Shadow mapping algorithms: Applications and limitations

This study provides an overview of popular and famous algorithms and techniques in shadow maps generation.Well- known techniques in shadow maps generation is described detail, along with a discussion of the advantages and drawbacks of each. Basic ideas, improvements and future works of the techniques are also comprehensively summarized and analyzed in depth. Often, programmers have difficulty selecting an appropriate shadow generation algorithm that is specific to their purpose. We have classified and systemized these techniques. The main goal of this paper is to provide researchers with background on a variety of shadow mapping techniques so as make it easier for them to choose the method best suited to their aims. It is al-so hoped that our analysis will help researchers find solutions to the shortcomings of each technique. © 2015 NSP Natural Sciences Publishing Co

Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects

© 2013 IEEE. Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of 'smart transport,' Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned

Interference-aware multipath video streaming in vehicular environments

The multipath transmission is one of the suitable transmission methods for high data rate oriented communication such as video streaming. Each video packets are split into smaller frames for parallel transmission via different paths. One path may interfere with another path due to these parallel transmissions. The multipath oriented interference is due to the route coupling which is one of the major challenges in vehicular traffic environments. The route coupling increases channel contention resulting in video packet collision. In this context, this paper proposes an Interference-aware Multipath Video Streaming (I-MVS) framework focusing on link and node disjoint optimal paths. Specifically, a multipath vehicular network model is derived. The model is utilized to develop interference-aware video streaming method considering angular driving statistics of vehicles. The quality of video streaming links is measured based on packet error rate considering non-circular transmission range oriented shadowing effects. Algorithms are developed as a complete operational I-MVS framework. The comparative performance evaluation attests the benefit of the proposed framework considering various video streaming related metrics

Behavior Analysis Using Enhanced Fuzzy Clustering and Deep Learning

Companies aim to offer customized treatments, intelligent care, and a seamless experience to their customers. Interactions between a company and its customers largely depend on the company’s ability to learn, understand, and predict customer behaviors. Customer behavior prediction is a pivotal factor in improving a company’s quality of services and thus its growth. Different machine learning techniques have been applied to gather customer data to predict behavioral patterns. Traditional methods are unable to discover hidden patterns in ideal situations and need to be improved to produce more accurate predictions. This work proposes a novel hybrid model comprised of two modules: a novel clustering module on the basis of an optimized fuzzy deep belief network and a customer behavior prediction module on the basis of a deep recurrent neural network. Customers’ previous purchasing characteristics and portfolio details were analyzed by applying learning parameters. In this paper, the deep learning techniques were optimized by applying the butterfly optimization method, which minimizes the maximum error classification problem. The performance of the system was evaluated using experimental analysis. The proposed approach was compared to other single and hybrid-model-based approaches and attained the highest performance in the respective metrics

Road aware geographical routing protocol coupled with distance, direction and traffic density metrics for urban vehicular ad hoc networks

The new information and communication technologies have changed the trend of communication in all fields. The transportation sector is one of the emerging field, where vehicles are communicating with each other or with infrastructure for different safety and comfort applications in the network. Vehicular ad hoc networks is one of the emerging multi-hop communication type of intelligent transportation field to deal with high mobility and dynamic vehicular traffic to deliver data packets in the network. The high mobility and dynamic topologies make the communication links unreliable and leads to frequent disconnectivity, delay and packet dropping issues in the network. To address these issues, we proposed a road aware geographical routing protocol for urban vehicular ad hoc networks. The proposed routing protocol uses distance, direction and traffic density routing metrics to forward the data towards the destination. The simulation results explore the better performance of proposed protocol in terms of data delivery, network delay and compared it with existing geographical routing protocols

Image encryption using a synchronous permutation-diffusion technique

In the past decade, the interest on digital images security has been increased among scientists. A synchronous permutation and diffusion technique is designed in order to protect gray-level image content while sending it through internet. To implement the proposed method, two-dimensional plain-image is converted to one dimension. Afterward, in order to reduce the sending process time, permutation and diffusion steps for any pixel are performed in the same time. The permutation step uses chaotic map and deoxyribonucleic acid (DNA) to permute a pixel, while diffusion employs DNA sequence and DNA operator to encrypt the pixel. Experimental results and extensive security analyses have been conducted to demonstrate the feasibility and validity of this proposed image encryption method

Extreme learning machine for prediction of heat load in district heating systems

District heating systems are important utility systems. If these systems are properly managed, they can ensure economic and environmental friendly provision of heat to connected customers. Potentials for further improvement of district heating systems' operation lie in improvement of present control strategies. One of the options is introduction of model predictive control. Multistep ahead predictive models of consumers' heat load are starting point for creating successful model predictive strategy. In this article, short-term, multistep ahead predictive models of heat load of consumer attached to district heating system were created. Models were developed using the novel method based on Extreme Learning Machine (ELM). Nine different ELM predictive models, for time horizon from 1 to 24 h ahead, were developed. Estimation and prediction results of ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ELM approach in comparison with GP and ANN. Moreover, achieved results indicate that developed ELM models can be used with confidence for further work on formulating novel model predictive strategy in district heating systems. The experimental results show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms

A clustering model based on an evolutionary algorithm for better energy use in crop production

Energy consumption and its negative environmental impacts are of interesting topics in the recent centuries. Agricultural systems are both energy users and suppliers in the form of bio energy and play a key role in world economics as well as food security. A high amount of energy from different sources is used in this sector while researchers who investigated energy flow in crops production especially in developing countries, have reported a high degree of inefficiency. In order to differentiate between efficient and inefficient farms, a clustering model based on imperialist competitive algorithm (ICA) has been developed and the surveyed watermelon farms have been clustered based on three features, i.e. greenhouse gas (GHG) emission, input energy and farm size. The results showed that of the three developed clusters, the best cluster performed 20 and 46 % better than the two other clusters in energy and 22 and 52 % in CO2 emissions. The average of total energy input and GHG emissions for the best cluster were calculated as 43,423 MJ per ha and 8,120 CO2eq. The results of this study demonstrate the successful application of ICA for better use of energy in cropping systems which can lead to a better environmental and energy performance

BRAIN-F: Beacon Rate Adaption Based on Fuzzy Logic in Vehicular Ad Hoc Network

Beacon rate adaption is a way to cope with congestion of the wireless link and it consequently decreases the beacon drop rate and the inaccuracy of information of each vehicle in the network. In a vehicular environment, the beacon rate adjustment is strongly dependent on the traffic condition. Due to this, we firstly propose a new model to detect traffic density based on the vehicle’s own status and the surrounding vehicle’s status. We also develop a model based on fuzzy logic namely the BRAIN-F, to adjust the frequency of beaconing. This model depends on three parameters including traffic density, vehicle status and location status. Channel congestion and information accuracy are considered the main criteria to evaluate the performance of BRAIN-F under both LOS and NLOS. Simulation results demonstrate that the BRAIN-F not only reduces the congestion of the wireless link but it also increases the information accuracy