Engfi Gate: An Indoor Guidance System using Marker-based Cyber-Physical Augmented-Reality

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 8, No. 1, February 2018, pp. 34~42
ISSN: 2088-8708, DOI: 10.11591/ijece.v8i1.pp34-42  34
Journal homepage: http://iaescore.com/journals/index.php/IJECE
Engfi Gate: An Indoor Guidance System using Marker-based
Cyber-Physical Augmented-Reality
Hanas Subakti1
, Herman Tolle2
, Muhammad Aswin3
1,3
Department of Computer Science and Information Engineering, National Central University, Taiwan
1,2
Departement of Electrical Engineering, University of Brawijaya, Malang, Indonesia
Article Info ABSTRACT
Article history:
Received Mar 13, 2017
Revised Jul 11, 2017
Accepted Jul 30, 2017
A guidance system is needed when freshmen explore their new building
environment. With the advancements of mobile technologies, a guidance
system using mobile computing devices such as mobile phones or tablets
could aid freshmen in locating the desired destination with ease. The
proposed system consists of three main subsystems: the marker-based cyber-
physical interaction (CPI) system, the indoor positioning (IP) system, and the
augmented-reality (AR) system. With the help of visible markers and
invisible markers, the CPI system allows the users to do interactions between
the physical and cyber environments; the IP system produces accurate user
position information; the AR system provides the users with good user
experiences. An Android application, named Engfi Gate, is developed to
realize the system design in the test environment. This paper also shows the
comparisons of the proposed system with other related systems. Furthermore,
the design architecture of Engfi Gate system can be used in other location-
based applications.
Keyword:
Augmented reality
Cyber physical system
Guidance system
Indoor positioning
Marker
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Hanas Subakti,
Department of Computer Science and Information Engineering,
National Central University,
No. 300, Jhongda Rd., Jhongli City, Taoyuan County 32001, Taiwan, ROC.
Email: hanas@g.ncu.edu,tw
1. INTRODUCTION
At the early visit, freshmen or new students need navigation guidance to ease them finding their
destination room in a building. The common current physical navigation signs are often confusing and even
more, make them lost inside the building. This problem makes freshman spend more effort to find their
intended destination. In order to address this problem, an interactive guidance system is needed.
In this work, we propose design and implementation of an interactive guidance system that enables
interaction between guidance system, building environment, and freshmen. This system provides general
information such as maps and images of the building, location-based information like directions, and
descriptions of specific places/rooms/facilities for helping new students (or users) explore their department
buildings. This system consists of three subsystems: the marker-based cyber-physical interaction (CPI)
system, the indoor positioning (IP) system, and the augmented-reality (AR) system. The CPI system uses
concept of the cyber-physical system (CPS) which collects sensor data from the physical world and links
them to various information sources for real-time analysis in the cyber environment [1]. The IP system
produces accurate user position updates so that the system can provide the users with proper and timely
location-based information. An AR system has high potential to improve the user experience of applications
in which users must access information that has a direct spatial relation to their immediate environment [2].
An Android application, named Engfi Gate, is developed to realize the system design in the test environment
of the Engineering Building five at the National Central University, Taiwan.

Int J Elec & Comp Eng ISSN: 2088-8708 
Engfi Gate: An Indoor Guidance System using Marker-based Cyber-Physical …. (Hanas Subakti)
35
There are several indoor guidance systems [3-7] are proposed in the literature. Some [3-5] of them
are related to the system proposed in this paper. They are the SmartPDR system [3], the PGPT system [4],
and the SumMap+ system [5]. They will be surveyed and compared with the proposed Engfi Gate system to
show the proposed system’s advantages.
The remainder of this paper is structured as follows: Section 2 introduces three systems that are
most related to the proposed one. Section 3 describes the proposed system design. Section 4 elaborates the
system implementation, and its comparisons with other related systems. Finally, the paper is concluded in
Section 5.
2. RELATED WORK
SmartPDR [3] is a smartphone-based pedestrian dead reckoning (PDR) system that tracks indoor
user's location. SmartPDR employs typical dead reckoning approach, which is widely adopted in localization
systems to solve the locating problems for anonymous places in indoor environments. SmartPDR is based on
the use of accelerometer, magnetometer, and gyroscope in a smartphone, which are able to observe the
pedestrian movement. SmartPDR provides an indoor localization model that computes the displacement
through step event detection, heading direction estimation, and step length estimation. The main difficulty of
the system is coping with situations that the map database is unknown in advance and available sensors are
inaccurate.
The pedestrian guidance for public transportation (PGPT) system [4] is a system for indoor
navigation in public transportation transfer buildings. It is an element of a dynamic seamless mobility
planning and travel guidance application for public transportation networks of metropolitan areas. Indoor
navigation is a key element of multi-modal door to door journeys, especially during transfer events between
public transportation vehicles at complex station buildings. Indoor positioning and navigation services are
realized by various components of an Android smartphone application. The system integrates the sensed data
of cameras, WiFi, GPS, and Bluetooth devices. It also integrates the pressure sensor and the inertial
measurement unit for PDR and 2.5D navigation in arbitrary building structures. Maps of multi-level
buildings are collected from escape/rescue floor maps and OpenStreetMap data are displayed. It provides
indoor routes over multiple levels, elevators, stairs and escalators, which are calculated by the combination of
a route search algorithm and a grid based pathfinding algorithm. The overall objective of the system is to
accomplish a multi-modal door-to-door journey planning and navigating application.
The SunMap+ system [5] running on Android smartphones can aid users to reach their intended
destinations in indoor environments. The system is used as a virtual guide that displays the Sunway
University campus 3D floor map and layout via the AR interface. In order to identify the current location of
the user in the 3D map, the image recognition technique is applied to initialize and identify locations in the
Sunway campus. After user location is determined initially through the visual recognition, SunMap+
continues tracking the user’s movement through the PDR scheme, which is regarded as a complementary
solution of the image recognition localization technique. Navigation paths to guide the users to reach their
intended destinations are also provided along with the 3D floor maps.
3. PROPOSED SYSTEM
The Engfi Gate indoor guidance system architecture is illustrated in Figure 1.
Figure 1. The System Architecture of the Proposed Indoor Guidance System
Invisible Marker
(BLE Beacon)
Visible Marker
(QR code)
Cyber
Physical
Interaction
Shortest
Path Finding
AR Generation
(Vuforia SDK)
User
Localization
Engfi Gate
Engine
Compensated
Weighted
Centroid Indoor
Positioning
Position and Path
Management
Actuators…
HMD
Generation
(Google VR SDK)
Light sensor
Humidity sensor
Marker Sensor
(Camera)
Phone
Display
Marker-Based
Cyber-Physical Interaction
System
Indoor Positioning
System
AR System
Web ServicesDatabases
Server
Marker Sensor
(Bluetooth Receiver)
Head
Mounted
Display
The User
Engfi Gate Guidance System
Internet Users

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 8, No. 1, February 2018 : 34 – 42
36
A marker is something used as a mark or an indication, such as a bookmark and a milestone [8]. The
proposed system uses two types of markers: the visible marker and the invisible marker. Visible markers hold
data in the format of image-based codes, such as the Quick Response (QR) code, which can be recognized by
the camera. Invisible markers hold messages that are sent periodically by wireless communication devices,
such as Bluetooth Low Energy (BLE) devices and IEEE 802.11 WiFi devices, and received properly by user
devices, such as smartphones. In the CPI system, the role of markers is to bridge the cyber and the physical
environments. For example, when a user’s device recognizes a QR code or receives a BLE message properly,
he/she can be aware of his/her position and related spatial information.
The marker-based CPI system gets inputs from visible markers, such as the QR code, and invisible
markers, such as BLE messages sent by the temperature, light, humidity, and sound sensors. So, the system is
marker-based and is aware of environmental conditions. Moreover, the CPI system also connects to
actuators, so it can control physical devices associated with the actuators. The CPI system also connects to
web or game services, which create a virtual environment or web pages corresponding to the physical world.
Internet users can roam in the virtual environment or browse the pages to interact with the physical world and
the Engfi Gate users. Such interactions are similar to those occurring in mixed reality environments [9]. The
web or game services can also access databases, so cyber conditions (e.g., user schedules and profiles) can be
retrieved and stored when user interactions proceed.
The IP system utilizes one or more markers, either visible or invisible, to decide the position of the
user. On the one hand, when a visible marker is recognized, the user is assumed to have the same position as
the marker. On the other hand, when three or more invisible markers are received, the user determines its
position by the WCL algorithm. The IP system also uses Dijkstra’s algorithm to find the shortest path from
the user’s current postion to his/her intended destination, as described later in Section IV.
The AR system provides two display modes: the phone mode and the head mounted display (HMD)
mode. In the phone mode, the guidance information is superimposed into the environmental images and the
user uses buttons on the phone screen to interact with the system. In the HMD mode, user’s phone is
embedded into a HMD device for users to have 3D view of environmental images. The guidance information
looks like floating above the images, and the user use the virtual button to interact with the system.
4. RESULTS AND ANALYSIS
This section describes a results and analysis of implementation of the above-mentioned Engfi Gate
system design, and also shows the comparisons of the system with other related systems.
4.1. Hardware/Software Specifications
The hardware/software specifications of the Engfi Gate system implementation are described below.
The system is implemented on an Android-based smartphone (Asus ZenFone 2 Laser, ZE550KL, running
Android 5.0 Lollipop) supporting Bluetooth 4.0. Some Raspberry Pi B+ devices, equipped with temperature,
humidity, light, and sound sensors, are deployed in a building to sense physical world environmental data.
The Raspberry Pi B+ devices are also equipped with Bluetooth Low Energy (BLE) USB dongles to send out
the sensed data through BLE beacon messages via BLE advertisement packets. Some Seekcy devices (model
M0L) [10], supporting BLE 4.0, are also deployed in the building to broadcast BLE beacon messages for the
purpose of indoor positioning. VR Box Virtual Reality [11], as shown in Figure 2, is used as the HMD.
Unity3D game engine is used to synthesize 3D scenes to be displyed as AR materials. Furthermore, Vuforia
SDK [12] is used for AR generation, while Google Virtual Reality (GVR) SDK [13] is used for the HMD
generation. The Engfi Gate system also involves a web server using the Codeigniter PHP framework and the
MySQL database.
Figure 2. VR Box Virtual Reality Head Mounted Display (HMD) [13].

37
4.2. Marker-based CPI System Implementation
With the help of markers, the marker-based CPI system is performed to connect cyber and physical
environments. There are two types of markers used in the system: visible markers and invisible markers.
4.2.1. Visible Marker: QR Code
In the system implementation of visible marker, image of a QR code is used. we encode the floor
number, X and Y coordinates into a QR code, as illustrated in Figure 3. The encoding is quite simple. There
are three values which are separated by separated by the hash sign “#”. The first value is the floor number,
the second is the X coordinate, and the third value is the Y coordinate. This type of marker is very useful for
determining the position of the user.
Figure 3. The QR Code Encoding of the Floor Number, X and Y Coordinates
4.2.2. Invisible Marker: BLE Beacon Message
There are two types of devices to send out the beacon messages as invisble marker. The first device
is the Raspberry Pi B+ device equipped with a BLE USB dongle, as shown in Figure 4. Such devices are
usually connected with sensors to sense the physical phenomena. The sensed data are embedded into BLE
beacon messages to be sent out. The BLE beacon message, called Pi beacon in our implementation, uses the
first 11 bytes of the BLE advertisement packet to store sensed data. The arrangement of the 11 bytes is as
follows: 4 bytes for the light sensor, 2 bytes for the humidity sensor, 3 bytes for the sound sensor, and 2 bytes
for the temperature sensor. On receiving Pi beacons, the user device (i.e., the smartphone) can retrieve
physical conditions of the surrounding environment and show them on the display. Figure 5 shows the
screenshots showing context information, namely the values of humidity, temperature, lightness, and
loudness, on the upper left corner.
Another device to send out BLE beacon messages is the Seekcy device. The position of the Seekcy
device is embedded into BLE beacon messages. On receiving beacon messages of Seekcy devices, the user
device (i.e., the smartphone) can then know the position of the Seekcy device sending out the beacon
message. This type of device is very useful for accurately determining the user position with the WCL
algorithm, as will be described later.
Figure 4. A Raspberry Pi B+ devcie (left) equiped
with sensors (right)
Figure 5. The Engfi Gate screenshot showing context
information, such as temperature, humidity,
lightness, and loudness, on the upper left corner
4.2.3. Cyber-Physical Communication and Interaction
There are two kinds of users in the Engfi Gate system: physical users and cyber users, as shown in
Figure 6. Physical users use smartphones to run Engfi Gate; they walk into the building and their positions

 ISSN: 2088-8708
38
are tracked by the system. Cyber users use desktop or laptop computers to access the Engfi Gate web page or
virtual environment through Internet. On the web page, cyber users can see physical users in a zone-by-zone
manner and can invite one or more physical users to communicate by texting or speaking. On the contrary, a
physical user can search for a specific cyber user and invite him/her to communicate by texting or speaking.
Figure 6. The Engfi Gate cyber-physical communication and interaction
The cyber-physical communication and interaction system is useful for physical users and cyber
users to communicate and interact. For example, when a new student uses Engfi Gate to explore the
department building, old students can invite him/her to communicate for giving advises or instructions.
Certainly, the new student can invite an old student or even a teacher to communicate for obtaining advises
or instructions. For special events, such as the freshman orientation, the department chair (if he/she cannot
show up in person) can invite all new students to communicate for giving them welcome messages by using
the privileged broadcast or multicast function through the Internet access.
4.3. Indoor Positioning (IP) System
The IP system utilizes either visible markers or invisible markers to determine the position of the
user. The positioning procedures are described below.
4.3.1. Positioning with Visible Markers
When a visible marker or a QR code image is successfully scanned by a user device’s camera, the
system can immediately determine the user’s position by retrieving the position information encoded in the
QR code. Thus, only one QR code can determine the user’s position properly. The accuracy of the
positioning depends on the distance that a QR code image can be scanned and recognized successfully by the
camera.
4.3.2. Positioning with Invisible Markers
When three or more different invisible markers or BLE beacon messages are received, the weighted
centroid localization (WCL) algorithm can be applied to determine the user’s position by using the Received
Signal Strength Indicator (RSSI) values of the messages. Below we describe the algorithm details.
The WCL algorithm [14] requires the user device to receive at the same time the BLE beacon
messages sent by three or more devices. Before describing the algorithm, we introduce some terminologies.
Below, we use the term “anchor node” to refer to a device sending out BLE beacon messages; we use
“unknown node” to refer to a device whose position is unknown and is to be determined by the WCL
algorithm.
After an unknown node receives a BLE beacon message sent by an anchor node i, the WCL
algorithm records the RSSI value RSSIi and the X and Y coordinates ai of the anchor node i, where ai = (xi, yi)
is embedded in the beacon message. After receiving BLE beacon messages from n, n  3, anchor nodes, the
algorithm computes the weight Wi for the anchor node i according to Equation (1).
√( )
∑ √( )
, (1)
Physical Engfi Gate Users
Web (Game) Server
Physical Building
Cyber Engfi Gate Users
Cyber Building

39
where g is the degree which determines the contribution of the RSSI values. As suggested in [14], g is set as
1. However, we set g as 2 after observing the results of extensive experiments. Afterwards, the algorithm
computes the position P of the user according to Equation (2).
∑ (2)
Figure 7 shows the screenshot of the Engfi Gate system showing the user position. The four 3D
green cubes at the corners indicate the position of anchor nodes, and the man-like object stands for the user
position. We perform extensive experiments to measure the positioning accuracy of our implementation of
the WCL algorithm in a room with the length of 9 m, the width of 5 m, and the height of 3 m. Four anchor
nodes are deployed on four ceiling corners, with coordinates (83,140), (85,738), (389,126), and (428,737),
respectively. The value of g is set as 1 or 2 for the sake of comparisons.
We arbitrarily choose 30 positions to perform indoor positioning using the WCL algorithm for
evaluating the accuracy of our implementation. Table 1 shows the experiment results. When g is set as 1, the
smallest error is 21 cm, the largest error is 188.5 cm, and the average error is 96.55 cm. When g is set as 2,
the smallest error is 12 cm, the largest error is 102.5 cm, and the average error is 46.8 cm. We can conclude
that setting g as 2 leads to lower errors and thus higher accuracies.
Figure 7. The Engfi Gate screenshot showing positioning information
Table 1. Positioning Errors for the WCL Algorithm
No Real location Calculation when g = 1 Calculation when g = 2
X(cm) Y(cm) X1(cm) Y1(cm) Accuracy error(cm) X2(cm) Y2(cm) Accuracy Error(cm)
1 97 210 222 375 145 130 169 37
2 110 394 222 450 84 130 494 60
3 92 690 197 575 110 105 719 21
4 408 290 297 225 88 355 244 49.5
5 230 376 272 425 45.5 280 382 28
6 67 230 212 247 81 136 243 41
7 101 373 237 422 92.5 136 418 40
8 25 500 162 422 107.5 136 543 77
9 33 643 212 522 150 136 618 64
10 38 775 187 547 188.5 136 668 102.5
11 141 695 262 497 159.5 136 668 16
12 272 698 312 547 95.5 261 668 20.5
13 318 767 312 572 100.5 286 693 53
14 334 662 387 572 71.5 330 643 11.5
15 450 712 312 612 119 411 718 22.5
16 441 587 287 447 147 361 668 80.5
17 358 510 312 422 67 336 418 57
18 359 429 337 472 32.5 261 493 81
19 408 325 262 322 74.5 386 268 39.5
20 370 233 262 297 86 361 218 12
21 413 100 312 222 111.5 361 143 47.5
22 335 101 337 222 61.5 311 143 33
23 263 140 312 247 78 261 168 15
24 191 60 237 297 141.5 161 168 69
25 100 70 262 272 182 136 168 67
26 278 235 287 322 48 311 193 37.5
27 267 360 262 397 21 236 368 19.5
28 307 464 262 422 43.5 286 418 33.5
29 186 509 287 422 94 236 643 92
30 179 289 262 347 70.5 261 218 76.5

 ISSN: 2088-8708
40
4.3.3. Path Finding and Travel Time Estimation
The Engfi Gate system can find a path for the user to go from his/her current position to the chosen
destination. The path finding task is done by applying the Dijkstra algorithm [15]. Each floor of the 3D map
of the Engfi Gate system is represented as a grid, which is a graph in which a node has four neighboring
nodes. With the help of grids, the path finding job can be achieved. Note that if the user’s current position is
on the floor different from the floor of the destination, the path finding will be achieved by finding a path on
one floor going from the current position to the entry of the elevator or the stairs, as well as a path on the
other floor going from the exit of the elevator or the stairs to the destination. After finding the shortest path,
the time needed to travel is estimated by calculating the ratio of the travel distance to the speed of the user.
Note that the travel time estimation is context-aware in the sense that the system considers the crowdedness
of people to do the estimation. The crowdedness can be derived from the number of users in nearby area if
most students use the Engfi Gate system. Alternatively, it can be derived by referring to the speed of nearby
users. The estimated travel time is helpful for users to decide to hurry up or to slow down when they are
walking. Figure 8 is the screenshot showing the navigation information for reaching the destination with the
estimated travel time on the upper right corner.
Figure 8. The Engfi Gate screenshot showing navigation information for reaching the destination with the
estimated travel time
4.4. Augmented Reality (AR) System
The AR system is in charge of synthesizing images to be shown on the user device’s display. As
mentioned earlier, there are two operation modes: the phone mode and the HMD mode. Below we describe
how the AR system deal with the two modes.
4.4.1. Phone Mode
The Unity 3D game engine and the Vuforia SDK together are used to synthesize 3D scenes and the
graphical user interface (GUI) to be shown on phone display. The screenshot in Figure 5 is for the phone
mode. The user interacts with the system by tapping buttons in the GUI.
4.4.2. HMD Mode
In addition to the Unity 3D game engine and the Vuforia SDK, the Google VR SDK is used in this
mode to split the phone screen into a left part and a right part. The two parts are for the left eye and the right
eye, respectively. Figure 9 shows a screenshot of this mode.
Figure 9. The screenshot of the HMD mode of the Engfi Gate system

41
This mode needs a head mounted device, such as VR Box Virtual Reality, to operate. The user
places his/her smartphone inside the head-mounted device and the user feels like seeing 3D objects with
depth information. This is because the user’s two eyes see slightly different views which are calculated by the
Google VR SDK to generate 3D illusions. Since the user cannot tap the phone which is put into the head
mounted device, this mode uses virtual buttons for the user to interact with the system. The user only needs
to take his/her finger in front of the camera to push or touch virtual buttons for the purpose of interaction.
4.5. Systems Comparison
We compare our Engfi Gate system with existing indoor guidance systems that are most related to
our system, namely the SmartPDR system [3], the PGPT system [4], and the SunMap+ system [5]. The
comparison results are shown in Table 2. By the table, we can observe that Engfi Gate has many advantages
over others. Specifically, SmartPDR and PGPT use 2D GUI, while SunMap+ and Engfi Gate use 3D AR
interface and provide 3D maps. Moreover, Engfi Gate is the only system providing the HMD mode and the
cyber-physical interaction. SmartPDR does not provide shortest path finding, but the others do provide. Engfi
Gate is the only context-aware system, since it considers the context information of crowdedness when
estimating the travel time for a user to reach his/her destination.
Table 2. Comparisons of Engfi Gate and other systems
5. CONCLUSION
The proposed guidance system Android application prototype named Engfi Gate System has been
developed to help freshmen explore Engineering Building V as their new environment. The full demo video
of the system running on an Android smartphone can be found in [16]. Compared with related work, this
system has richer features and technology systems in the guidance system area. This guidance system
consists 3 major components: Marker-Based CPI, IP system, and AR system. A Marker-Based CPI
subsystem brings new interaction between the cyber and the physical environment in the Engfi Gate System.
This subsystem roles are to connect the cyber and the physical environment. The IP system using QR Codes
and Bluetooth beacons achieves a good result. Indoor positioning uses QR codes to do building positioning.
When entering a room, Bluetooth beacons with WCL algorithm performed to gather a good accuracy and
real-time positioning. The room positioning needs more than two Bluetooth beacons achieves 46.8 cm
average accuracy error. Lastly, the freshman physical position information is sent to Engfi Gate system to
update their positions. AR subsystem is implemented because Engfi Gate system runs as the guidance system
in the spatial environment. There are two operational modes in this system: Phone and HMD mode. Phone
mode helps a new student explore building withholding their smartphone. HMD mode makes simpler
interaction by placing smartphone inside HMD device without holding the smartphone. Both operational
modes have its own advantages and disadvantages for freshmen.
ACKNOWLEDGEMENTS
This work was done by join degree collaboration between Department of Electrical Engineering
Brawijaya University with Advanced Computing and Networking Lab Department of Computer Science and
Information Engineering National Central University, Taiwan, under Professor Jehn-Ruey Jiang advise.
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Properties
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[3]
PGPT
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Localization
PDR +
Google Map
PDR + GPS + Camera +
Bluetooth + WiFi
Visual Recognition+ PDR BLE Beacon + QR code
User Interface 2D GUI 2D GUI 3D AR 3D AR & HMD
Cyber-Physical Interaction - - - V
Shortest Path Finding - V V V
3D Map - - V V
Context-Aware - - - V

 ISSN: 2088-8708
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[3] W Kang, Y Han, “SmartPDR: Smartphone-Based Pedestrian Dead Reckoning for Indoor Localization”, IEEE
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2016.
BIOGRAPHIES OF AUTHORS
Hanas Subakti received the MSc degree in Department of Computer Science and Information
Engineering, National Central University, Taiwan in 2016 as an International Dual Degree
Master student between University of Brawijaya, Indonesia and National Central University,
Taiwan. She completed her Bachelor degree in Department of Informatics Engineering,
University of Brawijaya, Indonesia in 2014. Her research interest area in the Augmented Reality
and Advance Computing and Networking.
Herman Tolle is a lecturer in Department of Electrical Engineering University of Brawijaya,
Indonesia. He completed his bachelor's degree in Department of Electrical Engineering
Brawijaya University, master's program in Information Systems Telecommunications ITB and
doctoral degree in Information Science, Saga University, Japan. His areas of expertise and
specialization includes; Web Development, Mobile Development, E-Learning, Multimedia,
Information Retrieval, Human-computer Interaction, Image Processing and Artificial
Intelligence.
Muhammad Aswin is a senior lecturer in Department of Electrical Engineering, University of
Brawijaya, Indonesia. He got Doctor degree from Brawijaya University, Indonesia. He presently
works in signal processing Laboratory, University of Brawijaya. His research interest area in the
areas of signal processing, technology of antenna, distributed systems, and sound processing.

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