03427nam 22005775 450 991036495040332120250609110559.09781484254431148425443010.1007/978-1-4842-5443-1(CKB)4100000010011623(MiAaPQ)EBC5990230(DE-He213)978-1-4842-5443-1(CaSebORM)9781484254431(PPN)24282109X(OCoLC)1142132599(OCoLC)on1142132599(MiAaPQ)EBC5990209(EXLCZ)99410000001001162320191205d2019 u| 0engurcn| |||||txtrdacontentcrdamediacrrdacarrierBasic Math for Game Development with Unity 3D A Beginner's Guide to Mathematical Foundations /by Kelvin Sung, Gregory Smith1st ed. 2019.Berkeley, CA :Apress :Imprint: Apress,2019.1 online resource (414 pages)9781484254424 1484254422 Includes bibliographical references.Chapter 1: Introduction and Learning Environment -- Chapter 2: Intervals and Bounding Boxes -- Chapter 3: Distances and Bounding Spheres -- Chapter 4: Vectors -- Chapter 5: Vector Dot Products -- Chapter 6: Vector Cross Products and 2D Planes -- Chapter 7: Conclusion.Use Unity-based examples to understand fundamental mathematical concepts and see how they are applied when building modern video game functionality. You will gain the theoretical foundation you need, and you will know how to examine and modify an implementation. This book covers points in a 3D Cartesian coordinate system, and then discusses vectors and the details of dot and cross products. Basic mathematical foundations are illustrated through Unity-based example implementations. Also provided are examples showing how the concepts are applied when implementing video game functionality, such as collision support, motion simulations, autonomous behaviors, shadow approximations, and reflection off arbitrary walls. Throughout this book, you learn and examine the concepts and their applications in a game engine. You will: Understand the basic concepts of points and vectors and their applications in game development Apply mathematical concepts to modern video game functionality, such as spherical and box colliders Implement autonomous behaviors, including following way points, facing a target, chasing an object, etc.Computer games—ProgrammingComputer science—MathematicsGame Developmenthttps://scigraph.springernature.com/ontologies/product-market-codes/I29040Math Applications in Computer Sciencehttps://scigraph.springernature.com/ontologies/product-market-codes/I17044Computer games—Programming.Computer science—Mathematics.Game Development.Math Applications in Computer Science.794.815Sung Kelvinauthttp://id.loc.gov/vocabulary/relators/aut926167Smith Gregoryauthttp://id.loc.gov/vocabulary/relators/autUMIUMIBOOK9910364950403321Basic Math for Game Development with Unity 3D2544820UNINA04965nam 2201153z- 450 991067404480332120220111(CKB)5400000000042630(oapen)https://directory.doabooks.org/handle/20.500.12854/76971(oapen)doab76971(EXLCZ)99540000000004263020202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierComputational Intelligence in HealthcareBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (226 p.)3-0365-2377-4 3-0365-2378-2 The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications.Information technology industriesbicssc1D poolingAlzheimer's diseaseartificial neural networkbody area networkclassificationclusteringcomputational intelligenceconvolutional neural networkCRISPRcross poolingdecision support systemsdeep learningdiabetic retinopathy (DR)diffusion tensor imaginge-healthearly detectionelectrocardiogramensemble learningevaluation metricseveryday walkingfault data eliminationfeature extractionfuzzy inference systemsgait analysisgait phasegenetic algorithmshealth offhealth status detectionhealth status predictionhealthcareIMUInternet of Medical Thingsinterpretable modelsleukemia nucleus imagelong-term monitoringmachine learningmachine learning algorithmmedical diagnosismedical informaticsMIMUmulti-modal deep featuresmulti-sensormulti-unitmultiple imputation by chained equationsmultistage support vector machine modeln/aneural networksnext-generation sequencingovarian cancerphysionet challengepre-trained deep ConvNetPremature ventricular contractionsegmentationsEMGsepsissoft computingsoft covering rough setSoftmax regressionsparse autoencoderSVM-based recursive feature eliminationtime synchronizationtransfer learningTri-Fog Health Systemuni-modal deep featuresunipolar depressionunsupervised learningInformation technology industriesCastellano Giovannaedt1339017Casalino GabriellaedtCastellano GiovannaothCasalino GabriellaothBOOK9910674044803321Computational Intelligence in Healthcare3059521UNINA