IEICE Trans. Electron.最新文献_第10页

Volume Integral Equations Combined with Orthogonality of Modes for Analysis of Two-Dimensional Optical Slab Waveguide 结合模正交性的体积积分方程分析二维平板光波导

IEICE Trans. Electron. Pub Date : 2022-01-01 DOI: 10.1587/transele.2021rep0002

Masahiro Tanaka

引用次数: 0

X-Band GaN Chipsets for Cost-Effective 20W T/R Modules 用于低成本20W T/R模块的x波段GaN芯片组

IEICE Trans. Electron. Pub Date : 2022-01-01 DOI: 10.1587/transele.2021ecp5024

J. Kamioka, Yoshifumi Kawamura, Ryota Komaru, M. Hangai, Y. Kamo, Tetsuo Kodera, S. Shinjo

{"title":"X-Band GaN Chipsets for Cost-Effective 20W T/R Modules","authors":"J. Kamioka, Yoshifumi Kawamura, Ryota Komaru, M. Hangai, Y. Kamo, Tetsuo Kodera, S. Shinjo","doi":"10.1587/transele.2021ecp5024","DOIUrl":"https://doi.org/10.1587/transele.2021ecp5024","url":null,"abstract":"SUMMARY This paper reports on X-band Gallium Nitride (GaN) chipsets for cost-e ﬀ ective 20W transmit-receive (T / R) modules. The chipset components include a GaN-on-Si monolithic microwave integrated circuit (MMIC) driver ampliﬁer (DA), a GaN-on-SiC high power ampli-ﬁer (HPA) with GaAs matching circuits, a high-gain GaN-on-Si HPA with a GaAs output matching circuit, and a GaN-on-Si MMIC switch (SW). By utilizing either combination of the DA or single high-gain HPA, the conﬁgurations of two T / R module types can be realized. The GaN-on-Si MMIC DA demonstrates an output power of 6.4–7.4W, an associate gain of 22.3–24.6dB and a power added e ﬃ ciency (PAE) of 32–36% over 9.0– 11.0GHz. A GaN-on-SiC HPA with GaAs matching circuits exhibited an output power of 20–28W, associate gain of 7.8–10.7dB, and a PAE of 40– 56% over 9.0–11.0GHz. The high-gain GaN-on-Si HPA with a GaAs output matching circuit exhibits an output power of 15–30W, associate gain of 27–30dB, and PAE of 26–33% over 9.0–11.0GHz. The GaN-on-Si MMIC switch demonstrates insertion losses of 1.1–1.3dB and isolation of 10.1– 14.7dB over 8.0–11.5GHz. By employing cost-e ﬀ ective circuit conﬁgu-rations, the costs of these chipsets are estimated to be about half that of conventional chipsets","PeriodicalId":13259,"journal":{"name":"IEICE Trans. Electron.","volume":"44 1","pages":"194-202"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85978954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of GaN MMIC Power Amplifier Technologies for Millimeter-Wave Applications 毫米波应用GaN MMIC功率放大器技术综述

IEICE Trans. Electron. Pub Date : 2022-01-01 DOI: 10.1587/transele.2022mmi0006

K. Nakatani, Y. Yamaguchi, Takuma Torii, M. Tsuru

引用次数: 4

SRAM: A Septum-Type Polarizer Design Method Based on Superposed Even- and Odd-Mode Excitation Analysis SRAM:一种基于奇偶模叠加激励分析的隔膜型偏振片设计方法

IEICE Trans. Electron. Pub Date : 2022-01-01 DOI: 10.1587/transele.2021ecp5012

Tomoki Kaneko, H. Saito, A. Hirose

引用次数: 2

A Discussion on Physical Optics Approximation for Edge Diffraction by A Conducting Wedge 关于导楔边缘衍射的物理光学近似的讨论

IEICE Trans. Electron. Pub Date : 2022-01-01 DOI: 10.1587/transele.2021ecp5031

Duc Minh Nguyen, H. Shirai

引用次数: 0

Deep-Learning-Assisted Single-Pixel Imaging for Gesture Recognition in Consideration of Privacy 考虑隐私的深度学习辅助单像素手势识别

IEICE Trans. Electron. Pub Date : 2022-01-01 DOI: 10.1587/transele.2021dii0002

Naoya Mukojima, Masaki Yasugi, Y. Mizutani, T. Yasui, Hirotsugu Yamamoto

{"title":"Deep-Learning-Assisted Single-Pixel Imaging for Gesture Recognition in Consideration of Privacy","authors":"Naoya Mukojima, Masaki Yasugi, Y. Mizutani, T. Yasui, Hirotsugu Yamamoto","doi":"10.1587/transele.2021dii0002","DOIUrl":"https://doi.org/10.1587/transele.2021dii0002","url":null,"abstract":"SUMMARY We have utilized single-pixel imaging and deep-learning to solve the privacy-preserving problem in gesture recognition for interactive display. Silhouette images of hand gestures were acquired by use of a display panel as an illumination. Reconstructions of gesture images have been performed by numerical experiments on single-pixel imaging by changing the number of illumination mask patterns. For the training and the image restoration with deep learning, we prepared reconstructed data with 250 and 500 illuminations as datasets. For each of the 250 and 500 illuminations, we prepared 9000 datasets in which original images and reconstructed data were paired. Of these data, 8500 data were used for training a neural network (6800 data for training and 1700 data for validation), and 500 data were used to evaluate the accuracy of image restoration. Our neural network, based on U-net, was able to restore images close to the original images even from reconstructed data with greatly reduced number of illuminations, which is 1/40 of the single-pixel imaging without deep learning. Compared restoration accuracy between cases using shadowgraph (black on white background) and negative-positive reversed images (white on black background) as silhouette image, the accuracy of the restored image was lower for negative-positive-reversed images when the number of illuminations was small. Moreover, we found that the restoration accuracy decreased in the order of rock, scissor, and paper. Shadowgraph is suitable for gesture silhouette, and it is necessary to prepare training data and construct neural networks, to avoid the restoration accuracy between gestures when further reducing the number of illuminations.","PeriodicalId":13259,"journal":{"name":"IEICE Trans. Electron.","volume":"735 1","pages":"79-85"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78763694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Analysis of Efficiency-Limiting Factors Resulting from Transistor Current Source on Class-F and Inverse Class-F Power Amplifiers f类和反f类功率放大器晶体管电流源限制效率的因素分析

IEICE Trans. Electron. Pub Date : 2022-01-01 DOI: 10.1587/transele.2022mmi0003

H. Yamamoto, K. Kikuchi, V. Vadalà, G. Bosi, A. Raffo, G. Vannini

引用次数: 3

28nm Atom-Switch FPGA: Static Timing Analysis and Evaluation 28nm原子开关FPGA:静态时序分析与评估

IEICE Trans. Electron. Pub Date : 2022-01-01 DOI: 10.1587/transele.2021fus0005

X. Bai, R. Nebashi, M. Miyamura, Kazunori Funahashi, N. Banno, K. Okamoto, Hideaki Numata, N. Iguchi, T. Sugibayashi, T. Sakamoto, M. Tada

引用次数: 0

A Self-Powered Flyback Pulse Resonant Circuit for Combined Piezoelectric and Thermoelectric Energy Harvesting 一种用于压电和热电联合能量收集的自供电反激脉冲谐振电路

IEICE Trans. Electron. Pub Date : 2022-01-01 DOI: 10.1587/transele.2021ecp5003

Huakang Xia, Yidie Ye, Xiudeng Wang, Ge Shi, Zhidong Chen, Libo Qian, Yinshui Xia

引用次数: 0

Sputtering Gas Pressure Dependence on the LaBxNy Insulator Formation for Pentacene-Based Back-Gate Type Floating-Gate Memory with an Amorphous Rubrene Passivation Layer 溅射气体压力对具有非晶Rubrene钝化层的五苯基背栅式浮栅存储器LaBxNy绝缘体形成的影响

IEICE Trans. Electron. Pub Date : 2022-01-01 DOI: 10.1587/transele.2021fup0005

E. Hong, K. Park, S. Ohmi

引用次数: 0