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At disaster sites, the use of Micro Unmanned Aerial Vehicles (MUAVs) is expected for human safety. One application is to support first-phase emergency restoration work conducted by teleoperated construction machines. To extend the operation time of a MUAV, the authors proposed a powerfeeding tethered MUAV to provide an overhead view of the site to operators. The target application is to be used outdoors,...
Binary classification is one of the most frequent studies in applied machine learning problems in various domains, from medicine to biology to meteorology to malware analysis. Many researchers use some performance metrics in their classification studies to report their success. However, the literature has shown a widespread confusion about the terminology and ignorance of the fundamental aspects behind...
The vision is considered the most important sense in humans, the visual field may be affected by various diseases or conditions that can even be congenital, resulting in a high percentage of people with visual problems, low vision to blindness. In the majority of cases can be corrected with conventional methods, however, if a suitable control could be prevented and up to early detection of various...
The engineering disciplines of industrial engineering, engineering management, and systems engineering are replete with abstract concepts without physical representations that are inherently difficult to learn. Hence, this study is motivated by engineering students' inability to understand abstract concepts and the need for continuous improvement of student learning. In this study, in the context...
The use of cognitive maps in support of solving practical problems of analysis of ill-structured situations and large-scale systems in economics, medicine, ecology, education and military-political sphere is growing. The problem of visual perception and understanding of cognitive maps is proven to be extremely urgent and practically important. The set of visualization methods that provide better perception...
Monocular ORB-SLAM has been proved to be one of the best open-source SLAM method. However, it is still unsatisfying especially in low illumination indoor environment, which is caused by scale recovery and wrong feature matching. In this paper, we proposed a vehicle model based monocular ORBSLAM method supplemented by April-Tag to improve the performance of original algorithm. This approach is practical...
Salient object detection aims to correctly highlight the most salient object(s) in an image. Combining fine-grained contrast prior with rough-grained object consistency, this paper proposes a Focusness Guided Salient object detection (FGS) algorithm. To obtain clean and precise contrast map, FGS uses the focusness prior to guide the contrast map. Combing different saliency priors, FGS utilizes a unified...
In this paper, we propose a computational modeling method to investigate head-eye coordination in face-to-face behavior. The method looks into probability density of individuals' head orientation during looking at others' face. We conducted experiment under two different scenarios in human-human interaction. Under each scenario, individuals' head orientation could be fitted with one Gaussian distribution...
The paper is concerned with the experimental and numerical investigations of a laminar jetting flow impacting immersed convex bodies, the so called Coanda effect. Numerical solutions of the coupled Navier-Stokes and diffusion equations are obtained for Hele-Shaw flow configurations, particularly designed to analyze the studied phenomenon, related to the spreading of a solute by an immersed water jet...
For the image of present surface defects in steel plates, a surface defect detection method based on human visual attention mechanism is proposed. Based on the physiological structure and function of human visual system, this paper establishes a steel surface defect detection model via Gaussian filter, Gabor filter and the calculation such as cross-scale reduction, cross-scale addition and normalization,...
Mobile robotics became an important knowledge area. Led by the technological advances, the life spectancy increases, rising the senior citizens ratio. At the same hand, the cities growth turn on a more intense and dangerous traffic. Autonomous mobile robots can help in these situations, but still have a jeopardized application. In this sense, this work presents a contribution to development of nonholonomic...
The development of a deep (stacked) convolutional auto-encoder in the Caffe deep learning framework is presented in this paper. We describe simple principles which we used to create this model in Caffe. The proposed model of convolutional auto-encoder does not have pooling/unpooling layers yet. The results of our experimental research show comparable accuracy of dimensionality reduction in comparison...
The aim of this work is to try to propose an alternative interpretation of the essence of digital money as a product of the development of information and telecommunication technologies, and to find the criteria for its quantitative assessment. The conducted research allowed us to trace the evolution of approaches to the interpretation of digital (electronic) money, which was modified under the influence...
Crowd event detection techniques aim at solving real-world surveillance problems, such as detecting crowd anomaly and tracking specific person in a highly dynamic crowd scene. In this paper, we proposed an innovate texture-based analysis method to model crowd dynamics and us it to distinguish the crowd behaviours. To describe complicated crowd scenes, homogeneous random features have been deployed...
Microbubbles as an ultrasound contrast agent have great diagnostic value, and can be used to visualize the vascularization in a variety of organs, e.g. imaging the neo-angiogenesis in tumors. Pulse inversion (PI) is a common method for detection of these bubbles, implemented in many ultrasound scanners. A dual band imaging technique named SURF has shown the ability to further enhance contrast-to-tissue...
Visualization is a flexible way to analyze simulated data and serves as a means for scientific discovery. Large scale neural simulations using high performance and distributed computing techniques produce huge amount of data for which visual analysis is generally difficult to perform. In this paper, a spiking neuron simulation environment was created to model and simulate networks of neurons of the...
From their beginning, people have always been prone to hiding something. Nowadays, the need for sending hidden messages has become the matter of pride and elite knowledge of managing how to send something to someone right in front of the eyes of others without others knowing about the communication. It opens a complete new perspective of the world, parallel to the existing one, where hidden messages...
Visualization of data in learning management systems became essential for easier analysis of learner's behavior and interpretation of results on such system. In this paper, we presented design and development process of dashboard for the prototype of program support-CM Tutor. After analysis of various approaches of designing a learning analytics dashboard, we selected most important items for display...
Accurate reporting of results is extremely important. This is especially relevant for energy and environmental data associated with government driven incentives and disincentives (such as tax benefits for energy efficiency or mandatory GHG emission reporting). Any abnormalities in the data can significantly affect reported results. It is therefore critically important to properly evaluate all data...
This paper presents the build design process used by students at Lincoln University to recreate an 1800's model of a harmonograph. One model of the harmonograph was tested and did not produce the desired visual outcome as expected. The second model was redesigned and will be built using 3D printed parts. The second model should improve reliability of the machine and students' understanding of physics...
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