Covid-19: Netherlands and Germany death rates stabilise, but Belgium still skyrockets

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Continuing with our Analytics team study of the virus on Western European countries, we present our findings for data up to week 15 (14 April).

As discussed in our previous articles, in order to provide an objective comparison per country, the algorithmic results need to be standardised around the population of each country in order to produce a more accurate deaths per million inhabitants rate. The figure shown below summarises the results.

As seen, Belgium’s mortality rate (red) is significantly higher than any of its neighbours. Germany (blue) and the Netherlands (green) have the lowest mortality rates, and appear to be levelling off. This suggests that the Dutch and German governments testing, health care systems and social distancing strategies appear to be paying off.

It’s not completely clear why Belgium’s mortality rate is so much higher than its neighbours, but a possible explanation may be due to insufficient testing and the virus hitting various elderly care homes. We’ll follow Belgium’s progress over the coming weeks, and report our findings.

The UK

As discussed in a previous article, the UK had a one-week head start on its neighbours. Therefore, shifting the UK data left by six days, we obtain an interesting picture of the UK’s situation:

Applying a prediction model to the UK data (dashed magenta line), notice how the UK’s data follows France’s data. Although long term predication models should be viewed with a degree of scepticism (as there are too many unknown factors to consider), the prediction suggests that the UK’s mortality rate should follow France’s mortality rate.  

The good news for the UK population, is that the emergency measures in place, appear to be working and are leading to a decline in deaths!

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6 Strengths of the ASN Filter Designer DSP platform

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6 reasons why ASN Filter Designer is a powerful real-time DSP platform e.g. life math scripting, tool creates your technical specification and documentation

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Covid-19: UK had 1 week headstart, but failed to act

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The Covid-19 virus has forced European governments to order millions to lockdown in the hope of limiting the spread of the virus, based on ‘expert scientific advice’.  The latest recent review of WHO data by Dutch data modelling specialist, Advanced Solutions Nederland (ASN) reveals that the UK could of adverted strain on services and avoided a sharp rise in Covid-19 cases by taking advantage of being six days behind the infection spread in Northern  Europe, but failed to put measures in place in time, due to flawed ‘expert’ predictions.

Central to government policies imposed are predictions being made from statistics that are essentially handling raw data ineffectively. Many models are based on raw measured values that are not adjusted for comparison with neighbouring countries, so called population standardisation, which can give a false perspective of the situation at hand.

– Director of Algorithms and Analytics, ASN, Dr. Sanjeev Sarpal

Ineffective use of modelling to predict virus trend

John Hopkins University (JHU) provide an open database of confirmed cases, deaths and number of recoveries, obtained from data from the World Health Organisation (WHO), various other health intuitions and governments. These datasets are broken down into countries and regions.

Analysis considered data obtained from the following five European countries populations: Germany (83 million), France (67 million) UK (66 million), the Netherlands (17 million), Belgium (11 million).

Our analysts found that by analysing the viral trend by doing a ‘like with like’ comparison of populations rather than the conventional method of non-standardisation, resulted in a totally contradicting set of results, implying that the UK governments response was not informed appropriately.

In order to provide an objective comparison per country, the algorithmics results were standardised around the population of each country in order to produce a more accurate deaths per million inhabitants rate. The figure shown below summarises the results.

Analysing the chart, it can be seen that all central countries considered herein all report first cases within days of each other, and have very similar contamination rate. The UK is the exception, as it is approximately 6 days behind mainland Europe.

By shifting the UK left by six days, we see that the UK also follows the same trend as its continental neighbours. The dashed line represents the algorithmic prediction of the number of confirmed cases for the next two days (short term prediction), which closely follows the other countries.

Thus, it can be concluded that despite the British government having advanced warning, they failed to adequately prepare themselves for the effects of the virus.

No magic long-term prediction model

There are a multitude of data modelling methods, each giving a different result depending on the interpretation required. For the Covid-19 virus, there is no ‘magic model’ that can be used to predict the long-term severity of the outbreak, as there are too many variables to consider, which are almost impossible to model and track as the pandemic unfolds.

External factors, such as emergency laws, increased public hygiene/diligence and better medical care facilities are but a few major factors that affect any long-term prediction model. These critical factors are generally not modelled when making a prediction model. The short-term prediction shown herein, was just for the next two days, but all prediction models must be viewed with a degree of scepticism, as it is not possible to model all of the unique circumstances that present themselves.  

ASN’s data analytics team will be closely monitoring the development of the Covid-19 virus, and providing regular updates via our blog.

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Power Blackouts: jargon buster

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Energy companies have struggled for years with meeting demand with supply with society’s increasing demand for energy. This been made even more challenging with more people using electric vehicles and smart cities demanding more lighting.

Modern IoT sensors and smart grid solutions help energy companies and consumers improve and optimize the modern grid for the 21st century. But what does all the jargon really mean?

Blackout

The UK National Grid recently experienced a major outage that left almost a million homes in the dark and forced trains to a standstill. The source of the blackout was traced back to two generators that failed, resulting in grid’s frequency falling below the critical 49.5Hz set by the regulator.

According to the media the UK blackout was triggered when the frequency slumped to 48.88Hz, which is well below the legal limits set by the regulatory agencies.

But what do these limits really mean?

Some background information

The energy grid frequency is 50Hz in Europe, 60Hz in the US. Japan has an unusual historical situation in that the East of the country runs on a European 50Hz system and the West of country runs on an American 60Hz system.

In all cases, in order to meet the energy requirements, several generators are needed to work in parallel and must be synchronised. Accurate frequency control is required to control the amount of power delivered by multiple generators in order to provide a stable power supply to consumers. The challenge for the energy companies is meeting the changes in supply and demand, since higher demand than supply will result in fall of frequency and vice versa.

Thus, the challenge for IoT sensors and algorithms is measuring the operating frequency and phase to a sufficient accuracy and adjusting the generators to meet the energy demand requirement at that particular time. But how?

A PMU (phase measurement unit) is typically used the measure and report back (typically 30-60 measurements per second) to the network operator what the actual frequency and phase of various points on the grid are. In order to synchronise the measurements, the PMU internal clocks are time synchronised via a GPS (global positioning system) unit, such that all reported frequency and phase measured across the grid are time aligned.

The frequency limits are shown below:

The challenge for energy managers

As seen above, the normal region in Europe is between 49.85 – 50.15Hz. If the generators exceed 50.15Hz (entering the orange region), there is too much energy and the generators need to be rolled back a little. If the frequency falls below 49.85Hz (also in the orange region), there is not enough energy to meet demand, and more energy is needed. In all cases, the frequency must never enter the red region, otherwise Blackouts will occur.

The energy company is legally obliged to keep the powerline frequency between 49.5 – 50.5Hz (± 1%). This is typically tracked to an accuracy of ± 1mHz resolution.

Blackouts

The UK blackout was triggered when the frequency slumped to 48.88Hz, which is well below the legal limits and in the blackout region. The damage to the UK economy has still yet to be determined, but National Grid UK should be considering adding extra redundancy safe guards in order restore public confidence.

Dips and swells tracking

Another common problem that occurs is that of energy dips, i.e. the voltage momentarily drops for a few cycles. Think about lights temporarily flickering in your house.

In factories running machinery, this usually occurs when a machine is started up, indicating imminent component failure. Swells are the opposite of dips, but are much less common.

ASN’s IoT sensor and algorithms play an essential role in keeping the grid healthy, as demonstrated in the video below.

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IoT Real-time dataset analysis with 5G: the big challenges

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5G’s claim of ultra-low latency, and suitability for real-time edge processing has created a fever of interest in the IoT market. But what does Real-time dataset analysis really mean for your IoT application?

It’s estimated that the global smart sensor market will have over 50 billion smart devices in 2020. All of these IoT smart sensors (temperature, pressure, gas, image, motion, loadcells) will be connected to Wifi, 5G, LoRa etc network services via embedded processors performing real-time signal processing on the captured datasets.

But there are a number of challenges….

IoT sensor measurement challenge

A common challenge is that many sensors used in these applications require a little bit of filtering in order to clean the measurement data in order to make it useful for analysis.

Let’s have a look at what sensor data really is…. All sensors produce measurement data. These measurement data contain two types of components:

  • Wanted components, i.e. information what we want to know
  • Unwanted components, measurement noise, 50/60Hz powerline interference, glitches etc – what we don’t want to know

Unwanted components degrade system performance and need to be removed.

So, how do we do it?

DSP means Digital Signal Processing and is a mathematical recipe (algorithm) that can be applied to IoT sensor measurement data in order to clean it and make it useful for analysis.

But that’s not all! DSP algorithms can also help in analysing data, producing more accurate results for decision making with ML (machine learning). They can also improve overall system performance with existing hardware (no need to redesign your hardware – a massive cost saving!), and can reduce the data sent off to the cloud by pre-analysing data and only sending what is necessary.

Do you have a practical example?

All analog sensor signals need to be sampled by a digital system in order to make them usable for analysis in the digital domain.  The choice of the sampling frequency is primarily goverend by the maximum frequency that needs to be analysed. But what are design rules?

Consider the following application for gas sensor measurement (see the figure below). The requirement is to determine the amplitude of the noisy sinusoid (shown in blue) in order to get an estimate of gas concentration, where the bigger amplitude, the more the gas concentration.

In order to clean the noisy sinusoid with a filtering algorithm (results shown in red), we first need to find what the frequency of the sinusoid is. The Nyquist sampling Theorem is used for determining this value, and states that,

the analog signal must be sampled at a least two times the maximum analog frequency component.

For our gas sensor, the frequency of the blue sinusoid is about 5Hz, so a minimum sampling frequency of 10Hz is required in order to perform valid analysis on the sampled dataset. However, many designers choose a value 10 times higher than Nyquist in order account for the effects of the noise component and not to be on the borderline of the Nyquist-sampling theorem.

The concept of sampling is demonstrated below:

 

What does Real-time really mean?

Many clients ask us to clarify what real-time really means.

Most people assume that an instant response to a button push or event means real-time. However, the reality is a little more complicated, as a real-time system means that the response is deterministic occurring within a known time frame. This could be seconds or even micro-seconds. In all cases, the response or action time is always known.

For the gas sensor discussed above, the sampling frequency must be constant in order to correctly follow the characteristics of the sinusoid. If the sampling rate varied over time, the sampled data wouldn’t match the design criteria of the algorithmic filtering blocks, and the data analysis would be invalid.

In recent years, much has been said about 5G’s potentially ultra-low latency, and suitability for real-time edge processing. Time will tell how far 5G’s low latency claim can be realised. However, latency in network/cloud services, means that no communication channel can be guaranteed to be real-time 100% of the time. This is further complicated by the requirement of meeting the Nyquist-sampling criteria for sampling analog sensors signals.

In light of all of these issues, our experience has shown that real-time sensor processing (especially for critical automotive or industrial control operations) should be performed at the edge on an embedded real-time processor for maximum reliability and safety.

Our close collaboration with leading technology companies, such as: Arm, Texas Instruments and KPN ensure that our 5G IoT solutions are built with the latest design paradigms using the best of today’s sensor and networking technology.

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Industry 4.0: Asset Track and Trace

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When considering an asset track and trace IoT application in a factory or warehouse, many think of the well-established Barcode or QR code. Although this technology is firmly embedded into modern society as a reliable, low cost and easy to understand pillar for tracking and tracing assets, many companies were quick to adopt the technology as an easy way of minimising human errors and increasing process efficiency.

However, when managing the location of thousands of assets, this simple system is somewhat limited in the overview that it can provide an ERP (enterprise requirements system) system. A significant aspect of Industry 4.0 is process transparency, providing the ERP and BI (business intelligence) systems with the most update-to-date information, allowing management to identify bottlenecks and potential areas of weakness.

Until several years ago, asset tracking was strengthened by combining RFID tags with GPS (global positioning system) technology. Although this was certainly a step in the right direction, the implementation costs were high and technology suffered from RF interference, short range and moderate location accuracy. GPS also had the big disadvantage of only being able to work outdoors and has a location accuracy of several metres – not really suitable!

Industry 4.0 real time location systems (RTLS)

Over the years, different technology has appeared as solution to providing real-time assets location information to the ERP system. As mentioned above, technologies, such as RFIDs, bar codes and GPS have certainly been a step in the right direction, but didn’t fully meet the requirements of modern businesses look to optimise their processes.

Ultrawide band (UWB) radar

With advances in radar technology over the last few years, a few silicon vendors are now producing affordable UWB radar devices suitable for trace and trace applications. Radar technology that used to cost thousands of Euros, and was primarily aimed a military technology, is now available for tens of Euros, making it viable candidate for track and trace applications.

UWB highlights

  • Ten times more accurate than GPS, Wi-Fi or Bluetooth with typical accuracies a good as 10cm.
  • Hundreds of metres range with data communication options.
  • Very low power and safe for humans – power emission typically a fraction of percent of a typical Wi-Fi router.
  • Licence free ISM band, meaning no complicated ETSI/FCC certification and lower implementation costs.
  • Penetrates walls and doors, making it ideal for warehouses and buildings.

Contemporary UWB based solutions finally allow for a true RTLS implementation, giving enterprises control over their personnel and assets. Whether tracking containers through a supply chain, optimising manufacturing processes, or providing asset traceability, an RTLS-UWB system provides an ERP system with real-time situation awareness that can be acted upon instantly.

Benefits for your enterprise

  • Inventory accuracy: achieve 99.9% inventory accuracy without the need for meticulous manual audits that can take hours or even days. An RTLS-UWB system provides you with all of your asset location information in real-time.
  • Live situation update: feed the ERP and BI systems with an accurate real-time picture of asset location and personnel trends.
  • Personnel safety: attaching tags to your employees helps track process efficiency and may also be used to alert personnel about entering dangerous areas. The tag locations are also invaluable in case of an emergency, such as fire, as the location of all personnel is known at all times.
  • e-paper and sensors: modern tags use e-paper technology to only display the most up-to-date information (e.g. QR code, sensor readings). Extra sensor information, such as temperature, humidity and vibration provide a simple way of establishing anti-tampering and asset health.

Advanced Solutions Nederland (ASN) BV is an international market leader in innovative IoT smart sensor and track and trace RTLS-UWB technological solutions.

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How drones fuel ‘smart air’: The eye in the sky

Besides delivery, drones are already used as an ‘eye in the sky’. Or, with a ultrawide band radar attached to the drone, you can fly the drone wherever you want, maybe land the drone and start measuring. For instance:

  • It helps farmer to get higher yields by giving them a literal overview which spots on their field are developing well and which spots are perfoming less. So that farmers can take action on the lower performing spots.
  • Drones have excellent use for finding spots for roads, waterworks, energy fields and other infrastructure.
  • Drones give a real-time situation overview. As an example: an overview of road congestion to aid the city council to take proper action
  • They can measure while covering large areas. For instance: a large crop field where only the first crops from the road are visible for a farmer. Furthermore, they have the advantage that big areas can be captured in one glance
  • At places where humans have difficulty or is dangerous to reach. Think about places in the jungle or large mountainous regions. But also: an aid in building and maintenance of buildings and constructions like large building sites, bridges and high towers. Or when action on dangerous gasses is needed. And maybe, drones can become an aid to perform reparations and make installations themselves
  • For better and worse, drones can also be used for guarding assets. With sensors, they can guard areas by looking for movement, and establish a protected zone. Unfortunately, the technology is also available for terrorists, who will also find a use for drones for maximising chaos

Privacy, Safety and Security

Especially in crowded areas, privacy is a big issuse. A big complaint is the noise that drones produce: in a 2017 study, NASA found out that people find the noise of drones more annoying than the sound of ‘normal’ traffic. Besides noise, privacy has another factor: the camera. Besides that, drones may fly unasked over your property, what do they register exactly? What if you don’t want to be filmed in your garden?

Another practical problem is the risk that a drone can drop its cargo. Or that it can fall out of air itself. Amazon is already experimenting with a self-destroying drone when the drone risks crashing. In crowded areas, the risk of damage or even worse: hitting someone can’t be overlooked.

For acceptance of the use of drones, these challenges have to be resolved for getting trust and acceptance. Legislation is expected to come in to regulate drone traffic.

Security

As anyone can and will buy a drone, security is another issue. Anyone can just buy one online or even in a toystore and fly with it anywhere they want. The annoying noise of a drone in natural parks might be a inconvenience. But of course, more harmful use of drones might take place as well, for instance when used by terrorists, who can use drones for unwanted inspection and creating chaos. But also: you can load anything on your drone, fly to your destination and place it into action. Governments can forbid the flying of drones in the proximity of (for instance) a nuclear powerplant, but how do you prevent someone actually flying it there? Where a lot of questions of drone-flying have some potential answers, this one is still unsolved.

In all cases, sensors play a pivotal role in solving the technical questions. ASN Filter Designer can help with sensor measurement with real-time feedback and the powerful signal analyzer. How? Look at ASN Filter Designer or mail our consultancy service at: designs@advsolned.com

Do you agree with this list? Do you have other suggestions? Please let us know!

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How drones fuel ‘smart air’: Delivery drones

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Where ‘smart traffic’ has already 417 billion hits on google, I only found ‘smart air’ for a kind of door lock and ‘smart drone’ for an advanced toy drone. But definitely, drones are so hot that they will become part of something called ‘smart air’. The SESAR project predicts that drones will make 250 billion hours of flight in the European Union alone. For comparison: this is far more than the air traffic of ‘normal’ airplanes today.

Because drones are using many sensors, we did some research how the use of drones can grow to maturity and fuel ‘smart air’. Today we talk about challenges for delivery drones.

Delivery drones

No wonder, drones have proven to be very convenient already and have even more promises in store. Soon, it will be commonplace that drones are delivering packages, from hot pizzas to even more urgent medicines. And even humans: the first drone taxis are already being tested. At this moment, drones are already used for drag-and-drop deliveries in some rural and faraway areas. Most articles on the internet talk about the use in drones in big city areas. And there they have the big advantage of an -still- almost empty sky instead of congested roads and overfull parking places. For that, delivery by drones will be faster and more predictable.

But until use of drones are entirely tried and tested, most drone developments will take place on rural environments. Because here the risk of large damage is a lot smaller when something will go wrong. In time, delivery drones will still be used in rural places. Maybe as a standalone, maybe in combination with self-driving trucks. Reach will not be a big problem, since the whole word is getting connected fast. So, reach will almost only depend on battery endurance. And for now, these batteries have only a limited capacity for distance and cargo.

Challenges while travelling

Like all delivery services, drone delivery has to a pick-up a package, travel to the destination and drop-of the package.  While travelling, drones have to know how to reach their destination. Meanwhile, there are some challenges:

  • Risk of colliding, with other drones, birds and other air users. Just like other traffic
  • And at point in time, some traffic rules have to be set in place. Sensors can help to let the drone follow these rules
  • How drones can stay on course, even with wind
  • Preventing drones to cross over forbidden (known) areas and unexpected ‘wrong’ areas (e.g. a building or a wood on fire)
  • How to prevent a package from falling? How to alert that a package will probably fall? Or maybe the drone itself? If so, measurement can be taken. Already, there are experiments with self-destruction. But maybe more practical solutions can be found to let the drone aim for a ‘safe area’, such as a park, river, etc. for an ‘emergency landing’
  • Acceptance of drones beside safety: how to guarentee privacy when drones are flying over peopled areas? Then there is the issue of noise: research shows that people find the noise of drones one of the most annoying forms of noise

Challenges with dropping the cargo

For now, the drop-of is literally done by dropping-of the cargo. Maybe with the aid of a cord which places the package as soft as possible to the ground. But anyhow: the drone stays in the air. So, technology has to get safe: for the package to be delivered undamaged. How does the drone know that the right person gets the package? And we have to prevent dogs from biting the package. And of course, to prevent that the dropped cargo will harm humans, animals or buildings or even worse.

The use of sensors

The application possibilities of drones are very promising for delivery uses. It is still in its experimental phase. But with developments going fast, soon it will reach the maturity phase. For this, there are two-fold kind of challenges.

Some are challenges on privacy, safety and security. These challenges have to be solved before the use of drones will get widespread trust and acceptance. The other are technical and communication issues: where multiple drones are being used – especially in cities- challenges how drones can and have to behave in traffic has to be solved.

In both challenges, sensors play a pivotal role in solving the technical questions. In all cases, ASN Filter Designer can help with sensor measurement with real-time feedback and the powerful signal analyzer. How? Look at ASN Filter Designer or mail ASN consultancy: designs@advsolned.com

Do you agree with this list? Do you have other suggestions? Please let us know!

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How to measure preventative maintenance

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Typical challenges faced by assets managers include:

  • How to measure mechanical component fatigue?
  • How to assess electrical wiring health?
  • How to reduce overall operating costs, but not comprise on public safety?
  • Risks posed by hackers & terrorists
  • Asset damage due to vandalism

Preventative Maintenance aims to solve the aforementioned problems by acting pre-emptively. This is achieved by constantly monitoring the performance of critical components (usually with sensors) and then alerting the maintenance team that a component is about to fail. The asset management team can then schedule maintenance in order to replace the failing component(s) with minimum disruption to the public, and overall lower operational costs.

  • Plan maintenance
  • Machine health care
  • Motor health care
  • Secure firmware updates and anti-tampering

Plan maintenance

Monitoring the health of critical component, such as a lamp, motor or machine component and input power supply. Our algorithms and analytics help asset management departments provide planned maintenance.

A better maintenance program is achieved by constantly monitoring the performance of critical components (usually with sensors or other devices) and then alerting the maintenance team that a component is about to fail.

Machine health care

The health of a machine can be determined by ‘listening’ to the sound it makes via microphones. Algorithms filter and compare recorded audio to fingerprint templates of known failures.

Motor health care

The health of an industrial motor be determined by analysis the phase currents. Algorithms filter and compare captured data to fingerprints templates of known failures. The phase current data can also be used to check for wire breaks or phase failure.

Secure firmware updates & Anti-tampering

ASN’s security module provides asset protection up to military grade, and while at the same time allowing for secure (encrypted) firmware updates.

ASN contactless measurement, smart algorithms and alerting offers the ideal condition for this programme. The asset management team can then schedule maintenance in order to replace the failing component(s) with minimum disruption to the public, and overall lower operational costs.

Let’s make an appointment to see how can help you create an effective maintenance programme and reduce your Total Cost of Ownership.

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