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Solid-state nanopores are a versatile class single-molecule sensors to electrically characterize a range of biological molecules. Nanopores operate on the simple premise that when a voltage is applied across a pore immersed in a salt solution, the passage of a biomolecule results in a transient blockage in the ionic current that provide information about the translocating molecule. This thesis presents studies employing various DNA nanostructures with solid-state nanopore electrical readout for the development of high sensitivity digital single-molecule assays to detect low-abundance biomarkers. Toward this ultimate goal, work presented in this thesis use nanopores to probe DNA nanostructures, their assembly, mechanical properties, and monitor their dynamics with time and temperature. DNA nanostructures are self-assembled via specific base pairing of DNA, their programmability make them particularly useful for applications including drug delivery, molecular computation and biosensing. Here, I first show results of translocation profiles and discuss folding characteristics, mobility, and molecular configuration during passage for different DNA nanostructures such as the short star-shaped DNA nanostructures and large helix-bundle DNA origami structures under various experimental conditions in an effort to understand the passage characteristics through nanopores of these structures before using them in biological assays. I conclude by presenting a magnetic bead-based immunoassay scheme using a digital solid-state nanopore readout. Nanopore has the ability to count molecules one at a time, this allows accurate and precise determination of the concentration of a biomarker in solution. Coupled with the use of specific choice of DNA nanostructures, as proxy labels for proteins of interest, I establish that nanopores sensors can reliably quantify the concentration of a protein biomarker from complex biofluids and overcome the traditional challenges associated with nanopore-based protein sensing, such as specificity, sensitivity, and consistency. I demonstrate the quantification of thyroid stimulating hormone (TSH) with a high degree of precision down to the femtomolar range by using a nanoparticle-based signal amplification strategy. The proposed assay scheme is generalizable to a framework for the detection and quantification of a wide range of target proteins, and given that its performance can further be improved with the use of parallelization, preconcentration, or miniaturization, it opens up exciting opportunities for the development of ultra-sensitive digital assay in a format that is compatible for point-of-care.

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Induced graphs are used to describe the structure of a graph, one such type of induced graph that has been studied are long paths. In this thesis we show a way to represent such graphs in terms of an array with two colors and a labeled graph. Using this representation and the techniques of Polya counting we will then be able to get upper and lower bounds for graphs containing a long path as an induced subgraph. In particular, if we let P(n,k) be the number of graphs on n+k vertices which contains P_n, a path on n vertices, as an induced subgraph then using our upper and lower bounds for P(n,k) we will show that for any fixed value of k that P(n,k)~2^(nk+k_C_2)/(2k!).

Visual analysis of dense crowds is particularly challenging due to large number of individuals, occlusions, clutter, and fewer pixels per person which rarely occur in ordinary surveillance scenarios. This dissertation aims to address these challenges in images and videos of extremely dense crowds containing hundreds to thousands of humans. The goal is to tackle the fundamental problems of counting, detecting and tracking people in such images and videos using visual and contextual cues that are automatically derived from the crowded scenes. For counting in an image of extremely dense crowd, we propose to leverage multiple sources of information to compute an estimate of the number of individuals present in the image. Our approach relies on sources such as low confidence head detections, repetition of texture elements (using SIFT), and frequency-domain analysis to estimate counts, along with confidence associated with observing individuals, in an image region. Furthermore, we employ a global consistency constraint on counts using Markov Random Field which caters for disparity in counts in local neighborhoods and across scales. We tested this approach on crowd images with the head counts ranging from 94 to 4543 and obtained encouraging results. Through this approach, we are able to count people in images of high-density crowds unlike previous methods which are only applicable to videos of low to medium density crowded scenes. However, the counting procedure just outputs a single number for a large patch or an entire image. With just the counts, it becomes difficult to measure the counting error for a query image with unknown number of people. For this, we propose to localize humans by finding repetitive patterns in the crowd image. Starting with detections from an underlying head detector, we correlate them within the image after their selection through several criteria: in a pre-defined grid, locally, or at multiple scales by automatically finding the patches that are most representative of recurring patterns in the crowd image. Finally, the set of generated hypotheses is selected using binary integer quadratic programming with Special Ordered Set (SOS) Type 1 constraints. Human Detection is another important problem in the analysis of crowded scenes where the goal is to place a bounding box on visible parts of individuals. Primarily applicable to images depicting medium to high density crowds containing several hundred humans, it is a crucial pre-requisite for many other visual tasks, such as tracking, action recognition or detection of anomalous behaviors, exhibited by individuals in a dense crowd. For detecting humans, we explore context in dense crowds in the form of locally-consistent scale prior which captures the similarity in scale in local neighborhoods with smooth variation over the image. Using the scale and confidence of detections obtained from an underlying human detector, we infer scale and confidence priors using Markov Random Field. In an iterative mechanism, the confidences of detections are modified to reflect consistency with the inferred priors, and the priors are updated based on the new detections. The final set of detections obtained are then reasoned for occlusion using Binary Integer Programming where overlaps and relations between parts of individuals are encoded as linear constraints. Both human detection and occlusion reasoning in this approach are solved with local neighbor-dependent constraints, thereby respecting the inter-dependence between individuals characteristic to dense crowd analysis. In addition, we propose a mechanism to detect different combinations of body parts without requiring annotations for individual combinations. Once human detection and localization is performed, we then use it for tracking people in dense crowds. Similar to the use of context as scale prior for human detection, we exploit it in the form of motion concurrence for tracking individuals in dense crowds. The proposed method for tracking provides an alternative and complementary approach to methods that require modeling of crowd flow. Simultaneously, it is less likely to fail in the case of dynamic crowd flows and anomalies by minimally relying on previous frames. The approach begins with the automatic identification of prominent individuals from the crowd that are easy to track. Then, we use Neighborhood Motion Concurrence to model the behavior of individuals in a dense crowd, this predicts the position of an individual based on the motion of its neighbors. When the individual moves with the crowd flow, we use Neighborhood Motion Concurrence to predict motion while leveraging five-frame instantaneous flow in case of dynamically changing flow and anomalies. All these aspects are then embedded in a framework which imposes hierarchy on the order in which positions of individuals are updated. The results are reported on eight sequences of medium to high density crowds and our approach performs on par with existing approaches without learning or modeling patterns of crowd flow. We experimentally demonstrate the efficacy and reliability of our algorithms by quantifying the performance of counting, localization, as well as human detection and tracking on new and challenging datasets containing hundreds to thousands of humans in a given scene.

As the world explores opportunities to develop offshore renewable energy capacity, there will be a growing need for pre-construction biological surveys and post-construction monitoring in the challenging marine environment. Underwater video is a powerful tool to facilitate such surveys, but the interpretation of the imagery is costly and time-consuming. Emerging technologies have improved automated analysis of underwater video, but these technologies are not yet accurate or accessible enough for widespread adoption in the scientific community or industries that might benefit from these tools. To address these challenges, prior research developed a website that allows to: (1) Quickly play and annotate underwater videos, (2) Create a short tracking video for each annotation that shows how an annotated concept moves in time, (3) Verify the accuracy of existing annotations and tracking videos, (4) Create a neural network model from existing annotations, and (5) Automatically annotate unwatched videos using a model that was previously created. It uses both validated and unvalidated annotations and automatically generated annotations from trackings to count the number of Rathbunaster californicus (starfish) and Strongylocentrotus fragilis (sea urchin) with count accuracy of 97% and 99%, respectively, and F1 score accuracy of 0.90 and 0.81, respectively. The thesis explores several improvements to the model above. First, a method to sync JavaScript video frames to a stable Python environment. Second, reinforcement training using marine biology experts and the verification feature. Finally, a hierarchical method that allows the model to combine predictions of related concepts. On average, this method improved the F1 scores from 0.42 to 0.45 (a relative increase of 7%) and count accuracy from 58% to 69% (a relative increase of 19%) for the concepts Umbellula Lindahli and Funiculina.

Within cosmology, there are two entirely independent pillars which can jointly drive this field towards precision: Astronomical observations of primordial element abundances and the detailed surveying of the cosmic microwave background. However, the comparatively large uncertainty stemming from the nuclear physics input is currently still hindering this effort, i.e. stemming from the 2H(p,γ)3He reaction. An accurate understanding of this reaction is required for precision data on primordial nucleosynthesis and an independent determination of the cosmological baryon density. Elsewhere, our Sun is an exceptional object to study stellar physics in general. While we are now able to measure solar neutrinos live on earth, there is a lack of knowledge regarding theoretical predictions of solar neutrino fluxes due to the limited precision (again) stemming from nuclear reactions, i.e. from the 3He(α,γ)7Be reaction. This thesis sheds light on these two nuclear reactions, which both limit our understanding of the universe. While the investigation of the 2H(p,γ)3He reaction will focus on the determination of its cross- section in the vicinity of the Gamow window for the Big Bang nucleosynthesis, the main aim for the 3He(α,γ)7Be reaction will be a measurement of its γ-ray angular distribution at astrophysically relevant energies. In addition, the installation of an ultra-low background counting setup will be reported which further enables the investigation of the physics of rare events. This is essential for modern nuclear astrophysics, but also relevant for double beta decay physics and the search for dark matter. The presented setup is now the most sensitive in Germany and among the most sensitive ones worldwide.

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In this paper, an algorithm for estimating lead-acid battery state of charge (SOC) is implemented. The algorithm, named “Improved Coulomb Counting Algorithm”, was developed within a master thesis project (M. M. Samolyk & J. Sobczak, “Development of an algorithm for estimating Lead-acid Battery State of Charge and State of Health”, M.S. thesis, Dept. Signal Processing, Blekinge Institute of Technology, Karlskrona, Sweden, 2013) with cooperation of a Swedish company – Micropower – Research and Development department.  Currently used method at Micropower is Coulomb Counting; implemented algorithm compares coulomb counting method with open circuit voltage method and uses current, terminal voltage and temperature measurements to finally produce improvement for the very same coulomb counting method and get a better estimation of SOC.  The algorithm was implemented on Micropower Access Battery Monitoring Unit (BMU) using C programming language, so that it can be tested in real time application of the regular battery operation. In the end specific gravity measurements were also presented to comparing the methods.