Difference between revisions of "Time"

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m (Put these in a hierarchical list based on commonality: * Autoregressive (AR): This algorithm uses past values of the time series to predict future values. It assumes that the current value is a linear function of previous values. For example, AR can be us)
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== Types ==
 
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There are different types of time based AI algorithms, depending on the goal and the method of the algorithm. Some of the most common ones are:
 
  
−
* Autoregressive (AR): This algorithm uses past values of the time series to predict future values. It assumes that the current value is a linear function of previous values. For example, AR can be used to forecast the weather based on historical data.
+
= Algorithm Types =
−
* Autoregressive Integrated Moving Average (ARIMA): This algorithm is an extension of AR that also accounts for the trend and the seasonality of the time series. It uses differencing to make the time series stationary (i.e., having constant mean and variance) and then applies AR and moving average (MA) models. For example, ARIMA can be used to forecast the sales of a product based on past sales and seasonal patterns.
 
−
* Seasonal Autoregressive Integrated Moving Average (SARIMA): This algorithm is a further extension of ARIMA that also accounts for the cyclic variations of the time series. It uses seasonal differencing and seasonal AR and MA models to capture the periodic fluctuations of the time series. For example, SARIMA can be used to forecast the electricity demand based on past demand and seasonal factors.
 
−
* Exponential Smoothing (ES): This algorithm uses weighted averages of past values of the time series to predict future values. It gives more weight to recent values than older values, and it can also incorporate trend and seasonality components. For example, ES can be used to forecast the inventory level based on past demand and supply.
 
−
* Prophet: This algorithm is a modern and flexible approach to time series forecasting developed by Facebook. It uses a decomposable model that consists of trend, seasonality, and holiday components, and it allows for adding custom effects and prior information. For example, Prophet can be used to forecast the web traffic for a data science blog website based on past traffic and special events.
 
−
* [[Recurrent Neural Network (RNN)]]: This algorithm is a type of deep learning model that can process sequential data such as time series. It uses a network of neurons that have feedback loops, which enable them to store information from previous inputs. For example, RNN can be used to forecast the prices of Bitcoin based on past prices and other factors.
 
−
** [[Gated Recurrent Unit (GRU)]]
 
−
** [[Sequence to Sequence (Seq2Seq)]] can map a variable-length input sequence to a variable-length output sequence. It is often used for natural language processing tasks, such as machine translation, text summarization, conversational models, and question answering. The Seq2Seq algorithm consists of two main components: an encoder and a decoder. The encoder reads the input sequence one timestep at a time and produces a hidden vector representation of the input. The decoder then uses the hidden vector as the initial state and generates the output sequence one timestep at a time, using the previous output as the input context.
 
−
* Transformer: This algorithm is a state-of-the-art deep learning model that can process sequential data such as time series. It uses layers of attention mechanisms that can learn how to focus on relevant parts of the input data, and it can handle long-term dependencies and parallel computations efficiently. For example, Transformer can be used to forecast the spread of COVID-19 based on past cases and interventions. Can process sequential data using layers of attention mechanisms, without using recurrent or convolutional layers. It can handle long-term dependencies and parallel computations efficiently, and it can achieve better results than RNN-based Seq2Seq models on various tasks
 
−
** [[Generative Pre-trained Transformer (GPT)]]
 
−
** [[Attention]] Mechanism: This allows the decoder to selectively focus on different parts of the input sequence when generating the output, instead of relying on a single fixed vector. This can improve the performance and accuracy of the Seq2Seq model, especially for long sequences 
 
−
*** [[Transformer-XL]]
 
−
** Beam search: This is a technique to find the most probable output sequence given the input sequence, by keeping track of multiple candidate sequences and expanding them based on their probabilities. This can improve the quality and diversity of the output, compared to using a greedy or random search.
 
−
* [[Long Short-Term Memory (LSTM)]]: This algorithm is a special type of RNN that can handle long-term dependencies in sequential data. It uses a memory cell that can store, update, and forget information over time, and it has gates that control the flow of information in and out of the cell. For example, LSTM can be used to forecast the generation of wind power based on past generation and weather conditions.
 
−
** [[Bidirectional Long Short-Term Memory (BI-LSTM)]]
 
−
** [[Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism]]
 
−
** [[Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)]]
 
−
* Convolutional Neural Network (CNN): This algorithm is another type of deep learning model that can process sequential data such as time series. It uses layers of filters that can extract features from local regions of the input data, and it can capture complex patterns and relationships in the data. For example, CNN can be used to forecast an avalanche in a famous ski resort based on past snowfall and temperature data.
 
−
* Gaussian Process (GP): This algorithm is a type of probabilistic model that can handle uncertainty and noise in time series data. It uses a function that defines how similar any two points in the input space are, and it produces a distribution over possible outputs for any given input. For example, GP can be used to forecast the depletion level of stocks in stores based on past sales and inventory data.
 
−
* [[End-to-End Speech]]
 
−
* [[Neural Turing Machine]]
 
−
* [[(Tree) Recursive Neural (Tensor) Network (RNTN)]]
 
−
* [[Temporal Difference (TD) Learning]]
 
 
* [[Forecasting#Time Series Forecasting - Statistical|Time Series Forecasting - Statistical]]  
 
* [[Forecasting#Time Series Forecasting - Statistical|Time Series Forecasting - Statistical]]  
 
* [[Forecasting#Time Series Forecasting - Deep Learning|Time Series Forecasting - Deep Learning]]
 
* [[Forecasting#Time Series Forecasting - Deep Learning|Time Series Forecasting - Deep Learning]]
−
* [[Spatial-Temporal Dynamic Network (STDN)]]
+
 
 +
There are different types of time based AI algorithms, depending on the goal and the method of the algorithm. Some of the most common ones are:
 +
 
 +
* Time Series Forecasting:
 +
    ** Statistical:
 +
        *** Autoregressive (AR): This algorithm uses past values of the time series to predict future values. It assumes that the current value is a linear function of previous values. For example, AR can be used to forecast the weather based on historical data.
 +
        ***  Autoregressive Integrated Moving Average (ARIMA): This algorithm is an extension of AR that also accounts for the trend and the seasonality of the time series. It uses differencing to make the time series stationary (i.e., having constant mean and variance) and then applies AR and moving average (MA) models. For example, ARIMA can be used to forecast the sales of a product based on past sales and seasonal patterns.
 +
        ***  Seasonal Autoregressive Integrated Moving Average (SARIMA): This algorithm is a further extension of ARIMA that also accounts for the cyclic variations of the time series. It uses seasonal differencing and seasonal AR and MA models to capture the periodic fluctuations of the time series. For example, SARIMA can be used to forecast the electricity demand based on past demand and seasonal factors.
 +
        ***  Exponential Smoothing (ES): This algorithm uses weighted averages of past values of the time series to predict future values. It gives more weight to recent values than older values, and it can also incorporate trend and seasonality components. For example, ES can be used to forecast the inventory level based on past demand and supply.
 +
    ** Deep Learning:
 +
        *** Prophet: This algorithm is a modern and flexible approach to time series forecasting developed by Facebook. It uses a decomposable model that consists of trend, seasonality, and holiday components, and it allows for adding custom effects and prior information. For example, Prophet can be used to forecast the web traffic for a data science blog website based on past traffic and special events.
 +
* Neural Networks:
 +
    ** [[Recurrent Neural Network (RNN)]]: This algorithm is a type of deep learning model that can process sequential data such as time series. It uses a network of neurons that have feedback loops, which enable them to store information from previous inputs. For example, RNN can be used to forecast the prices of Bitcoin based on past prices and other factors.
 +
        *** [[Gated Recurrent Unit (GRU)]]
 +
        *** [[Long Short-Term Memory (LSTM)]]: This algorithm is a special type of RNN that can handle long-term dependencies in sequential data. It uses a memory cell that can store, update, and forget information over time, and it has gates that control the flow of information in and out of the cell. For example, LSTM can be used to forecast the generation of wind power based on past generation and weather conditions:
 +
            **** [[Bidirectional Long Short-Term Memory (BI-LSTM)]]
 +
            **** Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism
 +
            **** [[Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)]]
 +
        *** [[Sequence to Sequence (Seq2Seq)]] can map a variable-length input sequence to a variable-length output sequence. It is often used for natural language processing tasks, such as machine translation, text summarization, conversational models, and question answering. The Seq2Seq algorithm consists of two main components: an encoder and a decoder. The encoder reads the input sequence one timestep at a time and produces a hidden vector representation of the input. The decoder then uses the hidden vector as the initial state and generates the output sequence one timestep at a time, using the previous output as the input context.
 +
    ** [[Transformer]]: This algorithm is a state-of-the-art deep learning model that can process sequential data such as time series. It uses layers of attention mechanisms that can learn how to focus on relevant parts of the input data, and it can handle long-term dependencies and parallel computations efficiently. For example, Transformer can be used to forecast the spread of COVID-19 based on past cases and interventions. Transformer can process sequential data using layers of attention mechanisms, without using recurrent or convolutional layers. It can handle long-term dependencies and parallel computations efficiently, and it can achieve better results than RNN-based Seq2Seq models on various tasks.
 +
        *** [[Generative Pre-trained Transformer (GPT)]]
 +
        *** [[Attention]] Mechanism: This allows the decoder to selectively focus on different parts of the input sequence when generating the output, instead of relying on a single fixed vector. This can improve the performance and accuracy of the Seq2Seq model, especially for long sequences 
 +
            **** [[Transformer-XL]]
 +
        *** Beam search: This is a technique to find the most probable output sequence given the input sequence, by keeping track of multiple candidate sequences and expanding them based on their probabilities. This can improve the quality and diversity of the output, compared to using a greedy or random search.
 +
    ** Convolutional Neural Network (CNN): This algorithm is another type of deep learning model that can process sequential data such as time series. It uses layers of filters that can extract features from local regions of the input data, and it can capture complex patterns and relationships in the data. For example, CNN can be used to forecast an avalanche in a famous ski resort based on past snowfall and temperature data.
 +
* Other:
 +
    ** Gaussian Process (GP): This algorithm is a type of probabilistic model that can handle uncertainty and noise in time series data. It uses a function that defines how similar any two points in the input space are, and it produces a distribution over possible outputs for any given input. For example, GP can be used to forecast the depletion level of stocks in stores based on past sales and inventory data.
 +
    ** [[End-to-End Speech]]
 +
    ** [[Neural Turing Machine]]
 +
    ** [[(Tree) Recursive Neural (Tensor) Network (RNTN)]]
 +
    ** [[Temporal Difference (TD) Learning]]
 +
    ** [[Spatial-Temporal Dynamic Network (STDN)]]
  
  

Revision as of 20:31, 9 June 2023

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Time-based Algorithms

Time based AI algorithms are algorithms that use time series data to make predictions or analyses. Time series data are data that are collected over time and have a temporal order. For example, the daily temperature, the stock prices, or the number of visitors to a website are all time series data.


Algorithm Types

There are different types of time based AI algorithms, depending on the goal and the method of the algorithm. Some of the most common ones are:

  • Time Series Forecasting:
   ** Statistical:
       *** Autoregressive (AR): This algorithm uses past values of the time series to predict future values. It assumes that the current value is a linear function of previous values. For example, AR can be used to forecast the weather based on historical data.
       ***  Autoregressive Integrated Moving Average (ARIMA): This algorithm is an extension of AR that also accounts for the trend and the seasonality of the time series. It uses differencing to make the time series stationary (i.e., having constant mean and variance) and then applies AR and moving average (MA) models. For example, ARIMA can be used to forecast the sales of a product based on past sales and seasonal patterns.
       ***  Seasonal Autoregressive Integrated Moving Average (SARIMA): This algorithm is a further extension of ARIMA that also accounts for the cyclic variations of the time series. It uses seasonal differencing and seasonal AR and MA models to capture the periodic fluctuations of the time series. For example, SARIMA can be used to forecast the electricity demand based on past demand and seasonal factors.
       ***  Exponential Smoothing (ES): This algorithm uses weighted averages of past values of the time series to predict future values. It gives more weight to recent values than older values, and it can also incorporate trend and seasonality components. For example, ES can be used to forecast the inventory level based on past demand and supply.
   ** Deep Learning:
       *** Prophet: This algorithm is a modern and flexible approach to time series forecasting developed by Facebook. It uses a decomposable model that consists of trend, seasonality, and holiday components, and it allows for adding custom effects and prior information. For example, Prophet can be used to forecast the web traffic for a data science blog website based on past traffic and special events.
  • Neural Networks:
   ** Recurrent Neural Network (RNN): This algorithm is a type of deep learning model that can process sequential data such as time series. It uses a network of neurons that have feedback loops, which enable them to store information from previous inputs. For example, RNN can be used to forecast the prices of Bitcoin based on past prices and other factors.
       *** Gated Recurrent Unit (GRU)
       *** Long Short-Term Memory (LSTM): This algorithm is a special type of RNN that can handle long-term dependencies in sequential data. It uses a memory cell that can store, update, and forget information over time, and it has gates that control the flow of information in and out of the cell. For example, LSTM can be used to forecast the generation of wind power based on past generation and weather conditions:
           **** Bidirectional Long Short-Term Memory (BI-LSTM)
           **** Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism
           **** Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM) 
       *** Sequence to Sequence (Seq2Seq) can map a variable-length input sequence to a variable-length output sequence. It is often used for natural language processing tasks, such as machine translation, text summarization, conversational models, and question answering. The Seq2Seq algorithm consists of two main components: an encoder and a decoder. The encoder reads the input sequence one timestep at a time and produces a hidden vector representation of the input. The decoder then uses the hidden vector as the initial state and generates the output sequence one timestep at a time, using the previous output as the input context.
   ** Transformer: This algorithm is a state-of-the-art deep learning model that can process sequential data such as time series. It uses layers of attention mechanisms that can learn how to focus on relevant parts of the input data, and it can handle long-term dependencies and parallel computations efficiently. For example, Transformer can be used to forecast the spread of COVID-19 based on past cases and interventions. Transformer can process sequential data using layers of attention mechanisms, without using recurrent or convolutional layers. It can handle long-term dependencies and parallel computations efficiently, and it can achieve better results than RNN-based Seq2Seq models on various tasks.
       *** Generative Pre-trained Transformer (GPT)
       *** Attention Mechanism: This allows the decoder to selectively focus on different parts of the input sequence when generating the output, instead of relying on a single fixed vector. This can improve the performance and accuracy of the Seq2Seq model, especially for long sequences  
           **** Transformer-XL
       *** Beam search: This is a technique to find the most probable output sequence given the input sequence, by keeping track of multiple candidate sequences and expanding them based on their probabilities. This can improve the quality and diversity of the output, compared to using a greedy or random search.
   ** Convolutional Neural Network (CNN): This algorithm is another type of deep learning model that can process sequential data such as time series. It uses layers of filters that can extract features from local regions of the input data, and it can capture complex patterns and relationships in the data. For example, CNN can be used to forecast an avalanche in a famous ski resort based on past snowfall and temperature data.
  • Other:
   ** Gaussian Process (GP): This algorithm is a type of probabilistic model that can handle uncertainty and noise in time series data. It uses a function that defines how similar any two points in the input space are, and it produces a distribution over possible outputs for any given input. For example, GP can be used to forecast the depletion level of stocks in stores based on past sales and inventory data.
   ** End-to-End Speech
   ** Neural Turing Machine
   ** (Tree) Recursive Neural (Tensor) Network (RNTN)
   ** Temporal Difference (TD) Learning
   ** Spatial-Temporal Dynamic Network (STDN)



Whenever we have developed better clocks, we’ve learned something new about the world.
- Alexander Smith New Time Dilation Phenomenon Revealed: Timekeeping Theory Combines Quantum Clocks and Einstein’s Relativity - Dartmouth College


The Earth's rotation is so accurate it varies only in milliseconds ...do you feel the Earth rotation slowing down?


Time is an indispensable concept within computer programs and applications. Without this concept, we would not be able to access any transport layer security (TLS) based websites, exchange data, or utilize various cryptographic algorithms. | Olga Hryniuk - Input Output


DARPA Making Progress on Miniaturized Atomic Clocks for Future PNT Applications | US Defense Advanced Research Projects Agency (DARPA)

TIME AI
Robert Edward Grant Crown Sterling https://timeai.io/

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This 3D Quantum Gas Clock Could Redefine Time
Seeker Time may be a human construct but that hasn't stopped physicists from perfecting it. Read More: JILA’s 3-D Quantum Gas Atomic Clock Offers New Dimensions in Measurement https://www.nist.gov/news-events/news... “JILA physicists have created an entirely new design for an atomic clock, in which strontium atoms are packed into a tiny three-dimensional (3-D) cube at 1,000 times the density of previous one-dimensional (1-D) clocks. In doing so, they are the first to harness the ultra-controlled behavior of a so-called “quantum gas” to make a practical measurement device.” Jun Ye: Let There Be Light (and Thus, Time) https://www.youtube.com/watch?v=bbBmk... Dr. Jun Ye, professor of physics at the University of Colorado at Boulder and a fellow of both the National Institute of Standards and Technology and JILA, explains how lasers are used to manipulate atoms inside and out for ultra-precise clocks. Ultra-Accurate Clocks Lead Search for New Laws of Physics https://www.quantamagazine.org/ultra-.. Atomic clocks are letting physicists tighten the lasso around elusive phenomena such as dark matter. Sign Up For The Seeker Newsletter Here - https://bit.ly/1UO1PxI Seeker inspires us to see the world through the lens of science and evokes a sense of curiosity, optimism and adventure. Visit the Seeker website https://www.seeker.com/

David Wineland Public Lecture: Keeping Better Time - The Era of Optical Atomic Clocks
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A brief History of the Calendar and Time Keeping
School of Business and Economics Tuesday 23 February, 20h00 • Aula Minderbroedersberg 4-6 • Dr. Donna Carroll, Lecturer of Physics, Maastricht University How many times a day do you check your calendar or look at your clock? These days our lives are driven by deadlines, schedules and timetables. Time and its many divisions (hours, days, weeks, months, and years) have completely shaped our lives and yet we seldom take the time to consider how these concepts arose. The calendar is inextricably linked to the mechanics of our solar system, and the way in which we describe our periods of time has arisen from ancient speculation in astronomy, mathematics and religion. In this talk, Donna Carroll will provide a brief history of our calendar and an introduction to time measurement. A fascinating field where astronomy, astrology, mathematics, politics, agriculture, superstition and religion all come together. For more information please visit: SG Maastricht: https://www.sg.unimaas.nl/ Talkin'Business: https://www.talkinbusiness.nl/ University Maastricht: https://www.maastrichtuniversity.nl/

WSU: Space, Time, and Einstein with Brian Greene
World Science Festival Join Brian Greene, acclaimed physicist and author, on a wild ride into the mind of Albert Einstein, revealing deep aspects of the world that defy everyday experience. Using a visually rich canvas of animations, Greene leads you through all the startling conclusions of special relativity, from time travel to space warps to E = mc2. In the span of 2+ hours, this short master class will change your conception of reality. This is a mostly non-mathematical version of the WSU Master Class “Special Relativity with Brian Greene.” https://youtu.be/XFV2feKDK9E #WorldSciU The Special Theory of Relativity - 00:05 Speed - 00:05:50 The Speed of Light - 00:18:23 Relativity of Simultaneity - 00:27:42 Time in Motion - 00:37:42 How Fast Does Time Slow? - 00:47:49 Time Dilation: Experimental Evidence - 01:05:31 The Reality of Past, Present, and Future - 01:14:37 Time Dilation: Intuitive Explanation - 01:28:38 Motion's Effect on Space - 01:32:34 The Pole in the Barn: Quantitative Details - 01:49:48 The Twin Paradox - 02:10:39 Implications for Mass - 02:19:17 Special Relativity - 02:29:06

Timing in Mission-Critical Systems
GPS World You’ll hear from our expert speaker panel about real-life timing challenges in mission-critical applications, such as satellite and military communications, test ranges and radar; time transfer accuracy and stability via GPS or PTP; and what technologies to look for in your next-generation instrument class clock to cost-effectively deliver accurate and stable time and frequency signal types, signal output flexibility and robust security. Speakers: Paul Skoog, Microsemi Corporation; Scott Williams, G.L. Williams Associates; and James L. Wright, Range Generation Next Original Broadcast Date: March 31, 2016

The Importance of Time Synchronization - I&C Short Tips
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Precision Time Protocol (PTP)

YouTube search... ...Google search

The Precision Time Protocol (PTP) is a protocol used to synchronize clocks throughout a computer network. On a local area network, it achieves clock accuracy in the sub-microsecond range, making it suitable for measurement and control systems.[1] PTP is currently employed to synchronize financial transactions, mobile phone tower transmissions, sub-sea acoustic arrays, and networks that require precise timing but lack access to satellite navigation signals.Wikipedia

Overall, its structure is similar to NTP in that there are different levels within it and GPS satellites can serve as its time source. However, the major difference between Network Time Protocol (NTP) and PTP is that PTP is accurate to microseconds, meaning that it is more exact than NTP


Precision Time Protocol (PTP) IEEE-1588
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How Computers Synchronize Their Clocks - NTP and PTP Explained
It is important for computers to know the correct time. Everything from online shopping to stock market trades rely on accurate time keeping. But how do computers know what time it is? Here is a quick look at how computers synchronize their clocks including a look at NTP and PTP.

Positioning, Navigation and Timing (PNT)

YouTube search... ...Google search

Navigation is a field of study that focuses on the process of monitoring and controlling the movement of a craft or vehicle from one place to another.[1] The field of navigation includes four general categories: land navigation, marine navigation, aeronautic navigation, and space navigation. Navigation | Wikipedia

Navigation Systems
ERAU SpecialVFR

How did Planes Fly Before GPS?
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Hosted PNT as a Service (PNTaaS)
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Global Positioning System (GPS)

YouTube search... ...Google search

GPS receivers that use the L5 band can pinpoint to within 30 centimeters or 11.8 inches. The GPS concept is based on time and the known position of GPS specialized satellites. The satellites carry very stable atomic clocks that are synchronized with one another and with the ground clocks. Any drift from time maintained on the ground is corrected daily. In the same manner, the satellite locations are known with great precision. GPS receivers have clocks as well, but they are less stable and less precise. Each GPS satellite continuously transmits a radio signal containing the current time and data about its position. Since the speed of radio waves is constant and independent of the satellite speed, the time delay between when the satellite transmits a signal and the receiver receives it is proportional to the distance from the satellite to the receiver. A GPS receiver monitors multiple satellites and solves equations to determine the precise position of the receiver and its deviation from true time. At a minimum, four satellites must be in view of the receiver for it to compute four unknown quantities (three position coordinates and clock deviation from satellite time). Global Positioning System | Wikipedia

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MIT's AI System helps to improve GPS navigation in places with limited map data | RoadTagger
Rajamanickam Antonimuthu An AI model developed at MIT and Qatar Computing Research Institute uses only satellite imagery to automatically tag road features in digital maps. This innovation could improve GPS navigation, especially in countries with limited map data.

GPS vs AI: The Challenges of Losing Satellite Signal | Roborace
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One technology you probably use everyday that wouldn't work if it wasn't for Einstein's theories is GPS, or the global positioning system.

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PCBWAY: https://www.pcbway.com/ Wanna help us out? GitHub Consider donating any amount: https://www.paypal.me/akarsh98 CETech@FB: https://www.facebook.com/CETech4u/ Our email: akarshagarwal98@gmail.com Track: Raiko - Revenger [NCS Release] Music provided by NoCopyrightSounds. Watch: https://youtu.be/Vj_V0RfdTSY Free Download / Stream: https://ncs.io/Revenger Facebook

AWS re:Invent 2017: GPS: Industry 4.0: AI and the Future of Manufacturing (GPSTEC326)
Advances in artificial intelligence, machine learning, and deep learning, along with the rapid deployment of Internet of Things (IoT) devices, are changing how physical products are designed and built. In this session, learn how Amazon AWS partners Siemens and Autodesk use AWS to enhance the design process and how they're incorporating AWS services into their products and smart factories. We explore how these trends impact the future of design and manufacturing.

Deep-Space Positioning System (DPS)

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Space Is Hard | There Is No GPS in Space
There is no mapping app for deep space, at least not yet. If we're going to explore there we'll need new navigation tools. Here's how NASA is going to keep its rockets on target. Still haven’t subscribed to WIRED on YouTube? https://wrd.cm/15fP7B7 CONNECT WITH WIRED Web: https://wired.com

Jamming and Spoofing

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2019-06-19_The History of GPS Spoofing
Incose Chesapeake Mr Dana Goward discussed the development of Global Positioning System (GPS) spoofing from a fiction account in film to being an everyday problem.

How to fool a GPS - Todd Humphreys
TED-Ed Todd Humphreys forecasts the near-future of geolocation when millimeter-accurate GPS "dots" will enable you to find pin-point locations, index-search your physical possessions ... or to track people without their knowledge. And the response to the sinister side of this technology may have unintended consequences of its own. (Filmed at TEDxAustin.) Talk by Todd Humphreys.

Orolia Presents: GPS World Webinar – Resilient PNT for a 5G World
Panelists will discuss key factors for the successful implementation of 5G technology for 5G infrastructures, automotive, and mission critical applications: 

- Testing requirements needed to ensure consistent operations
- Resilient Positioning, Navigation and Timing (PNT) technologies that can help ensure accurate, continuous operations for critical applications during interference or signal loss.

Which is the bigger problem? Risk assessment for PNT with Dana Goward
This webinar is part of the Resilient Positioning Navigation and Timing Seminar Series. Find out more about this series at: https://rin.org.uk/events/EventDetail... Presenter: Dana Goward, President & Director, Resilient Navigation and Timing Foundation

Assured-Positioning, Navigation and Timing (A-PNT)

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Like the GPS units in many automobiles today, a simple receiver and some processing power is all that is needed for accurate navigation. But, what if the GPS satellites suddenly became unavailable due to malfunction, enemy action or simple interference, such as driving into a tunnel? Unavailability of GPS would be inconvenient for drivers on the road, but could be disastrous for military missions. Extreme Miniaturization: Seven Devices, One Chip to Navigate without GPS | US Defense Advanced Research Projects Agency (DARPA)

National Positioning, Navigation, and Timing (PNT) Architecture | U.S. Department of Transportation


System Performance and Resilience or: What could possibly go wrong? with Prof Marek Ziebart
Royal Institute of Navigation. Presenter: Prof Marek Ziebart, Director, Space Geodesy and Navigation Group, University College London Website: https://rin.org.uk/

GPS/GNSS Spoofing and How To Detect It
GPSPATRON GNSS Interference Issues What is GNSS Spoofing? Spoofing Detection Solution In this video, we describe GNSS spoofing/jamming and how to detect it. GPSPATRON is facilitated to protect GNSS-dependent infrastructure against spoofing/jamming or other GNSS signals anomalies that cause time/position accuracy degradation More about GNSS spoofing detection: https://gpspatron.com/detecting-gnss-... Links: https://rntfnd.org/2018/04/24/5-gps-s... https://www.c4reports.org/aboveusonly... https://www.gpsworld.com/ https://insidegnss.com/ https://www.gps.gov/

When GNSS fails, what will you do? - MarRINav!
Royal Institute of Navigation. This webinar, called 'When GNSS fails, what will you do? - MarRINav! ' features presentations and comments form Jonathan Turner (NLA Int.), Dr Alan Grant (GLA), and Dana Goward (RNTF). The webinar provides analysis and insights from Phase 1 of the Maritime Resilience and Integrity of Navigation (MarRINav) project. To download the full transcript of Jonathan's presentation please follow this link: https://rin.org.uk/resource/resmgr/fi... Many thanks to all co-sponsors of this webinar: Resilient Navigation and Timing Foundation, Institute of Navigation, GPS World, The Maritime Executive, and of course the MarRINav project. Website: https://rin.org.uk/

The Coming Revolution in MEMS Gyroscopes and MEMS Inertial Sensors
Wireless Integrated MicroSensing & Systems - WIMS2 Relevant for automotive robotic drone wearable applications.

Enabling the Next Generation of GPS Technology with Supercorrelation with Dr Ramsey Faragher
Royal Institute of Navigation. Focal Point Positioning have developed a new method for processing GNSS radio signals called Supercorrelation which dramatically improves the performance of the earliest stage of radio processing. The software update removes multipath interference at the correlator level, and provides the ability to determine signal arrival angle without adding any new hardware to a standard GNSS device. In this webinar Dr Ramsey Faragher, Founder/CEO of FocalPoint will explain how the technology works, will cover some of the challenges that FocalPoint have overcome in deploying it on very low cost platforms, and will show off the new capabilities that it unlocks. Website: https://rin.org.uk/

AgilLOC Global Navigation Satellite System (GNSS) Anti-Jamming and Spoofing Capability
AgilLOC Antenna Element Compact (AEC) & Resilient Time Source (RTS) provide assured access to Position, Navigation and Timing (PNT) information for mission-critical systems. AEC provides GNSS anti-jam capability under denied environment through adaptive nulling of interference sources. RTS provides add-on GNSS anti-spoof capability and timing resiliency to ensure the integrity of the GNSS.

GRCon20 - Software defined radio based Global Navigation Satellite System real time spoofing....
Software defined radio based Global Navigation Satellite System real time spoofing detection and cancellation Presented by Jean-Michel Friedt,, D. Rabus and G. Goavec-Merou at GNU Radio Conference 2020 https://gnuradio.org/grcon20 Global Navigation Satellite Systems (GNSS) -- most significantly the Global Positioning System (GPS) -- have become ubiquitous to most daily activities, from positioning and navigation to long range time synchronization or distributed energy production ("smart grid"). While initially developed as a military system hardly accessible to civilians, the advent of Software Defined Radio jamming and spoofing capabilities emphasize the low security of GNSS weak signals emitted from satellites orbiting the Earth 20000 km away. While a properly spoofing signal cannot be detected after a consumer-grade receiver has decoded the radiofrequency signal, addressing at the radiofrequency wave level the signal integrity provides the solution of identifying spoofing with all satellites appearing at the same direction of arrival. This classical beamforming analysis -- Controlled Reception Pattern Antenna (CRPA) with multiple antenna reception and phase analysis -- is demonstrated using commercial, off the shelf software defined radio platform receivers (Ettus Research B210) running the real-time GNSS decoder gnss-sdr based on GNU Radio running on embedded boards such as the Raspberry Pi4.

PULP-DroNet -- Autonomous Artificial Intelligence-powered Nano-Drone
PULP-DroNet is a deep learning-powered visual navigation engine that enables autonomous navigation of a pocket-size quadrotor in a previously unseen environment. Thanks to PULP-DroNet the nano-drone can explore the environment, avoiding collisions also with dynamic obstacles, in complete autonomy -- no human operator, no ad-hoc external signals, and no remote laptop! This means that all the complex computations are done directly aboard the vehicle and very fast. The visual navigation engine is composed of both a software and a hardware part. The former is based on the previous DroNet [1] project developed by the RPG [2] from the University of Zürich (UZH). DroNet is a shallow convolutional neural network (CNN) which has been used to control a standard-size quadrotor in a set of environments via remote computation. The hardware soul of PULP-DroNet is embodied by the PULP-Shield an ultra-low power visual navigation module featuring a Parallel Ultra-Low-Power (PULP) GAP8 System-on-Chip (SoC) from GreenWaves Technologies [3], an ultra-low power camera, and off-chip Flash/DRAM memory; the shield is designed as a pluggable PCB for the Crazyflie 2.0 [4] nano-drone. Then, we developed a general methodology for deploying state-of-the-art deep learning algorithms on top of ultra-low power embedded computation nodes, like a miniaturized drone. Our novel methodology allowed us first to deploy DroNet on the PULP-Shield, and then demonstrating how it enables the execution the CNN on board the CrazyFlie 2.0 within only 64-284mW and with a throughput of 6-18 frame-per-second! Finally, we field-prove our methodology presenting a closed-loop fully working demonstration of vision-driven autonomous navigation relying only on onboard resources, and within an ultra-low power budget. We release here, as open source, all our code, hardware designs, datasets, and trained networks. Reference: D. Palossi, F. Conti, and L. Benini An Open Source and Open Hardware Deep Learning-powered Visual Navigation Engine for Autonomous Nano-UAVs Preprint: https://arxiv.org/abs/1905.04166 PULP-Platform Project Webpage: https://www.pulp-platform.org/

GPS Spoofing and Jamming: Learn how to protect against threats to GNSS systems
Steatite

Securing Positioning & Timing 7: Improving Performance by Augmenting GPS/GNSS
Royal Institute of Navigation. The seventh, and final, of a series of webinars from the Securing Positioning & Timing short course. This webinar covers Improving Performance by Augmenting GPS/GNSS. Presented by Prof Terry Moore. Supported by the UK Space Agency. Website: https://rin.org.uk/

Assess performance & reduce risks across a multisensor, multi-system integration with Peter Rylands
Royal Institute of Navigation. Presenter: Peter Rylands, Product Manager, Oxford Technical Solutions Ltd

Resilient PNT for Unmanned Systems
Orolia. Demand for unmanned systems is growing exponentially across defense and civil/commercial organizations, for applications ranging from military missions and intelligence surveillance to border security and precision agriculture.

Resilient Positioning, Navigation and Timing (PNT) data and GPS/GNSS signals are critical for unmanned systems in order to successfully pilot and control aircraft, vehicle and vessel onboard systems.

Geolocation: Locating GPS/GNSS Jamming and Spoofing

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Securing Positioning & Timing 3: Detecting and Characterising GPS/GNSS Jamming & Spoofing
Royal Institute of Navigation. The third of a series of webinars from the Securing Positioning & Timing short course. This webinar covers Detecting and Characterising GPS/GNSS Jamming & Spoofing. Presented by Dr Mark Dumville. Supported by the UK Space Agency. Website: https://rin.org.uk/

Securing Positioning & Timing 4: Locating GPS/GNSS Jamming and Spoofing
Royal Institute of Navigation. The fourth of a series of webinars from the Securing Positioning & Timing short course. This webinar covers Locating GPS/GNSS Jamming and Spoofing. Presented by Mike Jones. Supported by the UK Space Agency. Website: https://rin.org.uk/

GNSS Jamming - Crowd Sourcing Detection and Geolocation
GNSS Jamming - Crowd Sourcing Detection and Geolocation webinar by InsideGNSS

Harris Corporation - Detect and Locate GPS Jamming with Signal Sentry™ 1000
Harris Corporation The Global Positioning System—GPS—is an essential element of the global information infrastructure. GPS jamming devices are becoming cheaper and more accessible, creating a greater need to protect from a diverse range of threats. Harris Signal Sentry 1000 is a GPS interference detection and geolocation solution. It provides a web-based visualization tool to support timely and effective actionable intelligence.

Why isn’t my GPS receiver consistently more accurate? with John Pottle
Royal Institute of Navigation. Presenter: John Pottle, Director of the RIN Website: https://rin.org.uk/

Current threats to GNSS: An update of incidents and impacts with Guy Buesnel
Royal Institute of Navigation. Presenter: Guy Buesnel, PNT Security Technologist Spirent Communications plc. Website: https://rin.org.uk/

Software-defined Global Navigation Satellite Systems (GNSS)

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Dr. Carles Fernandez: An Open Source Global Navigation Satellite Systems Software-Defined Receiver
Software Defined Radio Academy GNSS-SDR (see https://gnss-sdr.org) is an open source, software-defined Global Navigation Satellite Systems (GNSS) receiver. This software application takes care of all the digital signal processing chain (from the output of the Analog-to-Digital Converter of a radio-frequency front-end, or from raw sam- ples stored in a file), performing signal acquisition and tracking of the available satellite signals, decoding the navigation message and computing the observables needed by positioning algorithms, which ultimately compute the navigation solution. Several outputs are provided in standard formats, including RINEX observation and navigation files, RTCM-104 v3.2 message streaming via TCP/IP and NMEA-0183, as well as KML, GeoJSON, and GPX files for Geographic Information Systems, map representation and Earth browsers. Currently, the software is able to process GLONASS L1 C/A, GPS L1 C/A, Galileo E1b/c, BeiDou B1I, BeiDou B3I, GLONASS L2 C/A, GPS L2C, GPS L5 and Galileo E5a signals, in all possible combinations, including multi-constellation and multi-frequency configurations. The software leverages on the GNU Radio framework, inheriting multithreading scheduling and a modular, scalable architecture. The software is designed to facilitate the inclusion of new signal processing techniques, offering an easy way to measure their impact in the overall receiver performance under fair conditions, as well as the expansion to other signals.

GRCon20 - Software defined radio based Global Navigation Satellite System real time spoofing....
Software defined radio based Global Navigation Satellite System real time spoofing detection and cancellation Presented by Jean-Michel Friedt,, D. Rabus and G. Goavec-Merou at GNU Radio Conference 2020 https://gnuradio.org/grcon20 Global Navigation Satellite Systems (GNSS) -- most significantly the Global Positioning System (GPS) -- have become ubiquitous to most daily activities, from positioning and navigation to long range time synchronization or distributed energy production ("smart grid"). While initially developed as a military system hardly accessible to civilians, the advent of Software Defined Radio jamming and spoofing capabilities emphasize the low security of GNSS weak signals emitted from satellites orbiting the Earth 20000 km away. While a properly spoofing signal cannot be detected after a consumer-grade receiver has decoded the radiofrequency signal, addressing at the radiofrequency wave level the signal integrity provides the solution of identifying spoofing with all satellites appearing at the same direction of arrival. This classical beamforming analysis -- Controlled Reception Pattern Antenna (CRPA) with multiple antenna reception and phase analysis -- is demonstrated using commercial, off the shelf software defined radio platform receivers (Ettus Research B210) running the real-time GNSS decoder gnss-sdr based on GNU Radio running on embedded boards such as the Raspberry Pi4.

Long Range Navigation (LORAN)

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Long Range Navigation (Loran-c and eLoran)
Wade Cumberland AVN400 Presentation

BBC Click no GPS radio eLoran instead. Filmed at the Port of Felixstowe
BBC Click report at the Port of Felixstowe demonstrating the loss of GPS and using eLoran as an alternative

Quantum Sensors in Navigation

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  • Quantum
  • UK Research and Innovation
  • Review of Quantum Navigation | Donghui Feng - IOP Conference Series: Earth and Environmental Science
  • Quantum Sensing Technology Growing Rapidly to Enable Ultra Sensitive Quantum RADARS, Imaging, and Navigation | Rajesh Uppal - International Defence Security & Technology

Typically, the performance of measurement devices is limited by deleterious effects such as thermal noise and vibration. Notable exceptions are atomic clocks, which operate very near their fundamental limits. Driving devices to their physical limits will open new application spaces critical to future DoD systems. Indeed, many defense-critical applications already require exceptionally precise time and frequency standards enabled only by atomic clocks. The Global Positioning System (GPS) and the internet are two key examples. Measurement systems based on atomic physics benefit from the exquisite properties of the atom. Among these are (a) precise frequency transitions, (b) the ability to initialize, control, and readout the atomic state and (c) environmental isolation. In addition, atomic properties are absolute, and do not “drift” over time. In this sense, atoms are self-calibrated, making them ideal for precision sensing. Quantum-Assisted Sensing and Readout (QuASAR) | US Defense Advanced Research Projects Agency (DARPA)

Quantum Sensors in Navigation with Roger McKinlay, George Shaw and Kai Bongs
Royal Institute of Navigation Co-hosted by the UK Quantum Technology Hub Sensors and Timing and the Royal Institute of Navigation Presenters: Professor Kai Bongs, Principal Investigator at the UK Quantum Technology Hub Sensors and Timing; Roger McKinlay, Challenge Director for Quantum Technologies, UK Research and Innovation; George Shaw, Principal Systems Engineer, General Lighthouse Authority In this trio of presentations Roger, George and Kai discuss quantum sensors in navigation... Roger covers systems considerations in PNT and vulnerabilities in GNSS, UK industry opportunities and IUK programmes George covers current maritime navigation challenges, the resilient PNT system-of-systems approach and opportunity for Quantum Sensor technology insertion Kai covers quantum Sensor developments towards navigation solutions Website: https://rin.org.uk/

Nanoscale Quantum Sensing - Prof. Jorg Wrachtrup
Bar-Ilan University Nanoscale Quantum Sensing - a lecture by Prof. Jorg Wrachtrup of the Institute for Quantum Science and Technology in Germany. This lecture was given during the conference QUEST - Quantum Entanglement Science & Technology, held by Bar-Ilan University's Physics Department in June 2017.


Spacetime

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Time Does Not Exist. Let me explain with a graph.
How do we really move through spacetime? Sadly the books have sold out.

IT'S NOT REAL! Physicists PROVE Time Does NOT Exist
We, humans, are fascinated with the idea of traveling back or ahead in time. Time Travel Machines and Devices have been a staple for a lot of science fiction and even fantasy stories catering to our desire to either change something in the past to make our lives better or just skip ahead in time to find out if life gets better on its own in the future. Ever wonder why we may never develop a Time Machine? Because Time probably doesn’t even EXIST! Welcome to Factnomenal, and today let’s find out why some physicists insist that TIME exists only in our heads.

This Is Why Time Might Not Actually Exist
Quantum Entanglement May Reveal a Reality We Can't Handle

What Actually Are Space And Time?
If you like this video, check out writer Geraint Lewis´ excellent book, co-written with Chris Ferrie: Where Did the Universe Come From? And Other Cosmic Questions: Our Universe, from the Quantum to the Cosmos AND check out his Youtube channel

Longitude

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Longitude
Dava Sobel's book is a captivating story, such a good film with an impressive cast. What a genius Mr Harrison was. Such determination. A lesson to us all..

The Clock That Changed the World (BBC History of the World)
SciShow It’s time for another leap second! Join SciShow as we celebrate by exploring the long and strange history of timekeeping. Hosted by: Michael Aranda Dooblydoo thanks go to the following Patreon supporters -- we couldn't make SciShow without them! Shout out to Justin Ove, Justin Lentz, David Campos, John Szymakowski, Peso255, Jeremy Peng, Avi Yaschin, and Fatima Iqbal. Like SciShow? Want to help support us, and also get things to put on your walls, cover your torso and hold your liquids? Check out our awesome products over at DFTBA Records: https://dftba.com/scishow Or help support us by becoming our patron on Patreon: https://www.patreon.com/scishow

Natural Navigation

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Henrik Mouritsen on How do migratory birds find their way?
Prof. Henrik Mouritsen (University of Oldenburg, Germany) on "How do migratory birds find their way?", at the FENS Hertie Winter School 2017 on Neural Control of Behaviour - Series 1: Navigation, 10-16 December 2017, Obergurgl, Austria.

Animal Navigation || Radcliffe Institute
Lizabeth Cohen 00:16 Dean, Radcliffe Institute for Advanced Study Howard Mumford Jones Professor of American Studies Department of History Harvard University John Huth 7:32 Donner Professor of Science Codirector of the Science Program Radcliffe Institute for Advanced Study Harvard University ANIMAL NAVIGATION 16:50 Susanne Åkesson 19:53 Professor and Principal Investigator Centre for Animal Movement Research, Lund University (Sweden) Introduced by Scott Edwards 17:51 Professor of Organismic and Evolutionary Biology Curator of Ornithology Alexander Agassiz Professor of Zoology in the Museum of Comparative Zoology Harvard University

Time & Music

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