In this paper, we systematically investigate the use of delays to optimize the throughput for the working Maximum-On-Ground (MOG) problem space. The MOG optimization refers to the management of the transport aircraft in-and-around an airfield. The working MOG refers to the fulfilling of the servicing requirements of the aircraft. The effective and efficient daily MOG management enables the U.S. Air Force (USAF) Air Mobility Command (AMC) to rapidly deploy and sustain the equipment, and personnel anywhere in the world. However, the seemingly solved problem can quickly grow out of hand when the number of interruptions exceed past a certain point; this due to the combinatorial nature of the scheduling problem, where the order, and the mission dependencies matter. The opportunistic delays optimization explores the trade-off space between the efficiency (throughput maximization) and the resilience to schedule disruptions.
{"title":"Working MOG optimization via opportunistic delays","authors":"Gennady Staskevich, Joseph Skufca","doi":"10.1117/12.3025179","DOIUrl":"https://doi.org/10.1117/12.3025179","url":null,"abstract":"In this paper, we systematically investigate the use of delays to optimize the throughput for the working Maximum-On-Ground (MOG) problem space. The MOG optimization refers to the management of the transport aircraft in-and-around an airfield. The working MOG refers to the fulfilling of the servicing requirements of the aircraft. The effective and efficient daily MOG management enables the U.S. Air Force (USAF) Air Mobility Command (AMC) to rapidly deploy and sustain the equipment, and personnel anywhere in the world. However, the seemingly solved problem can quickly grow out of hand when the number of interruptions exceed past a certain point; this due to the combinatorial nature of the scheduling problem, where the order, and the mission dependencies matter. The opportunistic delays optimization explores the trade-off space between the efficiency (throughput maximization) and the resilience to schedule disruptions.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"2 4","pages":"130580O - 130580O-11"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Laser Power Transfer (LPT) can serve as a potential solution to powering solar cells that are out of contact with the sun. It also has the potential to be combined with communications through beam modulation. This research aimed to integrate LPT and communications into a dual-use system, thus decreasing the Size, Weight, and Power (SWaP) of a rover, which would in turn reduce its cost. The two main focuses of this research were to characterize data rate and power transfer to a solar cell through the modulation of a laser beam by comparing different modulation methods. An off-the-shelf monocrystalline solar cell detected 30kbps of LED modulation with a maximum loss in power of 5.5%, and it detected 2.7kbps of laser modulation with a maximum loss in power of 20.1%.
激光功率传输（LPT）可以作为一种潜在的解决方案，为不与太阳接触的太阳能电池供电。它还有可能通过光束调制与通信相结合。这项研究旨在将激光功率传输和通信集成到一个两用系统中，从而减小漫游车的尺寸、重量和功率（SWaP），进而降低其成本。这项研究的两大重点是通过比较不同的调制方法，确定通过调制激光束向太阳能电池传输数据的速率和功率。一个现成的单晶硅太阳能电池能检测到 30kbps 的 LED 调制，最大功率损耗为 5.5%；它能检测到 2.7kbps 的激光调制，最大功率损耗为 20.1%。
{"title":"Integrating power beaming and communication through laser modulation","authors":"Daniel O'Flaherty, Mike Sanders, Charles Nelson","doi":"10.1117/12.3013604","DOIUrl":"https://doi.org/10.1117/12.3013604","url":null,"abstract":"Laser Power Transfer (LPT) can serve as a potential solution to powering solar cells that are out of contact with the sun. It also has the potential to be combined with communications through beam modulation. This research aimed to integrate LPT and communications into a dual-use system, thus decreasing the Size, Weight, and Power (SWaP) of a rover, which would in turn reduce its cost. The two main focuses of this research were to characterize data rate and power transfer to a solar cell through the modulation of a laser beam by comparing different modulation methods. An off-the-shelf monocrystalline solar cell detected 30kbps of LED modulation with a maximum loss in power of 5.5%, and it detected 2.7kbps of laser modulation with a maximum loss in power of 20.1%.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"56 s196","pages":"130620Q - 130620Q-11"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Single-frequency GNSS users are reliant on estimates of the Total Electron Content (TEC) along lines of sight to navigation satellites to correct for ionospheric propagation delay and the resulting positioning errors. The parametric correction methods in use (Klobuchar’s algorithm for GPS and the NeQuick-G model for Galileo) can compensate for a large fraction of the delay but are hindered by using only a few daily coefficients to describe the ground truth ionosphere state. This loss of state information is particularly detrimental during periods of high deviation from baseline TEC patterns, e.g. solar weather events. This work describes an autoregressive RNN/CNN approach for spatiotemporal TEC forecasting from windowed historical map products, preserving local temporal and geospatial dependence between samples. By leveraging a large dataset spanning from 2000-2020 and applying convolutional transformations over both the temporal and spatial dimensions of the data, this model exhibits improved performance for time horizons up to 48 hours, compared to neural network-based approaches described in the literature to date.
{"title":"Improvements to global ionospheric forecasting with a recurrent convolutional neural network","authors":"Joseph Dailey, Khanh D. Pham","doi":"10.1117/12.3023846","DOIUrl":"https://doi.org/10.1117/12.3023846","url":null,"abstract":"Single-frequency GNSS users are reliant on estimates of the Total Electron Content (TEC) along lines of sight to navigation satellites to correct for ionospheric propagation delay and the resulting positioning errors. The parametric correction methods in use (Klobuchar’s algorithm for GPS and the NeQuick-G model for Galileo) can compensate for a large fraction of the delay but are hindered by using only a few daily coefficients to describe the ground truth ionosphere state. This loss of state information is particularly detrimental during periods of high deviation from baseline TEC patterns, e.g. solar weather events. This work describes an autoregressive RNN/CNN approach for spatiotemporal TEC forecasting from windowed historical map products, preserving local temporal and geospatial dependence between samples. By leveraging a large dataset spanning from 2000-2020 and applying convolutional transformations over both the temporal and spatial dimensions of the data, this model exhibits improved performance for time horizons up to 48 hours, compared to neural network-based approaches described in the literature to date.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"3 1","pages":"130620C - 130620C-6"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sravani Varanasi, Tianye Zhai, Hong Gu, Yihong Yang, Fow-Sen Choa
Substance Use Disorder (SUD) is a complex condition with profound effects on brain function. Understanding the altered functional connectivity patterns in the brains of SUD patients is crucial for unraveling the neurological underpinnings of this disorder. This study employs Energy Landscape Analysis, an energy-based machine learning technique, to investigate whole brain Regions of Interest (ROI) functional connectivity differences between SUD patients and healthy controls. The challenge with Energy Landscape Analysis lies in selecting the appropriate ROI from the extensive brain atlas. In this study, seed-based connectivity was utilized to identify relevant ROIs, overcoming the limitation of analyzing only a limited number of ROIs. The dataset comprised 53 cocaine users and 52 age- and sex-matched healthy controls, with fMRI data preprocessed using the CONN toolbox. ROI-ROI seed-based pair connectivity was derived through first and second level analyses. The identified sub-ROIs were categorized into default CONN network affiliations and bundled into Superior Temporal Gyrus (STG), Inferior Temporal Gyrus, temporooccipital part (toITG), Visual Primary (VIS-P), Auditory (AUD), Cerebellum, Basal Ganglia (BSL), and Thalamus (THL). Significance testing revealed eight connectivity states among all above regions with p-values that satisfy Bonferroni correction between controls and patients. Notably, the connectivity states with the lowest p-values revealed a distinctive pattern: STG (auditory attention) toITG were disconnected from the rest of the networks. This finding underscores the importance of investigating specific network disruptions in SUD, shedding light on potential neural mechanisms underlying the disorder. In summary, our study utilizes Energy Landscape Analysis to explore whole brain ROI functional connectivity in SUD, revealing disrupted connectivity patterns that may have implications for understanding the neural basis of this disorder. These findings may ultimately inform targeted interventions and treatment strategies for individuals with SUD.
药物使用障碍（SUD）是一种对大脑功能有深远影响的复杂疾病。了解 SUD 患者大脑功能连接模式的改变对于揭示这种疾病的神经学基础至关重要。本研究采用能量景观分析（一种基于能量的机器学习技术）来研究 SUD 患者与健康对照组之间的全脑兴趣区（ROI）功能连接差异。能量景观分析的难点在于从广泛的脑图谱中选择合适的 ROI。在这项研究中，利用基于种子的连通性来识别相关的 ROI，克服了只能分析有限数量 ROI 的局限性。数据集包括 53 名可卡因使用者和 52 名年龄和性别匹配的健康对照者，并使用 CONN 工具箱对 fMRI 数据进行了预处理。通过一级和二级分析，得出了基于 ROI-ROI 种子对的连接性。确定的子 ROI 被归类为默认的 CONN 网络从属关系，并捆绑为颞上回（STG）、颞下回、颞枕部（toITG）、视觉初级（VIS-P）、听觉（AUD）、小脑、基底节（BSL）和丘脑（THL）。显著性检验显示，上述所有区域中存在八种连接状态，对照组和患者之间的 p 值符合 Bonferroni 校正。值得注意的是，p 值最低的连接状态显示了一种独特的模式：STG（听觉注意）到 ITG 与其他网络断开。这一发现强调了研究 SUD 中特定网络中断的重要性，从而揭示了该疾病的潜在神经机制。总之，我们的研究利用 "能量景观分析"（Energy Landscape Analysis）来探索 SUD 的全脑 ROI 功能连通性，揭示了连通性中断的模式，这可能对理解这种障碍的神经基础有影响。这些发现最终可能为针对 SUD 患者的针对性干预和治疗策略提供依据。
{"title":"Extracting functional connectivity signatures in substance use disorder using energy landscape analysis","authors":"Sravani Varanasi, Tianye Zhai, Hong Gu, Yihong Yang, Fow-Sen Choa","doi":"10.1117/12.3013694","DOIUrl":"https://doi.org/10.1117/12.3013694","url":null,"abstract":"Substance Use Disorder (SUD) is a complex condition with profound effects on brain function. Understanding the altered functional connectivity patterns in the brains of SUD patients is crucial for unraveling the neurological underpinnings of this disorder. This study employs Energy Landscape Analysis, an energy-based machine learning technique, to investigate whole brain Regions of Interest (ROI) functional connectivity differences between SUD patients and healthy controls. The challenge with Energy Landscape Analysis lies in selecting the appropriate ROI from the extensive brain atlas. In this study, seed-based connectivity was utilized to identify relevant ROIs, overcoming the limitation of analyzing only a limited number of ROIs. The dataset comprised 53 cocaine users and 52 age- and sex-matched healthy controls, with fMRI data preprocessed using the CONN toolbox. ROI-ROI seed-based pair connectivity was derived through first and second level analyses. The identified sub-ROIs were categorized into default CONN network affiliations and bundled into Superior Temporal Gyrus (STG), Inferior Temporal Gyrus, temporooccipital part (toITG), Visual Primary (VIS-P), Auditory (AUD), Cerebellum, Basal Ganglia (BSL), and Thalamus (THL). Significance testing revealed eight connectivity states among all above regions with p-values that satisfy Bonferroni correction between controls and patients. Notably, the connectivity states with the lowest p-values revealed a distinctive pattern: STG (auditory attention) toITG were disconnected from the rest of the networks. This finding underscores the importance of investigating specific network disruptions in SUD, shedding light on potential neural mechanisms underlying the disorder. In summary, our study utilizes Energy Landscape Analysis to explore whole brain ROI functional connectivity in SUD, revealing disrupted connectivity patterns that may have implications for understanding the neural basis of this disorder. These findings may ultimately inform targeted interventions and treatment strategies for individuals with SUD.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"26 1","pages":"1305909 - 1305909-6"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cheng-Ying Wu, Qi Zhao, Cheng-Yu Cheng, Yuchen Yang, Muhammad Qureshi, Hang Liu, Genshe Chen
Apache Storm is a popular open-source distributed computing platform for real-time big-data processing. However, the existing task scheduling algorithms for Apache Storm do not adequately take into account the heterogeneity and dynamics of node computing resources and task demands, leading to high processing latency and suboptimal performance. In this thesis, we propose an innovative machine learning-based task scheduling scheme tailored for Apache Storm. The scheme leverages machine learning models to predict task performance and assigns a task to the computation node with the lowest predicted processing latency. In our design, each node operates a machine learning-based monitoring mechanism. When the master node schedules a new task, it queries the computation nodes obtains their available resources, and processes latency predictions to make the optimal assignment decision. We explored three machine learning models, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Deep Belief Networks (DBN). Our experiments showed that LSTM achieved the most accurate latency predictions. The evaluation results demonstrate that Apache Storm with the proposed LSTM-based scheduling scheme significantly improves the task processing delay and resource utilization, compared to the existing algorithms.
{"title":"Machine learning-based real-time task scheduling for Apache Storm","authors":"Cheng-Ying Wu, Qi Zhao, Cheng-Yu Cheng, Yuchen Yang, Muhammad Qureshi, Hang Liu, Genshe Chen","doi":"10.1117/12.3021842","DOIUrl":"https://doi.org/10.1117/12.3021842","url":null,"abstract":"Apache Storm is a popular open-source distributed computing platform for real-time big-data processing. However, the existing task scheduling algorithms for Apache Storm do not adequately take into account the heterogeneity and dynamics of node computing resources and task demands, leading to high processing latency and suboptimal performance. In this thesis, we propose an innovative machine learning-based task scheduling scheme tailored for Apache Storm. The scheme leverages machine learning models to predict task performance and assigns a task to the computation node with the lowest predicted processing latency. In our design, each node operates a machine learning-based monitoring mechanism. When the master node schedules a new task, it queries the computation nodes obtains their available resources, and processes latency predictions to make the optimal assignment decision. We explored three machine learning models, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Deep Belief Networks (DBN). Our experiments showed that LSTM achieved the most accurate latency predictions. The evaluation results demonstrate that Apache Storm with the proposed LSTM-based scheduling scheme significantly improves the task processing delay and resource utilization, compared to the existing algorithms.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"110 8","pages":"130620I - 130620I-9"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the ocean, underwater currents are driven by various natural effects attributed to heat transfer through water. The movement of heat subsequently affects light propagation due to changes in the water’s refractive index leading to optical phase distortions. Applications implementing laser beams containing structured phase profiles are prone to being distorted by this underwater optical turbulence. Typical distortions of these beams can include beam wander, intensity and phase variations, and beam spreading that can limit their effectiveness for applications including free-space optical communication, imaging, or sensing. Experimental and theoretical studies have shown optical vortices, a form of structured light, propagate differently through optical turbulence compared with Gaussian beams. Changes in propagation are observed by varying the amount of Orbital Angular Momentum (OAM) a vortex beam carries that increases the beam size as OAM increases. This experimental study intends to fairly compare Laguerre-Gaussian (LG) beams to Gaussian beams after propagation through underwater turbulence by normalizing the initial beam size using the RMS radius. The metrics chosen are the mean scintillation, on-axis intensity, and intensity correlation. Results show the scintillation and on-axis intensity, when chosen at locations along the LG beam annuli, are similar for different LG beams. When the initial beam waist is normalized, the speckle field correlation width and peak correlation energy decreases as RMS radius increases. These results show that structured light is not independent of the effects of beam size and divergence, similar to Gaussian beams, to determine propagation effectiveness or robustness.
在海洋中，水下洋流是由各种自然效应驱动的，这些自然效应归因于水中的热传递。由于水的折射率发生变化，热量的流动随后会影响光的传播，从而导致光学相位失真。应用包含结构相位轮廓的激光束时，很容易受到这种水下光学湍流的影响而发生扭曲。这些光束的典型畸变包括光束漂移、强度和相位变化以及光束扩散，从而限制了它们在自由空间光通信、成像或传感等应用中的有效性。实验和理论研究表明，与高斯光束相比，结构光的一种形式--光漩涡在光湍流中的传播方式有所不同。通过改变旋涡光束所携带的轨道角动量（OAM），可以观察到光束传播的变化。本实验研究旨在通过使用均方根半径对初始光束大小进行归一化，对通过水下湍流传播后的拉盖尔-高斯（LG）光束和高斯光束进行公平比较。选择的指标是平均闪烁、轴向强度和强度相关性。结果表明，不同 LG 光束沿 LG 光束环的位置选择时，闪烁和轴上强度相似。当初始束腰归一化时，斑点场相关宽度和峰值相关能量随着有效值半径的增加而减小。这些结果表明，结构光与高斯光束类似，在决定传播效果或稳健性时，并不独立于光束大小和发散的影响。
{"title":"Propagation of Laguerre-Gaussian beams through underwater optical turbulence","authors":"Nathaniel Ferlic, A. Laux, Linda J. Mullen","doi":"10.1117/12.3013120","DOIUrl":"https://doi.org/10.1117/12.3013120","url":null,"abstract":"In the ocean, underwater currents are driven by various natural effects attributed to heat transfer through water. The movement of heat subsequently affects light propagation due to changes in the water’s refractive index leading to optical phase distortions. Applications implementing laser beams containing structured phase profiles are prone to being distorted by this underwater optical turbulence. Typical distortions of these beams can include beam wander, intensity and phase variations, and beam spreading that can limit their effectiveness for applications including free-space optical communication, imaging, or sensing. Experimental and theoretical studies have shown optical vortices, a form of structured light, propagate differently through optical turbulence compared with Gaussian beams. Changes in propagation are observed by varying the amount of Orbital Angular Momentum (OAM) a vortex beam carries that increases the beam size as OAM increases. This experimental study intends to fairly compare Laguerre-Gaussian (LG) beams to Gaussian beams after propagation through underwater turbulence by normalizing the initial beam size using the RMS radius. The metrics chosen are the mean scintillation, on-axis intensity, and intensity correlation. Results show the scintillation and on-axis intensity, when chosen at locations along the LG beam annuli, are similar for different LG beams. When the initial beam waist is normalized, the speckle field correlation width and peak correlation energy decreases as RMS radius increases. These results show that structured light is not independent of the effects of beam size and divergence, similar to Gaussian beams, to determine propagation effectiveness or robustness.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"19 11","pages":"130610G - 130610G-11"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alex McCafferty-Leroux, W. Hilal, S. A. Gadsden, Mohammad A. AlShabi
An inherent property of dynamic systems with real applications is their high degree of variability, manifesting itself in ways that are often harmful to system stability and performance. External disturbances, modeling error, and faulty components must be accounted for, either in the system design, or algorithmically through estimation and control methods. In orbital satellite systems, the ability to compensate for uncertainty and detect faults is vital. Satellites are responsible for many essential operations on Earth, including GPS tracking, radio communication/broadcasting, defense, and climate monitoring. They are also expensive to design and fabricate, to deploy, and currently impossible to fix if suddenly inoperable. In being subjected to unforeseen disturbances or minor system failures, communications with Earth can cease and valuable data can be lost. Researchers have been developing robust estimation and control strategies for several decades to mitigate the effects of these failure modes. For instance, fault detection methods can be employed in satellites to detect deviations in attitude or actuator states such that error or incorrect data does not propagate further across its long life cycle. The Kalman Filter (KF) is an optimal state estimation strategy with sub-optimal nonlinear variations, commonly applied in most dynamic systems, including satellites. However, in the presence of aforementioned uncertainties, these optimal estimators tend to degrade drastically in performance, and must be replaced for more robust methods. The newly developed Sliding-Innovation Filter (SIF) is one such candidate, as it has been demonstrated to perform state estimation robustly in faulty systems. Using an in-lab Nanosatellite Attitude Control Simulator (NACS), an adaptive hybrid formulation of the SIF and EKF is applied to a satellite system to detect faults and disturbances in experiments, based on the Normalized Innovation Squares (NIS) metric. This strategy was demonstrated to improve state estimation accuracy in the presence of multiple faults, compared to conventional methods.
{"title":"Adaptive SIF-EKF estimation for fault detection in attitude control experiments","authors":"Alex McCafferty-Leroux, W. Hilal, S. A. Gadsden, Mohammad A. AlShabi","doi":"10.1117/12.3013725","DOIUrl":"https://doi.org/10.1117/12.3013725","url":null,"abstract":"An inherent property of dynamic systems with real applications is their high degree of variability, manifesting itself in ways that are often harmful to system stability and performance. External disturbances, modeling error, and faulty components must be accounted for, either in the system design, or algorithmically through estimation and control methods. In orbital satellite systems, the ability to compensate for uncertainty and detect faults is vital. Satellites are responsible for many essential operations on Earth, including GPS tracking, radio communication/broadcasting, defense, and climate monitoring. They are also expensive to design and fabricate, to deploy, and currently impossible to fix if suddenly inoperable. In being subjected to unforeseen disturbances or minor system failures, communications with Earth can cease and valuable data can be lost. Researchers have been developing robust estimation and control strategies for several decades to mitigate the effects of these failure modes. For instance, fault detection methods can be employed in satellites to detect deviations in attitude or actuator states such that error or incorrect data does not propagate further across its long life cycle. The Kalman Filter (KF) is an optimal state estimation strategy with sub-optimal nonlinear variations, commonly applied in most dynamic systems, including satellites. However, in the presence of aforementioned uncertainties, these optimal estimators tend to degrade drastically in performance, and must be replaced for more robust methods. The newly developed Sliding-Innovation Filter (SIF) is one such candidate, as it has been demonstrated to perform state estimation robustly in faulty systems. Using an in-lab Nanosatellite Attitude Control Simulator (NACS), an adaptive hybrid formulation of the SIF and EKF is applied to a satellite system to detect faults and disturbances in experiments, based on the Normalized Innovation Squares (NIS) metric. This strategy was demonstrated to improve state estimation accuracy in the presence of multiple faults, compared to conventional methods.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"125 1","pages":"130620L - 130620L-15"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eric R. Languirand, Errie Parrilla, Nathaniel Smith, Matthew D. Collins, Angus Unruh, Lars Lefkowitz, Cecilia H. Phung, Ayusman Sen
Active matter, such as Janus micromotors have been used for applications such as self-assembly, pollution mitigation, and drug delivery. Metal-Organic Framework (MOF)-based Janus micromotors have been recently explored as a method to increase the rate of decontamination for chemical warfare agents in solution due to favorable MOF-chemical interactions. To achieve active-matter decontamination, SiO2@UiO66@Ag MOF-based Janus micromotors were synthesized. In addition to decontamination, the MOF-based micromotors have favorable surface topography for maintaining a localized surface plasmon. This work explores the plasmonic capabilities of Ag@MOF Janus micromotors by systematically changing the amount of Ag, the size of the microparticle that is being used for the plasmonic sensing, and the underlying MOF structure. By changing these parameters, MOF-based micromotors may be able to be used as sensors by utilizing techniques such as Surface Enhanced Raman Spectroscopy (SERS).
{"title":"Exploring MOF-based micromotors as SERS sensors","authors":"Eric R. Languirand, Errie Parrilla, Nathaniel Smith, Matthew D. Collins, Angus Unruh, Lars Lefkowitz, Cecilia H. Phung, Ayusman Sen","doi":"10.1117/12.3017251","DOIUrl":"https://doi.org/10.1117/12.3017251","url":null,"abstract":"Active matter, such as Janus micromotors have been used for applications such as self-assembly, pollution mitigation, and drug delivery. Metal-Organic Framework (MOF)-based Janus micromotors have been recently explored as a method to increase the rate of decontamination for chemical warfare agents in solution due to favorable MOF-chemical interactions. To achieve active-matter decontamination, SiO2@UiO66@Ag MOF-based Janus micromotors were synthesized. In addition to decontamination, the MOF-based micromotors have favorable surface topography for maintaining a localized surface plasmon. This work explores the plasmonic capabilities of Ag@MOF Janus micromotors by systematically changing the amount of Ag, the size of the microparticle that is being used for the plasmonic sensing, and the underlying MOF structure. By changing these parameters, MOF-based micromotors may be able to be used as sensors by utilizing techniques such as Surface Enhanced Raman Spectroscopy (SERS).","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"163 4","pages":"130590B - 130590B-6"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141375914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A number of research papers has been published using the architecture of adversarial neural networks to prove that communication between two neural net based on synchronized input can be achieved, and without knowledge of this synchronized information these systems can not be breached. In this paper we will try to evaluate these adversarial neural net architectures when a third party gain access to partial secret key, or a noisy secret key, or has knowledge about loss function, or loss values itself, or activation functions used during training of encryption layers. We explore the cryptanalysis side of it in which we will focus on vulnerabilities a neural-net based cryptography network can face. This can be used in future to improve the current neural net based cryptography architectures. In this paper we show that while the encryption key is necessary to decrypt the messages in neural network domain, the adversarial neural networks can occasionally decrypt messages or raise a concern which will require further training.
{"title":"Neural cryptography: vulnerabilities and attack strategies","authors":"L. Beshaj, Gaurav Tyagi","doi":"10.1117/12.3013669","DOIUrl":"https://doi.org/10.1117/12.3013669","url":null,"abstract":"A number of research papers has been published using the architecture of adversarial neural networks to prove that communication between two neural net based on synchronized input can be achieved, and without knowledge of this synchronized information these systems can not be breached. In this paper we will try to evaluate these adversarial neural net architectures when a third party gain access to partial secret key, or a noisy secret key, or has knowledge about loss function, or loss values itself, or activation functions used during training of encryption layers. We explore the cryptanalysis side of it in which we will focus on vulnerabilities a neural-net based cryptography network can face. This can be used in future to improve the current neural net based cryptography architectures. In this paper we show that while the encryption key is necessary to decrypt the messages in neural network domain, the adversarial neural networks can occasionally decrypt messages or raise a concern which will require further training.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"19 3","pages":"130580S - 130580S-8"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unmanned Aerial Vehicles (UAV) have been widely adopted in many applications, from surveillance to delivery. More UAV delivery businesses are expected to be launched in the foreseeable future to meet food, goods, and medicine needs for residents living in smart cities, remote areas, or places lacking runways. As the density of UAVs operating in a community increases, collision avoidance becomes critical concerning the safety of personnel, property, and UAVs. In the last decade, many solutions have been suggested for collision avoidance scenarios, where typical solutions require integrated sensing, information exchange, and on-board decision-making. However, including these essential components increases the cost and makes it unaffordable for small-size UAVs in terms of payload weight and power consumption. Inspired by the Metaverse-enabled by Digital Twins, Blockchain, Augmented Reality (AR)/Virtual Reality (VR), and the fifth generation (5G) wireless communication technologies; we propose LoCASM, a low-cost collision avoidance scheme in Microverse, a local-scale Metaverse, for UAV delivery networks. LoCASM only requests position (GPS), altitude, velocity, and direction (PAVAD) information from each UAV; relieving the burden of expensive and energy-consuming components. By mirroring UAVs’ PAVAD information and the city landscape in the Microverse, the computing-intensive tasks, including UAV tracking, trajectory prediction, and collision avoidance management, are migrated to the Microverse server on the ground. A proof-of-concept prototype of the LoCASM system has been built, and the simulation experimental study has validated the design.
{"title":"Low-cost collision avoidance in microverse for unmanned aerial vehicle delivery networks","authors":"Qian Qu, Yu Chen, Xiaohua Li, Erik Blasch, Genshe Chen, Erika Ardiles-Cruz","doi":"10.1117/12.3013124","DOIUrl":"https://doi.org/10.1117/12.3013124","url":null,"abstract":"Unmanned Aerial Vehicles (UAV) have been widely adopted in many applications, from surveillance to delivery. More UAV delivery businesses are expected to be launched in the foreseeable future to meet food, goods, and medicine needs for residents living in smart cities, remote areas, or places lacking runways. As the density of UAVs operating in a community increases, collision avoidance becomes critical concerning the safety of personnel, property, and UAVs. In the last decade, many solutions have been suggested for collision avoidance scenarios, where typical solutions require integrated sensing, information exchange, and on-board decision-making. However, including these essential components increases the cost and makes it unaffordable for small-size UAVs in terms of payload weight and power consumption. Inspired by the Metaverse-enabled by Digital Twins, Blockchain, Augmented Reality (AR)/Virtual Reality (VR), and the fifth generation (5G) wireless communication technologies; we propose LoCASM, a low-cost collision avoidance scheme in Microverse, a local-scale Metaverse, for UAV delivery networks. LoCASM only requests position (GPS), altitude, velocity, and direction (PAVAD) information from each UAV; relieving the burden of expensive and energy-consuming components. By mirroring UAVs’ PAVAD information and the city landscape in the Microverse, the computing-intensive tasks, including UAV tracking, trajectory prediction, and collision avoidance management, are migrated to the Microverse server on the ground. A proof-of-concept prototype of the LoCASM system has been built, and the simulation experimental study has validated the design.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"226 4","pages":"130620N - 130620N-12"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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