Pub Date : 2024-09-09DOI: 10.1038/s44172-024-00266-5
Humayun Zubair Khan, Abdul Jabbar, Jalil ur Rehman Kazim, Masood Ur Rehman, Muhammad Ali Imran, Qammer H. Abbasi
Linear polarization (LP) and circular polarization (CP) holds paramount importance in Ku, K, and Ka bands for satellite based communication, and remote sensing applications. Satellite based remote sensing applications face challenges like atmospheric attenuation, noise & interference, and signal degradation. Moreover, satellite based communication application demands CP in two distinct, non-adjacent frequency bands with orthogonal polarizations at greater oblique angles, considering the unpredictable incidence angles of electromagnetic (EM) waves. Addressing these challenges, an innovative metasurface polarization converter is proposed to operate efficiently across the Ku-band (13.5–18.0 GHz), K-band (18.0–26.5 GHz), and Ka-band (26.5–38.5 GHz) frequency ranges. The converter achieves left-handed circular polarization (LHCP) in the Ku- and Ka-bands within the frequency ranges of 14.57–15.65 GHz and 27.47–33.85 GHz for y-polarized incident EM waves. Additionally, it provides right-handed circular polarization (RHCP) in the K- and Ka-bands at 17.27–23.92 GHz and 35.87–38.32 GHz for y-polarized incident EM waves. The LP conversion ratio exceeds 95% in the frequency bands of 15.97–16.85 GHz, 24.70–26.65 GHz, and 34.37–35.45 GHz for y-polarized, LHCP, and RHCP incident EM waves, respectively. The metasurface exhibits robust performance up to incidence angles of 45 degrees under oblique conditions. Experimental validation using traditional board-circuit manufacturing demonstrates close agreement between measured co- and cross-polarized reflection coefficients and simulations in the 13.5–18 GHz, and 24–38.5 GHz frequency range. Thin metasurface with a thickness of only 0.64 = 0.013λo mm, the proposed design outperforms existing studies in the literature, establishing its competitive edge in terms of structure and performance. Humayun Zubair Khan and colleagues design a thin and efficient metasurface for polarisation conversion. Their solution can operate to up to 45-degree incidence angles and is suitable for satellite communications which use separated frequency bands with orthogonal circularly polarised waves.
线性极化（LP）和圆极化（CP）在 Ku、K 和 Ka 波段的卫星通信和遥感应用中至关重要。卫星遥感应用面临着大气衰减、噪声放大器、干扰和信号衰减等挑战。此外，考虑到电磁波（EM）的入射角难以预测，卫星通信应用需要在两个不同的、不相邻的频段上以更大的斜角进行正交极化。为应对这些挑战，我们提出了一种创新的元表面极化转换器，可在 Ku 波段（13.5-18.0 GHz）、K 波段（18.0-26.5 GHz）和 Ka 波段（26.5-38.5 GHz）频率范围内高效运行。该转换器可在 14.57-15.65 GHz 和 27.47-33.85 GHz 频率范围内的 Ku 波段和 Ka 波段实现左手圆极化（LHCP），适用于 y 极化入射电磁波。此外，它还能在 K 波段和 Ka 波段的 17.27-23.92 千兆赫和 35.87-38.32 千兆赫频率范围内为 Y 偏振入射电磁波提供右旋圆极化（RHCP）。在 15.97-16.85 GHz、24.70-26.65 GHz 和 34.37-35.45 GHz 频段，对于 y 偏振、LHCP 和 RHCP 入射电磁波，LP 转换率分别超过 95%。该元表面在入射角为 45 度的倾斜条件下表现出稳定的性能。使用传统电路板制造工艺进行的实验验证表明，在 13.5-18 GHz 和 24-38.5 GHz 频率范围内，测量的同极化和跨极化反射系数与模拟结果非常接近。薄元表面的厚度仅为 0.64 = 0.013λo 毫米，所提出的设计优于文献中的现有研究，从而确立了其在结构和性能方面的竞争优势。Humayun Zubair Khan 及其同事设计了一种用于极化转换的薄而高效的元表面。他们的解决方案入射角可达 45 度，适用于使用正交圆极化波分离频带的卫星通信。
{"title":"Multi-band ultrathin reflective metasurface for linear and circular polarization conversion in Ku, K, and Ka bands","authors":"Humayun Zubair Khan, Abdul Jabbar, Jalil ur Rehman Kazim, Masood Ur Rehman, Muhammad Ali Imran, Qammer H. Abbasi","doi":"10.1038/s44172-024-00266-5","DOIUrl":"10.1038/s44172-024-00266-5","url":null,"abstract":"Linear polarization (LP) and circular polarization (CP) holds paramount importance in Ku, K, and Ka bands for satellite based communication, and remote sensing applications. Satellite based remote sensing applications face challenges like atmospheric attenuation, noise & interference, and signal degradation. Moreover, satellite based communication application demands CP in two distinct, non-adjacent frequency bands with orthogonal polarizations at greater oblique angles, considering the unpredictable incidence angles of electromagnetic (EM) waves. Addressing these challenges, an innovative metasurface polarization converter is proposed to operate efficiently across the Ku-band (13.5–18.0 GHz), K-band (18.0–26.5 GHz), and Ka-band (26.5–38.5 GHz) frequency ranges. The converter achieves left-handed circular polarization (LHCP) in the Ku- and Ka-bands within the frequency ranges of 14.57–15.65 GHz and 27.47–33.85 GHz for y-polarized incident EM waves. Additionally, it provides right-handed circular polarization (RHCP) in the K- and Ka-bands at 17.27–23.92 GHz and 35.87–38.32 GHz for y-polarized incident EM waves. The LP conversion ratio exceeds 95% in the frequency bands of 15.97–16.85 GHz, 24.70–26.65 GHz, and 34.37–35.45 GHz for y-polarized, LHCP, and RHCP incident EM waves, respectively. The metasurface exhibits robust performance up to incidence angles of 45 degrees under oblique conditions. Experimental validation using traditional board-circuit manufacturing demonstrates close agreement between measured co- and cross-polarized reflection coefficients and simulations in the 13.5–18 GHz, and 24–38.5 GHz frequency range. Thin metasurface with a thickness of only 0.64 = 0.013λo mm, the proposed design outperforms existing studies in the literature, establishing its competitive edge in terms of structure and performance. Humayun Zubair Khan and colleagues design a thin and efficient metasurface for polarisation conversion. Their solution can operate to up to 45-degree incidence angles and is suitable for satellite communications which use separated frequency bands with orthogonal circularly polarised waves.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00266-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165803","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 : 2024-09-07DOI: 10.1038/s44172-024-00278-1
Gonzalo Arranz, Yuenong Ling, Sam Costa, Konrad Goc, Adrián Lozano-Durán
Computational fluid dynamics is an essential tool for accelerating the discovery and adoption of transformative designs across multiple engineering disciplines. Despite its many successes, no single approach consistently achieves high accuracy for all flow phenomena of interest, primarily due to limitations in the modeling assumptions. Here, we introduce a closure model for wall-modeled large-eddy simulation to address this challenge. The model, referred to as the Building-block Flow Model (BFM), rests on the premise that a finite collection of simple flows encapsulates the essential missing physics necessary to predict more complex scenarios. The BFM is designed to: (1) predict multiple flow regimes, (2) unify the closure model at solid boundaries and the rest of the flow, (3) ensure consistency with numerical schemes and gridding strategies by accounting for numerical errors, (4) be directly applicable to arbitrary complex geometries, and (5) be scalable to model additional flow physics in the future. The BFM is utilized to predict key quantities in five cases, including an aircraft in landing configuration, demonstrating similar or superior capabilities compared to previous state-of-the-art models. The design of BFM opens up new opportunities for developing closure models that can accurately represent various flow physics across different scenarios. Arranz and colleagues introduce a closure model for computational fluid dynamics. Their approach is implemented using artificial neural networks. It predicts multiple flow conditions, is directly applicable to complex geometries, and ensures consistency with numerical schemes.
{"title":"Building-block-flow computational model for large-eddy simulation of external aerodynamic applications","authors":"Gonzalo Arranz, Yuenong Ling, Sam Costa, Konrad Goc, Adrián Lozano-Durán","doi":"10.1038/s44172-024-00278-1","DOIUrl":"10.1038/s44172-024-00278-1","url":null,"abstract":"Computational fluid dynamics is an essential tool for accelerating the discovery and adoption of transformative designs across multiple engineering disciplines. Despite its many successes, no single approach consistently achieves high accuracy for all flow phenomena of interest, primarily due to limitations in the modeling assumptions. Here, we introduce a closure model for wall-modeled large-eddy simulation to address this challenge. The model, referred to as the Building-block Flow Model (BFM), rests on the premise that a finite collection of simple flows encapsulates the essential missing physics necessary to predict more complex scenarios. The BFM is designed to: (1) predict multiple flow regimes, (2) unify the closure model at solid boundaries and the rest of the flow, (3) ensure consistency with numerical schemes and gridding strategies by accounting for numerical errors, (4) be directly applicable to arbitrary complex geometries, and (5) be scalable to model additional flow physics in the future. The BFM is utilized to predict key quantities in five cases, including an aircraft in landing configuration, demonstrating similar or superior capabilities compared to previous state-of-the-art models. The design of BFM opens up new opportunities for developing closure models that can accurately represent various flow physics across different scenarios. Arranz and colleagues introduce a closure model for computational fluid dynamics. Their approach is implemented using artificial neural networks. It predicts multiple flow conditions, is directly applicable to complex geometries, and ensures consistency with numerical schemes.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00278-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143897","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 : 2024-09-05DOI: 10.1038/s44172-024-00259-4
Eisa Hedayati, Fatemeh Safari, George Verghese, Vito R. Ciancia, Daniel K. Sodickson, Seena Dehkharghani, Leeor Alon
Stroke is a leading cause of mortality and disability. Emergent diagnosis and intervention are critical, and predicated upon initial brain imaging; however, existing clinical imaging modalities are generally costly, immobile, and demand highly specialized operation and interpretation. Low-energy microwaves have been explored as a low-cost, small form factor, fast, and safe probe for tissue dielectric properties measurements, with both imaging and diagnostic potential. Nevertheless, challenges inherent to microwave reconstruction have impeded progress, hence conduction of microwave imaging remains an elusive scientific aim. Herein, we introduce a dedicated experimental framework comprising a robotic navigation system to translate blood-mimicking phantoms within a human head model. An 8-element ultra-wideband array of modified antipodal Vivaldi antennas was developed and driven by a two-port vector network analyzer spanning 0.6–9.0 GHz at an operating power of 1 mW. Complex scattering parameters were measured, and dielectric signatures of hemorrhage were learned using a dedicated deep neural network for prediction of hemorrhage classes and localization. An overall sensitivity and specificity for detection >0.99 was observed, with Rayleigh mean localization error of 1.65 mm. The study establishes the feasibility of a robust experimental model and deep learning solution for ultra-wideband microwave stroke detection. Eisa Hedayati, Fatemeh Safari and colleagues use an array of ultra-wideband microwave antennas to locate the haemorrhages in a human head phantom. The results of the measurements are processed by the deep neural network algorithm to classify the digital signatures for efficient detection and localization.
{"title":"An experimental system for detection and localization of hemorrhage using ultra-wideband microwaves with deep learning","authors":"Eisa Hedayati, Fatemeh Safari, George Verghese, Vito R. Ciancia, Daniel K. Sodickson, Seena Dehkharghani, Leeor Alon","doi":"10.1038/s44172-024-00259-4","DOIUrl":"10.1038/s44172-024-00259-4","url":null,"abstract":"Stroke is a leading cause of mortality and disability. Emergent diagnosis and intervention are critical, and predicated upon initial brain imaging; however, existing clinical imaging modalities are generally costly, immobile, and demand highly specialized operation and interpretation. Low-energy microwaves have been explored as a low-cost, small form factor, fast, and safe probe for tissue dielectric properties measurements, with both imaging and diagnostic potential. Nevertheless, challenges inherent to microwave reconstruction have impeded progress, hence conduction of microwave imaging remains an elusive scientific aim. Herein, we introduce a dedicated experimental framework comprising a robotic navigation system to translate blood-mimicking phantoms within a human head model. An 8-element ultra-wideband array of modified antipodal Vivaldi antennas was developed and driven by a two-port vector network analyzer spanning 0.6–9.0 GHz at an operating power of 1 mW. Complex scattering parameters were measured, and dielectric signatures of hemorrhage were learned using a dedicated deep neural network for prediction of hemorrhage classes and localization. An overall sensitivity and specificity for detection >0.99 was observed, with Rayleigh mean localization error of 1.65 mm. The study establishes the feasibility of a robust experimental model and deep learning solution for ultra-wideband microwave stroke detection. Eisa Hedayati, Fatemeh Safari and colleagues use an array of ultra-wideband microwave antennas to locate the haemorrhages in a human head phantom. The results of the measurements are processed by the deep neural network algorithm to classify the digital signatures for efficient detection and localization.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00259-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143872","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 : 2024-09-03DOI: 10.1038/s44172-024-00272-7
Jiankai Sun, Linjiang Huang, Hongsong Wang, Chuanyang Zheng, Jianing Qiu, Md Tauhidul Islam, Enze Xie, Bolei Zhou, Lei Xing, Arjun Chandrasekaran, Michael J. Black
Understanding a person’s behavior from their 3D motion sequence is a fundamental problem in computer vision with many applications. An important component of this problem is 3D action localization, which involves recognizing what actions a person is performing, and when the actions occur in the sequence. To promote the progress of the 3D action localization community, we introduce a new, challenging, and more complex benchmark dataset, BABEL-TAL (BT), for 3D action localization. Important baselines and evaluating metrics, as well as human evaluations, are carefully established on this benchmark. We also propose a strong baseline model, i.e., Localizing Actions with Transformers (LocATe), that jointly localizes and recognizes actions in a 3D sequence. The proposed LocATe shows superior performance on BABEL-TAL as well as on the large-scale PKU-MMD dataset, achieving state-of-the-art performance by using only 10% of the labeled training data. Our research could advance the development of more accurate and efficient systems for human behavior analysis, with potential applications in areas such as human-computer interaction and healthcare. Jiankai Sun, Michael J. Black and colleagues present a benchmark for human movement analysis. Their transformer-based approach, LocATe, learns to perform both temporal action localization and recognition.
{"title":"Localization and recognition of human action in 3D using transformers","authors":"Jiankai Sun, Linjiang Huang, Hongsong Wang, Chuanyang Zheng, Jianing Qiu, Md Tauhidul Islam, Enze Xie, Bolei Zhou, Lei Xing, Arjun Chandrasekaran, Michael J. Black","doi":"10.1038/s44172-024-00272-7","DOIUrl":"10.1038/s44172-024-00272-7","url":null,"abstract":"Understanding a person’s behavior from their 3D motion sequence is a fundamental problem in computer vision with many applications. An important component of this problem is 3D action localization, which involves recognizing what actions a person is performing, and when the actions occur in the sequence. To promote the progress of the 3D action localization community, we introduce a new, challenging, and more complex benchmark dataset, BABEL-TAL (BT), for 3D action localization. Important baselines and evaluating metrics, as well as human evaluations, are carefully established on this benchmark. We also propose a strong baseline model, i.e., Localizing Actions with Transformers (LocATe), that jointly localizes and recognizes actions in a 3D sequence. The proposed LocATe shows superior performance on BABEL-TAL as well as on the large-scale PKU-MMD dataset, achieving state-of-the-art performance by using only 10% of the labeled training data. Our research could advance the development of more accurate and efficient systems for human behavior analysis, with potential applications in areas such as human-computer interaction and healthcare. Jiankai Sun, Michael J. Black and colleagues present a benchmark for human movement analysis. Their transformer-based approach, LocATe, learns to perform both temporal action localization and recognition.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11372174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142127546","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 : 2024-09-03DOI: 10.1038/s44172-024-00276-3
Kambez H. Benam
Technology-based platforms offer crucial support for regulatory agencies in overseeing tobacco products to enhance public health protection. The use of electronic nicotine delivery systems (ENDS), such as electronic cigarettes, has surged exponentially over the past decade. However, the understanding of the impact of ENDS on lung health remains incomplete due to scarcity of physiologically relevant technologies for evaluating their toxicity. This review examines the societal and public health impacts of ENDS, prevalent preclinical approaches in pulmonary space, and the application of emerging Organ-on-Chip technologies and bioinspired robotics for assessing ENDS respiratory toxicity. It highlights challenges in ENDS inhalation toxicology and the value of multidisciplinary bioengineering approaches for generating reliable, human-relevant regulatory data at an accelerated pace. Kambez Benam reviews preclinical approaches to assess lung health impacts of e-cigarettes, highlighting limitations of current strategies in capturing 3D lung architecture and inhalation mechanics. The review article emphasizes the promise of Organs-on-Chips and Bioinspired Robotics.
{"title":"Multidisciplinary approaches in electronic nicotine delivery systems pulmonary toxicology: emergence of living and non-living bioinspired engineered systems","authors":"Kambez H. Benam","doi":"10.1038/s44172-024-00276-3","DOIUrl":"10.1038/s44172-024-00276-3","url":null,"abstract":"Technology-based platforms offer crucial support for regulatory agencies in overseeing tobacco products to enhance public health protection. The use of electronic nicotine delivery systems (ENDS), such as electronic cigarettes, has surged exponentially over the past decade. However, the understanding of the impact of ENDS on lung health remains incomplete due to scarcity of physiologically relevant technologies for evaluating their toxicity. This review examines the societal and public health impacts of ENDS, prevalent preclinical approaches in pulmonary space, and the application of emerging Organ-on-Chip technologies and bioinspired robotics for assessing ENDS respiratory toxicity. It highlights challenges in ENDS inhalation toxicology and the value of multidisciplinary bioengineering approaches for generating reliable, human-relevant regulatory data at an accelerated pace. Kambez Benam reviews preclinical approaches to assess lung health impacts of e-cigarettes, highlighting limitations of current strategies in capturing 3D lung architecture and inhalation mechanics. The review article emphasizes the promise of Organs-on-Chips and Bioinspired Robotics.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11372223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142127547","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}
Optical coherence tomography (OCT) can be used to image microstructures of human kidneys. However, current OCT probes exhibit inadequate field-of-view, leading to potentially biased kidney assessment. Here we present a robotic OCT system where the probe is integrated to a robot manipulator, enabling wider area (covers an area of 106.39 mm by 37.70 mm) spatially-resolved imaging. Our system comprehensively scans the kidney surface at the optimal altitude with preoperative path planning and OCT image-based feedback control scheme. It further parameterizes and visualizes microstructures of large area. We verified the system positioning accuracy on a phantom as 0.0762 ± 0.0727 mm and showed the clinical feasibility by scanning ex vivo kidneys. The parameterization reveals vasculatures beneath the kidney surface. Quantification on the proximal convoluted tubule of a human kidney yields clinical-relevant information. The system promises to assess kidney viability for transplantation after collecting a vast amount of whole-organ parameterization and patient outcomes data. Xihan Ma and colleagues expand the field-of-view for optical coherence tomography using a robotic manipulator to control the probe. They achieve high position precision in ex vivo demonstration.
光学相干断层扫描（OCT）可用于成像人体肾脏的微观结构。然而，目前的 OCT 探头视场不足，导致对肾脏的评估可能存在偏差。在这里，我们展示了一种机器人 OCT 系统，该系统将探头集成到机器人操纵器上，实现了更大面积（覆盖面积 106.39 毫米 x 37.70 毫米）的空间分辨成像。我们的系统通过术前路径规划和基于 OCT 图像的反馈控制方案，以最佳高度全面扫描肾脏表面。它还能进一步参数化和可视化大面积的微观结构。我们在模型上验证了系统的定位精度为 0.0762 ± 0.0727 毫米，并通过扫描体外肾脏证明了其临床可行性。参数化显示了肾脏表面下的血管。对人体肾脏近端曲小管的定量分析产生了与临床相关的信息。该系统有望在收集大量全器官参数化数据和患者预后数据后，评估肾脏在移植手术中的存活能力。
{"title":"Large area kidney imaging for pre-transplant evaluation using real-time robotic optical coherence tomography","authors":"Xihan Ma, Mousa Moradi, Xiaoyu Ma, Qinggong Tang, Moshe Levi, Yu Chen, Haichong K. Zhang","doi":"10.1038/s44172-024-00264-7","DOIUrl":"10.1038/s44172-024-00264-7","url":null,"abstract":"Optical coherence tomography (OCT) can be used to image microstructures of human kidneys. However, current OCT probes exhibit inadequate field-of-view, leading to potentially biased kidney assessment. Here we present a robotic OCT system where the probe is integrated to a robot manipulator, enabling wider area (covers an area of 106.39 mm by 37.70 mm) spatially-resolved imaging. Our system comprehensively scans the kidney surface at the optimal altitude with preoperative path planning and OCT image-based feedback control scheme. It further parameterizes and visualizes microstructures of large area. We verified the system positioning accuracy on a phantom as 0.0762 ± 0.0727 mm and showed the clinical feasibility by scanning ex vivo kidneys. The parameterization reveals vasculatures beneath the kidney surface. Quantification on the proximal convoluted tubule of a human kidney yields clinical-relevant information. The system promises to assess kidney viability for transplantation after collecting a vast amount of whole-organ parameterization and patient outcomes data. Xihan Ma and colleagues expand the field-of-view for optical coherence tomography using a robotic manipulator to control the probe. They achieve high position precision in ex vivo demonstration.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368928/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121259","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 : 2024-08-31DOI: 10.1038/s44172-024-00274-5
Nageswara Lalam, Sandeep Bukka, Hari Bhatta, Michael Buric, Paul Ohodnicki, Ruishu Wright
The development of advanced distributed optical fiber sensing systems that are capable of performing accurate and spatially resolved multiparameter measurements is of great interest to a wide range of scientific and industrial applications. Here, we propose and experimentally demonstrate a wavelength diversity based advanced distributed optical fiber sensor system to accomplish multiparameter sensing while greatly enhancing measurement accuracy. A suite of deep neural network (DNN) algorithms are developed and verified for data denoising, rapid Brillouin frequency shift estimation, and vibration data event classification. As a proof-of-concept, we demonstrate the effectiveness of the proposed advanced wavelength diversity distributed fiber sensor system assisted by DNN for simultaneous, independent measurements of static strain, temperature, and acoustic vibrations over a 25 km long sensing fiber at 3 m spatial resolution. These results suggest the potential for an intelligent multiparameter monitoring system with enhanced performance in advanced structural health monitoring applications. Nageswara Lalam and colleagues demonstrate a multiparameter distributed optical fibre sensing. They employ the wavelength multiplexing technique in Brillouin and Rayleigh scattering with the deep neural networks and achieve an improved performance of strain, temperature and vibration detection.
{"title":"Achieving precise multiparameter measurements with distributed optical fiber sensor using wavelength diversity and deep neural networks","authors":"Nageswara Lalam, Sandeep Bukka, Hari Bhatta, Michael Buric, Paul Ohodnicki, Ruishu Wright","doi":"10.1038/s44172-024-00274-5","DOIUrl":"10.1038/s44172-024-00274-5","url":null,"abstract":"The development of advanced distributed optical fiber sensing systems that are capable of performing accurate and spatially resolved multiparameter measurements is of great interest to a wide range of scientific and industrial applications. Here, we propose and experimentally demonstrate a wavelength diversity based advanced distributed optical fiber sensor system to accomplish multiparameter sensing while greatly enhancing measurement accuracy. A suite of deep neural network (DNN) algorithms are developed and verified for data denoising, rapid Brillouin frequency shift estimation, and vibration data event classification. As a proof-of-concept, we demonstrate the effectiveness of the proposed advanced wavelength diversity distributed fiber sensor system assisted by DNN for simultaneous, independent measurements of static strain, temperature, and acoustic vibrations over a 25 km long sensing fiber at 3 m spatial resolution. These results suggest the potential for an intelligent multiparameter monitoring system with enhanced performance in advanced structural health monitoring applications. Nageswara Lalam and colleagues demonstrate a multiparameter distributed optical fibre sensing. They employ the wavelength multiplexing technique in Brillouin and Rayleigh scattering with the deep neural networks and achieve an improved performance of strain, temperature and vibration detection.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00274-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142091208","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 : 2024-08-27DOI: 10.1038/s44172-024-00268-3
Bryan Holt, Kyle Oswalt, Alexa England, Richard Murphy, Isabella Owens, Micaela Finney, Natalie Wong, Sushil Adhikari, James McCann, John Beckmann
Mosquitoes and other biting arthropods transmit diseases worldwide, causing over 700,000 deaths each year, and costing about 3 billion USD annually for Aedes species alone. Insect vectored diseases also pose a considerable threat to agricultural animals. While clothing could provide a simple solution to vector-borne diseases, modern textiles do not effectively block mosquito bites. Here we have designed three micro-resolution knitted structures, with five adjustable parameters that can block mosquito bites. These designs, which exhibit significant bite reduction were integrated into a computer numerical control knitting robot for mass production of bite-blocking garments with minimal human labor. We then quantified the comfort of blocking garments. Our knits enable individuals to protect themselves from insects amidst their day-to-day activities without impacting the environment. Bryan Holt, Kyle Oswalt and colleagues design the mosquito bite-proof knitting pattern. Their approach can be implemented in programmable knitting robots for mass production.
{"title":"Computer numerical control knitting of high-resolution mosquito bite blocking textiles","authors":"Bryan Holt, Kyle Oswalt, Alexa England, Richard Murphy, Isabella Owens, Micaela Finney, Natalie Wong, Sushil Adhikari, James McCann, John Beckmann","doi":"10.1038/s44172-024-00268-3","DOIUrl":"10.1038/s44172-024-00268-3","url":null,"abstract":"Mosquitoes and other biting arthropods transmit diseases worldwide, causing over 700,000 deaths each year, and costing about 3 billion USD annually for Aedes species alone. Insect vectored diseases also pose a considerable threat to agricultural animals. While clothing could provide a simple solution to vector-borne diseases, modern textiles do not effectively block mosquito bites. Here we have designed three micro-resolution knitted structures, with five adjustable parameters that can block mosquito bites. These designs, which exhibit significant bite reduction were integrated into a computer numerical control knitting robot for mass production of bite-blocking garments with minimal human labor. We then quantified the comfort of blocking garments. Our knits enable individuals to protect themselves from insects amidst their day-to-day activities without impacting the environment. Bryan Holt, Kyle Oswalt and colleagues design the mosquito bite-proof knitting pattern. Their approach can be implemented in programmable knitting robots for mass production.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00268-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082743","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 : 2024-08-27DOI: 10.1038/s44172-024-00269-2
Linli Shi, Christina Mastracchio, Ilyas Saytashev, Meijun Ye
The neuromodulatory effects of >250 kHz ultrasound have been well-demonstrated, but the impact of lower-frequency ultrasound, which can transmit better through air and the skull, on the brain is unclear. This study investigates the biological impact of 40 kHz pulsed ultrasound on the brain using calcium imaging and electrophysiology in mice. Our findings reveal burst duration-dependent neural responses in somatosensory and auditory cortices, resembling responses to 12 kHz audible tone, in vivo. In vitro brain slice experiments show no neural responses to 300 kPa 40 kHz ultrasound, implying indirect network effects. Ketamine fully blocks neural responses to ultrasound in both cortices but only partially affects 12 kHz audible tone responses in the somatosensory cortex and has no impact on auditory cortex 12 kHz responses. This suggests that low-frequency ultrasound’s cortical effects rely heavily on NMDA receptors and may involve mechanisms beyond indirect auditory cortex activation. This research uncovers potential low-frequency ultrasound effects and mechanisms in the brain, offering a path for future neuromodulation. Dr Ye and colleagues investigate the biological impact low-frequency ultrasound pulses can have on the cortex of mice. They observe pulse duration dependent neural responses and find that ketamine can block parameter-dependent brain responses at certain frequencies.
{"title":"Low frequency ultrasound elicits broad cortical responses inhibited by ketamine in mice","authors":"Linli Shi, Christina Mastracchio, Ilyas Saytashev, Meijun Ye","doi":"10.1038/s44172-024-00269-2","DOIUrl":"10.1038/s44172-024-00269-2","url":null,"abstract":"The neuromodulatory effects of >250 kHz ultrasound have been well-demonstrated, but the impact of lower-frequency ultrasound, which can transmit better through air and the skull, on the brain is unclear. This study investigates the biological impact of 40 kHz pulsed ultrasound on the brain using calcium imaging and electrophysiology in mice. Our findings reveal burst duration-dependent neural responses in somatosensory and auditory cortices, resembling responses to 12 kHz audible tone, in vivo. In vitro brain slice experiments show no neural responses to 300 kPa 40 kHz ultrasound, implying indirect network effects. Ketamine fully blocks neural responses to ultrasound in both cortices but only partially affects 12 kHz audible tone responses in the somatosensory cortex and has no impact on auditory cortex 12 kHz responses. This suggests that low-frequency ultrasound’s cortical effects rely heavily on NMDA receptors and may involve mechanisms beyond indirect auditory cortex activation. This research uncovers potential low-frequency ultrasound effects and mechanisms in the brain, offering a path for future neuromodulation. Dr Ye and colleagues investigate the biological impact low-frequency ultrasound pulses can have on the cortex of mice. They observe pulse duration dependent neural responses and find that ketamine can block parameter-dependent brain responses at certain frequencies.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00269-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082744","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 : 2024-08-24DOI: 10.1038/s44172-024-00263-8
Ali Sekmen, Bahadir Bilgin
It is important to understand the mathematical foundations of neural networks and to include robustness in model evaluation. Here, we introduce algorithms based on manifold curvature estimation to assess neural network robustness. These algorithms rely solely on training data and do not require regular or adversarial test data. Initially, a metric is proposed to measure the curvature of discrete data manifolds by introducing weighted angles concept between subspaces. Following this, a robustness measure is introduced that is independent of network architecture or model parameters. Lastly, two additional methods are introduced, utilizing curvature estimation of special manifolds formed by using gradient vectors between output and input network layers, alongside manifold curvature estimation. A comprehensive evaluation is provided on multiple network models using the CIFAR-10 dataset. Manifold geometry-based robustness analysis may lead to the development of not only accurate but also robust neural network models. Bahadir Bilgin and Ali Sekmen build the framework for examining the post-training robustness of the neural network. Their method estimates the data curvature on the output layer and does not require knowledge of the black-box topology.
了解神经网络的数学基础并将鲁棒性纳入模型评估非常重要。在此，我们介绍基于流形曲率估计的算法，用于评估神经网络的鲁棒性。这些算法仅依赖于训练数据，不需要常规或对抗性测试数据。首先，通过引入子空间之间的加权角度概念，提出了一种度量离散数据流形曲率的方法。随后，引入了一种与网络架构或模型参数无关的鲁棒性测量方法。最后，除了流形曲率估算外，还介绍了另外两种方法，即利用输出和输入网络层之间的梯度向量形成的特殊流形的曲率估算。利用 CIFAR-10 数据集对多个网络模型进行了综合评估。基于流形几何的鲁棒性分析不仅能开发出准确的神经网络模型，还能开发出鲁棒性神经网络模型。Bahadir Bilgin 和 Ali Sekmen 建立了检查神经网络训练后鲁棒性的框架。他们的方法可以估计输出层的数据曲率，而且不需要黑盒拓扑知识。
{"title":"Manifold-based approach for neural network robustness analysis","authors":"Ali Sekmen, Bahadir Bilgin","doi":"10.1038/s44172-024-00263-8","DOIUrl":"10.1038/s44172-024-00263-8","url":null,"abstract":"It is important to understand the mathematical foundations of neural networks and to include robustness in model evaluation. Here, we introduce algorithms based on manifold curvature estimation to assess neural network robustness. These algorithms rely solely on training data and do not require regular or adversarial test data. Initially, a metric is proposed to measure the curvature of discrete data manifolds by introducing weighted angles concept between subspaces. Following this, a robustness measure is introduced that is independent of network architecture or model parameters. Lastly, two additional methods are introduced, utilizing curvature estimation of special manifolds formed by using gradient vectors between output and input network layers, alongside manifold curvature estimation. A comprehensive evaluation is provided on multiple network models using the CIFAR-10 dataset. Manifold geometry-based robustness analysis may lead to the development of not only accurate but also robust neural network models. Bahadir Bilgin and Ali Sekmen build the framework for examining the post-training robustness of the neural network. Their method estimates the data curvature on the output layer and does not require knowledge of the black-box topology.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00263-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142045344","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}
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