La teoria dei sistemi : presupposti, caratteristiche e sviluppi del pensiero sistematico / scritti di Ackoff, Angyal, Ashby ... [et al.] ; a cura di F.E. Emery

• Record Nr.: UNINA990004143370403321
• Pubbl/distr/stampa: Milano : FrancoAngeli, 1980
• Descrizione fisica: 436 p. ; 22 cm
• Locazione: FLFBC; DDCIC; SES
• Collocazione: P.1 PEP 4; XXVI 902; B/1 EME
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Computer Vision and Image Processing : 5th International Conference, CVIP 2020, Prayagraj, India, December 4-6, 2020, Revised Selected Papers, Part III / / edited by Satish Kumar Singh, Partha Roy, Balasubramanian Raman, P. Nagabhushan

• Record Nr.: UNINA9910484439203321
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021
• ISBN: 981-16-1103-3
• Edizione: [1st ed. 2021.]
• Descrizione fisica: 1 online resource (556 pages)
• Collana: Communications in Computer and Information Science, , 1865-0937 ; ; 1378
• Disciplina: 006.37
• Soggetti: Image processing - Digital techniques; Computer vision; Artificial intelligence; Computer engineering; Computer networks; Computers; Computer systems; Social sciences - Data processing; Computer Imaging, Vision, Pattern Recognition and Graphics; Artificial Intelligence; Computer Engineering and Networks; Computing Milieux; Computer System Implementation; Computer Application in Social and Behavioral Sciences
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: U-Net-Based Approach for Segmentation of Tables from Scanned Pages -- Air Writing: Tracking and Tracing -- Mars Surface Multi-Decadal Change Detection using ISRO’s Mars Color Camera (MCC) and Viking Orbiter Images -- Deep Over and Under Exposed Region Detection -- DeepHDR-GIF: Capturing Motion in High Dynamic Range Scenes -- Camera Based Parking Slot Detection For Autonomous Parking -- Hard-; Mining Loss based Convolutional Neural Network for Face Recognition -- Domain Adaptive Egocentric Person Re-identification -- Scene Text recognition in the wild with motion deblurring using deep networks -- Vision based Autonomous Drone Navigation through enclosed spaces -- Deep Learning-based Smart Parking Management System and Business Model -- Design and Implementation of Motion Envelope for a Moving Object using Kinect for Windows -- Software Auto Trigger Recording for Super Slow Motion Videos using Statistical Change Detection -- Using Class Activations to Investigate Semantic Segmentation -- Few Shots Learning: Caricature to Image Recognition using Improved Relation Network -- Recognition of Adavus in Bharatanatyam Dance -- Digital Borders: Design of an Animal Intrusion Detection System based on Deep Learning -- Automatic On-Road Object Detection in LiDAR-Point Cloud Data using Modified VoxelNet Architecture -- On the Performance of Convolutional Neural Networks under High and Low Frequency Information -- A Lightweight Multi-Label Image Classification Model Based on Inception Module -- Computer Vision based Animal Collision Avoidance Framework for Autonomous Vehicles -- L2PF - Learning to Prune Faster -- Efficient Ensemble Sparse Convolutional Neural Networks with Dynamic Batch Size -- Inferring Semantic Object Affordances from Videos -- An Unsupervised Approach for Estimating Depth of Outdoor Scenes from Monocular Image -- Age and Gender Prediction using Deep CNNs and Transfer Learning -- One Shot Learning Based Human Tracking in Multiple Surveillance Cameras -- Fast road sign detection and recognition using colour-based thresholding -- Dimensionality Reduction by Consolidated Sparse Representation and Fisher Criterion with Initialization for Recognition -- Deep Learning and Density Based Clustering Methods for Road Traffic Prediction -- Deep learning based Stabbing Action Detection in ATM Kiosks for intelligent Video Surveillance Applications -- An algorithm for semantic vectorization of video scenes -- Applications to Retrieval and Anomaly detection -- Meta-tracking and Dominant Motion Patterns at the Macroscopic Crowd Level -- Digital Video Encryption by Quasigroup on System on Chip (SoC) -- Detection based Multipath Correlation Filter for Visual Object Tracking -- Graph-based depth estimation in a monocular image using constrained 3D wireframe models -- AE-CNN based Supervised Image Classification -- Ensemble based Graph Convolutional Network for Semi supervised learning -- Regularized Deep Convolutional Generative Adversarial Network -- A Novel Approach for Video Captioning based on Semantic Cross Embedding and Skip-Connection -- Dual Segmentation Technique for Road Extraction on Unstructured Roads for Autonomous Mobile Robots -- Edge based Robust and Secure Perceptual Hashing Framework -- Real-Time Driver Drowsiness Detection Using GRU with CNN Features -- Detection of Concave Points in Closed Object Boundaries Aiming at Separation of Overlapped Objects -- High Performance Ensembled Convolutional Neural Network for Plant Species Recognition.
• Sommario/riassunto: This three-volume set (CCIS 1367-1368) constitutes the refereed proceedings of the 5th International Conference on Computer Vision and Image Processing, CVIP 2020, held in Prayagraj, India, in December 2020. Due to the COVID-19 pandemic the conference was partially held online. The 134 papers papers were carefully reviewed and selected from 352 submissions. The papers present recent research on such topics as biometrics, forensics, content protection, image enhancement/super-resolution/restoration, motion and tracking, image or video retrieval, image, image/video processing for autonomous vehicles, video scene understanding, human-computer ; interaction, document image analysis, face, iris, emotion, sign language and gesture recognition, 3D image/video processing, action and event detection/recognition, medical image and video analysis, vision-based human GAIT analysis, remote sensing, and more.

Statistical Tools for the Comprehensive Practice of Industrial Hygiene and Environmental Health Sciences

• Record Nr.: UNINA9910971929603321
• Autore: Johnson David L
• Pubbl/distr/stampa: New York : , : John Wiley & Sons, Incorporated, , 2017; ©2017
• ISBN: 9781119351351; 9781119143017
• Descrizione fisica: 1 online resource (395 pages)
• Disciplina: 363.110727
• Soggetti: Industrial hygiene--Statistical methods
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Cover -- Title Page -- Copyright -- Dedication -- Contents -- Preface -- Acknowledgments -- About the Author -- About the Companion Website -- Chapter 1 Some Basic Concepts -- 1.1 Introduction -- 1.2 Physical versus Statistical Sampling -- 1.3 Representative Measures -- 1.4 Strategies for Representative Sampling -- 1.5 Measurement Precision -- 1.6 Probability Concepts -- 1.6.1 The Relative Frequency Approach -- 1.6.2 The Classical Approach - Probability Based on Deductive Reasoning -- 1.6.3 Subjective Probability -- 1.6.4 Complement of a Probability -- 1.6.5 Mutually Exclusive Events -- 1.6.6 Independent Events -- 1.6.7 Events that Are Not Mutually Exclusive -- 1.6.8 Marginal and Conditional Probabilities -- 1.6.9 Testing for Independence -- 1.7 Permutations and Combinations -- 1.7.1 Permutations for Sampling without Replacement -- 1.7.2 Permutations for Sampling with Replacement -- 1.7.3 Combinations -- 1.8 Introduction to Frequency Distributions -- 1.8.1 The Binomial Distribution -- 1.8.2 The Normal Distribution -- 1.8.3 The Chi-Square ; Distribution -- 1.9 Confidence Intervals and Hypothesis Testing -- 1.10 Summary -- 1.11 Addendum: Glossary of Some Useful Excel Functions -- 1.12 Exercises -- References -- Chapter 2 Descriptive Statistics and Methods of Presenting Data -- 2.1 Introduction -- 2.2 Quantitative Descriptors of Data and Data Distributions -- 2.3 Displaying Data with Frequency Tables -- 2.4 Displaying Data with Histograms and Frequency Polygons -- 2.5 Displaying Data Frequency Distributions with Cumulative Probability Plots -- 2.6 Displaying Data with NED and Q - Q Plots -- 2.7 Displaying Data with Box-and-Whisker Plots -- 2.8 Data Transformations to Achieve Normality -- 2.9 Identifying Outliers -- 2.10 What to Do with Censored Values? -- 2.11 Summary -- 2.12 Exercises -- References -- Chapter 3 Analysis of Frequency Data.; 3.1 Introduction -- 3.2 Tests for Association and Goodness-of-Fit -- 3.2.1 r × c Contingency Tables and the Chi-Square Test -- 3.2.2 Fisher's Exact Test -- 3.3 Binomial Proportions -- 3.4 Rare Events and the Poisson Distribution -- 3.4.1 Poisson Probabilities -- 3.4.2 Confidence Interval on a Poisson Count -- 3.4.3 Testing for Fit with the Poisson Distribution -- 3.4.4 Comparing Two Poisson Rates -- 3.4.5 Type I Error, Type II Error, and Power -- 3.4.6 Power and Sample Size in Comparing Two Poisson Rates -- 3.5 Summary -- 3.6 Exercises -- References -- Chapter 4 Comparing Two Conditions -- 4.1 Introduction -- 4.2 Standard Error of the Mean -- 4.3 Confidence Interval on a Mean -- 4.4 The t-Distribution -- 4.5 Parametric One-Sample Test - Student's t-Test -- 4.6 Two-Tailed versus One-Tailed Hypothesis Tests -- 4.7 Confidence Interval on a Variance -- 4.8 Other Applications of the Confidence Interval Concept in IH/EHS Work -- 4.8.1 OSHA Compliance Determinations -- 4.8.2 Laboratory Analyses - LOB, LOD, and LOQ -- 4.9 Precision, Power, and Sample Size for One Mean -- 4.9.1 Sample Size Required to Estimate a Mean with a Stated Precision -- 4.9.2 Sample Size Required to Detect a Specified Difference in Student's t-Test -- 4.10 Iterative Solutions Using the Excel Goal Seek Utility -- 4.11 Parametric Two-Sample Tests -- 4.11.1 Confidence Interval for a Difference in Means: The Two-Sample t-Test -- 4.11.2 Two-Sample t-Test When Variances Are Equal -- 4.11.3 Verifying the Assumptions of the Two-Sample t-Test -- 4.11.4 Two-Sample t-Test with Unequal Variances - Welch's Test -- 4.11.5 Paired Sample t-Test -- 4.11.6 Precision, Power, and Sample Size for Comparing Two Means -- 4.12 Testing for Difference in Two Binomial Proportions -- 4.12.1 Testing a Binomial Proportion for Difference from a Known Value -- 4.12.2 Testing Two Binomial Proportions for Difference.; 4.13 Nonparametric Two-Sample Tests -- 4.13.1 Mann - Whitney U Test -- 4.13.2 Wilcoxon Matched Pairs Test -- 4.13.3 McNemar and Binomial Tests for Paired Nominal Data -- 4.14 Summary -- 4.15 Exercises -- References -- Chapter 5 Characterizing the Upper Tail of the Exposure Distribution -- 5.1 Introduction -- 5.2 Upper Tolerance Limits -- 5.3 Exceedance Fractions -- 5.4 Distribution Free Tolerance Limits -- 5.5 Summary -- 5.6 Exercises -- References -- Chapter 6 One-Way Analysis of Variance -- 6.1 Introduction -- 6.2 Parametric One-Way ANOVA -- 6.2.1 How the Parametric ANOVA Works - Sums of Squares and the F-Test -- 6.2.2 Post hoc Multiple Pairwise Comparisons in Parametric ANOVA -- 6.2.3 Checking the ANOVA Model Assumptions - NED Plots and Variance Tests -- 6.3 Nonparametric Analysis of Variance -- 6.3.1 Kruskal - Wallis Nonparametric One-Way ANOVA -- 6.3.2 Post hoc Multiple Pairwise Comparisons in Nonparametric ANOVA -- 6.4 ANOVA Disconnects -- 6.5 Summary -- 6.6 Exercises -- References -- Chapter 7 Two-Way ; Analysis of Variance -- 7.1 Introduction -- 7.2 Parametric Two-Way ANOVA -- 7.2.1 Two-Way ANOVA without Interaction -- 7.2.2 Checking for Homogeneity of Variance -- 7.2.3 Multiple Pairwise Comparisons When There Is No Interaction Term -- 7.2.4 Two-Way ANOVA with Interaction -- 7.2.5 Multiple Pairwise Comparisons with Interaction -- 7.2.6 Two-Way ANOVA without Replication -- 7.2.7 Repeated-Measures ANOVA -- 7.2.8 Two-Way ANOVA with Unequal Sample Sizes -- 7.3 Nonparametric Two-Way ANOVA -- 7.3.1 Rank Tests -- 7.3.2 Repeated-Measures Nonparametric ANOVA - Friedman's Test -- 7.4 More Powerful Non-ANOVA Approaches: Linear Modeling -- 7.5 Summary -- 7.6 Exercises -- References -- Chapter 8 Correlation Analysis -- 8.1 Introduction -- 8.2 Simple Parametric Correlation Analysis -- 8.2.1 Testing the Correlation Coefficient for Significance.; 8.2.2 Confidence Limits on the Correlation Coefficient -- 8.2.3 Power in Simple Correlation Analysis -- 8.2.4 Comparing Two Correlation Coefficients for Difference -- 8.2.5 Comparing More Than Two Correlation Coefficients for Difference -- 8.2.6 Multiple Pairwise Comparisons of Correlation Coefficients -- 8.3 Simple Nonparametric Correlation Analysis -- 8.3.1 Spearman Rank Correlation Coefficient -- 8.3.2 Testing Spearman's Rank Correlation Coefficient for Statistical Significance -- 8.3.3 Correction to Spearman's Rank Correlation Coefficient When There Are Tied Ranks -- 8.4 Multiple Correlation Analysis -- 8.4.1 Parametric Multiple Correlation -- 8.4.2 Nonparametric Multiple Correlation: Kendall's Coefficient of Concordance -- 8.5 Determining Causation -- 8.6 Summary -- 8.7 Exercises -- References -- Chapter 9 Regression Analysis -- 9.1 Introduction -- 9.2 Linear Regression -- 9.2.1 Simple Linear Regression -- 9.2.2 Nonconstant Variance - Transformations and Weighted Least Squares Regression -- 9.2.3 Multiple Linear Regression -- 9.2.4 Using Regression for Factorial ANOVA with Unequal Sample Sizes -- 9.2.5 Multiple Correlation Analysis Using Multiple Regression -- 9.2.6 Polynomial Regression -- 9.2.7 Interpreting Linear Regression Results -- 9.2.8 Linear Regression versus ANOVA -- 9.3 Logistic Regression -- 9.3.1 Odds and Odds Ratios -- 9.3.2 The Logit Transformation -- 9.3.3 The Likelihood Function -- 9.3.4 Logistic Regression in Excel -- 9.3.5 Likelihood Ratio Test for Significance of MLE Coefficients -- 9.3.6 Odds Ratio Confidence Limits in Multivariate Models -- 9.4 Poisson Regression -- 9.4.1 Poisson Regression Model -- 9.4.2 Poisson Regression in Excel -- 9.5 Regression with Excel Add-ons -- 9.6 Summary -- 9.7 Exercises -- References -- Chapter 10 Analysis of Covariance -- 10.1 Introduction -- 10.2 The Simple ANCOVA Model and Its Assumptions.; 10.2.1 Required Regressions -- 10.2.2 Checking the ANCOVA Assumptions -- 10.2.3 Testing and Estimating the Treatment Effects -- 10.3 The Two-Factor Covariance Model -- 10.4 Summary -- 10.5 Exercises -- Reference -- Chapter 11 Experimental Design -- 11.1 Introduction -- 11.2 Randomization -- 11.3 Simple Randomized Experiments -- 11.4 Experimental Designs Blocking on Categorical Factors -- 11.5 Randomized Full Factorial Experimental Design -- 11.6 Randomized Full Factorial Design with Blocking -- 11.7 Split Plot Experimental Designs -- 11.8 Balanced Experimental Designs - Latin Square -- 11.9 Two-Level Factorial Experimental Designs with Quantitative Factors -- 11.9.1 Two-Level Factorial Designs for Exploratory Studies -- 11.9.2 The Standard Order -- 11.9.3 Calculating Main Effects -- 11.9.4 Calculating Interactions -- 11.9.5 Estimating Standard Errors -- 11.9.6 Estimating Effects with REGRESSION in Excel -- 11.9.7 Interpretation -- 11.9.8 Cube, Surface, and NED Plots as an ; Aid to Interpretation -- 11.9.9 Fractional Factorial Two-Level Experiments -- 11.10 Summary -- 11.11 Exercises -- References -- Chapter 12 Uncertainty and Sensitivity Analysis -- 12.1 Introduction -- 12.2 Simulation Modeling -- 12.2.1 Propagation of Errors -- 12.2.2 Simple Bounding -- 12.2.3 Addition in Quadrature -- 12.2.4 LOD and LOQ Revisited - Dust Sample Gravimetric Analysis -- 12.3 Uncertainty Analysis -- 12.4 Sensitivity Analysis -- 12.4.1 One-at-a-Time (OAT) Analysis -- 12.4.2 Variance-Based Analysis -- 12.5 Further Reading on Uncertainty and Sensitivity Analysis -- 12.6 Monte Carlo Simulation -- 12.7 Monte Carlo Simulation in Excel -- 12.7.1 Generating Random Numbers in Excel -- 12.7.2 The Populated Spreadsheet Approach -- 12.7.3 Monte Carlo Simulation Using VBA Macros -- 12.8 Summary -- 12.9 Exercises -- References.; Chapter 13 Bayes' Theorem and Bayesian Decision Analysis.
• Sommario/riassunto: Reviews and reinforces concepts and techniques typical of a first statistics course with additional techniques useful to the IH/EHS practitioner. Includes both parametric and non-parametric techniques described and illustrated in a worker health and environmental protection practice context Illustrated through numerous examples presented in the context of IH/EHS field practice and research, using the statistical analysis tools available in Excel® wherever possible Emphasizes the application of statistical tools to IH/EHS-type data in order to answer IH/EHS-relevant questions Includes an instructor's manual that follows in parallel with the textbook, including PowerPoints to help prepare lectures and answers in the text as for the Exercises section of each chapter.