This study presents a history of the development and implementation of a punched card registration system in North Texas State College. The study also covers planning stages of registration materials, and a description of the various stages and processes involved in a typical semester from pre-registration preparations through posting the student's grades to the permanent record. To help fill the need for ready reference to methods used in various institutions across the nation, this study will present a history of the development and implementation of a punched card registration system in North Texas State College, with emphasis placed upon those areas of probable major interest to other colleges faced with a similar problem. The study will cover planning stages of registration materials, and will then present a a description of the various stages and processes involved in a typical semester from pre-registration preparations through posting the student's grades to the permanent record.

In this thesis we suggest a technique for detecting pulses by signal processing of a raw ECG signal registered from 4 electrodes located on the left upper arm. The signal processing is performed in Matlab and consists of normalization, lowpass filtering, highpass filtering, derivation, squaring and a moving average window to reduce interference. The technology is capable of extracting periods between heartbeats after an implemented detection algorithm. The thesis also includes reflections on the types of interferences that may affect an electrical development equipment and also methods of how major parts of the interference can be reduced by different shields. Before the technique is applied in a final product, further tests may need to be performed during the monitoring of a person's pulse. Finally, we believe that our development of pulse detection is the beginning of a new technology that in the future can save lives. / I den här rapporten föreslår vi en teknik för att detektera pulser med hjälp av att signalbehandla en rå EKG-signal registrerad från 4 elektroder placerade på vänster överarm. En signalbehandling utförd i Matlab som bland annat består av normering, lågpassfiltrering, högpassfiltrering, derivering, kvadrering samt ett glidande medelvärdesfönster för att reducera störningar. Tekniken är kapabel till att utvinna tider mellan hjärtslag efter en implementerad detekteringsalgoritm. Rapporten innefattar även reflektioner kring vilka typer av störningar som kan påverka en elektrisk utvecklingsutrustning samt metoder för hur större delar av störningarna kan reduceras med hjälp av olika skärmningar. Innan tekniken appliceras i en slutlig produkt kan ytterligare tester behöva utföras under monitorering av en persons puls. Slutligen anser vi att våran utveckling av pulsdetektion är en början på en ny teknik för att kunna rädda liv.

As more data sources containing 3-D information are becoming available, an increased interest in 3-D imaging has emerged. Among these is the 3-D reconstruction of buildings and other man-made structures. A necessary preprocessing step is the detection and isolation of individual buildings that subsequently can be reconstructed in 3-D using various methodologies. Applications for both building detection and reconstruction have commercial use for urban planning, network planning for mobile communication (cell phone tower placement), spatial analysis of air pollution and noise nuisances, microclimate investigations, geographical information systems, security services and change detection from areas affected by natural disasters. Building detection and reconstruction are also used in the military for automatic target recognition and in entertainment for virtual tourism. Previously proposed building detection and reconstruction algorithms solely utilized aerial imagery. With the advent of Light Detection and Ranging (LiDAR) systems providing elevation data, current algorithms explore using captured LiDAR data as an additional feasible source of information. Additional sources of information can lead to automating techniques (alleviating their need for manual user intervention) as well as increasing their capabilities and accuracy. Several building detection approaches surveyed in the open literature have fundamental weaknesses that hinder their use; such as requiring multiple data sets from different sensors, mandating certain operations to be carried out manually, and limited functionality to only being able to detect certain types of buildings. In this work, a building detection system is proposed and implemented which strives to overcome the limitations seen in existing techniques. The developed framework is flexible in that it can perform building detection from just LiDAR data (first or last return), or just nadir, color aerial imagery. If data from both LiDAR and aerial imagery are available, then the algorithm will use them both for improved accuracy. Additionally, the proposed approach does not employ severely limiting assumptions thus enabling the end user to apply the approach to a wider variety of different building types. The proposed approach is extensively tested using real data sets and it is also compared with other existing techniques. Experimental results are presented.

Automatic bin picking is a well-known problem in industrial automation and computer vision, where a robot picks an object from a bin and places it somewhere else. There is continuous ongoing research for many years to improve the contemporary solution. With camera technology advancing rapidly and available fast computation resources, solving this problem with deep learning has become a current interest for several researchers. This thesis intends to leverage the current state-of-the-art deep learning based methods of 3D instance segmentation and point cloud registration and combine them to improve the bin picking solution by improving the performance and make them robust. The problem of bin picking becomes complex when the bin contains identical objects with heavy occlusion. To solve this problem, a 3D instance segmentation is performed with Fast Point Cloud Clustering (FPCC) method to detect and locate the objects in the bin. Further, an extraction strategy is proposed to choose one predicted instance at a time. Inthe next step, a point cloud registration technique is implemented based on PointNetLK method to estimate the pose of the selected object from the bin. The above implementation is trained, tested, and evaluated on synthetically generated datasets. The synthetic dataset also contains several noisy point clouds to imitate a real situation. The real data captured at the company ’SICK IVP’ is also tested with the implemented model. It is observed that the 3D instance segmentation can detect and locate the objects available in the bin. In a noisy environment, the performance degrades as the noise level increase. However, the decrease in the performance is found to be not so significant. Point cloud registration is observed to register best with the full point cloud of the object, when compared to point cloud with missing points.

The development of the IEEE 802.22 standard is aimed at providing broadband access in rural areas by effectively utilizing the unused TV band, provided no harmful interference is caused to the incumbent operation. This thesis presents the analytical framework to evaluate the number of active customer premise equipments (CPEs) in a wireless regional area network. Initial ranging is the primary process in IEEE 802.22 networks for CPEs to access the network and establish their connections with the base station (BS). A comprehensive analysis of initial ranging mechanism is provided in this work and initial ranging request success probability is derived based on the number of contended CPEs and the initial contention window size. Further, the average ranging success delay is derived for the maximum backoff stages. The collision probability is highly dependent on the size of the initial contention window and the number of contended CPEs. To keep it at a specific level, it is necessary for the BS to schedule the required size of the initial contention window to facilitate the maximum number of CPEs to establish their connections with reasonable delay. Therefore, the optimized initial window size is proposed that meets the collision probability constraint for a particular number of contended CPEs. An analytical model is also developed to estimate the ranging request collision probability depending upon the size of initial contention window and the number of contended CPEs. Moreover, this approximation provides the threshold size for contention window to start the initial ranging process in the IEEE 802.22 network. / Bahauddin Zakariya University Multan, Pakistan.

Positron Emission Tomography (PET) is pivotal in medical imaging but is prone to artifactsfrom physiological movements, notably respiration. These motion artifacts both degradeimage quality and compromise precise attenuation correction. To counteract this, gatingstrategies partition PET data in synchronization with respiratory cycles, ensuring each gatenearly represents a static phase. Additionally, a 3D deep learning image registration modelcan be used for inter-gate motion correction, maximizing the use of the full acquired data. Thisstudy aimed to implement and evaluate two gating strategies: an external device-based approachand a data-driven centroid-of-distribution (COD) trace algorithm, and assess their impact on theperformance of the registration model. Analysis of clinical data from four subjects indicated thatthe external device approach outperformed its data-driven counterpart, which faced challengesin real-patient settings. Post motion compensation, both methods achieved results comparableto state-of-the-art reconstructions, suggesting the deep learning model addressed some data-driven method limitations. However, the motion corrected outputs did not exhibit significantimprovements in image quality over state-of-the-art standards. / Positronemissionstomografi (PET) är fundamentalt inom medicinsk avbildning men påverkasav artefakter orsakade av fysiologiska rörelser, framför allt andning. Dessa artefakter påverkarbildkvaliteten negativt och försvårar korrekt attenueringskorrigering. För att motverka dettakan tekniker för rörelsekorrigering tillämpas. Dessa innefattar gating-tekniker där PET-dataförst synkroniseras med andningscykeln för att därefter segmenterateras i olika så kalladegater som representerar en specifick respiratorisk fas. Vidare kan en 3D djupinlärningsmodellanvändas för att korrigera för rörelserna mellan gaterna, vilket optimerar användningen av allinsamlad data. Denna studie implementerade och undersökte två gating-tekniker: en externenhetsbaserad metod och en datadriven ”centroid-of-distribution (COD)” spår-algoritm, samtanalyserade hur dessa tekniker påverkar prestandan av bildregistreringsmodellen. Utifrånanalysen av kliniska data från fyra patienter visade sig metoden med den externa enhetenvara överlägsen den datadrivna metoden, som hade svårigheter i verkliga patient-situationer.Trots detta visade bildregistreringsmodellen potential att delvis kompensera för den datadrivnametodens begränsningar, då resultatet från båda strategeierna var jämförbara med befintligaklinisk bildrekonstruktion. Dock kunde ingen markant förbättring i bildkvalitet urskiljas av derörelsekorrigerade bilderna jämfört med nuvarande toppstandard.

Boron neutron capture therapy (BNCT) has the potential to specifically destroy tumor cells without damaging the tissues infiltrated by the tumor. BNCT is a binary treatment method based on the combination of two agents that have no effect when applied individually: 10B and thermal neutrons. Exclusively, the combination of both produces an effect, whose extent depends on the amount of 10B in the tumor but also on the organs at risk. It is not yet possible to determine the 10B concentration in a specific tissue using non-invasive methods. At present, it is only possible to measure the 10B concentration in blood and to estimate the boron concentration in tissues based on the assumption that there is a fixed uptake of 10B from the blood into tissues. On this imprecise assumption, BNCT can hardly be developed further. A therapeutic approach, combining the boron carrier for therapeutic purposes with an imaging tool, might allow us to determine the 10B concentration in a specific tissue using a non-invasive method. This review provides an overview of the current clinical protocols and preclinical experiments and results on how innovative drug development for boron delivery systems can also incorporate concurrent imaging. The last section focuses on the importance of proteomics for further optimization of BNCT, a highly precise and personalized therapeutic approach.

Alzheimer´s Disease (AD) is the most prevalent cause of dementia, signifying a progressive and degenerative brain disorder that causes cognitive function deterioration including memory loss, communication difficulties, impaired judgment, and changes in behavior and personality. Compared to normal aging, AD introduces more profound cognitive impairments and brain morphology changes. Understanding these morphological changes associated with both normal aging and AD holds pivotal significance for the study of brain health. In recent years, the flourishing development of Artificial Intelligence (AI) has facilitated the analysis of medical images and the study of longitudinal brain morphology evolution. Numerous advanced AI-based frameworks have emerged to generate unbiased and realistic medical templates that represent the common characteristics within a cohort, providing valuable insights for cohort studies. Among these, Atlas-GAN is a state-of-the-art framework which can generate high-quality conditional deformable templates using diffeomorphic registration. However, cohort studies are not sufficient for individualized healthcare and treatment as each patient has a unique condition. Fortunately, the introduction of a mathematical mechanism, parallel transport, enables the inference of individual brain morphological evolution from cohort-level longitudinal templates. This project proposed an image generator that integrates the pole ladder, a tool for parallel transport implementation, into Atlas-GAN, to translate the cohort-level brain morphological evolution onto individual subjects, enabling the synthesis of anatomically plausible and personalized longitudinal Magnetic Resonance (MR) images based on one individual Magnetic Resonance Imaging (MRI) scan. In clinics, the synthesized images empower the physicians to retrospectively understand the patient's premorbid brain states and prospectively predict their brain morphology changes over time. Such capabilities are of paramount importance for the prognosis, diagnosis, and early-stage intervention of AD, especially given the current absence of a cure for AD. The primary contributions of this project include: (1) Introduction of an image generator that combines parallel transport with Atlas-GAN to synthesize individual longitudinal MR images for both the normal aging cohort and the cohort suffering from AD with both anatomical plausibility and preservation of individualized characteristics; (2) exploration into the prediction of individual longitudinal MR images in the case of an individual undergoing a state transition using the proposed generator; (3) conduction of both qualitative and quantitative evaluations and analyses for the synthesized images. / AD är den mest framträdande orsaken till demens och innebär en progressiv och degenerativ hjärnsjukdom som resulterar i kognitiv försämring, inklusive minnesförlust, kommunikationssvårigheter, nedsatt omdöme samt förändringar i beteende och personlighet. I jämförelse med normal åldrande introducerar AD mer djupgående kognitiva störningar och förändringar i hjärnans morfologi. Att förstå dessa morfologiska förändringar i samband med både normalt åldrande och AD har avgörande betydelse för studien av järnhälsa. De senaste årens blomstrande utveckling inom AI har underlättat analysen av medicinska bilder och studiet av långsiktig hjärnmorfologi. Flera avancerade AI-baserade ramverk har utvecklats för att generera opartiska och realistiska medicinska mallar som representerar gemensamma egenskaper inom en kohort och ger värdefulla insikter for kohortstudier. Bland dessa ar Atlas-GAN ett framstående ramverk som kan generera högkvalitativa, konditionellt deformabla mallar med hjälp av diffeomorfisk registrering. Dock ar kohortstudier inte tillräckliga för individualiserad sjukvård och behandling, eftersom varje patient har en unik situation. Som tur är möjliggör introduktionen av en matematisk mekanism, parallell transport, att man kan dra slutsatser om individuell hjärnmorfologisk utveckling från kohortbaserade longitudinella mallar. I detta projekt föreslogs en bildgenerator som integrerar pole ladder", ett verktyg for implementering av parallell transport, i Atlas- GAN. Detta möjliggör att kohortbaserad hjärnmorfologisk utveckling kan översättas till individnivå, vilket gör det möjligt att syntetisera anatomiskt trovärdiga och personifierade longitudinella MR-bilder baserade på en individs MRI-skanning. Inom kliniken gör de syntetiserade bilderna det möjligt för läkare att retrospektivt förstå patientens premorbida hjärnstatus och prospektivt förutsäga deras hjärnmorfologiska förändringar över tiden. Sådana möjligheter är av avgörande betydelse för prognos, diagnos och tidig intervention vid AD, särskilt med tanke på den nuvarande bristen på en botemedel för AD. De huvudsakliga bidragen från detta projekt inkluderar: (1) Introduktion av en bildgenerator som kombinerar parallell transport med Atlas-GAN för att syntetisera individuella longitudinella MR-bilder för både kohorten med normalt åldrande och kohorten som lider av AD, med både anatomisk trovärdighet och bevarande av individualiserade egenskaper. Dessutom har de genererade bilderna genomgått både kvalitativa och kvantitativa utvärderingar och analyser; (2) Utforskning av förutsägelse av individuella longitudinella MR-bilder i fallet när en individ genomgår en tillståndsövergång med hjälp av det föreslagna generatorn.

Point cloud registration is a technique to align two sets of points with manifold applications across a range of industries. However, due to a lack of adequate sensing technology, this technique has seldom found applications in the automotive sector up to now. With the advent of solid-state Light Detection and Ranging (LiDAR) sensors that are easily integrable in series production vehicles as means to sense the surrounding environment, this technique can be functional to automate their operations. Maneuvering a vehicle in the proximity of a reference object is one such operation, which can only be performed by accurately estimating its position and orientation relative to the vehicle itself. This project deals with the design and the implementation of an algorithm to accurately locate an aircraft parked on an airfield apron in real time. This is achieved by registering the point cloud model of the plane to the measurement point cloud of the scene produced by the LiDAR sensors on board the vehicle. To this end, the Iterative Closest Point (ICP) algorithm is a well-established approach to register two sets of points without prior knowledge of the correspondences between pairs of points, which, however, is notoriously sensitive towards outliers and computationally expensive with large point clouds. In this work, different variants are presented that improve on the standard ICP algorithm, in terms of accuracy and runtime performance, by leveraging different data structures to index the reference model and outlier rejection strategies. The results show that the implemented algorithms can produce estimates of centimeter precision in milliseconds based only on partial observations of the aircraft, outperforming another established solution tested. / Punktmolnregistrering är en teknik för att anpassa två uppsättningar punkter med mångfaldiga applikationer inom en rad branscher. På grund av bristen på adekvat sensorsteknik har denna teknik hittills sällan används inom automotivesektorn. Med tillkomsten av solid-state LiDAR -sensorer som enkelt kan integreras i serieproduktionsfordon för att kunna känna av den omgivningen, kan denna teknik automatisera verksamheten. Att manövrera ett fordon i närheten av ett referensobjekt är en sådan operation, som bara kan utföras genom att exakt uppskatta dess position och orientering i förhållande till själva fordonet. Detta projekt handlar om design och implementering av en algoritm för att exakt lokalisera ett flygplan parkerat på ett flygfält i realtid. Detta uppnås genom att registrera planetens molnmodell till mätpunktsmolnet på scenen som produceras av LiDAR -sensorerna ombord på fordonet. För detta ändamålet är Iterative Closest Point (ICP) -algoritmen ett väletablerat tillvägagångssätt för att registrera två uppsättningar punkter utan föregående kännedom om överensstämmelserna mellan parpar, vilket dock är notoriskt känsligt för avvikelser och beräknat dyrt med stora punktmoln. I detta arbete presenteras olika varianter som förbättrar standard ICP - algoritmen, när det gäller noggrannhet och runtime performance, genom att utnyttja olika datastrukturer för att indexera referensmodellen och outlier -avvisningsstrategier. Resultaten visar att de implementerade algoritmerna kan producera uppskattningar av centimeters precision i millisekunder baserat endast på partiella observationer av flygplanet, vilket överträffar en annan etablerad lösning som testats.

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