Topology Conflict Detection Considering Incremental Updating of Multi-Scale Road Networks

Incremental updating is an important technical method used to maintain the data of road networks. Topology conflict detection of multiscale road networks in incremental updating is an important link. Most of the previous algorithms focus on a single scale road network, which cannot be applied to topology conflict detection for different scale road networks during incremental updating. Therefore, this study proposes a topology conflict detection algorithm that considers the incremental updating of multiscale networks. The algorithm designs a K-order topological neighborhood to judge incremental neighborhood links and builds a topology refinement model based on geometric measurement. Furthermore, we propose a network topology conflict detection rule considering the influence of cartographic generalization operator and use the improved topological distance to detect topology conflicts. The experimental results show that (1) the overall accuracy and recall rate of the proposed method are more than 90%; (2) after considering the topology conflict caused by cartography generalization, the accuracy was increased by 29.2%; and (3) the value of average path length of a network can be used as the basis for setting the best K value.

On the Detection of Structural Aesthetic Defects of Android Mobile User Interfaces with a Metrics-based Tool

Smartphone users are striving for easy-to-learn and use mobile apps user interfaces. Accomplishing these qualities demands an iterative evaluation of the Mobile User Interface (MUI). Several studies stress the value of providing a MUI with a pleasing look and feel to engaging end-users. The MUI, therefore, needs to be free from all kinds of structural aesthetic defects. Such defects are indicators of poor design decisions interfering with the consistency of a MUI and making it more difficult to use. To this end, we are proposing a tool (Aesthetic Defects DEtection Tool (ADDET)) to determine the structural aesthetic dimension of MUIs. Automating this process is useful to designers in evaluating the quality of their designs. Our approach is composed of two modules. (1) Metrics assessment is based on the static analysis of a tree-structured layout of the MUI. We used 15 geometric metrics (also known as structural or aesthetic metrics) to check various structural properties before a defect is triggered. (2) Defects detection: The manual combination of metrics and defects are time-consuming and user-dependent when determining a detection rule. Thus, we perceive the process of identification of defects as an optimization problem. We aim to automatically combine the metrics related to a particular defect and optimize the accuracy of the rules created by assigning a weight, representing the metric importance in detecting a defect. We conducted a quantitative and qualitative analysis to evaluate the accuracy of the proposed tool in computing metrics and detecting defects. The findings affirm the tool’s reliability when assessing a MUI’s structural design problems with 71% accuracy.

Implementing Facial Recognition by Interfacing MATLAB Along with Arduino

Facial recognition has become one of the recent trends in attracting abundant attention within the society of social media network. The face is flat and therefore needs plenty of mathematical computations. Facial knowledge has become one in every of the foremost necessary biometric, we tend to witness it from the day-to-day gadgets like mobile phones. Every transportable electronic device currently being discharged includes a camera embedded in it. Network access management via face recognition not solely makes hackers just about not possible to steal one's "password", however conjointly will increase the user-friendliness in human-computer interaction. For the applications of videophone and conference, the help of face recognition conjointly provides an additional economical secret writing theme. Face detection technologies are employed in an oversized kind of applications like advertising, diversion, video secret writing, digital cameras, CCTV police investigation, and even in military use. Totally different algorithms are used for biometric authentication. The Kanade-Lucas-Tomasi rule makes use of abstraction common intensity transformation to direct the deep explore for the position that shows the simplest match. Another common face detection rule is that the Viola-Jones rule that's the foremost wide used face detection rule. It's employed in most digital cameras and mobile phones to notice faces. It uses cascades to notice edges just like the nose, the ears, etc. Hence, during this paper, we've got planned the Viola-Jones rule because the best one supported our application. The rule is employed within the biometric authentication of individuals and also the pictures are kept during processing. The kept information is employed for recognizing the faces and if the information matches, an impression signal is given to the controller. The MATLAB software is employed to relinquish control signals to the motor, which is employed for gap and shutting the door. The input image is fed by a digital camera and also the image is processed within MATLAB. The output is given to the external controller interfaced with MATLAB. The image process field has several sub-fields, biometric authentication is one in each of them because it gains additional quality for security functions these days. The planned system can be employed in residential buildings, malls, and industrial sectors. Thus, this technique is helpful for homemakers to be safer in their homes.

Statistical Surveillance of Structural Breaks in Credit Rating Dynamics

The 2007–2008 financial crisis had severe consequences on the global economy and an intriguing question related to the crisis is whether structural breaks in the credit market can be detected. To address this issue, we chose firms’ credit rating transition dynamics as a proxy of the credit market and discuss how statistical process control tools can be used to surveil structural breaks in firms’ rating transition dynamics. After reviewing some commonly used Markovian models for firms’ rating transition dynamics, we present several surveillance rules for detecting changes in generators of firms’ rating migration matrices, including the likelihood ratio rule, the generalized likelihood ratio rule, the extended Shiryaev’s detection rule, and a Bayesian detection rule for piecewise homogeneous Markovian models. The effectiveness of these rules was analyzed on the basis of Monte Carlo simulations. We also provide a real example that used the surveillance rules to analyze and detect structural breaks in the monthly credit rating migration of U.S. firms from January 1986 to February 2017.

A hybrid feature extraction approach for the detection of melanoma using neural network

In spite of the gargantuan number of patients affected by melanoma every year, its detection at an early stage is still a challenging task. This paper illustrates a method which involves the combination of the existing ABCD (Involving symmetry, border, color, and diameter detection) rule and grey level co-occurrence matrix (GLCM) along with Local Binary Pattern (LBP) for identification of malignant melanoma skin lesion with greater accuracy. Several steps, such as image acquisition technique, pre-processing (RGB to HSV) techniques and segmentation processes are undertaken for the skin feature selection criteria to successfully determine the skin lesion's characteristic properties for classification. Texture features such as contrast, entropy, energy and homogeneity of the affected region is obtained using LBP and GLCM for discriminatory purposes of the two cases (melanoma and non-melanoma). Finally, the back propagation neural network (BPN) is used as the classifier to determine whether the dermoscopic image is benign or malignant.

AUTOMATIC FAULT RECOGNITION OF PHOTOVOLTAIC MODULES BASED ON STATISTICAL ANALYSIS OF UAV THERMOGRAPHY

As a malfunctioning PV (Photovoltaic) cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.