Sequence/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. These algorithms can be used for a variety of purposes, such as forecasting future values, detecting trends and patterns, and making informed decisions based on historical data. They can be applied to many different fields, including finance, economics, meteorology, and healthcare.

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

Sequence/Time-based Algorithms

There are different types of sequence/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): 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): 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): 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): 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: 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 Turing Machine (NTM): the fuzzy pattern matching capabilities of Neural Networks with the algorithmic power of programmable computers. NTMs are an instance of Memory Augmented Neural Networks, a new class of Recurrent Neural Network (RNN)s which decouple computation from memory by introducing an external memory unit. NTMs have demonstrated superior performance over Long Short-Term Memory Cells in several sequence learning tasks.
• Neural Networks:
• Recurrent Neural Network (RNN): 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): are a gating mechanism in Recurrent Neural Network (RNN) architecture. Like other RNNs, a GRU can process sequential data such as time series, natural language, and speech1. The GRU is similar to a Long Short-Term Memory (LSTM) with a forget gate, but has fewer parameters than LSTM, as it lacks an output gate. This means that GRUs are generally easier and faster to train than their LSTM counterparts. GRUs have been found to perform similarly to LSTMs on certain tasks such as polyphonic music modeling, speech signal modeling, and natural language processing. They have shown that gating is indeed helpful in general.
• Long Short-Term Memory (LSTM): 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): is a type of Recurrent Neural Network (RNN) architecture that processes data in both forward and backward directions. It consists of two LSTMs: one taking the input in a forward direction, and the other in a backward direction. BI-LSTMs effectively increase the amount of information available to the network, improving the context available to the algorithm. For example, knowing what words immediately follow and precede a word in a sentence. Compared to LSTM, BI-LSTM combines the forward hidden layer and the backward hidden layer, which can access both the preceding and succeeding contexts¹. This feature of flow of data in both directions makes the BI-LSTM different from other LSTMs. BI-LSTMs have been successfully applied to various tasks such as natural language processing, speech recognition, and traffic forecasting.
• Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism: is a type of Recurrent Neural Network (RNN) architecture that processes data in both forward and backward directions, and uses an attention mechanism to weigh the importance of different parts of the input sequence. The attention mechanism allows the network to focus on specific parts of the input sequence when making predictions, rather than treating all parts of the sequence equally. This can be particularly useful when dealing with long input sequences, where some parts of the sequence may be more relevant to the prediction than others. BI-LSTMs with Attention Mechanism have been successfully applied to various tasks such as text classification, Sentiment Analysis, and human activity recognition.
• Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM): is a variant of LSTM that employs DropConnect for regularization, as well as NT-ASGD for optimization. NT-ASGD stands for non-monotonically triggered averaged stochastic gradient descent, which returns an average of the last iterations of weights. AWD-LSTM has shown great results on both word-level and character-level models. It has been used in research papers on word-level models and has shown great results on character-level models as well.
• 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: 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): are a family of language models that use Deep Learning techniques to generate natural language text. They are based on the transformer architecture and can be fine-tuned for various natural language processing tasks such as text generation, language translation, and text classification. The first GPT was introduced in 2018 by the American artificial intelligence (AI) company OpenAI. GPT models are artificial Neural Networks that are based on the transformer architecture, pre-trained on large data sets of unlabelled text, and able to generate novel human-like content
• Attention Mechanism: 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: is a transformer-based language model that introduces the notion of recurrence to the deep self-attention network. It was designed to enable learning dependency beyond a fixed length without disrupting temporal coherence. The model consists of a segment-level recurrence mechanism and a novel positional encoding scheme. This method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation.
• Beam search: 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): 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.
• Spatial-Temporal Dynamic Network (STDN): a Deep Learning framework proposed to address the challenge of modeling complex spatial dependencies and temporal dynamics in traffic prediction. A flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically shifted attention mechanism is designed to handle long-term periodic temporal shifting. This approach has been shown to be effective in predicting taxi demand
• Other:
• Gaussian Process (GP): 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: translation is an approach to speech translation that is gaining high interest from the research world in the last few years. It consists of using a single Deep Learning model that learns to generate translated text of the input audio in an end-to-end fashion. This approach, known as “end-to-end” or “direct” ST, supposes many advantages over the former, such as avoiding the concatenation of errors, the direct use of prosodic from speech and a lower inference time.
• (Tree) Recursive Neural (Tensor) Network (RNTN): type of Neural Network that is mostly used for natural language processing. It has a tree structure with a neural net at each node. The purpose of these nets is to analyze data that have a hierarchy of structure. An RNTN is a powerful tool for deciphering and labeling patterns. Structurally, an RNTN is a binary tree with three nodes: a root and two leaves. The root and leaf nodes are not neurons, but instead, they are groups of neurons – the more complicated the input data, the more neurons are required. RNTNs have been successfully applied to Sentiment Analysis, where the input is a sentence in its parse tree structure, and the output is the classification for the input sentence, i.e., whether the meaning is very negative, negative, neutral, positive, or very positive
• Temporal Difference (TD) Learning: refers to a class of model-free Reinforcement Learning (RL) methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods, and perform updates based on current estimates, like dynamic programming methods. While Monte Carlo methods only adjust their estimates once the final outcome is known, TD methods adjust predictions to match later, more accurate, predictions about the future before the final outcome is known.

What Time Is It?

 TIME AI Robert Edward Grant Crown Sterling https://timeai.io/
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 TimeLine - A Brief Introduction To The History Of Timekeeping Devices SpotImageryLtd An animated documentary about how time was measured in the past, and how we measure it in present day.
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 David Wineland Public Lecture: Keeping Better Time - The Era of Optical Atomic Clocks Perimeter Institute for Theoretical Physics David Wineland, 2012 Nobel Laureate in Physics, will explore the theoretical and technological know-how needed to build ultra-precise atomic clocks during his Perimeter Institute Public Lecture on Wednesday, Nov. 4, 2015. Find information about future Perimeter institute Public Lectures here: https://ow.ly/UiOr5
 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 Affinity Energy Learn more at https://affinityenergy.com - Time synchronization is overlooked in terms of specification of applications in terms of hardware, proper controls, and data acquisition. Here's an example of why time synchronization is so important in a double ended substation.

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

Precision Time Protocol (PTP)

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 a basic overview of how the Precision Time Protocol (PTP) is used to synchronize, and set the correct time of day within a network. Potentially Nano Second accuracy can be achieved.
 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.

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

 How did Planes Fly Before GPS? How did Planes Fly Before GPS?: The Wright Brothers first took to the skies in 1903 but GPS wasn't publicly available until 1983, so how did planes traverse the world in those 80 years? From celestial navigation to dead reckoning, to the firsts forms of radio telemetry (like adock range stations and LORAN) we'll be discussing them all in this video. Discord: https://discord.gg/DUvyS8n Amazon Affiliate Link*: https://amzn.to/3kNTHhK
 PNT Requirements Definition – Some Principles with John Pottle Royal Institute of Navigation. Presenter: John Pottle, Director of the Royal Institute of Navigation
 Hosted PNT as a Service (PNTaaS) NAVSYS Corporation. The threat to GPS drives demand for a back-up position, navigation, and timing (PNT) solution that can operate in the event of denial of service to the GPS satellite signals. The military is dependent on PNT for their warfighting operations, and national critical infrastructure is also reliant on GPS for positioning and timing. In this video, we describe our PNT as a Service (PNTaaS) concept that will leverage capabilities already existing on certain commercial broadband internet satellite constellations to allow their signals to be used for PNT.

Global Positioning System (GPS)

 Why The US Military Made GPS Free-To-Use Create a free account on SimScale here: https://goo.gl/qByVRB Find all recordings of the Drone Design Workshop here: https://goo.gl/hSh5nA Listen to our new podcast at: Showmakers YouTube channel at: https://goo.gl/Ks1WMp
 Satellite Navigation Systems Overview with John Pottle Royal Institute of Navigation. John Pottle, Director of the Royal Institute of Navigation, will put into context what the hundreds of navigation satellites in space are all for and how they work together. This webinar will explain the similarities and differences between global and regional satellite navigation systems, how they are co-ordinated, and by whom. The space-based augmentation systems will also be covered: what are these and how do they help? *During the webinar Q&A there was a question about whether or not GNSS could be used for moon missions - please see: Website: https://rin.org.uk/
 GPS, How does it work? | ICT #12 GPS has already become an integral part of our lives, and you can see a few useful applications from these examples. GPS is really an interesting technology. It uses a system of 24 satellites continuously orbiting the earth, and requires at least four satellites to track your location; it uses an atomic clock, and the time error of your mobile phone is also a matter of great concern. Moreover, Albert Einstein’s theory of relativity plays an important role in GPS technology, finally, a real-life application for the theory of relativity! Let’s put aside all these complications and understand the technology of GPS in a step by step and logical manner. Be a Learn Engineering supporter or contributor: https://www.youtube.com/channel/UCqZQ...
 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.
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 Why GPS wouldn't work if we didn't know about relativity 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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 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)

 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

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.
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 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

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)

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

 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)

 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
• 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

 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

 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