TinyVers: A Tiny Versatile System-on-chip with State-Retentive eMRAM for ML Inference at the Extreme Edge

Extreme edge devices or Internet-of-thing nodes require both ultra-low power always-on processing as well as the ability to do on-demand sampling and processing. Moreover, support for IoT applications like voice recognition, machine monitoring, etc., requires the ability to execute a wide range of ML workloads. This brings challenges in hardware design to build flexible processors operating in ultra-low power regime. This paper presents TinyVers, a tiny versatile ultra-low power ML system-on-chip to enable enhanced intelligence at the Extreme Edge. TinyVers exploits dataflow reconfiguration to enable multi-modal support and aggressive on-chip power management for duty-cycling to enable smart sensing applications. The SoC combines a RISC-V host processor, a 17 TOPS/W dataflow reconfigurable ML accelerator, a 1.7 $\mu$W deep sleep wake-up controller, and an eMRAM for boot code and ML parameter retention. The SoC can perform up to 17.6 GOPS while achieving a power consumption range from 1.7 $\mu$W-20 mW. Multiple ML workloads aimed for diverse applications are mapped on the SoC to showcase its flexibility and efficiency. All the models achieve 1-2 TOPS/W of energy efficiency with power consumption below 230 $\mu$W in continuous operation. In a duty-cycling use case for machine monitoring, this power is reduced to below 10 $\mu$W.Comment: Accepted in IEEE Journal of Solid-State Circuit

Diseño y aplicaciones de sistemas de antenas inteligentes para redes inalámbricas en el contexto de la internet de las cosas

[SPA] Esta tesis doctoral se presenta bajo la modalidad de compendio de publicaciones. Las antenas de onda de fuga (LWA) consisten en una estructura de guía de onda que permite la fuga de parte de la potencia a lo largo de la estructura. Por esta razón, la radiación de la antena se produce por la fuga de energía. Para producir una radiación coherente, es necesario controlar esta tasa de radiación a lo largo de la estructura radiante. Así, ajustando con precisión la tasa de radiación, se controla la forma del diagrama de radiación. Las LWAs han sido ampliamente estudiadas por la comunidad científica debido a sus ventajas, tales como, red de alimentación simple, alta directividad y escaneo en frecuencia pasivo. Sin embargo, presentan ciertas desventajas entre las cuales, la más importante a destacar es el efecto de beam-squinting. Éste se produce por la propiedad dispersiva inherente a este tipo de antenas. Además, presentan dificultades a la hora de generar radiación coherente en las direcciones broadside y endfire, aumentando la complejidad del diseńo para la radiación en dichas direcciones. Las LWA han sido relativamente poco utilizadas en aplicaciones prácticas hasta la fecha, a pesar de sus ventajas. Las pocas aplicaciones en las que se han utilizado son los radares de onda continua modulada en frecuencia y los sistemas de enfoque controlado en frecuencia de campo cercano. Esta tesis propone el uso de las LWAs en aplicaciones prácticas aprovechando las ventajas mencionadas anteriormente y teniendo en cuenta los inconvenientes de este tipo de antenas para que su uso no sea limitado. Recientemente, las LWAs han sido propuestas para aplicaciones de localización de bajo coste, ya que permiten el diseńo de estructuras planas con haces directivos. Además, debido al aumento exponencial del uso de la tecnología, es necesario encontrar nuevas tecnologías para una transmisión de datos mayor, más rápida y más eficiente, manteniendo bajos costes de fabricación. Por lo tanto las LWAs pueden ser una solución crucial al mezclar bajos costes de fabricación, alta integrabilidad en diferentes sistemas debido a su tecnología impresa planar y alta directividad al mismo tiempo que se aprovecha su característica dispersiva que proporciona un escaneo pasivo en frecuencia. En este contexto, la principal aportación de esta Tesis consiste en el estudio, análisis, diseńo e integración de LWAs en aplicaciones reales y prácticas. Esta Tesis presenta las siguientes tres contribuciones principales, definidas en los tres bloques principales de este documento: • Estudio y análisis de LWAs para su uso en sistemas de estimación de dirección de llegada basados en técnicas de amplitud de monopulso. Comparar las características y prestaciones de las LWAs junto con las antenas comerciales más utilizadas. Para ello, diseńar y fabricar las HWM-LWAs con el fin de comparar sus prestaciones con las antenas de panel adquiridas comercialmente. Dado que cada aplicación requiere el diseńo de una HWM-LWA nueva y diferente, estudiar y proponer una técnica eficiente de análisis y diseńo de antenas para obtener fácilmente diagramas de radiación monopulso escaneados en frecuencia. • Una vez analizado que las HWM-LWA son una solución factible para su uso en aplicaciones reales de localización debido a sus diversas ventajas. Integrar las HWM-LWAs diseńadas en sistemas digitales para estimación del ángulo de llegada en interiores. Por lo tanto, diseńar, desarrollar, configurar e integrar las LWAs en diferentes sistemas basados en las bandas de frecuencia Wi-Fi ISM de 2,4 GHz y 5 GHz. Finalmente, comparar los resultados de estimación obtenidos con otras soluciones propuestas para corroborar que los LWAs pueden ser utilizados en aplicaciones reales. • Asimismo, debido a su bajo coste de fabricación y a su principal propiedad de escaneo en frecuencia. Ampliar el uso de las LWAs para la localización angular en redes de sensores inalámbricas (WSN) utilizando la banda de frecuencias UHF de 900 MHz. Utilizando así etiquetas RFID pasivas. También estudiar su aplicabilidad en WSNs utilizando etiquetas LoRa activas. Este documento se presenta como una Tesis por compendio, por lo que se presentarán y explicarán brevemente los 4 artículos de revistas que se han publicado durante el programa de doctorado. Además, también se presentarán algunos artículos de conferencias y otros trabajos en revisión para exponer algunas de las investigaciones que no han sido publicadas en revistas hasta la fecha de depósito de tesis. El documento está organizado como se indica a continuación: En la Introducción, se presenta una contextualización del estado del arte y una explicación rigurosa sobre las LWAs y las aplicaciones anteriormente mencionadas. Las dos partes siguientes se vi dedican a presentar y explicar brevemente los trabajos publicados que contribuyen a esta Tesis. En la parte II, se presentan los cuatro artículos que conforman el compendio. Esto es, el análisis de las LWAs para la estimación de la dirección del ángulo de llegada y la integración de las LWAs en sistemas de localización digital usando el protocolo Wi-Fi en el Capítulo 1, la banda de frecuencias ISM UHF 900 MHz se utiliza junto con los HWM-LWAs en el Capítulo 2, luego se implementa en un sistema en tiempo real para la estimación de la dirección de llegada de múltiples tags pasivos en el Capítulo 3 y la integración de LoRa en el Capítulo 4. Finalmente, en la Parte III, se discuten las conclusiones generales y las futuras líneas de investigación. [ENG] This doctoral dissertation has been presented in the form of thesis by publication. Leaky-Wave Antennas (LWA) consist on a waveguide structure which allows the leakage of part of the power along the structure. For this reason, the radiation of the antenna is produced by the leakage of power. In order to produce coherent radiation, it is necessary to control this leakage rate along the radiating structure. Thus, precisely adjusting the leakage rate, the shape of the radiation pattern is controlled. LWAs have been widely studied by the scientific community due to their advantages, such as, simple feeding network, high directivity and passive frequency-scanning performance. However, they present certain disadvantages among which, the most important to highlight is the beam-squinting effect. TThis is due to the inherent dispersion property of this type of antenna. In addition, LWAs present difficulties when generating coherent radiation in broadside and endfire directions, increasing the complexity of the design for radiation in these directions. LWAs have been relatively unused in practical applications to date, despite of their benefits. The few applications in which they have been used are frequency modulated continuous wave radars and near-field frequency controlled focusing systems.This thesis proposes the use of LWAs in practical applications by exploiting the advantages mentioned above while taking into account the drawbacks of this type of antennas so that their use is not limited. Recently, LWAs have been proposed for low-cost localization applications, as they allow the design of planar structures with directive beams. In addition, due to the exponential increase in the use of technology, it is necessary to find new technologies for higher, faster and more efficient data transmission while maintaining low manufacturing costs. Therefore, LWAs can be a crucial solution mixing low manufacturing costs, high integrability in different systems due to their planar printed technology and high directivity while taking advantage of their dispersive characteristic that provides passive frequency scanning. In this context, the main contribution of this Thesis consist of the study, analysis, design and integration of LWAs in real and practical applications. This Thesis presents the following three main contributions, defined in the three main blocks of this document: • Study and analysis of LWAs for its use in direction of arrival estimation systems based on monopulse amplitude techniques. Compare the characteristics and performance of LWAs along with widely used commercial antennas. For this purpose, design and manufacture the HWM-LWAs in order to compare their performance with commercially acquired panel antennas. Since each application requires the design of a new and different HWM-LWA, a main objective of this block is to study and propose an efficient antenna analysis and design technique to facilitate obtaining frequency-scanned monopulse patterns. • Once analyzed that LWAs are a feasible solution for its use in real localization applications due to their several advantages, integrate the designed half-width microstrip (HWM-LWAs) in digital indoor angle-of-arrival estimation systems. Therefore, design, develop, configure and integrate LWAs in different systems based on the Wi-Fi ISM 2.4 GHz and 5 GHz frequency bands. Finally, compare the obtained estimation results with other proposed solutions to corroborate that LWAs can be used in real applications. • Extending the use of antennas for angular localization in sensor networks using the 900 MHz UHF frequency band: the main properties of low manufacturing cost and passive frequency beam scanning can be used in other applications. Thus, the localization estimation of passive RFID tags is studied, as well as their application in Wireless Sensor Networks (WSNs) using active tags with LORA technology. This document is presented as a Thesis by compilation, so the 4 journal articles that have been published during the Ph.D program will be presented and briefly explained. Besides, some conference articles and other work under review will be also presented to expose some of the research that has not been published in journals. The document is organized as outlined hereafter: In Part I, a state-of-the-art contextualization, a rigorous explanation about LWAs and the previous applications mentioned above is presented. The next two parts are dedicated to present and briefly explain the published works included in this Thesis and their main contributions. In Part II the explanation of the four papers which compose the compendium are presented. This is, LWAs analysis for direction of arrival estimation and the integration of LWAs in digital Wi-Fi localization systems in chapter 1, the UHF 900 MHz ISM frequency band is used in conjunction with HWM-LWAs in chapter 2, then, it is implemented in a real time system for direction of arrival estimation of multi RFID tags in chapter 3 and LoRa integration in chapter 4. Finally, in Part III, the overall conclusions and the future research lines are discussed.Esta tesis doctoral se presenta bajo la modalidad de compendio de publicaciones. Está formada por un total de cuatro artículos. Article 1.-: A. Gil-Martinez, M. Poveda-Garcia, J. A. Lopez-Pastor, J. C. Sanchez-Aarnoutse and J. L. Gomez-Tornero, Wi-Fi Direction Finding with Frequency-Scanned Antenna and Channel Hopping Scheme IEEE sensors Journal, , vol. 22, no. 6, pp. 5210-5222, 2022. DOI: 10.1109/JSEN.2021.3122232. Article 2.-: A. Gil-Martinez, M. Poveda-Garcia, D. Cañete-Rebenaque, and J. L. Gomez-Tornero, Frequency-Scanned Monopulse Antenna for RSSI-based Direction Finding of UHF RFID tags IEEE Antennas and Wireless Propagation Letters,, vol. 21, no. 1, pp. 158-162, 2022. DOI: 10.1109/LAWP.2021.3122232. Article 3.-: A. Gil-Martinez, M. Poveda-Garcia, J. Garcia-Fernandez, M. Campo-Valera, D. Cañete-Rebenaque, and J. L. Gomez-Tornero, Direction Finding of RFID tags in UHF Band Using a Passive Beam-Scanning Leaky-Wave Antenna IEEE Journal of Radio Frequency Identi cation, doi: 10.1109/JRFID.2021.3122233. Article 4.-: J. L. Gomez-Tornero, A. Gil-Martinez, M. Poveda-Garcia and D. Cañete-Rebenaque, ARIEL: Passive Beam-Scanning Antenna TeRminal for Iridiscent and E cient LEO Satellite Connectivity in IEEE Antennas and Wireless Propagation Letters, doi: 10.1109/LAWP.2022.3193040.Escuela Internacional de Doctorado de la Universidad Politécnica de CartagenaUniversidad Politécnica de CartagenaPrograma Doctorado en Tecnologías de la Información y las Comunicacione

The Internet of Things Will Thrive by 2025

This report is the latest research report in a sustained effort throughout 2014 by the Pew Research Center Internet Project to mark the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-LeeThis current report is an analysis of opinions about the likely expansion of the Internet of Things (sometimes called the Cloud of Things), a catchall phrase for the array of devices, appliances, vehicles, wearable material, and sensor-laden parts of the environment that connect to each other and feed data back and forth. It covers the over 1,600 responses that were offered specifically about our question about where the Internet of Things would stand by the year 2025. The report is the next in a series of eight Pew Research and Elon University analyses to be issued this year in which experts will share their expectations about the future of such things as privacy, cybersecurity, and net neutrality. It includes some of the best and most provocative of the predictions survey respondents made when specifically asked to share their views about the evolution of embedded and wearable computing and the Internet of Things

A Preventive Medicine Framework for Wearable Abiotic Glucose Detection System

In this work, we present a novel abiotic glucose fuel cell with battery-less remote access. In the presence of a glucose analyte, we characterized the power generation and biosensing capabilities. This system is developed on a flexible substrate in bacterial nanocellulose with gold nanoparticles used as a conductive ink for piezoelectric deposition based printing. The abiotic glucose fuel cell is constructed using colloidal platinum on gold (Au-co-Pt) and a composite of silver oxide nanoparticles and carbon nanotubes as the anodic and cathodic materials. At a concentration of 20 mM glucose, the glucose fuel cell produced a maximum open circuit voltage of 0.57 V and supplied a maximum short circuit current density of 0.581 mA/cm2 with a peak power density of 0.087 mW/cm2 . The system was characterized by testing its performance using electrochemical techniques like linear sweep voltammetry, cyclic voltammetry, chronoamperometry in the presence of various glucose level at the physiological temperatures. An open circuit voltage (Voc) of 0.43 V, short circuit current density (Isc) of 0.405 mA/cm2 , and maximum power density (Pmax) of 0.055 mW/cm2 at 0.23 V were achieved in the presence of 5 mM physiologic glucose. The results indicate that glucose fuel cells can be employed for the development of a self-powered glucose sensor. The glucose monitoring device demonstrated sensitivity of 1.87 uA/mMcm2 and a linear dynamic range of 1 mM to 45 mM with a correlation coefficient of 0.989 when utilized as a self-powered glucose sensor. For wireless communication, the incoming voltage from the abiotic fuel cell was fed to a low power microcontroller that enables battery less communication using NFC technology. The voltage translates to the NFC module as the digital signals, which are displayed on a custom-built android application. The digital signals are converted to respective glucose concentration using a correlation algorithm that allows data to be processed and recorded for further analysis. The android application is designed to record the time, date stamp, and other independent features (e.g. age, height, weight) with the glucose measurement to allow the end-user to keep track of their glucose levels regularly. Analytics based on in-vitro testing were conducted to build a machine learning model that enables future glucose prediction for 15, 30 or 60 minutes

An inertial motion capture framework for constructing body sensor networks

Motion capture is the process of measuring and subsequently reconstructing the movement of an animated object or being in virtual space. Virtual reconstructions of human motion play an important role in numerous application areas such as animation, medical science, ergonomics, etc. While optical motion capture systems are the industry standard, inertial body sensor networks are becoming viable alternatives due to portability, practicality and cost. This thesis presents an innovative inertial motion capture framework for constructing body sensor networks through software environments, smartphones and web technologies. The first component of the framework is a unique inertial motion capture software environment aimed at providing an improved experimentation environment, accompanied by programming scaffolding and a driver development kit, for users interested in studying or engineering body sensor networks. The software environment provides a bespoke 3D engine for kinematic motion visualisations and a set of tools for hardware integration. The software environment is used to develop the hardware behind a prototype motion capture suit focused on low-power consumption and hardware-centricity. Additional inertial measurement units, which are available commercially, are also integrated to demonstrate the functionality the software environment while providing the framework with additional sources for motion data. The smartphone is the most ubiquitous computing technology and its worldwide uptake has prompted many advances in wearable inertial sensing technologies. Smartphones contain gyroscopes, accelerometers and magnetometers, a combination of sensors that is commonly found in inertial measurement units. This thesis presents a mobile application that investigates whether the smartphone is capable of inertial motion capture by constructing a novel omnidirectional body sensor network. This thesis proposes a novel use for web technologies through the development of the Motion Cloud, a repository and gateway for inertial data. Web technologies have the potential to replace motion capture file formats with online repositories and to set a new standard for how motion data is stored. From a single inertial measurement unit to a more complex body sensor network, the proposed architecture is extendable and facilitates the integration of any inertial hardware configuration. The Motion Cloud’s data can be accessed through an application-programming interface or through a web portal that provides users with the functionality for visualising and exporting the motion data

Multi-sensor data fusion in mobile devices for the identification of Activities of Daily Living

Following the recent advances in technology and the growing use of mobile devices such as smartphones, several solutions may be developed to improve the quality of life of users in the context of Ambient Assisted Living (AAL). Mobile devices have different available sensors, e.g., accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS) receiver, which allow the acquisition of physical and physiological parameters for the recognition of different Activities of Daily Living (ADL) and the environments in which they are performed. The definition of ADL includes a well-known set of tasks, which include basic selfcare tasks, based on the types of skills that people usually learn in early childhood, including feeding, bathing, dressing, grooming, walking, running, jumping, climbing stairs, sleeping, watching TV, working, listening to music, cooking, eating and others. On the context of AAL, some individuals (henceforth called user or users) need particular assistance, either because the user has some sort of impairment, or because the user is old, or simply because users need/want to monitor their lifestyle. The research and development of systems that provide a particular assistance to people is increasing in many areas of application. In particular, in the future, the recognition of ADL will be an important element for the development of a personal digital life coach, providing assistance to different types of users. To support the recognition of ADL, the surrounding environments should be also recognized to increase the reliability of these systems. The main focus of this Thesis is the research on methods for the fusion and classification of the data acquired by the sensors available in off-the-shelf mobile devices in order to recognize ADL in almost real-time, taking into account the large diversity of the capabilities and characteristics of the mobile devices available in the market. In order to achieve this objective, this Thesis started with the review of the existing methods and technologies to define the architecture and modules of the method for the identification of ADL. With this review and based on the knowledge acquired about the sensors available in off-the-shelf mobile devices, a set of tasks that may be reliably identified was defined as a basis for the remaining research and development to be carried out in this Thesis. This review also identified the main stages for the development of a new method for the identification of the ADL using the sensors available in off-the-shelf mobile devices; these stages are data acquisition, data processing, data cleaning, data imputation, feature extraction, data fusion and artificial intelligence. One of the challenges is related to the different types of data acquired from the different sensors, but other challenges were found, including the presence of environmental noise, the positioning of the mobile device during the daily activities, the limited capabilities of the mobile devices and others. Based on the acquired data, the processing was performed, implementing data cleaning and feature extraction methods, in order to define a new framework for the recognition of ADL. The data imputation methods were not applied, because at this stage of the research their implementation does not have influence in the results of the identification of the ADL and environments, as the features are extracted from a set of data acquired during a defined time interval and there are no missing values during this stage. The joint selection of the set of usable sensors and the identifiable set of tasks will then allow the development of a framework that, considering multi-sensor data fusion technologies and context awareness, in coordination with other information available from the user context, such as his/her agenda and the time of the day, will allow to establish a profile of the tasks that the user performs in a regular activity day. The classification method and the algorithm for the fusion of the features for the recognition of ADL and its environments needs to be deployed in a machine with some computational power, while the mobile device that will use the created framework, can perform the identification of the ADL using a much less computational power. Based on the results reported in the literature, the method chosen for the recognition of the ADL is composed by three variants of Artificial Neural Networks (ANN), including simple Multilayer Perceptron (MLP) networks, Feedforward Neural Networks (FNN) with Backpropagation, and Deep Neural Networks (DNN). Data acquisition can be performed with standard methods. After the acquisition, the data must be processed at the data processing stage, which includes data cleaning and feature extraction methods. The data cleaning method used for motion and magnetic sensors is the low pass filter, in order to reduce the noise acquired; but for the acoustic data, the Fast Fourier Transform (FFT) was applied to extract the different frequencies. When the data is clean, several features are then extracted based on the types of sensors used, including the mean, standard deviation, variance, maximum value, minimum value and median of raw data acquired from the motion and magnetic sensors; the mean, standard deviation, variance and median of the maximum peaks calculated with the raw data acquired from the motion and magnetic sensors; the five greatest distances between the maximum peaks calculated with the raw data acquired from the motion and magnetic sensors; the mean, standard deviation, variance, median and 26 Mel- Frequency Cepstral Coefficients (MFCC) of the frequencies obtained with FFT based on the raw data acquired from the microphone data; and the distance travelled calculated with the data acquired from the GPS receiver. After the extraction of the features, these will be grouped in different datasets for the application of the ANN methods and to discover the method and dataset that reports better results. The classification stage was incrementally developed, starting with the identification of the most common ADL (i.e., walking, running, going upstairs, going downstairs and standing activities) with motion and magnetic sensors. Next, the environments were identified with acoustic data, i.e., bedroom, bar, classroom, gym, kitchen, living room, hall, street and library. After the environments are recognized, and based on the different sets of sensors commonly available in the mobile devices, the data acquired from the motion and magnetic sensors were combined with the recognized environment in order to differentiate some activities without motion, i.e., sleeping and watching TV. The number of recognized activities in this stage was increased with the use of the distance travelled, extracted from the GPS receiver data, allowing also to recognize the driving activity. After the implementation of the three classification methods with different numbers of iterations, datasets and remaining configurations in a machine with high processing capabilities, the reported results proved that the best method for the recognition of the most common ADL and activities without motion is the DNN method, but the best method for the recognition of environments is the FNN method with Backpropagation. Depending on the number of sensors used, this implementation reports a mean accuracy between 85.89% and 89.51% for the recognition of the most common ADL, equals to 86.50% for the recognition of environments, and equals to 100% for the recognition of activities without motion, reporting an overall accuracy between 85.89% and 92.00%. The last stage of this research work was the implementation of the structured framework for the mobile devices, verifying that the FNN method requires a high processing power for the recognition of environments and the results reported with the mobile application are lower than the results reported with the machine with high processing capabilities used. Thus, the DNN method was also implemented for the recognition of the environments with the mobile devices. Finally, the results reported with the mobile devices show an accuracy between 86.39% and 89.15% for the recognition of the most common ADL, equal to 45.68% for the recognition of environments, and equal to 100% for the recognition of activities without motion, reporting an overall accuracy between 58.02% and 89.15%. Compared with the literature, the results returned by the implemented framework show only a residual improvement. However, the results reported in this research work comprehend the identification of more ADL than the ones described in other studies. The improvement in the recognition of ADL based on the mean of the accuracies is equal to 2.93%, but the maximum number of ADL and environments previously recognized was 13, while the number of ADL and environments recognized with the framework resulting from this research is 16. In conclusion, the framework developed has a mean improvement of 2.93% in the accuracy of the recognition for a larger number of ADL and environments than previously reported. In the future, the achievements reported by this PhD research may be considered as a start point of the development of a personal digital life coach, but the number of ADL and environments recognized by the framework should be increased and the experiments should be performed with different types of devices (i.e., smartphones and smartwatches), and the data imputation and other machine learning methods should be explored in order to attempt to increase the reliability of the framework for the recognition of ADL and its environments.Após os recentes avanços tecnológicos e o crescente uso dos dispositivos móveis, como por exemplo os smartphones, várias soluções podem ser desenvolvidas para melhorar a qualidade de vida dos utilizadores no contexto de Ambientes de Vida Assistida (AVA) ou Ambient Assisted Living (AAL). Os dispositivos móveis integram vários sensores, tais como acelerómetro, giroscópio, magnetómetro, microfone e recetor de Sistema de Posicionamento Global (GPS), que permitem a aquisição de vários parâmetros físicos e fisiológicos para o reconhecimento de diferentes Atividades da Vida Diária (AVD) e os seus ambientes. A definição de AVD inclui um conjunto bem conhecido de tarefas que são tarefas básicas de autocuidado, baseadas nos tipos de habilidades que as pessoas geralmente aprendem na infância. Essas tarefas incluem alimentar-se, tomar banho, vestir-se, fazer os cuidados pessoais, caminhar, correr, pular, subir escadas, dormir, ver televisão, trabalhar, ouvir música, cozinhar, comer, entre outras. No contexto de AVA, alguns indivíduos (comumente chamados de utilizadores) precisam de assistência particular, seja porque o utilizador tem algum tipo de deficiência, seja porque é idoso, ou simplesmente porque o utilizador precisa/quer monitorizar e treinar o seu estilo de vida. A investigação e desenvolvimento de sistemas que fornecem algum tipo de assistência particular está em crescente em muitas áreas de aplicação. Em particular, no futuro, o reconhecimento das AVD é uma parte importante para o desenvolvimento de um assistente pessoal digital, fornecendo uma assistência pessoal de baixo custo aos diferentes tipos de pessoas. pessoas. Para ajudar no reconhecimento das AVD, os ambientes em que estas se desenrolam devem ser reconhecidos para aumentar a fiabilidade destes sistemas. O foco principal desta Tese é o desenvolvimento de métodos para a fusão e classificação dos dados adquiridos a partir dos sensores disponíveis nos dispositivos móveis, para o reconhecimento quase em tempo real das AVD, tendo em consideração a grande diversidade das características dos dispositivos móveis disponíveis no mercado. Para atingir este objetivo, esta Tese iniciou-se com a revisão dos métodos e tecnologias existentes para definir a arquitetura e os módulos do novo método de identificação das AVD. Com esta revisão da literatura e com base no conhecimento adquirido sobre os sensores disponíveis nos dispositivos móveis disponíveis no mercado, um conjunto de tarefas que podem ser identificadas foi definido para as pesquisas e desenvolvimentos desta Tese. Esta revisão também identifica os principais conceitos para o desenvolvimento do novo método de identificação das AVD, utilizando os sensores, são eles: aquisição de dados, processamento de dados, correção de dados, imputação de dados, extração de características, fusão de dados e extração de resultados recorrendo a métodos de inteligência artificial. Um dos desafios está relacionado aos diferentes tipos de dados adquiridos pelos diferentes sensores, mas outros desafios foram encontrados, sendo os mais relevantes o ruído ambiental, o posicionamento do dispositivo durante a realização das atividades diárias, as capacidades limitadas dos dispositivos móveis. As diferentes características das pessoas podem igualmente influenciar a criação dos métodos, escolhendo pessoas com diferentes estilos de vida e características físicas para a aquisição e identificação dos dados adquiridos a partir de sensores. Com base nos dados adquiridos, realizou-se o processamento dos dados, implementando-se métodos de correção dos dados e a extração de características, para iniciar a criação do novo método para o reconhecimento das AVD. Os métodos de imputação de dados foram excluídos da implementação, pois não iriam influenciar os resultados da identificação das AVD e dos ambientes, na medida em que são utilizadas as características extraídas de um conjunto de dados adquiridos durante um intervalo de tempo definido. A seleção dos sensores utilizáveis, bem como das AVD identificáveis, permitirá o desenvolvimento de um método que, considerando o uso de tecnologias para a fusão de dados adquiridos com múltiplos sensores em coordenação com outras informações relativas ao contexto do utilizador, tais como a agenda do utilizador, permitindo estabelecer um perfil de tarefas que o utilizador realiza diariamente. Com base nos resultados obtidos na literatura, o método escolhido para o reconhecimento das AVD são as diferentes variantes das Redes Neuronais Artificiais (RNA), incluindo Multilayer Perceptron (MLP), Feedforward Neural Networks (FNN) with Backpropagation and Deep Neural Networks (DNN). No final, após a criação dos métodos para cada fase do método para o reconhecimento das AVD e ambientes, a implementação sequencial dos diferentes métodos foi realizada num dispositivo móvel para testes adicionais. Após a definição da estrutura do método para o reconhecimento de AVD e ambientes usando dispositivos móveis, verificou-se que a aquisição de dados pode ser realizada com os métodos comuns. Após a aquisição de dados, os mesmos devem ser processados no módulo de processamento de dados, que inclui os métodos de correção de dados e de extração de características. O método de correção de dados utilizado para sensores de movimento e magnéticos é o filtro passa-baixo de modo a reduzir o ruído, mas para os dados acústicos, a Transformada Rápida de Fourier (FFT) foi aplicada para extrair as diferentes frequências. Após a correção dos dados, as diferentes características foram extraídas com base nos tipos de sensores usados, sendo a média, desvio padrão, variância, valor máximo, valor mínimo e mediana de dados adquiridos pelos sensores magnéticos e de movimento, a média, desvio padrão, variância e mediana dos picos máximos calculados com base nos dados adquiridos pelos sensores magnéticos e de movimento, as cinco maiores distâncias entre os picos máximos calculados com os dados adquiridos dos sensores de movimento e magnéticos, a média, desvio padrão, variância e 26 Mel-Frequency Cepstral Coefficients (MFCC) das frequências obtidas com FFT com base nos dados obtidos a partir do microfone, e a distância calculada com os dados adquiridos pelo recetor de GPS. Após a extração das características, as mesmas são agrupadas em diferentes conjuntos de dados para a aplicação dos métodos de RNA de modo a descobrir o método e o conjunto de características que reporta melhores resultados. O módulo de classificação de dados foi incrementalmente desenvolvido, começando com a identificação das AVD comuns com sensores magnéticos e de movimento, i.e., andar, correr, subir escadas, descer escadas e parado. Em seguida, os ambientes são identificados com dados de sensores acústicos, i.e., quarto, bar, sala de aula, ginásio, cozinha, sala de estar, hall, rua e biblioteca. Com base nos ambientes reconhecidos e os restantes sensores disponíveis nos dispositivos móveis, os dados adquiridos dos sensores magnéticos e de movimento foram combinados com o ambiente reconhecido para diferenciar algumas atividades sem movimento (i.e., dormir e ver televisão), onde o número de atividades reconhecidas nesta fase aumenta com a fusão da distância percorrida, extraída a partir dos dados do recetor GPS, permitindo também reconhecer a atividade de conduzir. Após a implementação dos três métodos de classificação com diferentes números de iterações, conjuntos de dados e configurações numa máquina com alta capacidade de processamento, os resultados relatados provaram que o melhor método para o reconhecimento das atividades comuns de AVD e atividades sem movimento é o método DNN, mas o melhor método para o reconhecimento de ambientes é o método FNN with Backpropagation. Dependendo do número de sensores utilizados, esta implementação reporta uma exatidão média entre 85,89% e 89,51% para o reconhecimento das AVD comuns, igual a 86,50% para o reconhecimento de ambientes, e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão global entre 85,89% e 92,00%. A última etapa desta Tese foi a implementação do método nos dispositivos móveis, verificando que o método FNN requer um alto poder de processamento para o reconhecimento de ambientes e os resultados reportados com estes dispositivos são inferiores aos resultados reportados com a máquina com alta capacidade de processamento utilizada no desenvolvimento do método. Assim, o método DNN foi igualmente implementado para o reconhecimento dos ambientes com os dispositivos móveis. Finalmente, os resultados relatados com os dispositivos móveis reportam uma exatidão entre 86,39% e 89,15% para o reconhecimento das AVD comuns, igual a 45,68% para o reconhecimento de ambientes, e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão geral entre 58,02% e 89,15%. Com base nos resultados relatados na literatura, os resultados do método desenvolvido mostram uma melhoria residual, mas os resultados desta Tese identificam mais AVD que os demais estudos disponíveis na literatura. A melhoria no reconhecimento das AVD com base na média das exatidões é igual a 2,93%, mas o número máximo de AVD e ambientes reconhecidos pelos estudos disponíveis na literatura é 13, enquanto o número de AVD e ambientes reconhecidos com o método implementado é 16. Assim, o método desenvolvido tem uma melhoria de 2,93% na exatidão do reconhecimento num maior número de AVD e ambientes. Como trabalho futuro, os resultados reportados nesta Tese podem ser considerados um ponto de partida para o desenvolvimento de um assistente digital pessoal, mas o número de ADL e ambientes reconhecidos pelo método deve ser aumentado e as experiências devem ser repetidas com diferentes tipos de dispositivos móveis (i.e., smartphones e smartwatches), e os métodos de imputação e outros métodos de classificação de dados devem ser explorados de modo a tentar aumentar a confiabilidade do método para o reconhecimento das AVD e ambientes

Experimental Demonstration and Performance Enhancement of 5G NR Multiband Radio over Fiber System Using Optimized Digital Predistortion

This paper presents an experimental realization of multiband 5G new radio (NR) optical front haul (OFH) based radio over fiber (RoF) system using digital predistortion (DPD). A novel magnitude-selective affine (MSA) based DPD method is proposed for the complexity reduction and performance enhancement of RoF link followed by its comparison with the canonical piece wise linearization (CPWL), decomposed vector rotation method (DVR) and generalized memory polynomial (GMP) methods. Similarly, a detailed study is shown followed by the implementation proposal of novel neural network (NN) for DPD followed by its comparison with MSA, CPWL, DVR and GMP methods. In the experimental testbed, 5G NR standard at 20 GHz with 50 MHz bandwidth and flexible-waveform signal at 3 GHz with 20 MHz bandwidth is used to cover enhanced mobile broad band and small cells scenarios. A dual drive Mach Zehnder Modulator having two distinct radio frequency signals modulates a 1310 nm optical carrier using distributed feedback laser for 22 km of standard single mode fiber. The experimental results are presented in terms of adjacent channel power ratio (ACPR), error vector magnitude (EVM), number of estimated coefficients and multiplications. The study aims to identify those novel methods such as MSA DPD are a good candidate to deploy in real time scenarios for DPD in comparison to NN based DPD which have a slightly better performance as compared to the proposed MSA method but has a higher complexity levels. Both, proposed methods, MSA and NN are meeting the 3GPP Release 17 requirements

Authentication and Message Integrity Verification without Secrets

Embedding network capabilities in a plethora of new devices and infrastructures--the Internet-of-Things, vehicular and aviation networks, the critical national infrastructure, industrial plants--are dramatically transforming the modern way of living. The rapid deployment pace of these emerging applications has brought unprecedented security challenges related to data confidentiality, user privacy, and critical infrastructure availability. A significant portion of these threats is attributed to the broadcast nature of the wireless medium, which exposes systems to easy-to-launch passive and active attacks. The slow security standards rollout combined with the ever-shrinking time-to-market, the device heterogeneity and the lack of user-friendly input interfaces (screen, keyboard, etc.) only exacerbate the security challenges. In this dissertation, we address the fundamental problem of trust establishment in the context of emerging network applications. We present techniques integrating physical layer properties with cryptographic primitives to guarantee message integrity and bootstrap initial trust without relying on any prior secrets. We present the ``helper'' security paradigm in which security is outsourced to one or more dedicated devices to allow for the scalable pairing of off-the-shelf heterogeneous devices. In addition, we present our work on message integrity verification of navigation information for aircrafts (speed, location, and heading) by exploiting the Doppler spread of the wireless channel. Finally, we develop a secure and fast voting technique for distributed networks which allows fast coordination of a group of devices without the overhead of messaging