Project Coordinator: Prof. Dr. Klaus Werner Schmidt

Project Type: TUBITAK 1001

Project Budget: 1.378.113 TL

Project Duration: 36 Months

Project Start Date: 28 March 2025

Funded Personnel: 1 PhD Student (part-time), 2 MSc Students (part-time), 3 Undergraduate Students

Clothoids are curves that are inherently drivable by vehicles due to their linear change in curvature, enabling smooth transitions between straight and curved segments. This property makes them ideal for modeling road geometry and generating dynamically feasible trajectories in autonomous vehicle (AV) applications. This project introduces a unified trajectory framework for AVs based on the novel concept of clothoid-based path templates. By using elementary path components (ECs) with a small number of parameters, the framework enables efficient computation of both geometric and dynamic properties, supporting trajectory planning, tracking, and prediction using a single representation. In contrast to existing approaches that rely on inconsistent, task-specific models, this method eliminates the need for complex conversions and ensures seamless integration across various driving tasks.

The project involves the design, implementation, and validation of the proposed framework through software-in-the-loop simulations and real-world tests using both on-road and off-road vehicles. A lightweight embedded software library will be developed to ensure efficient road representation and real-time trajectory computation under dynamic driving constraints. The resulting framework is expected to make significant contributions to both academia and industry, with potential applications in transportation, construction, agriculture, and defense sectors.

Project Coordinator: Assoc. Prof. Ayşe Melda Yüksel Turgut

Project Type: TUBITAK 1001 Scientific and Technological Research Projects Funding Program

Project Budget: 631250 TL

Project Duration: 30 months

Project Start Date: 01.09.2022

Funded Personnel: 2 PhD Students (Full-Time), 1 MSc Students (Full-Time), 2 BSc Students (Part-Time)

Project Summary: Faster than Nyquist (FTN) is a promising physical layer technique that has the potential to significantly improve spectral efficiency (bit/s/Hz) in communication systems. In this transmission technique, information carrying pulses are transmitted faster than the Nyquist limit, and this transmission causes inter-symbol interference, which degrades the communication quality. The foundations of the FTN technique date back to the 1960s and 1970s. Mazo showed in 1975 that FTN can achieve the same error performance as Nyquist, provided that the acceleration of pulses does not exceed a certain limit and that inter-symbol interference is eliminated at the receiver. Significant advances have been made in silicon technology and signal processing techniques since 1975. On the other hand, the transmission rates required for future communication networks are very high. These mentioned developments and high-rate requirements make many experts in both academia and industry think that the FTN transmission technique is finally a technology whose time has come.

Project Coordinator: Assoc. Prof. Dr. Yeşim Serinağaoğlu Doğrusöz
Project Type: TUBITAK Bosphorus 2509, TÜBİTAK – the French Ministry of Foreign Affairs Bilateral Cooperation Project
Project Budget: To be Announced
Project Duration: 24 Months
Project Start Date: 01 June 2022
Funded Personnel: 1 PhD Student (Full-Time), 1 MSc Student (Full-Time)

Project Summary: Ventricular arrhythmias are a major cause of sudden cardiac death (SCD), which accounts for about half of cardiac mortality. INVERSE will focus on patients with an abnormal chronic substrate, which is largely underestimated and not addressed by current guidelines for preventive therapy. The electrical measurements performed with ECGI in this project will allow for a detailed non-invasive characterization of th e specific features for hearts with an arrhythmogenic substrate leading to SCD. Non-invasive ECGI detection of arrhythmogenic substrates will improve SCD risk stratification and offer preventive treatment of high-risk individuals by defibrillators, interventions or anti-arrhythmic therapies.

Ex-vivo torso tank recordings utilizing pig hearts, and clinical data from patients with primary electrical anomalies, who arrive for treatment at the Hôpital Haut Lévèque (Bordeaux, France), will be used for evaluating the ECGI methods. Geometric models will be generated from medical images, then subject-specific forward matrices will be computed. Inverse problem will be solved using novel ECGI methods such as Lp norm-based methods, spatio-temporal regularization, sparse representations and learning-based methods. Results will be evaluated qualitatively and quantitatively by comparing with the ground truth obtained from the experiments.

Online progress meetings will be organized every month with the members involved in this period to discuss potential issues and short-term experimentations. Five visits for general meetings will be organized with all the members involved in the project to discuss global progress, strategic decisions and future directions.

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Project Coordinator: Assoc. Prof. Dr. Yeşim Serinağaoğlu Doğrusöz
Project Type: TUBITAK 2540, TÜBİTAK – Slovak Academy of Sciences (SAS) Bilateral Cooperation Project
Project Budget: 756.252 TL
Project Duration: 36 Months
Project Start Date: 01 May 2021
Funded Personnel: 1 PhD Student (Full-Time), 2 MSc Students (Full-Time), 2 Undergraduate Students (Full-Time)

Project Summary: The project is focused on advanced non-invasive methods for the localization of the origin of an undesired ventricular activity known as the extrasystoles. The treatment of these arrhythmias involves an invasive procedure using an endocardial mapping, during which such origins are eliminated by the application of radiofrequency energy. The methods proposed in the project aim to shorten this time demanding invasive procedure, by guiding the clinicians to the correct regions of the arrhythmia origin.

Computational methods for obtaining information about the heart’s condition non-invasively from multiple-leads electrocardiography (ECG) measurements are called as the inverse problem of ECG or electrocardiographic imaging (ECGI). This problem is in general ill-posed, i.e. no unique solution exists corresponding to the measurements. Each research group participating in the project uses their own approaches to the inverse problem solution, which were previously evaluated for their accuracy on simulated or experimental animal data. Researchers from Middle East Technical University (METU), Turkey, use various regularization and statistical methods and obtain the results on the heart ventricles in the form of epicardial potentials. Researchers from the Institute of Measurement Science (IMS SAS), Slovakia, apply the method using a single dipole as representative of the local activation of the heart’s ventricles. In the proposed project, both research groups plan to apply their methods on the common clinical data from the patients with the diagnosis of premature ventricular contraction (PVC), who are indicated for invasive endocardial mapping and radiofrequency ablation. The computational results will be validated with the known position of the catheter in case of successful elimination of the origin of the undesired ventricular contraction. Coordination will be maintained by web meetings, and visits by each research group to their partner institution.

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Project Coordinator: Assoc. Prof. S. Figen Öktem

Project Type: TÜBİTAK 1001 Scientific and Technological Research Projects Funding Program

Project Budget: 869.686 TL

Project Duration: 36 Months

Project Start Date: 15 March 2021

Funded Personnel: 1 PhD student (full-time), 2 MSc students (full-time)

Project Summary: 

Computational imaging, a rapidly evolving interdisciplinary field, enables new forms of visual information in various applications in natural sciences. In a computational imaging system, an inverse problem has to be solved to reconstruct an image from the acquired raw data. For the solution of these high-dimensional inverse problems, commonly used fast direct inversion methods are not robust to noise. On the other hand, regularization-based methods can offer better reconstruction quality but with higher computational cost.

Recently deep-learning based approaches have been developed to achieve high accuracy with fast reconstruction. In this project, by using a general framework for various inverse problems in imaging, we will develop deep learning-based fast techniques that enable unprecedented reconstruction quality. Moreover, the advantages and disadvantages of the developed techniques will be investigated not only through numerical simulations but also experimentally. For this purpose, inverse problems will be studied by considering the following three general categories separately: a) two-dimensional (2D) linear problems, b) two-dimensional nonlinear problems, c) three- or more-dimensional linear problems. Because the existing deep learning-based methods are mostly developed for two-dimensional linear problems, this project focuses on the development of new methods mostly for the two-dimensional nonlinear problems, and three or more-dimensional linear problems. Likewise, deep learning-based techniques will be studied by considering the following general categories: i) learned direct reconstruction, ii) reconstruction with learned prior, iii) learned iterative reconstruction. Different type of techniques will be developed separately, and then will be compared with each other and commonly used analytical inversion methods. By demonstrating the pros and cons of each approach, it is expected to understand which approaches perform better for different inverse problems and measurement settings. 

Project Coordinator: Prof. Dr. Elif Uysal
Project Type: TUBITAK 2247-B Scientific and Technological Research Projects Funding Program
Project Duration: 24 months
Project Start Date: July 1, 2021
Funded Personnel: 2 PhD Students (Full-Time), 1 Postdoc, 1 Engineer

Project Summary: The Internet will soon be dominated by nodes using Machine-Type Communications (MTC) (e.g., industrial control, autonomous vehicles, social network apps). The key performance metric for MTC is sufficiently timely data, i.e., freshness of status updates. However, current networks have been optimized for maximizing throughput for a moderate number of high rate connections. So, how to re-architect networks to provide fresh information to an explosive number of status-update flows? This is a major challenge as classical network theory does not even have tools to address information freshness.

Age of Information (AoI) is a recently popularized metric for information freshness. The PI contributed to the original solution of controlling generalized AoI penalty under random delay or energy constraints. Our work revealed that, contrary to conventional wisdom, an age-optimal policy neither transmits at the highest possible rate nor requires the smallest end- to-end delay. We also showed how freshness-aware sampling of a stochastic process can improve accuracy of remote monitoring several-fold compared to uniform sampling. These results motivate a paradigm shift in re-engineering networks for MTC, based on AoI and more advanced freshness metrics to replace traditional performance indicators such as throughput or delay.

Propelled by these game-changing revelations, FRESHDATA will develop the theory of freshness-optimal network design through clean-slate formulations that innovate across network layers. Fundamental trade-offs between freshness, energy and reliability will be characterized. Revolutionary sampling, transmission and service policies for information flows will be devised. We also take on the challenge of demonstrating the impact of our new technologies on real-world IoT implementations. We envision the impact to reach beyond the communication networks research area, and influence the interface with control, robotics and data analytics, the main end users of MTC.

Keywords: Age of information for status updates, semantics of information, networked control systems, cross-layer wireless resource allocation, transmission scheduling, usage-limited estimation and control, finite blocklength, energy harvesting networks

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Project Coordinator: Assist. Prof. Elif Vural
Project Type: TUBITAK 1001 Scientific and Technological Research Projects Funding Program
Project Budget: 466 000 TL
Project Duration: 30 months
Project Start Date: January 15, 2021
Funded Personnel: 2 PhD Students (Full-Time), 2 MSc Students (Full-Time)

Brief Summary: Many applications nowadays involve the acquisition of data over certain time intervals on platforms such as social networks, communication networks and irregular sensor arrays. Such data types can be modeled as time-varying graph signals. In many practical applications, it is not possible to observe graph signals as a complete data set with no missing samples; hence the need for estimating the unavailable observations of the graph signal using the available ones arises as an important requirement. Among many applications involving time-varying graph signals, some examples are the prediction of how an epidemic will evolve in a society, the estimation of lost data in a sensor network due to sensor failure or communication problems, the prediction of the tendencies of individuals in a social network such as their consumption habits. The purpose of this project is to develop novel methods for the modeling and estimation of time-varying graph signals.

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Project Coordinator: Prof. Dr. Klaus Werner Schmidt

Project Type: TUBITAK 1001 Scientific and Technological Research Projects Funding Program

Project Budget: 373.910 TL

Project Duration: 36 months

Project Start Date: 01 November 2019

Funded Personnel: 1 PhD Student (Full-Time), 2 MSc Students (Full-Time)

Modern vehicles are considered as automotive Cyber Physical Systems (ACPS) with a strong interaction of the physical vehicle, its sensors and actuators, the communication technology and embedded software. The in-vehicle network is a vital part of an ACPS since it enables the information exchange between the system components such as electronic control units (ECUs). Hence, it must fulfill stringent requirements regarding reliability, timing, efficiency, cost and compatibility.

Existing in-vehicle protocols for ACPS address the stated requirements only partially. Accordingly, the subject of this project is the development of formal and systematic methods for the design, implementation and performance analysis of in-vehicle network protocols for ACPS that support the stringent requirements of deterministic real-time applications with periodic signal communication and that are compatible to existing standards. The major contributıons of the project are (1) the development of a general framework CANDS (Controller Area Network with Determinism and Synchronization support) for in-vehicle network protocols based on the novel idea of weak time division multiple access (TDMA); (2) a hierarchy of novel, fault tolerant clock synchronization algorithms with different accuracy levels; (3) the definition of different CANDS protocols with different levels of clock accuracy and implementation complexity that are fully compatible with CAN; (4) the formal modeling and verification of the general protocol operation as well as the specific CANDS protocols; (5) the development of new algorithms for the performance analysis and design of CANDS networks with ECUs of a certain CANDS protocol or for mixed CANDS ECUs; (6) the experimental evaluation of the CANDS framework based on software and hardware implementations of the proposed CANDS protocols.

As the end of the project, CANDS will realize fault-tolerant clock synchronization with different accuracy levels. For the first time, CANDS will employ the idea of weak TDMA for traffic shaping in order to achieve deterministic network access. Since CANDS is fully compatible to the existing CAN standard, it provides a highly reliable, low-cost in-vehicle protocol for modern ACPS.

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Project Coordinator: Asst. Prof. Ozan KEYSAN

Project Type: TUBITAK 1001 Scientific and Technological Research Projects Funding Program

Project Budget: 380.000 TL

Project Duration: 24 months

Project Start Date: 01 November 2019

Funded Personnel: 3 MSc Students (Full-Time)

In small power applications inductive wireless power transfer has been used in the consumer electronics and for high power applications (>1kW) many research outputs are presented especially in wireless electric vehicle charger. Other uses of contactless slip rings are the rotating systems of radar-guidance systems, pitch angle adjuster servo motors in wind turbines and field excitation slip rings. Although brushless exciters are used for field excitation of electrical machines (high power applications) these units have narrow operating speed range and they are not efficient. In the industry, contactless slip rings are produced by a few companies. However they require separate shafts and mounting. Moreover, these systems have single transmitter and receiver modules therefore their fault tolerances are low.

Modular structure: Using multi transmitter and multi receiver it is aimed to increase the tolerance to fault conditions and increase the operating speed range. In the case of a fault in single transmitter and receiver the unit need replacement whereas in the modular case like in Figure 1.b only the fault modüle needs replacement. Moreover with modular units the product can be mounted on a single shaft where in the case of Figure 1.a two seperate shafts are required. Series-series resonant converter: Using series-series resonant converter the overall system efficiency is aimed to be kept >90% for all conditions. Silicon Carbide (SiC): SiC have low switching losses and allows higher power to be switched. Hence it is thought to give the Project flexibility. Moreover SİC systems have much higher efficiency then  Si MOSFET and Si IGBTs. Also they can operate at higher temperatures.

The proposed project is about a contactless, modular slip ring alternative system which has wide operating speed range, high efficiency, modular and robust. In order to achieve wide operating speed range magnetic pad design minimizing magnetic coupling deviation is required. Series-series resonant converter will be designed. System will have a speed operating range of 0-3000 rpm hence a FEA model will be built. Resonant converters are usually considered difficult to design however they allow zero voltage/current switching which increases efficiency. The system will be fed from the grid and the overall efficiency is aimed to be >90% under all load and coupling conditions. Silicon Carbide (SiC) will be used as switching unit which have lower switching losses than Si MOSFET/IGBT and higher possible operating temperatures. 

At the end of the project the wireless power transfer unit will be capable of delivering 1kW of power. This module will be first designed to be used as a wind turbine slip ring and the prototype will be brought to TRL-6 level. The prototype can be also used in the industrial and military purposes.

    Figure 1: (a) Industrial contactless slip ring  (b) Proposed modular contactless power transfer slip ring design

Project Coordinator: Prof. Dr. Nevzat G. GENÇER

Project Type: TUBITAK 1001 Scientific and Technological Research Projects Funding Program

Project Budget: 720000 TL

Project Duration: 36 months

Project Start Date: 01 October 2019

Funded Personnel: 2 PhD Students (Full-Time), 2 PhD Students (Part-Time), 1 MSc Students (Full-Time)

Magneto-Acousto Electrical Tomography (MAET) is a new medical imaging modality to image the electrical properties (conductivity and permittivity) of body tissues. When ultrasound is applied to a body in a static magnetic field, electrical currents are generated. The current sources propagating in the direction of the ultrasound beam result in a potential  and magnetic field distributions. The voltages (magnetic fields) measured by the surface electrodes (receiver coils) are used to reconstruct images.  MAET has been applied to develop prototype systems using permanent magnets (~0.5 T), utilizing electrodes for voltage measurements, to obtain images with small field of view, and to obtain conductivity images at a single frequency.

In this project, magnetic measurements will be used for image reconstruction. The performance of multi-frequency electrical property imaging will be investigated  using LPA ultrasound transducers in high field magnets (1.5-3T). The system will be assessed using numerical simulations and experimental studies conducted with a larger field of view using novel receiver coils. A new data acquisition system will be developed with low noise amplifiers and associated signal processing units.

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Project Coordinator: Asst. Prof. Serdar Kocaman

Project Type: TUBITAK 1001 Scientific and Technological Research Projects Funding Program

Project Duration: 24 Months

Project Start Date:

Funded Personnel: 2 PhD Students (full-time)

In this project, for the first time in the literature, nBn type infrared detector with delta doping layers will be developed for InGaAs detector technology for expanded SWIR (eSWIR) wavelength (approximately 2.5 µm cut wavelength). In SWIR band, traditional single band 1.7 µm cut-off wavelength detectors have been successfully developed and megapixel focal planes are recently produced in Middle East Technical University Quantum Devices and Nanophotonics Research Laboratory (METU-KANAL) which is one of the few research groups in the world in terms of infrared materials technologies covered by its expertise. Here, eSWIR test detectors in the developing detector structure (nBn) will be developed.

Project Coordinator: Assoc. Prof. Dr. Murat Göl

Project Team: Asst. Prof. Dr. Ebru Aydin Göl (METU-CENG), Asst. Prof. Dr. Burcu Güldür Erkal (Hacettepe U.-Civil Eng.)

Project Type: TUBITAK 1001 Scientific and Technological Research Projects Funding Program

With the developing technology, the continuity of the electric energy has gained the utmost importance. Critical customers (hospitals, critical government departments, military units, large industrial establishments, etc.) who need electrical energy to maintain their operations in the event of a possible disaster need to be energized as soon as possible. This project aims to evaluate the disaster scenarios such as earthquakes and to develop a decision support method for the rapid elimination of the hazard related problems that may occur in the electricity distribution system.

Restoring the power distribution systems after the interruption is a problem that researchers have been working on for a long time. Especially with the developing communication technology, decision support activities in this area have improved considerably, however, restoration strategies after an event of a serious hazard still have a serious research potential. The main difference that separates disruptions from operational faults or disruptions in an event of a hazard is the changes that occur in the network structure. After a major hazard some components in the network (electric poles, distribution transformers, etc.) may become unusable, and above all the current status of these components may be unknown. In other words, the system operator may have limited knowledge about the topology of the network that is desired to be restored after an earthquake. In this project, a decision support method that helps the system operator to restore the earthquake damaged network with uncertain network topology will be developed. In this way, the energy will be delivered to the critical customers in the shortest time, the duration of the diesel generator usage will be minimized and more reliable and continuous energization will be ensured.

Critical system elements (power plant, power line, transformer center, etc.) will be evaluated for a probable earthquake situation and dynamic performance analysis will be performed in order to predict the post-earthquake response of these elements. With these analytical methods, the probability of these elements to remain usable after an earthquake will be determined. Thus, a procedure for performance evaluation will be established. The developed procedure will work online and will calculate the most realistic outcomes using the information of the location and severity of the earthquake. This information is foreseen to be received from the data servers of the observatories and the earthquake research centers via internet in real time.

A decision support method based on the Markov Decision Process that uses the failure probabilities obtained after the performed structural analysis will be developed within the scope of the project. This developed method works in real time to provide optimum decision support to the system operator. It will also recommend a strategy for restoration, which is constantly updated with the information received from the field (notifications, aerial imagery, feedback from teams, etc.). The improved decision support method will aim to restore the network smoothly by taking the electrical characteristics of the network into account.

Distributed production units, which are becoming more widespread in medium voltage distribution systems, will be evaluated by the developed decision support method. The ability of conventional production facilities to operate as micro-networks in terms of minimizing the restoration time will also be evaluated by this decision support method.

Project Coordinator: Assoc. Prof. Dr. Özgür Ergül

Project Type: TUBITAK 1001 Scientific and Technological Research Projects Funding Program

Project Duration: 30 Months

Project Start Date: 15 October  2018

Funded Personnel: 2 PhD Students (full-time), 3 MSc Student (full-time)

As opposed to the antennas used at radio and microwave frequencies, nanoantennas can be described, in terms of working principles, as small antennas that operate at THz and optical frequencies. In this perspective, a nanoantenna that operates as a receiver can gather electromagnetic waves at optical frequencies and direct to its terminals, enabling optical energy harvesting. When a nanoantenna operates as a transmitter, it makes it possible to detect particles in its terminal. This enables the detection of low-density but critical chemical substances that are used in biology and chemistry. In order to design nanoantennas that may have thousands of different geometries, their very accurate electromagnetic simulations are needed. However, due to the accuracy and efficiency issues of the solvers and software in the literature, complex nanoantenna structures cannot be modeled as in real life, leading to poor performances of the fabricated samples. In this project, an electromagnetic solver and optimization mechanism based on the direct application of the Maxwell’s equations without approximation, which can perform accurate, efficient, and fast solutions of complex nanoantenna problems, will be developed and used to obtain superior nanoantenna designs for diverse applications.

Project Coordinator: Assoc. Prof. Dr. Yeşim Serinağaoğlu Doğrusöz

Project Type: TUBITAK 1001 Scientific and Technological Research Projects Funding Program

Project Duration: 30 Months

Project Start Date:

Funded Personnel: 2 PhD Students (full-time), 1 MSc Student (full-time)

Conventional 12-lead electrocardiography (ECG), which is the primary method of diagnosing cardiac diseases, is widely used for detection of arrhythmias, but is insufficient to provide detailed distributions of electrical potentials, electrical conduction disturbances and sources of arrhythmia in the heart. More advanced diagnosis techniques are often invasive, their practice is at the very least uncomfortable to the patient, and can even lead to unwanted complications. One of the emerging diagnosis methods for heart arrhythmias is the noninvasive electrocardiographic imaging (ECGI). In this method, electrical potentials on the heart surface are estimated from body surface potentials (BSP) recorded with multi-channel ECG systems, and a mathematical model obtained using the geometrical structure of the body and the conductivity values ​​of tissues. In particular, ECGI focuses on a type of arrhythmia referred to as premature ventricular contraction (PVC), in which electrical activity in the heart originates from a focal point in conflict with the normal conduction system. In advance stages, it can lead to fibrillation and even death. In clinical practice, treatment of these arrhythmias is performed by detecting PVC foci using invasive electrocardiographic imaging methods and ablation. Noninvasive ECGI has the potential for guiding the ablation procedure and shortening its duration by estimating the heart potential distributions and locating these foci before the procedure. However, in order for ECGI to become a clinically acceptable method, it is still necessary to develop new and innovative methods that are robust to uncertainties in the models, and to test these methods using clinical data.

Our aim is to obtain images of the cardiac electrical activity on the heart surface, and to detect PVC foci at clinically acceptable accuracy levels by using statistical estimation techniques. In these approaches, every parameter with uncertainty in their values (specifically epicardial potentials, measurement errors, geometric model errors, parameters used in their modeling, etc.) can be included in the model by means of a priori probability density functions (pdf), so that solutions can be obtained with higher accuracy than by using deterministic methods. However, the success of these methods highly depends on the use of correct models and a priori pdf’s. In this project, we will address some open questions in the literature, such as automatically determining a priori pdf’s from the available data, incorporating the uncertainties in the model parameters, and evaluating the accuracy of the results based on statistical metrics.

Principle Investigator: Prof. Ece Güran Schmidt

Project Manager: Prof. Ece Güran Schmidt (METU), Yük. Müh. Alper Yazar (ASELSAN)

Project Type: TÜBİTAK/ARDEB 1003 – Primary Subjects R&D Funding Program

Project Duration: 36 Months

Project Start Date: April 2018

Funded Personnel: 1 PhD (full-time, 36 months), 2 MSc (full-time, 18 months) student(s)

The data centers of today are mostly cloud based with virtualized servers to provide on-demand scalability and flexibility of the available resources such as CPU, memory, data storage and network bandwidth.  A cloud data center provider may offer resources as IaaS, PaaS and SaaS.

In this respect, it is necessary to maintain a proper mapping of the virtual resources to the underlying physical hardware. This is a particularly difficult task, considering the cloud resource heterogeneity, the unpredictable nature of workload, and the diversified objectives of cloud users. Finally, it is desirable to always select the most appropriate resource type depending on the user requests.

FPGAs increase the popularity of hardware accelerators which can provide better performance and less energy consumption depending on the problem properties and size. To this end, the very recent research focuses on employing hardware resources in cloud based data centers. Integrating hardware resources in the cloud based data center should be seamless, together with virtualization and dynamic resource allocation capabilities.

This project proposes a novel architecture for cloud-based data centers that we call ACCLOUD (ACcelerated CLOUD). The proposed architecture features FPGA hardware resources that can be offered to users in the scope of IaaS, PaaS and SaaS. To this end, we propose augmenting the cloud servers with FPGA as well as to employ standalone FPGA units. The FPGA resources are virtualized using a number of run-time partially reconfigurable regions. We propose to use the OpenStack software framework to allocate these partially reconfigurable regions to the users together with other virtualized computing resources. To this end, the first major contribution of this project is offering hardware resources as IaaS, PaaS and SaaS in the ACCLOUD architecture different from all previous work.

The second major contribution is the entirely novel resource management approach ACCLOUD-MAN that incorporates the hardware resources into the existing CPU, memory, bandwidth and disk resources and coordinates the IaaS, PaaS and SaaS management. To this end, ACCLOUD-MAN achieves near globally optimum resource allocation together with increased performance by employing hardware resources for SaaS whenever appropriate.

The third major contribution of the project is the extensive optimization evaluation, simulation and real test bed implementation and evaluation of ACCLOUD and ACCLOUD-MAN.

The project is proposed in the form of one main project (METU) with one sub-project (ASELSAN) together with competent project teams. The METU project team has experience in research and development of high speed hardware architectures for computer networking and network processing. The ASELSAN project team has experience in the development and implementation of advanced FPGA-based architectures.

Project Coordinator: Assist. Prof. Serdar Kocaman

Project Duration: 36 Months

Project Start Date: 2017

Funded Personnel: 1 PHD and 3 MS Students will be involved.

Today’s common platforms have both electrical and optical components and the signal has to be converted from electrical domain to optical domain and from optical domain to electrical domain at every stage of the link. Furthermore, the electrical wires at their physical limit. Due to these two factors, the new generation systems will include all-optical components as much as it is practical.

In this project, optical modulators and switches will be designed and fabricated. The innovative devices will have a larger spectral width advantage in addition to being all optical. Designed structures will be able to be fabricated in the large foundries that are manufacturing electronic chips. Therefore, there is no additional infrastructure is urgently necessary and this makes the whole process cost efficient.

Project Coordinator: Assoc. Prof. Barış Bayram (ULTRAMEMS)

Project Duration: 36 Months

Project Start Date: 2017

Funded Personnel:Two MS/PhD/engineer (with a BS degree and above) and two undergraduate students will be appointed in the research activities regarding the project.

The proposed project is considered to be competitive in the imaging of microvascular structures and to be a pioneering work in the biomedical health field at international level with outstanding features.

Project Coordinator: Assist. Prof. S. Figen Öktem

Project Type: TÜBİTAK/ARDEB 3501 – Career Development Program

Project Duration: 36 Months

Project Start Date: October 2017

Funded Personnel: 1 PhD student (full-time), 1 MSc student (full-time), 2 undergraduate students

Spectral imaging, the sensing of spatial information as a function of wavelength, is a widely used diagnostic technique in diverse fields such as physics, chemistry, biology, medicine, astronomy, and remote sensing. However, the three-dimensional spectral image dataset is required to be obtained using two-dimensional detectors, and this poses intrinsic limitations on the spatio-spectral extent of the technique. For example, for conventional spectral imagers employing wavelength filters, spatial and spectral resolutions are inherently limited by the cost and manufacturability of its optical components.

In this project, we will develop a class of novel spectral imaging techniques that enable capabilities beyond the reach of conventional techniques. Each development will be based on computational imaging. This involves distributing the imaging task between an optical system (containing a photon sieve) and a computational system. The proposed novel optical systems will take multiplexed measurements, and these measurements will then be used in the computational processing unit to digitally form the spectral images by means of solving an inverse problem. Compressive sensing theory and the state-of-the-art image reconstruction approaches will be exploited for this purpose. By building prototypes, the performance of these novel spectral imaging modalities will also be demonstrated experimentally.

The project will include collaboration with University of Illinois at Urbana-Champaign and NASA Goddard Space Flight Center. Moreover, scholarships will be provided to one full-time M.S. and one full-time Ph.D. student as well as two undergraduates. This will enable new researchers in Turkey to join the field of computational imaging, which is an exciting field with several Nobel prizes.

Currently two undergraduate students are involved in the project as part of the METU EE STAR Program (http://star.eee.metu.edu.tr/current-program/), and we are seeking for highly motivated, full-time, M.S. and Ph.D. students to join our team.

Project Coordinator: Prof. Elif Uysal-Biyikoglu

Project Duration: 36 Months

Project Start Date: October 2017

Funded Personnel: Postdoctoral researcher (12 mo), 2 full time RAs (36 mo, 12 mo), 2 undergraduates (24 mo, 12 mo)

We are witnessing significant change in the structure and quality requirements of data that is transported on communication networks.  Technologies such as control and monitoring systems that run on the Internet, cellular and ad-hoc networks, applications running on social networks, environmental monitoring algorithms of autonomous vehicles, as well as automated decision making in health and finance depend on sufficiently frequent updates  (data packets bearing new samples/measurements/data). The proposed project aims to develop techniques and algorithms that approach theoretical limits of efficiently transporting update packets.

A common performance criterion for applications that depend on updates is how fresh the data at the node that processes the data and forms decisions is. On the other hand, traditionally, optimal design of data networks has been centered on the analysis of throughput and delay.  However, results in recent literature have shown that transmission policies that may be optimal in terms of throughput or delay may be sub-optimal in terms of achieving data freshness.

Age of Information is defined as the amount of time that has elapsed since the most recent update that has been received by the source was generated/sampled at the source, in other words, it  expresses how old the newest update at the receiver is.

The proposed project contains work in three main thrusts. The first is AoI optimal scheduling in single server and multi server queuing models. The second is real time sampling. In preliminary work leading to this project, it has been observed that increasing sampling rate does not always lead to reduction in Age, on the contrary it may sometimes increase it. We will develop a sampling theory for optimizing not only age but also error for remote estimation over a network.

The third stage is implementation and demonstration, that uses a variety of sensors that will collect and sample various physical disturbances (e.g. temperature, humidity, acceleration, motion, image). Wireless access points will collect and forward the data captured by the sensors and send it over the Internet to be processed by an application software, and be used for a remote control/automation system.

Among the expected outputs of the project, the first is groundbreaking scientific contributions in the form of theoretical limits for AoI, optimal sampling in the presence of network delay, and algorithms that approach these limits. The project will include collaboration with MIT and Ohio State University researchers.

Project Coordinator: Assoc. Prof. Barış Bayram (ULTRAMEMS)

Project Duration: 36 Months

Project Start Date: 2017

Funded Personnel: One MS/PhD and two undergraduate students will be appointed in the research activities regarding the project.

Detection of brain hemorrhage within the first 1.5 hours is critical for a patient to avoid permanent brain damage. However due to their high complexity, high cost devices like MRI and CT can hardly be available in this time frame. This project proposes a method for detection of blood within the brain.