A real-time power monitoring and energy-efficient network/interface selection tool for android smartphones

Energy efficiency in wireless and cellular networks has become one of the most important concerns for both academia and industry due to battery dependence of mobile devices. In this regard, Wireless Network Interface Cards (WNICs) of mobile devices have to be taken into account carefully as they consume an important chunk of the system's total energy. In this paper, we propose a real-time network power consumption profiler and an energy-aware network/interface selection tool for Android-based smartphones. The tool has been freely released on the Android Play Store. The proposed solution reports the power consumption levels of different network interfaces (Wi-Fi and Cellular) by making use of actual packet measurements and precise computations, and enables the devices to handover horizontally/vertically in order to improve the energy efficiency. In this context, widespread analyses have been executed to show the accuracy of the proposed tool. The results demonstrate that the proposed tool is very accurate for any type of IEEE 802.11 wireless or cellular stations, regardless of having different amount of channel utilization, transmission rates, signal strengths or traffic types

A novel method for lung segmentation on chest CT images: complex-valued artificial neural network with complex wavelet transform

Image segmentation is an important step in many computer vision algorithms. The objective of segmentation is to obtained an optimal region of convergences (ROC). Error in this stage will impact all higher level activities. This paper focuses on a new efficient method denoted as Complex-Valued Artificial Neural Network with Complex Wavelet Transform (CWT-CVANN) for the segmentation of lung region on chest CT images. In this combined architecture is composed of two cascade stages: feature extraction with various levels of complex wavelet transform, and segmentation with complex-valued artificial neural network. Hem, 32 CT images of 6 female and 26 male patients were recorded from, Baskent University Radiology Department. (This collection includes 10 images with benign nodules and 22 images with malign nodules. Averaged age of patients is 64. Each CT slice used in this study has dimensions of 752x 752 pixels with grey level) In only two seconds of processing time per each CT image, 99.79% averaged accuracy rate is obtained using 3(rd) level CWT-CVANN for segmentation of the lung region. Thus, it is concluded that CWT-CVANN is a comprising method in luny region segmentation problem

A New Method for 3D Thinning of Hybrid Shaped Porous Media Using Artificial Intelligence. Application to Trabecular Bone

Curve and surface thinning are widely-used skeletonization techniques for modeling objects in three dimensions. In the case of disordered porous media analysis, however, neither is really efficient since the internal geometry of the object is usually composed of both rod and plate shapes. This paper presents an alternative to compute a hybrid shape-dependant skeleton and its application to porous media. The resulting skeleton combines 2D surfaces and 1D curves to represent respectively the plate-shaped and rod-shaped parts of the object. For this purpose, a new technique based on neural networks is proposed: cascade combinations of complex wavelet transform (CWT) and complex-valued artificial neural network (CVANN). The ability of the skeleton to characterize hybrid shaped porous media is demonstrated on a trabecular bone sample. Results show that the proposed method achieves high accuracy rates about 99.78%-99.97%. Especially, CWT (2nd level)-CVANN structure converges to optimum results as high accuracy rate-minimum time consumption

COMPARISON OF DİSCRETE WAVELET TRANSFORM AND COMPLEX WAVELET TRANSFORM IN HYBRID SKELETONIZATION BASED ON CVANN

Curve and surface thinning are widely-used skeletonization techniques for modeling objects in 3 dimensions. In the case of disordered porous media analysis, however, neither is really efficient since the internal geometry of the object is usually composed of both rod and plate shapes. This paper concludes an application of discrete wavelet transform (WT) and complex wavelet transform (CWT) in image processing problem such as hybrid skeletonization of trabecular bone images. Hybrid skeleton combines 2D surfaces and 1D curve to represent respectively the plate-shaped and rod-shaped parts of the object. For hybrid skeletonization, two cascade structures are proposed. In these structures, features of images were extracted with discrete wavelet transform and complex wavelet transform. After that, obtained features were used as inputs of complex-valued artificial neural network (CVANN) which is multi-layered artificial neural networks with two dimensions (real and imaginary parts). Effects of the feature extraction methods are compared for ability of the hybrid skeletonization on a trabecular bone sample. Results show that the CWT succeeded to hybrid skeletonization with lower error rate than WT

Mitigation of indomethacin-induced gastric mucosal lesions by a potent specific type V phosphodiesterase inhibitor

AIM: To investigate the gastroprotective effect of vardenafil against indomethacin-induced gastric damage

Cerebral Venous Sinus Thrombosis as a Rare Complication of Systemic Lupus Erythematosus: Subgroup Analysis of the VENOST Study

Kozak, Hasan Huseyin/0000-0001-6904-8545; Sahin, Sevki/0000-0003-2016-9965; Batur Caglayan, Hale/0000-0002-3279-1842; GUNES, TASKIN/0000-0002-9343-0573; Afsar, Nazire/0000-0001-8123-8560; Uzuner, Nevzat/0000-0002-4961-4332WOS: 000498868800011PubMed: 31562041Aim: Systemic lupus erythematosus (SLE) is an unusual risk factor for cerebral venous sinus thrombosis (CVST). As few CVST patients with SLE have been reported, little is known regarding its frequency as an underlying etiology, clinical characteristics, or long-term outcome. We evaluated a large cohort of CVST patients with SLE in a multicenter study of cerebral venous thrombosis, the VENOST study, and their clinical characteristics. Material and Method: Among the 1144 CVST patients in the VENOST cohort, patients diagnosed with SLE were studied. Their demographic and clinical characteristics, etiological risk factors, venous involvement status, and outcomes were recorded. Results: In total, 15 (1.31%) of 1144 CVST patients had SLE. The mean age of these patients was 39.9 +/- 12.1 years and 13 (86.7%) were female. Presenting symptoms included headache (73.3%), visual field defects (40.0%), and altered consciousness (26.7%). The main sinuses involved were the transverse (60.0%), sagittal (40.0%), and sigmoid (20.0%) sinuses. Parenchymal involvement was not seen in 73.3% of the patients. On the modified Rankin scale, 92.9% of the patients scored 0-1 at the 1-month follow-up and 90.9% scored 0-1 at the 1-year follow-up. Conclusions: SLE was found in 1.31% of the CVST patients, most frequently in young women. Headache was the most common symptom and the CVST onset was chronic in the majority of cases. The patient outcomes were favorable. CVST should be suspected in SLE patients, even in those with isolated chronic headache symptoms with or without other neurological findings