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We present a revised pipe-line of the existing 3D object detection and pose estimation framework based on point pair feature matching. This framework proposed to represent 3D target object using self-similar point pairs, and then matching such model to 3D scene using efficient Hough-like voting scheme operating on the reduced pose parameter space. Even though this work produces great results and motivated...
This paper presents a labeled multi-Bernoulli filter for track-before-detect with a special focus on visual tracking of multiple targets in video. We show that labeled multi-Bernoulli distribution is a conjugate prior for an image likelihood function with a specific separable form. Following a previously formulated likelihood function (with the desirable separable form) using background subtraction,...
The paper describes the application of a brand new approach to evaluate magneto harmonic effects in round geometries. Largely employed regular modeling approaches such as Dowell or homogenization methods show important lacks of accuracy for 2D modeling. They are not able to reflect many physically existing phenomena like side effects and empty layers of conductors e.g, what require Finite Elements...
We investigate the accuracy of the nonrigorous symmetric second-order absorbing boundary condition (ABC) implemented in the higher order large domain finite element method (FEM) technique for electromagnetic analysis in the frequency domain. The electric field is expanded using edge based curl-conforming polynomial basis functions and geometrical modeling is done by curved hexahedral finite elements...
Practical models of lithographic processes are usually empirically calibrated, making their accuracy dependent on the total number of samples used to build the models, and more specifically on the selection of a representative set of samples for calibration. An inadequate number of samples can adversely impact model accuracy, but a broadly comprehensive set will excessively increase measurement cost...
Understanding and modeling the spread of influence is an important topic in social network analysis and therefore attracts many researchers to this area. It has several practical applications such as viral marketing. In this paper, we propose a new method (Linear Threshold Behavioral Model) for modeling the spread of influence in social networks. Experiments were conducted on three, real-world datasets...
A novel battery state of charge (SOC) estimation method is developed in the paper using sliding mode observer and the Nernst Equation based battery model. The method to design sliding mode observer is presented. The proposed estimator is less computational complex but it can obtain relatively accurate result. The performance of the system was verified by simulations and experiments.
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,...
Machine-learning test strategy has been developed in the last decade as an alternative to costly specification-driven tests for Analog, Mixed-Signal and RF circuits (AMS-RF). The concept is simple: powerful algorithms are used to map simple measurements onto specifications. But the proper execution requires an information-rich input space. This paper presents an efficient hybrid algorithm to select...
Multi-instance multi-label learning (MIML) is a framework that addresses label ambiguity when data contains bags, each bag contains instances, and a bag label set is provided for each bag. Instance annotation in the MIML setting is the problem of finding an instance level classifier given training data consisting of labeled bags of instances. Current approaches for instance annotation mainly focus...
Due to the predicted scarcity of the fossil fuels and their adverse effects to the environment, currently renewable energy generation is a major focus of the power and energy sector. Wind energy is one of the potential and rapidly growing sources of green energy. This is particularly due to the cost of the wind energy production has reduced by a factor of more than five over the last two decades....
In this paper, we present a new approach to computing the lower bound on the measurement of buffer overflow probability, when the buffer state is modeled as a semi-Markov process. In this commonly assumed model of buffer overflow we use this approach to explore the relationship between sampling rate and accuracy. Crucially we go on to show that a realistic simulation of a packet buffer reveals that...
Feature location is a program comprehension activity in which a developer inspects source code to locate the classes or methods that implement a feature of interest. Many feature location techniques (FLTs) are based on text retrieval models, and in such FLTs it is typical for the models to be trained on source code snapshots. However, source code evolution leads to model obsolescence and thus to the...
We explore here the suitability of a mode space tight binding algorithm to various III-V homo- and heterojunction nanowire devices. We show that in III-V materials, the number of unphysical modes to eliminate is very high compared to the Si case previously reported in the literature. Nevertheless, we demonstrate here the possibility to clean III-V mode space basis from the unphysical modes and achieve...
The low-frequency instrument of the SKA radiotelescope will be made of large arrays of log-periodic antennas. Their mutual coupling may be strong and needs to be modeled in terms of embedded element patterns. The HARP method is based on Macro Basis Functions and computes their interactions versus relative positions, based on a limited number of explicit calculations in the near field. This paper shows...
Naïve Bayes is a commonly used algorithm in text categorization because of its easy implementation and low complexity. Naïve Bayes has mainly two event models used for text categorization which are multivariate Bernoulli and multinomial models. A very large number of studies choose multinomial model and Laplace smoothing just based on the assumption that it performs better than multivariate model...
Medical diagnosis is an exciting are of research and many researchers have been working on the application of Artificial Intelligence techniques to develop disease recognition systems. They are analysing currently available information and also biochemical data collecting from clinical laboratories and experts for identifying pathological status of the patient. During the process of diagnosis, the...
We offer an automated way of estimating the author of a song using only its lyrics content. To this end, we introduce a complete text classification framework which takes raw lyrics data as input and report estimated songwriter. The performance of the system is evaluated based on its classification and retrieval ability on a large dataset of Turkish songs, which was collected in this study. The results...
In this paper, a novel analytical method based on CADET (covariance analysis description equation technique) is proposed to solve the computing problem of the precision of the three-dimensional shooting engine when evaluating the effectiveness of the three-dimensional virtual shooting system. This method statistical linearizes the nonlinear factors that will affect shooting accuracy, and then get...
Software code review is a process of developers inspecting new code changes made by others, to evaluate their quality and identify and fix defects, before integrating them to the main branch of a version control system. Modern Code Review (MCR), a lightweight and tool-based variant of conventional code review, is widely adopted in both open source and proprietary software projects. One challenge that...
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