<p>With the emergence of 5G/mm-Wave communication, there is a growing need for novel front-end electromechanical devices in filtering and carrier generation applications. CMOS-MEMS resonators fabricated using state-of-the-art Integrated Circuit (IC) manufacturing processes provide a significant advantage for power, area and cost savings. In this work, a comprehensive physics-based compact model capable of capturing the non-linear behaviour and other non-idealities has been developed for MEMS resonators seamlessly integrated in CMOS. As the first large signal model for CMOS-embedded resonators, it enables holistic design of MEMS components with advanced CMOS circuits as well as system-level performance evaluation within the framework of modern IC design tools. Global Foundries 14nm FinFET (GF14LPP) Resonant Body Transistors (fRBT) operating at 11.8 GHz are demonstrated and benchmarked against this large-signal electromechanical model. </p> <p><br></p> <p>Additionally, there is a growing interest in CMOS-integrable ferroelectric materials such as Hafnium Dioxide (HfO2) and Aluminum Scandium Nitride (AlScN) for next-generation memory and computation, as well as electromechanical transduction in CMOS-MEMS devices. This work also explores the performance of 700 MHz Ferroelectric Capacitor-based resonators in the Texas Instruments HPE035 process under high-power operating conditions. Identification of previously unreported characteristics, together with the first nonlinear large signal model for integrated ferroelectric resonators, provides insights on the design of frequency references and acoustic filters using ferroelectric transducers. </p> <p><br></p> <p>Extending the range of unreleased CMOS-MEMS resonators to lower frequency using novel design, we also investigate embedded transducers in chip-scale devices for physical sensing. We have simulated and modeled the transducer coupling for low-frequency propagating modes and benchmarked their projected performance against state-of-the-art conventional MEMS sensors. A new approach to phononic crystal (PnC) Interdigitated Transducers (IDTs) is presented emulating the acoustic dispersion in conventional ICs. Unloaded quality factors up to 15,000 have been measured in $\sim$80 MHz resonators, demonstrating their capacity for resonant rotation sensing. We present a unique methodology to amplify and collimate acoustic waves using CMOS-design-rule-compliant Graded Index (GRIN) Phononic IDTs. Ultimately, the CMOS-MEMS techniques presented in this work for both RF applications and physical sensing can facilitate additional functionality in standard CMOS and emerging 3D heterogeneously integrated (3DHI) ICs with minor or no modifications to manufacturing and packaging. This enables new paradigms in next-generation communications, internet of things (IoT), and hardware security.</p>

<p dir="ltr">There is growing research in analyzing human gait in the context of various applications. This has been aided by the improvement in sensing technologies and computation power. A complex motor skill that it is, gait has found its use in medicine for diagnosing different neurological ailments and injuries. In sports, gait can be used to provide feedback to the player/athlete to improve his/her skill and to prevent injuries. In biometrics, gait can be used to identify and authenticate individuals. This can be easier to scale to perform biometrics of individuals in large crowds compared to conventional biometric methods. In the field of Human Computer Interaction (HCI), gait can be an additional input that could be provided to be used in applications such as video games. Gait analysis has also been used for Human Activity Recognition (HAR) for purposes such as personal fitness, elderly care and rehabilitation. </p><p dir="ltr">The current state-of-the-art methods for gait analysis involves non-wearable technology due to its superior performance. The sophistication afforded in non-wearable technologies, such as cameras, is better able to capture gait information as compared to wearables. However, non-wearable systems are expensive, not scalable and typically, inaccessible to the general public. These systems sometimes need to be set up in specialized clinical facilities by experts. On the other hand, wearables offer scalability and convenience but are not able to match the performance of non-wearables. So the current work is a step in the direction to bridge the gap between the performance of non-wearable systems and the convenience of wearables. </p><p dir="ltr">A magnetic tracking system is developed to be applied for gait analysis. The system performs position and orientation tracking, i.e. 6 degrees of freedom or 6DoF tracking. One or more tracker modules, called Rx modules, is tracked with respect to a module called the Tx module. The Tx module mainly consists of a magnetic field generating coil, Inertial Measurement Unit (IMU) and magnetometer. The Rx module mainly consists of a tri-axis sensing coil, IMU and magnetometer. The system is minimally intrusive, works with Non-Line-of-Sight (NLoS) condition, low power consuming, compact and light weight. </p><p dir="ltr">The magnetic tracking system has been applied to the task of Human Activity Recognition (HAR) in this work as a proof-of-concept. The tracking system was worn by participants, and 4 activities - walking, walking with weight, marching and jogging - were performed. The Tx module was worn on the waist and the Rx modules were placed on the feet. To compare magnetic tracking with the most commonly used wearable sensors - IMUs + magnetometer - the same system was used to provide IMU and magnetometer data for the same 4 activities. The gait data was processed by 2 commonly used deep learning models - Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The magnetic tracking system shows an overall accuracy of 92\% compared to 86.69\% of the IMU + magnetometer system. Moreover, an accuracy improvement of 8\% is seen with the magnetic tracking system in differentiating between the walking and walking with weight activities, which are very similar in nature. This goes to show the improvement in gait information that 6DoF tracking brings, that manifests as increased classification accuracy. This increase in gait information will have a profound impact in other applications of gait analysis as well.</p>

<p>Precision livestock farming (PLF) has emerged as a solution to address global challenges related to food scarcity, increasing demand for animal products, slim profit margins in livestock production, and growing societal concerns regarding farm animal welfare. By offering individualized care for animals, PLF aims to provide labor savings, enhanced monitoring, and improved control capabilities within complex farming systems, enabled by digital technologies. The adoption of an individual-centric approach to farming through PLF is anticipated to enhance farm productivity and ensure ethical treatment of animals while mitigating concerns associated with labor shortages in modern intensive farming operations. Real-time continuous monitoring of each animal enables precise and accurate health and well-being management. However, to achieve these benefits, large-scale animal farms require commercially viable technological solutions for individualized care and welfare. Cyber-physical systems (CPSs) offer precise monitoring and control and present a promising avenue for PLF but pose significant implementation challenges.</p> <p>  In this work, a generalizable CPS architecture was formalized with active robotic nodes that can realize adaptive continuous real-time animal health monitoring to maximize productivity, animal welfare, and sustainability. Taking the example of dairy farming, a resource- and criticality-aware CPS was developed that enables real-time resource-aware sensing, adaptive control, and agile networking with an emphasis on handling emergencies autonomously. Using a decentralized approach, each node was made capable of optimizing its operation to be resource conscious, while also being able to identify emergency conditions in real-time. In this novel design, we accommodate the social dynamics of the herd and effectively address the various types of emergencies possible in PLF. Moreover, the communication was customized for the unique needs of animal agriculture, wherein it reduced latency and power consumption while ensuring collision-free two-way synchronization with adaptive range extension for emergency conditions. Further, since the CPS was centered around animals, a special robust security layer was also developed and implemented to protect the active embodied nodes against known and unknown malicious attacks. The proposed CPS reference architecture provides a foundation for implementing individualized care and welfare, ultimately improving the efficiency and sustainability of livestock operations.</p>

<p dir="ltr">The design of hardware platform in acoustic sensing is critical. The number and the spatial arrangement of microphones play a huge role in sensing performance. All microphones should be properly processed for simultaneous recording. This work introduces an FPGA-based acoustic transceiver system supporting acoustic sensing with custom acoustic signals. The system contains 16 microphones and a speaker synchronized during sensing processes. The microphones were arranged into an ‘L’ shape with eight microphones on each line for a better resolution of angle estimation on two dimensions. The microphones were placed on a specifically designed PCB to achieve an optimal distance of the half-wavelength of acoustic signals for optimized sensing performance. A microphone interface was implemented on Ultra96-V2 FPGA for handling the simultaneous high-speed data streams. The system features an implementation of full-level data transmission up to the top-level Python program. To evaluate the sensing performance of the system, we conducted an experiment used Frequency Modulated Continuous Wave (FMCW) as the transmitted acoustic signal. The result of evaluation shown the accurate sensing of range, velocity and relative angle of a moving hand on the two dimensions corresponding to the microphone groups.</p>

Aufgrund der steigenden Verkehrsnachfrage und der begrenzten Resourcen zum Ausbau der Straßenverkehrsnetze werden zukünftig größere Anforderungen an die Effektivität von Telematikanwendungen gestellt. Die Erhebung und Bereitstellung aktueller Verkehrsdaten durch geeignete Sensoren ist dazu eine entscheidende Voraussetzung. Gegenstand dieser Arbeit ist die großflächige Analyse des Straßenverkehrs auf der Basis bodengebundener und verteilter opto-elektronischer Sensoren. Es wird ein Konzept vorgestellt, dass eine von der Bilddatenerhebung bis zur Bereitstellung der Daten für Verkehrsanwendungen durchgehende Verarbeitungskette enthält. Der interdisziplinäre Ansatz bildet die Basis zur Verknüpfung eines solchen Sensorsystems mit Verkehrstelematik. Die Abbildung des Verkehrsgeschehens erfolgt im Gegensatz zu herkömmlichen bodengebundenen Messsystemen innerhalb größerer zusammenhängender Ausschnitte des Verkehrsraums. Dadurch können streckenbezogene Verkehrskenngrößen direkt bestimmt werden. Die Georeferenzierung der Verkehrsobjekte ist die Grundlage für eine optimale Verkehrsanalyse und Verkehrssteuerung. Die generierten Daten sind Basis zur Findung und Verifizierung von Theorien und Modellen sowie zur Entwicklung verkehrsadaptiver Steuerungsverfahren auf mikroskopischer Ebene. Es wird gezeigt, wie aus der Fusion gleichzeitig erhaltener Daten mehrerer Sensoren, die im Bereich des Sichtbaren und im thermalen Infrarot sensitiv sind, ein zusammengesetztes Abbildungsmosaik eines vergrößerten Verkehrsraums erzeugt werden kann. In diesem Abbildungsmosaik werden Verkehrsdatenmodelle unterschiedlicher räumlicher Kategorien abgeleitet. Die Darstellung des Abbildungsmosaiks mit seinen Daten erfolgt auf unterschiedlichen Informationsebenen in geokodierten Karten. Die Bewertung mikroskopischer Verkehrsprozesse wird durch die besondere Berücksichtigung der Zeitkomponente bei der Visualisierung möglich. Die vorgestellte Verarbeitungskette beinhaltet neue Anwendungsbereiche für geografische Informationssysteme (GIS). Der beschriebene Ansatz wurde konzeptionell bearbeitet, in der Programmiersprache IDL realisiert und erfolgreich getestet. / The growing demand of urban and interregional road traffic requires an improvement regarding the effectiveness of telematics systems. The use of appropriate sensor systems for traffic data acquisition is a decisive prerequisite for the efficiency of traffic control. This thesis focuses on analyzing road traffic based on stationary and distributed ground opto-electronic matrix sensors. A concept which covers all parts from image data acquisition up to traffic data provision is presented. This interdisciplinary approach establishes a basis for the integration of such a sensor system into telematics systems. Unlike conventional ground stationary sensors, the acquisition of traffic data is spread over larger areas in this case. As a result, road specific traffic data can be measured directly. Georeferencing of traffic objects is the basis for optimal road traffic analysis and road traffic control. This approach will demonstrate how to generate a spatial mosaic consisting of traffic data generated by several sensors with different spectral resolution. For traffic flow analysis the realisation of special 4D data visualisation methods on different information levels was an essential need. The data processing chain introduces new areas of application for geographical information systems (GIS). The approach utilised in this study has been worked out conceptually and also successfully tested and applied in the programming language IDL.

<p>Passive chipless wireless sensors have gained great attention in Radio Frequency Identification (RFID) applications, inventory tracking, and structural health monitoring, as they offer a prospective low-cost, scalable alternative to the state-of-the-art active sensors. While the popularity and demand for chipless sensors are on the rise, their applications are limited to low-noise environments and their caliber as low-cost, scalable devices has not been explored to a successful degree in challenging domains, such as precision agriculture, healthcare, and food packaging. Size, cost of materials, and complexity of the manufacturing process are the main obstacles to progress in the large-scale production of chipless sensors for practical applications. </p> <p><br></p> <p>Conventional manufacturing processes, such as photolithography, are costly, cumbersome, and time intensive. While additive manufacturing techniques, such as printing technologies, have circumvented some of these challenges, printing techniques require costly inks and complex post-processing steps, such as drying and sintering, which limit their large-scale utilization. To overcome these challenges, this dissertation focuses on investigating the possibility of utilizing laser processing of conventional metalized films and polymers to develop cost-effective chipless sensors. This Scalable Laser Assisted Manufacturing (SLAM) process offers a platform for large-scale roll-to-roll production of high-resolution sensors for precision agriculture, healthcare, and food packaging applications. </p> <p><br></p> <p>In this pursuit, the first study explores combining the SLAM process with 3D printing to develop a miniaturized, biodegradable, chipless sensor for soil moisture monitoring. In the second study, the SLAM process is further explored in the development of metalized stickers for healthcare applications focusing on urine bag management and early risk detection of urinary tract infections. In the third study, the capability of the SLAM process to form moisture-sensitive metal nanoparticles as a co-product of metal patterning is harnessed to develop a chipless humidity sensor. The SLAM process is further expanded in the fourth study by functionalizing metalized films with stimuli-responsive polymers to achieve specificity in detecting unique biomarkers of food spoilage. The SLAM platform described in this work opens up new avenues toward processing metalized fabric for the future of wearable electronics and implementing multi-functional sensors for precision agriculture.</p> <p>  </p>

<p dir="ltr">Uncovering the mechanisms of senescence in maize will give us a deeper understanding needed to drive future yield increases. Previous work on senescence response to sink disruption has identified a set of genes and biochemical mechanisms. Still, little is understood about the impact of this phenotype on yield and other commercially relevant traits. Uncovering the genetic basis of senescence in maize and testing the effect of these alleles on yield will provide a mechanistic framework for considering this trait to drive future yield increases.</p><p dir="ltr">Ear removal experiments demonstrated that senescence timing is insensitive to the presence or absence of an ear outside a critical window from 10 to 45 days after pollination. Nitrogen fertilization did not impact the SSRS response measured in the upper canopy. In further characterizing the SSRS phenotype, we have provided a spatial and temporal map of the B73 senescence response to sink disruption from the top of the plant to the ear leaf and discovered that this phenomenon is dose dependent and proportional to the size of the sink across two genotypes and years. This relationship was successfully used to predict kernel numbers and grain weight from spectral leaf properties as early as 4 weeks after pollination using remote sensing under agronomic conditions.</p><p dir="ltr">A population of 343 exPVP inbred lines was evaluated for source-sink regulated senescence and hybrid testcrosses were made for a subset of 200 inbred lines to testers for measurement of yield and ear photometry phenotypes. Source-sink regulated senescence of inbred parents was correlated with the yield of intra-family hybrids but was not generally correlated with the yield of hybrids made from crosses between two heterotic groups. The presence of multiple significant SNP association at the Bonferroni-corrected threshold at loci that are associated both with kernel traits and SSRS suggests shared genetic regulation of two traits that is likely driving the observed trait correlations of SSRS with kernel size and yield.</p><p dir="ltr">The maize nested association mapping (NAM) parents reveal a previously unknown breadth of SSRS phenotypes in the global diversity of maize germplasm. Mapping genes for SSRS in the NAM populations supports previously reported loci with large, dominant effects as well as evidence for previously unreported modifiers that are capable of suppressing the dominant alleles and producing a quantitative distribution in SSRS phenotypes. There are distinct alleles within sub-populations worth further study such as sweetcorn populations with non-senescence responses to sink disruption. A multi-factor analysis for QTL mapping, GWAS, and mutant variant sequencing identified highly significant loci on chromosomes 1 from 30.4Mbp to 35.8Mbp, chromosome 2 from 183.2Mbp to 190.8Mbp, chromosome 4 from 38.2Mbp to 134.8Mbp (crosses a centromere), chromosome 5 from 140.8Mbp to 233.9Mbp, chromosome 8 from 112.5Mbp to 123.8Mbp, and chromosome 8 from 155.7Mbp to 163.9Mbp. Candidate genes co-located with Bonferroni SNP in these regions may contribute to SSRS phenotypes through regulation of autophagy, accumulation of flavonoids, and sequestration of sugar in cell walls as an alternative sink. It is possible that co-regulation of these genes could cause all of them to be involved in the stress response of B73 to sugar accumulation. To find the causal variants for these traits, fine mapping and comparisons of near-isogenic lines will be required to narrow the list of candidate genes. Uncovering the alleles responsible for SSRS in global maize diversity could provide the building blocks for a physiological approach to increasing yields through optimizing the senescence responses to elevated sugar levels during grain-fill.</p>

<p>To facilitate indoor air quality (IAQ) monitoring, the research described herein develops and implements methods for the electronic integration of two types of gas sensor, each functionalized with a polymer blend tailored for CO₂ detection. A highly sensitive and tunable electronic chemiresistive sensor interface was developed and experimentally validated. This device achieved analog-to-digital conversion (ADC) through a pulse width modulated (PWM) signal, temporary data storage with an efficient data buffering system, and noise reduction and signal amplification utilizing an instrumentation amplifier integrator circuit. These techniques can used beyond CO₂-specific applications to compensate for certain undesirable chemiresistive sensor characteristics, such as low response magnitude and signal noise. Additionally, resonant mass sensing circuitry was combined with an on-chip field programmable gate array (FPGA) implemented frequency counter. Hz-level resolution was achieved with an Alorium Snō FPGA board and a Verilog data acquisition and communication program. This device can monitor up to 16 sensor channels simultaneously and has a straightforward interface with a controllable output. Furthermore, the functionality of each integrated sensor was experimentally validated. With additional work, these integrated designs have the potential to be inexpensive, low-power, highly sensitive devices that are suitable for practical use in IAQ monitoring applications.</p>

<p>  </p> <p>Cold chain logistics comprise a large portion of transported pharmaceutical medications and raw materials which must be preserved at specified temperatures to maintain consumer safety and efficacy. An immutable record of temperatures of transported pharmaceutical goods allows for mitigation of temperature-related issues of such drugs and their raw components. The recording of this information on a blockchain creates such an immutable record of this information which can be readily accessed by any relevant party. This can allow for any components which have not been kept at the appropriate temperatures to be removed from production. These data can also be used as inputs for smart contracts or for data analytic purposes. </p> <p>A theoretical framework for such a system, referred to as “TempEnsure” is described, which provides digital capture of the internal temperature of temperature-controlled shipping containers. The data are recorded in a blockchain system. Real world testing of this system was not possible due to monetary constraints, but the functional elements of the system, as well as potential improvements for the system, are discussed.</p>

<p dir="ltr">The market for infrared detectors has grown significantly in recent years due to the wide variety of applications from astronomy to medical thermography. Additionally, several emerging applications for high-speed infrared technologies are in development such as infrared LIDAR, autonomous vehicles, semiconductor device analysis, and free space communication. Improvements in the readout-speed and sensitivity of uncooled infrared detectors are required for some of these applications, and have been a long-standing goal in the field. Two technologies currently dominate the detection of infrared radiation, photodiodes and bolometers. Bolometers are extremely interesting as they are currently the most sensitive infrared detectors (either cooled or uncooled). We will propose and demonstrate a new type of bolometric infrared detector based on a highly structured spintronic material. The device's detection mechanism utilizes thermally activated magnetic transitions in a nanoscale magnetic device. We will also discuss a classification for detectors based on their digital-mode (discrete) or analog (continuous) readout signals. We develop a stochastic model to compare the sensitivity of these detectors. From this model we demonstrate several fundamental limits in the measurement of temperature by infrared detection.</p>