Advanced Intelligent Systems

Journal Information
ISSN / EISSN : 2640-4567 / 2640-4567
Published by: Wiley-Blackwell (10.1002)
Total articles ≅ 535
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Jae Gwang Kim, Jisoo Jeon, Rajamanickam Sivakumar, Jonggeon Lee, Yun Ho Kim, , ,
Published: 23 October 2021
Advanced Intelligent Systems; https://doi.org/10.1002/aisy.202100148

Abstract:
Azobenzene-functionalized liquid crystalline polymer networks (azo-LCNs) are promising candidates for light-fueled contactless manipulation of miniaturized soft robots through embedding photoactive molecular switches into alignment-programmable LCNs. In particular, the 3D helical geometry of azo-LCNs is reported to achieve rapid photomotility by introducing rolling resistance. However, the maximum height of the obstacle that soft robot can overcome is limited by the helix diameter and the stress–strain responsivity. Herein, the helical diameter per unit length and photogenerated stress through molecular engineering of photoactive molecular switches are maximized. The carbon number of aliphatic spacers in the photoactive molecular switches is varied from two to eight to systematically investigate the structure–property–performance relations by studying the molecular geometry, physical properties of polymers, and photomotility of polymers. Furthermore, a finite-element analysis simulation is presented to understand the rolling locomotion of helical torsional soft robots. Through molecular engineering, the helix diameter per unit length of 0.2 mg soft robots is maximized, demonstrating high Young's modulus (≈2 GPa) and photogenerated stress (>1 MPa), as well as large velocity per body length, compared with the previously reported soft robots. Finally, the molecularly engineered soft robots successfully climb stairs, which is a key task in robotic systems.
Finlay Walton, Eve McGlynn, Rupam Das, Hongze Zhong, ,
Published: 23 October 2021
Advanced Intelligent Systems; https://doi.org/10.1002/aisy.202100082

Abstract:
Electrical neurostimulation has been used successfully as a technique in both research and clinical contexts for over a century. Despite significant progress, inherent problems remain, hence there has been a drive for novel neurostimulation modalities including ultrasonic, magnetic, and optical, which have the potential to be less invasive, have enhanced biointegration, deeper stimulus penetration from the probe, and higher spatiotemporal resolution. Optogenetics—the optical stimulation of genetically photosensitized neurons, enables highly precise genetic targeting of the stimulus. Specifically, it allows for selective optical excitation and inhibition via different wavelengths. As such, optogenetics has become a prominent tool for neuroscience. Herein, the complementarity between different forms of neurostimulation is explored with a focus on cranial magnetic and optogenetic stimulation. Magnetic stimulation is complementary to optogenetics in that it does not require an electrochemical tissue interface like in the case of electrical stimulation. Furthermore, if incorporated onto the same probe as one with light emitters, its stimulation field can be orthogonal to the light emission field—allowing for complementary stimulus fields. Herein, dual optogenetic and magnetic modalities are proposed that can unite to yield a powerful and versatile tool for neural engineering.
Yuanzhao Wu, YouLin Zhou, Waqas Asghar, , Fali Li, Dandan Sun, Chao Hu, Zhenguang Wu, Jie Shang, Zhe Yu, et al.
Published: 21 October 2021
Advanced Intelligent Systems, Volume 3; https://doi.org/10.1002/aisy.202170073

Yichen Yan, Yusen Zhao, Yousif Alsaid, Bowen Yao, Yucheng Zhang, Shuwang Wu,
Published: 21 October 2021
Advanced Intelligent Systems, Volume 3; https://doi.org/10.1002/aisy.202170070

Abstract:
Artificial Phototropic Systems
Bonan Sun, Rong Jia, Hang Yang, Xi Chen, Kai Tan, ,
Published: 19 October 2021
Advanced Intelligent Systems; https://doi.org/10.1002/aisy.202100139

Abstract:
Magnetically driven small-scale soft robots are promising for applications in biomedicine, due to their fast, programmable deformation, and remote, untethered actuation to accomplish complicated tasks. Although diverse materials and designs have been proposed for magnetic soft robots with programmable shape transformation, it is still challenging to produce strong actuation by a small magnetic field. Inspired by arthropod species, magnetic soft millirobots with joint structures by 3D printing hydrogels have been developed. The joints can turn the bending deformation into the folding deformation, with the jointed region deforming locally. Different from homogeneous bending deformation, such local deformation allows larger motions of robots and reduces the overall energy consumption at the same time. Through experiments and numerical simulations, it is shown that the magnetic arthropod millirobots are capable of performing multimodal locomotion and programmed shape transformation, such as move, flip, catch, carry, and release. Finally, ex vivo experiments of removing a foreign object from porcine organs (e.g., aorta, stomach, and intestine) are presented to demonstrate the potential surgery application of magnetic arthropod millirobots.
XuLei Wu, Bingjie Dang, Hong Wang, ,
Published: 18 October 2021
Advanced Intelligent Systems; https://doi.org/10.1002/aisy.202100151

Abstract:
Speech recognition involves the ability to learn the audios which are closely related to event sequence. Although speech recognition has been widely implemented in software neural networks, a hardware implementation based on energy efficient computing architecture is still missing. Herein, W/MgO/SiO2/Mo memristor arrays with multilevel resistance states are fabricated, where the weights of the artificial synapses in the memristor array can be tuned precisely by voltage pulses. Based on the array, speech recognition in memristive spiking neural networks (SNNs) with improved supervised tempotron algorithm on Texas Instruments digit sequences (TIDIGITS) dataset is conducted, demonstrating software-comparable accuracy for speech recognition in the memristive SNN. It is envisioned that such memristive SNNs can pave the way to building a bioinspired spike-based neuromorphic system for audio learning.
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