The Second International Conference on Electrical ...

Conference Title

The Second International Conference on Electrical and Electronic Engineering, Telecommunication Engineering, and Mechatronics (EEETEM2016)

Conference Dates

February 24-26, 2016 Conference Venue

University of Perpetual Help System DALTA Las Piñas - Manila, Philippines ISBN

978-1-941968-30-7 ©2016 SDIWC

Published by

The Society of Digital Information and Wireless Communications (SDIWC) Wilmington, New Castle, DE 19801, USA www.sdiwc.net

Table of Contents

Dispersed Remote Vehicle Diagnostic System …………………………………………………………………………… 1 Modeling and Simulation for Orientation Control of an Under-actuated Drill Machine …………….. 8 Time Synchronization Algorithm for Asymmetric Optical Fiber Communication Link ………………… 15 Microcontroller - Based Embedded System for Drowsiness Detection and Vibration Alertness … 21 Block Based Technique for Detecting Copy-Move Digital Image Forgeries: Wavelet Transform and Zernike Moments ……………………………………………………………………………….. 26 Analysis and Performance Evaluation of Convolutional Codes over Binary Symmetric Channel Using MATLAB …………………………………………………………………………………………………………….. 34 A Study on the Stratospheric Environmental Factors in the Philippines that Affects the Quality of Data Gathered in Launching Near Space Balloon ………………………………………………… 46 Development of a City Planning Simulation System in the AR Space …………………………………………. 50 Development of the Intuitive and Seamless Synthetic System of the Interior and Exterior in a Building by Using AR ……………………………………………………………………………………………… 55 Prototyping an Intuitive Circuit Simulator Manipulated by Hand Gestures for Elementary Physics Education …………………………………………………………………………………………………… 61

Proceedings of The Second International Conference on Electrical and Electronic Engineering, Telecommunication Engineering, and Mechatronics, Philippines 2016

Dispersed Remote Vehicle Diagnostic System YoungJin Go1,a, ByongOk Jung2,b, Buhm Lee3,c, Wangrim Choi4,d, JaeHak Shim1,e, HwangWoo Byun1,f and Kyoung-Min Kim3,g,* 1 Dept. of Electrical Automation Eng., Suncheon Jeil College, Suncheon City, Republic of Korea 2 SOLWORKS CORP., YongIn City, Republic of Korea 3 Dept. of Electrical and Semiconductor Eng., Chonnam National Univ., Yeosu City, Republic of Korea 4 Dept. of Biomedical and Electronic Eng., Chonnam National Univ., Yeosu City, Republic of Korea a [email protected], [email protected], [email protected], [email protected], e [email protected], [email protected], [email protected] *Corresponding author

ABSTRACT There are a large number of devices that conduct diagnosis for abnormality of vehicle in the market. However, those devices support only C-CAN signal among the vehicle signals; therefore, it is not possible to diagnose parts that use B-CAN signal. A majority of vehicle parts generate analog signal in order to operate sensor and actuator. On that account, it is essential to collect electric signal for an accurate diagnosis. However, it is required to connect a number of individual devices in order to diagnose by using the existing equipment; thus, the level of reliability of collected data will be reduced. Moreover, it is also required for an user to undergo data extraction process manually in order to extract valid data from the collected data. As a result, it takes a lot of time and the efficiency of overall inspection and verification will be reduced. Those conventional vehicle diagnostic devices are used when a vehicle is not running. However, the frequency of abnormal signal of an actual vehicle is high while a vehicle is running. The time of occurrence is not uniform either. In general, the critical part is to monitor multiple vehicles simultaneously and finding abnormality of vehicle in this situation in terms of improving the quality of vehicle substantially. This paper developed the dispersed remote vehicle diagnostic system that would collect and analyze CCAN, B-CAN and analog signal. Also, this study secured the reliability of vehicle data with the differentiated performance from the conventional equipment through the synchronization with CAN communication and analog signal. The developed diagnostic system is able to diagnose abnormality of

ISBN: 978-1-941968-30-7 ©2016 SDIWC

parts and search the cause hereof through the linked analyzed of synchronized AI signal and the segmented diagnosis. It is also able to reduce the unit cost of a purchaser by integrating a large number of individual diagnostic devices. Moreover, it is installed in an actually running vehicle as a system allowing for a small-scale long-hour test. As a result, it is possible to identify the cause in a daily life. It is also believed that it can contribute to the development of vehicle and improvement in research reliability since it facilitates a remote test on vehicle status for a long time.

KEYWORDS Vehicle, Diagnostic, Disperse, Remote, CAN

1 Introduction Recently, primary parts manufacturers have been a variety of safety related issues due to abnormal signals in a vehicle. These abnormal signals are mainly caused by an increase in the amount of data in the network for vehicles with an increase in the number of electronic devices in a vehicle. Table 1. Classification for each type of communication Chassis CAN

Body CAN

Analog Signal

CANcommunication to transmit and receive driving information

CANcommunication transmitted/received by the module used in vehicle body

Basic input and output signal of all electrical equipment of vehicle

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The following information shows the essential signals for reading the abnormal signals in a vehicle.

synchronization is established conventional method.

with

the

1.1 C-CAN (Chassis-CAN) It is possible to acquire C-CAN data through the equipment called vehicle diagnostic equipment or vehicle diagnostic device. One can conduct self-diagnosis of a vehicle through connecting this equipment to OBD (On Board Diagnosis) port of a vehicle. Some of the prominent domestic devices thereof include 'Carmanscan' of Nextech and 'G-Scan' of GIT. C-CAN is able to measure airbag control module, parking guide module, vehicle diagnostic module, electronic parking brake module, tire pressure monitoring module, lane departure detection module, smart cruise control module, ABS control module, etc[1]. 1.2 B-CAN (Body-CAN) B-CAN data cannot be used as a vehicle diagnostic device due to rate difference. Thus, a separately manufactured device based on individual CAN data collection devices has been used so far. Some of these individual CAN data collection devices include ‘VN Series’ of Vector and ‘NI-CAN 8473’ of National Instruments. The modules to be measured by B-CAN are driving seat door module, passenger seat door module, power trunk module, steering tilt/telescopic module, smart key control module, etc[1]. 1.3 AI (Analog Signal) It is collected by using an oscilloscope or DAQ (data acquisition) equipment. As for vehicle parts, C-CAN, B-CAN and analog signals are generated simultaneously when they are running. However, it is difficult to conduct accurate analysis with the conventional method since it measures C-CAN, B-CAN and analog signals separately. That is to say, no proper data

ISBN: 978-1-941968-30-7 ©2016 SDIWC

Figure 1. Example of Measurement Signal of Equipment for Vehicle.

Those conventional vehicle diagnostic devices are used when a vehicle is not moving. However, there is actually a high occurrence rate of abnormal signals when a vehicle is moving. Thus, it is required to conduct a study on diagnosing problems of a vehicle accurately and determine presence of abnormality. The vehicle diagnostic method only based on the existing C-CAN information allowed for only one-directional diagnosis. However, the method proposed in this thesis makes it possible to diagnose abnormality of parts and search the cause hereof through the linkage analysis of synchronized AI signal and the segmented diagnosis. It also allows us to integrate those individual diagnostic devices. As a result, it will likely allow buyers to reduce unit cost. In addition, it is the system that allows for a small-scale long-hour test. Thus, it can be installed in an actually running vehicle in order to identify the causes in everyday life. That is to say, it makes it easier to conduct a remote test of vehicle status for a long time; thus, it is expected to contribute to developing a vehicle and improving research reliability. 2 Trend of Vehicle Analysis Device CAN, which can be regarded as vehicle network standard, was first proposed by Robert Bosch of Germany in SAE (Society of Automotive Engineers) in 1986[2][3]. In 1988,

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Bosch and Intel produced the vehicle network system. In 1991, CAN protocol 2.0 was developed. In 1992, Mercedes Benz released the car that employed CAN. ISO11898, the ISO international standard specification, was revealed in 1993[4]. However, it was not still possible to conduct analysis perfectly using only CAN. Thus, those signal measuring instrument used for the repair work of electronic units installed in a vehicle generally leverage an oscilloscope. Also, it is possible to analyze the waveform of detected signals easily through the screen. In contrast, it is not appropriate to analyze a large number of signals since the number of channels is fixed at 2 or 4. As for the signal detector having a different shape compared to an oscilloscope, light bulb tester is leveraged for the maintenance work of a vehicle. This light bulb tester detects whether the voltage of measurement point is on or off. As a result, it allows us to verify signal status by naked eyes. Nonetheless, the number of channels is fixed at one. Also, it is unstable to operate a vehicle due to a distortion of sensor signal when detecting sensor signal directly from the operating mode of a vehicle. Moreover, it makes it hard to measure those high speed pulse signals. The diagnostic devices have been improved to such an extent as to release OBDII. As a result, it is now possible to measure CAN signals generated from a vehicle. The number of electronic devices for vehicle has increased substantially in recent years. Therefore, there is a large quantity of data in a network for vehicle. Also, there is a high degree of demand for stability and reliability. On that account, even those arbitrarily established methods have emerged. Some of them are EOBD and KOBD. The United States in which OBD was first proposed made CAN compulsory with the communication method of OBD-II in all vehicles produced since 2008. In South Korea, Hyundai Motors has supported only CAN method in all vehicles produced since 2008. Even though there exist the five methods of OBD-II communication, the connectors have been unified to J1962[5][6][7].

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3 Controller Area Network(CAN) Communication 3.1 Features CAN Communication CAN communication is the serial network communication method designed for communication between micro controllers. Thus, it is an economic and reliable communication method through which multiple CAN devices can communicate with each other. However, it controls multiple ECUs with a single CAN interface; thus, it is able to reduce the overall cost and weight of a vehicle and improve the system control rate and reliability. Moreover, each device has CAN controller chip; thus, it is able to control each system efficiently. CAN is the standard protocol for both ISO (International Standards Organization) and SAE (Society Automotive Engineers). It conducts multi master communication. All of CAN controllers perform the role of a master; thus, they can be utilized when desired. Since only two lines are used, the length of wire to be used is short since there is almost no addition of line even though many controllers share bus. Moreover, it provides plug and play so that CAN controllers can be connected to and disconnected from bus. It has priority and it makes it convenient to apply distributed control of ECU. Also, it can selectively receive only those set IDs and it can conduct communication in distance of about 1km[8]. 3.2 CAN Hardware Structure CAN is not interfered by electromagnetic wave. However, when electromagnetism collides directly with bus, bus lines on both sides will be affected and there will be EMI (Electro Magnetic Interference). For impedance matching for preventing it, resistance treatment of 120R shall be conducted at the end node when configuring the node.

3


Figure 2. CAN BUS

3.3 Classification of CAN Communication H-CAN uses voltage of 2.5V to 3.5V and it conducts high-speed communication of more than 200Kbit/sec. L-CAN uses voltage of 1.5V t 2.5V and it is used with the speed between 33.33Kbit/sec and 125Kbit/sec. Table 2. Rate of CAN Communication Communication Method

Voltage Level

Communication Rate

H-CAN

2.5V~3.5V

Higher than 200Kbit/Sec

L-CAN

1.5V~2.5V

33.33Kbit/Sec ~125Kbit/Sec

Also, it should be within the values allowed by line resistance and line impedance. Lastly, line opening/closing and ground resistance must be in compliance with the pre-determined range. Furthermore, it is imperative to eliminate other failure causes separately from wiring due to the phase of CAN and arbitration mechanism. To prevent signal reflection, CAN bus should be terminated with one 120-Ohm resistance on both ends. In addition, the maximum bus length should not be exceeded. As mentioned before, it is required to eliminate additional causes of failure that would affect the operation and reliability of system through review when setting it. Of those, such cases as clear failure and confusion of signal lines, etc. cause communication problem. Moreover, the entire common-mode voltage and signal level, which were transmitted, are measured; therefore, it is also imperative to secure an sufficient level of quality of transmitted signals[9]. First, signals shall be placed within the range of 1.5V to 3V in accordance with the standard (ISO 11898-2, high speed), and VCAN_L shall be higher than -2V and VCAN_H shall be less than 7V as common-mode voltage. In particular, any failure caused by signal quality often cause serious problems that would result in a significant amount of cost to solve the causes hereof. 4 Proposed Vehicle Diagnostic System 4.1 Classification of CAN Communication

Figure 3. H-CAN, L-CAN Voltage level comparison

There are a variety of factors behind an occurrence of failure in CAN system. Of those, some of the most prominent factors include wiring of components, accurate endpoint of bus and compliance with the maximum bus length. Wiring should have no electrical connection between lines.

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Previously, a majority of measurement and data transmission works have been conducted only by using C-CAN. However, B-CAN is not equipped with a vehicle diagnostic module; thus, B-CAN communication module cannot easily conduct failure diagnosis just like CCAN module. As for B-CAN module, a vehicle maintenance technician should conduct dissemble work for failure diagnosis. Also, it is required to diagnose a failure with those

4


individually manufactured failure diagnosis for each B-CAN communication module or check analog signals generated by each B-CAN module by using such an analog signal test equipment as multi-meter or oscilloscope. It is required to extract valid data or signal manually out of the collected data or signals when a vehicle maintenance technician diagnoses a failure of B-CAN communication module by using an individually manufactured failure diagnostic equipment or analog signal test equipment. On that account, it has a lot of time consumption and it is inefficient. Also, the efficiency and reliability of overall inspection and verification work will be lowered. Moreover, such CAN communication modules as C-CAN module and B-CAN module are not able to generate analog signal through their own actuator (or sensor) when they are broken down. As a result, the corresponding actuator that could not receive the above-mentioned analog signal could not be operated even though it is in normal mode. In this case, the corresponding actuator might be unnecessarily repaired or replaced if a vehicle maintenance technician misjudges it as a failure. To solve the aforementioned problem, it will be required to analyze a failure after synchronizing analog signal and CAN signal. However, the current system stores CAN signal and analog signal separately for analysis. Thus, it has a difficulty of determining presence of abnormality for signals in the same time slot.

Figure 4. Holistic configuration diagram of dispersed remote vehicle diagnostic system

ISBN: 978-1-941968-30-7 ©2016 SDIWC

4.2 Program design for abnormal signal of vehicle

detection

of

Fig. 5 is the graph displaying the result of failure diagnosis. If CAN communication module outputting CAN signal and analog signal is normal, then analog signal is changed simultaneously as CAN signal is changed as shown in Fig. 5 (a) during the synchronization collection period, in other words, the predetermined time interval (t). Also, analog signal can be changed a certain time delay after CAN signal is changed as shown in Fig. 5 (b).

Figure 5. Result of Failure diagnosis

On the other hand, analog signal will not be changed even though CAN signal is changed as shown in Fig. 5(c) during the synchronization collection period, in other words, the predetermined time interval (t) when CAN communication module that generates CAN signal and analog signal is failed. Also, CAN signal is not changed even though analog signal is changed as shown in Fig. 5(d). As can be seen above, it is not necessary to use the conventional type of portable vehicle diagnostic device that was mentioned in the previous

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technologies, the failure diagnostic device produced separately for each communication module or such analog signal test devices as multi-meter and oscilloscope. As a result of detecting a change in the above CAN signal and analog signal that were collected during the synchronization collection period, in other words, the pre-determined time interval from the aforementioned signal collection device, the above diagnostic device determines that CAN communication module, which generates CAN signal and analog signal, is broken down unless a change in either of the two signals is not detected. Therefore, it is possible to diagnose promptly, accurately and easily a failure of all CAN communication modules installed in a vehicle regardless of whether it is a chassis-CAN communication module connected to a vehicle electronic control device through chassis-CAN or the above body-CAN communication module connected to a vehicle electronic control device through body-CAN regardless of the type of CAN that connects a vehicle electronic control device to CAN communication module.

The proposed instrument is an embedded system with a FPGA-based Real Time OS.

Figure 7. The front of system

Figure 8. The back of system

5 System Design LED indicating power status and error status is installed at the front side in order to identify the status of corresponding equipment. The back side is designed in a way that B-CAN, C-CAN and analog 24 channel can be used.

Figure 9. The inside of system

The system was used NIsbRIO, NI9853, NI9221 and LabVIEW. Table 3. Specification of system

Figure 6. Dispersed remote vehicle diagnostic equipment

ISBN: 978-1-941968-30-7 ©2016 SDIWC

NI sbRIO – 9626 Chassis

NI 9853 Module

NI 9221 Module

-Real time OS -Analog Input 16ch -Analog Output 4ch -Spartan-6 LX45 FPGA -40 ~ 85

-5VDC voltage -2 CAN Port -1 Mbits/s -Sleep / Wakeup mode

-Analog Input 8ch --60v ~ 60v range -12bit sampling rate 100 KS/s/Ch

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6 Conclusion The system developed in this thesis was designed to determine the presence of abnormality in a vehicle by receiving data through LTE network in addition to identifying abnormality after collecting vehicle related data. The technical configuration and attributes of the developed system are as follows. 1) It has the function of performing the status monitoring and saving of a vehicle with voltage signal by collecting both high-rate network signal and low-rate network signal of a vehicle. 2) The technical features of the above device operates and saves with its own battery while measuring vehicle data and electrical signal in a synchronized state. Also, it has the function of transmitting data using LTE network.

[4]

ISO 11898: Road Vehicles, Interchange of Digital Information – Controller Area Network(CAN) for High-speed Communication, 1992.

[5]

P.S. Park, M. G. Park, G. W. Kim, S. B. Park, J. Y. Lee and J. I. Jung, “Development of the ISO 15765based Integrated On-board Diagnostics Protocol Conversion Algorithm with System,” KSAE Spring Conference Proceedings, pp.1428-1433, 2011.

[6]

S. Lee, M. H. Kim and K. C. Lee “Survey on Invehicle Network System Researches,” Journal of the Korean Society for Precision Engineering, Vol.23, No.9, 2006, pp.7-14.

[7]

J. K. Park, “North America OBD-II Laws,” Auto Journal, KSAE, Vol.22, No.4, 2000, pp.39-43.

[8]

S. K. Lee, J. Y. Lee, D. H. Kim, K.J. Choi and J. I. Jung, “CAN Communication System Using CAN Protocol,” Transaction of KIICE, Vol.13, No.1, 2006, pp.1423-1426.

[9]

P. E. Dumont, A. Aitouche, R. Merzouki, M. Bayart, “Fault Tolerant Control on an Electric Vehicle”, Industrial Technology, ICT2006, IEEE International Conference on, 2006, pp.2450-2455.

3) It can be leveraged in all vehicle models. It utilizes the software based on the analysis algorithm in the data verification process. It is designed to minimize the required time in the verification process and to allow users to secure the expandability of vehicle signal (CAN signal). Acknowledgment This study was supported by the Ministry of Trade, Industry and Energy(MOTIE) through the Regional Innovation Centre Programme. REFERENCES [1]

K. Etschberger, Controller Area Network: Basics, Protocols, Chips and Applications,” IXXAT Automation GmbH., in press.

[2]

D&K I. C. Technology, “Multi-CAN Analyzer & Converter User’s Manual,” unpublished.

[3]

I. J. WON “An Analysis of Effect of Electric Wave on Automobile CAN Communication,” Master’s Thesis, Sungkyunkwan University, Seoul, Republic of Korea, p61, 2009.

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Modeling and Simulation for Orientation Control of an Under-actuated Drill Machine Atif Ali* and Mohammad Bilal Malik Department of Electrical Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan * Corresponding Author. +92-321-3657000. Email: [email protected]

ABSTRACT Modelling for Orientation control of a Drill Machine belonging to a class of under actuated system is presented. The orientation control along x-axis and y-axis of the drill machine is achieved through a single pair of electromagnetic poles. Based on the concept of pulse width modulation a controlling signal for the actuators which caters for all practical limitations of the system is generated. The derived discrete time equivalent model overcomes the practical limitation of control techniques used for such class of systems. It also opens a new dimension especially for under-actuated and non-linear systems which are otherwise difficult rather impossible through orthodox control schemes. A closed form based on discrete time equivalent model supported by simulation results will be helpful in designing overall feedback control for the orientation of the system.

improvements including addressing of practical problems, improve efficiency, decrease wastage in terms of materials, time and maintenance cost etc, to carryout performance and error analysis in existing and already developed techniques [1,2,3,4]. The extensive use of multi-axis drilling in the industry [5,6] especially in the field of precision machinery for the drilling of soft materials, PCBs or even drilling on the curved surfaces where the drill bit should be kept perpendicular to the surface is foremost important. A similar application was discussed in [7], where a two axis drill machine was used. The drill bit is free to move along X & Y axis as shown in figure 1, while it spins or rotates about its z-axis. The movement in X & Y axis are executed or implemented by electromagnetic

KEYWORDS Under Actuated System, Control, Pulse Equivalent Area, Pulse width Modulation

1. INTRODUCTION Multi-axis machining is almost two to three decades old but with innovative ideas, challenging machining requirements and invention of new type of materials keeps this technological field still fertile and green for scientist and engineers for further research and experimentation. The ideas are not limited only for exploring new & innovative and versatile means of machining but field is open and rather more importantly to carry out further

ISBN: 978-1-941968-30-7 ©2016 SDIWC

Figure 1. Under-actuated Drill Machine [7]

windings on the stator and permanent magnets mounted on the rotor which in this case is drill bit itself. The magnetic field produced by the

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poles of stator winding interacts with that of permanent magnets mounted on the drill bit produces a torque which in turns is used for changing the drill orientation. The stator poles are excited at a particular phase in every revolution. Due to the mounting and weight limitations a single pair of electromagnets was used and therefore the desired orientation is achieved only by using single actuation thereby reducing the dual axis actuation to single for a two degree of freedom control. Hence the overall system reduces to under-actuated system as the degrees of freedom are greater than the number of control inputs. Although the control of actuator as a rotor from the excitation of the stator windings is a well-established technique [8,9,10,11]. But all these techniques are based on fully actuated principles. The control of under-actuated systems is challenging and that is the reason it is one of the favorite topics in field of controls and dynamics [12,13,14,15,16,17,18]. However mostly these systems are time invariant but here the system under discussion is time variant. The approach of [7] utilizes discrete-time controller to determine the magnitude and the phase of actuation pulse for the desired orientation of the drill bit on the basis of sampled state feedback. A discrete-time equivalent model was developed by considering one complete revolution of drill bit as a discrete step. The model was derived in discrete system which makes it easier for implementation and overcoming under actuation limitations. Although the solution provided by [7] is very precise and effective in controlling the orientation, but has a major practical limitation that the duration of the pulse is fixed and the level or intensity of control force is translated into the magnitude of the pulse. In physical system such unlimited amplitude is not available as it leads to saturation. On the other hand if a constraint is put on the maximum amplitude of control signal then this will pose serious limitation on the control input which restricts the overall movement of the drill and leads to

ISBN: 978-1-941968-30-7 ©2016 SDIWC

stability issues too. The proposed remedy to this limitation is the use of a Pulse Width modulation technique [19, 25], in which a pulse of varying width having fixed amplitude is used. This resulted in overcoming the control effort limitation as well as provides a smooth control for precise movement of the drill bit. The remainder of the paper is organized as follows: section II covers the structure and construction of the drill machine followed by system modeling in section III. In section IV, the already established techniques which were and can be applied. Section V covers the discrete time equivalent model, and the conclusions are drawn in Section VI followed by references. 2. STRUCTURE & CONSTRUCTION The plant is a small drill machine and its construction is shown in the figure 1[7]. It spins or rotates about its z-axis while the up & down and right & left movements are about x-axis and y-axis respectively. Due to the mounting limitations mainly because of its miniature size only a single pair of magnets is used. As the drill bit is rotating therefore both axis can be excited but only one axis can be excited at one time, thus converting the fully actuated drill machine into an under actuated system. Detailed description of drill machine was explained in detail [7]. The stator is a cylindrical body comprising of single phase winding while the rotor is a permanent magnet mounted on a spherical joint which is allowing its motion

Y’

Y

X’ X

Z, Z’

Figure 2. Frames of References

about the three axes. The two frames of reference one that is attached with the rotor (𝑋 ′ , 𝑌 ′ , 𝑍 ′ ) and the other with the stator (𝑋, 𝑌, 𝑍)

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are shown in the Figure 2. When the stator coil is energized at specific roll instants a torque about the 𝑋 ′ axis of the rotor is generated. This results the rotation of drill bit about its 𝑌 ′ axis. As the rotor is spinning at ω radians per second therefore due to precession phenomenon the applied torque in one axis causes a motion in the axis which is perpendicular to itself and to the spin axis as well.

as 𝑢1 &𝑢2 . Finally the state space representation in stator frame of reference is given in (5). 𝑏

𝐻

𝑥2̇ =

𝐽

𝑢2

(5)

𝑢(𝑡) = 𝜏 𝑏

−𝐽 𝐻

𝐴=

𝐽

1 [ 0

(2)

𝐽𝜃̈𝑥 + 𝑏𝜃̇𝑥 + 𝐻𝜃̇𝑦 = 𝑘𝑖 𝜏 𝑐𝑜𝑠𝜔𝑡 (3)

The combined moment of inertia of the drill bit and permanent magnet along 𝑋 & 𝑌 axes is assumed to be same because of axis symmetry and is denoted by 𝐽 and 𝐽𝑧 about Z axis respectively. Whereas 𝑏 & 𝐾𝑖 represents the coefficient of friction and torque constant. H being the angular momentum is given by the following relationship:(4)

To represent (3) in state space form, 𝜃𝑥 &𝜃𝑦 are represented as 𝑥3 & 𝑥4 and 𝜃̇𝑥 &𝜃̇𝑦 as 𝑥1 & 𝑥2 . The applied torques 𝜏𝑥 &𝜏𝑦 are represented

ISBN: 978-1-941968-30-7 ©2016 SDIWC

(6)

𝑦(𝑡) = 𝐶 𝑥(𝑡)

Substituting (2) in (1) we have (3)

𝐻 = (𝐽𝑧 − 𝐽)𝜔

𝐽

The (5) can be written in matrix form (6), the output vector 𝑦(𝑡) is formed by 𝑥3 & 𝑥4 states and the input 𝑢(𝑡) is given by (7).

(1)

𝜏𝑥 = 𝑘𝑖 𝜏 𝑐𝑜𝑠𝜔𝑡

𝐽𝜃̈𝑦 + 𝑏𝜃̇𝑦 − 𝐻𝜃̇𝑥 = 𝑘𝑖 𝜏 𝑠𝑖𝑛𝜔𝑡

𝑘𝑖

𝑢1

𝑥̇ (𝑡) = 𝐴 𝑥(𝑡) + 𝐵 𝑢(𝑡)

𝜃𝑥 &𝜃𝑦 are the angular position of the bit about 𝑋 & 𝑌 axes respectively while 𝜏𝑥 & 𝜏𝑦 are the applied torques in stator frame of reference. Due to single pair of pole is mounted on the rotor, the applied torque available in stator frame is given by (2) 𝜏𝑦 = 𝑘𝑖 𝜏 𝑠𝑖𝑛𝜔𝑡

𝐽

𝑥4̇ = 𝑥2

𝐽𝜃̈𝑥 + 𝑏𝜃̇𝑥 + 𝐻𝜃̇𝑦 = 𝜏𝑥 𝐽𝜃̈𝑦 + 𝑏𝜃̇𝑦 − 𝐻𝜃̇𝑥 = 𝜏𝑦

𝑏

𝑥1 – 𝐽 𝑥2 +

𝑘𝑖

𝑥3̇ = 𝑥1

3. MATHEMATICAL MODELING A spinning drill bit with a high angular rate about z-axis resembles two degrees of freedom gyroscope, therefore following model of gyroscope [20] is used :-

𝐻

𝑥1̇ = − 𝐽 𝑥1 – 𝐽 𝑥2 +

𝐻

−𝐽

0 0

𝑏

(7) 𝐾𝑖 𝐽 𝐾𝑖

𝑐𝑜𝑠𝜔𝑡

0 0 , 𝐵 = 𝐽 𝑠𝑖𝑛𝜔𝑡 , 0 0 0 0 [ 0 ] 1 0 0] 0 0 1 0 𝐶=[ ] (8) 0 0 0 1

−𝐽

Using the transformation (9) the stator frame of reference is transformed into rotor frame of reference (11) by changing the state variables for time variant systems [22] through a rotation matrix (10). The output vector 𝑦̅(𝑡) comprises of angular positions 𝜃𝑥 & 𝜃𝑦 in stator frame of reference. 𝑧(𝑡) = 𝑇 −1 (𝑡) 𝑥(𝑡)

(9)

Where 𝑐𝑜𝑠𝜔𝑡 −𝑠𝑖𝑛𝜔𝑡 [ 0 0

𝑇 −1 (𝑡) = 𝑠𝑖𝑛𝜔𝑡 0 𝑐𝑜𝑠𝜔𝑡 0 0 𝑐𝑜𝑠𝜔𝑡 0 −𝑠𝑖𝑛𝜔𝑡

0 0 ] (10) 𝑠𝑖𝑛𝜔𝑡 𝑐𝑜𝑠𝜔𝑡

10


̅ 𝑧(𝑡) + 𝐵̅ 𝑢(𝑡) 𝑧̇ (𝑡) = 𝐴 𝑦̅(𝑡) = 𝐶̅ (𝑡) 𝑧(𝑡)

(11)

where −

𝑏 𝐽

𝐻 𝐴̅ = +𝜔 𝐽 1 [ 0

−

𝐻 −𝜔 𝐽 𝑏 − 𝐽 0 1

0

0

(𝐾+1)𝑇

0

0

0 𝜔

−𝜔 0 ]

∫𝐾𝑇

𝐽

1 0 ] 0 1

(12)

The output vector 𝑦̅ comprises of angular positions 𝜃𝑥 & 𝜃𝑦 in rotor frame of reference. 4. PULSE EQUIVALENT AREA The approach used in [7] for orientation control of the drill bit was based on state space approach of a discrete time equivalent model. The practical limitations of provision of unlimited magnitude, saturation and stability 𝑢(𝑡) T 𝑢𝑘 (K-1)T

KT

𝑢(𝑡) 𝑑𝑡 = 𝑈𝜎𝑘

(13)

Where 𝑈 is pulse amplitude, 𝑇 is the PEA internal, 𝑢(𝑡) is the given control signal to be converted into pulse signal and 𝜎𝑘 is the pulsewidth to be determined.

𝐾𝑖

0 0 𝐵̅ = 0 , 𝐶̅ = [ 0 0 0 [0]

that even when two input signals have different waveforms, they can still results in the similar outputs if they have the same areas [21]. When this concept is applied to a PWM signal whose pulse width is to be modified to achieve a PEA equivalent of a given control signal the relationship is governed by following [21]:-

Although PEA concept is practically implementable but is based on approximation. Another limitation is of high modulation frequency as the averaging response becomes closer to the given control signal at high frequencies. The higher model formulas are more difficult to convert using PEA concept [23, 24]. Even the minimum pulse width which cannot be ignored is a major limitation for developing exact PEA signal. At last some rules of thumb need to be established for setting design parameters to obtain desired and more accurate PEA signal. In the light of above two techniques which 𝑈

(K+1)T

KT

𝜎𝑘

(K+1)T

Figure 3. PEA based Pulse width Determination [21]

issues are serious in nature. Another probable approach for such a system is to use the Principle of Equivalent Areas (PEA) for Pulse width modulation (PWM) scheme. The PEA implies

ISBN: 978-1-941968-30-7 ©2016 SDIWC

have specific limitations a novel technique based on PWM principle is developed which is explained in the next section.

11


KT

(K+1)T III

II

I

α

Pw

‖𝑢(𝑡)‖ ∆

time t

T Figure 4. Single actuation cycle of period T

difference in approach from the [7] where a pulse with fixed width having varying amplitude was used for the actuation.

5. EQUIVALENT MODEL IN DISCRETE TIME Figure 4 depicts one actuation cycle of period T applied for one complete revolution of drill bit. Where ∆ defines the shift of rectangular pulse center from the start of revolution. It is actually the representation of phase of actuation. Whereas α is the fixed amplitude of the pulse. Pw defines the width of rectangular pulse and varies according to the requirement of the control signal magnitude on the lines of Pulse width modulation concept. This marks the Interval I : 𝐾𝑇 → (𝐾𝑇 + ∆ −

𝑃𝑤 2

) the states at (𝐾𝑇 + ∆ −

𝑥 (𝐾𝑇 + ∆ − Interval II : (𝐾𝑇 + ∆ −

𝑃𝑤 2

Interval III : (𝐾𝑇 + ∆ +

𝑃𝑤 2

𝑃𝑤 2

𝑃𝑤 2

𝑃𝑤 2

) using [22] are given by (14)

𝑃𝑤

) = 𝑒 𝐴(𝐾𝑇+∆− 2 −𝐾𝑇) 𝑥(𝐾𝑇)

) → (𝐾𝑇 + ∆ +

𝑥 (𝐾𝑇 + ∆ +

Using the input signal u(t) characteristics of figure (4) a closed form is built by considering one complete revolution of the drill bit from time KT to (K+1)T. This model is developed by dividing the signal into three separate intervals i.e., interval I, II and III. It must be noted that during interval I and III the system is unactuated.

𝑃𝑤 2

(14)

)using result of (14) we have (15) 𝑃𝑤

𝑃𝑤

𝑃𝑤

2 ) = 𝑒 𝐴𝑃𝑤 𝑒 𝐴(∆− 2 ) 𝑥(𝐾𝑇) + ∫−𝑃𝑤 𝑒 𝐴( 2 −∅) 𝑑∅𝐵𝑈

(15)

2

) → 𝑥((𝐾 + 1)𝑇) we have (16) 𝑃𝑤

𝑥((𝐾 + 1)𝑇) = 𝑒 𝐴(𝑇−∆− 2 ) 𝑥(𝐾𝑇 + ∆ +

𝑃𝑤 2

)

(16)

Substituting values from (15) into (16) we get:𝑃𝑤

𝑥((𝐾 + 1)𝑇) = 𝑒 𝐴𝑇 𝑥(𝐾𝑇) + 𝑒 𝐴(𝑇−∆) ∫ 2𝑃𝑤 𝑒 −𝐴∅ 𝑑∅𝐵𝑈 −

(17)

2

After further simplifying (17) we get:𝑥((𝐾 + 1)𝑇) = ̃ 𝑒 𝐴𝑇 𝑥(𝐾𝑇) + 𝑒 𝐴(𝑇−∆) (𝑃𝑤 +

ISBN: 978-1-941968-30-7 ©2016 SDIWC

𝐴2 𝑃𝑤 3 24

+

𝐴4 𝑃𝑤 5 1920

) 𝐵𝑈

(18)

12


Position X-axis

200 Actual Plant Derived Model 0

-200

-400

0

0.1

0.2

0.3

0.4

0.5 Time

0.6

0.7

0.8

0.9

1

0.4

0.5 Time

0.6

0.7

0.8

0.9

1

Position Y-axis

200 Actual Plant Derived Model

150 100 50 0

0

0.1

0.2

0.3

Figure 5. Comparison of Actual Plant with Derived model (18)

Thus (18) describe the states at time (T+1) for a specific Pw and ∆. This was also verified from a simulation results as shown in figure 5, when same input signal is given to the drill bit model (8) and its derived solution (18).

REFERENCES [1]

[2]

6. CONCLUSION A closed form in discrete time domain is presented for orientation control of an underactuated drill machine. PWM approach was used to overcome the practical limitation of controlling signal. Technique of averaging the control force over one revolution e.g., Principle of Equivalent Areas (PEA) is avoided to achieve precise control. The derived model is also verified by comparing it with actual model through simulations. The derived model is useful for designing the feedback control system for orientation control. An pulse equivalent area based novel technique [26] has already been developed for orientation control of similar model under discussion since the typical control techniques [27,28,29,30,31] are not applicable for this class of model. This derived model will be useful in developing fast optimization schemes for this technique.

[3]

[4]

[5]

[6]

[7]

ISBN: 978-1-941968-30-7 ©2016 SDIWC

C. Criens, F. Willems, M. Steinbuch, “Under actuated air path control of diesel engines for low emissions and high efficiency”, Proceedings of the FISITA 2012 World Automotive Congress, Vol. 189, pp 725-737, 2013. F. Farivar et.all, “Synchronization of Under actuated Unknown Heavy Symmetric Chaotic Gyroscopes via Optimal Gaussian Radial Basi Adaptive Variable Structure Control”, in IEEE Transactions on control systems technology, Vol.. 21, No. 6, pp. 2374-2379, 2013. W. Dong, G. Y. Gu, X. Zhu, H. Ding, “Solving the boundary value problem of an under-actuated quadrotor with subspace stabilization approach” in Journal of Intelligent & Robotic Systems, 2014. DOI 10.1007/s10846-014-0161-3. G. He, Z.Geng, “Dynamics and robust control of an under actuated torsional vibratory gyroscope actuated by electrostatic actuator”, IEEE/ASME Transactions on Mechatronics, 2014, DOI: 10.1109/TMECH.2014.2350535. Y. Sagara, N. Hori, “Experimental verification of roll-angle control using air-jet pulse actuation”, The 2nd International Conference on Computer and Automation Engineering (ICCAE), Vol. 5, pp. 418-422, 2010. J. He, F. Gao, D. Zhang, “Design and performance analysis of a novel parallel servo press with redundant actuation”, International Journal of Mechanics and Materials in Design, Vol. 10, Issue 2, pp 145-163, 2014. M. B. Malik, Fahad M. Malik, and Khalid Munawar, "Orientation control of a 3-d under-

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[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

actuated drill machine based on discrete-time equivalent model," International Journal of Robotics and Automation, 10.2316/Journal.206.2012.4.206-3324,2012. E. Anderson and M. Anderrs,”A Three Axis Torque Motor of very High Steady State and Dynamic Accuracy”, in Proc. IEEE Power Tech. Conf. Stockholm, pp. 304-309, 1995. R. Bedereson, R. Wallace and E. Schwartz, “A Miniature Pan-Tilt Actuator; The Spherical Pointing Motor”, Trans. Robot. Automat, vol. 10, pp. 298-308, 1994. K. Kaneko, I. Yamada and K. Itao, “A Spherical DC Servo Motor with Three Degrees of Freedom”, Trans. ASME, J. Dyn. Syst. Meas. Control, vol. 3, pp. 398-402, 1090. J. Wang, G. W. Jewell and D. Howe, “Analysis Design and Control of a Novel Spherical Permanent Magnet Actuator”, IEE Proc. Elec. Power. Appl. vol. 145, no 1, pp. 61-71, 1998. A. M. Bloch, M. Reyhanoglu and N. H. McClamroch, “Control and Stabilization of Nonholonomic Dynamic Systems”, IEEE Trans. Automatic. Control, vol. 37, No11, 1992. M. W. Spong, “The control of under-actuated mechanical systems”, First International Conference on Mechatronics, Mexico City, 1994. R. Olfati, Saber, “Normal Forms for Underactuated Mechanical Systems with Symmetry”, IEEE Trans. on automatic Control, Vol. 47, No. 2, Feburary, 2002 R. Olfati, Saber, “Nonlinear Control of Underactuated Mechanical Systems with Application to Robotics and Aerospace Vehicles”, PhD Dissertation, Massachusetts Institute of Technology, February, 2001. N. P. I. Aneke, “Control of Under-actuated Mechanical Systems”, PhD Dissertation, Eindhoven, The Netherlands, 2003. A. M. Bloch, “Non-holonomic Mechanics and Control”, Springer Science Business Media, LLC, 2003. F. M. Malik, “Sampled-Data Control Based On Discrete-Time Equivalent Model”, PhD Dissertation, National University of Sciences and Technology, 2009. Barr, Michael. "Pulse Width Modulation," Embedded Systems Programming, September 2001, pp. 103-104. C. T. Leondes, “Guidance and Control of Aerospace Vehicles”, McGraw-Hill Book Company, 1963, University of California Engineering and Sciences Extension Series. T. Suzuki, Toru Ueno, and Noriyuki Hori, “Experimental Verification of PEA-Based PWM Control Using On-Off Type Air-Jet Thrusters,”

ISBN: 978-1-941968-30-7 ©2016 SDIWC

[22] [23] [24]

[25]

[26]

[27] [28]

[29] [30] [31]

Proceedings of the International Conference on Information and Automation, December 15-18, 2005, Colombo, Sri Lanka. W. J. Rugh, “Linear System Theory”, 2nd Edition, Prentice Hall, 1996. R. E. Andeen, “The principle of equivalent areas,” Trans. AIEE, Vol.79, pp. 332-336, 1960. T. Sakamoto and N. Hori, “New PWM schemes based on the principle of equivalent areas,” IEEE International Symposium on Industrial Electronics, L'Aquila, Italy, pp. 505-510, 2002. T. Sakamoto, N. Hori and T. Ueno, “Closed-loop control using a low-frequency PWM signal,” Proc. IEEE Methods and Models in Automation and Robotics, Miedzyzdroje, Poland, pp. 627-632, 2003. A. Ali and M. B. Malik, “Pulse Equivalent Area based Orientation of a Class of Under-Actuated System under Constraints”, Under review in Mehran University Research Journal Of Engineering & Technology. C. T. Chen, Linear Systems: Theory and Design. Saunders College Publishing, third edition, 1984. G. F. Franklin, D. Powell, and M. L. Workman, “Digital Control of Dynamical Systems”, AddisonWesley, 1996 K. Ogata., “Modern Control Engineering”,5thEdition, Prentice Hall, 2009 H. Khalil., “Nonlinear Systems”, 3rd Edition, Prentice Hall, 2001 J. E. Slotine and W. Li.,“Applied Nonlinear Control”, Prentice Hall, Englewood Clis, NJ, 1991.

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Time Synchronization Algorithm for Asymmetric Optical Fiber Communication Link Peng Liangfu College of Electrical Information Engineering, Southwest University for Nationalities No.16, South 4th Section, 1st Ring Road, Chengdu, Sichuan, China, Post Code 610041 [email protected] ABSTRACT To solve the problem of delay asymmetry for IEEE 1588v2 implementation in a two-fiber bidirectional network, many methods have been proposed, such as the optical time-domain reflectometry method, the optical fiber switching method, and the singlefiber bidirectional transmission method, etc. However, none of the existing methods are suitable for an asymmetric optical fiber link with an optical amplifier or optical regenerator. We present a new time synchronization algorithm for IEEE 1588v2 to calculate the time offset in an asymmetric optical fiber link with an optical amplifier or optical regenerator. The new algorithm is based on the timestamp mechanism of IEEE 1588v2, and uses wavelength conversion to produce stamped messages, which are exchanged between the master and slave to enable the slave to calculate the time offset. The formula of the time offset between the master and slave is deduced, and the detailed steps and process are given. The new algorithm is not only suitable for a two-fiber bidirectional optical communication system, but also for the Wavelength Division Multiplexer system.

KEYWORDS IEEE 1588v2, asymmetric delay, optical amplifier, optical regenerator, Wavelength Division Multiplexer system.

1 INTRODUCTION The distribution of timing information is a key problem in the synchronization of the optical telecommunication network. At present, IEEE 1588v2 time synchronization technology, also called Precision Time Protocol (PTP), can

ISBN: 978-1-941968-30-7 ©2016 SDIWC

distribute time information with submicrosecond accuracy and low-cost implementation in Packet Transport Networks (PTN). Its applications and enhancements have been widely studied [1], [2]. However, many transmission networks currently used are in reality two-fiber bidirectional networks. The asymmetry caused by the unequal length of the two fibers in the transmitting and receiving directions, will directly affect the precision of IEEE 1588v2 time synchronization. The asymmetric length of two optical fibers in an up/down link is a difficult problem to overcome for IEEE 1588v2 [3], [4]. Therefore, achieving automatic compensation of asymmetric delay is very important and worth studying for the IEEE 1588v2 standard. This paper attempts to address these issues. A solution is proposed, involving a new algorithm to accurately estimate the offset of the time between the master clock and the slave clock. The new algorithm can not only be applied in a network environment where the lengths of the optical fibers are not symmetrical, but also can be applied to an asymmetric optical link with an optical amplifier or optical regenerator. 2 TIME SYNCHRONIZATION PROBLEM OF ASYMMETRIC OPTICAL FIBER LINK The time transfer accuracy is affected not only by the packet transmission delay variation, but also by the asymmetry in transit delay in the master-to-slave and slave-to-master directions. Asymmetry of the communication link is

15


caused by asymmetric operation modes (e.g., asymmetric digital subscriber line, ADSL), and asymmetric physical links (e.g., optical fiber or twisted pair). Asymmetric optical links can be divided into two cases: one is when the data rate in the upstream and downstream directions is not symmetrical, and the other is when the physical length of the uplink and downlink is not symmetrical. For the case of asymmetric data rate, the transmission rate downstream is usually greater than the transmission rate upstream. Many studies have deeply examined this case, and proposed various ways to compensate for the offset error due to the dynamically changing data rate of the link, and to enhance the accuracy of time synchronization [4], [5], [6], [7], [8]. The primary reason for asymmetric physical length is that the uplink and downlink length are never equal, even in the same fiber-optic cable. The case of asymmetric physical length has received much more thought and involved several research achievements in recent years. Key methods created by experts to address asymmetric delay due to physical length include the following: (1) The optical time domain reflection (OTDR) method, in which the transmission time delay is measured using the back scattering effect of the propagation of a light pulse in a fiber [9], [10]; (2) The method of switching two optical fibers to reverse the direction of time transfer, in which the communication mode of optical signal transfer in a single fiber is half duplex through using an optical switch[11], [12]; (3) The method of two-way optical time transfer in a single fiber, which uses Wavelength Division Multiplexer (WDM) technology in a single fiber, to support bidirectional transmission for the measurement of transmission time delay [13], [14]. Although each of the above methods for an asymmetric optical link has its own unique features, all of them have obvious shortfalls, because all the existing methods measuring asymmetric delay include a theoretical

ISBN: 978-1-941968-30-7 ©2016 SDIWC

presumption, that the optical signal allows twoway transport in a single fiber. Obviously, this assumption is only theoretically true and not in accord with actual conditions. None of the existing methods could be applied to an asymmetric optical link involving an optical amplifier or optical regenerator. This article discusses a time synchronization method targeting the above aspects, to calculate the time offset and enhance the accuracy of time synchronization. 3 SOLUTION FOR ASYMMETRIC DELAY OF FIBER LINK WITH OPTICAL AMPLIFIER OR REGENERATOR To extend the signal transmission distance, optical amplifiers and optical regenerators are most often used in optical fiber communication systems for signal amplification and shaping. An optical amplifier can be used not only in long distance backbone networks, but also in Wavelength Division Multiplexer (WDM) systems. Because the optical signal can be transmitted in only one direction when an optical amplifier or optical regenerator is applied (see Figure 1), none of the existing methods for asymmetric delay are applicable, and thus we need a new method to address this case.

TX

EDFA L1

EDFA

RX

Master RX

Slave REG

L2

REG

TX

Figure 1. Optical communication system with optical amplifier and regenerator.

We know that IEEE 1588v2 is the precision time protocol (PTP) between the master and slave clocks for clock synchronization in networked measurement and control systems. It is a master-slave synchronization protocol.

16


After a master-slave hierarchy has been established, timestamp messages are exchanged between the masters and slaves, to enable the slave to measure the time on the master clock. In this paper, we examine the timestamp mechanism and master-slave clock synchronization mechanism from IEEE 1588v2, and propose a new synchronization algorithm based on an additional algorithm, to accurately estimate the offset of the time between the master clock and slave clock, requiring no assumption of the symmetrical link delay. The new algorithm can be applied to an asymmetric optical link with optical amplifier or regenerator. We call it the method of wavelength conversion. Its principles and steps are as follows: Step 1: The master clock and slave clock exchange time messages on the communication path linking the master and the slave. First, the master sends a time message over fiber link L1 to the slave using wavelength λ1, and then, the slave sends a time message over fiber link L2 to the master using wavelength λ1, as shown in Figure 2. Thus, we can obtain the following equations: (1) T 1  t2  t1  DL11_ MS  offset T 2  t4  t3  DL21_ SM  offset

(2)

where t1,t3 are the respective timestamps when the master and the slave send their time messages; t4,t2 are the respective timestamps when the master and the slave receive the time messages; DL11_ MS is the link transmission delay from the master to the slave, when transmitting signal light of wavelength λ1 over fiber link L1; DL21_ SM is the link transmission delay from the

Master

Slave Timeline

t1 L1

t 2 –t 1 =T1

1

DL1_ MS

DL21 _ SM

t2 t3 t 4 –t 3 =T2

L2 t4

Figure 2. Schematic of step one describing the method of wavelength conversion.

Step 2: First, the master sends a time message over fiber link L1 to the slave using wavelength λ2, and then, the slave sends a time message over fiber link L2 to the master using wavelength λ2, as shown in Figure 3. Thus, we can obtain the following equations: (3) T 3  t6  t5  DL12_ MS  offset T 4  t8  t7  DL22_ SM  offset

(4)

where t5,t7 are the respective timestamps when the master and the slave send their time messages; t8,t6 are the respective timestamps when the master and the slave receive the time messages; DL12_ MS is the link transmission delay from the master to the slave, when transmitting signal light of wavelength λ2 over fiber link L1; and DL22_ SM is the link transmission delay from the slave to the master, when transmitting signal light of wavelength λ2 over fiber link L2. Master

Slave Timeline

t5 L1

t 6 –t 5 =T3

2

DL1_ MS

slave to the master, when transmitting signal light of wavelength λ1 over fiber link L2; and offset is the time synchronization deviation of the slave clock with respect to the master clock.

DL22_ SM L2

t6 t7 t 8 –t 7 =T4

t8

ISBN: 978-1-941968-30-7 ©2016 SDIWC

17


Figure 3. Schematic of step two describing the method of wavelength conversion.


(6)

where t9,t11 are the respective timestamps when the master and the slave send their time messages; t12,t10 are the respective timestamps when the master and the slave receive the time messages; DL11_ MS is the link transmission delay

where t13,t15 are the respective timestamps when the master and the slave send their time messages; t16,t14 are the respective timestamps when the master and the slave receive the time messages; DL12_ MS is the link transmission delay from the master to the slave, when transmitting signal light of wavelength λ2 over fiber link L1; and DL21_ SM is the link transmission delay from the slave to the master, when transmitting signal light of wavelength λ1 over fiber link L2. Master t 13 L1

Master

Slave Timeline

DL1_ MS

1 DL1_ MS

DL22_ SM L2

t 10 –t 9 =T5 t 10

L2

t 15 t 16 –t 15 =T8

t 16

Figure 5. Schematic of step four describing the method of wavelength conversion.

Eq.(10) can be obtained by adding Eq.(3) and Eq.(4): (10) T 3  T 4  DL12_ MS  DL22_ SM

t 11 t 12 –t 11 =T6

t 12

Figure 4. Schematic of step four describing the method of wavelength conversion.


DL21 _ SM

t 14

Eq.(9) can be obtained by adding Eq.(1) and Eq.(2): (9) T 1  T 2  DL11_ MS  DL21_ SM

t9 L1

t 14 –t 13 =T7

2

from the master to the slave, when transmitting signal light of wavelength λ1 over fiber link L1; and DL22_ SM is the link transmission delay from the slave to the master, when transmitting signal light of wavelength λ2 over fiber link L2.

Slave Timeline

(8)

ISBN: 978-1-941968-30-7 ©2016 SDIWC

Eq.(11) can be obtained by adding Eq.(5) and Eq.(6): (11) T 5  T 6  DL11_ MS  DL22_ SM Eq.(12) can be obtained by adding Eq.(7) and Eq.(8): (12) T 7  T 8  DL12_ MS  DL21_ SM It should be noted that the optical signal’s propagation delay time is different when it is transmitted in a fiber at different wavelengths. Let us denote the ratio of propagation delay time when the optical signals are transmitted in fiber link L1 at wavelength λ1 and at wavelength λ2 by R1, and let R2 be the ratio of

18


propagation delay time when the optical signals are transmitted in fiber link L2 at wavelength λ1 and at wavelength λ2. Thus, we can obtain the following equations: (13) R1  DL11_ MS / DL12_ MS R 2  DL21_ SM / DL22_ SM

(14)

In practice, R1 and R2 can be considered equal because L1 and L2 are usually two optical fibers in the same fiber-optic cable. Let R=R1=R2. Then R is given by (15) R  DL11_ MS / DL12_ MS  DL21_ SM / DL22_ SM Subtracting Eq.(11) from Eq.(9) and using Eq.(15), we have (16) (T 1  T 2)  (T 5  T 6)  ( R  1) DL22_ SM Subtracting Eq.(12) from Eq.(9) and using Eq.(15), we have (17) (T 1  T 2)  (T 7  T 8)  ( R  1) DL12_ MS Dividing Eq.(16) by Eq.(17) yields 2 (T 1  T 2)  (T 5  T 6) DL 2 _ SM  (T 1  T 2)  (T 7  T 8) DL12_ MS

(18)

By substituting Eq.(18) into Eq.(10), the time deviation of the slave clock with respect to the master clock, can be obtained as follows: offset  (T 3  T 4)(T 1  T 2)  T 3(T 5  T 6)  T 4(T 7  T 8) 2(T 1  T 2)  (T 5  T 6)  (T 7  T 8)

(19)

Now that we have derived the formula to calculate the time offset of the slave clock with respect to the master clock, based on Eq.(19), we can calculate the correct value of the offset in the slave clock for an asymmetric fiber link, with an optical amplifier or optical regenerator. The slave clock adjusts its time to minimize the offset value, thereby synchronizing with the master clock. It is easy to determine from the derivation of the formula, that the sequence of the four steps (from step 1 to step 4) is not fixed. In general, the four steps can be performed in any order without changing or affecting the final deduced result of Eq.(19).

ISBN: 978-1-941968-30-7 ©2016 SDIWC

4 CONCLUSION The conventional IEEE 1588 synchronization algorithm assumes symmetric communication links, and makes errors in calculating the time difference between the master clock and the slave clock for asymmetric links. Most approaches proposed to accurately estimate the time offset between the master clock and slave clock involve clock synchronization based on measurement of the asymmetric link delay, but these approaches are not suitable for an asymmetric link involving an optical amplifier or optical regenerator. In this paper, we have proposed a new method to calculate the time offset by changing the operating wavelengths of the optical signal between the uplink and downlink, and we have presented a time synchronization algorithm based on the timestamp mechanism and the master-slave clock synchronization mechanism of IEEE 1588v2. Although the wavelength of the optical signal is changed, the transmission direction of the optical signal does not change. Therefore, the new algorithm is fully applicable to the asymmetric link with optical amplifier or optical regenerator. It can be used not only in an optical communication system where the length of optical fiber is not symmetrical, but also in a WDM system. The proposed method and model can easily be integrated into IEEE 1588v2 or other similar protocols, as it only influences the processing of the collected data, not the basic mechanism of message exchange. Our future work is to try the new method we proposed in this paper to do an experiment. ACKNOWLEDGEMENTS The author appreciates the financial support from Science & Technology Department of Sichuan Province (No. 2014GZ0015). REFERENCES [1]

R. Subrahmanyan, “Implementation Considerations for IEEE 1588v2 Applications in Telecommunications,” 2007 International IEEE

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[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

Symposium on Precision Clock Synchronization (ISPCS) for Measurement, Control and Communication, Vienna, Austria, pp.148-154, October 2007. L. Xie, Y. Wu, and J. Wang, “Efficient Time Synchronization of 1588v2 Technology in Packet Network,” 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN), Xi’an, China, pp.181-185, May 2011. R. Subrahmanyan, “Timing Recovery for IEEE 1588 Applications in Telecommunications,” IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 6, pp.1858-1868, June 2009. S. Lv, Y. Lu and Y. Ji, “An Enhanced IEEE 1588 Time Synchronization for Asymmetric Communication Link in Packet Transport Network,” IEEE Communications Letters, vol. 14, no. 8, pp.764-766, August 2010. S. Lee, “An Enhanced IEEE 1588 Time Synchronization Algorithm for Asymmetric Communication Link using Block Burst Transmission” IEEE Communications Letters, vol. 12, no. 9, pp.687-689, August 2008. Z. Du, Y. Lu and Y Ji, “An Enhanced End-to-End Transparent Clock Mechanism with a Fixed Delay Ratio” IEEE Communications Letters, vol. 15, no. 8, pp.872-874, August 2011. S. Lee, S. Lee and C. Hong, “An Accuracy Enhanced IEEE 1588 Synchronization Protocol for Dynamically Changing and Asymmetric Wireless Links” IEEE Communications Letters, vol. 6, no. 2, pp.190-192, February 2012. R. Exel, “Mitigation of Asymmetric Link Delays in IEEE 1588 Clock Synchronization Systems” IEEE Communications Letters, vol. 18, no. 3, pp.507-510, March 2014. Zhongxing Telecommunication Equipment Corporation, “A synchronization system, method and optical module of master and slave to detect the asymmetry of optical fiber”, invention patent, patent number: CN201210472244.0, Nov. 20th,2012.

[13]

[14]

华为技术有限公司, “光纤不对称时主从设备间的时间偏差获取方法和通信系统”,发明专利,专利号: CN201110384844.7, 2011.11.28 X. Lai, B. Zhang, L. Lu and C. Xie, “Estimation of Transmission Delay in the Accurate Time Transfer System Based on Optical Link”, ACTA OPTICA SINICA, vol. 28, no.12, pp.170-173, 2008. 赖先主,张宝富,卢麟,谢畅, “高精度光纤链路授时估算”,光学学报,vol. 28, no.12, pp.170-173, 2008. A. Imaoka, M. Kihara, “Accurate time and frequency transfer method using bidirectional WDM transmission”, IEEE Transactions on Instrumentation and Measurement, vol. 47, no. 2, pp.537-542, April 1998.

中兴通讯公司, “检测光纤非对称性的同步系统、方法及主从光模块设备”,发明专利,专利号: CN201210472244.0, 2012.11.20 Zhongxing Telecommunication Equipment Corporation, “A time synchronization device and method for automatic detection of asymmetry of optical fiber”, invention patent, patent number: CN201210484075.2, Nov. 23rd, 2012. 中兴通讯公司, “自动检测光纤非对称性的时间同步装置和方法”,发明专利,专利号: CN201210484075.2, 2012.11.23 France Telecom, “A technique used to determine the propagation delay time of optical signal between two optical devices”, invention patent, patent number: CN201180058577.2, Oct. 3rd, 2011. 法国电信公司, “用于确定经光链路的两个光学设备之间的光信号的传播延迟的技术”,发明专利,专利号: CN201180058577.2, 2011.10.03 Huawei Technologies Co., Ltd., “A method and communication system to obtain time offset between master and slave devices when optical fiber is asymmetrical”, invention patent, patent number: CN201110384844.7, Nov. 28th, 2011.

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Microcontroller-Based Embedded System for Drowsiness Detection and Vibration Alertness Garcia, S. M. P., Santos, C. L. B., Calungsod M.J., Javier P.J.L., Duñas L.M.O., Acosta, E.G. Jr. University of Perpetual Help System – Dalta, Philippines Alabang-Zapote Avenue, Pamplona 3, Las Piñas City, 1740 [email protected]/[email protected]

ABSTRACT Microcontroller – Based Embedded System for Drowsiness Detection and Vibration Alertness is a prototype that helps in indicating an individual who has the signs of drowsiness. The purpose of this study is to design and develop a microcontrollerbased embedded system that can detect drowsiness and that can alert the nurses through vibration alertness. Shift workers like nurses often have the tendency to fall asleep thus also showing a lack of energy and a sense of fatigue due to drowsiness during working hours particularly at night or graveyard schedules. According to the proponents’ survey that was conducted for the nurses from Callejo Medical Clinic, 14 out of 15 nurses (93.33%) said that drowsiness affects their duties. This paper aims to develop a proposed study to produce a hardware that consists of a (1) Si1143 Proximity Sensor that would detect the pulse rate of a nurse and an embedded (2) PIC16F689 Microcontroller in which it would process the vibration when the pulse rate drops below its normal condition (i.e. 59 BPM and below). MPLAB X IDE v.3.10 was used as the software for programming, the microcontroller.

KEYWORDS Drowsiness, Microcontroller, Ambient Measurement, Pulse Oximeter, Toplexil

Light

1 INTRODUCTION The development of microcontroller-based embedded systems is also the development of technologies for detecting or preventing conditions like drowsiness. Drowsiness is a

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medical condition and sleep disorder wherein an affected individual begins to feel tired or sleepy in an unsuitable time and place on an unusual way (e.g. having drowsiness while at work, having drowsiness while driving on the road). Compared to normal shift workers, night shift workers such as nurses are commonly affected by drowsiness, making them prone to accidents and mistakes. A microcontrollerbased embedded system can be a countermeasure that has the function to prevent such events that are occurring on a real time environment. Microcontroller-based embedded system can monitor various parameters (e.g. pulse rate on the wrist) that are necessary in order to detect drowsiness. In case when the drowsiness is detected according to the measurement, sensible signals and movements (e.g. vibrations) are issued to alert the user. For the time being, microcontroller-based embedded system for drowsiness detection is used commonly for drivers. The capability of implementing microcontroller-based embedded system to other industries such as hospitals and nurses is of greater degree for the prevention of conditions like drowsiness. The aim of the study will be positioned on the design and development of the prototype that uses a microcontroller-based embedded system for nurses to improve their efficiency during work, and to lessen the human errors when considering drowsiness.

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2. Microcontroller-Based Embedded System for Drowsiness Detection and Vibration Alertness The study is primarily focused and used only for nurses from medical institutions. It emphasizes on drowsiness detection that can sense the pulse rate of the nurse using a Si1143 Proximity Sensor and on vibration alertness that can produce vibration through the use of a PIC16F689 microcontroller-based embedded system that is programmed in MPLAB X IDE v3.10 software. The vibration of the prototype will trigger only if the nurse manifests drowsiness or the pulse rate of the nurse reaches below sixty beats per minute (60 BPM). The Beats-Per-Minute (BPM) rate of the prototype has a normal condition of sixty (60 BPM) – eighty five (85 BPM) and has a drowsiness condition of fifty (50 BPM) – fifty nine (59 BPM). The pulse rate that will display in the prototype is only from fifty (50) BPM up to eighty five (85) BPM.

Inter-Integrated Circuit iIs a bus register that is used for transferring data that was initiated by a master device that controls the SCL (Clock line). Slaves device can only send data if it responded to the master (i.e. it cannot start a transfer of data over the I2C bus without the master). I2C commonly has two (2) wires, SCL (Clock) is used to initiate all the transfer of data over the I2C bus, and SDA (Data) is the line of data. The function of this component is to be the host interface of the Si1143 proximity sensor together with another line which is the INT (Interrupt) to interrupt the host processor, and its purpose is for the Si1443 to transfer and receive data that would control the operational state of the device.

3 HARDWARE DESIGN The fabrication of the prototype’s hardware is to first consider the components which are the key to operate the functions of the prototype and then to create its design. The diagram below illustrates the required components and its process to complete the prototype.

Figure 2. Inter-Integrated Circuit Si1143 Proximity Sensor is a 3.3 voltage chip power that features proximity measurements. The function of the Si1143 proximity sensor is to detect the pulse rate coming from the device through the use of light measurements and it is also provided with a wearable ground strap.

Figure 1. Conceptual Paradigm

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4 Software Design

Figure 3. Si1143 Proximity Sensor PIC16F689 microcontroller is an integrated circuit that contains programmable features such as input and output peripherals. The function of the PIC16F689 microcontroller is that when the pulse rate reaches below sixty (60) BPM coming from the device, it would then be processed in its program that will activate the vibration alarm.

Figure 4. PIC16F689 microcontroller

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After initializing the input of the components, if it’s true, the Si1143 Proximity Sensor will calculate the pulse rate and will update the display. If it’s false, the process will stop. Figure 38 illustrates on how to calculate the pulse rate. The baseline is at eighty five (85) BPM. The program will detect the pulse rate, if it’s true, the BPM will be calculated, if the BPM is below the lower threshold or the minimum value of the normal condition, the Vibrator Motor will be activated for vibration. If it’s false, the program will keep on reading the values coming from the sensor

Figure 5. Software Design

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4.1 GENERAL PROCEDURAL STEPS

REFERENCES 1.

“Microcontroller-Based Heart Rate Measurement from Fingertip” http://embeddedlab.com/blog/?p=1671

2.

Chin,Che,Bor,Chao,Chi and I (2010). “RealTime Wireless Brain-Computer Interface (BCI) System for Drowsiness Detection” http://ieeexplore.ieee.org/xpl/abstractAuthors.js p?tp=&arnumber=5456232&url=http%3A%2F %2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp %3Farnumber%3D5456232

3.

Vitabile, Paola and Sorbello (2011). “A RealTime Non-Intrusive Microcontroller-Based Embedded System with a FPGA (Field Programmable Gate Array)-Based Drowsiness Detection System” https://www.researchgate.net/publication/22006 8846_A_real-time_non-intrusive_FPGAbased_drowsiness_detection_system

4.

Kusuma and Sunitha (2014). “Drowsiness Detection using Non-Intrusive Physiological Measures” http://ijcsn.org/IJCSN-2014/31/Hybrid-Drowsiness-Detection-System-toPrevent-Accident-using-Non-IntrusivePhysiological-Measures.pdf

5.

Sahayadhas, Sundaraj and Murugappan (2012). “Detecting Driver Drowsiness Based on Sensors: A Review” http://www.ncbi.nlm.nih.gov/pmc/articles/PMC 3571819/

6.

Hsu (2014). “Seatbelt Sensors to Fight Drowsy Driving” http://spectrum.ieee.org/cars-thatthink/transportation/safety/buckle-up-for-asmart-car-that-monitors-heartbeats

7.

“Steering Wheel Movement (SWM)” http://www.ctvnews.ca/autos/why-steeringwheel-movement-s-key-to-detecting-driverdrowsiness-1.17973

8.

Rahim, Dalimi and Jaafar “Detecting Drowsy Driver Using Pulse Sensor” http://www.jurnalteknologi.utm.my/index.php/j urnalteknologi/article/view/4238

Table 1. General Procedures 6 CONCLUSION

The proponents concluded that the proposed study, entitled “Microcontroller-based Embedded System for Drowsiness Detection and Vibration Alertness” is capable of detecting pulse rates from nurses who are experiencing drowsiness which would alert them through vibration. The information gathered that was based on the surveys and interviews gave the proponents the necessary data for determining that a problem such as drowsiness occurs in the field of nursing. The information gathered was also helpful for the device to be accurate in measuring drowsiness. The proponents have concluded that the design was complied well in fitting all the components on a device that is suited on a wrist. The developed program was complied as the device will vibrate when the pulse of the nurse is below sixty beats per minute (60 BPM).

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9. “Drowsy nurse dropped newborn, causing skull fracture” http://www.ibtimes.com/drowsynurse-dropped-newborn-causing-skull-fracturereports-1994013

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Block Based Technique for Detecting Copy-Move Digital Image Forgeries: Wavelet Transform and Zernike Moments Thuong Le-Tien, 1MarieLuong, 2Tu Huynh-Kha, Long Pham-Cong-Hoan, An Tran-Hong Dept of Electronics and Electrical Eng., HoChiMinh City University of Technology, Vietnam 1 Laboratory L2TI, University Paris 13, France 2 International University, National University of HoChiMinh City, Vietnam 268 Ly Thuong Kiet, Dist 10, HCMC, Vietnam [email protected], [email protected], [email protected], [email protected], [email protected]

ABSTRACT In this paper, a combination of the Wavelet transform and the Zernike moments is presented for detecting copy–move regions in digital forged images. Assuming that tested images have the JPEG format on both before and after being tampered. The proposed method uses the Wavelet transform to remove unimportant details in the forged images by using the approximate components only. After that, the Zernike moments of each 16 x 16 image block are calculated then compared the Euclidean distances to get pairs of any similar blocks. In order to support efficiently the proposed algorithm, a morphological technique is applied to extract the foreground first as a preprocessing. The evaluation for the theory and effectiveness of the proposed algorithm are demonstrated via experimental results on various images with different properties.

KEYWORDS

both positive and negative reasons, and extremely dangerous if they are to be used to falsify evidences or altering proofs regarding legal issues or politics affair. Some softwares can alter an image so delicate that it cannot be easily recognized by naked eyes (e.g. in Figure 1). An effective counter measure has been thriving throughout the recent years. One of the effective approaches is the Zernike moments and it has been proved to be a superiority in the analysis of invariant points of digital images. However, the Zernike based algorithm requires a large amount of running time [1]; for example a 1536x2048 image divided to L x L blocks takes O(Nmoments) + O(Nblocks × Nmoments × L2) + O(Nmoments × Nblocks × logNblocks) [2] to get high accuracy which is incredibly long for a tested simulation as an example processed on a computer using Window 7 Ultimate 64-bit, CPU Intel Core i5 @ 1.8GHz and RAM 4GB.

Wavelet transform; block-based technique; Zernike moment; Euclidean distance; tempered images, Morphological Technique.

1.

INTRODUCTION (a)

Without a doubt, digital images have been the significant role in daily activities and social areas; therefore, its’ security and authentication should be checked. When various softwares for image processing are getting more sophisticated and easily accessed by anybody, then researches on forged image detection are getting more attention and becoming more importance. Tampered images can be used on

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(b)

(c) (d) Figure 1. Original Images and Forged Images by Photoshop: (a), (c) Original; (b), (d) Forged Images

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Recognizing the feasibility to improve the method for identifying tampered images by the Zernike moment in order to take advantages of all the features of the algorithm, we proposed an algorithm where the approximate component of images derived from the Wavelet transform is used for further analysis and improves the runtime of the processing. After the wavelet transform, overlapping blocks with fixed-sized are used to store the information of the image as vectors. These vectors are calculated by Zernike algorithm and then compared to find the similarities for detecting identical regions. In order to make the proposed algorithm happened; some related works of other scientists have been researched in section 2, the proposed algorithm is in section 3, the experimental results are in next section, the last section is the conclusion and future intention. 2.

RELATED PREVIOUS WORKS

2.1. Extracting Features directly from the tested Image without Transformations A survey was conducted in [2], Kha Tu Huynh et al. proposed that copy–move forgeries detection generally requires 7 steps using the block based technique; the steps go from dividing the input image into overlapping blocks then calculate features of blocks and final steps are comparing blocks for detecting forgery. An algorithm to extract image features was proposed by Weiqi Luo et al. [3], by using seven characteristics features computed from the statistical analysis of pixels in an image block. Three features are colors and the others are directions. The right matching is obtained by defining which vector has the highest change to occur. In 2013, Leida Li et al. [4] introduced a new method to extract the image features called Local Binary Pattern (LBP) operated on the gray scale image and low frequency features for stability. The feature matching is still defined based on threshold and the detecting process requires a specially designed filter and morphological operations. In the past, a forged image with copied areas rotated with arbitrary angles can be detected via the method proposed by Hailing Huang et al. [5] (2008). Their

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method is using the Scale Invariant Feature Transform (SIFT) to extracts keypoints from an unknown image; these points are invariant to changes in scale, location and rotation. Keypoints are described by a descriptor vectors which are then compared based on ED and distance ratio threshold to search for matched keypoints. The higher threshold value leads to more false matching. Applying the method of Zernike moments was suggested by Seung – Jin Ryu et al. in [6]. In this method, features are derived from overlapped blocks in the image then sorted for forgery detection. The similarity of two adjacent blocks is calculated using Euclidian distance and a threshold to find the candidates for the forgery. In the case of blocks with the similar Zernike moments (satisfied D1 condition in eq.(15)), to ensure the exactness of detection, they consider calculating the distance between of the actual blocks of image (D2, in eq.(16)). Although this technique was weak against scaling and other tampering type based on affine transform, they still choose this to improve the algorithm for its advance in the feature representation capability, rotation invariance, fast computation, multi-level representation for describing shapes of patterns and low noise sensitivity. 2.2. Extracting Features Directly from the tested Image with a Transformation. The Block-Based algorithm which is dividing image into small overlapping blocks was proposed by Jessica Fridrich et al. [7]. They called it “exact match” and as it’s called, this algorithm compares and only shows the result when there are two or more segments of the image are match exactly. This technique has shorter computation time then the exhausted search method but still consumes a large amount of time when it deals with large images. Another approach from [8] uses neighborhood sorting, in which G. Li et al. used the DWT (Discrete Wavelet Transform) and SVD (Singular Value Decomposition). All the algorithms based on block-matching can reduce significant computational time compared to the

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original exhausted search. However, they still have a complex computation and require the JPEG image compression in order to work properly [9]. E.S.Gopi et al. [10] had found a way to work around that issue by proposed a method using Auto Regressive coefficients and Artificial Neural Network. This method works on uncompressed TIFF images, GIF and even JPEG images with minimal compression. When it comes to direct features extraction, there are many algorithms to detect the forgery from an image. In this paper, we proposed an algorithm applied the DWT into the forged image detection therefore this part will present some related theory of the DWT as an overview for the topic. According to [11], the two-dimensional images which requires a two-dimensional scaling function, φ(x, y), and three two-dimensional wavelets, ψH (x, y), ψV (x, y) and ψD (x, y). They are the product of one-dimensional scaling function φ and corresponding wavelet ψ,

 ( x, y )   ( x) ( y )

(1)

 H ( x, y )   ( x) ( y )

(2)

 V ( x, y )   ( x) ( y )

(3)

 D ( x, y )   ( x) ( y )

(4)

In which ψH measures variations along horizontal edges, ψv measures variations along vertical edges and ψD measures variations along diagonals. After finding two-dimensional scaling and wavelet functions, based on onedimensional discrete wavelet transform, we define the scaled and translated basis functions:

 j ,m,n ( x, y )  2 j / 2  (2 j x  m, 2 j y  n)

(5)

 ij ,m,n ( x, y )  2 j / 2  (2 j x  m, 2 j y  n)

6)

i = {H, V, D} Therefore, the discrete wavelet transform of function f(x, y) of size M x N is [11],

W ( j0 , m, n) 

1 MN

M 1 N 1

 f ( x, y) x 0 y 0

j 0,m,n

( x, y) (7)

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Wi ( j, m, n) 

1 MN

M 1 N 1

 f ( x, y) x 0 y 0

i j ,m,n

( x, y )

(8)

i = {H, V, D} where j0 is an arbitrary starting scale and the Wφ(j0,m,n) coefficients define an approximation of f(x, y)at scale j0. The Wiψ(J, m, n) coefficients add horizontal, vertical, and diagonal details for scales j j0. Normally we let j0 = 0 and select N =M = 2J so that we can get j = 0, 1, 2..,J – 1 and m, n = 0, 1, 2,…, 2J – 1. Given the Wφ and Wiψ of 2 equations (7) (8), f(x, y) is obtained by inverse discrete wavelet transform [11]. 1 f ( x, y )   W ( j0 , m, n) j 0,m,n ( x, y)  MN m n (9)  1 i    W ( j0 , m, n) ij,m,n ( x, y) MN i  H ,V , D j  j 0 m n 3.

THE PROPOSED ALGORITHM

In Fig. 2, the proposed copy-move image forgery detection is shown in a block diagram. In the first step, the input image is converted into gray scale and goes through some morphological enhancement to extract the foreground components. In the second step, the Wavelets transform will be applied to take the approximation of the foreground component.

Fig.2. Block diagram of the proposed algorithm

This is the key step of the algorithm to decrease the calculation time of block based image forgery detection technique using the Zernike moments. After that, the Zernike moment calculation is applied to the approximate component to extract the features. In the last step, in order to detect the similar blocks, Euclidean distance of each pair of blocks will

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be computed. If the Euclidean distance is lower than a threshold, the pair of blocks will be marked similar. 3.1. Foreground Component Extraction In order to extract the foreground component, firstly, the input color image is converted into gray scale which is necessary for calculating Zernike moment lately. Emphasis on studying the geometrical structure of the components of images, the morphological technique is a useful tool for extracting image edge components. To pre-processing or post-processing the images containing shapes of interest, the morphological algorithm has been efficiently used in image processing. Hence, the proposed method used function “edge” and morphological technique to create a binary gradient mask in order to extract the foreground components. Then the binary gradient mask that contains the segmented components of the image will be enhanced with morphological functions. After that the mask is used to remove the background. For analyzing shapes in images, morphological technique is a useful tool. Some of the morphology functions have been used for extracting the foreground components such as dilation, erosion, image filling, etc. There are two basis operations of morphology which combination of those two can build all other operations: the dilation and erosion [12].





A  B  z ( Bˆ ) z  A  

(10)

Erosion is the opposite of dilation. If the effect of dilation is to “grow” or “thicken” objects, erosion’s effect is to “shrink” or “thin” the objects in a binary image or gray scale image. This thinning is also controlled by the structuring element.





AB  z ( Bˆ ) z  AC  

(11)

Figure 4. An example of erosion

To create the binary gradient mask to extract the foreground component, the function “edge” is applied first to retrieve the edges of the input image. Then these edges will be enhanced by dilation and filling holes, the result will be used as mask to extract the foreground component.

(a)

(b)

(c)

Figure 5. An example of creating a mask from the input image. (a) Input image, (b) Binary gradient mask, (c) Mask that used to extract foreground component.

3.2. Wavelets Approximation

Figure 3. An example of dilation

Dilation’s effect is to “grow” or “thicken” objects in a binary image. The size and shape of the structuring element (SE) are used to control the extension and direction of this thickening.

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This is the key step of this algorithm that make it different from other methods. In order to reduce the calculation time of block based image forgery detection techniques, the Wavelets approximation will be used instead of the original input image. The information of the image will be divided into approximation and detail sub-signal using Wavelets analysis [11, 13]. Using four-band filter bank for sub-

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band coding, the Low Pass Filter (LPF) is applied to the foreground component on rows (along x) to obtain the horizontal approximate, and High Pass Filter (HPF) on rows to obtain the horizontal detail. Then, in the horizontal approximate, LPF and HPF is applied on columns (along y) to get approximate image and vertical detail respectively (Fig.6 and Fig.7). Do the same thing for horizontal detail; the results are horizontal detail and diagonal detail. The general trend of pixel values is shown in the approximate sub-band and the three detail sub-band can be neglected. Hence, using the approximate sub-band for detecting forgery is possible.

technique. It is used to analyze and calculate the Zernike moment of each block of the testing image. In this proposed method, the approximate of foreground component will be used to calculate the moment instead of whole original image. The mappings of an image onto a set of complex Zernike polynomials are the Zernike moments. Complex Zernike functions constitute a set of orthogonal basis functions mapped over the unit circle. The Zernike moments of a pattern are constructed by projecting it onto those functions. There are three main properties [6, 14]: First is the orthogonal feature with the unique and independent contribution of each moment; Second is the rotation invariance and third is the robust to noise or deformation of small descriptors. The 2D Zernike moment of order n with repetition of m for a continuous image function f(x,y) that vanishes outside the unit circle is:

Figure 6. Wavelets transform flowchart

Z nm 

n 1



 f ( x, y)V

* nm

(12)

x 2 y 2 1

Where (n-|m|) is non-negative and even in which n is a non-negative integer and m is an integer. The Zernike basis function Vnm (  ,  ) is defined by: Vnm (  ,  )  Rnm (  ) exp( jm )

Figure 7. A two-dimensional, four-band filter bank for sub-band image coding [11]

After being filtered, the foreground component is decomposed into two parts- a detailed and an approximated part. The sub-signal produced from low filter will have a highest frequency equal to half of the original. Hence, after sampling, only half of the original samples is needed to reconstruct the signal. 3.3. Zernike Moment’s Properties Zernike moment calculation is an important part of block based image forgery detection

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(13)

Where Rnm (  ,  ) is the n-th order of Zernike radial polynomial given by: Rnm (  )  (n m ) / 2

  1

k

k 0

(n  k )! (14)  n 2k  n  2k  m  n  2k  m k!  ! ! 2 2   

Identical to the rotational moments and the complex moments, the magnitude of Zernike moments is unchanged under image rotation transformation. Suppose that we know moments Znm of f(,θ) up to a given order m. A discretized original image function f() whose moments are those of f(,θ) up to given

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order can be computed. The Zernike moments have rotational invariance and can be made scale and translational invariant, making them suitable for many applications. The Zernike moments are accurate descriptors even with relatively few data points. Because of the three properties of Zernike moments, they are more effective than other kind of moments in describing an image. In spite of the characteristic of Zernike moments, they have some problems; one of them is costly to compute as their order increased which can be resolve with the proposed method.

then the Zernike moments are extracted from the foreground. Using Euclidean distant and the actual distant of the pair of blocks, the copymove blocks are determined. Although the calculation time of Zernike moments is increased as the maximum order increased, the wavelets transform reduces the size of original image to a quarter and reduce the computational time for calculating the Zernike moments. These results show the feasibility and possibility of the proposed method (see Fig. 8).

3.4. Forgery Detection Conclusion Firstly, the M x M size tested image is divided into overlapped L x L sub-blocks. Zernike moment, Vij of degree n is extracted from each block. After the Zernike moments are calculated, each pair of moments will be compared using Euclidean distance [2].

 (Z

p

 Z p 1  D1

(a)

(b)

(c)

(d)

(e)

(f)

(15)

However, the blocks that near each other may have similar characteristic of Zernike moments, an actual distant between each block is calculated as:

i  k 2   j  l 2

 D2

(16)

Where Zp = Vij and Zp+1 = Vkl Using equ.(15) and equ.(16), the testing blocks are determined if they are copy-move regions. 4.

SIMULATION RESULTS

4.1. Results This proposed algorithm is run in MATLAB 2013a. All tested images have 200x200 size and in JPEG format. The original images are color and before applying the algorithm, are converted into gray scale. After that, the foreground is extracted from the gray scale image using some morphological structures and

ISBN: 978-1-941968-30-7 ©2016 SDIWC

(g) (h) Figure 8. Some simulation results (a), (c), (e), (g): Tested images; (b), (d), (f), (h): copy-move detection.

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In the simulations, the image is divided in to overlapping blocks 16x16 size, therefore, only copied region greater than 16x16 can be detect. Because the similar blocks may have the same characteristics, if the copied region is closer than the threshold value D2, the block cannot be detected. Moreover, the threshold used to determine the similarity between the vectors of each block is not dynamic for all size of images and can be appeared as a statistic values.

for comparing of the examined methods. Particularly, the proposed approach is presented with a significant reduction of the running time. Table 1. Results for copy-move image forgery detection (%). The values are the average values of tested images.

4.2. Comparison An appropriate measure is needed to evaluate the performance of copy-move image forgery detection method. In this paper, precision p, recall r and F are parameters used to evaluate the efficiency of the proposed method [1]. This method is often be used in the field. These parameters are defined as p  T p /(T p  F p )

(17)

r  T p /(T p  FN )

(18)

F  2 pr /( p  r )

(19)

Where TP is the true positive – the number of true forged pixels, FP is the false positive – the number of false forged pixels, and FN is the false negative – the number of miss forged pixels. F is the combination of precision and recall in a single value. The precision is the probability of correct forgery found and the recall is the probability of forged image detected. The proposed method is compared with the traditional method using only Zernike moment [1] and other DWT-based methods as shown in the references. Table 1 show the comparison results for the case and the values listed in the table are the average values of variety tested images. It can be seen from he Table 1, even the precision is reduced in the proposed method due to the forged objects are detected usually larger than the objects in the tested images, however the recall and the F are getting better

ISBN: 978-1-941968-30-7 ©2016 SDIWC

90.47 86.72

Recall (r) 67.09 88.57

77.04 87.64

88.71

94.78

91.65

Method

Precision(p)

Zernike DWT Proposed method

F

Table 2. Comparison average speed between the traditional Zernike method with the proposed method.

Method Average calculation time (sec.) Running time reduced (%)

5.

Zernike only

Proposed method

588.5

135.4 77%

CONCLUSION

In this work, a copy-move image forgery detection technique using block-based method with the combining of Zernike moments and Wavelet approximation is proposed. Although the precision is relatively a bit lower than the traditional method, where only Zernike moment is applied, and higher than the Discrete Wavelet Transform DWT, however the calculation time is significantly reduced as shown in the simulation results. In addition, to remove the background, the proposed method detects edges and morphological technique to create a binary gradient mask in order to extract the foreground components. Then the binary gradient mask that contains the segmented components of the image will be enhanced with morphological functions. After that the mask is used to remove the background. This technique provides an efficient detection of forged objects in images.

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A future work will be to increase the precision of the method by using suitable morphological edge enhancements combined with the Curvelets and/or Ridgelets transform and the Zernike moments.

Workshop on Computational Intelligence Industrial Application, Wuhan, China, 2008.

and

ACKNOWLEDGMENT

[6] Seung-Jin Ryu, Min-Jeong Lee, and Heung-Kyu Lee, “Detection of copy-rotate-move forgery using Zernike moments,” Lecture Note in Department of Computer Science, Korea Advanced Institute of Science and Technology Volume 6387, pp 51-65, 2010, Daejeon, Republic of Koea.

This Research is funded by Vietnam National University of HoChiMinh City under the Grant Number B2015-20-02

[7] Fridrich, D. Soukal, and J. Lukás, “Detection of copy move forgery in digital images,” in Proc. Digital Forensic Research Workshop, Aug. 2003.

REFERENCES

[8] G. Li, Q. Wu, D. Tu, and S. Sun, “A Sorted Neighborhood Approach for Detecting Duplicated Regions in Image Forgeries based on DWT and SVD,” in Proceedings of IEEE International Conference on Multimedia and Expo, Beijing China, July 2-5, 2007, pp. 1750-1753.

[1] Thuong Le – Tien, Tan Huynh – Ngoc, Tu Huynh – Kha, Luong Marie, “Zernike moment-based Approach for the Detecting Duplicated Image Regions by a Modifierd Method to reduce Geometrical and Numerical Errors,” Lecture Note in Computer Science (LNCS) – The 7th Int. Workshop on the Software Eng. Processes and Applications – SEPA-2015, Banff, Canada, Springer Computer Science Proceeding (16 pages), 22 – 25 June 2015.

[9] P.Subathro, A.Baskar, D. Senthil Kumar, “Detecting digital image forgeries using re-sampling by automatic Region of Interest (ROI),” ICTACT Journal on image and video processing, Vol. 2, issue. 4, May 2012

[2] Tu Huynh–Kha, Thuong Le–Tien, Khoa Van Huynh, Sy Chi Nguyen, “A survey on image forgery detection techniques,” The Proceeding of 11-th IEEE-RIVF International Conference on Computing and Communication Technologies," Can Tho, Vietnam, Jan 25-28 2015.

[11] Rafael C. Gonzalez, “Digital Image Processing,” Prentice-Hall, Inc, 2002, ISBN 0-201-18075-8.

[3] Weiqi Luo,Jiwu Huang, Guoping Qiu, “Robust detection of region-duplication forgery in digital image,” The proceeding of 18-th IEEE International Conference on Pattern Recognition, Hong Kong, p. 746 – 749, 2006. [4] Leida Li, Shushang Li, Hancheng Zhu, “An efficient scheme for detecting copy-move forged images by local binary patterns,” Journal of Information Hiding and Multimedia Signal Processing, Vol. 4, No. 1, pp. 46-56, January 2013.

[10] E. S. Gopi, N. Lakshmanan, T. Gokul, S. Kumara Ganesh, and P. R. Shah, “Digital Image Forgery Detection using Artificial Neural Network and Auto Regressive Coefficients,” Electrical and Computer Engineering, 2006, pp.194-197.

[12] Oge Marques, “Practical Image and Video Processing using Matlab,” John Wiley & Sons, 2011, ISBN 978-0-47—04815-3 [13] Alasdair McAndrew, “An introduction to digital image processing with MATLAB,” Lecture note for SCM2511 Image processing 1, School of computer science and mathematics, Victoria University of Technology, 2004, pp.195-200. [14] Sundus Y. Hasan, “Study of Zernike moments using analytical Zernike polynomials,” Pelagia Research Library, Iraq, 2012.

[5] Hailing Huang, Weiqiang Guo, Yu Zhang, “Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm,” IEEE Pacific-Asia

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Analysis and Performance Evaluation of Convolutional Codes over Binary Symmetric Channel Using MATLAB Moussa Hamdan1 and Abdulati Abdullah2 1,2 Electrical and Communications Department, Azzytuna University, Libya 1 [email protected], [email protected]

ABSTRACT The most common concern of any communication system is the data quality. There exist different components that can impact the quality of data during its conveying over the channel as noise, fading, etc. Forward error correcting codes (FEC) play a major role for overcoming this noise as it adds a control bits to the original data for error detection and correction. This paper aims at analyzing convolutional codes with different rates and evaluating its performance. Binary phase shift modulation (BPSK) scheme and binary symmetric channel (BSC) model are used. First a convolution encoder is presented and then additive white Gaussian noise (AWGN) is added. The paper uses maximum likelihood mechanism (Vetribi Algorithm) for decoding process. Simulations are carried out using MATLAB with Simulink tools. Bit error rate (BET) is used as testing parameter and results of system behavior for both coded and encoded are compared.

Keywords: Convolutional codes, Viterbi decoding, AWGN, Code rate and BPSK

1 INTRODUCTION The fundamental target of communication systems is to involve conveying the information through the channel to be received with as less error as possible. Digital communications have been adopted to perform such goal due to their capability of processing data faster than the conventional (analogy) communications and potentiality of extremely less error rate. One of the major reasons for the continuous growth in the use of wireless communication is to increase the capability to provide efficient communication links to almost any location, at constantly

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reducing cost with increasing power efficiency. For this reason, digital communication systems have experienced quick-replacement in the area of telecommunications [1]. To appreciate the wireless version of a digital communication system, firstly it is necessary to consider what the essential components of a digital communications system are. A general block diagram of a digital data communication system is shown below:

Figure 1 Block diagram of digital communication system

In this system, it is primarily concerned with measuring the probability of error and the mechanisms introduced to minimize it at the receiver side [2]. The source represents any entity that contains information to send such as, audio, image, data…etc. Whereas, the source encoder, provides digitization and compression in order to remove the redundant information that results in reducing the bandwidth [1]. Channel coding evolves adding the controlled redundancy (extra bits) to detect and correct the errors at the receiver side for instance; linear codes, convolution codes and turbo codes. Afterward, the baseband signal needs to be modulated to a conceivable format that matches the physical medium. Multiple access is a mechanism that involves more than one transceiver to share the same medium such as TDMA, FDMA CDMA,…etc, then, the

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physical channel where the signal typically experiences the distortion components, either Additive White Gaussian Noise (AWGN) process or multipath. The receiver side performs the complementary process of the transmitter with the capability of overcoming the interference produced through the channel [1] & [2]. 1.1 Coding Theory Overview Coding theory is a technique used to efficiently and accurately transfer the information from one point to another. This theory has been sophisticated for the purpose of several applications such as, minimizing noise from compact disc recorders, or sending of financial information across telephone line, data transfer among many computers in a networks or from one location in a memory to the central processor, and information transmission from a distance source such as a weather or communications satellite or the Voyager spacecraft which sent pictures of Jupiter and Saturn to Earth. Therefore, the prior task of coding theory is to deal with the issue of detecting and correcting transmission errors resulted from the noise that is introduced through the channel [3]. In practice, the control we have over this noise is the choice of a good channel to be used for transmission and the use of various noise filters to combat certain types of interference which may be encountered [3]. 1.2 Block Codes In block coding, the message is segmented into blocks, each of which has k bits information which is named dataword. Every single dataword (k) is appended an extra bits called redundancy (m) to form codeword (v) with a length of n as shown in the following figure.

Figure 2 Datawords and codewords in block coding

ISBN: 978-1-941968-30-7 ©2016 SDIWC

Each block of message is expressed by the binary of k - tuple u = (u1, u2,... u3) . In bock coding, the symbol u is used to express a k-bit message instead of the entire information sequence. As a result, there are 2K different possible messages. The encoder reforms each message u independently into an n-tuple by adding redundancy, so that the new form will be as: v= (v1,v2,…vk) of discrete symbols called a code word. Therefore, corresponding to the 2k different possible message, there are 2n different possible code words at the encoder output. This set of 2k code words of length n is block code called (n, k) block code . This ratio R =k/n is called a code rate, and can be interpreted as the number of information bits entering the encoder per transmitted symbol [3]. 1.3 Error correction mechanisms Wireless technology has experienced a considerable enhancement in terms of the achievement of the fast deployment at low expenses. Whereas, the utilization of poor quality of the channel usually demands retransmissions, that are administrated by using Automatic Repeat Request (ARQ) [4]. Although, the ARQ is an advantageous tool to mitigate the errors of the packet, it has some drawbacks including the increment of the power expenditure due to retransmission and also latency. However, since the attention was gradually being shifted to the forward error correction (FEC), it has provided several advantages compared to the use of ARQ scheme [3]. One of its advantages is the development of the performance of the error control which is obtained by applying the error correction code that is based on adding extra bits to the original data that is known as redundancy. This redundancy is the principle of forward error correction that provides the capability of error detection and also correction at the destination side without the need to retransmission or waiting for acknowledgment return compared to ARQ. Also, in case of

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propagation delay, the FEC has become more suitable selection than other schemes [4]. Therefore, appending the check bits will typically increase the bandwidth used for transmission or causing the packet delay or might be both, however to achieve multiple bits correction, it is advantageous to add more redundant bits. This strategy requires higher bandwidth of forward channel as well as more cost. Furthermore, the FEC techniques can be divided into two main categories which are the block coding and convolutional coding. The block coding consists of several coding shames such as, Reed Solomon, BCH and Hamming codes [3] & [4]. 2 CONVOLUTION CODES Convolution codes have been applied for several systems encompassing today’s common wireless standard as well as satellite communications. The adoption of these codes is the recent approach that express the well building block in more powerful and modern codes that are applied for wide area cellular structures such as, 3G, LTE, LTE-A, 4G. The main purpose of using convolution coding technique is to minimize the probability of errors over any noisy communication channel. It assists in recovering the most likely message from among the set of all possible transmitted messages [4]. 2.1 Implementation of convolutional code The convolution code encoder accepts k- bit block of information sequence u and generates an encoded sequence (codeword) v of n-symbol blocks. However, each encoded block depends on both the corresponding k-bit message block at the same time unit, and also on m previous message blocks. Hence, the encoder has a memory order of m. The number of encoded symbols is called an (n, k, m) convolution code. The ratio R=k/n is called the code rate. Block codes contains no memory which results in an independency for the consecutive codewords [5]. On the other hand, because of

ISBN: 978-1-941968-30-7 ©2016 SDIWC

dealing with data blocks, buffering memory and latency overheads are always associated with block codes. Block codes, as opposed to convolution codes, can be cross interleaved for reliable storage of data. Block codes can be concatenated with convolution codes or mapped together onto an iterative (turbo) configuration for higher performance over some channels [5]. Convolution encoders are typically simple state techniques to be implemented in hardware. Whereas, decoding part is complex because it is based on involving searching for a best path to reconstruct or recover the transmitted information, while it is more amenable to softdecision decoding compared to block codes, thereby better coding performance can be resulted. Hence convolution codes are conceivable for lower SNR channels, as well as transmitters use simple low power devices. Because the encoder consists of memory, it is essential to be constructed with the circuit of sequential logic. For binary convolution codes, control bits (redundant bits) are appended to data sequence to combat the channel noise in case of k