Distance and the associated parameters can be measured by specific sensors mobile device. Introduction The work focuses on the description of the sensors and the effects on their accuracy. To measure the distance, there is a number of good practices, which were described and demonstrated the application implemented source code for the Android platform. The project works with a total of three sensors, namely accelerometer, magnetometer and camera. The main objective of this work is the definition and application of methods for measuring distance using the appropriate sensors.

Disertační práce je zaměřena na metody a systémy pro měření vzdálenosti a lokalizaci RFID tagů pracujících v pásmu UHF. Úvod je věnován popisu současného stavu vědeckého poznání v oblasti RFID prostorové identifikace a stručnému shrnutí problematiky modelování a návrhu prototypů těchto systémů. Po specifikaci cílů disertace pokračuje práce popisem teorie modelování degenerovaného kanálu pro RFID komunikaci. Detailně jsou rozebrány metody měření vzdálenosti a odhadu směru příchodu signálu založené na zpracování fázové informace. Pro účely lokalizace je navrženo několik scénářů rozmístění antén. Modely degenerovaného kanálu jsou simulovány v systému MATLAB. Významná část této práce je věnována konceptu softwarově definovaného rádia (SDR) a specifikům jeho adaptace na UHF RFID, která využití běžných SDR systémů značně omezují. Diskutována je zejména problematika průniku nosné vysílače do přijímací cesty a požadavky na signál lokálního oscilátoru používaný pro směšování. Prezentovány jsou tři vyvinuté prototypy: experimentální dotazovač EXIN-1, měřicí systém založený na platformě Ettus USRP a anténní přepínací matice pro emulaci SIMO systému. Závěrečná část je zaměřena na testování a zhodnocení popisovaných lokalizačních technik, založených na měření komplexní přenosové funkce RFID kanálu. Popisuje úzkopásmové/širokopásmové měření vzdálenosti a metody odhadu směru signálu. Oba navržené scénáře rozmístění antén jsou v závěru ověřeny lokalizačním měřením v reálných podmínkách.

There is a sharp increase in the number of old people living alone throughout the world. More often than not, such people require continuous and immediate care and attention in their everyday lives, hence the need for round the clock monitoring, albeit in a respectful, dignified and non-intrusive way. For example, continuous care is required when they become frail and less active, and immediate attention is required when they fall or remain in the same position for a long time. To this extent, various monitoring technologies have been developed, yet there are major improvements still to be realised. Current technologies include indoor positioning systems (IPSs) and health monitoring systems. The former relies on defined configurations of various sensors to capture a person's position within a given space in real-time. The functionality of the sensors varies depending on receiving appropriate data using WiFi, radio frequency identification (RFIO), ultrawide band (UWB), dead reckoning (OR), infrared indoor (IR), Bluetooth (BLE), acoustic signal, visible light detection, and sound signal monitoring. The systems use various algorithms to capture proximity, location detection, time of arrival, time difference of arrival angle, and received signal strength data. Health monitoring technologies capture important health data using accelerometers and gyroscope sensors. In some studies, audio fingerprinting has been used to detect indoor environment sound variation and have largely been based on recognising TV sound and songs. This has been achieved using various staging methods, including pre-processing, framing, windowing, time/frequency domain feature extraction, and post-processing. Time/frequency domain feature extraction tools used include Fourier Transforms (FTs}, Modified Discrete Cosine Transform (MDCT}, Principal Component Analysis (PCA), Mel-Frequency Cepstrum Coefficients (MFCCs), Constant Q Transform (CQT}, Local Energy centroid (LEC), and Wavelet transform. Artificial intelligence (Al) and probabilistic algorithms have also been used in IPSs to classify and predict different activities, with interesting applications in healthcare monitoring. Several tools have been applied in IPSs and audio fingerprinting. They include Radial Basis Kernel (RBF), Support Vector Machine (SVM), Decision Trees (DTs), Hidden Markov Models (HMMs), Na'ive Bayes (NB), Gaussian Mixture Modelling (GMM), Clustering algorithms, Artificial Neural Networks (ANNs), and Deep Learning (DL). Despite all these attempts, there is still a major gap for a completely non-intrusive system capable of monitoring what an elderly person living alone is doing, where and for how long, and providing a quick traffic-like risk score prompting, therefore immediate action or otherwise. In this thesis, a cost-effective and completely non-intrusive indoor positioning and activity-monitoring system for elderly people living alone has been developed, tested and validated in a typical residential living space. The proposed system works based on five phases: (1)Set-up phase that defines the typical activities of daily living (TADLs). (2)Configuration phase that optimises the implementation of the required sensors in exemplar flat No.1. (3)Learning phase whereby sounds and position data of the TADLs are collected and stored in a fingerprint reference data set. (4)Listening phase whereby real-time data is collected and compared against the reference data set to provide information as to what a person is doing, when, and for how long. (5)Alert phase whereby a health frailty score varying between O unwell to 10 healthy is generated in real-time. Two typical but different residential flats (referred to here are Flats No.1 and 2) are used in the study. The system is implemented in the bathroom, living room, and bedroom of flat No.1, which includes various floor types (carpet, tiles, laminate) to distinguish between various sounds generated upon walking on such floors. The data captured during the Learning Phase yields the reference data set and includes position and sound fingerprints. The latter is generated from tests of recording a specific TADL, thus providing time and frequency-based extracted features, frequency peak magnitude (FPM), Zero Crossing Rate (ZCR), and Root Mean Square Error (RMSE). The former is generated from distance measurement. The sampling rate of the recorded sound is 44.1kHz. Fast Fourier Transform (FFT) is applied on 0.1 seconds intervals of the recorded sound with minimisation of the spectral leakage using the Hamming window. The frequency peaks are detected from the spectrogram matrices to get the most appropriate FPM between the reference and sample data. The position detection of the monitored person is based on the distance between that captured from the learning and listening phases of the system in real-time. A typical furnished one-bedroom flat (flat No.2) is used to validate the system. The topologies and floorings of flats No.1 and No.2 are different. The validation is applied based on "happy" and "unusual" but typical behaviours. Happy ones include typical TADLs of a healthy elderly person living alone with a risk metric higher than 8. Unusual one's mimic acute or chronic activities (or lack thereof), for example, falling and remaining on the floor, or staying in bed for long periods, i.e., scenarios when an elderly person may be in a compromised situation which is detected by a sudden drop of the risk metric (lower than 4) in real-time. Machine learning classification algorithms are used to identify the location, activity, and time interval in real-time, with a promising early performance of 94% in detecting the right activity and the right room at the right time.

La mesure de couplage dipolaire permet d’accéder à la structure tridimensionnelle d’un composé solide. Cependant, en présence d’une forte densité de spins couplés, le phénomène de troncature dipolaire rend difficile l’obtention de ces informations par RMN du solide. Ce problème peut être affranchi par l’étude de spins rares en abondance naturelle. En effet, avec une abondance naturelle de 1.1 %, la probabilité que trois 13C soient couplés, et avec elle la troncature dipolaire, devient négligeable. Une méthodologie basée sur la séquence de recouplage dipolaire POST-C7 permet d’accéder à des informations structurales d’échantillons en abondance naturelle sensibles à la fois à la conformation moléculaire et à l’empilement cristallin par mesure de couplages dipolaires 13C-13C. La sensibilité de détection des signaux RMN 13C est augmentée à l’aide la polarisation dynamique nucléaire ce qui permet de réduire considérablement les temps d’expériences. De plus, la séquence de recouplage R20_9_2 aidée de supercycles s’est montrée être plus robustes que POST-C7 face à de fortes anisotropies de déplacement chimique ou de forts couplages hétéronucléaires 1H-13C. La seconde problématique abordée concerne l’attribution de signaux 13C. En effet, il existe seulement quelques exemples de détermination de connectivités 13C -13C en abondance naturelle. Nous montrons ici que des spectres de corrélations dipolaires 13C-13C peuvent être obtenus en quelques jours à l’aide de la séquence de recouplage R20_9_2. Contrairement aux méthodologies basées sur le couplage J, notre séquence requiert un temps d’excitation DQ plus court ce qui la rend adaptée à l’étude de solides désordonnés. / Measurment of dipolar coupling provides 3D structural information of powder samples. However, in practice, the high density of spins in organic compounds prevents the measurements of long-range dipolar couplings in solid-state NMR by the so-called dipolar truncation effect. The study of rare spins on natural abundance allows to overcome this problem. In fact, with a natural abundance of 1.1 %, the probability for three 13C to be coupled is negligible. We developed a methodology based either on the dipolar recoupling NMR pulse sequence POST-C7 or on the dramatic increase in sensitivity provided by dynamic nuclear polarization. We demonstrated that its methodology provides a measure of 13C-13C dipolar couplings in natural abundance powder samples and that the so-obtained distance information is sensitive to both molecular conformation and crystal packing of powder samples. Moreover, we show that the recoupling pulse sequence R20_9_2 is more robust to strong chemical shift anisotropy and also to strong 1H-13C heteronuclear dipolar couplings than POST-C7. The second challenge involves 13C signal assignment for natural abundance. In fact, there are only a few examples of 13C-13C correlation spectra obtained for natural abundance samples. Here, we show that 13C-13C correlation spectra sequence based on the reintroduction of 13C−13C dipolar couplings can be obtained with standard MAS probe and within few days using R20_9_2 pulse sequence. Contrary to pulse sequences based on 13C-13C J coupling, our pulse sequence requires shorter DQ excitation time and hence, is more suitable for samples having short T2 relaxation times such as amorphous solids.