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This paper deals with modelling the Antenna Current Green's Function (ACGF) using the Singularity Expansion Method (SEM). The ACGF is the exact transfer function of an antenna in the spatial domain and is able to deal with both near and far fields. In order to study the spectral content of the ACGF, the authors suggest here applying the SEM on the ACGF to extract its poles (characteristic modes)....
This paper proposes a novel model for predicting the outdoor to indoor radio signal coverage. This model is based on a joint ray launching algorithm that adapts different resolutions for outdoor and indoor simulations. The performance of the joint ray launching method is evaluated by a measurement at 3.5 GHz frequency. This model appears to be efficient for a scenario with mixed resolutions, in terms...
The wireless channel in mobile communications changes the transmitted signal and thus must be properly modeled. If a multiple-input multiple-output (MIMO) system is considered, the modeling procedure becomes more challenging. In this paper, a novel radio channel model is implemented and validated using measurement data. On one hand, statistical properties of MIMO channels are modeled by geometry-based...
Ray-optical algorithms are an excellent choice to model the radio channel in a deterministic manner. Especially, in vehicular environments where the channel is time-variant and system designers potentially need to consider the non-stationarity of the channel, ray-optical tools are a welcome solution to evaluate the achievable system performance in specific scenarios. The main drawback of ray-optical...
A joint indoor to outdoor ray launching algorithm is proposed in this paper. Different resolutions are considered for indoor and outdoor simulations. Instead of using conventional sampling technique to extract rays from a finer resolution, a novel method, named Ray Aggregation, is applied to minimise the loss of accuracy while benefiting from the computational cost of a coarse resolution. Furthermore,...
The CBFM technique is applied to enhance the performances of a 3D full-wave model, based on the volumetric integral representation of the electric field, for electromagnetic scattering from forest environments. This paper addresses different approaches adopted to improve the performances of the CBFM when applied to this 3D model. These approaches encompass the implementation of the CBFM on a non-uniform...
Prediction of the schedule of cruise ships benefits the managements of cruise ships. In order to prove the feasibility and accuracy of the grey GM (0, N) which is applied to cruise ships service, the number of monthly cruise ships which left Hong Kong for Macau from 2014 to 2105 are forecasted. The precision of the model is tested and analyzed by grey precision grades. Additionally, an interrelationship...
By virtue of recent developments in machine learning techniques, higher-level information can now to be extracted from big data. To analyze big data, efficient and smart representations of data achieved by using sufficiently fast algorithms, as well as highly accurate results, are important. In this paper, we focus on extracting multiple semantic relations using light-weight processing through the...
Fuzzy Grey Cognitive Map (FGCM) is an innovative soft computing technique mixing Fuzzy Cognitive Maps and Grey Systems Theory. FGCMs are supervised learning fuzzy-neural systems typically modeled with signed fuzzy grey weighted digraphs, generally involving feedbacks. It is hard to find an accurate mathematical model to describe this decision-making because it includes a high uncertainty and the factors...
Various photovoltaic PV models of different complexity exist in the literature. The modeling accuracy for most of them is directly related to their complexity and computational effort. Recently, two newly developed PV models featuring low computational effort and high accuracy appeared in the literature. The first model is developed based on Gompertz model which is originally used to model human mortality,...
Network intrusion detection is the process of identifying malicious behaviors that target a network and its resources. Current systems implementing intrusion detection processes observe traffic at several data collecting points in the network but analysis is often centralized or partly centralized. These systems are not scalable and suffer from the single point of failure, i.e. attackers only need...
In machine learning, ensemble model is combining two or more models for obtaining the better prediction, accuracy and robustness as compared to individual model separately. Before getting ensemble model first we have to assign our training dataset into different models, after that we have to select the best model suited for our data sets. In this work we explored six machine learning parameter for...
Change in a software is crucial to incorporate defect correction and continuous evolution of requirements and technology. Thus, development of quality models to predict the change proneness attribute of a software is important to effectively utilize and plan the finite resources during maintenance and testing phase of a software. In the current scenario, a variety of techniques like the statistical...
Sentiment classification of Twitter data has been successfully applied in finding predictions in a variety of domains. However, using sentiment classification to predict stock market variables is still challenging and ongoing research. The main objective of this study is to compare the overall accuracy of two machine learning techniques (logistic regression and neural network) with respect to providing...
Contemporary media entail colour-coded information. While an average viewer takes colour images for granted, individuals with colour vision deficiency have difficulties in discriminating certain colour combinations and, consequently, have difficulties in perceiving image features. Well-established simulation tools allow us to see the image from their perspective that goes beyond the stereotypical...
Rainfall prediction is an important part of weather prediction. Compared to conventional methods predicting rainfall rate, the approach applying historical records and data mining technology shows obviously advantage in computing cost. Many excellent works have been done attempting to build predicting model with data mining methods, however, most of them just test the predicting accuracy on data set...
Author attribution has grown into an area that is more challenging from the past decade. It has become an inevitable task in many sectors like forensic analysis, law, journalism and many more as it helps to detect the author in every documentation. Here unigram/bigram features along with latent semantic features from word space were taken and the similarity of a particular document was tested using...
This paper presents an activity detection system using dendrite threshold logic neuron models. This method generates a dendrite weight matrix from the background image and detect the changes in the subsequent images through the trained neuron outputs. Using only one layer of dendrite neuron cells with simplistic threshold logic cells, an accuracy of 98% is reported in realistic imaging conditions...
Randomized algorithms have good performances for regression and classification problems by using random hidden weights and pseudoinverse computing for the output weights. They have one single hidden layer structure. On the other hand, deep learning techniques have been successfully used for pattern recognition due to their deep structure and effective unsupervised learning. In this paper, the randomized...
Real-time electromagnetic transient simulation system involves high computational tasks and is very difficult to achieve small time step. In this paper, we proposes the field programmable gate array (FPGA) design and implementation for real-time electromagnetic transient simulation system with double-precision floating-point calculation. The parallel computing and fully pipelined architecture of the...
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