Journal of Robotics, Networking and Artificial Life
Vol. 8(3); December (2021), pp. 186–192
DOI: https://doi.org/10.2991/jrnal.k.210922.007; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

End-to-End Deep Learning by MCU Implementation: Indoor
Localization by Sound Spectrum of Light Fingerprints
Chung-Wen Hung1,*, Hiroyuki Kobayashi2, Jun-Rong Wu1, Chau-Chung Song1,3
Department of Electrical Engineering, National Yunlin University of Science and Technology, Douliou, Taiwan
Department of System Design Engineering, Osaka Institute of Technology, Osaka, Japan3
Department of Aeronautical Engineering, National Formosa University, Yunlin, Taiwan
1
2

ARTICLE INFOABSTRACT

Article History

This paper introduces a low-cost indoor localization system using sound spectrum of light fingerprint. An Artificial Intelligence(AI), algorithm will be implemented in a low-cost Micro-Control Unit (MCU), to perform the localization function. The uniquelight fingerprints with complex and tiny differences are caused by the different characteristics of the discrete components usedin lighting devices. Only sound spectrum of light fingerprint is adopted for the identification of the lighting device to reduce thememory size requirement for implementation in a low-cost MCU. So, the grid search is used to optimize the hyperparameters
for the smallest AI model. The system architecture and algorithm development are discussed in this paper, and the experimental
results will be present to show the performance of the proposed system.

Received 13 November 2020
Accepted 09 July 2021
Keywords
Light fingerprint
machine learningindoorlocalization© 2021 The Authors. Published by Atlantis Press International B.V.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
Indoor localization is an issue in may application, such as automated guided vehicle, smart building. The traditional positioning
method, Global Positioning System (GPS), is not workable indoors.
Due to the infrastructure-less characteristics, several indoor localization methods have been proposed. In other side, how to modularize the localization method is another key point for using thetechnology in indoor environment.
It is noted that there have been several literatures on this issue.
A mobile phone was used to sense the light and convert it to the
Received Light Strength (RLS). This RLS would be comparedwith other RLSs on the map, then the position was located by thesimilar RLS [1]. According to Hu et al. [1], Liu et al. [2], Zhao
et al. [3], the spectrometer can be used to analyze the differencein the spectrum of the ambient light, the light detector resistor
can be used as the equipment to sense the light intensity, and acontinuous segment of moving position information can be also
used to achieve the positioning. Four Light Emitting Diodes(LED) placed on the ceiling were used to transmit the encodedand modulated optical signals with position coordinates, and thesignal received from photodiode receiver was analyzed to calculate
the positions of the objects [4–6]. In Hamidi-Rad et al. [7], lightsignals were acquired at high sampling frequency, up to 1 megasamples/second, and then mapped into the frequency domain
by Fast Fourier Transform (FFT). The k-nearest neighbor andConvolutional Neural Net (CNN) classifiers were discussed. The
hardware system is comprised of a high-frequency light sensor,
Corresponding author. Email: wenhung@yuntech.edu.tw*

Analog-to-Digital Converter (ADC) scope, and Raspberry-PI processor to improve the performance of the classifiers. In Kobayashi[8], the microphone block equipped on the mobile phone was used
to sample the light signal sensed by a Photodiode (PD), and thenthe sampled information was sent to the cloud server through the
network. The FFT preprocessing and 1-dimensional CNN were
performed on the server, to achieve the classification missions. Hu
et al. [1] and Chen et al. [4] used other characteristics for localization, and never mentioned about realization. In Hamidi-Rad
et al. [7], a modularized indoor localization system was designed;
however, the designed system included an extra high sampling
rate ADC. The method proposed in Kobayashi [8] only needed theacoustic frequency spectrum of the light, which means that only
lower sampling frequency was required. Notice that all the above
proposed localization algorithms were performed in cloud sever.
The proposed method in this paper is similar to Kobayashi [8]with extension and modification, and how to realize the method
into a module will be detailed.
It should be pointed out that the most studies on the indoor localization focused on the AI algorithm on applications, but few ofthem pay attention to the modularized realization of the indoor
localization. should be revised as “Besides, there are only few literatures on the AI implementation in Micro-Control Unit (MCU) or
System on a Chip (SoC) platform. In [9], a chamfering tool diagnostic AI algorithm was developed and installed into a SOC. A
smart grape with object shape classification function was proposed
and implemented into an MCU in Hung et al. [10]. The experimental results of both researches showed that the end-point machinelearning equipment could provide a complete AI function in realtime. The modularization will be also detailed in this paper.

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2. DATA ACQUISITION
Data acquisition has been an important issue in the machinelearning. How to sample useful data and extract feature to train
AI models has become a key topic. First, the sampling rate needs
to be considered, due to higher sample frequency means higher
cost and larger-memory requirement. Next, data preprocess is also
necessary to extract the key features. FFT is adopted in this paper
to translate sampled data from time domain to frequency domain.
After that, some portions of spectrum will be ignored because ofless features and reduction of memory and calculation.

2.1. Data Acquisition and Feature
Spectrum Extraction		   n f = f s NFigure 1 | The spectrum of the fluorescent tubes 1.

The light spectrums of these two fluorescent lamps are different.
Obviously, the most features of the spectrum locate at low frequency interval. Note, here is a huge spike at 120 Hz no matterlamp 1 or 2, because fluorescent lights is powered by magnetic ballasts with a frequency of 60 Hz (50 Hz in some country) and lamps
flicker with the double frequency. In other word, this phenomenon
might not appear if the lamp is LED based. Moreover, the portion
less than 150 Hz will not be used in localization. In the other side,
there are also some peaks around 32 kHz, this portion near Nyquist
frequency is also ignored. Only the spectrum from 150 Hz to
30 kHz is used to machine learning and localization.

2.2. Rescaling

In proposed system, the ADC, build in MCU is used to sample
the time domain data. First, higher sampling rate and the larger
number of sampling points is selected to confirm the feature
frequency. Then, the sampling rate and point will be optimized
to suitable for modularization, it means to minimize these two
number but keep the feature. The preprocessor is Discrete Fourier
Transform (DFT), to present the frequency feature of the samplingdata. Note, in most cases, the DFT is performed by FFT libraryfunction in development system, and the function also is called inthis project. The formula is shown in Equation (1), X[.] and x[.]represent respectively the frequency and time domain data; k andn denotes the k-th point frequency and n-th point sampling data,
and N is the totally sampling points. The frequency resolution,
Δf, of FFT is as Equation (2), here, fs indicates the sampling rate
(frequency). The typical FFT results of two fluorescent lamps areshown in Figures 1 and 2, the 16,384 points time domain data sampled at 65,536 Hz.
2pnk
N −1 
−iX[k] = ∑  x[n] × e N  , k = 0, 1, …, N − 1 

n=0 

187

For modularization, the artificial intelligence algorithm will be
implemented into an MCU, which calculation and memory capabilities are limited. The less node number of input layer the lessoperation and memory requirement. The reduction of the input
nodes but without losing frequency feature is important. As
shown in Figures 3 and 4, the low frequency spectrum contains

(1)

(2)

Figure 2 | The spectrum of the fluorescent lamp 2.

Figure 3 | The spectrum of 150 to 4000 Hz for the lamp 1.

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188

most information, so frequency rescaling equation will be adopted
to cover original frequency data to the characteristic vector [4].
The frequency axis will be rescaled to power of Ffactor as shown inEquation (3), and the Ffactor is shown as Equation (4). Here, fmax andfmin indicate the frequency range which is used in machine learning,
and NF_target is the target number of reduction. Finally, the amplitude of rescaling spectrum, A[i], is the summation of the amplitude which frequency between two rescaling frequency axis point[Equation (5)].
		 F (i) = (Ffactor )i 

(3)1

 f − f min  N F _ target 	 Ffactor =  max
f min 	   A[i] =Ffactor i £ j * Df £ Ffactor i +1åj

X[ j] (4)

3. IDENTIFICATION ALGORITHM AND GRIDSEARCH FOR HYPERPARAMETERS
3.1. Identification Algorithm
Convolutional neural net is a popular algorithm for classification
due to its ability to learn and extract the features, and less parameter memory and calculation requirement is suitable for modularization [10–13]. 1-D CNN is adopted in this paper due to the resource
limitation in a low-cost MCU, no matter memory or calculation
speed. The structure and calculation diagram of CNN is shown in
Figure 6. The filter Kernel in convolution layer is used to extract
certain features from rescaling spectrum, and the full connected
layers perform the classification, but the pooling layer is skippedin this application.

(5)

The rescaling results of frequency information from the two lamps
are presented in Figure 5. Here, fmax and fmin are set to 150 and
30 kHz, and rescaling points are 400. The rescaling results keep theoriginal features in low frequency spectrum, and high frequency’s
is summed into low resolution form. However, the input data are
reduced 75 times, this rescaling processing will reduce the requirement of memory and calculation dramatically. In other words, it isvery helpful for modularization in a low-cost MCU.

3.2. Grid Search of the Hyperparameter
Grid search is standard skill to get the hyperparameter combinationof the highest accuracy [13], such as CNN Filter Kernel number andsize. Different from general grid search which only focus on accuracy, the memory size of parameter is more important factor for
modularization. The grid search items and the range is expressed inTable 1, and here are totally 240 parameter combination.
The proposes localization system is designed for eight lamps position, and 100 averaged rescaling data is collected from each lamp
for training. To eliminate random errors caused by initial random
weights, every three training results for each parameter combination are averaged. The iteration number set to 1000, and early stopping is adopted to avoid overfitting per training round. The grid

Figure 4 | The spectrum of 150 to 30 kHz for the lamp 2.
Figure 6 | The CNN architecture diagram.
Table 1 | The items and parameters of grid search

Figure 5 | The rescaling result for the lamp 1 and 2.

Item

Parameter

CNN filter Kernel number
CNN filter size
Activation function of fully
connected layer
Node of fully connected layer

1, 2
2, 4, 8, 16, 32, 64, 128, 256
ReLU, Tanh, Sigmoid
64, 32, 16, 8, 4

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189

Table 2 | The top ten results grid searchTrain. acc.

Train. loss

Verified acc.

Filter Kernel

Filter size

Act. func.

Nodes of fully
connected layer

Memory size
(kB)

1
1
1
1
1
1
1
1
1
1

0.000577
0.010547
0.051400
0.010734
0.048613
0.010579
0.085243
0.011359
0.002533
0.010598

1
1
1
1
1
1
1
1
1
1

1
1
1
1
1
2
2
1
1
1

256
256
256
128
128
256
256
64
256
256

ReLU
Tanh
Sigmoid
Tanh
Sigmoid
Tanh
Sigmoid
Tanh
Tanh
Sigmoid

32/16/16
32/16/16
32/16/16
32/16/16
32/16/16
32/16/16
32/16/16
32/16/16
64/32/32
64/32/32

5.96875
5.96875
5.96875
9.71875
9.71875
10.4687
10.4687
11.5937
12.9375
12.9375

search results are sorted by verified accuracy and parameter filesize. In the 240 training results for all parameter combination, thetop 10 results are shown in Table 2.

Table 3 | The parameters of CNN model
Item

Parameter

The parameters of CNN model are detailed in Table 3, the parameters are got from the grid search. The settings of fully connected
layer number and activation function are 3 and Tanh. The numberof output node is eight, its form is likelihood by SoftMAX function.

CNN filter Kernel number
CNN filter size
Activation function of CNN
Node of fully connected layer 1
Node of fully connected layer 2
Node of fully connected layer 3
Activation function of fully connected layer
Node number of output layer
Activation function of output layer1
256 * 1
ReLU
32
16
16
Tanh
8
SoftMAX

4. IMPLEMENTATION
4.1. Hardware
The main target in this paper is a low-cost indoor localization
module. The specification of components used in the proposedsystem is listed in Table 4. The RX65N MCU, provided by Renesas
Electronic Corporation, is used to implement the proposed algorithm. The build-in 2 MB of read-only memory, and 640 KB
random access memory, are enough to install the optimized AI
model, and the high-speed operation with 32-bit floating point
could support the calculation requirement like the FFT and matrix
multiply in ANN.
And the operational amplifier, OPA1612A, is selected to its low
noise amplification function, 1.1 nV/(Hz)1/2. The feature spectrum
could be kept after the amplifier circuit. And its input voltage range
is wide from ±2.25 to ±18 V; its positive powered from the Vcc,
shared the power source with MCU. Besides, negative provide
from the charge pump. The PD used in the system is S6967. It cansense the wavelength range from 320 to 1060 nm, cover the range
from 400 to 800 nm, which range is used in the household lighting equipment. Moreover, the bandwidth of the S6967 is higher to
1 MHz to catch the feature in high frequency.

Table 4 | The hardware specification
Type

Specification

MCU
AmplifierCharge pump
PhotodiodeRX65N R5F565NEDDFPOPA1612A
TPS60403DBVR
S6967

Figure 7 | The spectrum of the fluorescent tubes.

The proposed system is shown in Figure 7, it includes a sensor
circuit, amplifier block and MCU. The sensor circuit consists of aphotodiode and resister, and the amplifier block is implemented
with operational amplifier to perform the gain and filter functions.
The system hardware is present as Figure 8. Note, the hardware is
only for experimentation but not size-optimization yet.
The signal processing flow is indicated in Figure 9, moreover,the processing time of every sub processing block is also shown.
The longest duration is data acquisition. However, it is necessary,
because the data is sampled three times, and there is 16,384 points
sampled at 65,536 Hz every time. The transfer time from time

Figure 8 | The photo of proposed hardware.

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190

Figure 9 | The signal processing flow is dedicated.

domain data to frequency domain may be shorted by faster MCU.
And before localization AI algorithm, the rescaling FFT results ofthree samples are averaged.

4.2. From AI Model to MCU CodeTo run machine learning models on microcontroller, MCU, is
important in this research, and how to translate the AI model to
C code will be the key. “TensorFlow Lite for Microcontrollers” isdesigned to run a TensorFlow AI model on a microcontroller [14],and it is a useful tool. However, the MCU platforms and AI models
supported by this tool is limited, nor includes the RX65N MCU
what is adopted in the proposed system. The alternative is the other
tool, e-AI Translator, provided by the MCU provider, Renesas, for
its productions [15]. It can translate the AI models (.pb or checkpoint) in the C-language code for the Renesas MCU. Moreover, thetool also performs the calculations of the memory size and amountof calculation required by the machine learning model are also
estimated, and the multiply and accumulation number calculation
when the AI model operates.

Figure 10 | Experiment in the EN303 room.

5. EXPERIMENT
5.1. Setup
The experiment was performed in the EN303 room on third floorof the Engineering sixth hall, National Yunlin University of Scienceand Technology, as shown in Figure 10. The proposed module islocated about 1.7 m below lamps, and the numbers in Figure 10
denote the lamps as also in Figure 11. The floor plan is revealed in
Figure 11, which also includes the location of lamps, the distance
between each lamps and from lamp to hall. Note, the ‘X’ marks
indicate the location when testing is below the lamp, and the ‘+’marks shows the position when testing is not below the lamps.

5.2. Results
The estimated likelihoods of classification are shown in Table 5,
the proposed module is placed below the lamps which are marked
by ‘X’ turn-by-turn. The number in the table indicates the most
likelihood lamp number, and the number in the parentheses showsthe estimated likelihood. The experiment ran 10 turns, and every‘X’ point is tested every turn. The localization accuracy performed
100%. The least likelihood in the table locates at lamp 4 in the turn8, however the likelihood still achieved 75.8%, it demonstrated the
localization is correct.
Table 6 presents an interesting estimation results, the average
likelihood of 10 turns is expressed by percentage, when the

Figure 11 | Floor Experiment in the EN303 room.

proposed module is placed at mark ‘+’ position. The two adjacent likelihoods shown in bold are expected to be highest two
number, because the testing position is located between the twolamps indicated in the first line of table. However, the expectation was correct such as position 9, 11 and 13, but incorrect forother points. The reason should be that the sample located theposition 9–14 never be adopted for AI model training and theestimated likelihood is not the interpolation of any two training points. The future work is to adopt such position sample tomachine learning to evaluate the localization effect if not sampled below the lighting source.

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191

Table 5 | Likelihood of classification of the turns
Turn

Lamp1

Lamp2

Lamp3

Lamp4

Lamp5

Lamp6

Lamp7

Lamp8

1
2
3
4
5
6
7
8
9
10

1 (98.8)
1 (98.6)
1 (98.2)
1 (98.5)
1 (97.4)
1 (98.9)
1 (98.6)
1 (98.8)
1 (98.7)
1 (98.9)

2 (98.9)
2 (98.9)
2 (98.9)
2 (98.9)2 (99.0)
2 (99.0)
2 (99.0)
2 (98.9)
2 (98.9)
2 (98.9)

3 (98.9)
3 (97.2)
3 (98.2)
3 (98.6)
3 (91.3)
3 (98.9)
3 (98.5)
3 (96.4)
3 (98.5)
3 (95.0)

4 (97.6)
4 (97.6)
4 (95.3)
4 (93.1)
4 (95.1)
4 (96.4)
4 (94.2)
4 (75.8)
4 (93.9)4 (90.8)

5 (96.2)
5 (98.1)
5 (98.2)
5 (96.9)
5 (97.3)
5 (98.2)
5 (98.2)
5 (98.2)
5 (98.0)
5 (98.1)

6 (98.9)
6 (98.8)
6 (98.8)
6 (98.9)
6 (98.9)
6 (98.8)
6 (98.9)
6 (98.9)
6 (98.8)
6 (98.8)

7 (98.4)
7 (96.5)
7 (98.5)
7 (98.3)
7 (98.5)
7 (98.5)
7 (98.5)
7 (98.5)
7 (98.5)
7 (98.3)

8 (99.1)
8 (99.1)
8 (99.0)
8 (99.1)
8 (98.9)8 (99.1)
8 (99.1)
8 (99.0)
8 (99.1)
8 (98.8)

Table 6 | Likelihood estimation between two lamps
Pos.

Lamp1

Lamp2

Lamp3

Lamp4

Lamp5

Lamp6

Lamp7

Lamp8

9
10
11
12
13
14

28.333
0.113
0.138
0.033
0.011
0.080

26.87980.002
0.054
0.652
1.321
0.034

1.589
7.090
38.109
0.017
0.046
0.004

3.464
0.288
60.799
0.123
0.267
0.193

36.366
0.033
0.490
0.509
0.757
0.657

1.612
10.3780.056
97.52122.136
0.976

1.506
1.790
0.300
0.863
74.460
1.443

0.251
0.306
0.055
0.283
1.001
96.620

6. CONCLUSION
In this paper, a low-cost indoor localization system using soundspectrum of light fingerprint is proposed. To implement this lightfingerprint-based localization indoor mobile equipment, modularization is the key consideration. So, the memory and calculationof the selected MCU are limited by cost and size. A simplified 1-D
CNN is performed to classify the lamps, and the rescaling frequency features only between 100 Hz to 30 kHz are used in classification. Herein, the hyperparameter of the machine learning modelis optimized to find the highest-accuracy and smallest-size model,
after grid search. The experiment results show that the system
could be implemented in a lost-cost MCU with few components.
The on-line tests present the localization accuracy achieve 100%
when the proposed device located below the lamps. However, notas expected, when testing position locates between lamps, the likelihood is not an interpolation of the nearest two lamps. It may be
the future works of this topic.

CONFLICTS OF INTEREST
The authors declare they have no conflicts of interest.

ACKNOWLEDGMENTS
The part of this work was financially supported by Ministry of
Science and Technology, Taiwan, under contract MOST 109-2218E-150-002-, 109-2221-E-224-023-, 108-2221-E-224-045- and 1082218-E-150-004-.

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AUTHORS INTRODUCTION
Prof. Chung-Wen HungHe received the PhD degree in ElectricalEngineeringfrom
National
TaiwanUniversity in 2006. Currently, he is a professor in National Yunlin University of Science& Technology. His research interests include
IoT, IIoT, Power Electronics, Motor Control,and AI Application.

Prof. Hiroyuki Kobayashi
He received PhD degree in Engineering from
Tokyo Institute of Technology, Japan in 2003.
He worked at Keio University and Tokyo
University of Technology, then moved to
Osaka Institute of Technology in 2006, where
he is now working as a professor. His interests
include mobile robots localization, AI andIoT applications.

Mr. Jun-Rong Wu
He received his B.S. in Electrical Engineeringfrom Southern Taiwan University of Scienceand Technology. Since September 2019, heis a graduate student at the National Yunlin
University of Science and Technology. His
research interests include Data Science andArtificial Intelligence Algorithms in auto
matic systems.

Prof. Chau-Chung SongHe received the PhD degree in Electricaland Control Engineering from National
Chiao Tung University in 2001 at Taiwan.
He is currently a distinguished professor in
Department of Aeronautical Engineering,
National Formosa University. His researchinterests include the integration application of networking
information system, the UAV technology, and the cloud network and Cyber-Physical System (CPS).