微機電元件(Gardner) (1版)

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【簡介】 微機電系統是基於整合微電子電路(IC)與機械、光學、生化、高頻、流體等跨領域的系統技術(SoC)，其可能包含致動器、感測器、訊號處理電路等諸多功能於一體，而成為智慧元件(Intelligent Device)。隨著奈米及生醫技術逐漸成為今日科技焦點，微機電元件扮演了強勁推手的角色，我們也期待其能創造如半導體IC產業般的龐大商機。 本書可作為大學部高年級生，研究所學生教科書，對於相關產業界有興趣的工程人員作為參考材料也相當適合。除了對基本微電子技術有所介紹，在體型微加工、面型微加工、立體微影等微機電元件標準製作技術也有相當深入的最新技術討論。特別是作者以 IDT 元件為例，詳盡說明聲波元件的各種理論、效應、量測與微感測器的製作及應用等，最後加入電子電路的高度整合，可以發展成智慧型微機電系統，其中扮演關鍵角色的即是所謂微系統元件。特別在如本書作者專精的生醫應用領域，如人工鼻等，搭配高分子材料的微機電技術發展已被證明是最適合的基礎元件。譯者相當看好生醫及高頻等跨領域技術整合的未來發展，本書無疑是通往此路的最佳領航員，希望大家好好研讀「微機電元件」一書 【目錄】 第1章 導論 第2章 電子材料與製程 第3章 MEMS材料和它們的製備 第4章 標準微電子技術 第5章 矽微加工：體型 第6章 矽微加工：面型 第7章 MEMS的微立體微影 第8章 微感測器 第9章 SAW元件導論 第10章 固體中的表面聲波 第11章 IDT微感測器參數量測 第12章 IDT微感測器製作 第13章 IDT微感測器 第14章 MEMS-IDT微感測器 第15章 聰明感測器和微機電系統

Self-Face Recognition and the Brain 1st 2024

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Self-Face Recognition and the Brain explores a fundamental cornerstone of human consciousness; how recognizing ourselves leads to a better understanding of the brain and higher-order thinking. Featuring contributions from an interdisciplinary range of researchers, each chapter provides a unique insight into one aspect of self-face recognition. The book begins by introducing readers to the concept of self-face recognition, covering issues like the mirror-test and whether animals can recognize themselves, before addressing the role of neural correlates and attempts at localizing consciousness. It then discusses various disorders and the impact they can have on self-face recognition before considering how neuroscience can heighten our understanding of the field. It will be an essential read for all researchers of self-face recognition, from psychology, philosophy, and neuroscience backgrounds.

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This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component.