周期反常性

除众所周知的氢在周期表中的位置不确定性、第二周期元素的性质的特殊性之外,本文将详细介绍d区元素的电子排布,第四周期非金属元素最高价态的不稳定性,惰性电子对效应,第五、六周期重过渡元素的相似性和次周期性以及第3族元素编排反常现象并作必要的探讨

元素国150周年盛典

借用拟人化的手法,以舞台剧的形式表现元素周期表的诞生、元素周期律及不同元素的某些特性。国家基础科学人才培养基金项目(J1310024)2017年厦门大学教学改革研究项目(JG20170222

ディジタルオーディオ信号のサンプル欠けの位置推定と修復

ディジタルオーディオ信号データの転送の際，クロック同期の設定を誤ると，サンプルの欠けや重複を生じてしまう．本論文では，このうちサンプル欠けに焦点をしぼり，修復を行う方法について述べる．まず，同期ミスによって起こるサンプル欠けの型を分類した後，一つの評価関数で，サンプル欠けの位置推定，サンプル欠けの型の判定，失われた信号値の復元を行う方法を提案する．次に，評価関数を得る方法として，線形予測誤差を規範とする方法，スパース性を規範とする方法，エントロピーを規範とする方法の三つを定式化する．最後に，それら三つを実験的に比較し，エントロピーを規範とする方法が最も優れていることを示す

ウェーブレットに基づいた地中レーダ受信信号のノイズ除去と埋設管位置標定に関する研究

電気通信大学201

ウェーブレットに基づいた地中レーダ受信信号のノイズ除去と埋設管位置標定に関する研究

電気通信大学201

室内植物表型平台及性状鉴定研究进展和展望

Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perceptivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorised according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future

嶺南通訊 Lingnan Newsletter (第3期)

https://commons.ln.edu.hk/lingnan_newsletter/1002/thumbnail.jp