Theses supervised by Prof. Dr. Ergun Erçelebi
68 theses · Gaziantep University
Development of multi-level inverter for power quality enhancement
The objective of this thesis is to develop and improve the performance of the multi-level inverter. Two paths have been followed in the development and improvement works. The first one represents the off grid connected multi-level inverter. The trend is focused to increase the number of the level at the output of the inverter while the inverter represents the main energy source. In the second path followed in the studies, the grid-connected multi-level inverter has been considered. Power quality enhancement has been done by developing the control unit of grid connected multi-level inverter in such a way to ensure the stability of the system against the system voltage sag. The multilevel inverters designed in the thesis are both simulated using a computer software program and implemented in hardware. The developed inverters are multilevel and hybrid inverters. Conventional inverters give five levels in the output waveform. These inverters contain two H-bridge circuits to generate different levels at the output while the hybrid inverter developed in the thesis includes three H-bridge circuits to form different levels at the output. Moreover, developed hybrid inverter can also operate in conventional mode when it is supplied with equal sources. The output of each H-bridge has been connected in a cascaded form. The sum of the individual H bridges voltages represents the final inverter voltage. Experimental results on the developed circuits gave satisfactory results and confirmed the accuracy of the design. Key Words: Low Voltage Ride Through (LVRT), Photovoltaic Panel (PV),Multi Level Inverter, Maximum Power Point Tracking (MPPT), Active And Reactive Power Control
Development of RW-UAV detection model using deep learning training with a synthetic dataset
The use of rotary wing unmanned aerial vehicles (UAV) is increasing day by day and their accessibility is getting easier. As a disadvantage of easy accessibility and usability, UAV technology threatens security and personal areas. This technology, which is easy to access, should be detected with a cheap and easy-to-install detector. The aim of the study is to develop the UAV detection sensor using camera and machine learning based systems. Deep learning method has been used in sensor technology, which has shown successful classification results recently. Artificial intelligence trained with deep learning method shows effective results in terms of performance. On the other hand, artificial intelligence (AI) training needs a lot of data. To overcome this obstacle, the data set needed by the deep learning algorithm was produced synthetically using game engine software. The original aspect of the study is that the artificial intelligence trained with synthetically produced images was tested on real data and achieved 90% success. The success of AI, which is directly trained with synthetic data and tested with real data, is very low. The reason for this is the difference between the synthetic and real image. To minimize this difference, 7 different experiments were designed for different feature extraction algorithms. One of the experiments is the Corner Detection and Nearest Three-point Selection (CDNTS) feature extraction layer that we originally developed. The CDNTS layer classified the real data with 90% success.
Design and analysis of wearable antennas for use in wireless biomedical telemetry systems
This thesis presents the design of a fractal printed circuit antenna construction for portable applications. A modified Hilbert patch was printed on a flexible polyamide substrate for the proposed antenna. An open loop with a defective electromagnetic band gap (EBG) surrounds the antenna. Experimental findings are compared to simulated results from a commercial software package based on Computer Simulation Technology Microwave Studio for the proposed antenna (CST MW). The experimental and simulated findings are found to be quite similar. The suggested antenna design is determined to be compatible with the frequency ranges of UHF and ISM portable systems. The antenna shows two bandwidths from 0.8GHz to 1GHz and from 2.3GHz to 2.6GHz with average gain of around 3.1dBi and 7dBi, respectively. The antenna performances are conducted based on analytical and numerical simulations. The fabricated prototype performances have been measured experimentally to show excellent agreement with the simulated results. Finally, it is found that the proposed antenna frequency resonance and bandwidth are insignificantly affected when mounted in close to the human body; therefore, it would be excellent candidate to be in use with biomedical applications. The proposed antenna shows several resonances around the Body Area Networks (BAN) and ISM frequency bands. The proposed antenna shows a suitable gain for short and medium wireless communication systems about 1dBi, 1.24dBi, 1.48dBi, 2.05dBi, and 4.11dBi at 403MHz, 433MH, 611Mz, 912MHz, and 2.45GHz, respectively. The antenna is printed using silver nanoparticle ink on a polymer substrate. The antenna size is reduced to 20×10mm2 to suit the different miniaturized wireless biomedical devices. The fabricated prototype has been tested experimentally on the human body. The main novelty with this design is suppressing the surface wave from the patch edges, allowing it to significantly reduce the back radiation toward the human body when used enclosed to it.The proposed antenna is located on the human head to determine the specific absorption rate (SAR). It is found in all cases, the proposed antenna shows low SAR effects on the human body.
4 wheel steering electronic control unit design
Ease of driving and flexibility in movements are very important for the developing automobile technology today. The aim of the study is to provide convenience to the driver by trying different modes in the steering system of a vehicle. In the thesis, an electronic board has been designed for the steering system for the 4 wheels in the vehicle. The card developed in the study provides choosing from 3 different modes as standard, circle and crab to driver. The driver will have the advantage of both comfort and safe driving with the mode chosen according to the road and load conditions. Also, the 4-wheel steering system improves handling stability and active safety at high speeds. Using 4-wheel steering at low speeds can reduce the turning radius on the steering wheel for more convenient maneuvers. Positive feedback was received from drivers who tried the card on heavy-duty vehicles. Tests continue on different heavy-duty vehicles. If the electronic board and software do not perform as expected in different heavy duty vehicles, improvements will be made in the design. However, the results obtained so far are satisfactory. It shows good response performances, also improves ride quality and stability
Building management system for distribution control center
As the complexity and the type of electrical applications perpetually expands, monitor and control will require new systems are based on some features in such feature of self-powered and distantly management. Building management systems (BMSs) are developed as efficient solutions for users to controls and monitor electronic devices. BMS is a method especially developed to monitor environmental events such as temperature, fire and humidity in the computer server room and to warn the operator in case of fire or if the temperature and humidity values exceed the specified threshold level. A BMS uses some inexpensive sensors and real-time modules to generate advance information about the climate inside the space that needs to be remotely controlled. In this thesis, a building management system for a computer server room has been developed using inexpensive sensors, prototyping cards and mini-computers. Raspberry Pi used as a mini computer has been utilized to collect information from sensors in the server room or data center and transfer them to the receiving side via ZigBee wireless modules. The Arduino prototyping card has been used to process the sensor data transmitted wirelessly from the server room and actuate the actuators such as ventilation fans and switches. The developed BMS has been tested under critical environmental conditions to evaluate its performance. Experimental results showed that the BMS performs robustly and precisely even in extremely unwanted conditions
A real-time face recognition based on mobileNetV2 model
Facial recognition technology is one of the fastest developing technologies. Thanks to its efficacy, ease of use, and extensive applications in health, protection, and surveillance, face recognition is the most widespread technology compared to other biometric ones. Technologies based on deep learning and neural networks have demonstrated superior efficiency and speed when compared to traditional approaches for recognizing persons. In this work, we propose a fast real-time facial recognition system that is applicable for mobile devices and devices of low computational power. It relies on today's latest convolution neural networks algorithms. The database is built based on photos from a collection of known people and some VggFace dataset celebrities. The proposed system is divided into several steps, starting with the detection of faces in the input images using the MTCNN algorithm, followed by their alignment and preprocessing, extracting the face characteristic vectors for each face using the mobileNetV2 model, and finally comparing, classifying and distinguishing the faces. The performance of the system has been evaluated using some examples. The system produced results with an accuracy of 92.67%, with an average of 11.68 frames per second. When compared to the state-of-art models, the experimental results demonstrated that mobilenetv2 model was 2 times faster than ResNet50 model and 4 times faster than VGG16.
Pre-surgical device for correction of the cleft lip
The first object of this thesis is to propose a new architecture that can be utilized in the pre-surgical treatment of cleft lip and palate. The architecture consists of two main parts mechanical and electronic. The proposed architecture provides an external pressure applied directly to the cleft lip areas through an airbag placed on the mechanical part of the architecture. The amount of pressure applied to the cleft lip is controlled by the electronic part. FEA method was used to analyze the effect of pressure applied to the cleft lip and cleft bone regions. We analyzed the effect of displacement distributions, the tensile and compressive strengths, and von Mises stress distributions on the cleft areas. The results proved that the external pressure led to bringing the incisions closer to each other. External pressure has contributed to bringing the incisions closer to the target position of the plastic surgeons. The results have shown that the proposed architecture can contribute to the presurgical treatment of clefts in the mouth and palate as an alternative to the traditional method. The second object of our thesis is to detect the cleft lip and extract its features that provide a second opinion for plastic surgeons for a vision of the cleft lip features using the combined method. The combined method consists of three algorithms for cleft lip detection and feature extraction (face detection, cleft lip classification, and cleft lip detection). The results show that our method has the best efficiency and high accuracy in lip detection and optimal feature extraction. Moreover, the proposed method demonstrates its effectiveness with a limited set of cleft lip images.
COVID-19 detection from signs and symptoms using machine learning
Effective SARS-CoV-2 (COVID-19) screening enables a speedy and precise diagnosis of COVID-19, lowering the load on the health care systems. Different machine learning models were developed for COVID-19 detection. These models are intended to aid physicians globally in the quarantining of patients, especially in poor areas. At the end of 2021, the global officially recorded COVID1-19 cases reached 286,582,541 with 5,430,949 confirmed deaths. This study aimed to deploy and test machine learning-based models for predicting COVID-19 diagnosis. Seven machine learning models have been utilized as Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). The models were trained and tested on data from 2,151,898 tested people, among whom 208,726 (9.7%) were found to have COVID-19. Simply six binary characteristics were used to predict COVID-19 testing results with acceptable accuracy. The data included five early COVID 19 clinical signs and symptoms, such as cough, fever, sore throat, shortness of breath, and headache. Also, the data from people in contact with a confirmed COVID-19 case were used. Overall, we evaluated the possibility of building a model that could be used for prioritizing testing for COVID-19 by asking simple questions depending on the data we acquired from GitHub. When using normal unbalanced data; we achieved 92.9% accuracy, 60.4% sensitivity, 96.4% specificity and 62.2% F-score with the MLP, DT, RF and XGBoost machine learning models. When using balanced classes, all the seven machine learning models produced 91.8% accuracy, 65.1% sensitivity, 94.7% specificity and 60.61% F-score results. When using only symptomatic cases (161.915), MLP, DT, RF and XGBoost models gave 70.8% accuracy, 89.1% sensitivity, 47.3% specificity, and 77.4% F-score results. In the three scenarios XGBoost, RF, DT, and MLP gave the best results. Regarding the training time, XGboost, RF and DT were faster than MLP. Key Words: Machine Learning, Classification, Diagnosis, COVID-19, SARS-COV-2, Signs and Symptoms
Design of frequency selective surface based on reflection coefficients in C and X bands using nested convolutional neural network
Frequency selective surfaces (FSS) are surfaces consisting of periodically placed shapes used to reflect, transmit and absorb electromagnetic waves. FSS has many uses such as increasing the antenna performance, reducing the radar cross section, filtering, and increasing the photovoltaic cell performance. Due to the use of FSS in many fields, it has attracted the attention of researchers and has led to studies in this field. The studies are generally aimed at obtaining the FSS surface with optimization algorithms and methods. For this purpose, the desired reflection coefficients (S11) are obtained by iteration. With the significant increase in computational power in recent years, the use of machine learning algorithms has become more widespread and successful results have been obtained in classification and regression problems. In this thesis, studies were carried out to obtain the FSS corresponding to the desired S11 with Decision trees, K-nearest neighbor, Deep learning and hybrid deep learning algorithms from machine learning algorithms. In the training process of machine learning algorithms, reflection coefficients between 6-14 GHz were used as input, while 50x50 FSS images were used as targets. Machine learning models were trained using 29722 reflection coefficients in total. As a result of the training process, the most successful result was obtained with the nested convolutional neural network model, which is a hybrid model. The mean absolute error value of the nested convolutional neural network model was 0.69, and the R2 value was 0.9.
Raspberry pi ve python ile gerçek zamanli plaka tanima sistemi
In order to provide reliable transportation with the increasing traffic density in the current time, it is necessary to detect dangerous situations with video analysis by using camera and remote control system. One such analysis is Automatic License Plate Recognition. Automatic license plate recognition systems consist of algorithms such as license plate detection, edge detection, character segmentation and optical character recognition. With the license plate recognition system developed in this thesis, after preprocessing the license plate image taken from the camera with image processing algorithms, license plate recognition has been made with the deep learning algorithm and the recognized license plate information has been sent to the defined e-mail address. In the developed license plate recognition system, Raspberry Pi, called mini computer, has been utilized as hardware, and the software has been developed in Python programming language. From edge detection methods; the performances of Canny, Fuzzy, Roberts, Sobel and Prewitt methods on the license plate recognition system were measured and the performance of the Canny edge detection method was found to be high and the algorithm of this method was embedded in the license plate recognition system. As a final step, Tesseract optical character recognition has been performed by scaling the frame. After recognition, a comparison has been made with the characters defined in the deep learning structure and the license plate recognition process has been completed. Tests were carried out to evaluate the developed license plate recognition system. As a result of these tests, the average accuracy rate of the system was 91.67% in real-time license plate recognition and edge detection, and 89.43% in character recognition. With the developed license plate recognition system, low cost and high accuracy recognition will be achieved.
Heart attack prediction using machine learning
According to the World Health Organization (WHO) 2020 report, more than 17.9 million people lose their lives due to heart attacks each year, making it one of the deadliest diseases worldwide; furthermore, heart attack is classified as a cardiovascular disease that affects the lives of many people globally. Machine learning, vital to the healthcare industry, enhances people's quality of life along with scientific advancements. In this thesis, studies on predicting heart attacks using machine learning algorithms were conducted. The data used in the studies were obtained from the UCI Machine Learning database. The prediction of heart attacks was carried out using five well-known machine learning models mentioned in the literature. Additionally, a new conceptual model integrating data preprocessing, hyperparameter tuning, and Logistic Regression was proposed for predicting heart attacks in this thesis. Tests conducted on the proposed model yielded satisfactory results, achieving an accuracy rate of 93%. Furthermore, the results produced by the proposed model were compared with the outcomes of other machine learning algorithms, demonstrating that the proposed model outperforms other algorithms.
Face detection and recognition based on raspberry Pi using HAAR cascading and convolution neural network
Face Detection is a form of biometric method that relates to the automatic detection of faces by computerized systems through observation of the face. It is a popular feature in biometrics, digital cameras, and social tagging. Face detection and recognition have received increased research focus in recent years. In this thesis, a face detection and recognition system have been proposed and developed for detecting and recognition faces through the hybridization of two algorithms: HAAR cascading algorithm and deep learning algorithm. The proposed system consists of two approaches. The first approach, the HAAR cascading algorithm, was developed by taking a shot of the face and reducing it several times to ensure that there is a face at each shrinking time. The second approach has proposed convolution neural network (CNN) model to increase accuracy of classification. In addition to improving each algorithm, hybridization of the two algorithms significantly improved the results of the classification. In proposed system two dataset was used: download dataset, and real dataset. The accuracy of modifying HAAR in detection reached 98.667% for real dataset, and 97.532 % for download dataset. The accuracy of proposed model of CNN in classification reached 96.23% for download dataset, and 100% for real dataset. The tests conducted on the developed facial recognition system have demonstrated that the proposed algorithms and the developed real-time facial recognition system yield satisfactory results. Key Words: Face Detection, Face Recognition, HAAR Cascading, CNN.
Development of counterfeit banknote recognition system using deep learning
Banknote counterfeiting undermines financial stability and public trust on the currency of a nation. Moreover, it also has various economic implications on financial institutions and businesses. The counterfeiters employ increasingly sophisticated techniques to mimic the genuine banknotes with the advancement of technology, which necessitates the development of fast and robust counterfeit banknote detection systems in order to ensure the integrity of financial systems as well as maintaining the public trust on currency transactions. In this thesis, a Turkish lira authentication system has been developed which locates the banknotes on images and classify the located banknotes as genuine or counterfeit as well as identifying their denominations. The localization part is developed using the classical and well-known digital image processing techniques, where a novel convolutional neural network architecture is developed for the classification part as the deep learning, which is a subset of artificial intelligence, has shown remarkable success in automated feature extraction and pattern recognition tasks. The conducted experiments shows that the proposed architecture outperforms some state-of-the-art deep learning algorithms in terms of accuracy, loss, inference speed, and file size.
Spectrum sensing and opportunistic medium access control protocol design for cognitive radio wireless networks
The explosive growths in the wireless communication field require more spectral resources, while the radio spectrum is a finite natural source. On the other hand, usable frequency bands are nearly fully occupied by the licensed users by the static allocation strategy and it is also known that most of the radio spectrum is inefficiently used. In cognitive radio (CR), frequency allocation and the access to radio spectrum is not assumed fixed but dynamic and thus cognitive radio technology utilizes this dynamic structure to improve spectrum efficiency and network performance. The primary objective of the thesis is to study spectrum sensing duty of cognitive radio attached with proper medium access control (MAC) design. A new wireless network scheme where the intentional background noise is added to the wireless region is proposed. With the proposed scheme, a blind sensing algorithm is introduced. The proposed scheme mainly addresses the sensing problem of secondary users as well as the primary users without any prior knowledge. Moreover, the problems coming from noise uncertainty and different kind of noise types are diminished because of added dominant noise characteristics. The performance analysis has been conducted using real data and the experimental observations have been obtained. The experimental results show that the proposed sensing algorithm outperforms the existing methods and is robust on noisy environments. By the novel method presented here, 90% true detection performance is achieved on the orders of nearly -10 dB SNR values. With nearly perfect sensing performance, the proposed CR-MAC provides simple, fast and accurate operating conditions.
Design and implementation of a teleconference system using an improved HEVC codec
Teleconferencing has become an indispensable element in any business system, because it offers the opportunity for collaborators to participate in a virtual group while remaining in divergent regions. Teleconferencing also increases productivity, minimizes travel expenses and saves travel time. This thesis presents a reliable Teleconference system that utilizes an improved high-efficiency video codec (HEVC) H.265 technology with a congestion control. The improvement is based on Coding Unit (CU) size decision and entropy coding, which provides an adequate approach to enhance the real-time video/IP technology in terms of improved video quality and increased compression ratio compared to the previous codec (H.264) and original HEVC. In the thesis, we proposed fast algorithms that reduce encoder complexity in a way more suitable for a real-time processing. Our system ensured that voice, video, text and other control packets are streamed efficiently to various individuals with smooth real-time communication. This thesis proposed a solution for a qualified server/client teleconference system. The system is an open, expandable platform and can work efficiently in an intranet/internet environment.
Transient stability improvement in multi-machine system using power system stabilizer (pss) and static var compensator (svc)
Modern power systems are equipped with auxiliary equipments to consider power system stability, especially transient stability and small disturbance. Transient stability plays more effect role in stability of power system, during fault and huge disturbance. In this thesis, we offers comparative between power system stabilizer (PSS) and static Var compensator (SVC) used to improve damping oscillation and transient stability. Power system stabilizer is one of the traditionally devices and common controls applied to damp out the low frequency oscillations. The efficiency of a power system stabilizer PSS connected to the exciter and/or governor unit in damping electromechanical oscillations of isolated synchronous generator is verified. Also, the static Var compensator SVC device is a member of shunt flexible AC transmission system (FACTS) family, it is the recent development of power electronics. SVC makes contribution to improve the stability of the power system by controlling the amount of reactive power injected into or absorbed from the power system. The designed model has been tested for multi machine contain of 4 synchronous machine 6 bus with difference load simulated by software program, and took several cases. All simulation results show that SVC is superior to PSS in improving transient stability and damping oscillation.
Development of embedded system for monitoring temperature of blood bank
The human blood is analyzed for three main components as plasma, platelets and red blood cell. Those components of blood must be safekeeping inside refrigerators. There are many refrigerators inside blood bank center. In this thesis, we have developed a embedded hardware and software for monitoring of temperature of 24 refrigerators inside blood bank center. Embedded hardware is implemented with microcontroller, oscillator circuit, CAN integrated circuit for CAN bus, and LCD. Due to the security of locations in the blood bank hall and difficulty of monitoring of each refrigerator separately, this work proposes a solution to monitor temperatures of all the blood bank refrigerators in one location. CAN-bus system has been used because it has many advantages. Especially it has been preferred due to easy in use, low cost, providing a reduction in wiring, fast to repair and easily expanding. PIC18F458 microcontroller has been used in embedded hardware design to execute embedded software. As a temperature sensor, LM35 integrated circuit was utilized. Embedded software's have been developed by using C language on MPLAB IDE. The designed system has ability of detecting temperature in the range between –55°C and +150°C. The temperature sensed by sensor is compared with the setting value by the user and if the temperature goes beyond the preset temperature, then buzzer and a red LED will be active. Also, the address of each refrigerator is sent to LCD display so operator knows which refrigerator fails.
Preventing campus network from excessive of unwanted packet traffic using VLAN technology
The internet has been established to provide wired or wireless online data trans-fer between devices that connected to networks. The purpose of this thesis is to estimate excessive unwanted Local Area Network (LAN) traffic and block it without using any traditional security devices. The pro-posed solution is formed from two parts. Network monitoring helps network employees to estimate excessive unwanted LAN traffic, so it is important for maintenance of online connectivity. Monitoring server is prepared with a few pieces of hardware and operating system. Some useful tools have been installed in order to monitor collected LAN performance measurements by means of reports and graphics. Moreover to obtain web-based graphics of network metrics, a software has been developed with Shell script, text processing language and server-side scripting language. Additionally we have prepared another software to take configuration and Address Resolution Protocol (ARP) table backup of switches automatically in predetermined time which transfers these backups in related date-time folder to Linux and Windows TFTP backup server. Switch security features and Access Control Lists (ACL) were used with assist of Virtual Local Area Network (VLAN) to block excessive unwanted traffic on backbone switch.
Design and implementation of a network for hospital information system
A special challenge is the coordination of the hospitals buildings with using the most common requirements in health care facilities, building control system and electric power distribution, hospital information system (HIS) , using computer network to implementation this hospital information system and using protection against intrusion. It is important to ensure that data in transit should not be accessed, modified or tampered by unauthorized persons. Unauthorized access to data may cause heavy damage. Hospital Information System (HIS) is considered as an important factor in health care sector for managing the administrative, financial and clinical aspects of a hospital. The purpose of this project is to research on new network design that has a security techniques in order to enhance the current network security structure of save a hospital information system (HIS). This is very important because, it will avoid the system from suffering any attack. Security architecture was optimized but there are need to keep researching on best means to protect the network from future attacks. In this final project research, security techniques were uncovered to produce best network security results by using internet security protocols and adding a firewall, when implemented in an integrated frame work.
Simulation of grid connected of hybrid renewable power system
In this thesis, modeling of grid connected hybrid power system consisting of different power sources as photovoltaic (PV), wind turbine based permanent magnet synchronous generator (PMSG), diesel generator and battery has been studied. Photovoltaic and wind turbine which are the renewable energy sources are composed of main components of hybrid power system. In this study, we have analyzed the architecture of power system sources to achieve a good performance of the individual sub models for the hybrid system. And we have chosen the source that can supply the load demand in order to minimize the kWh cost and maximum availability while preserving the reliability of the system. The control strategy has been adopted to the model considered in the study for operating the system (PV/wind/diesel/battery/grid) in the most efficient way. Simulation studies of hybrid power system for different power sources have been done.
Enhancement of power stability and damping oscillation in multi machine system using SSSC with POD controller
When power system is heavily loaded due to the continuous demand in this case causes active and reactive power to be instable and insufficient. In this thesis, a proper approach has been presented to compensate active and reactive power and also damping oscillations in a multi-machine system consisting of four machines, six buses using Static Synchronous Series Compensator (SSSC). SSSC can be considered one of the most significant series compensation of flexible alternating current transmission system (FACTS) family used in power transmission systems. Both single phase and three phase faults have been considered in the study. Simulation studies of the presented approach have done with SSSC, without SSSC, and finally with SSSC and Power System Damper (POD). Simulation results showed that the system became unstable with oscillations when SSSC has been not used. It can be concluded from simulation results that When SSSC is inserted in the power network, the power transfer is increased and oscillations are reduced. When SSSC is used with POD controller, the system becomes stable in faster way than without controller. In a result, the system performance has been greatly improved and power system oscillations have been reduced and damped out very quickly by using SSSC and POD.
Comparison of HVDC and HVAC transmission systems
Power transmission mainly depends on the Alternating Current in various fields of life. Especially (AC) is commonly used in the short and medium distances, but that the process of power transfer may face several problems when it's affirmative for power transmission to the distal distances. So these problems and obstacles necessitate to search new ideas and find alternative solutions. Because the truth is that relying on the system stream of (AC) accompanies many problems for the power transmission and also difficult to control, as well as increasing the cost transmission system. In this study, we have put forth advantages and disadvantages of them by making comparison of HVDC and HVAC transmission systems. Power losses have been calculated in both systems by considering different transmission line distances. In addition, the voltage variation between both sending and receiving sides has been found for each of two systems considering different transmission line distances. All tests that have been performed for putting forth disadvantage and advantage of the high voltage transmission systems have been done by the systems working under normal and abnormal situations. As a result of studies, HVAC transmission systems in short and medium distances, HVDC have been proven to be advantageous in case of long distances. Key words: HVAC power transmission system, HVDC power transmission system.
Design and implementation of real-time eye detection and tracking system based on gpu
Non-Contact and remote camera-vision based eye detection and tracking systems has been widely used in many applications such as fatigue detection for the drivers, gaze detection for human-machine interfaces, iris recognition, and face normalization for automatic face detection systems. For these applications have the limitation of the real-time, how to accelerate the speed of the eye detection has always been an important topic. Until now, there has been several algorithm developed to decrease the computing time. Since the computational throughput still limits the types of algorithms that can be implemented in real time. However, the developing current Graphical Processing Units (GPU) offers algorithms in real time due to allow parallel programming and decreases the computing time. In this thesis, a real time algorithm is designed and implemented for eye detection and tracking system. An hybrid eye detection algorithm consist of feature based passive eye detection method and IR based active eye detection method is proposed. A new fast eye detection approach having 10 times fast performance with evaluated SCFace database on near infrared based eye detection has been proposed as a contribution. Also, the tracking methods such as Cam-Shift and Optical Flow methods are investigated with a created database including 30 people for the algorithm. The designed algorithm is implemented both CPU based embedded systems as Raspberry Pi, Banana Pi Pro, Beagle Bone Black, LeMaker Guitar and GPU based system Jetson TK1 for comparison. . The parallel computing advantages are presented for GPUs having higher almost ten times fast computation than CPU based systems.
Development of cascade H-bridge multi-level inverter for photovoltaic panels
The aim of this thesis is to develop cascade H-bridge multi-level inverter for photovoltaic panels. The thesis consists of simulation and hardware development studies of H-bridge multi-level inverter. Firstly, the inverter including 150W PV panel, a buck-boost DC to DC converter, and Maximum Power Point Tracking (MPPT) algorithm has been simulated by a computer simulation program. In the simulation part, the PV panel is supplied with constant irradiation and temperature as input. The panel output was monitored with the algorithm used for maximum power point monitoring purpose. The output of the MPPT controller varies the duty cycle of the buck-boost converter's switch in order to maintain a constant voltage and a current level for continuous charging of the battery. Another output of this controller has been used for stopping the charging process as the battery reaches the desired voltage level. In the hardware development studies of the thesis, H-bridge multi-level inverter hardware was implemented. The inverter has five levels at its output waveform. Dry cell batteries have been used at the input of inverter for simulating PV panels. The inverter is composed of two H-bridges to form different levels at the output. Full wave charge balance control method has been utilized in the development phase of the inverter. The output of each H-bridge was connected in a series pattern. The final output is taken from the first and second H-bridges. Key Words: photovoltaic panel (PV), DC to DC converter, Maximum PowerPoint tracking (MPPT) algorithm.