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. Ozan Keysan

Project Type: TÜBİTAK/ARDEB 3501 – Career Development Program

Project Duration: 24 Months

Project Start Date: October 2017

Funded Personnel: 1 PhD (half-time) and 1 MSc (full-time) student.

Nowadays, electric motors constitute more than 50% of the total electric consumption. Variable frequency drives (VFD) have become widespread in the industry. Moreover, electric vehicles are expected to be much more common in the next decade. Several control mechanisms which have been achieved via hydraulic systems are now being replaced by electromagnetic systems in the aerospace industry. In addition, sensitive servo drives are substituting for mechanical parts rapidly in the military defense industry. The electric motors are driven by separate drives from outside which are connected via long cables in all these applications. This leads to poor power density (W/kg, W/cm³) values. These applications also require high reliability, redundancy and drives with high fault tolerance.

The aim of this project is to develop an Integrated Modular Motor Drive (IMMD) system where the electric motor and its drive are integrated into a single package. Within this scope:

• The motor and drive will be integrated into a single package which will lead to high power density values (5 kW/liter) which will be very important in electric traction, aerospace and defense industries.
• Both the motor and the drive will be composed of several modules operating in parallel so that the design and control will be more flexible, thermal management will be easier and redundancy of the system will increase.
• Fault tolerance is very important in mission critical systems (aerospace, defense etc.). A fault tolerant system may continue its operation under a faulty condition with reduced power rating. The motor drive system will be able to continue its operation in case one module is faulted thanks to the modularity of the system.

In this project, new generation wide band gap (WBG) Gallium Nitride (GaN) power semiconductor devices will be utilized at high operating frequencies. By doing so, the volume of the motor drive is aimed to be reduced by 30% and the efficiency of the motor drive is aimed to be enhanced by 2% compared to the conventional drives.

All the work within the context of this project are conducted in an open-source manner and can be accessed via https://github.com/mesutto/IMMD. Moreover, the project coordinator is a member of PowerLab research group the website of which is http://power.eee.metu.edu.tr/.

A PhD student is currently working in the project and we are looking for a full-time MSc. scholarship student. 10 undergraduate students are a part of this project with several topics who are performing METU EEE STAR (http://star.eee.metu.edu.tr/current-program/) program and are also members of the sub-research group called Research League (http://power.eee.metu.edu.tr/research-league/).

Project Coordinator: Prof. Nevzat G. Gençer

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

Project Duration: 30 Months

Project Start Date: October 2017

Funded Personnel: 1 Post-Doctoral Research Assistant, 3 PhD Students (half-time), 1 PhD Student (full-time), 1 MSc Student (full-time)

Harmonic Motion Microwave Doppler Imaging (HMMDI) is a novel imaging modality to image electrical and mechanical (elastic) properties of body tissues. In the proposed project, it is aimed to accelerate the elapsing time in measurements, improve the accuracy levels in HMMDI and enhance its clinical applications.

Conventional method for breast cancer diagnosis, Mammography, suffers from utilization of ionizing radiation, difficulty in imaging of dense breasts, inconclusive results and patient discomfort. Top and Gencer (2014) proposed the HMMDI method as promising hybrid method for breast cancer detection, which may overcome the limitations of the Mammography. In this method, local vibrations at a focal point are created by focused ultrasound waves. At the same time, a narrowband microwave signal is transmitted to the vibrating region. Due to local vibrations, Doppler component is observed in the spectrum of the received signal. The level of Doppler signal is related to the mechanical and electrical properties of the vibrating region. By scanning the focal region inside the breast volume, HMMDI image of the tissue can be constructed. Technical feasibility of the method has been shown via analytical (Top and Gencer, 2014), numerical and experimental (Top, 2013) (Top et. al, 2016) studies.

In this project, studies for improving accuracy level, safety and accelerating the experimental measurements will be conveyed. In order to improve the sensitivity, the main microwave component in the received signal component will be cancelled out using a active signal cancellation. Spatial accuracy will be improved via utilization of multiple antennas. To decrease the scanning time, feasibility of continuous mechanical scan of the ultrasound probe will be investigated. Numerical studies for investigating electronical scanning of the focus of ultrasound will also be done. In addition, studies for real time monitoring of system safety, such as cavitation onset, and temperature rise will be done using simulations and experimental set-ups. Moreover, by enhancing the hybridization of microwave imaging with HMMDI, dielectric distribution inside the tissue will be monitored yielding a better liability for tumor detection.

The studies will be conducted via phantom materials to investigate the feasibility of HMMDI in breast cancer diagnosis. Overcoming current encountered problems will accelerate the translation of this method to clinical studies. The study can later be developed to be adapted on cancer diagnosis over other tissues (liver, prostate, etc.).

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.

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: 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: Assoc. Prof. Barış Bayram (ULTRAMEMS team)

Project Duration: 36 months

Project Start Date: November 2016

Funded Personnel: 2 undergraduate and 1 MS students will engage in the research activities regarding the project.

This new microphone will provide
•Compact size
•High sensitivity
•Low energy consumption
•Durability to acceleration
•Long-term stable performance

Efficiency enhancement of silicon solar cells by photonic up-conversion

Project Coordinator: Assist. Prof. Selçuk Yerci

Project Duration: 24 months

Project Start Date: Jan. 2016

Funded Personnel: Project will be conducted by 1 Ph.D. and 2 M.S. students

Today, over 90% of the solar cells are produced using silicon (Si) material. There are three main reasons for the choice of Si: its relatively low material cost thanks to being the second most earth-abundant element after oxygen, its near-to-ideal band gap, and the well-developed silicon technology that allows advanced fabrication processes. However, silicon solar cells similar to all other solar cells made of single band gap material suffers from two fundamental losses: (1) non-absorbed photons with energies below the band gap of the absorber, and (2) thermalization losses due to the absorption of photons with energies well-above the band gap of the absorber. These fundamental losses can be reduced by spectrum reshaping in which a high energy photon can be converted to two or multiple lower energy photons (down-conversion) and/or two or more low energy photons can be converted to one high energy photon (up-conversion).

"In this project, we aim at increasing the efficiency of bi-facial Si solar cells by reducing the energy losses due to non-absorbed photons." said Selçuk Yerci to summarize the aim of the project. "In this study, first, we will fabricate bi-facial Si solar cells for the first time in Turkey. Next, we will fabricate up-conversion layer at the back of the bi-facial solar cells. Finally, the up-conversion efficiency of the up-converting layer will be enhanced using various photonic structures." 

Other than Asst. Prof. Selcuk Yerci, Prof. Rasit Turan, the director of GUNAM, Assoc. Prof. Ipek Kocer Guler from Cankaya University and Assoc. Prof. Sahin Kaya Ozdemir, a former graduate of EEMB (M.S. 1995 and B.S. 1992), from University of Washington at St. Louis will work as co-PI in the project.

At the moment, 2 undergraduate students are working along the direction of this topic in their STAR project. We are seeking for motivated M.S. and Ph.D. students to join our team.