“…The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks…”

“…Beginning with the foundational aspects of the theory, the book presents important concepts and useful techniques, discusses issues of basic interest, and shows how thermal field, linear response, kinetic theories and hydrodynamics emerge. …”

“…As a European Green Capital 2023, Tallinn has a unique momentum to set the foundations for its transition from a linear to a circular economy. The newly created Circular Economy Department in the city administration is a signal of this transformation. …”

“…Beginning with the foundational aspects of the theory, the book presents important concepts and useful techniques, discusses issues of basic interest, and shows how thermal field, linear response, kinetic theories and hydrodynamics emerge. …”

“…In this revised and expanded edition of his groundbreaking THINKERTOYS, creativity expert Michael Michalko reveals life-changing tools that will help you think like a genius. From the linear to the intuitive, this comprehensive handbook details ingenious creative-thinking techniques for approaching problems in unconventional ways. …”

“…This is a major interdisciplinary work that moves away from ideas of linear or innate racism and recasts our understanding of interethnic relations…”

“…While the presentation is rigorous and contains theorems and proofs, linear algebra is largely avoided and only a minimal amount of multivariable calculus is needed. …”

“…Using Wagner's manuscripts as a point of departure, Darcy discusses the formal, harmonic, and linear structure of the work. In so doing, he challenges a number of contemporary views about the opera, including those of Curt von Westernhagen and Carl Dahlhaus…”

“…La seconda parte, quella più lunga, prospetta una presentazione lineare delle opere che può servire da segnavia tematico per uno studio delle medesime; la terza invece, più sintetica, presenta in maniera articolata i punti più rilevanti e caratteristici del pensiero fabriano, sulla base della panoramica fornita dalla parte precedente. …”

“…Como lo hacen otros al negar la realidad nacional de Catalunya. Con este libro lo que pretendo es hacer un acto de justicia a esta realidad…”

“…En la segunda parte, el lector aprende, a través de ejemplos concretos, cómo diseñar herramientas para recuperar información del sistema, acceder a bases de datos, utilizar diferentes formatos de archivo (.odt, .csv, .ini, .tar, .zip...), generar documentos en formato texto, CSV o HTML e incluso generar datos aleatorios, etc. …”
Acceso con credenciales UPSA.

Tabla de Contenidos: “…Syed and Ghazanfar Ali Safdar -- 6.1 Fundamentals of Signal Processing 151 -- 6.1.1 Channel Model 151 -- 6.1.1.1 Additive Gaussian Noise Channel 151 -- 6.1.1.2 Linear Filter Channel 152 -- 6.1.1.3 Band Limited Channel 153 -- 6.1.2 Modulation Technique 153 -- 6.1.3 Error Probability 154 -- 6.2 Spectrum Sensing Techniques 155 -- 6.2.1 Primary Transmitter Detection 155 -- 6.2.1.1 Energy Detector 156 -- 6.2.1.2 Matched Filter Detection 158 -- 6.2.1.3 Cyclostationary Feature Detection 159 -- 6.2.1.4 Waveform Detection 160 -- 6.2.1.5 Wavelet Detection 161 -- 6.2.1.6 Hybrid Sensing 162 -- 6.2.1.7 Multi?]…”

Tabla de Contenidos: “…-- 1.3.1 VSAM access types -- 1.4 Major VSAM parts -- 1.4.1 Catalog management -- 1.4.2 Record management -- 1.5 VSAM terminology and concepts -- 1.5.1 Logical record -- 1.5.2 Key field -- 1.5.3 Ways to identify logical records -- 1.5.4 Physical record -- 1.5.5 Control interval -- 1.5.6 Control area -- 1.5.7 Spanned records -- 1.5.8 Component -- 1.5.9 Cluster -- 1.5.10 Sphere -- 1.5.11 Alternate indexes -- 1.5.12 Splits -- 1.5.13 VSAM buffering -- 1.6 VSAM data set organizations -- 1.6.1 Key-sequenced data set -- 1.6.2 Entry sequenced data set (ESDS) -- 1.6.3 Relative record data set -- 1.6.4 Variable relative record data set -- 1.6.5 Linear data set (LDS) -- 1.7 Comparing VSAM data set organizations -- 1.8 Choosing a VSAM data set type -- 1.9 Extended format data set -- 1.10 Extended addressability -- 1.11 Data striping -- 1.12 Processing a VSAM cluster -- 1.12.1 Allocating a VSAM cluster -- 1.12.2 Accessing VSAM cluster -- 1.12.3 Unallocation -- 1.13 VSAM exploiters -- 1.13.1 DB2 -- 1.13.2 Hierarchical file system (HFS) -- 1.13.3 zSeries File System (zFS) -- 1.13.4 CICS -- 1.13.5 DFSMShsm -- 1.13.6 DFSMSrmm -- 1.13.7 Java Record I/O (JRIO) -- Chapter 2. …”

Tabla de Contenidos: “…7.2.3 Object Detection with a Training Set 236 -- 7.2.4 Object Recognition Combined with Bottom-up Attention 239 -- 7.3 Object Detection and Recognition in Satellite Imagery 240 -- 7.3.1 Ship Detection based on Visual Attention 242 -- 7.3.2 Airport Detection in a Land Region 245 -- 7.3.3 Saliency and Gist Feature for Target Detection 248 -- 7.4 Image Retrieval via Visual Attention 250 -- 7.4.1 Elements of General Image Retrieval 251 -- 7.4.2 Attention Based Image Retrieval 253 -- 7.5 Applications of Visual Attention in Robots 256 -- 7.5.1 Robot Self-localization 257 -- 7.5.2 Visual SLAM System with Attention 259 -- 7.5.3 Moving Object Detection using Visual Attention 262 -- 7.6 Summary 265 -- References 265 -- 8 Application of Attention Models in Image Processing 271 -- 8.1 Attention-modulated Just Noticeable Difference 271 -- 8.1.1 JND Modelling 272 -- 8.1.2 Modulation via Non-linear Mapping 274 -- 8.1.3 Modulation via Foveation 276 -- 8.2 Use of Visual Attention in Quality Assessment 277 -- 8.2.1 Image/Video Quality Assessment 278 -- 8.2.2 Weighted Quality Assessment by Salient Values 279 -- 8.2.3 Weighting through Attention-modulated JND Map 280 -- 8.2.4 Weighting through Fixation 281 -- 8.2.5 Weighting through Quality Distribution 281 -- 8.3 Applications in Image/Video Coding 282 -- 8.3.1 Image and Video Coding 282 -- 8.3.2 Attention-modulated JND based Coding 284 -- 8.3.3 Visual Attention Map based Coding 285 -- 8.4 Visual Attention for Image Retargeting 287 -- 8.4.1 Literature Review for Image Retargeting 288 -- 8.4.2 Saliency-based Image Retargeting in the Compressed Domain 289 -- 8.5 Application in Compressive Sampling 292 -- 8.5.1 Compressive Sampling 293 -- 8.5.2 Compressive Sampling via Visual Attention 296 -- 8.6 Summary 300 -- References 300 -- PART IV SUMMARY 305 -- 9 Summary, Further Discussions and Conclusions 307 -- 9.1 Summary 308 -- 9.1.1 Research Results from Physiology and Anatomy 308 -- 9.1.2 Research from Psychology and Neuroscience 309 -- 9.1.3 Theory of Statistical Signal Processing 310.…”

“…Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning Register your product at informit.com/register for convenient access to downloads, updates, and/or corrections as they become available…”

“…As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. …”

“…They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. …”

“…What You Will LearnExamine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden statesBe familiar with practical applications of machine learning and deep learning in econometricsUnderstand theoretical framework and hypothesis development, and techniques for selecting appropriate modelsDevelop, test, validate, and improve key supervised (i.e., regression and classification) and unsupervised (i.e., dimension reduction and cluster analysis) machine learning models, alongside neural networks, Markov, and SEM modelsRepresent and interpret data and models Who This Book Is ForBeginning and intermediate data scientists, economists, machine learning engineers, statisticians, and business executives…”

“…Advanced Modern Control Systems Theory and Design briefly reviews introductory control system analysis concepts and then presents the methods for designing linear control sys-tems using single-degree and two-degrees-of-freedom compensation techniques. …”

“…Relationships between variables were investigated by bivariate correlation analysis, linear regression and mediation models. The results allow for evidence of a significant relationship between self-efficacy, teachers' moral norms, and adoption of inclusive educational practices, but these relationships are negatively mediated by resistance to change (fear and stress in the affective component, protests and objections against change toward equality)…”