Building AI Applications for Signals and Time Series Data

Building AI applications for Signals and Time-

Series Data

Esha Shah, MathWorks

Francis Tiong, MathWorks

ARTIFICIAL INTELLIGENCE

MACHINE LEARNINGSupervised and Unsupervised Statistical Models…

DEEP LEARNINGNeural networks, GANs, Autoencoders….

Machine Learning and Deep learning have grown rapidly over the

last decade

2

ARTIFICIAL INTELLIGENCE

MACHINE LEARNINGSupervised and Unsupervised Statistical Models…

DEEP LEARNINGNeural networks, GANs, Autoencoders….

Machine Learning and Deep learning have grown rapidly over the

last decade

2

4

Use of AI in signal processing applications is growing rapidly

Modulation Classification of RF waveforms

4


4

TRANSMITTER

(Software

Defined Radio)

RECEIVER

(Software

Defined Radio)


4

TRANSMITTER

(Software

Defined Radio)

RECEIVER

(Software

Defined Radio)


4

TRANSMITTER

(Software

Defined Radio)

Modulation

Type

RECEIVER

(Software

Defined Radio)


4

TRANSMITTER

(Software

Defined Radio)

Modulation

Type

RECEIVER

(Software

Defined Radio)

AI-driven system design

5


Data cleansing and

preparation

Simulation-

generated data

Human insight

Data Preparation

5


Model design and

tuning

Hardware

accelerated training

Interoperability

AI Modeling

Data cleansing and

preparation

Simulation-

generated data

Human insight

Data Preparation

5


Model design and

tuning

Hardware


Interoperability

AI Modeling

Data cleansing and

preparation

Simulation-

generated data

Human insight

Data Preparation

Enterprise systems

Embedded devices

Edge, cloud,

desktop

Deployment

5


Model design and

tuning

Hardware


Interoperability

AI Modeling

Data cleansing and

preparation

Simulation-

generated data

Human insight

Data Preparation

Enterprise systems

Embedded devices

Edge, cloud,

desktop

Deployment

5

Preparing and labelling data

Data cleansing and

preparation

Simulation-generated

data

Human insight

Data Preparation

16


Q. How to label collected data?Data cleansing and

preparation


data

Human insight

Data Preparation

17


Q. How to label collected data?

Q. What if it is not possible to collect

data?

Data cleansing and

preparation


data

Human insight

Data Preparation

Labeling Signals with Signal Labeler App

7

Generate Synthetic Data for various applications in MATLAB

8


8

Simulate data using Simulink models


8

Simulate data using Simulink models Generate wireless waveforms


8

Generate Radar Returns

Simulate data using Simulink models Generate wireless waveforms


8

Generate Radar Returns Generate and Augment Audio Data

Simulate data using Simulink models

text2speech

23

Generate wireless waveforms

9

Generation of wireless communication waveforms with impairments

9

•Modulate digital baseband signals using built-in functions•BPSK, QPSK, 8PSK, FM, DSB-AM, SSB-AM, GFSK,PAM4


9


•Easily account for various impairments•RF / Hardware impairments (Frequency/ Phase Offsets etc. )

• Channel Impairments (Multipath Fading Channels)


9


•Easily account for various impairments•RF / Hardware impairments (Frequency/ Phase Offsets etc. )

• Channel Impairments (Multipath Fading Channels)

• Generate Datasets for Deep Learning

• 5000 frames generated for each modulation type

• 80% data – Training; 10% data – Validation; 10% data - Test


Feature Extraction

Data cleansing and

preparation


data

Human insight

Data Preparation

Feature Extraction

Q. Can I use raw data?Data cleansing and

preparation


data

Human insight

Data Preparation

Feature Extraction

Q. Can I use raw data?

Q. How do I extract the right features

for my data?

Data cleansing and

preparation


data

Human insight

Data Preparation

Use of raw data for AI models

11


11

IQ waveform

I waveform

Q waveform


11

High

Dimensionality

Need for more data

Need for

specialized models

IQ waveform

I waveform

Q waveform

Challenges with Raw Data

Feature extraction with signal processing techniques

12


12


12


12


12

Building the AI models

Model design and

tuning

Hardware


Interoperability

AI Modeling


Q. How do I select the right model for

my application:Model design and

tuning

Hardware


Interoperability

AI Modeling


Q. How do I select the right model for

my application:• If I do not have enough data?

• If I do not have domain expertise?

• If I need an easily interpretable model?

…..

Model design and

tuning

Hardware


Interoperability

AI Modeling

Start by using published literature and MATLAB examples

14


14


14

Understanding tradeoffs for model selection

15


15

Da

ta V

olu

me

Time Required


15

Da

ta V

olu

me

Signal Processing /

Domain Knowledge

There are three ways to build AI models in MATLAB

16


16

Writing code

fitcauto/fitrauto


16

Interactively Design Models with

Apps

Writing code

fitcauto/fitrauto


16

Interactively Design Models with

Apps

Use Transfer Learning

for Deep Learning

Writing code

fitcauto/fitrauto

Iterate to find the best model with Experiment Manager App

17


17


17

Find optimal

training options


17

Find optimal

training options

Compare the

results of using

different data sets


17

Find optimal

training options

Compare the

results of using

different data sets

Compare the

results of using

different models

Selecting the Right Model : Understanding Tradeoffs

18

Da

ta V

olu

me

Signal Processing /

Domain Knowledge

Selecting the Right Model : Understanding Tradeoffs

18

Da

ta V

olu

me

Signal Processing /

Domain Knowledge

Continuous Wavelet Transform is used to extract the Time-

Frequency maps

19


Frequency maps

19

•One line of code for generating wavelet time-

frequency visualization in MATLAB. Works for any

signal >> cwt(inputSignal)


Frequency maps

19




•Localizes sharp transients and slowly varying

oscillations simultaneously


Frequency maps

19




•Localizes sharp transients and slowly varying

oscillations simultaneously

• Works with complex data

Using time-frequency maps as inputs to a pretrained CNN

20


20


20

Transfer Learning with Deep Network Designer App

21

Train and Test Deep Network

22

Test Deep Network

23

Testing network with connected hardware

24


24


24


24

AI-assisted system design

Model design and

tuning

Hardware


Interoperability

AI Modeling

Data cleansing and

preparation

Simulation-

generated data

Human insight

Data Preparation

Enterprise systems

Embedded devices

Edge, cloud,

desktop

Deployment

25

AI-assisted system design

Model design and

tuning

Hardware


Interoperability

AI Modeling

Data cleansing and

preparation

Simulation-

generated data

Human insight

Data Preparation

Enterprise systems

Embedded devices

Edge, cloud,

desktop

Deployment

25

Deep Learning can be used in each step of the AI workflow

26


26

Labeling assistance

Manually Correct

Neural Network

Auto Label

Inspect


26

Labeling assistance

Manually Correct

Neural Network

Auto Label

Inspect

classifySound (YAMNet),GoogLeNet,

fitcecoc(ResNet18)


26

Labeling assistance Synthetic Data Generation

Manually Correct

Neural Network

Auto Label

Inspect


fitcecoc(ResNet18)


26

Labeling assistance Synthetic Data Generation

Manually Correct

Neural Network

Auto Label

Inspect


fitcecoc(ResNet18)

Generative Adversarial Networks

(GANs)


27


27

Feature Extraction


27

Feature Extraction


27

Feature Extraction

vggFeatures, waveletScattering


27

Feature Extraction

Differentiable Signal Processing



27

Feature Extraction




27

Feature Extraction



dlstft (Differentiable STFT)


Model design and

tuning

Hardware


Interoperability

AI Modeling

Data cleansing and

preparation

Simulation-

generated data

Human insight

Data Preparation

Enterprise systems

Embedded devices

Edge, cloud,

desktop

Deployment

28

Deploy to any processor with best-in-class performance

29


Preprocessing, Feature

Extraction, AI Model

29


CPU

Code

Generation



29


CPU

GPUCode

Generation



29


FPGA

CPU

GPUCode

Generation



29

Deploying complete AI algorithms to embedded processors, GPUs

and FPGAs

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and FPGAs

30


and FPGAs

30


and FPGAs

30


and FPGAs

31

MATLAB supports the entire AI-driven system design

32

MATLAB supports the entire AI-driven system design

Signal Processing apps

Feature Extraction Techniques

Generate Data Quickly build models

Accelerate training

Deploy to targets with

code generation

32

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Date post:	21-Feb-2022
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Building AI Applications for Signals and Time Series Data

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