Master's Theses

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A 1GS/S, 9-BIT DAC I interleaved (2+1)-bit then 2-bit per cycle, reference free SAR ADC
(2019-01-02) El-Sawy, Salma H.; Tekin, Ahmet; Tekin, Ahmet; Uğurdağ, Hasan Fatih; Karalar, T.; Department of Electrical and Electronics Engineering; El-Sawy, Salma H.
This work presents a high speed, medium resolution Successive Approximation Register Analog to Digital Converter (SAR ADC) designed for low-noise, low- power satellite transceiver applications. The proposed system is a (2+1) then 2-bit per cycle SAR ADC of 1GS/s sampling rate, 9-bits resolution designed and characterized in a 65nm standard CMOS technology. The designed system resolves 9 bits with a special switching scheme in a total of 4 cycles per sample effectively. This is achieved by interleaving 4 Capacitive Digital to Analog Converter (C-DACs) of unit capacitance 1fF. Since the interleaving is limited to the passive DACs only which match well, the design does not suffer from the drawbacks of full interleaving. Hence, significantly better power efficiency and performance metrics have been obtained in comparison to regular interleaved ADCs. A special timing scheme with a single extra first-bit comparator is optimized to leave proper timing margins for every step from a single 4- GHz low noise clock source which is readily available in the 8- GHz direct conversion front-end. This comparator as well is reused as all the other active comparators in both interleaving phases. The proposed design achieves an effective number of bits (ENOB) of 8.5 bits at Nyquist with total power consumption of 15mW (1.25V supply), resulting in a Figure of Merit (FoM) of 38.37 fJ/conversion-step.
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3-D simulation of droplet impact on static and moving walls
(2021-01-13) Yılmaz, Anıl; Ertunç, Özgür; Ertunç, Özgür; Başol, Altuğ Melik; Güngör, A. G.; Department of Mechanical Engineering; Yılmaz, Anıl
In the present study, the contact angle model and origin of the parasitic current have been studied to provide accurate prediction of droplet surface interactions in Volume of Fluid (VOF) framework. We investigate the effect of "modelled" dynamic contact angle boundary conditions to 3D droplet spread&deposition size and the parasitic currents relation with grid distribution. According the simulation experience, Open- FOAM's static and classical dynamic contact angle calculations are insufficient to provide accurate droplet spread and deposition ratio. In fact, it was shown that the classical Kistler dynamic contact angle model did not perform well in low capillary numbers and in cases where the difference in advancing and receding angles was high. It has been possible to reduce the unphysical flow problem seen in the interface region in multiphase simulations with structural changes. It has been quantitatively shown that the number of neighboring cells sharing the same Faces influences the gradient calculations associated with the formation of parasitic current. I has been observed that the polyhedral cell structure delivers smoother the interface gradient distribution than the cartesian cell structure. After implementing, modified Kistler contact angle model in OpenFoam and using polyhedral cells for the simulations, we could succesfully validate transient droplet shapes formed upon impact with those obtained from experiments. Droplet outcomes, such as deposition, partial rebound and split deposition on stationary and moving smooth surfaces are obtained consistent with experimental results.
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A comprehensive human-agent negotiation framework : preferences, emotion & interaction
Keskin, Mehmet Onur; Aydoğan, Reyhan; Aydoğan, Reyhan; Sefer, Emre; Erzin, E.; Department of Artificial Intelligence
In today’s increasingly interconnected world, human-agent negotiation plays a pivotal role in reaching socially beneficial agreements when stakeholders need to make joint decisions. Developing intelligent agents capable of understanding not only human negotiators’ prefer ences but also attitudes is a significant prerequisite for effective human-agent interactions. Awareness of a human’s emotional state and ability to express an agent’s mood to influence the human negotiator might significantly affect the negotiation outcome. This thesis presents a comprehensive framework that revolutionizes the field of human agent negotiation, integrating two critical elements: Emotionally aware negotiation strategy and Conflict-Based Opponent Modeling (CBOM). By combining these novel approaches, the framework enhances negotiation outcomes and fosters cooperation between agents and human negotiators, ultimately leading to mutually advantageous agreements. The thesis establishes the research context and motivation, underscoring the escalating importance of human-agent negotiation in a world where collaborative decision-making is essential for addressing complex challenges. It highlights the need for advanced agents to accurately interpret human preferences and behaviors, enabling admissible settlements that serve joint interests. Shedding light on the limitations of conventional approaches that heavily rely on opponent offers and remaining time. Additionally, it explores the critical role of emotional awareness and opponent modeling strategies in human-agent negotia tion. The synthesis of existing research lays the groundwork for developing the proposed comprehensive framework. Emotional awareness takes center stage in the proposed negotiation strategy. Solver Agent: Emotional Extension of the Hybrid Agent bidding strategy is introduced. The Solver Agent considers the opponent’s emotional state during negotiation, leading to higher social welfare scores and faster agreement times. The experimental study emphasized the profound impact of emotional awareness on negotiation outcomes, particularly in human agent settings. CBOM efficiently extracts maximum information from limited interaction rounds in human-agent negotiation settings, surpassing traditional approaches in prediction performance. Experimental analyses confirmed the superiority of CBOM in human-agent and automated negotiation scenarios, even when the exploration of the outcome space is limited. The experimental findings establish CBOM as a powerful tool for modeling human behavior and preferences in negotiation. In conclusion, the comprehensive human-agent negotiation framework presented in this thesis represents a significant advancement in the field. By seamlessly combining Conflict Based Opponent Modeling and Emotional Awareness, the framework empowers intelligent agents to discern human preferences and behaviors more accurately, facilitating cooperative interactions and achieving mutually beneficial agreements. The framework’s effectiveness in human-agent and automated negotiation settings highlights its potential for designing ne gotiation agents that interact adeptly with human negotiators, fostering understanding and optimizing negotiation outcomes. The future of human-agent negotiation lies in forging a new era of cooperation, where intelligent agents serve as capable partners, promoting social welfare and driving positive change through admissible settlements that incorporate joint interests. This thesis contributes valuable insights towards realizing this vision, marking a significant step forward in the field of human-agent interaction.
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A data-driven approach to NFT trading : Q-learning based simulator
Kamalak, Süleyman; Albey, Erinç; Albey, Erinç; Önal, Mehmet; Güler, M. G.; Department of Data Science
Non-fungible tokens (NFTs) have garnered considerable attention in recent years due to their broad range of applications and potential as a lucrative investment oppor tunity. Given the nascent nature of the technology and the scarcity of comprehen sive studies, there is a pressing need for a holistic trading framework that not only addresses the complexities inherent in the trading process but also proposes viable solutions to existing challenges. This study introduces a novel approach for navi gating the NFT trading landscape, effectively confronting the various challenges and suggesting practical solutions. The trading environment is modeled using a Markov Decision Process (MDP), with Q-learning employed to simulate the environment and resolve the MDP problem. The study proposes machine learning models to tackle key challenges, including defining the market state, appraising NFT tokens, and ad dressing the illiquidity issue prevalent in the NFT market. The proposed approach yields an NFT trading strategy that has shown to outperform traditional strategies, generating substantial profits even amidst bearish market conditions. The Bored Ape Yacht Club (BAYC) collection serves as the primary data set, with the agent trained from June 1, 2021, through January 1, 2023. In testing period from January 1 to June 10, 2023, the proposed model outperformed traditional benchmarks, achieving a profit of 21.14% as opposed to a 20.39% loss for the best-performing benchmark. We assert that this forms a robust foundation for future research into NFT trading simulation and backtesting. We also identify potential areas for future enhancements, particularly possible improvements in the trading strategy and Q-learning approach. The insights gleaned significantly enhance the understanding of the importance of AI applications in the rapidly evolving field of NFT trading.
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A metaheuristic approach for multiple-item economic lot sizing problem with inventory dependent demand
Balpınarlı, Duru; Önal, Mehmet; Önal, Mehmet; Albey, Erinç; Atan, S. T.; Department of Industrial Engineering
In this study, we consider a multiple-item Economic Lot Sizing problem where the demands for items depend on their stock quantities. The objective is to find a production plan such that the resulting stock levels (and hence demands) maximize total profit over a finite planning horizon. The single-item version of this problem has been studied in the literature, and a polynomial time algorithm has been proposed when there are no bounds on production. It has also been proven that the single-item version is NP-hard even when there are constant (i.e, time-invariant) finite capacities on production. We extend this capacitated single-item model by considering multiple-item. Since the single-item capacitated version is NP-hard, the multiple-item capacitated version is NP-hard as well. In the context of this research, we propose a Lagrangian Relaxation method to find an initial solution to the problem, and a Tabu Search algorithm to find better solutions. The performance of the proposed metaheuristic model is compared with the performance of a standard commercial software that works on a mixed integer programming formulation of the problem. We show that our metaheuristic algorithm finds better solutions within a predetermined time limit.
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A multi depot vehicle routing problem with time window for daily planned maintenance and repairment planning
Toru, Elif; Albey, Erinç; Yılmaz, G.; Albey, Erinç; Önal, Mehmet; Güler, M. G.; Department of Industrial Engineering
A compressor manufacturer producing in Kocaeli/Dilovası region makes vehicle routing and employee planning daily to fulfill the maintenance and repair requests of the Marmara region and its surroundings the next day. The service types and times are agreed upon with the customer before service planning. The vehicles and their respective operators for a given planning day are known, with the service personnel's starting and ending points being the residences. All the planned services must be satisfied in the time windows customers give. We deal the issue as a multi-depot vehicle routing problem with time windows (MDVRPTW) and construct a mixed-integer linear programming framework. The mathematical model solution is sufficient to solve the company's 3.000-6.000 maintenance demand problems. A clustering heuristic that provides a good solution in a short time has been developed to solve large instances of malfunction and part replacement requests coming to the vehicle routing and after-sales service side.
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A numerical investigation of thermal comfort with different air diffusers at enclosed office space
Eraslan, Tolga Arda; Mengüç, Mustafa Pınar; Mengüç, Mustafa Pınar; Ertunç, Özgür; Ertürk, H.; Department of Mechanical Engineering
People spend most of their time in indoor environments, which highlights the significance of heating, ventilation, and air conditioning (HVAC) systems in these spaces. While ensuring the comfort of occupants in indoor spaces, it is also essential to maintain the efficient operation of these systems. Studies related to this topic are ongoing in the present day. HVAC should not only be seen as individual devices within it, but rather as a whole system. By doing so, the efficiency of each component can be enhanced. HVAC systems consist of various components, including heating and cooling units, ventilation units, dampers, air terminals, louvers, ducts, and pipes, among others. One of the most crucial elements that interact with the indoor environment is the air terminal. In this thesis, the interaction of air terminals with occupants in indoor spaces was observed and visualized using Computational Fluid Dynamics (CFD). Three types of diffusers were used in the study: square diffuser, four-way swirl diffuser, and a hexagonal diffuser designed by Cem Keskin. The interaction of these diffusers with the air inside the room, their airflow characteristics, and their impact on thermal comfort were investigated. The Fanger method's six variables were discussed in this context. Based on the findings, it was observed that the airflow characteristics of the hexagonal diffuser varied based on the openings, while the four-way swirl diffuser was found unsuitable for providing thermal comfort in an office environment. Additionally, when aiming for even air distribution, there is no energy difference among the diffusers. However, if only a specific area require conditioning, it's noticeable that the hexagonal diffuser consumes less energy compared to the other types of diffusers.
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A revised approach to cryptocurrency portfolio optimization using advanced Q-learning and policy iteration frameworks
Altok, Ceren; Albey, Erinç; Albey, Erinç; Önal, Mehmet; Güler, M. G.; Department of Data Science
Despite all the factors that cause concern among investors, such as volatility and de centralization of crypto world, the popularity of cryptocurrencies continues to grow steadily. The cryptocurrency market still holds its allure for many investors due to the high profit levels it has experienced in the past. With the entrance of numerous alt coins into the market, portfolio management becomes much more challenging. In the literature, we come across numerous studies proposing efficient portfolio management techniques for cryptocurrencies. This study presents proposed models developed based on policy iteration and Q-learning algorithms. Under Q-learning, three distinct sub-models are introduced: Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Double Dueling Q Network (DDDQN). All of these models are trained using 6-month training periods and compared using 10 different training and testing periods. Additionally, to eval uate both of proposed policy iteration and Q-learning models, baseline models were created for each algorithm, and the performance of the proposed models was assessed against these baseline models. The results indicate that among Policy Iteration models, the proposed model has the highest average ROI value of 3%, making it the top-performing model. Similarly, among Q-learning models, the proposed DQN model surpasses both baseline models and other Q-learning models, with an average ROI value of 2%. Considering all the models, the proposed Policy Iteration model achieves the highest average ROI value, while the proposed DQN and the proposed DDDQN model demonstrates the lowest volatility in terms of ROI standard deviations.
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A self establishing clustered network architecture for blockchain
Doğan, Orkun; Çakmakçı, Kübra Kalkan; Çakmakçı, Kübra Kalkan; Sözer, Hasan; Alagöz, F.; Department of Computer Science
Blockchain technology has branched out into many industries, such as healthcare, manufacturing, agriculture and entertainment, in the shape of both of its public and non-public variants. In principle, blockchain provides these industries with an immutable ledger, allowing the processes in its application environment to be taken care of in a decentralized manner. However, some challenges blockchain has faced to this day remain, such as the degree of its scalability, the level of security it provides and the transparency of the network transactions. In this thesis, a novel approach to a distributed, permission-less blockchain network is explored with the use of hierarchical clustering to gather the nodes based on the latency of their connection to one another. These clusters of nodes are allowed to work on their respective local chains and to add the verified local chains to the actual global chain that is used by the entire system. Network's throughput performance and overall latency are evaluated and compared with other blockchain applications, namely a simulation of the Bitcoin network itself and another approach that makes use of a method called Community Clustering. We collected the data for the correlation in the same environment for our work, Bitcoin and Community Clustering\cite{communityclustering} networks. The comparison of the collected data aligns with our work's clusters to improve the transaction throughput of the network, where an increase in average throughput and a drastic decrease in latency are observed.
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A4WP-QI combo system for continious wireless charging range coverage with resonance filtering
(2019-01-02) Sağlam, Üstün; Tekin, Ahmet; Tekin, Ahmet; Poyrazoğlu, Göktürk; Tamer, Ö.; Department of Electrical and Electronics Engineering; Sağlam, Üstün
Distribution of wireless power charging field uniformly on a large area pad is critical for receivers, especially for wearable devices because of their small form factor receiver coil. Since the receiver coil size is quite limited in these types of technologies; the device is very sensitive to the amount of field it could retain and it needs special placement or snapping to fix it at an optimum location for reliable charging. In order to overcome this problem, a dual-mode multi-coil power transceiver system is proposed; utilizing resonance filtering to increase the amount of total power delivered with rather uniform spacial distribution. Two concentric coils; one driven by 6,78-MHz high frequency driver (A4WP) and the other with a 200-KHz low frequency driver (Qi) with resonant blocker could transfer 30mW to 50mW standards compliant flat power to a 13-mm radius 30-turns wearable receiver coil everywhere across an 8cm radius charging pad area without any alignment requirement or snapping.
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Acceleration of image processing modules in wide-area aerial surveillance
Demirdağ, Zeynep Gülbeyaz; Ateş, Hasan Fehmi; Ateş, Hasan Fehmi; Aktürk, İsmail; Uğurdağ, Hasan Fatih; Güntürk, B. K.; Goularas, D.; Department of Electrical and Electronics Engineering
Wide Area Aerial Surveillance (WAAS) is a type of surveillance system that refers to monitoring of large geographical areas in real-time. A WAAS system is designed using multiple cameras and/or sensors mounted on an Unmanned Aerial vehicle (UAV). WAAS systems can scan an area of several square kilometers at once. However, the huge amount of data collected by WAAS systems can be challenging to process in real-time on board of the aircraft. This study proposes to use Graphics Processing Units (GPU) and multi-core programming techniques to accelerate the performance of the four key modules of a WAAS system: image matching/stitching, object detection, tracking, and super-resolution. Image matching/stitching involves registering and combining multiple images captured by cameras on UAV to create a panoramic image, in other words, the mosaic image of the area being monitored. Object detection and tracking modules involve identifying and tracking the movements of objects, such as cars, trucks, and people. Finally, the super-resolution module uses computational techniques to enhance the resolution of the images and provides more details on the images. The WAAS system's speed and efficiency are significantly improved by using the GPU and multi-core programming techniques to accelerate these modules. GPUs are well-suited to this task because they are designed for parallel processing, allowing them to process large amounts of data in real-time. The experiments show that using GPUs and multi-core programming techniques can significantly improve the performance of image stitching, object detection, tracking, and super-resolution, making it possible to execute these modules in parallel and process the large amount of data collected from WAAS in real-time. The accelerated modules are tested on NVIDIA Jetson AGX Xavier embedded GPU card for challenging test scenarios, demonstrating their potential for real-time surveillance on edge devices.
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An actor-critic reinforcement learning approach for bilateral negotiation
Arslan, Furkan; Aydoğan, Reyhan; Aydoğan, Reyhan; Öztop, Erhan; Uğur, E.; Department of Computer Science; Arslan, Furkan
Designing an effective and intelligent bidding strategy is one of the most compelling research challenges in automated negotiation, where software agents negotiate with each other to find a mutual agreement when there is a conflict of interests. Instead of designing a hand-crafted decision-making module, this thesis proposes a novel bidding strategy adopting an actor-critic reinforcement learning approach, which learns what to offer in a bilateral negotiation. An entropy reinforcement learning framework called \acrfull{sac} is applied to the bidding problem, and a self-play approach is employed to train the model determining the target utility of the coming offer based on previous offer exchanges and remaining time. Furthermore, an imitation learning approach called behavior cloning is adopted to speed up the learning process. Also, a novel reward function is introduced that does not only take the agent's own utility, but also the opponent's utility at the end of the negotiation. The developed agent is empirically evaluated. Thus, a large number of negotiation sessions are run against a variety of opponents selected in different domains varying in size and opposition. The agent's performance is compared with its opponents and the performance of the baseline agents negotiating with the same opponents. The empirical results show that our agent successfully negotiates against challenging opponents in different negotiation scenarios without requiring any former information about the opponent or domain in advance. Furthermore, it achieves better results than the baseline agents regarding the received utility at the end of the successful negotiations.
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Adapting bilateral networks to monocular depth estimation for real-time inference
Menteş, Sami; Kıraç, Mustafa Furkan; Kıraç, Mustafa Furkan; Aydoğan, Reyhan; Gökberk, B.; Department of Computer Science; Menteş, Sami
Monocular Depth Estimation (MDE) is a fundamental computer vision application area for many industry-related advances. Due to its deployment needs, the inference time of the depth estimation algorithm also plays a crucial role among other accuracy metrics. With the recent advances in Convolutional Neural Networks (CNNs) on other time-constrained computer vision tasks, many efficient feature extractors have been studied and adopted from MDE models as the backbone. Although those feature extractors have shown significant improvement in throughput, the widely-used encoder-decoder architecture used by Real-time MDE models also relies on a decoder network for upsampling. Following a similar approach, stacking multi-channel convolutional layers on a decoder hinders the inference time. This study investigates the benefits of Bilateral Networks in Real-time MDE tasks. During our research, we first manipulate the structure of a recently introduced real-time segmentation model (STDC-Seg) for the MDE problem. Once we attain real-time inference speed, we tailor the backbone structure and attention modules of the model for the needs of MDE to improve prediction accuracy. Finally, we train the models on the well-known KITTI dataset and compare our results with the models of the KITTI Eigen Split MDE Benchmark along with the previous real-time models. Our experimental results show that our real-time method achieves on-par metric performance with state-of-the-art models that are not subject to any time-constraint.
Open Access
Adaptive domain-specific service monitoring
(2014-01) Ünsal, Arda Ahmet; Sözer, Hasan; Aktemur, Tankut Barış; Sözer, Hasan; Ercan, Ali Özer; Aktemur, Tankut Barış; Kalıpsız, O.; Gürsun, G.; Department of Computer Science; Ünsal, Arda Ahmet
We propose an adaptive and domain-specific service monitoring approach to detect partner service errors in a cost-effective manner. Hereby, we not only consider generic errors such as file not found or connection timed out, but also take domain-specific errors into account. The detection of each type of error entails a different monitoring cost in terms of the consumed resources. To reduce costs, we adapt the monitoring frequency for each service and for each type of error based on the measured error rates and a cost model. We introduce an industrial case study from the broadcasting and content-delivery domain for improving the user-perceived reliability of Smart TV systems. We demonstrate the effectiveness of our approach with real data collected to be relevant for a commercial TV portal application. We present empirical results regarding the trade-off between monitoring overhead and error detection accuracy. Our results show that each service is usually subject to various types of errors with different error rates and exploiting this variation can reduce monitoring costs by up to 30\% with negligible compromise on the quality of monitoring.
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An adaptive large neighborhood search for the multi-compartment inventory routing problem
(2021-06-10) Gültekin, Ceren; Özener, Okan Örsan; Özener, Okan Örsan; Yanıkoğlu, İhsan; Ekici, Ali; Önal, Mehmet; Yakıcı, E.; Department of Industrial Engineering; Gültekin, Ceren
In this thesis study, we concentrate on an inventory routing problem with a fleet of multi-compartment vehicles which enables the distribution of different products to customers on a delivery route. Using separate compartments on a vehicle increases profitability and customer satisfaction when customer demands vary over product and period basis. We assume that the compartment that each product can be loaded is known and the capacities of the compartments are fixed. Customers have preset storage capacities and distribution plans should be made in a way that no customers would face stock-outs for any product on any day. We observe the practices of this variant in the distribution of foods with different temperature needs to groceries, feed distribution to livestock farms, and collection of different types of recyclable wastes. We examine this problem separately for three assumptions considering different cases of allowing/disallowing split delivery to customers. We propose a matheuristic ap proach to solve the addressed problem where we systematically integrate an Adaptive Large Neighborhood Search algorithm with mathematical programming models. We generate a set of instances and test the performance of our algorithm by comparing it with the results obtained by a flow formulation adapted from the literature. We observe that the best results we find for each instance are only %11.7 worse than the solutions found by the flow formulation on average.
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Adaptive multiple-input multiple-output (MIMO) techniques for visible light communications
Al-Nahhal, Mohamed; Uysal, Murat; Uysal, Murat; Durak, Kadir; Başar, E.; Department of Electrical and Electronics Engineering; Al-Nahhal, Mohamed
Visible Light Communication (VLC) technology is a promising solution of wireless communication systems in beyond 5G communication networks, which provides low implementation cost, high energy efficiency, and high-speed data transmission. The VLC systems can be considered as a complementary or alternative for the existing Radio Frequency (RF) based wireless communication systems to satisfy the demands of increasing high capacity. In the VLC systems, Light-Emitting Diodes (LEDs) are used as transmitters, and Photodetectors (PDs) are used as receivers. Multiple LEDs are installed to offer sufficient illumination for indoor environments. This feature can be utilized to implement Multiple-Input Multiple-Output (MIMO) communications systems in order to achieve high data rates by improving spectral efficiency. However, LEDs can only convey unipolar signals. Furthermore, the nature of the VLC channel is frequency-selective, which causes significant degradation in the performance of the VLC system due to critical Inter-Symbol Interference (ISI). Several schemes based on pulse modulation and Orthogonal Frequency Division Multiplexing (OFDM) have been proposed to overcome the ISI of the VLC system. A new Optical Orthogonal Frequency Division Multiplexing (O-OFDM), referred to as a Unipolar Orthogonal Frequency Division Multiplexing (U-OFDM), appears as an attractive solution for emerging VLC systems. In the future of 5G and beyond 5G communication networks, the spectral efficiency needs further improvement through MIMO with adaptive transmission technique. Motivated by these, we focus on improving the spectral efficiency and Bit Error Rate (BER) performance for the MIMO VLC systems in both the single-carrier and multi-carrier. Firstly, we propose a novel Adaptive Spatial Modulation (ASM) scheme, referred to as Flexible Generalized Spatial Modulation (FGSM), for single-carrier MIMO VLC systems to achieve better average Symbol Error Rate (SER) and higher spectral efficiency with a fixed overall number of LEDs compared to existing ASM and Generalized Spatial Modulation (GSM) schemes. The proposed system varies the modulation sizes over the available LEDs as well as the number of active LEDs. The selection of the modulation sizes is based on an optimization problem for the average SER under a predefined value of the spectral efficiency. We derive a closed-form expression of approximate average SER for the proposed system and evaluate its decoding complexity. We show that the FGSM scheme can be a potential candidate for future MIMO-VLC systems. Secondly, we firstly propose adaptive transmission technique for the MIMO VLC system in conjunction with U-OFDM to exploit the U-OFDM benefits. The proposed adaptive MIMO U-OFDM VLC system is implemented to support three different MIMO modes that enable a set of different modulation sizes. The considered MIMO modes are Repetition Coding (RC), Spatial Multiplexing (SMUX) and Spatial Modulation (SM). In the RC, the same signal is transmitted simultaneously from all LEDs, while independent signals are transmitted simultaneously from all LEDs in the SMUX. In the SM technique, a single LED is activated and the index of the activated LED carries information in addition to modulated signals. Depending on the received Signal-to-Noise Ratio (SNR) and target BER, the proposed adaptive transmission system switches between the available MIMO modes and adjusts its modulation size to achieve higher spectral efficiency. The proposed U-OFDM system is applied over different VLC MIMO setups with realistic channel models for 8×8, 4×4 and 2×2 MIMO systems. Recently, Generalized LED Index Modulation (GLIM) appears as an attractive MIMO OFDM scheme for emerging VLC systems. Therefore, in the last part of this thesis, we propose a Magnitude and Wrap-Phase OFDM (MW-OFDM) scheme for the MIMO VLC systems. The proposed MW-OFDM scheme relies on the conversion of complex signals into polar form with the magnitudes and wrap-phases to decrease the restriction on the number of LEDs compared to the conventional GLIM-OFDM. Moreover, the Maximum Likelihood (ML) estimator for the proposed scheme is derived. The proposed MW-OFDM scheme improves the average bit error rate and provides a significant reduction in the decoding complexity, compared to the conventional GLIM-OFDM. Moreover, a half number of LEDs are required for the proposed scheme to deliver the same spectral efficiency in a comparison with the conventional GLIM-OFDM.
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Adaptive OFDM-based acoustic underwater transmission: system design and experimental verification
(2017-08) Sadeghi, Mohammad; Uysal, Murat; Durak, Kadir; Uysal, Murat; Baykaş, T.; Department of Electrical and Electronics Engineering; Sadeghi, Mohammad
In this study, we design and implement an adaptive Underwater Acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system employing adaptive modulation and transmit power. Recently, UWA communications gain more attractions as there has been a growing interest in exploring the communications possibilities for underwater applications such as oil field monitoring, oceanographic data collection, maritime archaeology and disaster prevention. However, the harsh characteristic of underwater environments imposes various difficulties for communication in this medium. Thus, proposing a reliable UWA communication system which has a high data rate can improve these applications performance significantly. Since UWA communications suffer from low data rates, we propose an adaptive OFDM communication system to support higher data rates over UWA channel. OFDM is an attractive technique for underwater channels due to the fact that inter-symbol-interference resulted from frequency selective channels can be removed completely. Additionally, adaptive communications achieve significant improvements in transmission data rate. For the sake of implementation, we take advantages of LabVIEW software to generate OFDM blocks. The generated data is transmitted to the receiver side with the aid of Software defined radios (USRPs) and acoustic front-ends. The received data at the acoustic hydrophones are transferred to the USRPs and then processed in LabVIEW for information detection. We conduct our experiments at the pool to investigate the performance of the proposed system over a real UWA channel.
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Adolescent disclosure and secrecy behaviors and psychological well-being in parent and best friend relationship contexts: variable- and person-centered examinations
(2017-01) Uraloğlu, Safiye Ebra; Gözkan, Ayfer Dost; Kafescioğlu, Nilüfer; Özcan, N. A.; Gözkan, Ayfer Dost; Kafescioğlu, Nilüfer; Özcan, N. A.; Department of Psychology; Uraloğlu, Safiye Ebra
The present study examined adolescents' disclosure and secrecy behaviors in their close relationships and their psychological well-being (life satisfaction, problem-solving confidence, and (lower) trait anxiety). In a sample of 1232 adolescents (ages 11-19 years; 60.1 % girls), disclosure and secrecy across three relationship contexts were examined by variable- and person-centered approaches. With a variable-centered approach, the study examined the links between disclosure to and secrecy from mother, father, and best friend and psychological well-being using structural equational modeling (SEM) analysis. Results of SEM showed that higher disclosure and lower secrecy levels were related to higher psychological well-being. More specifically, variable-centered analysis results showed that higher disclosure and lower secrecy in relationship with father predicted better psychological well-being. Disclosure to and secrecy from mother were not found as much effective as the father in the model. Results did not support the relation between secrecy from best friend and well-being but high disclosure to best friend predicted higher well-being. With a person-centered approach, the study investigated adolescents' disclosure and secrecy behaviors in their relationships with their mother, father, and best friend through clusters. Cluster analysis yielded patterns in which adolescents share information with or keep secret from their parents which differ in levels of psychological well-being. The best friend-adolescent cluster was found to be significantly related to psychological well-being but the relation was weaker as compared to other clusters' relationship with psychological well-being indices. Findings are discussed by synthesizing the information yielded by variable- and person-centered analyses.
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Adult children's experiences with parental infidelity in childhood
Kökçü, Beyza; Gürmen, Münevver Selenga; Gürmen, Münevver Selenga; Saydam, Fehime Senem Zeytinoğlu; Üstünel, A. Ö.; Department of Psychology; Kökçü, Beyza
This qualitative study aims to investigate children's experiences with parental infidelity in their childhood by using the interpersonal trauma perspective as a framework. The sample consisted of ten participants, 3 men, and 7 women. A semi-structured interview was held and it took approximately 60 minutes to complete. The ten participants’ ages ranged from 25 to 30 years. The analysis revealed four main themes and 13 subthemes on two levels: intrapersonal and interpersonal. Three intrapersonal themes and related subthemes were identified, namely 1) growing up before their time, 1a) child as a bridge between parents 1b) emotional parentification 1c) knowledge about parental sexual experience; 2) emotional rollercoaster around parental infidelity 2a) repressed anger 2b) disgust about third-party 2c) fear of resembling parent(s) 3) coping with parental infidelity 3a) social support as a coping strategy 3b) hard to remember details of parental infidelity 3c) normalization of infidelity 3d) try to emotional cut off from offending parent and one interpersonal theme was 4) challenges in romantic relationship 4a) hardship in trusting others 4b) multigenerational transmission of infidelity 4c) selectivity in a romantic partner. These intrapersonal themes specified the individual's own process in terms of a new adult like position in the family system, emotional ambivalence around parental infidelity, and the mechanisms they used for dealing with the incidence. Additionally, the interpersonal theme indicated the romantic relationship level for adult children's experiences. The findings of this study provide valuable information for clinical practitioners who work with individuals and couples and families by using a trauma perspective. Future studies can focus more on gender-specific parental infidelity experiences. It can also be investigated by crystalizing parental relationship status after revealed infidelity.
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Advisor: an adaptive framework for test oracle automation of visual output systems
(2019-01-10) Genç, Ahmet Esat; Sözer, Hasan; Sözer, Hasan; Kıraç, Furkan; Tekin, Ahmet; Demiroğlu, Cenk; Aktaş, M.; Department of Electrical and Electronics Engineering; Genç, Ahmet Esat
Test oracles differentiate between the correct and incorrect system behavior. Automation of test oracles for visual output systems mainly involves image comparison, where a snapshot of the observed output during test is compared with respect to a reference image. Hereby, the captured snapshot can be subject to variations due to, for instance, scaling, shifting, rotation, or color saturation. These variations lead to incorrect evaluations. Existing approaches in the literature employ a combination of techniques from the computer vision domain to address a specific set of variations. However, some of these techniques might not be the most effective one for addressing a particular variation, while some other techniques might not be necessary in the absence of a particular variation, introducing an unnecessary performance overhead. In this paper, we introduce ADVISOR, an adaptive framework for test oracle automation of visual output systems. The framework allows the use of a flexible combination and configuration of alternative techniques from the computer vision domain. We evaluated several instances of our framework with respect to state-of-the-art tools. We achieved up to 3% better overall accuracy based on a benchmark dataset collected during the tests of real Digital TV systems. We also observed that the accuracy of tools can differ for particular variations in the captured images.

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