A new feature selection from lidar data and digital aerial images acquired for an urban/c environment using an ANFIS-based classification and a fuzzy rough set method

In this article, a new fuzzy rough set (FRS) method was proposed for extracting rules from an adaptive neuro-fuzzy inference system (ANFIS)-based classification procedure in order to select the optimum features. The proposed methodology was used to classify lidar data and digital aerial images acquired for an urban environment to detect four classes, including trees, buildings, roads, and natural grounds. In this regard, 16 potentially primary features were produced for classification using the lidar data and the digital aerial images. The training and checking inputs of the proposed ANFIS were collected from the generated features for further training and evaluation processes. Also, the fuzzy c-mean clustering algorithm was used to initialize the fuzzy inference system of the proposed ANFIS-based classification method. By considering all states of fuzzy rules for each training input, the fuzzy rule with the maximum firing value was selected. Accordingly, these fuzzy rules were used as the inputs of the Rough Set Theory. Accordingly, the optimum features were acquired by the basic minimal covering algorithm as the rule induction method. To validate our proposed methodology, the procedure of classification was repeated by the achieved optimum features. The results showed that the classification using the optimum features has reached better overall accuracy than those achieved by using the 16 potentially primary features. Also, comparing the results of our proposed methodology with the other well-known genetic-algorithm-based feature selection methods indicated the significance of the proposed FRS method to select optimum features with high accuracy in a short running time.     

Stealing PINs via mobile sensors: actual risk versus user perception

In this paper, we present the actual risks of stealing user PINs by using mobile sensors versus the perceived risks by users. First, we propose PINlogger.js which is a JavaScript-based side channel attack revealing user PINs on an Android mobile phone. In this attack, once the user visits a website controlled by an attacker, the JavaScript code embedded in the web page starts listening to the motion and orientation sensor streams without needing any permission from the user. By analysing these streams, it infers the user’s PIN using an artificial neural network. Based on a test set of fifty 4-digit PINs, PINlogger.js is able to correctly identify PINs in the first attempt with a success rate of 74% which increases to 86 and 94% in the second and third attempts, respectively. The high success rates of stealing user PINs on mobile devices via JavaScript indicate a serious threat to user security. With the technical understanding of the information leakage caused by mobile phone sensors, we then study users’ perception of the risks associated with these sensors. We design user studies to measure the general familiarity with different sensors and their functionality, and to investigate how concerned users are about their PIN being discovered by an app that has access to all these sensors. Our studies show that there is significant disparity between the actual and perceived levels of threat with regard to the compromise of the user PIN. We confirm our results by interviewing our participants using two different approaches, within-subject and between-subject, and compare the results. We discuss how this observation, along with other factors, renders many academic and industry solutions ineffective in preventing such side channel attacks.     

Managing Water Supply and Demand to Achieve Economic and Environmental Objectives: Application of Mathematical Programming and ANFIS Models

The integrated management of water supply and demand has been considered by many policymakers; due to its complexity the decision makers have faced many challenges so far. In this study, we proposed an efficient framework for managing water supply and demand in line with the economic and environmental objectives of the basin. To design this framework, a combination of ANFIS and multi-objective augmented ε-constraint programming models and TOPSIS were used. First, using hydrological data from 2001 to 2017, the rate of water release from the dam reservoir was estimated with the ANFIS model; afterwards, its allocation to agricultural areas was performed by combining multi-objective augmented ε-constraint models and TOPSIS. To prove the reliability of the proposed model, the southern Karkheh basin in Khuzestan province, Iran, was considered as a case study. The results have showed that this model is able to reduce irrigation water consumption and to improve its economic productivity in the basin.

     

An experimental study on motion error of hexarot parallel manipulator

In this paper, a novel 6-DOF parallel manipulator with coaxial actuated arms is introduced and investigated. This mechanism has six rotating arms, and by rotation of arms about the base, positioning and desired movement of the mechanism is achieved. Sine parallel mechanisms have a nonlinear motion while moving from an initial position to a desired position, investigation of nonlinear error in such mechanisms is of paramount importance. In this paper, inverse and forward kinematics of the mechanism are studied. Nonlinear error of the mechanism’s motion in its workspace is extracted using mid-oscillating circle and kinematic relations as well. Moreover, effective parameters on nonlinear motion error of mechanism are determined. The results obtained by theoretical method are further verified through image processing experimental tests. It is found that the results of the theoretical analysis and experimental test are in good consistency.     

A PC-Based Decision Tool for Roadway Incident Management

A quick-response PC tool has been developed to address a number of crucial transportation needs following major road incidents or urban disasters. Known as TEMPO (Transportation Emergency Management of Post-Incident Operations), the tool is capable of instantaneously identifying near-optimal traffic-diversion strategies around disruptions in an urban traffic network. TEMPO utilizes heuristic approaches to estimating the origin-destination (O-D) of the traffic on the closed links and reassigning the estimated O-D to the remainder of the network.
This article describes the following features of TEMPO: (1) the GIS-based data structure that allows graphic user interaction and network editing, including closed street links, changes in number of lanes, cordoned off areas, changes in street directionality, speed limits, and parking regulations, and (2) the algorithm for traffic diversion around the incident based on cordoning the affected region around the closure, estimation of the O-D matrix for the traffic on the closed links, and reassignment of the O-D to the network. Finally, a simulation-based calibration procedure is conducted to compare the TEMPO results with those generated by a popular planning software, TRANPLAN. Thirty-three incident scenarios in a generic test network are simulated by both TEMPO and TRANPLAN, and the results are compared statistically.