Automatic and semi-automatic extraction of curvilinear features from SAR images

Extraction of curvilinear features from synthetic aperture radar (SAR) images is important for automatic recognition of various targets, such as fences, surrounding the buildings. The bright pixels which constitute curvilinear features in SAR images are usually disrupted and also degraded by high amount of speckle noise which makes extraction of such curvilinear features very difficult. In this paper an approach for the extraction of curvilinear features from SAR images is presented. The proposed approach is based on searching the curvilinear features as an optimum unidirectional path crossing over the vertices of the features determined after a despeckling operation. The proposed method can be used in a semi-automatic mode if the user supplies the starting vertex or in an automatic mode otherwise. In the semi-automatic mode, the proposed method produces reasonably accurate real-time solutions for SAR images

The Role of Environmental Factors in Etiology of Attention- Deficit Hyperactivity Disorder

Environmental factors in etiology of ADHD Attention deficit and hyperactivity disorder (ADHD) is one of the most common developmental disorders of childhood. It was reported that it is a disease that affects 5.29% of children and adolescents in the entire world. Although ADHD is a disorder with high inheritability, genetic factors are not the only explanation to ADHD etiology. ADHD is a disorder etiology which has genetic and environmental components and gene-environment interaction. In spite of the fact that many environmental factors are linked to ADHD, the number of environmental factors that are proven to be in significant cause-effect relation is too small. In other words, in presence of proper genetic basis, disease appears in presence of many environmental factors each of which have a slight effect, its severity or prognosis is variable. Environmental factors that are most commonly linked to ADHD pathophysiology are; complications during pregnancy, natal and postnatal period, several toxins and food substances. It has been considered that exposure to risk factors that may affect development of the brain in any of these periods will have long-term effects on behavior. Along with mother’s cigarette or alcohol use during pregnancy, emotional difficulties, medical diseases and complications of pregnancy; natal complications, low birth weight, premature birth, post mature birth, physical traumas that may affect brain development in early childhood, psychosocial difficulties are also found to be related to ADHD. Studies of gene-environment interaction also note the importance of environmental factors. For example, a study showed that in cases which carry 7 repeated alleles of DRD4, exposure to prenatal cigarettes causes more severe symptoms of ADHD. The purpose of this paper is to evaluate the role of environmental factors in etiology of ADHD, review these factors in the light of related literature and, lastly, to mention gene-environment interaction

The Effect of Burnout on Organizational Citizenship Behaviour: The Mediating Role of Job Satisfaction

The purpose of this study was to investigate the effect of burnout on organizational citizenship behavior (OCB) in a mediating model in which the job satisfaction was contextual state.  Data were obtained from 257 nurses from three university hospitals. While the data involving burnout and job satisfaction were gathered from the nurses, OCB data were obtained from supervisors. The findings of hierarchical regression analysis demonstrated that the only contributor burnout dimension on OCB-O (ODB toward organization) was the reduced personal accomplishment while emotional exhaustion and depersonalization had no effect. Also the findings of mediation analysis showed that job satisfaction is a mediator in the relation between reduced personal accomplishment and OCB-O and it is not a mediating factor in relation between all three burnout dimensions and OCB-I (OCB toward individuals)

Sentetik açıklıklı radar görüntülerinde alan tabanlı hedef tespiti ve paralel gerçekleştirmesi (Region based target detection in synthetic aperture radar images and its parallel implementation)

Sentetik açıklıklı radar (SAR) görüntülerinde otomatik hedef tespiti yöntemleri görüntünün çözünürlüğüne, hedefin büyüklüğüne, parazit yankı karmaşıklığına ve benek gürültü seviyesine duyarlıdır. Gürbüz bir hedef tespiti yönteminin ise bu tür etkenlere daha az duyarlı olması istenir. Önerilen yöntem görüntünün öznitelik korumalı benek gürültü arındırma (feature preserving despeckling, FPD) yönteminden geçmiş hali üzerinden olası hedef bölgelerinin ve etrafındaki parazit yankı karmaşıklığının bulunması ve sabit yanlış alarm oranı elde edilecek şekilde eşiklenmesi esasına dayanmaktadır. Hesaplama verimliği OpenMP ve NVidia CUDA kullanılarak arttırılmış ve elde edilen hızlanmalar gösterilmiştir

Deconstruction, legibility and space: four experimental typographic practices

In this article we wish to present the typographic experimentations of four designers, each of whom looks at typography and its implementations from different viewpoints; however with similar goals – namely to investigate how typographic systems can be implemented, their attributes as carriers of semantic meaning be redefined, and/or their functions be improved upon within the digital medium that presents challenges as well as opportunities that enable graphic designers to reach well beyond the traditional medium of typographic work; i.e., printed paper. The article will examine these four projects under the umbrella concept of Deconstruction, also extending into a consideration of Legibility; setting them forth as examples of the impact that the digital medium has brought to bear upon typographic practice in recent decades

Robust and Computationally-Efficient Anomaly Detection using Powers-of-Two Networks

Robust and computationally efficient anomaly detection in videos is a problem in video surveillance systems. We propose a technique to increase robustness and reduce computational complexity in a Convolutional Neural Network (CNN) based anomaly detector that utilizes the optical flow information of video data. We reduce the complexity of the network by denoising the intermediate layer outputs of the CNN and by using powers-of-two weights, which replaces the computationally expensive multiplication operations with bit-shift operations. Denoising operation during inference forces small valued intermediate layer outputs to zero. The number of zeros in the network significantly increases as a result of denoising, we can implement the CNN about 10% faster than a comparable network while detecting all the anomalies in the testing set. It turns out that denoising operation also provides robustness because the contribution of small intermediate values to the final result is negligible. During training we also generate motion vector images by a Generative Adversarial Network (GAN) to improve the robustness of the overall system. We experimentally observe that the resulting system is robust to background motion