Global Stratification and Classification

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Hero, A. “Signal Detection and Classification” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press LLC, 1999 c  1999byCRCPressLLC 13 Signal Detection and Classification Alfred Hero University of Michigan 13.1 Introduction 13.2 Signal Detection TheROCCurve • DetectorDesignStrategies • LikelihoodRatio Test 13.3 Signal Classification 13.4 The Linear Multivariate Gaussian Model 13.5 Temporal Signals in Gaussian Noise Signal Detection: Known Gains • Signal Detection: Unknown Gains • Signal Detection: Random Gains • Signal Detection: Single Signal 13.6 Spatio-Temporal Signals Detection: Known Gains and Known Spatial Covariance • Detection: Unknown Gains andUnknown SpatialCovariance 13.7 Signal Classification Classifying Individual Signals • Classifying Presence of Multi- ple Signals References 13.1 Introduction Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform. Signal detection algo- rithms decide whether the waveform consists of “noise alone” or “signal masked by noise.” Signal classification algorithms decide whether a detected signal belongs to one or another of prespecified classes of signals. The objective of signal detection and classification theory is to specify systematic strategies for designing algorithms which minimize the average number of decision errors. This theory is grounded in the mathematical discipline of statistical decision theory where detection and classification are respectively called binary and M-ary hypothesis testing [1, 2]. However, signal pro- cessing engineers must also contend with the exceedingly large size of signal processing datasets, the absence of reliable and tractible signal models, the associated requirement of fast algorithms, and the requirement for real-time imbedding of unsupervised algorithms into specialized software or hardware. While ad hoc statistical detection algorithms were implemented by engineers before 1950, the systematic development of signal detection theory was first undertaken by radar and radio engineers in the early 1950s [3, 4]. This chapter provides a brief and limited overview of some of the theory and practice of signal detection and classification. The focus will be on the Gaussian observation model. For more details and examples see the cited references. c  1999 by CRC Press LLC 13.2 Signal Detection Assume that for some physical measurement a sensor produces an output waveform x ={x(t) : t ∈ [0,T]} over a time interval [0,T]. Assume that the waveform may have been produced by ambient noise alone or by an impinging signal of known form plus the noise. These two possibilities are called the null hypothesis H and the alternative hypothesis K, respectively, and are commonly written in the compact notation: H : x = noise alone K : x = signal + noise. The hypotheses H and K are called simple hypotheses when the statistical distributions of x under H and K involve no unknown parameters such as signal amplitude, signal phase, or noise power. When the statistical distribution of x under a hypothesis depends on unknown (nuisance) parameters the hypothesis is called a composite hypothesis. To decide between the null and alternative hypotheses one might apply a high threshold to the sensor output x and make a decision that the signal is present if and only if the threshold is exceeded at some time within [0,T]. The engineer is Global Stratification and Classification Global Stratification and Classification Bởi: OpenStaxCollege Just as America’s wealth is increasingly concentrated among its richest citizens while the middle class slowly disappears, global inequality involves the concentration of resources in certain nations, significantly affecting the opportunities of individuals in poorer and less powerful countries But before we delve into the complexities of global inequality, let’s consider how the three major sociological perspectives might contribute to our understanding of it The functionalist perspective is a macroanalytical view that focuses on the way that all aspects of society are integral to the continued health and viability of the whole A functionalist might focus on why we have global inequality and what social purposes it serves This view might assert, for example, that we have global inequality because some nations are better than others at adapting to new technologies and profiting from a globalized economy, and that when core nation companies locate in peripheral nations, they expand the local economy and benefit the workers Conflict theory focuses on the creation and reproduction of inequality A conflict theorist would likely address the systematic inequality created when core nations exploit the resources of peripheral nations For example, how many American companies take advantage of overseas workers who lack the constitutional protection and guaranteed minimum wages that exist in the United States? Doing so allows them to maximize profits, but at what cost? The symbolic interaction perspective studies the day-to-day impact of global inequality, the meanings individuals attach to global stratification, and the subjective nature of poverty Someone applying this view to global inequality would probably focus on understanding the difference between what someone living in a core nation defines as poverty (relative poverty, defined as being unable to live the lifestyle of the average person in your country) and what someone living in a peripheral nation defines as poverty (absolute poverty, defined as being barely able, or unable, to afford basic necessities, such as food) 1/11 Global Stratification and Classification Global Stratification While stratification in the United States refers to the unequal distribution of resources among individuals, global stratification refers to this unequal distribution among nations There are two dimensions to this stratification: gaps between nations and gaps within nations When it comes to global inequality, both economic inequality and social inequality may concentrate the burden of poverty among certain segments of the earth’s population (Myrdal 1970) As the chart below illustrates, people’s life expectancy depends heavily on where they happen to be born Statistics such as infant mortality rates and life expectancy vary greatly by country of origin (Central Intelligence Agency 2011) Country Infant Mortality Rate Life Expectancy Canada 4.9 deaths per 1000 live births 81 years Mexico 17.2 deaths per 1000 live births 76 years Democratic Republic of Congo 78.4 deaths per 1000 live births 55 years Most of us are accustomed to thinking of global stratification as economic inequality For example, we can compare China’s average worker’s wage to America’s average wage Social inequality, however, is just as harmful as economic discrepancies Prejudice and discrimination—whether against a certain race, ethnicity, religion, or the like—can create and aggravate conditions of economic equality, both within and between nations Think about the inequity that existed for decades within the nation of South Africa Apartheid, one of the most extreme cases of institutionalized and legal racism, created a social inequality that earned it the world’s condemnation When looking at inequity between nations, think also about the disregard of the crisis in Darfur by most Western nations Since few citizens of Western nations identified with the impoverished, non-white victims of the genocide, there has been little push to provide aid Gender inequity is another global concern Consider the controversy surrounding female genital mutilation Nations that practice this female circumcision procedure defend it as a longstanding cultural tradition in certain tribes and argue that the West shouldn’t interfere Western nations, however, decry the practice and are working to stop it Inequalities based on sexual orientation and gender identity exist around the globe According to Amnesty International, there are a number of crimes committed against individuals who not conform to traditional gender roles or sexual orientations (however those are culturally defined) From culturally sanctioned rape to statesanctioned executions, the abuses are serious These legalized and culturally accepted forms of prejudice and discrimination exist everywhere—from the United States to 2/11 Global Stratification and Classification ... Hero, A. “Signal Detection and Classification” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press LLC, 1999 c  1999byCRCPressLLC 13 Signal Detection and Classification Alfred Hero University of Michigan 13.1 Introduction 13.2 Signal Detection TheROCCurve • DetectorDesignStrategies • LikelihoodRatio Test 13.3 Signal Classification 13.4 The Linear Multivariate Gaussian Model 13.5 Temporal Signals in Gaussian Noise Signal Detection: Known Gains • Signal Detection: Unknown Gains • Signal Detection: Random Gains • Signal Detection: Single Signal 13.6 Spatio-Temporal Signals Detection: Known Gains and Known Spatial Covariance • Detection: Unknown Gains andUnknown SpatialCovariance 13.7 Signal Classification Classifying Individual Signals • Classifying Presence of Multi- ple Signals References 13.1 Introduction Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform. Signal detection algo- rithms decide whether the waveform consists of “noise alone” or “signal masked by noise.” Signal classification algorithms decide whether a detected signal belongs to one or another of prespecified classes of signals. The objective of signal detection and classification theory is to specify systematic strategies for designing algorithms which minimize the average number of decision errors. This theory is grounded in the mathematical discipline of statistical decision theory where detection and classification are respectively called binary and M-ary hypothesis testing [1, 2]. However, signal pro- cessing engineers must also contend with the exceedingly large size of signal processing datasets, the absence of reliable and tractible signal models, the associated requirement of fast algorithms, and the requirement for real-time imbedding of unsupervised algorithms into specialized software or hardware. While ad hoc statistical detection algorithms were implemented by engineers before 1950, the systematic development of signal detection theory was first undertaken by radar and radio engineers in the early 1950s [3, 4]. This chapter provides a brief and limited overview of some of the theory and practice of signal detection and classification. The focus will be on the Gaussian observation model. For more details and examples see the cited references. c  1999 by CRC Press LLC 13.2 Signal Detection Assume that for some physical measurement a sensor produces an output waveform x ={x(t) : t ∈ [0,T]} over a time interval [0,T]. Assume that the waveform may have been produced by ambient noise alone or by an impinging signal of known form plus the noise. These two possibilities are called the null hypothesis H and the alternative hypothesis K, respectively, and are commonly written in the compact notation: H : x = noise alone K : x = signal + noise. The hypotheses H and K are called simple hypotheses when the statistical distributions of x under H and K involve no unknown parameters such as signal amplitude, signal phase, or noise power. When the statistical distribution of x under a hypothesis depends on unknown (nuisance) parameters the hypothesis is called a composite hypothesis. To decide between the null and alternative hypotheses one might apply a high threshold to the sensor output x and make a decision that the signal is present if and only if the threshold is exceeded at some time within [0,T]. The engineer is Hero, A. “Signal Detection and Classification” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press LLC, 1999 c  1999byCRCPressLLC 13 Signal Detection and Classification Alfred Hero University of Michigan 13.1 Introduction 13.2 Signal Detection TheROCCurve • DetectorDesignStrategies • LikelihoodRatio Test 13.3 Signal Classification 13.4 The Linear Multivariate Gaussian Model 13.5 Temporal Signals in Gaussian Noise Signal Detection: Known Gains • Signal Detection: Unknown Gains • Signal Detection: Random Gains • Signal Detection: Single Signal 13.6 Spatio-Temporal Signals Detection: Known Gains and Known Spatial Covariance • Detection: Unknown Gains andUnknown SpatialCovariance 13.7 Signal Classification Classifying Individual Signals • Classifying Presence of Multi- ple Signals References 13.1 Introduction Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform. Signal detection algo- rithms decide whether the waveform consists of “noise alone” or “signal masked by noise.” Signal classification algorithms decide whether a detected signal belongs to one or another of prespecified classes of signals. The objective of signal detection and classification theory is to specify systematic strategies for designing algorithms which minimize the average number of decision errors. This theory is grounded in the mathematical discipline of statistical decision theory where detection and classification are respectively called binary and M-ary hypothesis testing [1, 2]. However, signal pro- cessing engineers must also contend with the exceedingly large size of signal processing datasets, the absence of reliable and tractible signal models, the associated requirement of fast algorithms, and the requirement for real-time imbedding of unsupervised algorithms into specialized software or hardware. While ad hoc statistical detection algorithms were implemented by engineers before 1950, the systematic development of signal detection theory was first undertaken by radar and radio engineers in the early 1950s [3, 4]. This chapter provides a brief and limited overview of some of the theory and practice of signal detection and classification. The focus will be on the Gaussian observation model. For more details and examples see the cited references. c  1999 by CRC Press LLC 13.2 Signal Detection Assume that for some physical measurement a sensor produces an output waveform x ={x(t) : t ∈ [0,T]} over a time interval [0,T]. Assume that the waveform may have been produced by ambient noise alone or by an impinging signal of known form plus the noise. These two possibilities are called the null hypothesis H and the alternative hypothesis K, respectively, and are commonly written in the compact notation: H : x = noise alone K : x = signal + noise. The hypotheses H and K are called simple hypotheses when the statistical distributions of x under H and K involve no unknown Viet Nam and Global Viet Nam and Global Warming (GW) Warming (GW) By: Hoang Thanh An Pham Ba Nhat Mai Quang Vinh Nguyen Minh Duc Objective  To inform effects of GW on Viet Nam  To give audiences Correct understanding about the situation of the Earth. Content Content  Nature: Nature: • Disasters Disasters • Temperature Temperature • Sea level rise Sea level rise • Biodiversity Biodiversity  Economy Economy Floods occur continuously Floods occur continuously Nature Nature Natural disasters Natural disasters Sources: www.tailieu.vn • In 2009: 4 big floods with great damages -Properties: 23.745 billions dong -People: 426 deaths, 1930 injured • 2005-2009: 2005-2009: -Properties: average -Properties: average 10-13 storms 10-13 storms attacked Viet Nam’s attacked Viet Nam’s continent/year continent/year -Damage: 12000 -Damage: 12000 billions Dong, 150 billions Dong, 150 deaths/ year deaths/ year Sources: www.tailieu.vn Disasters increase Nature Nature Natural disasters Natural disasters Nature Nature Natural disasters Natural disasters • In 2009: In 2009: - 20-30% droughty - 20-30% droughty area area • In next 10 years: In next 10 years: -10-30% unusable -10-30% unusable agricultural land agricultural land Sources: www.tailieu.vn Drought and desertification Content Content  Nature Nature  Disasters Disasters • Temperature Temperature • Sea level rise Sea level rise • Biodiversity Biodiversity  Economy Economy Effects on Recent changes temperature of Global and Viet Nam Recent changes temperature of Global and Viet Nam Nature Temperature Sources: www.tailieu.vn Content Content  Nature: Nature:  Disasters Disasters  Temperature Temperature • Sea level rise Sea level rise • Biodiversity Biodiversity  Economy Economy Effects on Sources: www.tailieu.vn Nature Sea level rise The fluctuation of sea level [...]... threatened by Global Warming Sources: www.tailieu.vn Nature Biodiversity • 350 plant species and 365 animal species can be extinct Sources: www.tailieu.vn Content Effects on  Nature: Disasters Temperature Sea level rise Biodiversity  Economy Economy  • • • • Vietnam could lose up to: 16 % of its land (Taiwan:5%, ranked 2nd) 35 % of its GDP (Thailand:22%, ranked 2nd ) 40 % of its population (Thailand:13%,... (Thailand:13%, ranked 2nd ) 23 % of its agriculture (Myanmar:12%, ranked 2nd ) Sources: www.tailieu.vn Conclusion Effects of Global Warming on Viet Nam  Nature • • • • Disasters Temperature Sea level rise Biodiversity  Economy   Viet Nam is facing with serious problem Viet Nam needs to reduce the effects of GW Reference   Website www.tailieu.vn Some pictures from Center prediction of climate ThankECONOMICS AND RESEARCH DEPARTMENT ERD WORKING PAPER SERIES NO. 3 Francisco Veloso Rajiv Kumar January 2002 Asian Development Bank The Automotive Supply Chain: Global Trends and Asian Perspectives 43 Francisco Veloso is with the Massachusetts Institute of Technology. Rajiv Kumar is the Principal Economist of the Operations Coordination Division, East and Central Asia Regional Department, Asian Development Bank. This background paper was prepared for RETA 5875: International Competitiveness of Asian Economies: A Cross-Country Study. ERD Working Paper No. 3 THE AUTOMOTIVE SUPPLY CHAIN: GLOBAL TRENDS AND ASIAN PERSPECTIVES Francisco Veloso Rajiv Kumar January 2002 ERD Working Paper No. 3 THE AUTOMOTIVE SUPPLY CHAIN: GLOBAL TRENDS AND ASIAN PERSPECTIVES 44 Asian Development Bank P.O. Box 789 0980 Manila Philippines 2002 by Asian Development Bank January 2002 ISSN 1655-5252 The views expressed in this paper are those of the author(s) and do not necessarily reflect the views or policies of the Asian Development Bank. 45 Foreword The ERD Working Paper Series is a forum for ongoing and recently completed research and policy studies undertaken in the Asian Development Bank or on its behalf. The Series is a quick-disseminating, informal publication meant to stimulate discussion and elicit feedback. Papers published under this Series could subsequently be revised for publication as articles in professional journals or chapters in books. 47 Contents Page I. Introduction 1 II. Major Drivers of the Automotive Industry 1 III. Assembler Strategies 6 IV. The New Supplier Roles 12 A. First Tier Suppliers 14 B. Component Suppliers 19 V. Focus on Asia 22 A. Prospects for the Asian Market 22 B. Major Trends in Regions and Countries 26 1. India 26 2. People’s Republic of China 28 3. Republic of Korea 30 4. Association of Southeast Asian Nations (ASEAN) 31 5. Taipei,China 34 VI. Understanding Automotive Supplier Performance 35 A. Focus of the Study 35 B. Evaluating Manufacturing Excellence 36 C. Analyzing Innovation Capabilities 39 References 41 1 I. Introduction T he objective of this paper is to provide an overview of the major trends taking place in the automotive industry across the world, with an emphasis on the Asian market. It is not a comprehensive report, but rather an informed view of the issues and a panorama of the behavior of the major players, both automakers and suppliers. In the final section, the paper presents some suggestions on how to measure firm competitiveness in this fast moving industry, focusing on automotive suppliers, particularly the smaller ones that make up most of the local autoparts industry in Asia. Besides this initial introduction, the paper has five additional sections. The second section describes the major drivers of the auto industry. It explains how today’s fast changing business environment, where the client is in charge, the technology evolves at breathtaking speed, and regulatory issues are pressing, is altering the industry characteristics, strategies, and products. The third and fourth sections address the behavior of the major players in the industry. The third section focuses on the responses of the automakers. These firms are the lead actors in the industry and have been on the first stage of industry evolution. The section summarizes the major strategies they have followed in the recent past, as well as those forecast for the near future. The .. .Global Stratification and Classification Global Stratification While stratification in the United States refers to the unequal distribution of resources among individuals, global stratification. .. are serious These legalized and culturally accepted forms of prejudice and discrimination exist everywhere—from the United States to 2/11 Global Stratification and Classification Somalia to Tibet—restricting... peripheral nation factories is using a _ perspective to understand the global economy functional 8/11 Global Stratification and Classification conflict theory feminist symbolic interactionist