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Emerging technologies such as network function virtualization and software-defined networking (SDN) have made a phenomenal breakthrough in network management by introducing softwarization. The provision of assets to each virtualized network functions autonomously as well as efficiently and searching for an optimal pattern for traffic routing challenges are still under consideration. Unfortunately, the traditional methods for estimating the desired performance indicators are insufficient for a self-driven SDN. In the last decade, a combination of machine learning and cognitive techniques construct a knowledge plane (KP) for the Internet which introduces numerous benefits to networking, like automation and recommendation. Furthermore, the inclusion of KP to the conventional three planes SDN architectures recently has added another knowledge defined networking (KDN) architecture to drive an SDN autonomously. In this article, a self-driving system has been proposed based on KDN to achieve the selection of an optimal path for the deployment of service function chaining (SFC) and reactive traffic routing among the edge clouds. Considering the limited resource of edge clouds, the proposed system also maintains a balance among edge cloud resources while orchestrating SFC resources. The graph neural network has been also applied in the proposed system to recognize the composite relationship concerning topology, traffic features, and routing patterns for accurate estimation of key performance indicators. The proposed system improves resource utilization efficiency for SFC deployment by 20%, maximum network throughput by 5%, and CPU load by 13%. [ABSTRACT FROM AUTHOR]

Perceptual Control Theory (PCT) theorizes that a creature's behaviour is varied so that their perception can reach and maintain certain fixed limits, despite external disturbances. The distinguishing characteristic of PCT is that the controlled variables are the inputs (perceptions, as opposed to the system outputs). This paper presents the first direct comparison of a PCT controller for a mobile robot (a two-wheeled 'inverted pendulum' balancing robot) with a classical control method, LQR. Simulations and experimental validation results show that the performance of the PCT controller is comparable to the LQR controller and better at disturbance rejection. [ABSTRACT FROM AUTHOR]

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Illegally parked vehicle detection systems are considered crucial elements in the development of any video-surveillance based traffic-management system. The major challenges in this task lie in making the end solution real time, illumination invariant and occlusion tolerant. A two-stage application framework is presented which efficiently identifies vehicles parked illegally in restricted parking-zones. A real-time approach has been followed and an improved foreground segmentation method based on Segmentation History Images (SHI) is developed to identify stationary objects. A three step pixel based classification method is applied on the background segmentation output to segment adjacent moving pixels that become stationary for certain periods of time. The process then locks on to all identified stationary pixel patches, parts of which overlap with the regions of interest marked interactively a priori. The second stage of the process is applied subsequently to track all the stationary pixel patches detected during the first stage using an adaptive edge orientation based tracking method. Experimental results show that the tracking technique gives more than a 90% detection success rate, even if objects are partially occluded. The technique has been tested on the UK Home Office i-LIDS Parked Vehicle video sequences along with the University of Sussex Traffic Dataset and results are compared with other available state of the art methods. [ABSTRACT FROM AUTHOR]

Objectives: Three-dimensional (3-D) selective- and relative-scale texture analysis (TA) was applied to structural magnetic resonance (MR) brain images to quantify the presence of grey-matter (GM) and white-matter (WM) textural abnormalities associated with schizophrenia.Materials and Methods: Brain TA comprised volume filtration using the Laplacian of Gaussian filter to highlight fine, medium and coarse textures within GM and WM, followed by texture quantification. Relative TA (e.g. ratio of fine to medium) was also computed. T1-weighted MR whole-brain images from 32 participants with diagnosis of schizophrenia (n = 10) and healthy controls (n = 22) were examined. Five patients possessed marker alleles (SZ8) associated with schizophrenia on chromosome 8 in the pericentriolar material 1 gene while the remaining five had not inherited any of the alleles (SZ0).Results: Filtered fine GM texture (mean grey-level intensity; MGI) most significantly differentiated schizophrenic patients from controls (P = 0.0058; area under the receiver-operating characteristic curve = 0.809, sensitivity = 90%, specificity = 70%). WM measurements did not distinguish the two groups. Filtered GM and WM textures (MGI) correlated with total GM and WM volume respectively. Medium-to-coarse GM entropy distinguished SZ0 from controls (P = 0.0069) while measures from SZ8 were intermediate between the two.Conclusions: 3-D TA of brain MR enables detection of subtle distributed morphological features associated with schizophrenia, determined partly by susceptibility genes. [ABSTRACT FROM AUTHOR]

: Space-variant imaging sensors have many advantages over conventional raster imaging sensors. They provide a large field of view for a given pixel count while maintaining a high resolution at the centre of the field of view and, in addition, produce a mapping that is scale and rotation invariant. The effectiveness of the sensor depends greatly upon the geometry used and the sampling methods employed. In this paper, we define a sensor geometry and introduce an ideal weighted sampling method, where the pixels in the image lying at the intersection of sensor cells, are subdivided into smaller sub-pixels, and an interpolation method using a variable width interpolation mask, whose size varies exponentially with the size and shape of the cells in the sensor array. We compare the computational requirements of these methods, and show that they are scale and rotation invariant, when the image is scaled or rotated about its centre, giving the sensor a functionality similar to that provided by the retinal mapping present in the mammalian retina. These results illustrate the advantages that can be obtained in real-time tracking applications in computer vision, where computational and memory requirements need to be kept to a minimum. [ABSTRACT FROM AUTHOR]

The aim was to undertake an initial study of the relationship between texture features in computed tomography (CT) images of non-small cell lung cancer (NSCLC) and tumour glucose metabolism and stage. This retrospective pilot study comprised 17 patients with 18 pathologically confirmed NSCLC. Non-contrast-enhanced CT images of the primary pulmonary lesions underwent texture analysis in 2 stages as follows: (a) image filtration using Laplacian of Gaussian filter to differentially highlight fine to coarse textures, followed by (b) texture quantification using mean grey intensity (MGI), entropy (E) and uniformity (U) parameters. Texture parameters were compared with tumour fluorodeoxyglucose (FDG) uptake (standardised uptake value (SUV)) and stage as determined by the clinical report of the CT and FDG-positron emission tomography imaging. Tumour SUVs ranged between 2.8 and 10.4. The number of NSCLC with tumour stages I, II, III and IV were 4, 4, 4 and 6, respectively. Coarse texture features correlated with tumour SUV (E: r = 0.51, p = 0.03; U: r = -0.52, p = 0.03), whereas fine texture features correlated with tumour stage (MGI: rs = 0.71, p = 0.001; E: rs = 0.55, p = 0.02; U: rs = -0.49, p = 0.04). Fine texture predicted tumour stage with a kappa of 0.7, demonstrating 100% sensitivity and 87.5% specificity for detecting tumours above stage II ( p = 0.0001). This study provides initial evidence for a relationship between texture features in NSCLC on non-contrast-enhanced CT and tumour metabolism and stage. Texture analysis warrants further investigation as a potential method for obtaining prognostic information for patients with NSCLC undergoing CT.