Electrical & Computer Engineering

Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/32

All faculty members in ECE engage in research and creative activity. Areas of research include embedded computing, mixed signal design, optics and optoelectronics, MEMS/MOEMS, acoustics and audio, complex systems and control, communication systems, digital signal processing, power systems, and power electronics.

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Toward polarization-enhanced water quality remote sensing measurements from UAVs
(SPIE, 2024-05) Morgan, P. Flint; Weller, Wyatt W.; Maxwell, Dylan J.; Hamp, Shannon M.; Venkatesulu, Erica; Shaw, Joseph A.; Whitaker, Bradley M.; Roddewig, Michael R.
Montana and similar regions contain numerous rivers and lakes that are too small to be spatially resolved by satellites that provide water quality estimates. Unoccupied Aerial Vehicles (UAVs) can be used to obtain such data with much higher spatial and temporal resolution. Water properties are traditionally retrieved from passively measured spectral radiance, but polarization has been shown to improve retrievals of the attenuation-to-absorption ratio to enable calculation of the scattering coefficient for in-water particulate matter. This feeds into improved retrievals of other parameters such as the bulk refractive index and particle size distribution. This presentation will describe experiments conducted to develop a data set for water remote sensing using combined UAV-based hyperspectral and polarization cameras supplemented with in-situ sampling at Flathead Lake in northwestern Montana and the results of preliminary data analysis. A symbolic regression model was used to derive two equations: one relating DoLP, AoP, and the linear Stokes parameters at wavelengths of 440 nm, 550 nm and 660 nm, to chlorophyll-a content, and one relating the same data to the attenuation-to-absorption ratio for 440 nm, 550 nm and 660 nm. Symbolic regression is a machine learning algorithm where the inputs are vectors and the output is an analytic expression, typically chosen by a genetic algorithm. An advantage of this approach is that the explainability of a simple equation can be combined with the accuracy of less explainable models, such as the genetic algorithm.
Benthic river algae mapping using hyperspectral imagery from unoccupied aerial vehicles
(SPIE-Intl Soc Optical Eng, 2024-06) Logan, Riley D.; Shaw, Joseph A.
The increasing prevalence of nuisance benthic algal blooms in freshwater systems has led to water quality monitoring programs based on the presence and abundance of algae. Large blooms of the nuisance filamentous algae, Cladophora glomerata, have become common in the waters of the Upper Clark Fork River in western Montana. To aid in the understanding of algal growth dynamics, unoccupied aerial vehicle (UAV)-based hyperspectral images were gathered at three field sites along the length of the river throughout the growing season of 2021. Select regions within images covering the spectral range of 400 to 850 nm were labeled based on a combination of professional judgment and spectral profiles and used to train a random forest classifier to identify benthic algal growth across several classes, including benthic growth dominated by Cladophora (Clado), benthic growth dominated by growth forms other than Cladophora (non-Clado), and areas below a visually detectable threshold of benthic growth (bare substrate). After classification, images were stitched together to produce spatial distribution maps of each river reach while also calculating the average percent cover for each reach, achieving an accuracy of approximately 99% relative to manually labeled images. Results of this analysis showed strong variability across each reach, both temporally (up to 40%) and spatially (up to 46%), indicating that UAV-based imaging with high-spatial resolution could augment and therefore improve traditional measurement techniques that are spatially limited, such as spot sampling.
Radiometry and the Friis transmission equation
(American Association of Physics Teachers, 2013) Shaw, Joseph
To more effectively tailor courses involving antennas, wireless communications, optics, and applied electromagnetics to a mixed audience of engineering and physics students, the Friis transmission equation—which quantifies the power received in a free-space communication link—is developed from principles of optical radiometry and scalar diffraction. This approach places more emphasis on the physics and conceptual understanding of the Friis equation than is provided by the traditional derivation based on antenna impedance. Specifically, it shows that the wavelength-squared dependence can be attributed to diffraction at the antenna aperture and illustrates the important difference between the throughput (product of area and solid angle) of a single antenna or telescope and the throughput of a transmitter-receiver pair.
Predicting quantum emitter fluctuations with time-series forecasting models
(Springer Science and Business Media LLC, 2024-03) Ramezani, Fereshteh; Strasbourg, Matthew; Parvez, Sheikh; Saxena, Ravindra; Jariwala, Deep; Borys, Nicholas J.; Whitaker, Bradley M.
2D materials have important fundamental properties allowing for their use in many potential applications, including quantum computing. Various Van der Waals materials, including Tungsten disulfide (WS2), have been employed to showcase attractive device applications such as light emitting diodes, lasers and optical modulators. To maximize the utility and value of integrated quantum photonics, the wavelength, polarization and intensity of the photons from a quantum emission (QE) must be stable. However, random variation of emission energy, caused by the inhomogeneity in the local environment, is a major challenge for all solid-state single photon emitters. In this work, we assess the random nature of the quantum fluctuations, and we present time series forecasting deep learning models to analyse and predict QE fluctuations for the first time. Our trained models can roughly follow the actual trend of the data and, under certain data processing conditions, can predict peaks and dips of the fluctuations. The ability to anticipate these fluctuations will allow physicists to harness quantum fluctuation characteristics to develop novel scientific advances in quantum computing that will greatly benefit quantum technologies.
Fast charging of commercial lithium-ion battery without lithium plating
(Elsevier BV, 2023-12) Thapa, Arun; Hedding, Noah; Gao, Hongwei
Rapid charging of lithium-ion batteries (LIBs) enables the devices or systems powered by the batteries to provide services at faster rates or higher frequencies. However, fast charging of LIBs can cause lithium plating, resulting in rapid capacity degradation and even thermal runaway or fire in the batteries. Fast charging and lithium plating in a LIB are anode-centric events. Therefore, an anode-centric electrochemical model is critical for deriving a fast charging protocol for LIBs. In this work, we developed an electric circuit model for the negative electrode using tests conducted on laboratory three-electrode lithium-ion cells, used the model to estimate the fast charging current, and compared the fast charging current derived using the model to the fast charging current obtained from measurement. The fast charging current obtained using the model agrees well with the measured fast charging current. Furthermore, we implemented this fast charging protocol on commercial 18650 LIBs for 350 cycles using custom-built charging hardware and software and achieved an 80 % state of charge in 29 min with acceptable temperature rise. The cell aging analysis revealed no significant capacity degradation nor lithium plating on the anode surface, as the protocol explicitly imposes control to protect the battery from lithium plating.
Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data
(MDPI AG, 2023-12) Vannoy, Trevor C.; Sweeney, Nathaniel B.; Shaw, Joseph A.; Whitaker, Bradley M.
Concerns about decreases in insect population and biodiversity, in addition to the need for monitoring insects in agriculture and disease control, have led to an increased need for automated, non-invasive monitoring techniques. To this end, entomological lidar systems have been developed and successfully used for detecting and classifying insects. However, the data produced by these lidar systems create several problems from a data analysis standpoint: the data can contain millions of observations, very few observations contain insects, and the background environment is non-stationary. This study compares the insect-detection performance of various supervised machine learning and unsupervised changepoint detection algorithms and provides commentary on the relative strengths of each method. We found that the supervised methods generally perform better than the changepoint detection methods, at the cost of needing labeled data. The supervised learning method with the highest Matthew’s Correlation Coefficient score on the testing set correctly identified 99.5% of the insect-containing images and 83.7% of the non-insect images; similarly, the best changepoint detection method correctly identified 83.2% of the insect-containing images and 84.2% of the non-insect images. Our results show that both types of methods can reduce the need for manual data analysis.
Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization
(MDPI AG, 2023-06) Radhoush, Sepideh; Vannoy, Trevor; Liyanage, Kaveen; Whitaker, Bradley M.; Nehrir, Hashem
Distribution System State Estimation (DSSE) techniques have been introduced to monitor and control Active Distribution Networks (ADNs). DSSE calculations are commonly performed using both conventional measurements and pseudo-measurements. Conventional measurements are typically asynchronous and have low update rates, thus leading to inaccurate DSSE results for dynamically changing ADNs. Because of this, smart measurement devices, which are synchronous at high frame rates, have recently been introduced to enhance the monitoring and control of ADNs in modern power networks. However, replacing all traditional measurement devices with smart measurements is not feasible over a short time. Thus, an essential part of the grid modernization process is to use both traditional and advanced measurements to improve DSSE results. In this paper, a new method is proposed to hybridize traditional and advanced measurements using an online machine learning model. In this work, we assume that an ADN has been monitored using traditional measurements and the Weighted Least Square (WLS) method to obtain DSSE results, and the voltage magnitude and phase angle at each bus are considered as state vectors. After a period of time, a network is modified by the installation of advanced measurement devices, such as Phasor Measurement Units (PMUs), to facilitate ADN monitoring and control with a desired performance. Our work proposes a method for taking advantage of all available measurements to improve DSSE results. First, a machine-learning-based regression model was trained from DSSE results obtained using only the traditional measurements available before the installation of smart measurement devices. After smart measurement devices were added to the network, the model predicted traditional measurements when those measurements were not available to enable synchronization between the traditional and smart sensors, despite their different refresh rates. We show that the regression model had improved performance under the condition that it continued to be updated regularly as more data were collected from the measurement devices. In this way, the training model became robust and improved the DSSE performance, even in the presence of more Distributed Generations (DGs). The results of the proposed method were compared to traditional measurements incorporated into the DSSE calculation using a sample-and-hold technique. We present the DSSE results in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values for all approaches. The effectiveness of the proposed method was validated using two case studies in the presence of DGs: one using a modified IEEE 33-bus distribution system that considered loads and DGs based on a Monte Carlo simulation and the other using a modified IEEE 69-bus system that considered actual data for loads and DGs. The DSSE results illustrate that the proposed method is better than the sample-and-hold method.
Agile collaboration: Citizen science as a transdisciplinary approach to heliophysics
(Frontiers Media SA, 2023-04) Ledvina, Vincent; Brandt, Laura; MacDonald, Elizabeth; Frissell, Nathaniel; Anderson, Justin; Chen, Thomas Y.; French, Ryan J.; Mare, Francesca Di; Grover, Andrea; Sigsbee, Kristine; Gallardo-Lacourt, Bea; Lach, Donna; Shaw, Joseph A.; Hunnekuhl, Michael; Kosar, Burcu; Barkhouse, Wayne; Young, Tim; Kedhambadi, Chandresh; Ozturk, Dogacan S.; Claudepierre, Seth G.; Dong, Chuanfei; Witteman, Andy; Kuzub, Jeremy; Sinha, Gunjan
Citizen science connects scientists with the public to enable discovery, engaging broad audiences across the world. There are many attributes that make citizen science an asset to the field of heliophysics, including agile collaboration. Agility is the extent to which a person, group of people, technology, or project can work efficiently, pivot, and adapt to adversity. Citizen scientists are agile; they are adaptable and responsive. Citizen science projects and their underlying technology platforms are also agile in the software development sense, by utilizing beta testing and short timeframes to pivot in response to community needs. As they capture scientifically valuable data, citizen scientists can bring expertise from other fields to scientific teams. The impact of citizen science projects and communities means citizen scientists are a bridge between scientists and the public, facilitating the exchange of information. These attributes of citizen scientists form the framework of agile collaboration. In this paper, we contextualize agile collaboration primarily for aurora chasers, a group of citizen scientists actively engaged in projects and independent data gathering. Nevertheless, these insights scale across other domains and projects. Citizen science is an emerging yet proven way of enhancing the current research landscape. To tackle the next-generation’s biggest research problems, agile collaboration with citizen scientists will become necessary.
Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network
(MDPI AG, 2023-02) Radhoush, Sepideh; Vannoy, Trevor; Liyanage, Kaveen; Whitaker, Bradley M.; Nehrir, Hashem
Distribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data.
Validation of a microwave energy meter to non-lethally estimate energetic reserves in adult sturgeon
(Oxford University Press, 2023-05) Daigle, Nicole J.; Djokic, Matea A.; Kappenman, Kevin M.; Gaylord, T Gibson; Quinn, Sierra; Verhille, Christine E.
Whole-body (WB) energetic reserves influence fish survival, growth, and reproduction but are typically quantified using lethal methods (i.e. proximate analyses) or interpreted through body condition indices. Energetic reserves can impact population dynamics through influences on growth rates, age-at-first-reproductive-maturity, and spawning periodicity at the individual-fish level, especially in long-lived sturgeon species. Therefore, a non-lethal tool to track the energetic reserves of endangered sturgeon populations could inform adaptive management and further our understanding of the sturgeon’s biology. The Distell Fatmeter is a microwave energy meter that has been validated to non-lethally estimate energetic reserves in some fish species, but never successfully for sturgeon. Here, stepwise linear regressions were applied to test commonly monitored body metrics and Fatmeter measurements at nine different anatomical sites on captive adult pallid sturgeon (Scaphirhynchus albus; total length of 790–1015 mm; WB lipid of 13.9–33.3%) compared with WB lipid and energy content determined by proximate analyses. Fatmeter measurements alone explained approximately 70% of the variation in WB energetic reserves, which outperformed models considering body metrics alone by a margin of approximately 20%. The top-ranked models based on AICc score (second-order Akaike Information Criterion) included a combination of body metrics and Fatmeter measurements and accounted for up to 76% of the variation in WB lipid and energy. We recommend the incorporation of Fatmeter measurements at a single site located dorsally to the lateral scutes at the posterior end of the fish above the pelvic fins (U-P) into conservation monitoring programs for adult pallid sturgeon (total length [TL] ≥ 790 mm; fork length [FL] ≥ 715 mm) and the cautious application of Fatmeter measurements for sturgeon between 435 and 790 mm TL (375–715 mm FL). Measurements at this U-P site combined with body mass explained approximately 75% of the variation in WB lipid and energy.

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