Graphene coated microfiber for temperature sensor

A temperature sensor based on graphene coated microfiber is proposed and demonstrated. By depositing graphene onto the microfiber, the transmission optical power changes linearly along the temperature with a sensitivity of 0.03 dB / C°7. © OSA 2014.

Development of thermo-analytical prediction and classification models for food in thermal devices using a multi sensor system and artificial neural networks

Die thermische Verarbeitung von Lebensmitteln beeinflusst deren Qualität und ernährungsphysiologischen Eigenschaften. Im Haushalt ist die Überwachung der Temperatur innerhalb des Lebensmittels sehr schwierig. Zudem ist das Wissen über optimale Temperatur- und Zeitparameter für die verschiedenen Speisen oft unzureichend. Die optimale Steuerung der thermischen Zubereitung ist maßgeblich abhängig von der Art des Lebensmittels und der äußeren und inneren Temperatureinwirkung während des Garvorgangs. Das Ziel der Arbeiten war die Entwicklung eines automatischen Backofens, der in der Lage ist, die Art des Lebensmittels zu erkennen und die Temperatur im Inneren des Lebensmittels während des Backens zu errechnen. Die für die Temperaturberechnung benötigten Daten wurden mit mehreren Sensoren erfasst. Hierzu kam ein Infrarotthermometer, ein Infrarotabstandssensor, eine Kamera, ein Temperatursensor und ein Lambdasonde innerhalb des Ofens zum Einsatz. Ferner wurden eine Wägezelle, ein Strom- sowie Spannungs-Sensor und ein Temperatursensor außerhalb des Ofens genutzt. Die während der Aufheizphase aufgenommen Datensätze ermöglichten das Training mehrerer künstlicher neuronaler Netze, die die verschiedenen Lebensmittel in die entsprechenden Kategorien einordnen konnten, um so das optimale Backprogram auszuwählen. Zur Abschätzung der thermische Diffusivität der Nahrung, die von der Zusammensetzung (Kohlenhydrate, Fett, Protein, Wasser) abhängt, wurden mehrere künstliche neuronale Netze trainiert. Mit Ausnahme des Fettanteils der Lebensmittel konnten alle Komponenten durch verschiedene KNNs mit einem Maximum von 8 versteckten Neuronen ausreichend genau abgeschätzt werden um auf deren Grundlage die Temperatur im inneren des Lebensmittels zu berechnen. Die durchgeführte Arbeit zeigt, dass mit Hilfe verschiedenster Sensoren zur direkten beziehungsweise indirekten Messung der äußeren Eigenschaften der Lebensmittel sowie KNNs für die Kategorisierung und Abschätzung der Lebensmittelzusammensetzung die automatische Erkennung und Berechnung der inneren Temperatur von verschiedensten Lebensmitteln möglich ist.

Temperature Sensing Using Colloidal-Core Photonic Crystal Fiber

We report on a temperature sensor based on the monitoring of the luminescence spectrum of CdSe/ZnS nanocrystals, dispersed in mineral oil and inserted into the core of a photonic crystal fiber. The high overlap between the pump light and the nanocrystals as well as the luminescence guiding provided by the fiber geometry resulted in relatively high luminescence powers and improved optical signal-to-noise ratio (OSNR). Also, both core end interfaces were sealed so as to generate a more stable and robust waveguide structure. Temperature sensitivity experiments indicated a 70 pm/degrees C spectral shift over the 5 degrees C to 90 degrees C range.

Temperature Variation Aware Energy Optimization in Heterogeneous MPSoCs

Thermal effects are rapidly gaining importance in nanometer heterogeneous integrated systems. Increased power density, coupled with spatio-temporal variability of chip workload, cause lateral and vertical temperature non-uniformities (variations) in the chip structure. The assumption of an uniform temperature for a large circuit leads to inaccurate determination of key design parameters. To improve design quality, we need precise estimation of temperature at detailed spatial resolution which is very computationally intensive. Consequently, thermal analysis of the designs needs to be done at multiple levels of granularity. To further investigate the flow of chip/package thermal analysis we exploit the Intel Single Chip Cloud Computer (SCC) and propose a methodology for calibration of SCC on-die temperature sensors. We also develop an infrastructure for online monitoring of SCC temperature sensor readings and SCC power consumption. Having the thermal simulation tool in hand, we propose MiMAPT, an approach for analyzing delay, power and temperature in digital integrated circuits. MiMAPT integrates seamlessly into industrial Front-end and Back-end chip design flows. It accounts for temperature non-uniformities and self-heating while performing analysis. Furthermore, we extend the temperature variation aware analysis of designs to 3D MPSoCs with Wide-I/O DRAM. We improve the DRAM refresh power by considering the lateral and vertical temperature variations in the 3D structure and adapting the per-DRAM-bank refresh period accordingly. We develop an advanced virtual platform which models the performance, power, and thermal behavior of a 3D-integrated MPSoC with Wide-I/O DRAMs in detail. Moving towards real-world multi-core heterogeneous SoC designs, a reconfigurable heterogeneous platform (ZYNQ) is exploited to further study the performance and energy efficiency of various CPU-accelerator data sharing methods in heterogeneous hardware architectures. A complete hardware accelerator featuring clusters of OpenRISC CPUs, with dynamic address remapping capability is built and verified on a real hardware.

Pressure, tilt, temperature and conductivity monitoring within the Immerged Santorini Caldera during Jul 2012 - Sep 2013

Bottom pressure, tilt and seawater physical-properties were monitored for a year using two instruments within the immerged Santorini caldera (Greece). Piggy-backed on the CALDERA2012 cruise, this geodetic experiment was designed to monitor evolution of the 2011-2012 Santorini unrest. Conducted during a quiescent period, it allowed us to study oceanographic and atmospheric signal in our data series. We observe periodic oceanographic signals associated with tides, and seiches that are likely linked to both the caldera and Cretan basin geometries. In winter, the caldera witnesses sudden cooling events that tilt an instrument towards the Southeast, indicating cold-water influx likely originating from the north-western passage between Thirasia and Oia. We do not obtain evidence of long-term vertical seafloor deformation from the pressure signal, although it may be masked by instrumental drift. However, tilt data suggests a local seafloor tilt event ~1 year after the end of the unrest period which could be consistent with inflation under or near Nea Kameni. Seafloor geodetic data recorded at the bottom of the Santorini caldera illustrates that the oceanographic signature is an important part of the signal, which needs to be considered for monitoring volcanic or geological seafloor deformation in shallow-water and/or nearshore areas.