Fuzzy logic traffic signal controller enhancement based on aggressive driver behavior classification

The rise in population worldwide and especially in Egypt, together with the increase in the number of vehicles present serious complications regarding traffic congestion and road safety. The elementary solution towards improving congestion is to expand road capacities by building new lanes. This, however, requires time and effort and therefore new methodologies are being implemented. Intelligent transportation systems (ITS) try to approach traffic congestion through the application of computational and engineering techniques. Traffic signal control is a branch of intelligent transportation systems which focuses on improving traffic signal conditions. A traffic signal controllers’ main objective is to improve this assignment in a way which reduces delays. This research proposes a new approach to enhancing traffic signal control and reducing delays of a single intersection, through the integration of an aggressive driving behavior classifier. Previous approaches dealt with traffic control and driver behavior separately, and therefore their successful integration is a new challenging area in the field. Multiple experiment sets were conducted to provide an indication to the effectiveness of our approach. Firstly, an aggressive driver behavior classifier using feed-forward neural network was successfully built utilizing Virginia Tech 100-car naturalistic driving study data. Its performance was compared against long short-term memory recurrent neural networks and support vector machines, and it resulted in better performance as shown by the area under the curve. To the best of our knowledge, this classifier is the first of its kind to be built on this 100-car study data. Secondly, a representation of aggressive driving behavior was constructed in the simulated environment, based on real life data and statistics. Finally, Mamdani’s fuzzy logic controller was modified to accommodate for the integration of the aggressive behavior classifier. The integration results were encouraging and yielded significant improvements at higher traffic flow volumes when compared against the built Mamdani’s controller. The results are promising and provide an initial step towards the integration of driver behavior classification and traffic signal control

MIGRATOM : geophysical tomography using wavefront migration and fuzzy constraints

"MIGRATOM is a third-generation PC-based computer program for geophysical tomograph. It was developed to enable users to address a variety of mining-related problems by mathematically reconstructing the distribution of physical properties in a rock mass, using measurements of travel time or amplitude of wave energy propagated through the rock mass. Examples of applications include litho-logic characterization, fracture detection, fluid monitoring, stress evaluation, and blast assessment. The resolution and accuracy of tomographic reconstructions generally depend very strongly on the geometry of source and receiver locations. In many practical applications, such as cross-borehole tomography, limited coverage results in nonunique reconstructions. MIGRATOM allows the user to constrain the inversion, in order to reduce the nonuniqueness problem. A new approach to application of constraints implemented in MIGRATOM is based on fuzzy logic techniques. Refraction of wave energy is modeled in MIGRATOM using an approach based on Huygens' Principle. Propagation of a continuous wavefront is modeled as a constructive interference phenomenon, which obviates the shadow-zone problem that affects conventional ray-tracing calculations." - NIOSHTIC-2NIOSHTIC no. 10005134199

Distance Measurement-Based Cooperative Source Localization: A Convex Range-Free Approach

One of the most essential objectives in WSNs is to determine the spatial coordinates of a source or a sensor node having information. In this study, the problem of range measurement-based localization of a signal source or a sensor is revisited. The main challenge of the problem results from the non-convexity associated with range measurements calculated using the distances from the set of nodes with known positions to a xed sen- sor node. Such measurements corresponding to certain distances are non-convex in two and three dimensions. Attempts recently proposed in the literature to eliminate the non- convexity approach the problem as a non-convex geometric minimization problem, using techniques to handle the non-convexity. This study proposes a new fuzzy range-free sensor localization method. The method suggests using some notions of Euclidean geometry to convert the problem into a convex geometric problem. The convex equivalent problem is built using convex fuzzy sets, thus avoiding multiple stable local minima issues, then a gradient based localization algorithm is chosen to solve the problem. Next, the proposed algorithm is simulated considering various scenarios, including the number of available source nodes, fuzzi cation level, and area coverage. The results are compared with an algorithm having similar fuzzy logic settings. Also, the behaviour of both algorithms with noisy measurements are discussed. Finally, future extensions of the algorithm are suggested, along with some guidelines

AN INTELLIGENT NAVIGATION SYSTEM FOR AN AUTONOMOUS UNDERWATER VEHICLE

The work in this thesis concerns with the development of a novel multisensor data fusion (MSDF) technique, which combines synergistically Kalman filtering, fuzzy logic and genetic algorithm approaches, aimed to enhance the accuracy of an autonomous underwater vehicle (AUV) navigation system, formed by an integration of global positioning system and inertial navigation system (GPS/INS). The Kalman filter has been a popular method for integrating the data produced by the GPS and INS to provide optimal estimates of AUVs position and attitude. In this thesis, a sequential use of a linear Kalman filter and extended Kalman filter is proposed. The former is used to fuse the data from a variety of INS sensors whose output is used as an input to the later where integration with GPS data takes place. The use of an adaptation scheme based on fuzzy logic approaches to cope with the divergence problem caused by the insufficiently known a priori filter statistics is also explored. The choice of fuzzy membership functions for the adaptation scheme is first carried out using a heuristic approach. Single objective and multiobjective genetic algorithm techniques are then used to optimize the parameters of the membership functions with respect to a certain performance criteria in order to improve the overall accuracy of the integrated navigation system. Results are presented that show that the proposed algorithms can provide a significant improvement in the overall navigation performance of an autonomous underwater vehicle navigation. The proposed technique is known to be the first method used in relation to AUV navigation technology and is thus considered as a major contribution thereof.J&S Marine Ltd., Qinetiq, Subsea 7 and South West Water PL

Mobility Analysis and Management for Heterogeneous Networks

The global mobile data traffic has increased tremendously in the last decade due to the technological advancement in smartphones. Their endless usage and bandwidth-intensive applications will saturate current 4G technologies and has motivated the need for concrete research in order to sustain the mounting data traffic demand. In this regard, the network densification has shown to be a promising direction to cope with the capacity demands in future 5G wireless networks. The basic idea is to deploy several low power radio access nodes called small cells closer to the users on the existing large radio foot print of macrocells, and this constitutes a heterogeneous network (HetNet). However, there are many challenges that operators face with the dense HetNet deployment. The mobility management becomes a challenging task due to triggering of frequent handovers when a user moves across the network coverage areas. When there are fewer users associated in certain small cells, this can lead to significant increase in the energy consumption. Intelligently switching them to low energy consumption modes or turning them off without seriously degrading user performance is desirable in order to improve the energy savings in HetNets. This dynamic power level switching in the small cells, however, may cause unnecessary handovers, and it becomes important to ensure energy savings without compromising handover performance. Finally, it is important to evaluate mobility management schemes in real network deployments, in order to find any problems affecting the quality of service (QoS) of the users. The research presented in this dissertation aims to address these challenges. First, to tackle the mobility management issue, we develop a closed form, analytical model to study the handover and ping-pong performance as a function of network parameters in the small cells, and verify its performance using simulations. Secondly, we incorporate fuzzy logic based game-theoretic framework to address and examine the energy efficiency improvements in HetNets. In addition, we design fuzzy inference rules for handover decisions and target base station selection is performed through a fuzzy ranking technique in order to enhance the mobility robustness, while also considering energy/spectral efficiency. Finally, we evaluate the mobility performance by carrying out drive test in an existing 4G long term evolution (LTE) network deployment using software defined radios (SDR). This helps to obtain network quality information in order to find any problems affecting the QoS of the users

Applying Rprop Neural Network for the Prediction of the Mobile Station Location

Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one need to integrate all available heterogeneous measurements to increase the location accuracy. In this paper we propose a novel algorithm that combines both time of arrival (TOA) and angle of arrival (AOA) measurements to estimate the MS in NLOS environments. The proposed algorithm utilizes the intersections of two circles and two lines, based on the most resilient back-propagation (Rprop) neural network learning technique, to give location estimation of the MS. The traditional Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and even if the measurements are fairly accurate, the performance of these algorithms depends highly on the relative position of the MS and BSs. Different NLOS models were used to evaluate the proposed methods. Numerical results demonstrate that the proposed algorithms can not only preserve the convergence solution, but obtain precise location estimations, even in severe NLOS conditions, particularly when the geometric relationship of the BSs relative to the MS is poor