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How do you get the best performance from your IoT smart sensor?

The global smart sensor market size is projected to grow from USD 36.6 billion in 2020 to USD 87.6 billion by 2025, at a CAGR of 19.0%. At least 80% of these IoT/IIoT smart sensors (temperature, pressure, gas, image, motion, loadcells) will use Arm’s Cortex-M technology.

IoT sensor measurement challenge

The challenge for most, is that many sensors used in these applications require 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. So ther’s no need to redesign your hardware: a massive cost saving!
  • To reduce the data sent off to the cloud by pre-analysing data. So send only the data which is necessary

Nevertheless, DSP has been considered by most to be a black art, limited only to those with a strong academic mathematical background. However, for many IoT/IIoT applications, DSP has been become a must in order to remain competitive and obtain high performance with relatively low cost hardware.

Do you have an example?

Consider the following application for gas sensor measurement (see the figure below). The requirement is to determine the amplitude of the sinusoid in order to get an estimate of gas concentration (bigger amplitude, more gas concentration etc). Analysing the figure, it is seen that the sinusoid is corrupted with measurement noise (shown in blue), and any estimate based on the blue signal will have a high degree of uncertainty about it – which is not very useful if getting an accurate reading of gas concentration!

Algorithms clean the sensor data

After ‘cleaning’ the sinusoid (red line) with a DSP filtering algorithm, we obtain a much more accurate and usable signal. Now we are able to estimate the amplitude/gas concentration. Notice how easy it is to determine the amplitude of red line.

This is only a snippet of what is possible with DSP algorithms for IoT/IIoT applications, but it should give you a good idea as to the possibilities of DSP.

How do I use this in my IoT application?

As mentioned at the beginning of this article, 80% of IoT smart sensor devices are deployed on Arm’s Cortex-M technology. The Arm Cortex-M4 is a very popular choice with hundreds of silicon vendors, as it offers DSP functionality traditionally found in more expensive DSPs. Arm and its partners provide developers with easy to use tooling and a free software framework (CMSIS-DSP). So, you’ll be up and running within minutes.

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A digital filter is a mathematical algorithm that operates on a digital dataset (e.g. sensor data) in order extract information of interest and remove any unwanted information. Applications of this type of technology, include removing glitches from sensor data or even cleaning up noise on a measured signal for easier data analysis. But how do we choose the best type of digital filter for our application? And what are the differences between a IIR filter and a FIR filter?

Digital filters are divided into the following two categories:

  • Infinite impulse response (IIR)
  • Finite impulse response (FIR)

As the names suggest, each type of filter is categorised by the length of its impulse response. However, before beginning with a detailed mathematical analysis, it is prudent to appreciate the differences in performance and characteristics of each type of filter.

Example

In order to illustrate the differences between an IIR and FIR, the frequency response of a 14th order FIR (solid line), and a 4th order Chebyshev Type I IIR (dashed line) is shown below in Figure 1.  Notice that although the magnitude spectra have a similar degree of attenuation, the phase spectrum of the IIR filter is non-linear in the passband (\(\small 0\rightarrow7.5Hz\)), and becomes very non-linear at the cut-off frequency, \(\small f_c=7.5Hz\). Also notice that the FIR requires a higher number of coefficients (15 vs the IIR’s 10) to match the attenuation characteristics of the IIR.

FIR vs IIR: frequency response of a 14th order FIR (solid line), and a 4th order Chebyshev Type I IIR (dashed line); Fir Filter, IIR Filter
Figure 1: FIR vs IIR: frequency response of a 14th order FIR (solid line), and a 4th order Chebyshev Type I IIR (dashed line)

These are just some of the differences between the two types of filters. A detailed summary of the main advantages and disadvantages of each type of filter will now follow.

IIR filters

IIR (infinite impulse response) filters are generally chosen for applications where linear phase is not too important and memory is limited. They have been widely deployed in audio equalisation, biomedical sensor signal processing, IoT/IIoT smart sensors and high-speed telecommunication/RF applications.

Advantages

  • Low implementation cost: requires less coefficients and memory than FIR filters in order to satisfy a similar set of specifications, i.e., cut-off frequency and stopband attenuation.
  • Low latency: suitable for real-time control and very high-speed RF applications by virtue of the low number of coefficients.
  • Analog equivalent: May be used for mimicking the characteristics of analog filters using s-z plane mapping transforms.

Disadvantages

  • Non-linear phase characteristics: The phase charactersitics of an IIR filter are generally nonlinear, especially near the cut-off frequencies. All-pass equalisation filters can be used in order to improve the passband phase characteristics.
  • More detailed analysis: Requires more scaling and numeric overflow analysis when implemented in fixed point. The Direct form II filter structure is especially sensitive to the effects of quantisation, and requires special care during the design phase.
  • Numerical stability: Less numerically stable than their FIR (finite impulse response) counterparts, due to the feedback paths.

FIR filters

FIR (finite impulse response) filters are generally chosen for applications where linear phase is important and a decent amount of memory and computational performance are available. They have a widely deployed in audio and biomedical signal enhancement applications. Their all-zero structure (discussed below) ensures that they never become unstable for any type of input signal, which gives them a distinct advantage over the IIR.

Advantages

  • Linear phase: FIRs can be easily designed to have linear phase. This means that no phase distortion is introduced into the signal to be filtered, as all frequencies are shifted in time by the same amount – thus maintaining their relative harmonic relationships (i.e. constant group and phase delay). This is certainly not case with IIR filters, that have a non-linear phase characteristic.   
  • Stability: As FIRs do not use previous output values to compute their present output, i.e. they have no feedback, they can never become unstable for any type of input signal, which is gives them a distinct advantage over IIR filters.
  • Arbitrary frequency response: The Parks-McClellan and ASN FilterScript’s firarb() function allow for the design of an FIR with an arbitrary magnitude response. This means that an FIR can be customised more easily than an IIR.
  • Fixed point performance: the effects of quantisation are less severe than that of an IIR.

Disadvantages

  • High computational and memory requirement: FIRs usually require many more coefficients for achieving a sharp cut-off than their IIR counterparts. The consequence of this is that they require much more memory and significantly a higher amount of MAC (multiple and accumulate) operations. However, modern microcontroller architectures based on the Arm’s Cortex-M cores now include DSP hardware support via SIMD (signal instruction, multiple data) that expedite the filtering operation significantly.
  • Higher latency: the higher number of coefficients, means that in general an FIR is less suitable than an IIR for fast high throughput applications. This becomes problematic for real-time closed-loop control applications, where an FIR filter may have too much group delay to achieve loop stability.
  • No analog equivalent: using the Bilinear, matched z-transform (s-z mapping), an analog filter can be easily be transformed into an equivalent IIR filter.  However, this is not possible for an FIR as it has no analog equivalent.

Mathematical definitions

As discussed in the introduction, the name IIR and FIR originate from the mathematical definitions of each type of filter, i.e. an IIR filter is categorised by its theoretically infinite impulse response,

\(\displaystyle
y(n)=\sum_{k=0}^{\infty}h(k)x(n-k)
\)

and an FIR categorised by its finite impulse response,

\(\displaystyle
y(n)=\sum_{k=0}^{N-1}h(k)x(n-k)
\)

We will now analyse the mathematical properties of each type of filter in turn.

IIR definition

As seen above, an IIR filter is categorised by its theoretically infinite impulse response,

\(\displaystyle y(n)=\sum_{k=0}^{\infty}h(k)x(n-k) \)

Practically speaking, it is not possible to compute the output of an IIR using this equation. Therefore, the equation may be re-written in terms of a finite number of poles \(\small p\) and zeros \(\small q\), as defined by the linear constant coefficient difference equation given by:

\(\displaystyle
y(n)=\sum_{k=0}^{q}b_k x(n-k)-\sum_{k=1}^{p}a_ky(n-k)
\)

where, \(\small a_k\) and \(\small b_k\) are the filter’s denominator and numerator polynomial coefficients, who’s roots are equal to the filter’s poles and zeros respectively. Thus, a relationship between the difference equation and the z-transform (transfer function) may therefore be defined by using the z-transform delay property such that,

\(\displaystyle
\sum_{k=0}^{q}b_kx(n-k)-\sum_{k=1}^{p}a_ky(n-k)\quad\stackrel{\displaystyle\mathcal{Z}}{\longleftrightarrow}\quad\frac{\sum\limits_{k=0}^q b_kz^{-k}}{1+\sum\limits_{k=1}^p a_kz^{-k}}
\)

As seen, the transfer function is a frequency domain representation of the filter. Notice also that the poles act on the output data, and the zeros on the input data. Since the poles act on the output data, and affect stability, it is essential that their radii remain inside the unit circle (i.e. <1) for BIBO (bounded input, bounded output) stability. The radii of the zeros are less critical, as they do not affect filter stability. This is the primary reason why all-zero FIR (finite impulse response) filters are always stable.

BIBO stability

A linear time invariant (LTI) system (such as a digital filter) is said to be bounded input, bounded output stable, or BIBO stable, if every bounded input gives rise to a bounded output, as

\(\displaystyle \sum_{k=0}^{\infty}\left|h(k)\right|<\infty \)

Where, \(\small h(k)\) is the LTI system’s impulse response. Analyzing this equation, it should be clear that the BIBO stability criterion will only be satisfied if the system’s poles lie inside the unit circle, since the system’s ROC (region of convergence) must include the unit circle. Consequently, it is sufficient to say that a bounded input signal will always produce a bounded output signal if all the poles lie inside the unit circle.

The zeros on the other hand, are not constrained by this requirement, and as a consequence may lie anywhere on z-plane, since they do not directly affect system stability. Therefore, a system stability analysis may be undertaken by firstly calculating the roots of the transfer function (i.e., roots of the numerator and denominator polynomials) and then plotting the corresponding poles and zeros upon the z-plane.

An interesting situation arises if any poles lie on the unit circle, since the system is said to be marginally stable, as it is neither stable or unstable. Although marginally stable systems are not BIBO stable, they have been exploited by digital oscillator designers, since their impulse response provides a simple method of generating sine waves, which have proved to be invaluable in the field of telecommunications.

Biquad IIR filters

The IIR filter implementation discussed herein is said to be biquad, since it has two poles and two zeros as illustrated below in Figure 2. The biquad implementation is particularly useful for fixed point implementations, as the effects of quantization and numerical stability are minimised. However, the overall success of any biquad implementation is dependent upon the available number precision, which must be sufficient enough in order to ensure that the quantised poles are always inside the unit circle.

Direct Form I (biquad) IIR filter realization and transfer function.; Direct Form; Biquad filter

Figure 2: Direct Form I (biquad) IIR filter realization and transfer function.

Analysing Figure 2, it can be seen that the biquad structure is actually comprised of two feedback paths (scaled by \(\small a_1\) and \(\small a_2\)), three feed forward paths (scaled by \(\small b_0, b_1\) and \(\small b_2\)) and a section gain, \(\small K\). Thus, the filtering operation of Figure 1 can be summarised by the following simple recursive equation:

\(\displaystyle y(n)=K\times\Big[b_0 x(n) + b_1 x(n-1) + b_2 x(n-2)\Big] – a_1 y(n-1)-a_2 y(n-2)\)

Analysing the equation, notice that the biquad implementation only requires four additions (requiring only one accumulator) and five multiplications, which can be easily accommodated on any Cortex-M microcontroller. The section gain, \(\small K\) may also be pre-multiplied with the forward path coefficients before implementation.

A collection of Biquad filters is referred to as a Biquad Cascade, as illustrated below.

Biquad Cascade; Biquad filter

The ASN Filter Designer can design and implement a cascade of up to 50 biquads (Professional edition only).

Floating point implementation

When implementing a filter in floating point (i.e. using double or single precision arithmetic) Direct Form II structures are considered to be a better choice than the Direct Form I structure. The Direct Form II Transposed structure is considered the most numerically accurate for floating point implementation, as the undesirable effects of numerical swamping are minimised as seen by analysing the difference equations.

Direct Form II Transposed strucutre, transfer function and difference equations; IIR Filters; Biquad Filters

Figure 3 – Direct Form II Transposed strucutre, transfer function and difference equations

The filter summary (shown in Figure 4) provides the designer with a detailed overview of the designed filter, including a detailed summary of the technical specifications and the filter coefficients, which presents a quick and simple route to documenting your design.

The ASN Filter Designer supports the design and implementation of both single section and Biquad (default setting) IIR filters.

Biquad filter ASN Filter Designer DSP
Figure 4: detailed specification.

FIR definition

Returning the IIR’s linear constant coefficient difference equation, i.e.

\(\displaystyle
y(n)=\sum_{k=0}^{q}b_kx(n-k)-\sum_{k=1}^{p}a_ky(n-k)
\)

Notice that when we set the \(\small a_k\) coefficients (i.e. the feedback) to zero, the definition reduces to our original the FIR filter definition, meaning that the FIR computation is just based on past and present inputs values, namely:

\(\displaystyle
y(n)=\sum_{k=0}^{q}b_kx(n-k)
\)

Implementation

Although several practical implementations for FIRs exist, the direct form structure and its transposed cousin are perhaps the most commonly used, and as such, all designed filter coefficients are intended for implementation in a Direct form structure.

The Direct form structure and associated difference equation are shown below. The Direct Form is advocated for fixed point implementation by virtue of the single accumulator concept.

\(\displaystyle y(n) = b_0x(n) + b_1x(n-1) + b_2x(n-2) + …. +b_qx(n-q) \)

Direct form; Direct form structure

The recommended (default) structure within the ASN Filter Designer is the Direct Form Transposed structure, as this offers superior numerical accuracy when using floating point arithmetic. This can be readily seen by analysing the difference equations below (used for implementation), as the undesirable effects of numerical swamping are minimised, since floating point addition is performed on numbers of similar magnitude.

\(\displaystyle \begin{eqnarray}y(n) & = &b_0x(n) &+& w_1(n-1) \\ w_1(n)&=&b_1x(n) &+& w_2(n-1) \\ w_2(n)&=&b_2x(n) &+& w_3(n-1) \\ \vdots\quad &=& \quad\vdots &+&\quad\vdots \\ w_q(n)&=&b_qx(n) \end{eqnarray}\)

Direct form Transposed

What have we learned?

Digital filters are divided into the following two categories:

  • Infinite impulse response (IIR)
  • Finite impulse response (FIR)

IIR (infinite impulse response) filters are generally chosen for applications where linear phase is not too important and memory is limited. They have been widely deployed in audio equalisation, biomedical sensor signal processing, IoT/IIoT smart sensors and high-speed telecommunication/RF applications.

FIR (finite impulse response) filters are generally chosen for applications where linear phase is important and a decent amount of memory and computational performance are available. They have a widely deployed in audio and biomedical signal enhancement applications.

ASN Filter Designer provides engineers with everything they need to design, experiment and deploy complex IIR and FIR digital filters for a variety of sensor measurement applications. These advantages coupled with automatic documentation and code generation functionality allow engineers to design and validate an IIR/FIR digital filter within minutes rather than hours.

 

 

Download demo now

Licencing information

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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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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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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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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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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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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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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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