No description available.

No description available.

In this research, an AHPL (A Hardware Programming Language) based automation system is used to design and verify the Intel-8080 microprocessor from the RTL (Register Transfer Level) hardware description through the network list of transistors. The HPSIM is used as a RTL simulator which interprets the AHPL description and executes the connections, branches, and register transfer, and prints line or register values for each circuit clock period. After the AHPL description has been translated to switch-level link list, ESIM is applied for more detailed simulation to ensure the digital behavior in this microprocessor design is correct. The ESIM is an event-driven switch-level simulator which accepts commands from the user, and executes each command before reading the next one. After performing these different levels of simulations, a comparison is discussed at the end.

The use of Computer Hardware Description Languages plays an important role in the design automation process of digital systems. These languages help hardware engineers to provide a precise description of the internal structure of a system, and one of the most significant uses of these languages is as a means of input to a system simulator. AHPL is a hardware description language that describes a digital system as a set of modules and units. This language is supported by a function-level simulator (HPSIM2), but the simulator only provides support to the module descriptions of a system. This paper presents an improved version of the simulator that supports the use of unit descriptions called Combinational Logic Units or CLUNITs. The syntax and structure of a CLUNIT is analyzed, the operation and data structure of the simulator is given; and several examples are given to support these discussions.

No description available.

No description available.

No description available.

No description available.

梁佩嫦. / 手稿本 (cops. 283 複印本) / Thesis (M.A.)--香港中文大學硏究院語文學部. / Shou gao ben (cops. 283 fu yin ben) / Includes bibliographical references (leaves 61-75). / Liang Peichang. / Thesis (M.A.)--Xianggang Zhong wen da xue yan jiu yuan yu wen xue bu. / 引言 --- p.1-3 / Chapter 第一章 --- 『物色』的定義及溯源 --- p.4-85 / Chapter 甲 --- 定義 --- p.5-27 / Chapter 乙 --- 溯源 --- p.28-85 / Chapter 1 --- 詩經中的即物起興 --- p.28-47 / Chapter 2 --- 屈宗辭賦中的景物　 --- p.48-69 / Chapter 3 --- 漢賦中情景之結合 --- p.70-85 / Chapter 第二章 --- 《文選》「物色賦」四篇分析 --- p.86-171 / Chapter 甲 --- 宗玉《風賦》 --- p.95-118 / Chapter 乙 --- 潘岳〈秋興賦〉 --- p.119-138 / Chapter 丙 --- 謝惠連〈雪賦〉　 --- p.139-152 / Chapter 丁 --- 謝莊〈月賦〉 --- p.153-171 / Chapter 第三章 --- 六朝賦「物色」選例　　 --- p.172-263 / Chapter 甲 --- 時代喪亂的寫照 --- p.175-188 / Chapter 1 --- 曹丕〈感物賦〉 / Chapter 2 --- 王粲〈登樓賦〉 / Chapter 3 --- 鮑照〈蕪城賦〉 / Chapter 乙 --- 佛老思想的反映 --- p.189-201 / Chapter 1 --- 孫綽〈登天台小賦〉 / Chapter 2 --- 湛方生〈風賦〉〈懷春賦〉等 / Chapter 3 --- 謝靈運〈山居賦〉 / Chapter 丙 --- 描述隱居山林　 --- p.202-206 / Chapter 1 --- 潘岳〈閒居賦〉 / Chapter 2 --- 陸倕〈歸田賦〉 / Chapter 丁 --- 記敘羈旅官遊 --- p.206-221 / Chapter 1 --- 陸機〈述思賦〉等 / Chapter 2 --- 謝靈運〈歸塗賦〉 / Chapter 3 --- 鮑照〈遊思賦〉 / Chapter 4 --- 江淹〈去故鄉賦〉等 / Chapter 戊 --- 抒發生離死別 --- p.222-246 / Chapter 1 --- 曹丕〈柳賦〉等 / Chapter 2 --- 陸機〈思親賦〉等 / Chapter 3 --- 江淹〈別賦〉〈恨賦〉 / Chapter 4 --- 梁簡文帝〈秋興賦〉〈臨秋賦〉、梁元帝〈蕩婦秋思賦〉 / Chapter 己　 --- 應制酬唱 --- p.247-263 / Chapter 1 --- 二曹〈愁霖賦〉〈喜霽賦〉等 / Chapter 2 --- 謝朓〈七夕賦奉護軍命作〉 / Chapter 3 --- 庾信〈春賦〉 / Chapter 第四章 --- 「物色」與六朝人生活與風尚的關係 --- p.264-307 / Chapter 甲 --- 貴遊風氣 --- p.264-285 / Chapter 乙 --- 登山臨水，巖棲山居 --- p.226-301 / Chapter 丙 --- 構築庭園　 --- p.302-307 / Chapter 第五章 --- 六朝賦中「物色」的技巧 --- p.308-371 / Chapter 甲 --- 設色 --- p.310-326 / Chapter 乙 --- 練字 --- p.327-357 / Chapter 丙 --- 音律 --- p.358-371 / Chapter 第六章 --- 結論 --- p.372-388 / 注釋 --- p.1-60 / 參考書目 --- p.61-74

Anomaly detection is a fundamental research topic that has been widely investigated. From critical industrial systems, e.g., network intrusion detection systems, to people’s daily activities, e.g., mobile fraud detection, anomaly detection has become the very first vital resort to protect and secure public and personal properties. Although anomaly detection methods have been under consistent development over the years, the explosive growth of data volume and the continued dramatic variation of data patterns pose great challenges on the anomaly detection systems and are fuelling the great demand of introducing more intelligent anomaly detection methods with distinct characteristics to cope with various needs. To this end, this thesis starts with presenting a thorough review of existing anomaly detection strategies and methods. The advantageous and disadvantageous of the strategies and methods are elaborated. Afterward, four distinctive anomaly detection methods, especially for time series, are proposed in this work aiming at resolving specific needs of anomaly detection under different scenarios, e.g., enhanced accuracy, interpretable results, and self-evolving models. Experiments are presented and analysed to offer a better understanding of the performance of the methods and their distinct features. To be more specific, the abstracts of the key contents in this thesis are listed as follows: 1) Support Vector Data Description (SVDD) is investigated as a primary method to fulfill accurate anomaly detection. The applicability of SVDD over noisy time series datasets is carefully examined and it is demonstrated that relaxing the decision boundary of SVDD always results in better accuracy in network time series anomaly detection. Theoretical analysis of the parameter utilised in the model is also presented to ensure the validity of the relaxation of the decision boundary. 2) To support a clear explanation of the detected time series anomalies, i.e., anomaly interpretation, the periodic pattern of time series data is considered as the contextual information to be integrated into SVDD for anomaly detection. The formulation of SVDD with contextual information maintains multiple discriminants which help in distinguishing the root causes of the anomalies. 3) In an attempt to further analyse a dataset for anomaly detection and interpretation, Convex Hull Data Description (CHDD) is developed for realising one-class classification together with data clustering. CHDD approximates the convex hull of a given dataset with the extreme points which constitute a dictionary of data representatives. According to the dictionary, CHDD is capable of representing and clustering all the normal data instances so that anomaly detection is realised with certain interpretation. 4) Besides better anomaly detection accuracy and interpretability, better solutions for anomaly detection over streaming data with evolving patterns are also researched. Under the framework of Reinforcement Learning (RL), a time series anomaly detector that is consistently trained to cope with the evolving patterns is designed. Due to the fact that the anomaly detector is trained with labeled time series, it avoids the cumbersome work of threshold setting and the uncertain definitions of anomalies in time series anomaly detection tasks.