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1. Improved Continuum Joint Configuration Estimation Using a Linear Combination of Length Measurements and Optimization of Sensor Placement

   https://doi.org/10.3389/frobt.2021.637301  

   Rupert, Levi; Duggan, Timothy; Killpack, Marc D. (April 2021, Frontiers in Robotics and AI)

   This paper presents methods for placing length sensors on a soft continuum robot joint as well as a novel configuration estimation method that drastically minimizes configuration estimation error. The methods utilized for placing sensors along the length of the joint include a single joint length sensor, sensors lined end-to-end, sensors that overlap according to a heuristic, and sensors that are placed by an optimization that we describe in this paper. The methods of configuration estimation include directly relating sensor length to a segment of the joint's angle, using an equal weighting of overlapping sensors that cover a joint segment, and using a weighted linear combination of all sensors on the continuum joint. The weights for the linear combination method are determined using robust linear regression. Using a kinematic simulation we show that placing three or more overlapping sensors and estimating the configuration with a linear combination of sensors resulted in a median error of 0.026% of the max range of motion or less. This is over a 500 times improvement as compared to using a single sensor to estimate the joint configuration. This error was computed across 80 simulated robots of different lengths and ranges of motion. We also found that the fully optimized sensor placement performed only marginally better than the placement of sensors according to the heuristic. This suggests that the use of a linear combination of sensors, with weights found using linear regression is more important than the placement of the overlapping sensors. Further, using the heuristic significantly simplifies the application of these techniques when designing for hardware. 

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2. Optimizing Seasonal‐To‐Decadal Analog Forecasts With a Learned Spatially‐Weighted Mask

   https://doi.org/10.1029/2023GL104983  

   Rader, Jamin_K; Barnes, Elizabeth_A (November 2023, Geophysical Research Letters)

   Abstract Seasonal‐to‐decadal climate prediction is crucial for decision‐making in a number of industries, but forecasts on these timescales have limited skill. Here, we develop a data‐driven method for selecting optimal analogs for seasonal‐to‐decadal analog forecasting. Using an interpretable neural network, we learn a spatially‐weighted mask that quantifies how important each grid point is for determining whether two climate states will evolve similarly. We show that analogs selected using this weighted mask provide more skillful forecasts than analogs that are selected using traditional spatially‐uniform methods. This method is tested on two prediction problems using the Max Planck Institute for Meteorology Grand Ensemble: multi‐year prediction of North Atlantic sea surface temperatures, and seasonal prediction of El Niño Southern Oscillation. This work demonstrates a methodical approach to selecting analogs that may be useful for improving seasonal‐to‐decadal forecasts and understanding their sources of skill. 

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3. An Adaptive Search Algorithm for Detecting Respiratory Artifacts Using a Wireless Passive Wearable Device

   O’Neill, Patrick; Mongan, William; Ross, Robert; Acharya, Sayandeep; Fontecchio, Adam; Dandekar, Kapil R. (January 2019, 2019 IEEE Signal Processing in Medicine and Biology Symposium)

   With the use of a wireless, wearable, passive knitted smart fabric device as a strain gauge sensor, the proposed algorithm can estimate biomedical feedback such as respiratory activity. Variations in physical properties of Radio Frequency Identification (RFID) signals can be used to wirelessly detect physiological processes and states. However, it is typical for ambient noise artifacts to appear in the RFID signal making it difficult to identify physiological processes. This paper introduces a new technique for finding these repetitive physiological signals and identifying them into two states, active and inactive, using k- means clustering. The algorithm detects these biomedical events without the need to completely remove the noise components using a semi-unsupervised approach, and with these results, predict the next biomedical event using these classification results. This approach enables real-time noninvasive monitoring for use with actuating medical devices for therapy. Using this approach, the algorithm predicts the onset of respiratory activity in a simulated environment within approximately one second. 

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4. Improved efficiency of vibration-based sound power computation through multi-layered radiation resistance matrix symmetry

   https://doi.org/10.1121/10.0015355  

   Ebeling, John_C; Bacon, Ian_C; Bates, Trent_P; Sommerfeldt, Scott_D; Blotter, Jonathan_D (December 2022, JASA Express Letters)

   Computing sound power using complex-valued surface velocities involves using a geometry-dependent acoustic radiation resistance matrix multiplied by a velocity vector to compute sound power for a given frequency range. Using a laser scan grid with constant spacing and a scalar radiator area approximation, a multi-layered Toeplitz symmetry exists in the radiation resistance matrix. An innovative approach was developed to exploit this Toeplitz symmetry. This approach preserved accuracy and resulted in a maximum of ∼1300% computation time reduction for curved plate calculations and a ∼9600% computation time reduction for cylindrical shell sound power calculations. 

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5. Big data approaches to knot theory: Understanding the structure of the Jones polynomial

   https://doi.org/10.1142/S021821652250095X  

   Levitt, Jesse S.; Hajij, Mustafa; Sazdanovic, Radmila (November 2022, Journal of Knot Theory and Its Ramifications)

   In this paper, we examine the properties of the Jones polynomial using dimensionality reduction learning techniques combined with ideas from topological data analysis. Our data set consists of more than 10 million knots up to 17 crossings and two other special families up to 2001 crossings. We introduce and describe a method for using filtrations to analyze infinite data sets where representative sampling is impossible or impractical, an essential requirement for working with knots and the data from knot invariants. In particular, this method provides a new approach for analyzing knot invariants using Principal Component Analysis. Using this approach on the Jones polynomial data, we find that it can be viewed as an approximately three-dimensional subspace, that this description is surprisingly stable with respect to the filtration by the crossing number, and that the results suggest further structures to be examined and understood. 

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