Search results for "Landscape of Next Generation Sequencing Using Pattern Recognition"
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On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities
In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.
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De-Sequencing
Are you your genes? De-Sequencing: Identity Work with Genes explores this perplexing question, showing how different forms of knowledge must be contextualized to become meaningful. It is generally assumed that the genomic sequence adds up to the identity-forming material life is made of. Yet identity cannot itself adopt the form of a sequence. As the authors in this volume show, the genome must be ‘de-sequenced’ by human language to render it interpretable and meaningful in a social context. The book unpacks this type of ‘sequence-speech’ in engaging detail, adopting a personal, social, cultural, and bio-political approach to examine the transformation of human identity and reflexivity in the era of genetic citizenship.
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Speech Recognition and Coding
Based on a NATO Advanced Study Institute held in 1993, this book addresses recent advances in automatic speech recognition and speech coding. The book contains contributions by many of the most outstanding researchers from the best laboratories worldwide in the field. The contributions have been grouped into five parts: on acoustic modeling; language modeling; speech processing, analysis and synthesis; speech coding; and vector quantization and neural nets. For each of these topics, some of the best-known researchers were invited to give a lecture. In addition to these lectures, the topics were complemented with discussions and presentations of the work of those attending. Altogether, the reader is given a wide perspective on recent advances in the field and will be able to see the trends for future work.
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Generative AI with Python and PyTorch
Master GenAI techniques to create images and text using variational autoencoders (VAEs), generative adversarial networks (GANs), LSTMs, and large language models (LLMs)Key FeaturesImplement real-world applications of LLMs and generative AIFine-tune models with PEFT and LoRA to speed up trainingExpand your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndexPurchase of the print or Kindle book includes a free eBook in PDF formatBook DescriptionBecome an expert in Generative AI through immersive, hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision. Generative AI with Python and PyTorch is your end-to-end guide to creating advanced AI applications, made easy by Raghav Bali, a seasoned data scientist with multiple patents in AI, and Joseph Babcock, a PhD and machine learning expert. Through business-tested approaches, this book simplifies complex GenAI concepts, making learning both accessible and immediately applicable.From NLP to image generation, this second edition explores practical applications and the underlying theories that power these technologies. By integrating the latest advancements in LLMs, it prepares you to design and implement powerful AI systems that transform data into actionable intelligence.You’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models.Whether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI.What you will learnGrasp the core concepts and capabilities of LLMsCraft effective prompts using chain-of-thought, ReAct, and prompt query language to guide LLMs toward your desired outputsUnderstand how attention and transformers have changed NLPOptimize your diffusion models by combining them with VAEsBuild text generation pipelines based on LSTMs and LLMsLeverage the power of open-source LLMs, such as Llama and Mistral, for diverse applicationsWho this book is forThis book is for data scientists, machine learning engineers, and software developers seeking practical skills in building generative AI systems. A basic understanding of math and statistics and experience with Python coding is required.
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Robustness in Statistical Pattern Recognition
This book is concerned with important problems of robust (stable) statistical pat tern recognition when hypothetical model assumptions about experimental data are violated (disturbed). Pattern recognition theory is the field of applied mathematics in which prin ciples and methods are constructed for classification and identification of objects, phenomena, processes, situations, and signals, i. e. , of objects that can be specified by a finite set of features, or properties characterizing the objects (Mathematical Encyclopedia (1984)). Two stages in development of the mathematical theory of pattern recognition may be observed. At the first stage, until the middle of the 1970s, pattern recogni tion theory was replenished mainly from adjacent mathematical disciplines: mathe matical statistics, functional analysis, discrete mathematics, and information theory. This development stage is characterized by successful solution of pattern recognition problems of different physical nature, but of the simplest form in the sense of used mathematical models. One of the main approaches to solve pattern recognition problems is the statisti cal approach, which uses stochastic models of feature variables. Under the statistical approach, the first stage of pattern recognition theory development is characterized by the assumption that the probability data model is known exactly or it is esti mated from a representative sample of large size with negligible estimation errors (Das Gupta, 1973, 1977), (Rey, 1978), (Vasiljev, 1983)).
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Hands-On Image Processing and Computer Vision with Python
Explore the world of image processing, computer vision, and generative AI with Python—from fundamental concepts and classical methods to deep learning, modern vision systems, and real-world visual content generation.Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesMaster end-to-end image processing and computer vision workflows using PythonBuild visual AI systems with classical, deep learning, and generative AI techniquesApply theory with production-ready implementations using leading Python librariesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAnalyzing and understanding visual data has become essential in modern applications such as healthcare, security, remote sensing, manufacturing, and digital media. This book provides a hands-on guide to image processing and computer vision using Python, following a practical approach that bridges theory with implementation.As you progress through the chapters, you will develop proficiency in Python 3 and implement algorithms spanning classical image processing, modern computer vision, and state-of-the-art (SOTA) deep learning and generative AI. The book covers image enhancement, restoration, filtering, segmentation, feature extraction, classification, and object detection using libraries including NumPy, OpenCV, PIL, SciPy, scikit-image, scikit-learn, TensorFlow, Keras, and PyTorch.Advanced chapters introduce CNNs, Vision Transformers, transformer-based segmentation, modern detection frameworks, GANs, diffusion models, foundation models, image-to-image translation, super-resolution, and multimodal vision-language understanding. Real-world applications span medical imaging, remote sensing, banking, augmented reality, autonomous driving, industrial inspection, and intelligent visual analytics. By the end of the book, you will be equipped to design and implement real-world visual computing solutions.*Email sign-up and proof of purchase requiredWhat you will learnBuild image processing and computer vision pipelinesApply image enhancement, restoration, and segmentationImplement image classification and object detection modelsExplore CNNs, Vision Transformers, and attention modelsGenerate and edit images using GANs and diffusion modelsDevelop multimodal vision-language AI applicationsApply visual AI across diverse real-world domainsImplement super-resolution, style transfer, and image-to-image translationWho this book is forPython developers, engineers, applied researchers, students, and AI practitioners who want to build end-to-end image processing and computer vision systems. A working knowledge of Python is required, while familiarity with linear algebra, calculus, and basic machine learning concepts will help you get the most from the advanced topics.
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Graphic Recognition. Current Trends and Challenges
This book constitutes the thoroughly refereed post-conference proceedings of the 11th International Workshop on Graphics Recognition, GREC 2015, held in Nancy, France, in August 2015. The 10 revised full papers presented were carefully reviewed and selected from 19 initial submissions. They contain both classical and emerging topics of Graphics Recognition, namely symbol spotting; recognition in context; perceptual based approaches and grouping; low level processing; off-line to on-line and interactive systems; structure based approaches; performance evaluation and ground truthing; content based retrieval.
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Automated Pattern Recognition of Communication Behaviour in Electronic Business Negotiations
The world of digitalisation is changing the way how people and business companies communicate with each other. Electronic negotiations represent one of the most important forms of business communication and can influence the successes and failures of companies in a significant way, whether in interorganisational or intraorganisational processes. Analysing negotiation interactions to determine pattern-based peculiarities in the communication offers new value-adding information concerning the management of optimised communication processes, even though the machine-based processing of communication data bears a series of challenges. The present book develops a new approach to analyse the automated pattern recognition potential of Machine Learning methods in unstructured negotiation communication. It presents holistic research frameworks for the effective detection of structural patterns and reveals the pattern labelling potential in high-dimensional communication data by analytically implementing a series of Machine Learning methods.
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Beginners Guide To Bioinformatics For High Throughput Sequencing
Biologists find computing bewildering; yet they are expected to be able to process the voluminous data available from the machines they buy and the datasets that has accumulated in genomic databanks worldwide. It is now increasingly difficult for them to avoid dealing with large volumes of data, that goes beyond just doing manual programming.Most books in this realm are full of equations and complex code but this book gives a much gentler entry point particularly for biologists, with code snippets users can use to cut and paste, and run on their Linux or MacOSX operating system or cloud instance. It also provides a step by step installation instructions which they can easily follow. Those who are in the field of genome sequencing and already familiar with the procedures of analysis, may also find this book useful in closing some knowledge gaps.High throughput sequencing requires high throughput and high performance computing. This book provides a gentle entry to high throughput sequencing by dealing with simple skills which the average biologist is increasingly required to master. You will find this book a breeze to read, and some suggestions in this book maybe new to you, something you might want to try out.
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Fundamentals of Image, Audio, and Video Processing Using MATLAB®
Fundamentals of Image, Audio, and Video Processing Using MATLAB® introduces the concepts and principles of media processing and its applications in pattern recognition by adopting a hands-on approach using program implementations. The book covers the tools and techniques for reading, modifying, and writing image, audio, and video files using the data analysis and visualization tool MATLAB®. Key Features: Covers fundamental concepts of image, audio, and video processing Demonstrates the use of MATLAB® on solving problems on media processing Discusses important features of Image Processing Toolbox, Audio System Toolbox, and Computer Vision Toolbox MATLAB® codes are provided as answers to specific problems Illustrates the use of Simulink for audio and video processing Handles processing techniques in both the Spatio-Temporal domain and Frequency domainThis is a perfect companion for graduate and post-graduate students studying courses on image processing, speech and language processing, signal processing, video object detection and tracking, and related multimedia technologies, with a focus on practical implementations using programming constructs and skill developments. It will also appeal to researchers in the field of pattern recognition, computer vision and content-based retrieval, and for students of MATLAB® courses dealing with media processing, statistical analysis, and data visualization.Dr. Ranjan Parekh, PhD (Engineering), is Professor at the School of Education Technology, Jadavpur University, Calcutta, India, and is involved with teaching subjects related to Graphics and Multimedia at the post-graduate level. His research interest includes multimedia information processing, pattern recognition, and computer vision.
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Patterns, Thinking, and Cognition – A Theory of Judgment
What happens when we think? How do people make judgments? While different theories abound--and are heatedly debated--most are based on an algorithmic model of how the brain works. Howard Margolis builds a fascinating case for a theory that thinking is based on recognizing patterns and that this process is intrinsically a-logical. Margolis gives a Darwinian account of how pattern recognition evolved to reach human cognitive abilities. Illusions of judgment--standard anomalies where people consistently misjudge or misperceive what is logically implied or really present--are often used in cognitive science to explore the workings of the cognitive process. The explanations given for these anomalous results have generally explained only the anomaly under study and nothing more. Margolis provides a provocative and systematic analysis of these illusions, which explains why such anomalies exist and recur. Offering empirical applications of his theory, Margolis turns to historical cases to show how an individual's cognitive repertoire--the available cognitive patterns and their relation to cues--changes or resists changes over time. Here he focuses on the change in worldview occasioned by the Copernican discovery: not only how an individual might come to see things in a radically new way, but how it is possible for that new view to spread and become the dominant one. A reanalysis of the trial of Galileo focuses on social cognition and its interactions with politics. In challenging the prevailing paradigm for understanding how the human mind works, Patterns, Thinking, and Cognition is certain to stimulate fruitful debate.
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Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
This book constitutes the thoroughly refereed post-workshop proceedings of the First IAPR TC3 Workshop on Pattern Recognition of Social Signals in Human-Computer-Interaction (MPRSS2012), held in Tsukuba, Japan in November 2012, in collaboration with the NLGD Festival of Games. The 21 revised papers presented during the workshop cover topics on facial expression recognition, audiovisual emotion recognition, multimodal Information fusion architectures, learning from unlabeled and partially labeled data, learning of time series, companion technologies and robotics.
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Computer Recognition Systems 4
The computer recognition systems are nowadays one of the most promising directions in artificial intelligence. This book is the most comprehensive study of this field. It contains a collection of 78 carefully selected articles contributed by experts of pattern recognition. It reports on current research with respect to both methodology and applications. In particular, it includes the following sections:Biometrics,Features, learning and classifiers,Image processing and computer vision,Knowledge acquisition based on reasoning methodsMedical applications,Miscellaneous applications,This book is a great reference tool for scientists who deal with the problems of designing computer pattern recognition systems. Its target readers can be as well researchers as students of computer science, artificial intelligence or robotics.
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Designing the AI-Driven Data Foundations
Build AI-powered data platforms that unify analytics and intelligence The playbook for data and AI architecture has been rewritten. This is the guide to what comes next. For decades, data architectures were optimized for a predictable set of workloads. AI changes every layer of the stack where autonomous agents, not just humans, consume data. The architectures that worked for traditional analytics cannot meet these demands. Designing the AI-Driven Data Foundations is a comprehensive guide to architecting platforms for this new reality. Drawing on his experience as principal analyst at SanjMo and former VP of Research at Gartner, where he advised thousands of enterprises on data strategy, renowned analyst Sanjeev Mohan delivers vendor-neutral guidance for navigating a landscape where yesterday's best practices no longer apply. The book systematically unpacks each layer of contemporary data stacks, from operational and analytical data stores through ingestion, integration, analytics, generative AI, governance, security, privacy, and operations. The data architecture landscape is experiencing unprecedented disruption. AI has catalyzed changes across the entire data management stack: hardware optimized for vector operations, engineering practices reimagined for unstructured content, and consumption patterns transformed by autonomous agents. Designing the AI-Driven Data Foundations examines how the rise of unstructured data and AI agents as first-class consumers are redefining architectural principles that have guided the industry for decades. You’ll also discover: A unified and comprehensive data and AI strategyTechniques to avoid vendor lock-inFrameworks for evaluating data storesContextual data integration pattern that replaces ETL for AI workloadsCoverage of AI-ready data governance, quality and securityDataOps and observability practices that operationalize trust at scale Perfect for data architects, and technical leaders who must make consequential platform decisions, Designing the AI-Driven Data Foundations translates the complexity of data and AI infrastructure into actionable architectural decisions that serve both human analysts and autonomous agents.
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Graphics Recognition. Recent Advances
This edited volume contains refereed and improved versions of select papers 1 that were presented at the third IAPR Workshop on Graphics Recognition (GREC’99), held at Rambagh Palace in Jaipur, India, 26–27, September 1999. The workshop was organized by the TC10 (Technical Committee on Graphics Recognition) of the IAPR. Edited volumes from the previous two workshops in this series are also available as Lecture Notes in Computer Science (volumes 1072 and 1389). Graphics recognition is the study of techniques for computer interpretation of images of line drawings and symbols. This includes methods such as vectori- tion, symbol recognition, and table and chart recognition for applications such as engineering drawings, schematics, logic drawings, maps, diagrams, and musical scores. Some recently developed techniques include graphics-based information or drawing retrieval and recognition of online graphical strokes. With the recent advances in the ?eld, there is now a need to develop benchmarks for evaluating and comparing algorithms and systems. Graphics recognition is a growing ?eld of interest in the broader document image recognition community. The GREC’99 workshop was attended by ?fty-?ve people from ?fteen co- tries. The workshop program consisted of six technical sessions. Each session began with a half-hour invited talk which was followed by several short talks. Each session closed with a half-hour panel discussion where the authors ?elded questions from the other participants. Several interesting new research directions were discussed at the workshop.
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Agentic Design Patterns
This book is a practical resource designed to help developers master the art of building sophisticated AI agents. As artificial intelligence evolves from simple reactive programs to autonomous entities capable of understanding context and making complex decisions, this book provides the essential Design Patterns and proven techniques needed to construct intelligent systems effectively. Each of the 21 Design Patterns represents a fundamental building block for creating agents that can perceive their environment, make informed decisions, and execute actions autonomously. Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems is structured as a comprehensive hands-on guide, with each chapter dedicated to a single agentic pattern. Within each chapter, you will find a detailed pattern overview, practical applications and use cases, one or more hands-on code example, and key takeaways for quick review. From foundational concepts such as Prompt Chaining and Tool Use to advanced topics like Multi-Agent Collaboration and Self-Correction, readers will gain practical knowledge they can immediately apply. While the chapters build on each other, you can also use the book as a handy reference, jumping to patterns that address your specific challenges. To provide a tangible "canvas" for the code examples, this guide utilizes three prominent agent development frameworks: LangChain and its extension LangGraph, which offer a flexible way to build complex operational sequences; Crew AI, which provides a structured framework for orchestrating multiple agents; and the Google Agent Developer Kit (Google ADK), which offers tools for building, evaluating, and deploying agents. By showcasing examples across these tools, you will gain a broad understanding of how these patterns can be applied in any technical environment. Building effective agentic systems requires more than just a powerful language model; it demands structure and design. Agentic patterns provide reusable, battle-tested solutions to common challenges, much like design patterns in software engineering. They offer a common language that makes an agent's logic clearer, more maintainable, and more robust. By the end of this journey, you will possess both the theoretical understanding and the practical skills to implement these 21 essential patterns, enabling you to build more intelligent, capable, and autonomous systems on your chosen development canvas.
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Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging
This book constitutes the thoroughly refereed workshop proceedings of the Second International Workshop on Medical Computer Vision, MCV 2012, held in Nice, France, October 2012 in conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012. The 24 papers have been selected out of 42 submissions. At MCV 2012, 12 papers were presented as a poster and 12 as a poster together with a plenary talk. The book also features four selected papers which were presented at the previous CVPR Medical Computer Vision workshop held in conjunction with the International Conference on Computer Vision and Pattern Recognition on June 21 2012 in Providence, Rhode Island, USA. The papers explore the use of modern computer vision technology in tasks such as automatic segmentation and registration, localization of anatomical features and detection of anomalies, as well as 3D reconstruction and biophysical model personalization.
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First Aid Clinical Pattern Recognition for the USMLE Step 1
This groundbreaking new guide helps you logically associate and link symptoms to likely diagnoses/conditions—a critical skill for passing the USMLE Step 1 Despite the recent changes to the USMLE Step 1, it remains a very important exam for medical students. In order to navigate the vignette-based questions, students must be able to determine a diagnosis based on the vignette, then understand the science behind the diagnosis—a process called pattern recognition. This innovative guide will help you master pattern recognition—a crucial skill to develop in order to thrive throughout the medical school curriculum. Organized by symptom to reflect exactly what you’ll see on test day, First Aid Clinical Pattern Recognition for the USMLE Step 1 provides overviews for more than 50 symptoms, discussing differential diagnosis and the principles that should inform your thinking about each symptom. Overviews are followed by Step 1-style vignettes for every potential diagnosis that may be included in the Step 1 exam. Then, each vignette is followed by the diagnosis—and competing diagnoses—along with explanations of why the patterns fit for the correct diagnosis. Guiding principles provide important information (such as which diagnoses are common, which are rare, which are acute, which are chronic, etc.) to enhance the way you think about each symptom. Each vignette contains classic history/physical exam and lab/test findings that would be observed in a typical presentation of the disease.
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First Aid Clinical Pattern Recognition for the USMLE Step 2 CK
The most trusted name in USME review—just ask any student!First Aid Clinical Pattern Recognition for the USMLE Step 2 CK provides the information you need to tie clinical facts together in order to recognize patterns and build illness scripts for various high-yield conditions. This essential resource features classic textbook-style vignettes of syndromes and cases commonly tested on the USMLE 2 CK. You'll learn how to determine the next best step, perform a patient workup, and effectively manage patient care. By presenting context that ties different symptoms, signs, and conditions together, the text encourages you to consider a differential diagnosis for a chief complain, explore how different conditions may be related to one another, and more. • All key conditions began with a high-yield overview and then move into case vignettes for that particular disorder type• Robust differential diagnosis sections provide targeted information on how to narrow down a diagnosis• Case vignettes are followed by sections on evaluation, drug treatment and key clinical considerations• Useful illustrations and flow charts help clarify complex concepts and processes• Separate chapters on pediatrics, surgery and emergency medicine cover content highly represented on the Step 2 CK exam
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Machine Learning and Computer Vision for Renewable Energy
As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of hybrid solar and battery storage systems is more critical than ever. In response to this challenge, advanced Artificial Intelligence (AI) techniques are emerging as a solution, promising to revolutionize the renewable energy landscape. Machine Learning and Computer Vision for Renewable Energy presents a deep exploration of AI modeling, analysis, performance prediction, and control approaches dedicated to overcoming the pressing issues in renewable energy systems. Transitioning from the complexities of energy prediction to the promise of advanced technology, the book sets its sights on the game-changing potential of computer vision (CV) in the realm of renewable energy. Amidst the struggle to enhance sustainability across industries, CV technology emerges as a powerful ally, collecting invaluable data from digital photos and videos. This data proves instrumental in achieving better energy management, predicting factors affecting renewable energy, and optimizing overall sustainability. Readers, including researchers, academicians, and students, will find themselves immersed in a comprehensive understanding of the AI approaches and CV methodologies that hold the key to resolving the challenges faced by renewable energy systems. Machine Learning and Computer Vision for Renewable Energy positions itself as a catalyst for this change. The book not only addresses the immediate concerns of the energy sector but also details how to achieve a more sustainable future. By emphasizing breakthroughs in CV and AI, the objective is clear: to drive societal progress through research, innovation, and technological advancements in the domain of renewable energy. Academic researchers, professors, college students, and business professionals focused on the intersection of digital transformation and renewable energy will find this book to be an indispensable guide to navigating the challenges and opportunities that lie ahead. With a diverse array of recommended topics, this book stands as a testament to the evolving landscape of AI and computer vision, shaping a sustainable energy future for generations to come.
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Automatic Speech and Speaker Recognition
Research in the field of automatic speech and speaker recognition has made a number of significant advances in the last two decades, influenced by advances in signal processing, algorithms, architectures, and hardware. These advances include: the adoption of a statistical pattern recognition paradigm; the use of the hidden Markov modeling framework to characterize both the spectral and the temporal variations in the speech signal; the use of a large set of speech utterance examples from a large population of speakers to train the hidden Markov models of some fundamental speech units; the organization of speech and language knowledge sources into a structural finite state network; and the use of dynamic, programming based heuristic search methods to find the best word sequence in the lexical network corresponding to the spoken utterance. Automatic Speech and Speaker Recognition: Advanced Topics groups together in a single volume a number of important topics on speech and speaker recognition, topics which are of fundamental importance, but not yet covered in detail in existing textbooks. Although no explicit partition is given, the book is divided into five parts: Chapters 1-2 are devoted to technology overviews; Chapters 3-12 discuss acoustic modeling of fundamental speech units and lexical modeling of words and pronunciations; Chapters 13-15 address the issues related to flexibility and robustness; Chapter 16-18 concern the theoretical and practical issues of search; Chapters 19-20 give two examples of algorithm and implementational aspects for recognition system realization. Audience: A reference book for speech researchers and graduate students interested in pursuing potential research on the topic. May also be used as a text for advanced courses on the subject.
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Digital Health and Wireless Solutions: Integrating AI, LLMs and Multimodal Health Data for Next-Generation Decision Support
This open access volume set CCIS 3009-3011 constitutes the refereed proceedings of the Second Nordic Conference on Digital Health and Wireless Solutions, NCDHWS 2026, held in Oulu, Finland, during June 16-17, 2026. The total of 86 full and 21 short papers included in the proceedings was carefully reviewed and selected from 159 submissions. They were organized in topical sections as follows: Part I: Digital Health Transformation, Innovations & Future Visions; Personalised Digital Health – From Omics to Interventions, Telemedicine and eRehabilitation; Health Technology Assessment & Impact Evaluation; User Experience, Acceptance & Adoption of Health Information Systems; Education in Digital Health; Part II: Data‑Driven and Modelling‑Based Decision Support & Multimodal Health Data Analysis; AI and LLMs in Healthcare: Tools for Professionals, Support for Citizens;Part III: Wearable Sensors, Antennas and Physiological Measurement Systems; Next‑Generation Wireless Health Technologies, Wearable Sensing, and Digital Twin Innovations; Connectivity Architectures and Cybersecurity in Digital Health.
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Next Generation Wireless Systems and Networks
Next Generation Wireless Systems and Networks offers an expert view of cutting edge Beyond 3rd Generation (B3G) wireless applications. This self-contained reference combines the basics of wireless communications, such as 3G wireless standards, spread spectrum and CDMA systems, with a more advanced level research-oriented approach to B3G communications, eliminating the need to refer to other material. This book will provide readers with the most up-to-date technological developments in wireless communication systems/networks and introduces the major 3G standards, such as W-CDMA, CDMA2000 and TD-SCDMA. It also includes a focus on cognitive radio technology and 3GPP E-UTRA technology; areas which have not been well covered elsewhere. Covers many hot topics in the area of next generation wireless from the authors’ own research, including: Bluetooth, all-IP wireless networking, power-efficient and bandwidth-efficient air-link technologies, and multi-user signal processing in B3G wirelessClear, step-by-step progression throughout the book will provide the reader with a thorough grounding in the basic topics before moving on to more advanced materialAddresses various important topics on wireless communication systems and networks that have emerged only very recently, such as Super-3G technology, 4G wireless, UWB, OFDMA and MIMOIncludes a wealth of explanatory tables and illustrations This essential reference will prove invaluable to senior undergraduate and postgraduate students, academics and researchers. It will also be of interest to telecommunications engineers wishing to further their knowledge in this field.
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Generation Next
**The bestselling debut novel from YouTube sensation Oli White. CONTAINS EXCLUSIVE BONUS CONTENT!**Things haven't been easy for Jack recently - life as a teenager has its ups and downs. But when he meets a new group of friends, who are every bit as geek as they are chic, his luck seems to be changing. Each of the group is talented and when they pool together to create Generation Next, an incredible new kind of social media platform, it's clear that they're on to something special.What if your Instagram account grew by hundreds of thousands of followers overnight, and big companies were fighting each other to offer you photoshoots? When GenNext suddenly goes viral, Jack and his friends are thrust into a crazy world of fame which is as terrifying as it is awesome. Because someone out there is determined to trip Jack up at every step. If he doesn't stop them, soon everyone he cares about - his friends, his family, and the girl he's falling for - will be in danger...
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Sequencing?
The importance of the financial system in economic development has been frequently neglected by analysts and poorly understood by policymakers. Are there policy reforms, or any particular sequence of reform measures, that will contribute to the successful functioning of the financial system and thus spur long-term economic growth? What kind of regulatory changes are appropriate as countries move toward financial liberalization and as government development banks decline in importance compared to private banks and nonbank financial institutions? What broad lessons can be discerned from the experience of financial reform in Asia and Latin America for the transitional countries of Russia and Eastern Europe? The world's financial system has been buffeted in recent years by the crisis in the U.S. savings and loan industry, the implosion of the Japanese ""bubble economy"" of the late 1980s, the Mexican peso crisis, and other events. The experience of Western nations in adapting to financial liberalization can provide useful insights for the many countries embarking on a course of market reforms and beginning to build the financial infrastructure for a market economy. This volume analyzes the recent financial reforms and reform strategies in Asia, Latin America, Eastern Europe, and Russia. The chapters draw on the extensive practical experience of the authors and reflect the most recent empirical research in the field. The contributors are Gerald Caprio, Jr., Dimitri Vittas, and Ross Levine, the World Bank; David C. Cole and Betty F. Slade, Harvard Institute for International Development; Maxwell J. Fry, University of Birmingham at Edgbaston; Claudio Gonzalez-Vega, Ohio State University; Stephan Haggard, University of California, San Diego; R. Barry Johnston, International Monetary Fund; Philip A. Wellons, Harvard Law School; Lawrence J. White, New York University; and Alison Harwood.
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