Browsing by Subject "Targets"
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Item Open Access Characterization of the transcriptional and metabolic responses of pediatric high grade gliomas to mTOR-HIF-1a...(Impact Journals LLC, 2017-03-23) Nguyen, A.; Moussallieh, F. M.; Mackay, A.; Cicek, A. E.; Coca, A.; Chenard, M. P.; Weingertner, N.; Lhermitte, B.; Letouzé, E.; Guérin, E.; Pencreach, E.; Jannier, S.; Guenot, D.; Namer, I. J.; Jones, C.; Entz-Werlé, N.Pediatric high grade glioma (pHGGs), including sus-tentorial and diffuse intrinsic pontine gliomas, are known to have a very dismal prognosis. For instance, even an increased knowledge on molecular biology driving this brain tumor entity, there is no treatment able to cure those patients. Therefore, we were focusing on a translational pathway able to increase the cell resistance to treatment and to reprogram metabolically tumor cells, which are, then, adapting easily to a hypoxic microenvironment. To establish, the crucial role of the hypoxic pathways in pHGGs, we, first, assessed their protein and transcriptomic deregulations in a pediatric cohort of pHGGs and in pHGG’s cell lines, cultured in both normoxic and hypoxic conditions. Secondly, based on the concept of a bi-therapy targeting in pHGGs mTORC1 (rapamycin) and HIF-1α (irinotecan), we hypothesized that the balanced expressions between RAS/ERK, PI3K/AKT and HIF-1α/HIF-2α/MYC proteins or genes may provide a modulation of the cell response to this double targeting. Finally, we could evidence three protein, genomic and metabolomic profiles of response to rapamycin combined with irinotecan. The pattern of highly sensitive cells to mTOR/HIF-1α targeting was linked to a MYC/ERK/HIF-1α over-expression and the cell resistance to a major hyper-expression of HIF-2α.Item Open Access Differentiation and localization of targets using infrared sensors(Elsevier, 2002) Aytaç, T.; Barshan, B.This study investigates the use of low-cost infrared emitters and detectors in the differentiation and localization of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders. The intensity readings obtained with such systems are highly dependent on target location and properties in a way which cannot be represented in a simple manner, making the differentiation and localization process difficult. In this paper, we propose the use of angular intensity scans and present an algorithm to process them. This approach can determine the target type independent of its position. Once the target type is identified, its position can also be estimated. The method is verified experimentally. An average correct classification rate of 97% over all target types is achieved and targets are localized within absolute range and azimuth errors of 0.8 cm and 1.6°, respectively. The method demonstrated shows that simple infrared sensors, when coupled with appropriate processing, can be used to extract a significantly greater amount of information than that which they are commonly employed for.Item Open Access On the performance of linear least-squares estimation in wireless positioning systems(IEEE, 2008-05) Gezici, Sinan; Güvenç, İ.; Sahinoğlu, Z.A common technique for wireless positioning is to estimate time-of-arrivals (TOAs) of signals traveling between a target node and a number of reference nodes, and then to determine the position of the target node based on those TOA parameters. In determining the position of the target node from TOA parameters, linear or nonlinear least-squares (LS) estimation techniques can be employed. Although the linear LS techniques are suboptimal in general, they facilitate low-complexity position estimation. In this paper, performance of various linear LS techniques are compared, and suboptimality of the linear approach is quantified in terms of the Cramer-Rao lower bound (CRLB). Simulations are performed to compare the performance of the linear LS approaches versus the CRLBs for linear and nonlinear techniques. ©2008 IEEE.Item Open Access A particle swarm optimization based SAR motion compensation algorithm for target image reconstruction(IEEE, 2010) Uğur, Salih; Arıkan, OrhanA new SAR motion compensation algorithm is proposed for robust reconstruction of target images even under large deviations of the platform from intended flight path. Phase error due to flight path deviations is estimated as a solution to an optimization problem in terms of the positions of the reflectivity centers of the target. Particle swarm optimization is used to obtain phase error estimates efficiently. The quality of the reconstructions is demonstrated by using simulation studies. © 2010 IEEE.Item Open Access Simultaneous extraction of geometry and surface properties of targets using infrared intensity signals(IEEE, 2005) Aytaç, Tayfun; Barshan, BillurWe propose the use of angular intensity signals obtained with low-cost infrared sensors and present an algorithm to simultaneously extract the geometry and surface properties of commonly encountered targets in indoor environments. The method is verified experimentally with planes, 90° corners, and 90° edges covered with aluminum, white cloth, and Styrofoam packaging material. An average correct classification rate of 80% of both geometry and surface over all target types is achieved and targets are localized within absolute range and azimuth errors of 1.5 cm and 1.1°, respectively. Taken separately, the geometry and surface type of targets can be correctly classified with rates of 99% and 81%, respectively, indicating that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor. The method demonstrated shows that simple infrared sensors, when coupled with appropriate signal processing, can be used to extract substantially more information than such devices are commonly employed for.Item Open Access Tag suggestr: Automatic photo tag expansion using visual information for photo sharing websites(Springer, 2008-12) Küçüktunç, Onur; Sevil, Sare G.; Tosun, A. Burak; Zitouni, Hilal; Duygulu, Pınar; Can, FazlıIn this paper, we propose an automatic photo tag expansion system for the community photo collections, such as Flickr. Our aim is to suggest relevant tags for a target photograph uploaded to the system by a user, by incorporating the visual and textual cues from other related photographs. As the first step, the system requires the user to add only a few initial tags for each uploaded photo. These initial tags are used to retrieve related photos including the same tags in their tag lists. Then the set of candidate tags collected from a large pool of photos is weighted according to the similarity of the target photo to the retrieved photo including the tag. Finally, the tags in the highest rankings are used to automatically expand the tags of the target photo. The experimental results on Flickr photos show that, the use of visual similarity of semantically relevant photos to recommend tags improves the quality of suggested tags compared to only text-based systems. © 2008 Springer Berlin Heidelberg.Item Open Access Target classification with simple infrared sensors using artificial neural networks(IEEE, 2008-10) Aytaç, T.; Barshan, BillurThis study investigates the use of low-cost infrared (IR) sensors for the determination of geometry and surface properties of commonly encountered features or targets in indoor environments, such as planes, corners, edges, and cylinders using artificial neural networks (ANNs). The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way which cannot be represented by a simple analytical relationship, therefore complicating the localization and classification process. We propose the use of angular intensity scans and feature vectors obtained by modeling of angular intensity scans and present two different neural network based approaches in order to classify the geometry and/or the surface type of the targets. In the first case, where planes, 90° corners, and 90° edges covered with aluminum, white cloth, and Styrofoam packaging material are differentiated, an average correct classification rate of 78% of both geometry and surface over all target types is achieved. In the second case, where planes, 90° edges, and cylinders covered with different surface materials are differentiated, an average correct classification rate of 99.5% is achieved. The method demonstrated shows that ANNs can be used to extract substantially more information than IR sensors are commonly employed for. © 2008 IEEE.Item Open Access Target detection and classification in SAR images using region covariance and co-difference(SPIE, 2009-04) Duman, Kaan; Eryıldırım, Abdulkadir; Çetin, A. EnisIn this paper, a novel descriptive feature parameter extraction method from synthetic aperture radar (SAR) images is proposed. The new approach is based on region covariance (RC) method which involves the computation of a covariance matrix whose entries are used in target detection and classification. In addition the region co-difference matrix is also introduced. Experimental results of object detection in MSTAR (moving and stationary target recognition) database are presented. The RC and region co-difference method delivers high detection accuracy and low false alarm rates. It is also experimentally observed that these methods produce better results than the commonly used principal component analysis (PCA) method when they are used with different distance metrics introduced. © 2009 SPIE.Item Open Access Target differentiation and localization using infrared sensors(SPIE, 2003-08) Aytaç, Tayfun; Barshan, BillurWe discuss the use of low-cost infrared sensors in differentiating and localizing commonly encountered target primitives in indoor environments, such as planes, corners, edges, and cylinders. Single intensity readings are highly dependent on target location and properties and this dependence cannot be represented simply. We propose a method that can achieve position-invariant target differentiation without relying on absolute intensity readings and verify it experimentally. The correct identification rates for planes, 90° corners and edges, and cylinders are 90%, 100%, 82.5%, and 92.5%, respectively. The distance of the target can be estimated with an average error of 0.59 cm and the azimuth angle can be estimated with an error of 1.58°.