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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An Agent-Based Hierarchical Bargaining Framework for Power Management of Multiple Cooperative Microgrids
(2019-01) Deghganpour, Kaveh; Nehrir, Hashem
In this paper, we propose an agent-based hierarchical power management model in a power distribution system composed of several microgrids (MGs). At the lower level of the model, multiple MGs bargain with each other to cooperatively obtain a fair, and Pareto-optimal solution to their power management problem, employing the concept of Nash bargaining solution and using a distributed optimization framework. At the highest level of the model, a distribution system power supplier, e.g., a utility company, interacts with both the cluster of the MGs and the wholesale market. The goal of the utility company is to facilitate power exchange between the regional distribution network consisting of multiple MGs and the wholesale market to achieve its own private goals. The power exchange is controlled through dynamic energy pricing at the distribution level, at the day-ahead and real-time stages. To implement energy pricing at the utility company level, an iterative machine learning mechanism is employed, where the utility company develops a price-sensitivity model of the aggregate response of the MGs to the retail price signal through a learning process. This learned model is then used to perform optimal energy pricing. To verify its applicability, the proposed decision model is tested on a system with multiple MGs, with each MG having different load/generation data.
Agent-Based Modeling of Retail Electrical Energy Markets with Demand Response
(2018-08-01) Nehrir, Hashem; Dehghanpour, Kaveh; Sheppard, John W.; Kelly, Nathan
In this paper, we study the behavior of a Day-Ahead (DA) retail electrical energy market with price-based Demand Response (DR) from Air Conditioning (AC) loads through a hierarchical multiagent framework, employing a machine learning approach. At the top level of the hierarchy, a retailer agent buys energy from the DA wholesale market and sells it to the consumers. The goal of the retailer agent is to maximize its profit by setting the optimal retail prices, considering the response of the price-sensitive loads. Upon receiving the retail prices, at the lower level of the hierarchy, the AC agents employ a Q-learning algorithm to optimize their consumption patterns through modifying the temperature set-points of the devices, considering both consumption costs and users' comfort preferences. Since the retailer agent does not have direct access to the AC loads' underlying dynamics and decision process (i.e., incomplete information) the data privacy of the consumers becomes a source of uncertainty in the retailer's decision model. The retailer relies on techniques from the field of machine learning to develop a reliable model of the aggregate behavior of the price-sensitive loads to reduce the uncertainty of the decision-making process. Hence, a multiagent framework based on machine learning enables us to address issues such as interoperability and decision-making under incomplete information in a system that maintains the data privacy of the consumers. We will show that using the proposed model, all the agents are able to optimize their behavior simultaneously. Simulation results show that the proposed approach leads to a reduction in overall power consumption cost as the system converges to its equilibrium. This also coincides with maximization in the retailer's profit. We will also show that the same decision architecture can be used to reduce peak load to defer/avoid distribution system upgrades under high penetration of Photo-Voltaic (PV) power in the distribution feeder.
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.
Airborne lidar detection of an underwater thermal vent
(2017-08) Roddewig, Michael R.; Churnside, James H.; Shaw, Joseph A.
We report the lidar detection of an underwater feature that appears to be a thermal vent in Yellowstone Lake, Yellowstone National Park, USA, with the Montana State University Fish Lidar. The location of the detected vent was 30 m from the closest vent identified in a United States Geological Survey of Yellowstone Lake in 2008. A second possible vent is also presented, and the appearance of both vents in the lidar data is compared to descriptions of underwater thermal vents in Yellowstone Lake from the geological literature. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
All-sky polarization imaging of cloud thermodynamic phase
(2019-02) Eshelman, Laura M.; Tauc, Martin J.; Shaw, Joseph A.
Knowing the cloud thermodynamic phase (if a cloud is composed of ice crystals or liquid droplets) is crucial for many cloud remote sensing measurements. Further, this knowledge can help in simulating and interpreting cloud radiation measurements to better understand the role of clouds in climate, weather, and optical propagation. Knobelspiesse et al. [Atmos. Meas. Tech. 8, 1537 (2015)] showed that, for simulated zenith observations, the algebraic sign of the S1 Stokes parameter (related to the difference between perpendicular and parallel linear polarization in the scattering plane) can be used to detect cloud thermodynamic phase when observed with a ground-based passive polarimeter. In this paper, we describe the use of our all-sky imaging polarimeter to experimentally test this proposed method of detecting cloud thermodynamic phase in the entire sky dome. The zenith cloud phase was validated with a dual-polarization lidar instrument.
Automatic detection of multilayer hexagonal boron nitride in optical images using deep learning-based computer vision
(Springer Science and Business Media LLC, 2023-01) Ramezani, Fereshteh; Parvez, Sheikh; Fix, J. Pierce; Battaglin, Arthur; Whyte, Seamus; Borys, Nicholas J.; Whitaker, Bradley M.
Computer vision algorithms can quickly analyze numerous images and identify useful information with high accuracy. Recently, computer vision has been used to identify 2D materials in microscope images. 2D materials have important fundamental properties allowing for their use in many potential applications, including many in quantum information science and engineering. One such material is hexagonal boron nitride (hBN), an isomorph of graphene with a very indistinguishable layered structure. In order to use these materials for research and product development, the most effective method is mechanical exfoliation where single-layer 2D crystallites must be prepared through an exfoliation procedure and then identified using reflected light optical microscopy. Performing these searches manually is a time-consuming and tedious task. Deploying deep learning-based computer vision algorithms for 2D material search can automate the flake detection task with minimal need for human intervention. In this work, we have implemented a new deep learning pipeline to classify crystallites of hBN based on coarse thickness classifications in reflected-light optical micrographs. We have used DetectoRS as the object detector and trained it on 177 images containing hexagonal boron nitride (hBN) flakes of varying thickness. The trained model achieved a high detection accuracy for the rare category of thin flakes (<50 atomic layers thick). Further analysis shows that our proposed pipeline could be generalized to various microscope settings and is robust against changes in color or substrate background.
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.
Cobleigh hall weather station data, 2005-present [dataset]
(2015-11) Shaw, Joseph A.
This record links to data collected by a weather station operated by the Optical Remote Sensor Laboratory at Montana State University, under the direction of Dr. Joseph Shaw. The weather station is on top of Cobleigh Hall on the campus of Montana State University in Bozeman. The latitude is 45.67° N and the longitude is 111.05° W. The elevation on the roof is 5000ft (1524m).
Comparing Online to Face-to-Face Delivery of Undergraduate Digital Circuits Content
(2017-08) LaMeres, Brock J.; Plumb, Carolyn
This paper presents a comparison of online to traditional face-to-face delivery of undergraduate digital systems material. Two specific components of digital content were compared and evaluated: a sophomore logic circuits course with no laboratory, and a microprocessor laboratory component of a junior-level computer systems course. For each of these, a baseline level of student understanding was evaluated when they were being taught using traditional, face-to-face delivery. The course and lab component were then converted to being fully online, and the level of student understanding was again measured. In both cases, the same purpose-developed assessment tools were used to carry out the measurement of understanding. This paper presents the details of how the course components were converted to online delivery, including a discussion of the technology used to accomplish remote access of the electronic test equipment used in the laboratory. A comparison is then presented between the control and the experimental groups, including a statistical analysis of whether the delivery approach impacted student learning. Finally, student satisfaction is discussed, and instructor observations are given for the successful remote delivery of this type of class and laboratory.
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.
Correcting for focal-plane-array temperature dependence in microbolometer infrared cameras lacking thermal stabilization
(2013-01) Nugent, Paul W.; Shaw, Joseph A.; Pust, Nathan J.
Advances in microbolometer detectors have led to the development of infrared cameras that operate without active temperature stabilization. The response of these cameras varies with the temperature of the camera’s focal plane array (FPA). This paper describes a method for stabilizing the camera’s response through software processing. This stabilization is based on the difference between the camera’s response at a measured temperature and at a reference temperature. This paper presents the mathematical basis for such a correction and demonstrates the resulting accuracy when applied to a commercially available long-wave infrared camera. The stabilized camera was then radiometrically calibrated so that the digital response from the camera could be related to the radiance or temperature of objects in the scene. For FPA temperature deviations within ±7.2°C changing by 0.5°C/min, this method produced a camera calibration with spatial-temporal rms variability of 0.21°C, yielding a total calibration uncertainty of 0.38°C limited primarily by the 0.32°C uncertainty in the blackbody source emissivity and temperature.
Detection of polarization neutral points in observations of the combined corona and sky during the 21 August 2017 total solar eclipse
(2020-07) Snik, Frans; Bos, Steven P.; Brackenhoff, Stefanie A.; Doelman, David S.; Por, Emiel H.; Bettonvil, Felix; Rodenhuis, Michiel; Vorobiev, Dmitry; Eshelman, Laura M.; Shaw, Joseph A.
We report the results of polarimetric observations of the total solar eclipse of 21 August 2017 from Rexburg, Idaho (USA). We use three synchronized DSLR cameras with polarization filters oriented at 0°, 60°, and 120° to provide high-dynamic-range RGB polarization images of the corona and surrounding sky. We measure tangential coronal polarization and vertical sky polarization, both as expected. These observations provide detailed detections of polarization neutral points above and below the eclipsed Sun where the coronal polarization is canceled by the sky polarization. We name these special polarization neutral points after Minnaert and Van de Hulst.
Digital all-sky polarization imaging of the total solar eclipse on 21 August 2017 in Rexburg, Idaho, USA
(2020-07) Eshelman, Laura M.; Tauc, Martin Jan; Hashimoto, Taiga; Gillis, Kendra; Weiss, William; Stanley, Bryan; Hooser, Preston; Shaw, Glenn E.; Shaw, Joseph A.
All-sky polarization images were measured from sunrise to sunset and during a cloud-free totality on 21 August 2017 in Rexburg, Idaho using two digital three-camera all-sky polarimeters and a time-sequential liquid-crystal-based all-sky polarimeter. Twenty-five polarimetric images were recorded during totality, revealing a highly dynamic evolution of the distribution of skylight polarization, with the degree of linear polarization becoming nearly zenith-symmetric by the end of totality. The surrounding environment was characterized with an infrared cloud imager that confirmed the complete absence of clouds during totality, an AERONET solar radiometer that measured aerosol properties, a portable weather station, and a hand-held spectrometer with satellite images that measured surface reflectance at and near the observation site. These observations confirm that previously observed totality patterns are general and not unique to those specific eclipses. The high temporal image resolution revealed a transition of a neutral point from the zenith in totality to the normal Babinet point just above the Sun after third contact, providing the first indication that the transition between totality and normal daytime polarization patterns occurs over of a time period of approximately 13 s.
Discrimination of herbicide-resistant kochia with hyperspectral imaging
(2018-03) Nugent, Paul W.; Shaw, Joseph A.; Jha, Prashant; Scherrer, Bryan; Donelick, Andrew; Kumar, Vipan
A hyperspectral imager was used to differentiate herbicide-resistant versus herbicide-susceptible biotypes of the agronomic weed kochia, in different crops in the field at the Southern Agricultural Research Center in Huntley, Montana. Controlled greenhouse experiments showed that enough information was captured by the imager to classify plants as either a crop, herbicidesusceptible or herbicide-resistant kochia. The current analysis is developing an algorithm that will work in more uncontrolled outdoor situations. In overcast conditions, the algorithm correctly identified dicamba-resistant kochia, glyphosate-resistant kochia, and glyphosate-and dicamba-susceptible kochia with 67%, 76%, and 80% success rates, respectively. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
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.
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.
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.
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.
Dynamic performance of microelectromechanical systems deformable mirrors for use in an active/adaptive two-photon microscope
(2016-12) Archer-Zhang, Christian Chunzi; Foster, Warren B.; Downey, Ryan D.; Arrasmith, Christopher L.; Dickensheets, David L.
Active optics such as deformable mirrors can be used to control both focal depth and aberrations during scanning laser microscopy. If the focal depth can be changed dynamically during scanning, then imaging of oblique surfaces becomes possible. If aberrations can be corrected dynamically during scanning, an image can be optimized throughout the field of view. Here, we characterize the speed and dynamic precision of a Boston Micromachines Corporation Multi-DM 140 element aberration correction mirror and a Revibro Optics 4-zone focus control mirror to assess suitability for use in an active and adaptive two-photon microscope. Tests for the multi-DM include both step response and sinusoidal frequency sweeps of specific Zernike modes (defocus, spherical aberration, coma, astigmatism, and trefoil). We find wavefront error settling times for mode amplitude steps as large as 400 nm to be less than 52 mu s, with 3 dB frequencies ranging from 6.5 to 10 kHz. The Revibro Optics mirror was tested for step response only, with wavefront error settling time less than 80 mu s for defocus steps up to 3000 nm, and less than 45 mu s for spherical aberration steps up to 600 nm. These response speeds are sufficient for intrascan correction at scan rates typical of two-photon microscopy. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
Eye-Safe Diode-Laser-Based Micropulse Differential Absorption Lidar (DIAL) for Water Vapor Profiling in the Lower Troposphere
(2011-02) Nehrir, Amin R.; Repasky, Kevin S.; Carlsten, John L.
A second-generation diode-laser-based master oscillator power amplifier (MOPA) configured micropulse differential absorption lidar (DIAL) instrument for profiling of lower-tropospheric water vapor is presented. The DIAL transmitter is based on a continuous wave (cw) external cavity diode laser (ECDL) master oscillator that is used to injection seed two cascaded tapered semiconductor optical power amplifiers, which deliver up to 2-μJ pulse energies over a 1-μs pulse duration at 830 nm with an average power of ∼40 mW at a pulse repetition frequency of 20 kHz. The DIAL receiver utilizes a commercial 28-cm-diameter Schmidt–Cassegrain telescope, a 250-pm narrowband optical filter, and a fiber-coupled single-photon-counting Avalanche photodiode (APD) detector, yielding a far-field full-angle field of view of 170 μrad. A detailed description of the second-generation Montana State University (MSU) DIAL instrument is presented. Water vapor number density profiles and time–height cross sections collected with the water vapor DIAL instrument are also presented and compared with collocated radiosonde measurements, demonstrating the instruments ability to measure night- and daytime water vapor profiles in the lower troposphere.