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As the physical size of a communication system for satellites or unmanned aerial vehicles demands to be reduced, a compact antenna with high directivity is proposed as a core element essential to the wireless device. Instead of using a horn or an array antenna, a unit planar antenna is combined with a surface-modulated lens to convert a low antenna gain to a high antenna gain. The lens is not a metal-patterned PCB but is dielectric, which is neither curved nor very wide. This palm-sized lens comprises pixels with different heights from the backside of PolyPhenylene Sulfide (PPS) as the dielectric base. The antenna gain from the unit antenna of 4.5 cm × 4.5 cm is enhanced by 10 dB with the help of a compact dielectric lens of 7.5 cm × 7.5 cm at 24.5 GHz as the frequency of interest. The antenna design is verified by far-field measurement as well as near-field observation, including sensing a metal object behind a blocking wall by using an RF test bench. Moreover, antenna performance is understood by making a comparison with conventional designs of antennas in terms of directivity and physical sizes. [ABSTRACT FROM AUTHOR]

Graphene epitaxy on the Si face of a SiC wafer offers monolayer graphene with unique crystal orientation at the wafer-scale. However, due to carrier scattering near vicinal steps and excess bilayer stripes, the size of electrically uniform domains is limited to the width of the terraces extending up to a few microns. Nevertheless, the origin of carrier scattering at the SiC vicinal steps has not been clarified so far. A layer-resolved graphene transfer (LRGT) technique enables exfoliation of the epitaxial graphene formed on SiC wafers and transfer to flat Si wafers, which prepares crystallographically single-crystalline monolayer graphene. Because the LRGT flattens the deformed graphene at the terrace edges and permits an access to the graphene formed at the side wall of vicinal steps, components that affect the mobility of graphene formed near the vicinal steps of SiC could be individually investigated. Here, we reveal that the graphene formed at the side walls of step edges is pristine, and scattering near the steps is mainly attributed by the deformation of graphene at step edges of vicinalized SiC while partially from stripes of bilayer graphene. This study suggests that the two-step LRGT can prepare electrically single-domain graphene at the wafer-scale by removing the major possible sources of electrical degradation.

An array antenna for millimeter-wave communication and an array antenna for millimeter-wave sensing are designed and put together into one structure. Because millimeter-wave signals become weaker fast with the increasing distance and any kind of error in the required functions of the antenna has to be minimized, pointing error from the target direction should be prevented. The device is a millimeter-wave sensing antenna with high directivity to check the straight link between the TX and RX sides of wireless communication. A 24 GHz 8-by-16 array antenna which generates stronger signals for sensing resolves the drawback of a 28 GHz 1-by-4 array antenna that is commonly seen in 5G wireless terminals. The sensing and communication antennas are integrated as a planar structure mountable on platforms, which is investigated with regard to forming wireless links over a distance of several meters with an input power of less than 0 dBm. Additionally, in the event of a reflecting surface disturbing the straight path and worsening the pointing error in RF signal transfer, the dual-capability of the combination is presented on the basis of intuitive electromagnetic experiments. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) is increasingly being applied in pathology and cytology, showing promising results. We collected a large dataset of whole slide images (WSIs) of thyroid fine-needle aspiration cytology (FNA), incorporating z-stacking, from institutions across the nation to develop an AI model. Methods: We conducted a multicenter retrospective diagnostic accuracy study using thyroid FNA dataset from the Open AI Dataset Project that consists of digitalized images samples collected from 3 university hospitals and 215 Korean institutions through extensive quality check during the case selection, scanning, labeling, and reviewing process. Multiple z-layer images were captured using three different scanners and image patches were extracted from WSIs and resized after focus fusion and color normalization. We pretested six AI models, determining Inception ResNet v2 as the best model using a subset of dataset, and subsequently tested the final model with total datasets. Additionally, we compared the performance of AI and cytopathologists using randomly selected 1031 image patches and reevaluated the cytopathologists' performance after reference to AI results. Results: A total of 10,332 image patches from 306 thyroid FNAs, comprising 78 malignant (papillary thyroid carcinoma) and 228 benign from 86 institutions were used for the AI training. Inception ResNet v2 achieved highest accuracy of 99.7%, 97.7%, and 94.9% for training, validation, and test dataset, respectively (sensitivity 99.9%, 99.6%, and 100% and specificity 99.6%, 96.4%, and 90.4% for training, validation, and test dataset, respectively). In the comparison between AI and human, AI model showed higher accuracy and specificity than the average expert cytopathologists beyond the two-standard deviation (accuracy 99.71% [95% confidence interval (CI), 99.38–100.00%] vs. 88.91% [95% CI, 86.99–90.83%], sensitivity 99.81% [95% CI, 99.54–100.00%] vs. 87.26% [95% CI, 85.22–89.30%], and specificity 99.61% [95% CI, 99.23–99.99%] vs. 90.58% [95% CI, 88.80–92.36%]). Moreover, after referring to the AI results, the performance of all the experts (accuracy 96%, 95%, and 96%, respectively) and the diagnostic agreement (from 0.64 to 0.84) increased. Conclusions: These results suggest that the application of AI technology to thyroid FNA cytology may improve the diagnostic accuracy as well as intra- and inter-observer variability among pathologists. Further confirmatory research is needed. [ABSTRACT FROM AUTHOR]

Carrier mobility is one of the most important parameters to evaluate the quality and uniformity of graphene. The mobility of graphene is typically extracted from the transconductance of a field-effect transistor fabricated with the graphene layer. However, the mobility value evaluated by this method is imprecise when the contact resistance is non-negligible, or the contact resistance is modulated by the gate bias, which is the case for typical graphene field-effect transistors. Here, we suggest a method for extracting the precise intrinsic field-effect mobility by considering the effective bias across the channel and its gate-induced modulation. We show that the contact resistances of typical graphene field-effect transistors are significantly modulated by gate bias and conventional methods can, therefore, cause a considerable error in the evaluation of the mobility. The proposed method in which the contact-induced error is removed gives a channel-length-independent intrinsic field-effect mobility. This method can be generally used to correctly evaluate the field-effect mobility of nano-scale or low-dimensional materials. [ABSTRACT FROM AUTHOR]