This internship's objective is to implement modeling of uncut chip thicknesses and milling forces in 3-axis milling, and apply it to orbital drilling. The goal is to understand deeper the process, and develop a tool which permits to model a wide range of end-mill tool geometries, and most of 3-axis milling operations.In this report, the following axis will be developed: • The modeling of the complex tool geometry • The modeling of uncut chip thicknesses in slot milling, and for any 3-axis milling operation • The strategy which has been chosen to identify cutting forces coefficients, and their use. • The obtained results and prospects for development.

This paper will explore the speed of execution, memory foot-print and the maturity of WebAssembly Runtimes (WasmRT).For this study, the WasmRT will be Wasmer1and Wasmtime.2Initially, benchmarks were run on a Raspberry Pi 3 model Bto simulate a more hardware capable IoT-device. Tests per-formed on a Raspberry Pi shows that there are many instanceswhere a WasmRT outperforms a similar Docker+C solution.WasmRT has a very clear use case for IoT devices, specifi-cally short jobs, the results from our research will show thatWasmRT can be up to almost 70 times as fast as a similarDocker solution. WasmRT has a very strong use case thatother container solutions can not contend with. This paperwill show how effective a lightweight, portable, and fast Was-merRT can be, but also to highlight its pain points and whenother container solutions may make more sense

Deepfakes are an emerging problem in social media and for celebrities and political profiles, it can be devastating to their reputation if the technology ends up in the wrong hands. Creating deepfakes is becoming increasingly easy. Attempts have been made at detecting whether a face in an image is real or not but training these machine learning models can be a very time-consuming process. This research proposes a solution to training deepfake detection models cooperatively on the edge. This is done in order to evaluate if the training process, among other things, can be made more efficient with this approach.  The feasibility of edge training is evaluated by training machine learning models on several different types of iPhone devices. The models are trained using the YOLOv2 object detection system.  To test if the YOLOv2 object detection system is able to distinguish between real and fake human faces in images, several models are trained on a computer. Each model is trained with either different number of iterations or different subsets of data, since these metrics have been identified as important to the performance of the models. The performance of the models is evaluated by measuring the accuracy in detecting deepfakes.  Additionally, the deepfake detection models trained on a computer are ensembled using the bagging ensemble method. This is done in order to evaluate the feasibility of cooperatively training a deepfake detection model by combining several models.  Results show that the proposed solution is not feasible due to the time the training process takes on each mobile device. Additionally, each trained model is about 200 MB, and the size of the ensemble model grows linearly by each model added to the ensemble. This can cause the ensemble model to grow to several hundred gigabytes in size.

This master´s thesis is concerning the problem of manufacturing technology of the metal shim, which is a part of automobile brake mechanism. Shim will be made of the S460MC steel and the expected production series is 800 000 pieces per year. This quantity, demanded quality and accuracy of blank are the main reasons for application of fineblanking technology process with pressure edge. The blank will be making by triple–action press produced by Feintool, marked HFAplus4500LT. This press produces two blanks per one action.

This thesis deals with mathematical modelling of water flow through the safety object. It explains the principle of mathematical modelling and its laws. There is also present-ed the mathematical models used by numerical modelling. The results of mathemati-cal model Koryčany are included in this thesis compared with hydraulic calculations and physical model.

Diplomová práce pojednává o návrhu nového hardwaru využívající komparátor ve zpětné vazbě systému pro digitální předzkreslování signálu. Vybrané vlastnosti navrhované architektury jsou ověřeny pomocí simulací a následně jsou zvoleny komponenty vhodné pro vysokofrekvenční použití za účelem implementace. Na bázi předložené architektury je navržen akviziční modul včetně obvodové realizace a vytvoření plošného spoje. Zhotovený plošný spoj je osazen a připraven pro další testování. Dále je navržen příslušný firmware pro příjem a vysílání signálu a získávání naměřených dat. Obdržené výsledky jsou určeny pro zhodnocení vlastností hardwaru a budoucího využití architektury v systémech digitálních předzkreslovačů.

Cloud VR/AR/MR (Virtual Reality, Augmented Reality, and Mixed Reality) services representa high-level architecture that combines large scale computer resources in a data-center structurestyle set up to render VR/AR/MR services using a combination of very high bandwidth, ultralow latency, high throughput, latest 5G (5th Generation) mobile networks to the end users.  VR refers to a three-dimensional computer-generated virtual environment made up ofcomputers, which can be explored by people for real time interaction. AR amplifies humanperception of the real world through overlapping of computer-generated graphics or interactivedata on a real-world image for enhanced experience.  According to the Virtual Reality Society’s account of the history of VR, it started from the360-degree murals from the nineteenth century [18]. Historically, live application of AR wasdisplayed when Myron Kruger used a combination of video cameras and projector in aninteractive environment in 1974. In 1998, AR was put into live display with the casting of avirtual yellow line marker during an NFL game. However, personal, and commercial use ofVR/AR was made possible starting with release of a DIY (Do it Yourself) headset calledGoogle Cardboard in 2014 by Google, which made use of a smartphone for the VR experience.In 2014, Samsung also introduced Gear VR which officially started the competition for VRdevices. Subsequently In 2014, Facebook acquired Oculus VR with the major aim ofdominating the high-end spectrum of VR headset [18]. Furthermore, wider adoption of ARbecame enhanced with the introduction of Apple’s ARKit (Augmented Reality Kit) whichserves as a development framework for AR applications for iPhones and iPads [18].  The first application of VR devices in the health industry was made possible due to healthworkers’ need to visualize complex medical data during surgery and planning of surgery in1994. Since then, commercial production of VR devices and availability of advanced networkand faster broadband have increased the adoption of VR services in the healthcare industryespecially in planning of surgery and during surgery itself [16]. Overall, the wide availabilityof VR/AR terminals, displays, controllers, development kits, advanced network, and robustbandwidth have contributed to making VR and AR services to be of valuable and importanttechnologies in the area of digital entertainment, information, games, health, military and soon. However, the solutions or services needed for the technology required an advancedprocessing platform which in most cases is not cost efficient in single-use scenarios.  The kind of devices, hardware, software required for the processing and presentation ofimmersive experiences is often expensive and dedicated to the current application itself.Technological improvement in realism and immersion means increase in cost of ownershipwhich often affected cost-benefit consideration, leading to slower adoption of the VR services[14] [15]. This is what has led to development of cloud VR services, a form of data-centerbased system, which serves as a means of providing VR services to end users from the cloudanywhere in the world, using its fast and stable transport networks. The content of the VR isstored in the cloud, after which the output in form of audio-visuals is coded and compressedusing suitable encoding technology, and thereafter transmitted to the terminals. The industrywide acceptance of the cloud VR services, and technology has made available access to payper-use-basis and hence access to high processing capability offered, which is used in iipresenting a more immersive, imaginative, and interactive experience to end users [11] [12].However, cloud VR services has a major challenge in form of network latency introduced fromcloud rendering down to the display terminal itself. This is most often caused by otherperformance indicators such as network bandwidth, coding technology, RTT (Return TripTime) and so on [19]. This is the major problem which this thesis is set to find out.  The research methodology used was a combination of empirical and experimental method,using quantitative approach as it entails the generation of data in quantitative form availablefor quantitative analysis. The research questions are: Research Question 1 (RQ1): What are the latency related performance indicators ofnetworked immersive media in mobile health applications? Research Question 2 (RQ2): What are the suitable network structures to achieve an efficientlow latency VR health application?  The answers gotten from the result analysis at the end of the simulation, show thatbandwidth, frame rate, and resolution are very crucial performance indicator to achieve theoptimal latency required for hitch-free cloud VR user experience, while the importance of otherindicators such as resolution and coding standard cannot be overemphasized. Combination ofedge and cloud architecture also proved to more efficient and effective for the achievement ofa low-latency cloud VR application functionality.  Conclusively, the answer to research question one was that, the latency relatedperformance indicators of networked immersive media in mobile health applications arebandwidth, frame rate, resolution, coding technology. For research question two, suitablenetwork structures includes edge network, cloud network and combination of cloud and edgenetwork, but in order to achieve an optimally low-latency network for cloud VR mobile healthapplication in education, combination of edge and cloud network architecture is recommended

This diploma thesis inspects problems with specification location of edges with higher accuracy then one pixel (subpixel accuracy). In terms of this assignment has been created program, which generates three different shapes of objects. With change of parameters in program is measuring location of gravitational center on objects with subpixel accuracy. Obtained data of gravitational center deviations are depictured in graphs.

This work deals with the possibilities of employing a genetic algorithm to edge detection. There is introduced a project which uses enhanced image divided into sub-regions, on which detection by genetic algorithm is applied. To achieving our goals are used individuals in two-dimensional bit arrays, for which are specially adjusted mutation and crossover operators. Cost minimization approach is used as fitness function. The project was created and tested in Matlab environment.

This thesis deals with applying methods on object detection in image. Separation of objects off the background is often needed during the image processing. It isolates the region of interest that can be worked with. The main purpose of this paper is the explanation of principles of pre-processing and segmentation of image, resulting in object detection using the wavelet transformation. This wavelet transformation is described more in detail, because it is the base of the primary used method. In the practical part of this thesis the main method was implemented to MATLAB environment and tested on set of images. The method was tested for robustness against noise and blur of image. It was compared with commonly used methods, using the edge detectors and thresholding. A simulation program was created for comparison of methods efficiency, including user interface.