923 resultados para Low-power links
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The dielectric porcelain is usually obtained by mixing various raw materials proportions and is used in the production of electronic equipment for various applications, from capacitors of high and low Power to insulators for low, medium, high and extra high voltage, which are used in distribution lines and transmission of electricity.This work was directed to the s tudy of technological properties of technic porcelain, made from raw materials extracted from pegmatites found in the regions of Seridó and the Alto Oeste of Rio Grande do Norte, which are made of kaolin, quartz and feldspar, abundant and high quality in these regions. The technic ceramics were obtained by mixing in appropriate levels, kaolin, feldspar, quartz and clay, the last item from a pottery in the city of Sao Gonçalo do Amarante, Rio Grande do Norte. During the development the following characterizations correlated to raw materials were made: laser particle sizing, x-ray diffraction, DTA and TG. The compositions studied were formed by uniaxial pressing at a pressure of 50 MPa and sintered at temperatures ranging from 1150 to 1350ºC and levels (times) of sintering between 30, 60, 90 and 120 minutes. The characterization of the samples were taken from the analysis of weight loss, linear shrinkage, porosity, stoneware curve, bulk density, flexural strength of three points, SEM and X-ray diffraction, TMA, Dielectric and cross Resistivity. The studied materials can be employed in producing the objects used in electrical engineering such as: insulators for low, medium and high-voltage electrical systems, command devices, bushing insulation for transformers, power capacitors, spark plugs, receptacles for fluorescent and incandescent light bulbs and others
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The use of binders in the soil for the production of solid bricks is an old construction technique that has been used by several civilizations over time. At the same time, the need for environmental preservation and the tendency of scarcity of natural resources make the construction invest in researching new concepts, methods and materials for building systems for the sustainability of their economic activities. Thus arises the need to obtain building materials with low power consumption, capable of reducing the growing housing shortage of rural and urban population. Currently, research has been conducted on this topic to better understand the cementitious and pozzolanic reactions that occur in the formation of the microstructure of the soil-cement when added to other materials such as, for example, lime, and the relationship between microstructure and formed interfaces with the physical, mechanical and chemical analysis in compounds made from these ternary compositions. In this context, this study aimed to analyze the results of the influence of the incorporation of lime to the soil-cement to form a ternary mixture to produce soil-cement bricks and mortar without structural purposes. From the inclusion of contents of 6 %, 8 %, 10% and 12% lime to the soil, and soil-cement mixes in amounts of 2 %, 3 %, 4 % and 5 % were shaped-bodies of -cylindrical specimens to determine the optimum moisture content and maximum dry apparent specific weight. Then they were cured, and subjected to the tests of compressive strength, absorption and durability modified. Compositions obtained the best results in the tests performed on the bodies-of-proof cylindrical served as a parameter for molding of solid bricks, which underwent the same experimental methodology previously cited. The raw materials used, as well as compositions in which the bricks were molded solid, were characterized by physical and chemical tests, X-ray diffraction and scanning electron microscopy. The results obtained in the study indicate that the compositions studied, that showed the best results in terms of compressive strength, water absorption and durability ternary composition was soil, 10 % cement and 2 % lime
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Doutoramento em Economia
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Hardware vendors make an important effort creating low-power CPUs that keep battery duration and durability above acceptable levels. In order to achieve this goal and provide good performance-energy for a wide variety of applications, ARM designed the big.LITTLE architecture. This heterogeneous multi-core architecture features two different types of cores: big cores oriented to performance and little cores, slower and aimed to save energy consumption. As all the cores have access to the same memory, multi-threaded applications must resort to some mutual exclusion mechanism to coordinate the access to shared data by the concurrent threads. Transactional Memory (TM) represents an optimistic approach for shared-memory synchronization. To take full advantage of the features offered by software TM, but also benefit from the characteristics of the heterogeneous big.LITTLE architectures, our focus is to propose TM solutions that take into account the power/performance requirements of the application and what it is offered by the architecture. In order to understand the current state-of-the-art and obtain useful information for future power-aware software TM solutions, we have performed an analysis of a popular TM library running on top of an ARM big.LITTLE processor. Experiments show, in general, better scalability for the LITTLE cores for most of the applications except for one, which requires the computing performance that the big cores offer.
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Ambient mechanical vibrations have emerged as a viable energy source for low-power wireless sensor nodes aiming the upcoming era of the ‘Internet of Things’. Recently, purposefully induced dynamical nonlinearities have been exploited to widen the frequency spectrum of vibration energy harvesters. Here we investigate some critical inconsistencies between the theoretical formulation and applications of the bistable Duffing nonlinearity in vibration energy harvesting. A novel nonlinear vibration energy harvesting device with the capability to switch amidst individually tunable bistable-quadratic, monostable-quartic and bistable-quartic potentials has been designed and characterized. Our study highlights the fundamentally different large deflection behaviors of the theoretical bistable-quartic Duffing oscillator and the experimentally adapted bistable-quadratic systems, and underlines their implications in the respective spectral responses. The results suggest enhanced performance in the bistable-quartic potential in comparison to others, primarily due to lower potential barrier and higher restoring forces facilitating large amplitude inter-well motion at relatively lower accelerations.
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Fully articulated hand tracking promises to enable fundamentally new interactions with virtual and augmented worlds, but the limited accuracy and efficiency of current systems has prevented widespread adoption. Today's dominant paradigm uses machine learning for initialization and recovery followed by iterative model-fitting optimization to achieve a detailed pose fit. We follow this paradigm, but make several changes to the model-fitting, namely using: (1) a more discriminative objective function; (2) a smooth-surface model that provides gradients for non-linear optimization; and (3) joint optimization over both the model pose and the correspondences between observed data points and the model surface. While each of these changes may actually increase the cost per fitting iteration, we find a compensating decrease in the number of iterations. Further, the wide basin of convergence means that fewer starting points are needed for successful model fitting. Our system runs in real-time on CPU only, which frees up the commonly over-burdened GPU for experience designers. The hand tracker is efficient enough to run on low-power devices such as tablets. We can track up to several meters from the camera to provide a large working volume for interaction, even using the noisy data from current-generation depth cameras. Quantitative assessments on standard datasets show that the new approach exceeds the state of the art in accuracy. Qualitative results take the form of live recordings of a range of interactive experiences enabled by this new approach.
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Ce mémoire porte sur la participation des aînés à l’Université du troisième âge de Québec (UTAQ) et son lien avec la norme du bien vieillir véhiculée dans la politique québécoise sur le vieillissement. Quinze étudiants de l’UTAQ ont été rencontrés lors d’entrevues semi-dirigées pour recueillir leurs propos, tant sur ce qui les amène à fréquenter cette institution que sur le vieillissement lui-même et sa prise en charge sociale. Le cadre conceptuel de cette recherche s’inspire de l’anthropology of policy et permet d’envisager la manière dont nous sommes gouvernés dans une société néolibérale. Il est constitué des écrits foucaldiens sur le rapport savoir-pouvoir et les normes. De la littérature récente sur le néolibéralisme, je retiens également la technique de responsabilisation, centrale à ce mode de gouvernement. Les récits des étudiants de l’UTAQ montrent qu’ils sont très sensibles aux discours de la politique sur le vieillissement et qu’ils adhèrent à la vision de la vieillesse qui en découle tout en demeurant très critiques à son endroit. Enfin, mon analyse montre que l’UTAQ peut servir de relais de la politique du bien vieillir, et ce, de multiples façons.
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This paper presents the study and experimental tests for the viability analysis of using multiple wireless technologies in urban traffic light controllers in a Smart City environment. Communication drivers, different types of antennas, data acquisition methods and data processing for monitoring the network are presented. The sensors and actuators modules are connected in a local area network through two distinct low power wireless networks using both 868 MHz and 2.4 GHz frequency bands. All data communications using 868 MHz go through a Moteino. Various tests are made to assess the most advantageous features of each communication type. The experimental results show better range for 868 MHz solutions, whereas the 2.4 GHz presents the advantage of self-regenerating the network and mesh. The different pros and cons of both communication methods are presented.
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Resumo: Para execução do processo de raleamento na Caatinga é importante utilizar máquinas do tipo rotores trituradores acoplados em tratores de baixa potência, visando a diminuição de tempo e mão-de-obra dos agricultores familiares na, implantação de sistemas de exploração sustentáveis, sistemas agrossilvipastoris. Para o projeto de uma máquina que seja acessível a estes produtores, o primeiro passo é se conhecer a potência de corte necessária para a operação de raleamento. O objetivo deste trabalho foi desenvolver e avaliar uma metodologia de ensaio para medir a potência durante o corte de uma espécie arbórea, podendo assim avaliar os parâmetros a serem utilizados futuramente no projeto da máquina. Como espécie teste, utilizou-se o eucalyptus citriodora, por ser uma madeira que apresenta resistência de corte elevada e similar ao das espécies da Caatinga. Observou-se que a metodologia proposta, ao padronizar os procedimentos, permitiu adquirir de forma rápida e precisa os valores dos parâmetros de importância no projeto de máquinas destinadas a cortar e triturar espécies arbóreas. Também a utilização dos dados obtidos por meio da aplicação da metodologia, possibilitam maior confiabilidade e precisão no projeto de máquinas destinadas a este tipo de trabalho. [Methodology for obtaining the cutting power of a forest crusher]. Abstract: For executions the thinning process in Caatinga is important to use machines as Rotors Crushers coupled in low- power tractors, aimed at decrease of time and hand labor of Family Farmers in the implementation of sustainable exploration systems, agrosylvopastoral Systems. For the project for the design of a machine that is accessible these producers, the first step is knowing the cut power needed for a thinning operation. The objective of this study was to develop and evaluate a test methodology to measure the power during the cutting of a tree species and can evaluate the cutting parameters used in the future in machine design. As a test species, we used the eucalyptus citriodora, as this wood has cut high resistance and similar to Species of Caatinga. It was observed that the methodology proposal, to the standardize procedures, allows to get quickly and accurately the values of importance parameters in the design of machines designed to cut and grind tree species. Also the use of data obtained through the application of the methodology , enable greater reliability and precision in machine design intended for this type of work.
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Radars are expected to become the main sensors in various civilian applications, especially for autonomous driving. Their success is mainly due to the availability of low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. This thesis focuses on the study and the development of different deterministic and learning based techniques for colocated multiple-input multiple-output (MIMO) radars. In particular, after providing an overview on the architecture of these devices, the problem of detecting and estimating multiple targets in stepped frequency continuous wave (SFCW) MIMO radar systems is investigated and different deterministic techniques solving it are illustrated. Moreover, novel solutions, based on an approximate maximum likelihood approach, are developed. The accuracy achieved by all the considered algorithms is assessed on the basis of the raw data acquired from low power wideband radar devices. The results demonstrate that the developed algorithms achieve reasonable accuracies, but at the price of different computational efforts. Another important technical problem investigated in this thesis concerns the exploitation of machine learning and deep learning techniques in the field of colocated MIMO radars. In this thesis, after providing a comprehensive overview of the machine learning and deep learning techniques currently being considered for use in MIMO radar systems, their performance in two different applications is assessed on the basis of synthetically generated and experimental datasets acquired through a commercial frequency modulated continuous wave (FMCW) MIMO radar. Finally, the application of colocated MIMO radars to autonomous driving in smart agriculture is illustrated.
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The aim of TinyML is to bring the capability of Machine Learning to ultra-low-power devices, typically under a milliwatt, and with this it breaks the traditional power barrier that prevents the widely distributed machine intelligence. TinyML allows greater reactivity and privacy by conducting inference on the computer and near-sensor while avoiding the energy cost associated with wireless communication, which is far higher at this scale than that of computing. In addition, TinyML’s efficiency makes a class of smart, battery-powered, always-on applications that can revolutionize the collection and processing of data in real time. This emerging field, which is the end of a lot of innovation, is ready to speed up its growth in the coming years. In this thesis, we deploy three model on a microcontroller. For the model, datasets are retrieved from an online repository and are preprocessed as per our requirement. The model is then trained on the split of preprocessed data at its best to get the most accuracy out of it. Later the trained model is converted to C language to make it possible to deploy on the microcontroller. Finally, we take step towards incorporating the model into the microcontroller by implementing and evaluating an interface for the user to utilize the microcontroller’s sensors. In our thesis, we will have 4 chapters. The first will give us an introduction of TinyML. The second chapter will help setup the TinyML Environment. The third chapter will be about a major use of TinyML in Wake Word Detection. The final chapter will deal with Gesture Recognition in TinyML.
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Embedding intelligence in extreme edge devices allows distilling raw data acquired from sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in which end-nodes no longer depend entirely on the Cloud, offers undeniable benefits, driving a large research area (TinyML) to deploy leading Machine Learning (ML) algorithms on micro-controller class of devices. To fit the limited memory storage capability of these tiny platforms, full-precision Deep Neural Networks (DNNs) are compressed by representing their data down to byte and sub-byte formats, in the integer domain. However, the current generation of micro-controller systems can barely cope with the computing requirements of QNNs. This thesis tackles the challenge from many perspectives, presenting solutions both at software and hardware levels, exploiting parallelism, heterogeneity and software programmability to guarantee high flexibility and high energy-performance proportionality. The first contribution, PULP-NN, is an optimized software computing library for QNN inference on parallel ultra-low-power (PULP) clusters of RISC-V processors, showing one order of magnitude improvements in performance and energy efficiency, compared to current State-of-the-Art (SoA) STM32 micro-controller systems (MCUs) based on ARM Cortex-M cores. The second contribution is XpulpNN, a set of RISC-V domain specific instruction set architecture (ISA) extensions to deal with sub-byte integer arithmetic computation. The solution, including the ISA extensions and the micro-architecture to support them, achieves energy efficiency comparable with dedicated DNN accelerators and surpasses the efficiency of SoA ARM Cortex-M based MCUs, such as the low-end STM32M4 and the high-end STM32H7 devices, by up to three orders of magnitude. To overcome the Von Neumann bottleneck while guaranteeing the highest flexibility, the final contribution integrates an Analog In-Memory Computing accelerator into the PULP cluster, creating a fully programmable heterogeneous fabric that demonstrates end-to-end inference capabilities of SoA MobileNetV2 models, showing two orders of magnitude performance improvements over current SoA analog/digital solutions.
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Nowadays, application domains such as smart cities, agriculture or intelligent transportation, require communication technologies that combine long transmission ranges and energy efficiency to fulfill a set of capabilities and constraints to rely on. In addition, in recent years, the interest in Unmanned Aerial Vehicles (UAVs) providing wireless connectivity in such scenarios is substantially increased thanks to their flexible deployment. The first chapters of this thesis deal with LoRaWAN and Narrowband-IoT (NB-IoT), which recent trends identify as the most promising Low Power Wide Area Networks technologies. While LoRaWAN is an open protocol that has gained a lot of interest thanks to its simplicity and energy efficiency, NB-IoT has been introduced from 3GPP as a radio access technology for massive machine-type communications inheriting legacy LTE characteristics. This thesis offers an overview of the two, comparing them in terms of selected performance indicators. In particular, LoRaWAN technology is assessed both via simulations and experiments, considering different network architectures and solutions to improve its performance (e.g., a new Adaptive Data Rate algorithm). NB-IoT is then introduced to identify which technology is more suitable depending on the application considered. The second part of the thesis introduces the use of UAVs as flying Base Stations, denoted as Unmanned Aerial Base Stations, (UABSs), which are considered as one of the key pillars of 6G to offer service for a number of applications. To this end, the performance of an NB-IoT network are assessed considering a UABS following predefined trajectories. Then, machine learning algorithms based on reinforcement learning and meta-learning are considered to optimize the trajectory as well as the radio resource management techniques the UABS may rely on in order to provide service considering both static (IoT sensors) and dynamic (vehicles) users. Finally, some experimental projects based on the technologies mentioned so far are presented.
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The Structural Health Monitoring (SHM) research area is increasingly investigated due to its high potential in reducing the maintenance costs and in ensuring the systems safety in several industrial application fields. A growing demand of new SHM systems, permanently embedded into the structures, for savings in weight and cabling, comes from the aeronautical and aerospace application fields. As consequence, the embedded electronic devices are to be wirelessly connected and battery powered. As result, a low power consumption is requested. At the same time, high performance in defects or impacts detection and localization are to be ensured to assess the structural integrity. To achieve these goals, the design paradigms can be changed together with the associate signal processing. The present thesis proposes design strategies and unconventional solutions, suitable both for real-time monitoring and periodic inspections, relying on piezo-transducers and Ultrasonic Guided Waves. In the first context, arrays of closely located sensors were designed, according to appropriate optimality criteria, by exploiting sensors re-shaping and optimal positioning, to achieve improved damages/impacts localisation performance in noisy environments. An additional sensor re-shaping procedure was developed to tackle another well-known issue which arises in realistic scenario, namely the reverberation. A novel sensor, able to filter undesired mechanical boundaries reflections, was validated via simulations based on the Green's functions formalism and FEM. In the active SHM context, a novel design methodology was used to develop a single transducer, called Spectrum-Scanning Acoustic Transducer, to actively inspect a structure. It can estimate the number of defects and their distances with an accuracy of 2[cm]. It can also estimate the damage angular coordinate with an equivalent mainlobe aperture of 8[deg], when a 24[cm] radial gap between two defects is ensured. A suitable signal processing was developed in order to limit the computational cost, allowing its use with embedded electronic devices.
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In next generation Internet-of-Things, the overhead introduced by grant-based multiple access protocols may engulf the access network as a consequence of the proliferation of connected devices. Grant-free access protocols are therefore gaining an increasing interest to support massive multiple access. In addition to scalability requirements, new demands have emerged for massive multiple access, including latency and reliability. The challenges envisaged for future wireless communication networks, particularly in the context of massive access, include: i) a very large population size of low power devices transmitting short packets; ii) an ever-increasing scalability requirement; iii) a mild fixed maximum latency requirement; iv) a non-trivial requirement on reliability. To this aim, we suggest the joint utilization of grant-free access protocols, massive MIMO at the base station side, framed schemes to let the contention start and end within a frame, and succesive interference cancellation techniques at the base station side. In essence, this approach is encapsulated in the concept of coded random access with massive MIMO processing. These schemes can be explored from various angles, spanning the protocol stack from the physical (PHY) to the medium access control (MAC) layer. In this thesis, we delve into both of these layers, examining topics ranging from symbol-level signal processing to succesive interference cancellation-based scheduling strategies. In parallel with proposing new schemes, our work includes a theoretical analysis aimed at providing valuable system design guidelines. As a main theoretical outcome, we propose a novel joint PHY and MAC layer design based on density evolution on sparse graphs.