Lorenzo Cenceschi, C. D. Santina, Giuseppe Averta, M. Garabini, Qiushi Fu, M. Santello, M. Bianchi, A. Bicchi
In the execution of repetitive tasks, humans can capitalize on experience to improve their motor performance. Prominent examples of this ability can be recognized in our capacity of grasping and manipulating in uncertain conditions. With the aim of providing a mathematical description for such behavior, experiments are considered where participants are required to lift an object with an unexpected mass distribution. By repeating multiple times the same lifting action, participants can learn the correct motor command for task accomplishment. Three models are proposed that combine reactive terms and a learned anticipatory action to explain experimental data. The models feature intratrial and intertrial memory, and the effect of slowly and fast adaptive sensory receptors. The architectures’ effectiveness in explaining experimental data is compared with a general‐purpose state of the art model. The proposed algorithms conspicuously outperform the state of the art in all the considered validation routines. Global and within‐trial human behavior is predicted with 88% of accuracy in nominal conditions. When the object's center of mass is moved, the accuracy is maintained up to 83%. Finally, convergence properties of proposed algorithms are analytically discussed, and their stability and robustness against measurement noise are evaluated in simulation.
{"title":"Modeling Previous Trial Effect in Human Manipulation through Iterative Learning Control","authors":"Lorenzo Cenceschi, C. D. Santina, Giuseppe Averta, M. Garabini, Qiushi Fu, M. Santello, M. Bianchi, A. Bicchi","doi":"10.1002/aisy.201900074","DOIUrl":"https://doi.org/10.1002/aisy.201900074","url":null,"abstract":"In the execution of repetitive tasks, humans can capitalize on experience to improve their motor performance. Prominent examples of this ability can be recognized in our capacity of grasping and manipulating in uncertain conditions. With the aim of providing a mathematical description for such behavior, experiments are considered where participants are required to lift an object with an unexpected mass distribution. By repeating multiple times the same lifting action, participants can learn the correct motor command for task accomplishment. Three models are proposed that combine reactive terms and a learned anticipatory action to explain experimental data. The models feature intratrial and intertrial memory, and the effect of slowly and fast adaptive sensory receptors. The architectures’ effectiveness in explaining experimental data is compared with a general‐purpose state of the art model. The proposed algorithms conspicuously outperform the state of the art in all the considered validation routines. Global and within‐trial human behavior is predicted with 88% of accuracy in nominal conditions. When the object's center of mass is moved, the accuracy is maintained up to 83%. Finally, convergence properties of proposed algorithms are analytically discussed, and their stability and robustness against measurement noise are evaluated in simulation.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90208478","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}
Y. Kan‐Tor, Nir Zabari, Ity Erlich, Adi Szeskin, Tamar Amitai, D. Richter, Y. Or, Z. Shoham, A. Hurwitz, I. Har-Vardi, M. Gavish, A. Ben-Meir, A. Buxboim
In in vitro fertilization (IVF) treatments, early identification of embryos with high implantation potential is required for shortening time to pregnancy while avoiding clinical complications to the newborn and the mother caused by multiple pregnancies. Current classification tools are based on morphological and morphokinetic parameters that are manually annotated using time‐lapse video files. However, manual annotation introduces interobserver and intraobserver variability and provides a discrete representation of preimplantation development while ignoring dynamic features that are associated with embryo quality. A fully automated and standardized classifiers are developed by training deep neural networks directly on the raw video files of >6200 blastulation‐labeled and >5500 implantation‐labeled embryos. Prediction of embryo implantation is more accurate than the current state‐of‐the‐art morphokientic classifier. Embryo classification improves with video length where the most predictive images show only partial association with morphological features. Deep learning substitute to human evaluation of embryo developmental competence thus contributes to implementing single embryo transfer methodology.
{"title":"Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep‐Learning","authors":"Y. Kan‐Tor, Nir Zabari, Ity Erlich, Adi Szeskin, Tamar Amitai, D. Richter, Y. Or, Z. Shoham, A. Hurwitz, I. Har-Vardi, M. Gavish, A. Ben-Meir, A. Buxboim","doi":"10.1002/aisy.202000080","DOIUrl":"https://doi.org/10.1002/aisy.202000080","url":null,"abstract":"In in vitro fertilization (IVF) treatments, early identification of embryos with high implantation potential is required for shortening time to pregnancy while avoiding clinical complications to the newborn and the mother caused by multiple pregnancies. Current classification tools are based on morphological and morphokinetic parameters that are manually annotated using time‐lapse video files. However, manual annotation introduces interobserver and intraobserver variability and provides a discrete representation of preimplantation development while ignoring dynamic features that are associated with embryo quality. A fully automated and standardized classifiers are developed by training deep neural networks directly on the raw video files of >6200 blastulation‐labeled and >5500 implantation‐labeled embryos. Prediction of embryo implantation is more accurate than the current state‐of‐the‐art morphokientic classifier. Embryo classification improves with video length where the most predictive images show only partial association with morphological features. Deep learning substitute to human evaluation of embryo developmental competence thus contributes to implementing single embryo transfer methodology.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83679544","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}
F. Bachmann, J. Giltinan, Agnese Codutti, S. Klumpp, M. Sitti, D. Faivre
Magnetic microswimmers are promising devices for biomedical and environmental applications. Bacterium flagella‐inspired magnetic microhelices with perpendicular magnetizations are currently considered standard for propulsion at low Reynolds numbers because of their well‐understood dynamics and controllability. Deviations from this system have recently emerged: randomly shaped magnetic micropropellers with nonlinear swimming behaviors show promise in sensing, sorting, and directional control. The current progresses in 3D micro/nanoprinting allow the production of arbitrary 3D microstructures, increasing the accessible deterministic design space for complex micropropeller morphologies. Taking advantage of this, a shape is systematically reproduced that was formerly identified while screening randomly shaped propellers. Its nonlinear behavior, which is called frequency‐induced reversal of swimming direction (FIRSD), allows a propeller to swim in opposing directions by only changing the applied rotating field's frequency. However, the identically shaped swimmers do not only display the abovementioned swimming property but also exhibit a variety of swimming behaviors that are shown to arise from differences in their magnetic moment orientations. This underlines not only the role of shape in microswimmer behavior but also the importance of determining magnetic properties of future micropropellers that act as intelligent devices, as single‐shape templates with different magnetic moments can be utilized for different operation modes.
{"title":"Selection for Function: From Chemically Synthesized Prototypes to 3D‐Printed Microdevices","authors":"F. Bachmann, J. Giltinan, Agnese Codutti, S. Klumpp, M. Sitti, D. Faivre","doi":"10.1002/aisy.202000078","DOIUrl":"https://doi.org/10.1002/aisy.202000078","url":null,"abstract":"Magnetic microswimmers are promising devices for biomedical and environmental applications. Bacterium flagella‐inspired magnetic microhelices with perpendicular magnetizations are currently considered standard for propulsion at low Reynolds numbers because of their well‐understood dynamics and controllability. Deviations from this system have recently emerged: randomly shaped magnetic micropropellers with nonlinear swimming behaviors show promise in sensing, sorting, and directional control. The current progresses in 3D micro/nanoprinting allow the production of arbitrary 3D microstructures, increasing the accessible deterministic design space for complex micropropeller morphologies. Taking advantage of this, a shape is systematically reproduced that was formerly identified while screening randomly shaped propellers. Its nonlinear behavior, which is called frequency‐induced reversal of swimming direction (FIRSD), allows a propeller to swim in opposing directions by only changing the applied rotating field's frequency. However, the identically shaped swimmers do not only display the abovementioned swimming property but also exhibit a variety of swimming behaviors that are shown to arise from differences in their magnetic moment orientations. This underlines not only the role of shape in microswimmer behavior but also the importance of determining magnetic properties of future micropropellers that act as intelligent devices, as single‐shape templates with different magnetic moments can be utilized for different operation modes.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85803367","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}
Isabella De Bellis, Bin Ni, D. Martella, C. Parmeggiani, P. Keller, D. Wiersma, Min‐Hui Li, S. Nocentini
Nature provides well‐engineered and evolutionary optimized examples of brilliant structural colors in animals and plants. Morpho butterflies are among the well‐known species possessing iridescent bright blue coloration due to multiple optical effects generated by the complex structuration of the wing scales. Such surprising solution can be replicated to fabricate efficient devices. Maybe even more interesting, novel approaches can be developed to combine wings with synthetic smart materials to achieve complex structures responsive to external stimuli. This study demonstrates the proof of concept of an innovative biotic–abiotic hybrid smart structure made by the integration of a butterfly wing with thermoresponsive liquid crystalline elastomers, and their capability to actuate the mechanical action of the wing, thus controlling its spectral response. Exploiting two fabrication strategies, it is demonstrated how different mechanisms of color tuning can be achieved by temperature control. In addition, due to the thermally induced mechanical deformation of the elastomer and superhydrophobic properties of the wing, a potential self‐cleaning behavior of the bilayer material is demonstrated.
{"title":"Color Modulation in Morpho Butterfly Wings Using Liquid Crystalline Elastomers","authors":"Isabella De Bellis, Bin Ni, D. Martella, C. Parmeggiani, P. Keller, D. Wiersma, Min‐Hui Li, S. Nocentini","doi":"10.1002/aisy.202000035","DOIUrl":"https://doi.org/10.1002/aisy.202000035","url":null,"abstract":"Nature provides well‐engineered and evolutionary optimized examples of brilliant structural colors in animals and plants. Morpho butterflies are among the well‐known species possessing iridescent bright blue coloration due to multiple optical effects generated by the complex structuration of the wing scales. Such surprising solution can be replicated to fabricate efficient devices. Maybe even more interesting, novel approaches can be developed to combine wings with synthetic smart materials to achieve complex structures responsive to external stimuli. This study demonstrates the proof of concept of an innovative biotic–abiotic hybrid smart structure made by the integration of a butterfly wing with thermoresponsive liquid crystalline elastomers, and their capability to actuate the mechanical action of the wing, thus controlling its spectral response. Exploiting two fabrication strategies, it is demonstrated how different mechanisms of color tuning can be achieved by temperature control. In addition, due to the thermally induced mechanical deformation of the elastomer and superhydrophobic properties of the wing, a potential self‐cleaning behavior of the bilayer material is demonstrated.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"100 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89732996","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}
Josie Hughes, A. Spielberg, Mark Chounlakone, Gloria Chang, W. Matusik, D. Rus
Wearable devices have many applications ranging from health analytics to virtual and mixed reality interaction, to industrial training. For wearable devices to be practical, they must be responsive, deformable to fit the wearer, and robust to the user's range of motion. Signals produced by the wearable must also be informative enough to infer the precise physical state or activity of the user. Herein, a fully soft, wearable glove is developed, which is capable of real‐time hand pose reconstruction, environment sensing, and task classification. The design is easy to fabricate using low cost, commercial off‐the‐shelf items in a manner that is amenable to automated manufacturing. To realize such capabilities, resisitive and fluidic sensing technologies with machine learning neural architectures are merged. The glove is formed from a conductive knit which is strain sensitive, providing information through a network of resistance measurements. Fluidic sensing captured via pressure changes in fibrous sewn‐in flexible tubes, measuring interactions with the environment. The system can reconstruct user hand pose and identify sensory inputs such as holding force, object temperature, conductability, material stiffness, and user heart rate, all with high accuracy. The ability to identify complex environmentally dependent tasks, including held object identification and handwriting recognition is demonstrated.
{"title":"A Simple, Inexpensive, Wearable Glove with Hybrid Resistive‐Pressure Sensors for Computational Sensing, Proprioception, and Task Identification","authors":"Josie Hughes, A. Spielberg, Mark Chounlakone, Gloria Chang, W. Matusik, D. Rus","doi":"10.1002/aisy.202000002","DOIUrl":"https://doi.org/10.1002/aisy.202000002","url":null,"abstract":"Wearable devices have many applications ranging from health analytics to virtual and mixed reality interaction, to industrial training. For wearable devices to be practical, they must be responsive, deformable to fit the wearer, and robust to the user's range of motion. Signals produced by the wearable must also be informative enough to infer the precise physical state or activity of the user. Herein, a fully soft, wearable glove is developed, which is capable of real‐time hand pose reconstruction, environment sensing, and task classification. The design is easy to fabricate using low cost, commercial off‐the‐shelf items in a manner that is amenable to automated manufacturing. To realize such capabilities, resisitive and fluidic sensing technologies with machine learning neural architectures are merged. The glove is formed from a conductive knit which is strain sensitive, providing information through a network of resistance measurements. Fluidic sensing captured via pressure changes in fibrous sewn‐in flexible tubes, measuring interactions with the environment. The system can reconstruct user hand pose and identify sensory inputs such as holding force, object temperature, conductability, material stiffness, and user heart rate, all with high accuracy. The ability to identify complex environmentally dependent tasks, including held object identification and handwriting recognition is demonstrated.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"1164 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72691769","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}
Soft Robotics has emerged as a new and rapidly evolving interdisciplinary research area. This technology can provide a wide range of opportunities to create machines with unprecedented mechanical functionalities, as well as robots that are intrinsically safe to interact with human beings. However, the potential of this technology has not been fully realized as it is still a significant challenge to design, model and control such robots. This special issue, building on a workshop co-organized by the guest editors at the 2019 IEEE International Conference on Robotics and Automation in Montreal, Canada, focuses on recent advancements in soft robotics. The set of accepted papers highlights the opportunities and critical challenges of this field. Successfully realized soft robotics technologies could have a major impact on numerous industries and human activities (1900166, 1900171). Indeed, soft robotics offers the potential to be much more conformable and adaptable through novel sensing (1900080, 1900171, 1900178, 2000002; see Figure 1 A,B) and actuation mechanisms (1900177, 1900163; see Figure 1 C,D). As a result, these robots will be able to demonstrate significantly higher dexterity and manipulation capabilities than their traditional rigid counterparts. For example, grippers/gloves with embedded soft sensors can empower service robots to manipulate a broad range of objects (1900080; see Figure 1 A) or enable computational proprioception and task identification (2000002; see Figure 1 B). Bio-inspired soft robots can also significantly benefit search and rescue and exploratory operations as they can potentially negotiate across much more complicated terrestrial and aquatic terrains with soft bodies (1900183, 1900154, 1900186; see Figure 1 E,F).
{"title":"Opportunities and Challenges in Soft Robotics","authors":"H. Marvi, G. Z. Lum, I. Walker","doi":"10.1002/aisy.202000072","DOIUrl":"https://doi.org/10.1002/aisy.202000072","url":null,"abstract":"Soft Robotics has emerged as a new and rapidly evolving interdisciplinary research area. This technology can provide a wide range of opportunities to create machines with unprecedented mechanical functionalities, as well as robots that are intrinsically safe to interact with human beings. However, the potential of this technology has not been fully realized as it is still a significant challenge to design, model and control such robots. This special issue, building on a workshop co-organized by the guest editors at the 2019 IEEE International Conference on Robotics and Automation in Montreal, Canada, focuses on recent advancements in soft robotics. The set of accepted papers highlights the opportunities and critical challenges of this field. Successfully realized soft robotics technologies could have a major impact on numerous industries and human activities (1900166, 1900171). Indeed, soft robotics offers the potential to be much more conformable and adaptable through novel sensing (1900080, 1900171, 1900178, 2000002; see Figure 1 A,B) and actuation mechanisms (1900177, 1900163; see Figure 1 C,D). As a result, these robots will be able to demonstrate significantly higher dexterity and manipulation capabilities than their traditional rigid counterparts. For example, grippers/gloves with embedded soft sensors can empower service robots to manipulate a broad range of objects (1900080; see Figure 1 A) or enable computational proprioception and task identification (2000002; see Figure 1 B). Bio-inspired soft robots can also significantly benefit search and rescue and exploratory operations as they can potentially negotiate across much more complicated terrestrial and aquatic terrains with soft bodies (1900183, 1900154, 1900186; see Figure 1 E,F).","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87296553","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}
Hai Peng Wang, Y. Li, He Li, Shu-Yue Dong, Che Liu, Shi Jin, T. Cui
Metasurfaces have been developed as a promising approach for manipulating electromagnetic waves. Recently, deep learning algorithms have been introduced to design metasurfaces, but the network can only output one solution for each desired input and suffers from nonunique issue. To overcome the aforementioned challenges, a deep neural network model for inverse designs of anisotropic metasurfaces with full phase properties in ultrawideband is proposed. Given the target reflection spectra as inputs, the candidate metasurface patterns are generated through a generative adversarial network (GAN), and the corresponding predictions are simply achieved by the accurate forward neural network model to match the target spectra in the whole band with high fidelity. By training the generator and discriminator in GAN in an alternating order combined with setting a threshold of discriminator loss to trigger the phase prediction, the proposed method is much more efficient and consumes less time in the training process. Numerical simulations and experimental results demonstrate that the reflection phases of the generated meta‐atoms have excellent agreements with the given targets, providing an efficient way in automatically designing metasurfaces. The most important advantage of this approach over the previous schemes is to improve the design speed significantly with very good accuracy.
{"title":"Deep Learning Designs of Anisotropic Metasurfaces in Ultrawideband Based on Generative Adversarial Networks","authors":"Hai Peng Wang, Y. Li, He Li, Shu-Yue Dong, Che Liu, Shi Jin, T. Cui","doi":"10.1002/aisy.202000068","DOIUrl":"https://doi.org/10.1002/aisy.202000068","url":null,"abstract":"Metasurfaces have been developed as a promising approach for manipulating electromagnetic waves. Recently, deep learning algorithms have been introduced to design metasurfaces, but the network can only output one solution for each desired input and suffers from nonunique issue. To overcome the aforementioned challenges, a deep neural network model for inverse designs of anisotropic metasurfaces with full phase properties in ultrawideband is proposed. Given the target reflection spectra as inputs, the candidate metasurface patterns are generated through a generative adversarial network (GAN), and the corresponding predictions are simply achieved by the accurate forward neural network model to match the target spectra in the whole band with high fidelity. By training the generator and discriminator in GAN in an alternating order combined with setting a threshold of discriminator loss to trigger the phase prediction, the proposed method is much more efficient and consumes less time in the training process. Numerical simulations and experimental results demonstrate that the reflection phases of the generated meta‐atoms have excellent agreements with the given targets, providing an efficient way in automatically designing metasurfaces. The most important advantage of this approach over the previous schemes is to improve the design speed significantly with very good accuracy.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"61 5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77563230","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}
S. Mohanty, Q. Jin, G. P. Furtado, Arijit Ghosh, Gayatri J. Pahapale, I. Khalil, D. Gracias, S. Misra
The development of magnetically powered microswimmers that mimic the swimming mechanisms of microorganisms is important for lab‐on‐a‐chip devices, robotics, and next‐generation minimally invasive surgical interventions. Governed by their design, most previously described untethered swimmers can be maneuvered only by varying the direction of applied rotational magnetic fields. This constraint makes even state‐of‐the‐art swimmers incapable of reversing their direction of motion without a prior change in the direction of field rotation, which limits their autonomy and ability to adapt to their environments. Also, due to constant magnetization profiles, swarms of magnetic swimmers respond in the same manner, which limits multiagent control only to parallel formations. Herein, a new class of microswimmers are presented which are capable of reversing their direction of swimming without requiring a reversal in direction of field rotation. These swimmers exploit heterogeneity in their design and composition to exhibit reversible bidirectional motion determined by the field precession angle. Thus, the precession angle is used as an independent control input for bidirectional swimming. Design variability is explored in the systematic study of two swimmer designs with different constructions. Two different precession angles are observed for motion reversal, which is exploited to demonstrate independent control of the two swimmer designs.
{"title":"Bidirectional Propulsion of Arc‐Shaped Microswimmers Driven by Precessing Magnetic Fields","authors":"S. Mohanty, Q. Jin, G. P. Furtado, Arijit Ghosh, Gayatri J. Pahapale, I. Khalil, D. Gracias, S. Misra","doi":"10.1002/aisy.202000064","DOIUrl":"https://doi.org/10.1002/aisy.202000064","url":null,"abstract":"The development of magnetically powered microswimmers that mimic the swimming mechanisms of microorganisms is important for lab‐on‐a‐chip devices, robotics, and next‐generation minimally invasive surgical interventions. Governed by their design, most previously described untethered swimmers can be maneuvered only by varying the direction of applied rotational magnetic fields. This constraint makes even state‐of‐the‐art swimmers incapable of reversing their direction of motion without a prior change in the direction of field rotation, which limits their autonomy and ability to adapt to their environments. Also, due to constant magnetization profiles, swarms of magnetic swimmers respond in the same manner, which limits multiagent control only to parallel formations. Herein, a new class of microswimmers are presented which are capable of reversing their direction of swimming without requiring a reversal in direction of field rotation. These swimmers exploit heterogeneity in their design and composition to exhibit reversible bidirectional motion determined by the field precession angle. Thus, the precession angle is used as an independent control input for bidirectional swimming. Design variability is explored in the systematic study of two swimmer designs with different constructions. Two different precession angles are observed for motion reversal, which is exploited to demonstrate independent control of the two swimmer designs.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"114 10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83660352","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}
T. Vuorinen, K. Noponen, Vala Jeyhani, M. A. Aslam, J. Junttila, M. Tulppo, K. Kaikkonen, H. Huikuri, T. Seppänen, M. Mäntysalo, A. Vehkaoja
Continuous monitoring of vital signs can be a life‐saving matter for different patient groups. The development is going toward more intelligent and unobtrusive systems to improve the usability of body‐worn monitoring devices. Body‐worn devices can be skin‐conformable, patch‐type monitoring systems that are comfortable to use even for prolonged periods of time. Herein, an intelligent and wearable, out‐of‐hospital, and in‐hospital four‐electrode electrocardiography (ECG) and respiration measurement and monitoring system is proposed. The system consists of a conformable screen‐printed disposable patch, a measurement unit, gateway unit, and cloud‐based analysis tools with reconfigurable signal processing pipelines. The performance of the ECG patch and the measurement unit was tested with cardiac patients and compared with a Holter monitoring device and discrete, single‐site electrodes.
{"title":"Unobtrusive, Low‐Cost Out‐of‐Hospital, and In‐Hospital Measurement and Monitoring System","authors":"T. Vuorinen, K. Noponen, Vala Jeyhani, M. A. Aslam, J. Junttila, M. Tulppo, K. Kaikkonen, H. Huikuri, T. Seppänen, M. Mäntysalo, A. Vehkaoja","doi":"10.1002/aisy.202000030","DOIUrl":"https://doi.org/10.1002/aisy.202000030","url":null,"abstract":"Continuous monitoring of vital signs can be a life‐saving matter for different patient groups. The development is going toward more intelligent and unobtrusive systems to improve the usability of body‐worn monitoring devices. Body‐worn devices can be skin‐conformable, patch‐type monitoring systems that are comfortable to use even for prolonged periods of time. Herein, an intelligent and wearable, out‐of‐hospital, and in‐hospital four‐electrode electrocardiography (ECG) and respiration measurement and monitoring system is proposed. The system consists of a conformable screen‐printed disposable patch, a measurement unit, gateway unit, and cloud‐based analysis tools with reconfigurable signal processing pipelines. The performance of the ECG patch and the measurement unit was tested with cardiac patients and compared with a Holter monitoring device and discrete, single‐site electrodes.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78070799","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}
Manu Suvarna, Lennart Büth, Johannes Hejny, M. Mennenga, Jie Li, Y. Ng, C. Herrmann, Xiaonan Wang
With ‘smart’ being the order of the day, the shift in the landscape of a typical production‐oriented manufacturing environment to a more data‐oriented, automated and smart manufacturing is imminent. However, what is meant by smart manufacturing? And how can smart manufacturing contribute to a bigger picture by acting as enablers of smart cities? Given the paucity in literature that seeks to make sense in this direction, herein, first, six indices that represent or define a smart city are identified. Then, a holistic perspective of smart manufacturing is presented by collectively dwelling into the concepts of cyber physical production systems (CPPS) and industrial symbiosis—the recent and ongoing developments, applications, and relevant examples. In each subsequent section, the Review addresses how smart manufacturing contributes to smart cities, not just from a technology perspective, but also by satisfying the ergonometric factors and sustainability issues which are equally important indices that make up a smart city. A brief overview of Singapore as a smart nation and smart manufacturing hub is presented toward the end, along with highlights of a real‐world smart manufacturing platform called the Model Factory and its relevant modules.
{"title":"Smart Manufacturing for Smart Cities—Overview, Insights, and Future Directions","authors":"Manu Suvarna, Lennart Büth, Johannes Hejny, M. Mennenga, Jie Li, Y. Ng, C. Herrmann, Xiaonan Wang","doi":"10.1002/aisy.202000043","DOIUrl":"https://doi.org/10.1002/aisy.202000043","url":null,"abstract":"With ‘smart’ being the order of the day, the shift in the landscape of a typical production‐oriented manufacturing environment to a more data‐oriented, automated and smart manufacturing is imminent. However, what is meant by smart manufacturing? And how can smart manufacturing contribute to a bigger picture by acting as enablers of smart cities? Given the paucity in literature that seeks to make sense in this direction, herein, first, six indices that represent or define a smart city are identified. Then, a holistic perspective of smart manufacturing is presented by collectively dwelling into the concepts of cyber physical production systems (CPPS) and industrial symbiosis—the recent and ongoing developments, applications, and relevant examples. In each subsequent section, the Review addresses how smart manufacturing contributes to smart cities, not just from a technology perspective, but also by satisfying the ergonometric factors and sustainability issues which are equally important indices that make up a smart city. A brief overview of Singapore as a smart nation and smart manufacturing hub is presented toward the end, along with highlights of a real‐world smart manufacturing platform called the Model Factory and its relevant modules.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73310508","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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