The recent development of human–machine interface (HMI) involves advances in wearable devices that safely interact with the human body while providing high mechanical compliance. Various cutting‐edge technologies such as highly stretchable electronics, multiple sensor fusion, and wearable exoskeletons have enabled a higher level of interactivity. Notably, recent developments using machine intelligence have achieved unprecedented performance and solved various challenges. Herein, the recent progresses in stretchable HMI including stretchable sensors, stretchable actuating systems, and machine intelligence‐aided stretchable devices are presented, and their principles and working mechanisms are discussed.
{"title":"Smart Stretchable Electronics for Advanced Human–Machine Interface","authors":"K. Kim, Y. Suh, S. Ko","doi":"10.1002/aisy.202000157","DOIUrl":"https://doi.org/10.1002/aisy.202000157","url":null,"abstract":"The recent development of human–machine interface (HMI) involves advances in wearable devices that safely interact with the human body while providing high mechanical compliance. Various cutting‐edge technologies such as highly stretchable electronics, multiple sensor fusion, and wearable exoskeletons have enabled a higher level of interactivity. Notably, recent developments using machine intelligence have achieved unprecedented performance and solved various challenges. Herein, the recent progresses in stretchable HMI including stretchable sensors, stretchable actuating systems, and machine intelligence‐aided stretchable devices are presented, and their principles and working mechanisms are discussed.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"437 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75798400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. M. Nasab, Siavash Sharifi, Shuai Chen, Yang Jiao, W. Shan
Materials with tunable properties, especially dynamically tunable stiffness, have been of great interest for the field of soft robotics. Herein, a novel design concept of robust three‐component elastomer–particle–fiber composite system with tunable mechanical stiffness and electrical conductivity is introduced. These smart materials are capable of changing their mechanical stiffness rapidly and reversibly when powered with electrical current. One implementation of the composite system demonstrated here is composed of a polydimethylsiloxane (PDMS) matrix, Field's metal (FM) particles, and nickel‐coated carbon fibers (NCCF). It is demonstrated that the mechanical stiffness and the electrical conductivity of the composite are highly tunable and dependent on the volume fraction of the three components and the temperature, and can be reasonably estimated using effective medium theory. Due to its superior electrical conductivity, Joule heating can be used as the activation mechanism to realize ≈20× mechanical stiffness changes in seconds. The performance of the composites is thermally and mechanically robust. The shape memory effect of these composites is also demonstrated. The combination of tunable mechanical and electrical properties makes these composites promising candidates for sensing and actuation applications for soft robotics.
{"title":"Robust Three‐Component Elastomer–Particle–Fiber Composites with Tunable Properties for Soft Robotics","authors":"A. M. Nasab, Siavash Sharifi, Shuai Chen, Yang Jiao, W. Shan","doi":"10.1002/aisy.202000166","DOIUrl":"https://doi.org/10.1002/aisy.202000166","url":null,"abstract":"Materials with tunable properties, especially dynamically tunable stiffness, have been of great interest for the field of soft robotics. Herein, a novel design concept of robust three‐component elastomer–particle–fiber composite system with tunable mechanical stiffness and electrical conductivity is introduced. These smart materials are capable of changing their mechanical stiffness rapidly and reversibly when powered with electrical current. One implementation of the composite system demonstrated here is composed of a polydimethylsiloxane (PDMS) matrix, Field's metal (FM) particles, and nickel‐coated carbon fibers (NCCF). It is demonstrated that the mechanical stiffness and the electrical conductivity of the composite are highly tunable and dependent on the volume fraction of the three components and the temperature, and can be reasonably estimated using effective medium theory. Due to its superior electrical conductivity, Joule heating can be used as the activation mechanism to realize ≈20× mechanical stiffness changes in seconds. The performance of the composites is thermally and mechanically robust. The shape memory effect of these composites is also demonstrated. The combination of tunable mechanical and electrical properties makes these composites promising candidates for sensing and actuation applications for soft robotics.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84830056","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}
Kazutoshi Tanaka, Shihao Yang, Yuji Tokudome, Yuna Minami, Yuyao Lu, T. Arie, S. Akita, K. Takei, K. Nakajima
Flapping‐wing unmanned aerial vehicles have potential advantages, such as consuming lower energy by leveraging the force of wind. Since the flapping movements of the soft wings contain information about the wind, measuring the movement of each part of the wings allows these vehicles to distinguish the direction of the wind. To confirm this prediction, herein, the detection of wind flow from the flapping‐wing motion of a bird robot using an integrated flexible strain sensor on its wing and a physical reservoir computing analysis is presented. In the presence of different wind directions, the movement of the flapping‐wings is measured using flexible strain sensors, and the current wind direction is detected by capitalizing on the intrinsic wing dynamics. As a result, it is found that the detection accuracy using our embedded flexible strain sensors is significantly high, showing a similar level of accuracy with a high‐speed camera recorded from the fixed position in the environment. The results indicate that flapping‐wing unmanned aerial vehicles can recognize wind direction by exploiting the natural dynamics of their wings.
{"title":"Flapping‐Wing Dynamics as a Natural Detector of Wind Direction","authors":"Kazutoshi Tanaka, Shihao Yang, Yuji Tokudome, Yuna Minami, Yuyao Lu, T. Arie, S. Akita, K. Takei, K. Nakajima","doi":"10.1002/aisy.202000174","DOIUrl":"https://doi.org/10.1002/aisy.202000174","url":null,"abstract":"Flapping‐wing unmanned aerial vehicles have potential advantages, such as consuming lower energy by leveraging the force of wind. Since the flapping movements of the soft wings contain information about the wind, measuring the movement of each part of the wings allows these vehicles to distinguish the direction of the wind. To confirm this prediction, herein, the detection of wind flow from the flapping‐wing motion of a bird robot using an integrated flexible strain sensor on its wing and a physical reservoir computing analysis is presented. In the presence of different wind directions, the movement of the flapping‐wings is measured using flexible strain sensors, and the current wind direction is detected by capitalizing on the intrinsic wing dynamics. As a result, it is found that the detection accuracy using our embedded flexible strain sensors is significantly high, showing a similar level of accuracy with a high‐speed camera recorded from the fixed position in the environment. The results indicate that flapping‐wing unmanned aerial vehicles can recognize wind direction by exploiting the natural dynamics of their wings.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89257793","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}
Many soft robots are composed of soft fluidic actuators that are fabricated from silicone rubbers and use hydraulic or pneumatic actuation. The strong nonlinearities and complex geometries of soft actuators hinder the development of analytical models to describe their motion. Finite element modeling provides an effective solution to this issue and allows the user to predict performance and optimize soft actuator designs. Herein, the literature on a finite element analysis of soft actuators is reviewed. First, the required nonlinear elasticity concepts are introduced with a focus on the relevant models for soft robotics. In particular, the procedure for determining material constants for the hyperelastic models from material testing and curve fitting is explored. Then, a comprehensive review of constitutive model parameters for the most widely used silicone rubbers in the literature is provided. An overview of the procedure is provided for three commercially available software packages (Abaqus, Ansys, and COMSOL). The combination of modeling procedures, material properties, and design guidelines presented in this article can be used as a starting point for soft robotic actuator design.
{"title":"Finite Element Modeling of Soft Fluidic Actuators: Overview and Recent Developments","authors":"Matheus S. Xavier, A. Fleming, Y. Yong","doi":"10.1002/aisy.202000187","DOIUrl":"https://doi.org/10.1002/aisy.202000187","url":null,"abstract":"Many soft robots are composed of soft fluidic actuators that are fabricated from silicone rubbers and use hydraulic or pneumatic actuation. The strong nonlinearities and complex geometries of soft actuators hinder the development of analytical models to describe their motion. Finite element modeling provides an effective solution to this issue and allows the user to predict performance and optimize soft actuator designs. Herein, the literature on a finite element analysis of soft actuators is reviewed. First, the required nonlinear elasticity concepts are introduced with a focus on the relevant models for soft robotics. In particular, the procedure for determining material constants for the hyperelastic models from material testing and curve fitting is explored. Then, a comprehensive review of constitutive model parameters for the most widely used silicone rubbers in the literature is provided. An overview of the procedure is provided for three commercially available software packages (Abaqus, Ansys, and COMSOL). The combination of modeling procedures, material properties, and design guidelines presented in this article can be used as a starting point for soft robotic actuator design.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77900762","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}
Cagri Yalcin, M. Sam, Yifeng Bu, Moran Amit, A. Skalsky, Michael C. Yip, T. Ng, H. Garudadri
Spasticity is a pathological condition that can occur in people with neuromuscular disorders. Objective, repeatable metrics are needed for evaluation to provide appropriate treatment and to monitor patient condition. Herein, an instrumented bimodal glove with force and movement sensors for spasticity assessment is presented. To mitigate noise artifacts, machine learning techniques are used, specifically a multitask neural network, to calibrate the instrumented glove signals against the ground truth from sensors integrated in a robotic arm. The motorized robotic arm system offers adjustable resistance to simulate different levels of muscle stiffness in spasticity, and the sensors on the robot provide ground‐truth measurements of angular displacement and force applied during flexion and extension maneuvers. The robotic sensor measurements are used to train the instrumented glove data through multitask learning. After processing through the neural network, the Pearson correlation coefficients between the processed signals and the ground truth are above 0.92, demonstrating successful signal calibration and noise mitigation.
{"title":"Artifacts Mitigation in Sensors for Spasticity Assessment","authors":"Cagri Yalcin, M. Sam, Yifeng Bu, Moran Amit, A. Skalsky, Michael C. Yip, T. Ng, H. Garudadri","doi":"10.1002/aisy.202000106","DOIUrl":"https://doi.org/10.1002/aisy.202000106","url":null,"abstract":"Spasticity is a pathological condition that can occur in people with neuromuscular disorders. Objective, repeatable metrics are needed for evaluation to provide appropriate treatment and to monitor patient condition. Herein, an instrumented bimodal glove with force and movement sensors for spasticity assessment is presented. To mitigate noise artifacts, machine learning techniques are used, specifically a multitask neural network, to calibrate the instrumented glove signals against the ground truth from sensors integrated in a robotic arm. The motorized robotic arm system offers adjustable resistance to simulate different levels of muscle stiffness in spasticity, and the sensors on the robot provide ground‐truth measurements of angular displacement and force applied during flexion and extension maneuvers. The robotic sensor measurements are used to train the instrumented glove data through multitask learning. After processing through the neural network, the Pearson correlation coefficients between the processed signals and the ground truth are above 0.92, demonstrating successful signal calibration and noise mitigation.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"695 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81783013","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}
G. Moretti, S. Rosset, R. Vertechy, I. Anderson, M. Fontana
Dielectric elastomer generator systems (DEGSs) are a class of electrostatic soft‐transducers capable of converting oscillating mechanical power from different sources into usable electricity. Over the past years, a diversity of DEGSs has been conceived, integrated, and tested featuring diverse topologies and implementation characteristics tailored on different applications. Herein, the recent advances on DEGSs are reviewed and illustrated in terms of design of hardware architectures, power electronics, and control, with reference to the different application targets, including large‐scale systems such as ocean wave energy converters, and small‐scale systems such as human motion or ambient vibration energy harvesters. Finally, challenges and perspectives for the advancement of DEGSs are identified and discussed.
{"title":"A Review of Dielectric Elastomer Generator Systems","authors":"G. Moretti, S. Rosset, R. Vertechy, I. Anderson, M. Fontana","doi":"10.1002/aisy.202000125","DOIUrl":"https://doi.org/10.1002/aisy.202000125","url":null,"abstract":"Dielectric elastomer generator systems (DEGSs) are a class of electrostatic soft‐transducers capable of converting oscillating mechanical power from different sources into usable electricity. Over the past years, a diversity of DEGSs has been conceived, integrated, and tested featuring diverse topologies and implementation characteristics tailored on different applications. Herein, the recent advances on DEGSs are reviewed and illustrated in terms of design of hardware architectures, power electronics, and control, with reference to the different application targets, including large‐scale systems such as ocean wave energy converters, and small‐scale systems such as human motion or ambient vibration energy harvesters. Finally, challenges and perspectives for the advancement of DEGSs are identified and discussed.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89270853","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}
Nazek El‐atab, R. B. Mishra, Fhad Al-Modaf, Lana Joharji, Aljohara A. Alsharif, Haneen Alamoudi, Marlon Diaz, N. Qaiser, M. Hussain
Soft robotics technologies are paving the way toward robotic abilities which are vital for a wide range of applications, including manufacturing, manipulation, gripping, human–machine interaction, locomotion, and more. An essential component in a soft robot is the soft actuator which provides the system with a deformable body and allows it to interact with the environment to achieve a desired actuation pattern, such as locomotion. This Review article aims to provide researchers interested in the soft robotics field with a reference guide about the various state‐of‐the‐art soft actuation methodologies that are developed with a wide range of stimuli including light, heat, applied electric and magnetic fields with a focus on their various applications in soft robotics. The underlying principles of the soft actuators are discussed with a focus on the resulting motion complexities, deformations, and multi‐functionalities. Finally, various promising applications and examples of the different soft actuators are discussed in addition to their further development potential.
{"title":"Soft Actuators for Soft Robotic Applications: A Review","authors":"Nazek El‐atab, R. B. Mishra, Fhad Al-Modaf, Lana Joharji, Aljohara A. Alsharif, Haneen Alamoudi, Marlon Diaz, N. Qaiser, M. Hussain","doi":"10.1002/aisy.202000128","DOIUrl":"https://doi.org/10.1002/aisy.202000128","url":null,"abstract":"Soft robotics technologies are paving the way toward robotic abilities which are vital for a wide range of applications, including manufacturing, manipulation, gripping, human–machine interaction, locomotion, and more. An essential component in a soft robot is the soft actuator which provides the system with a deformable body and allows it to interact with the environment to achieve a desired actuation pattern, such as locomotion. This Review article aims to provide researchers interested in the soft robotics field with a reference guide about the various state‐of‐the‐art soft actuation methodologies that are developed with a wide range of stimuli including light, heat, applied electric and magnetic fields with a focus on their various applications in soft robotics. The underlying principles of the soft actuators are discussed with a focus on the resulting motion complexities, deformations, and multi‐functionalities. Finally, various promising applications and examples of the different soft actuators are discussed in addition to their further development potential.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81620586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The explosive growth of data and information has motivated technological developments in computing systems that utilize them for efficiently discovering patterns and gaining relevant insights. Inspired by the structure and functions of biological synapses and neurons in the brain, neural network algorithms that can realize highly parallel computations have been implemented on conventional silicon transistor‐based hardware. However, synapses composed of multiple transistors allow only binary information to be stored, and processing such digital states through complicated silicon neuron circuits makes low‐power and low‐latency computing difficult. Therefore, the attractiveness of the emerging memories and switches for synaptic and neuronal elements, respectively, in implementing neuromorphic systems, which are suitable for performing energy‐efficient cognitive functions and recognition, is discussed herein. Based on a literature survey, recent progress concerning memories shows that novel strategies related to materials and device engineering to mitigate challenges are presented to primarily achieve nonvolatile analog synaptic characteristics. Attempts to emulate the role of the neuron in various ways using compact switches and volatile memories are also discussed. It is hoped that this review will help direct future interdisciplinary research on device, circuit, and architecture levels of neuromorphic systems.
{"title":"Recent Advancements in Emerging Neuromorphic Device Technologies","authors":"Jiyong Woo, Jeong Hun Kim, J. Im, Seung Eon Moon","doi":"10.1002/aisy.202000111","DOIUrl":"https://doi.org/10.1002/aisy.202000111","url":null,"abstract":"The explosive growth of data and information has motivated technological developments in computing systems that utilize them for efficiently discovering patterns and gaining relevant insights. Inspired by the structure and functions of biological synapses and neurons in the brain, neural network algorithms that can realize highly parallel computations have been implemented on conventional silicon transistor‐based hardware. However, synapses composed of multiple transistors allow only binary information to be stored, and processing such digital states through complicated silicon neuron circuits makes low‐power and low‐latency computing difficult. Therefore, the attractiveness of the emerging memories and switches for synaptic and neuronal elements, respectively, in implementing neuromorphic systems, which are suitable for performing energy‐efficient cognitive functions and recognition, is discussed herein. Based on a literature survey, recent progress concerning memories shows that novel strategies related to materials and device engineering to mitigate challenges are presented to primarily achieve nonvolatile analog synaptic characteristics. Attempts to emulate the role of the neuron in various ways using compact switches and volatile memories are also discussed. It is hoped that this review will help direct future interdisciplinary research on device, circuit, and architecture levels of neuromorphic systems.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89287292","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}
This work studies a computation in‐memory concept for binary multiply‐accumulate operations based on complementary resistive switches (CRS). By exploiting the in‐memory boolean exclusive OR (XOR) operation of single CRS devices, the Hamming Distance (HD) can be calculated if the center electrodes of multiple CRS cells are connected. This HD is linearly encoded in the voltage drop of the common electrode, and from it the result of a binary multiply‐accumulate operation can be calculated. A small‐scale demonstration is experimentally realized and the feasibility of the in‐memory computation concept is confirmed. A simulation study identifies the low resistance state (LRS) variability as the main reason for the variations in the output voltage. The application as a potential hardware accelerator for the inference step of binary neural networks is investigated. Therefore, a 1‐layer fully connected neural network is trained on a binarized version of the MNIST data set and the inference step of the test data set is simulated. The concept achieves a prediction accuracy of approximately 86%.
{"title":"In‐Memory Binary Vector–Matrix Multiplication Based on Complementary Resistive Switches","authors":"T. Ziegler, R. Waser, D. Wouters, S. Menzel","doi":"10.1002/aisy.202000134","DOIUrl":"https://doi.org/10.1002/aisy.202000134","url":null,"abstract":"This work studies a computation in‐memory concept for binary multiply‐accumulate operations based on complementary resistive switches (CRS). By exploiting the in‐memory boolean exclusive OR (XOR) operation of single CRS devices, the Hamming Distance (HD) can be calculated if the center electrodes of multiple CRS cells are connected. This HD is linearly encoded in the voltage drop of the common electrode, and from it the result of a binary multiply‐accumulate operation can be calculated. A small‐scale demonstration is experimentally realized and the feasibility of the in‐memory computation concept is confirmed. A simulation study identifies the low resistance state (LRS) variability as the main reason for the variations in the output voltage. The application as a potential hardware accelerator for the inference step of binary neural networks is investigated. Therefore, a 1‐layer fully connected neural network is trained on a binarized version of the MNIST data set and the inference step of the test data set is simulated. The concept achieves a prediction accuracy of approximately 86%.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"105 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91183459","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}
Qilai Chen, Ying Zhang, Shuzhi Liu, Tingting Han, Xinhui Chen, Yanqing Xu, Ziqi Meng, Guanglei Zhang, Xuejun Zheng, Jinjin Zhao, G. Cao, Gang Liu
Machine vision is an indispensable part of today's artificial intelligence. The artificial visual systems used in industrial production and domestic daily life rely significantly on cameras and image‐processing components for live monitoring and target identifying. They, however, often suffer from bulky volume, high energy consumption, and more critically, lack of adaptive responsiveness under extreme lighting conditions and thus possible mortal visual disability of flash blinding or nyctalopia for applications such as auto‐piloting. Herein, it is demonstrated that perovskite switchable photovoltaic devices are used to effectively construct all‐in‐one sensory neural network. Arising from the spontaneous and electric field‐induced ion‐migration effect, the photoresponsivity of the perovskite device can be reconfigured over the wide range of 540–1270%, which not only allows high‐fidelity adaptive image sensing of the visual information but also acts as updatable synaptic weight to enable the sensor array for performing machine‐learning tasks. With the bioinspired electronic pupil regulation function achieved through adjustable photoresponsivity of the perovskite sensor array, a proof‐of‐concept adaptive machine vision system with a maximum 263% enhancement of the object recognition accuracy for compact, mobile yet delay‐sensitive applications is demonstrated.
{"title":"Switchable Perovskite Photovoltaic Sensors for Bioinspired Adaptive Machine Vision","authors":"Qilai Chen, Ying Zhang, Shuzhi Liu, Tingting Han, Xinhui Chen, Yanqing Xu, Ziqi Meng, Guanglei Zhang, Xuejun Zheng, Jinjin Zhao, G. Cao, Gang Liu","doi":"10.1002/aisy.202000122","DOIUrl":"https://doi.org/10.1002/aisy.202000122","url":null,"abstract":"Machine vision is an indispensable part of today's artificial intelligence. The artificial visual systems used in industrial production and domestic daily life rely significantly on cameras and image‐processing components for live monitoring and target identifying. They, however, often suffer from bulky volume, high energy consumption, and more critically, lack of adaptive responsiveness under extreme lighting conditions and thus possible mortal visual disability of flash blinding or nyctalopia for applications such as auto‐piloting. Herein, it is demonstrated that perovskite switchable photovoltaic devices are used to effectively construct all‐in‐one sensory neural network. Arising from the spontaneous and electric field‐induced ion‐migration effect, the photoresponsivity of the perovskite device can be reconfigured over the wide range of 540–1270%, which not only allows high‐fidelity adaptive image sensing of the visual information but also acts as updatable synaptic weight to enable the sensor array for performing machine‐learning tasks. With the bioinspired electronic pupil regulation function achieved through adjustable photoresponsivity of the perovskite sensor array, a proof‐of‐concept adaptive machine vision system with a maximum 263% enhancement of the object recognition accuracy for compact, mobile yet delay‐sensitive applications is demonstrated.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83159674","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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