Increase in population and limited resources have created a growing demand for a futuristic living environment where technology enables the efficient utilization and management of resources in order to increase quality of life. One characteristic of such a society, which is often referred to as a ‘Smart City’, is that the people are well informed about their physiological being as well as the environment around them, which makes them better equipped to handle crisis situations. There is a need, therefore, to develop wireless sensors which can provide early warnings and feedback during calamities such as floods, fires, and industrial leaks, and provide remote health care facilities. For these situations, low-cost sensor nodes with small form factors are required. For this purpose, the use of a low-cost, mass manufacturing technique such as inkjet printing can be beneficial due to its digitally controlled additive nature of depositing material on a variety of substrates. Inkjet printing can permit economical use of material on cheap flexible substrates that allows for the development of miniaturized freeform electronics. This thesis describes how low-cost, inkjet-printed, wireless sensors have been developed for real-time monitoring applications. A 3D buoyant mobile wireless sensor node has been demonstrated that can provide early warnings as well as real-time data for flood monitoring. This disposable paper-based module can communicate while floating in water up to a distance of 50 m, regardless of its orientation in the water. Moreover, fully inkjet-printed sensors have been developed to monitor temperature, humidity and gas levels for wireless environmental monitoring. The sensors are integrated and packaged using 3D inkjet printing technology. Finally, in order to demonstrate the benefits of such wireless sensor systems for health care applications, a low-cost, wearable, wireless sensing system has been developed for chronic wound monitoring. The system called ‘Smart Bandage’ can provide early warnings and long term data for medical diagnoses. These demonstrations show that inkjet printing can enable the development of low-cost wireless sensors that can be dispersed in the environment or worn on the human body to enable an internet of things (IoT), which can facilitate better and safer living.

<p>This dissertation explores the world of non-intrusive wireless sensing for diet and fitness activity monitoring, in addition to assessing security risks in human activity recognition (HAR). It delves into the use of WiFi and millimeter wave (mmWave) signals for monitoring eating behaviors, discerning intricate eating activities, and observing fitness movements. The proposed systems harness variations in wireless signal propagation to record human behavior while providing exhaustive details on dietary and exercise habits. Significant contributions encompass unsupervised learning methodologies for detecting dietary and fitness activities, implementing soft-decision and deep neural networks for assorted activity recognition, constructing tiny motion mechanisms for subtle mouth muscle movement recovery, employing space-time-velocity features for multi-person tracking, as well as utilizing generative adversarial networks and domain adaptation structures to enable less cumbersome training efforts and cross-domain deployments. A series of comprehensive tests validate the efficacy and precision of the proposed non-intrusive wireless sensing systems. Additionally, the dissertation probes the security vulnerabilities in mmWave-based HAR systems and puts forth various sophisticated adversarial attacks - targeted, untargeted, universal, and black-box. It designs adversarial perturbations aiming to deceive the HAR models whilst striving to minimize detectability. The research offers powerful insights into issues and efficient solutions relative to non-intrusive sensing tasks and security challenges linked with wireless sensing technologies.</p>

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The way in which infants play with objects can be indicative of their developmental progress and may serve as an early indicator for developmental delays. However, the observation of children interacting with toys for the purpose of quantitative analysis can be a difficult task. To better quantify how play may serve as an early indicator, researchers have conducted retrospective studies examining the differences in object play behaviors among infants. However, such studies require that researchers repeatedly inspect videos of play often at speeds much slower than real-time to indicate points of interest. The research presented in this dissertation examines whether a combination of sensors embedded within toys and automatic pattern recognition of object play behaviors can help expedite this process. For my dissertation, I developed the Child'sPlay system which uses augmented toys and statistical models to automatically provide quantitative measures of object play interactions, as well as, provide the PlayView interface to view annotated play data for later analysis. In this dissertation, I examine the hypothesis that sensors embedded in objects can provide sufficient data for automatic recognition of certain exploratory, relational, and functional object play behaviors in semi-naturalistic environments and that a continuum of recognition accuracy exists which allows automatic indexing to be useful for retrospective review. I designed several augmented toys and used them to collect object play data from more than fifty play sessions. I conducted pattern recognition experiments over this data to produce statistical models that automatically classify children's object play behaviors. In addition, I conducted a user study with twenty participants to determine if annotations automatically generated from these models help improve performance in retrospective review tasks. My results indicate that these statistical models increase user performance and decrease perceived effort when combined with the PlayView interface during retrospective review. The presence of high quality annotations are preferred by users and promotes an increase in the effective retrieval rates of object play behaviors.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19299号 / 工博第4096号 / 新制||工||1631(附属図書館) / 32301 / 京都大学大学院工学研究科建築学専攻 / (主査)教授 中島 正愛, 教授 川瀬 博, 教授 竹脇 出 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

This dissertation investigates Ultra-Wideband (UWB) techniques for the next generation Radio Frequency Identification (RFID) towards the Internet-of-Things (IoT). In particular, an ultra-high frequency (UHF) wireless-powered UWB radio (UHF/UWB hybrid) with asymmetric links is explored from system architecture to circuit implementation. Context-aware, location-aware, and energy-aware computing for the IoT demands future micro-devices (e.g., RFID tags) with capabilities of sensing, processing, communication, and positioning, which can be integrated into everyday objects including paper documents, as well as food and pharmaceutical packages. To this end, reliable-operating and maintenance-free wireless networks with low-power and low-cost radio transceivers are essential. In this context, state-of-the-art passive RFID technologies provide limited data rate and positioning accuracy, whereas active radios suffer from high complexity and power-hungry transceivers. Impulse Radio UWB (IR-UWB) exhibits significant advantages that are expected to overcome these limitations. Wideband signals offer robust communications and high-precision positioning; duty-cycled operations allow link scalability; and baseband-like architecture facilitates extremely simple and low-power transmitters. However, the implementation of the IR-UWB receiver is still power-hungry and complex, and thus is unacceptable for self-powered or passive tags. To cope with μW level power budget in wireless-powered systems, this dissertation proposes an UHF/UWB hybrid radio architecture with asymmetric links. It combines the passive UHF RFID and the IR-UWB transmitter. In the downlink (reader-tag), the tag is powered and controlled by UHF signals as conventional passive UHF tags, whereas it uses an IR-UWB transmitter to send data for a short time at a high rate in the uplink (tag-reader). Such an innovative architecture takes advantage of UWB transmissions, while the tag avoids the complex UWB receiver by shifting the burden to the reader. A wireless-powered tag providing -18.5 dBm sensitivity UHF downlink and 10 Mb/s UWB uplink is implemented in 180 nm CMOS. At the reader side, a non-coherent energy detection IR-UWB receiver is designed to pair the tag. The receiver is featured by high energy-efficiency and flexibility that supports multi-mode operations. A novel synchronization scheme based on the energy offset is suggested. It allows fast synchronization between the reader and tags, without increasing the hardware complexity. Time-of-Arrival (TOA) estimation schemes are analyzed and developed for the reader, which enables tag localization. The receiver prototype is fabricated in 90 nm CMOS with 16.3 mW power consumption and -79 dBm sensitivity at 10 Mb/s data rate. The system concept is verified by the link measurement between the tag and the reader. Compared with current passive UHF RFID systems, the UHF/UWB hybrid solution provides an order of magnitude improvement in terms of the data rate and positioning accuracy brought by the IR-UWB uplink. / QC 20120110

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