Computação massivamente paralela para identificação de marcadores RFID

Mestrado em Engenharia de Computadores e TelemáticaNos dias que correm, tem-se assistido a uma grande evolução dos sistemas de identificação através de marcadores RFID, frequentemente sem se dar a devida importância à componente de privacidade nos mesmos. A presente dissertação pretende explorar um paradigma de identificação de marcadores com o intuito de colmatar esta lacuna, recorrendo à utilização de uma função dificilmente invertível, criptográfica ou de síntese, para a geração no marcador de um identificador pseudo-aleatório a partir do identificador real do mesmo, bem como de um conjunto de números aleatórios gerados pelo marcador e pelo leitor. Contudo, torna-se necessária uma pesquisa ao longo de todos os identificadores atribuídos, que por questões de desempenho é realizado de uma forma massivamente paralela. Desta forma, impede-se o seguimento de objectos ou pessoas associados ao marcador por entidades Ilegítimas, que não tenham acesso a uma base de dados de todos os identificadores atribuídos.In recent years, there has been a large evolution of identification systems through the use of RFID tags, often with some disregard for privacy concerns. In this dissertation a paradigm will be explored focusing on the use of a well known cryptographic standard or hashing function to generate a pseudo-random identifier from the real identifier as well as a set of random nonces from the tag and reader. However, a search is required along the set of assigned identifiers, which for the sake of performance shall be done resorting to a massively parallel approach. This way, it becomes unfeasible for an illegitimate reader to relate two activation sessions of the same tag without access to the database of all the assigned identifiers

Online Semantic Labeling of Deformable Tissues for Medical Applications

University of Minnesota Ph.D. dissertation. May 2017. Major: Mechanical Engineering. Advisor: Timothy Kowalewski. 1 computer file (PDF); ix, 133 pages.Surgery remains dangerous, and accurate knowledge of what is presented to the surgeon can be of great importance. One technique to automate this problem is non-rigid tracking of time-of-flight camera scans. This requires accurate sensors and prior information as well as an accurate non-rigid tracking algorithm. This thesis presents an evaluation of four algorithms for tracking and semantic labeling of deformable tissues for medical applications, as well as additional studies on a stretchable flexible smart skin and dynamic 3D bioprinting. The algorithms were developed and tested for this study, and were evaluated in terms of speed and accuracy. The algorithms tested were affine iterative closest point, nested iterative closest point, affine fast point feature histograms, and nested fast point feature histograms. The algorithms were tested against simulated data as well as direct scans. The nested iterative closest point algorithm provided the best balance of speed and accuracy while providing semantic labeling in both simulation as well as using directly scanned data. This shows that fast point feature histograms are not suitable for nonrigid tracking of geometric feature poor human tissues. Secondary experiments were also performed to show that the graphics processing unit provides enough speed to perform iterative closest point algorithms in real-time and that time of flight depth sensing works through an endoscope. Additional research was conducted on related topics, leading to the development of a novel stretchable flexible smart skin sensor and an active 3D bioprinting system for moving human anatomy

Advances in Biomedical Applications and Assessment of Ultrasound Nonrigid Image Registration.

Image volume based registration (IVBaR) is the process of determining a one-to-one transformation between points in two images that relates the information in one image to that in the other image quantitatively. IVBaR is done primarily to spatially align the two images in the same coordinate system in order to allow better comparison and visualization of changes. The potential use of IVBaR has been explored in three different contexts. In a preliminary study on identification of biometric from internal finger structure, semi-automated IVBaR-based study provided a sensitivity and specificity of 0.93 and 1.00 respectively. Visual matching of all image pairs by four readers yielded 96% successful match. IVBaR could potentially be useful for routine breast cancer screening and diagnosis. Nearly whole breast ultrasound (US) scanning with mammographic-style compression and successful IVBaR were achieved. The image volume was registered off-line with a mutual information cost function and global interpolation based on the non-rigid thin-plate spline deformation. This Institutional Review Board approved study was conducted on 10 patients undergoing chemotherapy and 14 patients with a suspicious/unknown mass scheduled to undergo biopsy. IVBaR was successful with mean registration error (MRE) of 5.2±2 mm in 12 of 17 ABU image pairs collected before, during or after 115±14 days of chemotherapy. Semi-automated tumor volume estimation was performed on registered image volumes giving 86±8% mean accuracy compared with a radiologist hand-segmented tumor volume on 7 cases with correlation coefficient of 0.99 (p<0.001). In a reader study by 3 radiologists assigned to mark the tumor boundary, significant reduction in time taken (p<0.03) was seen due to IVBaR in 6 cases. Three new methods were developed for independent validation of IVBaR based on Doppler US signals. Non-rigid registration tools were also applied in the field of interventional guidance of medical tools used in minimally invasive surgery. The mean positional error in a CT scanner environment improved from 3.9±1.5 mm to 1.0±0.3 mm (p<0.0002). These results show that 3D image volumes and data can be spatially aligned using non-rigid registration for comparison as well as quantification of changes.Ph.D.Applied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64802/1/gnarayan_1.pd