Lecture Notes in Electrical Engineering 492

Daniel Thalmann · N Subhashini K. Mohanaprasad · M S Bala MuruganEditors

Intelligent Embedded SystemsSelect Proceedings of ICNETS2, Volume II

Lecture Notes in Electrical Engineering

Volume 492

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Daniel Thalmann • N SubhashiniK. Mohanaprasad • M S Bala MuruganEditors

Intelligent EmbeddedSystemsSelect Proceedings of ICNETS2, Volume II

123

EditorsDaniel ThalmannComputer Graphics LabEPFLLausanneSwitzerland

N SubhashiniSchool of Electronics EngineeringVIT UniversityChennai, Tamil NaduIndia

K. MohanaprasadSchool of Electronics EngineeringVIT UniversityChennai, Tamil NaduIndia

M S Bala MuruganSchool of Electronics EngineeringVIT UniversityChennai, Tamil NaduIndia

ISSN 1876-1100 ISSN 1876-1119 (electronic)Lecture Notes in Electrical EngineeringISBN 978-981-10-8574-1 ISBN 978-981-10-8575-8 (eBook)https://doi.org/10.1007/978-981-10-8575-8

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Preface

The first edition of the International Conference on NextGen ElectronicTechnologies: Silicon to Software (ICNETS2) was held at Vellore Institute ofTechnology, Chennai (VIT Chennai), during March 23–25, 2017. VIT being thehighest ranked private engineering and research institution in India was the aptsetting for the conference and its discussions on emerging technologies and itsapplications in both today’s and tomorrow’s society. The Intelligent EmbeddedSystems (Symposium-B) was one of the six symposia hosted by ICNETS2 andrepresented the upper layers of the computing stack culminating in system archi-tectures as well as software design. This symposium was an avenue for researchersworking in areas centered around Embedded Systems with more focus onIntelligence, to disseminate their research efforts to a wide audience including pro-fessionals and pioneers in the industry and academia. Intelligent Embedded Systemsis the ability of a product or process to gain knowledge of the operational capabilitiesand limitations by itself and its ecosystem and utilize this knowledge in enhancing itsfunctions and performance. This aspect was prominently at display during thesymposium, which discussed both conceptual systems and also practicalconsumer-centric systems in a wide set of horizontals including smart health care,automotive systems focusing on safety, precision agriculture, smart grids, andconsumer electronics. A total of 52 papers were presented during the 3 days of thesymposium, which attracted researchers from around the globe both to present theirwork and to deliver invited talks in cutting-edge research areas in several aspects ofEmbedded Systems. All the participants, a significant majority of whom were stu-dents, had the opportunity to engage in many fruitful discussions that paved way tothe success of the conference. Each day of the conference started with keynoteaddresses, and each session began with invited talks. The symposium was co-chairedby Dr. Daniel Thalmann (Director, Virtual Reality Lab, EPFL, Switzerland), whoalso gave a keynote address on the advances in Human–Computer Interaction. Therewere three invited talks covering the different system design and communicationtechnologies: Eric Torres (Tata Communications) talked on the LoRaWAN standardfor low-power wide-area networking in Internet of Things (IoT) applications, AdamuMurtala Zungeru (Botswana International University of Science and Technology)

v

on underground sensor networks, and Elizabeth Chang (The University of NewSouth Wales) on important aspects of trust and security on IoT. All in all, the veryfirst ICNETS2 was very successful and set a milestone in conferences organized inIndia. The plenary lectures and other invited talks bridged the gap between aspects ofEmbedded Systems and also enthused participants to produce more engagingresearch work in the future. Also, since most of the participants were students, newperspectives were discussed on how to embed intelligence into systems and thepracticality of such systems in various application domains. The next ICNETS2 willtake place in VIT Chennai in 2019. Given the explosive growth of the Internet ofThings and the associated analytics and machine learning components, Intelligencein Embedded Systems is bound to grow in leaps and bounds, and in the same vein,we expect that the future ICNETS2, and particularly the symposium on IntelligentEmbedded Systems, will be as stimulating as the current one was, as indicated by thecontributions presented in this proceedings volume.

Lausanne, Switzerland Daniel ThalmannChennai, India N SubhashiniChennai, India K. MohanaprasadChennai, India M S Bala Murugan

vi Preface

Acknowledgements

The organization of a conference, especially on the scale of ICNETS2, is asstressful as it is rewarding. Managing every detail, right down to the smallest issues,requires immense effort which would have been unlikely if not for the tremendoussupport rendered to the organizing committee. Organizing a conference covering allaspects of Electronics and Communication as part of six distinct symposia is not aneasy task. On behalf of Symposium-B (Intelligent Embedded Systems), we wouldlike to thank the management of VIT University, Chennai, for enabling the con-ference at every juncture. The conference would not have had its visibility is not forthe partnership of Nadia Thalmann (Director, MIRALab, University of Geneva,Switzerland) and Daniel Thalmann (Director, VRlab, EPFL, Switzerland), whowere co-chairs for the conference. We take this opportunity to thank SRSPrabaharan (Chair, ICNETS2) and Kanchana Bhaaskaran (Co-chair, ICNETS2), fortheir invaluable guidance in organizing our symposium. Thanks also go to theinvited speakers, Elizabeth Chang, Eric Torres, and Adamu Murtala Zungeru, whoshared their expertise to the eager participants of the symposium. We also thank theadvisory committee, reviewers, and session chairs who took time off from theirbusy schedules to contribute to the symposium. The participants and presenters atthe sessions only enhanced the quality of the symposium, and their willingness topublish their work as part of a fledgling symposium is highly appreciated. As thewhole conference and our symposium was the result of well-coordinated teamwork,it is only fair that we acknowledge the efforts of the entire organizing team of morethan 60 distinguished faculty members of the School of Electronics Engineering atVIT Chennai, who worked day and night for 8 months, way beyond their academicschedules. The contributions of the laboratory technicians and other maintenancestaff at VIT Chennai should also be noted. Tata Communications, our corporatelegend supporter, and Tenet Technetronics, our premium partner, provided thenecessary technical and financial impetus in moving the conference forward. Wealso acknowledge the endorsements of ISRO, CSIR, INSA, and the Department of

vii

State (USA) in increasing the conference visibility. Lastly, the acceptance ofSpringer to publish the proceedings of the conference and all its symposia estab-lished the credibility of the event, which aims at setting the standard for futureconferences organized in the country.

viii Acknowledgements

Contents

Design and Implementation of Dialysate Temperature ControlSystem for Hemodialysis: A Pilot Study . . . . . . . . . . . . . . . . . . . . . . . . . 1Mohamed Haroon Abdul Jabbar, S. Anandan Shanmugamand Poi Sim Khiew

Raspberry Pi in Computer Science and Engineering Education . . . . . . . 11S. Alex David, S. Ravikumar and A. Rizwana Parveen

Advanced Tele-surgery with IoT Approach . . . . . . . . . . . . . . . . . . . . . . 17N. Shabana and G. Velmathi

Xilinx System Generator-Based FPGA Control of Power Flowfor DC/DC Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Anurag Sharma, Rajesh Gupta and Muskan Gupta

An Improved Algorithm for the Estimation of Multibody Motion . . . . . 37K. Raghavan and R. Prithiviraj

An IoT-Based Smart Shopping Cart Using the ContemporaryBarcode Scanner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45V. N. Prithvish, Shraddha Agrawal and John Sahaya Rani Alex

Voting System for India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Shrikant Subhash Warghade and B. Karthikeyan

Human–Robot Interaction Using Three-Dimensional Gestures . . . . . . . . 67K. Ponmani and S. Sridharan

Integration of the Smart Phone and IOT for Smart Public ToiletHygiene Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Prashant Namekar and B. Karthikeyan

Hyperelliptic Curve Cryptography-Based Lightweight Privacy-AwareSecure Authentication Scheme for Vehicular Ad Hoc Network . . . . . . . 83Kirti A. Yadav and P. Vijayakumar

ix

A Dynamic Approach of Energy Saving Control Strategyin Smart Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91S. Sofana Reka and S. K. Pranesh

A Novel Approach for Night-Time Vehicle Detectionin Real-Time Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99M. Aswin and G. Suganthi Brindha

Local Visualization for Troubleshooting the RF Mesh Networkin a Wireless Metering System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107Parvathi L. Prabhakar, Kiran Thomas, S. Sreekumarand S. Muthulakshmi

Train Collision Avoidance System for Automatic TrainProtection Using Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115Mohit Savner and G. Gugapriya

Automatic Driver and Vehicle Safety Monitoring System . . . . . . . . . . . 129S. Vijay Kumar and Abraham Sudharson Ponraj

Emergency and Traffic Congestion Avoidance UsingVehicle-to-Vehicle Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147Anirban Das, Mahadev Desai, Nilkanth Mugatkarand Abraham Sudharson Ponraj

Smart Mobile Diagnostic Laboratory and Doctor AnnunciationSystem in Ambulances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155Nikita Bais, R. Shubha, V. Yamuna and M. Kalyan Chakravarthi

Magnetic Braking System for Automotives . . . . . . . . . . . . . . . . . . . . . . 163Arjun Nair and K. Srivatsan

Raspberry Pi-Based Surveillance System with IoT . . . . . . . . . . . . . . . . . 173Arvin Joseph Kumar Jayakumar and S. Muthulakshmi

Development of Roads Pothole Detection SystemUsing Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187Harshad Sawalakhe and Ramchandran Prakash

Automated Interoperability Testing of Optical NetworkTerminals for VoIP Call Features Using Robot Framework . . . . . . . . . 197Kavya Ajith, Kalaiselvan Ramalingam and Muddukrishna Dandu

Design and Implementation of Smart Helmet Using Low PowerMSP430 Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211Yogya Indupuru, K. Venkatasubramanian and V. Umamaheswari

Vision Intelligence System for Power Management UsingHuman Activity Detection System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225Sukanya B. Pasupuleti and Prakash Ramachandran

x Contents

Embedded System for Classification of Upper Limb MovementDuring Action Using EEG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241Navya Tummala, K. Venkatasubramanian and V. Umamaheswari

Intelligent Digital Signage System Based on Gender Identification . . . . . 251Riya Elizabeth Abraham and M. Robert Kennedy

Speech Recognition System Using Open-Source Speech Enginefor Indian Names . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263Nitin Arun Kallole and R. Prakash

Energy Estimation of Embedded Systems . . . . . . . . . . . . . . . . . . . . . . . 275Anagha Ram and M S Bala Murugan

Design of Communicating Power Supplies and Controllingthe Electronic Devices Using Internet and Mobile Application . . . . . . . . 285Gunta Krishna Kishore and M S Bala Murugan

Real-Time Human Detection and Tracking Using Quadcopter . . . . . . . . 301Rana Praful George and V. Prakash

Sonar Data Processing Using Multicore Architecture Processorand Embedded Linux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313Varun K. Jayan and A. K. Mohamed Husain

A Novel Black Box System for Automobiles . . . . . . . . . . . . . . . . . . . . . . 325S. Sriram and V. Prakash

IOT-Based Automated Aeroponics System . . . . . . . . . . . . . . . . . . . . . . . 337Felin Francis, P. L. Vishnu, Manish Jha and Bharghava Rajaram

Contents xi

About the Editors

Prof. Daniel Thalmann is a Swiss and Canadian Computer Scientist. He is oneof the most highly cited scientists in Computer Graphics. He is currently HonoraryProfessor at EPFL, Switzerland, and was since 2009 with the Institute for MediaInnovation, Nanyang Technological University, Singapore. Pioneer in research onVirtual Humans, his current research interests also include Social Robots, CrowdSimulation, and Virtual Reality. He has been the Founder of Virtual Reality Lab(VRlab) at EPFL, Switzerland; Professor at University of Montreal; and VisitingProfessor/Researcher at CERN, University of Nebraska, University of Tokyo, andNational University of Singapore. He is Co-Editor-in-Chief of the Journal ofComputer Animation and Virtual Worlds and member of the editorial board of 12other journals. He was Program Chair and Co-Chair of several conferencesincluding IEEE-VR, ACM-VRST, and ACM-VRCAI. He has published more than600 papers in Graphics, Animation, and Virtual Reality. He is co-editor of 30 booksand co-author of several books including ‘Crowd Simulation’ (second edition 2012)and ‘Stepping into Virtual Reality’ (2007), published by Springer. He received hisPh.D. in Computer Science in 1977 from the University of Geneva and an HonoraryDoctorate from Paul Sabatier University, Toulouse, France, in 2003. He alsoreceived the Eurographics Distinguished Career Award in 2010, the 2012 CanadianHuman Computer Communications Society Achievement Award, and the CGI2015 Career Achievement.

N Subhashini received her B.E. in Electronics and Communication Engineeringfrom the University of Madras, Chennai. She obtained her master’s degree inSystems Engineering and Operations Research from the College of Engineering,Guindy, Chennai. She was awarded a gold medal in PG for securing the highestrank in the University and also awarded a gold medal for being the Best OutgoingStudent in the year 2006. With over 12 years of experience in teaching UG and PGstudents, she is currently working as Assistant Professor in the School ofElectronics Engineering, Vellore Institute of Technology (VIT), Chennai. She hasguided a number of UG and PG students in many projects. Her research interestsinclude Optical Metro/Access Networks, Fiber-to-the-X (FTTX) Technologies,

xiii

Next-Generation Architectures and Services, Optical Fiber Technology andWavelength-Division Multiplexing (WDM) Systems. She has presented and pub-lished a number of papers in peer-reviewed journals and conferences. She is cur-rently working in optical networks and her research focuses on the design andperformance evaluation of optical networks, especially passive optical networks.She is an active member of the Optical and Microwave Research Group in VIT,Chennai, and has organized several workshops and seminars in the field of opticalnetworks.

Dr. K. Mohanaprasad graduated from VIT University, Vellore, Tamil Nadu,India, in the field of Signal Processing. He is currently associated with the School ofElectronics Engineering, VIT University, Chennai, Tamil Nadu, India, as AssociateProfessor. His major research interests are in the areas of Speech Processing, SignalProcessing, Wavelet Transform, Image Processing, and Biomedical SignalProcessing. He has co-authored a chapter and published over 20 reputed interna-tional journal/conference papers, several of them winning best paper awards. He isa regular reviewer of several top signal processing journals.

M S Bala Murugan has 10 years of experience in teaching industry, with a year ofresearch experience at Centre for Electronics Design and Technology (CEDT), IISc,Bangalore, India. He is currently working as Assistant Professor at VIT University,Chennai, and also chairs the IoT working committee in the School of ElectronicsEngineering. His areas of interest include Embedded Systems and IoT. He haspublished ten papers in international conferences and journals. His recent researchincludes deploying real-time operating system (RTOS) in heterogeneous multicorearchitectures and multistandard protocol gateway for IoT.

xiv About the Editors

Design and Implementation of DialysateTemperature Control Systemfor Hemodialysis: A Pilot Study

Mohamed Haroon Abdul Jabbar, S. Anandan Shanmugamand Poi Sim Khiew

Abstract In hemodialysis, the control of body temperature by altering the dialysatetemperature would reduce the intradialytic complications. Several studies show thatthe constant dialysate temperature affects the patient’s quality of life due to theirdifferent temperature threshold. Thus, these factors serve as a motivation factor todesign an individualized dialysate temperature control for hemodialysis patients,which can actively control the body temperature even in the case of any externaldisturbances. In this paper, a novel dialysate proportioning model has been pro-posed. Then, initial implementation of proposed model was designed with fuzzylogic control and implemented on a low-cost microcontroller—Raspberry Pi 3.A Simulink model was also designed by incorporating fuzzy logic control toimplement in real time. The pumps’ flow rates are varied using Pulse WidthModulation (PWM) according to the controller signal, while the temperature sen-sors are placed to acquire actual temperature in this model. Subsequently, it hasbeen tested and verified by comparing the simulation and experimental results.Furthermore, the dialysate temperature trend was studied for various input condi-tions to analyze its controller behavior in real-time implementation. The resultsshowed the potential to develop robust control by optimizing the fuzzy rules andmembership functions. The system response time is found to be minimal (less than300 ms), and the performance error is acceptable (less than 0.55%). Further work isongoing to implement the dialysate temperature controller incorporating in vitrostudies.

Keywords Hemodialysis � Temperature controller � Fuzzy control systemRaspberry Pi � MATLAB/Simulink

M. H. A. Jabbar (&) � S. Anandan ShanmugamDepartment of Electrical and Electronic Engineering, University of Nottingham MalaysiaCampus, Semenyih, Malaysiae-mail: kecx4mhr@nottingham.edu.my

P. S. KhiewDivision of Materials, Mechanics and Structures, University of Nottingham MalaysiaCampus, Semenyih, Malaysia

© Springer Nature Singapore Pte Ltd. 2018D. Thalmann et al. (eds.), Intelligent Embedded Systems, Lecture Notesin Electrical Engineering 492, https://doi.org/10.1007/978-981-10-8575-8_1

1

1 Introduction

Over many years, hemodialysis (HD) had been recognized asmost effective treatmentfor patients suffering from kidney failure. Yet, it has been associated with frequentintradialytic complications, while intradialytic hypotension (IDH) remains the mostcommon complication in HD [1]. In addition to toxin clearance, there is also a heattransfer taking place in dialyzer other than the heat loss from the blood tubing to theenvironment. This tends to fluctuate body temperature during HD, subsequentlyinterrupting the patient’s thermal equilibrium. However, if the core temperaturechanges beyond a critical threshold, the increase in the thermoregulatory mechanismswill lead to IDH and an increased risk of morbidity. Likewise, the temperaturethreshold differs in individual patients. A long-term study showed that the highestmortality was observed in patients whose post-dialysis body temperature increased ordecreased, irrespective of baseline body temperature [2]. This serves as strong evi-dence in mortality due to fluctuation in body temperature during HD. Therefore, thecontrol of body temperature plays a vital role in the onset of hypotension.

The most common practice to control body temperature is to alter the dialysatetemperature in extracorporeal circuit, which was first described by Maggiore et al.in the 1980s [3]. Moreover, maintaining the dialysate temperature within thephysiological range is vital for patients’ safety. Until recently, a dialysate temper-ature of 37 °C was considered as standard temperature, which is somewhat higherthan the average physiological body temperature. Hence, warm dialysate (37–37.5 °C) frequently causes an increase in body temperature of approximately 0.3–0.5 °C [4]. In contrary, studies have confirmed that mild cool dialysate improvesthe hemodynamic stability compared to warm dialysate [5, 6]. But the use of cooldialysate in the range of 35–35.5 °C showed unpleasant effects in some patientssuch as shivering and cold sensation [7]. These tolerability can be optimized by anindividualized approach to dialysate temperature prescription.

Until now, there has been only one commercially available system inhemodialysis machine that was able to measure and control body temperature—Blood Temperature Monitor (BTM) by Fresenius, Germany. The control of BTMregulates the temperature of the dialysate to compensate for increase or decrease inbody temperature. Even though, it shows impressive improvements in hemody-namic stability during the treatment, there is a possibility for further improvement.In the recent past, studies have been published on dialysate temperature controlsystem with the intention to minimize complications during the treatment [8, 9].Based on these literatures, it is probably best to prevent a fluctuation in bodytemperature during HD, when concerned about optimal dialysate temperature. It canalso be seen that the idea of active regulation of dialysate temperature according tothe patients’ body temperature is much needed for our current society. The controlwhich does not require the predialysis body temperature prescription to be inputtedunlike BTM would be one of the major developments in hemodialysis machinetechnology. The fuzzy logic control (FLC) system is found to be the most suitableadaptive controller for this application due to its decision-making capability.

2 M. H. A. Jabbar et al.

In this paper, a novel dialysate proportioning model has been proposed for theeffective temperature control. So, the desired dialysate temperature can be achievedby a suitable controller. Later, a preliminary controller model was designed withfuzzy logic control and then implemented on a low-cost microcontroller—Raspberry Pi 3. The main aim of this pilot study was to analyze the performanceand verify the controller behavior in real-time environment. Thereby, the samestrategy can be further applied to implement in full-fledged dialysate model.

2 Proposed Model

The model which consists of two dialysate tanks at constant temperature of 35 and37 °C, respectively, has been proposed for the benefit of temperature controller asshown in Fig. 1. As the dialysate temperature range is narrow (35–37 °C), theefficient way to control the temperature is by varying the flow rates using peristalticpumps. Contrary, the control of temperature can also be made possible by imple-menting heating elements through the tubing, which would be ineffective in activeregulation. Moreover, the response time and accuracy of this narrow temperaturerange would be challenging to control using heating element. Hence, the control of

Fig. 1 Proposed model for individualized dialysate temperature control module

Design and Implementation of Dialysate Temperature Control … 3

peristaltic pumps by varying flow rates would be superior to conventional heatersfor this application.

This dialysate proportioning model was designed as an external module that caninterface in existing HD machines. This module consists of three temperaturesensors, three flow sensors, and two peristaltic pumps to ensure the dialysate flow todialyzer in an efficient way. The temperature sensors were placed in each tem-perature tanks and at the inlet of dialyzer, while the flow sensors were positioned tomonitor the flow rates of both tanks and final flow rate to dialyzer. Since theefficiency of dialysis depends on dialysate flow rate [10], extra care needs to betaken to control the final dialysate flow rate. Therefore, the required dialysatetemperature can be achieved using a robust controller, which controls body tem-perature in HD by regulating the two dialysate flow rates effectively.

3 Implementation Design

As this is an ongoing project, this paper presents a pilot model of the proposeddesign, consisting of two tanks with temperature sensor, a peristaltic pump withencoder and Raspberry Pi as shown in Fig. 2. Raspberry Pi 3 model B was selectedas the microcontroller in this design due to its high processing power and peripheralinterface. The other components such as sensors and actuator were interfaced toRaspberry Pi using the predefined functions provided by MATLAB/Simulink.A 12-V peristaltic pump was chosen, which can drive the fluid up to 400 mL/minsuitable for this application. However, a switching circuit was constructed to createthe interface between the pump and Raspberry Pi. To measure the flow ratenon-invasively, a 3D-printed encoder wheel with 16 evenly spaced holes and

Fig. 2 Block diagram of the implementation design

4 M. H. A. Jabbar et al.

infrared sensor are attached to the shaft of the DC motor. An additional limitation isthat the Raspberry Pi does not have an in-built analog-to-digital converter (ADC).Hence, two DS18B20 digital temperature sensors were selected, which are accurateand waterproof for this model. Meanwhile, water was used to mimic dialysate fluidin this study as it is the major component in dialysate.

3.1 Fuzzy Logic Control Design

In this study, a multiple-input and single-output (MISO) fuzzy logic control wasdesigned using fuzzy logic toolbox in MATLAB. In hemodialysis, the inputparameters should be capable of reflecting the hemodynamic status of patient andshould be measurable by using non-invasive sensors, while the output parametersshould be adjustable. Our earlier simulation study showed the comparison ofTakagi–Sugeno and Mamdani fuzzy inference techniques. Accordingly, this paperfocuses on Mamdani model due to its high reliability. The inputs ‘TS1’ and ‘TS2’denote the two temperature sensors, while the output ‘Pump’ denotes the PWMvalue to vary the speed of pump. By considering these properties, membershipfunctions were defined for each of the input and output variables as shown in Fig. 3.

Fig. 3 Membership function for input (above) and output (below)

Design and Implementation of Dialysate Temperature Control … 5

One of the most important factors that depend on the efficiency of controller isbased on fuzzy rule base. In this system, rule base was defined randomly with thepurpose of analyzing its behavior in the hardware. Hence, a total of 25 rules werecreated for this initial implementation design as shown in Table 1. In future, byanalyzing its performance, the rules can be continuously added to improve theaccuracy of this system.

3.2 Simulink Model

Simulink provides the environment to deploy it to hardware directly and also able torun it in external mode. This makes the fuzzy logic control implementation effectiveusing fuzzy logic control Simulink block. The MATLAB functions were used toread the temperature from the sensors using 1-wire communication bus, whereas thePWM signals were generated using SFunction block along with WiringPi librariesfor the pump output. Since infrared sensor is a digital sensor, a predefinedRaspberry Pi block was used to read the signals based on the speed. This speed wasthen calibrated with known tachometer and flow meter to convert the rpm to flowrate. The overall Simulink model is shown in Fig. 4.

Then, the temperature trend was studied for various input conditions to analyzethe fuzzy logic controller performance. Further tests were carried out to verify theresults between the simulation and experimental PWM values for random inputswith the help of oscilloscope. Moreover, the time response was also analyzed byinitializing trigger blocks at random inputs and corresponding outputs.

4 Results and Discussion

The Simulink model was simulated and deployed to Raspberry Pi by defining itsunique IP address. The fuzzy rule viewer and other scope were able to monitor thecorresponding readings while running the controller. This helps to analyze theoverall behavior of the model. In external mode, the temperature and flow rate

Table 1 Rule base of the implementation design

TS1 TS2

Very low (VL) Low (L) Normal (N) High (H) Very high (VH)

VL VS VS F VF VF

L S S F F VF

N F N N N F

H VF F S S S

VH VF VF S VS VS

6 M. H. A. Jabbar et al.

reading were analyzed, and it can be seen that the flow rate varies with the changein temperature in agreement with fuzzy rule as shown in Fig. 5. Additionally, itshows that the output has the potential to adapt to various operating conditions anddisturbances. Thus, the fuzzy logic control allows more flexibility to the changingenvironment.

To further strengthen the effective implementation, the simulated results ofPWM must show a strong agreement with its experimental results. As the outputfrom fuzzy logic is PWM signal, it is best practice to compare the raw data with theintention to verify the fuzzy logic implementation. The comparison of these resultswas shown in Table 2. It was confirmed that the error between the actual andsimulation results is quite negligible. However, the maximum error was found to be0.55%, which can be regarded as acceptable.

The response time is considered as one of the vital parameter in hemodialysismachine technology. This is to ensure that the output changes when the inputtriggers within a fraction of second. In this design, the response time was analyzedby increasing and decreasing the defined trigger input values. The change in stateand time taken to reach 235 rpm when increasing and decreasing the temperature isshown in Fig. 6. Overall, the response time for random inputs was shown inTable 3. The maximum time taken to alter the speed of pump with increased inputis 300 ms, while the maximum when decreased is 270 ms. It was also noticed that

Fig. 4 Simulink model of the implementation design

Design and Implementation of Dialysate Temperature Control … 7

the rapid change in the input results in faster response time to trigger the outputvalue.

The real-time implementation of fuzzy logic on Raspberry Pi showed us thepossibility of controlling the hemodynamic parameters during hemodialysis. Theseresults motivate us to develop the proposed model of dialysate temperature control.The improvements in fuzzy control design have high potential to make it a robustcontrol by optimizing the fuzzy rules and membership functions. Therefore, our

Fig. 5 Output of temperatures (above) and flow rate (below) from Simulink

Table 2 Validation of output with simulation and experimental results

TS1 (°C) TS2 (°C) Simulation Experiment Relative error (%)

PWM value Duty cycle PWM value

27 34.5 240.5 0.94 239.2 0.55

28 31.1 216.2 0.84 215.2 0.48

30 31.1 204.9 0.80 204.0 0.44

30.6 30.4 191.3 0.75 191.0 0.16

29.8 28.6 179.6 0.70 179.0 0.33

34.5 32.2 152.8 0.60 152.0 0.52

34.5 34.5 148 0.58 148.0 0.00

8 M. H. A. Jabbar et al.

future study shall implement fuzzy logic control system on dialysate temperaturebiofeedback system to maintain stable body temperature during hemodialysis.

5 Conclusion

The body temperature is one of the vital parameters to be controlled duringhemodialysis. Thus, an innovative design for the active regulation of dialysatetemperature by varying flow rates has been proposed. Moreover, it is evident thatthe fuzzy logic control system has been successfully implemented in a low-costmicrocontroller—Raspberry Pi 3. It also showed that the performance is encour-aging with faster response time (less than 300 ms) and minimum error (less than0.55%). Further work is ongoing to develop the proposed dialysate model and itsin vitro studies.

Acknowledgements This work was supported by Faculty of Engineering, University ofNottingham, Malaysia Campus.

Fig. 6 Response time for the output at 235 rpm when input increasing (left) and decreasing(right)

Table 3 Time response for various increasing and decreasing inputs

TS1(°C)

TS2(°C)

TriggeredRPM

Temperature (increase/decrease)

Time response (inms)

29.1 27.3 215 Increase 300

Decrease 270

27.3 30.4 235 Increase 300

Decrease 220

27.6 32.5 255 Increase 280

Decrease 160

Design and Implementation of Dialysate Temperature Control … 9

References

1. Bradshaw W, Bennett PN (2015) Asymptomatic intradialytic hypotension: the need forpre-emptive intervention. Nephrol Nurs J 42(5):479–485 (quiz 486)

2. Usvyat LA et al (2012) Relation between trends in body temperature and outcome in incidenthemodialysis patients. Nephrol Dial Transplant 27(8):3255–3263

3. Maggiore Q et al (1982) Blood temperature and vascular stability during hemodialysis andhemofiltration. Trans Am Soc Artif Intern Organs 28(1):523–527

4. Pergola PE, Habiba NM, Johnson JM (2004) Body temperature regulation duringhemodialysis in long-term patients: Is it time to change dialysate temperature prescription?Am J Kidney Dis 44(1):155–165

5. Korkor AB, Bretzmann CM, Eastwood D (2010) Effect of dialysate temperature onintradialytic hypotension. Dial Transplant 39(9):377–385

6. Chesterton LJ, Selby NM, Burton JO, McIntyre CW (2009) Cool dialysate reducesasymptomatic intradialytic hypotension and increases baroreflex variability. Hemodial Int 13(2):189–196

7. Van Der Sande FM et al (2009) Control of core temperature and blood pressure stabilityduring hemodialysis. Clin J Am Soc Nephrol 4(1):93–98

8. Busono Ario PF, Handoyo T, Barkah A, Suryana Y, Riyanto R, Febryarto R (2015)Development of fuzzy logic based temperature controller for dialysate preparation system. In:Proceeding of the electrical engineering computer science and informatics, 2015, vol 2.EECSI, pp 264–268

9. De Capua C, Fabbiano L, Morello R, Vacca G (2014) Optimized procedure to evaluate thethermal energy transfer in hemodialysis treatment. Instrum Sci Technol 42(4):458–468

10. Albalate M et al (2015) Is it useful to increase dialysate flow rate to improve the delivered Kt?BMC Nephrol 16(20):1–6

10 M. H. A. Jabbar et al.

Raspberry Pi in Computer Scienceand Engineering Education

S. Alex David, S. Ravikumar and A. Rizwana Parveen

Abstract Sustainable Environmental Development is one of the hot topicsnowadays. Industries were advised to reduce and control pollution by using pol-lution control equipment. Computers are also contributing some amount in thepollution and power consumption. On the other hand, miniaturization, less powerconsumption, and environment-friendly devices are invented by the researchersevery day. One such device is Raspberry Pi. Size of the Raspberry Pi is not biggerthan a credit card with high computing capacity and low power consumption. Pi canrun Linux as well as Windows 10 in its higher versions. This paper aims to suggestthe Pi can be used for practicing most of the laboratory courses in the computerscience engineering curriculum. Most laboratory courses are practiced in C, C++,and Java languages. Apart from above-mentioned languages, some laboratorycourses make use of front end and back end tools. Above-mentioned languages canbe executed in Raspberry Pi. Following points are analyzed between normalcomputer and Pi, execution time, power consumption, and environmental effect. Inall the comparison, Pi gives much higher advantage over existing system.

Keywords Raspberry Pi � Engineering education � Eco-friendly computer

S. Alex David (&) � S. Ravikumar � A. Rizwana ParveenDepartment of Computer Science and Engineering, Vel Tech University,Avadi, Chennai, Indiae-mail: alex_art2002@yahoo.co.uk

S. Ravikumare-mail: ravikumar.086@gmail.com

A. Rizwana Parveene-mail: rizwanaparveen64@gmail.com

© Springer Nature Singapore Pte Ltd. 2018D. Thalmann et al. (eds.), Intelligent Embedded Systems, Lecture Notesin Electrical Engineering 492, https://doi.org/10.1007/978-981-10-8575-8_2

11

1 Introduction

Computers are much useful in the education. Most of the complex problems can beexpressed in simple manner with a help of computers. For example, in childhoodeverybody learned the poem by seeing the book with some pictures. Reading andunderstanding was made easy with the help of those pictures. Later when animationwas introduced in the education, it takes the understanding one step higher. Inschool days, many of us who studied during the 1990s used the picture to under-stand the working heart. Now, the same has been available as animated video showsthe flow of blood, arrangements of nerves clearly; hence, the complex structuresbecome very easy to understand. Nowadays, the miniaturization, cost-effective,pollution controlled, or environment-friendly device attracted the researchers overpast one decade. The size of the first computer occupies a room, and then thechanges comes in the hardware technologies reduced the size of the computer tohold with in the palm. A team of students from University of Cambridge invented acredit card size computer in the year 2012. It competes in the computer world tomake a place permanently in this field. It is cheap, compact and more computingpower compare to other computers. The name of this credit card size computer is“Raspberry Pi” [1].

This paper aims to give some idea about Raspberry Pi versions. What roleRaspberry Pi can play in the educational institute in India. The following chaptersdiscussed the laboratory courses in the curriculum in major intuitions in India, thesoftware which is used in each laboratory and what laboratory courses can make useof Raspberry Pi [2]. The advantages of Raspberry Pi over the other computers arediscussed in the conclusion.

2 Raspberry Pi an Intro

Raspberry Pi has an ARM processor as a computing component in single board,which makes use of low power to boot and run and occupies the size not more thanthe size of a credit card. Raspberry Pi comes in many versions: Raspberry Pi A, A+,B, B+, and Pi 2. At present, A+, B+, and Pi 2 are available in the market.Remaining were versions stopped from the production. Raspberry Pi can be con-nected to TV through HDMI or RCA. It has a computing power equal to few yearsback UNIX workstation. Many operating systems come based on Linux kernel andin last version; i.e., Raspberry Pi 2 can run Windows 10 operating system. Otherhardware can be interfaced with Raspberry Pi with I/O pins which makes theRaspberry Pi as server. It can be used as a super computer by clustering number ofRaspberry Pi’s. Graphics capabilities are more or less equal to some Xbox whichcomes during 2000–2001. Model A versions require very low power and it can beused in school level embedded project. Model B version can be used in many areaslike development, high computing environment [3].

12 S. Alex David et al.

All versions (except Raspberry Pi 2) of the RPi are based on the SoC BroadcomBCM2835; it has an ARM CPU together with a VideoCore IV GPU. The RAM isshared between the CPU and the GPU. The model A has 256 Mbytes of RAM, andmodel B/B+ has 512 Mbytes [4].

The Raspberry Pi 2 is based on Broadcom BCM2836 system on chip (SoC) thathas 1 GB RAM (Fig. 1).

Raspberry Pi 2 has the following features 4 USB ports, 40 GPIO pins, fullHDMI port, Ethernet port, combined 3.5 mm audio jack and composite video,camera interface (CSI), display interface (DSI), micro SD card slot, VideoCore IV3D graphics core. Comparison between all B models is listed below.

SoCModel B: Broadcom BCM2835 SoCModel B+: Broadcom BCM2835 SoC Model Pi 2: BroadcomBCM2836 SoC

CPUModel B: 700 MHz single-core ARM1176JZF-SModel B+: 700 MHz single-coreARM1176JZF-S Model Pi 2: 900 MHz quad-coreARMCortex-A

GPUModel B: Broadcom VideoCore IV @250 MHz, OpenGL ES 2.0Model B: Broadcom VideoCore IV @250 MHz, OpenGL ES 2.0Model Pi 2: Broadcom VideoCore IV @250 MHz, OpenGL ES 2.0

Primary Memory (SDRAM)Model B: 256 MB in first version of model B, 512 MB SDRAM@ 400 MHz in thesecond version of model BModel B+: 512 MB SDRAM@ 400 MHzModel Pi 2: 1 GB SDRAM@ 400 MHz

Fig. 1 Raspberry Pi 2

Raspberry Pi in Computer Science and Engineering Education 13

StorageModel B+: SD/MMC/SDIO card slotModel B+: Micro SDModel Pi 2: Micro SD

USB PortsModel B: 2Model B+: 2Model Pi 2: 4

GPIOModel B: 26 pinModel B+: 40 pinModel Pi 2: 40 pin

PowerModel B: 700 mAModel B+: 600 mAModel Pi 2: 800 mA

Operating systemModel B: Linux, OpenELEC, XBMC, RetroPie, RISC OS, Firefox OS, Plan 9,Android

Model B+: Linux, OpenELEC, XBMC, RetroPie, RISC OS, Firefox OS, Plan 9,Android

Model Pi 2: Linux, OpenELEC, XBMC, RetroPie, RISC OS, Firefox OS, Plan 9,Android, Windows 10.

Among all the B models, only Pi 2 has additional support that it can run Windows10 operating system.

3 Laboratory Courses in Computer Engineering

Bench marking of computer laboratory courses in India is shown in Table 1.From the above benchmarking among the universities and top engineering

college, it is clearly showing most of the practical executed in C or C++ envi-ronment. Most of the programs have been executed in Pi, and the average executiontime has been calculated in order to compare with other existing system [5].

Computer Practice Laboratory: In all the engineering degree programs, thiscourse is mandatory. Every freshman will undergo this course. Basic concepts inthe C programming will be taught and practiced in the laboratory sessions [6].

Data Structures Laboratory: In this course, learners will practice all theexperiment in C or C++.

14 S. Alex David et al.

Tab

le1

Com

puterlabo

ratory

coursesin

engineering

S. No.

Practical

name

Ann

aUniversity

,Chenn

ai,

Tam

ilNadu

JNTU,Hyd

rabed,

Telun

gana/AP

NIT

Silchar,

Assam

SRM

University

,Chenn

ai,Tam

ilNadu/

Delhi

Jadavp

urUniversity

.Kolkata,WestBengal

1Com

puterPractices

Laboratory

C/C++

lang

uage

C/C++

lang

uage

C/C++

lang

uage

C/C++

lang

uage

C/C++

lang

uage

2DataStructures

Lab

’’’’

’’’’

’’

3Operatin

gSy

stem

sLab

’’’’

’’’’

’’

4Com

puterNetworks

Lab

’’’’

’’’’

’’

5Com

piler

Llabo

ratory

C,YACC

andLex

C,Y

ACCandLex

C,Y

ACCandLex

C,YACC

andLex

C,YACC

andLex

6Com

puterGraph

ics

Laboratory

C/C++

lang

uage

C/C++

lang

uage

C/C++

lang

uage

C/C++

lang

uage

C/C++

lang

uage

7Java

Prog

ramming

JDK

JDK

JDK

JDK

JDK

8DataBase

Managem

entSy

stem

MyS

QL/Oracle

MyS

QL/Oracle

MyS

QL/Oracle

MyS

QL/Oracle

MyS

QL/Oracle

9Web

Techn

olog

yLaboratory

HTML,A

pplet,Java

Script

HTML,App

let,

Java

Script

HTML,App

let,

Java

Script

HTML,App

let,Java

Script

HTML,App

let,Java

Script

10CASE

Too

lsLab

Rationalsuite

open

source

alternatives:ArgoU

ML

Rationalrose

Rationalrose

Rationalrose

Rationalrose

11VisualProg

ramming

Microsoftvisual

stud

ioMicrosoftvisual

stud

ioMicrosoftvisual

stud

ioMicrosoftvisual

stud

ioMicrosoftvisual

stud

io

12Mob

ileApp

lications

Develop

mentLab

ADK,iOSdevelopm

ent

kit,JA

VA

ADK,iOS

developm

entkit,

JAVA

ADK,iOS

developm

entkit,

JAVA

ADK,iOSdevelopm

ent

kit,JA

VA

ADK,iOS

developm

entkit,

JAVA

Raspberry Pi in Computer Science and Engineering Education 15

Operating Systems Laboratory: Shell programming and C programming will beexecuted in the Linux environment.

Compiler Laboratory: Various phases of the compiler will be executed in theLinux C environment.

Computer Graphics Laboratory: Graphical library had been used to experimentthe basic graphics concepts such as 2D primitives and 2D, 3D transformation.

Java Programming Laboratory: Basic Java concepts will be executed in the JavaDevelopment Kit (JDK).

Web Technology Laboratory: Basic concepts of HTML and Java Script will bepracticed in this course.

All the above courses can be executed in Raspberry Pi B model. Some of theprograms were executed in high-end computer and Raspberry Pi in order to com-pare the execution time. Most of the programs had the execution time more or lessequal to the high-end system.

A. Advantages of using Raspberry Pi:

• Power consumption very less than high-end system• Cost: Very less than high-end system• Environment: Raspberry Pi produces less heat than high-end system.

4 Conclusion

Raspberry Pi has a computing capacity equal to the high-end systems. It can beused for most of the laboratory courses in the engineering studies. Comparison onexecution time has been made between high-end system and Raspberry Pi. Resultsclearly show that Raspberry Pi can be used for laboratory courses. In many ways,Raspberry Pi shows advantage over existing laboratory system.

References

1. Alex David S, Grace Priyanka J (2014) Study on Raspberry Pi. IJMEIT 2(7) July 20142. Paramanathan A, Pahlevani P, Thorsteinsson S, Hundebøll M, Lucani DE, Fitzek FH Sharing

the Pi: testbed description and performance evaluation of network coding on the Raspberry Pi3. Bruce RF, Brock JD, Reiser SL (2015) Make space for the pi. IEEE SoutheastCon, 9–12 Apr

2015, Fort Lauderdale, Florida4. The Making of Pi [Online]. Available: http://www.raspberrypi.org/about/. Accessed 22 Jan

20155. Sharma A, Williams B (2014) Build your own supercomputer at home with Raspberry Pi

computers. In Proceedings of the southern association for information systems conference,Macon, GA, 21–22 Mar 2014

6. Cunningham J (2014, February 28) Tech in the classroom: Raspberry Pi, (Education World),[Online]. Available: http://www.educationworld.com/a_tech/tech-inthe-classroom/raspberry-pi.shtml. Accessed 23 Jan 2015

16 S. Alex David et al.