The role of MEMS in passenger safety and comfort, sensors for automotive vehicle stability control applications and automotive tire pressure monitoring systems are considered, along with pressure and flow sensors for engine management, and RF MEMS for automotive radar sensors. Part two then goes on to explore MEMS for

  2. MEMS for Space Flight Applications

    Science.gov (United States)

    Lawton, R.

    1998-01-01

    Micro-Electrical Mechanical Systems (MEMS) are entering the stage of design and verification to demonstrate the utility of the technology for a wide range of applications including sensors and actuators for military, space, medical, industrial, consumer, automotive and instrumentation products.

  3. Piezoelectric MEMS: Ferroelectric thin films for MEMS applications

    Science.gov (United States)

    Kanno, Isaku

    2018-04-01

    In recent years, piezoelectric microelectromechanical systems (MEMS) have attracted attention as next-generation functional microdevices. Typical applications of piezoelectric MEMS are micropumps for inkjet heads or micro-gyrosensors, which are composed of piezoelectric Pb(Zr,Ti)O3 (PZT) thin films and have already been commercialized. In addition, piezoelectric vibration energy harvesters (PVEHs), which are regarded as one of the key devices for Internet of Things (IoT)-related technologies, are promising future applications of piezoelectric MEMS. Significant features of piezoelectric MEMS are their simple structure and high energy conversion efficiency between mechanical and electrical domains even on the microscale. The device performance strongly depends on the function of the piezoelectric thin films, especially on their transverse piezoelectric properties, indicating that the deposition of high-quality piezoelectric thin films is a crucial technology for piezoelectric MEMS. On the other hand, although the difficulty in measuring the precise piezoelectric coefficients of thin films is a serious obstacle in the research and development of piezoelectric thin films, a simple unimorph cantilever measurement method has been proposed to obtain precise values of the direct or converse transverse piezoelectric coefficient of thin films, and recently this method has become to be the standardized testing method. In this article, I will introduce fundamental technologies of piezoelectric thin films and related microdevices, especially focusing on the deposition of PZT thin films and evaluation methods for their transverse piezoelectric properties.

  4. NeuroMEMS: Neural Probe Microtechnologies

    Directory of Open Access Journals (Sweden)

    Sam Musallam

    2008-10-01

    Full Text Available Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer’s, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultralong multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies.

  5. MEMS applications in space exploration

    Science.gov (United States)

    Tang, William C.

    1997-09-01

    Space exploration in the coming century will emphasize cost effectiveness and highly focused mission objectives, which will result in frequent multiple missions that broaden the scope of space science and to validate new technologies on a timely basis. MEMS is one of the key enabling technology to create cost-effective, ultra-miniaturized, robust, and functionally focused spacecraft for both robotic and human exploration programs. Examples of MEMS devices at various stages of development include microgyroscope, microseismometer, microhygrometer, quadrupole mass spectrometer, and micropropulsion engine. These devices, when proven successful, will serve as models for developing components and systems for new-millennium spacecraft.

  6. MEMS fundamental technology and applications

    CERN Document Server

    Choudhary, Vikas

    2013-01-01

    "The book editors have managed to assemble a group of extraordinary authors to provide their expertise to this book. While giving an excellent overview of the history and the state of the art of MEMS technology, this book also focuses on current trends and topics such as gyroscopes that currently experience significant and increasing popularity in research and in industry. It is well written and the material is presented in a well-structured way making it easily accessible to any reader with a technical background."-Boris Stoeber, The University of British Columbia, Vancouver, Canada.

  7. Partially flexible MEMS neural probe composed of polyimide and sucrose gel for reducing brain damage during and after implantation

    International Nuclear Information System (INIS)

    Jeon, Myounggun; Yoon, Eui-Sung; Cho, Il-Joo; Cho, Jeiwon; Jung, Dahee; Kim, Yun Kyung; Shin, Sehyun

    2014-01-01

    This paper presents a flexible microelectromechanical systems (MEMS) neural probe that minimizes neuron damage and immune response, suitable for chronic recording applications. MEMS neural probes with various features such as high electrode densities have been actively investigated for neuron stimulation and recording to study brain functions. However, successful recording of neural signals in chronic application using rigid silicon probes still remains challenging because of cell death and macrophages accumulated around the electrodes over time from continuous brain movement. Thus, in this paper, we propose a new flexible MEMS neural probe that consists of two segments: a polyimide-based, flexible segment for connection and a rigid segment composed of thin silicon for insertion. While the flexible connection segment is designed to reduce the long-term chronic neuron damage, the thin insertion segment is designed to minimize the brain damage during the insertion process. The proposed flexible neural probe was successfully fabricated using the MEMS process on a silicon on insulator wafer. For a successful insertion, a biodegradable sucrose gel is coated on the flexible segment to temporarily increase the probe stiffness to prevent buckling. After the insertion, the sucrose gel dissolves inside the brain exposing the polyimide probe. By performing an insertion test, we confirm that the flexible probe has enough stiffness. In addition, by monitoring immune responses and brain histology, we successfully demonstrate that the proposed flexible neural probe incurs fivefold less neural damage than that incurred by a conventional silicon neural probe. Therefore, the presented flexible neural probe is a promising candidate for recording stable neural signals for long-time chronic applications. (paper)

  8. Neural Networks Integrated Circuit for Biomimetics MEMS Microrobot

    Directory of Open Access Journals (Sweden)

    Ken Saito

    2014-06-01

    Full Text Available In this paper, we will propose the neural networks integrated circuit (NNIC which is the driving waveform generator of the 4.0, 2.7, 2.5 mm, width, length, height in size biomimetics microelectromechanical systems (MEMS microrobot. The microrobot was made from silicon wafer fabricated by micro fabrication technology. The mechanical system of the robot was equipped with small size rotary type actuators, link mechanisms and six legs to realize the ant-like switching behavior. The NNIC generates the driving waveform using synchronization phenomena such as biological neural networks. The driving waveform can operate the actuators of the MEMS microrobot directly. Therefore, the NNIC bare chip realizes the robot control without using any software programs or A/D converters. The microrobot performed forward and backward locomotion, and also changes direction by inputting an external single trigger pulse. The locomotion speed of the microrobot was 26.4 mm/min when the step width was 0.88 mm. The power consumption of the system was 250 mWh when the room temperature was 298 K.

  9. MEMS Micro-Valve for Space Applications

    Science.gov (United States)

    Chakraborty, I.; Tang, W. C.; Bame, D. P.; Tang, T. K.

    1998-01-01

    We report on the development of a Micro-ElectroMechanical Systems (MEMS) valve that is designed to meet the rigorous performance requirements for a variety of space applications, such as micropropulsion, in-situ chemical analysis of other planets, or micro-fluidics experiments in micro-gravity. These systems often require very small yet reliable silicon valves with extremely low leak rates and long shelf lives. Also, they must survive the perils of space travel, which include unstoppable radiation, monumental shock and vibration forces, as well as extreme variations in temperature. Currently, no commercial MEMS valve meets these requirements. We at JPL are developing a piezoelectric MEMS valve that attempts to address the unique problem of space. We begin with proven configurations that may seem familiar. However, we have implemented some major design innovations that should produce a superior valve. The JPL micro-valve is expected to have an extremely low leak rate, limited susceptibility to particulates, vibration or radiation, as well as a wide operational temperature range.

  10. MEMS-Reconfigurable Metamaterials and Antenna Applications

    Directory of Open Access Journals (Sweden)

    Tomislav Debogovic

    2014-01-01

    Full Text Available This paper reviews some of our contributions to reconfigurable metamaterials, where dynamic control is enabled by microelectromechanical systems (MEMS technology. First, we show reconfigurable composite right-/left-handed transmission lines (CRLH-TLs having state of the art phase velocity variation and loss, thereby enabling efficient reconfigurable phase shifters and leaky-wave antennas (LWA. Second, we present very low loss metasurface designs with reconfigurable reflection properties, applicable in reflectarrays and partially reflective surface (PRS antennas. All the presented devices have been fabricated and experimentally validated. They operate in X- and Ku-bands.

  11. Glassy carbon MEMS for novel origami-styled 3D integrated intracortical and epicortical neural probes

    Science.gov (United States)

    Goshi, Noah; Castagnola, Elisa; Vomero, Maria; Gueli, Calogero; Cea, Claudia; Zucchini, Elena; Bjanes, David; Maggiolini, Emma; Moritz, Chet; Kassegne, Sam; Ricci, Davide; Fadiga, Luciano

    2018-06-01

    We report on a novel technology for microfabricating 3D origami-styled micro electro-mechanical systems (MEMS) structures with glassy carbon (GC) features and a supporting polymer substrate. GC MEMS devices that open to form 3D microstructures are microfabricated from GC patterns that are made through pyrolysis of polymer precursors on high-temperature resisting substrates like silicon or quartz and then transferring the patterned devices to a flexible substrate like polyimide followed by deposition of an insulation layer. The devices on flexible substrate are then folded into 3D form in an origami-fashion. These 3D MEMS devices have tunable mechanical properties that are achieved by selectively varying the thickness of the polymeric substrate and insulation layers at any desired location. This technology opens new possibilities by enabling microfabrication of a variety of 3D GC MEMS structures suited to applications ranging from biochemical sensing to implantable microelectrode arrays. As a demonstration of the technology, a neural signal recording microelectrode array platform that integrates both surface (cortical) and depth (intracortical) GC microelectrodes onto a single flexible thin-film device is introduced. When the device is unfurled, a pre-shaped shank of polyimide automatically comes off the substrate and forms the penetrating part of the device in a 3D fashion. With the advantage of being highly reproducible and batch-fabricated, the device introduced here allows for simultaneous recording of electrophysiological signals from both the brain surface (electrocorticography—ECoG) and depth (single neuron). Our device, therefore, has the potential to elucidate the roles of underlying neurons on the different components of µECoG signals. For in vivo validation of the design capabilities, the recording sites are coated with a poly(3,4-ethylenedioxythiophene)—polystyrene sulfonate—carbon nanotube composite, to improve the electrical conductivity of the

  12. Adaptive Global Sliding Mode Control for MEMS Gyroscope Using RBF Neural Network

    Directory of Open Access Journals (Sweden)

    Yundi Chu

    2015-01-01

    Full Text Available An adaptive global sliding mode control (AGSMC using RBF neural network (RBFNN is proposed for the system identification and tracking control of micro-electro-mechanical system (MEMS gyroscope. Firstly, a new kind of adaptive identification method based on the global sliding mode controller is designed to update and estimate angular velocity and other system parameters of MEMS gyroscope online. Moreover, the output of adaptive neural network control is used to adjust the switch gain of sliding mode control dynamically to approach the upper bound of unknown disturbances. In this way, the switch item of sliding mode control can be converted to the output of continuous neural network which can weaken the chattering in the sliding mode control in contrast to the conventional fixed gain sliding mode control. Simulation results show that the designed control system can get satisfactory tracking performance and effective estimation of unknown parameters of MEMS gyroscope.

  13. MEMS Sensor Arrays for Cryogenic Propellant Applications, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — KWJ offers this proposal for a low-power, practical and versatile MEMS sensor platform for NASA applications. The proposed nano-sensor platform is ultra-low power...

  14. MEMS and MOEMS for national security applications

    Science.gov (United States)

    Scott, Marion W.

    2003-01-01

    Major opportunities for microsystem insertion into commercial applications, such as telecommunications and medical prosthesis, are well known. Less well known are applications that ensure the security of our nation, the protection of its armed forces, and the safety of its citizens. Microsystems enable entirely new possibilities to meet National Security needs, which can be classed along three lines: anticipating security needs and threats, deterring the efficacy of identified threats, and defending against the application of these threats. In each of these areas, specific products that are enabled by MEMS and MOEMS are discussed. In the area of anticipating needs and threats, sensored microsystems designed for chem/bio/nuclear threats, and sensors for border and asset protection can significantly secure our borders, ports, and transportation systems. Key features for these applications include adaptive optics and spectroscopic capabilities. Microsystems to monitor soil and water quality can be used to secure critical infrastructure, food safety can be improved by in-situ identification of pathogens, and sensored buildings can ensure the architectural safety of our homes and workplaces. A challenge to commercializing these opportunities, and thus making them available for National Security needs, is developing predictable markets and predictable technology roadmaps. The integrated circuit manufacturing industry provides an example of predictable technology maturation and market insertion, primarily due to the existence of a "unit cell" that allows volume manufacturing. It is not clear that microsystems can follow an analogous path. The possible paths to affordable low-volume production, as well as the prospects of a microsystems unit cell, are discussed.

  15. Applications of MEMS for Space Exploration

    Science.gov (United States)

    Tang, William C.

    1998-03-01

    Space exploration in the coming century will emphasize cost effectiveness and highly focused mission objectives, which will result in frequent multiple missions that broaden the scope of space science and to validate new technologies on a timely basis. Micro Electro Mechanical Systems (MEMS) is one of the key enabling technologies to create cost-effective, ultra-miniaturized, robust, and functionally focused spacecraft for both robotic and human exploration programs. Examples of MEMS devices at various stages of development include microgyroscope, microseismometer, microhygrometer, quadrupole mass spectrometer, and micropropulsion engine. These devices, when proven successful, will serve as models for developing components and systems for new-millennium spacecraft.

  16. Polycrystalline-Diamond MEMS Biosensors Including Neural Microelectrode-Arrays

    Directory of Open Access Journals (Sweden)

    Donna H. Wang

    2011-08-01

    Full Text Available Diamond is a material of interest due to its unique combination of properties, including its chemical inertness and biocompatibility. Polycrystalline diamond (poly-C has been used in experimental biosensors that utilize electrochemical methods and antigen-antibody binding for the detection of biological molecules. Boron-doped poly-C electrodes have been found to be very advantageous for electrochemical applications due to their large potential window, low background current and noise, and low detection limits (as low as 500 fM. The biocompatibility of poly-C is found to be comparable, or superior to, other materials commonly used for implants, such as titanium and 316 stainless steel. We have developed a diamond-based, neural microelectrode-array (MEA, due to the desirability of poly-C as a biosensor. These diamond probes have been used for in vivo electrical recording and in vitro electrochemical detection. Poly-C electrodes have been used for electrical recording of neural activity. In vitro studies indicate that the diamond probe can detect norepinephrine at a 5 nM level. We propose a combination of diamond micro-machining and surface functionalization for manufacturing diamond pathogen-microsensors.

  17. Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation

    Directory of Open Access Journals (Sweden)

    Yuzheng Yang

    2014-01-01

    Full Text Available An adaptive sliding controller using radial basis function (RBF network to approximate the unknown system dynamics microelectromechanical systems (MEMS gyroscope sensor is proposed. Neural controller is proposed to approximate the unknown system model and sliding controller is employed to eliminate the approximation error and attenuate the model uncertainties and external disturbances. Online neural network (NN weight tuning algorithms, including correction terms, are designed based on Lyapunov stability theory, which can guarantee bounded tracking errors as well as bounded NN weights. The tracking error bound can be made arbitrarily small by increasing a certain feedback gain. Numerical simulation for a MEMS angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive neural control scheme and demonstrate the satisfactory tracking performance and robustness.

  18. MEMS for Tunable Photonic Metamaterial Applications

    Science.gov (United States)

    Stark, Thomas

    Photonic metamaterials are materials whose optical properties are derived from artificially-structured sub-wavelength unit cells, rather than from the bulk properties of the constituent materials. Examples of metamaterials include plasmonic materials, negative index materials, and electromagnetic cloaks. While advances in simulation tools and nanofabrication methods have allowed this field to grow over the past several decades, many challenges still exist. This thesis addresses two of these challenges: fabrication of photonic metamaterials with tunable responses and high-throughput nanofabrication methods for these materials. The design, fabrication, and optical characterization of a microelectromechanical systems (MEMS) tunable plasmonic spectrometer are presented. An array of holes in a gold film, with plasmon resonance in the mid-infrared, is suspended above a gold reflector, forming a Fabry-Perot interferometer of tunable length. The spectra exhibit the convolution of extraordinary optical transmission through the holes and Fabry-Perot resonances. Using MEMS, the interferometer length is modulated from 1.7 mum to 21.67 mum , thereby tuning the free spectral range from about 2900 wavenumbers to 230.7 wavenumbers and shifting the reflection minima and maxima across the infrared. Due to its broad spectral tunability in the fingerprint region of the mid-infrared, this device shows promise as a tunable biological sensing device. To address the issue of high-throughput, high-resolution fabrication of optical metamaterials, atomic calligraphy, a MEMS-based dynamic stencil lithography technique for resist-free fabrication of photonic metamaterials on unconventional substrates, has been developed. The MEMS consists of a moveable stencil, which can be actuated with nanometer precision using electrostatic comb drive actuators. A fabrication method and flip chip method have been developed, enabling evaporation of metals through the device handle for fabrication on an

  19. Thermal energy harvesting for application at MEMS scale

    CERN Document Server

    Percy, Steven; McGarry, Scott; Post, Alex; Moore, Tim; Cavanagh, Kate

    2014-01-01

    This book discusses the history of thermal heat generators and focuses on the potential for these processes using micro-electrical mechanical systems (MEMS) technology for this application. The main focus is on the capture of waste thermal energy for example from industrial processes, transport systems or the human body to generate useable electrical power.  A wide range of technologies is discussed, including external combustion heat cycles at MEMS ( Brayton, Stirling and Rankine), Thermoacoustic, Shape Memory Alloys (SMAs), Multiferroics, Thermionics, Pyroelectric, Seebeck, Alkali Metal Thermal, Hydride Heat Engine, Johnson Thermo Electrochemical Converters, and the Johnson Electric Heat Pipe.

  20. Sputtered highly oriented PZT thin films for MEMS applications

    Science.gov (United States)

    Kalpat, Sriram S.

    Recently there has been an explosion of interest in the field of micro-electro-mechanical systems (MEMS). MEMS device technology has become critical in the growth of various fields like medical, automotive, chemical, and space technology. Among the many applications of ferroelectric thin films in MEMS devices, microfluidics is a field that has drawn considerable amount of research from bio-technology industries as well as chemical and semiconductor manufacturing industries. PZT thin films have been identified as best suited materials for micro-actuators and micro-sensors used in MEMS devices. A promising application for piezoelectric thin film based MEMS devices is disposable drug delivery systems that are capable of sensing biological parameters, mixing and delivering minute and precise amounts of drugs using micro-pumps or micro mixers. These devices call for low driving voltages, so that they can be battery operated. Improving the performance of the actuator material is critical in achieving battery operated disposal drug delivery systems. The device geometry and power consumption in MEMS devices largely depends upon the piezoelectric constant of the films, since they are most commonly used to convert electrical energy into a mechanical response of a membrane or cantilever and vice versa. Phenomenological calculation on the crystal orientation dependence of piezoelectric coefficients for PZT single crystal have reported a significant enhancement of the piezoelectric d33 constant by more than 3 times along [001] in the rhombohedral phase as compared to the conventionally used orientation PZT(111) since [111] is the along the spontaneous polarization direction. This could mean considerable improvement in the MEMS device performance and help drive the operating voltages lower. The motivation of this study is to investigate the crystal orientation dependence of both dielectric and piezoelectric coefficients of PZT thin films in order to select the appropriate

  1. Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

    Directory of Open Access Journals (Sweden)

    Bin Xu

    2017-01-01

    Full Text Available This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.

  2. New Trends on MEMS Sensor Technology for Harsh Environment Applications

    Directory of Open Access Journals (Sweden)

    Patricia M. NIEVA

    2007-10-01

    Full Text Available MEMS and NEMS sensor systems that can operate in the presence of high temperatures, corrosive media, and/or high radiation hold great promise for harsh environment applications. They would reduce weight, improve machine reliability and reduce cost in strategic market sectors such as automotive, avionics, oil well logging, and nuclear power. This paper presents a review of the recent advances in harsh-environment MEMS and NEMS sensors focusing on materials and devices. Special emphasis is put on high-temperature operation. Wide-bandgap semiconductor materials for high temperature applications are discussed from the device point of view. Micro-opto mechanical systems (MOEMS are presented as a new trend for high temperature applications. As an example of a harsh environment MOEMS sensor, a vibration sensor is presented.

  3. MEMS-Based Power Generation Techniques for Implantable Biosensing Applications

    Directory of Open Access Journals (Sweden)

    Jonathan Lueke

    2011-01-01

    Full Text Available Implantable biosensing is attractive for both medical monitoring and diagnostic applications. It is possible to monitor phenomena such as physical loads on joints or implants, vital signs, or osseointegration in vivo and in real time. Microelectromechanical (MEMS-based generation techniques can allow for the autonomous operation of implantable biosensors by generating electrical power to replace or supplement existing battery-based power systems. By supplementing existing battery-based power systems for implantable biosensors, the operational lifetime of the sensor is increased. In addition, the potential for a greater amount of available power allows additional components to be added to the biosensing module, such as computational and wireless and components, improving functionality and performance of the biosensor. Photovoltaic, thermovoltaic, micro fuel cell, electrostatic, electromagnetic, and piezoelectric based generation schemes are evaluated in this paper for applicability for implantable biosensing. MEMS-based generation techniques that harvest ambient energy, such as vibration, are much better suited for implantable biosensing applications than fuel-based approaches, producing up to milliwatts of electrical power. High power density MEMS-based approaches, such as piezoelectric and electromagnetic schemes, allow for supplemental and replacement power schemes for biosensing applications to improve device capabilities and performance. In addition, this may allow for the biosensor to be further miniaturized, reducing the need for relatively large batteries with respect to device size. This would cause the implanted biosensor to be less invasive, increasing the quality of care received by the patient.

  4. MEMS-based power generation techniques for implantable biosensing applications.

    Science.gov (United States)

    Lueke, Jonathan; Moussa, Walied A

    2011-01-01

    Implantable biosensing is attractive for both medical monitoring and diagnostic applications. It is possible to monitor phenomena such as physical loads on joints or implants, vital signs, or osseointegration in vivo and in real time. Microelectromechanical (MEMS)-based generation techniques can allow for the autonomous operation of implantable biosensors by generating electrical power to replace or supplement existing battery-based power systems. By supplementing existing battery-based power systems for implantable biosensors, the operational lifetime of the sensor is increased. In addition, the potential for a greater amount of available power allows additional components to be added to the biosensing module, such as computational and wireless and components, improving functionality and performance of the biosensor. Photovoltaic, thermovoltaic, micro fuel cell, electrostatic, electromagnetic, and piezoelectric based generation schemes are evaluated in this paper for applicability for implantable biosensing. MEMS-based generation techniques that harvest ambient energy, such as vibration, are much better suited for implantable biosensing applications than fuel-based approaches, producing up to milliwatts of electrical power. High power density MEMS-based approaches, such as piezoelectric and electromagnetic schemes, allow for supplemental and replacement power schemes for biosensing applications to improve device capabilities and performance. In addition, this may allow for the biosensor to be further miniaturized, reducing the need for relatively large batteries with respect to device size. This would cause the implanted biosensor to be less invasive, increasing the quality of care received by the patient.

  5. Neural Networks: Implementations and Applications

    OpenAIRE

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  6. MEMS packaging

    CERN Document Server

    Hsu , Tai-Ran

    2004-01-01

    MEMS Packaging discusses the prevalent practices and enabling techniques in assembly, packaging and testing of microelectromechanical systems (MEMS). The entire spectrum of assembly, packaging and testing of MEMS and microsystems, from essential enabling technologies to applications in key industries of life sciences, telecommunications and aerospace engineering is covered. Other topics included are bonding and sealing of microcomponents, process flow of MEMS and microsystems packaging, automated microassembly, and testing and design for testing.The Institution of Engineering and Technology is

  7. MEMS temperature scanner: principles, advances, and applications

    Science.gov (United States)

    Otto, Thomas; Saupe, Ray; Stock, Volker; Gessner, Thomas

    2010-02-01

    Contactless measurement of temperatures has gained enormous significance in many application fields, ranging from climate protection over quality control to object recognition in public places or military objects. Thereby measurement of linear or spatially temperature distribution is often necessary. For this purposes mostly thermographic cameras or motor driven temperature scanners are used today. Both are relatively expensive and the motor drive devices are limited regarding to the scanning rate additionally. An economic alternative are temperature scanner devices based on micro mirrors. The micro mirror, attached in a simple optical setup, reflects the emitted radiation from the observed heat onto an adapted detector. A line scan of the target object is obtained by periodic deflection of the micro scanner. Planar temperature distribution will be achieved by perpendicularly moving the target object or the scanner device. Using Planck radiation law the temperature of the object is calculated. The device can be adapted to different temperature ranges and resolution by using different detectors - cooled or uncooled - and parameterized scanner parameters. With the basic configuration 40 spatially distributed measuring points can be determined with temperatures in a range from 350°C - 1000°C. The achieved miniaturization of such scanners permits the employment in complex plants with high building density or in direct proximity to the measuring point. The price advantage enables a lot of applications, especially new application in the low-price market segment This paper shows principle, setup and application of a temperature measurement system based on micro scanners working in the near infrared range. Packaging issues and measurement results will be discussed as well.

  8. Cyber integrated MEMS microhand for biological applications

    Science.gov (United States)

    Weissman, Adam; Frazier, Athena; Pepen, Michael; Lu, Yen-Wen; Yang, Shanchieh Jay

    2009-05-01

    Anthropomorphous robotic hands at microscales have been developed to receive information and perform tasks for biological applications. To emulate a human hand's dexterity, the microhand requires a master-slave interface with a wearable controller, force sensors, and perception displays for tele-manipulation. Recognizing the constraints and complexity imposed in developing feedback interface during miniaturization, this project address the need by creating an integrated cyber environment incorporating sensors with a microhand, haptic/visual display, and object model, to emulates human hands' psychophysical perception at microscale.

  9. Scanning Micromirror Platform Based on MEMS Technology for Medical Application

    Directory of Open Access Journals (Sweden)

    Eakkachai Pengwang

    2016-02-01

    Full Text Available This topical review discusses recent development and trends on scanning micromirrors for biomedical applications. This also includes a biomedical micro robot for precise manipulations in a limited volume. The characteristics of medical scanning micromirror are explained in general with the fundamental of microelectromechanical systems (MEMS for fabrication processes. Along with the explanations of mechanism and design, the principle of actuation are provided for general readers. In this review, several testing methodology and examples are described based on many types of actuators, such as, electrothermal actuators, electrostatic actuators, electromagnetic actuators, pneumatic actuators, and shape memory alloy. Moreover, this review provides description of the key fabrication processes and common materials in order to be a basic guideline for selecting micro-actuators. With recent developments on scanning micromirrors, performances of biomedical application are enhanced for higher resolution, high accuracy, and high dexterity. With further developments on integrations and control schemes, MEMS-based scanning micromirrors would be able to achieve a better performance for medical applications due to small size, ease in microfabrication, mass production, high scanning speed, low power consumption, mechanical stable, and integration compatibility.

  10. Micro-fabrication technology for piezoelectric film formation and its application to MEMS

    OpenAIRE

    一木, 正聡; 曹, 俊杰; 張, 麓〓; 王, 占杰; 前田, 龍太郎; Masaaki, ICHIKI; Jiunn Jye, TSAUR; Lulu, ZHANG; Zhang Jie, WANG; Ryutaro, MAEDA; 産業技術総合研究所; 産業技術総合研究所; 産業技術総合研究所; 東北大学; 産業技術総合研究所

    2005-01-01

    Technological problems for realization of Micro Electro-mechanical System (MEMS) are discussed and an introduction of smart materials (PZT) is encouraged. The film formation and micromaching technology are discussed in integration of PZT thin films into MEMS. Further developments are proposed on PZT micro sensors and actuators with special emphasis laid on exploration of new application fields of MEMS, such as scanning mirror. Internal stress is estimated and analyzed for the improvement of d...

  11. Standard semiconductor packaging for high-reliability low-cost MEMS applications

    Science.gov (United States)

    Harney, Kieran P.

    2005-01-01

    Microelectronic packaging technology has evolved over the years in response to the needs of IC technology. The fundamental purpose of the package is to provide protection for the silicon chip and to provide electrical connection to the circuit board. Major change has been witnessed in packaging and today wafer level packaging technology has further revolutionized the industry. MEMS (Micro Electro Mechanical Systems) technology has created new challenges for packaging that do not exist in standard ICs. However, the fundamental objective of MEMS packaging is the same as traditional ICs, the low cost and reliable presentation of the MEMS chip to the next level interconnect. Inertial MEMS is one of the best examples of the successful commercialization of MEMS technology. The adoption of MEMS accelerometers for automotive airbag applications has created a high volume market that demands the highest reliability at low cost. The suppliers to these markets have responded by exploiting standard semiconductor packaging infrastructures. However, there are special packaging needs for MEMS that cannot be ignored. New applications for inertial MEMS devices are emerging in the consumer space that adds the imperative of small size to the need for reliability and low cost. These trends are not unique to MEMS accelerometers. For any MEMS technology to be successful the packaging must provide the basic reliability and interconnection functions, adding the least possible cost to the product. This paper will discuss the evolution of MEMS packaging in the accelerometer industry and identify the main issues that needed to be addressed to enable the successful commercialization of the technology in the automotive and consumer markets.

  12. Vibrations of Elastic Systems With Applications to MEMS and NEMS

    CERN Document Server

    Magrab, Edward B

    2012-01-01

    This work presents a unified approach to the vibrations of elastic systems as applied to MEMS devices, mechanical components, and civil structures. Applications include atomic force microscopes, energy harvesters, and carbon nanotubes and consider such complicating effects as squeeze film damping, viscous fluid loading, in-plane forces, and proof mass interactions with their elastic supports. These effects are analyzed as single degree-of-freedom models and as more realistic elastic structures. The governing equations and boundary conditions for beams, plates, and shells with interior and boundary attachments are derived by applying variational calculus to an expression describing the energy of the system. The advantages of this approach regarding the generation of orthogonal functions and the Rayleigh-Ritz method are demonstrated. A large number of graphs and tables are given to show the impact of various factors on the systems’ natural frequencies, mode shapes, and responses.

  13. Long-term neural recordings using MEMS based moveable microelectrodes in the brain

    Directory of Open Access Journals (Sweden)

    Nathan Jackson

    2010-06-01

    Full Text Available One of the critical requirements of the emerging class of neural prosthetic devices is to maintain good quality neural recordings over long time periods. We report here a novel (Micro-ElectroMechanical Systems based technology that can move microelectrodes in the event of deterioration in neural signal to sample a new set of neurons. Microscale electro-thermal actuators are used to controllably move microelectrodes post-implantation in steps of approximately 9 µm. In this study, a total of 12 moveable microelectrode chips were individually implanted in adult rats. Two of the 12 moveable microelectrode chips were not moved over a period of 3 weeks and were treated as control experiments. During the first three weeks of implantation, moving the microelectrodes led to an improvement in the average SNR from 14.61 ± 5.21 dB before movement to 18.13 ± 4.99 dB after movement across all microelectrodes and all days. However, the average RMS values of noise amplitudes were similar at 2.98 ± 1.22 µV and 3.01 ± 1.16 µV before and after microelectrode movement. Beyond three weeks, the primary observed failure mode was biological rejection of the PMMA (dental cement based skull mount resulting in the device loosening and eventually falling from the skull. Additionally, the average SNR for functioning devices beyond three weeks was 11.88 ± 2.02 dB before microelectrode movement and was significantly different (p<0.01 from the average SNR of 13.34 ± 0.919 dB after movement. The results of this study demonstrate that MEMS based technologies can move microelectrodes in rodent brains in long-term experiments resulting in improvements in signal quality. Further improvements in packaging and surgical techniques will potentially enable movable microelectrodes to record cortical neuronal activity in chronic experiments.

  14. Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements

    Directory of Open Access Journals (Sweden)

    Jiahong Zhang

    2015-05-01

    Full Text Available This paper provides a novel and effective compensation method by improving the hardware design and software algorithm to achieve optimization of piezoresistive pressure sensors and corresponding measurement systems in order to measure pressure more accurately and stably, as well as to meet the application requirements of the meteorological industry. Specifically, GE NovaSensor MEMS piezoresistive pressure sensors within a thousandth of accuracy are selected to constitute an array. In the versatile compensation method, the hardware utilizes the array of MEMS pressure sensors to reduce random error caused by sensor creep, and the software adopts the data fusion technique based on the wavelet neural network (WNN which is improved by genetic algorithm (GA to analyze the data of sensors for the sake of obtaining accurate and complete information over the wide temperature and pressure ranges. The GA-WNN model is implemented in hardware by using the 32-bit STMicroelectronics (STM32 microcontroller combined with an embedded real-time operating system µC/OS-II to make the output of the array of MEMS sensors be a direct digital readout. The results of calibration and test experiments clearly show that the GA-WNN technique can be effectively applied to minimize the sensor errors due to the temperature drift, the hysteresis effect and the long-term drift because of aging and environmental changes. The maximum error of the low cost piezoresistive MEMS-array pressure transmitter proposed by us is within 0.04% of its full-scale value, and it can satisfy the meteorological pressure measurement.

  15. Micro-Electromechanical-Systems (MEMS) technologies for aerospace applications in Canada

    International Nuclear Information System (INIS)

    Pimprikar, M.

    2001-01-01

    During the last decade, research and development of Micro-Electro-Mechanical Systems (MEMS) have shown significant promise for a variety of aerospace applications. The advantages of drastic size and weight reduction of MEMS enables consideration of developing low-cost, high-performance, ultra-portable, MEMS-based devices and systems for aircraft, space and defense requirements. 'Microelectromechanical Systems, or MEMS', are integrated microdevices or systems combining electrical and mechanical components, fabricated using integrated circuit compatible batch-processing techniques, and varying in size from micrometers to millimeters. In the 1990's, MEMS were used as laboratory curiosities with very low power, short lifetimes and few concrete applications. One decade later, MEMS have taken major roles in several industries, the total world market is expected to grow from $14 billion to over $40 billion by the year 2002. A typical device contains micromechanical structures that move by flexing (membranes, cantilevers, springs) and MEMS/MOEMS level where the integration of microelectronics, micromechanics and optics form a complete system (sensor, actuator, photonic device). (author)

  16. MEMS-based transmission lines for microwave applications

    Science.gov (United States)

    Wu, Qun; Fu, Jiahui; Gu, Xuemai; Shi, Huajuan; Lee, Jongchul

    2003-04-01

    This paper mainly presents a briefly review for recent progress in MEMS-based transmission lines for use in microwave and millimeterwave range. MEMS-based transmission lines including different transmission line structure such as membrane-supported microstrip line microstrip line, coplanar microshield transmission line, LIGA micromachined planar transmission line, micromachined waveguides and coplanar waveguide are discussed. MEMS-based transmission lines are characterized by low propagation loss, wide operation frequency band, low dispersion and high quality factor, in addition, the fabrication is compatible with traditional processing of integrated circuits (IC"s). The emergence of MEMS-based transmission lines provided a solution for miniaturizing microwave system and monolithic microwave integrated circuits.

  17. Effects Of Environmental And Operational Stresses On RF MEMS Switch Technologies For Space Applications

    Science.gov (United States)

    Jah, Muzar; Simon, Eric; Sharma, Ashok

    2003-01-01

    Micro Electro Mechanical Systems (MEMS) have been heralded for their ability to provide tremendous advantages in electronic systems through increased electrical performance, reduced power consumption, and higher levels of device integration with a reduction of board real estate. RF MEMS switch technology offers advantages such as low insertion loss (0.1- 0.5 dB), wide bandwidth (1 GHz-100 GHz), and compatibility with many different process technologies (quartz, high resistivity Si, GaAs) which can replace the use of traditional electronic switches, such as GaAs FETS and PIN Diodes, in microwave systems for low signal power (x technologies, the unknown reliability, due to the lack of information concerning failure modes and mechanisms inherent to MEMS devices, create an obstacle to insertion of MEMS technology into high reliability applications. All MEMS devices are sensitive to moisture and contaminants, issues easily resolved by hermetic or near-hermetic packaging. Two well-known failure modes of RF MEMS switches are charging in the dielectric layer of capacitive membrane switches and contact interface stiction of metal-metal switches. Determining the integrity of MEMS devices when subjected to the shock, vibration, temperature extremes, and radiation of the space environment is necessary to facilitate integration into space systems. This paper will explore the effects of different environmental stresses, operational life cycling, temperature, mechanical shock, and vibration on the first commercially available RF MEMS switches to identify relevant failure modes and mechanisms inherent to these device and packaging schemes for space applications. This paper will also describe RF MEMS Switch technology under development at NASA GSFC.

  18. MEMS Accelerometers Sensors: an Application in Virtual Reality

    Directory of Open Access Journals (Sweden)

    Daniel CORRÊA

    2010-09-01

    Full Text Available The measurement of a particular human body member position is extremely important in many applications. The human behavior understanding typically involves the body posture analysis or estimation, as well as the generated corresponding gestures. This behavior characterization allows analyzing, interpreting, and animating human actions and therefore enables us the use of experimental methodologies. Using the virtual reality devices to facilitate people’s lives, they can help to train and improve the actions of an Olympic athlete, for example and imitation of human actions by robotic systems. The systems development to monitor human body members’ movements is a growing interesting area, both in entertainment and in systems to help physically disabled people, as that developing assistive technology. To contribute to this area, this paper presents the experimental development of an instrumented glove prototype of low cost for the recognition of hand inclination movements, using a Micro-Electro-Mechanical Systems (MEMS accelerometer, by virtual reality concepts for demonstration in real time. We present the hardware that was developed, the calibration procedures, the achieved results with their statistical corresponding validation. The results allowed to state that the system is suitable for the inclination measurement in a 2D plan, thus allowing its use in entertainment systems and as an auxiliary device for assistive technology system.

  19. A MEMS Electrochemical Bellows Actuator for Fluid Metering Applications

    Science.gov (United States)

    Sheybani, Roya; Gensler, Heidi; Meng, Ellis

    2013-01-01

    We present a high efficiency wireless MEMS electrochemical bellows actuator capable of rapid and repeatable delivery of boluses for fluid metering and drug delivery applications. Nafion®-coated Pt electrodes were combined with Parylene bellows filled with DI water to form the electrolysis-based actuator. The performance of actuators with several bellows configurations was compared for a range of applied currents (1-10 mA). Up to 75 boluses were delivered with an average pumping flow rate of 114.40 ± 1.63 μL/min. Recombination of gases into water, an important factor in repeatable and reliable actuation, was studied for uncoated and Nafion®-coated actuators. Real-time pressure measurements were conducted and the effects of temperature, physiological back pressure, and drug viscosity on delivery performance were investigated. Lastly, we present wireless powering of the actuator using a class D inductive powering system that allowed for repeatable delivery with less than 2% variation in flow rate values. PMID:22833156

  20. Neural fields theory and applications

    CERN Document Server

    Graben, Peter; Potthast, Roland; Wright, James

    2014-01-01

    With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

  1. Sound Source Localization through 8 MEMS Microphones Array Using a Sand-Scorpion-Inspired Spiking Neural Network.

    Science.gov (United States)

    Beck, Christoph; Garreau, Guillaume; Georgiou, Julius

    2016-01-01

    Sand-scorpions and many other arachnids perceive their environment by using their feet to sense ground waves. They are able to determine amplitudes the size of an atom and locate the acoustic stimuli with an accuracy of within 13° based on their neuronal anatomy. We present here a prototype sound source localization system, inspired from this impressive performance. The system presented utilizes custom-built hardware with eight MEMS microphones, one for each foot, to acquire the acoustic scene, and a spiking neural model to localize the sound source. The current implementation shows smaller localization error than those observed in nature.

  2. Sound Source Localization Through 8 MEMS Microphones Array Using a Sand-Scorpion-Inspired Spiking Neural Network

    Directory of Open Access Journals (Sweden)

    Christoph Beck

    2016-10-01

    Full Text Available Sand-scorpions and many other arachnids perceive their environment by using their feet to sense ground waves. They are able to determine amplitudes the size of an atom and locate the acoustic stimuli with an accuracy of within 13° based on their neuronal anatomy. We present here a prototype sound source localization system, inspired from this impressive performance. The system presented utilizes custom-built hardware with eight MEMS microphones, one for each foot, to acquire the acoustic scene, and a spiking neural model to localize the sound source. The current implementation shows smaller localization error than those observed in nature.

  3. Infrastructure for the design and fabrication of MEMS for RF/microwave and millimeter wave applications

    Science.gov (United States)

    Nerguizian, Vahe; Rafaf, Mustapha

    2004-08-01

    This article describes and provides valuable information for companies and universities with strategies to start fabricating MEMS for RF/Microwave and millimeter wave applications. The present work shows the infrastructure developed for RF/Microwave and millimeter wave MEMS platforms, which helps the identification, evaluation and selection of design tools and fabrication foundries taking into account packaging and testing. The selected and implemented simple infrastructure models, based on surface and bulk micromachining, yield inexpensive and innovative approaches for distributed choices of MEMS operating tools. With different educational or industrial institution needs, these models may be modified for specific resource changes using a careful analyzed iteration process. The inputs of the project are evaluation selection criteria and information sources such as financial, technical, availability, accessibility, simplicity, versatility and practical considerations. The outputs of the project are the selection of different MEMS design tools or software (solid modeling, electrostatic/electromagnetic and others, compatible with existing standard RF/Microwave design tools) and different MEMS manufacturing foundries. Typical RF/Microwave and millimeter wave MEMS solutions are introduced on the platform during the evaluation and development phases of the project for the validation of realistic results and operational decision making choices. The encountered challenges during the investigation and the development steps are identified and the dynamic behavior of the infrastructure is emphasized. The inputs (resources) and the outputs (demonstrated solutions) are presented in tables and flow chart mode diagrams.

  4. PECVD silicon carbide surface micromachining technology and selected MEMS applications

    NARCIS (Netherlands)

    Rajaraman, V.; Pakula, L.S.; Yang, H.; French, P.J.; Sarro, P.M.

    2011-01-01

    Attractive material properties of plasma enhanced chemical vapour deposited (PECVD) silicon carbide (SiC) when combined with CMOS-compatible low thermal budget processing provides an ideal technology platform for developing various microelectromechanical systems (MEMS) devices and merging them with

  5. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

  6. BurstMem: A High-Performance Burst Buffer System for Scientific Applications

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Teng [Auburn University, Auburn, Alabama; Oral, H Sarp [ORNL; Wang, Yandong [Auburn University, Auburn, Alabama; Settlemyer, Bradley W [ORNL; Atchley, Scott [ORNL; Yu, Weikuan [Auburn University, Auburn, Alabama

    2014-01-01

    The growth of computing power on large-scale sys- tems requires commensurate high-bandwidth I/O system. Many parallel file systems are designed to provide fast sustainable I/O in response to applications soaring requirements. To meet this need, a novel system is imperative to temporarily buffer the bursty I/O and gradually flush datasets to long-term parallel file systems. In this paper, we introduce the design of BurstMem, a high- performance burst buffer system. BurstMem provides a storage framework with efficient storage and communication manage- ment strategies. Our experiments demonstrate that BurstMem is able to speed up the I/O performance of scientific applications by up to 8.5 on leadership computer systems.

  7. A versatile multi-user polyimide surface micromachinning process for MEMS applications

    KAUST Repository

    Carreno, Armando Arpys Arevalo

    2015-04-01

    This paper reports a versatile multi-user micro-fabrication process for MEMS devices, the \\'Polyimide MEMS Multi-User Process\\' (PiMMPs). The reported process uses polyimide as the structural material and three separate metallization layers that can be interconnected depending on the desired application. This process enables for the first time the development of out-of-plane compliant mechanisms that can be designed using six different physical principles for actuation and sensing on a wafer from a single fabrication run. These principles are electrostatic motion, thermal bimorph actuation, capacitive sensing, magnetic sensing, thermocouple-based sensing and radio frequency transmission and reception. © 2015 IEEE.

  8. A MEMS-based super fast dew point hygrometer—construction and medical applications

    International Nuclear Information System (INIS)

    Jachowicz, Ryszard S; Weremczuk, Jerzy; Paczesny, Daniel; Tarapata, Grzegorz

    2009-01-01

    The paper shows how MEMS (micro-electro-mechanical system) technology and a modified principle of fast temperature control (by heat injection instead of careful control of cooling) can considerably improve the dynamic parameters of dew point hygrometers. Some aspects of MEMS-type integrated sensor construction and technology, whole measurement system design, the control algorithm to run the system as well as empirical dynamic parameters from the tests are discussed too. The hygrometer can easily obtain five to six measurements per second with an uncertainty of less than 0.3 K. The meter range is between −10 °C and 40 °C dew point. In the second part of the paper (section 2), two different successful applications in medicine based on fast humidity measurements have been discussed. Some specific constructions of these super fast dew point hygrometers based on a MEMS sensor as well as limited empirical results from clinical tests have been reported too

  9. A MEMS-based super fast dew point hygrometer—construction and medical applications

    Science.gov (United States)

    Jachowicz, Ryszard S.; Weremczuk, Jerzy; Paczesny, Daniel; Tarapata, Grzegorz

    2009-12-01

    The paper shows how MEMS (micro-electro-mechanical system) technology and a modified principle of fast temperature control (by heat injection instead of careful control of cooling) can considerably improve the dynamic parameters of dew point hygrometers. Some aspects of MEMS-type integrated sensor construction and technology, whole measurement system design, the control algorithm to run the system as well as empirical dynamic parameters from the tests are discussed too. The hygrometer can easily obtain five to six measurements per second with an uncertainty of less than 0.3 K. The meter range is between -10 °C and 40 °C dew point. In the second part of the paper (section 2), two different successful applications in medicine based on fast humidity measurements have been discussed. Some specific constructions of these super fast dew point hygrometers based on a MEMS sensor as well as limited empirical results from clinical tests have been reported too.

  10. MEMS Coupled Resonator for Filter Application in Air

    KAUST Repository

    Ilyas, Saad; Jaber, Nizar; Younis, Mohammad I.

    2017-01-01

    We present a mechanically coupled MEMS H resonator capable of performing simultaneous amplification and filter operation in air. The device comprises of two doubly clamped polyimide microbeams joined through the middle by a coupling beam of the same size. The resonator is fabricated via a multilayer surface micromachining process. A special fabrication process and device design is employed to enable the device's operation in air and to achieve mechanical amplification of the output response. Moreover, mixed-frequency excitation is used to demonstrate a tunable wide band filter. The device design combined with the mixed-frequency excitation is used to demonstrate simultaneous amplification and filtering in air.

  11. MEMS Coupled Resonator for Filter Application in Air

    KAUST Repository

    Ilyas, Saad

    2017-11-03

    We present a mechanically coupled MEMS H resonator capable of performing simultaneous amplification and filter operation in air. The device comprises of two doubly clamped polyimide microbeams joined through the middle by a coupling beam of the same size. The resonator is fabricated via a multilayer surface micromachining process. A special fabrication process and device design is employed to enable the device\\'s operation in air and to achieve mechanical amplification of the output response. Moreover, mixed-frequency excitation is used to demonstrate a tunable wide band filter. The device design combined with the mixed-frequency excitation is used to demonstrate simultaneous amplification and filtering in air.

  12. Neural networks and applications tutorial

    Science.gov (United States)

    Guyon, I.

    1991-09-01

    The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

  13. Neural Networks Methodology and Applications

    CERN Document Server

    Dreyfus, Gérard

    2005-01-01

    Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts ands seemlessly edited to present a coherent and comprehensive, yet not redundant, practically-oriented...

  14. Biomaterials for MEMS

    CERN Document Server

    Chiao, Mu

    2011-01-01

    This book serves as a guide for practicing engineers, researchers, and students interested in MEMS devices that use biomaterials and biomedical applications. It is also suitable for engineers and researchers interested in MEMS and its applications but who do not have the necessary background in biomaterials.Biomaterials for MEMS highlights important features and issues of biomaterials that have been used in MEMS and biomedical areas. Hence this book is an essential guide for MEMS engineers or researchers who are trained in engineering institutes that do not provide the background or knowledge

  15. Mechanical and electromechanical properties of graphene and their potential application in MEMS

    International Nuclear Information System (INIS)

    Khan, Zulfiqar H; Kermany, Atieh R; Iacopi, Francesca; Öchsner, Andreas

    2017-01-01

    Graphene-based micro-electromechanical systems (MEMS) are very promising candidates for next generation miniaturized, lightweight, and ultra-sensitive devices. In this review, we review the progress to date of the assessment of the mechanical, electromechanical, and thermomechanical properties of graphene for application in graphene-based MEMS. Graphene possesses a plethora of outstanding properties—such as a 1 TPa Young’s modulus, exceptionally high 2D failure strength that stems from its sp 2 hybridization, and strong sigma bonding between carbon atoms. Such exceptional mechanical properties can enable, for example, graphene-based sound sources capable of generating sound beyond the audible range. The recently engineered piezoelectric properties of atomic force microscope tip-pressed graphene membranes or supported graphene on SiO 2 substrates, have paved the way in fabricating graphene-based nano-generators and actuators. On the other hand, graphene’s piezoresistive properties have enabled miniaturized pressure and strain sensors. 2D graphene nano-mechanical resonators can potentially measure ultralow forces, charges and potentially detect single atomic masses. The exceptional tribology of graphene can play a significant role in achieving superlubricity. In addition, the highest reported thermal conductivity of graphene is amenable for use in chips and providing better performing MEMS, as heat is efficiently dissipated. On top of that, graphene membranes could be nano-perforated to realize specialized applications like DNA translocation and desalination. Finally, to ensure stability and reliability of the graphene-based MEMS, adhesion is an important mechanical property that should be considered. In general, graphene could be used as a structural material in resonators, sensors, actuators and nano-generators with better performance and sensitivity than conventional MEMS. (topical review)

  16. Development, characterization and application of compact spectrometers based on MEMS with in-plane capacitive drives

    Science.gov (United States)

    Kenda, A.; Kraft, M.; Tortschanoff, A.; Scherf, Werner; Sandner, T.; Schenk, Harald; Luettjohann, Stephan; Simon, A.

    2014-05-01

    With a trend towards the use of spectroscopic systems in various fields of science and industry, there is an increasing demand for compact spectrometers. For UV/VIS to the shortwave near-infrared spectral range, compact hand-held polychromator type devices are widely used and have replaced larger conventional instruments in many applications. Still, for longer wavelengths this type of compact spectrometers is lacking suitable and affordable detector arrays. In perennial development Carinthian Tech Research AG together with the Fraunhofer Institute for Photonic Microsystems endeavor to close this gap by developing spectrometer systems based on photonic MEMS. Here, we review on two different spectrometer developments, a scanning grating spectrometer working in the NIR and a FT-spectrometer accessing the mid-IR range up to 14 μm. Both systems are using photonic MEMS devices actuated by in-plane comb drive structures. This principle allows for high mechanical amplitudes at low driving voltages but results in gratings respectively mirrors oscillating harmonically. Both systems feature special MEMS structures as well as aspects in terms of system integration which shall tease out the best possible overall performance on the basis of this technology. However, the advantages of MEMS as enabling technology for high scanning speed, miniaturization, energy efficiency, etc. are pointed out. Whereas the scanning grating spectrometer has already evolved to a product for the point of sale analysis of traditional Chinese medicine products, the purpose of the FT-spectrometer as presented is to demonstrate what is achievable in terms of performance. Current developments topics address MEMS packaging issues towards long term stability, further miniaturization and usability.

  17. Recent Advances of MEMS Resonators for Lorentz Force Based Magnetic Field Sensors: Design, Applications and Challenges

    Directory of Open Access Journals (Sweden)

    Agustín Leobardo Herrera-May

    2016-08-01

    Full Text Available Microelectromechanical systems (MEMS resonators have allowed the development of magnetic field sensors with potential applications such as biomedicine, automotive industry, navigation systems, space satellites, telecommunications and non-destructive testing. We present a review of recent magnetic field sensors based on MEMS resonators, which operate with Lorentz force. These sensors have a compact structure, wide measurement range, low energy consumption, high sensitivity and suitable performance. The design methodology, simulation tools, damping sources, sensing techniques and future applications of magnetic field sensors are discussed. The design process is fundamental in achieving correct selection of the operation principle, sensing technique, materials, fabrication process and readout systems of the sensors. In addition, the description of the main sensing systems and challenges of the MEMS sensors are discussed. To develop the best devices, researches of their mechanical reliability, vacuum packaging, design optimization and temperature compensation circuits are needed. Future applications will require multifunctional sensors for monitoring several physical parameters (e.g., magnetic field, acceleration, angular ratio, humidity, temperature and gases.

  18. Development of a Multi-User Polyimide-MEMS Fabrication Process and its Application to MicroHotplates

    KAUST Repository

    Lizardo, Ernesto B.

    2013-05-08

    Micro-electro-mechanical systems (MEMS) became possible thanks to the silicon based technology used to fabricate integrated circuits. Originally, MEMS fabrication was limited to silicon based techniques and materials, but the expansion of MEMS applications brought the need of a wider catalog of materials, including polymers, now being used to fabricate MEMS. Polyimide is a very attractive polymer for MEMS fabrication due to its high temperature stability compared to other polymers, low coefficient of thermal expansion, low film stress and low cost. The goal of this thesis is to expand the Polyimide usage as structural material for MEMS by the development of a multi-user fabrication process for the integration of this polymer along with multiple metal layers on a silicon substrate. The process also integrates amorphous silicon as sacrificial layer to create free-standing structures. Dry etching is used to release the devices and avoid stiction phenomena. The developed process is used to fabricate platforms for micro-hotplate gas sensors. The fabrication steps for the platforms are described in detail, explaining the process specifics and capabilities. An initial testing of the micro-hotplate is presented. As the process was also used as educational tool, some designs made by students and fabricated with the Polyimide-MEMS process are also presented.

  19. Improved mechanical reliability of MEMS electret based vibration energy harvesters for automotive applications

    International Nuclear Information System (INIS)

    Renaud, M; Goedbloed, M; De Nooijer, C; Van Schaijk, R; Fujita, T

    2014-01-01

    Current commercial wireless tire pressure monitoring systems (TPMS) require a battery as electrical power source. The battery limits the lifetime of the TPMS. This limit can be circumvented by replacing the battery by a vibration energy harvester. Autonomous wireless TPMS powered by MEMS electret based vibration energy harvester have been demonstrated. A remaining technical challenge to attain the grade of commercial product with these autonomous TPMS is the mechanical reliability of the MEMS harvester. It should survive the harsh conditions imposed by the tire environment, particularly in terms of mechanical shocks. As shown in this article, our first generation of harvesters has a shock resilience of 400 g, which is far from being sufficient for the targeted application. In order to improve this aspect, several types of shock absorbing structures are investigated. With the best proposed solution, the shock resilience of the harvesters is brought above 2500 g

  20. Epitaxial Pb(Zr,Ti)O3 thin films for a MEMS application

    International Nuclear Information System (INIS)

    Nguyen, Minh D; Vu, Hung N; Blank, Dave H A; Rijnders, Guus

    2011-01-01

    This research presents the deposition and device fabrication of epitaxial Pb(Zr,Ti)O 3 (PZT) thin films for applications in microelectromechanical systems (MEMS). A piezoelectric micro-membrane is described as an example. Using the pulsed laser deposition (PLD) technique and the MEMS microfabrication process, the piezo-membranes with diameters ranging from 200 to 500 μm were obtained. The displacement of piezo-membranes increased from 5.1 to 17.5 nm V −1 with a piezoelectric-membrane diameter in the range of 200–500 μm. Furthermore, the effect of PZT film-thickness on the mechanical properties has been investigated. By using the conductive-oxide SrRuO 3 (SRO) layers as the electrodes, the degradation of both ferroelectric and piezoelectric properties is prevented up to 10 10 switching cycles

  1. Centimeter-scale MEMS scanning mirrors for high power laser application

    Science.gov (United States)

    Senger, F.; Hofmann, U.; v. Wantoch, T.; Mallas, C.; Janes, J.; Benecke, W.; Herwig, Patrick; Gawlitza, P.; Ortega-Delgado, M.; Grune, C.; Hannweber, J.; Wetzig, A.

    2015-02-01

    A higher achievable scan speed and the capability to integrate two scan axes in a very compact device are fundamental advantages of MEMS scanning mirrors over conventional galvanometric scanners. There is a growing demand for biaxial high speed scanning systems complementing the rapid progress of high power lasers for enabling the development of new high throughput manufacturing processes. This paper presents concept, design, fabrication and test of biaxial large aperture MEMS scanning mirrors (LAMM) with aperture sizes up to 20 mm for use in high-power laser applications. To keep static and dynamic deformation of the mirror acceptably low all MEMS mirrors exhibit full substrate thickness of 725 μm. The LAMM-scanners are being vacuum packaged on wafer-level based on a stack of 4 wafers. Scanners with aperture sizes up to 12 mm are designed as a 4-DOF-oscillator with amplitude magnification applying electrostatic actuation for driving a motor-frame. As an example a 7-mm-scanner is presented that achieves an optical scan angle of 32 degrees at 3.2 kHz. LAMM-scanners with apertures sizes of 20 mm are designed as passive high-Q-resonators to be externally excited by low-cost electromagnetic or piezoelectric drives. Multi-layer dielectric coatings with a reflectivity higher than 99.9 % have enabled to apply cw-laser power loads of more than 600 W without damaging the MEMS mirror. Finally, a new excitation concept for resonant scanners is presented providing advantageous shaping of intensity profiles of projected laser patterns without modulating the laser. This is of interest in lighting applications such as automotive laser headlights.

  2. A Capacitance-To-Digital Converter for MEMS Sensors for Smart Applications.

    Science.gov (United States)

    Pérez Sanjurjo, Javier; Prefasi, Enrique; Buffa, Cesare; Gaggl, Richard

    2017-06-07

    The use of MEMS sensors has been increasing in recent years. To cover all the applications, many different readout circuits are needed. To reduce the cost and time to market, a generic capacitance-to-digital converter (CDC) seems to be the logical next step. This work presents a configurable CDC designed for capacitive MEMS sensors. The sensor is built with a bridge of MEMS, where some of them function with pressure. Then, the capacitive to digital conversion is realized using two steps. First, a switched-capacitor (SC) preamplifier is used to make the capacitive to voltage (C-V) conversion. Second, a self-oscillated noise-shaping integrating dual-slope (DS) converter is used to digitize this magnitude. The proposed converter uses time instead of amplitude resolution to generate a multibit digital output stream. In addition it performs noise shaping of the quantization error to reduce measurement time. This article shows the effectiveness of this method by measurements performed on a prototype, designed and fabricated using standard 0.13 µm CMOS technology. Experimental measurements show that the CDC achieves a resolution of 17 bits, with an effective area of 0.317 mm², which means a pressure resolution of 1 Pa, while consuming 146 µA from a 1.5 V power supply.

  3. An investigation into graphene exfoliation and potential graphene application in MEMS devices

    Science.gov (United States)

    Fercana, George; Kletetschka, Gunther; Mikula, Vilem; Li, Mary

    2011-02-01

    The design of microelectromecanical systems (MEMS) and micro-opto-electromechanical systems (MOEMS) are often materials-limited with respect to the efficiency and capability of the material. Graphene, a one atom thick honeycomb lattice of carbon, is a highly desired material for MEMS applications. Relevant properties of graphene include the material's optical transparency, mechanical strength, energy efficiency, and electrical and thermal conductivity due to its electron mobility. Aforementioned properties make graphene a strong candidate to supplant existing transparent electrode technology and replace the conventionally used material, indium-tin oxide. In this paper we present preliminary results on work toward integration of graphene with MEMS structures. We are studying mechanical exfoliation of highly ordered pyrolytic graphite (HOPG) crystals by repeatedly applying and separating adhesive materials from the HOPG surface. The resulting graphene sheets are then transferred to silicon oxide substrate using the previously applied adhesive material. We explored different adhesive options, particularly the use of Kapton tape, to improve the yield of graphene isolation along with chemical cross-linking agents which operate on a mechanism of photoinsertion of disassociated nitrene groups. These perfluorophenyl nitrenes participate in C=C addition reactions with graphene monolayers creating a covalent binding between the substrate and graphene. We are focusing on maximizing the size of isolated graphene sheets and comparing to conventional exfoliation. Preliminary results allow isolation of few layer graphene (FLG) sheets (ntechnology to be used in future deep space telescopes.

  4. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    are examined. The models are separated into three groups representing input/output descriptions as well as state space descriptions: - Models, where all in- and outputs are measurable (static networks). - Models, where some inputs are non-measurable (recurrent networks). - Models, where some in- and some...... outputs are non-measurable (recurrent networks with incomplete state information). The three groups are ordered in increasing complexity, and for each group it is shown how to solve the problems concerning training and application of the specific model type. Of particular interest are the model types...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...

  5. CMOS compatible fabrication process of MEMS resonator for timing reference and sensing application

    Science.gov (United States)

    Huynh, Duc H.; Nguyen, Phuong D.; Nguyen, Thanh C.; Skafidas, Stan; Evans, Robin

    2015-12-01

    Frequency reference and timing control devices are ubiquitous in electronic applications. There is at least one resonator required for each of this device. Currently electromechanical resonators such as crystal resonator, ceramic resonator are the ultimate choices. This tendency will probably keep going for many more years. However, current market demands for small size, low power consumption, cheap and reliable products, has divulged many limitations of this type of resonators. They cannot be integrated into standard CMOS (Complement metaloxide- semiconductor) IC (Integrated Circuit) due to material and fabrication process incompatibility. Currently, these devices are off-chip and they require external circuitries to interface with the ICs. This configuration significantly increases the overall size and cost of the entire electronic system. In addition, extra external connection, especially at high frequency, will potentially create negative impacts on the performance of the entire system due to signal degradation and parasitic effects. Furthermore, due to off-chip packaging nature, these devices are quite expensive, particularly for high frequency and high quality factor devices. To address these issues, researchers have been intensively studying on an alternative for type of resonator by utilizing the new emerging MEMS (Micro-electro-mechanical systems) technology. Recent progress in this field has demonstrated a MEMS resonator with resonant frequency of 2.97 GHz and quality factor (measured in vacuum) of 42900. Despite this great achievement, this prototype is still far from being fully integrated into CMOS system due to incompatibility in fabrication process and its high series motional impedance. On the other hand, fully integrated MEMS resonator had been demonstrated but at lower frequency and quality factor. We propose a design and fabrication process for a low cost, high frequency and a high quality MEMS resonator, which can be integrated into a standard

  6. Novel designs for application specific MEMS pressure sensors.

    Science.gov (United States)

    Fragiacomo, Giulio; Reck, Kasper; Lorenzen, Lasse; Thomsen, Erik V

    2010-01-01

    In the framework of developing innovative microfabricated pressure sensors, we present here three designs based on different readout principles, each one tailored for a specific application. A touch mode capacitive pressure sensor with high sensitivity (14 pF/bar), low temperature dependence and high capacitive output signal (more than 100 pF) is depicted. An optical pressure sensor intrinsically immune to electromagnetic interference, with large pressure range (0-350 bar) and a sensitivity of 1 pm/bar is presented. Finally, a resonating wireless pressure sensor power source free with a sensitivity of 650 KHz/mmHg is described. These sensors will be related with their applications in harsh environment, distributed systems and medical environment, respectively. For many aspects, commercially available sensors, which in vast majority are piezoresistive, are not suited for the applications proposed.

  7. Novel Designs for Application Specific MEMS Pressure Sensors

    Directory of Open Access Journals (Sweden)

    Erik V. Thomsen

    2010-10-01

    Full Text Available In the framework of developing innovative microfabricated pressure sensors, we present here three designs based on different readout principles, each one tailored for a specific application. A touch mode capacitive pressure sensor with high sensitivity (14 pF/bar, low temperature dependence and high capacitive output signal (more than 100 pF is depicted. An optical pressure sensor intrinsically immune to electromagnetic interference, with large pressure range (0–350 bar and a sensitivity of 1 pm/bar is presented. Finally, a resonating wireless pressure sensor power source free with a sensitivity of 650 KHz/mmHg is described. These sensors will be related with their applications in  harsh environment, distributed systems and medical environment, respectively. For many aspects, commercially available sensors, which in vast majority are piezoresistive, are not suited for the applications proposed.

  8. MICROSTRUCTURING OF SU-8 RESIST FOR MEMS AND BIO-APPLICATIONS

    OpenAIRE

    Dey, P.K.; Pramanick, B.; RaviShankar, A.; Ganguly, P.; Das, S.

    2017-01-01

    Some studies on the fabrication of micro-needles, micro-pillers, and micro-channels using SU-8 negative photoresist for MEMS and bio-applications are reported. The SU-8 processing technology was standardized for the purpose. Micro-pillars were fabricated on SU-8 polymer by soft lithographic technique. Micro-needles were realized on SU-8 film utilizing lensing effect of the etched groove structure of the glass substrate. Micro-channel was fabricated by molding of PDMS polymer on patterned SU-8...

  9. Advanced Mechatronics and MEMS Devices

    CERN Document Server

    2013-01-01

    Advanced Mechatronics and MEMS Devicesdescribes state-of-the-art MEMS devices and introduces the latest technology in electrical and mechanical microsystems. The evolution of design in microfabrication, as well as emerging issues in nanomaterials, micromachining, micromanufacturing and microassembly are all discussed at length in this volume. Advanced Mechatronics also provides a reader with knowledge of MEMS sensors array, MEMS multidimensional accelerometer, artificial skin with imbedded tactile components, as well as other topics in MEMS sensors and transducers. The book also presents a number of topics in advanced robotics and an abundance of applications of MEMS in robotics, like reconfigurable modular snake robots, magnetic MEMS robots for drug delivery and flying robots with adjustable wings, to name a few. This book also: Covers the fundamentals of advanced mechatronics and MEMS devices while also presenting new state-of-the-art methodology and technology used in the application of these devices Prese...

  10. Fuzzy neural network theory and application

    CERN Document Server

    Liu, Puyin

    2004-01-01

    This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he

  11. Integrated design of MEMS

    DEFF Research Database (Denmark)

    De Grave, Arnaud; Brissaud, Daniel

    2007-01-01

    Emerging technologies of Micro-Electromechanical Systems (MEMS) are applications such as airbag accelerometers. Micro-products present many physical differences from macro-products. Moreover, there is a high level of integration in multiple fields of physics with strongly coupled effects...... industrial immersion to propose a socio-technological description of the design process and MEMS design tools....

  12. Spray-coatable negative photoresist for high topography MEMS applications

    International Nuclear Information System (INIS)

    Arnold, Markus; Haas, Sven; Schwenzer, Falk; Schwenzer, Gunther; Reuter, Danny; Geßner, Thomas; Voigt, Anja; Gruetzner, Gabi

    2017-01-01

    In microsystem technology, the lithographical processing of substrates with a topography is very important. Interconnecting lines, which are routed over sloped topography sidewalls from the top of the protecting wafer to the contact pads of the device wafer, are one example of patterning over a topography. For structuring such circuit paths, a photolithography process, and therefore a process for homogeneous photoresist coating, is required. The most flexible and advantageous way of depositing a homogeneous photoresist film over structures with high topography steps is spray-coating. As a pattern transfer process for circuit paths in cavities, the lift-off process is widely used. A negative resist, like ma-N (MRT) or AZnLOF (AZ) is favoured for lift-off processes due to the existing negative angle of the sidewalls. Only a few sprayable negative photoresists are commercially available. In this paper, the development of a novel negative resist spray-coating based on a commercially available single-layer lift-off resist for spin-coating, especially for the patterning of structures inside the cavity and on the cavity wall, is presented. A variety of parameters influences the spray-coating process, and therefore the patterning results. Besides the spray-coating tool and the parameters, the composition of the resist solution itself also influences the coating results. For homogeneous resist coverage over the topography of the substrate, different solvent combinations for diluting the resist solution, different chuck temperatures during the coating process, and also the softbake conditions, are all investigated. The solvent formulations and the process conditions are optimized with respect to the homogeneity of the resist coverage on the top edge of the cavities. Finally, the developed spray-coating process, the resist material and the process stability are demonstrated by the following applications: (i) lift-off, (ii) electroplating, (iii) the wet and (iv) the dry

  13. Thick and low-stress PECVD amorphous silicon for MEMS applications

    International Nuclear Information System (INIS)

    Iliescu, Ciprian; Chen Bangtao

    2008-01-01

    This paper presents a solution for the deposition of thick amorphous silicon (α-Si:H) in PECVD reactors for MEMS applications, such as sacrificial layer or mask layer for dry or wet etching of glass. This achievement was possible by tuning the deposition parameters to a 'zero' value of the residual stress in the α-Si:H layer. The influence of the process parameters, such as power, frequency mode, temperature, pressure and SiH 4 /Ar flow rates for tuning the residual stress and for a good deposition rate is analyzed. The deposition of low-stress and thick (more than 12 µm in our case) α-Si:H layers was possible without generation of hillock defects (previously reported in literature for layers thicker then 2 µm). Finally, the paper presents some MEMS applications of such a deposited α-Si:H layer: masking layer for deep wet etching as well as dry etching of glass, and sacrificial layer for dry or wet release

  14. 5 V Compatible Two-Axis PZT Driven MEMS Scanning Mirror with Mechanical Leverage Structure for Miniature LiDAR Application.

    Science.gov (United States)

    Ye, Liangchen; Zhang, Gaofei; You, Zheng

    2017-03-05

    The MEMS (Micro-Electronical Mechanical System) scanning mirror is an optical MEMS device that can scan laser beams across one or two dimensions. MEMS scanning mirrors can be applied in a variety of applications, such as laser display, bio-medical imaging and Light Detection and Ranging (LiDAR). These commercial applications have recently created a great demand for low-driving-voltage and low-power MEMS mirrors. However, no reported two-axis MEMS scanning mirror is available for usage in a universal supplying voltage such as 5 V. In this paper, we present an ultra-low voltage driven two-axis MEMS scanning mirror which is 5 V compatible. In order to realize low voltage and low power, a two-axis MEMS scanning mirror with mechanical leverage driven by PZT (Lead zirconate titanate) ceramic is designed, modeled, fabricated and characterized. To further decrease the power of the MEMS scanning mirror, a new method of impedance matching for PZT ceramic driven by a two-frequency mixed signal is established. As experimental results show, this MEMS scanning mirror reaches a two-axis scanning angle of 41.9° × 40.3° at a total driving voltage of 4.2 Vpp and total power of 16 mW. The effective diameter of reflection of the mirror is 2 mm and the operating frequencies of two-axis scanning are 947.51 Hz and 1464.66 Hz, respectively.

  15. Design of Diaphragm Based MEMS Pressure Sensor with Sensitivity Analysis for Environmental Applications

    Directory of Open Access Journals (Sweden)

    A. Nallathambi

    2015-05-01

    Full Text Available In this paper Micro-electromechanical System (MEMS diaphragm based pressure sensor for environmental applications is discussed. The main focus of this paper is to design, simulate and analyze the sensitivity of MEMS based diaphragm using different structures to measure the low and high pressure values. The simulation is done through the finite element tool and specifications related the maximum convinced stress; deflection and sensitivity of the diaphragms have been analyzed using the software INTELLISUITE 8.7v. The change in pressure is to bending of the diaphragm that modifies the measured displacement between the substrate and the diaphragm. This change in displacement gives the measure of the pressure in that environment. The design of these studies can be used to improve the sensitivity of these devices. Here the diaphragm based pressure sensor produced better displacement, sensitivity and stress output responses are obtained from the square diaphragm. The pressure range from 0.6 MPa to 25 MPa and its maximum displacement is accordingly 59 mm over a pressure range of 0 to 2 MPa. Its sensitivity is therefore 2.35 [10E-12/Pa].

  16. Flexible-CMOS and biocompatible piezoelectric AlN material for MEMS applications

    International Nuclear Information System (INIS)

    Jackson, Nathan; Keeney, Lynette; Mathewson, Alan

    2013-01-01

    The development of a CMOS compatible flexible piezoelectric material is desired for numerous applications and in particular for biomedical MEMS devices. Aluminum nitride (AlN) is the most commonly used CMOS compatible piezoelectric material, which is typically deposited on Si in order to enhance the c-axis (002) crystal orientation which gives AlN its high piezoelectric properties. This paper reports on the successful deposition of AlN on polyimide (PI-2611) material. The AlN deposited has a FWHM (002) value of 5.1° and a piezoelectric d 33 value of 1.12 pm V −1 , and SEM images show high quality columnar grains. The highly crystalline AlN material is due to the semi-crystalline properties of the polyimide film used. Cytotoxicity testing showed the AlN/polyimide material to be non-toxic to 3T3 cells and primary neurons. Surface properties of the AlN/polyimide film were evaluated as they have a significant effect on the adhesion of cells to the film. The results show neurons adhering to the AlN surface. The results of this paper show the characterization of a new flexible-CMOS and biocompatible AlN/polyimide material for MEMS devices with improved crystallinity and piezoelectric properties. (paper)

  17. Tailoring design and fabrication of capacitive RF MEMS switches for K-band applications

    Science.gov (United States)

    Quaranta, Fabio; Persano, Anna; Capoccia, Giovanni; Taurino, Antonietta; Cola, Adriano; Siciliano, Pietro; Lucibello, Andrea; Marcelli, Romolo; Proietti, Emanuela; Bagolini, Alvise; Margesin, Benno; Bellutti, Pierluigi; Iannacci, Jacopo

    2015-05-01

    Shunt capacitive radio-frequency microelectromechanical (RF MEMS) switches were modelled, fabricated and characterized in the K-band domain. Design allowed to predict the RF behaviour of the switches as a function of the bridge geometric parameters. The modelled switches were fabricated on silicon substrate, using a surface micromachining approach. In addition to the geometric parameters, the material structure in the bridge-actuator area was modified for switches fabricated on the same wafer, thanks to the removal/addition of two technological steps of crucial importance for RF MEMS switches performance, which are the use of the sacrificial layer and the deposition of a floating metal layer on the actuator. Surface profilometry analysis was used to check the material layer structure in the different regions of the bridge area as well as to investigate the mechanical behaviour of the moveable bridge under the application of a loaded force. The RF behaviour of all the fabricated switches was measured, observing the impact on the isolation of the manipulation of the bridge size and of the variations in the fabrication process.

  18. Nanotechnology: MEMS and NEMS and their applications to smart systems and devices

    Science.gov (United States)

    Varadan, Vijay K.

    2003-10-01

    civil strutures and food and medical industries. This unique combination of technologies also results in novel conformal sensors that can be remotely sensed by an antenna system with the advantage of no power requirements at the sensor site. This paper provides a brief review of MEMS and NEMS based smart systems for various applications mentioned above. Carbon Nano Tubes (CNT) with their unique structure, have already proven to be valuable in their application as tips for scanning probe microscopy, field emission devices, nanoelectronics, H2-storage, electromagnetic absorbers, ESD, EMI films and coatings and structural composites. For many of these applications, highly purified and functionalized CNT which are compatible with many host polymers are needed. A novel microwave CVD processing technique to meet these requirements has been developed at Penn State Center for the Engineering of Electronic and Acoustic Materials and Devices (CEEAMD). This method enables the production of highly purified carbon nano tubes with variable size (from 5 - 40 nm) at low cost (per gram) and high yield. Whereas, carbon nano tubes synthesized using the laser ablation or arc discharge evaporation method always include impurity due to catalyst or catalyst support. The Penn State research is based on the use of zeolites over other metal/metal oxides in the microwave field for a high production and uniformity of the product. An extended coventional purification method has been employed to purify our products in order to remove left over impurity. A novel composite structure can be tailored by functionalizing carbon nano tubes and chemically bonding them with the polymer matrix e.g. block or graft copolymer, or even cross-linked copolymer, to impart exceptional structural, electronic and surface properties. Bio- and Mechanical-MEMS devices derived from this hybrid composites will be presented.

  19. Decentralized neural control application to robotics

    CERN Document Server

    Garcia-Hernandez, Ramon; Sanchez, Edgar N; Alanis, Alma y; Ruz-Hernandez, Jose A

    2017-01-01

    This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural i...

  20. MEMBRAIN NEURAL NETWORK FOR VISUAL PATTERN RECOGNITION

    Directory of Open Access Journals (Sweden)

    Artur Popko

    2013-06-01

    Full Text Available Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.

  1. EDITORIAL: International MEMS Conference 2006

    Science.gov (United States)

    Tay, Francis E. H.; Jianmin, Miao; Iliescu, Ciprian

    2006-04-01

    The International MEMS conference (iMEMS2006) organized by the Institute of Bioengineering and Nanotechnology and Nanyang Technological University aims to provide a platform for academicians, professionals and industrialists in various related fields from all over the world to share and learn from each other. Of great interest is the incorporation of the theme of life sciences application using MEMS. It is the desire of this conference to initiate collaboration and form network of cooperation. This has continued to be the objective of iMEMS since its inception in 1997. The technological advance of MEMS over the past few decades has been truly exciting in terms of development and applications. In order to participate in this rapid development, a conference involving delegates from within the MEMS community and outside the community is very meaningful and timely. With the receipt of over 200 articles, delegates related to MEMS field from all over the world will share their perspectives on topics such as MEMS/MST Design, MEMS Teaching and Education, MEMS/MST Packaging, MEMS/MST Fabrication, Microsystems Applications, System Integration, Wearable Devices, MEMSWear and BioMEMS. Invited speakers and delegates from outside the field have also been involved to provide challenges, especially in the life sciences field, for the MEMS community to potentially address. The proceedings of the conference will be published as an issue in the online Journal of Physics: Conference Series and this can reach a wider audience and will facilitate the reference and citation of the work presented in the conference. We wish to express our deep gratitude to the International Scientific Committee members and the organizing committee members for contributing to the success of this conference. We would like to thank all the delegates, speakers and sponsors from all over the world for presenting and sharing their perspectives on topics related to MEMS and the challenges that MEMS can

  2. EDITORIAL: The Fourth International Workshop on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS 2004)

    Science.gov (United States)

    Tanaka, Shuji; Toriyama, Toshiyuki

    2005-09-01

    This special issue of the Journal of Micromechanics and Microengineering features papers selected from the Fourth International Workshop on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS 2004). The workshop was held in Kyoto, Japan, on 28-30 November 2004, by The Ritsumeikan Research Institute of Micro System Technology in cooperation with The Global Emerging Technology Institute, The Institute of Electrical Engineers of Japan, The Sensors and Micromachines Society, The Micromachine Center and The Kyoto Nanotech Cluster. Power MEMS is one of the newest categories of MEMS, which encompasses microdevices and microsystems for power generation, energy conversion and propulsion. The first concept of power MEMS was proposed in the late 1990s by Epstein's group at the Massachusetts Institute of Technology, where they continue to study MEMS-based gas turbine generators. Since then, the research and development of power MEMS have been promoted by the need for compact power sources with high energy and power density. Since its inception, power MEMS has expanded to include not only various MEMS-based power generators but also small energy machines and microdevices for macro power generators. At the last workshop, various devices and systems, such as portable fuel cells and their peripherals, micro and small turbo machinery, energy harvesting microdevices, and microthrusters, were presented. Their power levels vary from ten nanowatts to hundreds of watts, spanning ten orders of magnitude. The first PowerMEMS workshop was held in 2000 in Sendai, Japan, and consisted of only seven invited presentations. The workshop has grown since then, and in 2004 there were 5 invited, 20 oral and 29 poster presentations. From the 54 papers in the proceedings, 12 papers have been selected for this special issue. I would like to express my appreciation to the members of the Organizing Committee and Technical Program Committee. This special issue was

  3. Applications of neural network to numerical analyses

    International Nuclear Information System (INIS)

    Takeda, Tatsuoki; Fukuhara, Makoto; Ma, Xiao-Feng; Liaqat, Ali

    1999-01-01

    Applications of a multi-layer neural network to numerical analyses are described. We are mainly concerned with the computed tomography and the solution of differential equations. In both cases as the objective functions for the training process of the neural network we employed residuals of the integral equation or the differential equations. This is different from the conventional neural network training where sum of the squared errors of the output values is adopted as the objective function. For model problems both the methods gave satisfactory results and the methods are considered promising for some kind of problems. (author)

  4. Fabrication and performance analysis of MEMS-based Variable Emissivity Radiator for Space Applications

    International Nuclear Information System (INIS)

    Lee, Changwook; Oh, Hyung-Ung; Kim, Taegyu

    2014-01-01

    All Louver was typically representative as the thermal control device. The louver was not suitable to be applied to small satellite, because it has the disadvantage of increase in weight and volume. So MEMS-based variable radiator was developed to support the disadvantage of the louver MEMS-based variable emissivity radiator was designed for satellite thermal control. Because of its immediate response and low power consumption. Also MEMS- based variable emissivity radiator has been made smaller by using MEMS process, it could be solved the problem of the increase in weight and volume, and it has a high reliability and immediate response by using electrical control. In this study, operation validation of the MEMS radiator had been carried out, resulting that emissivity could be controlled. Numerical model was also designed to predict the thermal control performance of MEMS-based variable emissivity radiator

  5. Design and fabrication of non silicon substrate based MEMS energy harvester for arbitrary surface applications

    Energy Technology Data Exchange (ETDEWEB)

    Balpande, Suresh S., E-mail: balpandes@rknec.edu [Ph.D.. Scholar, Department of Electronics Engineering Shri Ramdeobaba College of Engineering & Management, Nagpur-13, (M.S.) (India); Pande, Rajesh S. [Professor, Department of Electronics Engineering Shri Ramdeobaba College of Engineering & Management, Nagpur-13, (M.S.) (India)

    2016-04-13

    Internet of Things (IoT) uses MEMS sensor nodes and actuators to sense and control objects through Internet. IOT deploys millions of chemical battery driven sensors at different locations which are not reliable many times because of frequent requirement of charging & battery replacement in case of underground laying, placement at harsh environmental conditions, huge count and difference between demand (24 % per year) and availability (energy density growing rate 8% per year). Energy harvester fabricated on silicon wafers have been widely used in manufacturing MEMS structures. These devices require complex fabrication processes, costly chemicals & clean room. In addition to this silicon wafer based devices are not suitable for curved surfaces like pipes, human bodies, organisms, or other arbitrary surface like clothes, structure surfaces which does not have flat and smooth surface always. Therefore, devices based on rigid silicon wafers are not suitable for these applications. Flexible structures are the key solution for this problems. Energy transduction mechanism generates power from free surrounding vibrations or impact. Sensor nodes application has been purposefully selected due to discrete power requirement at low duty cycle. Such nodes require an average power budget in the range of about 0.1 microwatt to 1 mW over a period of 3-5 seconds. Energy harvester is the best alternate source in contrast with battery for sensor node application. Novel design of Energy Harvester based on cheapest flexible non silicon substrate i.e. cellulose acetate substrate have been modeled, simulated and analyzed on COMSOL multiphysics and fabricated using sol-gel spin coating setup. Single cantilever based harvester generates 60-75 mV peak electric potential at 22Hz frequency and approximately 22 µW power at 1K-Ohm load. Cantilever array can be employed for generating higher voltage by replicating this structure. This work covers design, optimization, fabrication of

  6. Design and fabrication of non silicon substrate based MEMS energy harvester for arbitrary surface applications

    Science.gov (United States)

    Balpande, Suresh S.; Pande, Rajesh S.

    2016-04-01

    Internet of Things (IoT) uses MEMS sensor nodes and actuators to sense and control objects through Internet. IOT deploys millions of chemical battery driven sensors at different locations which are not reliable many times because of frequent requirement of charging & battery replacement in case of underground laying, placement at harsh environmental conditions, huge count and difference between demand (24 % per year) and availability (energy density growing rate 8% per year). Energy harvester fabricated on silicon wafers have been widely used in manufacturing MEMS structures. These devices require complex fabrication processes, costly chemicals & clean room. In addition to this silicon wafer based devices are not suitable for curved surfaces like pipes, human bodies, organisms, or other arbitrary surface like clothes, structure surfaces which does not have flat and smooth surface always. Therefore, devices based on rigid silicon wafers are not suitable for these applications. Flexible structures are the key solution for this problems. Energy transduction mechanism generates power from free surrounding vibrations or impact. Sensor nodes application has been purposefully selected due to discrete power requirement at low duty cycle. Such nodes require an average power budget in the range of about 0.1 microwatt to 1 mW over a period of 3-5 seconds. Energy harvester is the best alternate source in contrast with battery for sensor node application. Novel design of Energy Harvester based on cheapest flexible non silicon substrate i.e. cellulose acetate substrate have been modeled, simulated and analyzed on COMSOL multiphysics and fabricated using sol-gel spin coating setup. Single cantilever based harvester generates 60-75 mV peak electric potential at 22Hz frequency and approximately 22 µW power at 1K-Ohm load. Cantilever array can be employed for generating higher voltage by replicating this structure. This work covers design, optimization, fabrication of harvester and

  7. Design and fabrication of non silicon substrate based MEMS energy harvester for arbitrary surface applications

    International Nuclear Information System (INIS)

    Balpande, Suresh S.; Pande, Rajesh S.

    2016-01-01

    Internet of Things (IoT) uses MEMS sensor nodes and actuators to sense and control objects through Internet. IOT deploys millions of chemical battery driven sensors at different locations which are not reliable many times because of frequent requirement of charging & battery replacement in case of underground laying, placement at harsh environmental conditions, huge count and difference between demand (24 % per year) and availability (energy density growing rate 8% per year). Energy harvester fabricated on silicon wafers have been widely used in manufacturing MEMS structures. These devices require complex fabrication processes, costly chemicals & clean room. In addition to this silicon wafer based devices are not suitable for curved surfaces like pipes, human bodies, organisms, or other arbitrary surface like clothes, structure surfaces which does not have flat and smooth surface always. Therefore, devices based on rigid silicon wafers are not suitable for these applications. Flexible structures are the key solution for this problems. Energy transduction mechanism generates power from free surrounding vibrations or impact. Sensor nodes application has been purposefully selected due to discrete power requirement at low duty cycle. Such nodes require an average power budget in the range of about 0.1 microwatt to 1 mW over a period of 3-5 seconds. Energy harvester is the best alternate source in contrast with battery for sensor node application. Novel design of Energy Harvester based on cheapest flexible non silicon substrate i.e. cellulose acetate substrate have been modeled, simulated and analyzed on COMSOL multiphysics and fabricated using sol-gel spin coating setup. Single cantilever based harvester generates 60-75 mV peak electric potential at 22Hz frequency and approximately 22 µW power at 1K-Ohm load. Cantilever array can be employed for generating higher voltage by replicating this structure. This work covers design, optimization, fabrication of

  8. Application of neural networks in CRM systems

    Directory of Open Access Journals (Sweden)

    Bojanowska Agnieszka

    2017-01-01

    Full Text Available The central aim of this study is to investigate how to apply artificial neural networks in Customer Relationship Management (CRM. The paper presents several business applications of neural networks in software systems designed to aid CRM, e.g. in deciding on the profitability of building a relationship with a given customer. Furthermore, a framework for a neural-network based CRM software tool is developed. Building beneficial relationships with customers is generating considerable interest among various businesses, and is often mentioned as one of the crucial objectives of enterprises, next to their key aim: to bring satisfactory profit. There is a growing tendency among businesses to invest in CRM systems, which together with an organisational culture of a company aid managing customer relationships. It is the sheer amount of gathered data as well as the need for constant updating and analysis of this breadth of information that may imply the suitability of neural networks for the application in question. Neural networks exhibit considerably higher computational capabilities than sequential calculations because the solution to a problem is obtained without the need for developing a special algorithm. In the majority of presented CRM applications neural networks constitute and are presented as a managerial decision-taking optimisation tool.

  9. Neural networks advances and applications 2

    CERN Document Server

    Gelenbe, E

    1992-01-01

    The present volume is a natural follow-up to Neural Networks: Advances and Applications which appeared one year previously. As the title indicates, it combines the presentation of recent methodological results concerning computational models and results inspired by neural networks, and of well-documented applications which illustrate the use of such models in the solution of difficult problems. The volume is balanced with respect to these two orientations: it contains six papers concerning methodological developments and five papers concerning applications and examples illustrating the theoret

  10. N-type doped nano-diamond in a first MEMS application

    Energy Technology Data Exchange (ETDEWEB)

    Dipalo, M.; Kusterer, J.; Janischowsky, K.; Kohn, E. [Dept. of Electron Devices and Circuits, University of Ulm, Albert Einstein Allee 45, 89081 Ulm (Germany)

    2006-09-15

    Nanocrystalline diamond is an interesting material for MEMS applications especially due to its outstanding mechanical, electrical and electrochemical properties. The current choice for doping is boron, resulting in p-type conduction. It has two difficulties: firstly, at high concentration (as needed for full activation) the lattice becomes highly stressed and may degrade the material's quality. Secondly, it contaminates the growth chamber, resulting in a memory effect. A recent alternative is n-type nitrogen doping, avoiding these disadvantages. However, nitrogen is mainly incorporated in the grain boundaries and thus inhomogeneously distributed. In turn this may limit the material's stability. Here we present a first trial to use nitrogen-doped nanocrystalline diamond (NCD), grown by hot filament CVD, in a water microjet as heater element. No stability problems were encountered even at high overdrive power. (copyright 2006 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  11. Application of neural network to CT

    International Nuclear Information System (INIS)

    Ma, Xiao-Feng; Takeda, Tatsuoki

    1999-01-01

    This paper presents a new method for two-dimensional image reconstruction by using a multilayer neural network. Multilayer neural networks are extensively investigated and practically applied to solution of various problems such as inverse problems or time series prediction problems. From learning an input-output mapping from a set of examples, neural networks can be regarded as synthesizing an approximation of multidimensional function (that is, solving the problem of hypersurface reconstruction, including smoothing and interpolation). From this viewpoint, neural networks are well suited to the solution of CT image reconstruction. Though a conventionally used object function of a neural network is composed of a sum of squared errors of the output data, we can define an object function composed of a sum of residue of an integral equation. By employing an appropriate line integral for this integral equation, we can construct a neural network that can be used for CT. We applied this method to some model problems and obtained satisfactory results. As it is not necessary to discretized the integral equation using this reconstruction method, therefore it is application to the problem of complicated geometrical shapes is also feasible. Moreover, in neural networks, interpolation is performed quite smoothly, as a result, inverse mapping can be achieved smoothly even in case of including experimental and numerical errors, However, use of conventional back propagation technique for optimization leads to an expensive computation cost. To overcome this drawback, 2nd order optimization methods or parallel computing will be applied in future. (J.P.N.)

  12. RF-MEMS Technology for High-Performance Passives; The challenge of 5G mobile applications

    Science.gov (United States)

    Iannacci, Jacopo

    2017-11-01

    Commencing with a review of the characteristics of RF-MEMS in relation to 5G, the book proceeds to develop practical insight concerning the design and development of RF-MEMS including case studies of design concepts. Including multiphysics simulation and animated figures, the book will be essential reading for both academic and industrial researchers and engineers.

  13. Power gating of VLSI circuits using MEMS switches in low power applications

    KAUST Repository

    Shobak, Hosam; Ghoneim, Mohamed T.; El Boghdady, Nawal; Halawa, Sarah; Iskander, Sophinese M.; Anis, Mohab H.

    2011-01-01

    -designed MEMS switch to power gate VLSI circuits, such that leakage power is efficiently reduced while accounting for performance and reliability. The designed MEMS switch is characterized by an 0.1876 ? ON resistance and requires 4.5 V to switch. As a result

  14. Characterization of low temperature deposited atomic layer deposition TiO2 for MEMS applications

    NARCIS (Netherlands)

    Huang, Y.; Pandraud, G.; Sarro, P.M.

    2012-01-01

    TiO2 is an interesting and promising material for micro-/nanoelectromechanical systems (MEMS/NEMS). For high performance and reliable MEMS/NEMS, optimization of the optical characteristics, mechanical stress, and especially surface smoothness of TiO2 is required. To overcome the roughness issue of

  15. DLC nano-dot surfaces for tribological applications in MEMS devices

    International Nuclear Information System (INIS)

    Singh, R. Arvind; Na, Kyounghwan; Yi, Jin Woo; Lee, Kwang-Ryeol; Yoon, Eui-Sung

    2011-01-01

    With the invention of miniaturized devices like micro-electro-mechanical systems (MEMS), tribological studies at micro/nano-scale have gained importance. These studies are directed towards understanding the interactions between surfaces at micro/nano-scales, under relative motion. In MEMS devices, the critical forces, namely adhesion and friction restrict the smooth operation of the elements that are in relative motion. These miniaturized devices are traditionally made from silicon (Si), whose tribological properties are not good. In this paper, we present a short investigation of nano- and micro-tribological properties of diamond-like carbon (DLC) nano-dot surfaces. The investigation was undertaken to evaluate the potential of these surfaces for their possible application to the miniaturized devices. The tribological evaluation of the DLC nano-dot surfaces was done in comparison with bare Si (1 0 0) surfaces and DLC coated silicon surfaces. A commercial atomic force microscope (AFM) was used to measure adhesion and friction properties of the test materials at the nano-scale, whereas a custom-built micro-tribotester was used to measure their micro-friction property. Results showed that the DLC nano-dot surfaces exhibited superior tribological properties with the lowest values of adhesion force, and friction force both at the nano- and micro-scales, when compared to the bare Si (1 0 0) surfaces and DLC coated silicon surfaces. In addition, the DLC nano-dot surfaces showed no observable wear at the micro-scale, unlike the other two test materials. The superior tribological performance of the DLC nano-dot surfaces is attributed to their hydrophobic nature and the reduced area of contact projected by them.

  16. DLC nano-dot surfaces for tribological applications in MEMS devices

    Energy Technology Data Exchange (ETDEWEB)

    Singh, R. Arvind; Na, Kyounghwan [Nano-Bio Research Center, Korea Institute of Science and Technology, 39-1, Hawolgok-dong, Seongbuk-gu, Seoul 136-791 (Korea, Republic of); Yi, Jin Woo; Lee, Kwang-Ryeol [Computational Science Center, Korea Institute of Science and Technology, 39-1, Hawolgok-dong, Seongbuk-gu, Seoul 136-791 (Korea, Republic of); Yoon, Eui-Sung, E-mail: esyoon@kist.re.kr [Nano-Bio Research Center, Korea Institute of Science and Technology, 39-1, Hawolgok-dong, Seongbuk-gu, Seoul 136-791 (Korea, Republic of)

    2011-02-01

    With the invention of miniaturized devices like micro-electro-mechanical systems (MEMS), tribological studies at micro/nano-scale have gained importance. These studies are directed towards understanding the interactions between surfaces at micro/nano-scales, under relative motion. In MEMS devices, the critical forces, namely adhesion and friction restrict the smooth operation of the elements that are in relative motion. These miniaturized devices are traditionally made from silicon (Si), whose tribological properties are not good. In this paper, we present a short investigation of nano- and micro-tribological properties of diamond-like carbon (DLC) nano-dot surfaces. The investigation was undertaken to evaluate the potential of these surfaces for their possible application to the miniaturized devices. The tribological evaluation of the DLC nano-dot surfaces was done in comparison with bare Si (1 0 0) surfaces and DLC coated silicon surfaces. A commercial atomic force microscope (AFM) was used to measure adhesion and friction properties of the test materials at the nano-scale, whereas a custom-built micro-tribotester was used to measure their micro-friction property. Results showed that the DLC nano-dot surfaces exhibited superior tribological properties with the lowest values of adhesion force, and friction force both at the nano- and micro-scales, when compared to the bare Si (1 0 0) surfaces and DLC coated silicon surfaces. In addition, the DLC nano-dot surfaces showed no observable wear at the micro-scale, unlike the other two test materials. The superior tribological performance of the DLC nano-dot surfaces is attributed to their hydrophobic nature and the reduced area of contact projected by them.

  17. A liquid crystal polymer membrane MEMS sensor for flow rate and flow direction sensing applications

    International Nuclear Information System (INIS)

    Kottapalli, A G P; Tan, C W; Olfatnia, M; Miao, J M; Barbastathis, G; Triantafyllou, M

    2011-01-01

    The paper reports the design, fabrication and experimental results of a liquid crystal polymer (LCP) membrane-based pressure sensor for flow rate and flow direction sensing applications. Elaborate experimental testing results demonstrating the sensors' performance as an airflow sensor have been illustrated and validated with theory. MEMS sensors using LCP as a membrane structural material show higher sensitivity and reliability over silicon counterparts. The developed device is highly robust for harsh environment applications such as atmospheric wind flow monitoring and underwater flow sensing. A simple, low-cost and repeatable fabrication scheme has been developed employing low temperatures. The main features of the sensor developed in this work are a LCP membrane with integrated thin film gold piezoresistors deposited on it. The sensor developed demonstrates a good sensitivity of 3.695 mV (ms −1 ) −1 , large operating range (0.1 to >10 ms −1 ) and good accuracy in measuring airflow with an average error of only 3.6% full-scale in comparison with theory. Various feasible applications of the developed sensor have been demonstrated with experimental results. The sensor was tested for two other applications—in clinical diagnosis for breath rate, breath velocity monitoring, and in underwater applications for object detection by sensing near-field spatial flow pressure

  18. PREFACE: 14th International Conference on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS 2014)

    Science.gov (United States)

    2014-11-01

    It is our great pleasure to welcome you to the 14th International Conference on Micro- and Nano-Technology for Power Generation and Energy Conversion Applications, or PowerMEMS 2014, in Awaji Island, Japan. The aim of PowerMEM is to present the latest research results in the field of miniature, micro- and nano-scale technologies for power generation and energy conversion. The conference will also- give us the opportunity to exchange informations and new ideas in the field of Power MEMS/NEMS. The current status of the field of PowerMEMS spans the full spectrum from basic research to practical applications. We will enjoy valuable discussions not only from the viewpoint of academia but from commercial and industrial perspectives. In the conference, three invited speakers lead the technical program. We received 172 abstracts and after a careful reviewing process by the Technical Program Committee a total of 133 papers were selected for presentation. These have been organized into 16 Oral sessions in two parallel streams and two poster sessions including some late-news papers. The oral and regular poster papers are published by the Institute of Physics (IOP). We have also organized a PowerMEMS School in Kobe-Sannomiya contiguous to the main conference. This two-day school will cover various topics of energy harvesting. World leading experts will give invited lectures on their main topics. This is a new experiment to broaden the technology remit of our conference by organizing mini symposiums that aim to gather the latest research on the following topics by the organizers: Microscale Combustion, Wideband Vibration Energy Harvesting, RF Energy Transfer and Industrial Application. We hope this, and other activities will make PowerMEMS2014 a memorable success. One of the important programs in an international conference is the social program, and we prepare the PowerMEMS2014 banquet in the banquet room at the Westin Awaji Island Hotel. This will provide an opportunity to

  19. Development of a Multi-User Polyimide-MEMS Fabrication Process and its Application to MicroHotplates

    KAUST Repository

    Lizardo, Ernesto B.

    2013-01-01

    Micro-electro-mechanical systems (MEMS) became possible thanks to the silicon based technology used to fabricate integrated circuits. Originally, MEMS fabrication was limited to silicon based techniques and materials, but the expansion of MEMS

  20. Application of neural networks in experimental physics

    International Nuclear Information System (INIS)

    Kisel', I.V.; Neskromnyj, V.N.; Ososkov, G.A.

    1993-01-01

    The theoretical foundations of numerous models of artificial neural networks (ANN) and their applications to the actual problems of associative memory, optimization and pattern recognition are given. This review contains also numerous using of ANN in the experimental physics both as the hardware realization of fast triggering systems for even selection and for the following software implementation of the trajectory data recognition

  1. Hollow MEMS

    DEFF Research Database (Denmark)

    Larsen, Peter Emil

    Miniaturization of electro mechanical sensor systems to the micro range and beyond has shown impressive sensitivities measuring sample properties like mass, viscosity, acceleration, pressure and force just to name a few applications. In order to enable these kinds of measurements on liquid samples...... a hollow MEMS sensor has been designed, fabricated and tested. Combined density, viscosity, buoyant mass spectrometry and IR absorption spectroscopy are possible on liquid samples and micron sized suspended particles (e.g. single cells). Measurements are based on changes in the resonant behavior...... of these sensors. Optimization of the microfabrication process has led to a process yield of almost 100% .This is achieved despite the fact, that the process still offers a high degree of flexibility. By simple modifications the Sensor shape can be optimized for different size ranges and sensitivities...

  2. Application of radial basis neural network for state estimation of ...

    African Journals Online (AJOL)

    An original application of radial basis function (RBF) neural network for power system state estimation is proposed in this paper. The property of massive parallelism of neural networks is employed for this. The application of RBF neural network for state estimation is investigated by testing its applicability on a IEEE 14 bus ...

  3. Some metal oxides and their applications for creation of Microsystems (MEMS) and Energy Harvesting Devices (EHD)

    International Nuclear Information System (INIS)

    Denishev, K

    2016-01-01

    This is a review of a part of the work of the Technological Design Group at Technical University of Sofia, Faculty of Electronic Engineering and Technologies, Department of Microelectronics. It is dealing with piezoelectric polymer materials and their application in different microsystems (MEMS) and Energy Harvesting Devices (EHD), some organic materials and their applications in organic (OLED) displays, some transparent conductive materials etc. The metal oxides Lead Zirconium Titanate (PZT) and Zinc Oxide (ZnO) are used as piezoelectric layers - driving part of different sensors, actuators and EHD. These materials are studied in term of their performance in dependence on the deposition conditions and parameters. They were deposited as thin films by using RF Sputtering System. As technological substrates, glass plates and Polyethylenetherephtalate (PET) foils were used. For characterization of the materials, a test structure, based on Surface Acoustic Waves (SAW), was designed and prepared. The layers were characterized by Fourier Transform Infrared spectroscopy (FTIR). The piezoelectric response was tested at variety of mechanical loads (tensile strain, stress) in static and dynamic (multiple bending) mode. The single-layered and double-layered structures were prepared for piezoelectric efficiency increase. A structure of piezoelectric energy transformer is proposed and investigated. (paper)

  4. A MEMS electret generator with electrostatic levitation for vibration-driven energy-harvesting applications

    International Nuclear Information System (INIS)

    Suzuki, Yuji; Miki, Daigo; Edamoto, Masato; Honzumi, Makoto

    2010-01-01

    In this paper, we propose a passive gap-spacing control method in order to avoid stiction between top and bottom structures in in-plane sensor/actuator/generator applications. A patterned electret using a high-performance perfluoro polymer material is employed to induce a repulsive electrostatic force. An out-of-plane repulsive force is successfully demonstrated with our early prototype, in both air and liquid. By using the present electret-based levitation method to keep the air gap, a MEMS electret generator has been developed for energy-harvesting applications. A dual-phase electrode arrangement is adopted in order to reduce the horizontal electrostatic damping force. With the present prototype, about 0.5 µW is obtained for both phases of the generator, resulting in a total power output of 1.0 µW at an acceleration of 2 g with 63 Hz. With our electromechanical model of the generator, we have confirmed that the model can mimic the response of the generator prototype

  5. A low-noise MEMS accelerometer for unattended ground sensor applications

    Science.gov (United States)

    Speller, Kevin E.; Yu, Duli

    2004-09-01

    A low-noise micro-machined servo accelerometer has been developed for use in Unattended Ground Sensors (UGS). Compared to conventional coil-and-magnet based velocity transducers, this Micro-Electro-Mechanical System (MEMS) accelerometer offers several key benefits for battlefield monitoring. Many UGS require a compass to determine deployment orientation with respect to magnetic North. This orientation information is critical for determining the bearing of incoming signals. Conventional sensors with sensing technology based on a permanent magnet can cause interference with a compass when used in close proximity. This problem is solved with a MEMS accelerometer which does not require any magnetic materials. Frequency information below 10 Hz is valuable for identification of signal sources. Conventional seismometers used in UGS are typically limited in frequency response from 20 to 200 Hz. The MEMS accelerometer has a flat frequency response from DC to 5 kHz. The wider spectrum of signals received improves detection, classification and monitoring on the battlefield. The DC-coupled output of the MEMS accelerometer also has the added benefit of providing tilt orientation data for the deployed UGS. Other performance parameters of the MEMS accelerometer that are important to UGS such as size, weight, shock survivability, phase response, distortion, and cross-axis rejection will be discussed. Additionally, field test data from human footsteps recorded with the MEMS accelerometer will be presented.

  6. Numerical Simulation of a Novel Electroosmotic Micropump for Bio-MEMS Applications

    Directory of Open Access Journals (Sweden)

    Alireza Alishahi

    2014-12-01

    Full Text Available High lamination in microchannel is one of the main challenges in Lab-On-a-Chip’s components like micro total analyzer systems and any miniaturization of fluid channels intensify the viscose effects. In chip-scale, the electroosmotic flow is more efficient. Therefore, this study presents a MEMS-based low-voltage micropump for low-conductive biological samples and solutions, where twelve narrow miniaturized microchannels designed in one unit to efficiently using the electroosmotic effects which generated near the walls. Four microelectrodes are mounted in lateral sides of the microchannel and excited by low-voltage potential to generate pumping process inside the channel. We sweep the voltage amplitude and a linear variation of fluid velocity achieved by Finite-Element-Method (FEM simulation. We obtain a net average velocity of 0.1 mm/s; by applying 2 V and -2 V to the electrodes. Therefore, the proposed low-voltage design is able to pumping the low-conductive biofluids for conventional lab-on-a-chip applications.

  7. MEMS-based micro-fuel processor for application in a cell phone

    Energy Technology Data Exchange (ETDEWEB)

    Kundu, Arunabha; Jang, Jae Hyuk; Lee, Hong Ryul; Kim, Sung-Han; Gil, Jae Hyoung; Jung, Chang Ryul; Oh, Yong Soo [Micro-Fuel Cell Team, Electro-Material and Device (eMD) Laboratory, Corporate R and D Center, Samsung Electro-Mechanics, 314 Maetan-Dong, Yeongtong-Gu, Suwon, Gyunngi-Do 443-743 (Korea, Republic of)

    2006-11-08

    The operation of a micro-electro-mechanical system (MEMS)-based micro-reformer was investigated for application in a cell phone. Different aspects like the time required to attain the desired temperature of the system, the time required to get the required hydrogen flow, catalyst durability, flow uniformity of the mixture of methanol and water and volume of the total system were considered. A loading procedure for the catalyst in the micro-reformer was developed. Catalyst deactivation was observed after operating continuously for 8h, but it regained its original activity after the reformer was shut down for at least 2h. The deactivation of the catalyst was analyzed by catalyst characterization. The comparison of the performance between a parallel channeled and serpentine channeled micro-reformer was carried out. The performance with the serpentine channeled micro-reformer was always higher than with parallel channeled micro-reformer. The shorter residence time in the parallel-channeled micro-reformer may be one of the reasons behind its low activity. (author)

  8. Humidity influence on the adhesion of SU-8 polymer from MEMS applications

    Directory of Open Access Journals (Sweden)

    Birleanu Corina

    2017-01-01

    Full Text Available In this paper, the adhesion behaviors of SU-8 polymer thin film from MEMS application were investigated as a function of relative humidity. The adhesion test between the AFM tip and SU-8 polymer have been extensively studied using the atomic force microscope (AFM, for a relative humidity (RH varying between 20 and 90%. The samples for tests are SU-8 polymers hard baked at different temperatures. The hard bake temperature changes the tribo-mechanical properties of polymers. The paper reports the measurements and the modeling of adhesion forces versus humidity in controlled ranges between 20 to 90%RH. To investigate the effect of relative humidity on adhesion for SU-8 polymer hard baked we used an analytical method which encompasses the effect of capillarity as well as the solid-to-solid interaction. While the capillary force expression is considered to be the sum of the superficial tension and the Laplace force for the solid-solid interaction is expressed by the Derjagin, Muller and Toropov (DMT model of solids adhesion. The analytical results obtained are in accordance with those obtained experimentally.

  9. Aluminum Nitride Micro-Channels Grown via Metal Organic Vapor Phase Epitaxy for MEMs Applications

    Energy Technology Data Exchange (ETDEWEB)

    Rodak, L.E.; Kuchibhatla, S.; Famouri, P.; Ting, L.; Korakakis, D.

    2008-01-01

    Aluminum nitride (AlN) is a promising material for a number of applications due to its temperature and chemical stability. Furthermore, AlN maintains its piezoelectric properties at higher temperatures than more commonly used materials, such as Lead Zirconate Titanate (PZT) [1, 2], making AlN attractive for high temperature micro and nanoelectromechanical (MEMs and NEMs) applications including, but not limited to, high temperature sensors and actuators, micro-channels for fuel cell applications, and micromechanical resonators. This work presents a novel AlN micro-channel fabrication technique using Metal Organic Vapor Phase Epitaxy (MOVPE). AlN easily nucleates on dielectric surfaces due to the large sticking coefficient and short diffusion length of the aluminum species resulting in a high quality polycrystalline growth on typical mask materials, such as silicon dioxide and silicon nitride [3,4]. The fabrication process introduced involves partially masking a substrate with a silicon dioxide striped pattern and then growing AlN via MOVPE simultaneously on the dielectric mask and exposed substrate. A buffered oxide etch is then used to remove the underlying silicon dioxide and leave a free standing AlN micro-channel. The width of the channel has been varied from 5 ìm to 110 ìm and the height of the air gap from 130 nm to 800 nm indicating the stability of the structure. Furthermore, this versatile process has been performed on (111) silicon, c-plane sapphire, and gallium nitride epilayers on sapphire substrates. Reflection High Energy Electron Diffraction (RHEED), Atomic Force Microscopy (AFM), and Raman measurements have been taken on channels grown on each substrate and indicate that the substrate is influencing the growth of the AlN micro-channels on the SiO2 sacrificial layer.

  10. New Results on Plasma Activated Bonding of Imprinted Polymer Features for Bio MEMS Applications

    International Nuclear Information System (INIS)

    Kettner, P; Pelzer, R L; Glinsner, T; Farrens, S; Lee, D

    2006-01-01

    Nanoimprint Lithography is a well-acknowledged low cost, high resolution, large area 3D patterning process for polymers. It includes the most promising methods: high pressure hot embossing (HE) and UV-Nanoimprint Lithography (UV-NIL). Curing of the imprinted structures is either done by cooling down below the glass transition temperature of the thermoplastic polymer in case of HE or by subsequent UV-light exposure and cross-linking in case of UV-NIL. Both techniques allow rapid prototyping for high volume production of fully patterned substrates for a wide range of materials. The advantages of using polymer substrates over common Micro-Electro-Mechanical Systems (MEMS) processing materials like glass, silicon or quartz are: bio-compatible surfaces, easy manufacturability, low cost for high volume production, suitable for use in micro- and nano-fabrication, low conductivity, wide range of optical properties just to name a few. We will present experimental results on HE processes with PMMA as well as UV-NIL imprints in selected UV-curable resists. In the second part of the work we will describe the bonding techniques for packaging of the micro or nano structures. Packaging of the imprinted features is a key technology for a wide variety of field of applications: μ-TAS, biochemistry, micro-mixers, micro-reactors, electrophoresis cells, life science, micro-optical and nano-optical applications (switches) nanofluidics, data storage, etc. for features down to sub-100 nm range. Most bonding techniques for polymer use adhesives as intermediate layers. We will demonstrate a promising technique for dense and very strong bonds using plasma activation of polymers and glass. This bonding technology allows for bonding at low temperatures well below the glass transition temperature of the polymers, which will ensure that the structures are not deformed

  11. Application of neural networks to group technology

    Science.gov (United States)

    Caudell, Thomas P.; Smith, Scott D. G.; Johnson, G. C.; Wunsch, Donald C., II

    1991-08-01

    Adaptive resonance theory (ART) neural networks are being developed for application to the industrial engineering problem of group technology--the reuse of engineering designs. Two- and three-dimensional representations of engineering designs are input to ART-1 neural networks to produce groups or families of similar parts. These representations, in their basic form, amount to bit maps of the part, and can become very large when the part is represented in high resolution. This paper describes an enhancement to an algorithmic form of ART-1 that allows it to operate directly on compressed input representations and to generate compressed memory templates. The performance of this compressed algorithm is compared to that of the regular algorithm on real engineering designs and a significant savings in memory storage as well as a speed up in execution is observed. In additions, a `neural database'' system under development is described. This system demonstrates the feasibility of training an ART-1 network to first cluster designs into families, and then to recall the family when presented a similar design. This application is of large practical value to industry, making it possible to avoid duplication of design efforts.

  12. Improved Extension Neural Network and Its Applications

    Directory of Open Access Journals (Sweden)

    Yu Zhou

    2014-01-01

    Full Text Available Extension neural network (ENN is a new neural network that is a combination of extension theory and artificial neural network (ANN. The learning algorithm of ENN is based on supervised learning algorithm. One of important issues in the field of classification and recognition of ENN is how to achieve the best possible classifier with a small number of labeled training data. Training data selection is an effective approach to solve this issue. In this work, in order to improve the supervised learning performance and expand the engineering application range of ENN, we use a novel data selection method based on shadowed sets to refine the training data set of ENN. Firstly, we use clustering algorithm to label the data and induce shadowed sets. Then, in the framework of shadowed sets, the samples located around each cluster centers (core data and the borders between clusters (boundary data are selected as training data. Lastly, we use selected data to train ENN. Compared with traditional ENN, the proposed improved ENN (IENN has a better performance. Moreover, IENN is independent of the supervised learning algorithms and initial labeled data. Experimental results verify the effectiveness and applicability of our proposed work.

  13. The 13th International Conference on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS 2013)

    Science.gov (United States)

    Mitcheson, Paul; Beeby, Steve

    2013-12-01

    It is a pleasure to welcome you to The Royal Society in London and the 13th International Conference on Micro- and Nano-Technology for Power Generation and Energy Conversion Applications, or PowerMEMS 2013. The objective of PowerMEMS 2013 is to catalyse innovation in miniature, micro- and nano-scale technologies for power generation and energy conversion. The conference aims to stimulate the exchange of insights and information, and the development of new ideas in the Power MEMS/NEMS field as well as at the meso-scale. It will allow the attendees to interact and network within our multidisciplinary community that includes professionals from many branches of science and engineering. The technical program is led by four invited speakers covering inductive power transfer, chip scale power sources, thermal energy harvesting and implantable biofuel cells. We received 177 abstracts and following a careful reviewing process by the Technical Program Committee a total of 137 papers were selected for presentation. These have been organised into 16 oral sessions in two parallel streams and two poster sessions that have been augmented by 10 late news papers. The oral and regular poster papers are, for the first time, being published by the Institute of Physics. We have made every effort to make PowerMEMS 2013 the busiest yet and have included for the first time the PowerMEMS School. This two-day school held at Imperial College London covered a wide range of power-MEMS topics including technologies for power generation, power transmission, energy storage, power electronics interfaces and metrology. Registrations for the School exceeded our expectations and it was full by early November. We hope this, and other activities such as the Discussion Panel and the inclusion of late news papers, will make PowerMEMS 2013 a memorable success. We have also reached out to new communities, such as those working in wireless power transfer and RF harvesting to broaden the technology remit of

  14. Performance Enhancement MEMS Based INS/GPS Integrated System Implemented on a FPGA for Terrestrial Applications

    OpenAIRE

    Garcia Quinchia, Alex

    2014-01-01

    Hoy en día con el desarrollo de sensores inerciales basados en Sistemas Micro\\-electromecánicos (MEMS), podemos encontrar acelerómetros y giróscopos embebidos en diferentes dispositivos y plataformas, teniéndolos en relojes, teléfonos inteligentes, consolas de video juego hasta sistemas de navegación terrestre y vehículos aéreos no tripulados (UAVs), {\\em etc}. A pesar del amplio rango de aplicaciones donde están siendo utilizados, los sensores inerciales de bajo costo (grado MEMs) son afecta...

  15. 16th International Conference on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS 2016)

    International Nuclear Information System (INIS)

    2016-01-01

    It is our great pleasure to welcome you to PowerMEMS’16 - the 16th International Conference on Micro- and Nano-Technology for Power Generation and Energy Conversion Applications - in Paris at the UIC Espace Congrès, a few meters away from the Eiffel Tower. The objective of the PowerMEMS conference is to catalyse innovation in micro- and nano- scale technologies for the energy domain. The scope of the meeting ranges from basic principles, to materials and fabrication, to devices and systems, to applications. The many applications of Power MEMS concern the harvesting, storage, conversion and conditioning of energy, to integrated systems that manage these processes, to actuation, pumping, and propulsion. Our Conference aims to stimulate the exchange of insights and information, as well as the development of new ideas, in the Power MEMS field. Our goal is to allow the attendees to interact and network within our multidisciplinary community that includes professionals from many branches of science and engineering, as well as energy, policy, and entrepreneurial specialists interested in the commercialization of Power MEMS technologies. This year's technical program is highlighted by four plenary talks from prominent experts on M/NEMS for ultra-low power in electronics, advanced nanomaterial for solar cells and thermal transistor. The contributed program received 159 abstract submissions this year. After careful review by the 33-members of the Technical Program Committee, a total of 123 papers will be presented. The 40 contributed oral presentations are arranged in two parallel sessions. The 83 posters are arranged in a ''two-in-one'' poster session in which the poster session time is divided in two; half the posters will be presented during each half-session, allowing the poster presenters to also browse the posters during the poster session. Posters will remain up during the meeting, so please feel free to peruse them at your leisure. The

  16. Neural network application to diesel generator diagnostics

    International Nuclear Information System (INIS)

    Logan, K.P.

    1990-01-01

    Diagnostic problems typically begin with the observation of some system behavior which is recognized as a deviation from the expected. The fundamental underlying process is one involving pattern matching cf observed symptoms to a set of compiled symptoms belonging to a fault-symptom mapping. Pattern recognition is often relied upon for initial fault detection and diagnosis. Parallel distributed processing (PDP) models employing neural network paradigms are known to be good pattern recognition devices. This paper describes the application of neural network processing techniques to the malfunction diagnosis of subsystems within a typical diesel generator configuration. Neural network models employing backpropagation learning were developed to correctly recognize fault conditions from the input diagnostic symptom patterns pertaining to various engine subsystems. The resulting network models proved to be excellent pattern recognizers for malfunction examples within the training set. The motivation for employing network models in lieu of a rule-based expert system, however, is related to the network's potential for generalizing malfunctions outside of the training set, as in the case of noisy or partial symptom patterns

  17. Artificial neural network applications in ionospheric studies

    Directory of Open Access Journals (Sweden)

    L. R. Cander

    1998-06-01

    Full Text Available The ionosphere of Earth exhibits considerable spatial changes and has large temporal variability of various timescales related to the mechanisms of creation, decay and transport of space ionospheric plasma. Many techniques for modelling electron density profiles through entire ionosphere have been developed in order to solve the "age-old problem" of ionospheric physics which has not yet been fully solved. A new way to address this problem is by applying artificial intelligence methodologies to current large amounts of solar-terrestrial and ionospheric data. It is the aim of this paper to show by the most recent examples that modern development of numerical models for ionospheric monthly median long-term prediction and daily hourly short-term forecasting may proceed successfully applying the artificial neural networks. The performance of these techniques is illustrated with different artificial neural networks developed to model and predict the temporal and spatial variations of ionospheric critical frequency, f0F2 and Total Electron Content (TEC. Comparisons between results obtained by the proposed approaches and measured f0F2 and TEC data provide prospects for future applications of the artificial neural networks in ionospheric studies.

  18. MEMS-based wavelength and orbital angular momentum demultiplexer for on-chip applications

    DEFF Research Database (Denmark)

    Lyubopytov, Vladimir; Porfirev, Alexey P.; Gurbatov, Stanislav O.

    2017-01-01

    Summary form only given. We demonstrate a new tunable MEMS-based WDM&OAM Fabry-Pérot filter for simultaneous wavelength (WDM) and Orbital Angular Momentum (OAM) (de)multiplexing. The WDM&OAM filter is suitable for dense on-chip integration and dedicated for the next generation of optical...

  19. Advancing MEMS Technology Usage through the MUMPS (Multi-User MEMS Processes) Program

    Science.gov (United States)

    Koester, D. A.; Markus, K. W.; Dhuler, V.; Mahadevan, R.; Cowen, A.

    1995-01-01

    In order to help provide access to advanced micro-electro-mechanical systems (MEMS) technologies and lower the barriers for both industry and academia, the Microelectronic Center of North Carolina (MCNC) and ARPA have developed a program which provides users with access to both MEMS processes and advanced electronic integration techniques. The four distinct aspects of this program, the multi-user MEMS processes (MUMP's), the consolidated micro-mechanical element library, smart MEMS, and the MEMS technology network are described in this paper. MUMP's is an ARPA-supported program created to provide inexpensive access to MEMS technology in a multi-user environment. It is both a proof-of-concept and educational tool that aids in the development of MEMS in the domestic community. MUMP's technologies currently include a 3-layer poly-silicon surface micromachining process and LIGA (lithography, electroforming, and injection molding) processes that provide reasonable design flexibility within set guidelines. The consolidated micromechanical element library (CaMEL) is a library of active and passive MEMS structures that can be downloaded by the MEMS community via the internet. Smart MEMS is the development of advanced electronics integration techniques for MEMS through the application of flip chip technology. The MEMS technology network (TechNet) is a menu of standard substrates and MEMS fabrication processes that can be purchased and combined to create unique process flows. TechNet provides the MEMS community greater flexibility and enhanced technology accessibility.

  20. Advanced Applications of Neural Networks and Artificial Intelligence: A Review

    OpenAIRE

    Koushal Kumar; Gour Sundar Mitra Thakur

    2012-01-01

    Artificial Neural Network is a branch of Artificial intelligence and has been accepted as a new computing technology in computer science fields. This paper reviews the field of Artificial intelligence and focusing on recent applications which uses Artificial Neural Networks (ANN’s) and Artificial Intelligence (AI). It also considers the integration of neural networks with other computing methods Such as fuzzy logic to enhance the interpretation ability of data. Artificial Neural Networks is c...

  1. Going Fabless with MEMS

    Directory of Open Access Journals (Sweden)

    Bhaskar CHOUBEY

    2011-04-01

    Full Text Available The Microelectromechanical sensors are finding increasing applications in everyday life. However, each MEMS sensor is generally fabricated on its own individual process. This leads to high cost per sensor. It has been suggested the MEMS should and would follow the path of integrated circuits industry, wherein fabless firms could concentrate on design leading pure-foundries to perfect the manufacturing process. With several designs being manufactured on the same process, the installation cost of fabrication would be evenly shared. Simultaneously, multiple project wafer runs are being offered for MEMS processes to encourage design activity in universities as well as startups. This paper reviews the present state of this transition through an experience of designing and manufacturing microelectromechanical resonators on different processes.

  2. Neural network application for illicit substances identification

    International Nuclear Information System (INIS)

    Nunes, Wallace V.; Silva, Ademir X. da; Crispim, Verginia R.; Schirru, Roberto

    2000-01-01

    Thermal neutron activation analysis is based on neutron capture prompt gamma-ray analysis and has been used in wide variety of fields, for examples, for inspection of checked airline baggage and for detection of buried land mines. In all of these applications, the detected γ-ray intensities from the elements present are used to estimate their concentrations. A study about application using a trained neutral network is presented to determine the presence of illicit substances, such as explosives and drugs, carried out in the luggages. The illicit substances emit characteristic detected γ-ray which are the fingerprint of each isotope. The fingerprint data-base of the gamma-ray spectrum of substances is obtained via Monte Carlo N-Particle Transport code, MCNP, version 4B. It was possible to train the neural network to determine the presence of explosives and narcotics even hidden by several materials. (author)

  3. Applications of neural networks to mechanics

    International Nuclear Information System (INIS)

    1997-01-01

    Neural networks have become powerful tools in engineer's techniques. The aim of this conference was to present their application to concrete cases in the domain of mechanics, including the preparation and use of materials. Artificial neurons are non-linear organs which provide an output signal that depends on several differently weighted input signals. Their connection into networks allows to solve problems for which the driving laws are not well known. The applications discussed during this conference deal with: the driving of machines or processes, the control of machines, materials or products, the simulation and forecasting, and the optimization. Three papers dealing with the control of spark ignition engines, the regulation of heating floors and the optimization of energy consumptions in industrial buildings were selected for ETDE and one paper dealing with the optimization of the management of a reprocessed plutonium stock was selected for INIS. (J.S.)

  4. MEMS and the microbe

    NARCIS (Netherlands)

    Ingham, C.J.; Vlieg, J.E.T.V.H.

    2008-01-01

    In recent years, relatively simple MEMS fabrications have helped accelerate our knowledge of the microbial cell. Current progress and challenges in the application of lab-on-a-chip devices to the viable microbe are reviewed. Furthermore, the degree to which microbiologists are becoming the engineers

  5. Power gating of VLSI circuits using MEMS switches in low power applications

    KAUST Repository

    Shobak, Hosam

    2011-12-01

    Power dissipation poses a great challenge for VLSI designers. With the intense down-scaling of technology, the total power consumption of the chip is made up primarily of leakage power dissipation. This paper proposes combining a custom-designed MEMS switch to power gate VLSI circuits, such that leakage power is efficiently reduced while accounting for performance and reliability. The designed MEMS switch is characterized by an 0.1876 ? ON resistance and requires 4.5 V to switch. As a result of implementing this novel power gating technique, a standby leakage power reduction of 99% and energy savings of 33.3% are achieved. Finally the possible effects of surge currents and ground bounce noise are studied. These findings allow longer operation times for battery-operated systems characterized by long standby periods. © 2011 IEEE.

  6. Design, simulation and analysis of piezoresistive MEMS pressure sensor for fast reactor applications

    International Nuclear Information System (INIS)

    Patankar, Mahesh Kumar; Murali, N.; Satya Murty, S.A.V.; Kalyana Rao, K.; Sridhar, S.

    2013-01-01

    To exploit the extraordinary benefits of MEMS technology in fast reactor domain, a piezoresistive MEMS pressure sensor was designed, simulated and analyzed using Intellisuite Software to measure the RCB air pressure in 0 - 1.25 bar (a) range. For sensing the pressure, a thin square silicon diaphragm of size of 800 x 800 μm 2 with thickness of 20 μm was optimized using FEM analysis and to transfer the mechanical stress, induce in the diaphragm due to pressure, into electrical output voltage signal, a set of piezoresistors were arranged on top side of the diaphragm in full active wheatstone bridge configuration for obtaining the higher sensitivity. The simulation results were compared with the analytical results which were found in line of expectations and electrical sensitivity was obtained at 15 mV/V.bar. (author)

  7. A Survey on Modeling and Simulation of MEMS Switches and Its Application in Power Gating Techniques

    OpenAIRE

    Pramod Kumar M.P; A.S. Augustine Fletcher

    2014-01-01

    Large numbers of techniques have been developed to reduce the leakage power, including supply voltage scaling, varying threshold voltages, smaller logic banks, etc. Power gating is a technique which is used to reduce the static power when the sleep transistor is in off condition. Micro Electro mechanical System (MEMS) switches have properties that are very close to an ideal switch, with infinite off-resistance due to an air gap and low on-resistance due to the ohmic metal to m...

  8. Protein patterning on polycrystalline silicon-germanium via standard UV lithography for bioMEMS applications

    Energy Technology Data Exchange (ETDEWEB)

    Lenci, S., E-mail: silvia.lenci@gmail.com [Dipartimento di Ingegneria dell' Informazione, University of Pisa, Via G. Caruso 16, I-56122 Pisa (Italy); imec, Kapeldreef 75, Leuven B-3001 (Belgium); Tedeschi, L.; Domenici, C.; Lande, C. [Istituto di Fisiologia Clinica, CNR, via G. Moruzzi 1, Pisa I-56124 (Italy); Nannini, A.; Pennelli, G.; Pieri, F. [Dipartimento di Ingegneria dell' Informazione, University of Pisa, Via G. Caruso 16, I-56122 Pisa (Italy); Severi, S. [imec, Kapeldreef 75, Leuven B-3001 (Belgium)

    2010-10-12

    Polycrystalline silicon-germanium (poly-SiGe) is a promising structural material for the post-processing of micro electro-mechanical systems (MEMS) on top of complementary metal-oxide-semiconductor (CMOS) substrates. Combining MEMS and CMOS allows for the development of high-performance devices. We present for the first time selective protein immobilization on top of poly-SiGe surfaces, an enabling technique for the development of novel poly-SiGe based MEMS biosensors. Active regions made of 3-aminopropyl-triethoxysilane (APTES) were defined using silane deposition onto photoresist patterns followed by lift-off in organic solvents. Subsequently, proteins were covalently bound on the created APTES patterns. Fluorescein-labeled human serum albumin (HSA) was used to verify the immobilization procedure while the binding capability of the protein layer was tested by an antigen-labeled antibody pair. Inspection by fluorescence microscopy showed protein immobilization inside the desired bioactive areas and low non-specific adsorption outside the APTES pattern. Furthermore, the quality of the silane patches was investigated by treatment with 30 nm-diameter gold nanoparticles and scanning electron microscope observation. The developed technique is therefore a promising first step towards the realization of poly-SiGe based biosensors.

  9. Application of MEMS Accelerometers and Gyroscopes in Fast Steering Mirror Control Systems

    Directory of Open Access Journals (Sweden)

    Jing Tian

    2016-03-01

    Full Text Available In a charge-coupled device (CCD-based fast steering mirror (FSM tracking control system, high control bandwidth is the most effective way to enhance the closed-loop performance. However, the control system usually suffers a great deal from mechanical resonances and time delays induced by the low sampling rate of CCDs. To meet the requirements of high precision and load restriction, fiber-optic gyroscopes (FOGs are usually used in traditional FSM tracking control systems. In recent years, the MEMS accelerometer and gyroscope are becoming smaller and lighter and their performance have improved gradually, so that they can be used in a fast steering mirror (FSM to realize the stabilization of the line-of-sight (LOS of the control system. Therefore, a tentative approach to implement a CCD-based FSM tracking control system, which uses MEMS accelerometers and gyroscopes as feedback components and contains an acceleration loop, a velocity loop and a position loop, is proposed. The disturbance suppression of the proposed method is the product of the error attenuation of the acceleration loop, the velocity loop and the position loop. Extensive experimental results show that the MEMS accelerometers and gyroscopes can act the similar role as the FOG with lower cost for stabilizing the LOS of the FSM tracking control system.

  10. Application of neural networks in coastal engineering

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.

    the neural network attractive. A neural network is an information processing system modeled on the structure of the dynamic process. It can solve the complex/nonlinear problems quickly once trained by operating on problems using an interconnected number...

  11. Fuzzy logic and neural networks basic concepts & application

    CERN Document Server

    Alavala, Chennakesava R

    2008-01-01

    About the Book: The primary purpose of this book is to provide the student with a comprehensive knowledge of basic concepts of fuzzy logic and neural networks. The hybridization of fuzzy logic and neural networks is also included. No previous knowledge of fuzzy logic and neural networks is required. Fuzzy logic and neural networks have been discussed in detail through illustrative examples, methods and generic applications. Extensive and carefully selected references is an invaluable resource for further study of fuzzy logic and neural networks. Each chapter is followed by a question bank

  12. Micromachined sensor for stress measurement and micromechanical study of free-standing thin films for MEMS applications

    Science.gov (United States)

    Zhang, Ping

    Microelectromechanical systems (MEMS) have a wide range of applications. In the field of wireless and microwave technology, considerable attention has been given to the development and integration of MEMS-based RF (radio frequency) components. An RF MEMS switch requires low insertion loss, high isolation, and low actuation voltage - electrical aspects that have been extensively studied. The mechanical requirements of the switch, such as low sensitivity to built-in stress and high reliability, greatly depend on the micromechanical properties of the switch materials, and have not been thoroughly explored. RF MEMS switches are typically in the form of a free-standing thin film structure. Large stress gradients and across-wafer stress variations developed during fabrication severely degrade their electrical performance. A micromachined stress measurement sensor has been developed that can potentially be employed for in-situ monitoring of stress evolution and stress variation. The sensors were micromachined using five masks on two wafer levels, each measuring 5x3x1 mm. They function by means of an electron tunneling mechanism, where a 2x2 mm silicon nitride membrane elastically deflects under an applied deflection voltage via an external feedback circuitry. For the current design, the sensors are capable of measuring tensile stresses up to the GPa range under deflection voltages of 50--100 V. Sensor functionality was studied by finite element modeling and a theoretical analysis of square membrane deflection. While the mechanical properties of thin films on substrates have been extensively studied, studies of free-standing thin films have been limited due to the practical difficulties in sample handling and testing. Free-standing Al and Al-Ti thin films specimens have been successfully fabricated and microtensile and stress relaxation tests have been performed using a custom-designed micromechanical testing apparatus. A dedicated TEM (transmission electron microscopy

  13. RF MEMS

    Indian Academy of Sciences (India)

    At the bare die level the insertion loss, return loss and the isolation ... ing and packaging of a silicon on glass based RF MEMS switch fabricated using DRIE. ..... follows the power law based on the asperity deformation model given by Pattona & ... Surface mount style RF packages (SMX series 580465) from Startedge Corp.

  14. Introduction to spiking neural networks: Information processing, learning and applications.

    Science.gov (United States)

    Ponulak, Filip; Kasinski, Andrzej

    2011-01-01

    The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.

  15. Wafer-Level Packaging Method for RF MEMS Applications Using Pre-Patterned BCB Polymer

    Directory of Open Access Journals (Sweden)

    Zhuhao Gong

    2018-02-01

    Full Text Available A radio-frequency micro-electro-mechanical system (RF MEMS wafer-level packaging (WLP method using pre-patterned benzo-cyclo-butene (BCB polymers with a high-resistivity silicon cap is proposed to achieve high bonding quality and excellent RF performance. In this process, the BCB polymer was pre-defined to form the sealing ring and bonding layer by the spin-coating and patterning of photosensitive BCB before the cavity formation. During anisotropic wet etching of the silicon wafer to generate the housing cavity, the BCB sealing ring was protected by a sputtered Cr/Au (chromium/gold layer. The average measured thickness of the BCB layer was 5.9 μm. In contrast to the conventional methods of spin-coating BCB after fabricating cavities, the pre-patterned BCB method presented BCB bonding layers with better quality on severe topography surfaces in terms of increased uniformity of thickness and better surface flatness. The observation of the bonded layer showed that no void or gap formed on the protruding coplanar waveguide (CPW lines. A shear strength test was experimentally implemented as a function of the BCB widths in the range of 100–400 μm. The average shear strength of the packaged device was higher than 21.58 MPa. A RF MEMS switch was successfully packaged using this process with a negligible impact on the microwave characteristics and a significant improvement in the lifetime from below 10 million to over 1 billion. The measured insertion loss of the packaged RF MEMS switch was 0.779 dB and the insertion loss deterioration caused by the package structure was less than 0.2 dB at 30 GHz.

  16. Plasma Etching of Tapered Features in Silicon for MEMS and Wafer Level Packaging Applications

    International Nuclear Information System (INIS)

    Ngo, H-D; Hiess, Andre; Seidemann, Volker; Studzinski, Daniel; Lange, Martin; Leib, Juergen; Shariff, Dzafir; Ashraf, Huma; Steel, Mike; Atabo, Lilian; Reast, Jon

    2006-01-01

    This paper is a brief report of plasma etching as applied to pattern transfer in silicon. It will focus more on concept overview and strategies for etching of tapered features of interest for MEMS and Wafer Level Packaging (WLP). The basis of plasma etching, the dry etching technique, is explained and plasma configurations are described elsewhere. An important feature of plasma etching is the possibility to achieve etch anisotropy. The plasma etch process is extremely sensitive to many variables such as mask material, mask openings and more important the plasma parameters

  17. Wafer-Level Packaging Method for RF MEMS Applications Using Pre-Patterned BCB Polymer

    OpenAIRE

    Zhuhao Gong; Yulong Zhang; Xin Guo; Zewen Liu

    2018-01-01

    A radio-frequency micro-electro-mechanical system (RF MEMS) wafer-level packaging (WLP) method using pre-patterned benzo-cyclo-butene (BCB) polymers with a high-resistivity silicon cap is proposed to achieve high bonding quality and excellent RF performance. In this process, the BCB polymer was pre-defined to form the sealing ring and bonding layer by the spin-coating and patterning of photosensitive BCB before the cavity formation. During anisotropic wet etching of the silicon wafer to gener...

  18. From MEMS to nanomachine

    International Nuclear Information System (INIS)

    Esashi, Masayoshi; Ono, Takahito

    2005-01-01

    Practically applicable microelectromechanical systems (MEMS) and nanomachines have been developed by applying dry processes. Deep reactive ion etching (RIE) of silicon and its applications to an electrostatically levitated rotational gyroscope, a fibre optic blood pressure sensor and in micro-actuated probes are described. High density electrical feedthrough in glass is made using deep RIE of glass and electroplating of metal. Multi-probe data storage system has been developed using the high density electrical feedthrough in glass. Chemical vapour deposition (CVD) of different materials have been developed for MEMS applications; trench-refill using SiO 2 CVD, microstructures using Silicon carbide CVD for glass mold press and selective CVD of carbon nanotube for electron field emitter. Multi-column electron beam lithography system has been developed using the electron field emitter. (topical review)

  19. On using the dynamic snap-through motion of MEMS initially curved microbeams for filtering applications

    KAUST Repository

    Ouakad, Hassen M.; Younis, Mohammad I.

    2014-01-01

    Numerical and experimental investigations of the dynamics of micromachined shallow arches (initially curved microbeams) and the possibility of using their dynamic snap-through motion for filtering purposes are presented. The considered MEMS arches are actuated by a DC electrostatic load along with an AC harmonic load. Their dynamics is examined numerically using a Galerkin-based reduced-order model when excited near both their first and third natural frequencies. Several simulation results are presented demonstrating interesting jumps and dynamic snap-through behavior of the MEMS arches and their attractive features for uses as band-pass filters, such as their sharp roll-off from pass-bands to stop-bands and their flat response. Experimental work is conducted to test arches realized of curved polysilicon microbeams when excited by DC and AC loads. Experimental data of the micromachined curved beams are shown for the softening and hardening behavior near the first and third natural frequencies, respectively, as well as dynamic snap-through motion. © 2013 Elsevier Ltd.

  20. Mechanical characterization of poly-SiGe layers for CMOS–MEMS integrated application

    International Nuclear Information System (INIS)

    Modlinski, Robert; Witvrouw, Ann; Verbist, Agnes; De Wolf, Ingrid; Puers, Robert

    2010-01-01

    Measuring mechanical properties at the microscale is essential to understand and to fabricate reliable MEMS. In this paper a tensile testing system and matching microscale test samples are presented. The test samples have a dog-bone-like structure. They are designed to mimic standard macro-tensile test samples. The micro-tensile tests are used to characterize 0.9 µm thick polycrystalline silicon germanium (poly-SiGe) films. The poly-SiGe film, that can be considered as a close equivalent to polycrystalline silicon (poly-Si), is studied as a very promising material for use in CMOS/MEMS integration in a single chip due to its low-temperature LPCVD deposition (T < 450 °C). The fabrication process of the poly-SiGe micro-tensile test structure is explained in detail: the design, the processing and post-processing, the testing and finally the results' discussion. The poly-SiGe micro-tensile results are also compared with nanoindentation data obtained on the same poly-SiGe films as well as with results obtained by other research groups

  1. The application of structural nonlinearity in the development of linearly tunable MEMS capacitors

    International Nuclear Information System (INIS)

    Shavezipur, M; Khajepour, A; Hashemi, S M

    2008-01-01

    Electrostatically actuated parallel-plate tunable capacitors are the most desired MEMS capacitors because of their smaller sizes and higher Q-factors. However, these capacitors suffer from low tunability and exhibit high sensitivity near the pull-in voltage which counters the concept of tunability. In this paper, a novel design for parallel-plate tunable capacitors with high tunability and linear capacitance–voltage (C–V) response is developed. The design uses nonlinear structural rigidities to relieve intrinsic electrostatic nonlinearity in MEMS capacitors. Based on the force–displacement characteristic of an ideally linear capacitor, a real beam-like nonlinear spring model is developed. The variable stiffness coefficients of such springs improve the linearity of the C–V curve. Moreover, because the structural stiffness increases with deformations, the pull-in is delayed and higher tunability is achieved. Finite element simulations reveal that capacitors with air gaps larger than 4 µm and supporting beams thinner than 1 µm can generate highly linear C–V responses and tunabilities over 120%. Experimental results for capacitors fabricated by PolyMUMPs verify the effect of weak nonlinear geometric stiffness on improving the tunability for designs with a small air gap and relatively thick structural layers

  2. Multi frequency excited MEMS cantilever beam resonator for Mixer-Filter applications

    KAUST Repository

    Chandran, Akhil A.; Younis, Mohammad I.

    2016-01-01

    Wireless communication uses Radio Frequency waves to transfer information from one point to another. The modern RF front end devices are implementing MEMS in their designs so as to exploit the inherent properties of MEMS devices, such as its low mass, low power consumption, and small size. Among the components in the RF transceivers, band pass filters and mixers play a vital role in achieving the optimum RF performance. And this paper aims at utilizing an electrostatically actuated micro cantilever beam resonator's nonlinear frequency mixing property to realize a Mixer-Filter configuration through multi-frequency excitation. The paper studies about the statics and dynamics of the device. Simulations are carried out to study the added benefits of multi frequency excitation. The modelling of the cantilever beam has been done using a Reduced Order Model of the Euler-Bernoulli's beam equation by implementing the Galerkin discretization. The device is shown to be able to down-convert signals from 960 MHz of frequency to an intermediate frequency around 50 MHz and 70 MHz in Phase 1 and 2, respectively. The simulation showed promising results to take the project to the next level. © 2016 IEEE.

  3. Key Processes of Silicon-On-Glass MEMS Fabrication Technology for Gyroscope Application.

    Science.gov (United States)

    Ma, Zhibo; Wang, Yinan; Shen, Qiang; Zhang, Han; Guo, Xuetao

    2018-04-17

    MEMS fabrication that is based on the silicon-on-glass (SOG) process requires many steps, including patterning, anodic bonding, deep reactive ion etching (DRIE), and chemical mechanical polishing (CMP). The effects of the process parameters of CMP and DRIE are investigated in this study. The process parameters of CMP, such as abrasive size, load pressure, and pH value of SF1 solution are examined to optimize the total thickness variation in the structure and the surface quality. The ratio of etching and passivation cycle time and the process pressure are also adjusted to achieve satisfactory performance during DRIE. The process is optimized to avoid neither the notching nor lag effects on the fabricated silicon structures. For demonstrating the capability of the modified CMP and DRIE processes, a z-axis micro gyroscope is fabricated that is based on the SOG process. Initial test results show that the average surface roughness of silicon is below 1.13 nm and the thickness of the silicon is measured to be 50 μm. All of the structures are well defined without the footing effect by the use of the modified DRIE process. The initial performance test results of the resonant frequency for the drive and sense modes are 4.048 and 4.076 kHz, respectively. The demands for this kind of SOG MEMS device can be fulfilled using the optimized process.

  4. Multi frequency excited MEMS cantilever beam resonator for Mixer-Filter applications

    KAUST Repository

    Chandran, Akhil A.

    2016-09-15

    Wireless communication uses Radio Frequency waves to transfer information from one point to another. The modern RF front end devices are implementing MEMS in their designs so as to exploit the inherent properties of MEMS devices, such as its low mass, low power consumption, and small size. Among the components in the RF transceivers, band pass filters and mixers play a vital role in achieving the optimum RF performance. And this paper aims at utilizing an electrostatically actuated micro cantilever beam resonator\\'s nonlinear frequency mixing property to realize a Mixer-Filter configuration through multi-frequency excitation. The paper studies about the statics and dynamics of the device. Simulations are carried out to study the added benefits of multi frequency excitation. The modelling of the cantilever beam has been done using a Reduced Order Model of the Euler-Bernoulli\\'s beam equation by implementing the Galerkin discretization. The device is shown to be able to down-convert signals from 960 MHz of frequency to an intermediate frequency around 50 MHz and 70 MHz in Phase 1 and 2, respectively. The simulation showed promising results to take the project to the next level. © 2016 IEEE.

  5. Mechanical characterization of poly-SiGe layers for CMOS-MEMS integrated application

    Science.gov (United States)

    Modlinski, Robert; Witvrouw, Ann; Verbist, Agnes; Puers, Robert; De Wolf, Ingrid

    2010-01-01

    Measuring mechanical properties at the microscale is essential to understand and to fabricate reliable MEMS. In this paper a tensile testing system and matching microscale test samples are presented. The test samples have a dog-bone-like structure. They are designed to mimic standard macro-tensile test samples. The micro-tensile tests are used to characterize 0.9 µm thick polycrystalline silicon germanium (poly-SiGe) films. The poly-SiGe film, that can be considered as a close equivalent to polycrystalline silicon (poly-Si), is studied as a very promising material for use in CMOS/MEMS integration in a single chip due to its low-temperature LPCVD deposition (T < 450 °C). The fabrication process of the poly-SiGe micro-tensile test structure is explained in detail: the design, the processing and post-processing, the testing and finally the results' discussion. The poly-SiGe micro-tensile results are also compared with nanoindentation data obtained on the same poly-SiGe films as well as with results obtained by other research groups.

  6. Introduction to neural networks with electric power applications

    International Nuclear Information System (INIS)

    Wildberger, A.M.; Hickok, K.A.

    1990-01-01

    This is an introduction to the general field of neural networks with emphasis on prospects for their application in the power industry. It is intended to provide enough background information for its audience to begin to follow technical developments in neural networks and to recognize those which might impact on electric power engineering. Beginning with a brief discussion of natural and artificial neurons, the characteristics of neural networks in general and how they learn, neural networks are compared with other modeling tools such as simulation and expert systems in order to provide guidance in selecting appropriate applications. In the power industry, possible applications include plant control, dispatching, and maintenance scheduling. In particular, neural networks are currently being investigated for enhancements to the Thermal Performance Advisor (TPA) which General Physics Corporation (GP) has developed to improve the efficiency of electric power generation

  7. A phononic crystal strip based on silicon for support tether applications in silicon-based MEMS resonators and effects of temperature and dopant on its band gap characteristics

    Directory of Open Access Journals (Sweden)

    Thi Dep Ha

    2016-04-01

    Full Text Available Phononic crystals (PnCs and n-type doped silicon technique have been widely employed in silicon-based MEMS resonators to obtain high quality factor (Q as well as temperature-induced frequency stability. For the PnCs, their band gaps play an important role in the acoustic wave propagation. Also, the temperature and dopant doped into silicon can cause the change in its material properties such as elastic constants, Young’s modulus. Therefore, in order to design the simultaneous high Q and frequency stability silicon-based MEMS resonators by two these techniques, a careful design should study effects of temperature and dopant on the band gap characteristics to examine the acoustic wave propagation in the PnC. Based on these, this paper presents (1 a proposed silicon-based PnC strip structure for support tether applications in low frequency silicon-based MEMS resonators, (2 influences of temperature and dopant on band gap characteristics of the PnC strips. The simulation results show that the largest band gap can achieve up to 33.56 at 57.59 MHz and increase 1280.13 % (also increase 131.89 % for ratio of the widest gaps compared with the counterpart without hole. The band gap properties of the PnC strips is insignificantly effected by temperature and electron doping concentration. Also, the quality factor of two designed length extensional mode MEMS resonators with proposed PnC strip based support tethers is up to 1084.59% and 43846.36% over the same resonators with PnC strip without hole and circled corners, respectively. This theoretical study uses the finite element analysis in COMSOL Multiphysics and MATLAB softwares as simulation tools. This findings provides a background in combination of PnC and dopant techniques for high performance silicon-based MEMS resonators as well as PnC-based MEMS devices.

  8. A phononic crystal strip based on silicon for support tether applications in silicon-based MEMS resonators and effects of temperature and dopant on its band gap characteristics

    Energy Technology Data Exchange (ETDEWEB)

    Ha, Thi Dep, E-mail: hathidep@yahoo.com [School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731 (China); Faculty of Electronic Technology, Industrial University of Ho Chi Minh City, Hochiminh City (Viet Nam); Bao, JingFu, E-mail: baojingfu@uestc.edu.cn [School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731 (China)

    2016-04-15

    Phononic crystals (PnCs) and n-type doped silicon technique have been widely employed in silicon-based MEMS resonators to obtain high quality factor (Q) as well as temperature-induced frequency stability. For the PnCs, their band gaps play an important role in the acoustic wave propagation. Also, the temperature and dopant doped into silicon can cause the change in its material properties such as elastic constants, Young’s modulus. Therefore, in order to design the simultaneous high Q and frequency stability silicon-based MEMS resonators by two these techniques, a careful design should study effects of temperature and dopant on the band gap characteristics to examine the acoustic wave propagation in the PnC. Based on these, this paper presents (1) a proposed silicon-based PnC strip structure for support tether applications in low frequency silicon-based MEMS resonators, (2) influences of temperature and dopant on band gap characteristics of the PnC strips. The simulation results show that the largest band gap can achieve up to 33.56 at 57.59 MHz and increase 1280.13 % (also increase 131.89 % for ratio of the widest gaps) compared with the counterpart without hole. The band gap properties of the PnC strips is insignificantly effected by temperature and electron doping concentration. Also, the quality factor of two designed length extensional mode MEMS resonators with proposed PnC strip based support tethers is up to 1084.59% and 43846.36% over the same resonators with PnC strip without hole and circled corners, respectively. This theoretical study uses the finite element analysis in COMSOL Multiphysics and MATLAB softwares as simulation tools. This findings provides a background in combination of PnC and dopant techniques for high performance silicon-based MEMS resonators as well as PnC-based MEMS devices.

  9. Tribo-functionalizing Si and SU8 materials by surface modification for application in MEMS/NEMS actuator-based devices

    International Nuclear Information System (INIS)

    Singh, R A; Satyanarayana, N; Sinha, S K; Kustandi, T S

    2011-01-01

    Micro/nano-electro-mechanical-systems (MEMS/NEMS) are miniaturized devices built at micro/nanoscales. At these scales, the surface/interfacial forces are extremely strong and they adversely affect the smooth operation and the useful operating lifetimes of such devices. When these forces manifest in severe forms, they lead to material removal and thereby reduce the wear durability of the devices. In this paper, we present a simple, yet robust, two-step surface modification method to significantly enhance the tribological performance of MEMS/NEMS materials. The two-step method involves oxygen plasma treatment of polymeric films and the application of a nanolubricant, namely perfluoropolyether. We apply the two-step method to the two most important MEMS/NEMS structural materials, namely silicon and SU8 polymer. On applying surface modification to these materials, their initial coefficient of friction reduces by ∼4-7 times and the steady-state coefficient of friction reduces by ∼2.5-3.5 times. Simultaneously, the wear durability of both the materials increases by >1000 times. The two-step method is time effective as each of the steps takes the time duration of approximately 1 min. It is also cost effective as the oxygen plasma treatment is a part of the MEMS/NEMS fabrication process. The two-step method can be readily and easily integrated into MEMS/NEMS fabrication processes. It is anticipated that this method will work for any kind of structural material from which MEMS/NEMS are or can be made.

  10. Tribo-functionalizing Si and SU8 materials by surface modification for application in MEMS/NEMS actuator-based devices

    Science.gov (United States)

    Singh, R. A.; Satyanarayana, N.; Kustandi, T. S.; Sinha, S. K.

    2011-01-01

    Micro/nano-electro-mechanical-systems (MEMS/NEMS) are miniaturized devices built at micro/nanoscales. At these scales, the surface/interfacial forces are extremely strong and they adversely affect the smooth operation and the useful operating lifetimes of such devices. When these forces manifest in severe forms, they lead to material removal and thereby reduce the wear durability of the devices. In this paper, we present a simple, yet robust, two-step surface modification method to significantly enhance the tribological performance of MEMS/NEMS materials. The two-step method involves oxygen plasma treatment of polymeric films and the application of a nanolubricant, namely perfluoropolyether. We apply the two-step method to the two most important MEMS/NEMS structural materials, namely silicon and SU8 polymer. On applying surface modification to these materials, their initial coefficient of friction reduces by ~4-7 times and the steady-state coefficient of friction reduces by ~2.5-3.5 times. Simultaneously, the wear durability of both the materials increases by >1000 times. The two-step method is time effective as each of the steps takes the time duration of approximately 1 min. It is also cost effective as the oxygen plasma treatment is a part of the MEMS/NEMS fabrication process. The two-step method can be readily and easily integrated into MEMS/NEMS fabrication processes. It is anticipated that this method will work for any kind of structural material from which MEMS/NEMS are or can be made.

  11. Hidden neural networks: application to speech recognition

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1998-01-01

    We evaluate the hidden neural network HMM/NN hybrid on two speech recognition benchmark tasks; (1) task independent isolated word recognition on the Phonebook database, and (2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how hidden neural networks...

  12. Investigation of surface roughness in micro-electro discharge machining of nonconductive ZrO2 for MEMS application

    International Nuclear Information System (INIS)

    Sabur, A; Moudood, A; Ali, M Y; Maleque, M A

    2013-01-01

    Micro-electro discharge machining technique, a noncontact machining process, is applied for drilling blind hole on nonconductive ZrO 2 ceramic for MEMS application. A conductive layer of adhesive copper is applied on the workpiece surface to initiate the sparks. Kerosene is used as dielectric for creation of continuous conductive pyrolytic carbon layer on the machined surface. Experiments are conducted by varying the voltage (V), capacitance (C) and rotational speed (N). Correlating these variables a mathematical model for surface roughness (SR) is developed using Taguchi method. The results showed that the V and C are the significant parameters of SR in micro-EDM for nonconductive ZrO 2 ceramic. The model also showed that SR increases with the increase of V and C

  13. Neural networks and their application to nuclear power plant diagnosis

    International Nuclear Information System (INIS)

    Reifman, J.

    1997-01-01

    The authors present a survey of artificial neural network-based computer systems that have been proposed over the last decade for the detection and identification of component faults in thermal-hydraulic systems of nuclear power plants. The capabilities and advantages of applying neural networks as decision support systems for nuclear power plant operators and their inherent characteristics are discussed along with their limitations and drawbacks. The types of neural network structures used and their applications are described and the issues of process diagnosis and neural network-based diagnostic systems are identified. A total of thirty-four publications are reviewed

  14. Neural Network Based Models for Fusion Applications

    Science.gov (United States)

    Meneghini, Orso; Tema Biwole, Arsene; Luda, Teobaldo; Zywicki, Bailey; Rea, Cristina; Smith, Sterling; Snyder, Phil; Belli, Emily; Staebler, Gary; Canty, Jeff

    2017-10-01

    Whole device modeling, engineering design, experimental planning and control applications demand models that are simultaneously physically accurate and fast. This poster reports on the ongoing effort towards the development and validation of a series of models that leverage neural-­network (NN) multidimensional regression techniques to accelerate some of the most mission critical first principle models for the fusion community, such as: the EPED workflow for prediction of the H-Mode and Super H-Mode pedestal structure the TGLF and NEO models for the prediction of the turbulent and neoclassical particle, energy and momentum fluxes; and the NEO model for the drift-kinetic solution of the bootstrap current. We also applied NNs on DIII-D experimental data for disruption prediction and quantifying the effect of RMPs on the pedestal and ELMs. All of these projects were supported by the infrastructure provided by the OMFIT integrated modeling framework. Work supported by US DOE under DE-SC0012656, DE-FG02-95ER54309, DE-FC02-04ER54698.

  15. Miniaturized GPS/MEMS IMU integrated board

    Science.gov (United States)

    Lin, Ching-Fang (Inventor)

    2012-01-01

    This invention documents the efforts on the research and development of a miniaturized GPS/MEMS IMU integrated navigation system. A miniaturized GPS/MEMS IMU integrated navigation system is presented; Laser Dynamic Range Imager (LDRI) based alignment algorithm for space applications is discussed. Two navigation cameras are also included to measure the range and range rate which can be integrated into the GPS/MEMS IMU system to enhance the navigation solution.

  16. MEMS-based Circuits and Systems for Wireless Communication

    CERN Document Server

    Kaiser, Andreas

    2013-01-01

    MEMS-based Circuits and Systems for Wireless Communication provides comprehensive coverage of RF-MEMS technology from device to system level. This edited volume places emphasis on how system performance for radio frequency applications can be leveraged by Micro-Electro-Mechanical Systems (MEMS). Coverage also extends to innovative MEMS-aware radio architectures that push the potential of MEMS technology further ahead.  This work presents a broad overview of the technology from MEMS devices (mainly BAW and Si MEMS resonators) to basic circuits, such as oscillators and filters, and finally complete systems such as ultra-low-power MEMS-based radios. Contributions from leading experts around the world are organized in three parts. Part I introduces RF-MEMS technology, devices and modeling and includes a prospective outlook on ongoing developments towards Nano-Electro-Mechanical Systems (NEMS) and phononic crystals. Device properties and models are presented in a circuit oriented perspective. Part II focusses on ...

  17. Neural networks and their potential application in nuclear power plants

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1991-01-01

    A neural network is a data processing system consisting of a number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks have emerged in the past few years as an area of unusual opportunity for research, development and application to a variety of real world problems. Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. Examples include reading Japanese Kanji characters and human handwriting, reading a typewritten manuscript aloud, compensating for alignment errors in robots, interpreting very noise signals (e.g., electroencephalograms), modeling complex systems that cannot be modeled mathematically, and predicting whether proposed loans will be good or fail. This paper presents a brief tutorial on neural networks and describes research on the potential applications to nuclear power plants

  18. Design, fabrication and actuation of a MEMS-based image stabilizer for photographic cell phone applications

    International Nuclear Information System (INIS)

    Chiou, Jin-Chern; Hung, Chen-Chun; Lin, Chun-Ying

    2010-01-01

    This work presents a MEMS-based image stabilizer applied for anti-shaking function in photographic cell phones. The proposed stabilizer is designed as a two-axis decoupling XY stage 1.4 × 1.4 × 0.1 mm 3 in size, and adequately strong to suspend an image sensor for anti-shaking photographic function. This stabilizer is fabricated by complex fabrication processes, including inductively coupled plasma (ICP) processes and flip-chip bonding technique. Based on the special designs of a hollow handle layer and a corresponding wire-bonding assisted holder, electrical signals of the suspended image sensor can be successfully sent out with 32 signal springs without incurring damage during wire-bonding packaging. The longest calculated traveling distance of the stabilizer is 25 µm which is sufficient to resolve the anti-shaking problem in a three-megapixel image sensor. Accordingly, the applied voltage for the 25 µm moving distance is 38 V. Moreover, the resonant frequency of the actuating device with the image sensor is 1.123 kHz.

  19. A CMOS-MEMS clamped–clamped beam displacement amplifier for resonant switch applications

    Science.gov (United States)

    Liu, Jia-Ren; Lu, Shih-Chuan; Tsai, Chun-Pu; Li, Wei-Chang

    2018-06-01

    This paper presents a micromechanical clamped–clamped beam (CC-beam) displacement amplifier based on a CMOS-MEMS fabrication process platform. In particular, a 2.0 MHz resonant displacement amplifier composed of two identical CC-beams coupled by a mechanical beam at locations where the two beams have mismatched velocities exhibits a larger displacement, up to 9.96×, on one beam than that of the other. The displacement amplification prevents unwanted input impacting—the structure switches only to the output but not the input—required by resonant switch-based mechanical circuits (Kim et al 2009 22nd IEEE Int. Conf. on Micro Electro Mechanical Systems; Lin et al 2009 15th Int. Conf. on Solid-State Sensors, Actuators, & Microsystems (TRANSDUCERS’09) Li et al 2013 17th Int. Conf. on Solid-State Sensors, Actuators, & Microsystems (TRANSDUCERS’13)). Compared to a single CC-beam displacement amplifier, theory predicts that the displacement amplifying CC-beam array yields a larger overall output displacement for displacement gain beyond 1.13 thanks to the preserved input driving force. A complete analytical model predicts the resultant stiffness and displacement gain of the coupled CC-beam displacement amplifier that match well with finite element analysis (FEA) prediction and measured results.

  20. MEMS fiber-optic Fabry-Perot pressure sensor for high temperature application

    Science.gov (United States)

    Fang, G. C.; Jia, P. G.; Cao, Q.; Xiong, J. J.

    2016-10-01

    We design and demonstrate a fiber-optic Fabry-Perot pressure sensor (FOFPPS) for high-temperature sensing by employing micro-electro-mechanical system (MEMS) technology. The FOFPPS is fabricated by anodically bonding the silicon wafer and the Pyrex glass together and fixing the facet of the optical fiber in parallel with the silicon surface by glass frit and organic adhesive. The silicon wafer can be reduced through dry etching technology to construct the sensitive diaphragm. The length of the cavity changes with the deformation of the diaphragm due to the loaded pressure, which leads to a wavelength shift of the interference spectrum. The pressure can be gauged by measuring the wavelength shift. The pressure experimental results show that the sensor has linear pressure sensitivities ranging from 0 kPa to 600 kPa at temperature range between 20°C to 300°C. The pressure sensitivity at 300°C is approximately 27.63 pm/kPa. The pressure sensitivities gradually decrease with increasing the temperature. The sensor also has a linear thermal drift when temperature changes from 20°C - 300°C.

  1. In-flight performance analysis of MEMS GPS receiver and its application to precise orbit determination of APOD-A satellite

    Science.gov (United States)

    Gu, Defeng; Liu, Ye; Yi, Bin; Cao, Jianfeng; Li, Xie

    2017-12-01

    An experimental satellite mission termed atmospheric density detection and precise orbit determination (APOD) was developed by China and launched on 20 September 2015. The micro-electro-mechanical system (MEMS) GPS receiver provides the basis for precise orbit determination (POD) within the range of a few decimetres. The in-flight performance of the MEMS GPS receiver was assessed. The average number of tracked GPS satellites is 10.7. However, only 5.1 GPS satellites are available for dual-frequency navigation because of the loss of many L2 observations at low elevations. The variations in the multipath error for C1 and P2 were estimated, and the maximum multipath error could reach up to 0.8 m. The average code noises are 0.28 m (C1) and 0.69 m (P2). Using the MEMS GPS receiver, the orbit of the APOD nanosatellite (APOD-A) was precisely determined. Two types of orbit solutions are proposed: a dual-frequency solution and a single-frequency solution. The antenna phase center variations (PCVs) and code residual variations (CRVs) were estimated, and the maximum value of the PCVs is 4.0 cm. After correcting the antenna PCVs and CRVs, the final orbit precision for the dual-frequency and single-frequency solutions were 7.71 cm and 12.91 cm, respectively, validated using the satellite laser ranging (SLR) data, which were significantly improved by 3.35 cm and 25.25 cm. The average RMS of the 6-h overlap differences in the dual-frequency solution between two consecutive days in three dimensions (3D) is 4.59 cm. The MEMS GPS receiver is the Chinese indigenous onboard receiver, which was successfully used in the POD of a nanosatellite. This study has important reference value for improving the MEMS GPS receiver and its application in other low Earth orbit (LEO) nanosatellites.

  2. Neural networks and their potential application to nuclear power plants

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1991-01-01

    A network of artificial neurons, usually called an artificial neural network is a data processing system consisting of a number of highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks exhibit characteristics and capabilities not provided by any other technology. Neural networks may be designed so as to classify an input pattern as one of several predefined types or to create, as needed, categories or classes of system states which can be interpreted by a human operator. Neural networks have the ability to recognize patterns, even when the information comprising these patterns is noisy, sparse, or incomplete. Thus, systems of artificial neural networks show great promise for use in environments in which robust, fault-tolerant pattern recognition is necessary in a real-time mode, and in which the incoming data may be distorted or noisy. The application of neural networks, a rapidly evolving technology used extensively in defense applications, alone or in conjunction with other advanced technologies, to some of the problems of operating nuclear power plants has the potential to enhance the safety, reliability and operability of nuclear power plants. The potential applications of neural networking include, but are not limited to diagnosing specific abnormal conditions, identification of nonlinear dynamics and transients, detection of the change of mode of operation, control of temperature and pressure during start-up, signal validation, plant-wide monitoring using autoassociative neural networks, monitoring of check valves, modeling of the plant thermodynamics, emulation of core reload calculations, analysis of temporal sequences in NRC's ''licensee event reports,'' and monitoring of plant parameters

  3. Advances in Artificial Neural Networks – Methodological Development and Application

    Directory of Open Access Journals (Sweden)

    Yanbo Huang

    2009-08-01

    Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological

  4. A review of vibration-based MEMS piezoelectric energy harvesters

    Energy Technology Data Exchange (ETDEWEB)

    Saadon, Salem; Sidek, Othman [Collaborative Microelectronic Design Excellence Center (CEDEC), School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang (Malaysia)

    2011-01-15

    The simplicity associated with the piezoelectric micro-generators makes it very attractive for MEMS applications, especially for remote systems. In this paper we reviewed the work carried out by researchers during the last three years. The improvements in experimental results obtained in the vibration-based MEMS piezoelectric energy harvesters show very good scope for MEMS piezoelectric harvesters in the field of power MEMS in the near future. (author)

  5. The application of artificial neural networks to TLD dose algorithm

    International Nuclear Information System (INIS)

    Moscovitch, M.

    1997-01-01

    We review the application of feed forward neural networks to multi element thermoluminescence dosimetry (TLD) dose algorithm development. A Neural Network is an information processing method inspired by the biological nervous system. A dose algorithm based on a neural network is a fundamentally different approach from conventional algorithms, as it has the capability to learn from its own experience. The neural network algorithm is shown the expected dose values (output) associated with a given response of a multi-element dosimeter (input) many times.The algorithm, being trained that way, eventually is able to produce its own unique solution to similar (but not exactly the same) dose calculation problems. For personnel dosimetry, the output consists of the desired dose components: deep dose, shallow dose, and eye dose. The input consists of the TL data obtained from the readout of a multi-element dosimeter. For this application, a neural network architecture was developed based on the concept of functional links network (FLN). The FLN concept allowed an increase in the dimensionality of the input space and construction of a neural network without any hidden layers. This simplifies the problem and results in a relatively simple and reliable dose calculation algorithm. Overall, the neural network dose algorithm approach has been shown to significantly improve the precision and accuracy of dose calculations. (authors)

  6. Robust neural network with applications to credit portfolio data analysis.

    Science.gov (United States)

    Feng, Yijia; Li, Runze; Sudjianto, Agus; Zhang, Yiyun

    2010-01-01

    In this article, we study nonparametric conditional quantile estimation via neural network structure. We proposed an estimation method that combines quantile regression and neural network (robust neural network, RNN). It provides good smoothing performance in the presence of outliers and can be used to construct prediction bands. A Majorization-Minimization (MM) algorithm was developed for optimization. Monte Carlo simulation study is conducted to assess the performance of RNN. Comparison with other nonparametric regression methods (e.g., local linear regression and regression splines) in real data application demonstrate the advantage of the newly proposed procedure.

  7. Complex-valued neural networks advances and applications

    CERN Document Server

    Hirose, Akira

    2013-01-01

    Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and

  8. Challenges in the Packaging of MEMS

    Energy Technology Data Exchange (ETDEWEB)

    Malshe, A.P.; Singh, S.B.; Eaton, W.P.; O' Neal, C.; Brown, W.D.; Miller, W.M.

    1999-03-26

    The packaging of Micro-Electro-Mechanical Systems (MEMS) is a field of great importance to anyone using or manufacturing sensors, consumer products, or military applications. Currently much work has been done in the design and fabrication of MEMS devices but insufficient research and few publications have been completed on the packaging of these devices. This is despite the fact that packaging is a very large percentage of the total cost of MEMS devices. The main difference between IC packaging and MEMS packaging is that MEMS packaging is almost always application specific and greatly affected by its environment and packaging techniques such as die handling, die attach processes, and lid sealing. Many of these aspects are directly related to the materials used in the packaging processes. MEMS devices that are functional in wafer form can be rendered inoperable after packaging. MEMS dies must be handled only from the chip sides so features on the top surface are not damaged. This eliminates most current die pick-and-place fixtures. Die attach materials are key to MEMS packaging. Using hard die attach solders can create high stresses in the MEMS devices, which can affect their operation greatly. Low-stress epoxies can be high-outgassing, which can also affect device performance. Also, a low modulus die attach can allow the die to move during ultrasonic wirebonding resulting to low wirebond strength. Another source of residual stress is the lid sealing process. Most MEMS based sensors and devices require a hermetically sealed package. This can be done by parallel seam welding the package lid, but at the cost of further induced stress on the die. Another issue of MEMS packaging is the media compatibility of the packaged device. MEMS unlike ICS often interface with their environment, which could be high pressure or corrosive. The main conclusion we can draw about MEMS packaging is that the package affects the performance and reliability of the MEMS devices. There is a

  9. Hybrid digital signal processing and neural networks applications in PWRs

    International Nuclear Information System (INIS)

    Eryurek, E.; Upadhyaya, B.R.; Kavaklioglu, K.

    1991-01-01

    Signal validation and plant subsystem tracking in power and process industries require the prediction of one or more state variables. Both heteroassociative and auotassociative neural networks were applied for characterizing relationships among sets of signals. A multi-layer neural network paradigm was applied for sensor and process monitoring in a Pressurized Water Reactor (PWR). This nonlinear interpolation technique was found to be very effective for these applications

  10. Ultra-high-aspect-orthogonal and tunable three dimensional polymeric nanochannel stack array for BioMEMS applications

    Science.gov (United States)

    Heo, Joonseong; Kwon, Hyukjin J.; Jeon, Hyungkook; Kim, Bumjoo; Kim, Sung Jae; Lim, Geunbae

    2014-07-01

    Nanofabrication technologies have been a strong advocator for new scientific fundamentals that have never been described by traditional theory, and have played a seed role in ground-breaking nano-engineering applications. In this study, we fabricated ultra-high-aspect (~106 with O(100) nm nanochannel opening and O(100) mm length) orthogonal nanochannel array using only polymeric materials. Vertically aligned nanochannel arrays in parallel can be stacked to form a dense nano-structure. Due to the flexibility and stretchability of the material, one can tune the size and shape of the nanochannel using elongation and even roll the stack array to form a radial-uniformly distributed nanochannel array. The roll can be cut at discretionary lengths for incorporation with a micro/nanofluidic device. As examples, we demonstrated ion concentration polarization with the device for Ohmic-limiting/overlimiting current-voltage characteristics and preconcentrated charged species. The density of the nanochannel array was lower than conventional nanoporous membranes, such as anodic aluminum oxide membranes (AAO). However, accurate controllability over the nanochannel array dimensions enabled multiplexed one microstructure-on-one nanostructure interfacing for valuable biological/biomedical microelectromechanical system (BioMEMS) platforms, such as nano-electroporation.Nanofabrication technologies have been a strong advocator for new scientific fundamentals that have never been described by traditional theory, and have played a seed role in ground-breaking nano-engineering applications. In this study, we fabricated ultra-high-aspect (~106 with O(100) nm nanochannel opening and O(100) mm length) orthogonal nanochannel array using only polymeric materials. Vertically aligned nanochannel arrays in parallel can be stacked to form a dense nano-structure. Due to the flexibility and stretchability of the material, one can tune the size and shape of the nanochannel using elongation and even

  11. Two-layer radio frequency MEMS fractal capacitors in PolyMUMPS for S-band applications

    KAUST Repository

    Elshurafa, Amro M.; Salama, Khaled N.

    2012-01-01

    In this Letter, the authors fabricate for the first time MEMS fractal capacitors possessing two layers and compare their performance characteristics with the conventional parallel-plate capacitor and previously reported state-of-the-art single

  12. An SOI CMOS-Based Multi-Sensor MEMS Chip for Fluidic Applications.

    Science.gov (United States)

    Mansoor, Mohtashim; Haneef, Ibraheem; Akhtar, Suhail; Rafiq, Muhammad Aftab; De Luca, Andrea; Ali, Syed Zeeshan; Udrea, Florin

    2016-11-04

    An SOI CMOS multi-sensor MEMS chip, which can simultaneously measure temperature, pressure and flow rate, has been reported. The multi-sensor chip has been designed keeping in view the requirements of researchers interested in experimental fluid dynamics. The chip contains ten thermodiodes (temperature sensors), a piezoresistive-type pressure sensor and nine hot film-based flow rate sensors fabricated within the oxide layer of the SOI wafers. The silicon dioxide layers with embedded sensors are relieved from the substrate as membranes with the help of a single DRIE step after chip fabrication from a commercial CMOS foundry. Very dense sensor packing per unit area of the chip has been enabled by using technologies/processes like SOI, CMOS and DRIE. Independent apparatuses were used for the characterization of each sensor. With a drive current of 10 µA-0.1 µA, the thermodiodes exhibited sensitivities of 1.41 mV/°C-1.79 mV/°C in the range 20-300 °C. The sensitivity of the pressure sensor was 0.0686 mV/(V excit kPa) with a non-linearity of 0.25% between 0 and 69 kPa above ambient pressure. Packaged in a micro-channel, the flow rate sensor has a linearized sensitivity of 17.3 mV/(L/min) -0.1 in the tested range of 0-4.7 L/min. The multi-sensor chip can be used for simultaneous measurement of fluid pressure, temperature and flow rate in fluidic experiments and aerospace/automotive/biomedical/process industries.

  13. An SOI CMOS-Based Multi-Sensor MEMS Chip for Fluidic Applications

    Science.gov (United States)

    Mansoor, Mohtashim; Haneef, Ibraheem; Akhtar, Suhail; Rafiq, Muhammad Aftab; De Luca, Andrea; Ali, Syed Zeeshan; Udrea, Florin

    2016-01-01

    An SOI CMOS multi-sensor MEMS chip, which can simultaneously measure temperature, pressure and flow rate, has been reported. The multi-sensor chip has been designed keeping in view the requirements of researchers interested in experimental fluid dynamics. The chip contains ten thermodiodes (temperature sensors), a piezoresistive-type pressure sensor and nine hot film-based flow rate sensors fabricated within the oxide layer of the SOI wafers. The silicon dioxide layers with embedded sensors are relieved from the substrate as membranes with the help of a single DRIE step after chip fabrication from a commercial CMOS foundry. Very dense sensor packing per unit area of the chip has been enabled by using technologies/processes like SOI, CMOS and DRIE. Independent apparatuses were used for the characterization of each sensor. With a drive current of 10 µA–0.1 µA, the thermodiodes exhibited sensitivities of 1.41 mV/°C–1.79 mV/°C in the range 20–300 °C. The sensitivity of the pressure sensor was 0.0686 mV/(Vexcit kPa) with a non-linearity of 0.25% between 0 and 69 kPa above ambient pressure. Packaged in a micro-channel, the flow rate sensor has a linearized sensitivity of 17.3 mV/(L/min)−0.1 in the tested range of 0–4.7 L/min. The multi-sensor chip can be used for simultaneous measurement of fluid pressure, temperature and flow rate in fluidic experiments and aerospace/automotive/biomedical/process industries. PMID:27827904

  14. Type-2 fuzzy neural networks and their applications

    CERN Document Server

    Aliev, Rafik Aziz

    2014-01-01

    This book deals with the theory, design principles, and application of hybrid intelligent systems using type-2 fuzzy sets in combination with other paradigms of Soft Computing technology such as Neuro-Computing and Evolutionary Computing. It provides a self-contained exposition of the foundation of type-2 fuzzy neural networks and presents a vast compendium of its applications to control, forecasting, decision making, system identification and other real problems. Type-2 Fuzzy Neural Networks and Their Applications is helpful for teachers and students of universities and colleges, for scientis

  15. Neural networks: Application to medical imaging

    Science.gov (United States)

    Clarke, Laurence P.

    1994-01-01

    The research mission is the development of computer assisted diagnostic (CAD) methods for improved diagnosis of medical images including digital x-ray sensors and tomographic imaging modalities. The CAD algorithms include advanced methods for adaptive nonlinear filters for image noise suppression, hybrid wavelet methods for feature segmentation and enhancement, and high convergence neural networks for feature detection and VLSI implementation of neural networks for real time analysis. Other missions include (1) implementation of CAD methods on hospital based picture archiving computer systems (PACS) and information networks for central and remote diagnosis and (2) collaboration with defense and medical industry, NASA, and federal laboratories in the area of dual use technology conversion from defense or aerospace to medicine.

  16. Applications of neural networks in training science.

    Science.gov (United States)

    Pfeiffer, Mark; Hohmann, Andreas

    2012-04-01

    Training science views itself as an integrated and applied science, developing practical measures founded on scientific method. Therefore, it demands consideration of a wide spectrum of approaches and methods. Especially in the field of competitive sports, research questions are usually located in complex environments, so that mainly field studies are drawn upon to obtain broad external validity. Here, the interrelations between different variables or variable sets are mostly of a nonlinear character. In these cases, methods like neural networks, e.g., the pattern recognizing methods of Self-Organizing Kohonen Feature Maps or similar instruments to identify interactions might be successfully applied to analyze data. Following on from a classification of data analysis methods in training-science research, the aim of the contribution is to give examples of varied sports in which network approaches can be effectually used in training science. First, two examples are given in which neural networks are employed for pattern recognition. While one investigation deals with the detection of sporting talent in swimming, the other is located in game sports research, identifying tactical patterns in team handball. The third and last example shows how an artificial neural network can be used to predict competitive performance in swimming. Copyright © 2011 Elsevier B.V. All rights reserved.

  17. Selected papers from the 12th International Workshop on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS 2012) (Atlanta, GA, USA, 2-5 December 2012)

    Science.gov (United States)

    Allen, Mark G.; Lang, Jeffrey

    2013-11-01

    Welcome to this special section of the Journal of Micromechanics and Microengineering (JMM). This section, co-edited by myself and by Professor Jeffrey Lang of the Massachusetts Institute of Technology, contains expanded versions of selected papers presented at the Power MEMS meeting held in Atlanta, GA, USA, in December of 2012. Professor Lang and I had the privilege of co-chairing Power MEMS 2012, the 12th International Workshop on Micro and Nanotechnology for Power Generation and Energy Conversion Applications. The scope of the PowerMEMS series of workshops ranges from basic principles, to materials and fabrication, to devices and systems, to applications. The many applications of power MEMS (microelectromehcanical systems) range from MEMS-enabled energy harvesting, storage, conversion and conditioning, to integrated systems that manage these processes. Why is the power MEMS field growing in importance? Smaller-scale power and power supplies (microwatts to tens of watts) are gaining in prominence due to many factors, including the ubiquity of low power portable electronic equipment and the proliferation of wireless sensor nodes that require extraction of energy from their embedding environment in order to function. MEMS manufacturing methods can be utilized to improve the performance of traditional power supply elements, such as allowing batteries to charge faster or shrinking the physical size of passive elements in small-scale power supplies. MEMS technologies can be used to fabricate energy harvesters that extract energy from an embedding environment to power wireless sensor nodes, in-body medical implants and other devices, in which the harvesters are on the small scales that are appropriately matched to the overall size of these microsystems. MEMS can enable the manufacturing of energy storage elements from nontraditional materials by bringing appropriate structure and surface morphology to these materials as well as fabricating the electrical interfaces

  18. Effect of tungsten (W) on structural and magnetic properties of electroplated NiFe thin films for MEMS applications

    Science.gov (United States)

    Kannan, R.; Devaki, P.; Premkumar, P. S.; Selvambikai, M.

    2018-04-01

    Electrodeposition of nanocrystalline NiFe and NiFeW thin films were carried out from ammonium citrate bath at a constant current density and controlled pH of 8 by varying the bath temperature from 40 °C to 70 °C. The surface morphology and chemical composition of the electrodeposited NiFe and NiFeW soft magnetic thin films were studied by using SEM and EDAX. The SEM micrographs of the films coated at higher electrodeposited bath temperature have no micro cracks and also the films have more uniform surface morphology. The existence of crystalline nature of the coated films were analysed by XRD. The presence of predominant peaks in x-ray diffraction pattern (compared with JCPDS data) reveal that the average crystalline size was in the order of few tens of nano meters. The magnetic properties such as coercivity, saturation magnetization and magnetic flux density have been calculated from vibrating sample magnetometer analysis. The VSM result shows that the NiFeW thin film synthesised at 70 °C exhibit the lower coercivity with higher saturation magnetization. The hardness and adhesion of the electroplated films have been investigated. Reasons for variation in magnetic properties and structural characteristics are also discussed. The electroplated NiFe and NiFeW thin films can be used for Micro Electro Mechanical System (MEMS) applications due to their excellent soft magnetic behaviour.

  19. Potential applications of neural networks to nuclear power plants

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1991-01-01

    Application of neural networks to the operation of nuclear power plants is being investigated under a US Department of Energy sponsored program at the University of Tennessee. Projects include the feasibility of using neural networks for the following tasks: diagnosing specific abnormal conditions, detection of the change of mode of operation, signal validation, monitoring of check valves, plant-wide monitoring using autoassociative neural networks, modeling of the plant thermodynamics, emulation of core reload calculations, monitoring of plant parameters, and analysis of plant vibrations. Each of these projects and its status are described briefly in this article. The objective of each of these projects is to enhance the safety and performance of nuclear plants through the use of neural networks

  20. SU-8 Based MEMS Process with Two Metal Layers using α-Si as a Sacrificial Material

    KAUST Repository

    Ramadan, Khaled S.

    2012-01-01

    MEMS applications. α-Si can be deposited at large thicknesses for MEMS applications and also can be released in a dry method using XeF2 which can solve stiction problems related to MEMS applications. In this thesis, an SU-8 MEMS process is developed

  1. PREFACE: The 15th International Conference on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS 2015)

    Science.gov (United States)

    Livermore, C.; Velásquez-García, L. F.

    2015-12-01

    Greetings, and welcome to Boston, MA and PowerMEMS 2015 - the 15th International Conference on Micro and Nanotechnology for Power Generation and Energy Conversion Applications! The objective of PowerMEMS 2015 is to catalyze innovation in micro- and nano-scale technologies for the energy domain. The scope of the meeting ranges from basic principles, to materials and fabrication, to devices and systems, to applications. The many applications of Power MEMS range from the harvesting, storage, conversion and conditioning of energy, to integrated systems that manage these processes, to actuation, pumping, and propulsion. Our Conference aims to stimulate the exchange of insights and information, as well as the development of new ideas, in the Power MEMS field. Our goal is to allow the attendees to interact and network within our multidisciplinary community that includes professionals from many branches of science and engineering, as well as energy, policy, and entrepreneurial specialists interested in the commercialization of Power MEMS technologies. Since the first PowerMEMS in Sendai, Japan in 2000, the Conference has grown in size, reputation, impact, and technical breadth. This continuing growth is evident in this year's technical program, which includes an increasing number of papers on nanomaterials, additive manufacturing for energy systems, actuators, energy storage, harvesting strategies and integrated energy harvesting systems, for example. This year's technical program is highlighted by six plenary talks from prominent experts on piezoelectrics, robotic insects, thermoelectrics, photovoltaics, nanocomposite cathodes, and thermal energy conversion systems. The contributed program received a large number of abstract submissions this year, 169 in total. After careful review by the 34-member Technical Program Committee, a total of 135 papers were selected for presentation. The 60 contributed oral presentations are arranged in two parallel sessions. The 75 posters

  2. Neural networks. A new analytical tool, applicable also in nuclear technology

    International Nuclear Information System (INIS)

    Stritar, A.

    1992-01-01

    The basic concept of neural networks and back propagation learning algorithm are described. The behaviour of typical neural network is demonstrated on a simple graphical case. A short literature survey about the application of neural networks in nuclear science and engineering is made. The application of the neural network to the probability density calculation is shown. (author) [sl

  3. EDITORIAL: The 6th International Workshop on Micro and Nanotechnologies for Power Generation and Energy Conversion Applications (PowerMEMS 2006)

    Science.gov (United States)

    Fréchette, Luc G.

    2007-09-01

    Energy is a sector of paramount importance over the coming decades if we are to ensure sustainable development that respects our environment. The research and development of novel approaches to convert available energy into usable forms using micro and nanotechnologies can contribute towards this goal and meet the growing need for power in small scale portable applications. The dominant power sources for handheld and other portable electronics are currently primary and rechargeable batteries. Their limited energy density and adverse effects on the environment upon disposal suggest that alternative approaches need to be explored. This special issue will showcase some of the leading work in this area, initially presented at PowerMEMS 2006, the 6th International Workshop on Micro and Nanotechnologies for Power Generation and Energy Conversion Applications. Power MEMS are defined as microsystems for electrical power generation and other energy conversion applications, including propulsion and cooling. The range of power MEMS technologies includes micro thermodynamic machines, such as microturbines, miniature internal combustion engines and micro-coolers; solid-state direct energy conversion, such as thermoelectric and photovoltaic microstructures; micro electrochemical devices, such as micro fuel cells and nanostructure batteries; vibration energy harvesting devices, such as piezoelectric, magnetic or electrostatic micro generators, as well as micro thrusters and rocket engines for propulsion. These can either be driven by scavenging thermal, mechanical or solar energy from the environment, or from a stored energy source, such as chemical fuel or radioactive material. The unique scope leads to unique challenges in the development of power MEMS, ranging from the integration of novel materials to the efficient small scale implementation of energy conversion principles. In this special issue, Mitcheson et al provide a comparative assessment of three inertial vibration

  4. A Teaching - Learning Framework for MEMS Education

    International Nuclear Information System (INIS)

    Sheeparamatti, B G; Angadi, S A; Sheeparamatti, R B; Kadadevaramath, J S

    2006-01-01

    Micro-Electro-Mechanical Systems (MEMS) technology has been identified as one of the most promising technologies in the 21st century. MEMS technology has opened up a wide array of unforeseen applications. Hence it is necessary to train the technocrats of tomorrow in this emerging field to meet the industrial/societal demands. The drive behind fostering of MEMS technology is the reduction in the cost, size, weight, and power consumption of the sensors, actuators, and associated electronics. MEMS is a multidisciplinary engineering and basic science area which includes electrical engineering, mechanical engineering, material science and biomedical engineering. Hence MEMS education needs a special approach to prepare the technocrats for a career in MEMS. The modern education methodology using computer based training systems (CBTS) with embedded modeling and simulation tools will help in this direction. The availability of computer based learning resources such as MATLAB, ANSYS/Multiphysics and rapid prototyping tools have contributed to proposition of an efficient teaching-learning framework for MEMS education presented in this paper. This paper proposes a conceptual framework for teaching/learning MEMS in the current technical education scenario

  5. Biomaterial applications in neural therapy and repair

    Institute of Scientific and Technical Information of China (English)

    Harmanvir Ghuman; Michel Modo

    2017-01-01

    The use of biomaterials,such as hydrogels,as a scaffold to deliver cells and drugs is becoming increasingly common to treat neurological conditions,including stroke.With a limited intrinsic ability to regenerate after injury,innovative tissue engineering strategies have shown the potential of biomaterials in facilitating neural tissue regeneration and functional recovery.Using biomaterials can not only promote the survival and integration of transplanted cells in the existing circuitry,but also support controlled site specific delivery of therapeutic drugs.This review aims to provide the reader an understanding of the brain tissue microenvironment after injury,biomaterial criteria that support tissue repair,commonly used natural and synthetic biomaterials,benefits of incorporating cells and neurotrophic factors,as well as the potential of endogenous neurogenesis in repairing the injured brain.

  6. Two-layer radio frequency MEMS fractal capacitors in PolyMUMPS for S-band applications

    KAUST Repository

    Elshurafa, Amro M.

    2012-07-23

    In this Letter, the authors fabricate for the first time MEMS fractal capacitors possessing two layers and compare their performance characteristics with the conventional parallel-plate capacitor and previously reported state-of-the-art single-layer MEMS fractal capacitors. Explicitly, a capacitor with a woven structure and another with an interleaved configuration were fabricated in the standard PolyMUMPS surface micromachining process and tested at S-band frequencies. The self-resonant frequencies of the fabricated capacitors were close to 10GHz, which is better than that of the parallel-plate capacitor, which measured only 5.5GHz. Further, the presented capacitors provided a higher capacitance when compared with the state-of-the-art-reported MEMS fractal capacitors created using a single layer at the expense of a lower quality factor. © 2012 The Institution of Engineering and Technology.

  7. Applications of neural networks in high energy physics

    International Nuclear Information System (INIS)

    Cutts, D.; Hoftun, J.S.; Nesic, D.; Sornborger, A.; Johnson, C.R.; Zeller, R.T.

    1990-01-01

    Neural network techniques provide promising solutions to pattern recognition problems in high energy physics. We discuss several applications of back propagation networks, and in particular describe the operation of an electron algorithm based on calorimeter energies. 5 refs., 5 figs., 1 tab

  8. Application of artificial neural networks to improve power transfer ...

    African Journals Online (AJOL)

    Application of artificial neural networks to improve power transfer capability through OLTC. ... International Journal of Engineering, Science and Technology ... Numerical results show that the setting of OLTC transformer in terms of the load model has a major effect on the maximum power transfer in power systems and the ...

  9. Approaches and Challenges of Engineering Implantable Microelectromechanical Systems (MEMS Drug Delivery Systems for in Vitro and in Vivo Applications

    Directory of Open Access Journals (Sweden)

    Ken-Tye Yong

    2012-11-01

    Full Text Available Despite the advancements made in drug delivery systems over the years, many challenges remain in drug delivery systems for treating chronic diseases at the personalized medicine level. The current urgent need is to develop novel strategies for targeted therapy of chronic diseases. Due to their unique properties, microelectromechanical systems (MEMS technology has been recently engineered as implantable drug delivery systems for disease therapy. This review examines the challenges faced in implementing implantable MEMS drug delivery systems in vivo and the solutions available to overcome these challenges.

  10. Movable MEMS Devices on Flexible Silicon

    KAUST Repository

    Ahmed, Sally

    2013-01-01

    Flexible electronics have gained great attention recently. Applications such as flexible displays, artificial skin and health monitoring devices are a few examples of this technology. Looking closely at the components of these devices, although MEMS

  11. Active mems microbeam device for gas detection

    KAUST Repository

    Bouchaala, Adam M.; Jaber, Nizar; Younis, Mohammad I.

    2017-01-01

    Sensors and active switches for applications in gas detection and other fields are described. The devices are based on the softening and hardening nonlinear response behaviors of microelectromechanical systems (MEMS) clamped-clamped microbeams

  12. Non-linear feedback neural networks VLSI implementations and applications

    CERN Document Server

    Ansari, Mohd Samar

    2014-01-01

    This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known that the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.

  13. Accurate and wide field of view MEMS-based sun sensor for industrial applications

    OpenAIRE

    Delgado, Francisco; Quero, J.M.; Garcia Ortega, Juan; López Tarrida, Cristina; Ortega Villasclaras, Pablo Rafael; Bermejo Broto, Sandra

    2012-01-01

    This paper describes the design, fabrication, sim- ulation, and experimental results of an improved miniaturized two-axis sun sensor for industrial applications, created by adapt- ing a technology used previously in satellite applications. The sensor for each axis is composed of six photodiodes integrated in a crystalline-silicon substrate and a layer of cover glass, which is used to protect the silicon and to hold the windows. The high preci...

  14. Magnetron sputtered Cu{sub 3}N/NiTiCu shape memory thin film heterostructures for MEMS applications

    Energy Technology Data Exchange (ETDEWEB)

    Kaur, Navjot; Choudhary, Nitin [Indian Institute of Technology Roorkee, Roorkee, Functional Nanomaterials Research Lab, Department of Physics and Centre of Nanotechnology (India); Goyal, Rajendra N. [Indian Institute of Technology, Roorkee, Department of Chemistry (India); Viladkar, S. [Indian Institute of Technology Roorkee, Roorkee, Functional Nanomaterials Research Lab, Department of Physics and Centre of Nanotechnology (India); Matai, I.; Gopinath, P. [Indian Institute of Technology, Roorkee, Centre for Nanotechnology (India); Chockalingam, S. [Indian Institute of Technology, Guwahati, Department of Biotechnology (India); Kaur, Davinder, E-mail: dkaurfph@iitr.ernet.in [Indian Institute of Technology Roorkee, Roorkee, Functional Nanomaterials Research Lab, Department of Physics and Centre of Nanotechnology (India)

    2013-03-15

    In the present study, for the first time, Cu{sub 3}N/NiTiCu/Si heterostructures were successfully grown using magnetron sputtering technique. Nanocrystalline copper nitride (Cu{sub 3}N with thickness {approx}200 nm) thin films and copper nanodots were subsequently deposited on the surface of 2-{mu}m-thick NiTiCu shape memory thin films in order to improve the surface corrosion and nickel release properties of NiTiCu thin films. Interestingly, the phase transformation from martensite phase to austenite phase has been observed in Cu{sub 3}N/NiTiCu heterostructures with corresponding change in texture and surface morphology of top Cu{sub 3}N films. Field emission scanning electron microscopy and atomic force microscope images of the heterostructures reveals the formation of 20-nm-sized copper nanodots on NiTiCu surface at higher deposition temperature (450 Degree-Sign C) of Cu{sub 3}N. Cu{sub 3}N passivated NiTiCu films possess low corrosion current density with higher corrosion potential and, therefore, better corrosion resistance as compared to pure NiTiCu films. The concentration of Ni released from the Cu{sub 3}N/NiTiCu samples was observed to be much less than that of pure NiTiCu film. It can be reduced to the factor of about one-ninth after the surface passivation resulting in smooth, homogeneous and highly corrosion resistant surface. The antibacterial and cytotoxicity of pure and Cu{sub 3}N coated NiTiCu thin films were investigated through green fluorescent protein expressing E. coli bacteria and human embryonic kidney cells. The results show the strong antibacterial property and non cytotoxicity of Cu{sub 3}N/NiTiCu heterostructure. This work is of immense technological importance due to variety of BioMEMS applications.

  15. Real-time applications of neural nets

    International Nuclear Information System (INIS)

    Spencer, J.E.

    1989-05-01

    Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas, e.g., improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. Such issues are considered, examples given and possibilities discussed. 21 refs., 6 figs

  16. Real-time applications of neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Spencer, J.E.

    1989-05-01

    Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas, e.g., improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. Such issues are considered, examples given and possibilities discussed. 21 refs., 6 figs.

  17. Real-time applications of neural nets

    International Nuclear Information System (INIS)

    Spencer, J.E.

    1989-01-01

    Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas e.g. improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. In this paper, such issues are considered, examples given and possibilities discussed

  18. Integrated electronics and fluidic MEMS for bioengineering

    Science.gov (United States)

    Fok, Ho Him Raymond

    Microelectromechanical systems (MEMS) and microelectronics have become enabling technologies for many research areas. This dissertation presents the use of fluidic MEMS and microelectronics for bioengineering applications. In particular, the versatility of MEMS and microelectronics is highlighted by the presentation of two different applications, one for in-vitro study of nano-scale dynamics during cell division and one for in-vivo monitoring of biological activities at the cellular level. The first application of an integrated system discussed in this dissertation is to utilize fluidic MEMS for studying dynamics in the mitotic spindle, which could lead to better chemotherapeutic treatments for cancer patients. Previous work has developed the use of electrokinetic phenomena on the surface of a glass-based platform to assemble microtubules, the building blocks of mitotic spindles. Nevertheless, there are two important limitations of this type of platform. First, an unconventional microfabrication process is necessary for the glass-based platform, which limits the utility of this platform. In order to overcome this limitation, in this dissertation a convenient microfluidic system is fabricated using a negative photoresist called SU-8. The fabrication process for the SU-8-based system is compatible with other fabrication techniques used in developing microelectronics, and this compatibility is essential for integrating electronics for studying dynamics in the mitotic spindle. The second limitation of the previously-developed glass-based platform is its lack of bio-compatibility. For example, microtubules strongly interact with the surface of the glass-based platform, thereby hindering the study of dynamics in the mitotic spindle. This dissertation presents a novel approach for assembling microtubules away from the surface of the platform, and a fabrication process is developed to assemble microtubules between two self-aligned thin film electrodes on thick SU-8

  19. SU-8-based microneedles for in vitro neural applications

    International Nuclear Information System (INIS)

    Altuna, Ane; Tijero, María; Berganzo, Javier; Salido, Rafa; Fernández, Luis J; Gabriel, Gemma; Guimerá, Anton; Villa, Rosa; Menéndez de la Prida, Liset

    2010-01-01

    This paper presents novel design, fabrication, packaging and the first in vitro neural activity recordings of SU-8-based microneedles. The polymer SU-8 was chosen because it provides excellent features for the fabrication of flexible and thin probes. A microprobe was designed in order to allow a clean insertion and to minimize the damage caused to neural tissue during in vitro applications. In addition, a tetrode is patterned at the tip of the needle to obtain fine-scale measurements of small neuronal populations within a radius of 100 µm. Impedance characterization of the electrodes has been carried out to demonstrate their viability for neural recording. Finally, probes are inserted into 400 µm thick hippocampal slices, and simultaneous action potentials with peak-to-peak amplitudes of 200–250 µV are detected.

  20. RF-MEMS for future mobile applications: experimental verification of a reconfigurable 8-bit power attenuator up to 110 GHz

    International Nuclear Information System (INIS)

    Iannacci, J; Tschoban, C

    2017-01-01

    RF-MEMS technology is proposed as a key enabling solution for realising the high-performance and highly reconfigurable passive components that future communication standards will demand. In this work, we present, test and discuss a novel design concept for an 8-bit reconfigurable power attenuator, manufactured using the RF-MEMS technology available at the CMM-FBK, in Italy. The device features electrostatically controlled MEMS ohmic switches in order to select/deselect the resistive loads (both in series and shunt configuration) that attenuate the RF signal, and comprises eight cascaded stages (i.e. 8-bit), thus implementing 256 different network configurations. The fabricated samples are measured (S-parameters) from 10 MHz to 110 GHz in a wide range of different configurations, and modelled/simulated with Ansys HFSS. The device exhibits attenuation levels (S21) in the range from  −10 dB to  −60 dB, up to 110 GHz. In particular, S21 shows flatness from 15 dB down to 3–5 dB and from 10 MHz to 50 GHz, as well as fewer linear traces up to 110 GHz. A comprehensive discussion is developed regarding the voltage standing wave ratio, which is employed as a quality indicator for the attenuation levels. The margins of improvement at design level which are needed to overcome the limitations of the presented RF-MEMS device are also discussed. (paper)

  1. A novel design and analysis of a MEMS ceramic hot-wire anemometer for high temperature applications

    International Nuclear Information System (INIS)

    Nagaiah, N R; Sleiti, A K; Rodriguez, S; Kapat, J S; An, L; Chow, L

    2006-01-01

    This paper attempts to prove the feasibility of high temperature MEMS hot-wire anemometer for gas turbine environment. No such sensor exists at present. Based on the latest improvement in a new type of Polymer-Derived Ceramic (PDC) material, the authors present a Novel design, structural and thermal analysis of MEMS hot-wire anemometer (HWA) based on PDC material, and show that such a sensor is indeed feasible. This MEMS Sensor is microfabricated by using three types of PDC materials such as SiAlCN, SiCN (lightly doped) and SiCN (heavily doped) for sensing element (hot-wire), support prongs and connecting leads respectively. This novel hot wire anemometer can perform better than a conventional HWA in which the hot wire is made of tungsten or platinum-iridium. This type of PDC-HWA can be used in harsh environment due to its high temperature resistance, tensile strength and resistance to oxidation. This HWA is fabricated using microstereolithography as a novel microfabrication technique to manufacture the proposed MEMS Sensor

  2. MEMS monocrystalline-silicon based thermal devices for chemical and microfluidic applications

    NARCIS (Netherlands)

    Mihailovic, M.

    2011-01-01

    This thesis explores the employment of monocrystalline silicon in microsystems as an active material for different thermal functions, such as heat generation and heat transfer by conduction. In chapter 1 applications that need thermal micro devices, micro heaters and micro heat exchangers, are

  3. Low Voltage, High-Q SOI MEMS Varactors for RF Applications

    DEFF Research Database (Denmark)

    Yalcinkaya, Arda Deniz; Jensen, Søren; Hansen, Ole

    2003-01-01

    A micro electromechanical tunable capacitor with a low control voltage, a wide tuning range and high electrical quality factor is presented with detailed characterizations. A 50μm thick single-crystalline silicon layer was etched using deep reactive ion etching (DRIE) for obtaining high-aspect ra...... is a suitable passive component to be used in band-pass filtering, voltage controlled oscillator or impedance matching applications on the very high frequency(VHF) and ultra high frequency (UHF) bands....

  4. Sandia Agile MEMS Prototyping, Layout Tools, Education and Services Program

    Energy Technology Data Exchange (ETDEWEB)

    Schriner, H.; Davies, B.; Sniegowski, J.; Rodgers, M.S.; Allen, J.; Shepard, C.

    1998-05-01

    Research and development in the design and manufacture of Microelectromechanical Systems (MEMS) is growing at an enormous rate. Advances in MEMS design tools and fabrication processes at Sandia National Laboratories` Microelectronics Development Laboratory (MDL) have broadened the scope of MEMS applications that can be designed and manufactured for both military and commercial use. As improvements in micromachining fabrication technologies continue to be made, MEMS designs can become more complex, thus opening the door to an even broader set of MEMS applications. In an effort to further research and development in MEMS design, fabrication, and application, Sandia National Laboratories has launched the Sandia Agile MEMS Prototyping, Layout Tools, Education and Services Program or SAMPLES program. The SAMPLES program offers potential partners interested in MEMS the opportunity to prototype an idea and produce hardware that can be used to sell a concept. The SAMPLES program provides education and training on Sandia`s design tools, analysis tools and fabrication process. New designers can participate in the SAMPLES program and design MEMS devices using Sandia`s design and analysis tools. As part of the SAMPLES program, participants` designs are fabricated using Sandia`s 4 level polycrystalline silicon surface micromachine technology fabrication process known as SUMMiT (Sandia Ultra-planar, Multi-level MEMS Technology). Furthermore, SAMPLES participants can also opt to obtain state of the art, post-fabrication services provided at Sandia such as release, packaging, reliability characterization, and failure analysis. This paper discusses the components of the SAMPLES program.

  5. EDITORIAL: Selected papers from the 11th International Workshop on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS 2011) Selected papers from the 11th International Workshop on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS 2011)

    Science.gov (United States)

    Cho, Young-Ho

    2012-09-01

    This special section of Journal of Micromechanics and Microengineering features papers selected from the 11th International Workshop on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS 2011), held at Sejong Hotel in Seoul, Korea during 15-18 November 2011. Since the first PowerMEMS workshop held in Sendai, Japan in 2000, the workshop has developed as the premier forum for reporting research results in micro and nanotechnology for power generation, energy conversion, harvesting and processing applications, including in-depth technical issues on nanostructures and materials for small-scale high-density energy and thermal management. Potential PowerMEMS applications cover not only portable power devices for consumer electronics and remote sensors, but also micro engines, impulsive thrusters and fuel cells for systems ranging from the nanometer to the millimeter scale. The 2011 technical program consists of 1 plenary talk, 4 invited talks and 118 contributed presentations. The 48 oral and 70 poster presentations, selected by 27 Technical Program Committee Members from 131 submitted abstracts, have stimulated lively discussion maximizing the interaction between participants. Among them, this special section includes 9 papers covering micro-scale power generators, energy converters, harvesters, thrusters and thermal coolers. Finally, we are grateful to the members of the International Steering Committee, the Technical Program Committee, and the Local Organizing Committee for their efforts and contributions to PowerMEMS 2011. We also thank the two companies Samsung Electro-Mechanics and LG Elite for technical tour arrangements. Special thanks go to Dr Ian Forbes, the editorial staff of the Journal of Micromechanics and Microengineering, as well as to the staff of IOP Publishing for making this special section possible.

  6. Three dimensional MEMS supercapacitors

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Wei

    2011-10-15

    and the specific power of 0.58 mWcm-2 at 20mVs-1 scan rate. The 3D MEMS supercapacitor fabricated in this project has the specific capacitance and the specific power of 0.029 F cm-2 and 2.2 mWcm-2 respectively at a relative large discharge rate of 5 m Acm-2. It is also found vi that the supercapacitors have the performance of broad frequency range up to 300Hz. For DRIE based 3D MEMS supercapacitor, the innovative designs were developed based on silicon micromachining process flow which includes the key processes such as thermal oxidation, RF sputtering, wet etching, DRIE, electroless plating and P Py polymerization. The optimized P Py electrode doping with Tos- performed ideal super capacitor properties in NaCl electrolyte. The single PPy electrode of the 3D MEMS supercapacitors can provide 0.128 F cm-2 specific capacitance and 1.28 mWcm-2 specific power at 20 mvs-1 scan rate. The specific capacitance of the 3D MEMS supercapacitors equals 0.056 F cm-2, and the specific power at 20 mvs-1 scan rate equals 0.56 mWcm-2. In addition, novel supercapacitors based on wafer level process are designed for flexible integration in applications such as high temperature electronics and hybrid power system for electric vehicles. Experimental work on TiO2 anodic oxidation, which enables the fabrication of the one of these designs, has been carried out. Dense TiO2 nano holes with diameters ranged from about 90 to 270 nm were obtained in 0.05 wt% HF aqueous solutions with two-step anodic oxidation method. Comparing to the published specific capacitance (about 2 mFcm-2) for microsupercapacitors [33], I have achieved much larger specific capacitance (typically 0.029 to 0.056 F cm-2) for 3D MEMS supercapacitors. The above results have been presented in 3 international conferences. Total 4 journal articles have been published, and one has been submitted. The article in the Journal of Power Source (appendix 3) has been cited 9 times after published in April 2009, and the article in

  7. Micro electromechanical systems (MEMS) for mechanical engineers

    Energy Technology Data Exchange (ETDEWEB)

    Lee, A. P., LLNL

    1996-11-18

    The ongoing advances in Microelectromechanical Systems (MEMS) are providing man-kind the freedom to travel to dimensional spaces never before conceivable. Advances include new fabrication processes, new materials, tailored modeling tools, new fabrication machines, systems integration, and more detailed studies of physics and surface chemistry as applied to the micro scale. In the ten years since its inauguration, MEMS technology is penetrating industries of automobile, healthcare, biotechnology, sports/entertainment, measurement systems, data storage, photonics/optics, computer, aerospace, precision instruments/robotics, and environment monitoring. It is projected that by the turn of the century, MEMS will impact every individual in the industrial world, totaling sales up to $14 billion (source: System Planning Corp.). MEMS programs in major universities have spawned up all over the United States, preparing the brain-power and expertise for the next wave of MEMS breakthroughs. It should be pointed out that although MEMS has been initiated by electrical engineering researchers through the involvement of IC fabrication techniques, today it has evolved such that it requires a totally multi-disciplinary team to develop useful devices. Mechanical engineers are especially crucial to the success of MEMS development, since 90% of the physical realm involved is mechanical. Mechanical engineers are needed for the design of MEMS, the analysis of the mechanical system, the design of testing apparatus, the implementation of analytical tools, and the packaging process. Every single aspect of mechanical engineering is being utilized in the MEMS field today, however, the impact could be more substantial if more mechanical engineers are involved in the systems level designing. In this paper, an attempt is made to create the pathways for a mechanical engineer to enter in the MEMS field. Examples of application in optics and medical devices will be used to illustrate how mechanical

  8. Modelling and Simulation of Novel Three Arm MEMS Actuators and Its Application

    International Nuclear Information System (INIS)

    Pandiyan, Jagadeesh; Umapathy, M; Balachandar, S; Arumugam, M; Ramasamy, S; Gajjar, Nilesh C

    2006-01-01

    This paper presents the design and Finite Element Model (FEM) simulation of a novel electrothermal microactuators and arrays. It is a single material microactuator which deflects at its tips by differential thermal expansion of its constituent parts. The electrothermal actuator consists of three thin arms, three thin blades and two electrical connection pads. The goal of this coupled electrothermal actuator design was to multiply the force by adding the individual contributions of all the three actuators. The difference in magnitude of blade deflections depends on the geometrical characteristics of the actuators. The thermal deformation and thermal stability are easily controllable. The simulation employing ANSYS/Multiphysics software results include force, deflection, thermal stress, ideal electrothermal actuator and array geometries. The main advantage of this electrothermal actuator is large deflection of blades with very low actuation voltage in comparison with electrostatic actuators. A typical application in a micromirror is shown to illustrate the utility of these actuators and arrays

  9. Optical inspection of hidden MEMS structures

    Science.gov (United States)

    Krauter, Johann; Gronle, Marc; Osten, Wolfgang

    2017-06-01

    Micro-electro-mechanical system's (MEMS) applications have greatly expanded over the recent years, and the MEMS industry has grown almost exponentially. One of the strongest drivers are the automotive and consumer markets. A 100% test is necessary especially in the production of automotive MEMS sensors since they are subject to safety relevant functions. This inspection should be carried out before dicing and packaging since more than 90% of the production costs are incurred during these steps. An electrical test is currently being carried out with each MEMS component. In the case of a malfunction, the defect can not be located on the wafer because the MEMS are no longer optically accessible due to the encapsulation. This paper presents a low coherence interferometer for the topography measurement of MEMS structures located within the wafer stack. Here, a high axial and lateral resolution is necessary to identify defects such as stuck or bent MEMS fingers. First, the boundary conditions for an optical inspection system will be discussed. The setup is then shown with some exemplary measurements.

  10. Advanced mechatronics and MEMS devices II

    CERN Document Server

    Wei, Bin

    2017-01-01

    This book introduces the state-of-the-art technologies in mechatronics, robotics, and MEMS devices in order to improve their methodologies. It provides a follow-up to "Advanced Mechatronics and MEMS Devices" (2013) with an exploration of the most up-to-date technologies and their applications, shown through examples that give readers insights and lessons learned from actual projects. Researchers on mechatronics, robotics, and MEMS as well as graduate students in mechanical engineering will find chapters on: Fundamental design and working principles on MEMS accelerometers Innovative mobile technologies Force/tactile sensors development Control schemes for reconfigurable robotic systems Inertial microfluidics Piezoelectric force sensors and dynamic calibration techniques ...And more. Authors explore applications in the areas of agriculture, biomedicine, advanced manufacturing, and space. Micro-assembly for current and future industries is also considered, as well as the design and development of micro and intel...

  11. Application of Artificial Neural Networks for estimating index floods

    Science.gov (United States)

    Šimor, Viliam; Hlavčová, Kamila; Kohnová, Silvia; Szolgay, Ján

    2012-12-01

    This article presents an application of Artificial Neural Networks (ANNs) and multiple regression models for estimating mean annual maximum discharge (index flood) at ungauged sites. Both approaches were tested for 145 small basins in Slovakia in areas ranging from 20 to 300 km2. Using the objective clustering method, the catchments were divided into ten homogeneous pooling groups; for each pooling group, mutually independent predictors (catchment characteristics) were selected for both models. The neural network was applied as a simple multilayer perceptron with one hidden layer and with a back propagation learning algorithm. Hyperbolic tangents were used as an activation function in the hidden layer. Estimating index floods by the multiple regression models were based on deriving relationships between the index floods and catchment predictors. The efficiencies of both approaches were tested by the Nash-Sutcliffe and a correlation coefficients. The results showed the comparative applicability of both models with slightly better results for the index floods achieved using the ANNs methodology.

  12. Application of neural networks and cellular automata to calorimetric problems

    Energy Technology Data Exchange (ETDEWEB)

    Brenton, V; Fonvieille, H; Guicheney, C; Jousset, J; Roblin, Y; Tamin, F; Grenier, P

    1994-09-01

    Computing techniques based on parallel processing have been used to treat the information from the electromagnetic calorimeters in SLAC experiments E142/E143. Cluster finding and separation of overlapping showers are performed by a cellular automaton, pion and electron identification is done by using a multilayered neural network. Both applications are presented and their resulting performances are shown to be improved compared to more standard approaches. (author). 9 refs.; Submitted to Nuclear Instruments and Methods (NL).

  13. Application of neural networks and cellular automata to calorimetric problems

    International Nuclear Information System (INIS)

    Brenton, V.; Fonvieille, H.; Guicheney, C.; Jousset, J.; Roblin, Y.; Tamin, F.; Grenier, P.

    1994-09-01

    Computing techniques based on parallel processing have been used to treat the information from the electromagnetic calorimeters in SLAC experiments E142/E143. Cluster finding and separation of overlapping showers are performed by a cellular automaton, pion and electron identification is done by using a multilayered neural network. Both applications are presented and their resulting performances are shown to be improved compared to more standard approaches. (author)

  14. Application of artificial neural networks in particle physics

    International Nuclear Information System (INIS)

    Kolanoski, H.

    1995-04-01

    The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the use of feed-forward nets for event classification and function approximation. This network type is best suited for a hardware implementation and special VLSI chips are available which are used in fast trigger processors. Also discussed are fully connected networks of the Hopfield type for pattern recognition in tracking detectors. (orig.)

  15. Workshop on environmental and energy applications of neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hashem, S.

    1995-03-01

    This report consists of the abstracts for the papers given at the conference. Applications of neural networks in the environmental, energy and biomedical fields are discussed. Some of the topics covered are: predicting atmospheric pollutant concentrations due to fossil-fired electric power generation; hazardous waste characterization; nondestructive TRU (transuranic) waste assay; risk analysis; load forecasting for electric utilities; design of a wind power storage and generation system; nuclear fuel management; etc.

  16. Workshop on environmental and energy applications of neural networks

    International Nuclear Information System (INIS)

    Hashem, S.

    1995-03-01

    This report consists of the abstracts for the papers given at the conference. Applications of neural networks in the environmental, energy and biomedical fields are discussed. Some of the topics covered are: predicting atmospheric pollutant concentrations due to fossil-fired electric power generation; hazardous waste characterization; nondestructive TRU (transuranic) waste assay; risk analysis; load forecasting for electric utilities; design of a wind power storage and generation system; nuclear fuel management; etc

  17. Progress and profit through microtechnologies: commercial applications of MEMS/MOEMS

    Science.gov (United States)

    Ehrfeld, Wolfgang; Ehrfeld, Ursula

    2001-09-01

    Micro technology deals with miniaturization and integration in all areas of technology outside of microelectronics like micro mechanics, micro optics, micro acoustics, micro fluid technology, micro reaction technology and further disciplines which are focused on technical components and systems with characteristic dimensions in the micrometer range. Within a period of about ten years a multi-billion dollar market has been set up with many products for daily life. The growth rate of the market of micro technology will remain on a high level for the years to come. Mega trends resulting from fundamental human wishes for health, information, mobility and sustainable development are creating a further growing basis for micro technical products. A broad spectrum of production processes and materials has been developed to meet the requirements of a strongly diversified range of applications. For the development of new components and systems the importance of software tools for simulation of functional properties, production processes and comprehensive optimization is growing rapidly. Micro devices are meanwhile used extensively in information, automotive, and medical technologies. In addition, micro technology is generating a completely novel basis for chemical engineering, life sciences, industrial automation and optical communication, to mention only a few disciplines where future innovation will be dominated by miniaturization.

  18. Nano-tribology and materials in MEMS

    CERN Document Server

    Satyanarayana, N; Lim, Seh

    2013-01-01

    This book brings together recent developments in the areas of MEMS tribology, novel lubricants and coatings for nanotechnological applications, biomimetics in tribology and fundamentals of micro/nano-tribology. Tribology plays important roles in the functioning and durability of machines at small length scales because of the problems associated with strong surface adhesion, friction, wear etc. Recently, a number of studies have been conducted to understand tribological phenomena at nano/micro scales and many new tribological solutions for MEMS have been proposed.

  19. Digital holography for MEMS and microsystem metrology

    CERN Document Server

    Asundi, Anand

    2011-01-01

    Approaching the topic of digital holography from the practical perspective of industrial inspection, Digital Holography for MEMS and Microsystem Metrology describes the process of digital holography and its growing applications for MEMS characterization, residual stress measurement, design and evaluation, and device testing and inspection. Asundi also provides a thorough theoretical grounding that enables the reader to understand basic concepts and thus identify areas where this technique can be adopted. This combination of both practical and theoretical approach will ensure the

  20. Review On Applications Of Neural Network To Computer Vision

    Science.gov (United States)

    Li, Wei; Nasrabadi, Nasser M.

    1989-03-01

    Neural network models have many potential applications to computer vision due to their parallel structures, learnability, implicit representation of domain knowledge, fault tolerance, and ability of handling statistical data. This paper demonstrates the basic principles, typical models and their applications in this field. Variety of neural models, such as associative memory, multilayer back-propagation perceptron, self-stabilized adaptive resonance network, hierarchical structured neocognitron, high order correlator, network with gating control and other models, can be applied to visual signal recognition, reinforcement, recall, stereo vision, motion, object tracking and other vision processes. Most of the algorithms have been simulated on com-puters. Some have been implemented with special hardware. Some systems use features, such as edges and profiles, of images as the data form for input. Other systems use raw data as input signals to the networks. We will present some novel ideas contained in these approaches and provide a comparison of these methods. Some unsolved problems are mentioned, such as extracting the intrinsic properties of the input information, integrating those low level functions to a high-level cognitive system, achieving invariances and other problems. Perspectives of applications of some human vision models and neural network models are analyzed.

  1. Application of neural networks to waste site screening

    International Nuclear Information System (INIS)

    Dabiri, A.E.; Garrett, M.; Kraft, T.; Hilton, J.; VanHammersveld, M.

    1993-02-01

    Waste site screening requires knowledge of the actual concentrations of hazardous materials and rates of flow around and below the site with time. The present approach consists primarily of drilling boreholes near contaminated sites and chemically analyzing the extracted physical samples and processing the data. This is expensive and time consuming. The feasibility of using neural network techniques to reduce the cost of waste site screening was investigated. Two neural network techniques, gradient descent back propagation and fully recurrent back propagation were utilized. The networks were trained with data received from Westinghouse Hanford Corporation. The results indicate that the network trained with the fully recurrent technique shows satisfactory generalization capability. The predicted results are close to the results obtained from a mathematical flow prediction model. It is possible to develop a new tool to predict the waste plume, thus substantially reducing the number of the bore sites and samplings. There are a variety of applications for this technique in environmental site screening and remediation. One of the obvious applications would be for optimum well siting. A neural network trained from the existing sampling data could be utilized to decide where would be the best position for the next bore site. Other applications are discussed in the report

  2. Methodology for neural networks prototyping. Application to traffic control

    Energy Technology Data Exchange (ETDEWEB)

    Belegan, I.C.

    1998-07-01

    The work described in this report was carried out in the context of the European project ASTORIA (Advanced Simulation Toolbox for Real-World Industrial Application in Passenger Management and Adaptive Control), and concerns the development of an advanced toolbox for complex transportation systems. Our work was focused on the methodology for prototyping a set of neural networks corresponding to specific strategies for traffic control and congestion management. The tool used for prototyping is SNNS (Stuggart Neural Network Simulator), developed at the University of Stuggart, Institute for Parallel and Distributed High Performance Systems, and the real data from the field were provided by ZELT. This report is structured into six parts. The introduction gives some insights about traffic control and its approaches. The second chapter discusses the various control strategies existing. The third chapter is an introduction to the field of neural networks. The data analysis and pre-processing is described in the fourth chapter. In the fifth chapter, the methodology for prototyping the neural networks is presented. Finally, conclusions and further work are presented. (author) 14 refs.

  3. Tutorial on neural network applications in high energy physics: A 1992 perspective

    International Nuclear Information System (INIS)

    Denby, B.

    1992-04-01

    Feed forward and recurrent neural networks are introduced and related to standard data analysis tools. Tips are given on applications of neural nets to various areas of high energy physics. A review of applications within high energy physics and a summary of neural net hardware status are given

  4. Modularly Integrated MEMS Technology

    National Research Council Canada - National Science Library

    Eyoum, Marie-Angie N

    2006-01-01

    Process design, development and integration to fabricate reliable MEMS devices on top of VLSI-CMOS electronics without damaging the underlying circuitry have been investigated throughout this dissertation...

  5. Neural chips, neural computers and application in high and superhigh energy physics experiments

    International Nuclear Information System (INIS)

    Nikityuk, N.M.; )

    2001-01-01

    Architecture peculiarity and characteristics of series of neural chips and neural computes used in scientific instruments are considered. Tendency of development and use of them in high energy and superhigh energy physics experiments are described. Comparative data which characterize the efficient use of neural chips for useful event selection, classification elementary particles, reconstruction of tracks of charged particles and for search of hypothesis Higgs particles are given. The characteristics of native neural chips and accelerated neural boards are considered [ru

  6. Neural network application to aircraft control system design

    Science.gov (United States)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural network as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research identified to enhance the practical applicability of neural networks to flight control design.

  7. Neural network application to aircraft control system design

    Science.gov (United States)

    Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

    1991-01-01

    The feasibility of using artificial neural networks as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research are identified to enhance the practical applicability of neural networks to flight control design.

  8. Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Biaobiao Zhang

    2011-01-01

    Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

  9. A Nuclear Microbattery for MEMS Devices

    International Nuclear Information System (INIS)

    Blanchard, James; Henderson, Douglass; Lal, Amit

    2002-01-01

    This project was designed to demonstrate the feasibility of producing on-board power for MEMS devices using radioisotopes. MEMS is a fast growing field, with hopes for producing a wide variety of revolutionary applications, including ''labs on a chip,'' micromachined scanning tunneling microscopes, microscopic detectors for biological agents, microsystems for DNA identification, etc. Currently, these applications are limited by the lack of an on-board power source. Research is ongoing to study approaches such as fuel cells, fossil fuels, and chemical batteries, but all these concepts have limitations. For long-lived, high energy density applications, on-board radioisotope power offers the best choice. We have succeeded in producing such devices using a variety of isotopes, incorporation methods, and device geometries. These experiments have demonstrated the feasibility of using radioisotope power and that there are a variety of options available for MEMS designers. As an example of an integrated, self-powered application, we have created an oscillating cantilever beam that is capable of consistent, periodic oscillations over very long time periods without the need for refueling. Ongoing work will demonstrate that this cantilever is capable of radio frequency transmission, allowing MEMS devices to communicate with one another wirelessly. Thus, this will be the first self-powered wireless transmitter available for use in MEMS devices, permitting such applications as sensors embedded in buildings for continuous monitoring of the building performance and integrity

  10. Artificial neural nets application in the cotton yarn industry

    Directory of Open Access Journals (Sweden)

    Gilberto Clóvis Antoneli

    2016-06-01

    Full Text Available The competitiveness in the yarn production sector has led companies to search for solutions to attain quality yarn at a low cost. Today, the difference between them, and thus the sector, is in the raw material, meaning processed cotton and its characteristics. There are many types of cotton with different characteristics due to its production region, harvest, storage and transportation. Yarn industries work with cotton mixtures, which makes it difficult to determine the quality of the yarn produced from the characteristics of the processed fibers. This study uses data from a conventional spinning, from a raw material made of 100% cotton, and presents a solution with artificial neural nets that determine the thread quality information, using the fibers’ characteristics values and settings of some process adjustments. In this solution a neural net of the type MultiLayer Perceptron with 11 entry neurons (8 characteristics of the fiber and 3 process adjustments, 7 output neurons (yarn quality and two types of training, Back propagation and Conjugate gradient descent. The selection and organization of the production data of the yarn industry of the cocamar® indústria de fios company are described, to apply the artificial neural nets developed. In the application of neural nets to determine yarn quality, one concludes that, although the ideal precision of absolute values is lacking, the presented solution represents an excellent tool to define yarn quality variations when modifying the raw material composition. The developed system enables a simulation to define the raw material percentage mixture to be processed in the plant using the information from the stocked cotton packs, thus obtaining a mixture that maintains the stability of the entire productive process.

  11. Applications of artificial neural networks in medical science.

    Science.gov (United States)

    Patel, Jigneshkumar L; Goyal, Ramesh K

    2007-09-01

    Computer technology has been advanced tremendously and the interest has been increased for the potential use of 'Artificial Intelligence (AI)' in medicine and biological research. One of the most interesting and extensively studied branches of AI is the 'Artificial Neural Networks (ANNs)'. Basically, ANNs are the mathematical algorithms, generated by computers. ANNs learn from standard data and capture the knowledge contained in the data. Trained ANNs approach the functionality of small biological neural cluster in a very fundamental manner. They are the digitized model of biological brain and can detect complex nonlinear relationships between dependent as well as independent variables in a data where human brain may fail to detect. Nowadays, ANNs are widely used for medical applications in various disciplines of medicine especially in cardiology. ANNs have been extensively applied in diagnosis, electronic signal analysis, medical image analysis and radiology. ANNs have been used by many authors for modeling in medicine and clinical research. Applications of ANNs are increasing in pharmacoepidemiology and medical data mining. In this paper, authors have summarized various applications of ANNs in medical science.

  12. Application of artificial neural nets to Shashlik calorimetry

    International Nuclear Information System (INIS)

    Bonesini, M.; Paganoni, M.; Terranova, F.

    1997-01-01

    Artificial neural networks (ANN) are powerful tools widely used in high-energy physics to solve track finding and particle identification problems. An entirely new class of application is related to the problem of recovering the information lost during data taking or signal transmission. Good performances can be reached by ANN when the events are described by quite regular patterns. Such a method was used for the DELPHI luminosity monitor (STIC) to recover calorimeter dead channels. A comparison with more traditional techniques is also given. (orig.)

  13. Application of the minimum fuel neural network to music signals

    DEFF Research Database (Denmark)

    Harbo, Anders La-Cour

    2004-01-01

    ) for finding sparse representations of music signals. This method is a set of two ordinary differential equations. We argue that the most important parameter for optimal use of this method is the discretization step size, and we demonstrate that this can be a priori determined. This significantly speeds up......Finding an optimal representation of a signal in an over-complete dictionary is often quite difficult. Since general results in this field are not very application friendly it truly helps to specify the framework as much as possible. We investigate the method Minimum Fuel Neural Network (MFNN...

  14. Neural computation and particle accelerators research, technology and applications

    CERN Document Server

    D'Arras, Horace

    2010-01-01

    This book discusses neural computation, a network or circuit of biological neurons and relatedly, particle accelerators, a scientific instrument which accelerates charged particles such as protons, electrons and deuterons. Accelerators have a very broad range of applications in many industrial fields, from high energy physics to medical isotope production. Nuclear technology is one of the fields discussed in this book. The development that has been reached by particle accelerators in energy and particle intensity has opened the possibility to a wide number of new applications in nuclear technology. This book reviews the applications in the nuclear energy field and the design features of high power neutron sources are explained. Surface treatments of niobium flat samples and superconducting radio frequency cavities by a new technique called gas cluster ion beam are also studied in detail, as well as the process of electropolishing. Furthermore, magnetic devises such as solenoids, dipoles and undulators, which ...

  15. Recurrent Neural Network Based Boolean Factor Analysis and its Application to Word Clustering

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Húsek, Dušan; Polyakov, P.Y.

    2009-01-01

    Roč. 20, č. 7 (2009), s. 1073-1086 ISSN 1045-9227 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : recurrent neural network * Hopfield-like neural network * associative memory * unsupervised learning * neural network architecture * neural network application * statistics * Boolean factor analysis * concepts search * information retrieval Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.889, year: 2009

  16. Amorphous Diamond MEMS and Sensors

    Energy Technology Data Exchange (ETDEWEB)

    SULLIVAN, JOHN P.; FRIEDMANN, THOMAS A.; ASHBY, CAROL I.; DE BOER, MAARTEN P.; SCHUBERT, W. KENT; SHUL, RANDY J.; HOHLFELDER, ROBERT J.; LAVAN, D.A.

    2002-06-01

    This report describes a new microsystems technology for the creation of microsensors and microelectromechanical systems (MEMS) using stress-free amorphous diamond (aD) films. Stress-free aD is a new material that has mechanical properties close to that of crystalline diamond, and the material is particularly promising for the development of high sensitivity microsensors and rugged and reliable MEMS. Some of the unique properties of aD include the ability to easily tailor film stress from compressive to slightly tensile, hardness and stiffness 80-90% that of crystalline diamond, very high wear resistance, a hydrophobic surface, extreme chemical inertness, chemical compatibility with silicon, controllable electrical conductivity from insulating to conducting, and biocompatibility. A variety of MEMS structures were fabricated from this material and evaluated. These structures included electrostatically-actuated comb drives, micro-tensile test structures, singly- and doubly-clamped beams, and friction and wear test structures. It was found that surface micromachined MEMS could be fabricated in this material easily and that the hydrophobic surface of the film enabled the release of structures without the need for special drying procedures or the use of applied hydrophobic coatings. Measurements using these structures revealed that aD has a Young's modulus of {approx}650 GPa, a tensile fracture strength of 8 GPa, and a fracture toughness of 8 MPa{center_dot}m {sup 1/2}. These results suggest that this material may be suitable in applications where stiction or wear is an issue. Flexural plate wave (FPW) microsensors were also fabricated from aD. These devices use membranes of aD as thin as {approx}100 nm. The performance of the aD FPW sensors was evaluated for the detection of volatile organic compounds using ethyl cellulose as the sensor coating. For comparable membrane thicknesses, the aD sensors showed better performance than silicon nitride based sensors. Greater

  17. Ultra-nanocrystalline diamond electrodes: optimization towards neural stimulation applications.

    Science.gov (United States)

    Garrett, David J; Ganesan, Kumaravelu; Stacey, Alastair; Fox, Kate; Meffin, Hamish; Prawer, Steven

    2012-02-01

    Diamond is well known to possess many favourable qualities for implantation into living tissue including biocompatibility, biostability, and for some applications hardness. However, conducting diamond has not, to date, been exploited in neural stimulation electrodes due to very low electrochemical double layer capacitance values that have been previously reported. Here we present electrochemical characterization of ultra-nanocrystalline diamond electrodes grown in the presence of nitrogen (N-UNCD) that exhibit charge injection capacity values as high as 163 µC cm(-2) indicating that N-UNCD is a viable material for microelectrode fabrication. Furthermore, we show that the maximum charge injection of N-UNCD can be increased by tailoring growth conditions and by subsequent electrochemical activation. For applications requiring yet higher charge injection, we show that N-UNCD electrodes can be readily metalized with platinum or iridium, further increasing charge injection capacity. Using such materials an implantable neural stimulation device fabricated from a single piece of bio-permanent material becomes feasible. This has significant advantages in terms of the physical stability and hermeticity of a long-term bionic implant.

  18. Buffering Implications for the Design Space of Streaming MEMS Storage

    NARCIS (Netherlands)

    Khatib, M.G.; Abelmann, Leon; Preas, Kathy

    2011-01-01

    Emerging nanotechnology-based systems encounter new non-functional requirements. This work addresses MEMS storage, an emerging technology that promises ultrahigh density and energy-efficient storage devices. We study the buffering requirement of MEMS storage in streaming applications. We show that

  19. Research on artificial neural network applications for nuclear power plants

    International Nuclear Information System (INIS)

    Chang, Soon-Heung; Cheon, Se-Woo

    1992-01-01

    Artificial neural networks (ANNs) are an emerging computational technology which can significantly enhance a number of applications. These consist of many interconnected processing elements that exhibit human-like performance, i.e., learning, pattern recognition and associative memory skills. Several application studies on ANNs devoted to nuclear power plants have been carried out at the Korea Advanced Institute of Science and Technology since 1989. These studies include the feasibility of using ANNs for the following tasks: (1) thermal power prediction, (2) transient identification, (3) multiple alarm processing and diagnosis, (4) core thermal margin prediction, and (5) prediction of core parameters for fuel reloading. This paper introduces the back-propagation network (BPN) model which is the most commonly used algorithm, and summarizes each of the studies briefly. (author)

  20. Additive manufacturing of three-dimensional (3D) microfluidic-based microelectromechanical systems (MEMS) for acoustofluidic applications.

    Science.gov (United States)

    Cesewski, Ellen; Haring, Alexander P; Tong, Yuxin; Singh, Manjot; Thakur, Rajan; Laheri, Sahil; Read, Kaitlin A; Powell, Michael D; Oestreich, Kenneth J; Johnson, Blake N

    2018-06-13

    Three-dimensional (3D) printing now enables the fabrication of 3D structural electronics and microfluidics. Further, conventional subtractive manufacturing processes for microelectromechanical systems (MEMS) relatively limit device structure to two dimensions and require post-processing steps for interface with microfluidics. Thus, the objective of this work is to create an additive manufacturing approach for fabrication of 3D microfluidic-based MEMS devices that enables 3D configurations of electromechanical systems and simultaneous integration of microfluidics. Here, we demonstrate the ability to fabricate microfluidic-based acoustofluidic devices that contain orthogonal out-of-plane piezoelectric sensors and actuators using additive manufacturing. The devices were fabricated using a microextrusion 3D printing system that contained integrated pick-and-place functionality. Additively assembled materials and components included 3D printed epoxy, polydimethylsiloxane (PDMS), silver nanoparticles, and eutectic gallium-indium as well as robotically embedded piezoelectric chips (lead zirconate titanate (PZT)). Electrical impedance spectroscopy and finite element modeling studies showed the embedded PZT chips exhibited multiple resonant modes of varying mode shape over the 0-20 MHz frequency range. Flow visualization studies using neutrally buoyant particles (diameter = 0.8-70 μm) confirmed the 3D printed devices generated bulk acoustic waves (BAWs) capable of size-selective manipulation, trapping, and separation of suspended particles in droplets and microchannels. Flow visualization studies in a continuous flow format showed suspended particles could be moved toward or away from the walls of microfluidic channels based on selective actuation of in-plane or out-of-plane PZT chips. This work suggests additive manufacturing potentially provides new opportunities for the design and fabrication of acoustofluidic and microfluidic devices.

  1. Application of neural networks and its prospect. 1. General comment on application to nuclear fusion and plasma researches

    International Nuclear Information System (INIS)

    Takeda, Tatsuoki

    2006-01-01

    The back ground of application of neutral networks to R and D of scientific field and increasing of application fields are stated. A definition of neural networks, the kinds of neural networks and functions, error back propagation, and generalization are explained. An application of multi-layer neural networks to nuclear fusion and plasma researches are described by inverse problem, interpolation, time series prediction, and computerized tomography. Some examples of researches such as MHD of plasma from magnetic probe data of fusion reactor systems, parameter prediction of distribution of the impurity spectra and the charge exchange neutral particle energy spectra, disruption prediction, and residual minimization training neural network are commented. (S.Y.)

  2. An application of neural networks to process and materials control

    International Nuclear Information System (INIS)

    Howell, J.A.; Whiteson, R.

    1991-01-01

    Process control consists of two basic elements: a model of the process and knowledge of the desired control algorithm. In some cases the level of the control algorithm is merely supervisory, as in an alarm-reporting or anomaly-detection system. If the model of the process is known, then a set of equations may often be solved explicitly to provide the control algorithm. Otherwise, the model has to be discovered through empirical studies. Neural networks have properties that make them useful in this application. They can learn (make internal models from experience or observations). The problem of anomaly detection in materials control systems fits well into this general control framework. To successfully model a process with a neutral network, a good set of observables must be chosen. These observables must in some sense adequately span the space of representable events, so that a signature metric can be built for normal operation. In this way, a non-normal event, one that does not fit within the signature, can be detected. In this paper, we discuss the issues involved in applying a neural network model to anomaly detection in materials control systems. These issues include data selection and representation, network architecture, prediction of events, the use of simulated data, and software tools. 10 refs., 4 figs., 1 tab

  3. Recurrent Neural Network Applications for Astronomical Time Series

    Science.gov (United States)

    Protopapas, Pavlos

    2017-06-01

    The benefits of good predictive models in astronomy lie in early event prediction systems and effective resource allocation. Current time series methods applicable to regular time series have not evolved to generalize for irregular time series. In this talk, I will describe two Recurrent Neural Network methods, Long Short-Term Memory (LSTM) and Echo State Networks (ESNs) for predicting irregular time series. Feature engineering along with a non-linear modeling proved to be an effective predictor. For noisy time series, the prediction is improved by training the network on error realizations using the error estimates from astronomical light curves. In addition to this, we propose a new neural network architecture to remove correlation from the residuals in order to improve prediction and compensate for the noisy data. Finally, I show how to set hyperparameters for a stable and performant solution correctly. In this work, we circumvent this obstacle by optimizing ESN hyperparameters using Bayesian optimization with Gaussian Process priors. This automates the tuning procedure, enabling users to employ the power of RNN without needing an in-depth understanding of the tuning procedure.

  4. An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

    Science.gov (United States)

    1996-01-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

  5. APPLICATION OF NEURAL NETWORK ALGORITHMS FOR BPM LINEARIZATION

    Energy Technology Data Exchange (ETDEWEB)

    Musson, John C. [JLAB; Seaton, Chad [JLAB; Spata, Mike F. [JLAB; Yan, Jianxun [JLAB

    2012-11-01

    Stripline BPM sensors contain inherent non-linearities, as a result of field distortions from the pickup elements. Many methods have been devised to facilitate corrections, often employing polynomial fitting. The cost of computation makes real-time correction difficult, particulalry when integer math is utilized. The application of neural-network technology, particularly the multi-layer perceptron algorithm, is proposed as an efficient alternative for electrode linearization. A process of supervised learning is initially used to determine the weighting coefficients, which are subsequently applied to the incoming electrode data. A non-linear layer, known as an activation layer, is responsible for the removal of saturation effects. Implementation of a perceptron in an FPGA-based software-defined radio (SDR) is presented, along with performance comparisons. In addition, efficient calculation of the sigmoidal activation function via the CORDIC algorithm is presented.

  6. Noves dades sobre el mecanisme neural de la consolidació de la memòria: una oportunitat per a atenuar l’impacte emocional de les experiències traumàtiques

    OpenAIRE

    Lanuza, Enrique

    2016-01-01

    Durant la major part del segle xx, els estudis científics sobre la memòria assumien que les memòries eren immutables una vegada es consolidaven, tot i que podien perdre qualitat amb el pas del temps. Dades recents han revelat que les memòries, tot i estar ja consolidades, es tornen làbils durant la seua rememoració. Aquest descobriment implica que poden ser modificades i per tant emmagatzemades de nou amb aquestes modificacions. Aquestes investigacions obrin noves possibilitats...

  7. EDITORIAL: Selected papers from the 10th International Workshop on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS 2010) Selected papers from the 10th International Workshop on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS 2010)

    Science.gov (United States)

    Reynaerts, Dominiek; Vullers, Ruud

    2011-10-01

    This special section of Journal of Micromechanics and Microengineering features papers selected from the 10th International Workshop on Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS 2010). The workshop was organized in Leuven, Belgium from 30 November to 3 December 2010 by Katholieke Universiteit Leuven and the imec/Holst Centre. This was a special PowerMEMS Workshop, for several reasons. First of all, we celebrated the 10th anniversary of the workshop: the first PowerMEMS meeting was organized in Sendai, Japan in 2000. None of the organizers or participants of this first meeting could have predicted the impact of the workshop over the next decade. The second reason was that, for the first time, the conference organization spanned two countries: Belgium and the Netherlands. Thanks to the advances in information technology, teams from Katholieke Universiteit Leuven (Belgium) and the imec/Holst Centre in Eindhoven (the Netherlands) have been able to work together seamlessly as one team. The objective of the PowerMEMS Workshop is to stimulate innovation in micro and nanotechnology for power generation and energy conversion applications. Its scope ranges from integrated microelectromechanical systems (MEMS) for power generation, dissipation, harvesting, and management, to novel nanostructures and materials for energy-related applications. True to the objective of the PowerMEMSWorkshop, the 2010 technical program covered a broad range of energy related research, ranging from the nanometer to the millimeter scale, discussed in 5 invited and 52 oral presentations, and 112 posters. This special section includes 14 papers covering vibration energy harvesters, thermal applications and micro power systems. Finally, we wish to express sincere appreciation to the members of the International Steering Committee, the Technical Program Committee and last but not least the Local Organizing Committee. This special issue was edited in

  8. Neural networks. A new analytical tool, applicable also in nuclear technology

    Energy Technology Data Exchange (ETDEWEB)

    Stritar, A [Inst. Jozef Stefan, Ljubljana (Slovenia)

    1992-07-01

    The basic concept of neural networks and back propagation learning algorithm are described. The behaviour of typical neural network is demonstrated on a simple graphical case. A short literature survey about the application of neural networks in nuclear science and engineering is made. The application of the neural network to the probability density calculation is shown. (author) [Slovenian] Opisana je osnova nevronskih mrez in back propagation nacina njihovega ucenja. Obnasanje enostavne nevronske mreze je prikazano na graficnem primeru. Podan je kratek pregled literaure o uporabi nevronskih mrez v jedrski znanosti in tehnologiji. Prikazana je tudi uporaba nevronske mreze pri izracunu verjetnostne porazdelitve. (author)

  9. RF MEMS theory, design, and technology

    CERN Document Server

    Rebeiz, Gabriel M

    2003-01-01

    Ultrasmall Radio Frequency and Micro-wave Microelectromechanical systems (RF MEMs), such as switches, varactors, and phase shifters, exhibit nearly zero power consumption or loss. For this reason, they are being developed intensively by corporations worldwide for use in telecommunications equipment. This book acquaints readers with the basics of RF MEMs and describes how to design practical circuits and devices with them. The author, an acknowledged expert in the field, presents a range of real-world applications and shares many valuable tricks of the trade.

  10. New dynamic silicon photonic components enabled by MEMS technology

    Science.gov (United States)

    Errando-Herranz, Carlos; Edinger, Pierre; Colangelo, Marco; Björk, Joel; Ahmed, Samy; Stemme, Göran; Niklaus, Frank; Gylfason, Kristinn B.

    2018-02-01

    Silicon photonics is the study and application of integrated optical systems which use silicon as an optical medium, usually by confining light in optical waveguides etched into the surface of silicon-on-insulator (SOI) wafers. The term microelectromechanical systems (MEMS) refers to the technology of mechanics on the microscale actuated by electrostatic actuators. Due to the low power requirements of electrostatic actuation, MEMS components are very power efficient, making them well suited for dense integration and mobile operation. MEMS components are conventionally also implemented in silicon, and MEMS sensors such as accelerometers, gyros, and microphones are now standard in every smartphone. By combining these two successful technologies, new active photonic components with extremely low power consumption can be made. We discuss our recent experimental work on tunable filters, tunable fiber-to-chip couplers, and dynamic waveguide dispersion tuning, enabled by the marriage of silicon MEMS and silicon photonics.

  11. Optical MEMS for earth observation payloads

    Science.gov (United States)

    Rodrigues, B.; Lobb, D. R.; Freire, M.

    2017-11-01

    An ESA study has been taken by Lusospace Ltd and Surrey Satellite Techonoly Ltd (SSTL) into the use of optical Micro Eletro-Mechanical Systems (MEMS) for earth Observation. A review and analysis was undertaken of the Micro-Optical Electro-Mechanical Systems (MOEMS) available in the market with potential application in systems for Earth Observation. A summary of this review will be presented. Following the review two space-instrument design concepts were selected for more detailed analysis. The first was the use of a MEMS device to remove cloud from Earth images. The concept is potentially of interest for any mission using imaging spectrometers. A spectrometer concept was selected and detailed design aspects and benefits evaluated. The second concept developed uses MEMS devices to control the width of entrance slits of spectrometers, to provide variable spectral resolution. This paper will present a summary of the results of the study.

  12. MEMS tunable grating micro-spectrometer

    Science.gov (United States)

    Tormen, Maurizio; Lockhart, R.; Niedermann, P.; Overstolz, T.; Hoogerwerf, A.; Mayor, J.-M.; Pierer, J.; Bosshard, C.; Ischer, R.; Voirin, G.; Stanley, R. P.

    2017-11-01

    The interest in MEMS based Micro-Spectrometers is increasing due to their potential in terms of flexibility as well as cost, low mass, small volume and power savings. This interest, especially in the Near-Infrared and Mid- Infrared, ranges from planetary exploration missions to astronomy, e.g. the search for extra solar planets, as well as to many other terrestrial fields of application such as, industrial quality and surface control, chemical analysis of soil and water, detection of chemical pollutants, exhausted gas analysis, food quality control, process control in pharmaceuticals, to name a few. A compact MEMS-based Spectrometer for Near- Infrared and Mid-InfraRed operation have been conceived, designed and demonstrated. The design based on tunable MEMS blazed grating, developed in the past at CSEM [1], achieves state of the art results in terms of spectral resolution, operational wavelength range, light throughput, overall dimensions, and power consumption.

  13. Stability, Nonlinearity and Reliability of Electrostatically Actuated MEMS Devices

    Directory of Open Access Journals (Sweden)

    Di Chen

    2007-05-01

    Full Text Available Electrostatic micro-electro-mechanical system (MEMS is a special branch with a wide range of applications in sensing and actuating devices in MEMS. This paper provides a survey and analysis of the electrostatic force of importance in MEMS, its physical model, scaling effect, stability, nonlinearity and reliability in detail. It is necessary to understand the effects of electrostatic forces in MEMS and then many phenomena of practical importance, such as pull-in instability and the effects of effective stiffness, dielectric charging, stress gradient, temperature on the pull-in voltage, nonlinear dynamic effects and reliability due to electrostatic forces occurred in MEMS can be explained scientifically, and consequently the great potential of MEMS technology could be explored effectively and utilized optimally. A simplified parallel-plate capacitor model is proposed to investigate the resonance response, inherent nonlinearity, stiffness softened effect and coupled nonlinear effect of the typical electrostatically actuated MEMS devices. Many failure modes and mechanisms and various methods and techniques, including materials selection, reasonable design and extending the controllable travel range used to analyze and reduce the failures are discussed in the electrostatically actuated MEMS devices. Numerical simulations and discussions indicate that the effects of instability, nonlinear characteristics and reliability subjected to electrostatic forces cannot be ignored and are in need of further investigation.

  14. Methodology of Neural Design: Applications in Microwave Engineering

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2006-06-01

    Full Text Available In the paper, an original methodology for the automatic creation of neural models of microwave structures is proposed and verified. Following the methodology, neural models of the prescribed accuracy are built within the minimum CPU time. Validity of the proposed methodology is verified by developing neural models of selected microwave structures. Functionality of neural models is verified in a design - a neural model is joined with a genetic algorithm to find a global minimum of a formulated objective function. The objective function is minimized using different versions of genetic algorithms, and their mutual combinations. The verified methodology of the automated creation of accurate neural models of microwave structures, and their association with global optimization routines are the most important original features of the paper.

  15. Application of a Shallow Neural Network to Short-Term Stock Trading

    OpenAIRE

    Madahar, Abhinav; Ma, Yuze; Patel, Kunal

    2017-01-01

    Machine learning is increasingly prevalent in stock market trading. Though neural networks have seen success in computer vision and natural language processing, they have not been as useful in stock market trading. To demonstrate the applicability of a neural network in stock trading, we made a single-layer neural network that recommends buying or selling shares of a stock by comparing the highest high of 10 consecutive days with that of the next 10 days, a process repeated for the stock's ye...

  16. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    International Nuclear Information System (INIS)

    Saini, K. K.; Saini, Sanju

    2008-01-01

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  17. Characterization of dielectric charging in RF MEMS

    NARCIS (Netherlands)

    Herfst, R.W.; Huizing, H.G.A.; Steeneken, P.G.; Schmitz, Jurriaan

    2005-01-01

    Capacitive RF MEMS switches show great promise for use in wireless communication devices such as mobile phones, but the successful application of these switches is hindered by the reliability of the devices: charge injection in the dielectric layer (SiN) can cause irreversible stiction of the moving

  18. Neural network and its application to CT imaging

    Energy Technology Data Exchange (ETDEWEB)

    Nikravesh, M.; Kovscek, A.R.; Patzek, T.W. [Lawrence Berkeley National Lab., CA (United States)] [and others

    1997-02-01

    We present an integrated approach to imaging the progress of air displacement by spontaneous imbibition of oil into sandstone. We combine Computerized Tomography (CT) scanning and neural network image processing. The main aspects of our approach are (I) visualization of the distribution of oil and air saturation by CT, (II) interpretation of CT scans using neural networks, and (III) reconstruction of 3-D images of oil saturation from the CT scans with a neural network model. Excellent agreement between the actual images and the neural network predictions is found.

  19. A MEMS-based, wireless, biometric-like security system

    Science.gov (United States)

    Cross, Joshua D.; Schneiter, John L.; Leiby, Grant A.; McCarter, Steven; Smith, Jeremiah; Budka, Thomas P.

    2010-04-01

    We present a system for secure identification applications that is based upon biometric-like MEMS chips. The MEMS chips have unique frequency signatures resulting from fabrication process variations. The MEMS chips possess something analogous to a "voiceprint". The chips are vacuum encapsulated, rugged, and suitable for low-cost, highvolume mass production. Furthermore, the fabrication process is fully integrated with standard CMOS fabrication methods. One is able to operate the MEMS-based identification system similarly to a conventional RFID system: the reader (essentially a custom network analyzer) detects the power reflected across a frequency spectrum from a MEMS chip in its vicinity. We demonstrate prototype "tags" - MEMS chips placed on a credit card-like substrate - to show how the system could be used in standard identification or authentication applications. We have integrated power scavenging to provide DC bias for the MEMS chips through the use of a 915 MHz source in the reader and a RF-DC conversion circuit on the tag. The system enables a high level of protection against typical RFID hacking attacks. There is no need for signal encryption, so back-end infrastructure is minimal. We believe this system would make a viable low-cost, high-security system for a variety of identification and authentication applications.

  20. Piezoelectric MEMS resonators

    CERN Document Server

    Piazza, Gianluca

    2017-01-01

    This book introduces piezoelectric microelectromechanical (pMEMS) resonators to a broad audience by reviewing design techniques including use of finite element modeling, testing and qualification of resonators, and fabrication and large scale manufacturing techniques to help inspire future research and entrepreneurial activities in pMEMS. The authors discuss the most exciting developments in the area of materials and devices for the making of piezoelectric MEMS resonators, and offer direct examples of the technical challenges that need to be overcome in order to commercialize these types of devices. Some of the topics covered include: Widely-used piezoelectric materials, as well as materials in which there is emerging interest Principle of operation and design approaches for the making of flexural, contour-mode, thickness-mode, and shear-mode piezoelectric resonators, and examples of practical implementation of these devices Large scale manufacturing approaches, with a focus on the practical aspects associate...

  1. MEMS fluidic actuator

    Science.gov (United States)

    Kholwadwala, Deepesh K [Albuquerque, NM; Johnston, Gabriel A [Trophy Club, TX; Rohrer, Brandon R [Albuquerque, NM; Galambos, Paul C [Albuquerque, NM; Okandan, Murat [Albuquerque, NM

    2007-07-24

    The present invention comprises a novel, lightweight, massively parallel device comprising microelectromechanical (MEMS) fluidic actuators, to reconfigure the profile, of a surface. Each microfluidic actuator comprises an independent bladder that can act as both a sensor and an actuator. A MEMS sensor, and a MEMS valve within each microfluidic actuator, operate cooperatively to monitor the fluid within each bladder, and regulate the flow of the fluid entering and exiting each bladder. When adjacently spaced in a array, microfluidic actuators can create arbitrary surface profiles in response to a change in the operating environment of the surface. In an embodiment of the invention, the profile of an airfoil is controlled by independent extension and contraction of a plurality of actuators, that operate to displace a compliant cover.

  2. Designing Computer Systems with MEMS-Based Storage

    National Research Council Canada - National Science Library

    Schlosser, Steven

    2000-01-01

    .... An exciting new storage technology based on microelectromechanical systems (MEMS) is poised to fill a large portion of this performance gap, significantly reduce power consumption, and enable many new classes of applications...

  3. The application of artificial neural networks in astronomy

    Science.gov (United States)

    Li, Li-Li; Zhang, Yan-Xia; Zhao, Yong-Heng; Yang, Da-Wei

    2006-12-01

    Artificial Neural Networks (ANNs) are computer algorithms inspired from simple models of human central nervous system activity. They can be roughly divided into two main kinds: supervised and unsupervised. The supervised approach lays the stress on "teaching" a machine to do the work of a mention human expert, usually by showing examples for which the true answer is supplied by the expert. The unsupervised one is aimed at learning new things from the data, and most useful when the data cannot easily be plotted in a two or three dimensional space. ANNs have been used widely and successfully in various fields, for instance, pattern recognition, financial analysis, biology, engineering and so on, because they have many merits such as self-learning, self-adapting, good robustness and dynamically rapid response as well as strong capability of dealing with non-linear problems. In the last few years there has been an increasing interest toward the astronomical applications of ANNs. In this paper, the authors firstly introduce the fundamental principle of ANNs together with the architecture of the network and outline various kinds of learning algorithms and network toplogies. The specific aspects of the applications of ANNs in astronomical problems are also listed, which contain the strong capabilities of approximating to arbitrary accuracy, any nonlinear functional mapping, parallel and distributed storage, tolerance of faulty and generalization of results. They summarize the advantages and disadvantages of main ANN models available to the astronomical community. Furthermore, the application cases of ANNs in astronomy are mainly described in detail. Here, the focus is on some of the most interesting fields of its application, for example: object detection, star/galaxy classification, spectral classification, galaxy morphology classification, the estimation of photometric redshifts of galaxies and time series analysis. In addition, other kinds of applications have been

  4. Application of genetic neural network in steam generator fault diagnosing

    International Nuclear Information System (INIS)

    Lin Xiaogong; Jiang Xingwei; Liu Tao; Shi Xiaocheng

    2005-01-01

    In the paper, a new algorithm which neural network and genetic algorithm are mixed is adopted, aiming at the problems of slow convergence rate and easily falling into part minimums in network studying of traditional BP neural network, and used in the fault diagnosis of steam generator. The result shows that this algorithm can solve the convergence problem in the network trains effectively. (author)

  5. Advances in Artificial Neural Networks - Methodological Development and Application

    Science.gov (United States)

    Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other ne...

  6. Application of a neural network for reflectance spectrum classification

    Science.gov (United States)

    Yang, Gefei; Gartley, Michael

    2017-05-01

    Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

  7. Application of neural networks to connectional expert system for identification of transients in nuclear power plants

    International Nuclear Information System (INIS)

    Cheon, Se Woo; Kim, Wan Joo; Chang, Soon Heung; Roh, Myung Sub

    1991-01-01

    The Back-propagation Neural Network (BPN) algorithm is applied to connectionist expert system for the identification of BWR transients. Several powerful features of neural network-based expert systems over traditional rule-based expert systems are described. The general mapping capability of the neural networks enables to identify transients easily. A number of case studies were performed with emphasis on the applicability of the neural networks to the diagnostic domain. It is revealed that the BPN algorithm can identify transients properly, even when incomplete or untrained symptoms are given. It is also shown that multiple transients are easily identified

  8. Optical MEMS for Earth observation

    Science.gov (United States)

    Liotard, Arnaud; Viard, Thierry; Noell, Wilfried; Zamkotsian, Frédéric; Freire, Marco; Guldimann, Benedikt; Kraft, Stefan

    2017-11-01

    Due to the relatively large number of optical Earth Observation missions at ESA, this area is interesting for new space technology developments. In addition to their compactness, scalability and specific task customization, optical MEMS could generate new functions not available with current technologies and are thus candidates for the design of future space instruments. Most mature components for space applications are the digital mirror arrays, the micro-deformable mirrors, the programmable micro diffraction gratings and tiltable micromirrors. A first selection of market-pull and techno-push concepts is done. In addition, some concepts are coming from outside Earth Observation. Finally two concepts are more deeply analyzed. The first concept is a programmable slit for straylight control for space spectro-imagers. This instrument is a push-broom spectroimager for which some images cannot be exploited because of bright sources in the field-of-view. The proposed concept consists in replacing the current entrance spectrometer slit by an active row of micro-mirrors. The MEMS will permit to dynamically remove the bright sources and then to obtain a field-of-view with an optically enhanced signal-to-noise ratio. The second concept is a push-broom imager for which the acquired spectrum can be tuned by optical MEMS. This system is composed of two diffractive elements and a digital mirror array. The first diffractive element spreads the spectrum. A micromirror array is set at the location of the spectral focal plane. By putting the micro-mirrors ON or OFF, we can select parts of field-of-view or spectrum. The second diffractive element then recombines the light on a push-broom detector. Dichroics filters, strip filter, band-pass filter could be replaced by a unique instrument.

  9. Applications of neural networks in environmental and energy sciences and engineering. Proceedings of the 1995 workshop on environmental and energy applications of neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hashem, S.; Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

    1995-12-31

    These proceedings contain edited versions of the technical presentations of the Workshop on Environmental and Energy Applications of Neural Networks, held on March 30--31, 1995, in Richland, Washington. The purpose of the workshop was to provide a forum for discussing environmental, energy, and biomedical applications of neural networks. Panels were held to discuss various research and development issues relating to real-world applications in each of the three areas. The applications covered in the workshop were: Environmental applications -- modeling and predicting soil, air and water pollution, environmental sensing, spectroscopy, hazardous waste handling and cleanup; Energy applications -- process monitoring and optimization of power systems, modeling and control of power plants, environmental monitoring for power systems, power load forecasting, fault location and diagnosis of power systems; and Biomedical applications -- medical image and signal analysis, medical diagnosis, analysis of environmental health effects, and modeling biological systems. Selected papers are indexed separately for inclusion in the Energy Science and Technology Database.

  10. Nondestructive surface profiling of hidden MEMS using an infrared low-coherence interferometric microscope

    Science.gov (United States)

    Krauter, Johann; Osten, Wolfgang

    2018-03-01

    There are a wide range of applications for micro-electro-mechanical systems (MEMS). The automotive and consumer market is the strongest driver for the growing MEMS industry. A 100 % test of MEMS is particularly necessary since these are often used for safety-related purposes such as the ESP (Electronic Stability Program) system. The production of MEMS is a fully automated process that generates 90 % of the costs during the packaging and dicing steps. Nowadays, an electrical test is carried out on each individual MEMS component before these steps. However, after encapsulation, MEMS are opaque to visible light and other defects cannot be detected. Therefore, we apply an infrared low-coherence interferometer for the topography measurement of those hidden structures. A lock-in algorithm-based method is shown to calculate the object height and to reduce ghost steps due to the 2π -unambiguity. Finally, measurements of different MEMS-based sensors are presented.

  11. MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: a review on research activities in Italy.

    Science.gov (United States)

    Ciuti, Gastone; Ricotti, Leonardo; Menciassi, Arianna; Dario, Paolo

    2015-03-17

    Over the past few decades the increased level of public awareness concerning healthcare, physical activities, safety and environmental sensing has created an emerging need for smart sensor technologies and monitoring devices able to sense, classify, and provide feedbacks to users' health status and physical activities, as well as to evaluate environmental and safety conditions in a pervasive, accurate and reliable fashion. Monitoring and precisely quantifying users' physical activity with inertial measurement unit-based devices, for instance, has also proven to be important in health management of patients affected by chronic diseases, e.g., Parkinson's disease, many of which are becoming highly prevalent in Italy and in the Western world. This review paper will focus on MEMS sensor technologies developed in Italy in the last three years describing research achievements for healthcare and physical activity, safety and environmental sensing, in addition to smart systems integration. Innovative and smart integrated solutions for sensing devices, pursued and implemented in Italian research centres, will be highlighted, together with specific applications of such technologies. Finally, the paper will depict the future perspective of sensor technologies and corresponding exploitation opportunities, again with a specific focus on Italy.

  12. MEMS Sensor Technologies for Human Centred Applications in Healthcare, Physical Activities, Safety and Environmental Sensing: A Review on Research Activities in Italy

    Directory of Open Access Journals (Sweden)

    Gastone Ciuti

    2015-03-01

    Full Text Available Over the past few decades the increased level of public awareness concerning healthcare, physical activities, safety and environmental sensing has created an emerging need for smart sensor technologies and monitoring devices able to sense, classify, and provide feedbacks to users’ health status and physical activities, as well as to evaluate environmental and safety conditions in a pervasive, accurate and reliable fashion. Monitoring and precisely quantifying users’ physical activity with inertial measurement unit-based devices, for instance, has also proven to be important in health management of patients affected by chronic diseases, e.g., Parkinson’s disease, many of which are becoming highly prevalent in Italy and in the Western world. This review paper will focus on MEMS sensor technologies developed in Italy in the last three years describing research achievements for healthcare and physical activity, safety and environmental sensing, in addition to smart systems integration. Innovative and smart integrated solutions for sensing devices, pursued and implemented in Italian research centres, will be highlighted, together with specific applications of such technologies. Finally, the paper will depict the future perspective of sensor technologies and corresponding exploitation opportunities, again with a specific focus on Italy.

  13. Corrosion of tungsten microelectrodes used in neural recording applications.

    Science.gov (United States)

    Patrick, Erin; Orazem, Mark E; Sanchez, Justin C; Nishida, Toshikazu

    2011-06-15

    In neuroprosthetic applications, long-term electrode viability is necessary for robust recording of the activity of neural populations used for generating communication and control signals. The corrosion of tungsten microwire electrodes used for intracortical recording applications was analyzed in a controlled bench-top study and compared to the corrosion of tungsten microwires used in an in vivo study. Two electrolytes were investigated for the bench-top electrochemical analysis: 0.9% phosphate buffered saline (PBS) and 0.9% PBS containing 30 mM of hydrogen peroxide. The oxidation and reduction reactions responsible for corrosion were found by measurement of the open circuit potential and analysis of Pourbaix diagrams. Dissolution of tungsten to form the tungstic ion was found to be the corrosion mechanism. The corrosion rate was estimated from the polarization resistance, which was extrapolated from the electrochemical impedance spectroscopy data. The results show that tungsten microwires in an electrolyte of PBS have a corrosion rate of 300-700 μm/yr. The corrosion rate for tungsten microwires in an electrolyte containing PBS and 30 mM H₂O₂ is accelerated to 10,000-20,000 μm/yr. The corrosion rate was found to be controlled by the concentration of the reacting species in the cathodic reaction (e.g. O₂ and H₂O₂). The in vivo corrosion rate, averaged over the duration of implantation, was estimated to be 100 μm/yr. The reduced in vivo corrosion rate as compared to the bench-top rate is attributed to decreased rate of oxygen diffusion caused by the presence of a biological film and a reduced concentration of available oxygen in the brain. Copyright © 2011 Elsevier B.V. All rights reserved.

  14. Proceedings of the workshop cum symposium on applications of neural networks in nuclear science and industry

    International Nuclear Information System (INIS)

    1993-01-01

    The Workshop cum Symposium on Application of Neural Networks in Nuclear Science and Industry was held at Bombay during November 24-26. 1993. The past decade has seen many important advances in the design and technology of artificial neural networks in research and industry. Neural networks is an interdisciplinary field covering a broad spectrum of applications in surveillance, diagnosis of nuclear power plants, nuclear spectroscopy, speech and written text recognition, robotic control, signal processing etc. The objective of the symposium was to promote awareness of advances in neural network research and applications. It was also aimed at conducting the review of the present status and giving direction for future technological developments. Contributed papers have been organized into the following groups: a) neural network architectures, learning algorithms and modelling, b) computer vision and image processing, c) signal processing, d) neural networks and fuzzy systems, e) nuclear applications and f) neural networks and allied applications. Papers relevant to INIS are indexed separately. (M.K.V.)

  15. Chaotic Hopfield Neural Network Swarm Optimization and Its Application

    Directory of Open Access Journals (Sweden)

    Yanxia Sun

    2013-01-01

    Full Text Available A new neural network based optimization algorithm is proposed. The presented model is a discrete-time, continuous-state Hopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos and Hopfield neural networks: particles learn from their own experience and the experiences of surrounding particles, their search behavior is ergodic, and convergence of the swarm is guaranteed. The effectiveness of the proposed approach is demonstrated using simulations and typical optimization problems.

  16. Application of artificial neural network in radiographic diagnosis

    International Nuclear Information System (INIS)

    Piraino, D.; Amartur, S.; Richmond, B.; Schils, J.; Belhobek, G.

    1990-01-01

    This paper reports on an artificial neural network trained to rate the likelihood of different bone neoplasms when given a standard description of a radiograph. A three-layer back propagation algorithm was trained with descriptions of examples of bone neoplasms obtained from standard radiographic textbooks. Fifteen bone neoplasms obtained from clinical material were used as unknowns to test the trained artificial neural network. The artificial neural network correctly rated the pathologic diagnosis as the most likely diagnosis in 10 of the 15 unknown cases

  17. Microelectromechanical Systems (MEMS)

    Indian Academy of Sciences (India)

    As a field, Microelectromechanical Systems (MEMS) has matured over the last two decades to have several scientific journals dedicated to it. These journals are instrumental in bringing out the interdisciplinary nature of research that the field demands. In the beginning, most papers were process centric where realization of ...

  18. Microelectromechanical Systems (MEMS)

    Indian Academy of Sciences (India)

    to have several scientific journals dedicated to it. These journals are instrumental in bringing out the interdisciplinary nature of research that the field demands. In the beginning, most papers were process centric where realization of a MEMS device or structure using conven- tional CMOS processes or their variants was the ...

  19. MEMS Solar Generators

    OpenAIRE

    Grbovic, Dragoslav; Osswald, Sebastian

    2011-01-01

    Approved for public release; distribution is unlimited Using MEMS bimaterial structures to build highly efficient solar energy generators. This is a novel approach that utilizes developments in the area of bimaterial sensors and applies them in the field of solar energy harvesting.

  20. European MEMS foundries

    Science.gov (United States)

    Salomon, Patric R.

    2003-01-01

    According to the latest release of the NEXUS market study, the market for MEMS or Microsystems Technology (MST) is predicted to grow to $68B by the year 2005, with systems containing these components generating even higher revenues and growth. The latest advances in MST/MEMS technology have enabled the design of a new generation of microsystems that are smaller, cheaper, more reliable, and consume less power. These integrated systems bring together numerous analog/mixed signal microelectronics blocks and MEMS functions on a single chip or on two or more chips assembled within an integrated package. In spite of all these advances in technology and manufacturing, a system manufacturer either faces a substantial up-front R&D investment to create his own infrastructure and expertise, or he can use design and foundry services to get the initial product into the marketplace fast and with an affordable investment. Once he has a viable product, he can still think about his own manufacturing efforts and investments to obtain an optimized high volume manufacturing for the specific product. One of the barriers to successful exploitation of MEMS/MST technology has been the lack of access to industrial foundries capable of producing certified microsystems devices in commercial quantities, including packaging and test. This paper discusses Multi-project wafer (MPW) runs, requirements for foundries and gives some examples of foundry business models. Furthermore, this paper will give an overview on MST/MEMS services that are available in Europe, including pure commercial activities, European project activities (e.g. Europractice), and some academic services.

  1. Controlled neural network application in track-match problem

    International Nuclear Information System (INIS)

    Baginyan, S.A.; Ososkov, G.A.

    1993-01-01

    Track-match problem of high energy physics (HEP) data handling is formulated in terms of incidence matrices. The corresponding Hopfield neural network is developed to solve this type of constraint satisfaction problems (CSP). A special concept of the controlled neural network is proposed as a basis of an algorithm for the effective CSP solution. Results of comparable calculations show the very high performance of this algorithm against conventional search procedures. 8 refs.; 1 fig.; 1 tab

  2. A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing

    Directory of Open Access Journals (Sweden)

    Yi-Qing Wang

    2015-09-01

    Full Text Available Recent years have seen a surge of interest in multilayer neural networks fueled by their successful applications in numerous image processing and computer vision tasks. In this article, we describe a C++ implementation of the stochastic gradient descent to train a multilayer neural network, where a fast and accurate acceleration of tanh(· is achieved with linear interpolation. As an example of application, we present a neural network able to deliver state-of-the-art performance in image demosaicing.

  3. Radioisotope Power Sources for MEMS Devices,

    International Nuclear Information System (INIS)

    Blanchard, J.P.

    2001-01-01

    Microelectromechanical systems (MEMS) comprise a rapidly expanding research field with potential applications varying from sensors in airbags to more recent optical applications. Depending on the application, these devices often require an on-board power source for remote operation, especially in cases requiring operation for an extended period of time. Previously suggested power sources include fossil fuels and solar energy, but nuclear power sources may provide significant advantages for certain applications. Hence, the objective of this study is to establish the viability of using radioisotopes to power realistic MEMS devices. A junction-type battery was constructed using silicon and a 63 Ni liquid source. A source volume containing 64 microCi provided a power of ∼0.07 nW. A more novel application of nuclear sources for MEMS applications involves the creation of a resonator that is driven by charge collection in a cantilever beam. Preliminary results have established the feasibility of this concept, and future work will optimize the design for various applications

  4. Applications of artificial neural networks in Nuclear Medicine

    International Nuclear Information System (INIS)

    Maddalena, D.J.

    1993-01-01

    Artificial neural networks (ANNs) are computer-based mathematical models developed to have analogous functions to idealized simple biological nervous systems. They consist of layers of processing elements, which are considered to be analogous to the nerve cells (neurons) and these are interconnected to form a network which is in essence a parallel computer even though they are most likely to be run on non-parallel computers such as personal computers or workstations. The parallel processing nature of the ANNs gives them the characteristics of speed, reliability and generalisation. The speed occurs because many bits of information can be input and analysed simultaneously. Reliability occurs because the networks can produce reasonable results even when some input data are missing or inaccurate. Generalisation is the ability of the network to estimate reasonable results when faced with new data outside its normal range of experience. There are two main classes of ANN - supervised and un-supervised. Supervised ANNs are trained to build internal algorithms relating patterns of inputs to outputs. After learning the relationship between the inputs and outputs they are able to classify patterns and make decisions of predictions based upon new patterns of inputs. The most frequently used ANN for biomedical applications is a supervised type called the back propagation ANN which has an excellent ability to predict and classify data and is becoming commonly used throughout the biomedical field. This article will discuss back propagation ANN structure. Its use for image analysis and diagnostic classification in various imaging modalities including Single Photon Emission Computed Tomography and Positron Emission Tomography 17 refs., 2 figs

  5. Application of Artificial Neural Networks to Complex Groundwater Management Problems

    International Nuclear Information System (INIS)

    Coppola, Emery; Poulton, Mary; Charles, Emmanuel; Dustman, John; Szidarovszky, Ferenc

    2003-01-01

    As water quantity and quality problems become increasingly severe, accurate prediction and effective management of scarcer water resources will become critical. In this paper, the successful application of artificial neural network (ANN) technology is described for three types of groundwater prediction and management problems. In the first example, an ANN was trained with simulation data from a physically based numerical model to predict head (groundwater elevation) at locations of interest under variable pumping and climate conditions. The ANN achieved a high degree of predictive accuracy, and its derived state-transition equations were embedded into a multiobjective optimization formulation and solved to generate a trade-off curve depicting water supply in relation to contamination risk. In the second and third examples, ANNs were developed with real-world hydrologic and climate data for different hydrogeologic environments. For the second problem, an ANN was developed using data collected for a 5-year, 8-month period to predict heads in a multilayered surficial and limestone aquifer system under variable pumping, state, and climate conditions. Using weekly stress periods, the ANN substantially outperformed a well-calibrated numerical flow model for the 71-day validation period, and provided insights into the effects of climate and pumping on water levels. For the third problem, an ANN was developed with data collected automatically over a 6-week period to predict hourly heads in 11 high-capacity public supply wells tapping a semiconfined bedrock aquifer and subject to large well-interference effects. Using hourly stress periods, the ANN accurately predicted heads for 24-hour periods in all public supply wells. These test cases demonstrate that the ANN technology can solve a variety of complex groundwater management problems and overcome many of the problems and limitations associated with traditional physically based flow models

  6. Carbon microelectromechanical systems (C-MEMS) based microsupercapacitors

    KAUST Repository

    Agrawal, Richa

    2015-05-18

    The rapid development in miniaturized electronic devices has led to an ever increasing demand for high-performance rechargeable micropower scources. Microsupercapacitors in particular have gained much attention in recent years owing to their ability to provide high pulse power while maintaining long cycle lives. Carbon microelectromechanical systems (C-MEMS) is a powerful approach to fabricate high aspect ratio carbon microelectrode arrays, which has been proved to hold great promise as a platform for energy storage. C-MEMS is a versatile technique to create carbon structures by pyrolyzing a patterned photoresist. Furthermore, different active materials can be loaded onto these microelectrode platforms for further enhancement of the electrochemical performance of the C-MEMS platform. In this article, different techniques and methods in order to enhance C-MEMS based various electrochemical capacitor systems have been discussed, including electrochemical activation of C-MEMS structures for miniaturized supercapacitor applications, integration of carbon nanostructures like carbon nanotubes onto C-MEMS structures and also integration of pseudocapacitive materials such as polypyrrole onto C-MEMS structures. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

  7. Carbon microelectromechanical systems (C-MEMS) based microsupercapacitors

    KAUST Repository

    Agrawal, Richa; Beidaghi, Majid; Chen, Wei; Wang, Chunlei

    2015-01-01

    The rapid development in miniaturized electronic devices has led to an ever increasing demand for high-performance rechargeable micropower scources. Microsupercapacitors in particular have gained much attention in recent years owing to their ability to provide high pulse power while maintaining long cycle lives. Carbon microelectromechanical systems (C-MEMS) is a powerful approach to fabricate high aspect ratio carbon microelectrode arrays, which has been proved to hold great promise as a platform for energy storage. C-MEMS is a versatile technique to create carbon structures by pyrolyzing a patterned photoresist. Furthermore, different active materials can be loaded onto these microelectrode platforms for further enhancement of the electrochemical performance of the C-MEMS platform. In this article, different techniques and methods in order to enhance C-MEMS based various electrochemical capacitor systems have been discussed, including electrochemical activation of C-MEMS structures for miniaturized supercapacitor applications, integration of carbon nanostructures like carbon nanotubes onto C-MEMS structures and also integration of pseudocapacitive materials such as polypyrrole onto C-MEMS structures. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

  8. Optimization and simulation of MEMS rectilinear ion trap

    Directory of Open Access Journals (Sweden)

    Huang Gang

    2015-04-01

    Full Text Available In this paper, the design of a MEMS rectilinear ion trap was optimized under simulated conditions. The size range of the MEMS rectilinear ion trap’s electrodes studied in this paper is measured at micron scale. SIMION software was used to simulate the MEMS rectilinear ion trap with different sizes and different radio-frequency signals. The ion-trapping efficiencies of the ion trap under these different simulation conditions were obtained. The ion-trapping efficiencies were compared to determine the performance of the MEMS rectilinear ion trap in different conditions and to find the optimum conditions. The simulation results show that for the ion trap at micron scale or smaller, the optimized length–width ratio was 0.8, and a higher frequency of radio-frequency signal is necessary to obtain a higher ion-trapping efficiency. These results have a guiding role in the process of developing MEMS rectilinear ion traps, and great application prospects in the research fields of the MEMS rectilinear ion trap and the MEMS mass spectrometer.

  9. Advanced MEMS systems for optical communication and imaging

    International Nuclear Information System (INIS)

    Horenstein, M N; Sumner, R; Freedman, D S; Datta, M; Kani, N; Miller, P; Stewart, J B; Cornelissen, S

    2011-01-01

    Optical communication and adaptive optics have emerged as two important uses of micro-electromechanical (MEMS) devices based on electrostatic actuation. Each application uses a mirror whose surface is altered by applying voltages of up to 300 V. Previous generations of adaptive-optic mirrors were large (∼1 m) and required the use of piezoelectric transducers. Beginning in the mid-1990s, a new class of small MEMS mirrors (∼1 cm) were developed. These mirrors are now a commercially available, mature technology. This paper describes three advanced applications of MEMS mirrors. The first is a mirror used for corona-graphic imaging, whereby an interferometric telescope blocks the direct light from a distant star so that nearby objects such as planets can be seen. We have developed a key component of the system: a 144-channel, fully-scalable, high-voltage multiplexer that reduces power consumption to only a few hundred milliwatts. In a second application, a MEMS mirror comprises part of a two-way optical communication system in which only one node emits a laser beam. The other node is passive, incorporating a retro-reflective, electrostatic MEMS mirror that digitally encodes the reflected beam. In a third application, the short (∼100-ns) pulses of a commercially-available laser rangefinder are returned by the MEMS mirror as a digital data stream. Suitable low-power drive systems comprise part of the system design.

  10. RF MEMS: status of the industry and roadmaps

    Science.gov (United States)

    Bouchaud, Jeremie; Wicht, Henning

    2005-01-01

    Microsystems for Radio Frequency applications, known as RF MEMS, have entered the commercialization phase in 2003. Bulk Acoustic Wave filters are already produced in series and first commercial samples of switches are available. On the other hand, reliability and packaging problems are still a major hurdle especially for switches and tunable capacitors. Will RF MEMS hold their promise to be one of the future major businesses for MEMS? The presentation will give an overview on RF MEMS applications and market players. WTC will highlight technical challenges that still have to be solved to open mass markets such as mobile telephony and WLAN. WTC will also present applications of RF MEMS and opportunities in niche markets with high added value like military and space applications. WTC will provide a regional analysis and compare R&D focus and public funding situation in North America, Europe and Asia. Finally, WTC will present an updated product roadmap market forecast for RF MEMS devices for the 2004-2008 time period.

  11. State-of-the-art of applications of neural networks in the nuclear industry

    International Nuclear Information System (INIS)

    Zwingelstein, G.; Masson, M.H.

    1990-01-01

    Artificial neural net models have been extensively studied for many years in various laboratories to try to simulate with computer programs the human brain performances. The first applications were developed in the fields of speech and image recognition. The aims of these studies were mainly to classify rapidly patterns corrupted by noises or partly missing. Neural networks with the development of new net topologies and algorithms and parallel computing hardwares and softwares are to-day very promising for applications in many industries. In the introduction, this paper presents the anticipated benefits of the uses of neural networks for industrial applications. Then a brief overview of the main neural networks is provided. Finally a short review of neural networks applications in the nuclear industry is given. It covers domains such as: predictive maintenance for vibratory surveillance of rotating machinery, signal processing, operator guidance and eddy current inspection. In conclusion recommendations are made to use with efficiency neural networks for practical applications. In particular the need for supercomputing will be pinpointed. (author)

  12. Application of artificial neural network for medical image recognition and diagnostic decision making

    International Nuclear Information System (INIS)

    Asada, N.; Eiho, S.; Doi, K.; MacMahon, H.; Montner, S.M.; Giger, M.L.

    1989-01-01

    An artificial neural network has been applied for pattern recognition and used as a tool in an expert system. The purpose of this study is to examine the potential usefulness of the neural network approach in medical applications for image recognition and decision making. The authors designed multilayer feedforward neural networks with a back-propagation algorithm for our study. Using first-pass radionuclide ventriculograms, we attempted to identify the right and left ventricles of the heart and the lungs by training the neural network from patterns of time-activity curves. In a preliminary study, the neural network enabled identification of the lungs and heart chambers once the network was trained sufficiently by means of repeated entries of data from the same case

  13. Application of neural networks to multiple alarm processing and diagnosis in nuclear power plants

    International Nuclear Information System (INIS)

    Cheon, Se Woo; Chang Soon Heung; Chung, Hak Yeong

    1992-01-01

    This paper presents feasibility studies of multiple alarm processing and diagnosis using neural networks. The back-propagation neural network model is applied to the training of multiple alarm patterns for the identification of failure in a reactor coolant pump (RCP) system. The general mapping capability of the neural network enables to identify a fault easily. The case studies are performed with emphasis on the applicability of the neural network to pattern recognition problems. It is revealed that the neural network model can identify the cause of multiple alarms properly, even when untrained or sensor-failed alarm symptoms are given. It is also shown that multiple failures are easily identified using the symptoms of multiple alarms

  14. Application of neural network technology to setpoint control of a simulated reactor experiment loop

    International Nuclear Information System (INIS)

    Cordes, G.A.; Bryan, S.R.; Powell, R.H.; Chick, D.R.

    1991-01-01

    This paper describes the design, implementation, and application of artificial neural networks to achieve temperature and flow rate control for a simulation of a typical experiment loop in the Advanced Test Reactor (ATR) located at the Idaho National Engineering Laboratory (INEL). The goal of the project was to research multivariate, nonlinear control using neural networks. A loop simulation code was adapted for the project and used to create a training set and test the neural network controller for comparison with the existing loop controllers. The results for the best neural network design are documented and compared with existing loop controller action. The neural network was shown to be as accurate at loop control as the classical controllers in the operating region represented by the training set. 5 refs., 8 figs., 3 tabs

  15. Converting MEMS technology into profits

    Science.gov (United States)

    Bryzek, Janusz

    1998-08-01

    This paper discusses issues related to transitioning a company from the advanced technology development phase (with a particular focus on MEMS) to a profitable business, with emphasis on start-up companies. It includes several case studies from (primarily) NovaSensor MEMS development history. These case studies illustrate strategic problems with which advanced MEMS technology developers have to be concerned. Conclusions from these case studies could be used as checkpoints for future MEMS developers to increase probability of profitable operations. The objective for this paper is to share the author's experience from multiple MEMS start-ups to accelerate development of the MEMS market by focusing state- of-the-art technologists on marketing issues.

  16. Statistical modelling of neural networks in γ-spectrometry applications

    International Nuclear Information System (INIS)

    Vigneron, V.; Martinez, J.M.; Morel, J.; Lepy, M.C.

    1995-01-01

    Layered Neural Networks, which are a class of models based on neural computation, are applied to the measurement of uranium enrichment, i.e. the isotope ratio 235 U/( 235 U + 236 U + 238 U). The usual method consider a limited number of Γ-ray and X-ray peaks, and require previously calibrated instrumentation for each sample. But, in practice, the source-detector ensemble geometry conditions are critically different, thus a means of improving the above convention methods is to reduce the region of interest: this is possible by focusing on the K α X region where the three elementary components are present. Real data are used to study the performance of neural networks. Training is done with a Maximum Likelihood method to measure uranium 235 U and 238 U quantities in infinitely thick samples. (authors). 18 refs., 6 figs., 3 tabs

  17. MEMS Actuators for Improved Performance and Durability

    Science.gov (United States)

    Yearsley, James M.

    Micro-ElectroMechanical Systems (MEMS) devices take advantage of force-scaling at length scales smaller than a millimeter to sense and interact with directly with phenomena and targets at the microscale. MEMS sensors found in everyday devices like cell-phones and cars include accelerometers, gyros, pressure sensors, and magnetic sensors. MEMS actuators generally serve more application specific roles including micro- and nano-tweezers used for single cell manipulation, optical switching and alignment components, and micro combustion engines for high energy density power generation. MEMS rotary motors are actuators that translate an electric drive signal into rotational motion and can serve as rate calibration inputs for gyros, stages for optical components, mixing devices for micro-fluidics, etc. Existing rotary micromotors suffer from friction and wear issues that affect lifetime and performance. Attempts to alleviate friction effects include surface treatment, magnetic and electrostatic levitation, pressurized gas bearings, and micro-ball bearings. The present work demonstrates a droplet based liquid bearing supporting a rotary micromotor that improves the operating characteristics of MEMS rotary motors. The liquid bearing provides wear-free, low-friction, passive alignment between the rotor and stator. Droplets are positioned relative to the rotor and stator through patterned superhydrophobic and hydrophilic surface coatings. The liquid bearing consists of a central droplet that acts as the motor shaft, providing axial alignment between rotor and stator, and satellite droplets, analogous to ball-bearings, that provide tip and tilt stable operation. The liquid bearing friction performance is characterized through measurement of the rotational drag coefficient and minimum starting torque due to stiction and geometric effects. Bearing operational performance is further characterized by modeling and measuring stiffness, environmental survivability, and high

  18. Disorder generated by interacting neural networks: application to econophysics and cryptography

    International Nuclear Information System (INIS)

    Kinzel, Wolfgang; Kanter, Ido

    2003-01-01

    When neural networks are trained on their own output signals they generate disordered time series. In particular, when two neural networks are trained on their mutual output they can synchronize; they relax to a time-dependent state with identical synaptic weights. Two applications of this phenomenon are discussed for (a) econophysics and (b) cryptography. (a) When agents competing in a closed market (minority game) are using neural networks to make their decisions, the total system relaxes to a state of good performance. (b) Two partners communicating over a public channel can find a common secret key

  19. The application of neural networks for fault diagnosis in nuclear reactors

    International Nuclear Information System (INIS)

    Jalel, N.A.; Nicholson, H.

    1990-11-01

    In recent years considerable work have been done in the field of neural networks due to the recent development of effective learning algorithms, and the results of their applications have suggested that they can provide useful tools for solving practical problems. Artificial neural networks are mathematical models of theorized mind and brain activity. They are aimed to explore and reproduce human information processing tasks such as speech, vision, knowledge processing and control. The possibility of using artificial neural networks for fault and accident diagnosis in the Loss Of Fluid Test (LOFT) reactor, a small scale pressurised water reactor, is examined and explained in the paper. (author)

  20. Development and application of deep convolutional neural network in target detection

    Science.gov (United States)

    Jiang, Xiaowei; Wang, Chunping; Fu, Qiang

    2018-04-01

    With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected.

  1. Neural networks and its application in biomedical engineering

    International Nuclear Information System (INIS)

    Husnain, S.K.; Bhatti, M.I.

    2002-01-01

    Artificial network (ANNs) is an information processing system that has certain performance characteristics in common with biological neural networks. A neural network is characterized by connections between the neurons, method of determining the weights on the connections and its activation functions while a biological neuron has three types of components that are of particular interest in understanding an artificial neuron: its dendrites, soma, and axon. The actin of the chemical transmitter modifies the incoming signal. The study of neural networks is an extremely interdisciplinary field. Computer-based diagnosis is an increasingly used method that tries to improve the quality of health care. Systems on Neural Networks have been developed extensively in the last ten years with the hope that medical diagnosis and therefore medical care would improve dramatically. The addition of a symbolic processing layer enhances the ANNs in a number of ways. It is, for instance, possible to supplement a network that is purely diagnostic with a level that recommends or nodes in order to more closely simulate the nervous system. (author)

  2. Application of neural networks in coastal engineering - An overview

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Patil, S.G.; Manjunatha, Y.R.; Hegde, A.V.

    Artificial Neural Network (ANN) is being applied to solve a wide variety of coastal/ocean engineering problems. In practical terms ANNs are non-linear modeling tools and they can be used to model complex relationship between the input and output...

  3. Application of Neural Networks to Higgs Boson Search

    Czech Academy of Sciences Publication Activity Database

    Hakl, František; Hlaváček, M.; Kalous, R.

    2003-01-01

    Roč. 502, - (2003), s. 489-491 ISSN 0168-9002 R&D Projects: GA MPO RP-4210/69/97 Institutional research plan: AV0Z1030915 Keywords : neural network s * Higgs search * genetic optimization Subject RIV: BA - General Mathematics Impact factor: 1.166, year: 2003

  4. Neural network application to the neutral meson recognition

    International Nuclear Information System (INIS)

    Lefevre, F.; Delagrange, H.; Merrouch, R.; Ostendorf, R.; Schutz, Y.; Matulewicz, T.

    1991-01-01

    The combinatorial background produced by high photon multiplicities expected in TAPS experiments causes problems in precise meson recognition. We use neural networks to reduce this background. First we give a description of this technique, hereafter the first results obtained by applying this method to simulated events and future perspective will be discussed [fr

  5. Neural network based satellite tracking for deep space applications

    Science.gov (United States)

    Amoozegar, F.; Ruggier, C.

    2003-01-01

    The objective of this paper is to provide a survey of neural network trends as applied to the tracking of spacecrafts in deep space at Ka-band under various weather conditions and examine the trade-off between tracing accuracy and communication link performance.

  6. Application of design of experiments and artificial neural networks ...

    African Journals Online (AJOL)

    This paper discusses the use of Distance based optimal designs in the design of experiments (DOE) and artificial neural networks (ANN) in optimizing the stacking sequence for simply supported laminated composite plate under uniformly distributed load (UDL) for minimizing the deflections and stresses. A number of finite ...

  7. Application of neural networks to seismic active control

    International Nuclear Information System (INIS)

    Tang, Yu.

    1995-01-01

    An exploratory study on seismic active control using an artificial neural network (ANN) is presented in which a singledegree-of-freedom (SDF) structural system is controlled by a trained neural network. A feed-forward neural network and the backpropagation training method are used in the study. In backpropagation training, the learning rate is determined by ensuring the decrease of the error function at each training cycle. The training patterns for the neural net are generated randomly. Then, the trained ANN is used to compute the control force according to the control algorithm. The control strategy proposed herein is to apply the control force at every time step to destroy the build-up of the system response. The ground motions considered in the simulations are the N21E and N69W components of the Lake Hughes No. 12 record that occurred in the San Fernando Valley in California on February 9, 1971. Significant reduction of the structural response by one order of magnitude is observed. Also, it is shown that the proposed control strategy has the ability to reduce the peak that occurs during the first few cycles of the time history. These promising results assert the potential of applying ANNs to active structural control under seismic loads

  8. High Volume Manufacturing and Field Stability of MEMS Products

    Science.gov (United States)

    Martin, Jack

    Low volume MEMS/NEMS production is practical when an attractive concept is implemented with business, manufacturing, packaging, and test support. Moving beyond this to high volume production adds requirements on design, process control, quality, product stability, market size, market maturity, capital investment, and business systems. In a broad sense, this chapter uses a case study approach: It describes and compares the silicon-based MEMS accelerometers, pressure sensors, image projection systems, and gyroscopes that are in high volume production. Although they serve several markets, these businesses have common characteristics. For example, the manufacturing lines use automated semiconductor equipment and standard material sets to make consistent products in large quantities. Standard, well controlled processes are sometimes modified for a MEMS product. However, novel processes that cannot run with standard equipment and material sets are avoided when possible. This reliance on semiconductor tools, as well as the organizational practices required to manufacture clean, particle-free products partially explains why the MEMS market leaders are integrated circuit manufacturers. There are other factors. MEMS and NEMS are enabling technologies, so it can take several years for high volume applications to develop. Indeed, market size is usually a strong function of price. This becomes a vicious circle, because low price requires low cost - a result that is normally achieved only after a product is in high volume production. During the early years, IC companies reduced cost and financial risk by using existing facilities for low volume MEMS production. As a result, product architectures are partially determined by capabilities developed for previous products. This chapter includes a discussion of MEMS product architecture with particular attention to the impact of electronic integration, packaging, and surfaces. Packaging and testing are critical, because they are

  9. SU-8 Based MEMS Process with Two Metal Layers using α-Si as a Sacrificial Material

    KAUST Repository

    Ramadan, Khaled S.

    2012-04-01

    Polymer based microelectromechanical systems (MEMS) micromachining is finding more interest in research and applications. This is due to its low cost and less time processing compared with silicon MEMS. SU-8 is a photo-patternable polymer that is used as a structural layer for MEMS and microfluidic devices. In addition to being processed with low cost, it is a biocompatible material with good mechanical properties. Also, amorphous silicon (α-Si) has found use as a sacrificial layer in silicon MEMS applications. α-Si can be deposited at large thicknesses for MEMS applications and also can be released in a dry method using XeF2 which can solve stiction problems related to MEMS applications. In this thesis, an SU-8 MEMS process is developed using amorphous silicon (α-Si) as a sacrificial layer. Electrostatic actuation and sensing is used in many MEMS applications. SU-8 is a dielectric material which limits its direct use in electrostatic actuation. This thesis provides a MEMS process with two conductive metal electrodes that can be used for out-of-plane electrostatic applications like MEMS switches and variable capacitors. The process provides the fabrication of dimples that can be conductive or non-conductive to facilitate more flexibility for MEMS designers. This SU-8 process can fabricate SU-8 MEMS structures of a single layer of two different thicknesses. Process parameters were tuned for two sets of thicknesses which are thin (5-10μm) and thick (130μm). Chevron bent-beam structures and different suspended beams (cantilevers and bridges) were fabricated to characterize the SU-8 process through extracting the density, Young’s Modulus and the Coefficient of Thermal Expansion (CTE) of SU-8. Also, the process was tested and used as an educational tool through which different MEMS structures were fabricated including MEMS switches, variable capacitors and thermal actuators.

  10. Waveguide-Integrated MEMS Concepts for Tunable Millimeter-Wave Systems

    OpenAIRE

    Baghchehsaraei, Zargham

    2014-01-01

    This thesis presents two families of novel waveguide-integrated components based on millimeter-wave microelectromechanical systems (MEMS) for reconfigurable systems. The first group comprises V-band (50–75 GHz) and W-band (75–110 GHz) waveguide switches and switchable irises, and their application as switchable cavity resonators, and tunable bandpass filters implemented by integration of novel MEMS-reconfigurable surfaces into a rectangular waveguide. The second category comprises MEMS-based ...

  11. Recent advances in MEMS radiation detectors for improving radiation safety in nuclear reactors

    International Nuclear Information System (INIS)

    Bhisikar, Abhay

    2016-01-01

    MEMS (micro-electro-mechanical-system) is a core technology that leverages integrated circuit (IC) fabrication technology, builds ultra-miniaturized components and, enables radical new system applications. When considering MEMS radiation detectors; they are the specific micromechanical structures which are designed to sense doses of radiations. The present article reviews the most recent progress made in the domain of MEMS ionizing radiation sensors at international level for nuclear reactors which can be relevant to Indian context. (author)

  12. A Bootstrap Neural Network Based Heterogeneous Panel Unit Root Test: Application to Exchange Rates

    OpenAIRE

    Christian de Peretti; Carole Siani; Mario Cerrato

    2010-01-01

    This paper proposes a bootstrap artificial neural network based panel unit root test in a dynamic heterogeneous panel context. An application to a panel of bilateral real exchange rate series with the US Dollar from the 20 major OECD countries is provided to investigate the Purchase Power Parity (PPP). The combination of neural network and bootstrapping significantly changes the findings of the economic study in favour of PPP.

  13. An absorptive single-pole four-throw switch using multiple-contact MEMS switches and its application to a monolithic millimeter-wave beam-forming network

    International Nuclear Information System (INIS)

    Lee, Sanghyo; Kim, Jong-Man; Kim, Yong-Kweon; Kwon, Youngwoo

    2009-01-01

    In this paper, a new absorptive single-pole four-throw (SP4T) switch based on multiple-contact switching is proposed and integrated with a Butler matrix to demonstrate a monolithic beam-forming network at millimeter waves (mm waves). In order to simplify the switching driving circuit and reduce the number of unit switches in an absorptive SP4T switch, the individual switches were replaced with long-span multiple-contact switches using stress-free single-crystalline-silicon MEMS technology. This approach improves the mechanical stability as well as the manufacturing yield, thereby allowing successful integration into a monolithic beam former. The fabricated absorptive SP4T MEMS switch shows insertion loss less than 1.3 dB, return losses better than 11 dB at 30 GHz and wideband isolation performance higher than 39 dB from 20 to 40 GHz. The absorptive SP4T MEMS switch is integrated with a 4 × 4 Butler matrix on a single chip to implement a monolithic beam-forming network, directing beam into four distinct angles. Array factors from the measured data show that the proposed absorptive SPnT MEMS switch can be effectively used for high-performance mm-wave beam-switching systems. This work corresponds to the first demonstration of a monolithic beam-forming network using switched beams

  14. Quantitative Accelerated Life Testing of MEMS Accelerometers.

    Science.gov (United States)

    Bâzu, Marius; Gălăţeanu, Lucian; Ilian, Virgil Emil; Loicq, Jerome; Habraken, Serge; Collette, Jean-Paul

    2007-11-20

    Quantitative Accelerated Life Testing (QALT) is a solution for assessing thereliability of Micro Electro Mechanical Systems (MEMS). A procedure for QALT is shownin this paper and an attempt to assess the reliability level for a batch of MEMSaccelerometers is reported. The testing plan is application-driven and contains combinedtests: thermal (high temperature) and mechanical stress. Two variants of mechanical stressare used: vibration (at a fixed frequency) and tilting. Original equipment for testing at tiltingand high temperature is used. Tilting is appropriate as application-driven stress, because thetilt movement is a natural environment for devices used for automotive and aerospaceapplications. Also, tilting is used by MEMS accelerometers for anti-theft systems. The testresults demonstrated the excellent reliability of the studied devices, the failure rate in the"worst case" being smaller than 10 -7 h -1 .

  15. Review on the Modeling of Electrostatic MEMS

    Directory of Open Access Journals (Sweden)

    Wan-Chun Chuang

    2010-06-01

    Full Text Available Electrostatic-driven microelectromechanical systems devices, in most cases, consist of couplings of such energy domains as electromechanics, optical electricity, thermoelectricity, and electromagnetism. Their nonlinear working state makes their analysis complex and complicated. This article introduces the physical model of pull-in voltage, dynamic characteristic analysis, air damping effect, reliability, numerical modeling method, and application of electrostatic-driven MEMS devices.

  16. Packaging of MEMS/MOEMS and nanodevices: reliability, testing, and characterization aspects

    Science.gov (United States)

    Tekin, Tolga; Ngo, Ha-Duong; Wittler, Olaf; Bouhlal, Bouchaib; Lang, Klaus-Dieter

    2011-02-01

    The last decade witnessed an explosive growth in research and development efforts devoted to MEMS devices and packaging. The successfully developed MEMS devices are, for example inkjet, pressure sensors, silicon microphones, accelerometers, gyroscopes, MOEMS, micro fuel cells and emerging MEMS. For the next decade, MEMS/MOEMS and nanodevice based products will penetrate into IT, telecommunications, automotive, defense, life sciences, medical and implantable applications. Forecasts say the MEMS market to be $14 billion by 2012. The packaging cost of MEMS/MOEMS products in general is about 70 percent. Unlike today's electronics IC packaging, their packaging are custom-built and difficult due to the moving structural elements. In order for the moving elements of a MEMS device to move effectively in a well-controlled atmosphere, hermetic sealing of the MEMS device in a cap is necessary. For some MEMS devices, such as resonators and gyroscopes, vacuum packaging is required. Usually, the cap is processed at the wafer level, and thus MEMS packaging is truly a wafer level packaging. In terms of MEMS/MOEMS and nanodevice packaging, there are still many critical issues need to be addressed due to the increasing integration density supported by 3D heterogeneous integration of multi-physic components/layers consisting of photonics, electronics, rf, plasmonics, and wireless. The infrastructure of MEMS/MOEMS and nanodevices and their packaging is not well established yet. Generic packaging platform technologies are not available. Some of critical issues have been studied intensively in the last years. In this paper we will discuss about processes, reliability, testing and characterization of MEMS/MOEMS and nanodevice packaging.

  17. Practical Application of Neural Networks in State Space Control

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon

    the networks, although some modifications are needed for the method to apply to the multilayer perceptron network. In connection with the multilayer perceptron networks it is also pointed out how instantaneous, sample-by-sample linearized state space models can be extracted from a trained network, thus opening......In the present thesis we address some problems in discrete-time state space control of nonlinear dynamical systems and attempt to solve them using generic nonlinear models based on artificial neural networks. The main aim of the work is to examine how well such control algorithms perform when...... theoretic notions followed by a detailed description of the topology, neuron functions and learning rules of the two types of neural networks treated in the thesis, the multilayer perceptron and the neurofuzzy networks. In both cases, a Least Squares second-order gradient method is used to train...

  18. Inner and Outer Recursive Neural Networks for Chemoinformatics Applications.

    Science.gov (United States)

    Urban, Gregor; Subrahmanya, Niranjan; Baldi, Pierre

    2018-02-26

    Deep learning methods applied to problems in chemoinformatics often require the use of recursive neural networks to handle data with graphical structure and variable size. We present a useful classification of recursive neural network approaches into two classes, the inner and outer approach. The inner approach uses recursion inside the underlying graph, to essentially "crawl" the edges of the graph, while the outer approach uses recursion outside the underlying graph, to aggregate information over progressively longer distances in an orthogonal direction. We illustrate the inner and outer approaches on several examples. More importantly, we provide open-source implementations [available at www.github.com/Chemoinformatics/InnerOuterRNN and cdb.ics.uci.edu ] for both approaches in Tensorflow which can be used in combination with training data to produce efficient models for predicting the physical, chemical, and biological properties of small molecules.

  19. Application of artificial neural network for NHR fault diagnosis

    International Nuclear Information System (INIS)

    Yu Haitao; Zhang Liangju; Xu Xiangdong

    1999-01-01

    The author makes researches on 200 MW nuclear heating reactor (NHR) fault diagnosis system using artificial neural network, and use the tendency value and real value of the data under the accidents to train and test two BP networks respectively. The final diagnostic result is the combination of the results of the two networks. The compound system can enhance the accuracy and adaptability of the diagnosis comparing to the single network system

  20. An analog integrated front-end amplifier for neural applications

    OpenAIRE

    Zhou, Zhijun; Warr, Paul

    2017-01-01

    The front-end amplifier forms the critical element for signal detection and pre-processing within neural monitoring systems. It determines not only the fidelity of the biosignal, but also impacts power consumption and detector size. In this paper, a combined feedback loop-controlled approach is proposed to neutralize for the input leakage currents generated by low noise amplifiers when in integrated circuit form, alongside signal leakage into the input bias network. Significantly, this loop t...

  1. Artificial neural networks application for solid fuel slagging intensity predictions

    Directory of Open Access Journals (Sweden)

    Kakietek Sławomir

    2017-01-01

    Full Text Available Slagging issues present in pulverized steam boilers very often lead to heat transfer problems, corrosion and not planned outages of boilers which increase the cost of energy production and decrease the efficiency of energy production. Slagging especially occurs in regions with reductive atmospheres which nowadays are very common due to very strict limitations in NOx emissions. Moreover alternative fuels like biomass which are also used in combustion systems from two decades in order to decrease CO2 emissions also usually increase the risk of slagging. Thus the prediction of slagging properties of fuels is not the minor issue which can be neglected before purchasing or mixing of fuels. This however is rather difficult to estimate and even commonly known standard laboratory methods like fusion temperature determination or special indexers calculated on the basis of proximate and ultimate analyses, very often have no reasonable correlation to real boiler fuel behaviour. In this paper the method of determination of slagging properties of solid fuels based on laboratory investigation and artificial neural networks were presented. A fuel data base with over 40 fuels was created. Neural networks simulations were carried out in order to predict the beginning temperature and intensity of slagging. Reasonable results were obtained for some of tested neural networks, especially for hybrid feedforward networks with PCA technique. Consequently neural network model will be used in Common Intelligent Boiler Operation Platform (CIBOP being elaborated within CERUBIS research project for two BP-1150 and BB-1150 steam boilers. The model among others enables proper fuel selection in order to minimize slagging risk.

  2. Application of neural networks to waste site screening

    Energy Technology Data Exchange (ETDEWEB)

    Dabiri, A.E.; Kraft, T.; Hilton, J.M. [Science Applications International Corp., San Diego, CA (United States)

    1993-03-01

    Waste site screening requires knowledge of the actual concentrations of hazardous materials and rates of flow around and below the site with time. The present approach to site screening consists primarily of drilling, boreholes near contaminated site and chemically analyzing the extracted physical samples and processing the data. In addition, hydraulic and geochemical soil properties are obtained so that numerical simulation models can be used to interpret and extrapolate the field data. The objective of this work is to investigate the feasibility of using neural network techniques to reduce the cost of waste site screening. A successful technique may lead to an ability to reduce the number of boreholes and the number of samples analyzed from each borehole to properly screen the waste site. The analytic tool development described here is inexpensive because it makes use of neural network techniques that can interpolate rapidly and which can learn how to analyze data rather than having to be explicitly programmed. In the following sections, data collection and data analyses will be described, followed by a section on different neural network techniques used. The results will be presented and compared with mathematical model. Finally, the last section will summarize the research work performed and make several recommendations for future work.

  3. Topology optimized RF MEMS switches

    DEFF Research Database (Denmark)

    Philippine, M. A.; Zareie, H.; Sigmund, Ole

    2013-01-01

    Topology optimization is a rigorous and powerful method that should become a standard MEMS design tool - it can produce unique and non-intuitive designs that meet complex objectives and can dramatically improve the performance and reliability of MEMS devices. We present successful uses of topology...

  4. Electromagnetic actuation in MEMS switches

    DEFF Research Database (Denmark)

    Oliveira Hansen, Roana Melina de; Mátéfi-Tempfli, Mária; Chemnitz, Steffen

    . Electromagnetic actuation is a very promising approach to operate such MEMS and Power MEMS devices, due to the long range, reproducible and strong forces generated by this method, among other advantages. However, the use of electromagnetic actuation in such devices requires the use of thick magnetic films, which...

  5. EDITORIAL: The 7th International Workshop on Micro and Nanotechnologies for Power Generation and Energy Conversion Applications (PowerMEMS 2007)

    Science.gov (United States)

    Hebling, C.; Woias, P.

    2008-10-01

    This special issue of Journal of Micromechanics and Microengineering (JMM) contains a selection of papers from the 7th International Workshop on Micro and Nanotechnologies for Power Generation and Energy Conversion (PowerMEMS 2007). The workshop was held in Freiburg, Germany on 27-29 November 2007 under the joint organization of the Fraunhofer Institute for Solar Energy Systems (FhG-ISE), Freiburg and the Department of Microsystems Engineering (IMTEK) of the Albert-Ludwig-University of Freiburg. PowerMEMS 2007 continues a series of workshops initiated in 2000 in Japan to create an annual discussion forum in the emerging field of micro energy technology. With a single exception in 2001, the workshop has continued as an annual meeting ever since, with a continuous increase in the number of presentations and participants. The program of PowerMEMS 2007 was composed of 2 invited talks, 25 oral talks and 61 poster presentations. From these 88 presentations 16 have been selected for this special issue. It was at the end of 1959 when the Caltech physicist Richard Feynman gave his famous lecture entitled 'There Is Plenty of Room at the Bottom' in which he discussed the possibilities of miniaturization for both storage capacity ('Encyclopaedia Britannica on the head of a pin') as well as micro machining ('rearranging the atoms'), although there were absolutely no technological possibilities in sight for an adequate realization of such ideas. Now, nearly 50 years later, we not only have incredible knowledge about the nanoworld, but even more we are now able to generate microelectromechanical devices which, next to their electronic properties, can integrate physical and analytical functions. Today, Feynman might easily have added a second lecture entitled 'There is Plenty of Energy at the Bottom'. Micro energy technology has seen a tremendous rise in MEMS and material sciences and is regarded today as one of their hot topics. Also, there are more and more companies in this

  6. Automated implementation of rule-based expert systems with neural networks for time-critical applications

    Science.gov (United States)

    Ramamoorthy, P. A.; Huang, Song; Govind, Girish

    1991-01-01

    In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

  7. MEMS Reliability: Infrastructure, Test Structures, Experiments, and Failure Modes

    Energy Technology Data Exchange (ETDEWEB)

    TANNER,DANELLE M.; SMITH,NORMAN F.; IRWIN,LLOYD W.; EATON,WILLIAM P.; HELGESEN,KAREN SUE; CLEMENT,J. JOSEPH; MILLER,WILLIAM M.; MILLER,SAMUEL L.; DUGGER,MICHAEL T.; WALRAVEN,JEREMY A.; PETERSON,KENNETH A.

    2000-01-01

    The burgeoning new technology of Micro-Electro-Mechanical Systems (MEMS) shows great promise in the weapons arena. We can now conceive of micro-gyros, micro-surety systems, and micro-navigators that are extremely small and inexpensive. Do we want to use this new technology in critical applications such as nuclear weapons? This question drove us to understand the reliability and failure mechanisms of silicon surface-micromachined MEMS. Development of a testing infrastructure was a crucial step to perform reliability experiments on MEMS devices and will be reported here. In addition, reliability test structures have been designed and characterized. Many experiments were performed to investigate failure modes and specifically those in different environments (humidity, temperature, shock, vibration, and storage). A predictive reliability model for wear of rubbing surfaces in microengines was developed. The root causes of failure for operating and non-operating MEMS are discussed. The major failure mechanism for operating MEMS was wear of the polysilicon rubbing surfaces. Reliability design rules for future MEMS devices are established.

  8. Development of the micro pixel chamber based on MEMS technology

    Science.gov (United States)

    Takemura, T.; Takada, A.; Kishimoto, T.; Komura, S.; Kubo, H.; Matsuoka, Y.; Miuchi, K.; Miyamoto, S.; Mizumoto, T.; Mizumura, Y.; Motomura, T.; Nakamasu, Y.; Nakamura, K.; Oda, M.; Ohta, K.; Parker, J. D.; Sawano, T.; Sonoda, S.; Tanimori, T.; Tomono, D.; Yoshikawa, K.

    2018-02-01

    Micro pixel chambers (μ-PIC) are gaseous two-dimensional imaging detectors originally manufactured using printed circuit board (PCB) technology. They are used in MeV gamma-ray astronomy, medicalimaging, neutron imaging, the search for dark matter, and dose monitoring. The position resolution of the present μ-PIC is approximately 120 μm (RMS), however some applications require a fine position resolution of less than 100 μm. To this end, we have started to develop a μ-PIC based on micro electro mechanical system (MEMS) technology, which provides better manufacturing accuracy than PCB technology. Our simulation predicted the gains of MEMS μ-PICs to be twice those of PCB μ-PICs at the same anode voltage. We manufactured two MEMS μ-PICs and tested them to study their behavior. In these experiments, we successfully operated the fabricatedMEMS μ-PICs and we achieved a maximum gain of approximately 7×103 and collected their energy spectra under irradiation of X-rays from 55Fe. However, the measured gains of the MEMS μ-PICs were less than half of the values predicted in the simulations. We postulated that the gains of the MEMS μ-PICs are diminished by the effect of the silicon used as a semiconducting substrate.

  9. Digital reflection holography based systems development for MEMS testing

    Science.gov (United States)

    Singh, Vijay Raj; Liansheng, Sui; Asundi, Anand

    2010-05-01

    MEMS are tiny mechanical devices that are built onto semiconductor chips and are measured in micrometers and nanometers. Testing of MEMS device is an important part in carrying out their functional assessment and reliability analysis. Development of systems based on digital holography (DH) for MEMS inspection and characterization is presented in this paper. Two DH reflection systems, table-top and handheld types, are developed depending on the MEMS measurement requirements and their capabilities are presented. The methodologies for the systems are developed for 3D profile inspection and static & dynamic measurements, which is further integrated with in-house developed software that provides the measurement results in near real time. The applications of the developed systems are demonstrated for different MEMS devices for 3D profile inspection, static deformation/deflection measurements and vibration analysis. The developed systems are well suitable for the testing of MEMS and Microsystems samples, with full-field, static & dynamic inspection as well as to monitor micro-fabrication process.

  10. Active mems microbeam device for gas detection

    KAUST Repository

    Bouchaala, Adam M.

    2017-10-05

    Sensors and active switches for applications in gas detection and other fields are described. The devices are based on the softening and hardening nonlinear response behaviors of microelectromechanical systems (MEMS) clamped-clamped microbeams. In that context, embodiments of gas-triggered MEMS microbeam sensors and switches are described. The microbeam devices can be coated with a Metal-Organic Framework to achieve high sensitivity. For gas sensing, an amplitude-based tracking algorithm can be used to quantify an amount of gas captured by the devices according to frequency shift. Noise analysis is also conducted according to the embodiments, which shows that the microbeam devices have high stability against thermal noise. The microbeam devices are also suitable for the generation of binary sensing information for alarming, for example.

  11. MEMS cost analysis from laboratory to industry

    CERN Document Server

    Freng, Ron Lawes

    2016-01-01

    The World of MEMS; Chapter 2: Basic Fabrication Processes; Chapter 3: Surface Microengineering. High Aspect Ratio Microengineering; Chapter 5: MEMS Testing; Chapter 6: MEMS Packaging. Clean Rooms, Buildings and Plant; Chapter 8: The MEMSCOST Spreadsheet; Chapter 9: Product Costs - Accelerometers. Product Costs - Microphones. MEMS Foundries. Financial Reporting and Analysis. Conclusions.

  12. Neural attractor network for application in visual field data classification

    International Nuclear Information System (INIS)

    Fink, Wolfgang

    2004-01-01

    The purpose was to introduce a novel method for computer-based classification of visual field data derived from perimetric examination, that may act as a ' counsellor', providing an independent 'second opinion' to the diagnosing physician. The classification system consists of a Hopfield-type neural attractor network that obtains its input data from perimetric examination results. An iterative relaxation process determines the states of the neurons dynamically. Therefore, even 'noisy' perimetric output, e.g., early stages of a disease, may eventually be classified correctly according to the predefined idealized visual field defect (scotoma) patterns, stored as attractors of the network, that are found with diseases of the eye, optic nerve and the central nervous system. Preliminary tests of the classification system on real visual field data derived from perimetric examinations have shown a classification success of over 80%. Some of the main advantages of the Hopfield-attractor-network-based approach over feed-forward type neural networks are: (1) network architecture is defined by the classification problem; (2) no training is required to determine the neural coupling strengths; (3) assignment of an auto-diagnosis confidence level is possible by means of an overlap parameter and the Hamming distance. In conclusion, the novel method for computer-based classification of visual field data, presented here, furnishes a valuable first overview and an independent 'second opinion' in judging perimetric examination results, pointing towards a final diagnosis by a physician. It should not be considered a substitute for the diagnosing physician. Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available

  13. Artificial intelligence. Application of the Statistical Neural Networks computer program in nuclear medicine

    International Nuclear Information System (INIS)

    Stefaniak, B.; Cholewinski, W.; Tarkowska, A.

    2005-01-01

    Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer application of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. In this paper practical aspects of scientific application of ANN in medicine using the Statistical Neural Networks Computer program, were presented. Several steps of data analysis with the above ANN software package were discussed shortly, from material selection and its dividing into groups to the types of obtained results. The typical problems connected with assessing scintigrams by ANN were also described. (author)

  14. Application of neural computing paradigms for signal validation

    International Nuclear Information System (INIS)

    Upadhyaya, B.R.; Eryurek, E.; Mathai, G.

    1989-01-01

    Signal validation and process monitoring problems often require the prediction of one or more process variables in a system. The feasibility of applying neural network paradigms to relate one variable with a set of other related variables is studied. The backpropagation network (BPN) is applied to develop models of signals from both a commercial power plant and the EBR-II. Modification of the BPN algorithm is studied with emphasis on the speed of network training and the accuracy of prediction. The prediction of process variables in a Westinghouse PWR is presented in this paper

  15. Application of neural networks for the prediction of multidirectional magnetostriction

    CERN Document Server

    Baumgartinger, N; Pfützner, H; Krismanic, G

    2000-01-01

    This paper describes attempts to use artificial neural networks (ANNs) for the prediction of magnetostriction (MS) characteristics of transformer core materials. In this first approach, the ANNs were trained with data from a rotational single-sheet tester to predict MS in rolling direction (r.d.) as a function of material grade, amplitude and shape of multidirectional magnetisation as well as the level of additional mechanical stress. It is shown that ANNs are able to forecast the corresponding relative MS changes in an approximate way.

  16. Modeling of a Piezoelectric MEMS Micropump Dedicated to Insulin Delivery and Experimental Validation Using Integrated Pressure Sensors: Application to Partial Occlusion Management

    Directory of Open Access Journals (Sweden)

    S. Fournier

    2017-01-01

    Full Text Available A numerical model based on equivalent electrical networks has been built to simulate the dynamic behavior of a positive-displacement MEMS micropump dedicated to insulin delivery. This device comprises a reservoir in direct communication with the inlet check valve, a pumping membrane actuated by a piezo actuator, two integrated piezoresistive pressure sensors, an anti-free-flow check valve at the outlet, and finally a fluidic pathway up to the patient cannula. The pressure profiles delivered by the sensors are continuously analyzed during the therapy in order to detect failures like occlusion. The numerical modeling is a reliable way to better understand the behavior of the micropump in case of failure. The experimental pressure profiles measured during the actuation phase have been used to validate the numerical modeling. The effect of partial occlusion on the pressure profiles has been also simulated. Based on this analysis, a new management of partial occlusion for MEMS micropump is finally proposed.

  17. Movable MEMS Devices on Flexible Silicon

    KAUST Repository

    Ahmed, Sally

    2013-05-05

    Flexible electronics have gained great attention recently. Applications such as flexible displays, artificial skin and health monitoring devices are a few examples of this technology. Looking closely at the components of these devices, although MEMS actuators and sensors can play critical role to extend the application areas of flexible electronics, fabricating movable MEMS devices on flexible substrates is highly challenging. Therefore, this thesis reports a process for fabricating free standing and movable MEMS devices on flexible silicon substrates; MEMS flexure thermal actuators have been fabricated to illustrate the viability of the process. Flexure thermal actuators consist of two arms: a thin hot arm and a wide cold arm separated by a small air gap; the arms are anchored to the substrate from one end and connected to each other from the other end. The actuator design has been modified by adding etch holes in the anchors to suit the process of releasing a thin layer of silicon from the bulk silicon substrate. Selecting materials that are compatible with the release process was challenging. Moreover, difficulties were faced in the fabrication process development; for example, the structural layer of the devices was partially etched during silicon release although it was protected by aluminum oxide which is not attacked by the releasing gas . Furthermore, the thin arm of the thermal actuator was thinned during the fabrication process but optimizing the patterning and etching steps of the structural layer successfully solved this problem. Simulation was carried out to compare the performance of the original and the modified designs for the thermal actuators and to study stress and temperature distribution across a device. A fabricated thermal actuator with a 250 μm long hot arm and a 225 μm long cold arm separated by a 3 μm gap produced a deflection of 3 μm before silicon release, however, the fabrication process must be optimized to obtain fully functioning

  18. Artificial neural network applications in the calibration of spark-ignition engines: An overview

    Directory of Open Access Journals (Sweden)

    Richard Fiifi Turkson

    2016-09-01

    Full Text Available Emission legislation has become progressively tighter, making the development of new internal combustion engines very challenging. New engine technologies for complying with these regulations introduce an exponential dependency between the number of test combinations required for obtaining optimum results and the time and cost outlays. This makes the calibration task very expensive and virtually impossible to carry out. The potential use of trained neural networks in combination with Design of Experiments (DoE methods for engine calibration has been a subject of research activities in recent times. This is because artificial neural networks, compared with other data-driven modeling techniques, perform better in satisfying a majority of the modeling requirements for engine calibration including the curse of dimensionality; the use of DoE for obtaining few measurements as practicable, with the aim of reducing engine calibration costs; the required flexibility that allows model parameters to be optimized to avoid overfitting; and the facilitation of automated online optimization during the engine calibration process that eliminates the need for user intervention. The purpose of this review is to give an overview of the various applications of neural networks in the calibration of spark-ignition engines. The identified and discussed applications include system identification for rapid prototyping, virtual sensing, use of neural networks as look-up table surrogates, emerging control strategies and On-Board Diagnostic (OBD applications. The demerits of neural networks, future possibilities and alternatives were also discussed.

  19. Image Finder Mobile Application Based on Neural Networks

    Directory of Open Access Journals (Sweden)

    Nabil M. Hewahi

    2017-04-01

    Full Text Available Nowadays taking photos via mobile phone has become a very important part of everyone’s life. Almost each and every person who has a smart phone also has thousands of photos in their mobile device. At times it becomes very difficult to find a particular photo from thousands of photos, and it takes time. This research was done to come up with an innovative solution that could solve this problem. The solution will allow the user to find the required photo by simply drawing a sketch on the objects in the required picture, for example a tree or car, etc. Two types of supervised Artificial Neural Networks are used for this purpose; one is trained to identify the handmade sketches and other is trained to identify the images. The proposed approach introduces a mechanism to relate the sketches with the images by matching them after training. The experimentation results for testing the trained neural networks reached 100% for the sketches, and 84% for the images of two objects as a case study.

  20. The application of particle swarm optimization to identify gamma spectrum with neural network

    International Nuclear Information System (INIS)

    Shi Dongsheng; Di Yuming; Zhou Chunlin

    2006-01-01

    Aiming at the shortcomings that BP algorithm is usually trapped to a local optimum and it has a low speed of convergence in the application of neural network to identify gamma spectrum, according to the advantage of the globe optimal searching of particle swarm optimization, this paper put forward a new algorithm for neural network training by combining BP algorithm and Particle Swarm Optimization-mixed PSO-BP algorithm. In the application to identify gamma spectrum, the new algorithm overcomes the shortcoming that BP algorithm is usually trapped to a local optimum and the neural network trained by it has a high ability of generalization with identification result of one hundred percent correct. Practical example shows that the mixed PSO-BP algorithm can effectively and reliably be used to identify gamma spectrum. (authors)

  1. Application of artificial neural networks to evaluate weld defects of nuclear components

    International Nuclear Information System (INIS)

    Amin, E.S.

    2007-01-01

    Artificial neural networks (ANNs) are computational representations based on the biological neural architecture of the brain. ANNs have been successfully applied to a wide range of engineering and scientific applications, such as signal, image processing and data analysis. Although Radiographic testing is widely used for welding defects, it is unsuccessful in identifying some welding defects because of the nature of image formation and quality. Neoteric algorithms have been used for the purpose of weld defects identifications in radiographic images to replace the expert knowledge. The application of artificial neural networks in noise detection of radiographic films is used. Radial Basis (RB) and learning vector quantization (LVQ) were applied. The method shows good performance in weld defects recognition and classification problems.

  2. Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

    Science.gov (United States)

    Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

    2015-01-01

    Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

  3. A nuclear micro battery for Mems devices

    International Nuclear Information System (INIS)

    Lal, A.; Bilbao Y Leon, R.M.; Guo, H.; Li, H.; Santanam, S.; Yao, R.; Blanchard, J.; Henderson, D.

    2001-01-01

    Micro-electromechanical Systems (MEMS) have not gained wide use because they lack the on-device power required by many important applications. Several forms of energy could be considered to supply this needed power (solar, fossil fuels, etc), but nuclear sources provide an intriguing option in terms of power density and lifetime. This paper describes several approaches for establishing the viability of nuclear sources for powering realistic MEMS devices. Isotopes currently being used include alpha and low-energy beta emitters. The sources are in both solid and liquid form, and a technique for plating a solid source from a liquid source has been investigated. Several approaches are being explored for the production of MEMS power sources. The first concept is a junction-type battery. The second concept involves a more direct use of the charged particles produced by the decay: the creation of a resonator by inducing movement due to attraction or repulsion resulting from the collection of charged particles. Performance results are provided for each of these concepts. (authors)

  4. Heterogeneous MEMS device assembly and integration

    Science.gov (United States)

    Topart, Patrice; Picard, Francis; Ilias, Samir; Alain, Christine; Chevalier, Claude; Fisette, Bruno; Paultre, Jacques E.; Généreux, Francis; Legros, Mathieu; Lepage, Jean-François; Laverdière, Christian; Ngo Phong, Linh; Caron, Jean-Sol; Desroches, Yan

    2014-03-01

    In recent years, smart phone applications have both raised the pressure for cost and time to market reduction, and the need for high performance MEMS devices. This trend has led the MEMS community to develop multi-die packaging of different functionalities or multi-technology (i.e. wafer) approaches to fabricate and assemble devices respectively. This paper reports on the fabrication, assembly and packaging at INO of various MEMS devices using heterogeneous assembly at chip and package-level. First, the performance of a giant (e.g. about 3 mm in diameter), electrostatically actuated beam steering mirror is presented. It can be rotated about two perpendicular axes to steer an optical beam within an angular cone of up to 60° in vector scan mode with an angular resolution of 1 mrad and a response time of 300 ms. To achieve such angular performance relative to mirror size, the microassembly was performed from sub-components fabricated from 4 different wafers. To combine infrared detection with inertial sensing, an electroplated proof mass was flip-chipped onto a 256×1 pixel uncooled bolometric FPA and released using laser ablation. In addition to the microassembly technology, performance results of packaged devices are presented. Finally, to simulate a 3072×3 pixel uncooled detector for cloud and fire imaging in mid and long-wave IR, the staggered assembly of six 512×3 pixel FPAs with a less than 50 micron pixel co-registration is reported.

  5. A MEMS sensor for microscale force measurements

    International Nuclear Information System (INIS)

    Majcherek, S; Aman, A; Fochtmann, J

    2016-01-01

    This paper describes the development and testing of a new MEMS-based sensor device for microscale contact force measurements. A special MEMS cell was developed to reach higher lateral resolution than common steel-based load cells with foil-type strain gauges as mechanical-electrical converters. The design provided more than one normal force measurement point with spatial resolution in submillimeter range. Specific geometric adaption of the MEMS-device allowed adjustability of its measurement range between 0.5 and 5 N. The thin film nickel-chromium piezo resistors were used to achieve a mechanical-electrical conversion. The production process was realized by established silicon processing technologies such as deep reactive ion etching and vapor deposition (sputtering). The sensor was tested in two steps. Firstly, the sensor characteristics were carried out by application of defined loads at the measurement points by a push-pull tester. As a result, the sensor showed linear behavior. A measurement system analysis (MSA1) was performed to define the reliability of the measurement system. The measured force values had the maximal relative deviation of 1% to average value of 1.97 N. Secondly, the sensor was tested under near-industrial conditions. In this context, the thermal induced relaxation behavior of the electrical connector contact springs was investigated. The handling of emerging problems during the characterization process of the sensor is also described. (paper)

  6. Ultra-compact MEMS FTIR spectrometer

    Science.gov (United States)

    Sabry, Yasser M.; Hassan, Khaled; Anwar, Momen; Alharon, Mohamed H.; Medhat, Mostafa; Adib, George A.; Dumont, Rich; Saadany, Bassam; Khalil, Diaa

    2017-05-01

    Portable and handheld spectrometers are being developed and commercialized in the late few years leveraging the rapidly-progressing technology and triggering new markets in the field of on-site spectroscopic analysis. Although handheld devices were commercialized for the near-infrared spectroscopy (NIRS), their size and cost stand as an obstacle against the deployment of the spectrometer as spectral sensing components needed for the smart phone industry and the IoT applications. In this work we report a chip-sized microelectromechanical system (MEMS)-based FTIR spectrometer. The core optical engine of the solution is built using a passive-alignment integration technique for a selfaligned MEMS chip; self-aligned microoptics and a single detector in a tiny package sized about 1 cm3. The MEMS chip is a monolithic, high-throughput scanning Michelson interferometer fabricated using deep reactive ion etching technology of silicon-on-insulator substrate. The micro-optical part is used for conditioning the input/output light to/from the MEMS and for further light direction to the detector. Thanks to the all-reflective design of the conditioning microoptics, the performance is free of chromatic aberration. Complemented by the excellent transmission properties of the silicon in the infrared region, the integrated solution allows very wide spectral range of operation. The reported sensor's spectral resolution is about 33 cm-1 and working in the range of 1270 nm to 2700 nm; upper limited by the extended InGaAs detector. The presented solution provides a low cost, low power, tiny size, wide wavelength range NIR spectral sensor that can be manufactured with extremely high volumes. All these features promise the compatibility of this technology with the forthcoming demand of smart portable and IoT devices.

  7. Absolute stability of nonlinear systems with time delays and applications to neural networks

    Directory of Open Access Journals (Sweden)

    Xinzhi Liu

    2001-01-01

    Full Text Available In this paper, absolute stability of nonlinear systems with time delays is investigated. Sufficient conditions on absolute stability are derived by using the comparison principle and differential inequalities. These conditions are simple and easy to check. In addition, exponential stability conditions for some special cases of nonlinear delay systems are discussed. Applications of those results to cellular neural networks are presented.

  8. Artificial Neural Networks: A New Approach for Predicting Application Behavior. AIR 2001 Annual Forum Paper.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    This paper examines how predictive modeling can be used to study application behavior. A relatively new technique, artificial neural networks (ANNs), was applied to help predict which students were likely to get into a large Research I university. Data were obtained from a university in Iowa. Two cohorts were used, each containing approximately…

  9. MEMS digital parametric loudspeaker

    KAUST Repository

    Carreno, Armando Arpys Arevalo

    2016-03-23

    This paper reports on the design and fabrication of MEMS actuator arrays suitable for Digital Sound reconstruction and Parametric Directional Loudspeakers. Two distinct versions of the device were fabricated: one using the electrostatic principle actuation and the other one, the piezoelectric principle. Both versions used similar membrane dimensions, with a diameter of 500 μm. These devices are the smallest Micro-Machined Ultrasound Transducer (MUT) arrays that can be operated for both modes: Digital Sound Reconstruction and Parametric Loudspeaker. The chips consist of an array with 256 transducers, in a footprint of 12 mm by 12 mm. The total single chip size is: 2.3 cm by 2.3 cm, including the contact pads. © 2016 IEEE.

  10. Tribology and MEMS

    International Nuclear Information System (INIS)

    Williams, J A; Le, H R

    2006-01-01

    Micro-electro-mechanical system, MEMS, is a rapidly growing interdisciplinary technology dealing with the design and manufacture of miniaturized machines with the major dimensions at the scale of tens, to perhaps hundreds, of micrometres. Because they depend on the cube of a representative dimension, component masses and inertias rapidly become small as size decreases whereas surface and tribological effects, which often depend on area, become increasingly important. Although our explanations of macroscopic tribological phenomena often involve individual events occurring at the micro-scale, when the overall component size is itself miniaturized it may be necessary to re-evaluate some conventional tribological solutions. While the absolute loads are small in such micro-devices, the tribological requirements, especially in terms of longevity-which may be limited by wear rather than friction-are particularly demanding and will require imaginative and novel solutions. (topical review)

  11. MEMS digital parametric loudspeaker

    KAUST Repository

    Carreno, Armando Arpys Arevalo; Castro, David; Conchouso Gonzalez, David; Kosel, Jü rgen; Foulds, Ian G.

    2016-01-01

    This paper reports on the design and fabrication of MEMS actuator arrays suitable for Digital Sound reconstruction and Parametric Directional Loudspeakers. Two distinct versions of the device were fabricated: one using the electrostatic principle actuation and the other one, the piezoelectric principle. Both versions used similar membrane dimensions, with a diameter of 500 μm. These devices are the smallest Micro-Machined Ultrasound Transducer (MUT) arrays that can be operated for both modes: Digital Sound Reconstruction and Parametric Loudspeaker. The chips consist of an array with 256 transducers, in a footprint of 12 mm by 12 mm. The total single chip size is: 2.3 cm by 2.3 cm, including the contact pads. © 2016 IEEE.

  12. Stability Analysis and Application for Delayed Neural Networks Driven by Fractional Brownian Noise.

    Science.gov (United States)

    Zhou, Wuneng; Zhou, Xianghui; Yang, Jun; Zhou, Jun; Tong, Dongbing

    2018-05-01

    This paper deals with two types of the stability problem for the delayed neural networks driven by fractional Brownian noise (FBN). The existence and the uniqueness of the solution to the main system with respect to FBN are proved via fixed point theory. Based on Hilbert-Schmidt operator theory and analytic semigroup principle, the mild solution of the stochastic neural networks is obtained. By applying the stochastic analytic technique and some well-known inequalities, the asymptotic stability criteria and the exponential stability condition are established. Both numerical example and practical application for synchronization control of multiagent system are provided to illustrate the effectiveness and potential of the proposed techniques.

  13. Application of neural network in τ→ρυτ polarization analysis

    International Nuclear Information System (INIS)

    Zhang Ziping; Wang Yifang; Innocente, V.

    1994-01-01

    An artificial neutral network was built to select events in the τ→ρυ τ polarization analysis at LEP/L3, much better selection efficiency has been achieved. Detailed studies show that no systematic errors or bias have been introduced by the application of neural network. A polarization of P τ = -0.129 +- 0.050 +- 0.050 for this channel was obtained by using a sample of 8977 τ + τ - pairs collected near the peak of Z 0 resonance. The neural network training method and some details are described

  14. Development and Evaluation of Micro-Electrocorticography Arrays for Neural Interfacing Applications

    Science.gov (United States)

    Schendel, Amelia Ann

    Neural interfaces have great promise for both electrophysiological research and therapeutic applications. Whether for the study of neural circuitry or for neural prosthetic or other therapeutic applications, micro-electrocorticography (micro-ECoG) arrays have proven extremely useful as neural interfacing devices. These devices strike a balance between invasiveness and signal resolution, an important step towards eventual human application. The objective of this research was to make design improvements to micro-ECoG devices to enhance both biocompatibility and device functionality. To best evaluate the effectiveness of these improvements, a cranial window imaging method for in vivo monitoring of the longitudinal tissue response post device implant was developed. Employment of this method provided valuable insight into the way tissue grows around micro-ECoG arrays after epidural implantation, spurring a study of the effects of substrate geometry on the meningeal tissue response. The results of the substrate footprint comparison suggest that a more open substrate geometry provides an easy path for the tissue to grow around to the top side of the device, whereas a solid device substrate encourages the tissue to thicken beneath the device, between the electrode sites and the brain. The formation of thick scar tissue between the recording electrode sites and the neural tissue is disadvantageous for long-term recorded signal quality, and thus future micro-ECoG device designs should incorporate open-architecture substrates for enhanced longitudinal in vivo function. In addition to investigating improvements for long-term device reliability, it was also desired to enhance the functionality of micro-ECoG devices for neural electrophysiology research applications. To achieve this goal, a completely transparent graphene-based device was fabricated for use with the cranial window imaging method and optogenetic techniques. The use of graphene as the conductive material provided

  15. MEMS: A new approach to micro-optics

    Energy Technology Data Exchange (ETDEWEB)

    Sniegowski, J.J.

    1997-12-31

    MicroElectroMechanical Systems (MEMS) and their fabrication technologies provide great opportunities for application to micro-optical systems (MOEMS). Implementing MOEMS technology ranges from simple, passive components to complicated, active systems. Here, an overview of polysilicon surface micromachining MEMS combined with optics is presented. Recent advancements to the technology, which may enhance its appeal for micro-optics applications are emphasized. Of all the MEMS fabrication technologies, polysilicon surface micromachining technology has the greatest basis in and leverages the most the infrastructure for silicon integrated circuit fabrication. In that respect, it provides the potential for very large volume, inexpensive production of MOEMS. This paper highlights polysilicon surface micromachining technology in regards to its capability to provide both passive and active mechanical elements with quality optical elements.

  16. Finite element modeling of micromachined MEMS photon devices

    Science.gov (United States)

    Evans, Boyd M., III; Schonberger, D. W.; Datskos, Panos G.

    1999-09-01

    The technology of microelectronics that has evolved over the past half century is one of great power and sophistication and can now be extended to many applications (MEMS and MOEMS) other than electronics. An interesting application of MEMS quantum devices is the detection of electromagnetic radiation. The operation principle of MEMS quantum devices is based on the photoinduced stress in semiconductors, and the photon detection results from the measurement of the photoinduced bending. These devices can be described as micromechanical photon detectors. In this work, we have developed a technique for simulating electronic stresses using finite element analysis. We have used our technique to model the response of micromechanical photon devices to external stimuli and compared these results with experimental data. Material properties, geometry, and bimaterial design play an important role in the performance of micromechanical photon detectors. We have modeled these effects using finite element analysis and included the effects of bimaterial thickness coating, effective length of the device, width, and thickness.

  17. Finite Element Modeling of Micromachined MEMS Photon Devices

    International Nuclear Information System (INIS)

    Datskos, P.G.; Evans, B.M.; Schonberger, D.

    1999-01-01

    The technology of microelectronics that has evolved over the past half century is one of great power and sophistication and can now be extended to many applications (MEMS and MOEMS) other than electronics. An interesting application of MEMS quantum devices is the detection of electromagnetic radiation. The operation principle of MEMS quantum devices is based on the photoinduced stress in semiconductors, and the photon detection results from the measurement of the photoinduced bending. These devices can be described as micromechanical photon detectors. In this work, we have developed a technique for simulating electronic stresses using finite element analysis. We have used our technique to model the response of micromechanical photon devices to external stimuli and compared these results with experimental data. Material properties, geometry, and bimaterial design play an important role in the performance of micromechanical photon detectors. We have modeled these effects using finite element analysis and included the effects of bimaterial thickness coating, effective length of the device, width, and thickness

  18. Application of recurrent neural networks for drought projections in California

    Science.gov (United States)

    Le, J. A.; El-Askary, H. M.; Allali, M.; Struppa, D. C.

    2017-05-01

    We use recurrent neural networks (RNNs) to investigate the complex interactions between the long-term trend in dryness and a projected, short but intense, period of wetness due to the 2015-2016 El Niño. Although it was forecasted that this El Niño season would bring significant rainfall to the region, our long-term projections of the Palmer Z Index (PZI) showed a continuing drought trend, contrasting with the 1998-1999 El Niño event. RNN training considered PZI data during 1896-2006 that was validated against the 2006-2015 period to evaluate the potential of extreme precipitation forecast. We achieved a statistically significant correlation of 0.610 between forecasted and observed PZI on the validation set for a lead time of 1 month. This gives strong confidence to the forecasted precipitation indicator. The 2015-2016 El Niño season proved to be relatively weak as compared with the 1997-1998, with a peak PZI anomaly of 0.242 standard deviations below historical averages, continuing drought conditions.

  19. Application of Artificial Neural Networks in Canola Crop Yield Prediction

    Directory of Open Access Journals (Sweden)

    S. J. Sajadi

    2014-02-01

    Full Text Available Crop yield prediction has an important role in agricultural policies such as specification of the crop price. Crop yield prediction researches have been based on regression analysis. In this research canola yield was predicted using Artificial Neural Networks (ANN using 11 crop year climate data (1998-2009 in Gonbad-e-Kavoos region of Golestan province. ANN inputs were mean weekly rainfall, mean weekly temperature, mean weekly relative humidity and mean weekly sun shine hours and ANN output was canola yield (kg/ha. Multi-Layer Perceptron networks (MLP with Levenberg-Marquardt backpropagation learning algorithm was used for crop yield prediction and Root Mean Square Error (RMSE and square of the Correlation Coefficient (R2 criterions were used to evaluate the performance of the ANN. The obtained results show that the 13-20-1 network has the lowest RMSE equal to 101.235 and maximum value of R2 equal to 0.997 and is suitable for predicting canola yield with climate factors.

  20. Application of neural networks to quantitative spectrometry analysis

    International Nuclear Information System (INIS)

    Pilato, V.; Tola, F.; Martinez, J.M.; Huver, M.

    1999-01-01

    Accurate quantitative analysis of complex spectra (fission and activation products), relies upon experts' knowledge. In some cases several hours, even days of tedious calculations are needed. This is because current software is unable to solve deconvolution problems when several rays overlap. We have shown that such analysis can be correctly handled by a neural network, and the procedure can be automated with minimum laboratory measurements for networks training, as long as all the elements of the analysed solution figure in the training set and provided that adequate scaling of input data is performed. Once the network has been trained, analysis is carried out in a few seconds. On submitting to a test between several well-known laboratories, where unknown quantities of 57 Co, 58 Co, 85 Sr, 88 Y, 131 I, 139 Ce, 141 Ce present in a sample had to be determined, the results yielded by our network classed it amongst the best. The method is described, including experimental device and measures, training set designing, relevant input parameters definition, input data scaling and networks training. Main results are presented together with a statistical model allowing networks error prediction

  1. Fast and compact optical-resolution photoacoustic microscopy using a water-proofing two-axis MEMS scanner, and a step forward to clinical applications

    Science.gov (United States)

    Kim, Jin Young; Lee, Changho; Lim, Geunbae; Kim, Chulhong

    2016-03-01

    Optical-resolution photoacoustic microscopy (OR-PAM) is a novel microscopic tool to provide in vivo optically sensitive images in biomedical research. Conventional OR-PAM systems are typically slow and bulky because of the linear scanning stages with stepping motors. For practical purposes, however, fast imaging speed and small footprint are crucial. To address these issues, we have developed a real-time compact OR-PAM system equipped with a waterproof two-axis MEMS scanner. The OR-PAM system consists of key components such as an ultrasonic transducer with a bandwidth of 50 MHz, an opto-acoustic beam combiner (BC), and an MEMS scanner. These are all installed inside a small water tank, with dimensions of 30 mm × 90 mm × 30 mm along the x-, y-, and z-axes, respectively. A pulsed laser with a repetition rate of 50 kHz is confocally aligned with the photoacoustic (PA) waves in the BC to maximize the SNRs. The fast scanning ability of the MEMS scanner fully utilizes the A-scan speed of 50 kHz. For instance, the B- and C-scan imaging speeds are 125 Hz and 0.625 Hz, respectively, when the acquired PA maximum amplitude projection image has 200 × 200 pixels along the x- and y-axes, respectively. The measured lateral resolution of 3.6 μm and axial resolution of 27 μm are sufficient to resolve the small capillaries. Finally, we have successfully obtained in vivo PA images of iris microvasculatures in mice. This real-time and compact OR-PAM system is optimized to examine small animals in clinical studies.

  2. Applications of neural networks to monitoring and decision making in the operation of nuclear power plants

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1990-01-01

    Application of neural networks to monitoring and decision making in the operation of nuclear power plants is being investigated under a US Department of Energy sponsored program at the University of Tennessee. Projects include the feasibility of using neural networks for the following tasks: (1) diagnosing specific abnormal conditions or problems in nuclear power plants, (2) detection of the change of mode of operation of the plant, (3) validating signals coming from detectors, (4) review of ''noise'' data from TVA's Sequoyah Nuclear Power Plant, and (5) examination of the NRC's database of ''Letter Event Reports'' for correlation of sequences of events in the reported incidents. Each of these projects and its status are described briefly in this paper. This broad based program has as its objective the definition of the state-of-the-art in using neural networks to enhance the performance of commercial nuclear power plants

  3. Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

    Science.gov (United States)

    Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

    2017-11-01

    In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

  4. Application of Artificial Neural Network to Predict the use of Runway at Juanda International Airport

    Science.gov (United States)

    Putra, J. C. P.; Safrilah

    2017-06-01

    Artificial neural network approaches are useful to solve many complicated problems. It solves a number of problems in various areas such as engineering, medicine, business, manufacturing, etc. This paper presents an application of artificial neural network to predict a runway capacity at Juanda International Airport. An artificial neural network model of backpropagation and multi-layer perceptron is adopted to this research to learning process of runway capacity at Juanda International Airport. The results indicate that the training data is successfully recognizing the certain pattern of runway use at Juanda International Airport. Whereas, testing data indicate vice versa. Finally, it can be concluded that the approach of uniformity data and network architecture is the critical part to determine the accuracy of prediction results.

  5. Application of General Regression Neural Network to the Prediction of LOD Change

    Science.gov (United States)

    Zhang, Xiao-Hong; Wang, Qi-Jie; Zhu, Jian-Jun; Zhang, Hao

    2012-01-01

    Traditional methods for predicting the change in length of day (LOD change) are mainly based on some linear models, such as the least square model and autoregression model, etc. However, the LOD change comprises complicated non-linear factors and the prediction effect of the linear models is always not so ideal. Thus, a kind of non-linear neural network — general regression neural network (GRNN) model is tried to make the prediction of the LOD change and the result is compared with the predicted results obtained by taking advantage of the BP (back propagation) neural network model and other models. The comparison result shows that the application of the GRNN to the prediction of the LOD change is highly effective and feasible.

  6. Applications of self-organizing neural networks in virtual screening and diversity selection.

    Science.gov (United States)

    Selzer, Paul; Ertl, Peter

    2006-01-01

    Artificial neural networks provide a powerful technique for the analysis and modeling of nonlinear relationships between molecular structures and pharmacological activity. Many network types, including Kohonen and counterpropagation, also provide an intuitive method for the visual assessment of correspondence between the input and output data. This work shows how a combination of neural networks and radial distribution function molecular descriptors can be applied in various areas of industrial pharmaceutical research. These applications include the prediction of biological activity, the selection of screening candidates (cherry picking), and the extraction of representative subsets from large compound collections such as combinatorial libraries. The methods described have also been implemented as an easy-to-use Web tool, allowing chemists to perform interactive neural network experiments on the Novartis intranet.

  7. MEMS (Micro-Electro-Mechanical Systems) for Automotive and Consumer Electronics

    Science.gov (United States)

    Marek, Jiri; Gómez, Udo-Martin

    MEMS sensors gained over the last two decades an impressive width of applications: (a) ESP: A car is skidding and stabilizes itself without driver intervention (b) Free-fall detection: A laptop falls to the floor and protects the hard drive by parking the read/write drive head automatically before impact. (c) Airbag: An airbag fires before the driver/occupant involved in an impending automotive crash impacts the steering wheel, thereby significantly reducing physical injury risk. MEMS sensors are sensing the environmental conditions and are giving input to electronic control systems. These crucial MEMS sensors are making system reactions to human needs more intelligent, precise, and at much faster reaction rates than humanly possible. Important prerequisites for the success of sensors are their size, functionality, power consumption, and costs. This technical progress in sensor development is realized by micro-machining. The development of these processes was the breakthrough to industrial mass-production for micro-electro-mechanical systems (MEMS). Besides leading-edge micromechanical processes, innovative and robust ASIC designs, thorough simulations of the electrical and mechanical behaviour, a deep understanding of the interactions (mainly over temperature and lifetime) of the package and the mechanical structures are needed. This was achieved over the last 20 years by intense and successful development activities combined with the experience of volume production of billions of sensors. This chapter gives an overview of current MEMS technology, its applications and the market share. The MEMS processes are described, and the challenges of MEMS, compared to standard IC fabrication, are discussed. The evolution of MEMS requirements is presented, and a short survey of MEMS applications is shown. Concepts of newest inertial sensors for ESP-systems are given with an emphasis on the design concepts of the sensing element and the evaluation circuit for achieving

  8. Micro-electro-mechanical systems (MEMS: Technology for the 21st century

    Directory of Open Access Journals (Sweden)

    Đakov Tatjana A.

    2014-01-01

    Full Text Available Micro-electro-mechanical systems (MEMS are miniturized devices that can sense the environment, process and analyze information, and respond with a variety of mechanical and electrical actuators. MEMS consists of mechanical elements, sensors, actuators, electrical and electronics devices on a common silicon substrate. Micro-electro-mechanical systems are becoming a vital technology for modern society. Some of the advantages of MEMS devices are: very small size, very low power consumption, low cost, easy to integrate into systems or modify, small thermal constant, high resistance to vibration, shock and radiation, batch fabricated in large arrays, improved thermal expansion tolerance. MEMS technology is increasingly penetrating into our lives and improving quality of life, similar to what we experienced in the microelectronics revolution. Commercial opportunities for MEMS are rapidly growing in broad application areas, including biomedical, telecommunication, security, entertainment, aerospace, and more in both the consumer and industrial sectors on a global scale. As a breakthrough technology, MEMS is building synergy between previously unrelated fields such as biology and microelectronics. Many new MEMS and nanotechnology applications will emerge, expanding beyond that which is currently identified or known. MEMS are definitely technology for 21st century.

  9. An effective approach for restraining electrochemical corrosion of polycrystalline silicon caused by an HF-based solution and its application for mass production of MEMS devices

    International Nuclear Information System (INIS)

    Liu, Yunfei; Xie, Jing; Zhao, Hui; Luo, Wei; Yang, Jinling; An, Ji; Yang, Fuhua

    2012-01-01

    This paper presents a novel method to effectively protect the structural material polycrystalline silicon (polysilicon) from electrochemical corrosion, which often occurs when the MEMS device is released in HF-based solutions, especially when the device contains a noble metal. This corrosion seriously degrades the electrical and mechanical performance as well as the reliability of MEMS devices. In this method, a photoresist (PR) is employed to cover the noble metal, which is electrically coupled with the underlying polysilicon layer. This PR cover can effectually prevent an HF-based solution from diffusing through and arriving at the surface of the noble metal, thus cutting off the electrical current of the electrochemical corrosion reaction. The polysilicon is well protected for longer than 80 min in 49% concentrated HF solutions by a 3 µm-thick AZ 6130 PR film. This fabrication process is simple, reliable and suitable for mass production of high-end micromechanical disk resonators. Benefiting from the technology breakthrough mentioned above, a novel low-cost microfabrication method for disk resonators with high performance has been developed, and the VHF polysilicon disk resonators with resonance frequencies around 282 MHz and Q values larger than 2000 at atmosphere have been produced at wafer level. (paper)

  10. Design and application of a metal wet-etching post-process for the improvement of CMOS-MEMS capacitive sensors

    International Nuclear Information System (INIS)

    Tsai, Ming-Han; Sun, Chih-Ming; Liu, Yu-Chia; Fang, Weileun; Wang, Chuanwei

    2009-01-01

    This study presents a process design methodology to improve the performance of a CMOS-MEMS gap-closing capacitive sensor. In addition to the standard CMOS process, the metal wet-etching approach is employed as the post-CMOS process to realize the present design. The dielectric layers of the CMOS process are exploited to form the main micro mechanical structures of the sensor. The metal layers of the CMOS process are used as the sensing electrodes and sacrificial layers. The advantages of the sensor design are as follows: (1) the parasitic capacitance is significantly reduced by the dielectric structure, (2) in-plane and out-of-plane sensing gaps can be reduced to increase the sensitivity, and (3) plate-type instead of comb-type out-of-plane sensing electrodes are available to increase the sensing electrode area. To demonstrate the feasibility of the present design, a three-axis capacitive CMOS-MEMS accelerometers chip is implemented and characterized. Measurements show that the sensitivities of accelerometers reach 11.5 mV G −1 (in the X-, Y-axes) and 7.8 mV G −1 (in the Z-axis), respectively, which are nearly one order larger than existing designs. Moreover, the detection of 10 mG excitation using the three-axis accelerometer is demonstrated for both in-plane and out-of-plane directions

  11. Application of neural network to multi-dimensional design window search in reactor core design

    International Nuclear Information System (INIS)

    Kugo, Teruhiko; Nakagawa, Masayuki

    1999-01-01

    In the reactor core design, many parametric survey calculations should be carried out to decide an optimal set of basic design parameter values. They consume a large amount of computation time and labor in the conventional way. To support design work, we investigate a procedure to search efficiently a design window, which is defined as feasible design parameter ranges satisfying design criteria and requirements, in a multi-dimensional space composed of several basic design parameters. The present method is applied to the neutronics and thermal hydraulics fields. The principle of the present method is to construct the multilayer neural network to simulate quickly a response of an analysis code through a training process, and to reduce computation time using the neural network without parametric study using analysis codes. To verify the applicability of the present method to the neutronics and the thermal hydraulics design, we have applied it to high conversion water reactors and examined effects of the structure of the neural network and the number of teaching patterns on the accuracy of the design window estimated by the neural network. From the results of the applications, a guideline to apply the present method is proposed and the present method can predict an appropriate design window in a reasonable computation time by following the guideline. (author)

  12. Ball driven type MEMS SAD for artillery fuse

    International Nuclear Information System (INIS)

    Seok, Jin Oh; Jeong, Ji-hun; Eom, Junseong; Lee, Seung S; Lee, Chun Jae; Ryu, Sung Moon; Oh, Jong Soo

    2017-01-01

    The SAD (safety and arming device) is an indispensable fuse component that ensures safe and reliable performance during the use of ammunition. Because the application of electronic devices for smart munitions is increasing, miniaturization of the SAD has become one of the key issues for next-generation artillery fuses. Based on MEMS technology, various types of miniaturized SADs have been proposed and fabricated. However, none of them have been reported to have been used in actual munitions due to their lack of high impact endurance and complicated explosive train arrangements. In this research, a new MEMS SAD using a ball driven mechanism, is successfully demonstrated based on a UV LIGA (lithography, electroplating and molding) process. Unlike other MEMS SADs, both high impact endurance and simple structure were achieved by using a ball driven mechanism. The simple structural design also simplified the fabrication process and increased the processing yield. The ball driven type MEMS SAD performed successfully under the desired safe and arming conditions of a spin test and showed fine agreement with the FEM simulation result, conducted prior to its fabrication. A field test was also performed with a grenade launcher to evaluate the SAD performance in the firing environment. All 30 of the grenade samples equipped with the proposed MEMS SAD operated successfully under the high-G setback condition. (paper)

  13. Ball driven type MEMS SAD for artillery fuse

    Science.gov (United States)

    Seok, Jin Oh; Jeong, Ji-hun; Eom, Junseong; Lee, Seung S.; Lee, Chun Jae; Ryu, Sung Moon; Oh, Jong Soo

    2017-01-01

    The SAD (safety and arming device) is an indispensable fuse component that ensures safe and reliable performance during the use of ammunition. Because the application of electronic devices for smart munitions is increasing, miniaturization of the SAD has become one of the key issues for next-generation artillery fuses. Based on MEMS technology, various types of miniaturized SADs have been proposed and fabricated. However, none of them have been reported to have been used in actual munitions due to their lack of high impact endurance and complicated explosive train arrangements. In this research, a new MEMS SAD using a ball driven mechanism, is successfully demonstrated based on a UV LIGA (lithography, electroplating and molding) process. Unlike other MEMS SADs, both high impact endurance and simple structure were achieved by using a ball driven mechanism. The simple structural design also simplified the fabrication process and increased the processing yield. The ball driven type MEMS SAD performed successfully under the desired safe and arming conditions of a spin test and showed fine agreement with the FEM simulation result, conducted prior to its fabrication. A field test was also performed with a grenade launcher to evaluate the SAD performance in the firing environment. All 30 of the grenade samples equipped with the proposed MEMS SAD operated successfully under the high-G setback condition.

  14. BioMEMS –Advancing the Frontiers of Medicine

    Directory of Open Access Journals (Sweden)

    Dentcho V. Ivanov

    2008-09-01

    Full Text Available Biological and medical application of micro-electro-mechanical-systems (MEMS is currently seen as an area of high potential impact. Integration of biology and microtechnology has resulted in the development of a number of platforms for improving biomedical and pharmaceutical technologies. This review provides a general overview of the applications and the opportunities presented by MEMS in medicine by classifying these platforms according to their applications in the medical field.

  15. Prospects for MEMS in the Automotive Industry

    Directory of Open Access Journals (Sweden)

    Richard DIXON

    2007-12-01

    Full Text Available An automotive sector as a growth market for MEMS sensors is analyzed in the article. The automotive sector accounted for $1.6 billion, making this the second biggest opportunity after IT peripherals and inkjet print heads. By 2011 the market will top $2.2 billion, a CAGR of around 7%. The main applications in revenues terms are, in order, pressure sensors, gyroscopes, accelerometers and flow sensors and this will remain so for the foreseeable future. Automotive companies are forced to innovate as a result of competition and price pressures.

  16. Neural Spike Train Synchronisation Indices: Definitions, Interpretations and Applications.

    Science.gov (United States)

    Halliday, D M; Rosenberg, J R

    2017-04-24

    A comparison of previously defined spike train syncrhonization indices is undertaken within a stochastic point process framework. The second order cumulant density (covariance density) is shown to be common to all the indices. Simulation studies were used to investigate the sampling variability of a single index based on the second order cumulant. The simulations used a paired motoneurone model and a paired regular spiking cortical neurone model. The sampling variability of spike trains generated under identical conditions from the paired motoneurone model varied from 50% { 160% of the estimated value. On theoretical grounds, and on the basis of simulated data a rate dependence is present in all synchronization indices. The application of coherence and pooled coherence estimates to the issue of synchronization indices is considered. This alternative frequency domain approach allows an arbitrary number of spike train pairs to be evaluated for statistically significant differences, and combined into a single population measure. The pooled coherence framework allows pooled time domain measures to be derived, application of this to the simulated data is illustrated. Data from the cortical neurone model is generated over a wide range of firing rates (1 - 250 spikes/sec). The pooled coherence framework correctly characterizes the sampling variability as not significant over this wide operating range. The broader applicability of this approach to multi electrode array data is briefly discussed.

  17. MEMS and mil/aero: technology push and market pull

    Science.gov (United States)

    Clifford, Thomas H.

    2001-04-01

    MEMS offers attractive solutions to high-density fluidics, inertial, optical, switching and other demanding military/aerospace (mil/aero) challenges. However, full acceptance must confront the realities of production-scale producibility, verifiability, testability, survivability, as well as long-term reliability. Data on these `..ilities' are crucial, and are central in funding and deployment decisions. Similarly, mil/aero users must highlight specific missions, environmental exposures, and procurement issues, as well as the quirks of its designers. These issues are particularly challenging in MEMS, because of the laws of physics and business economics, as well as the risks of deploying leading-edge technology into no-fail applications. This paper highlights mil/aero requirements, and suggests reliability/qualification protocols, to guide development effort and to reassure mil/aero users that MEMS labs are mindful of the necessary realities.

  18. Videometrics-based Detection of Vibration Linearity in MEMS Gyroscope

    Directory of Open Access Journals (Sweden)

    Yong Zhou

    2011-05-01

    Full Text Available MEMS gyroscope performs as a sort of sensor to detect angular velocity, with diverse applications in engineering including vehicle and intelligent traffic etc. A balanced vibration of driving module excited by electrostatic driving signal is the base MEMS gyroscope's performance. In order to analyze the linear property of vibration in MEMS Gyroscope, a method of computer vision measuring is applied with the help of high-speed vidicon to obtain video of linear vibration of driving module in gyroscope, under the driving voltage signal of inherent frequency and amplitude linearly increasing. By means of image processing, target identifying, and motion parameter extracting from the obtained video, vibration curve with time variation is acquired. And then, linearity of this vibration system can be analyzed by focusing on the amplitude value of vibration responding to the amplitude variation of driving voltage signal.

  19. Integrated Magnetic MEMS Relays: Status of the Technology

    Directory of Open Access Journals (Sweden)

    Giuseppe Schiavone

    2014-08-01

    Full Text Available The development and application of magnetic technologies employing microfabricated magnetic structures for the production of switching components has generated enormous interest in the scientific and industrial communities over the last decade. Magnetic actuation offers many benefits when compared to other schemes for microelectromechanical systems (MEMS, including the generation of forces that have higher magnitude and longer range. Magnetic actuation can be achieved using different excitation sources, which create challenges related to the integration with other technologies, such as CMOS (Complementary Metal Oxide Semiconductor, and the requirement to reduce power consumption. Novel designs and technologies are therefore sought to enable the use of magnetic switching architectures in integrated MEMS devices, without incurring excessive energy consumption. This article reviews the status of magnetic MEMS technology and presents devices recently developed by various research groups, with key focuses on integrability and effective power management, in addition to the ability to integrate the technology with other microelectronic fabrication processes.

  20. Use of neural networks in process engineering. Thermodynamics, diffusion, and process control and simulation applications

    International Nuclear Information System (INIS)

    Otero, F

    1998-01-01

    This article presents the current status of the use of Artificial Neural Networks (ANNs) in process engineering applications where common mathematical methods do not completely represent the behavior shown by experimental observations, results, and plant operating data. Three examples of the use of ANNs in typical process engineering applications such as prediction of activity in solvent-polymer binary systems, prediction of a surfactant self-diffusion coefficient of micellar systems, and process control and simulation are shown. These examples are important for polymerization applications, enhanced-oil recovery, and automatic process control

  1. Image processing using pulse-coupled neural networks applications in Python

    CERN Document Server

    Lindblad, Thomas

    2013-01-01

    Image processing algorithms based on the mammalian visual cortex are powerful tools for extraction information and manipulating images. This book reviews the neural theory and translates them into digital models. Applications are given in areas of image recognition, foveation, image fusion and information extraction. The third edition reflects renewed international interest in pulse image processing with updated sections presenting several newly developed applications. This edition also introduces a suite of Python scripts that assist readers in replicating results presented in the text and to further develop their own applications.

  2. Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.

    Science.gov (United States)

    Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun

    2017-08-01

    The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P < 0.001. The areas under the ROC curve (AUC) and 95 % CI of prediction set from Fisher discrimination analysis and BP neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

  3. Neural network retrieval of soil moisture: application to SMOS

    Science.gov (United States)

    Rodriguez-Fernandez, Nemesio; Richaume, Philippe; Aires, Filipe; Prigent, Catherine; Kerr, Yann; Kolasssa, Jana; Jimenez, Carlos; Cabot, Francois; Mahmoodi, Ali

    2014-05-01

    We present an efficient statistical soil moisture (SM) retrieval method using SMOS brightness temperatures (BTs) complemented with MODIS NDVI and ASCAT backscattering data. The method is based on a feed-forward neural network (hereafter NN) trained with SM from ECMWF model predictions or from the SMOS operational algorithm. The best compromise to retrieve SM with NNs from SMOS brightness temperatures in a large fraction of the swath (~ 670 km) is to use incidence angles from 25 to 60 degrees (in 7 bins of 5 deg width) for both H and V polarizations. The correlation coefficient (R) of the SM retrieved by the NN and the reference SM dataset (ECMWF or SMOS L3) is 0.8. The correlation coefficient increases to 0.91 when adding as input MODIS NDVI, ECOCLIMAP sand and clay fractions and one of the following data: (i) active microwaves observations (ASCAT backscattering coefficient at 40 deg incidence angle), (ii) ECMWF soil temperature. Finally, the correlation coefficient increases to R=0.94 when using a normalization index computed locally for each latitude-longitude point with the maximum and minimum BTs and the associated SM values from the local time series. Global maps of SM obtained with NNs reproduce well the spatial structures present in the reference SM datasets, implying that the NN works well for a wide range of ecosystems and physical conditions. In addition, the results of the NNs have been evaluated at selected locations for which in situ measurements are available such as the USDA-ARS watersheds (USA), the OzNet network (AUS) and USDA-NRCS SCAN network (USA). The time series of SM obtained with NNs reproduce the temporal behavior measured with in situ sensors. For well known sites where the in situ measurement is representative of a 40 km scale like the Little Washita watershed, the NN models show a very high correlation of (R = 0.8-0.9) and a low standard deviation of 0.02-0.04 m3/m3 with respect to the in situ measurements. When comparing with all the in

  4. CMOS MEMS Fabrication Technologies and Devices

    Directory of Open Access Journals (Sweden)

    Hongwei Qu

    2016-01-01

    Full Text Available This paper reviews CMOS (complementary metal-oxide-semiconductor MEMS (micro-electro-mechanical systems fabrication technologies and enabled micro devices of various sensors and actuators. The technologies are classified based on the sequence of the fabrication of CMOS circuitry and MEMS elements, while SOI (silicon-on-insulator CMOS MEMS are introduced separately. Introduction of associated devices follows the description of the respective CMOS MEMS technologies. Due to the vast array of CMOS MEMS devices, this review focuses only on the most typical MEMS sensors and actuators including pressure sensors, inertial sensors, frequency reference devices and actuators utilizing different physics effects and the fabrication processes introduced. Moreover, the incorporation of MEMS and CMOS is limited to monolithic integration, meaning wafer-bonding-based stacking and other integration approaches, despite their advantages, are excluded from the discussion. Both competitive industrial products and state-of-the-art research results on CMOS MEMS are covered.

  5. A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization.

    Science.gov (United States)

    Liu, Qingshan; Guo, Zhishan; Wang, Jun

    2012-02-01

    In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

  6. An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking.

    Science.gov (United States)

    Ding, Lei; Xiao, Lin; Liao, Bolin; Lu, Rongbo; Peng, Hua

    2017-01-01

    To obtain the online solution of complex-valued systems of linear equation in complex domain with higher precision and higher convergence rate, a new neural network based on Zhang neural network (ZNN) is investigated in this paper. First, this new neural network for complex-valued systems of linear equation in complex domain is proposed and theoretically proved to be convergent within finite time. Then, the illustrative results show that the new neural network model has the higher precision and the higher convergence rate, as compared with the gradient neural network (GNN) model and the ZNN model. Finally, the application for controlling the robot using the proposed method for the complex-valued systems of linear equation is realized, and the simulation results verify the effectiveness and superiorness of the new neural network for the complex-valued systems of linear equation.

  7. Microelectromechanical (MEM) thermal actuator

    Science.gov (United States)

    Garcia, Ernest J [Albuquerque, NM; Fulcher, Clay W. G. [Sandia Park, NM

    2012-07-31

    Microelectromechanical (MEM) buckling beam thermal actuators are disclosed wherein the buckling direction of a beam is constrained to a desired direction of actuation, which can be in-plane or out-of-plane with respect to a support substrate. The actuators comprise as-fabricated, linear beams of uniform cross section supported above the substrate by supports which rigidly attach a beam to the substrate. The beams can be heated by methods including the passage of an electrical current through them. The buckling direction of an initially straight beam upon heating and expansion is controlled by incorporating one or more directional constraints attached to the substrate and proximal to the mid-point of the beam. In the event that the beam initially buckles in an undesired direction, deformation of the beam induced by contact with a directional constraint generates an opposing force to re-direct the buckling beam into the desired direction. The displacement and force generated by the movement of the buckling beam can be harnessed to perform useful work, such as closing contacts in an electrical switch.

  8. MEMS Reliability Assurance Activities at JPL

    Science.gov (United States)

    Kayali, S.; Lawton, R.; Stark, B.

    2000-01-01

    An overview of Microelectromechanical Systems (MEMS) reliability assurance and qualification activities at JPL is presented along with the a discussion of characterization of MEMS structures implemented on single crystal silicon, polycrystalline silicon, CMOS, and LIGA processes. Additionally, common failure modes and mechanisms affecting MEMS structures, including radiation effects, are discussed. Common reliability and qualification practices contained in the MEMS Reliability Assurance Guideline are also presented.

  9. NEURAL NETWORKS, FUZZY LOGIC AND GENETIC ALGORITHMS: APPLICATIONS AND POSSIBILITIES IN FINANCE AND ACCOUNTING

    Directory of Open Access Journals (Sweden)

    José Alonso Borba

    2010-04-01

    Full Text Available There are problems in Finance and Accounting that can not be easily solved by means of traditional techniques (e.g. bankruptcy prediction and strategies for investing in common stock. In these situations, it is possible to use methods of Artificial Intelligence. This paper analyzes empirical works published in international journals between 2000 and 2007 that present studies about the application of Neural Networks, Fuzzy Logic and Genetic Algorithms to problems in Finance and Accounting. The objective is to identify and quantify the relationships established between the available techniques and the problems studied by the researchers. Analyzing 258 papers, it was noticed that the most used technique is the Artificial Neural Network. The most researched applications are from the field of Finance, especially those related to stock exchanges (forecasting of common stock and indices prices.

  10. Application of neural networks for finding the relation between stress and operational parameters of NPP Temelin

    International Nuclear Information System (INIS)

    Ruzek, L.

    2003-01-01

    Quick and sufficiently precise determination of stresses and strains measured by I and C, TMDS a CHEMIS is very important for on-line assessment of continuous damage of material under operating conditions. The application of some of the artificial intelligence methods, viz. neural network, is convenient in this context. A practical example of the application of this method is presented and the advantages in comparison with the finite element method (FEM) are discussed. The approach to the selection of characteristic loading used for the preparation of training data is also shown. The paper presents the results of actual calculation and analyses the merits of the attained coincidence for the determination of the tensor of stresses by FEM and neural networks

  11. Modeling nonlinearities in MEMS oscillators.

    Science.gov (United States)

    Agrawal, Deepak K; Woodhouse, Jim; Seshia, Ashwin A

    2013-08-01

    We present a mathematical model of a microelectromechanical system (MEMS) oscillator that integrates the nonlinearities of the MEMS resonator and the oscillator circuitry in a single numerical modeling environment. This is achieved by transforming the conventional nonlinear mechanical model into the electrical domain while simultaneously considering the prominent nonlinearities of the resonator. The proposed nonlinear electrical model is validated by comparing the simulated amplitude-frequency response with measurements on an open-loop electrically addressed flexural silicon MEMS resonator driven to large motional amplitudes. Next, the essential nonlinearities in the oscillator circuit are investigated and a mathematical model of a MEMS oscillator is proposed that integrates the nonlinearities of the resonator. The concept is illustrated for MEMS transimpedance-amplifier- based square-wave and sine-wave oscillators. Closed-form expressions of steady-state output power and output frequency are derived for both oscillator models and compared with experimental and simulation results, with a good match in the predicted trends in all three cases.

  12. Model-based design of MEMS resonant pressure sensors

    NARCIS (Netherlands)

    Suijlen, M.A.G.

    2011-01-01

    The massive integration of micromechanical structures on ICs to allow microsystems to sense and control the environment is expected to be one of the most important technological breakthroughs of the future. At present, cheap and small MEMS sensors are emerging in countless applications. Automotive

  13. Biogeography-inspired multiobjective optimization for helping MEMS synthesis

    Directory of Open Access Journals (Sweden)

    Di Barba Paolo

    2017-09-01

    Full Text Available The aim of the paper is to assess the applicability of a multi-objective biogeography-based optimisation algorithm in MEMS synthesis. In order to test the performances of the proposed method in this research field, the optimal shape design of an electrostatic micromotor, and two different electro-thermo-elastic microactuators are considered as the case studies.

  14. RF sputtering: A viable tool for MEMS fabrication

    Indian Academy of Sciences (India)

    being prepared by RF sputtering and their application in MEMS being explored. ... crystallographic properties were evaluated using XRD analysis (CuKα radiation ..... Bhatt V, Pal P, Chandra S 2005 Feasibility study of RF sputtered ZnO film for ...

  15. Wavelet neural networks with applications in financial engineering, chaos, and classification

    CERN Document Server

    Alexandridis, Antonios K

    2014-01-01

    Through extensive examples and case studies, Wavelet Neural Networks provides a step-by-step introduction to modeling, training, and forecasting using wavelet networks. The acclaimed authors present a statistical model identification framework to successfully apply wavelet networks in various applications, specifically, providing the mathematical and statistical framework needed for model selection, variable selection, wavelet network construction, initialization, training, forecasting and prediction, confidence intervals, prediction intervals, and model adequacy testing. The text is ideal for

  16. Artificial neural networks application for analysis of gamma ray spectrum obtained from the scintillation detectors

    International Nuclear Information System (INIS)

    Stegowski, Z.

    2002-01-01

    Scintillation detectors are commonly used for the gamma ray detection. Actually the small peak resolution and the significant Compton effect fraction limit their utilization in the gamma ray spectrometry analysis. This article presents the artificial neural networks (ANN) application to the analysis of the gamma ray spectra acquired from scintillation detectors. The obtained results validate the effectiveness of the ANN method to spectrometry analysis. (author)

  17. Structured synthesis of MEMS using evolutionary approaches

    DEFF Research Database (Denmark)

    Fan, Zhun; Wang, Jiachuan; Achiche, Sofiane

    2008-01-01

    In this paper, we discuss the hierarchy that is involved in a typical MEMS design and how evolutionary approaches can be used to automate the hierarchical synthesis process for MEMS. The paper first introduces the flow of a structured MEMS design process and emphasizes that system-level lumped...

  18. A six degrees of freedom mems manipulator

    NARCIS (Netherlands)

    de Jong, B.R.

    2006-01-01

    This thesis reports about a six degrees of freedom (DOF) precision manipulator in MEMS, concerning concept generation for the manipulator followed by design and fabrication (of parts) of the proposed manipulation concept in MEMS. Researching the abilities of 6 DOF precision manipulation in MEMS is

  19. Electrostatic MEMS devices with high reliability

    Science.gov (United States)

    Goldsmith, Charles L; Auciello, Orlando H; Sumant, Anirudha V; Mancini, Derrick C; Gudeman, Chris; Sampath, Suresh; Carlilse, John A; Carpick, Robert W; Hwang, James

    2015-02-24

    The present invention provides for an electrostatic microelectromechanical (MEMS) device comprising a dielectric layer separating a first conductor and a second conductor. The first conductor is moveable towards the second conductor, when a voltage is applied to the MEMS device. The dielectric layer recovers from dielectric charging failure almost immediately upon removal of the voltage from the MEMS device.

  20. Investigation and modeling on protective textiles using artificial neural networks for defense applications

    International Nuclear Information System (INIS)

    Ramaiah, Gurumurthy B.; Chennaiah, Radhalakshmi Y.; Satyanarayanarao, Gurumurthy K.

    2010-01-01

    Kevlar 29 is a class of Kevlar fiber used for protective applications primarily by the military and law enforcement agencies for bullet resistant vests, hence for these reasons military has found that armors reinforced with Kevlar 29 multilayer fabrics which offer 25-40% better fragmentation resistance and provide better fit with greater comfort. The objective of this study is to investigate and develop an artificial neural network model for analyzing the performance of ballistic fabrics made from Kevlar 29 single layer fabrics using their material properties as inputs. Data from fragment simulation projectile (FSP) ballistic penetration measurements at 244 m/s has been used to demonstrate the modeling aspects of artificial neural networks. The neural network models demonstrated in this paper is based on back propagation (BP) algorithm which is inbuilt in MATLAB 7.1 software and is used for studies in science, technology and engineering. In the present research, comparisons are also made between the measured values of samples selected for building the neural network model and network predicted results. The analysis of the results for network predicted and experimental samples used in this study showed similarity.

  1. Applications of Mesenchymal Stem Cells and Neural Crest Cells in Craniofacial Skeletal Research

    Directory of Open Access Journals (Sweden)

    Satoru Morikawa

    2016-01-01

    Full Text Available Craniofacial skeletal tissues are composed of tooth and bone, together with nerves and blood vessels. This composite material is mainly derived from neural crest cells (NCCs. The neural crest is transient embryonic tissue present during neural tube formation whose cells have high potential for migration and differentiation. Thus, NCCs are promising candidates for craniofacial tissue regeneration; however, the clinical application of NCCs is hindered by their limited accessibility. In contrast, mesenchymal stem cells (MSCs are easily accessible in adults, have similar potential for self-renewal, and can differentiate into skeletal tissues, including bones and cartilage. Therefore, MSCs may represent good sources of stem cells for clinical use. MSCs are classically identified under adherent culture conditions, leading to contamination with other cell lineages. Previous studies have identified mouse- and human-specific MSC subsets using cell surface markers. Additionally, some studies have shown that a subset of MSCs is closely related to neural crest derivatives and endothelial cells. These MSCs may be promising candidates for regeneration of craniofacial tissues from the perspective of developmental fate. Here, we review the fundamental biology of MSCs in craniofacial research.

  2. The application of neural networks to flow regime identification

    International Nuclear Information System (INIS)

    Embrechts, M.; Yapo, T.C.; Lahey, R.T. Jr.

    1993-01-01

    This paper deals with the application of a Kohonen map for the identification of two-phase flow regimes where a mixture of gas and fluid flows through a horizontal tube. Depending on the relative flow velocities of the gas and the liquid phase, four distinct flow regimes can be identified: Wavy flow, plug flow, slug flow and annular flow. A schematic of these flow regimes is presented. The objective identification of two-phase flow regimes constitutes an important and challenging problem for the design of safe and reliable nuclear power plants. Previous attempts to classify these flow regimes are reviewed by Franca and Lahey. The authors describe how a Kohonen map can be applied to distinguish between flow regimes based on the Fourier power spectra and wavelet transforms of pressure drop fluctuations. The Fourier power spectra allowed the Kohonen map to identify the flow regimes successfully. In contrast, the Kohonen maps based on a wavelet transform could only distinguish between wavy and annular flows. An analysis of typical two-phase pressure drop data for an air/water mixture in a horizontal pipe is presented. Use of the wavelet transform and the Kohonen feature map are discussed

  3. RF-MEMS for high-performance and widely reconfigurable passive components – A review with focus on future telecommunications, Internet of Things (IoT and 5G applications

    Directory of Open Access Journals (Sweden)

    Jacopo Iannacci

    2017-10-01

    This work frames the current state of RF-MEMS market exploitation, analysing the main reasons impairing in past years the proper employment of Microsystem technology based RF passive components. Moreover, highlights on further expansion of RF-MEMS solutions in mobile and telecommunication systems will be briefly provided and discussed.

  4. Application of artificial neural networks for decision support in medicine.

    Science.gov (United States)

    Larder, Brendan; Wang, Dechao; Revell, Andy

    2008-01-01

    The emergence of drug resistant pathogens can reduce the efficacy of drugs commonly used to treat infectious diseases. Human immunodeficiency virus (HIV) is particularly sensitive to drug selection pressure, rapidly evolving into drug resistant variants on exposure to anti-HIV drugs. Over 200 mutations within the genetic material of HIV have been shown to be associated with drug resistance to date, and complex mutational patterns have been found in HIV isolates from infected patients exposed to multiple antiretroviral drugs. Genotyping is commonly used in clinical practice as a tool to identify drug resistance mutations in HIV from individual patients. This information is then used to help guide the choice of future therapy for patients whose drug regimen is failing because of the development of drug resistant HIV. Many sets of rules and algorithms are available to predict loss of susceptibility to individual antiretroviral drugs from genotypic data. Although this approach has been helpful, the interpretation of genotypic data remains challenging. We describe here the development and application of ANN models as alternative tools for the interpretation of HIV genotypic drug resistance data. A large amount of clinical and virological data, from around 30,000 patients treated with antiretroviral drugs, has been collected by the HIV Resistance Response Database Initiative (RDI, www.hivrdi.org) in a centralized database. Treatment change episodes (TCEs) have been extracted from these data and used along with HIV drug resistance mutations as the basic input variables to train ANN models. We performed a series of analyses that have helped define the following: (1) the reliability of ANN predictions for HIV patients receiving routine clinical care; (2) the utility of ANN models to identify effective treatments for patients failing therapy; (3) strategies to increase the accuracy of ANN predictions; and (4) performance of ANN models in comparison to the rules-based methods

  5. Modeling of biaxial gimbal-less MEMS scanning mirrors

    Science.gov (United States)

    von Wantoch, Thomas; Gu-Stoppel, Shanshan; Senger, Frank; Mallas, Christian; Hofmann, Ulrich; Meurer, Thomas; Benecke, Wolfgang

    2016-03-01

    One- and two-dimensional MEMS scanning mirrors for resonant or quasi-stationary beam deflection are primarily known as tiny micromirror devices with aperture sizes up to a few Millimeters and usually address low power applications in high volume markets, e.g. laser beam scanning pico-projectors or gesture recognition systems. In contrast, recently reported vacuum packaged MEMS scanners feature mirror diameters up to 20 mm and integrated high-reflectivity dielectric coatings. These mirrors enable MEMS based scanning for applications that require large apertures due to optical constraints like 3D sensing or microscopy as well as for high power laser applications like laser phosphor displays, automotive lighting and displays, 3D printing and general laser material processing. This work presents modelling, control design and experimental characterization of gimbal-less MEMS mirrors with large aperture size. As an example a resonant biaxial Quadpod scanner with 7 mm mirror diameter and four integrated PZT (lead zirconate titanate) actuators is analyzed. The finite element method (FEM) model developed and computed in COMSOL Multiphysics is used for calculating the eigenmodes of the mirror as well as for extracting a high order (n system inputs and scanner displacement as system output. By applying model order reduction techniques using MATLABR a compact state space system approximation of order n = 6 is computed. Based on this reduced order model feedforward control inputs for different, properly chosen scanner displacement trajectories are derived and tested using the original FEM model as well as the micromirror.

  6. An Examination of Application of Artificial Neural Network in Cognitive Radios

    Science.gov (United States)

    Bello Salau, H.; Onwuka, E. N.; Aibinu, A. M.

    2013-12-01

    Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined.

  7. [Algorithms of artificial neural networks--practical application in medical science].

    Science.gov (United States)

    Stefaniak, Bogusław; Cholewiński, Witold; Tarkowska, Anna

    2005-12-01

    Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer applications of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. This paper presents practical aspects of scientific application of ANN in medicine using widely available algorithms. Several main steps of analysis with ANN were discussed starting from material selection and dividing it into groups, to the quality assessment of obtained results at the end. The most frequent, typical reasons for errors as well as the comparison of ANN method to the modeling by regression analysis were also described.

  8. An Examination of Application of Artificial Neural Network in Cognitive Radios

    International Nuclear Information System (INIS)

    Salau, H Bello; Onwuka, E N; Aibinu, A M

    2013-01-01

    Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined

  9. Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

    Directory of Open Access Journals (Sweden)

    Tomasz Ciszewski

    2006-01-01

    Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

  10. A nuclear micro battery for Mems devices

    International Nuclear Information System (INIS)

    Blanchard, J.; Lal, A.; Henderson, D.; Bilbao Y Leon, R.; Guo, H.; Li, H.; Santanam, S.; Yao, R.

    2001-01-01

    Micro-electromechanical Systems (MEMS) have not gained wide use because they lack the on-device power required by many important applications. Several forms of energy could be considered to supply this needed power (solar, fossil fuels, etc), but nuclear sources provide an intriguing option in terms of power density and lifetime. This paper describes several approaches for establishing the viability of nuclear sources for powering realistic MEMS devices. Isotopes currently being used include low-energy beta emitters (solid and liquid) and alpha emitters (solid). Several approaches are being explored for the production of MEMS power sources. The first concept is a junction-type battery. In this case, the charged particles emitted from the decay of the radioisotopes are absorbed by a semiconductor and dissipate most of their energy as ionization of the atoms in the solid. The carriers generated in this fashion are in excess of the number permitted by thermodynamic equilibrium and, if they diffuse to the vicinity of a rectifying junction, induce a voltage across the junction. The second concept involves a more direct use of the charged particles produced by the decay: the creation of a resonator by inducing movement due to attraction or repulsion resulting from the collection of charged particles. As the charge is collected, the deflection of a cantilever beam increases until it contacts a grounded element, thus discharging the beam and causing it to return to its original position. This process will repeat as long as the source is active. One final concept relies on temperature gradients produced by the sources, along with appropriate insulation, to create power using a Peltier device. The source is isolated in order to allow it to reach sufficient temperatures, and the temperature difference between the source and the rest of the device is exploited using the Peltier effect. Performance results will be provided for each of these concepts. (author)

  11. MEMS linear and nonlinear statics and dynamics

    CERN Document Server

    Younis, Mohammad I

    2011-01-01

    MEMS Linear and Nonlinear Statics and Dynamics presents the necessary analytical and computational tools for MEMS designers to model and simulate most known MEMS devices, structures, and phenomena. This book also provides an in-depth analysis and treatment of the most common static and dynamic phenomena in MEMS that are encountered by engineers. Coverage also includes nonlinear modeling approaches to modeling various MEMS phenomena of a nonlinear nature, such as those due to electrostatic forces, squeeze-film damping, and large deflection of structures. The book also: Includes examples of nume

  12. "Geo-statistics methods and neural networks in geophysical applications: A case study"

    Science.gov (United States)

    Rodriguez Sandoval, R.; Urrutia Fucugauchi, J.; Ramirez Cruz, L. C.

    2008-12-01

    The study is focus in the Ebano-Panuco basin of northeastern Mexico, which is being explored for hydrocarbon reservoirs. These reservoirs are in limestones and there is interest in determining porosity and permeability in the carbonate sequences. The porosity maps presented in this study are estimated from application of multiattribute and neural networks techniques, which combine geophysics logs and 3-D seismic data by means of statistical relationships. The multiattribute analysis is a process to predict a volume of any underground petrophysical measurement from well-log and seismic data. The data consist of a series of target logs from wells which tie a 3-D seismic volume. The target logs are neutron porosity logs. From the 3-D seismic volume a series of sample attributes is calculated. The objective of this study is to derive a set of attributes and the target log values. The selected set is determined by a process of forward stepwise regression. The analysis can be linear or nonlinear. In the linear mode the method consists of a series of weights derived by least-square minimization. In the nonlinear mode, a neural network is trained using the select attributes as inputs. In this case we used a probabilistic neural network PNN. The method is applied to a real data set from PEMEX. For better reservoir characterization the porosity distribution was estimated using both techniques. The case shown a continues improvement in the prediction of the porosity from the multiattribute to the neural network analysis. The improvement is in the training and the validation, which are important indicators of the reliability of the results. The neural network showed an improvement in resolution over the multiattribute analysis. The final maps provide more realistic results of the porosity distribution.

  13. Identification of complex systems by artificial neural networks. Applications to mechanical frictions

    International Nuclear Information System (INIS)

    Dominguez, Manuel

    1998-01-01

    In the frame of complex systems modelization, we describe in this report the contribution of neural networks to mechanical friction modelization. This thesis is divided in three parts, each one corresponding to every stage of the realized work. The first part takes stock of the properties of neural networks by replacing them in the statistic frame of learning theory (particularly: non-linear and non-parametric regression models) and by showing the existing links with other more 'classic' techniques from automatics. We show then how identification models can be integrated in the neural networks description as a larger nonlinear model class. A methodology of neural networks use have been developed. We focused on validation techniques using correlation functions for non-linear systems, and on the use of regularization methods. The second part deals with the problematic of friction in mechanical systems. Particularly, we present the main current identified physical phenomena, which are integrated in advanced friction modelization. Characterization of these phenomena allows us to state a priori knowledge to be used in the identification stage. We expose some of the most well-known friction models: Dahl's model, Reset Integrator and Canuda's dynamical model, which are then used in simulation studies. The last part links the former one by illustrating a real-world application: an electric jack from SFIM-Industries, used in the Very Large Telescope (VLT) control scheme. This part begins with physical system presentation. The results are compared with more 'classic' methods. We finish using neural networks compensation scheme in closed-loop control. (author) [fr

  14. Study of functional viability of SU-8-based microneedles for neural applications

    International Nuclear Information System (INIS)

    Fernández, Luis J; Altuna, Ane; Tijero, Maria; Vilares, Roman; Berganzo, Javier; Blanco, F J; Gabriel, Gemma; Villa, Rosa; Rodríguez, Manuel J; Batlle, Montse

    2009-01-01

    This paper presents the design, fabrication, packaging and first test results of SU-8-based microneedles for neural applications. By the use of photolithography, sputtering and bonding techniques, polymer needles with integrated microchannels and electrodes have been successfully fabricated. The use of photolithography for the patterning of the fluidic channel integrated in the needle allows the design of multiple outlet ports at the needle tip, minimizing the possibility of being blocked by the tissue. Furthermore, the flexibility of the polymer reduces the risk of fracture and tissue damage once the needle is inserted, while it is still rigid enough to allow a perfect insertion into the neural tissue. Fluidic and electric characterization of the microneedles has shown their viability for drug delivery and monitoring in neural applications. First drug delivery tests in ex vivo tissue demonstrated the functional viability of the needle to deliver drugs to precise points. Furthermore, in vivo experiments have demonstrated lower associated damages during insertion than those by stereotaxic standard needles

  15. Application of neural network in market segmentation: A review on recent trends

    Directory of Open Access Journals (Sweden)

    Manojit Chattopadhyay

    2012-04-01

    Full Text Available Despite the significance of Artificial Neural Network (ANN algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000–2010 and proposed a classification scheme for the articles. One thousands (1000 articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.

  16. Study on application of adaptive fuzzy control and neural network in the automatic leveling system

    Science.gov (United States)

    Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

    2015-04-01

    This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

  17. Parzen neural networks: Fundamentals, properties, and an application to forensic anthropology.

    Science.gov (United States)

    Trentin, Edmondo; Lusnig, Luca; Cavalli, Fabio

    2018-01-01

    A novel, unsupervised nonparametric model of multivariate probability density functions (pdf) is introduced, namely the Parzen neural network (PNN). The PNN is intended to overcome the major limitations of traditional (either statistical or neural) pdf estimation techniques. Besides being profitably simple, the PNN turns out to have nice properties in terms of unbiased modeling capability, asymptotic convergence, and efficiency at test time. Several matters pertaining the practical application of the PNN are faced in the paper, too. Experiments are reported, involving (i) synthetic datasets, and (ii) a challenging sex determination task from 1400 scout-view CT-scan images of human crania. Incidentally, the empirical evidence entails also some conclusions of high anthropological relevance. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Application of fuzzy neural network technologies in management of transport and logistics processes in Arctic

    Science.gov (United States)

    Levchenko, N. G.; Glushkov, S. V.; Sobolevskaya, E. Yu; Orlov, A. P.

    2018-05-01

    The method of modeling the transport and logistics process using fuzzy neural network technologies has been considered. The analysis of the implemented fuzzy neural network model of the information management system of transnational multimodal transportation of the process showed the expediency of applying this method to the management of transport and logistics processes in the Arctic and Subarctic conditions. The modular architecture of this model can be expanded by incorporating additional modules, since the working conditions in the Arctic and the subarctic themselves will present more and more realistic tasks. The architecture allows increasing the information management system, without affecting the system or the method itself. The model has a wide range of application possibilities, including: analysis of the situation and behavior of interacting elements; dynamic monitoring and diagnostics of management processes; simulation of real events and processes; prediction and prevention of critical situations.

  19. The applications of deep neural networks to sdBV classification

    Science.gov (United States)

    Boudreaux, Thomas M.

    2017-12-01

    With several new large-scale surveys on the horizon, including LSST, TESS, ZTF, and Evryscope, faster and more accurate analysis methods will be required to adequately process the enormous amount of data produced. Deep learning, used in industry for years now, allows for advanced feature detection in minimally prepared datasets at very high speeds; however, despite the advantages of this method, its application to astrophysics has not yet been extensively explored. This dearth may be due to a lack of training data available to researchers. Here we generate synthetic data loosely mimicking the properties of acoustic mode pulsating stars and we show that two separate paradigms of deep learning - the Artificial Neural Network And the Convolutional Neural Network - can both be used to classify this synthetic data effectively. And that additionally this classification can be performed at relatively high levels of accuracy with minimal time spent adjusting network hyperparameters.

  20. Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation

    Directory of Open Access Journals (Sweden)

    Chunqing Li

    2012-01-01

    Full Text Available The computer simulation of the membrane bioreactor MBR has become the research focus of the MBR simulation. In order to compensate for the defects, for example, long test period, high cost, invisible equipment seal, and so forth, on the basis of conducting in-depth study of the mathematical model of the MBR, combining with neural network theory, this paper proposed a three-dimensional simulation system for MBR wastewater treatment, with fast speed, high efficiency, and good visualization. The system is researched and developed with the hybrid programming of VC++ programming language and OpenGL, with a multifactor linear regression model of affecting MBR membrane fluxes based on neural network, applying modeling method of integer instead of float and quad tree recursion. The experiments show that the three-dimensional simulation system, using the above models and methods, has the inspiration and reference for the future research and application of the MBR simulation technology.

  1. Comments on "The multisynapse neural network and its application to fuzzy clustering".

    Science.gov (United States)

    Yu, Jian; Hao, Pengwei

    2005-05-01

    In the above-mentioned paper, Wei and Fahn proposed a neural architecture, the multisynapse neural network, to solve constrained optimization problems including high-order, logarithmic, and sinusoidal forms, etc. As one of its main applications, a fuzzy bidirectional associative clustering network (FBACN) was proposed for fuzzy-partition clustering according to the objective-functional method. The connection between the objective-functional-based fuzzy c-partition algorithms and FBACN is the Lagrange multiplier approach. Unfortunately, the Lagrange multiplier approach was incorrectly applied so that FBACN does not equivalently minimize its corresponding constrained objective-function. Additionally, Wei and Fahn adopted traditional definition of fuzzy c-partition, which is not satisfied by FBACN. Therefore, FBACN can not solve constrained optimization problems, either.

  2. Integrated Electromechanical Transduction Schemes for Polymer MEMS Sensors

    Directory of Open Access Journals (Sweden)

    Damien Thuau

    2018-04-01

    Full Text Available Polymer Micro ElectroMechanical Systems (MEMS have the potential to constitute a powerful alternative to silicon-based MEMS devices for sensing applications. Although the use of commercial photoresists as structural material in polymer MEMS has been widely reported, the integration of functional polymer materials as electromechanical transducers has not yet received the same amount of interest. In this context, we report on the design and fabrication of different electromechanical schemes based on polymeric materials ensuring different transduction functions. Piezoresistive transduction made of carbon nanotube-based nanocomposites with a gauge factor of 200 was embedded within U-shaped polymeric cantilevers operating either in static or dynamic modes. Flexible resonators with integrated piezoelectric transduction were also realized and used as efficient viscosity sensors. Finally, piezoelectric-based organic field effect transistor (OFET electromechanical transduction exhibiting a record sensitivity of over 600 was integrated into polymer cantilevers and used as highly sensitive strain and humidity sensors. Such advances in integrated electromechanical transduction schemes should favor the development of novel all-polymer MEMS devices for flexible and wearable applications in the future.

  3. Additive direct-write microfabrication for MEMS: A review

    Science.gov (United States)

    Teh, Kwok Siong

    2017-12-01

    manufacturing technologies, it is possible to fabricate unsophisticated micrometer scale structures at adequate resolutions and precisions using materials that range from polymers, metals, ceramics, to composites. In both academia and industry, direct-write additive manufacturing offers extraordinary promises to revolutionize research and development in microfabrication and MEMS technologies. Importantly, direct-write additive manufacturing could appreciably augment current MEMS fabrication technologies, enable faster design-to-product cycle, empower new paradigms in MEMS designs, and critically, encourage wider participation in MEMS research at institutions or for individuals with limited or no access to cleanroom facilities. This article aims to provide a limited review of the current landscape of direct-write additive manufacturing techniques that are potentially applicable for MEMS microfabrication.

  4. Development of an SU-8 MEMS process with two metal electrodes using amorphous silicon as a sacrificial material

    KAUST Repository

    Ramadan, Khaled S.

    2013-02-08

    This work presents an SU-8 surface micromachining process using amorphous silicon as a sacrificial material, which also incorporates two metal layers for electrical excitation. SU-8 is a photo-patternable polymer that is used as a structural layer for MEMS and microfluidic applications due to its mechanical properties, biocompatibility and low cost. Amorphous silicon is used as a sacrificial layer in MEMS applications because it can be deposited in large thicknesses, and can be released in a dry method using XeF2, which alleviates release-based stiction problems related to MEMS applications. In this work, an SU-8 MEMS process was developed using ;-Si as a sacrificial layer. Two conductive metal electrodes were integrated in this process to allow out-of-plane electrostatic actuation for applications like MEMS switches and variable capacitors. In order to facilitate more flexibility for MEMS designers, the process can fabricate dimples that can be conductive or nonconductive. Additionally, this SU-8 process can fabricate SU-8 MEMS structures of a single layer of two different thicknesses. Process parameters were optimized for two sets of thicknesses: thin (5-10 m) and thick (130 m). The process was tested fabricating MEMS switches, capacitors and thermal actuators. © 2013 IOP Publishing Ltd.

  5. Development of an SU-8 MEMS process with two metal electrodes using amorphous silicon as a sacrificial material

    KAUST Repository

    Ramadan, Khaled S.; Nasr, Tarek Adel Hosny; Foulds, Ian G.

    2013-01-01

    method using XeF2, which alleviates release-based stiction problems related to MEMS applications. In this work, an SU-8 MEMS process was developed using ;-Si as a sacrificial layer. Two conductive metal electrodes were integrated in this process to allow

  6. MEMS based digital transform spectrometers

    Science.gov (United States)

    Geller, Yariv; Ramani, Mouli

    2005-09-01

    Earlier this year, a new breed of Spectrometers based on Micro-Electro-Mechanical-System (MEMS) engines has been introduced to the commercial market. The use of these engines combined with transform mathematics, produces powerful spectrometers at unprecedented low cost in various spectral regions.

  7. MEMS reliability: coming of age

    Science.gov (United States)

    Douglass, Michael R.

    2008-02-01

    In today's high-volume semiconductor world, one could easily take reliability for granted. As the MOEMS/MEMS industry continues to establish itself as a viable alternative to conventional manufacturing in the macro world, reliability can be of high concern. Currently, there are several emerging market opportunities in which MOEMS/MEMS is gaining a foothold. Markets such as mobile media, consumer electronics, biomedical devices, and homeland security are all showing great interest in microfabricated products. At the same time, these markets are among the most demanding when it comes to reliability assurance. To be successful, each company developing a MOEMS/MEMS device must consider reliability on an equal footing with cost, performance and manufacturability. What can this maturing industry learn from the successful development of DLP technology, air bag accelerometers and inkjet printheads? This paper discusses some basic reliability principles which any MOEMS/MEMS device development must use. Examples from the commercially successful and highly reliable Digital Micromirror Device complement the discussion.

  8. A Musical instrument in MEMS

    NARCIS (Netherlands)

    Engelen, Johannes Bernardus Charles; de Boer, Hans L.; de Boer, H.; Beekman, J.G.; Been, A.J.; Folkertsma, Gerrit Adriaan; Folkertsma, G.A.; Fortgens, L.; de Graaf, D.; Vocke, S.; Woldering, L.A.; Abelmann, Leon; Elwenspoek, Michael Curt

    In this work we describe a MEMS instrument that resonates at audible frequencies, and with which music can be made. The sounds are generated by mechanical resonators and capacitive displacement sensors. Damping by air scales unfavourably for generating audible frequencies with small devices.

  9. HARM processing techniques for MEMS and MOEMS devices using bonded SOI substrates and DRIE

    Science.gov (United States)

    Gormley, Colin; Boyle, Anne; Srigengan, Viji; Blackstone, Scott C.

    2000-08-01

    Silicon-on-Insulator (SOI) MEMS devices (1) are rapidly gaining popularity in realizing numerous solutions for MEMS, especially in the optical and inertia application fields. BCO recently developed a DRIE trench etch, utilizing the Bosch process, and refill process for high voltage dielectric isolation integrated circuits on thick SOI substrates. In this paper we present our most recently developed DRIE processes for MEMS and MOEMS devices. These advanced etch techniques are initially described and their integration with silicon bonding demonstrated. This has enabled process flows that are currently being utilized to develop optical router and filter products for fiber optics telecommunications and high precision accelerometers.

  10. Miniaturized Rotary Actuators Using Shape Memory Alloy for Insect-Type MEMS Microrobot

    Directory of Open Access Journals (Sweden)

    Ken Saito

    2016-03-01

    Full Text Available Although several types of locomotive microrobots have been developed, most of them have difficulty locomoting on uneven surfaces. Thus, we have been focused on microrobots that can locomote using step patterns. We are studying insect-type microrobot systems. The locomotion of the microrobot is generated by rotational movements of the shape memory alloy-type rotary actuator. In addition, we have constructed artificial neural networks by using analog integrated circuit (IC technology. The artificial neural networks can output the driving waveform without using software programs. The shape memory alloy-type rotary actuator and the artificial neural networks are constructed with silicon wafers; they can be integrated by using micro-electromechanical system (MEMS technology. As a result, the MEMS microrobot system can locomote using step patterns. The insect-type MEMS microrobot system is 0.079 g in weight and less than 5.0 mm in size, and its locomotion speed is 2 mm/min. The locomotion speed is slow because the heat of the shape memory alloy conducts to the mechanical parts of the MEMS microrobot. In this paper, we discuss a new rotary actuator compared with the previous model and show the continuous rotation of the proposed rotary actuator.

  11. MEMS Integrated Submount Alignment for Optoelectronics

    Science.gov (United States)

    Shakespeare, W. Jeffrey; Pearson, Raymond A.; Grenestedt, Joachim L.; Hutapea, Parsaoran; Gupta, Vikas

    2005-02-01

    One of the most expensive and time-consuming production processes for single-mode fiber-optic components is the alignment of the photonic chip or waveguide to the fiber. The alignment equipment is capital intensive and usually requires trained technicians to achieve desired results. Current technology requires active alignment since tolerances are only ~0.2 μ m or less for a typical laser diode. This is accomplished using piezoelectric actuated stages and active optical feedback. Joining technologies such as soldering, epoxy bonding, or laser welding may contribute significant postbond shift, and final coupling efficiencies are often less than 80%. This paper presents a method of adaptive optical alignment to freeze in place directly on an optical submount using a microelectromechanical system (MEMS) shape memory alloy (SMA) actuation technology. Postbond shift is eliminated since the phase change is the alignment actuation. This technology is not limited to optical alignment but can be applied to a variety of MEMS actuations, including nano-actuation and nano-alignment for biomedical applications. Experimental proof-of-concept results are discussed, and a simple analytical model is proposed to predict the stress strain behavior of the optical submount. Optical coupling efficiencies and alignment times are compared with traditional processes. The feasibility of this technique in high-volume production is discussed.

  12. Frequency adjustable MEMS vibration energy harvester

    Science.gov (United States)

    Podder, P.; Constantinou, P.; Amann, A.; Roy, S.

    2016-10-01

    Ambient mechanical vibrations offer an attractive solution for powering the wireless sensor nodes of the emerging “Internet-of-Things”. However, the wide-ranging variability of the ambient vibration frequencies pose a significant challenge to the efficient transduction of vibration into usable electrical energy. This work reports the development of a MEMS electromagnetic vibration energy harvester where the resonance frequency of the oscillator can be adjusted or tuned to adapt to the ambient vibrational frequency. Micro-fabricated silicon spring and double layer planar micro-coils along with sintered NdFeB micro-magnets are used to construct the electromagnetic transduction mechanism. Furthermore, another NdFeB magnet is adjustably assembled to induce variable magnetic interaction with the transducing magnet, leading to significant change in the spring stiffness and resonance frequency. Finite element analysis and numerical simulations exhibit substantial frequency tuning range (25% of natural resonance frequency) by appropriate adjustment of the repulsive magnetic interaction between the tuning and transducing magnet pair. This demonstrated method of frequency adjustment or tuning have potential applications in other MEMS vibration energy harvesters and micromechanical oscillators.

  13. Piezoelectric Zinc Oxide Based MEMS Acoustic Sensor

    Directory of Open Access Journals (Sweden)

    Aarti Arora

    2008-04-01

    Full Text Available An acoustic sensors exhibiting good sensitivity was fabricated using MEMS technology having piezoelectric zinc oxide as a dielectric between two plates of capacitor. Thin film zinc oxide has structural, piezoelectric and optical properties for surface acoustic wave (SAW and bulk acoustic wave (BAW devices. Oxygen effficient films are transparent and insulating having wide applications for sensors and transducers. A rf sputtered piezoelectric ZnO layer transforms the mechanical deflection of a thin etched silicon diaphragm into a piezoelectric charge. For 25-micron thin diaphragm Si was etched in tetramethylammonium hydroxide solution using bulk micromachining. This was followed by deposition of sandwiched structure composed of bottom aluminum electrode, sputtered 3 micron ZnO film and top aluminum electrode. A glass having 1 mm diameter hole was bonded on backside of device to compensate sound pressure in side the cavity. The measured value of central capacitance and dissipation factor of the fabricated MEMS acoustic sensor was found to be 82.4pF and 0.115 respectively, where as the value of ~176 pF was obtained for the rim capacitance with a dissipation factor of 0.138. The response of the acoustic sensors was reproducible for the devices prepared under similar processing conditions under different batches. The acoustic sensor was found to be working from 30Hz to 8KHz with a sensitivity of 139µV/Pa under varying acoustic pressure.

  14. Characterisation and Modelling of MEMS Ultrasonic Transducers

    International Nuclear Information System (INIS)

    Teng, M F; Hariz, A J

    2006-01-01

    Silicon ultrasonic transducer micro arrays based on micro-electro-mechanicalsystem (MEMS) technologies are gaining popularity for applications in sonar sensing and excitation. A current challenge for many researchers is modelling the dynamic performance of these and other micro-mechanical devices to ascertain their performance and explain experimental observations reported. In this work, the performance simulation of a MEMS ultrasonic transducer array made from silicon nitride has been successfully carried out using CoventorWare package. The dynamic response of the entire transducer array was characterised, and the results were compared with theoretical predictions. Individual elements were found to vibrate with Bessel-like displacement patterns, and they were resonant at approximately 3 MHz, depending on thickness and lateral dimensions. The frequency shows a linear dependence around the common thickness of 2 μm. Peak displacement levels were examined as a function of frequency, DC bias voltage, and AC drive voltage. Accounting for fabrication variations, and uniformity variations across the wafer, the full array showed minimal variations in peak out-of-plane displacement levels across the device, and isolated elements that were over-responsive and under-responsive. Presently, the effect of observed variations across the array on the performance of the transducers and their radiated fields are being examined

  15. Frequency adjustable MEMS vibration energy harvester

    International Nuclear Information System (INIS)

    Podder, P; Constantinou, P; Roy, S; Amann, A

    2016-01-01

    Ambient mechanical vibrations offer an attractive solution for powering the wireless sensor nodes of the emerging “Internet-of-Things”. However, the wide-ranging variability of the ambient vibration frequencies pose a significant challenge to the efficient transduction of vibration into usable electrical energy. This work reports the development of a MEMS electromagnetic vibration energy harvester where the resonance frequency of the oscillator can be adjusted or tuned to adapt to the ambient vibrational frequency. Micro-fabricated silicon spring and double layer planar micro-coils along with sintered NdFeB micro-magnets are used to construct the electromagnetic transduction mechanism. Furthermore, another NdFeB magnet is adjustably assembled to induce variable magnetic interaction with the transducing magnet, leading to significant change in the spring stiffness and resonance frequency. Finite element analysis and numerical simulations exhibit substantial frequency tuning range (25% of natural resonance frequency) by appropriate adjustment of the repulsive magnetic interaction between the tuning and transducing magnet pair. This demonstrated method of frequency adjustment or tuning have potential applications in other MEMS vibration energy harvesters and micromechanical oscillators. (paper)

  16. Application of neural network technology to nuclear plant thermal efficiency improvement

    International Nuclear Information System (INIS)

    Doremus, Rick; Allen Ho, S.; Bailey, James V.; Roman, Harry

    2004-01-01

    to determine which plant systems and components should be manipulated to increase electric output. Each parameter from the current data is replaced, one at a time, with an optimum value found among all the data used to train the neural networks. Each new data record, with one altered parameter, is then processed through the neural network, which calculates a predicted electric output These predictions are then ranked by order of increased electric output. Thermal Performance Engineers then review and perform selected recommendations An initial feasibility study was recently completed. The next phase of the project is the creation of a demonstration application to be tested at Salem Nuclear Generating Station. (author)

  17. Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

    Directory of Open Access Journals (Sweden)

    Melike Bildirici

    2014-01-01

    Full Text Available The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100. Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray’s MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray’s MS-GARCH model. Therefore, the models are promising for various economic applications.

  18. Gait Phases Recognition from Accelerations and Ground Reaction Forces: Application of Neural Networks

    Directory of Open Access Journals (Sweden)

    S. Rafajlović

    2009-06-01

    Full Text Available The goal of this study was to test the applicability of accelerometer as the sensor for assessment of the walking. We present here the comparison of gait phases detected from the data recorded by force sensing resistors mounted in the shoe insoles, non-processed acceleration and processed acceleration perpendicular to the direction of the foot. The gait phases in all three cases were detected by means of a neural network. The output from the neural network was the gait phase, while the inputs were data from the sensors. The results show that the errors were in the ranges: 30 ms (2.7% – force sensors; 150 ms (13.6% – nonprocessed acceleration, and 120 ms (11% – processed acceleration data. This result suggests that it is possible to use the accelerometer as the gait phase detector, however, with the knowledge that the gait phases are time shifted for about 100 ms with respect the neural network predicted times.

  19. Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Krzysztof Gajowniczek

    2018-04-01

    Full Text Available Artificial neural networks are currently one of the most commonly used classifiers and over the recent years they have been successfully used in many practical applications, including banking and finance, health and medicine, engineering and manufacturing. A large number of error functions have been proposed in the literature to achieve a better predictive power. However, only a few works employ Tsallis statistics, although the method itself has been successfully applied in other machine learning techniques. This paper undertakes the effort to examine the q -generalized function based on Tsallis statistics as an alternative error measure in neural networks. In order to validate different performance aspects of the proposed function and to enable identification of its strengths and weaknesses the extensive simulation was prepared based on the artificial benchmarking dataset. The results indicate that Tsallis entropy error function can be successfully introduced in the neural networks yielding satisfactory results and handling with class imbalance, noise in data or use of non-informative predictors.

  20. U.S. Army Corrosion Office's storage and quality requirements for military MEMS program

    Science.gov (United States)

    Zunino, J. L., III; Skelton, D. R.

    2007-04-01

    As the Army transforms into a more lethal, lighter and agile force, the technologies that support these systems must decrease in size while increasing in intelligence. Micro-electromechanical systems (MEMS) are one such technology that the Army and DOD will rely on heavily to accomplish these objectives. Conditions for utilization of MEMS by the military are unique. Operational and storage environments for the military are significantly different than those found in the commercial sector. Issues unique to the military include; high G-forces during gun launch, extreme temperature and humidity ranges, extended periods of inactivity (20 years plus) and interaction with explosives and propellants. The military operational environments in which MEMS will be stored or required to function are extreme and far surpass any commercial operating conditions. Security and encryption are a must for all MEMS communication, tracking, or data reporting devices employed by the military. Current and future military applications of MEMS devices include safety and arming devices, fuzing devices, various guidance systems, sensors/detectors, inertial measurement units, tracking devices, radio frequency devices, wireless Radio Frequency Identifications (RFIDs) and network systems, GPS's, radar systems, mobile base systems and information technology. MEMS embedded into these weapons systems will provide the military with new levels of speed, awareness, lethality, and information dissemination. The system capabilities enhanced by MEMS will translate directly into tactical and strategic military advantages.

  1. Feasibility of Frequency-Modulated Wireless Transmission for a Multi-Purpose MEMS-Based Accelerometer

    Directory of Open Access Journals (Sweden)

    Alessandro Sabato

    2014-09-01

    Full Text Available Recent advances in the Micro Electro-Mechanical System (MEMS technology have made wireless MEMS accelerometers an attractive tool for Structural Health Monitoring (SHM of civil engineering structures. To date, sensors’ low sensitivity and accuracy—especially at very low frequencies—have imposed serious limitations for their application in monitoring large-sized structures. Conventionally, the MEMS sensor’s analog signals are converted to digital signals before radio-frequency (RF wireless transmission. The conversion can cause a low sensitivity to the important low-frequency and low-amplitude signals. To overcome this difficulty, the authors have developed a MEMS accelerometer system, which converts the sensor output voltage to a frequency-modulated signal before RF transmission. This is achieved by using a Voltage to Frequency Conversion (V/F instead of the conventional Analog to Digital Conversion (ADC. In this paper, a prototype MEMS accelerometer system is presented, which consists of a transmitter and receiver circuit boards. The former is equipped with a MEMS accelerometer, a V/F converter and a wireless RF transmitter, while the latter contains an RF receiver and a F/V converter for demodulating the signal. The efficacy of the MEMS accelerometer system in measuring low-frequency and low-amplitude dynamic responses is demonstrated through extensive laboratory tests and experiments on a flow-loop pipeline.

  2. Feasibility of frequency-modulated wireless transmission for a multi-purpose MEMS-based accelerometer.

    Science.gov (United States)

    Sabato, Alessandro; Feng, Maria Q

    2014-09-05

    Recent advances in the Micro Electro-Mechanical System (MEMS) technology have made wireless MEMS accelerometers an attractive tool for Structural Health Monitoring (SHM) of civil engineering structures. To date, sensors' low sensitivity and accuracy--especially at very low frequencies--have imposed serious limitations for their application in monitoring large-sized structures. Conventionally, the MEMS sensor's analog signals are converted to digital signals before radio-frequency (RF) wireless transmission. The conversion can cause a low sensitivity to the important low-frequency and low-amplitude signals. To overcome this difficulty, the authors have developed a MEMS accelerometer system, which converts the sensor output voltage to a frequency-modulated signal before RF transmission. This is achieved by using a Voltage to Frequency Conversion (V/F) instead of the conventional Analog to Digital Conversion (ADC). In this paper, a prototype MEMS accelerometer system is presented, which consists of a transmitter and receiver circuit boards. The former is equipped with a MEMS accelerometer, a V/F converter and a wireless RF transmitter, while the latter contains an RF receiver and a F/V converter for demodulating the signal. The efficacy of the MEMS accelerometer system in measuring low-frequency and low-amplitude dynamic responses is demonstrated through extensive laboratory tests and experiments on a flow-loop pipeline.

  3. A design philosophy for multi-layer neural networks with applications to robot control

    Science.gov (United States)

    Vadiee, Nader; Jamshidi, MO

    1989-01-01

    A system is proposed which receives input information from many sensors that may have diverse scaling, dimension, and data representations. The proposed system tolerates sensory information with faults. The proposed self-adaptive processing technique has great promise in integrating the techniques of artificial intelligence and neural networks in an attempt to build a more intelligent computing environment. The proposed architecture can provide a detailed decision tree based on the input information, information stored in a long-term memory, and the adapted rule-based knowledge. A mathematical model for analysis will be obtained to validate the cited hypotheses. An extensive software program will be developed to simulate a typical example of pattern recognition problem. It is shown that the proposed model displays attention, expectation, spatio-temporal, and predictory behavior which are specific to the human brain. The anticipated results of this research project are: (1) creation of a new dynamic neural network structure, and (2) applications to and comparison with conventional multi-layer neural network structures. The anticipated benefits from this research are vast. The model can be used in a neuro-computer architecture as a building block which can perform complicated, nonlinear, time-varying mapping from a multitude of input excitory classes to an output or decision environment. It can be used for coordinating different sensory inputs and past experience of a dynamic system and actuating signals. The commercial applications of this project can be the creation of a special-purpose neuro-computer hardware which can be used in spatio-temporal pattern recognitions in such areas as air defense systems, e.g., target tracking, and recognition. Potential robotics-related applications are trajectory planning, inverse dynamics computations, hierarchical control, task-oriented control, and collision avoidance.

  4. Parallelization of learning problems by artificial neural networks. Application in external radiotherapy

    International Nuclear Information System (INIS)

    Sauget, M.

    2007-12-01

    This research is about the application of neural networks used in the external radiotherapy domain. The goal is to elaborate a new evaluating system for the radiation dose distributions in heterogeneous environments. The al objective of this work is to build a complete tool kit to evaluate the optimal treatment planning. My st research point is about the conception of an incremental learning algorithm. The interest of my work is to combine different optimizations specialized in the function interpolation and to propose a new algorithm allowing to change the neural network architecture during the learning phase. This algorithm allows to minimise the al size of the neural network while keeping a good accuracy. The second part of my research is to parallelize the previous incremental learning algorithm. The goal of that work is to increase the speed of the learning step as well as the size of the learned dataset needed in a clinical case. For that, our incremental learning algorithm presents an original data decomposition with overlapping, together with a fault tolerance mechanism. My last research point is about a fast and accurate algorithm computing the radiation dose deposit in any heterogeneous environment. At the present time, the existing solutions used are not optimal. The fast solution are not accurate and do not give an optimal treatment planning. On the other hand, the accurate solutions are far too slow to be used in a clinical context. Our algorithm answers to this problem by bringing rapidity and accuracy. The concept is to use a neural network adequately learned together with a mechanism taking into account the environment changes. The advantages of this algorithm is to avoid the use of a complex physical code while keeping a good accuracy and reasonable computation times. (author)

  5. The application of neural network techniques to magnetic and optical inverse problems

    International Nuclear Information System (INIS)

    Jones, H.V.

    2000-12-01

    The processing power of the computer has increased at unimaginable rates over the last few decades. However, even today's fastest computer can take several hours to find solutions to some mathematical problems; and there are instances where a high powered supercomputer may be impractical, with the need for near instant solutions just as important (such as in an on-line testing system). This led us to believe that such complex problems could be solved using a novel approach, whereby the system would have prior knowledge about the expected solutions through a process of learning. One method of approaching this kind of problem is through the use of machine learning. Just as a human can be trained and is able to learn from past experiences, a machine is can do just the same. This is the concept of neural networks. The research which was conducted involves the investigation of various neural network techniques, and their applicability to solve some known complex inverse problems in the field of magnetic and optical recording. In some cases a comparison is also made to more conventional methods of solving the problems, from which it was possible to outline some key advantages of using a neural network approach. We initially investigated the application of neural networks to transverse susceptibility data in order to determine anisotropy distributions. This area of research is proving to be very important, as it gives us information about the switching field distribution, which then determines the minimum transition width achievable in a medium, and affects the overwrite characteristics of the media. Secondly, we investigated a similar situation, but applied to an optical problem. This involved the determination of important compact disc parameters from the diffraction pattern of a laser from a disc. This technique was then intended for use in an on-line testing system. Finally we investigated another area of neural networks with the analysis of magnetisation maps and

  6. Application of artificial neural network with extreme learning machine for economic growth estimation

    Science.gov (United States)

    Milačić, Ljubiša; Jović, Srđan; Vujović, Tanja; Miljković, Jovica

    2017-01-01

    The purpose of this research is to develop and apply the artificial neural network (ANN) with extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. The economic growth forecasting was analyzed based on agriculture, manufacturing, industry and services value added in GDP. The results were compared with ANN with back propagation (BP) learning approach since BP could be considered as conventional learning methodology. The reliability of the computational models was accessed based on simulation results and using several statistical indicators. Based on results, it was shown that ANN with ELM learning methodology can be applied effectively in applications of GDP forecasting.

  7. Radial basis function neural networks with sequential learning MRAN and its applications

    CERN Document Server

    Sundararajan, N; Wei Lu Ying

    1999-01-01

    This book presents in detail the newly developed sequential learning algorithm for radial basis function neural networks, which realizes a minimal network. This algorithm, created by the authors, is referred to as Minimal Resource Allocation Networks (MRAN). The book describes the application of MRAN in different areas, including pattern recognition, time series prediction, system identification, control, communication and signal processing. Benchmark problems from these areas have been studied, and MRAN is compared with other algorithms. In order to make the book self-contained, a review of t

  8. The application of neural networks for optimization of the configuration of fuel assemblies in PWR reactors

    International Nuclear Information System (INIS)

    Sadighi, M.; Setayeshi, S.; Salehi, A.A.

    2002-01-01

    This paper presents a new method to solve the problem of finding the best configuration of fuel assemblies in a PWR (Pressurized Water Reactor) core. Finding an optimum solution requires a huge amount of calculations in classical methods. It has been shown that the application of continuous Hop field neural network accompanied by the Simulated Annealing method to this problem not only reduces the volume of the calculations, but also guarantees finding the best solution. In this study flattening of neutron flux inside the reactor core of Brusher NPP is considered as an objective function. The result shows the optimum core configuration which is in agreement with the pattern proposed by the designer

  9. Fuzzy logic and artificial neural networks for nuclear power plant applications

    International Nuclear Information System (INIS)

    Berkan, R.C.; Eryurek, E.; Upadhyaya, B.R.

    1992-01-01

    This paper discusses the feasibility of applying fuzzy logic and neural networks to plant-wide monitoring, diagnostics, and control problems. Different data sets are gathered from several sources including two commercial Pressurized Water Reactors (PWR), the Experimental Breeder Reactor-II (EBR-II), and the conceptual design of Modular Liquid-Metal Reactor (PRISM). These data sets are used to illustrate applications to operating processes, and to PRISM design. The results show that the artificial intelligence approach to a number of operational tasks can considerably improve the safety and availability of nuclear power generation

  10. Fast tunable blazed MEMS grating for external cavity lasers

    Science.gov (United States)

    Tormen, Maurizio; Niedermann, Philippe; Hoogerwerf, Arno; Shea, Herbert; Stanley, Ross

    2017-11-01

    Diffractive MEMS are interesting for a wide range of applications, including displays, scanners or switching elements. Their advantages are compactness, potentially high actuation speed and in the ability to deflect light at large angles. We have designed and fabricated deformable diffractive MEMS grating to be used as tuning elements for external cavity lasers. The resulting device is compact, has wide tunability and a high operating speed. The initial design is a planar grating where the beams are free-standing and attached to each other using leaf springs. Actuation is achieved through two electrostatic comb drives at either end of the grating. To prevent deformation of the free-standing grating, the device is 10 μm thick made from a Silicon on Insulator (SOI) wafer in a single mask process. At 100V a periodicity tuning of 3% has been measured. The first resonant mode of the grating is measured at 13.8 kHz, allowing high speed actuation. This combination of wide tunability and high operating speed represents state of the art in the domain of tunable MEMS filters. In order to improve diffraction efficiency and to expand the usable wavelength range, a blazed version of the deformable MEMS grating has been designed. A key issue is maintaining the mechanical properties of the original device while providing optically smooth blazed beams. Using a process based on anisotropic KOH etching, blazed gratings have been obtained and preliminary characterization is promising.

  11. Modeling of MEMS Mirrors Actuated by Phase-Change Mechanism

    Directory of Open Access Journals (Sweden)

    David Torres

    2017-04-01

    Full Text Available Given the multiple applications for micro-electro-mechanical system (MEMS mirror devices, most of the research efforts are focused on improving device performance in terms of tilting angles, speed, and their integration into larger arrays or systems. The modeling of these devices is crucial for enabling a platform, in particular, by allowing for the future control of such devices. In this paper, we present the modeling of a MEMS mirror structure with four actuators driven by the phase-change of a thin film. The complexity of the device structure and the nonlinear behavior of the actuation mechanism allow for a comprehensive study that encompasses simpler electrothermal designs, thus presenting a general approach that can be adapted to most MEMS mirror designs based on this operation principle. The MEMS mirrors presented in this work are actuated by Joule heating and tested using optical techniques. Mechanical and thermal models including both pitch and roll displacements are developed by combining theoretical analysis (using both numerical and analytical tools with experimental data and subsequently verifying with quasi-static and dynamic experiments.

  12. A Widely-Accessible Distributed MEMS Processing Environment. The MEMS Exchange Program

    Science.gov (United States)

    2012-10-29

    all of these patterns in advance, we made a new cost model, called the Python Code cost model, which utilizes the power of a high level programming ...document entitled “The Beginners Guide to MEMS Processing” on the MEMSNet and MEMS Exchange The MEMS Exchange Program Final Technical Report October 29...from the Government is absolutely necessary. As said The MEMS Exchange Program Final Technical Report October 29, 2012 Page 57 of 58 before

  13. Application of a hybrid model of neural networks and genetic algorithms to evaluate landslide susceptibility

    Science.gov (United States)

    Wang, H. B.; Li, J. W.; Zhou, B.; Yuan, Z. Q.; Chen, Y. P.

    2013-03-01

    is 93.02%, whereas units without landslide occurrence are predicted with an accuracy of 81.13%. To sum up, the verification shows satisfactory agreement with an accuracy of 86.46% between the susceptibility map and the landslide locations. In the landslide susceptibility assessment, ten new slopes were predicted to show potential for failure, which can be confirmed by the engineering geological conditions of these slopes. It was also observed that some disadvantages could be overcome in the application of the neural networks with back propagation, for example, the low convergence rate and local minimum, after the network was optimized using genetic algorithms. To conclude, neural networks with back propagation that are optimized by genetic algorithms are an effective method to predict landslide susceptibility with high accuracy.

  14. Improving Pattern Recognition and Neural Network Algorithms with Applications to Solar Panel Energy Optimization

    Science.gov (United States)

    Zamora Ramos, Ernesto

    Artificial Intelligence is a big part of automation and with today's technological advances, artificial intelligence has taken great strides towards positioning itself as the technology of the future to control, enhance and perfect automation. Computer vision includes pattern recognition and classification and machine learning. Computer vision is at the core of decision making and it is a vast and fruitful branch of artificial intelligence. In this work, we expose novel algorithms and techniques built upon existing technologies to improve pattern recognition and neural network training, initially motivated by a multidisciplinary effort to build a robot that helps maintain and optimize solar panel energy production. Our contributions detail an improved non-linear pre-processing technique to enhance poorly illuminated images based on modifications to the standard histogram equalization for an image. While the original motivation was to improve nocturnal navigation, the results have applications in surveillance, search and rescue, medical imaging enhancing, and many others. We created a vision system for precise camera distance positioning motivated to correctly locate the robot for capture of solar panel images for classification. The classification algorithm marks solar panels as clean or dirty for later processing. Our algorithm extends past image classification and, based on historical and experimental data, it identifies the optimal moment in which to perform maintenance on marked solar panels as to minimize the energy and profit loss. In order to improve upon the classification algorithm, we delved into feedforward neural networks because of their recent advancements, proven universal approximation and classification capabilities, and excellent recognition rates. We explore state-of-the-art neural network training techniques offering pointers and insights, culminating on the implementation of a complete library with support for modern deep learning architectures

  15. Wafer level packaging of MEMS

    International Nuclear Information System (INIS)

    Esashi, Masayoshi

    2008-01-01

    Wafer level packaging plays many important roles for MEMS (micro electro mechanical systems), including cost, yield and reliability. MEMS structures on silicon chips are encapsulated between bonded wafers or by surface micromachining, and electrical interconnections are made from the cavity. Bonding at the interface, such as glass–Si anodic bonding and metal-to-metal bonding, requires electrical interconnection through the lid vias in many cases. On the other hand, lateral electrical interconnections on the surface of the chip are used for bonding with intermediate melting materials, such as low melting point glass and solder. The cavity formed by surface micromachining is made using sacrificial etching, and the openings needed for the sacrificial etching are plugged using deposition sealing methods. Vacuum packaging methods and the structures for electrical feedthrough for the interconnection are discussed in this review. (topical review)

  16. Application of Artificial Neural Networks to Rainfall Forecasting in Queensland, Australia

    Institute of Scientific and Technical Information of China (English)

    John ABBOT; Jennifer MAROHASY

    2012-01-01

    In this study,the application of artificial intelligence to monthly and seasonal rainfall forecasting in Queensland,Australia,was assessed by inputting recognized climate indices,monthly historical rainfall data,and atmospheric temperatures into a prototype stand-alone,dynamic,recurrent,time-delay,artificial neural network.Outputs,as monthly rainfall forecasts 3 months in advance for the period 1993 to 2009,were compared with observed rainfall data using time-series plots,root mean squared error (RMSE),and Pearson correlation coefficients.A comparison of RMSE values with forecasts generated by the Australian Bureau of Meteorology's Predictive Ocean Atmosphere Model for Australia (POAMA)-1.5 general circulation model (GCM) indicated that the prototype achieved a lower RMSE for 16 of the 17 sites compared.The application of artificial neural networks to rainfall forecasting was reviewed.The prototype design is considered preliminary,with potential for significant improvement such as inclusion of output from GCMs and experimentation with other input attributes.

  17. Neural prostheses in clinical applications--trends from precision mechanics towards biomedical microsystems in neurological rehabilitation.

    Science.gov (United States)

    Stieglitz, T; Schuettler, M; Koch, K P

    2004-04-01

    Neural prostheses partially restore body functions by technical nerve excitation after trauma or neurological diseases. External devices and implants have been developed since the early 1960s for many applications. Several systems have reached nowadays clinical practice: Cochlea implants help the deaf to hear, micturition is induced by bladder stimulators in paralyzed persons and deep brain stimulation helps patients with Parkinson's disease to participate in daily life again. So far, clinical neural prostheses are fabricated with means of precision mechanics. Since microsystem technology opens the opportunity to design and develop complex systems with a high number of electrodes to interface with the nervous systems, the opportunity for selective stimulation and complex implant scenarios seems to be feasible in the near future. The potentials and limitations with regard to biomedical microdevices are introduced and discussed in this paper. Target specifications are derived from existing implants and are discussed on selected applications that has been investigated in experimental research: a micromachined implant to interface a nerve stump with a sieve electrode, cuff electrodes with integrated electronics, and an epiretinal vision prosthesis.

  18. Application of RBF neural network improved by peak density function in intelligent color matching of wood dyeing

    International Nuclear Information System (INIS)

    Guan, Xuemei; Zhu, Yuren; Song, Wenlong

    2016-01-01

    According to the characteristics of wood dyeing, we propose a predictive model of pigment formula for wood dyeing based on Radial Basis Function (RBF) neural network. In practical application, however, it is found that the number of neurons in the hidden layer of RBF neural network is difficult to determine. In general, we need to test several times according to experience and prior knowledge, which is lack of a strict design procedure on theoretical basis. And we also don’t know whether the RBF neural network is convergent. This paper proposes a peak density function to determine the number of neurons in the hidden layer. In contrast to existing approaches, the centers and the widths of the radial basis function are initialized by extracting the features of samples. So the uncertainty caused by random number when initializing the training parameters and the topology of RBF neural network is eliminated. The average relative error of the original RBF neural network is 1.55% in 158 epochs. However, the average relative error of the RBF neural network which is improved by peak density function is only 0.62% in 50 epochs. Therefore, the convergence rate and approximation precision of the RBF neural network are improved significantly.

  19. Prospects of application of artificial neural networks for forecasting of cargo transportation volume in transport systems

    Directory of Open Access Journals (Sweden)

    D. T. Yakupov

    2017-01-01

    Full Text Available The purpose of research – to identify the prospects for the use of neural network approach in relation to the tasks of economic forecasting of logistics performance, in particular of volume freight traffic in the transport system promiscuous regional freight traffic, as well as to substantiate the effectiveness of the use of artificial neural networks (ANN, as compared with the efficiency of traditional extrapolative methods of forecasting. The authors consider the possibility of forecasting to use ANN for these economic indicators not as an alternative to the traditional methods of statistical forecasting, but as one of the available simple means for solving complex problems.Materials and methods. When predicting the ANN, three methods of learning were used: 1 the Levenberg-Marquardt algorithm-network training stops when the generalization ceases to improve, which is shown by the increase in the mean square error of the output value; 2 Bayes regularization method - network training is stopped in accordance with the minimization of adaptive weights; 3 the method of scaled conjugate gradients, which is used to find the local extremum of a function on the basis of information about its values and gradient. The Neural Network Toolbox package is used for forecasting. The neural network model consists of a hidden layer of neurons with a sigmoidal activation function and an output neuron with a linear activation function, the input values of the dynamic time series, and the predicted value is removed from the output. For a more objective assessment of the prospects of the ANN application, the results of the forecast are presented in comparison with the results obtained in predicting the method of exponential smoothing.Results. When predicting the volumes of freight transportation by rail, satisfactory indicators of the verification of forecasting by both the method of exponential smoothing and ANN had been obtained, although the neural network

  20. MEMS Bragg grating force sensor

    DEFF Research Database (Denmark)

    Reck, Kasper; Thomsen, Erik Vilain; Hansen, Ole

    2011-01-01

    We present modeling, design, fabrication and characterization of a new type of all-optical frequency modulated MEMS force sensor based on a mechanically amplified double clamped waveguide beam structure with integrated Bragg grating. The sensor is ideally suited for force measurements in harsh...... environments and for remote and distributed sensing and has a measured sensitivity of -14 nm/N, which is several times higher than what is obtained in conventional fiber Bragg grating force sensors. © 2011 Optical Society of America....

  1. MEMS Stirling Cooler Development Update

    Science.gov (United States)

    Moran, Matthew E.; Wesolek, Danielle

    2003-01-01

    This presentation provides an update on the effort to build and test a prototype unit of the patented MEMS Stirling cooler concept. A micro-scale regenerator has been fabricated by Polar Thermal Technologies and is currently being integrated into a Stirling cycle simulator at Johns Hopkins University Applied Physics Laboratory. A discussion of the analysis, design, assembly, and test plans for the prototype will be presented.

  2. Surface chemistry and tribology of MEMS.

    Science.gov (United States)

    Maboudian, Roya; Carraro, Carlo

    2004-01-01

    The microscopic length scale and high surface-to-volume ratio, characteristic of microelectro-mechanical systems (MEMS), dictate that surface properties are of paramount importance. This review deals with the effects of surface chemical treatments on tribological properties (adhesion, friction, and wear) of MEMS devices. After a brief review of materials and processes that are utilized in MEMS technology, the relevant tribological and chemical issues are discussed. Various MEMS microinstruments are discussed, which are commonly employed to perform adhesion, friction, and wear measurements. The effects of different surface treatments on the reported tribological properties are discussed.

  3. Synchronization of Markovian jumping inertial neural networks and its applications in image encryption.

    Science.gov (United States)

    Prakash, M; Balasubramaniam, P; Lakshmanan, S

    2016-11-01

    This study is mainly concerned with the problem on synchronization criteria for Markovian jumping time delayed bidirectional associative memory neural networks and their applications in secure image communications. Based on the variable transformation method, the addressed second order differential equations are transformed into first order differential equations. Then, by constructing a suitable Lyapunov-Krasovskii functional and based on integral inequalities, the criteria which ensure the synchronization between the uncontrolled system and controlled system are established through designed feedback controllers and linear matrix inequalities. Further, the proposed results proved that the error system is globally asymptotically stable in the mean square. Moreover, numerical illustrations are provided to validate the effectiveness of the derived analytical results. Finally, the application of addressed system is explored via image encryption/decryption process. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Applications of artificial neural networks for thermal analysis of heat exchangers - A review

    International Nuclear Information System (INIS)

    Mohanraj, M.; Jayaraj, S.; Muraleedharan, C.

    2015-01-01

    Artificial neural networks (ANN) have been widely used for thermal analysis of heat exchangers during the last two decades. In this paper, the applications of ANN for thermal analysis of heat exchangers are reviewed. The reported investigations on thermal analysis of heat exchangers are categorized into four major groups, namely (i) modeling of heat exchangers, (ii) estimation of heat exchanger parameters, (iii) estimation of phase change characteristics in heat exchangers and (iv) control of heat exchangers. Most of the papers related to the applications of ANN for thermal analysis of heat exchangers are discussed. The limitations of ANN for thermal analysis of heat exchangers and its further research needs in this field are highlighted. ANN is gaining popularity as a tool, which can be successfully used for the thermal analysis of heat exchangers with acceptable accuracy. (authors)

  5. Engineering applications of fpgas chaotic systems, artificial neural networks, random number generators, and secure communication systems

    CERN Document Server

    Tlelo-Cuautle, Esteban; de la Fraga, Luis Gerardo

    2016-01-01

    This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. Readers will b...

  6. Feedback Control of MEMS to Atoms

    CERN Document Server

    Shapiro, Benjamin

    2012-01-01

    Feedback Control of MEMS to Atoms illustrates the use of control and control systems as an essential part of functioning integrated miniaturized systems. The book is organized according to the dimensional scale of the problem, starting with microscale systems and ending with atomic-scale systems. Similar to macroscale machines and processes, control systems can play a major role in improving the performance of micro- and nanoscale systems and in enabling new capabilities that would otherwise not be possible. The majority of problems at these scales present many new challenges that go beyond the current state-of-the-art in control theory and engineering. This is a result of the multidisciplinary nature of micro/nanotechnology, which requires the merging of control engineering with physics, biology and chemistry. This book: Shows how the utilization of feedback control in nanotechnology instrumentation can yield results far better than passive systems can Discusses the application of control systems to problems...

  7. Application of neural networks to the petroleum refining industry; Aplicando redes neurais a industria de refino de petroleo

    Energy Technology Data Exchange (ETDEWEB)

    Silva, R M.C.F. da [PETROBRAS, Rio de Janeiro, RJ (Brazil); Chaves, C. [Fundacao Gorceix, Belo Horizonte, MG (Brazil)

    2000-07-01

    Neural Network technology is an approach for describing behavior from process data, using mathematical algorithms and statistical techniques. The use of Neural Network in industrial process modeling and property estimation of feedstocks or products, is increasing in several kinds of chemical industries. This paper comments about critical successful factors, advantages and disadvantages of this methodology. Moreover, it presents some applications in Hydrotreating Process of the petroleum refining industry. In Hydrotreating of feedstocks, knowledge about characteristics of process regarding product property estimation, hydrogen consumption and removal of contaminants (sulfur, nitrogen, aromatics), are very important to process optimization, product specification and environment protection. The Neural Network technique has been used to model the behaviour of the chemical hydrogen consumption, the conversions of the hydrogenation of aromatic hydrocarbons, hydrodesulfurization and hydro denitrogenation reactions and the physical properties of product with operational conditions and feedstock properties. In addition, Neural Networks have been built to predict the cetane number of feedstocks. (author)

  8. A MEMS AC current sensor for residential and commercial electricity end-use monitoring

    International Nuclear Information System (INIS)

    Leland, E S; Wright, P K; White, R M

    2009-01-01

    This paper presents a novel prototype MEMS sensor for alternating current designed for monitoring electricity end-use in residential and commercial environments. This new current sensor design is comprised of a piezoelectric MEMS cantilever with a permanent magnet mounted on the cantilever's free end. When placed near a wire carrying AC current, the magnet is driven sinusoidally, producing a voltage in the cantilever proportional to the current being measured. Analytical models were developed to predict the applicable magnetic forces and piezoelectric voltage output in order to guide the design of a sensor prototype. This paper also details the fabrication process for this sensor design. Released piezoelectric MEMS cantilevers have been fabricated using a four-mask process and aluminum nitride as the active piezoelectric material. Dispenser-printed microscale composite permanent magnets have been integrated, resulting in the first MEMS-scale prototypes of this current sensor design

  9. Thermal Desalination using MEMS and Salinity-Gradient Solar Pond Technology

    Science.gov (United States)

    Lu, H.; Walton, J. C.; Hein, H.

    2002-08-01

    MEMS (multi-effect, multi-stage) flash desalination (distillation) driven by thermal energy derived from a salinity-gradient solar pond is investigated in this study for the purpose of improving the thermodynamic efficiency and economics of this technology. Three major tasks are performed: (1) a MEMS unit is tested under various operating conditions at the El Paso Solar Pond site; (2) the operation and maintenance procedures of the salinity-gradient solar pond coupled with the MEMS operation is studied; and (3) previous test data on a 24-stage, falling-film flash distillation unit (known as the Spinflash) is analyzed and compared with the performance of the MEMS unit. The data and information obtained from this investigation is applicable to a variety of thermal desalination processes using other solar options and/or waste heat.

  10. Department of Defense need for a micro-electromechanical systems (MEMS) reliability assessment program

    Science.gov (United States)

    Zunino, James L., III; Skelton, Donald

    2005-01-01

    As the United States (U.S.) Army transforms into a lighter, more lethal, and more agile force, the technologies that support both legacy and emerging weapon systems must decrease in size while increasing in intelligence. Micro-electromechanical systems (MEMS) are one such technology that the Army as well as entire DOD will heavily rely on in achieving these objectives. Current and future military applications of MEMS devices include safety and arming devices, guidance systems, sensors/detectors, inertial measurement units, tracking devices, radio frequency devices, wireless radio frequency identification (RFID), etc. Even though the reliance on MEMS devices has been increasing, there have been no studies performed to determine their reliability and failure mechanisms. Furthermore, no standardized test protocols exist for assessing reliability. Accordingly, the U.S. Army Corrosion Office at Picatinny, NJ has initiated the MEMS Reliability Assessment Program to address this issue.

  11. Large-area multiplexed sensing using MEMS and fiber optics

    Science.gov (United States)

    Miller, Michael B.; Clark, Richard L., Jr.; Bell, Clifton R.; Russler, Patrick M.

    2000-06-01

    Micro-electro-mechanical (MEMS) technology offers the ability to implement local and independent sensing and actuation functions through the coordinated response of discrete micro-electro-mechanical 'basis function' elements. The small size of micromechanical components coupled with the ability to reduce costs using volume manufacturing techniques opens up significant potential not only in military applications such as flight and engine monitoring and control, but in autonomous vehicle control, smart munitions, airborne reconnaissance, LADAR, missile guidance, and even in intelligent transportation systems and automotive guidance applications. In this program, Luna Innovations is developing a flexible, programmable interface which can be integrated direction with different types of MEMS sensors, and then used to multiplex many sensors ona single optical fiber to provide a unique combination of functions that will allow larger quantities of sensory input with better resolution than ever before possible.

  12. An improved superconducting neural circuit and its application for a neural network solving a combinatorial optimization problem

    International Nuclear Information System (INIS)

    Onomi, T; Nakajima, K

    2014-01-01

    We have proposed a superconducting Hopfield-type neural network for solving the N-Queens problem which is one of combinatorial optimization problems. The sigmoid-shape function of a neuron output is represented by the output of coupled SQUIDs gate consisting of a single-junction and a double-junction SQUIDs. One of the important factors for an improvement of the network performance is an improvement of a threshold characteristic of a neuron circuit. In this paper, we report an improved design of coupled SQUID gates for a superconducting neural network. A step-like function with a steep threshold at a rising edge is desirable for a neuron circuit to solve a combinatorial optimization problem. A neuron circuit is composed of two coupled SQUIDs gates with a cascade connection in order to obtain such characteristics. The designed neuron circuit is fabricated by a 2.5 kA/cm 2 Nb/AlOx/Nb process. The operation of a fabricated neuron circuit is experimentally demonstrated. Moreover, we discuss about the performance of the neural network using the improved neuron circuits and delayed negative self-connections.

  13. Use of thermal cycling to reduce adhesion of OTS coated coated MEMS cantilevers

    Science.gov (United States)

    Ali, Shaikh M.; Phinney, Leslie M.

    2003-01-01

    °Microelectromechanical systems (MEMS) have enormous potential to contribute in diverse fields such as automotive, health care, aerospace, consumer products, and biotechnology, but successful commercial applications of MEMS are still small in number. Reliability of MEMS is a major impediment to the commercialization of laboratory prototypes. Due to the multitude of MEMS applications and the numerous processing and packaging steps, MEMS are exposed to a variety of environmental conditions, making the prediction of operational reliability difficult. In this paper, we investigate the effects of operating temperature on the in-use adhesive failure of electrostatically actuated MEMS microcantilevers coated with octadecyltrichlorosilane (OTS) films. The cantilevers are subjected to repeated temperature cycles and electrostatically actuated at temperatures between 25°C and 300°C in ambient air. The experimental results indicate that temperature cycling of the OTS coated cantilevers in air reduces the sticking probability of the microcantilevers. The sticking probability of OTS coated cantilevers was highest during heating, which decreased during cooling, and was lowest during reheating. Modifications to the OTS release method to increase its yield are also discussed.

  14. Refining mass formulas for astrophysical applications: A Bayesian neural network approach

    Science.gov (United States)

    Utama, R.; Piekarewicz, J.

    2017-10-01

    Background: Exotic nuclei, particularly those near the drip lines, are at the core of one of the fundamental questions driving nuclear structure and astrophysics today: What are the limits of nuclear binding? Exotic nuclei play a critical role in both informing theoretical models as well as in our understanding of the origin of the heavy elements. Purpose: Our aim is to refine existing mass models through the training of an artificial neural network that will mitigate the large model discrepancies far away from stability. Methods: The basic paradigm of our two-pronged approach is an existing mass model that captures as much as possible of the underlying physics followed by the implementation of a Bayesian neural network (BNN) refinement to account for the missing physics. Bayesian inference is employed to determine the parameters of the neural network so that model predictions may be accompanied by theoretical uncertainties. Results: Despite the undeniable quality of the mass models adopted in this work, we observe a significant improvement (of about 40%) after the BNN refinement is implemented. Indeed, in the specific case of the Duflo-Zuker mass formula, we find that the rms deviation relative to experiment is reduced from σrms=0.503 MeV to σrms=0.286 MeV. These newly refined mass tables are used to map the neutron drip lines (or rather "drip bands") and to study a few critical r -process nuclei. Conclusions: The BNN approach is highly successful in refining the predictions of existing mass models. In particular, the large discrepancy displayed by the original "bare" models in regions where experimental data are unavailable is considerably quenched after the BNN refinement. This lends credence to our approach and has motivated us to publish refined mass tables that we trust will be helpful for future astrophysical applications.

  15. Design and evaluation of carbon nanofiber and silicon materials for neural implant applications

    Science.gov (United States)

    McKenzie, Janice L.

    Reduction of glial scar tissue around central nervous system implants is necessary for improved efficacy in chronic applications. Design of materials that possess tunable properties inspired by native biological tissue and elucidation of pertinent cellular interactions with these materials was the motivation for this study. Since nanoscale carbon fibers possess the fundamental dimensional similarities to biological tissue and have attractive material properties needed for neural biomaterial implants, this present study explored cytocompatibility of these materials as well as modifications to traditionally used silicon. On silicon materials, results indicated that nanoscale surface features reduced astrocyte functions, and could be used to guide neurite extension from PC12 cells. Similarly, it was determined that astrocyte functions (key cells in glial scar tissue formation) were reduced on smaller diameter carbon fibers (125 nm or less) while PC12 neurite extension was enhanced on smaller diameter carbon fibers (100 nm or less). Further studies implicated laminin adsorption as a key mechanism in enhancing astrocyte adhesion to larger diameter fibers and at the same time encouraging neurite extension on smaller diameter fibers. Polycarbonate urethane (PCU) was then used as a matrix material for the smaller diameter carbon fibers (100 and 60 nm). These composites proved very versatile since electrical and mechanical properties as well as cell functions and directionality could be influenced by changing bulk and surface composition and features of these matrices. When these composites were modified to be smooth at the micronscale and only rough at the nanoscale, P19 cells actually submerged philopodia, extensions, or whole cells bodies beneath the PCU in order to interact with the carbon nanofibers. These carbon nanofiber composites that have been formulated are a promising material to coat neural probes and thereby enhance functionality at the tissue interface. This

  16. Strong Motion Seismograph Based On MEMS Accelerometer

    Science.gov (United States)

    Teng, Y.; Hu, X.

    2013-12-01

    The MEMS strong motion seismograph we developed used the modularization method to design its software and hardware.It can fit various needs in different application situation.The hardware of the instrument is composed of a MEMS accelerometer,a control processor system,a data-storage system,a wired real-time data transmission system by IP network,a wireless data transmission module by 3G broadband,a GPS calibration module and power supply system with a large-volumn lithium battery in it. Among it,the seismograph's sensor adopted a three-axis with 14-bit high resolution and digital output MEMS accelerometer.Its noise level just reach about 99μg/√Hz and ×2g to ×8g dynamically selectable full-scale.Its output data rates from 1.56Hz to 800Hz. Its maximum current consumption is merely 165μA,and the device is so small that it is available in a 3mm×3mm×1mm QFN package. Furthermore,there is access to both low pass filtered data as well as high pass filtered data,which minimizes the data analysis required for earthquake signal detection. So,the data post-processing can be simplified. Controlling process system adopts a 32-bit low power consumption embedded ARM9 processor-S3C2440 and is based on the Linux operation system.The processor's operating clock at 400MHz.The controlling system's main memory is a 64MB SDRAM with a 256MB flash-memory.Besides,an external high-capacity SD card data memory can be easily added.So the system can meet the requirements for data acquisition,data processing,data transmission,data storage,and so on. Both wired and wireless network can satisfy remote real-time monitoring, data transmission,system maintenance,status monitoring or updating software.Linux was embedded and multi-layer designed conception was used.The code, including sensor hardware driver,the data acquisition,earthquake setting out and so on,was written on medium layer.The hardware driver consist of IIC-Bus interface driver, IO driver and asynchronous notification driver. The

  17. Programmable differential capacitance-to-voltage converter for MEMS accelerometers

    Science.gov (United States)

    Royo, G.; Sánchez-Azqueta, C.; Gimeno, C.; Aldea, C.; Celma, S.

    2017-05-01

    Capacitive MEMS sensors exhibit an excellent noise performance, high sensitivity and low power consumption. They offer a huge range of applications, being the accelerometer one of its main uses. In this work, we present the design of a capacitance-to-voltage converter in CMOS technology to measure the acceleration from the capacitance variations. It is based on a low-power, fully-differential transimpedance amplifier with low input impedance and a very low input noise.

  18. MEMS-Based Micro Gas Chromatography: Design, Fabrication and Characterization

    OpenAIRE

    Zareian-Jahromi, Mohammad Amin

    2009-01-01

    This work is focused on the design, fabrication and characterization of high performance MEMS-based micro gas chromatography columns having wide range of applications in the pharmaceutical industry, environmental monitoring, petroleum distillation, clinical chemistry, and food processing. The first part of this work describes different approaches to achieve high-performance microfabricated silicon-glass separation columns for micro gas chromatographic (µGC) systems. The capillary width effec...

  19. Regenerative medicine using adult neural stem cells: the potential for diabetes therapy and other pharmaceutical applications

    Institute of Scientific and Technical Information of China (English)

    Tomoko Kuwabara; Makoto Asashima

    2012-01-01

    Neural stem cells (NSCs),which are responsible for continuous neurogenesis during the adult stage,are present in human adults.The typical neurogenic regions are the hippocampus and the subventricular zone; recent studies have revealed that NSCs also exist in the olfactory bulb.Olfactory bulb-derived neural stem cells (OB NSCs) have the potential to be used in therapeutic applications and can be easily harvested without harm to the patient.Through the combined influence of extrinsic cues and innate programming,adult neurogenesis is a finely regulated process occurring in a specialized cellular environment,a niche.Understanding the regulatory mechanisms of adult NSCs and their cellular niche is not only important to understand the physiological roles of neurogenesis in adulthood,but also to provide the knowledge necessary for developing new therapeutic applications using adult NSCs in other organs with similar regulatory environments.Diabetes is a devastating disease affecting more than 200 million people worldwide.Numerous diabetic patients suffer increased symptom severity after the onset,involving complications such as retinopathy and nephropathy.Therefore,the development of treatments for fundamental diabetes is important.The utilization of autologous cells from patients with diabetes may address challenges regarding the compatibility of donor tissues as well as provide the means to naturally and safely restore function,reducing future risks while also providing a long-term cure.Here,we review recent findings regarding the use of adult OB NSCs as a potential diabetes cure,and discuss the potential of OB NSC-based pharmaceutical applications for neuronal diseases and mental disorders.

  20. Applications of autoassociative neural networks for signal validation in accident management

    International Nuclear Information System (INIS)

    Fantoni, P.; Mazzola, A.

    1994-01-01

    The OECD Halden Reactor Project has been working for several years with computer based systems for determination on plant status including early fault detection and signal validation. The method here presented explores the possibility to use a neural network approach to validate important process signals during normal and abnormal plant conditions. In BWR plants, signal validation has two important applications: reliable thermal limits calculation and reliable inputs to other computerized systems that support the operator during accident scenarious. This work shows how a properly trained autoassociative neural network can promptly detect faulty process signal measurements and produce a best estimate of the actual process value. Noise has been artificially added to the input to evaluate the network ability to respond in a very low signal to noise ratio environment. Training and test datasets have been simulated by the real time transient simulator code APROS. Future development addresses the validation of the model through the use of real data from the plant. (author). 5 refs, 17 figs

  1. Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors

    Directory of Open Access Journals (Sweden)

    N. Sriraam

    2011-01-01

    Full Text Available A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

  2. The application of expert systems and neural networks to gas turbine prognostics and diagnostics

    Energy Technology Data Exchange (ETDEWEB)

    DePold, H.R.; Gass, F.D.

    1999-10-01

    Condition monitoring of engine gas generators plays an essential role in airline fleet management. Adaptive diagnostic systems are becoming available that interpret measured data, furnish diagnosis of problems, provide a prognosis of engine health for planning purposes, and rank engines for scheduled maintenance. More than four hundred operations worldwide currently use versions of the first or second generation diagnostic tools. Development of a third generation system is underway which will provide additional system enhancements and combine the functions of the existing tools. Proposed enhancements include the use of artificial intelligence to automate, improve the quality of the analysis, provide timely alerts, and the use of an Internet link for collaboration. One objective of these enhancements is to have the intelligent system do more of the analysis and decision making, while continuing to support the depth of analysis currently available at experienced operations. This paper presents recent developments in technology and strategies in engine condition monitoring including: (1) application of statistical analysis and artificial neural network filters to improve data quality, (2) neural networks for trend change detection, and classification to diagnose performance change, and (3) expert systems to diagnose, provide alerts and to rank maintenance action recommendations.

  3. Evaluation of the maximum-likelihood adaptive neural system (MLANS) applications to noncooperative IFF

    Science.gov (United States)

    Chernick, Julian A.; Perlovsky, Leonid I.; Tye, David M.

    1994-06-01

    This paper describes applications of maximum likelihood adaptive neural system (MLANS) to the characterization of clutter in IR images and to the identification of targets. The characterization of image clutter is needed to improve target detection and to enhance the ability to compare performance of different algorithms using diverse imagery data. Enhanced unambiguous IFF is important for fratricide reduction while automatic cueing and targeting is becoming an ever increasing part of operations. We utilized MLANS which is a parametric neural network that combines optimal statistical techniques with a model-based approach. This paper shows that MLANS outperforms classical classifiers, the quadratic classifier and the nearest neighbor classifier, because on the one hand it is not limited to the usual Gaussian distribution assumption and can adapt in real time to the image clutter distribution; on the other hand MLANS learns from fewer samples and is more robust than the nearest neighbor classifiers. Future research will address uncooperative IFF using fused IR and MMW data.

  4. An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models

    Directory of Open Access Journals (Sweden)

    Alex Alexandridis

    2018-01-01

    Full Text Available This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the fuzzy means (PSO-NSFM algorithm so that an approximation of the inverse system dynamics is obtained. PSO-NSFM offers models of high accuracy combined with small network structures. Next, the applicability domain concept is suitably tailored and embedded into the proposed control structure in order to ensure that extrapolation is avoided in the controller predictions. Finally, an error correction term, estimating the error produced by the unmodeled dynamics and/or unmeasured external disturbances, is included to the control scheme to increase robustness. The resulting controller guarantees bounded input-bounded state (BIBS stability for the closed loop system when the open loop system is BIBS stable. The proposed methodology is evaluated on two different control problems, namely, the control of an experimental armature-controlled direct current (DC motor and the stabilization of a highly nonlinear simulated inverted pendulum. For each one of these problems, appropriate case studies are tested, in which a conventional neural controller employing inverse models and a PID controller are also applied. The results reveal the ability of the proposed control scheme to handle and manipulate diverse data through a data fusion approach and illustrate the superiority of the method in terms of faster and less oscillatory responses.

  5. Extruded Bread Classification on the Basis of Acoustic Emission Signal With Application of Artificial Neural Networks

    Science.gov (United States)

    Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata

    2015-04-01

    The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.

  6. Application of Entropy Ensemble Filter in Neural Network Forecasts of Tropical Pacific Sea Surface Temperatures

    Directory of Open Access Journals (Sweden)

    Hossein Foroozand

    2018-03-01

    Full Text Available Recently, the Entropy Ensemble Filter (EEF method was proposed to mitigate the computational cost of the Bootstrap AGGregatING (bagging method. This method uses the most informative training data sets in the model ensemble rather than all ensemble members created by the conventional bagging. In this study, we evaluate, for the first time, the application of the EEF method in Neural Network (NN modeling of El Nino-southern oscillation. Specifically, we forecast the first five principal components (PCs of sea surface temperature monthly anomaly fields over tropical Pacific, at different lead times (from 3 to 15 months, with a three-month increment for the period 1979–2017. We apply the EEF method in a multiple-linear regression (MLR model and two NN models, one using Bayesian regularization and one Levenberg-Marquardt algorithm for training, and evaluate their performance and computational efficiency relative to the same models with conventional bagging. All models perform equally well at the lead time of 3 and 6 months, while at higher lead times, the MLR model’s skill deteriorates faster than the nonlinear models. The neural network models with both bagging methods produce equally successful forecasts with the same computational efficiency. It remains to be shown whether this finding is sensitive to the dataset size.

  7. Artificial neural network application for predicting soil distribution coefficient of nickel

    International Nuclear Information System (INIS)

    Falamaki, Amin

    2013-01-01

    The distribution (or partition) coefficient (K d ) is an applicable parameter for modeling contaminant and radionuclide transport as well as risk analysis. Selection of this parameter may cause significant error in predicting the impacts of contaminant migration or site-remediation options. In this regards, various models were presented to predict K d values for different contaminants specially heavy metals and radionuclides. In this study, artificial neural network (ANN) is used to present simplified model for predicting K d of nickel. The main objective is to develop a more accurate model with a minimal number of parameters, which can be determined experimentally or select by review of different studies. In addition, the effects of training as well as the type of the network are considered. The K d values of Ni is strongly dependent on pH of the soil and mathematical relationships were presented between pH and K d of nickel recently. In this study, the same database of these presented models was used to verify that neural network may be more useful tools for predicting of K d . Two different types of ANN, multilayer perceptron and redial basis function, were used to investigate the effect of the network geometry on the results. In addition, each network was trained by 80 and 90% of the data and tested for 20 and 10% of the rest data. Then the results of the networks compared with the results of the mathematical models. Although the networks trained by 80 and 90% of the data the results show that all the networks predict with higher accuracy relative to mathematical models which were derived by 100% of data. More training of a network increases the accuracy of the network. Multilayer perceptron network used in this study predicts better than redial basis function network. - Highlights: ► Simplified models for predicting K d of nickel presented using artificial neural networks. ► Multilayer perceptron and redial basis function used to predict K d of nickel in

  8. Artificial neural networks application for horizontal and vertical forecasting radionuclides transport

    International Nuclear Information System (INIS)

    Khil'ko, O.S.; Kovalenko, V.I.; Kundas, S.P.

    2010-01-01

    Artificial neural networks approach for horizontal and vertical radionuclide transport forecasting was proposed. Runoff factors analysis was considered. Additional artificial neural network structures for physical-chemical properties recognition were used. (authors)

  9. Wavelength tunable MEMS VCSELs for OCT imaging

    DEFF Research Database (Denmark)

    Sahoo, Hitesh Kumar; Ansbæk, Thor; Ottaviano, Luisa

    2018-01-01

    MEMS VCSELs are one of the most promising swept source (SS) lasers for optical coherence tomography (OCT) and one of the best candidates for future integration with endoscopes, surgical probes and achieving an integrated OCT system. However, the current MEMS-based SS are processed on the III...

  10. Evolution of a MEMS Photoacoustic Chemical Sensor

    National Research Council Canada - National Science Library

    Pellegrino, Paul M; Polcawich, Ronald G

    2003-01-01

    .... Initial MEMS work is centered on fabrication of a lead zirconate titanate (PZT) microphone subsystem to be incorporated in the full photoacoustic device. Preliminary results were very positive for the macro-photoacoustic cell, PZT membrane microphones design / fabrication and elementary monolithic MEMS photoacoustic cavity.

  11. MEMS-Based Waste Vibrational Energy Harvesters

    Science.gov (United States)

    2013-06-01

    MEMS energy- harvesting device. Although PZT is used more prevalently due to its higher piezoelectric coefficient and dielectric constant, AlN has...7 1. Lead Zirconium Titanate ( PZT ) .........................................................7 2. Aluminum...Laboratory PiezoMUMPS Piezoelectric Multi-User MEMS Processes PZT Lead Zirconate Titanate SEM Scanning Electron Microscopy SiO2 Silicon

  12. The Micronium-A Musical MEMS instrument

    NARCIS (Netherlands)

    Engelen, Johannes Bernardus Charles; de Boer, Hans L.; de Boer, Hylco; Beekman, Jethro G.; Fortgens, Laurens C.; de Graaf, Derk B.; Vocke, Sander; Abelmann, Leon

    The Micronium is a musical instrument fabricated from silicon using microelectromechanical system (MEMS) technology. It is—to the best of our knowledge—the first musical micro-instrument fabricated using MEMS technology, where the actual sound is generated by mechanical microstructures. The

  13. Measurement of the Earth tides with a MEMS gravimeter.

    Science.gov (United States)

    Middlemiss, R P; Samarelli, A; Paul, D J; Hough, J; Rowan, S; Hammond, G D

    2016-03-31

    The ability to measure tiny variations in the local gravitational acceleration allows, besides other applications, the detection of hidden hydrocarbon reserves, magma build-up before volcanic eruptions, and subterranean tunnels. Several technologies are available that achieve the sensitivities required for such applications (tens of microgal per hertz(1/2)): free-fall gravimeters, spring-based gravimeters, superconducting gravimeters, and atom interferometers. All of these devices can observe the Earth tides: the elastic deformation of the Earth's crust as a result of tidal forces. This is a universally predictable gravitational signal that requires both high sensitivity and high stability over timescales of several days to measure. All present gravimeters, however, have limitations of high cost (more than 100,000 US dollars) and high mass (more than 8 kilograms). Here we present a microelectromechanical system (MEMS) device with a sensitivity of 40 microgal per hertz(1/2) only a few cubic centimetres in size. We use it to measure the Earth tides, revealing the long-term stability of our instrument compared to any other MEMS device. MEMS accelerometers--found in most smart phones--can be mass-produced remarkably cheaply, but none are stable enough to be called a gravimeter. Our device has thus made the transition from accelerometer to gravimeter. The small size and low cost of this MEMS gravimeter suggests many applications in gravity mapping. For example, it could be mounted on a drone instead of low-flying aircraft for distributed land surveying and exploration, deployed to monitor volcanoes, or built into multi-pixel density-contrast imaging arrays.

  14. Application of Artificial Neural Networks to the Analysis of NORM Samples

    International Nuclear Information System (INIS)

    Moser, H.; Peyrés, V.; Mejuto, M.; García-Toraño, E.

    2015-01-01

    This work describes the application of artificial neural networks (ANNs) to analyze the raw data of gamma-ray spectra of NORM samples and decide if the activity content of a certain nuclide is above or below the exemption limit of 1 Bq/g. The main advantage of using an ANN for this purpose is that for the user no specialized knowledge in the field of gamma-ray spectrometry is necessary. In total a number of 635 spectra consisting of varying activity concentrations, seven different materials and three densities each have been generated by Monte Carlo simulation to provide training material to the ANN. These spectra have been created using the simulation code PENELOPE. Validation was carried out with a number of NORM samples previously characterized by conventional gamma-ray spectrometry with peak fitting

  15. Natural gas demand forecast system based on the application of artificial neural networks

    International Nuclear Information System (INIS)

    Sanfeliu, J.M.; Doumanian, J.E.

    1997-01-01

    Gas Natural BAN, as a distribution gas company since 1993 in the north and west area of Buenos Aires Argentina, with 1,000,000 customers, had to develop a gas demand forecast system which should comply with the following basic requirements: Be able to do reliable forecasts with short historical information (2 years); Distinguish demands in areas of different characteristics, i.e. mainly residential, mainly industrial; Self-learning capability. To accomplish above goals, Gas Natural BAN chose in view of its own necessities, an artificial intelligence application (neural networks). 'SANDRA', the gas demand forecast system for gas distribution used by Gas Natural BAN, has the following features: Daily gas demand forecast, Hourly gas demand forecast and Breakdown of both forecast for each of the 3 basic zones in which the distribution area of Gas Natural BAN is divided. (au)

  16. Fault tolerance of artificial neural networks with applications in critical systems

    Science.gov (United States)

    Protzel, Peter W.; Palumbo, Daniel L.; Arras, Michael K.

    1992-01-01

    This paper investigates the fault tolerance characteristics of time continuous recurrent artificial neural networks (ANN) that can be used to solve optimization problems. The principle of operations and performance of these networks are first illustrated by using well-known model problems like the traveling salesman problem and the assignment problem. The ANNs are then subjected to 13 simultaneous 'stuck at 1' or 'stuck at 0' faults for network sizes of up to 900 'neurons'. The effects of these faults is demonstrated and the cause for the observed fault tolerance is discussed. An application is presented in which a network performs a critical task for a real-time distributed processing system by generating new task allocations during the reconfiguration of the system. The performance degradation of the ANN under the presence of faults is investigated by large-scale simulations, and the potential benefits of delegating a critical task to a fault tolerant network are discussed.

  17. Application of artificial neural network to predict the optimal start time for heating system in building

    International Nuclear Information System (INIS)

    Yang, In-Ho; Yeo, Myoung-Souk; Kim, Kwang-Woo

    2003-01-01

    The artificial neural network (ANN) approach is a generic technique for mapping non-linear relationships between inputs and outputs without knowing the details of these relationships. This paper presents an application of the ANN in a building control system. The objective of this study is to develop an optimized ANN model to determine the optimal start time for a heating system in a building. For this, programs for predicting the room air temperature and the learning of the ANN model based on back propagation learning were developed, and learning data for various building conditions were collected through program simulation for predicting the room air temperature using systems of experimental design. Then, the optimized ANN model was presented through learning of the ANN, and its performance to determine the optimal start time was evaluated

  18. Synchronization of Switched Interval Networks and Applications to Chaotic Neural Networks

    Directory of Open Access Journals (Sweden)

    Jinde Cao

    2013-01-01

    Full Text Available This paper investigates synchronization problem of switched delay networks with interval parameters uncertainty, based on the theories of the switched systems and drive-response technique, a mathematical model of the switched interval drive-response error system is established. Without constructing Lyapunov-Krasovskii functions, introducing matrix measure method for the first time to switched time-varying delay networks, combining Halanay inequality technique, synchronization criteria are derived for switched interval networks under the arbitrary switching rule, which are easy to verify in practice. Moreover, as an application, the proposed scheme is then applied to chaotic neural networks. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.

  19. Artificial Neural Networks to reconstruct incomplete satellite data: application to the Mediterranean Sea Surface Temperature

    Directory of Open Access Journals (Sweden)

    E. Pisoni

    2008-02-01

    Full Text Available Satellite data can be very useful in applications where extensive spatial information is needed, but sometimes missing data due to presence of clouds can affect data quality. In this study a methodology for pre-processing sea surface temperature (SST data is proposed. The methodology, that processes measures in the visible wavelength, is based on an Artificial Neural Network (ANN system. The effectiveness of the procedure has been also evaluated comparing results obtained using an interpolation method. After the methodology has been identified, a validation is performed on 3 different episodes representative of SST variability in the Mediterranean sea. The proposed technique can process SST NOAA/AVHRR data to simulate severe storm episodes by means of prognostic meteorological models.

  20. A systematic FPGA acceleration design for applications based on convolutional neural networks

    Science.gov (United States)

    Dong, Hao; Jiang, Li; Li, Tianjian; Liang, Xiaoyao

    2018-04-01

    Most FPGA accelerators for convolutional neural network are designed to optimize the inner acceleration and are ignored of the optimization for the data path between the inner accelerator and the outer system. This could lead to poor performance in applications like real time video object detection. We propose a brand new systematic FPFA acceleration design to solve this problem. This design takes the data path optimization between the inner accelerator and the outer system into consideration and optimizes the data path using techniques like hardware format transformation, frame compression. It also takes fixed-point, new pipeline technique to optimize the inner accelerator. All these make the final system's performance very good, reaching about 10 times the performance comparing with the original system.