Z SYMBOL NAME ETYMOLOGY SELECTED APPLICATIONS 61 PM Promethium after the TitanPrometheus, who Nuclear batteries brought fire to mortals. 62 SM Samarium after mine official,Vasili Samarsky-Bykhovets. 63 EU Europium after the continent of Europe. 64 GD 65 TB 66 DY 67 Rare-earth magnets, lasers, neutron capture, masers Red and blue phosphors, lasers, mercury-vapor lamps, fluorescent lamps, NMR relaxation agent Gadolinium after Johan Gadolin (1760– Rare-earth magnets, high refractive index glass or garnets, lasers, X-ray 1852), to honor his investigation tubes,computer memories, neutron capture, MRI contrast of rare earths. agent, NMR relaxation agent, magnetostrictive alloys such as Galfenol, steel additive Terbium after the village of Ytterby, Sweden. Dysprosium from the Greek "dysprositos", meaning hard to get. Green phosphors, lasers, fluorescent lamps, magnetostrictive alloys such as Terfenol-D Rare-earth magnets, lasers, magnetostrictive alloys such as Terfenol-D HO Holmium Latin, "Holmia"), native city of one of its discoverers. Lasers, wavelength calibration standards for optical spectrophotometers, magnets 68 ER Erbium 69 TM Thulium after the village of Ytterby, Infrared lasers, vanadium steel, fiber-optic technology Sweden. after the mythological northern Portable X-ray machines, metal-halide lamps, lasers land of Thule. 70 YB Ytterbium 71 LU Lutetium after the village of Ytterby, Sweden. after Lutetia, the city which later became Paris. Infrared lasers, chemical reducing agent, decoy flares, stainless steel, stress gauges, nuclear medicine Positron emission tomography – PET scan detectors, high refractive index glass,lutetium tantalate hosts for phosphors
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Secure Sensor Communication Sensor Sensor n Data Manager Sensor a Data Sensor Communication Subsystem Sensor Sensor on Data Manager Sensor a Data Cluster A: Unclassified Sensors Sensor Data Manager aSensor Data Communication Subsystem Sensor Sensor n Data Manager Cluster B: Classified Sensors a Sensor Data
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Example Electronic Sensor Systems • Components vary with application – digital sensor within an instrument • microcontroller – signal timing – data storage sensor µC sensor signal timing memory keypad display handheld instrument – analog sensor analyzed by a PC sensor sensor interface e.g., RS232 A/D, communication signal processing PC comm. card – multiple sensors displayed over internet internet sensor processor comm. sensor bus PC sensor bus comm. card ECE 480, Prof. A. Mason sensor processor comm. Sensors p.4
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Observations vs. Models Other considerations for observational data accuracy - how well the sensor measures the environment in an absolute sense resolution / precision - the ability of a sensor to see small differences in readings e.g. a temperature sensor may have a resolution of 0.000,01º C, but only be accurate to 0.001º C range - the maximum and minimum value range over which a sensor works well. repeatability - ability of a sensor to repeat a measurement when put back in the same environment - often directly related to accuracy drift/stability - low frequency change in a sensor with time - often associated with electronic aging of components or reference standards in the sensor hysteresis - present state depends on previous state; e.g., a linear up and down input to a sensor, results in output that lags the input, i.e., one curve on increasing pressure, and another on decreasing response time - typically estimated as the frequency response of a sensor assuming exponential behavior self heating - to measure the resistance in the thermistor to measure temperature, we need to put current through it settling time - the time for the sensor to reach a stable output once it is turned on Sundermeyer MAR 550 Spring 2013 13
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Gas Sensor Gyro Accelerometer Pendulum Resistive Tilt Sensors Metal Detector Piezo Bend Sensor Gieger-Muller Radiation Sensor Pyroelectric Detector UV Detector Resistive Bend Sensors Digital Infrared Ranging CDS Cell Resistive Light Sensor Pressure Switch Miniature Polaroid Sensor Limit Switch Touch Switch Mechanical Tilt Sensors IR Pin Diode IR Sensor w/lens Thyristor IR Reflection Sensor Magnetic Sensor Magnetic Reed Switch IR Amplifier Sensor Hall Effect Magnetic Field Sensors Polaroid Sensor Board IRDA Transceiver Lite-On IR Remote Receiver Radio Shack Remote Receiver IR Modulator Receiver Copyright Prentice Hall, 2001 Solar Cell Compass Compass 2 Piezo Ultrasonic Transducers
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Magnetostrictive Pos. Sensor • Pulse sent down magnetostrictive material • Pulse reflects off position magnet’s field • Position is proportional to trcvd - tsent • Pulse propagates at ~2800 m/s • Resolution is ~.001” with tupdate ~1msec/in.
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Magnetostrictive Pos. Sensor • Pulse sent down magnetostrictive material • Pulse reflects off position magnet’s field • Position is proportional to trcvd - tsent • Pulse propagates at ~2800 m/s • Resolution is ~.001” with tupdate ~1msec/in.
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Magnetostrictive Pos. Sensor • Pulse sent down magnetostrictive material • Pulse reflects off position magnet’s field • Position is proportional to trcvd - tsent • Pulse propagates at ~2800 m/s • Resolution is ~.001” with tupdate ~1msec/in.
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Comparison against a Flat Sensor NoC Infrastructure Core and SDP num. Latency type Flat sensor NoC (cycles) Our method (cycles) Latency reduction w.r.t. flat sensor NoC (%) 256 core (4 SDP) Inter-SDP 45.43 7.85 82.72 Total 62.82 25.24 59.82 512 core (8 SDP) Inter-SDP 67.57 11.37 83.17 Total 84.96 28.76 66.15 1024 core (16 SDP) Inter-SDP 90.36 14.38 84.08 107.75 31.77 70.52 Total • Simulated using a modified Popnet, one SDP per 64 core is chosen, sensor data from the memory layer included • Compare the latency of our infrastructure versus a flat sensor NoC infrastructure – Only sensor routers, no SDP NoC – Packets between SDPs (inter-SDP) are transmitted using sensor routers • Our infrastructure scientifically reduce the inter-SDP (>82%) and total latency (>59%) • Hardware cost increase with respect to the flat sensor NoC less than 6% • Our infrastructure provides higher throughput versus the flat sensor NoC 27
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Navigation Path Example = screen = state Diagnostics Menu •User moves cursor to Control Panel or Graph Graph = screen = state Control panel = screen = state • User selects data group and • User selects functionality of sensors type of graph Define = screen = state • User defines a sensor event from a list of events Enable • User can enable a sensor event from a list of sensor events List of events • User selects event(s) Bernd Bruegge & Allen H. Dutoit Disable • User can disable a sensor event from a list of sensor events List of sensor events • User selects sensor event(s) Selection = screen = state • User selects data group • Field site • Car • Sensor group • Time range • User selects type of graph • time line • histogram • pie chart Visualize • User views graph • User can add data groups for being viewed Link • User makes a link (doclink) Object-Oriented Software Engineering: Using UML, Patterns, and Java 23
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Maji Safi Jason Arble, Peter Balkam, Kaitlin Menzie, Minh Tran Faculty Advisor: Prof. Paul Siqueira Abstract System Overview Maji Safi is a remotely controlled device allowing users with a SMS-enabled cell phone to access measurements of physical water parameters where the device is deployed. Users text the device to issue commands to detect the presence of water, return turbidity, temperature, and pH measurements. The device then texts a web server (running Twilio’s web API) to report measurements. The server interprets the measurements and reports back to users via SMS. Our device targets countries experiencing economic water scarcity, where lack of information regarding the water quality of unimproved water resources leads to a poor water-delivery infrastructure. Admins ● Water Detector (yellow) ● Temperature Sensor (violet) ● Turbidity Sensor (green) ● pH Sensor (red) Data can be used to supplement chemical testing, allowing officials to monitor sanitation and correlate water conditions over time with growth of bacteria. People Scientists Device Control Request Block Diagram Application Control Signals Sensor Readings Sensor Readings Sensing The diagram (above left) are example use cases. We intend to have it be used as a scientific instrument, tool in the design and regulation of water distribution infrastructure, and a means by which locals learn about the water around them. Above right illustrates possible deployment sites: refined wells, unprotected wells, and small bodies of exposed surface water. Device Specifications Parameters Goal Actual/Dev. Device Volume 4400 ㎠ 4000 ㎠ Housing Water-Resistant, Buoyant Water-Resistant, Buoyant Server-User Response Time < 5 minutes < 1 minute Phone-User Response Time < 1 minute < 30 seconds Turbidity Sensor Accuracy ± 0.01-5 NTUs. ± 100 NTU pH Sensor Accuracy ± 0.5 ±1 Temperature Sensor Accuracy ± 3°C ± 3°C Power Consumption ≅ 1.4 W 1.35 W Battery Life 12 hours 18.5 hours Deployment Duration 1-3 Months 1-3 months SMS Text($)/Day $0.01-$0.03 (1 SMS/Day) $1.00-$5.00 (1 SMS/Day) Cost/Device <$100 $227.92 ● Solar panel extends the deployment time, allowing continuous measurements to be taken for weeks or months. ● Sensors are fairly accurate, power efficient, but could be improved upon or specialized for different applications Water Detector The water detector uses simple voltage divider (shown right) whose output voltage is read by the microcontroller. If this voltage is between a threshold between one and two volts and makes a binary decision on whether or not there is water. This threshold was determined from an experiment involving measuring the resistance of different states of water and calculating the output voltage. This data is shown in the table to the right. Substance Impedance (1 cm) Output Voltage Tap water 86 kΩ 1.50 V Sugar Water 99 kΩ 1.65 V Oatmeal Water 75 kΩ 1.36 V Garlic Salt Water Rice Water 65 kΩ 1.23 V 90 kΩ 1.55 V Mud 4 MΩ 4.76 V Acknowledgement Special thanks to our advisor Professor Paul Siqueira, our evaluators Sandip Kundu and Hossein Pishro-Nik, Kris Hollot, Francis Caron and Professor John E.Tobiason. Department of Electrical and Computer Engineering ECE 415/ECE 416 – SENIOR DESIGN PROJECT 2016 College of Engineering - University of Massachusetts Amherst SDP16
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