This article reports the functionality of Paint/PMN-PT and Paint/PLZT composite films for use in pyroelectric infrared sensors and energy conversion devices. Smart Paint/Lead Magnesium Niobate-Lead Titanate (Paint/PMN-PT) and Paint/Lead Lanthanum Zirconate Titanate (Paint-PLZT) nanocomposite films have been fabricated by the conventional paint-brushing technique on copper substrate. The pyroelectric, piezoelectric, and dielectric properties of the composite films were measured for their use in uncooled infrared detectors and thermal energy conversion devices. The properties investigated include: dielectric constants (epsilon' and epsilon''); pyroelectric coefficient (p); and energy conversion performance. From the foregoing parameters, material’s figure-of-merits, for infrared detection and thermal energy conversion, were calculated. The results indicated that paint composite films are functional and figure-of-merits increase with increase in amount of PMN-PT and PLZT nanoparticles in paint matrix. Based on the preliminary results obtained, composite films are reasonably attractive for use in uncooled thermal sensing elements, and thermal energy conversion devices for low power applications, especially in applications where flexible and curved surface sensors are required. With these factors in consideration, a novel cantilever system is designed and examined for its performance. The highest voltage output and power accomplished were 65 mV and 1 nano-Watts, respectively for a particular structure with a broad frequency response operating in the 31 mode of Paint/PMN-PT based harvester. Efforts have been made to investigate the performance of nanocomposite films on copper substrate to mechanical vibrations and thermal variations as well. Thus, could be utilized for energy scavenging combining piezoelectric and pyroelectric effects.
In this paper we will discuss the utilization of a set of waveforms derived from chaotic dynamical systems for
compression and feature recognition in digital images. We will also describe the design and testing of an embedded
systems implementation of the algorithm. We will show that a limited set of combined chaotic oscillations are sufficient
to form a basis for the compression of thousands of digital images. We will demonstrate this in the analysis of images
extracted from the solar heliospheric observatory (SOHO), showing that we are able to detect coronal mass ejections
(CMEs) in quadrants of the image data during a severe solar event. We undertake hardware design in order to optimize
the speed of the algorithm, taking advantage of its parallel nature. We compare the calculation speed of the algorithm in
compiled C, enhanced Matlab, Simulink, and in hardware.
Solar images taken at different wavelengths enable scientists to visualize and analyze the suns activities. The Solar
Dynamics Observatory (SDO) provides high-resolution images of the sun, with cadence in seconds, taken at varying
wavelengths, resulting in finely detailed, almost continuous data for researcher's examination. We propose an
approach to find active regions and coronal holes that involves shifted means based segmentation, and voting based
edge linking to link fragments combined with Moore's neighbor tracing algorithm to highlight the regions of
interest. This approach is illustrated by using the images taken by the AIA telescopes onboard of the SDO mission.
We obtain a segmented image that clearly isolates the active regions. Moreover this method is comparatively faster
than the commonly used fuzzy logic based methods. This method is capable of forming a foundation for the analysis
of various other features of the sun like detection of prominences.
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