GaN-based MIS-HEMTs with Al2O3 dielectric deposited by low-cost and environmental-friendly mist-CVD technique

We report on the fabrication and characterization of AlGaN/GaN metal-insulator-semiconductor (MIS) capacitors and high-electron-mobility transistors (MIS-HEMTs) using a 5 nm thick Al2O3 dielectric deposited by cost-effective and environmental-friendly mist chemical vapor deposition (mist-CVD) technique. Practically hysteresis-free capacitance–voltage profiles were obtained from the fabricated two-terminal MIS-capacitors indicating high quality of the mist-Al2O3/AlGaN interface. Compared with reference Schottky-gate HEMTs, mist MIS-HEMTs exhibited much improved performance including higher drain current on-to-off ratio, much lower gate leakage current in both forward and reverse directions and lower subthreshold swing. These results demonstrate the potential and viability of non-vacuum mist-CVD Al2O3 in the development of high-performance GaN-based MIS-HEMTs

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001.Peer reviewe

Rapid detection of papillary thyroid carcinoma by fluorescence imaging using a γ-glutamyltranspeptidase-specific probe: a pilot study

Abstract Background Nodular lesions of the thyroid gland, including papillary thyroid carcinoma (PTC), may be difficult to diagnose by imaging, such as in ultrasonic echo testing, or by needle biopsy. Definitive diagnosis is made by pathological examination but takes several days. A more rapid and simple method to clarify whether thyroid nodular lesions are benign or malignant is needed. Fluorescence imaging with γ-glutamyl hydroxymethyl rhodamine green (gGlu-HMRG) uses γ-glutamyltranspeptidase (GGT), a cell-surface enzyme, to hydrolyze the γ-glutamyl peptide and transfer the γ-glutamyl group. GGT is overexpressed in several cancers, such as breast, lung, and liver cancers. This imaging method is rapid and useful for detecting such cancers. In this study, we tried to develop a rapid fluorescence detection method for clinical samples of thyroid cancer, especially papillary carcinoma. Methods Fluorescence imaging with gGlu-HMRG was performed to detect PTC using 23 surgically resected clinical samples. A portable imaging device conveniently captured white-light images and fluorescence images with blue excitation light. Hematoxylin-eosin (HE) staining was used to evaluate which fluorescent regions coincided with cancer, and immunohistochemical examination was used to detect GGT expression. Results All 16 PTC samples exhibited fluorescence after topical application of gGlu-HMRG, whereas the normal sections of each sample showed no fluorescence. HE staining revealed that each fluorescent region corresponded to a region with carcinoma. The PTC samples also exhibited GGT expression, as confirmed by immunohistochemistry. Conclusions All PTC samples were detected by fluorescence imaging with gGlu-HMRG. Thus, fluorescence imaging with gGlu-HMRG is a rapid, simple, and powerful detection tool for PTC

Status of ITER Remote Experimentation Centre

The ITER Remote Experimentation Centre (REC) project (one of the three sub-projects of the InternationalFusion Energy Research Centre (IFERC)) is progressing under the agreement between the Government of Japanand the European Atomic Energy Community for the joint implementation of the Broader Approach (BA) activitiesin the field of fusion energy research. The objectives of the REC activity are to identify the functions andsolve the technical issues for the construction of the REC for ITER at Rokkasho, and to develop the remoteexperiment system and verify the functions required for remote experimentation by using the Satellite Tokamak(JT-60SA) facilities to facilitate the future exploitation of ITER and JT-60SA. The functions of REC will be tested,and the total system will be demonstrated using JT-60SA and existing facilities in the EU, such as JET and WEST.The hardware of the REC has been prepared in Rokkasho Japan, which has the remote experiment room witha large video wall to show the plasma and operation status, IT equipment and a storage system by the reuse ofthe Helios supercomputer tape library. A broadband network infrastructure of 10Gbps has been installed connectedto SINET5. Using this network system, fast data transfer from ITER to REC was examined in 2016, and thetransfer of the data volumes expected for the initial ITER experiments has been demonstrated. A secure remoteexperimentation system has been developed, using JT-60SA, that has functions for preparing and setting of shotparameters, viewing the status of control data, streaming of the plasma status, data-exchange function of shotevents, and monitoring of the facility operation. Remote data analysis techniques, data visualisation software, adocumentation management and experiment planning system and numerical simulation codes for the preparationand performance estimation of discharges have also been developed

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements