YODA – Your Only Design Assistant

Converting user interface designs created by graphic designers into computer code is a typical job of a front end engineer in order to develop functional web and mobile applications. This conversion process can often be extremely tedious, slow and prone to human error. In this project, deep learning based object detection along with optical character recognition is used to generate platform ready prototypes directly from design sketches. Also, a new design language is introduced to facilitate expressive prototyping and allowing the creation of more expressive and functional designs. It is observed that the AI powered application along with modern web technology can significantly help streamline and automate the overall product development routine and eliminate hurdles from the product development process

Deep Space Network information system architecture study

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control

Integrating passive ubiquitous surfaces into human-computer interaction

Mobile technologies enable people to interact with computers ubiquitously. This dissertation investigates how ordinary, ubiquitous surfaces can be integrated into human-computer interaction to extend the interaction space beyond the edge of the display. It turns out that acoustic and tactile features generated during an interaction can be combined to identify input events, the user, and the surface. In addition, it is shown that a heterogeneous distribution of different surfaces is particularly suitable for realizing versatile interaction modalities. However, privacy concerns must be considered when selecting sensors, and context can be crucial in determining whether and what interaction to perform.Mobile Technologien ermöglichen den Menschen eine allgegenwärtige Interaktion mit Computern. Diese Dissertation untersucht, wie gewöhnliche, allgegenwärtige Oberflächen in die Mensch-Computer-Interaktion integriert werden können, um den Interaktionsraum über den Rand des Displays hinaus zu erweitern. Es stellt sich heraus, dass akustische und taktile Merkmale, die während einer Interaktion erzeugt werden, kombiniert werden können, um Eingabeereignisse, den Benutzer und die Oberfläche zu identifizieren. Darüber hinaus wird gezeigt, dass eine heterogene Verteilung verschiedener Oberflächen besonders geeignet ist, um vielfältige Interaktionsmodalitäten zu realisieren. Bei der Auswahl der Sensoren müssen jedoch Datenschutzaspekte berücksichtigt werden, und der Kontext kann entscheidend dafür sein, ob und welche Interaktion durchgeführt werden soll

Distributed network of meteostations with LoRa

Současná práce analyzuje možnosti nasazení distribuované sítě meteostanic v městském prostředí. Cílem této práce je návrh a implementace zařízení s důrazem na jednoduchost instalace, minimální spotřebu, bezdrátový přenos dat a využití alternativního zdroje energie. V rámci této práce byl také implementován algoritmus založený na architektuře neuronové sítě LSTM, schopný generovat předpověď měřených parametrů. Kromě toho na hostingu Amazon byla nasazena infrastruktura, která kombinuje centralizovaný sběr dat ze všech zařízení, předpovídání měřených parametrů, sdílení dat s komunitními projekty monitorování počasí a navíc bylo poskytnuto webové rozhraní pro zobrazování měřených a předpovězených dat. Vyvinutý systém byl úspěšně otestován v reálných klimatických podmínkách. Nakonec byla provedena srovnávací analýza vyvinutého zařízení a komerčních analogů ze stejné a vyšší cenové kategorie. Výsledkem této práce je systém, který má komerční potenciál a je schopen konkurovat populárním stávajícím řešením.The present work analyzes the possibilities of deploying a distributed network of meteostations in an urban environment. The aim of this work is the design and implementation of a device with an emphasis on the simplest possible installation, minimum power consumption, wireless data transmission and the use of alternative power source. Also, within the framework of this work, an algorithm based on the LSTM neural network architecture has been implemented, capable of generating a forecast of the measured parameters. In addition, an infrastructure was deployed on Amazon hosting, combining both centralized data collection from all devices, predicting measured parameters, sharing data with community weather monitoring projects, and, moreover, the web interface was implemented displaying both device data along with measured and predicted parameters. The developed system has been successfully tested in real climatic conditions. Finally, a comparative analysis of the developed device and commercial counterparts from the same and premium price segments was carried out. The result of the present work is a system with commercial potential and the ability to compete with popular existing solutions

Deep Learning in the Classification and Recognition of Cardiac Activity Patterns

Electrocardiography is an examination performed frequently in patients experiencing symptoms of heart disease. Upon a detailed analysis, it has shown potential to detect and identify various activities. In this article, we present a deep learning approach that can be used to analyze ECG signals. Our research shows promising results in recognizing activity and disease patterns with nearly 90% accuracy. In this paper, we present the early results of our analysis, indicating the potential of using deep learning algorithms in the analysis of both onedimensional and two–dimensional data. The methodology we present can be utilized for ECG data classification and can be extended to wearable devices. Conclusions of our study pave the way for exploring live data analysis through wearable devices in order to not only predict specific cardiac conditions, but also a possibility of using them in alternative and augmentedcommunication frameworks

Prioritizing Content of Interest in Multimedia Data Compression

Image and video compression techniques make data transmission and storage in digital multimedia systems more efficient and feasible for the system's limited storage and bandwidth. Many generic image and video compression techniques such as JPEG and H.264/AVC have been standardized and are now widely adopted. Despite their great success, we observe that these standard compression techniques are not the best solution for data compression in special types of multimedia systems such as microscopy videos and low-power wireless broadcast systems. In these application-specific systems where the content of interest in the multimedia data is known and well-defined, we should re-think the design of a data compression pipeline. We hypothesize that by identifying and prioritizing multimedia data's content of interest, new compression methods can be invented that are far more effective than standard techniques. In this dissertation, a set of new data compression methods based on the idea of prioritizing the content of interest has been proposed for three different kinds of multimedia systems. I will show that the key to designing efficient compression techniques in these three cases is to prioritize the content of interest in the data. The definition of the content of interest of multimedia data depends on the application. First, I show that for microscopy videos, the content of interest is defined as the spatial regions in the video frame with pixels that don't only contain noise. Keeping data in those regions with high quality and throwing out other information yields to a novel microscopy video compression technique. Second, I show that for a Bluetooth low energy beacon based system, practical multimedia data storage and transmission is possible by prioritizing content of interest. I designed custom image compression techniques that preserve edges in a binary image, or foreground regions of a color image of indoor or outdoor objects. Last, I present a new indoor Bluetooth low energy beacon based augmented reality system that integrates a 3D moving object compression method that prioritizes the content of interest.Doctor of Philosoph

Enriching BIM models with fire safety equipment using keypoint-based symbol detection in escape plans

In the context of fire safety inspections, Building Information Modeling (BIM) models enriched with Fire Safety Equipment (FSE) components can be used to complete compliance checks and other analyses. However, BIM models often lack the required FSE information. To address this issue, escape plans are a convenient source of data, as they show the position and type of FSE on floor plans. Therefore, this study proposes an automated method to analyze escape plans and extract FSE component information to enrich existing BIM models. The method employs the deep learning model Keypoint R-CNN for symbol detection. Symbol locations are then translated into physical positions within the BIM model. Through a real-building case study, the method demonstrates promising results. Future research may focus on improving the symbol detection performance and the registration between the BIM models and fire escape plans, as well as utilizing the extracted information for actual fire safety analyses