Probabilistic position and orientation estimation of rigid objects using an RGB-D sensor

The recent emergence of low-cost RGB-D sensors has brought new opportunities for robotics by providing affordable devices that can provide synchronized images with both color and depth information. In this thesis, recent work on pose estimation utilizing RGBD sensors is reviewed. Also, a pose recognition system for rigid objects using RGB-D data is implemented. The implementation uses half-edge primitives extracted from the RGB-D images for pose estimation. The system is based on the probabilistic object representation framework by Detry et al., which utilizes Nonparametric Belief Propagation for pose inference. Experiments are performed on household objects to evaluate the performance and robustness of the system.

Imbuing airborne electronic warfare self-protection : possibilities to disguise

Tutkielman nimi käännettynä suomeksi on Lentokoneiden omasuojajärjestelmien kyllästäminen – mahdollisuuksia kätkeytymiseen. Siinä keskitytään etsimään aktiivisia, ilmauhkaa kohti säteileviä keinoja ilmahyökkääjän, lentokoneen tai helikopterin, omasuojajärjestelmän harhauttamiseksi. Järjestelmät pyrkivät varoittamaan lentäjää laser- ja tutkasäteilyhavainnoista sekä ohjuksen laukaisusta ja lähestymisestä. Omasuojajärjestelmiin kuuluviin vastakeinoihin tutkimus ei keskity. Tavoitteena harhauttamisella on ilmapuolustuksen todellisen määrän ja sijainnin suojaaminen. Epätietoisuus voi saada lentäjän tekemään vääriä johtopäätöksiä. Työ ei fokusoi harhauttamisen taktiseen kehykseen työn kannalta tarpeellista määrää enempää. Tietosuojasyistä tutkimus ei käsittele käytössä olevien omasuojajärjestelmien operatiivisia ominaisuuksia vaan käsittelee teoriaa niiden taustalla. Tutkimus tehtiin käyttäen taustatutkimukselle enemmän tyypillistä kirjallisuustutkimusta. Jo tutki-muksen alkuvaiheessa oli oletettavaa, että elektronisiin sensoreihin perustuvaa omasuojajärjestelmää pystytään harhauttamaan. Systeemianalysoinnilla pyrittiin löytämään vastauksia tähän olettamukseen. Omasuojajärjestelmästä muodostettiin jo tutkimuksen varhaisessa vaiheessa malli. Tieteellistä kirjallisuutta omasuojajärjestelmistä on olemassa jonkin verran, ja niistä löydettyä tietoa omasuojajärjestelmien ominaisuuksista ja sensoreista yhdistettiin malliin niin, että siitä saatiin mahdollisimman tarkka systeemin kuvaus todellisesta omasuojajärjestelmästä. Analyysin tavoitteena oli löytää niitä kriteereitä, joilla omasuojajärjestelmä saataisiin kohtuullisen tehokkaasti uskomaan harhautusta oikeaksi hälytykseksi. Ohjuksen laukaisusta varoittava sensori perustuu ohjuksen moottorin muodostaman pilven lämpö-säteilyyn. Säteily kuitenkin muuttuu lennon eri vaiheissa, mikä tuottaa haasteita järjestelmälle. Millimetrialueen tutkan käyttö varoittimen sensorina on myös yksi vaihtoehto. Laser-varoittimet toimivat koko sillä taajuusalueella, mitä sotilaskäytössä tulenjohtamiseen ja ohjusten ohjaamiseen käytetään. Tutkavaroittimen tutkimus on vielä kesken. Löydettyjä tuloksia analysoimalla tulen tässä vaiheessa hieman ristiriitaisiin tuloksiin. Lämpösäteilyn käytön suurin haaste on sen eteneminen ilmakehässä. Varoittimen tavoitekaan ei ole toimia kymmentä kilometriä pidemmälle. Yksi mahdollinen ratkaisu on suunnitella ja toteuttaa raketti, jonka tuottaa lämpösäteilyä kuten tietty puolustavan joukon käytössä oleva ohjus. Jos koneessa on kuitenkin myös millimetrialueen tutka tukemassa varoitusjärjestelmää, hankaloituu rakettiharhautus merkittävästi, koska sen pitäisi oletettavasti olla lentokoneen kanssa melko tarkasti kohtaavalla reitillä. Laser-varoitin on ilmeisesti herkin järjestelmistä, koska se voi tietyissä olosuhteissa ja varsinkin matalalla lentokorkeudella aiheuttaa paljon vääriä hälytyksiä ilman tarkoituksellista harhauttamista. Laserin käyttö yhdistettynä raketin laukaisuun saattaisi tuottaa halutun tuloksen. Tutkavaroittimen harhautus onnistuu, jos valelaitteen signaali on uskottavan tarkka.

Sensor communication in Smart cities and regions: An efficient IoT-based remote health monitoring system

Recent advances in Information and Communication Technology (ICT), especially those related to the Internet of Things (IoT), are facilitating smart regions. Among many services that a smart region can offer, remote health monitoring is a typical application of IoT paradigm. It offers the ability to continuously monitor and collect health-related data from a person, and transmit the data to a remote entity (for example, a healthcare service provider) for further processing and knowledge extraction. An IoT-based remote health monitoring system can be beneficial in rural areas belonging to the smart region where people have limited access to regular healthcare services. The same system can be beneficial in urban areas where hospitals can be overcrowded and where it may take substantial time to avail healthcare. However, this system may generate a large amount of data. In order to realize an efficient IoT-based remote health monitoring system, it is imperative to study the network communication needs of such a system; in particular the bandwidth requirements and the volume of generated data. The thesis studies a commercial product for remote health monitoring in Skellefteå, Sweden. Based on the results obtained via the commercial product, the thesis identified the key network-related requirements of a typical remote health monitoring system in terms of real-time event update, bandwidth requirements and data generation. Furthermore, the thesis has proposed an architecture called IReHMo - an IoT-based remote health monitoring architecture. This architecture allows users to incorporate several types of IoT devices to extend the sensing capabilities of the system. Using IReHMo, several IoT communication protocols such as HTTP, MQTT and CoAP has been evaluated and compared against each other. Results showed that CoAP is the most efficient protocol to transmit small size healthcare data to the remote servers. The combination of IReHMo and CoAP significantly reduced the required bandwidth as well as the volume of generated data (up to 56 percent) compared to the commercial product. Finally, the thesis conducted a scalability analysis, to determine the feasibility of deploying the combination of IReHMo and CoAP in large numbers in regions in north Sweden.

Use of airborne laser scanner data in demanding forest conditions

Most of the applications of airborne laser scanner data to forestry require that the point cloud be normalized, i.e., each point represents height from the ground instead of elevation. To normalize the point cloud, a digital terrain model (DTM), which is derived from the ground returns in the point cloud, is employed. Unfortunately, extracting accurate DTMs from airborne laser scanner data is a challenging task, especially in tropical forests where the canopy is normally very thick (partially closed), leading to a situation in which only a limited number of laser pulses reach the ground. Therefore, robust algorithms for extracting accurate DTMs in low-ground-point-densitysituations are needed in order to realize the full potential of airborne laser scanner data to forestry. The objective of this thesis is to develop algorithms for processing airborne laser scanner data in order to: (1) extract DTMs in demanding forest conditions (complex terrain and low number of ground points) for applications in forestry; (2) estimate canopy base height (CBH) for forest fire behavior modeling; and (3) assess the robustness of LiDAR-based high-resolution biomass estimation models against different field plot designs. Here, the aim is to find out if field plot data gathered by professional foresters can be combined with field plot data gathered by professionally trained community foresters and used in LiDAR-based high-resolution biomass estimation modeling without affecting prediction performance. The question of interest in this case is whether or not the local forest communities can achieve the level technical proficiency required for accurate forest monitoring. The algorithms for extracting DTMs from LiDAR point clouds presented in this thesis address the challenges of extracting DTMs in low-ground-point situations and in complex terrain while the algorithm for CBH estimation addresses the challenge of variations in the distribution of points in the LiDAR point cloud caused by things like variations in tree species and season of data acquisition. These algorithms are adaptive (with respect to point cloud characteristics) and exhibit a high degree of tolerance to variations in the density and distribution of points in the LiDAR point cloud. Results of comparison with existing DTM extraction algorithms showed that DTM extraction algorithms proposed in this thesis performed better with respect to accuracy of estimating tree heights from airborne laser scanner data. On the other hand, the proposed DTM extraction algorithms, being mostly based on trend surface interpolation, can not retain small artifacts in the terrain (e.g., bumps, small hills and depressions). Therefore, the DTMs generated by these algorithms are only suitable for forestry applications where the primary objective is to estimate tree heights from normalized airborne laser scanner data. On the other hand, the algorithm for estimating CBH proposed in this thesis is based on the idea of moving voxel in which gaps (openings in the canopy) which act as fuel breaks are located and their height is estimated. Test results showed a slight improvement in CBH estimation accuracy over existing CBH estimation methods which are based on height percentiles in the airborne laser scanner data. However, being based on the idea of moving voxel, this algorithm has one main advantage over existing CBH estimation methods in the context of forest fire modeling: it has great potential in providing information about vertical fuel continuity. This information can be used to create vertical fuel continuity maps which can provide more realistic information on the risk of crown fires compared to CBH.

Hydrogen Sensor Application of Anodic Titanium Oxide Nanostructures

Hydrogen (H2) fuel cells have been considered a promising renewable energy source. The recent growth of H2 economy has required highly sensitive, micro-sized and cost-effective H2 sensor for monitoring concentrations and alerting to leakages due to the flammability and explosiveness of H2 Titanium dioxide (TiO2) made by electrochemical anodic oxidation has shown great potential as a H2 sensing material. The aim of this thesis is to develop highly sensitive H2 sensor using anodized TiO2. The sensor enables mass production and integration with microelectronics by preparing the oxide layer on suitable substrate. Morphology, elemental composition, crystal phase, electrical properties and H2 sensing properties of TiO2 nanostructures prepared on Ti foil, Si and SiO2/Si substrates were characterized. Initially, vertically oriented TiO2 nanotubes as the sensing material were obtained by anodizing Ti foil. The morphological properties of tubes could be tailored by varying the applied voltages of the anodization. The transparent oxide layer creates an interference color phenomena with white light illumination on the oxide surface. This coloration effect can be used to predict the morphological properties of the TiO2 nanostructures. The crystal phase transition from amorphous to anatase or rutile, or the mixture of anatase and rutile was observed with varying heat treatment temperatures. However, the H2 sensing properties of TiO2 nanotubes at room temperature were insufficient. H2 sensors using TiO2 nanostructures formed on Si and SiO2/Si substrates were demonstrated. In both cases, a Ti layer deposited on the substrates by a DC magnetron sputtering method was successfully anodized. A mesoporous TiO2 layer obtained on Si by anodization in an aqueous electrolyte at 5°C showed diode behavior, which was influenced by the work function difference of Pt metal electrodes and the oxide layer. The sensor enabled the detection of H2 (20-1000 ppm) at low operating temperatures (50–140°C) in ambient air. A Pd decorated tubular TiO2 layer was prepared on metal electrodes patterned SiO2/Si wafer by anodization in an organic electrolyte at 5°C. The sensor showed significantly enhanced H2 sensing properties, and detected hydrogen in the range of a few ppm with fast response/recovery time. The metal electrodes placed under the oxide layer also enhanced the mechanical tolerance of the sensor. The concept of TiO2 nanostructures on alternative substrates could be a prospect for microelectronic applications and mass production of gas sensors. The gas sensor properties can be further improved by modifying material morphologies and decorating it with catalytic materials.

Life without Phd? – Phd Oxygen Sensor Proteins in Hypoxic Carcinoma Cell Survival

The microenvironment within the tumor plays a central role in cellular signaling. Rapidly proliferating cancer cells need building blocks for structures as well as nutrients and oxygen for energy production. In normal tissue, the vasculature effectively transports oxygen, nutrient and waste products, and maintains physiological pH. Within a tumor however, the vasculature is rarely sufficient for the needs of tumor cells. This causes the tumor to suffer from lack of oxygen (hypoxia) and nutrients as well as acidification, as the glycolytic end product lactate is accumulated. Cancer cells harbor mutations enabling survival in the rough microenvironment. One of the best characterized mutations is the inactivation of the von Hippel-Lindau protein (pVHL) in clear cell renal cell carcinoma (ccRCC). Inactivation causes constitutive activation of hypoxia-inducible factor HIF which is an important survival factor regulating glycolysis, neovascularization and apoptosis. HIFs are normally regulated by HIF prolyl hydroxylases (PHDs), which in the presence of oxygen target HIF α-subunit to ubiquitination by pVHL and degradation by proteasomes. In my thesis work, I studied the role of PHDs in the survival of carcinoma cells in hypoxia. My work revealed an essential role of PHD1 and PHD3 in cell cycle regulation through two cyclin-dependent kinase inhibitors (CKIs) p21 and p27. Depletion of PHD1 or PHD3 caused a cell cycle arrest and subjected the carcinoma cells to stress and impaired the survival.