A simple guide to technology and analytics. (2021)

• Record Type: Book
• Further Information: Note: Brian J. Evans.
• Authors: Evans, Brian J, 1945-
• Contents: Acknowledgements About the Author Preface Learning Outcomes Background to technology 1.1 Overview of basic technology and why the rapid increase in network speed is important. 1.1.1 Application in industry. 1.2 Basic physics of everyday technology innovations. 1.2.1 Basic history of technology development leading to data analytics. 1.2.2 The physics from analogue TV to smart viewing- LED, QLED, LCD, OLED. 1.2.3 Smart phones, towers and radiation. 1.3 Frequency and the basics of sampling speed. 1.3.1 Use of the frequency spectrum- more than just colours. 1.3.2 What you can see (and hear) - is not what you always get. 1.3.3 Basics of Clock Rate (aka timing frequency or sample speed). 1.4 Further reading. 1.6. Exercise - what we mean by clock rate/timing frequency/sample rate and aliasing. Tracking and triangulation- it’s simple 2.1 Tracking real-time position. 2.1.1 Animations of a moving object using multiple fixed-cameras. 2.1.2. Calculating speed (velocity) of a moving object using multiple fixed-cameras. 2.1.3. Calculating multiple body tracking. 2.2 Calculating position from the TV screen view. 2.2.1. Tracking in 2D on the sports field. 2.2.2. Tracking in 3D from multiple observation points. 2.2.3. Doppler positioning by satellite. 2.3 Further reading. 2.4 Exercise on object tracking a car- better to have one camera in the line of travel. Pattern Recognition and its applications 3.1 Introduction to number (data) representation. 3.1.1. Basics of data analysis- the time series. 3.1.2. Number systems using BITS and BYTES. 3.1.3. Higher sampling rate of bits versus accuracy 3.1.4. Bit resolution, formats and storage. 3.1.5. Rebuilding the analog graphic using a digital number series (DAC). 3.2 Correlation of image data. 3.2.1. Simple correlation of two sets of numbers- recognition of a signature is simple. 3.2.2. Flagging a good number series correlation versus a weak correlation. 3.2.3. Effect of higher sample rate on accuracy of correlations. 3.2.4. Increasing resolution through pixel mixing. 3.3 Application using a pixelated-matrix display. 3.3.1. Facial recognition (on a TV or monitor screen) 3.3.2. EM data scanning for financial and other transactions. 3.4 Correlation applications and use in security devices- from crowds to eye-balls. 3.4.1. Application of correlation to pictures (face in the crowd). 3.4.2. Application of correlation to sound. 3.4.3. Application of correlation to EM fields (credit card or door key). 3.4.4. Application of correlation in industry. 3.5. Further reading. 3.6 Exercise- determine the computer word for the number series 3, 1, -2.8, 0, assuming 0.1 is the value of the basic binary integer. 3.7 Exercise – We have installed a new exhaust pipe system on our car. How much in real numbers, has the sound amplitude level of the exhaust system changed if we say it has changed by -24 dB? 3.8 Exercise on pattern recognition (Passport photo versus Immigration Gate photo). Average track and prediction of future location 4.1 Understanding the meaning of average track. 4.1.1. Average versus mean, versus RMS track. 4.1.2. Filtering data. 4.2 Rule-based track prediction. 4.2.1. Curve fitting. 4.2.2. Application to ball tracking 4.3 Basic regression analysis and predictions of future data. 4.3.1. Regression and least squares (best fit). 4.3.2. Conical equations. 4.3.3. Accuracy of a line of regression. 4.3.4. Probability of a prediction being correct. 4.3.5. Bayes Theorem of probability or chance. 4.4 Further reading. 4.5 Exercise in predicting future apartment prices. Track Prediction in sports and industry 5.1 Present day sports (cricket, tennis, baseball and football technology). 5.1.1. Track prediction of a cricket ball. 5.1.2. Track prediction during tennis games. 5.1.3. Sports analytics and prediction- baseball. 5.1.4. Track prediction in football and application to gaming. 5.1.5. The use of spider-cam and drones. 5.2 Prediction in player performance and team tracking. 5.2.1. Team selection. 5.2.2. Tracking team performance. 5.3. Prediction in Industry 5.3.1. Prediction using sensor data in process control operations. 5.3.2. Computer control system terminology. 5.3.3. Prediction using past stock market values in the financial industry. 5.4 The use of Reality Technology. 5.4.1. Computer 3D visualisations and virtual reality (VR). 5.4.2. Computer Augmented Reality (AR) and its use in industry. 5.5 Product tracking in industry, RFID. 5.5.1. Electronic signatures of products- barcodes. 5.5.2. Radio Frequency IDentification of products (RFID). 5.6 Further reading. 5.7 Exercise in tracking a product from New York to London, and on to Cairo. Most common active and passive sensors 6.1 Sensors- the basics. 6.1.1. All of the Natural sensors. 6.1.2. Most of the Passive sensors. 6.1.3. Most of the Active sensors. 6.2 Simple explanation of active sensor technology. 6.2.1. Sensors responding to temperature. 6.2.2. Sensors responding to pressure. 6.2.3. Sensors responding to sound pressure and vibration. 6.2.4. Sensors responding to light (visible EM radiation). 6.2.5. Sensors respond
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2021
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 670.427; Automation; Electronic apparatus and appliances; Technological innovations; Mathematical statistics
• Languages: English
• ISBNs: 9781000449334; 9781000449297; 9781003108443
• Related ISBNs: 9780367608613
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.