Pub Date : 2024-11-25DOI: 10.1109/JMASS.2024.3504992
{"title":"2024 Index IEEE Journal on Miniaturization for Air and Space Systems Vol. 5","authors":"","doi":"10.1109/JMASS.2024.3504992","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3504992","url":null,"abstract":"","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 4","pages":"274-281"},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10766876","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-20DOI: 10.1109/JMASS.2024.3496303
{"title":"The Journal of Miniaturized Air and Space Systems","authors":"","doi":"10.1109/JMASS.2024.3496303","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3496303","url":null,"abstract":"","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 4","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10759326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1109/JMASS.2024.3479151
Ju Gao;Zhangziyi Jin;Zonghui Li;Zixian Chen;Qingwang Wang
As unmanned aerial vehicles (UAVs) continue to play an increasingly critical role in reconnaissance missions, establishing dependable communication links between UAVs and ground stations has become imperative. Nevertheless, ensuring reliable communication remains a great challenge, particularly in environments characterized by weak signals or high levels of electromagnetic interference. To tackle this challenge, this study presents a design and optimization approach for a miniature UAV antenna. This antenna achieves significant performance improvements by optimizing the magnetic field (MF) distribution and convergence within its central section. Specifically with the aim of capturing and amplifying signals in a specified direction, the antenna enhances reception sensitivity, especially in challenging operational settings. The structure ensures robust and consistent signal reception with a maximum gain of up to 12.8 dB and a converging MF magnitude of 2279 A/m at its center. Furthermore, it operates effectively within the C band, exhibiting a relative bandwidth of 12.2%. This capability empowers UAV to transmit reconnaissance data accurately and swiftly, regardless of the distance traveled or the complexity of the electromagnetic environment. This advancement not only enhances UAV capabilities but also opens new possibility for applications requiring dependable communication in diverse and demanding scenarios.
{"title":"Broadband Miniaturized Antenna Based on Enhanced Magnetic Field Convergence in UAV","authors":"Ju Gao;Zhangziyi Jin;Zonghui Li;Zixian Chen;Qingwang Wang","doi":"10.1109/JMASS.2024.3479151","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3479151","url":null,"abstract":"As unmanned aerial vehicles (UAVs) continue to play an increasingly critical role in reconnaissance missions, establishing dependable communication links between UAVs and ground stations has become imperative. Nevertheless, ensuring reliable communication remains a great challenge, particularly in environments characterized by weak signals or high levels of electromagnetic interference. To tackle this challenge, this study presents a design and optimization approach for a miniature UAV antenna. This antenna achieves significant performance improvements by optimizing the magnetic field (MF) distribution and convergence within its central section. Specifically with the aim of capturing and amplifying signals in a specified direction, the antenna enhances reception sensitivity, especially in challenging operational settings. The structure ensures robust and consistent signal reception with a maximum gain of up to 12.8 dB and a converging MF magnitude of 2279 A/m at its center. Furthermore, it operates effectively within the C band, exhibiting a relative bandwidth of 12.2%. This capability empowers UAV to transmit reconnaissance data accurately and swiftly, regardless of the distance traveled or the complexity of the electromagnetic environment. This advancement not only enhances UAV capabilities but also opens new possibility for applications requiring dependable communication in diverse and demanding scenarios.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 4","pages":"265-273"},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679355","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}
Deep neural network-based image classification models are vulnerable to adversarial examples, which are meticulously crafted to mislead the model by adding perturbations to clean images. Although adversarial training demonstrates outstanding performance in enhancing models robustness against adversarial examples, it often incurs the expense of accuracy. To address this problem, this article proposes a strategy to achieve a better tradeoff between accuracy and robustness, which mainly consists of symbol perturbations and examples mixing. First, we employ a symbol processing approach for randomly generated initial perturbations, which makes model identify the correct parameter attack direction faster during the training process. Second, we put forward a methodology that utilizes a mixture of different examples to generate more distinct adversarial features. Further, we utilize scaling conditions for tensor feature modulation, enabling the model to achieve both improved accuracy and robustness after learning more diverse adversarial features. Finally, we conduct extensive experiments to show the feasibility and effectiveness of the proposed methods.
{"title":"Toward a Better Tradeoff Between Accuracy and Robustness for Image Classification via Adversarial Feature Diversity","authors":"Wei Xue;Yonghao Wang;Yuchi Wang;Yue Wang;Mingyang Du;Xiao Zheng","doi":"10.1109/JMASS.2024.3462548","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3462548","url":null,"abstract":"Deep neural network-based image classification models are vulnerable to adversarial examples, which are meticulously crafted to mislead the model by adding perturbations to clean images. Although adversarial training demonstrates outstanding performance in enhancing models robustness against adversarial examples, it often incurs the expense of accuracy. To address this problem, this article proposes a strategy to achieve a better tradeoff between accuracy and robustness, which mainly consists of symbol perturbations and examples mixing. First, we employ a symbol processing approach for randomly generated initial perturbations, which makes model identify the correct parameter attack direction faster during the training process. Second, we put forward a methodology that utilizes a mixture of different examples to generate more distinct adversarial features. Further, we utilize scaling conditions for tensor feature modulation, enabling the model to achieve both improved accuracy and robustness after learning more diverse adversarial features. Finally, we conduct extensive experiments to show the feasibility and effectiveness of the proposed methods.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 4","pages":"254-264"},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679371","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}
Pub Date : 2024-08-29DOI: 10.1109/JMASS.2024.3451477
Li Zhao;Chuan Qin;Qiuni Li;Chongchong Han;Jialong Jian;Yuanfei Liu
An improved dynamic surface control (IDSC) method is proposed for a class of strict-feedback nonlinear systems with internal uncertainties and external disturbances. First, compared with the typical first-order sliding-mode differentiator, this article presents an improved method to obtain the first-order differential approximation of the virtual control signals, which tackles the obstacle of “explosion of complexity.” Second, to eliminate the effect of filtering errors that exist in traditional dynamic surface control method, in this article, the tracking errors are directly constructed using the virtual control signal. Third, composite disturbances were estimated and compensated by designing a novel disturbance observer, which eliminates the limitations that the disturbance terms must be differentiable or even slow tensors. Finally, to illustrate that the proposed method has a great ability to suppress fast time-varying and nondifferentiable disturbances, the simulation results of a numerical example and a practical example of a modern advanced fighter jet system were presented.
{"title":"Improved Dynamic Surface Control for Uncertain Nonlinear Systems With Application to Fighter Jet System","authors":"Li Zhao;Chuan Qin;Qiuni Li;Chongchong Han;Jialong Jian;Yuanfei Liu","doi":"10.1109/JMASS.2024.3451477","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3451477","url":null,"abstract":"An improved dynamic surface control (IDSC) method is proposed for a class of strict-feedback nonlinear systems with internal uncertainties and external disturbances. First, compared with the typical first-order sliding-mode differentiator, this article presents an improved method to obtain the first-order differential approximation of the virtual control signals, which tackles the obstacle of “explosion of complexity.” Second, to eliminate the effect of filtering errors that exist in traditional dynamic surface control method, in this article, the tracking errors are directly constructed using the virtual control signal. Third, composite disturbances were estimated and compensated by designing a novel disturbance observer, which eliminates the limitations that the disturbance terms must be differentiable or even slow tensors. Finally, to illustrate that the proposed method has a great ability to suppress fast time-varying and nondifferentiable disturbances, the simulation results of a numerical example and a practical example of a modern advanced fighter jet system were presented.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 4","pages":"246-253"},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679334","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}
Pub Date : 2024-08-23DOI: 10.1109/JMASS.2024.3449071
Elisa Robert;Mathieu Barthelemy;Thierry Sequies
The satellites for auroral tomography in space (SATIS) project is a mission concept that proposes to perform auroral tomography from space using imagers placed on a constellation of satellites. Auroral tomography is particularly interesting for reconstructing the flux of particles precipitating into the atmosphere. The advantage of space observations is that they avoid cloud cover problems, allowing larger set of data and with a dedicated ground-based infrastructure ensure quasi-continuous monitoring. However, the main difficulty of this mission is to synchronize orbits and attitudes of the satellites in order to observe the same volume of emission at the same time and from different perspectives. The attitude and determination control system will thus have to be very precise and stable. The data volume is also an issue especially in a monitoring point of view. Furthermore, atmospheric drag will have to be correctly considered to limit orbit disturbances and keep satellites synchronized. We present here the preliminary study of this project and the initial requirements identified to be able to perform this mission concept.
{"title":"The Satellites for Auroral Tomography in Space (SATIS) Project: Tomographic Reconstruction of the Auroral Emissions From Space","authors":"Elisa Robert;Mathieu Barthelemy;Thierry Sequies","doi":"10.1109/JMASS.2024.3449071","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3449071","url":null,"abstract":"The satellites for auroral tomography in space (SATIS) project is a mission concept that proposes to perform auroral tomography from space using imagers placed on a constellation of satellites. Auroral tomography is particularly interesting for reconstructing the flux of particles precipitating into the atmosphere. The advantage of space observations is that they avoid cloud cover problems, allowing larger set of data and with a dedicated ground-based infrastructure ensure quasi-continuous monitoring. However, the main difficulty of this mission is to synchronize orbits and attitudes of the satellites in order to observe the same volume of emission at the same time and from different perspectives. The attitude and determination control system will thus have to be very precise and stable. The data volume is also an issue especially in a monitoring point of view. Furthermore, atmospheric drag will have to be correctly considered to limit orbit disturbances and keep satellites synchronized. We present here the preliminary study of this project and the initial requirements identified to be able to perform this mission concept.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 4","pages":"237-245"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679377","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}
Pub Date : 2024-08-23DOI: 10.1109/JMASS.2024.3448433
Heng Zhang;Zhemin Sun;Chaoqun Yang;Xianghui Cao
Mobile edge computing (MEC) revolutionizes data processing by shifting it from the network core to the edge, significantly reducing latency and ensuring Quality of Service. Integrating the agile and flexible unmanned- aerial-vehicle (UAV) technology with MEC offers new opportunities and challenges in decision making for dynamic and complex environments due to the UAVs’ mobility and Line of Sight advantages. Motivated by the potential of UAV-assisted MEC systems with caching mechanisms, this study addresses the optimization problem under uncertain conditions and user demand. To tackle the complex nonconvex sequential decision problem, a deep reinforcement learning framework named delay hybrid action actor-critic is proposed, possessing the capability to handle scenarios requiring both continuous and discrete actions. Comprehensive simulations are conducted to validate the capability of the proposed framework, demonstrating its superiority over traditional methods.
{"title":"Latency Optimization in UAV-Assisted Mobile Edge Computing Empowered by Caching Mechanisms","authors":"Heng Zhang;Zhemin Sun;Chaoqun Yang;Xianghui Cao","doi":"10.1109/JMASS.2024.3448433","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3448433","url":null,"abstract":"Mobile edge computing (MEC) revolutionizes data processing by shifting it from the network core to the edge, significantly reducing latency and ensuring Quality of Service. Integrating the agile and flexible unmanned- aerial-vehicle (UAV) technology with MEC offers new opportunities and challenges in decision making for dynamic and complex environments due to the UAVs’ mobility and Line of Sight advantages. Motivated by the potential of UAV-assisted MEC systems with caching mechanisms, this study addresses the optimization problem under uncertain conditions and user demand. To tackle the complex nonconvex sequential decision problem, a deep reinforcement learning framework named delay hybrid action actor-critic is proposed, possessing the capability to handle scenarios requiring both continuous and discrete actions. Comprehensive simulations are conducted to validate the capability of the proposed framework, demonstrating its superiority over traditional methods.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 4","pages":"228-236"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679335","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}
Pub Date : 2024-08-22DOI: 10.1109/JMASS.2024.3440776
{"title":"The Journal of Miniaturized Air and Space Systems","authors":"","doi":"10.1109/JMASS.2024.3440776","DOIUrl":"https://doi.org/10.1109/JMASS.2024.3440776","url":null,"abstract":"","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"5 3","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643755","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-21DOI: 10.1109/JMASS.2024.3447457
Gazali Bashir;Amit K. Singh;Ankit Dubey
This article introduces a compact wideband beam-switching digital metasurface reflector (MSR) array antenna featuring extreme offset illumination for satellite communications in the Ka band. The MSR comprises phase-modulating subwave length unit cell elements. The unit cell consists of a cross dipole loaded with curved stubs. The arrangement of the stubs across the dipole modulates the phase characteristics of the incident electric field. An MSR composed of $15times 15$