Magnetic Field as a Characterization of Wide and Narrow Spaces in a Real Challenging Scenario Using Dynamic Time Warping

Ponencia presentada en 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 24-27 Sept. 2018This paper presents a study of indoor positioning in public zones of the Parc Taulí Hospital in Sabadell. It is a challenging scenario because: (1) it combines wide spaces with middle sized and narrow spaces; (2) it is a shielded zone where no signals are available, and therefore, no WiFi signal can be used for positioning; and (3) it is not possible to deploy beacons for positioning. The goal of this work is to test whether it is possible to get indoor positioning in a real and challenging scenario by using only the magnetic field. The positioning precision requires to locate the part of the hospital where the user is. The proposed solution defines “virtual corridors” to improve positioning in wide areas. To validate the work, magnetic field data have been recorded from the scenario, using different smartphones and by different persons. The obtained magnetic data curves have been compared by using dynamic time warping distance. Results show that it is possible to characterize every path with the magnetic field. The main contributions of the present paper are: (1) defining “virtual corridors” as a way to position using magnetic field in 2D spaces; and (2) showing that even in wide spaces, like the hall of a hospital, it is possible to find magnetic anomalies linked to positions

Thermophysical Phenomena in Metal Additive Manufacturing by Selective Laser Melting: Fundamentals, Modeling, Simulation and Experimentation

Among the many additive manufacturing (AM) processes for metallic materials, selective laser melting (SLM) is arguably the most versatile in terms of its potential to realize complex geometries along with tailored microstructure. However, the complexity of the SLM process, and the need for predictive relation of powder and process parameters to the part properties, demands further development of computational and experimental methods. This review addresses the fundamental physical phenomena of SLM, with a special emphasis on the associated thermal behavior. Simulation and experimental methods are discussed according to three primary categories. First, macroscopic approaches aim to answer questions at the component level and consider for example the determination of residual stresses or dimensional distortion effects prevalent in SLM. Second, mesoscopic approaches focus on the detection of defects such as excessive surface roughness, residual porosity or inclusions that occur at the mesoscopic length scale of individual powder particles. Third, microscopic approaches investigate the metallurgical microstructure evolution resulting from the high temperature gradients and extreme heating and cooling rates induced by the SLM process. Consideration of physical phenomena on all of these three length scales is mandatory to establish the understanding needed to realize high part quality in many applications, and to fully exploit the potential of SLM and related metal AM processes

Mobile localization : approach and applications

textLocalization is critical to a number of wireless network applications. In many situations GPS is not suitable. This dissertation (i) develops novel localization schemes for wireless networks by explicitly incorporating mobility information and (ii) applies localization to physical analytics i.e., understanding shoppers' behavior within retail spaces by leveraging inertial sensors, Wi-Fi and vision enabled by smart glasses. More specifically, we first focus on multi-hop mobile networks, analyze real mobility traces and observe that they exhibit temporal stability and low-rank structure. Motivated by these observations, we develop novel localization algorithms to effectively capture and also adapt to different degrees of these properties. Using extensive simulations and testbed experiments, we demonstrate the accuracy and robustness of our new schemes. Second, we focus on localizing a single mobile node, which may not be connected with multiple nodes (e.g., without network connectivity or only connected with an access point). We propose trajectory-based localization using Wi-Fi or magnetic field measurements. We show that these measurements have the potential to uniquely identify a trajectory. We then develop a novel approach that leverages multi-level wavelet coefficients to first identify the trajectory and then localize to a point on the trajectory. We show that this approach is highly accurate and power efficient using indoor and outdoor experiments. Finally, localization is a critical step in enabling a lot of applications --- an important one is physical analytics. Physical analytics has the potential to provide deep-insight into shoppers' interests and activities and therefore better advertisements, recommendations and a better shopping experience. To enable physical analytics, we build ThirdEye system which first achieves zero-effort localization by leveraging emergent devices like the Google-Glass to build AutoLayout that fuses video, Wi-Fi, and inertial sensor data, to simultaneously localize the shoppers while also constructing and updating the product layout in a virtual coordinate space. Further, ThirdEye comprises of a range of schemes that use a combination of vision and inertial sensing to study mobile users' behavior while shopping, namely: walking, dwelling, gazing and reaching-out. We show the effectiveness of ThirdEye through an evaluation in two large retail stores in the United States.Computer Science

ZATLAB : recognizing gestures for artistic performance interaction

Most artistic performances rely on human gestures, ultimately resulting in an elaborate interaction between the performer and the audience. Humans, even without any kind of formal analysis background in music, dance or gesture are typically able to extract, almost unconsciously, a great amount of relevant information from a gesture. In fact, a gesture contains so much information, why not use it to further enhance a performance? Gestures and expressive communication are intrinsically connected, and being intimately attached to our own daily existence, both have a central position in our (nowadays) technological society. However, the use of technology to understand gestures is still somehow vaguely explored, it has moved beyond its first steps but the way towards systems fully capable of analyzing gestures is still long and difficult (Volpe, 2005). Probably because, if on one hand, the recognition of gestures is somehow a trivial task for humans, on the other hand, the endeavor of translating gestures to the virtual world, with a digital encoding is a difficult and illdefined task. It is necessary to somehow bridge this gap, stimulating a constructive interaction between gestures and technology, culture and science, performance and communication. Opening thus, new and unexplored frontiers in the design of a novel generation of multimodal interactive systems. This work proposes an interactive, real time, gesture recognition framework called the Zatlab System (ZtS). This framework is flexible and extensible. Thus, it is in permanent evolution, keeping up with the different technologies and algorithms that emerge at a fast pace nowadays. The basis of the proposed approach is to partition a temporal stream of captured movement into perceptually motivated descriptive features and transmit them for further processing in Machine Learning algorithms. The framework described will take the view that perception primarily depends on the previous knowledge or learning. Just like humans do, the framework will have to learn gestures and their main features so that later it can identify them. It is however planned to be flexible enough to allow learning gestures on the fly. This dissertation also presents a qualitative and quantitative experimental validation of the framework. The qualitative analysis provides the results concerning the users acceptability of the framework. The quantitative validation provides the results about the gesture recognizing algorithms. The use of Machine Learning algorithms in these tasks allows the achievement of final results that compare or outperform typical and state-of-the-art systems. In addition, there are also presented two artistic implementations of the framework, thus assessing its usability amongst the artistic performance domain. Although a specific implementation of the proposed framework is presented in this dissertation and made available as open source software, the proposed approach is flexible enough to be used in other case scenarios, paving the way to applications that can benefit not only the performative arts domain, but also, probably in the near future, helping other types of communication, such as the gestural sign language for the hearing impaired.Grande parte das apresentações artísticas são baseadas em gestos humanos, ultimamente resultando numa intricada interação entre o performer e o público. Os seres humanos, mesmo sem qualquer tipo de formação em música, dança ou gesto são capazes de extrair, quase inconscientemente, uma grande quantidade de informações relevantes a partir de um gesto. Na verdade, um gesto contém imensa informação, porque não usá-la para enriquecer ainda mais uma performance? Os gestos e a comunicação expressiva estão intrinsecamente ligados e estando ambos intimamente ligados à nossa própria existência quotidiana, têm uma posicão central nesta sociedade tecnológica actual. No entanto, o uso da tecnologia para entender o gesto está ainda, de alguma forma, vagamente explorado. Existem já alguns desenvolvimentos, mas o objetivo de sistemas totalmente capazes de analisar os gestos ainda está longe (Volpe, 2005). Provavelmente porque, se por um lado, o reconhecimento de gestos é de certo modo uma tarefa trivial para os seres humanos, por outro lado, o esforço de traduzir os gestos para o mundo virtual, com uma codificação digital é uma tarefa difícil e ainda mal definida. É necessário preencher esta lacuna de alguma forma, estimulando uma interação construtiva entre gestos e tecnologia, cultura e ciência, desempenho e comunicação. Abrindo assim, novas e inexploradas fronteiras na concepção de uma nova geração de sistemas interativos multimodais . Este trabalho propõe uma framework interativa de reconhecimento de gestos, em tempo real, chamada Sistema Zatlab (ZtS). Esta framework é flexível e extensível. Assim, está em permanente evolução, mantendo-se a par das diferentes tecnologias e algoritmos que surgem num ritmo acelerado hoje em dia. A abordagem proposta baseia-se em dividir a sequência temporal do movimento humano nas suas características descritivas e transmiti-las para posterior processamento, em algoritmos de Machine Learning. A framework descrita baseia-se no facto de que a percepção depende, principalmente, do conhecimento ou aprendizagem prévia. Assim, tal como os humanos, a framework terá que aprender os gestos e as suas principais características para que depois possa identificá-los. No entanto, esta está prevista para ser flexível o suficiente de forma a permitir a aprendizagem de gestos de forma dinâmica. Esta dissertação apresenta também uma validação experimental qualitativa e quantitativa da framework. A análise qualitativa fornece os resultados referentes à aceitabilidade da framework. A validação quantitativa fornece os resultados sobre os algoritmos de reconhecimento de gestos. O uso de algoritmos de Machine Learning no reconhecimento de gestos, permite a obtençãoc¸ ˜ao de resultados finais que s˜ao comparaveis ou superam outras implementac¸ ˜oes do mesmo g´enero. Al ´em disso, s˜ao tamb´em apresentadas duas implementac¸ ˜oes art´ısticas da framework, avaliando assim a sua usabilidade no dom´ınio da performance art´ıstica. Apesar duma implementac¸ ˜ao espec´ıfica da framework ser apresentada nesta dissertac¸ ˜ao e disponibilizada como software open-source, a abordagem proposta ´e suficientemente flex´ıvel para que esta seja usada noutros cen´ arios. Abrindo assim, o caminho para aplicac¸ ˜oes que poder˜ao beneficiar n˜ao s´o o dom´ınio das artes performativas, mas tamb´em, provavelmente num futuro pr ´oximo, outros tipos de comunicac¸ ˜ao, como por exemplo, a linguagem gestual usada em casos de deficiˆencia auditiva

Characterization of multiphase flows integrating X-ray imaging and virtual reality

Multiphase flows are used in a wide variety of industries, from energy production to pharmaceutical manufacturing. However, because of the complexity of the flows and difficulty measuring them, it is challenging to characterize the phenomena inside a multiphase flow. To help overcome this challenge, researchers have used numerous types of noninvasive measurement techniques to record the phenomena that occur inside the flow. One technique that has shown much success is X-ray imaging. While capable of high spatial resolutions, X-ray imaging generally has poor temporal resolution. This research improves the characterization of multiphase flows in three ways. First, an X-ray image intensifier is modified to use a high-speed camera to push the temporal limits of what is possible with current tube source X-ray imaging technology. Using this system, sample flows were imaged at 1000 frames per second without a reduction in spatial resolution. Next, the sensitivity of X-ray computed tomography (CT) measurements to changes in acquisition parameters is analyzed. While in theory CT measurements should be stable over a range of acquisition parameters, previous research has indicated otherwise. The analysis of this sensitivity shows that, while raw CT values are strongly affected by changes to acquisition parameters, if proper calibration techniques are used, acquisition parameters do not significantly influence the results for multiphase flow imaging. Finally, two algorithms are analyzed for their suitability to reconstruct an approximate tomographic slice from only two X-ray projections. These algorithms increase the spatial error in the measurement, as compared to traditional CT; however, they allow for very high temporal resolutions for 3D imaging. The only limit on the speed of this measurement technique is the image intensifier-camera setup, which was shown to be capable of imaging at a rate of at least 1000 FPS. While advances in measurement techniques for multiphase flows are one part of improving multiphase flow characterization, the challenge extends beyond measurement techniques. For improved measurement techniques to be useful, the data must be accessible to scientists in a way that maximizes the comprehension of the phenomena. To this end, this work also presents a system for using the Microsoft Kinect sensor to provide natural, non-contact interaction with multiphase flow data. Furthermore, this system is constructed so that it is trivial to add natural, non-contact interaction to immersive visualization applications. Therefore, multiple visualization applications can be built that are optimized to specific types of data, but all leverage the same natural interaction. Finally, the research is concluded by proposing a system that integrates the improved X-ray measurements, with the Kinect interaction system, and a CAVE automatic virtual environment (CAVE) to present scientists with the multiphase flow measurements in an intuitive and inherently three-dimensional manner

Camera positioning for 3D panoramic image rendering

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Virtual camera realisation and the proposition of trapezoidal camera architecture are the two broad contributions of this thesis. Firstly, multiple camera and their arrangement constitute a critical component which affect the integrity of visual content acquisition for multi-view video. Currently, linear, convergence, and divergence arrays are the prominent camera topologies adopted. However, the large number of cameras required and their synchronisation are two of prominent challenges usually encountered. The use of virtual cameras can significantly reduce the number of physical cameras used with respect to any of the known camera structures, hence adequately reducing some of the other implementation issues. This thesis explores to use image-based rendering with and without geometry in the implementations leading to the realisation of virtual cameras. The virtual camera implementation was carried out from the perspective of depth map (geometry) and use of multiple image samples (no geometry). Prior to the virtual camera realisation, the generation of depth map was investigated using region match measures widely known for solving image point correspondence problem. The constructed depth maps have been compare with the ones generated using the dynamic programming approach. In both the geometry and no geometry approaches, the virtual cameras lead to the rendering of views from a textured depth map, construction of 3D panoramic image of a scene by stitching multiple image samples and performing superposition on them, and computation of virtual scene from a stereo pair of panoramic images. The quality of these rendered images were assessed through the use of either objective or subjective analysis in Imatest software. Further more, metric reconstruction of a scene was performed by re-projection of the pixel points from multiple image samples with a single centre of projection. This was done using sparse bundle adjustment algorithm. The statistical summary obtained after the application of this algorithm provides a gauge for the efficiency of the optimisation step. The optimised data was then visualised in Meshlab software environment, hence providing the reconstructed scene. Secondly, with any of the well-established camera arrangements, all cameras are usually constrained to the same horizontal plane. Therefore, occlusion becomes an extremely challenging problem, and a robust camera set-up is required in order to resolve strongly the hidden part of any scene objects. To adequately meet the visibility condition for scene objects and given that occlusion of the same scene objects can occur, a multi-plane camera structure is highly desirable. Therefore, this thesis also explore trapezoidal camera structure for image acquisition. The approach here is to assess the feasibility and potential of several physical cameras of the same model being sparsely arranged on the edge of an efficient trapezoid graph. This is implemented both Matlab and Maya. The quality of the depth maps rendered in Matlab are better in Quality

Developing a person guidance module for hospital robots

This dissertation describes the design and implementation of the Person Guidance Module (PGM) that enables the IWARD (Intelligent Robot Swarm for attendance, Recognition, Cleaning and delivery) base robot to offer route guidance service to the patients or visitors inside the hospital arena. One of the common problems encountered in huge hospital buildings today is foreigners not being able to find their way around in the hospital. Although there are a variety of guide robots currently existing on the market and offering a wide range of guidance and related activities, they do not fit into the modular concept of the IWARD project. The PGM features a robust and foolproof non-hierarchical sensor fusion approach of an active RFID, stereovision and cricket mote sensor for guiding a patient to the X-ray room, or a visitor to a patient’s ward in every possible scenario in a complex, dynamic and crowded hospital environment. Moreover, the speed of the robot can be adjusted automatically according to the pace of the follower for physical comfort using this system. Furthermore, the module performs these tasks in any unconstructed environment solely from a robot’s onboard perceptual resources in order to limit the hardware installation costs and therefore the indoor setting support. Similar comprehensive solution in one single platform has remained elusive in existing literature. The finished module can be connected to any IWARD base robot using quick-change mechanical connections and standard electrical connections. The PGM module box is equipped with a Gumstix embedded computer for all module computing which is powered up automatically once the module box is inserted into the robot. In line with the general software architecture of the IWARD project, all software modules are developed as Orca2 components and cross-complied for Gumstix’s XScale processor. To support standardized communication between different software components, Internet Communications Engine (Ice) has been used as middleware. Additionally, plug-and-play capabilities have been developed and incorporated so that swarm system is aware at all times of which robot is equipped with PGM. Finally, in several field trials in hospital environments, the person guidance module has shown its suitability for a challenging real-world application as well as the necessary user acceptance

A Digital Manufacturing Process For Three-Dimensional Electronics

Additive manufacturing (AM) offers the ability to produce devices with a degree of three-dimensional complexity and mass customisation previously unachievable with subtractive and formative approaches. These benefits have not transitioned into the production of commercial electronics that still rely on planar, template-driven manufacturing, which prevents them from being tailored to the end user or exploiting conformal circuitry for miniaturisation. Research into the AM fabrication of 3D electronics has been demonstrated; however, because of material restrictions, the durability and electrical conductivity of such devices was often limited. This thesis presents a novel manufacturing approach that hybridises the AM of polyetherimide (PEI) with chemical modification and selective light-based synthesis of silver nanoparticles to produce 3D electronic systems. The resulting nanoparticles act as a seed site for the electroless deposition of copper. The use of high-performance materials for both the conductive and dielectric elements created devices with the performance required for real-world applications. For printing PEI, a low-cost fused filament fabrication (FFF); also known as fused deposition modelling (FDM), printer with a unique inverted design was developed. The orientation of the printer traps hot air within a heated build environment that is open on its underside allowing the print head to deposit the polymer while keeping the sensitive components outside. The maximum achievable temperature was 120 °C and was found to reduce the degree of warping and the ultimate tensile strength of printed parts. The dimensional accuracy was, on average, within 0.05 mm of a benchmark printer and fine control over the layer thickness led to the discovery of flexible substrates that can be directly integrated into rigid parts. Chemical modification of the printed PEI was used to embed ionic silver into the polymer chain, sensitising it to patterning with a 405 nm laser. The rig used for patterning was a re-purposed vat-photopolymerisation printer that uses a galvanometer to guide the beam that is focused to a spot size of 155 µm at the focal plane. The positioning of the laser spot was controlled with an open-sourced version of the printers slicing software. The optimal laser patterning parameters were experimentally validated and a link between area-related energy density and the quality of the copper deposition was found. In tests where samples were exposed to more than 2.55 J/cm^2, degradation of the polymer was experienced which produced blistering and delamination of the copper. Less than 2.34 J/cm^2 also had negative effect and resulted in incomplete coverage of the patterned area. The minimum feature resolution produced by the patterning setup was 301 µm; however, tests with a photomask demonstrated features an order of magnitude smaller. The non-contact approach was also used to produce conformal patterns over sloped and curved surfaces. Characterisation of the copper deposits found an average thickness of 559 nm and a conductivity of 3.81 × 107 S/m. Tape peel and bend fatigue testing showed that the copper was ductile and adhered well to the PEI, with flexible electronic samples demonstrating over 50,000 cycles at a minimum bend radius of 6.59 mm without failure. Additionally, the PEI and copper combination was shown to survive a solder reflow with peak temperatures of 249°C. Using a robotic pick and place system a test board was automatically populated with surface mount components as small as 0201 resistors which were affixed using high-temperature, Type-V Tin-Silver-Copper solder paste. Finally, to prove the process a range of functional demonstrators were built and evaluated. These included a functional timer circuit, inductive wireless power coils compatible with two existing standards, a cylindrical RF antenna capable of operating at several frequencies below 10 GHz, flexible positional sensors, and multi-mode shape memory alloy actuators

지자기 지문을 이용한 비용 효율적이고 실용적인 실내 측위 시스템

학위논문(박사)--서울대학교 대학원 :공과대학 전기·컴퓨터공학부,2020. 2. 권태경.지난 십수 년 동안 학계와 산업 영역을 막론하여 실내 측위 시스템이 널리 연구되어 왔다. 실내 측위 시스템을 설계할 때 WiFi, Bluetooth, 관성 센서와 같은 다양한 종류의 센서나 무선 인터페이스들을 활용할 수 있는데, 그 중에서도 지자기 센서에서 측정한 자기장의 패턴을 측위에 사용하는 시스템은 정확성과 안정성 측면에서 타 시스템에 비해 큰 장점을 지니고 있다. 철골 구조에 기반하여 건축된 현대 건축물들의 실내 공간에는 지자기장의 왜곡이 발생하며 이는 곧 실내의 개별 공간들에 고유하고 안정적인 지자기 패턴을 발생시킨다. 이를 실내 측위의 맥락에서는 지자기 지문이라고 정의하는데 이 지자기 지문은 사용자의 움직임, 문과 창문의 여닫힘 등과 같은 일상적인 환경 변화에 강건하며, 특히 WiFi 등 무선 신호와 비교하였을 때 높은 수준의 안정성을 보인다. 그러나 이와 같은 안정성에도 불구하고 지자기 지문을 이용하여 실내 측위 시스템을 설계할 때에는 몇가지 고려해야할 사항들이 있다. 먼저 지자기 지문은 센서 데이터의 낮은 차원 개수로 인하여 AP 개수에 따라 데이터의 차원이 수십, 수백개에 달하는 무선 신호에 비하여 구별성이 매우 낮다. 대부분의 기존 연구들은 많은 양의 연산을 수행하는 복잡한 알고리즘과 많은 센서를 이용하여 이를 극복하였다. 또 대상 공간의 지자기 지문을 수집하기 위한 사전 조사 비용과 주기적인 지자기 지문 재수집에 드는 관리 비용 역시 지자기 지문을 사용할 때 고려해야할 문제점이다. 따라서 본 논문은 이 두 가지 문제점을 해결하는 데에 초점을 맞추어 작성되었다. 먼저 사물 인터넷 (IoT, Internet of Things) 환경에서 실내 측위를 위해 지자기 지문을 활용하는 에너지 효율적인 경량 시스템을 연구하였다. BLE 인터페이스와 센서 2개(지자기 및 가속도계)만 탑재한 새로운 하드웨어 설계 방식을 제안하였으며, 이 기기는 코인 크기의 배터리를 사용할 경우 1년 동안 동작 가능하였다. 또한 최소한의 센서 데이터만을 이용하여 강건한 사용자 보행 모델과 효율적인 알고리즘을 도입한 파티클 필터 프레임워크를 제안하였다. 직접 설계한 사물인터넷 기기와 알고리즘을 적용한 결과, 본 시스템은 낮은 계산 복잡성과 높은 에너지 효율을 보여주면서 일반적인 사무실 공간에 대하여 평균 1.62m의 측위 정확도를 달성하였다. 다음으로는 크라우드소싱 방식을 활용하는 지자기 지문 기반의 실내 측위 시스템을 제안하였다. 실내 측위의 맥락에서 크라우드소싱은 명시적인 대상 공간의 사전 조사 과정 없이 지자기 지문 데이터베이스를 구성하는 방법이다. 지난 십수년 간 실내 측위 연구를 위해 크라우드소싱 방법론이 활발하게 연구되어 왔으나, 크라우드소싱에 기반한 기존 측위 시스템은 일반적으로 사전 조사 기반 시스템보다 위치 정확도가 낮게 측정되어왔다. 크라우드소싱 기반 시스텝의 낮은 측위 성능을 극복하기 위해, 본 연구에서는 지자기 기반 크라우드소싱 데이터를 사용한 실내 측위 시스템을 제안한다. 명시적인 수집 과정 없이 스마트폰 사용자가 일상생활에서 자연스럽게 지자기 지문 데이터베이스를 구축할 수 있도록 HMM 기반의 새로운 학습 모델을 구현하였으며, 주로 복도 위주로 구성된 실내 공간에서의 평가 결과, 제안된 시스템이 96.47\%의 학습 정확도와 0.25m의 중간값 측위 정확도를 달성하였다.Over the decades, indoor localization system has been widely studied in the academic and also in the industrial area. Many sensors or wireless signals such as WiFi, Bluetooth, and inertial sensors are available when designing an indoor localization system, but among them, the systems using the geomagnetic field has advantages concerning accuracy and stability. Every spatial point in an indoor space has its own distinct and stable fingerprint, which arises owing to the distortion of the magnetic field induced by the surrounding steel and iron structures. The magnetic fingerprint is robust to environmental changes like pedestrian activities and door/window movements, particularly compared with radio signals such as WiFi. This phenomenon makes many indoor positioning techniques rely on the magnetic field as an essential source of localization. Despite the robustness, there are some challenges when leveraging the magnetic fingerprint to design the indoor localization system. Due to lower discernibility of the magnetic fingerprint, most of the existing studies have exploited high computational algorithms and many sensors. Also, the cost of a site survey to collect the fingerprints and periodic management of target spaces is still problematic when using magnetic fingerprints. This dissertation thus focuses on these two challenges. First, we present an energy-efficient and lightweight system that utilizes the magnetic field for indoor positioning in the Internet of Things (IoT) environments. We propose a new hardware design of an IoT device that only has a BLE interface and two sensors (magnetometer and accelerometer), with the lifetime of one year when using a coin-size battery. We further propose an augmented particle filter framework that features a robust motion model and algorithmic-efficient localization heuristics with minimal sensory data. The prototype-based evaluation shows that the proposed system achieves a median accuracy of 1.62 m for an office building while exhibiting low computational complexity and high energy efficiency. Next, we propose a magnetic fingerprint-based indoor localization system leveraging a crowdsourcing approach. In the aspect of indoor localization, crowdsourcing is a method to construct the fingerprint database without the explicit site-survey process. Over the past decade, crowdsourcing has been actively studied for indoor localization. However, the existing localization systems based on crowdsourcing usually achieve lower location accuracy than the site survey based systems. To overcome the low performance of the crowdsourcing based approaches, we design an indoor positioning system using the crowdsourced data of the magnetic field. We substantiate a novel HMM-based learning model to construct a database of magnetic field fingerprints from smartphone users. Experiments in an indoor space consisting of aisles show that the proposed system achieves the learning accuracy of 96.47\% and median positioning accuracy of 0.25m.Chapter 1 Introduction 1p - Motivation 1p - Indoor Localization Overview 2p - Magnetic Field based Systems 5p - Organization of Dissertation 7p Chapter 2 An Energy-efficient and Lightweight Indoor Localization System for Internet-of-Things (IoT) Environments 8p - Introduction 8p - Related Work 11p - Issues on Using Magnetic Field 12p - Characteristics of Magnetic Field 12p - Variation Issues 13p - Sensing Rate 17p - Design of Energy-efficient Device for Localization 18p - Hardware Design 18p - Structure of the BLE Beacon Frame 21p - Processing Sensor Data 22p - System Architecture 25p - Overview 25p - Site Survey Methodology 25p - Particle Filter Framework 26p - Evaluation 36p - Implementation 37p - Experiment Setup 38p - Localization Performance 38p - Energy and Algorithmic Efficiency 44p - Discussion 46p Chapter 3 Magnetic Field based Indoor Localization System:A Crowdsourcing Approach 51p - Introduction 51p - Characteristics of Magnetic Field 54p - Robustness 54p - Distinctness 54p - Diversity issues 55p - Design of HMM for Crowdsoucing 56p - Basic Model 56p - Issues in HMM Learning 59p - Preliminary Experiments 59p - System Architecture 63p - Enhanced Learning Model 63p - Pre-processing Crowdsourced Data 65p - Allocating Initial HMM Parameters 67p - Comparing Similarity between the Magnetic Fingerprints 70p - Evaluation 71p - Experimental Settings 71p - Learning Accuracy 71p - Positioning Accuracy 73p - Algorithmic Efficiency 75p Chapter 4 Discussion and Future Work 76p - Open Space Issue 76p Chapter 5 Conclusion 82p Bibliography 84p 초록 92pDocto