-optical study of nanoscale Al-Si-truncated conical photodetector with subwavelength aperture," J. Nanophoton. 11(4), 046021 (2017), doi: 10.1117/1.JNP.11.046021.
A new nanoscale silicon-based modulator has been investigated at different temperatures. In addition to these two advantages, nanoscale dimensions (versus MEMS temperature sensors) and integrated silicon-based material (versus polymers), the third novelty of such optoelectronic device is that it can be activated as a Silicon-On-Insulator Photoactivated Modulator (SOIPAM) or as a Silicon-On-Insulator Thermoactivated Modulator (SOITAM). In this work, static and time dependent temperature effects on the current have been investigated. The aim of the time dependent temperature simulation was to set a temporal pulse and to check, for given dimensions, how much time would it take for the temperature profile and for the change in the electrons’ concentration to come back to the steady state. Assuring that the thermal response is fast enough, the device can be operated as a modulator via thermal stimulation or, on the other hand, can be used as thermal sensor/imager. We present here the design, simulation, and model of the second generation which seems capable of speeding up the processing capabilities. This novel device can serve as a building block towards the development of optical/thermal data processing while breaking through the way to all optic processors based on silicon chips that are fabricated via typical microelectronics fabrication process.
Aim/Purpose: The aim of this study was to develop a prototype of an information-generating computer tool designed to automatically map the dental restorations in a panoramic radiograph. Background: A panoramic radiograph is an external dental radiograph of the oro-maxillofacial region, obtained with minimal discomfort and significantly lower radiation dose compared to full mouth intra-oral radiographs or cone-beam computed tomography (CBCT) imaging. Currently, however, a radiologic informative report is not regularly designed for a panoramic radiograph, and the referring doctor needs to interpret the panoramic radiograph manually, according to his own judgment. Methodology: An algorithm, based on techniques of computer vision and machine learning, was developed to automatically detect and classify dental restorations in a panoramic radiograph, such as fillings, crowns, root canal treatments and implants. An experienced dentist evaluated 63 panoramic anonymized images and marked on them, manually, 316 various restorations. The images were automatically cropped to obtain a region of interest (ROI) containing only the upper and lower alveolar ridges. The algorithm automatically segmented the restorations using a local adaptive threshold. In order to improve detection of the dental restorations, morphological operations such as opening, closing and hole-filling were employed. Since each restoration is characterized by a unique shape and unique gray level distribution, 20 numerical features describing the contour and the texture were extracted in order to classify the restorations. Twenty-two different machine learning models were evaluated, using a cross-validation approach, to automatically classify the dental restorations into 9 categories. Contribution: The computer tool will provide automatic detection and classification of dental restorations, as an initial step toward automatic detection of oral pathologies in a panoramic radiograph. The use of this algorithm will aid in generating a radiologic report which includes all the information required to improve patient management and treatment outcome. Findings: The automatic cropping of the ROI in the panoramic radiographs, in order to include only the alveolar ridges, was successful in 97% of the cases. The developed algorithm for detection and classification of the dental restorations correctly detected 95% of the restorations. ‘Weighted k-NN’ was the machine-learning model that yielded the best classification rate of the dental restorations - 92%. Impact on Society: Information that will be extracted automatically from the panoramic image will provide a reliable, reproducible radiographic report, currently unavailable, which will assist the clinician as well as improve patients’ reliance on the diagnosis. Future Research: The algorithm for automatic detection and classification of dental restorations in panoramic imaging must be trained on a larger dataset to improve the results. This algorithm will then be used as a preliminary stage for automatically detecting incidental oral pathologies exhibited in the panoramic images.
An advanced Surface-Enhanced Raman Scattering (SERS) Nanosensor Array, dedicated to serve in the future as a pH imager for the real-time detection of chemical reaction, is presented. The full flow of elementary steps—architecture, design, simulations, fabrication, and preliminary experimental results of structural characterization (Focused Ion Beam (FIB), TEM and SEM)—show an advanced SERS pixel array that is capable of providing spatially resolved measurements of chemical pH in a fluid target that became more than desirable in this period. Ultimately, the goal will be to provide real-time monitoring of a chemical reaction. The pixels consist of a nanostructured substrate composed of an array of projections or cavities. The shape of the nanostructures and the thickness of the metallic (Ag or Au) layer can be tuned to give maximal enhancement at the desired wavelength. The number and arrangement of nanostructures is optimized to obtain maximal responsivity.
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