Forward by Prof.Xu Mengxia(Research Center of Information Seienee, Peking University, Beijing, China) Prof.Yao Qingdong(DePt.Information and Eleetronies Eng., Zhejiang University, Hangzhou, China) Prof.Gu Funian(DePt.Eleetronies Eng., shanghai Jiaotung University, Shanghai, China) Prof.Yuan Baozhong(Researeh Inst.Information Seienee, Northern Jiaotung University, China) (SePtember 20, 1986)

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Further study of the trace representation of Bent sequences families by KE Pin-hui1, ZHANG Jie2, WEN Qiao-yan2

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A statictical approach to Walfisch-Ikegami model by ZENG Yan-jun1, ZHU Dong-zhao2, QIAN Jing 1, WANG Jia-yi1

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OFDM系统新的无损峰均值比降低方案 附视频 by 卢光跃, 邵朝, 周诠, 1.西安邮电学院通信工程系, 2.西安邮电学院通信工程系, 3.西安空间无线电技术研究所空间微波技术国家重点实验室 陕西西安710061, 4.陕西西安710061, 5.陕西西安710000

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多描述编码的研究现状及其展望 附视频 by 赵谊虹, 余松煜, 程国华, 1.上海交通大学图像通信与信息处理研究所, 2.上海交通大学图像通信与信息处理研究所, 3.上海交通大学图像通信与信息处理研究所 上海200030, 4.上海200030, 5.上海200030

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采用遗传算法的低轨区域通信星座优化设计 附视频... by 郦苏丹, 朱江, 李广侠, 1.解放军理工大学通信工程学院, 2.解放军理工大学通信工程学院, 3.解放军理工大学通信工程学院 江苏南京210007, 4.江苏南京210007, 5.江苏南京210007

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The Studies of Buffer Control Strategy of 128 kb/s Videophone System by 王海松, 何芸

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The Only Way into Digital Audio Broadcasting by 孙金荣, 李晓芳

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视频故事结构的描述与浏览 附视频 by 王东辉, 钱徽, 朱淼良, 1.浙江大学计算机科学与技术学院, 2.浙江大学计算机科学与技术学院, 3.浙江大学计算机科学与技术学院 浙江杭州310027, 4.浙江杭州310027, 5.浙江杭州310027

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A multi-dimensional student performance prediction model (MSPP): An advanced framework for accurate academic classification and analysis by V. Balachandar, K. Venkatesh

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Silicon Nitride Spot-Size Converter with Coupling Loss < 1.5 dB for Both Polarizations at 1W Optical Input by Enge Zhang, Yu Zhang, Lei Zhang, Xu Yang

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Projection-Type Holographic Display With Observation Area Expanded Using Time-Division Reproduced Light Generation System by Tomoki Kanno, Yasuyuki Ichihashi, Takashi Kakue, Koki Wakunami, Ryutaro Oi, Tomoyoshi Shimobaba, Tomoyoshi Ito

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Ecological Protection Technology of Spraying Vegetation Concrete on Carbonaceous Rock Slope Experimental Research and Application by Zhenchao Chang, Junhui Luo, Qiongzhen Tang, Ling Zeng, Hongri Zhang, Youjun Li, Gexiao Yang, Liming Zhang

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Reversible Spectral Speech Watermarking with Variable Embedding Locations Against Spectrum-Based Attacks by Xuping Huang, Akinori Ito

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Managing natural disasters: An analysis of technological advancements, opportunities, and challenges by Moez Krichen, Mohamed S. Abdalzaher, Mohamed Elwekeil, Mostafa M. Fouda

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AI anxiety: Explication and exploration of effect on state anxiety when interacting with AI doctors by Hyun Yang, S. Shyam Sundar

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Towards Resilient Marine Ecosystems: EcoSensitivity as an Operational Model for Strategic Oil Spill Management by Anneliis Kõivupuu, Mihhail Fetissov, Robert Aps, Helen Orav-Kotta, Valtteri Laine, Mirka Laurila-Pant, Merli Rätsep, Jonne Kotta

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Fault-Tolerant One-Way Noiseless Amplification for Microwave Bosonic Quantum Information Processing by Hany Khalifa, Riku Jantti, Gheorghe Sorin Paraoanu

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Electromagnetic signal modulation recognition technology based on lightweight deep neural network by Sicheng ZHANG, Yun LIN, Ya TU, Shiwen Mao

“…In response to the trend that in the 6th generation wireless (6G) era,mobile communications and artificial intelligence will be closely integrated,and a huge number of edge intelligent signal processing nodes will be deployed,an efficient and intelligent electromagnetic signal recognition model was proposed,which could be deployed on resource-constrained edge devices.The constellation diagram of electromagnetic signal was firstly drawn to visualize electromagnetic signal as a two-dimensional image,and color the constellation diagram according to the normalized point density to achieve feature enhancement.Then,a binary deep neural network was adopted to recognize the colored constellation diagrams.It was shown that the approach can guarantee a high recognition accuracy,which significantly reduced the model storage and calculation costs.For verification,the proposed approach was applied to the problem of electromagnetic signal modulation recognition.The experiment uses eight commonly used digital modulation signals and selects additive white Gaussian noise as the channel environment.The experimental results show that the scheme can achieve a comprehensive recognition rate of 96.1% under the noise condition of -6～6 dB,while the size of the network model is only 166 KB.Further,the execution time,when executed on a Raspberry Pi 4B,is only 290 ms.Compared to a full-precision network of the same scale,the accuracy is increased by 0.6%,the model is reduced to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <mfrac> <mtext>1</mtext> <mrow> <mtext>26</mtext><mo>.…”
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Optimal downlink power allocation schemes for OFDM-NOMA-based Internet of things by Ya Gao, Fei Yu, Haoran Zhang, Yongpeng Shi, Yujie Xia

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