Journal of Computer and Robotics
Volume:13 Issue: 1, Winter and Spring 2020

An effective approach is introduced to apply Transmission Line Model (TLM) to calculate induced voltage and current on a buried pipeline due to electrical power line. The approach can be applied to the parallel or nonparallel pipelines. The method also extended for any complexity system with different conditions and locations of the pipeline. A two ports circuitry network or model is achieved for a parallel pipeline. This two ports network is then modified for each small length, straight and ramped pipeline relative to the power line, which are used for circuitry modelling a complicated configuration of the pipeline. Two numerical services is proceeded to show the simplicity and effectiveness.

Internet of Vehicles (IOV) is a section of the Internet of Things (IoT) which makes road transportation smart and provides security for the passengers traveling along the roads. Fog computation can be considered as a complement for IOV because it is close to the user and can communicate with Road Side Units (RSU) and process information with low latency. IOV employs a wireless network for message exchange which is a security flaw and an opportunity for the adversaries since that can modify the transmitted data. Thus, data authentication between the transmitter and the receiver has become a challenge in this context. We propose a secure mutual authentication protocol with the ability to key exchange in this paper, which does not use the hash function. We compared this design with other protocols in terms of security requirements and communication and processing costs. To the security analysis of the proposed Automated Validation of Internet Security Protocols and Applications (AVISPA) tool is used. The results show that the proposed protocol is more resistant to other methods of active and passive attacks but Computation and communication costs have increased.

Time series consists of a sequence of observations, measured at moments in time, sorted chronologically, and evenly spaced from each other, so the data are usually dependent on each other. Uncertainty is the consequence of imperfection of knowledge about a state or a process. The time series is an important class of time-based data objects and it can be easily obtained from scientific and financial applications. Main carrier of time series forecasting is which constitutes the level of uncertainty human knowledge, with its intrinsic ambiguity and vagueness in complex and non-stationary criteria. In this study, a comprehensive revision on the existing time series pattern analysis research is given. They are generally categorized into representation and indexing, similarity measure, uncertainty modeling, visualization and mining. Various Fuzzy Time Series (FTS) models have been proposed in scientific literature during the past decades or so. Among the most accurate FTS models found in literature are the high order models. However, three fundamental issues need to be resolved with regards to the high order models. The primary objective of this paper is to serve as a glossary for interested researchers to have an overall depiction on the current time series prediction and fuzzy time-series models development.

Because of the changing from traditional paper-based systems to a digital systems and the evolution of online storage and cloud computing, datacenters are becoming fundamental to almost every sector of the economy and the main energy consumers in the universe. With the acceptance of High Performance Computing (HPC) and cloud computing, the area and number of cloud datacenter grow quickly; hence, it has become significant to optimize datacenter energy consumption. With modern energy efficient design in cloud datacenter infrastructure and cooling devices, active items like servers and cooling devices consume most of the power. In many researches, it was shown that cloud datacenters consume enormous energy; therefore researchers are looking for metrics of energy efficiency. The goal of energy efficient researches is to sufficiently take benefit of reachable resources such as processors and network devices, or to reduce thermal cooling expenses and energy consumption. In this paper, we discuss the state of the art researches and provide an overview of energy efficient solutions and architectures for cloud servers in processor design, power distribution unit, and server cooling management.

Face recognition is a widely used identification method in the machine learning field because face biometrics are distinctive enough for detection and have more accessibility compared to other biometrics. Despite their merits, face biometrics have various challenges. Mainly, these challenges are divided into local and global categories. Local challenges can be addressed using sustainable methods against change while global challenges such as illumination challenges require powerful pre-processing methods. Therefore, in this study, a sustainable method against light changes has been proposed. In this method, two stages of the Difference of Gaussian have been utilized for the illumination normalization. Then, the features of the normalized image are extracted using Histogram of Oriented Gradient (HOG) and the feature vectors are classified using 3 k-nearest neighbor classifiers and the support vector machine with linear kernel, and the support vector machine with Radial Basis Function (RBF) kernel. Testing the proposed method on Computer Vision and Biometric Laboratory (CVBL) data indicated that the recognition rate, at best for the illumination challenge in the whole face and a part of the face is 98.6 % and 97.9% respectively.

Multiple description (MD) coding has appeared to be an attractive technique to decrease impact of network failures and increase the robustness of multimedia communications. Very common model of this technique is multiple-description lattice vector quantization, which is the best choice for robust data transmission over the unreliable network channels. However, MD coinciding lattice vector quantizer (MDCLVQ) is not considered discrete network conditions, so in this scheme, all videos are received or are not received. In this paper, this scheme is implemented in real network environment. So, raw video will be send in various packet, packets send independently and packets lose independently. The possibility of lossing all packets together is close to zero. Our object for increasing of resistance transfer in error- prone communication channels are used. This technique has been tested for standard videos "Akiyo", "Carphone", "Miss-America" and "Foreman". This results show that the quality of the reconstructed videos from the average PSNR values of the central decoder and the side decoders has been reached to grate degree, so increases error resilience over error-prone communication channels.