Everyday technology is constantly changing, and it’s hard to keep up with it at times. What is all this talk about automation, STEM, analytics and super-computers, and how will it really affect my daily life at work and in the home?
This book is a simple guide to everyday technology and analytics written in plain language. It starts with explaining how computer networks are increasing in speed so fast that we can do more in less time than ever before. It explains the analytical jargon in plain English and why robotics in the home will be aided by the new technology of the quantum computer.
Richly furnished with over 200 illustrations, photos and with minimal equations, A Simple Guide to Technology and Analytics is a ready reference book for those times when you don’t really understand the technology and analytics being talked about. It explains complicated topics such as automated character recognition in a very simple way, and has simple exercises for the reader to fully understand the technology (with answers at the back). It even has explanations on how home appliances work, which are very useful the next time you go shopping for a microwave or TV. Even the Glossary at the back can be used as a quick look-up explanation for those on the go.
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 responding to any EM energy.
6.2.6. Sensors responding to electrical transmission.
6.2.7. Sensors responding to chemical flow.
6.2.8. Haptic (tactile) sensors and their future potential.
6.3 Digitizing sensor data, using conventional computer interfaces and quantum computing.
6.3.1. Digitizing sensor data.
6.3.2. Sending the output to a recording and computing control point.
6.3.3. Speed of modern control operations.
6.3.4. Computer programs and toolboxes for analytics, Python and R.
6.3.5. How Quantum computing will change the world.
6.4. Further reading.
6.5 Exercise on using sensors.
Automation and simulations
7.1 Monitoring, feedback loops and remote control.
7.1.1. Sensing data and local control systems.
7.1.2. Transmitting and multiplexing local control to remote control.
7.1.3. The remote control centre and its operation.
7.1.4. Recording, analysing and predicting future data.
7.1.5. Effect on operating, repairs and maintenance scheduling.
7.2 Automated process control systems, PFDs to P&IDs.
7.2.1. Explaining a simple automated system as a PFD.
7.2.2. P&ID schematics.
7.2.3. P&ID diagrams for automated control.
7.3 Automating a continuous process.
7.3.1. Issues in continuous processing operations.
7.3.2. Approach to automating the control of a process- Heath Robinson approach.
7.3.3. Basic hardware and software control requirements.
7.4 The digital twin (aka ghosting/shadowing) and simulations.
7.4.1. Development of simulators.
7.4.2. Numerical simulation.
7.4.3. Running a simulation in parallel with a process system- the Digital Twin.
7.4.4. Block-chain and its use in administering developments.
7.4.5. Cyber issues.
7.5 Further reading.
7.6 Exercise in control systems.
Technology of Household appliances
8.1 The microwave oven.
8.2 The refrigerator and freezer.
8.3 The cook-top, infra-red ceramic v induction.
8.4 The steam oven and Bar-B-Que.
8.5 The dish washer.
8.6 The automatic washing machine and the clothes dryer.
8.7 The electric/oil/water heater v reverse-cycle air-conditioner.
8.8 The vacuum cleaner- suction/vibration v barrel cyclone v robot.
8.9 The hot water system- electric immersive storage v instantaneous heating.
8.10 The hot water kettle- immersive element v induction.
8.11 Drip coffee v percolator coffee machine and the espresso coffee machine with pods.
8.12 The home printer and scanner- 3D printing.
8.13 The hot air fryer and the Thermomix.
8.14 Further reading.
The future of analytics and automation
9.1 Smart applications to 2D and 3D sport and society benefits
9.1.1. Some applications of 2D analytics to sport and their consequences
9.1.2. Some applications of quantum 3D analytics to society
9.2 Gaming and simulations changing sport.
9.2.1. Simulations in real-time allowing new games to be developed.
9.2.2. Reality 3D sports using haptic sensor suits and automated refereeing.
9.2.3. The avatar, soccer with Pele, cricket with Bradman, baseball with Babe Ruth.
9.3 Smart technology changing industry.
9.3.1. Communications.
9.3.2. Traffic and transport.
9.3.3. Everyday living with robots and technology.
9.4 An automated world and a robotic future.
9.5 The smart jobs associated with future technologically controlled processes.
9.6 Further reading
Exercise Answers
Glossary
Index
Biography
Dr. Brian John Evans is a John Curtin Distinguished Emeritus Professor of Curtin University.
