Luis Miguel Samaniego Campoverde, Arijit Dutta, M. Tropea, F. De Rango
The air pollution, with its impacts on human health and the environment, is a growing global issue. In this article, we propose the implementation of a Multi-Interface Mobile Gateway (MIMG) with LPWAN technology in public transportation vehicles for monitoring air quality. The idea is to use a mobile monitoring system that can reduce the cost of the classical fixed air pollution and environmental monitoring stations. This approach addresses challenges such as data transfer, interference, and data pre-processing to reduce the amount of data sent over the remote data management center. We conducted a system emulation to evaluate some data forwarding strategies and to evaluate the overall traffic load generated by the mobile station over the overall network. Furthermore, the MIMG manages the use of the communication interface, uses data aggregation techniques to reduce the amount of data to be transmitted, and utilizes machine learning to enhance the accuracy of the low-cost sensor readings. Our approach has significant applications in urban air quality management.
{"title":"Multi-interface mobile gateways for LPWAN-based air pollution monitoring","authors":"Luis Miguel Samaniego Campoverde, Arijit Dutta, M. Tropea, F. De Rango","doi":"10.1117/12.3016789","DOIUrl":"https://doi.org/10.1117/12.3016789","url":null,"abstract":"The air pollution, with its impacts on human health and the environment, is a growing global issue. In this article, we propose the implementation of a Multi-Interface Mobile Gateway (MIMG) with LPWAN technology in public transportation vehicles for monitoring air quality. The idea is to use a mobile monitoring system that can reduce the cost of the classical fixed air pollution and environmental monitoring stations. This approach addresses challenges such as data transfer, interference, and data pre-processing to reduce the amount of data sent over the remote data management center. We conducted a system emulation to evaluate some data forwarding strategies and to evaluate the overall traffic load generated by the mobile station over the overall network. Furthermore, the MIMG manages the use of the communication interface, uses data aggregation techniques to reduce the amount of data to be transmitted, and utilizes machine learning to enhance the accuracy of the low-cost sensor readings. Our approach has significant applications in urban air quality management.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"3 3","pages":"1305819 - 1305819-10"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380488","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}
Brian M. Concannon, Aaron G. Meldrum, D. Illig, Benjamin M. Decker, Aaron D. Pyrah, Anton Vasilyev
During a twelve day field test west of the continental shelf off the coast of Washington state, we conducted multiple environmental data collection flights in a 150 km by 150 km area. We operated a scanning lidar system optimized for ocean profiling collecting near surface atmospheric return signal, surface reflections and optical profiles to several optical depths. The along and across track spatial resolution was approximately 10 meters and the vertical resolution was approximately 0.1 meters. We also deployed ten single use temperature profiling buoys during the test. We will present comparisons of the spatial-temporal lidar data to the buoy data and other public source data, such as satellite derived k-diffuse and Argo float data. It is our expectation that the lidar data will reveal complex and changing vertical optical structures on sub-kilometer horizontal scales that are not adequately captured by other ocean sensing techniques.
{"title":"Comparison of 20 million airborne lidar optical profiles to anything else","authors":"Brian M. Concannon, Aaron G. Meldrum, D. Illig, Benjamin M. Decker, Aaron D. Pyrah, Anton Vasilyev","doi":"10.1117/12.3013116","DOIUrl":"https://doi.org/10.1117/12.3013116","url":null,"abstract":"During a twelve day field test west of the continental shelf off the coast of Washington state, we conducted multiple environmental data collection flights in a 150 km by 150 km area. We operated a scanning lidar system optimized for ocean profiling collecting near surface atmospheric return signal, surface reflections and optical profiles to several optical depths. The along and across track spatial resolution was approximately 10 meters and the vertical resolution was approximately 0.1 meters. We also deployed ten single use temperature profiling buoys during the test. We will present comparisons of the spatial-temporal lidar data to the buoy data and other public source data, such as satellite derived k-diffuse and Argo float data. It is our expectation that the lidar data will reveal complex and changing vertical optical structures on sub-kilometer horizontal scales that are not adequately captured by other ocean sensing techniques.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"78 4","pages":"1306107 - 1306107-7"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377507","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}
Allyson R. Tesky, Sujan Aryal, Julia Molitor, Anupama Kaul
Graphene, a single sheet of carbon atoms arranged in a two-dimensional (2-D) honeycomb lattice extracted from bulk three-dimensional (3-D) graphite, has shown great promise towards low-profile sensing applications. Several studies have demonstrated its potential in acquiring 2-D electrophysiological measurements of the human body including the use of electromyography (EMG). Electromyograms require a minimum of two electrodes, making them a cost-effective option for the study of 2-D conductors interfaced to the human body. Although EMG signals are typically no more than 5 mV, they can be easily visualized through amplification with a gain resistor on a prototype circuit. In this study, preliminary EMG measurements of antagonist-agonist muscle pairs are collected through utilization of commercial electrodes to yield statistically significant results on the effect of gain on the Signal-to-Noise-Ratio (SNR) and on quantitative measurements of muscle force and associated amplitude. This information is then applied towards the exploration of producing graphene electrodes for biosensing. Presently, there have been limited studies on inkjet-printed electrodes for this purpose, with methods typically favoring screen-printing techniques. Therefore, there is value in analyzing reliable fabrication methods with graphene ink towards the production of devices for strain-dependent sensing and biosensing. To do this, graphene ink was processed via liquid-phase exfoliation with a mixture of graphite powder with typical solvents and other additives. This ink was printed on an SiO2/Si substrate to form electrodes for voltage testing in addition to electrode formation on flexible substrates for dynamic strain sensing. Here the conductivity was verified through strain-dependent testing, and the flexible graphene devices demonstrated live current changes at variable bending angles and in opposite profiles which we discuss in this work.
{"title":"Inkjet-printed 2-D conductors for electromyography (EMG) electrodes in biosensing applications","authors":"Allyson R. Tesky, Sujan Aryal, Julia Molitor, Anupama Kaul","doi":"10.1117/12.3011853","DOIUrl":"https://doi.org/10.1117/12.3011853","url":null,"abstract":"Graphene, a single sheet of carbon atoms arranged in a two-dimensional (2-D) honeycomb lattice extracted from bulk three-dimensional (3-D) graphite, has shown great promise towards low-profile sensing applications. Several studies have demonstrated its potential in acquiring 2-D electrophysiological measurements of the human body including the use of electromyography (EMG). Electromyograms require a minimum of two electrodes, making them a cost-effective option for the study of 2-D conductors interfaced to the human body. Although EMG signals are typically no more than 5 mV, they can be easily visualized through amplification with a gain resistor on a prototype circuit. In this study, preliminary EMG measurements of antagonist-agonist muscle pairs are collected through utilization of commercial electrodes to yield statistically significant results on the effect of gain on the Signal-to-Noise-Ratio (SNR) and on quantitative measurements of muscle force and associated amplitude. This information is then applied towards the exploration of producing graphene electrodes for biosensing. Presently, there have been limited studies on inkjet-printed electrodes for this purpose, with methods typically favoring screen-printing techniques. Therefore, there is value in analyzing reliable fabrication methods with graphene ink towards the production of devices for strain-dependent sensing and biosensing. To do this, graphene ink was processed via liquid-phase exfoliation with a mixture of graphite powder with typical solvents and other additives. This ink was printed on an SiO2/Si substrate to form electrodes for voltage testing in addition to electrode formation on flexible substrates for dynamic strain sensing. Here the conductivity was verified through strain-dependent testing, and the flexible graphene devices demonstrated live current changes at variable bending angles and in opposite profiles which we discuss in this work.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"8 3","pages":"1305907 - 1305907-8"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378661","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}
Muscular myopathies such as woody or Wooden Breast (WB), which impair the eating quality and marketability of poultry products, are threatening the profitability of poultry industries worldwide, with an estimated annual loss exceeding $500 million for the United States (U.S.) poultry industry. WB-affected fillets are characterized by abnormal tissue hardness and muscle rigidity with varying degrees of severity. The assessment of WB conditions at processing facilities currently relies on tactile palpation combined with a visual examination by trained personnel. This approach is subjective, labor-intensive, costly, and may induce contamination due to physical contact. Optical imaging technology offers a promising alternative for objective and non-invasive quality assessment of broiler meat. This study presents a proof-of-concept evaluation of a new scattering imaging technique that captures light-scattering characteristics of meat tissues for the detection of WB conditions in broiler breast fillets. Broadband scattering images, generated under the illumination of a highly focused broadband beam, were acquired from broiler meat samples. Two types of image features, i.e., 1) deep-learning-based and 2) hand-crafted scattering features, were extracted for building classification models using regularized linear discriminant analysis to differentiate meat samples into two categories, i.e., “Normal (no WB)” and “Defective”, according to WB conditions. Deep-learning-based features yielded an overall classification accuracy of 80.9%, while an improved accuracy of 88.7% was obtained by hand-crafted scattering features, representing a significant improvement of 7.8% (P ⪅ 0.01). Furthermore, feature selection based on Minimum Redundancy Maximum Relevance (MRMR) was conducted to select a subset of scattering image features for discriminant modeling, leading to a further accuracy improvement to 90.5% with top-ranked 65 features. This study has demonstrated the promise of the light scattering imaging technique for WB detection in broiler breast meats.
{"title":"Detection of woody breast condition in broiler breast fillets using light scattering imaging","authors":"Jiaxu Cai, Yuzhen Lu","doi":"10.1117/12.3013464","DOIUrl":"https://doi.org/10.1117/12.3013464","url":null,"abstract":"Muscular myopathies such as woody or Wooden Breast (WB), which impair the eating quality and marketability of poultry products, are threatening the profitability of poultry industries worldwide, with an estimated annual loss exceeding $500 million for the United States (U.S.) poultry industry. WB-affected fillets are characterized by abnormal tissue hardness and muscle rigidity with varying degrees of severity. The assessment of WB conditions at processing facilities currently relies on tactile palpation combined with a visual examination by trained personnel. This approach is subjective, labor-intensive, costly, and may induce contamination due to physical contact. Optical imaging technology offers a promising alternative for objective and non-invasive quality assessment of broiler meat. This study presents a proof-of-concept evaluation of a new scattering imaging technique that captures light-scattering characteristics of meat tissues for the detection of WB conditions in broiler breast fillets. Broadband scattering images, generated under the illumination of a highly focused broadband beam, were acquired from broiler meat samples. Two types of image features, i.e., 1) deep-learning-based and 2) hand-crafted scattering features, were extracted for building classification models using regularized linear discriminant analysis to differentiate meat samples into two categories, i.e., “Normal (no WB)” and “Defective”, according to WB conditions. Deep-learning-based features yielded an overall classification accuracy of 80.9%, while an improved accuracy of 88.7% was obtained by hand-crafted scattering features, representing a significant improvement of 7.8% (P ⪅ 0.01). Furthermore, feature selection based on Minimum Redundancy Maximum Relevance (MRMR) was conducted to select a subset of scattering image features for discriminant modeling, leading to a further accuracy improvement to 90.5% with top-ranked 65 features. This study has demonstrated the promise of the light scattering imaging technique for WB detection in broiler breast meats.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"108 1","pages":"1306002 - 1306002-13"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376237","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}
The idea of Subspace Learning Machine (SLM) has been a powerful tool for Machine Learning (ML), and it has been successfully applied to the task of image classification. Recently, a novel SLM method was proposed, which (i) projects high-dimensional feature vectors into a 1D feature subspace, and (ii) partitions it into two disjoint sets. SLM with soft partitioning (SLM/SP) extends this approach by learning an adaptive Soft Decision Tree (SDT) structure using local greedy subspace partitioning. After meeting the stopping criteria for all child nodes and determining the tree structure, it updates all Projection Vectors (PVs) globally. It enables efficient training, high classification accuracy, and a small model size. It is applied to experimental data to show its performance as a lightweight and high-performance classification method.
{"title":"Methodology of soft partition for image classification","authors":"Vinod K. Mishra, C.-C. Jay Kuo","doi":"10.1117/12.3012728","DOIUrl":"https://doi.org/10.1117/12.3012728","url":null,"abstract":"The idea of Subspace Learning Machine (SLM) has been a powerful tool for Machine Learning (ML), and it has been successfully applied to the task of image classification. Recently, a novel SLM method was proposed, which (i) projects high-dimensional feature vectors into a 1D feature subspace, and (ii) partitions it into two disjoint sets. SLM with soft partitioning (SLM/SP) extends this approach by learning an adaptive Soft Decision Tree (SDT) structure using local greedy subspace partitioning. After meeting the stopping criteria for all child nodes and determining the tree structure, it updates all Projection Vectors (PVs) globally. It enables efficient training, high classification accuracy, and a small model size. It is applied to experimental data to show its performance as a lightweight and high-performance classification method.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"76 3‐4","pages":"130580D - 130580D-14"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376669","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}
Michael J. Reale, Daniel P. Murphy, Maria Cornacchia, Jamie Vazquez Madera
The ability to create and detect synthetic video is becoming critically important to scene understanding. Techniques for synthetic manipulation and augmentation of data increase diversity within available datasets, while not requiring laborious labeling efforts. That is, the ability to create synthetic video can enable augmentation of small realistic datasets on which to further train Artificial Intelligence and Machine Learning (AI/ML) algorithms. Thus, it may be desirable to add, remove, or modify vehicles in satellite and overhead video. In our previous work, we leveraged Generative Adversarial Networks (GANs) to transform cars into trucks (and vice versa) in static images. We utilized an attention-based masking approach that assists the network in transformation of the object and not background. In addition, we demonstrated the benefits of numerous data augmentation procedures, including presenting a new artificial dataset of vehicles from an aerial perspective and introducing novel augmentation techniques appropriate for our network architectures. This work extends the applied techniques from still imagery to video. We employ a few different architectures: (1) a fully dynamic 3D convolutional discriminator network with static generators, (2) a fully dynamic 3D convolutional discriminator and generator network, and (3) an architecture that computes "warp" between frames for input to a static generator. Additionally, to help enforce consistency, we experiment with an interframe classifier that verifies whether two frames belong to the same video sequence or not. We run experiments on a real-world dataset, presenting promising results in terms of FID, KID, and metrics developed from a classifier trained on our dataset.
{"title":"Video modification in drone and satellite imagery","authors":"Michael J. Reale, Daniel P. Murphy, Maria Cornacchia, Jamie Vazquez Madera","doi":"10.1117/12.3013881","DOIUrl":"https://doi.org/10.1117/12.3013881","url":null,"abstract":"The ability to create and detect synthetic video is becoming critically important to scene understanding. Techniques for synthetic manipulation and augmentation of data increase diversity within available datasets, while not requiring laborious labeling efforts. That is, the ability to create synthetic video can enable augmentation of small realistic datasets on which to further train Artificial Intelligence and Machine Learning (AI/ML) algorithms. Thus, it may be desirable to add, remove, or modify vehicles in satellite and overhead video. In our previous work, we leveraged Generative Adversarial Networks (GANs) to transform cars into trucks (and vice versa) in static images. We utilized an attention-based masking approach that assists the network in transformation of the object and not background. In addition, we demonstrated the benefits of numerous data augmentation procedures, including presenting a new artificial dataset of vehicles from an aerial perspective and introducing novel augmentation techniques appropriate for our network architectures. This work extends the applied techniques from still imagery to video. We employ a few different architectures: (1) a fully dynamic 3D convolutional discriminator network with static generators, (2) a fully dynamic 3D convolutional discriminator and generator network, and (3) an architecture that computes \"warp\" between frames for input to a static generator. Additionally, to help enforce consistency, we experiment with an interframe classifier that verifies whether two frames belong to the same video sequence or not. We run experiments on a real-world dataset, presenting promising results in terms of FID, KID, and metrics developed from a classifier trained on our dataset.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"24 30","pages":"1305813 - 1305813-10"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380140","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}
Eungchan Kim, Sang-Yeon Kim, Chang-Hyup Lee, Sungjay Kim, Xianghui Xin, Seul-Ki Lee, J. Cho, Ghiseok Kim
We utilized hyperspectral imaging technology, which is commonly used for nondestructive quality assessment in agriculture, to predict SSC (Brix, %) and also the firmness (N) of apples. In this research, various regression models were applied based on machine learning and deep learning with hyperspectral (400~1000 nm) spectrum data to predict SSC and firmness of apple fruits. To evaluate the prediction accuracy of each model, coefficient of determination (r square) and Root Mean Square Error (RMSE) was used. For this purpose, spectral data of apple fruits was acquired and prediction models using various regression models such as PLSR were developed. Also, various preprocessing methods were applied, including extracting meaningful pixels, MSC (Multiplicative Scatter Correction), SNV (Standard Normal Variate), to enhance the accuracy of regression models. Through these process, SSC and firmness prediction performance of each model was analyzed and compared with various combination of preprocessing methods.
{"title":"Determination of optimal harvest timing for field-grown apple fruits using hyperspectral imaging technology","authors":"Eungchan Kim, Sang-Yeon Kim, Chang-Hyup Lee, Sungjay Kim, Xianghui Xin, Seul-Ki Lee, J. Cho, Ghiseok Kim","doi":"10.1117/12.3014570","DOIUrl":"https://doi.org/10.1117/12.3014570","url":null,"abstract":"We utilized hyperspectral imaging technology, which is commonly used for nondestructive quality assessment in agriculture, to predict SSC (Brix, %) and also the firmness (N) of apples. In this research, various regression models were applied based on machine learning and deep learning with hyperspectral (400~1000 nm) spectrum data to predict SSC and firmness of apple fruits. To evaluate the prediction accuracy of each model, coefficient of determination (r square) and Root Mean Square Error (RMSE) was used. For this purpose, spectral data of apple fruits was acquired and prediction models using various regression models such as PLSR were developed. Also, various preprocessing methods were applied, including extracting meaningful pixels, MSC (Multiplicative Scatter Correction), SNV (Standard Normal Variate), to enhance the accuracy of regression models. Through these process, SSC and firmness prediction performance of each model was analyzed and compared with various combination of preprocessing methods.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"120 1","pages":"130600G - 130600G-4"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376777","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}
Patrick Jungwirth, W. M. Crowe, Tom Barnett, Linton Salmon. Darpassith, Program Manager
Saltzer and Schroeder’s security principles define complete mediation as to verify all access rights and authority. Conventional architectures focus on speed at all costs using predictors, caches, out-of-order execution, speculative execution, etc. A new approach is required to overcome the limitations of conventional architectures: the clock speed differential between a microprocessor and memory, and the resulting self-imposed, never-ending cyber security problems. The Aberdeen Architecture uses the cache bank pipeline memory architecture from the Redstone Architecture to overcome some of the speed differential between a microprocessor and memory. The trusted computing base uses hardware state machine monitors (hardware-based nano-operating system kernels). The state machine monitors use register and memory tags to manage and track information flows during instruction execution. The Aberdeen Architecture tracks and monitors four information flows: data flow integrity, memory access flow integrity, control flow integrity, and instruction execution flow integrity. All information flows are data flow driven. The state machine monitors completely virtualize the execution pipeline. The Aberdeen Architecture achieves near complete mediation for instruction execution. This paper focuses on data flow integrity and memory access flow integrity.
{"title":"Aberdeen architecture: information flow monitoring and tracking","authors":"Patrick Jungwirth, W. M. Crowe, Tom Barnett, Linton Salmon. Darpassith, Program Manager","doi":"10.1117/12.3014162","DOIUrl":"https://doi.org/10.1117/12.3014162","url":null,"abstract":"Saltzer and Schroeder’s security principles define complete mediation as to verify all access rights and authority. Conventional architectures focus on speed at all costs using predictors, caches, out-of-order execution, speculative execution, etc. A new approach is required to overcome the limitations of conventional architectures: the clock speed differential between a microprocessor and memory, and the resulting self-imposed, never-ending cyber security problems. The Aberdeen Architecture uses the cache bank pipeline memory architecture from the Redstone Architecture to overcome some of the speed differential between a microprocessor and memory. The trusted computing base uses hardware state machine monitors (hardware-based nano-operating system kernels). The state machine monitors use register and memory tags to manage and track information flows during instruction execution. The Aberdeen Architecture tracks and monitors four information flows: data flow integrity, memory access flow integrity, control flow integrity, and instruction execution flow integrity. All information flows are data flow driven. The state machine monitors completely virtualize the execution pipeline. The Aberdeen Architecture achieves near complete mediation for instruction execution. This paper focuses on data flow integrity and memory access flow integrity.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"68 1","pages":"130580N - 130580N-17"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378241","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}
Jacob Romeo, Dylan Ballback, Kyle Fox, Sergey V. Drakunov
ISAAC is a 3D-printed pneumatic spacecraft for attitude control system development in a 3-axis gimbal ring. This allows for simulated free-space movement of a cold gas thruster-controlled probe in a controlled test environment. The purpose of this open-sourced control platform is to allow students, professors, and researchers to test their control algorithms on real hardware in real-time. The end goal is to have a website allowing anyone to upload their code and watch it run via live stream. The spacecraft uses a pneumatic system to mimic cold gas thrusters by using compressed air as a means of propulsion. The delivery system uses solenoids to control the thrust, stabilizing the craft. The hardware is simple and consists of custom Arduino Printed Circuit Boards (PCB), a Raspberry Pi, an Inertial Measurement Unit (IMU) for total orientation data, and 2 LiPo batteries. The craft is entirely 3D printed, including the mounts for the components, to be accessible for future research and upgrades. The attitude controller will be integrated into the website easycontrols.org, which will allow anyone interested, both students and researchers alike, to upload their Python control algorithm and watch it run on hardware in real-time. The website will have built-in functions and examples, allowing the user to create their algorithm easily. A proof of concept of this system has been the application of a sliding mode controller in one axis of the gimbal rings. Future work can include the application of more modern control methods for students and facilities to display and follow.
ISAAC 是一个三维打印的气动航天器，用于在三轴万向节环中开发姿态控制系统。这使得冷气体推进器控制的探测器能够在受控测试环境中进行模拟自由空间运动。这个开源控制平台的目的是让学生、教授和研究人员能够在真实硬件上实时测试他们的控制算法。最终目标是建立一个网站，允许任何人上传代码，并通过直播观看代码运行。航天器使用气动系统模仿冷气推进器，以压缩空气作为推进手段。传送系统使用螺线管来控制推力，从而稳定飞船。硬件非常简单，包括定制的 Arduino 印刷电路板（PCB）、一个树莓派（Raspberry Pi）、一个用于获取总方位数据的惯性测量单元（IMU）和两块锂电池。该飞行器完全是 3D 打印的，包括组件的支架，以便将来进行研究和升级。姿态控制器将被集成到网站 easycontrols.org，任何感兴趣的人，包括学生和研究人员，都可以通过该网站上传自己的 Python 控制算法，并实时观察其在硬件上的运行情况。网站将提供内置功能和示例，让用户轻松创建自己的算法。该系统的概念验证是在万向节环的一个轴上应用滑动模式控制器。未来的工作可以包括应用更现代的控制方法，供学生和设备展示和跟踪。
{"title":"Integrated spacecraft autonomous attitude control testbed","authors":"Jacob Romeo, Dylan Ballback, Kyle Fox, Sergey V. Drakunov","doi":"10.1117/12.3013739","DOIUrl":"https://doi.org/10.1117/12.3013739","url":null,"abstract":"ISAAC is a 3D-printed pneumatic spacecraft for attitude control system development in a 3-axis gimbal ring. This allows for simulated free-space movement of a cold gas thruster-controlled probe in a controlled test environment. The purpose of this open-sourced control platform is to allow students, professors, and researchers to test their control algorithms on real hardware in real-time. The end goal is to have a website allowing anyone to upload their code and watch it run via live stream. The spacecraft uses a pneumatic system to mimic cold gas thrusters by using compressed air as a means of propulsion. The delivery system uses solenoids to control the thrust, stabilizing the craft. The hardware is simple and consists of custom Arduino Printed Circuit Boards (PCB), a Raspberry Pi, an Inertial Measurement Unit (IMU) for total orientation data, and 2 LiPo batteries. The craft is entirely 3D printed, including the mounts for the components, to be accessible for future research and upgrades. The attitude controller will be integrated into the website easycontrols.org, which will allow anyone interested, both students and researchers alike, to upload their Python control algorithm and watch it run on hardware in real-time. The website will have built-in functions and examples, allowing the user to create their algorithm easily. A proof of concept of this system has been the application of a sliding mode controller in one axis of the gimbal rings. Future work can include the application of more modern control methods for students and facilities to display and follow.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"153 1‐3","pages":"130580T - 130580T-9"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381148","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}
As the population of the earth grows, the demand for food grows proportionally. Early and cost-effective detection of plant diseases can result in less food loss throughout the world. The current methods for image-based plant disease detection tend to fail in field conditions. Our method uses region proposal networks to localize diseased leaves for detection. We discard no prior anchor boxes, which increases the average recall of the network, resulting in better localization.
{"title":"Localizing plant leaves using maximum anchor boxes in region proposal convolutional neural networks","authors":"debojyoti Misra, Prakash Duraisamy, Tushar Sandan","doi":"10.1117/12.3015526","DOIUrl":"https://doi.org/10.1117/12.3015526","url":null,"abstract":"As the population of the earth grows, the demand for food grows proportionally. Early and cost-effective detection of plant diseases can result in less food loss throughout the world. The current methods for image-based plant disease detection tend to fail in field conditions. Our method uses region proposal networks to localize diseased leaves for detection. We discard no prior anchor boxes, which increases the average recall of the network, resulting in better localization.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"29 30","pages":"1306004 - 1306004-9"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379339","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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