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This MATLAB exercise book accompanies the textbook Control Engineering, providing a platform for students to practice problem solving in the analysis and design of continuous and discrete control problems reflected in the main textbook. The book starts off with a brief introduction to MATLAB, control toolbox and Simulink. Subsequent chapters include a short theoretical summary of the topic followed by exercises on solving complex problems using MATLAB commands. These exercises are ideal for students in computer laboratory classes.
Understand and Solve Your Customers' Real Problems with Agile Business Analysis To deliver real value, you must understand what your customers truly value, and solve the problems they really need solved. Business analysis can help you do this-and it's as crucial in agile environments now as it always has been. In Business Analysis Agility, leading experts James Robertson and Suzanne Robertson show how to perform business analysis in an agile way: trying new things, adapting to changes and discoveries, staying flexible, and being quick. Drawing on their unsurpassed experience of hundreds of projects and organizations, the Robertsons help you prioritize relentlessly, focus investments on delivering value, and learn in ways that improve your results. Uncover the real customer problems hidden behind assumptions and conventional solutions Hypothesize potential solutions and quickly test them with safe-to-fail probes Understand how people, hardware, software, organizations, and other components come together in an optimal customer experience Write stories that help you find solutions that deliver more value to customers and the business Think about problems and projects in more agile, nimble, and open-minded ways The Robertsons' approach to analytical thinking will be valuable to anyone who wants to build better software in agile environments: analysts, developers, team leads, project managers, software architects, and other team members and stakeholders at all levels of experience.
Computer technology has transformed modern society, yet curators wishing to reflect those changes face difficult challenges in terms of both collecting and exhibiting. Collecting and Exhibiting Computer-Based Technology examines how curators at the history and technology museums of the Smithsonian Institution have met these challenges. Focusing on the curatorial process, the book explores the ways in which curators at the institution have approached the accession and display of technological artifacts. Such collections often have comparatively few precedents, and can pose unique dilemmas. In analysing the Smithsonian's approach, Foti takes in diverse collection case studies ranging from DNA analyzers to Herbie Hancock's music synthesizers, from iPods to born-digital photographs, from the laptop used during the filming of the television program Sex and the City to "Stanley" the self-driving car. Using her proposed model of "expert curation", she synthesizes her findings into a more universal framework for undertanding the curatorial methods associated with computer technology and reflects on what it means to be a curator in a postdigital world. Collecting and Exhibiting Computer-Based Technology offers a detailed analysis of curatorial practice in a relatively new field that is set to grow exponentially. It will be useful reading for curators, scholars, and students alike.
Our contemporary age is confronted by a profound contradiction: on the one hand, our lives as workers, consumers and citizens have become ever more monitored by new technologies. On the other, big business and finance become increasingly less regulated and controllable. What does this technocratic ideology and surveillance-heavy culture reveal about the deeper reality of modern society? Monitored investigates the history and implications of this modern accountability paradox. Peter Bloom reveals pervasive monitoring practices which mask how at its heart, the elite remains socially and ethically out of control. Challenging their exploitive 'accounting power', Bloom demands that the systems that administer our lives are oriented to social liberation and new ways of being in the world.
This book provides the most up-to-date research and development on wearable computing, wireless body sensor networks, wearable systems integrated with mobile computing, wireless networking and cloud computing This book has a specific focus on advanced methods for programming Body Sensor Networks (BSNs) based on the reference SPINE project. It features an on-line website (http://spine.deis.unical.it) to support readers in developing their own BSN application/systems and covers new emerging topics on BSNs such as collaborative BSNs, BSN design methods, autonomic BSNs, integration of BSNs and pervasive environments, and integration of BSNs with cloud computing. The book provides a description of real BSN prototypes with the possibility to see on-line demos and download the software to test them on specific sensor platforms and includes case studies for more practical applications. Provides a future roadmap by learning advanced technology and open research issues Gathers the background knowledge to tackle key problems, for which solutions will enhance the evolution of next-generation wearable systems References the SPINE web site (http://spine.deis.unical.it) that accompanies the text Includes SPINE case studies and span topics like human activity recognition, rehabilitation of elbow/knee, handshake detection, emotion recognition systems Wearable Systems and Body Sensor Networks: from modeling to implementation is a great reference for systems architects, practitioners, and product developers. Giancarlo Fortino is currently an Associate Professor of Computer Engineering (since 2006) at the Department of Electronics, Informatics and Systems (DEIS) of the University of Calabria (Unical), Rende (CS), Italy. He was recently nominated Guest Professor in Computer Engineering of Wuhan University of Technology on April, 18 2012 (the term of appointment is three years). His research interests include distributed computing and networks, wireless sensor networks, wireless body sensor networks, agent systems, agent oriented software engineering, streaming content distribution networks, distributed multimedia systems, GRID computing. Raffaele Gravina received the B.Sc. and M.S. degrees both in computer engineering from the University of Calabria, Rende, Italy, in 2004 and 2007, respectively. Here he also received the Ph.D. degree in computer engineering. He's now a Postdoctoral research fellow at University of Calabria. His research interests are focused on high-level programming methods for WSNs, specifically Wireless Body Sensor Networks. He wrote almost 30 scientific/technical articles in the area of the proposed Book. He is co-founder of SenSysCal S.r.l., a spin-off company of the University of Calabria, and CTO of the wearable computing area of the company. Stefano Galzarano received the B.S. and M.S. degrees both in computer engineering from the University of Calabria, Rende, Italy, in 2006 and 2009, respectively. He is currently pursuing a joint Ph.D. degree in computer engineering with University of Calabria and Technical University of Eindhoven (The Netherlands). His research interests are focused on high-level programming methods for wireless sensor networks and, specifically, novel methods and frameworks for autonomic wireless body sensor networks.
The interplay between computability and randomness has been an active area of research in recent years, reflected by ample funding in the USA, numerous workshops, and publications on the subject. The complexity and the randomness aspect of a set of natural numbers are closely related. Traditionally, computability theory is concerned with the complexity aspect. However, computability theoretic tools can also be used to introduce mathematical counterparts for the intuitive notion of randomness of a set. Recent research shows that, conversely, concepts and methods originating from randomness enrich computability theory. The book covers topics such as lowness and highness properties, Kolmogorov complexity, betting strategies and higher computability. Both the basics and recent research results are desribed, providing a very readable introduction to the exciting interface of computability and randomness for graduates and researchers in computability theory, theoretical computer science, and measure theory.
This fully revised second edition of Machine Learning with TensorFlow teaches you the foundational concepts of machine learning and how to utilize the TensorFlow library to rapidly build powerful ML models. You'll learn the basics of regression, classification, and clustering algorithms, applying them to solve real-world challenges. New and revised content expands coverage of core machine learning algorithms and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. Key Features * Visualizing algorithms with TensorBoard * Understanding and using neural networks * Reproducing and employing predictive science * Downloadable Jupyter Notebooks for all examples * Questions to test your knowledge * Examples use the super-stable 1.14.1 branch of TensorFlow Developers experienced with Python and algebraic concepts like vectors and matrices. About the technology TensorFlow, Google's library for large-scale machine learning, makes powerful ML techniques easily accessible. It simplifies often-complex computations by representing them as graphs that are mapped to machines in a cluster or to the processors of a single machine. Offering a complete ecosystem for all stages and types of machine learning, TensorFlow's end-to-end functionality empowers machine learning engineers of all skill levels to solve their problems with ML. Chris Mattmann is the Deputy Chief Technology and Innovation Officer at NASA Jet Propulsion Lab, where he has been recognised as JPL's first Principal Scientist in the area of Data Science. Chris has applied TensorFlow to challenges he's faced at NASA, including building an implementation of Google's Show & Tell algorithm for image captioning using TensorFlow. He contributes to open source as a former Director at the Apache Software Foundation, and teaches graduate courses at USC in Content Detection and Analysis, and in Search Engines and Information Retrieval. Nishant Shukla wrote the first edition of Machine Learning with TensorFlow.
Service design is the activity of planning and organizing people, infrastructure, communication and material components of a service in order to improve its quality and the interaction between service provider and customers. It is now a growing field of both practice and academic research. Designing for Service brings together a wide range of international contributors to map the field of service design and identify key issues for practitioners and researchers such as identity, ethics and accountability. Designing for Service aims to problematize the field in order to inform a more critical debate within service design, thereby supporting its development beyond the pure methodological discussions that currently dominate the field. The contributors to this innovative volume consider the practice of service design, ethical challenges designers may encounter, and the new spaces opened up by the advent of modern digital technologies.
THE DESIGN COLLECTION REVEALED CREATIVE CLOUD provides comprehensive step-by-step instruction and in-depth explanation for three of today's most widely used design and layout programs: Adobe (R) InDesign (R) Creative Cloud, Adobe (R) Photoshop (R) Creative Cloud, and Adobe (R) Illustrator (R) Creative Cloud. Your students will gain practical experience with the software as they work through end-of-chapter learning projects and step-by-step tutorials. An integration chapter demonstrates how to move from one application to the other. Full-color illustrations and a user-friendly design combine to create a robust learning experience that reveals how to master the latest features of Adobe's popular design suite.
This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overview for students.
The series of MFCS symposia, organized in rotation by Poland, Slovakia, and the Czech Republic since 1972, has a long and well-established tradition. The symposiaencouragehigh-qualityresearchinallbranchesoftheoreticalcomputer science.Their broadscopeprovidesanopportunityto bring together researchers whodonotusuallymeetatspecialized conferences. The 35th International Symposium on Mathematical Foundations of C- puter Science (MFCS 2010) was organized in parallel with the 19th EACSL Annual Conference on Computer Science Logic (CSL 2010). The federated MFCS and CSL 2010 conference had shared plenary sessions and social events forallparticipants,butthescienti?cprogramandtheproceedingswereprepared independently for both events. Out of 149 regular submissions to MFCS 2010, the Program Committee - lected 56 papers for presentation at the conference and publication in this v- ume. Each paper was reviewed by at least three Program Committee members with the help of outside experts, and the actual selection was based on a sub- quent electronic discussion. In addition to the contributed papers, the scienti?c program of MFCS 2010 included ?ve MFCS and CSL plenary talks delivered by David Basin (ETH Z. . urich),HerbertEdelsbrunner (IST Austria andDuke University),ErichGrad .. el (RWTH Aachen), Bojan Mohar (University of Ljubljana and Simon Fraser U- versity),andJosephSifakis (CNRS), andthree invitedMFCS lecturesby Andris Ambainis (University of Latvia), Juraj Hromkovi?c(ETHZur .. ich), and Daniel Lokshtanov (Universitetet i Bergen). We are grateful to the invited speakers for accepting our invitation and sharing their knowledge and skills with all MFCS 2010 participants.
Geomatics is a field of science that has been intimately intertwined with our daily lives for almost 30 years, to the point where we often forget all the challenges it entails. Who does not have a navigation application on their phone or regularly engage with geolocated data? What is more, in the coming decades, the accumulation of geo-referenced data is expected to increase significantly. This book focuses on the notion of the imperfection of geographic data, an important topic in geomatics. It is essential to be able to define and represent the imperfections that are encountered in geographical data. Ignoring these imperfections can lead to many risks, for example in the use of maps which may be rendered inaccurate. It is, therefore, essential to know how to model and treat the different categories of imperfection. A better awareness of these imperfections will improve the analysis and the use of this type of data.
An Introduction to Mathematical Cryptography provides an introduction to public key cryptography and underlying mathematics that is required for the subject. Each of the eight chapters expands on a specific area of mathematical cryptography and provides an extensive list of exercises.
It is a suitable text for advanced students in pure and applied mathematics and computer science, or the book may be used as a self-study. This book also provides a self-contained treatment of mathematical cryptography for the reader with limited mathematical background.
Specification by Example and Gherkin offer programmers, designers, and managers an inclusive environment for clear communication, discovering requirements, and building a documentation system. Writing Great Specifications is an example-rich tutorial that teaches readers how to write good Gherkin specification documents that take advantage of Specification by Example's benefits. Engineers and testers will find it helpful in striking a stronger chord with nontechnical audiences through automated specifications. Key Features: * Teaches good practices to refactor Gherkin documents in legacy projects * Example-rich tutorial * In-depth introduction This book is a teaching resource for product and design people, programmers and testers. About the Technology: Specification by Example is a collaborative approach to defining and illustrating software requirements using concrete examples. Gherkin is a business-readable DSL that you use to describe software's behaviour as executable test cases that are easy for non-technical folks to understand.
As a field, computer science occupies a unique scientific space, in that its subject matter can exist in both physical and abstract realms. An artifact such as software is both tangible and not, and must be classified as something in between, or "liminal." The study and production of liminal artifacts allows for creative possibilities that are, and have been, possible only in computer science. In It Began With Babbage, Subrata Dasgupta examines the unique history of computer science in terms of its creative innovations, spanning back to Charles Babbage in 1819. Since all artifacts of computer science are conceived with a use in mind, the computer scientist is not concerned with the natural laws that govern disciplines like physics or chemistry; the computer scientist is more concerned with the concept of purpose. This requirement lends itself to a type of creative thinking that, as Dasgupta shows us, has exhibited itself throughout the history of computer science. From Babbage's Difference Engine, through the Second World War, to the establishment of the term "Computer Science" in 1956, It Began With Babbage traces a lively and complete history of computer science.
Introducing a NEW addition to our growing library of computer science titles, Algorithm Design and Applications, by Michael T. Goodrich & Roberto Tamassia! Algorithms is a course required for all computer science majors, with a strong focus on theoretical topics. Students enter the course after gaining hands-on experience with computers, and are expected to learn how algorithms can be applied to a variety of contexts. This new book integrates application with theory. Goodrich & Tamassia believe that the best way to teach algorithmic topics is to present them in a context that is motivated from applications to uses in society, computer games, computing industry, science, engineering, and the internet. The text teaches students about designing and using algorithms, illustrating connections between topics being taught and their potential applications, increasing engagement.
Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple's ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications. Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book's clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models--both pre-trained and user-built--with Apple's CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers: Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming Develop skills in data acquisition and modeling, classification, and regression. Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS) Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn & Keras models with CoreML Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps.
This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.
Originally designed as neutral entities, computerized bots are increasingly being used maliciously by online criminals in mass spamming events, fraud, extortion, identity theft, and software theft. Malicious Bots: An Inside Look into the Cyber-Criminal Underground of the Internet explores the rise of dangerous bots and exposes the nefarious methods of "botmasters". This valuable resource assists information security managers in understanding the scope, sophistication, and criminal uses of bots. With sufficient technical detail to empower IT professionals, this volume provides in-depth coverage of the top bot attacks against financial and government networks over the last several years. The book presents exclusive details of the operation of the notorious Thr34t Krew, one of the most malicious bot herder groups in recent history. Largely unidentified by anti-virus companies, their bots spread globally for months, launching massive distributed denial of service (DDoS) attacks and warez (stolen software distributions). For the first time, this story is publicly revealed, showing how the botherders got arrested, along with details on other bots in the world today. Unique descriptions of the criminal marketplace - how criminals make money off of your computer - are also a focus of this exclusive book! With unprecedented detail, the book goes on to explain step-by-step how a hacker launches a botnet attack, providing specifics that only those entrenched in the cyber-crime investigation world could possibly offer. Authors Ken Dunham and Jim Melnick serve on the front line of critical cyber-attacks and countermeasures as experts in the deployment of geopolitical and technical bots. Their work involves advising upper-level government officials and executives who control some of the largest networks in the world. By examining the methods of Internet predators, information security managers will be better able to proactively prote
Functional programming is a way of thinking about programs that emphasizes functions, while avoiding state mutation. C# includes a number of functional features and libraries, enabling us to take advantage of these benefits. Functional Programming in C# teaches readers to apply functional thinking to real-world scenarios. They'll start by learning the principles of functional programming, and how they translate in the C# language. By the end of this book, readers will be able to integrate functional techniques, making their C# programs robust and maintainable, and helping them to become more well rounded developers. Key Features: * Introduction to functional programming * Real-world examples * Integrate functional techniques * Become a well rounded developer This book is designed to help C# programmers with an OOP background understand functional thinking. About the Technology: Functional programming is a way of thinking about programs that emphasizes functions, while avoiding state mutation. It allows us to write elegant, intention-revealing code, that shines in testability and support for concurrency.
AQA Computing offers complete coverage and support through a variety of truly blended printed and online resources. Learning Objectives, clearly referenced to the related statements in the AQA specification, let students know exactly what they'll need to learn and understand in that topic. Learning Activities in the student's books are enhanced by electronic animations, simulations and videos. Study tips provide essential advice on common errors and exam preparation. Summary questions promote independent learning and develop students' exam techniques through practice, preparation and study tips.
This book presents the latest findings on one of the most intensely investigated subjects in computational mathematics--the traveling salesman problem. It sounds simple enough: given a set of cities and the cost of travel between each pair of them, the problem challenges you to find the cheapest route by which to visit all the cities and return home to where you began. Though seemingly modest, this exercise has inspired studies by mathematicians, chemists, and physicists. Teachers use it in the classroom. It has practical applications in genetics, telecommunications, and neuroscience.
The authors of this book are the same pioneers who for nearly two decades have led the investigation into the traveling salesman problem. They have derived solutions to almost eighty-six thousand cities, yet a general solution to the problem has yet to be discovered. Here they describe the method and computer code they used to solve a broad range of large-scale problems, and along the way they demonstrate the interplay of applied mathematics with increasingly powerful computing platforms. They also give the fascinating history of the problem--how it developed, and why it continues to intrigue us.
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