Sensor

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A sensor is a device that measures a physical quantity and converts it into a signal which can be read by an observer or by an instrument. (Wikipedia.org)






Conferences related to Sensor

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2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)

The goal of the 14th ASME/IEEE MESA2018 is to bring together experts from the fields of mechatronic and embedded systems, disseminate the recent advances in the area, discuss future research directions, and exchange application experience. The main achievement of MESA2018 is to bring out and highlight the latest research results and developments in the IoT (Internet of Things) era in the field of mechatronics and embedded systems.


2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)

IEEE CCNC 2018 will present the latest developments and technical solutions in the areas of home networking, consumer networking, enabling technologies (such as middleware) and novel applications and services. The conference will include a peer-reviewed program of technical sessions, special sessions, business application sessions, tutorials, and demonstration sessions


2018 15th International Workshop on Advanced Motion Control (AMC)

1. Advanced Motion Control2. Haptics, Robotics and Human-Machine Systems3. Micro/Nano Motion Control Systems4. Intelligent Motion Control Systems5. Nonlinear, Adaptive and Robust Control Systems6. Motion Systems for Robot Intelligence and Humanoid Robotics7. CPG based Feedback Control, Morphological Control8. Actuators and Sensors in Motion System9. Motion Control of Aerial/Ground/Underwater Robots10. Advanced Dynamics and Motion Control11. Motion Control for Assistive and Rehabilitative Robots and Systems12. Intelligent and Advanced Traffic Controls13. Computer Vision in Motion Control14. Network and Communication Technologies in Motion Control15. Motion Control of Soft Robots16. Automation Technologies in Primary Industries17. Other Topics and Applications Involving Motion Dynamics and Control


2018 20th European Conference on Power Electronics and Applications (EPE'18 ECCE Europe)

Energy conversion and conditioning technologies, power electronics, adjustable speed drives and their applications, power electronics for smarter grid, energy efficiency,technologies for sustainable energy systems, converters and power supplies


2018 European Conference on Antennas and Propagation (EuCAP)

Antennas & related topics e.g. theoretical methods, systems, wideband, multiband, UWBPropagation & related topics e.g. modelling/simulation, HF, body-area, urbanAntenna & RCS measurement techniques


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Periodicals related to Sensor

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Applied Superconductivity, IEEE Transactions on

Contains articles on the applications and other relevant technology. Electronic applications include analog and digital circuits employing thin films and active devices such as Josephson junctions. Power applications include magnet design as well asmotors, generators, and power transmission


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Circuits and Systems I: Regular Papers, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


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Most published Xplore authors for Sensor

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Xplore Articles related to Sensor

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Vehdoop: A Scalable Analytical Processing Framework for Vehicular Sensor Networks

[{u'author_order': 1, u'affiliation': u'Computer Science Department, City University of Hong Kong, Hong Kong.', u'full_name': u'Wendi Nie'}, {u'author_order': 2, u'affiliation': u'College of Computer Science, Chongqing University, Chongqing 400040, China (e-mail: liukai0807@cqu.edu.cn).', u'full_name': u'Kai Liu'}, {u'author_order': 3, u'affiliation': u'Computer Science Department, City University of Hong Kong, Hong Kong.', u'full_name': u'Victor C. S. Lee'}, {u'author_order': 4, u'affiliation': u'Computer Science Department, City University of Hong Kong, Hong Kong.', u'full_name': u'Yaoxin Duan'}, {u'author_order': 5, u'affiliation': u'Vidyasirimedhi Institute of Science and Technology, Rayong 21210, Thailand (e-mail: snutanon@vistec.ac.th).', u'full_name': u'Sarana Nutanong'}] IEEE Transactions on Intelligent Transportation Systems, None

The vehicular sensor network (VSN) technology empowers intelligent transportation systems (ITSs) to support a wide range of road safety and traffic management applications. By taking advantage of the information collection and communication capabilities offered by VSNs, information, such as speed, travel time, dash-camera video, and so on, can be gathered from sensors embedded in vehicles and then delivered to the ...


MEMS Flow Sensor Using Suspended Graphene Diaphragm With Microhole Arrays

[{u'author_order': 1, u'affiliation': u'Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011 USA, and also with the Microelectronics Research Center, Iowa State University, Ames, IA 50011 USA.', u'full_name': u'Qiugu Wang'}, {u'author_order': 2, u'affiliation': u'Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011 USA, and also with the Microelectronics Research Center, Iowa State University, Ames, IA 50011 USA.', u'full_name': u'Yifei Wang'}, {u'author_order': 3, u'affiliation': u'Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011 USA, and also with the Microelectronics Research Center, Iowa State University, Ames, IA 50011 USA (e-mail: ldong@iastate.edu).', u'full_name': u'Liang Dong'}] Journal of Microelectromechanical Systems, None

This letter reports a miniature flow sensor using a suspended composite membrane of graphene and silicon nitride containing an array of microscale through-holes. As a fluid flow passes through these microholes, the graphene layer of the composite membrane is stressed to change its electrical resistance due to the piezoresistive effect of graphene. The creation of the microholes in graphene allows ...


Superresolution Line Scan Image Sensor for Multimodal Microscopy

[{u'author_order': 1, u'affiliation': u'Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada', u'full_name': u'Chengzhi Winston Liu'}, {u'author_order': 2, u'affiliation': u'Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada', u'full_name': u'Arshya Feizi'}, {u'author_order': 3, u'affiliation': u'Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada', u'full_name': u'Navid Sarhangnejad'}, {u'author_order': 4, u'affiliation': u'Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada', u'full_name': u'Glenn Gulak'}, {u'author_order': 5, u'affiliation': u'Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada', u'full_name': u'Roman Genov'}] IEEE Transactions on Biomedical Circuits and Systems, 2018

A low-cost contact scanning microscope is presented which performs optical imaging of millimeter-scale samples with multiple sensory modalities at a spatial resolution better than the pixel size in both x and y dimensions. The 7.5 mm $times$ 3.2 mm 0.35 $mu$m CMOS image sensor is comprised of 214 scanning lines of 256 pixels, each line horizontally shifted by 300 nm ...


Optimization on PVDF Film Force Sensor for Steel Ball Forging Fault Diagnosis

[{u'author_order': 1, u'affiliation': u'School of Mechanical Engineering, University of Jinan, Jinan, 250022, PR China', u'full_name': u'Yingjun Li'}, {u'author_order': 2, u'affiliation': u'School of Mechanical Engineering, University of Jinan, Jinan, 250022, PR China', u'full_name': u'Guicong Wang'}, {u'author_order': 3, u'affiliation': u'School of Mechanical Engineering, University of Jinan, Jinan, 250022, PR China', u'full_name': u'Huanyong Cui'}, {u'author_order': 4, u'affiliation': u'School of Mechanical Engineering, University of Jinan, Jinan, 250022, PR China', u'full_name': u'Xiangyu Wang'}] 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE), None

Bearing capacity and sensitivity are two important indicators of force sensor measurement performance. In this paper, the parameters optimization and performance analysis of the piezoelectric film force sensor for steel ball cold heading machine are carried out for the piezoelectric film force sensor for detecting cold ball force of steel ball. In order to avoid local stress concentration, a modified ...


A Prototype of Wireless Sensor for Data Acquisition in Energy Management Systems

[{u'author_order': 1, u'affiliation': u'National Research Council of Italy', u'full_name': u'Massimiliano Luna'}, {u'author_order': 2, u'affiliation': u'National Research Council of Italy', u'full_name': u'Giuseppe La Tona'}, {u'author_order': 3, u'affiliation': u'National Research Council of Italy', u'full_name': u'Maria Carmela di Piazza'}, {u'author_order': 4, u'affiliation': u'National Research Council of Italy', u'full_name': u'Marcello Pucci'}, {u'author_order': 5, u'affiliation': u'National Research Council of Italy', u'full_name': u'Angelo Accetta'}, {u'author_order': 6, u'affiliation': u'National Research Council of Italy', u'full_name': u'Davide Taibi'}, {u'author_order': 7, u'affiliation': u'DMI, University of Palermo, Palermo, Italy', u'full_name': u'Calogero Vetro'}, {u'author_order': 8, u'affiliation': u'DMI, University of Palermo, Palermo, Italy', u'full_name': u'Riccardo La Grassa'}] 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), None

A prototype of a wireless sensor for monitoring electrical loads in a smart building is designed and implemented. The sensor can acquire the main electrical parameters of the connected load and, optionally, other physical quantities (e.g., room temperature). Unlike other wireless sensors in literature, the proposed sensor is cheap and small, exploits the Wi-Fi network that is commonly available inside ...


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Educational Resources on Sensor

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eLearning

No eLearning Articles are currently tagged "Sensor"

IEEE.tv Videos

Skillful Manipulation Based on High-Speed Sensory-Motor Fusion
ICASSP 2010 - Advances in Neural Engineering
Robot Redux: Lego's Mindstorms NXT in action
Performance, Environment, Actuators, Sensors (PEAS)
NeXOS: Observations Supporting Ocean Sustainability
Generation of Models for Wireless Sensor Network Assessment
Women In Engineering Focus on Technical Activities - Christina Schober - Sections Congress 2017
Cooperative Localization in Sensor Networks
Yuan-ting Zhang AMA EMBS Individualized Health
Recording and Using 3D Object Models with RoboEarth
NeXOS: Observations Supporting Ocean Sustainability (short version)
Alexandros Fragkiadakis: Trust-based Scheme Employing Evidence Reasoning for IoT Architectures: WF-IoT 2016
The Fundamentals of Compressive Sensing, Part I: Introduction
Technology for Health Summit 2017 - Panel I: Introducing the technological revolution in health and wellbeing
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson - 7 of 7 - SQUID-based noise thermometers for sub-Kelvin thermometry
Living the Future Now: The challenges and promise of pervasive computing
Compressive Sensing Tutorial: A Game Changing Technology for Energy Efficient IoT Sensor Networks: WF-IoT 2016
Mapping Human to Robot Motion with Functional Anthropomorphism for Teleoperation and Telemanipulation with Robot Arm Hand Systems
EMB AMA Medical technology and Individualized healthcare - Joseph Kvedar
Multiple Sensor Fault Detection and Isolation in Complex Distributed Dynamical Systems

IEEE-USA E-Books

  • Sensor Systems for PHM

    This chapter introduces the fundamentals of sensors and their sensing principles. It discusses the key attributes of sensor systems for prognostics and health management (PHM) implementation. The chapter describes some state‐of‐the‐art of PHM sensor systems. It presents the emerging trends in sensor system technologies. From the point of view of sensing principles, sensors are classified into three major groups: physical, chemical, and biological. The physical principles or effects involved in detecting a measurand include thermal, electrical,mechanical, chemical, biological, optical, and magnetic. A PHM sensor system will typically have internal or external sensors, internal or external power, a microprocessor with analog‐to‐digital (A/D) converters,memory, and data transmission. Sensor systems can be divided into two main categories with respect to their power sources: non‐battery‐powered sensor systems and battery‐powered sensor systems. Onboard memory is the memory contained within the sensor system. Memory management allows one to configure, allocate, monitor, and optimize the utilization of memory.

  • Commercially Available Sensor Systems for PHM

    <p><b>AN INDISPENSABLE GUIDE FOR ENGINEERS AND DATA SCIENTISTS IN DESIGN, TESTING, OPERATION, MANUFACTURING, AND MAINTENANCE</b> <p>A road map to the current challenges and available opportunities for the research and development of Prognostics and Health Management &#40;PHM&#41;, this important work covers all areas of electronics and explains how to: <ul> <li>assess methods for damage estimation of components and systems due to field loading conditions</li> <li>assess the cost and benefits of prognostic implementations</li> <li>develop novel methods for in situ monitoring of products and systems in actual life&#45;cycle conditions</li> <li>enable condition&#45;based &#40;predictive&#41; maintenance</li> <li>increase system availability through an extension of maintenance cycles and/or timely repair actions</li> <li>obtain knowl dge of load history for future design, qualification, and root cause analysis</li> <li>reduce the occurrence of no fault found &#40;NFF&#41;</li> <li>subtract life&#45;cycle costs of equipment from reduction in inspection costs, downtime, and inventory</li> </ul> <p><i>Prognostics and Health Management of Electronics</i> also explains how to understand statistical techniques and machine learning methods used for diagnostics and prognostics. Using this valuable resource, electrical engineers, data scientists, and design engineers will be able to fully grasp the synergy between IoT, machine learning, and risk assessment.

  • CMOS Image Sensor

    The solid‐state image sensor is the critical component of photo‐electronic devices such as mobile phones, digital video cameras, automotive imaging, surveillance, and biometrics. Two types of solid‐state image sensor technologies have been developed: charged coupled devices (CCD) and Complementary Metal Oxide Semiconductor (CMOS) image sensors (CIS). This chapter introduces the low‐noise CIS sensor design for biomedical application. It analyzes the key sensor design block pixel and associated noise sources. The chapter then discusses the different sensor readout architectures. Noise appearing in a reproduced image, which is "fixed" at certain spatial positions, is referred to as fixed pattern noise (FPN), usually caused by the CIS readout circuitry. Dark current FPN due to the mismatches in the pixel photodiode leakage currents tends to dominate the non‐coherent component of FPN, especially with long exposure times. The chapter also introduces 3 Meg pixel CIS design, and evaluates sensor performance for lens less imaging system.

  • CMOS Dual&#x2010;mode pH&#x2010;Image Sensor

    This chapter develops a dual‐mode sensor to provide an image as well as pH information for sample analysis. In addition to the pH sensing, the chapter introduces optical sensing for the Complementary Metal Oxide Semiconductor (CMOS) ion‐sensitive field‐effect transistor (ISFET). The chapter compares the schematics for 4 T‐ CMOS Image Sensors (CIS) pixel and ISFET pixel with the proposed dual‐mode pixel. When the chip area of a CMOS sensor array is fixed, the only way to improve throughput is to reduce the pixel size and increase the pixel number. The correlated double sampling (CDS) readout circuit for CIS is realized through the signal chain of CIS pixel, column S/H, and switched‐capacitor amplifier. The chapter illustrates top architecture of the dual‐mode sensor, including a 64 × 64 dual‐mode pixel array, S/H circuit, and global switched‐capacitor operational amplifier for CDS readout, 12‐bit pipelined analog‐to‐digital conversion (ADC), and row/column decoders.

  • CMOS Ultrasound Sensor

    This chapter presents a two‐channel ultrasound sensor unit cell as the analog front‐end (AFE) for 320 x 320 capacitive micro‐machined ultrasound transducer (CMUT) array towards high‐frequency 3‐D ultrasound medical imaging applications. The first AFE IC is used to drive the transmitting CMUT with a high‐voltage (HV) signal, while the second AFE IC is placed close to the receiving CMUT in oil to amplify the weak current signal resulting from the echo signal. The ultrasound image sensing system is ultimately targeted for 3‐D ultrasound bio‐microscope application to obtain a high‐resolution image of a patient's eye to diagnose glaucoma with high frame rate while minimizing the discomfort given to the patient. The chapter demonstrates the HV, high‐frequency two‐channel AFE IC cell for CMUT interface, and highlights that it can be utilized as a unit cell for future 2‐D multi‐array AFE IC development for 3‐D high‐resolution ultrasound systems.

  • CMOS 3&#x2010;D&#x2010;Integrated MEMS Sensor

    This chapter presents one TSV‐less 3‐D complimentary metal‐oxide semiconductor (CMOS)‐on‐micro‐electro‐mechanical system (MEMS) integration technique using direct metal bonding. The CMOS‐on‐MEMS integration leads to a simultaneous formation of electrical, mechanical, and hermetic bonds, eliminates chip‐to‐chip wire‐bonding, and hence presents competitive advantages over hybrid or monolithic solutions. The MEMS sensor is a capacitive accelerometer. The basic working principle of the MEMS accelerometer is the displacement of a small proof mass etched into the silicon surface of the integrated circuit and suspended by small beams. The CMOS readout circuit for MEMS consists of a low‐noise, band‐pass gain stage, a fully differential synchronous demodulator, and an off‐chip, low‐pass filter. The chapter illustrates a figure of the heterogeneous 3‐D TSV‐less accelerometer structure. The CMOS readout circuit is stacked on the MEMS accelerometer using face‐to‐face (F2F) direct metal bonding, which provides smaller form factor, latency, and power.

  • CMOS Dual&#x2010;mode Energy&#x2010;harvesting&#x2010;image Sensor

    Recently, with the benefit of low power, high speed, and feasibility of system‐on‐chip (SoC) integration, Complementary Metal Oxide Semiconductor (CMOS) image sensors (CIS) are replacing power‐hungry charge‐coupled devices (CCD) in many biomedical applications. This chapter introduces an energy harvesting type ultra‐low‐power CIS design with an integrated power management system (PMS) towards personal diagnosis application. It explains the design details of the new energy high‐energy harvesting image (EHI) pixel structure and pixel operations. The chapter describes the details of the readout circuitry block design, as well as other ultra‐low power functional imaging and energy harvesting blocks. It also introduces the overall architecture of the EHI imager with PMS. With the 96 × 96 sensor array under 1‐V power supply, the power consumption is only 6 μW with 5 fps speed, and simulated and measured performance characteristics of the EHI CIS are also presented.

  • CMOS Impedance Sensor

    This chapter discusses a high‐density complimentary metal‐oxide semiconductor (CMOS) electrical‐impedance spectroscopy (EIS) biosensor array for precise counting of breast cancer MCF‐7 cells. The device consists of a 96 × 96 array of densely packed active microelectrodes in an area of 3 mm × 3 mm to enable counting over a wide range of MCF‐7 cells. The chapter analyses the impedance percentage change over a large number of working electrodes by incubating with a large number of cells. The results indicated clearly that the impedance change of the electrode covered by cells is more than 7%, whereas the impedance change in the control experiment by changing the phosphate buffered saline (PBS) solution is negligible. The chapter describes CMOS impedance pixel array, equivalent circuit model, and the working principle, and then employs a readout scheme. It illustrates the overall architecture of the 96 × 96 impedance sensing system.

  • CMOS Terahertz Sensor

    Recently, a great deal of attention has been paid to the terahertz (THz) spectroscopy and imaging system due to the moderate wavelength of THz wave that can leverage the advantages of both millimeter‐waves and light waves. This chapter shows that a high reflection coefficient of differential T‐line (DTL)‐split ring resonator (SRR) can be directly transferred into a high Q. It presents DTL‐SRR resonator with the standing‐wave resonator and compares the same using a coplanar stripline (CPS). Cell testing has attracted intensive attention recently due to the need for early detection of diseases such as cancer. The chapter investigates split‐ring resonator (SRR)‐based plasmonic sensors with layout designs in the standard 65‐nm complimentary metal‐oxide semiconductor (CMOS) process at sub‐THz. Two super‐regenerative receivers (SRX) working at 96 GHz and 135 GHz are implemented into the CMOS process to demonstrate the advantages of applying quench‐controlled oscillators with metamaterial resonators in SRX.

  • CMOS Sensor Design

    This chapter introduces basic column circuit blocks, such as the column amplifier and single‐slope analog‐to‐digital converter (ADC). It then introduces readout strategies, correlated double sampling, and correlated multiple sampling for high‐performance sensing. The chapter discusses row or column timing control blocks that determine operations and readout of the pixel array. It presents the widely used low‐power and high‐speed interface, low‐voltage differential signaling (LVDS) that meets the requirements of modern high‐throughput applications. The chapter illustrates the origin of thermal noise, flicker noise, and shot noise in circuit design. It discloses two types of the popular structures, including the Nyquist‐rate single‐slope ADC for area‐efficient sensing and oversampling sigma‐delta ADC for low‐noise and high‐resolution detection. The chapter proposes interface standards, such as voltage‐mode logic (VML), current‐mode logic (CML), and LVDS. It introduces the widely‐used LVDS readout strategy found in most large array complimentary metal‐oxide semiconductor (CMOS) sensors.



Standards related to Sensor

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IEEE Standard for Ultrawideband Radar Definitions

This document organizes and standardizes the terms and definitions used in the field of ultrawideband (UWB) radar.


IEEE Standard for Ultrawideband Radar Definitions - Corrigendum 1

This document organizes and standardizes the terms and definitions used in the field of ultrawideband(UWB) radar.


IEEE Standard Letter Designations for Radar-Frequency Bands

Radar systems operate in frequency bands that since World War II have been identified by letter designations. To recognize and preserve accepted usage, the proposed revision would re-affirm the letter designations for radar, revising the current standard to update it regarding current International Telecommunication Union (ITU) radar band allocations and comments. No change in scope from the current standard is ...


IEEE Standard Radar Definitions

This standard is devoted to providing radar definitions. The standard includes terms formerly found in IEEE Std 172-1971, with the exception of a few terms that are common in both fields, and new and updated terms. IEEE Std 172-1983 was withdrawn in 1983. As radar technology and literature evolve, new terms will be added and obsolete terms deleted.



Jobs related to Sensor

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