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Advanced Algorithms and Data Structures
by Marcello La RoccaAdvanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing.Summary As a software engineer, you&’ll encounter countless programming challenges that initially seem confusing, difficult, or even impossible. Don&’t despair! Many of these &“new&” problems already have well-established solutions. Advanced Algorithms and Data Structures teaches you powerful approaches to a wide range of tricky coding challenges that you can adapt and apply to your own applications. Providing a balanced blend of classic, advanced, and new algorithms, this practical guide upgrades your programming toolbox with new perspectives and hands-on techniques. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Can you improve the speed and efficiency of your applications without investing in new hardware? Well, yes, you can: Innovations in algorithms and data structures have led to huge advances in application performance. Pick up this book to discover a collection of advanced algorithms that will make you a more effective developer. About the book Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. You&’ll discover cutting-edge approaches to a variety of tricky scenarios. You&’ll even learn to design your own data structures for projects that require a custom solution. What's inside Build on basic data structures you already know Profile your algorithms to speed up application Store and query strings efficiently Distribute clustering algorithms with MapReduce Solve logistics problems using graphs and optimization algorithms About the reader For intermediate programmers. About the author Marcello La Rocca is a research scientist and a full-stack engineer. His focus is on optimization algorithms, genetic algorithms, machine learning, and quantum computing. Table of Contents 1 Introducing data structures PART 1 IMPROVING OVER BASIC DATA STRUCTURES 2 Improving priority queues: d-way heaps 3 Treaps: Using randomization to balance binary search trees 4 Bloom filters: Reducing the memory for tracking content 5 Disjoint sets: Sub-linear time processing 6 Trie, radix trie: Efficient string search 7 Use case: LRU cache PART 2 MULTIDEMENSIONAL QUERIES 8 Nearest neighbors search 9 K-d trees: Multidimensional data indexing 10 Similarity Search Trees: Approximate nearest neighbors search for image retrieval 11 Applications of nearest neighbor search 12 Clustering 13 Parallel clustering: MapReduce and canopy clustering PART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER 14 An introduction to graphs: Finding paths of minimum distance 15 Graph embeddings and planarity: Drawing graphs with minimal edge intersections 16 Gradient descent: Optimization problems (not just) on graphs 17 Simulated annealing: Optimization beyond local minima 18 Genetic algorithms: Biologically inspired, fast-converging optimization
Advanced Analytics and AI: Impact, Implementation, and the Future of Work (Wiley Finance)
by Tony BoobierBe prepared for the arrival of automated decision making Once thought of as science fiction, major corporations are already beginning to use cognitive systems to assist in providing wealth advice and also in medication treatment. The use of Cognitive Analytics/Artificial Intelligence (AI) Systems is set to accelerate, with the expectation that it’ll be considered ‘mainstream’ in the next 5 – 10 years. It’ll change the way we as individuals interact with data and systems—and the way we run our businesses. Cognitive Analysis and AI prepares business users for the era of cognitive analytics / artificial intelligence. Building on established texts and commentary, it specifically prepares you in terms of expectation, impact on personal roles, and responsibilities. It focuses on the specific impact on key industries (retail, financial services, utilities and media) and also on key professions (such as accounting, operational management, supply chain and risk management). Shows you how users interact with the system in natural language Explains how cognitive analysis/AI can source ‘big data’ Provides a roadmap for implementation Gets you up to speed now before you get left behind If you’re a decision maker or budget holder within the corporate context, this invaluable book helps you gain an advantage from the deployment of cognitive analytics tools.
Advanced Analytics and Deep Learning Models (Next Generation Computing and Communication Engineering)
by Amit Kumar Tyagi Archana Mire Shaveta MalikAdvanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.
Advanced Analytics and Learning on Temporal Data: 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers (Lecture Notes in Computer Science #11986)
by Vincent Lemaire Simon Malinowski Anthony Bagnall Alexis Bondu Thomas Guyet Romain TavenardThis book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Würzburg, Germany, in September 2019. The 7 full papers presented together with 9 poster papers were carefully reviewed and selected from 31 submissions. The papers cover topics such as temporal data clustering; classification of univariate and multivariate time series; early classification of temporal data; deep learning and learning representations for temporal data; modeling temporal dependencies; advanced forecasting and prediction models; space-temporal statistical analysis; functional data analysis methods; temporal data streams; interpretable time-series analysis methods; dimensionality reduction, sparsity, algorithmic complexity and big data challenge; and bio-informatics, medical, energy consumption, on temporal data.
Advanced Analytics and Learning on Temporal Data: 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers (Lecture Notes in Computer Science #12588)
by Vincent Lemaire Georgiana Ifrim Simon Malinowski Anthony Bagnall Thomas Guyet Romain TavenardThis book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Ghent, Belgium, in September 2020. The 15 full papers presented in this book were carefully reviewed and selected from 29 submissions. The selected papers are devoted to topics such as Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Early Classification of Temporal Data; Deep Learning and Learning Representations for Temporal Data; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Space-Temporal Statistical Analysis; Functional Data Analysis Methods; Temporal Data Streams; Interpretable Time-Series Analysis Methods; Dimensionality Reduction, Sparsity, Algorithmic Complexity and Big Data Challenge; and Bio-Informatics, Medical, Energy Consumption, Temporal Data.
Advanced Analytics and Learning on Temporal Data: 6th ECML PKDD Workshop, AALTD 2021, Bilbao, Spain, September 13, 2021, Revised Selected Papers (Lecture Notes in Computer Science #13114)
by Vincent Lemaire Georgiana Ifrim Simon Malinowski Anthony Bagnall Thomas Guyet Romain TavenardThis book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic. The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection.
Advanced Analytics and Learning on Temporal Data: 7th ECML PKDD Workshop, AALTD 2022, Grenoble, France, September 19–23, 2022, Revised Selected Papers (Lecture Notes in Computer Science #13812)
by Vincent Lemaire Georgiana Ifrim Simon Malinowski Anthony Bagnall Thomas Guyet Patrick ShaferThis book constitutes the refereed proceedings of the 7th ECML PKDD Workshop, AALTD 2022, held in Grenoble, France, during September 19–23, 2022.The 12 full papers included in this book were carefully reviewed and selected from 21 submissions. They were organized in topical sections as follows: Oral presentation and poster presentation.
Advanced Analytics and Learning on Temporal Data: 8th ECML PKDD Workshop, AALTD 2023, Turin, Italy, September 18–22, 2023, Revised Selected Papers (Lecture Notes in Computer Science #14343)
by Vincent Lemaire Georgiana Ifrim Simon Malinowski Anthony Bagnall Thomas Guyet Romain Tavenard Patrick SchaeferThis volume LNCS 14343 constitutes the refereed proceedings of the 8th ECML PKDD Workshop, AALTD 2023, in Turin, Italy, in September 2023. The 20 full papers were carefully reviewed and selected from 28 submissions. They are organized in the following topical section as follows: Machine Learning; Data Mining; Pattern Analysis; Statistics to Share their Challenges and Advances in Temporal Data Analysis.
Advanced Analytics and Learning on Temporal Data: 9th ECML PKDD Workshop, AALTD 2024, Vilnius, Lithuania, September 9–13, 2024, Revised Selected Papers (Lecture Notes in Computer Science #15433)
by Vincent Lemaire Georgiana Ifrim Simon Malinowski Anthony Bagnall Thomas Guyet Romain Tavenard Patrick SchäferThis book constitutes the refereed proceedings of the 9th ECML PKDD workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2024, held in Vilnius, Lithuania, during September 9-13, 2024. The 8 full papers presented here were carefully reviewed and selected from 15 submissions. The papers focus on recent advances in Temporal Data Analysis, Metric Learning, Representation Learning, Unsupervised Feature Extraction, Clustering, and Classification.
Advanced Analytics for Industry 4.0: Traditional Industries
by Ali SoofastaeiThe evolution of modern technology has affected all the industry dimensions. Mother industries play a critical role in providing the precursor materials for other industries, and a small improvement in these can make a big change in others. This book covers the analytics revolution in Industry 4.0 for the mother industries, such as mining, oil and gas, and steel. It focuses on the use of advanced analytics and artificial intelligence to improve the business decisions aimed at increasing the quality and quantity of mother industries' products. It helps to design and implement their digital transformation strategies in these industries.Key Features: Provides a concise overview of state of the art for mother industries' executives and managers. Highlights and describes critical opportunity areas for industry operations optimization. Explains how to implement advanced data analytics through case studies and examples. Provides approaches and methods to improve data-driven decision-making. Brings experience and learning in digital transformation from adjacent sectors. This book is aimed at researchers, professionals, and graduate students in data science, manufacturing, automation, and computer engineering.
Advanced Analytics in Mining Engineering: Leverage Advanced Analytics in Mining Industry to Make Better Business Decisions
by Ali SoofastaeiIn this book, Dr. Soofastaei and his colleagues reveal how all mining managers can effectively deploy advanced analytics in their day-to-day operations- one business decision at a time.Most mining companies have a massive amount of data at their disposal. However, they cannot use the stored data in any meaningful way. The powerful new business tool-advanced analytics enables many mining companies to aggressively leverage their data in key business decisions and processes with impressive results.From statistical analysis to machine learning and artificial intelligence, the authors show how many analytical tools can improve decisions about everything in the mine value chain, from exploration to marketing.Combining the science of advanced analytics with the mining industrial business solutions, introduce the “Advanced Analytics in Mining Engineering Book” as a practical road map and tools for unleashing the potential buried in your company’s data.The book is aimed at providing mining executives, managers, and research and development teams with an understanding of the business value and applicability of different analytic approaches and helping data analytics leads by giving them a business framework in which to assess the value, cost, and risk of potential analytical solutions. In addition, the book will provide the next generation of miners – undergraduate and graduate IT and mining engineering students – with an understanding of data analytics applied to the mining industry. By providing a book with chapters structured in line with the mining value chain, we will provide a clear, enterprise-level view of where and how advanced data analytics can best be applied. This book highlights the potential to interconnect activities in the mining enterprise better. Furthermore, the book explores the opportunities for optimization and increased productivity offered by better interoperability along the mining value chain – in line with the emerging vision of creating a digital mine with much-enhanced capabilities for modeling, simulation, and the use of digital twins – in line with leading “digital” industries.
Advanced Analytics in Power BI with R and Python: Ingesting, Transforming, Visualizing
by Ryan WadeThis easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard, if not impossible, to do using native Power BI tools. For example, you will learn to score Power BI data using custom data science models and powerful models from Microsoft Cognitive Services. The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration but become easier by leveraging the capabilities of R and Python. If you are a business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you do that. What You Will Learn Create advanced data visualizations via R using the ggplot2 package Ingest data using R and Python to overcome some limitations of Power Query Apply machine learning models to your data using R and Python without the need of Power BI premium compacity Incorporate advanced AI in Power BI without the need of Power BI premium compacity via Microsoft Cognitive Services, IBM Watson Natural Language Understanding, and pre-trained models in SQL Server Machine Learning Services Perform advanced string manipulations not otherwise possible in Power BI using R and Python Who This Book Is For Power users, data analysts, and data scientists who want to go beyond Power BI’s built-in functionality to create advanced visualizations, transform data in ways not otherwise supported, and automate data ingestion from sources such as SQL Server and Excel in a more concise way
Advanced Analytics with PySpark: Patterns for Learning from Data at Scale Using Python and Spark
by Uri Laserson Josh Wills Sandy Ryza Sean Owen Akash TandonThe amount of data being generated today is staggering and growing. Apache Spark has emerged as the de facto tool to analyze big data and is now a critical part of the data science toolbox. Updated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark's Python API, and other best practices in Spark programming.Data scientists Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills offer an introduction to the Spark ecosystem, then dive into patterns that apply common techniques-including classification, clustering, collaborative filtering, and anomaly detection, to fields such as genomics, security, and finance. This updated edition also covers NLP and image processing.If you have a basic understanding of machine learning and statistics and you program in Python, this book will get you started with large-scale data analysis.Familiarize yourself with Spark's programming model and ecosystemLearn general approaches in data scienceExamine complete implementations that analyze large public datasetsDiscover which machine learning tools make sense for particular problemsExplore code that can be adapted to many uses
Advanced Analytics with Spark
by Uri Laserson Josh Wills Sandy Ryza Sean OwenIn this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example.You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques--classification, collaborative filtering, and anomaly detection among others--to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find these patterns useful for working on your own data applications.Patterns include:Recommending music and the Audioscrobbler data setPredicting forest cover with decision treesAnomaly detection in network traffic with K-means clusteringUnderstanding Wikipedia with Latent Semantic AnalysisAnalyzing co-occurrence networks with GraphXGeospatial and temporal data analysis on the New York City Taxi Trips dataEstimating financial risk through Monte Carlo simulationAnalyzing genomics data and the BDG projectAnalyzing neuroimaging data with PySpark and Thunder
Advanced Analytics with Spark: Patterns for Learning from Data at Scale
by Uri Laserson Josh Wills Sandy Ryza Sean OwenIn the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming.You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—including classification, clustering, collaborative filtering, and anomaly detection—to fields such as genomics, security, and finance.If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find the book’s patterns useful for working on your own data applications.With this book, you will:Familiarize yourself with the Spark programming modelBecome comfortable within the Spark ecosystemLearn general approaches in data scienceExamine complete implementations that analyze large public data setsDiscover which machine learning tools make sense for particular problemsAcquire code that can be adapted to many uses
Advanced Analytics with Transact-SQL: Exploring Hidden Patterns and Rules in Your Data
by Dejan SarkaLearn about business intelligence (BI) features in T-SQL and how they can help you with data science and analytics efforts without the need to bring in other languages such as R and Python. This book shows you how to compute statistical measures using your existing skills in T-SQL. You will learn how to calculate descriptive statistics, including centers, spreads, skewness, and kurtosis of distributions. You will also learn to find associations between pairs of variables, including calculating linear regression formulas and confidence levels with definite integration. No analysis is good without data quality. Advanced Analytics with Transact-SQL introduces data quality issues and shows you how to check for completeness and accuracy, and measure improvements in data quality over time. The book also explains how to optimize queries involving temporal data, such as when you search for overlapping intervals. More advanced time-oriented information in the book includes hazard and survival analysis. Forecasting with exponential moving averages and autoregression is covered as well. Every web/retail shop wants to know the products customers tend to buy together. Trying to predict the target discrete or continuous variable with few input variables is important for practically every type of business. This book helps you understand data science and the advanced algorithms use to analyze data, and terms such as data mining, machine learning, and text mining.Key to many of the solutions in this book are T-SQL window functions. Author Dejan Sarka demonstrates efficient statistical queries that are based on window functions and optimized through algorithms built using mathematical knowledge and creativity. The formulas and usage of those statistical procedures are explained so you can understand and modify the techniques presented. T-SQL is supported in SQL Server, Azure SQL Database, and in Azure Synapse Analytics. There are so many BI features in T-SQL that it might become your primary analytic database language. If you want to learn how to get information from your data with the T-SQL language that you already are familiar with, then this is the book for you. What You Will LearnDescribe distribution of variables with statistical measuresFind associations between pairs of variablesEvaluate the quality of the data you are analyzingPerform time-series analysis on your dataForecast values of a continuous variablePerform market-basket analysis to predict customer purchasing patternsPredict target variable outcomes from one or more input variablesCategorize passages of text by extracting and analyzing keywordsWho This Book Is ForDatabase developers and database administrators who want to translate their T-SQL skills into the world of business intelligence (BI) and data science. For readers who want to analyze large amounts of data efficiently by using their existing knowledge of T-SQL and Microsoft’s various database platforms such as SQL Server and Azure SQL Database. Also for readers who want to improve their querying by learning new and original optimization techniques.
Advanced Applications in Heat Exchanger Technologies: AI, Machine Learning, and Additive Manufacturing
by Sunil Kumar, Kavita Rathore, and Debjyoti BanerjeeAdvanced Applications in Heat Exchanger Technologies presents the most recent developments in enhancing heat exchanger performance, reliability, and resilience, including the implementation of Artificial Intelligence, Machine Learning, and Additive Manufacturing.Covering the essential parts of many commercial endeavors, ranging from aerospace to marine applications to oil-and-gas, the book discusses various heat exchanger types and interdisciplinary industry applications. It encompasses several different techniques, such as nanofluids, microchannel heat exchangers, computer modeling, advanced manufacturing, and optimization. The book addresses real-world concerns that impact long-term heat exchanger performance and dependability such as fouling, corrosion prevention, and maintenance measures.This book is intended for researchers and graduate students who are interested in heat exchangers R&D and the diverse range of industrial applications of heat exchanger technologies in contemporary practice.
Advanced Applications of Blockchain Technology (Studies in Big Data #60)
by Ganesh Chandra Deka Shiho KimThis contributed volume discusses diverse topics to demystify the rapidly emerging and evolving blockchain technology, the emergence of integrated platforms and hosted third-party tools, and the development of decentralized applications for various business domains. It presents various applications that are helpful for research scholars and scientists who are working toward identifying and pinpointing the potential of as well as the hindrances to this technology.
Advanced Applications of Computational Mathematics: Theoretical Advances And Advanced Applications (De Gruyter Series On The Applications Of Mathematics In Engineering And Information Sciences Ser. #3)
by Mangey Ram Akshay Kumar Hari Mohan SrivastavaThis book “Advanced Applications of Computational Mathematics” covers multidisciplinary studies containing advanced research in the field of computational and applied mathematics. The book includes research methodology, techniques, applications, and algorithms. The book will be very useful to advanced students, researchers and practitioners who are involved in the areas of computational and applied mathematics and engineering.
Advanced Applications of Natural Language Processing for Performing Information Extraction (SpringerBriefs in Speech Technology)
by António Teixeira Mário RodriguesThis book explains how can be created information extraction (IE) applications that are able to tap the vast amount of relevant information available in natural language sources: Internet pages, official documents such as laws and regulations, books and newspapers, and social web. Readers are introduced to the problem of IE and its current challenges and limitations, supported with examples. The book discusses the need to fill the gap between documents, data, and people, and provides a broad overview of the technology supporting IE. The authors present a generic architecture for developing systems that are able to learn how to extract relevant information from natural language documents, and illustrate how to implement working systems using state-of-the-art and freely available software tools. The book also discusses concrete applications illustrating IE uses. · Provides an overview of state-of-the-art technology in information extraction (IE), discussing achievements and limitations for the software developer and providing references for specialized literature in the area · Presents a comprehensive list of freely available, high quality software for several subtasks of IE and for several natural languages · Describes a generic architecture that can learn how to extract information for a given application domain
Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection
by Umberto MichelucciDevelop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level.What You Will Learn See how convolutional neural networks and object detection workSave weights and models on diskPause training and restart it at a later stage Use hardware acceleration (GPUs) in your codeWork with the Dataset TensorFlow abstraction and use pre-trained models and transfer learningRemove and add layers to pre-trained networks to adapt them to your specific projectApply pre-trained models such as Alexnet and VGG16 to new datasets Who This Book Is For Scientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.
Advanced Arduino Techniques in Science: Refine Your Skills and Projects with PCs or Python-Tkinter
by Richard J. SmytheIf you’re already a comfortable programmer, familiar with your single board computer and microcontroller, and are ready to refine your projects, then let’s get started! This book covers advanced methods and techniques for creating, implementing, monitoring and controlling your experiments and projects with your Raspberry Pi and Arduino. Projects will use Python and the Tkinter GUI and will also cover software development for adding real time data display to the Raspberry Pi.You'll review concepts of frequency occurring in nature and the techniques used to measure the frequency of electrically varying signal voltages. You'll also study procedures for safe design, implementation and operation of experimental measurement systems operating at high heats and high temperatures. Throughout the book you'll look at sources and types of errors, and best practices for minimizing and reducing them.Often times there are simple environmental issues hindering what would seem to be simple projects: high temperatures, controlling the power for elevated temperature with the proportional integral and derivative (PID) algorithm, and the limitations imposed by eight bit code, the influence of noise and errors in measured data, and many more. Advanced Arduino Techniques in Science provides the best tools to move past those restrictions. What You’ll LearnImplement an experimental control system and graphical data display for the Raspberry Pi and ArduinoManage experimental control with PID algorithm implementation, tuning and limitations imposed by eight bit digital signalsBuild an analytical front end Examine data smoothing capability of the Kalman filterExplore available methods for measuring both high and low frequency values in electronic signalsWho This Book Is ForEducators, researchers, students, makers, citizen scientists, or hobbyists can all extend their measuring capability or improve upon the quality of their collected data. The book is directed to those with intermediate skills in programming and those who are comfortable with Python programming and Arduino C.
Advanced Artificial Intelligence and Robo-Justice
by Georgios I. ZekosThe book deals with digital technology which is transforming the landscape of dispute resolution. It illustrates the application of AI in the legal field and shows the future prospect of robo-justice for an AAI society in the advanced artificial intelligence era. In other words, the present justice system and the influence of current AI upon courts and arbitration are investigated. The transforming role of AI on all legal fields is examined thoroughly by giving answers concerning AI legal personality and liability. The analysis shows that digital technology is generating an ever-growing number of disputes and at the same time is challenging the effectiveness and reach of traditional dispute resolution avenues. To that extent, the book presents in tandem the impact of AI upon courts and arbitration, and reveals the role of AAI in generating a new robo-justice system.Finally, the end of the perplexing relation of courts and arbitration is evidenced methodically and comprehensively.
Advanced Audio Visualization Using ThMAD: Creating Amazing Graphics with Open Source Software
by Peter SpäthLearn advanced techniques and improve your audio visualization skills with Thinking Machine Audio Dreams (ThMAD). With this book, you can concentrate on advanced examples and usage patterns, including using shaders in a more profound way, and how to incorporate ThMAD into a tool chain using the professional sound server JACK.
Advanced Backend Code Optimization
by Sid Touati Benoit Dupont de DinechinThis book is a summary of more than a decade of research in the area of backend optimization. It contains the latest fundamental research results in this field. While existing books are often more oriented toward Masters students, this book is aimed more towards professors and researchers as it contains more advanced subjects.It is unique in the sense that it contains information that has not previously been covered by other books in the field, with chapters on phase ordering in optimizing compilation; register saturation in instruction level parallelism; code size reduction for software pipelining; memory hierarchy effects and instruction level parallelism.Other chapters provide the latest research results in well-known topics such as register need, and software pipelining and periodic register allocation.