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  Active and Ultrasensitive Chemical and Biosensing through Optothermally Generated Microbubble

  Farzia Karim

  In this work a cost-effective, label free and ultra-sensitive chemical and biosensing method has been demonstrated for active sensing of analytes. Development of an affordable and ultrasensitive sensing methods is critical. The most challenge in this area is the efficient management of detection time and sensitivity of sensor. Most of the sensing systems reported in the literature usually apply a passive sensing method in which binding of analytes occurs after waiting for the analytes to freely diffuse towards the sensor. Due to this free diffusion, analytes usually take very longer times to diffuse on the sensor, and therefore becomes a diffusion-limited method. In order to overcome this diffusion limit, active sensing method can be used in which analytes are forced towards the sensor for active diffusion. In this work, a cost-effective and ultrasensitive chemical and biosensing platform has been developed under ambient condition to demonstrate an active sensing method. This method works based on an optothermally generated microbubble (OGMB); a micron-sized bubble which is generated on a liquid-solid interface through laser heating of metallic nanoparticles solution. Due to a strong convective flow induced by OGMB, nanoparticles are attracted towards OGMB and rapidly deposited on the surface of a substrate to fabricate a nanogap-rich structure. This structure forms many nanogaps which are ideal for surface enhance Raman scattering (SERS) enhancement due to the plasmonic resonance. Liquid solutions containing an analyte is attached on nanogap-rich structure to develop the chemical and biosensing platform. In addition, OGMB is used to locally concentrate anaylytes around nanogap-rich structure for active sensing. Active sensing can improve the detection limit of analytes by one order of magnitude compared to passive sensing. This active sensing method can overcome the diffusion limit of conventional sensing methods and paves a new way for advanced chemical and bio-sensing application.

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  Additive manufactured Ti-6Al-4V lattice structures for use in orthopedic implants

  Dimitri Papazoglou

  The call for orthopedic implants is a growing concern with an ever-rising aging population. Current orthopedic implants provide good mechanical strength and stability, with some offering surface area bone growth. Lattice structures manufactured via laser powder bed fusion offer patient specific orthopedic implants with mechanical properties similar to bone, less weight and promotion of internal bone growth for better fixation. Two different lattice structures of cubic and diamond were printed in Ti-6Al-4V via an open architecture selective laser melting machine. These lattice structures have varied pore sizes of 400, 500, 600 and 900µm. Compression and tensile testing were performed to identify mechanical properties.. Properties needed to promote osseointegration are reviewed, such as pore size and geometry .

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  Additive manufacturing through opto-thermomechanical printing of nanoparticles at the nanoscales

  Md. Shah Alam

  Additive manufacturing or 3D printing at macro- and micro scale is well developed and widely used in different areas such as aerospace, automotive, military, optics and medical industries. However, 3D printing at nanoscale is still very challenging and researchers are striving to improve the manufacturing speed, accuracy, resolution at the nanoscales. This work demonstrates a cost-effective and rapid nanomanufacturing technique through opto-thermomechanical printing of nanoparticles which is capable of 3D printing at nanoscales. In this technique, a droplet of colloidal metallic nanoparticles solution is dried on a PDMS coated glass donor substrate. Nanoparticles are attached to the surface of the substrate due to Van der Walls attraction force. The individual nanoparticles are sequentially exposed to a focused laser beam. The exposed nanoparticle absorbs laser light and heats donor substrate which creates a thermal expansion force on the nanoparticle. As a result, nanoparticle is rapidly transferred to the receiver substrate. This technique enables selectively pick different types and sizes of nanoparticles in sequence and print them on the receiver substrate in a 2D or 3D patterns. One of the major distinct features of this technique is that the unwanted printed nanoparticles on the receiver substrate can further be removed by again applying opto-thermomecanical force, which can be used as correction step. The flexibility and versatility of this technique can facilitate the manufacturing of 2-D and 3-D devices for nanophotonics, microelectronics and energy harvesting.

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  A Fast Algorithm for Solving the Kinematics of Hyper Redundant Robots

  Tiangang Chen

  Hyper redundant robots consist of many equal length rigid links connected by a large number of revolute joints. This significant number of joints gives the robot many degrees of freedom enabling it to function in highly constrained environments. This work introduces a methodology to solve the kinematics of a hyper redundant robot. Addressing the kinematics includes two issues. The first issue is to approximate a desired curve that specifies the configuration or shape of the robot. The second issue is to accurately position the tool at the end of the robot. These two issues are addressed by analyzing the desired curve describing the hyper redundant robot as piecewise linear similar to the analysis for generating target profiles in shape-changing mechanism theory. There are two advantages to this approach. First, the error will be small. Second, the speed of the calculation is fast.

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  An Efficient Iterative Approach for Determining the Post-Necking True Stress-Strain Response of Aerospace Metals

  Luke Hoover

  To numerically simulate the plastic deformation of aerospace metals during extreme events (e.g., turbine engine blade-out/rotor-burst events and automotive crashworthiness assessment), accurate experimental knowledge of the metal’s hardening behavior at large strains is requisite. Tensile tests on thin (plane stress) specimens are frequently used for this purpose, with the metal’s large-strain plasticity ultimately captured by an equivalent true stress vs. equivalent true plastic strain curve. It is now well known that if axial strain is measured using an extensometer (either physical or virtual), the equivalent true stress-strain curve is valid only up to the onset of diffuse necking, when the strain field heterogeneously localizes in the specimen gage. A number of approaches have been proposed to correct the post-necking strain hardening response. Perhaps the most widely used technique involves inputting a suite of candidate post-necking true stress-strain curves into finite-element software; a tensile test simulation is run for each candidate curve, and the curve that produces the best agreement between simulation and experiment is ultimately adopted. In this talk, a novel variation of this iterative approach is presented that addresses some of its key deficiencies. Notably, we use local/pointwise in-plane Hencky (true) strain data from digital image correlation to generate an upper bound for the iterative simulation process, resulting in an efficient and computationally inexpensive post-necking correction procedure. Our approach is successfully demonstrated using experimental data for both wrought and additively manufactured Ti-6Al-4V titanium alloy.

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  A Novel Approach to Design Planar Four-Bar Linkages for Approximation Motion Synthesis

  Yizhen Cai

  A planar four-bar linkage can be synthesized to achieve at most five positions. Most useful design problems involve many more positions than this with the expectation that the four-bar mechanism will not accomplish the task exactly but will be close. Several methodologies have been proposed in the literature for solving this approximate motion synthesis challenge. Each of these methodologies has a metric central to it. This metric measures how well the mechanism performs at reaching the positions. As each methodology has its own metric, each produces a different optimal design. This research proposes a way to design a four-bar linkage for approximate motion synthesis that does not rely on a position metric. Not only does this produce a unique best solution, but it also provides a method for evaluating other approaches to solve the problem.

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  A Portable Impedimetric Biosensor for Determination of Bisphenol A in Drinking Water

  Birhanu Desta Alemayehu

  Bisphenol A (BPA) is an important chemical used predominantly as a monomer in the production of polycarbonate plastics, epoxy resins and food packaging. BPA can migrate out of polycarbonate (packaging) and contaminate food and drinks. Intake of BPA is potentially toxic to human health, even at low concentrations. Hence, a device that can detect BPA at trace levels is needed. However, current analytical methods for BPA detection require sample pre-treatment steps, time consuming, expensive and cannot be performed on-site. We present the development of a portable, rapid, cost-effective and ultra-sensitive impedimetric biosensor to determine the concentration of BPA in drinking/ tap water at trace levels. 2D materials or Transition Metal Dichalcogenides (TMDCs) are used as conductive elements to fabricate electrodes/ films. Pulsed laser deposition is selected as thin film deposition technique due to the low temperatures involved and uniformity of the film, thus allowing deposition on any substrates. To enhance the sensitive and selectivity of the biosensor, the surface of the interdigitated electrode would be functionalized with enzyme. The ultimate goal of this work is to determine BPA at trace levels by analyzing the impedance spectrum of water diluted with different concentration of BPA over a range of frequencies and then publish the impedance data to a cloud computing interface where the data is logged and processed.

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  Assessment of the Structural Suitability of Tensegrity Aircraft Wings

  Austin Mills

  This research investigates the suitability of tensegrity aircraft wing concepts and compares their simulated structural performance to a baseline conventional wing structure. Tensegrity systems consist of a series of compressed struts connected by tensioned cables that place the system in a self-equilibrium state. With all components being loaded axially, a tensegrity system has a potentially high strength-to-weight ratio. Of specific interest, tensegrity systems may provide pathway to morphing aircraft structures through the actuation of cables. Aircraft with wings that are able to alter their sweep, span, chord, and camber are particularly attractive for their ability to change between high maneuverability to high lift to low drag configurations. With an eye towards this application, the present study compares two tensegrity-based wing designs, generated through designer insights and structural topology optimization methods, to the aluminum Van’s RV-4 aircraft rib/spar wing structure, chosen as the baseline performance case.

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  Automated Design of Truss-Based Mechanical Components Using Topology Optimization

  Robert McCarren

  The goal of this research is to develop a design strategy, and associated algorithms, that take advantage of the topology optimization package within SolidWorks to create easily producible parts. Topology optimization (TO) is a numerical procedure that accepts an initial design space, which includes loads and constraints, and produces a part optimized for structural performance. The optimization objective is commonly posed as maximizing rigidity based on a desired weight percentage, subject to maximum stress and other design constraints. One difficulty with commercial packages, such as SolidWorks, is that the final designs are generally difficult to manufacture without using additive manufacturing (AM) due to the organic nature of the TO results. AM is impractical for many applications and the TO results must be converted to a practical design using conventional manufacturing operations. A consistent method for converting the TO results into manufacturable parts does not exist. Experienced design engineers can produce considerably different practical designs from the TO results. This research focuses on automating the conversion from TO results to practical design using visual basic coding in SolidWorks. TO results will generally resemble truss-like shapes due to the strong nature of trusses. As such, the code produces a three-dimensional sketch of the truss from a Matlab visual processing of the TO result and then uses the weldment tool to create the truss geometry with tubing so the part can be more easily produced by conventional methods.

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  Autonomous Model Update Scheme for Deep Learning-Based Network Traffic Classifiers

  Jielun Zhang

  Network traffic classification is essential in network management and measurement in access networks, e.g., network intrusion detection, network resource allocation, etc. State-of-the-art Deep Learning based classifiers achieve high classification accuracy even when dealing with encrypted data packets. Such classifiers would need to be updated when a new application appears in the network traffic. However, it is challenging to build and label a dataset of the unknown application so that the current network traffic classifier can be updated. In this paper, we propose an autonomous model update scheme to (i) build a dataset of new application packets from active network traffic; and (ii) update the current network traffic classifier. In particular, the core of the proposed scheme is a discriminator includes a statistical filter and a Convolutional Neural Network (CNN) based binary classifier to filter and build a dataset of new application packets from active network traffic. Evaluation is conducted based on an open dataset (ISCX VPN-nonVPN dataset). The results demonstrated that our proposed autonomous classifier update scheme can successfully filter packets of a new application from network traffic and build a new training dataset. Moreover, the packet classifier can be effectively updated through transfer learning. The success of the proposed update scheme can be adopted in the access network for efficient and adaptive network measurement and management.

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  Comparative Study of Region Localization Methods with Image Enhancement for Computer Vision

  Quinn Graehling

  Region localization is one of the main tasks within computer vision and pattern recognition. Early forms of region localization relied on basic pixel intensity thresholding while later versions used machine learning methods to locate and segment objects of interest within an image. Today the region localization fields are dominated by adaptive progressive thresholding methods, region growing segmentation and neural networks designed for semantic segmentation. With the creation of new image enhancement methods, such as the Retinex method, and with the increase in demand for quick image segmentation for use in artificial autonomy, the need for methods that can quickly and accurately segment images has grown exponentially. This presentation aims to analyze modern image segmentation methods and determine which method performs the quickest and with the highest accuracy. This presentation will also look at the difference in results between segmentation of raw images and segmentation of images with contrast enhancement via Retinex image enhancement.

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  Comparison of the Environmental Impact in Production of Lithium-Ion and Lead-Acid Batteries

  Toni Josipovic, Christopher Hartnagel, Gavin Swink

  Batteries are humanity's tools for personal exergy storage in our device enabled world. Exergy allows work to be done. We gather exergy from energy-carrying substances in the natural world. The two most popular types of natural resources for battery cell chemistries are lithium and lead metal. While energy is conserved, the exergetic portion can be destroyed when it undergoes energy conversion. Although both store exergy, each has unique drawbacks and advantages. The chemistry chosen has environmental consequences, knowing the full range of impacts may assist in efforts to decouple energy use from environmental damage. Properties that differ include raw material input, manufacturing technique and performance characteristics. Raw materials such as lithium cobalt oxide and lithium titanate are expensive, rare earth metals that are used in the production of lithium-ion batteries. Lead-acid batteries are typically made up of lead, barium sulfate, lead sulfate, and sulfuric acid. Though less expensive than lithium-ion, the efficiency compared to its counterpart is much lower. Also, the lead-based ionic compound byproduct is very toxic. To quantify our comparison, an economic input-output hybrid life cycle assessment (EIO-LCA) will be performed. Economic activity, conventional air pollutants, greenhouse gas emissions, and energy consumption for the manufacturing stage of both types of battery cell chemistries will be discussed. Knowledge of this EIO-LCA will inform the public on how battery selection is coupled with environmental damage. The production and use of different battery types would fall under UN Sustainable Development Goals relating to industry, innovation, and infrastructure and responsible consumption and production.

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  Constitutive and Predictive Modeling of cDLP Additively Manufactured Hyperelastic Polymers for Soft Robotics

  Kevin Lawson

  Recent developments in polymer chemistries along with timely advancements in the field of additive manufacturing (AM) have expanded the possibilities for soft material application. Specifically, novel UV-curable, ultra-stretchable (hyperelastic) liquid resins have been generated for compatibility with continuous digital light processing (cDLP) AM, a subset of vat photopolymerization (VP). However, the use of these materials in relevant applications is impeded by a lack of thorough mechanical testing and subsequent material modeling to clarify behavior. This project aims to address this issue through (1) building a framework of multimodal experimental test data, (2) fashioning descriptive constitutive (material) models, and (3) scripting representative finite element simulations, all towards the implementation of hyperelastic materials in soft robotics.

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  Co-Op Dayton- Creating an Economy that Works for All.

  Connor M. Dushane

  Co-Op Dayton is a non-profit founded in 2015 that incubates cooperative businesses to create and retain quality jobs in our region, building a local economy that works for all. The cooperative business model puts the power of decision making in the hands of the employees themselves giving them greater control over the future of their business. Co-Op Dayton is best known for its flagship project the Gem City Market. Over the past year, I have been working with them to develop new cooperative businesses in the manufacturing, construction, and agricultural industries. Figuring out how Co-Op Dayton’s work impacts the economic growth of the city of Dayton, and comparing it to the very successful Mondragon Corporation in Spain, creates an opportunity to see how investing resources in worker or community-owned companies could spur further economic growth in Dayton and beyond. This poster will dive into how cooperatives can grow businesses by analyzing different Dayton small businesses that either are or are in the process of transitioning to the cooperative model and how their business can grow with the benefits of the coop model, as well as analyzing the current economical climate of the city to understand how shifting to worker-owned could spur regrowth of certain industries. The goal of this poster is to provide a greater understanding of how cooperatives can change the economy for the better and meet the goals of positive social change while working to overcome some of today’s economic challenges.

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  DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation

  Nina Marie Varney

  We present DALES, a new large scale aerial LiDAR data set with over a half-billion hand-labeled points, spanning 10 $km^2$ of area and eight object categories. Large annotated point cloud data sets have become the standard for evaluating deep learning methods. However, most focus on data collected from a mobile or terrestrial scanner with few focusing on aerial data. Point cloud data collected from an Aerial Laser Scanner (ALS) presents a new set of challenges and applications in areas such as 3D urban modeling and large scale surveillance. DALES is the largest publicly available ALS data set with over 400 times the number of points and six times the per meter resolution than other currently available annotated aerial point cloud data sets. We describe the nature of our data and the annotation workflow as well as provide a benchmark of current state-of-the-art algorithms and their performance on our data set. This data set gives a critical number of expert verified hand-labeled points for the evaluation of new 3D deep learning algorithms, helping to expand the focus of current algorithms to aerial data.

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  Designing Fictional Spaces: Questionable Architecture that Supports Sustainable Design

  Noël J. Michel

  This thesis presents the modeling of spaces described in short stories that are difficult to visualize. The three stories are Kafka's "The Burrow", Borges's "The Library of Babel" and Barthelme's "The Balloon." Three dimensional models were created based on the details provided by the authors in each story. Several 2-D images are then generated from these models to match specific scenes. This consideration of these works of fiction provokes the asking of several questions about the science, mathematics and engineering that underpins the stories. In all cases, questions about sustainability arise.

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  Design Modeling of Spatial Shape-Change Linkages

  Chengwei Shi

  The goal of this research is to develop mechanical designs of spatial shape-changing linkages. Mechanical systems often benefit from the capacity to vary between specific shapes in a controlled manner, such as a morphing aircraft wing that can adapt to different in-flight requirements. Spatial shape-changing linkages consist of a chain of three-dimensional bodies connected with ball joints. When the chain segments are repositioned, they match a set of arbitrary spatial curves. These chains are composed of two segments types: a twisted rigid segment and a helical segment with constant curvature and torsion but varying length. The research project involves creating the mechanical designs of the segments and motion control schemes that move the chain from the origin position to the target position. Animations are created in SolidWorks that demonstrate various motion schemes and illustrate the chain’s approximation to the target spatial curves.

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  Design of a Trike for Paraplegics Powered By Functional Electrical Stimulation of Leg Muscles

  Andy Bazler, Nicholas Andrew Lanese

  The goal of this project is to design a performance tricycle for paraplegics whose leg muscles are stimulated to pedal via Functional Electrical Stimulation (FES). FES stimulates muscle contraction with small electrical currents and has proven useful in building muscle in patients while relieving soreness and promoting cardiovascular health. An FES-stimulated cyclist produces approximately 25 Watts of power, nearly 20 times less than a typical rider. At these reduced power levels, the challenges of pedaling are amplified. For example, as the pedal follows the traditional circular path, there are portions referred to as dead zones, where neither FES-stimulated leg actively propels the bike forward. One possibility for reducing or eliminating dead zones is to redesign the circular path of the pedaling motion. Bicycles have recently been marketed that feature pedaling mechanisms that employ alternate pedaling motions. In addition to addressing dead zones, these bikes also optimize the muscle capacity of the rider to deliver torque to the wheels. These new bikes achieve alternate pedaling paths through the introduction of more complicated mechanisms including four-bar and ratchet-and-pawl linkages. Such alternates are being considered for the redesign of the performance tricycle piloted by FES-stimulated riders. To investigate possible changes to the tricycle, quasi-static models have been developed for traditional and alternate cycling mechanisms. This allows for a comparison of torque generation between the mechanisms which facilitates selecting the optimal design. Such a tricycle is viewed as beneficial due to the health advantages, improved mobility, and independence created for the end user.

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  Development of Safety Performance Functions for Two-Lane Rural Highways in the State of Ohio

  Abdulrahman Faden

  The Highway Safety Manual (HSM), which is the guidance document for state departments of transportation (DOTs), was published in 2010 and one of its sections, called Part C of HSM, it involves the development of crash prediction methods. The goal of the predictive method is to develop and calibrate safety performance functions (SPFs). SPFs are mostly regression models that correlate the expected number of crashes quantitatively with traffic exposure and geometric characteristics of the road. However, HSM's default prediction models are not suitable for all states or jurisdictions because each state and jurisdiction have different characteristics, such as terrain, driver behaviors, weather conditions, etc. Hence, the principal objective of this study is to develop a prediction method for producing Ohio-specific SPF models to use for rural two-lane highways in the state of Ohio. This study aims to create SPFs or jurisdiction-specific SPFs for two-lane rural highway segments as the first study for this type of roadway facility in the state of Ohio. Almost 28,700 miles of highway geometric data were obtained from the Highway Safety Information System (HSIS) to create these new models using negative binomial regression. The most critical variables to be used for analyzing and creating the best models for the state of Ohio are average annual daily traffic (AADT), segment length, lane width, shoulder width, posted speed limit, presence of curves and grades.

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  Dimensioning Mechanical Press Architectures for Improved Dwell using Advanced Algebraic Techniques

  Tianze Xu

  A mechanical press is a machine that shapes parts by driving a ram into metal and deforming the material into a desirable shape. As this is an incredibly common process for forming metal parts, from pop cans to car fenders, presses see significant use in industry. This research project seeks to develop a numerical algebraic method for determining mechanical press dimensions from a desired dwell displacement pattern. This dwell pattern occurs when the ram lingers near the bottom of the stroke while the rest of the press stays in motion. Longer dwell produces improved part forming at no additional cost. This study focuses on knuckle presses architectures to test the proposed method on a variety of systems and to produce the most feasible design. Numerical algebraic methods are particularly relevant here due to their capacity to accurately describe mechanical press architectures while allowing solutions via current numerical methods that guarantee the determination of all solutions to a set of algebraic equations. As such, there are a significant number of companies designing and building mechanical presses to meet a variety of end used needs. A particularly common need is dwell, the capacity of the press to hold the position on one of its parts while the rest of the machine stays in motion. Dimensioning a new architecture for a mechanical press that produces significantly improved dwell allows for manufacturing parts at a higher rate with lower operating costs.

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  Dimensioning Mechanical Presses Driven by a Geared Five-Bar for Desired Dwell using Advanced Algebraic Techniques

  Xingyu Zhu

  A mechanical press uses a linkage that oscillates a ram in order to form or cut sheet metal. This research develops design theories that use a unique mechanical linkage to obtain alternative ram oscillation patterns, such as a prolonged dwell. A geared five-bar press with sliding output is proposed to produce these alternative motions. In one alternative motion, an extended dwell involves a ram that remains near the bottom of the stroke while the crank continues to rotate. A prolonged dwell is ideal for coining operations. Non-linear loop closure equations are generated using isotropic coordinates. After specifying a desired motion pattern, an algorithm that uses the closure equations with numerical algebraic geometry obtains all possible sets of appropriate dimensions for the links. Lastly, a process to determine the best possible set is formulated.

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  DIMLabyrinths: Printable 3D Cube Mazes Designed in MATLAB

  Adam Steven Wicks

  DIMLabyrinths are 3-dimensional marble mazes designed for 3D printing. The maze body is acube with an evenly-spaced grid of round holes from top-to-bottom, left-to-right, and front-to-back. The holes are one of two sizes inside the cube, either too small for the marble to passthrough or just big enough to allow the marble passage. As such, the solver can see the marble atall times as it moves through the maze embedded in the cube. The design of the maze itself isgenerated using an algorithm developed in MATLAB. The maze is guaranteed to visit everylocation in the cube on a path that connects the top-front-left corner to the bottom-back-rightcorner. This unique geometry is well-suited for manufacturing via 3D printing. DIMLabyrinthfiles suitable for rapid prototyping are available for free download on the DIMLab My MiniFactory site. The result is a unique puzzle, partially designed in MATLAB, that can be 3Dprinted at home for free.

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  Electrical Characterization of Tantalum Oxide Based Memristor

  Yassine Jaoudi

  Training deep learning models is computationally expensive due to the need for a tremendous volume of data and complex math. Graphical Processing Units (GPUs) are typically used and require about 200W of power at least, thus making them unusable in portable applications. Neuromorphic computing approaches based on memristor devices can drastically reduce this power and allow low power devices (edge computing and IoT devices) to learn and thus become much smarter. This work presents collected characteristics data of real memristor devices and modeling for memristor-based circuit and system design. Memristors – a relatively recent class on nanoscale devices that can be programmed and can retain their data even when the power is turned off. Memristor based online circuits is a popular research topic currently, but these are generally based on ideal devices behaviors. Therefore, the acquired device properties are used to update the memristor model used in previous circuit simulations and examine its impact on Artificial Intelligence learning circuits.

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  Emerging Mobility Services

  Baraah Qawasmeh

  In recent decades social and cultural trends have been rapidly and constantly changing and technological advancements such as smart phones, large-scale electronic devices, The Internet of Things (IOT), etc., have also experienced a more rapidly and accelerated growth. These rapid changes have also brought up some new innovative ideas on how to provide efficient and safe transportation services that can leverage emerging technologies. These opportunities can make transportation affordable and equitable with improved mobility options available to all types of travelers.

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  Environmental Impact of Freshwater Fish versus Chicken Farming

  Lukas Christopher Funk, Griffin L. Barger, Zachary A. Gerlach

  Using an environmentally extended Life Cycle Assessment (LCA), we’re able to evaluate the production and farming of freshwater fish on energy requirements, greenhouse gas emissions, and other environmental indicators. We will compare these results to analysis of the farming and production of chicken to see if our system represents an improvement. We are examining the 14th UN goal, which is to reduce marine pollution and unregulated fishing and to conserve coastal and marine areas.