Vasteajan mittausjärjestelmän suunnittelu, toteutus ja testaus

A touchscreen is a commonly used medium for the interaction between a user and a device. Response to user's action is often indicated visually on the screen after a certain delay. This interface latency is inherent in any computer system. Studies indicate that the latency has a major contribution on how users perceive the interaction with the device. While modern commercial touchscreen devices manifest latencies ranging between 50 ms and 200 ms, research indicates that the user performance for tapping tasks deteriorates at considerably lower levels and users are able to discern the latency as low as 3 ms. In this Thesis we present a novel solution for Android operated mobile devices to expose factors behind the feedback latency of a tap event. We start by reviewing the main components of the Android operating system. Next we describe the internal system elements which partake in the interaction between the user's touch input event and its corresponding visual presentation on the screen of the device. Propelled by the obtained information, we implement an affordable, fully automated system that is capable of collecting both temporal and environmental data. The constructed measurement system provided revealing results. We discovered that most of the feedback latency on a mobile device is accumulated by the internal components which are involved in presenting the visual feedback to the user. We also identified two main user action patterns which impose a huge effect upon system's responsiveness. Firstly, the location of touch is reflected in the amount of feedback latency. Secondly, the interval between two consecutive touch events might cause even unexpected results. Our study demonstrated that the latency can vary a lot between different devices by ranging from no effect on one device to a five-fold difference on another device. The study concludes that, despite the feedback latency is affected by multiple factors, the latency can be measured very precisely with the system that can be built even by an average Joe.Kosketusnäyttö on yleisesti käytetty kanava käyttäjän ja laitteen välisessä vuorovaikutuksessa. Järjestelmän palaute käyttäjän antamaan syötteeseen esitetään usein visuaalisesti laitteen näytöllä. Vasteen tuottamisessa syntyy kuitenkin jonkin verran viivettä eli latenssia. Tutkimusten mukaan viiveellä on suuri vaikutus käyttäjäkokemukseen. Nykyisten kosketuslaitteiden latenssi vaihtelee yleensä 50 ja 200 millisekunnin välillä. Kosketuspohjaisten tapahtumien suorittamisen on todettu heikentyvät jo huomattavasti pienemmän viiveen johdosta ja jopa alle kolme millisekuntia kestävä viive on vielä havaittavissa. Tässä diplomityössä esitetään Android-pohjaisille mobiililaitteille luotu edullinen järjestelmä, jonka avulla pystytään mittaamaan käyttäjän näytölle luoman kosketuksen ja sitä vastaavan järjestelmän antaman visuaalisen palautteen välistä viivettä. Työssä esitetellään ensin Android-käyttöjärjestelmän komponentit, jotka osallistuvat tämän tapahtumaketjun suorittamiseksi vaadittaviin toimintoihin. Tietojen pohjalta luodaan järjestelmä, jolla voidaan kerätä automaattisesti dataa viiveen eri syntykohdista ja sen ympäristöön littyvistä seikoista. Datan avulla pystytään aiempaa paremmin arvioimaan viiveen syntyyn vaikuttavia tekijöitä. Saatua tietoa voidaan hyödyntää yleisesti viiveen hallitsemiseen tähtääviin toimenpiteisiin ja siten lopulta käyttäjäkokemuksen parantamiseen. Järjestelmällä mitatuista tuloksista selviää, että suurin osa tapahtumaketjun latenssista syntyy käyttäjälle esitettävän visuaalisen palautteen vaatimiin toimenpiteisiin. Lisäksi työ tuo esille kaksi käyttäjän syötteen antamiseen liittyvää toimintatapaa, joilla on suuri vaikutus latenssiin. Kosketuksen sijainti ruudulla ja kahden peräkkäisen kosketuksen välinen aika vaikuttavat vasteaikaan. Latenssi ei aina muodostu suoraviivaisesti ja se voi ilmentää jopa yllättäviä piirteitä eri laitteiden välillä: toimintatapa yhdessä laitteessa ei vaikuta tulokseen, mutta saattaa toisessa laitteessa näkyä moninkertaisena erona. Vaikka latenssin syntyyn vaikuttaa monta eri tekijää, sitä voidaan onneksi mitata erittäin tarkasti järjestelmällä, jonka jopa Matti Meikäläinen pystyy rakentamaan

The design of personal ambient displays

Thesis (S.M.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1999.Includes bibliographical references (leaves 58-59).The goal of this thesis is to investigate the design of personal ambient displays. These are small, physical devices worn to display information to a person in a subtle, persistent, and private manner. They can be small enough to be carried in a pocket, worn as a watch, or even adorned like jewelry. In my implementations, information is displayed solely through tactile modalities such as thermal change (heating and cooling), movement (shifting and vibration), and change of shape (expanding, contracting, and deformation). Using a tactile display allows information to be kept private and reduces the chance of overloading primary visual and auditory activities. The display can remain ambient, transmitting information in the background of a person's perception through simple, physical means. The specific focus of this thesis is to create a number of these tactile displays, to identify and implement applications they can serve, and to evaluate aspects of their effectiveness. I have created a group of small, wireless objects that can warm up and cool down or gently move or shift. Users can reconfigure each display so that information sources like stock data or the activity of people on the internet are mapped to these different tactile modalities. Furthermore, in this thesis I consider the implications that human perception have on the design of these displays and examine potential application areas for further implementations.Craig Alexander Wisneski.S.M

Design of Analog Front-End of Touch-Screen Controller with Enhanced Noise Immunity and Configurable SNR and Frame Rate

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 정덕균.A design of analog front-end (AFE) for touch-screen controller (TSC) with highly enhanced noise immunity and configurable signal-to-noise ratio (SNR) and frame rate is proposed. First, the AFE for the mobile TSC is presented, which provides a configurable SNR and frame rate. The AFE configures its SNR and frame rate by adjusting the sampling cycles of the employed ADC. The test chip is fabricated in a 0.18-μm CMOS process and occupies a 2.2-mm2 active area. The test chip achieves 60-dB SNR and 200-Hz frame rate with 12 × 8 TSP. The SNR can be adjusted from 40 to 67 dB, while the frame rate is then inversely scaled from 50 Hz to 6.4 kHz. The test chip consumes 6.26 mW from a 3.3-V supply. The AFE for the tablet TSC is also presented, which provides highly enhanced noise immunity and configurable SNR and frame rate. The proposed AFE provides TX channels of 36 and RX channels of 64 in order to support a large-size TSP. A multi-driving TX structure with frequency-hopping signal generator is employed to improve the SNR and noise immunity. For a suppression of severe noise interference injected through the TSP, the RX sensing block adopts pre-filtering differential sensing method and high-order noise filtering structure. The AFE supports configurable SNR and frame rate with on-chip frame-rate controller. The test chip is fabri-cated in a 0.18-μm CMOS process. The active area of the test chip is 36 mm2. A 12.2-inch TSP with TX channels of 36 and RX channels of 64 is used in the measurement. The test chip achieves 54-dB SNR and 120-Hz frame rate with a finger touch. The frame rate can be adjusted from 85 to 385 Hz. The test chip achieves up to 20-Vpp noise immunity. The test chip consumes 94.5 mW with a 3.3-V supply.CHAPTER 1 INTRODUCTION 1 1.1 MOTIVATION 1 1.2 THESIS ORGANIZATION 3 CHAPTER 2 BASIC STUDY ON TOUCH-SCREEN CONTROLLER 5 2.1 TOUCH-SCREEN PANEL 5 2.2 TOUCH-SCREEN CONTROLLER 8 2.2.1 OVERVIEW ON TSC 8 2.3 ANALOG FRONT-END OF TSC 11 2.3.1 PERFORMANCE METRIC 12 2.3.2 DESIGN ISSUES OF AFE 15 CHAPTER 3 AFE OF MOBILE TSC WITH CONFIGURABLE SNR AND FRAME RATE 18 3.1 OVERVIEW 18 3.2 SYSTEM ARCHITECTURE 21 3.3 CONFIGURABLE SNR AND FRAME RATE 23 3.4 MEASUREMENT RESULTS 29 CHAPTER 4 AFE OF TABLET TSC WITH ENHANCED NOISE IMMUNITY 35 4.1 OVERVIEW 35 4.2 DESIGN ISSUES BY LARGE-SIZE TSP 38 4.3 DESIGN ISSUES BY NOISE INTERFERENCE 40 4.3.1 NOISE INTERFERENCE 40 4.3.2 DISPLAY NOISE REJECTION TECHNIQUE 43 4.3.3 CHARGER NOISE FILTERING TECHNIQUE 46 4.3.4 HIGH-VOLTAGE TX TECHNIQUE 50 4.3.5 MULTI-DRIVING TX TECHNIQUE 52 4.4 PROPOSED ARCHITECTURE 66 4.4.1 TX DRIVING ARCHITECTURE 67 4.4.2 RX SENSING ARCHITECTURE 71 4.5 MULTI-DRIVING TX STRUCTURE 75 4.5.1 CONSIDERATIONS FOR TX MODULATION SEQUENCE 75 4.5.2 COMPARISON OF MODULATION SEQUENCES 76 4.5.3 MODIFIED BUSH-TYPE HADAMARD MATRIX 79 4.6 NOISE FILTERING RX 83 4.6.1 PRE-FILTERING DIFFERENTIAL SENSING METHOD 83 4.6.2 NOISE-IMMUNE SENSING STRUCTURE 87 4.6.3 CONFIGURABLE SNR AND FRAME RATE 106 4.6.4 RX MODULATION 112 4.7 CIRCUIT IMPLEMENTATION 120 4.7.1 CHARGE AMPLIFIER AND BAND-PASS FILTER 121 4.7.2 CAPACITIVE DIFFERENTIAL AMPLIFIER 123 4.7.3 MIXER AND RX MODULATION 125 4.7.4 LOW-PASS FILTER 127 4.7.5 INCREMENTAL ΔΣ ADC 128 4.7.6 DIGITAL DEMODULATION 130 4.7.7 TX DRIVING BLOCK 131 4.8 MEASUREMENT RESULTS 132 4.8.1 TOUCH-SCREEN PANEL (TSP) 132 4.8.2 MEASUREMENT ENVIRONMENTS 133 4.8.3 FABRICATED AFE 134 4.8.4 OPERATION OF THE FABRICATED AFE 135 4.8.5 SNR MEASUREMENT 139 4.8.6 CONFIGURABLE SNR AND FRAME RATE 139 4.8.7 NOISE IMMUNITY 141 4.8.8 COMPARISON WITH OTHER WORKS 157 CHAPTER 5 CONCLUSION 158 BIBLIOGRAPHY 160 초 록 170Docto

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Small business innovation research. Abstracts of completed 1987 phase 1 projects

Non-proprietary summaries of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA in the 1987 program year are given. Work in the areas of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robotics, computer sciences, information systems, spacecraft systems, spacecraft power supplies, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Sensitive and Makeable Computational Materials for the Creation of Smart Everyday Objects

The vision of computational materials is to create smart everyday objects using the materi- als that have sensing and computational capabilities embedded into them. However, today’s development of computational materials is limited because its interfaces (i.e. sensors) are unable to support wide ranges of human interactions , and withstand the fabrication meth- ods of everyday objects (e.g. cutting and assembling). These barriers hinder citizens from creating smart every day objects using computational materials on a large scale. To overcome the barriers, this dissertation presents the approaches to develop compu- tational materials to be 1) sensitive to a wide variety of user interactions, including explicit interactions (e.g. user inputs) and implicit interactions (e.g. user contexts), and 2) makeable against a wide range of fabrication operations, such cutting and assembling. I exemplify the approaches through five research projects on two common materials, textile and wood. For each project, I explore how a material interface can be made to sense user inputs or activities, and how it can be optimized to balance sensitivity and fabrication complexity. I discuss the sensing algorithms and machine learning model to interpret the sensor data as high-level abstraction and interaction. I show the practical applications of developed computational materials. I demonstrate the evaluation study to validate their performance and robustness. In the end of this dissertation, I summarize the contributions of my thesis and discuss future directions for the vision of computational materials