Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Quality Real-Time Global Illumination in Augmented Reality

High-quality image synthesis, indistinguishable from reality, has been one of the most important problems in computer graphics from its beginning. Image synthesis in augmented reality (AR) poses an even more challenging problem, because coherence of virtual and real objects is required. Especially, visual coherence plays an important role in AR. Visual coherence can be achieved by calculating global illumination which introduces the light interaction between virtual and real objects. Correct light interaction provides precise information about spatial location, radiometric properties, and geometric details of inserted virtual objects. In order to calculate light interaction accurately, high-quality global illumination is required. However, high-quality global illumination algorithms have not been suitable for real-time AR due to their high computational cost. Global illumination in AR can be beneficial in many areas including automotive or architectural design, medical therapy, rehabilitation, surgery, education, movie production, and others. This thesis approaches the problem of visual coherence in augmented reality by adopting the physically based rendering algorithms and presenting a novel GPU implementation of these algorithms. The developed rendering algorithms calculate the two solutions of global illumination, required for rendering in AR, in one pass by using a novel one-pass differential rendering algorithm. The rendering algorithms, presented in this thesis, are based on GPU ray tracing which provides high quality results. The developed rendering system computes various visual features in high quality. These features include depth of field, shadows, specular and diffuse global illumination, reflections, and refractions. Moreover, numerous improvements of the physically based rendering algorithms are presented which allow fast and accurate light transport calculation in AR. Additionally, this thesis presents the differential progressive path tracing algorithm which can calculate the unbiased AR solution in a progressive fashion. Finally, the presented methods are compared to the state of the art in real-time global illumination for AR. The results show that our high-quality global illumination outperforms other methods in terms of accuracy of the rendered images. Additionally, the human perception of developed global illumination methods for AR is evaluated. The impact of the presented rendering algorithms to visual realism and to the sense of presence is studied in this thesis. The results suggest that high-quality global illumination has a positive impact on the realism and presence perceived by users in AR. Thus, future AR applications can benefit from the algorithms developed in this thesis.
Tuning of Power System Stabilizers (PSS's) using Phenomenological Models and Optimization Techniques Sintonizacion de Estabilizadores de Sistemas de Potencia (PSSs) empleando Modelos Fenomenologicos y Tecnicas de Optimizacion

In this research, a methodology to coordinate power system stabilizers (PSSs) in order to damp out oscillations is proposed. It is based on the minimization of an objective function including equality constraints and transient studies. The minimization is carried out by Genetic Algorithms, requiring time domain simulations. The effectiveness and robustness of the procedure are demonstrated through digital simulations and real time simulations for several power system, taken different operating conditions into account and analyzing faults at different locations
Some reflections on applying workflow technology to software process

Process Technologies (e.g., Workflow and Software Process) support co-operative work, where humans and Information Systems participate within an organisation. In the last two decades Workflow Management Systems have been demonstrated to be useful in the automation of Business Processes. We think Software Process is part of a more complex Business Process, and it is composed by several sub-processes, but with different granularity of detail. In recent years, environments have been created to automate Software Processes, but only few products are available today in the market, and they have shortcomings that can be addressed by Workflow Technology. Such technology can improve Software Process Technology leading to a Process Technology able to manage and automate other kind of Processes. The aim of this article is to reflect our ideas about the possibility of applying Workflow Technology to Software Process and vice versa.
Study on fabrication and sensing characteristics of a D-shaped fiber long-period fiber grating

A long-period fiber grating (LPFG) structure based on D-shaped fiber is proposed, where a graphene oxide (GO) film layer is deposited on the fiber by the dipping method to jointly construct a new humidity sensor. The D-shaped fiber structure can provide a stronger evanescent field than conventional fibers, and therefore offer much higher sensitivity. The maximum average sensitivity achieved by the sensor in the range of ambient humidity of 20-80% RH (relative humidity) is -0.1908 nm/% RH, and the linearity is 0.998. The experimental results show that the humidity sensor has higher response sensitivity, and improved linear response characteristics. It provides a beneficial exploration for the development of low-cost, easy-to-manufacture, and highly sensitive fiber-optic humidity sensors.
Transaction Consistency in the Cloud: Old Paradigms Revisited

Initial distributed and replicated databases used some mechanisms and paradigms that were slowly replaced by new and more efficient techniques that make cluster-based databases successful. Recently, cloud computing environments appeared, demanding database solutions with higher availability and scalability than those offered by cluster-based systems. In this search for proper solutions, current management techniques are again set aside, and looks are focusing on those initial and previously discarded mechanisms in order to adapt them to the elastic nature of the cloud.
Item Response Theory Principles And Applications

Thank you for reading item response theory principles and applications. As you may know, people have look hundreds times for their chosen novels like this item response theory principles and applications, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their laptop. item response theory principles and applications is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the item response theory principles and applications is universally compatible with any devices to read.
Identification of E. coli collected from water of various farms from Akola region

The present study was to identify the E. coli collected from Akola district. Twenty one water samples were collected for morphological, bacteriological and biochemical investigations for isolation and identification of E. coli. The samples were cultured on nutrient broth and after culturing the same samples transferred to specific media EMB & MC agar. Biochemical properties of the isolates were considered and response in TSI test incline was likewise watched. The samples which give particular morphological and staining characteristic were chosen for the further biochemical examination. From the present study it was concluded that the general prevalence of E. coli was recorded as 66.66% from the poultry farms. The outcomes recommended that water samples collected from the various farms were shown to be a significant wellspring of E. coli infections in the field conditions. The presence of E. coli at the recorded rate in water samples poses significant alarming for the public health issue if not maintain proper hygienic steps taken place.
An Optimized Data Association Solution for SLAM

Joint compatibility branch and bound(JCBB) owns some disadvantages like highly computational complexity. Three improvements are introduced to optimize JCBB's performance on accuracy and computational complexity.Firstly,data association accuracy is improved with the help of mutual exclusion rule and optimization rule.Secondly,the data association is limited in potential local region,which is determined by robot pose and sensor measurement range.Thirdly,data association is adaptively realized in a divisive manner.Simulation results indicate that optimized JCBB(OJCBB) can ensure accuracy and reduce computational complexity simultaneously.Experimental results with Victoria Park Dataset indicate that the OJCBB data association results are reliable,and the computational efficiency of OJCBB is much better than mat of JCBB.
Kano Model Analysis Required in APP Interactive Design based on Mobile User Experience

The user experience in APP interactive designdraws more and more attention in the APP market where the user population expands continuously and the competition becomes increasingly intense, butcurrent studies mostly focus on the user website experience. This paper is intended to classify and study user needs in APP interactive design based on Kano model and Better-Worse coefficient, and gain Kano two-dimensional attribute classification of user needsin the design. In addition, it carries out analysis on these needs based on the better-worse coefficient.
Super Twisting Sliding Mode Control-Based Impedance Control for Robot Arm End-Effector Force Tracking

Conventional impedance control is essentially used in a wide-ranging industrial robot-environment interaction application. While interacting, the robot arm must regulate the interactive forces to achieve the desired compliance. In this research, the impedance control is designed for a six-degree-of-freedom robot (one-DOF prismatic, telescopic mast, and five-DOF robot manipulator) end-effector force tracking while nuclear power plant's reactor vessel internals (RVI) dismantling operations. Initially, sliding mode control (SMC) was implemented but resulted in chattering in force tracking response. Therefore, super twisting sliding mode control (STSMC)-based force tracking impedance control is designed and implemented for enhanced end-effector force tracking. STSMC is a second-order SMC that does not require system dynamics information. The control input of the STSMC is composed of a continuous and discrete component. Continues control input regulates control performance against uncertainties, whereas discrete provides a switching algorithm. STSMC then eliminates the chattering effect and reduces the force tracking error. Furthermore, the impedance control parameters were auto-tuned using particle swarm optimization (PSO) to get the optimal parameters. The parameters regulation is achieved based on the desired force, current interaction force, and PSO cost-function. The performance of SMC-based and STSMC-based impedance control is compared, and the comparison shows that the STSMC-based impedance control provides enhanced performance.
Inner Dynamics of Particle Swarm Optimization Interpreted by Complex Network Analysis

In this paper, we present the relation between the inner dynamics of the particle swarm optimization algorithm and the properties of a complex network recording the information transfer in the population. Using population diversity as an example, we argue that the complex network analysis is a viable tool for assessing the state of the population and the eventual necessity of an adaptive intervention.
USING HYBRID OF EIGENFACE AND IMPERIALIST COMPETITION ALGORITHM FOR FEATURE SELECTION IN FACE RECOGNITION

Design of a robust Face Recognition (FR) system is greatly affected due to varying illumination and pose conditions. The accuracy of FR system can be increased by normalizing and compensating the illumination variations in the pre-processing stage. To improve the robustness of FR systems against illumination variations, a method is proposed in this study which is based on Contourlet Transform (CT), hybrid of Principal Component Analysis (PCA) and Imperialist Competition Algorithm (ICA) techniques for feature reduction, and an ICA-optimized Multi-Layer Perceptron (MLP) classifier. First, each face is decomposed using the CT. So, the contourlet coefficients of low and high frequency in different scales and various angles are obtained. The frequency coefficients are employed as a feature vector for further processing. The PCA is then used to reduce the dimensionality of the feature vector. The ICA is also exploited to search the feature space for an optimal feature subset. Then, the reduced-size feature vector is applied to the face classifier that is based on MLP neural network whose structure and learning rate are optimized by ICA. The proposed method is robust to variation of imaging conditions and pose variations. The proposed technique provides better results when tested on ORL and Extended Yale-B databases as compared with other existing techniques such as hybrid model based on discrete wavelet transform and PCA (in terms of precision, sensitivity, and accuracy) and different state-of-the-art methods.
Formalization and Verification of Shared Memory

Shared memory verification, checking the conformance of an implementation to a shared memory model, is an important, albeit complex on many levels, problem. One of the major reasons for this complexity is the implicit manipulation of semantic constructs to verify a memory model, instead of the desired syntactic methods, as they are amenable to be mechanized. The work presented in this dissertation is mainly aimed at reformulating shared memory verification through a new formalization so that the modified presentation of the problem manifests itself as purely syntactic. (Shared) memories are viewed as structures that define relations over the set of programs, an ordered set of instructions, and their executions, an ordered set of responses. As such, specifications (basically memory models that describe the set of executions considered correct with respect to a program) and implementations (that describe how an execution relates to a program both temporally and logically) have the same semantic basis. However, whereas a specification itself is described as a relation, an implementation is modelled by a transducer, where the relation it realizes is its language. This conscientious effort to distinguish between specification and implementation is not without merit: a memory model needs to be described and formalized only once, regardless of the implementation whose conformance is to be verified. Once the framework is constructed, shared memory verification reduces to language inclusion; that is, checking whether the relation realized by the implementation is a subset of the memory model. The observation that a specification can be approximated by an infinite hierarchy of finite-state transducers (implementations), called the memory model machines, results in the aforementioned syntactic formulation of the problem: regular language inclusion between two finite-state automata where one automaton has the same language (relation) as the implementation and the other has the same language as one of the memory model machines. On a different level but still related to shared memory verification, the problem of checking the interleaved-sequentiality of an execution (an execution is interleaved-sequential if it can be generated by a sequentially consistent memory), is considered. The problem is transformed into an equivalent constraint satisfaction problem. Thanks to this transformation, it is proved that if a memory implementation generates a non interleaved-sequential and unambiguous execution (no two writes in the execution have the same address and data values), then it necessarily generates one such execution of bounded size, the bound being a function of the address and the data spaces of the implementation.
Nonstationary and Sparse Linear Regression for State Prediction of Artificial Systems

When we use regression learning to predict some state variables in artificial systems, it is important to consider nonstationarity, sparsity, interpretability, reliability, and so on. This paper propose a method that takes them into account, called online linear Nonstationary Sparse Bayesian Learning (OL-NSBL), and considered its application to oil plant data as an example of an artificial system. We first investigated the performance and properties of the proposed method on an artificially generated data set, and demonstrated its effectiveness. Then we applied it to the plant data and it showed high performance.
Examining Learners’ Interaction in an Open Online Course Through the Community of Inquiry Framework

Abstract Open online courses are becoming more prevalent at local level and for and professional development objectives. Proper instructional design combined with use of online tools can promote learner interaction in online environments. Using the Community of Inquiry (CoI) framework, this study aimed at examining learners’ interaction and their perceptions of teaching presence, social presence, and cognitive presence in an open online course offered for professional development in three Swedish universities. The course was free and open to all, attracting participants from all over the world. In order to understand the online interactions of the course, three presences of CoI were matched to three types of interaction (Moore, 1989). Data were collected through a slightly revised version of the CoI instrument and open-ended questions were added. Survey results showed that participants had high perceptions of the three presences in the course. Results also yielded significant relationships between teaching presence and cognitive presence, as well as social presence and cognitive presence. The findings suggest that deploying a set of online tools combined with appropriate pedagogical approaches in designing open online courses could foster learner interaction especially learner-content interaction and cognitive presence.
Subset Precoding and Tapering on Phased Arrays for High Throughput Satellite Systems

Beamforming with phased arrays gained popularity for the design of new high throughput satellite (HTS) systems. In contrast to horn antenna arrays, they clearly offer more flexibility in space and provide the possibility to reduce interference while still aiming for full frequency re-use on ground. Often, tapering is also used for these antennas to control the side lobe level. In this work, we apply the traditional beam precoding, as interference mitigation scheme on ground along with tapering in the forward link and determine possible options for the reduction of computational complexity by subset precoding. The performance evaluation is executed on a direct radiating array and an array fed reflector antenna. Apart from additive white Gaussian noise, also an adaptive coding and modulation margin and channel state information error are considered to create realistic channel conditions.
Ensemble Learning Approach for Author Profiling Notebook for PAN at CLEF 2014

With the evolution of internet, author profiling has become a topic of great interest in the field of forensics, security, marketing, plagiarism detection etc. However the task of identifying the characteristics of the author just based on a text document has its own limitations and challenges. This paper reports on the design, techniques and learning models we adopted for the PAN-2014 Author Profiling challenge. To identify the age and gender of an author from a document we employed ensemble learning approach by training a Random Forest classifier with the training data provided by PAN organizers for English language only. Our work indicate that readability metrics, function words and structural features play a vital role in identifying the age and gender of an author.
Video Image Enhancement Method Research in the Dust Environment

For the problem of serious decline in the quality of the video image, sharpness and contrast reduction, edge blur and etc due to the atmospheric scattering of dust particles in dust environment, the paper proposes a dust image enhancement method, which combine the spatial domain and transform domain. First convert the degraded image into fuzzy domain to conduct PAL fuzzy image enhancement of global dust image; Then in the spatial domain, use the band-limited local histogram adaptive equalization algorithm to enhance the local component; Finally apply POSHE algorithm to enhance the local details. The experiment results show that the proposed method can effectively improve the dust image contrast, details and edge information, enhance the overall visual effect of the image.
Multivariate Analysis Techniques In Social Science Research From Problem To Analysis

Thank you very much for reading multivariate analysis techniques in social science research from problem to analysis. As you may know, people have search numerous times for their chosen books like this multivariate analysis techniques in social science research from problem to analysis, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some infectious bugs inside their computer.
A Parallel Computing Method for Entity Recognition based on MapReduce

With the rapid development of industrial automation, there are huge amounts of duplicate data refer to the same entity in the data sets have brought enormous challenges in data analysis. To accommodate the entity recognition of huge amounts of data, this paper presents a parallel computing method for entity recognition based on MapReduce. Through the detailed introduction to the MapReduce framework, running the applications on the Hadoop platform and parallel processing data sets to recognize the data entities. The experiments show that the proposed method greatly enhanced the recognition speed and accuracy, which has better effectiveness to meet the demand for entity recognition than other methods.
Impacts of English Learning Strategies, Interests, and Anxieties on English Learning Achievements: Taking Example from Chinese College Students

This article explores the correlations between Chinese college students’ English achievements and their learning strategies, learning interests, and learning anxieties, in a bid to analyze further the differences made by the effects of respondents’ individual variables (gender, age, expertise, and allowance) on students’ learning achievements. Based on literature review and questionnaire analysis, the following findings are obtained: 1. While the percentage of respondents who ranked their English achievements as medium or above reached 72.67%, the percentage of those who actually passed CET-4 or CET-6 was merely at 57.00%. 2. These percentages of respondents, who possessed learning strategies, learning interests, and learning anxieties, took up 65.67%, 84.67%, 60.33% respectively. 3. College students produced a noticeably positive correlation between their English achievements and their learning strategies and learning interests. 4. These respondents’ age and expertise cast a significant impact on their achievements, as their expertise also affected learning strategies, and their age and expertise affected learning interests conspicuously. Therefore, this article urges teachers, government agencies, and scholars to formulate pedagogy, policies, and research addressing the need of differentiated education based on students’ learning achievements, strategies, and interests, and other individual variables. Keywords—English learning strategies, English learning achievements, English learning interests, English learning anxieties