## UI experience 2020 pack

After downloading the ASN Filter Designer, most people just want to play with the tool, in order to get a feeling of whether it works for them. But how do you get started with the ASN Filter Designer? Based on some great user feedback, ASNFD v4.4 now comes with the UI experience 2020 pack. This pack includes detailed coaching tips, an enhanced user experience and step-by-step instructions to get you started with your design.

A quick overview of the ASN Filter Designer v4.4 is given below, and we’re sure that you’ll agree that it’s an awesome tool for DSP IIR/FIR digital filter design!

The ASN Filter Designer has a fast, intuitive user interface. Interactively design, validate and deploy your digital filter within minutes rather than hours. However, getting started with DSP Filter Design can be difficult, especially when you don’t have deep knowledge of digital signal processing. Most people just want to experiment with a tool to get a feeling whether it works for them (sure, there are tutorials and videos). But where do you start?

## Start experimenting with filter design immediately

That’s why we have developed the UI Experience 2020 pack. Based on user feedback, we’ve created detailed tooltips and animations of key functionality. Within minutes, you’ll get a kick-start into functionalities such as chart zooming, panning and design markers.

## Coaching tips, enhanced user experience, step-by-step instructions

Based on user feedback, the UI Experience 2020 pack includes:

• Extensive coaching tips
• Detailed explanations of design methods and types of filters
• Enhanced user experience:
• cursors
• animations
• visual effects
• Short cuts to detailed worked solutions, tutorials and step-by-step instructions

Feedback from the user community has been very positive indeed! By providing detailed tooltips and animations of key functionality, the initial hurdle of designing a filter with your desired specifications has now been significantly simplified.

So start with the ASN Filter Designer right away, and cut your development costs by up to 75%!

## How to export designed IIR/FIR filters to Python

For many IoT sensor measurement applications, an IIR or FIR filter is just one of the many components needed for an algorithm. This could be a powerline interference canceller for a biomedical application or even a simpler DC loadcell filter. In many cases, it is necessary to integrate a filter into a complete algorithm in another domain.

Python is a very popular general-purpose programming language with support for numerical computing, allowing for the design of algorithms and performing data analysis. The language’s numpy and signal add-on modules attempt to bridge the gap between numerical algorithmic languages, such as Matlab and more traditional programming languages, such as C/C++. As such, it is much more appealing to experienced programmers, who are used to C/C++ data types, syntax and functionality, rather than Matlab’s scripting language that is more aimed at mathematicans developing algorithmic concepts.

## ASN Filter Designer automatic code generator for Python

The ASN Filter Designer greatly simplifies exporting a designed filter to Python via its automatic code generator. The code generator supports all aspects of the ASN Filter Designer, allowing for a complete design comprised of H1, H2 and H3 filters and math operators to be fully integrated with an algorithm in Python.

The Python code generator can be accessed via the filter summary options (as shown on the right). Selecting this option will automatically generate a Python .py file based on the current design settings.

In order to use the generated code in a Python project, the following two framework files are provided in the
ASN Filter Designer’s installation directory in the \Python directory:

ASNFDFilterData.py
ASNFDImport.py

A convenient shortcut to the relevant Framework files and examples is available in the Filter summary toolbar via the folder symbol (see left).

Using the two framework files, you may build a demo of your choice based on the exported filter(s) specifications. The framework supports both Real and Complex filters in floating point only, and is built on ASN IP blocks, rather than Python’s signal module, which was seen to struggle with managing complex data. Thus, in order to expedite algorithm development with the framework, the following three demos are provided:

ASNFDPythonDemo: main demo file with various examples
RMSmeterDemo: An RMS amplitude powerline meter demo
EMGDataDemo: An EMG biomedical demo with a HPF, 50Hz notch filter and averaging

These framework files require the following Python dependency modules:

matplotlib.pyplot
numpy

An example of the generated code for use with the ASNFD-Python framework is shown below.

As seen, it is as simple as copying and pasting the filter coefficients from the ASN Filter Designer’s filter summary into a Python project script.

## Drones and DC motor control – How the ASN Filter Designer can save you a lot of time and effort

Drones are one of the golden nuggets in IoT. No wonder, they can play a pivotal role in congested cities and far away areas for delivery. Further, they can be a great help to give an overview of a large area or places which are difficult or dangerous to reach. However, most of the technology is still in its experimental stage.

Because drones have a lot of sensors, Advanced Solutions Nederland did some research on how drone producing companies have solved questions regarding their sensor technology, especially regarding DC motor control.

### Until now: solutions developed with great difficulty

We found out that most producers spend weeks or even months on finding solutions for their sensor technology challenges. With the ASN Filter Designer, he/she could have come to a solution within days or maybe even hours. Besides, we expect that the measurement would be better too.

The biggest time coster is that until now algorithms were developed by handwork, i.e. they were developed in a lab environment and then tested in real-life. With the result of the test, the algorithm would be tweaked again until the desired results were reached. However, yet another challenge stems from the fact that a lab environment is where testing conditions are stable, so it’s very hard to make models work in real life. These steps result in rounds and rounds of ‘lab development’ and ‘real life testing’ in order to make any progress -which isn’t ideal!

### How the ASN Filter Designer can help save a lot of time and effort

The ASN Filter Designer can help a lot of time in the design and testing of algorithms in the following ways:

• Design, analyse and implement filters for drone sensor applications with real-time feedback and our powerful signal analyser.
• Design filters for speed and positioning control for sensorless BLDC (brushless DC) motor applications.
• Speed up deployment to Arm Cortex-M embedded processors.

### Real-time feedback and powerful signal analyser

One of the key benefits of the ASN Filter Designer and signal analyser is that it gives real-time feedback. Once an algorithm is developed, it can easily be tested on real-life data. To analyse the real-life data, the ASN Filter Designer has a powerful signal analyser in place.

### Design and analyse filters the easy way

You can easily design, analyse and implement filters for a variety of drone sensor applications, including: loadcells, strain gauges, torque, pressure, temperature, vibration, and ultrasonic sensors and assess their dynamic performance in real-time for a variety of input conditions.  With the ASN Filter Designer, you don’t have do to any coding yourself or break your head with specifications: you just have to draw the filter magnitude specification and the tool will calculate the coefficients itself.

### Speed up deployment

Perform detailed time/frequency analysis on captured test datasets and fine-tune your design. Our Arm CMSIS-DSP and C/C++ code generators and software frameworks speed up deployment to a DSP, FPGA or micro-controller.

## An example: designing BLDC motor control algorithms

BLDC (brushless DC) BLDC motors have found use in a variety of application areas, including: robotics, drones and cars. They have significant advantages over brushed DC motors and induction motors, such as: better speed-torque characteristics, high reliability, longer operating life, noiseless operation, and reduction of electromagnetic interference (EMI).

One advantage of BLDC motor control compared to standard DC motors is that the motor’s speed can be controlled very accurately using six-step commutation, making it a good choice for precision motion applications, such as robotics and drones.

### Sensorless back-EMF and digital filtering

For most applications, monitoring of the back-EMF (back-electromotive force) signal of the unexcited phase winding is easier said than done, since it has significant noise distortion from PWM (pulse width modulation) commutation from the other energised windings. The  coupling  between  the  motor parameters, especially inductances, can induce ripple in the back-EMF signal that is synchronous with the PWM commutation.  As a consequence, this induced ripple on the back EMF signal leads to faulty commutation. Thus, the measurement challenge is how to accurately measure the zero-crossings of the back-EMF signal in the presence of PWM signals?

A standard solution is to use digital filtering, i.e. IIR, FIR or even a median (majority) filter. However, the challenge for most designers is how to find the best filter type and optimal filter specification for the motor under consideration.

### The solution

The ASN Filter Designer allows engineers to work on speed and position sensorless BLDC motor control applications based on back-EMF filtering to easily experiment and see the filtering results on captured test datasets in real-time for various IIR, FIR and median (majority filtering) digital filtering schemes. The tool’s signal analyser implements a robust zero-crossings detector, allowing engineers to evaluate and fine-tune a complete sensorless BLDC control algorithm quickly and simply.

So, if you have a measurement problem, ask yourself:

Can I save time and money, and reduce the headache of design and implementation with an investment in new tooling?

Our licensing solutions start from just 125 EUR for a 3-month licence.

Find out what we can do for you, and learn more by visiting the ASN Filter Designer’s product homepage.

## The moving average filter

The moving average (MA) filter is perhaps one of the most widely used FIR filters due to its conceptual simplicity and ease of implementation. As seen in the diagram below, notice that the filter doesn’t require any multiplications, just additions and a delay line, making it very suitable for many extreme low-power embedded devices with basic computational capabilities.

However, despite its simplicity, the moving average filter is optimal for reducing random noise while retaining a sharp step response, making it a versatile building block for smart sensor signal processing applications.

A moving average filter of length $$L$$ for an input signal $$x(n)$$ may be defined as follows:

$$y(n)=\large{\frac{1}{L}}\normalsize{\sum\limits_{k=0}^{L-1}x(n-k)}$$ for $$\normalsize{n=0,1,2,3….}$$

Where, a simple rule of thumb states that the amount of noise reduction is equal to the square-root of the number of points in the average. For example, an MA of length 9 will result in a factor 3 noise reduction.

Frequency response of an MA filter of length 9. Notice the poor stopband attentuation at around -20dB.

• Most commonly used digital lowpass filter.
• Optimal for reducing random noise while retaining a sharp step response.
• Good smoother (time domain).
• Unity valued filter coefficients, no MAC (multiply and accumulate) operations required.
• Conceptually simple to implement.

• Inflexible frequency response: nudging a conjugate zero pair results in non-unity coefficients.
• Poor lowpass filter (frequency domain): slow roll-off and terrible stopband attenuation characteristics.

## Implementation

The MA filter may be implemented in ASN FilterScript as follows:

ClearH1;  // clear primary filter from cascade

Main()
Hd=movaver(8,"symbolic");  // design a MA of length 8
Num = getnum(Hd);   // define numerator coefficients
Den = {1};          // define denominator coefficients
Gain = getgain(Hd); // define gain