| 111. |
- Özalay, Özgun, et al.
(författare)
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Cortical thickness and VBM in young women at risk for familial depression and their depressed mothers with positive family history
- 2016
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Ingår i: Psychiatry Research. - : Elsevier. - 0925-4927 .- 1872-7506. ; 252, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- It has been demonstrated that compared to low-risk subjects, high-risk subjects for depression have structural and functional alterations in their brain scans even before the disease onset. However, it is not known if these alterations are related to vulnerability to depression or epiphenomena. One way to resolve this ambiguity is to detect the structural alterations in the high-risk subjects and determine if the same alterations are present in the probands. In this study, we recruited 24 women with the diagnosis of Major Depressive Disorder (MDD) with recurrent episodes and their healthy daughters (the high-risk for familial depression group; HRFD). We compared structural brain scans of the patients and HRFG group with those of 24 age-matched healthy mothers and their healthy daughters at similar ages to the HRFD group; respectively. Both cortical gray matter (GM) volume and thickness analyses revealed that HRFD daughters and their MDD mothers had similar GM differences in two regions: the right temporoparietal region and the dorsomedial prefrontal cortex. These results suggested that the observed alterations may be related to trait clinical and neurophysiological characteristics of MDD and may present before the onset of illness.
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| 112. |
- Özcelikkale, Ayca
(författare)
-
Sparse Recovery with Non-Linear Fourier Features
- 2020
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Ingår i: ICASSP 2020. - 9781509066315 - 9781509066322 ; , s. 5715-5719
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Konferensbidrag (refereegranskat)abstract
- Random non-linear Fourier features have recently shown remarkable performance in a wide-range of regression and classification applications. Motivated by this success, this article focuses on a sparse non-linear Fourier feature (NFF) model. We provide a characterization of the sufficient number of data points that guarantee perfect recovery of the unknown parameters with high-probability. In particular, we show how the sufficient number of data points depends on the kernel matrix associated with the probability distribution function of the input data. We compare our results with the recoverability bounds for the bounded orthonormal systems and provide examples that illustrate sparse recovery under the NFF model.
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| 113. |
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| 114. |
- Özeren, Hüsamettin Deniz
(författare)
-
Plasticization of Biobased Polymers: A Combined Experimental and Simulation Approach
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The field of bio-based plastics has developed significantly in recent decades and there is an increasing demand for industries to shift from petrochemical to biobased polymers. Biobased polymers offer competitive properties, and in many cases have advantages in terms of cost. Thermoplastic starch is already commercially available, while wheat-gluten protein-based materials are considered to be promising candidates for commercial use.Biobased materials can, however, have several drawbacks that have to be handled. Starch-based materials are, in general, brittle due to the stiff glucose-based molecular chain and hydrogen bond network. This is the case also for proteins (due to the stiff peptide bond, bulky side groups and hydrogen bond network), like for example gluten. These issues can, however, be resolved with effective compatible plasticizers. But in order to be able to optimize the choice of the right plasticizer for a specific polymer, there is a need for an increased understanding of the plasticizer mechanisms. Besides, a methodology for prediction of the plasticizer amount needed, as well as to be able to rank possible plasticizer candidates, based on their effectiveness. As a part of the development of a methodology (based on the combination of experimental and molecular-dynamics simulations) for prediction of plasticization and to investigate and understand plasticizer mechanisms, the main material investigated was starch, but also wheat gluten, both plasticized with glycerol. The main plasticizer used to date for biobased polymer materials is glycerol, because of its effectiveness, stability and low cost. In addition, it is also a large byproduct of biodiesel production. A number of other plasticizer candidates were also studied for the starch system to see if the developed methodology could be used to rank plasticizers. Diols were tested in the starch system as plasticizers, but they had no or little plasticization effect. Nevertheless, they gave rise to unexpected structures and properties. Several techniques were used to determine the experimental properties of the bio-based films, including calorimetry, gravimetry, dynamic mechanical analysis, and tensile testing.The results (based on mechanical and thermal properties) showed that the methodology could be used to rank plasticizers in terms of their effectiveness. It was also possible to predict the amount of plasticizer needed for effective softening. With the help of the simulations, the emollient effect could be studied in detail and largely explained by hydrogen bonding effects. The methodology was also developed to be able to predict from simulation not only trends in mechanical properties but also absolute values in stiffness and strength at elongation rates corresponding to experimental measurements.
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| 115. |
- Özger, Mustafa, et al.
(författare)
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Energy-Efficient Transmission Range and Duration for Cognitive Radio Sensor Networks
- 2021
-
Ingår i: IEEE Transactions on Cognitive Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2332-7731. ; , s. 1-1
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Tidskriftsartikel (refereegranskat)abstract
- Cognitive Radio (CR) promises an efficient utilization of radio spectrum resources by enabling dynamic spectrum access to overcome the spectrum scarcity problem. Cognitive Radio Sensor Networks (CRSNs) are one type of Wireless Sensor Networks (WSNs) equipped with CR capabilities. CRSN nodes need to operate energy-efficiently to extend network lifetime due to their limited battery capacity. In this paper, for the first time in literature, we formulate the problem of finding a common energy-efficient transmission range and transmission duration for all CRSN nodes and network deployment that would minimize the energy consumed per goodput per meter toward the sink in a greedy forwarding scenario. Results reveal non-trivial relations for energy-efficient CRSN transmission range and duration as a function of nine critical network parameters such as primary user activity levels. These relations provide valuable insights for detailed CRSN designs prior to deployment.
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| 116. |
- Özger, Mustafa, et al.
(författare)
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Towards Beyond Visual Line of Sight Piloting of UAVs with Ultra Reliable Low Latency Communication
- 2018
-
Ingår i: 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538647271
-
Konferensbidrag (refereegranskat)abstract
- In this paper, we propose a model for beyond visual line of sight (BVLOS) operation for remote piloting of unmanned aerial vehicles (UAVs), which utilizes different technologies such as mobile edge computing and augmented reality. Ultra reliable low latency communication (URLLC) is a key service of 5G that enables safe BVLOS operation. Since message size of piloting signal is finite and communication channel is altitude dependent, we study reliability and latency under finite blocklength regime for different altitudes. In our numerical study, we find that for message sizes 30 and 50 bits, coded packet size, i.e., blocklength, should be in the range of 200 and 300 bits to enable BVLOS operation. We also found that minimum distance between UAVs to avoid any crash should be around 0.2 m for 15 m/s UAV speed and different altitudes ranging from 1.5 m to 120 m. According to our study, BVLOS operation of UAVs can be realized by URLLC by providing error probability in the vicinity of 10(-3), and latency on the order of milliseconds for downlink communication with blocklength of tens to hundred bits.
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| 117. |
- Özgü, Alay, et al.
(författare)
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End to End 5G Measurements with MONROE: Challenges and Opportunities
- 2018
-
Ingår i: 4th International Forum on Research and Technologies for Society and Industry (IEEE RTSI 2018). - : IEEE. - 9781538662823
-
Konferensbidrag (refereegranskat)abstract
- To be able to support diverse requirements of massive number of connected devices while also ensuring good user experience, 5G networks will leverage multi-access technolo- gies, deploy supporting operational mechanisms such as SDN and NFV, and require enhanced protocols and algorithms. For 4G networks, MONROE has been key to provide a common measurement platform and a set of methodologies available to the wider community. Such common grounds will become even more important and more challenging with 5G. In this paper, we elaborate on some key requirements for the design and implementation of 5G technologies and highlight the key challenges and needs for new solutions as seen in the context of 5G end-to-end measurements. We then discuss the opportunities that MONROE provides and more specifically, how a 5G-capable MONROE platform could facilitate these efforts.
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| 118. |
- Özkale, M. Revan, et al.
(författare)
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The stochastic restricted ridge estimator in generalized linear models
- 2021
-
Ingår i: Statistical papers. - : Springer Science and Business Media LLC. - 0932-5026 .- 1613-9798. ; 62, s. 1421-1460
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Tidskriftsartikel (refereegranskat)abstract
- Many researchers have studied restricted estimation in the context of exact and stochastic restrictions in linear regression. Some ideas in linear regression, where the ridge and restricted estimations are the well known, were carried to the generalized linear models which provide a wide range of models, including logistic regression, Poisson regression, etc. This study considers the estimation of generalized linear models under stochastic restrictions on the parameters. Furthermore, the sampling distribution of the estimators under the stochastic restriction, the compatibility test and choice of the biasing parameter are given. A real data set is analyzed and simulation studies concerning Binomial and Poisson distributions are conducted. The results show that when stochastic restrictions and ridge idea are simultaneously applied to the estimation methods, the new estimator gains efficiency in terms of having smaller variance and mean square error.
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| 119. |
- Özkan, Emre, et al.
(författare)
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Non-Parametric Bayesian Measurement Noise Density Estimation in Non-Linear Filtering
- 2011
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Ingår i: Acoustics, Speech and Signal Processing (ICASSP), 2011. - : IEEE. - 9781457705380 - 9781457705373 ; , s. 5924-5927
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Konferensbidrag (refereegranskat)abstract
- In this study, we investigate online Bayesian estimation of the measurement noise density of a given state space model using particle filters and Dirichlet process mixtures. Dirichlet processes are widely used in statistics for nonparametric density estimation. In the proposed method, the unknown noise is modeled as a Gaussian mixture with unknown number of components. The joint estimation of the state and the noise density is done via particle filters. Furthermore, the number of components and the noise statistics are allowed to vary in time. An extension of the method for the estimation of time varying noise characteristics is also introduced.
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| 120. |
- Özkirimli, Umut
(författare)
-
Denial
- 2018
-
Ingår i: Ahval.
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Tidskriftsartikel (populärvet., debatt m.m.)
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