Produkt zum Begriff Processing:
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Digital Video Processing
Over the years, thousands of engineering students and professionals relied on Digital Video Processing as the definitive, in-depth guide to digital image and video processing technology. Now, Dr. A. Murat Tekalp has completely revamped the first edition to reflect today’s technologies, techniques, algorithms, and trends. Digital Video Processing, Second Edition, reflects important advances in image processing, computer vision, and video compression, including new applications such as digital cinema, ultra-high-resolution video, and 3D video. This edition offers rigorous, comprehensive, balanced, and quantitative coverage of image filtering, motion estimation, tracking, segmentation, video filtering, and compression. Now organized and presented as a true tutorial, it contains updated problem sets and new MATLAB projects in every chapter. Coverage includes Multi-dimensional signals/systems: transforms, sampling, and lattice conversion Digital images and video: human vision, analog/digital video, and video quality Image filtering: gradient estimation, edge detection, scaling, multi-resolution representations, enhancement, de-noising, and restoration Motion estimation: image formation; motion models; differential, matching, optimization, and transform-domain methods; and 3D motion and shape estimation Video segmentation: color and motion segmentation, change detection, shot boundary detection, video matting, video tracking, and performance evaluation Multi-frame filtering: motion-compensated filtering, multi-frame standards conversion, multi-frame noise filtering, restoration, and super-resolution Image compression: lossless compression, JPEG, wavelets, and JPEG2000 Video compression: early standards, ITU-T H.264/MPEG-4 AVC, HEVC, Scalable Video Compression, and stereo/multi-view approaches
Preis: 97.36 € | Versand*: 0 € -
Digital Signal Processing
Modern coverage of the fundamentals, implementation and applications of digital signal processing techniques from a practical point of view.The past ten years has seen a significant growth in DSP applications throughout all areas of technology and this growth is expected well into the next millennium. This successful textbook covers most aspects of DSP found in undergraduate electrical, electronic or communications engineering courses. Unlike many other texts, it also covers a number of DSP techniques which are of particular relevance to industry such as adaptive filtering and multirate processing. The emphasis throughout the book is on the practical aspects of DSP.
Preis: 80.14 € | Versand*: 0 € -
Discrete-Time Signal Processing
For senior/graduate-level courses in Discrete-Time Signal Processing.THE definitive, authoritative text on DSP — ideal for those with an introductory-level knowledge of signals and systems. Written by prominent DSP pioneers, it provides thorough treatment of the fundamental theorems and properties of discrete-time linear systems, filtering, sampling, and discrete-time Fourier Analysis. By focusing on the general and universal concepts in discrete-time signal processing, it remains vital and relevant to the new challenges arising in the field.
Preis: 101.01 € | Versand*: 0 € -
Real-World Natural Language Processing
Voice assistants, automated customer service agents, and other cutting-edge human-to-computer interactions rely on accurately interpreting language as it is written and spoken. Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you’ll explore the core tools and techniques required to build a huge range of powerful NLP apps.about the technologyNatural language processing is the part of AI dedicated to understanding and generating human text and speech. NLP covers a wide range of algorithms and tasks, from classic functions such as spell checkers, machine translation, and search engines to emerging innovations like chatbots, voice assistants, and automatic text summarization. Wherever there is text, NLP can be useful for extracting meaning and bridging the gap between humans and machines.about the bookReal-world Natural Language Processing teaches you how to create practical NLP applications using Python and open source NLP libraries such as AllenNLP and Fairseq. In this practical guide, you’ll begin by creating a complete sentiment analyzer, then dive deep into each component to unlock the building blocks you’ll use in all different kinds of NLP programs. By the time you’re done, you’ll have the skills to create named entity taggers, machine translation systems, spelling correctors, and language generation systems. what's insideDesign, develop, and deploy basic NLP applicationsNLP libraries such as AllenNLP and FairseqAdvanced NLP concepts such as attention and transfer learningabout the readerAimed at intermediate Python programmers. No mathematical or machine learning knowledge required.about the authorMasato Hagiwara received his computer science PhD from Nagoya University in 2009, focusing on Natural Language Processing and machine learning. He has interned at Google and Microsoft Research, and worked at Baidu Japan, Duolingo, and Rakuten Institute of Technology. He now runs his own consultancy business advising clients, including startups and research institutions.
Preis: 58.84 € | Versand*: 0 €
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Was ist die Processing-Programmiersprache?
Die Processing-Programmiersprache ist eine Open-Source-Sprache, die speziell für die kreative Programmierung und die Erstellung von interaktiven Grafiken, Animationen und visuellen Effekten entwickelt wurde. Sie basiert auf Java und bietet eine einfache Syntax und eine umfangreiche Bibliothek, um komplexe visuelle Projekte zu erstellen. Processing wird häufig von Künstlern, Designern und Pädagogen verwendet.
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Was sind die wichtigsten Anwendungsgebiete von Natural Language Processing?
Die wichtigsten Anwendungsgebiete von Natural Language Processing sind die automatische Übersetzung von Texten, die Sentiment-Analyse in sozialen Medien und die Chatbot-Entwicklung für den Kundenservice. NLP wird auch für die Extraktion von Informationen aus großen Textmengen, wie z.B. in der Medizin oder im Finanzwesen, eingesetzt. Zudem spielt NLP eine wichtige Rolle bei der automatischen Zusammenfassung von Texten und der Erkennung von Sprachmustern für die Verbesserung von Suchalgorithmen.
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Wie plotte ich in Processing Java einen Graphen?
Um einen Graphen in Processing Java zu plotten, kannst du die Funktion `line()` verwenden, um Linien zwischen den einzelnen Punkten zu zeichnen. Du musst die x- und y-Koordinaten der Punkte berechnen und dann die `line()`-Funktion aufrufen, um die Linien zu zeichnen. Du kannst auch die `beginShape()`- und `endShape()`-Funktionen verwenden, um einen geschlossenen Graphen zu erstellen.
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Was sind häufige Fehler für Anfänger in Java Processing?
Häufige Fehler für Anfänger in Java Processing sind zum Beispiel das Vergessen von Semikolons am Ende von Anweisungen, das Verwechseln von Groß- und Kleinschreibung bei Variablennamen oder Methodenaufrufen und das Fehlen von Import-Anweisungen für benötigte Klassen oder Bibliotheken. Ein weiterer häufiger Fehler ist das Verwenden von nicht initialisierten Variablen, was zu Laufzeitfehlern führen kann.
Ähnliche Suchbegriffe für Processing:
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Digital Image Processing, Global Edition
For courses in Image Processing and Computer Vision.For years, Image Processing has been the foundational text for the study of digital image processing. The book is suited for students at the college senior and first-year graduate level with prior background in mathematical analysis, vectors, matrices, probability, statistics, linear systems, and computer programming. As in all earlier editions, the focus of this edition of the book is on fundamentals.The 4th Edition is based on an extensive survey of faculty, students, and independent readers in 5 institutions from 3 countries. Their feedback led to epanded or new coverage of topics such as deep learning and deep neural networks, including convolutional neural nets, the scale-invariant feature transform (SIFT), maimally-stable etremal regions (MSERs), graph cuts, k-means clustering and superpiels, active contours (snakes and level sets), and eact histogram matching. Major improvements were made in reorganising the material on image transforms into a more cohesive presentation, and in the discussion of spatial kernels and spatial filtering.
Preis: 96.09 € | Versand*: 0 € -
Transfer Learning for Natural Processing
Building and training deep learning models from scratch is costly, time-consuming, and requires massive amounts of data. To address this concern, cutting-edge transfer learning techniques enable you to start with pretrained models you can tweak to meet your exact needs. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre takes you hands-on with customizing these open source resources for your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results even when working with limited label data, all while saving on training time and computational costs.about the technologyTransfer learning enables machine learning models to be initialized with existing prior knowledge. Initially pioneered in computer vision, transfer learning techniques have been revolutionising Natural Language Processing with big reductions in the training time and computation power needed for a model to start delivering results. Emerging pretrained language models such as ELMo and BERT have opened up new possibilities for NLP developers working in machine translation, semantic analysis, business analytics, and natural language generation.about the bookTransfer Learning for Natural Language Processing is a practical primer to transfer learning techniques capable of delivering huge improvements to your NLP models. Written by DARPA researcher Paul Azunre, this practical book gets you up to speed with the relevant ML concepts before diving into the cutting-edge advances that are defining the future of NLP. You’ll learn how to adapt existing state-of-the art models into real-world applications, including building a spam email classifier, a movie review sentiment analyzer, an automated fact checker, a question-answering system and a translation system for low-resource languages. what's insideFine tuning pretrained models with new domain dataPicking the right model to reduce resource usageTransfer learning for neural network architecturesFoundations for exploring NLP academic literatureabout the readerFor machine learning engineers and data scientists with some experience in NLP.about the authorPaul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. He founded Algorine Inc., a Research Lab dedicated to advancing AI/ML and identifying scenarios where they can have a significant social impact. Paul also co-founded Ghana NLP, an open source initiative focused using NLP and Transfer Learning with Ghanaian and other low-resource languages. He frequently contributes to major peer-reviewed international research journals and serves as a program committee member at top conferences in the field.
Preis: 49.21 € | Versand*: 0 € -
Böker Magnum Knochensäge HL Processing Saw
Knochensäge mit Dreifachschliff. Griff in Signalfarbe. Abgerundete Sägeblattspitze. Blattlänge 13,5 cm. Gesamtlänge 19,4 cm. Gewicht 92 g. Lieferung mit passender Scheide.
Preis: 34.90 € | Versand*: 5.95 € -
Practical Applications in Digital Signal Processing
The Only DSP Book 100% Focused on Step-by-Step Design and Implementation of Real Devices and Systems in Hardware and Software Practical Applications in Digital Signal Processing is the first DSP title to address the area that even the excellent engineering textbooks of today tend to omit. This book fills a large portion of that omission by addressing circuits and system applications that most design engineers encounter in the modern signal processing industry. This book includes original work in the areas of Digital Data Locked Loops (DLLs), Digital Automatic Gain Control (dAGC), and the design of fast elastic store memory used for synchronizing independently clocked asynchronous data bit streams. It also contains detailed design discussions on Cascaded Integrator Comb (CIC) filters, including the seldom-covered topic of bit pruning. Other topics not extensively covered in other modern textbooks, but detailed here, include analog and digital signal tuning, complex-to-real conversion, the design of digital channelizers, and the techniques of digital frequency synthesis. This book also contains an appendix devoted to the techniques of writing mixed-language CC++ Fortran programs. Finally, this book contains very extensive review material covering important engineering mathematical tools such as the Fourier series, the Fourier transform, the z transform, and complex variables. Features of this book include • Thorough coverage of the complex-to-real conversion of digital signals • A complete tutorial on digital frequency synthesis • Lengthy discussion of analog and digital tuning and signal translation • Detailed coverage of the design of elastic store memory • A comprehensive study of the design of digital data locked loops • Complete coverage of the design of digital channelizers • A detailed treatment on the design of digital automatic gain control • Detailed techniques for the design of digital and multirate filters • Extensive coverage of the CIC filter, including the topic of bit pruning • An extensive review of complex variables • An extensive review of the Fourier series, and continuous and discrete Fourier transforms • An extensive review of the z transform
Preis: 66.33 € | Versand*: 0 €
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Wie wird Natural Language Processing eingesetzt, um maschinenverständliche Informationen aus menschlicher Sprache zu extrahieren? Welche Anwendungen hat Natural Language Processing in verschiedenen Bereichen?
Natural Language Processing wird eingesetzt, um menschliche Sprache in maschinenverständliche Daten umzuwandeln, indem Algorithmen verwendet werden, um Texte zu analysieren, zu verstehen und zu verarbeiten. In verschiedenen Bereichen wie der Medizin, der Finanzwelt, der Kundenbetreuung und der Automobilindustrie wird Natural Language Processing eingesetzt, um automatisierte Chatbots, Übersetzungssoftware, Sentiment-Analyse-Tools und Spracherkennungssysteme zu entwickeln. Durch die Anwendung von Natural Language Processing können Unternehmen effizienter arbeiten, bessere Entscheidungen treffen und ihren Kunden eine verbesserte Benutzererfahrung bieten.
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Wie kann man in Java Processing eine Kollision zwischen zwei Objekten programmieren?
Um eine Kollision zwischen zwei Objekten in Java Processing zu programmieren, musst du die Positionen und Größen der beiden Objekte überprüfen. Du kannst dies tun, indem du die Koordinaten der Objekte vergleichst und überprüfst, ob sie sich überschneiden. Wenn dies der Fall ist, kannst du entsprechende Aktionen ausführen, wie zum Beispiel das Ändern der Farbe oder das Stoppen der Bewegung der Objekte.
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Was sind die wichtigsten Funktionen und Merkmale einer Central Processing Unit (CPU)?
Die wichtigsten Funktionen einer CPU sind die Ausführung von Befehlen, die Verarbeitung von Daten und die Steuerung anderer Hardwarekomponenten. Zu den Merkmalen einer CPU gehören die Taktfrequenz, die Anzahl der Kerne und der Cache-Speicher. Eine CPU ist das Herzstück eines Computers und bestimmt maßgeblich die Leistungsfähigkeit des Systems.
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Was sind die Anwendungsmöglichkeiten von Natural Language Processing in der heutigen Gesellschaft?
Natural Language Processing wird in der heutigen Gesellschaft für Chatbots und virtuelle Assistenten eingesetzt, um die Kommunikation mit Kunden zu verbessern. Es wird auch für die automatische Übersetzung von Texten, die Analyse von sozialen Medien und die Extraktion von Informationen aus großen Textmengen verwendet. Darüber hinaus wird NLP in der medizinischen Forschung eingesetzt, um Krankheiten frühzeitig zu erkennen und Behandlungen zu verbessern.
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