DSpace@RPI is a repository of Rensselaer Polytechnic Institute's theses and dissertations which are available in digital format, largely from 2006 to present, along with other selected resources.

Recent Submissions

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    The use of creative analogies in a complex problem situation
    (Springer, 2014-08-01) Damaskinos, Melanie; Lutsevich, Alexander; Do¨rner, Dietrich; Schmid, Ute; Gu¨ss, C. Dominik
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    Reducing the Cognitive Load of Visual Analytics of Networks Using Concentrically Arranged Multi-surface Projections Focusing Immersive Real-time Exploration
    (2018-06-01) Ameres, Eric
    The analysis of “Big Data” stretches traditional visualization to its breaking point. This is especially true of highly interconnected relational data that pervades the field. Seemingly in response to that Visual Analytics (VA) and the graphical visualization of data in general are often used only with the goal of simplification and presentation rather than as a tool for rich study and discovery. To maximize the use of visualization as an effective tool for generating new knowledge from complex data, we must understand and address issues of design based on human sensing, perception and overall cognitive processing especially with regard to learning. The Campfire and the visualization paradigm I have developed based on its form (Concentrical-ly Arranged Multi-surface Projections Focusing Immersive Real-time Exploration aka “CAMPFIRE”) are novel, and provide a form and affordances that inspire new methods for the exploration and structured visualization of data that are immersive and visuo-spatially rich. However, novelty is not a measure of effectiveness. Instructional media, instructional methods and their associated cognitive tasks such as evaluating a graph, chart or other visual information carry loading costs of different types that need to be mitigated and moderated by informed design. The proposal was that through the application of Cognitive Load Theory and by specifically designing with careful attention to the effect of split attention and increasing the use of spatiality as a distinct modality, it is possible to reduce cognitive load for certain types of visual analytics tasks. It should be possible to promote the efficacy and engagement of old and new methods of visual analytics by re-imagining their use of space and form, and by applying these theories and practices with that in mind. This is especially true for the radial visualization technique that relies heavily on form, and for display paradigms that can potentially instill a sense of spatial 3-dimensionality in the user. This thesis demonstrates and tests the Campfire style of visualization on a simulated network (graph) visualization type task (e.g., visually inspecting and comparing nodes in a connected graph). It shows that it is possible to better engage the user through heightened dimensionality vs. traditional flat display by creating affordances that offload certain types of spatial processing (rotation and translation) from the user back into the visualization system. This thesis also provides design-based analysis of a variety of cases to give insight into “best practices” and design recommendations with regard to Campfire style visual analytics. It also demonstrates the connections and parallels that this method has to other traditional visualization and information and statistical graphic theory and practice.
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    Evolving a rapid prototyping environment for visually and analytically exploring large-scale Linked Open Data
    (2011-12-01) Downie, Mark; Kaiser, Paul; Enloe, Dylan; Fox, Peter; Hendler, James A.; Ameres, Eric; Goebel, Johannes
    The lack of development environments for interdisciplinary research conducted on large-scale datasets hampers research at every stage. Projects incur large startup costs as disparate infrastructure is assembled; experimentation slows when software components and environment are mismatched for specific research tasks; and findings are disseminated in forms that are hard to examine, learn from, and reuse. Behind these problems is a common cause - the lack of good tools. When large, heterogeneous and distributed data is added to the equation, further frustration, at the least, ensues. As a result using existing platforms, the programmers of 21 st century interactive visualizations are reduced to working in the same fashion with the same tools as 20 th century database programmers. Our contribution is to bring the tools of digital artists to bear on the aforementioned data analysis and visualization challenges. Here we report on the current state of progress in adapting Field for large-scale, web-based scientific data analysis and visualization with an emphasis on Linked Open Data [1] and especially the current data hosted by RPI [2].
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    MortalityMinder: Visualization and AI Interpretations of Social Determinants of Premature Mortality in the United States
    (MDPI, 2024-04-30) Bhanot, Karan; Erickson, John S.; Bennett, Kristin P.
    MortalityMinder enables healthcare researchers, providers, payers, and policy makers to gain actionable insights into where and why premature mortality rates due to all causes, cancer, cardiovascular disease, and deaths of despair rose between 2000 and 2017 for adults aged 25–64. MortalityMinder is designed as an open-source web-based visualization tool that enables interactive analysis and exploration of social, economic, and geographic factors associated with mortality at the county level. We provide case studies to illustrate how MortalityMinder finds interesting relationships between health determinants and deaths of despair. We also demonstrate how GPT-4 can help translate statistical results from MortalityMinder into actionable insights to improve population health. When combined with MortalityMinder results, GPT-4 provides hypotheses on why socio-economic risk factors are associated with mortality, how they might be causal, and what actions could be taken related to the risk factors to improve outcomes with supporting citations. We find that GPT-4 provided plausible and insightful answers about the relationship between social determinants and mortality. Our work is a first step towards enabling public health stakeholders to automatically discover and visualize relationships between social determinants of health and mortality based on available data and explain and transform these into meaningful results using artificial intelligence.
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    Low-power time-to-digital converters for high-precision measurement
    (Rensselaer Polytechnic Institute, Troy, NY, 2023-12) Tong, Xing; Hella, Mona, M
    Time-to-digital converters (TDCs), which converts time delays into digital signals, have garnered significant interest for their diverse applications in fields such as high-energy nuclear physics, time-of-flight (ToF) sensors, time-domain analog-to-digital converters (TD-ADCs), and all-digital phase-locked loops (ADPLLs). The performance of a TDC is primarily measured by its resolution, range, sampling rate, and power consumption. To simultaneously achieve a fine resolution and a long range, or a high dynamic range, in an area and power efficient manner, hierarchical architectures with the potential to combine the advantages of several different approaches has been studied. This thesis investigated three different hierarchical designs implemented in technologies ranging from 350nm CMOS to 45nm SOI, each suitable for its specific applications. This work first presents a hierarchical ADC-assisted TDC with reconfigurable resolution. The reconfigurable resolution and range are achieved by adjusting reference currents in the time-to-voltage converter (TVC) and the reference voltages in the ADC. The proposed resolution-reconfigurable approach combined with a two-step hierarchical architecture can be employed in a wide range of applications with different spatial range and resolution requirements. Fabricated using a 350nm CMOS process with a core area of 0.15mm², prototype chips yielded a resolution of 39ps with a 100MHz reference clock or 78ps with a 50MHz reference clock. In both cases, the measurement rate is 384kS/s while consuming less than 6.7mW from a 3.3V supply. Secondly, a multi-channel 4-tier TDC design combining gated-ring oscillators (GRO) coarse measurement stage, time amplifier, and 2D vernier fine measurement stage designed an simulated in 90nm SOI SiPh process. A dual-counter correction scheme is proposed to address the parallel-output-misalignment (POM) error in multi-phase clock based TDCs. The finer two tiers employ time amplifiers and 2D vernier lines to measure the residual signal, achieving a sub-gate-delay resolution while keeping a high conversion rate. Post-layout extracted simulation on the proposed TDC design shows a 2ps LSB size, while consuming 5.11-mW per additional channel from a 1.2V supply when operating at the maximum sampling rate of 500MS/s. Compared to state-of-the-art TDC designs, the proposed architecture shows an improvement in quantization step, conversion time, and dynamic range. The idea of multiphase-clock-based multichannel coarse measurement is further explored in a DLL-based TDC implemented in 45nm SOI technology. Lastly, a hierarchical pipeline TDC in 45nm SOI technology, optimized for high-speed and high-precision applications, is introduced. A novel analytical model for the cross-coupled time amplifier (TA) in the pipeline TDC is formulated. Based on this model, a gain calibration scheme is proposed. To validate the time amplifier analysis, a hierarchical TDC with pipeline fine measurement is designed and fabricated in 45nm SOI technology. With look-up table correction, measurement results of the TDC demonstrate a resolution of 0.95ps, a range of 0.8ns, and a DNL/INL range of 2.14 LSB and 2.13 LSB, respectively. The device operates on 8.851mW from a 1.0V supply at a sampling rate of 120MHz, and it achieves a maximum sampling rate of at least 250MHz, highlighting its capability to simultaneously deliver high speed and fine resolution.

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