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Balandra A, Doll Y, Hirose S, Kajiwara T, Kashino Z, Inami M, Koshimizu S, Fukaki H, Watahiki MK. P-MIRU, a Polarized Multispectral Imaging System, Reveals Reflection Information on the Biological Surface. Plant Cell Physiol 2023; 64:1311-1322. [PMID: 37217180 DOI: 10.1093/pcp/pcad045] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/12/2023] [Accepted: 05/20/2023] [Indexed: 05/24/2023]
Abstract
Reflection light forms the core of our visual perception of the world. We can obtain vast information by examining reflection light from biological surfaces, including pigment composition and distribution, tissue structure and surface microstructure. However, because of the limitations in our visual system, the complete information in reflection light, which we term 'reflectome', cannot be fully exploited. For example, we may miss reflection light information outside our visible wavelengths. In addition, unlike insects, we have virtually no sensitivity to light polarization. We can detect non-chromatic information lurking in reflection light only with appropriate devices. Although previous studies have designed and developed systems for specialized uses supporting our visual systems, we still do not have a versatile, rapid, convenient and affordable system for analyzing broad aspects of reflection from biological surfaces. To overcome this situation, we developed P-MIRU, a novel multispectral and polarization imaging system for reflecting light from biological surfaces. The hardware and software of P-MIRU are open source and customizable and thus can be applied for virtually any research on biological surfaces. Furthermore, P-MIRU is a user-friendly system for biologists with no specialized programming or engineering knowledge. P-MIRU successfully visualized multispectral reflection in visible/non-visible wavelengths and simultaneously detected various surface phenotypes of spectral polarization. The P-MIRU system extends our visual ability and unveils information on biological surfaces.
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Affiliation(s)
| | - Yuki Doll
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, 113-0033 Japan
| | - Shogo Hirose
- Faculty of Agriculture, Meijo University, Shiogamaguchi 1-501, Tempaku-ku, Nagoya, 468-0073 Japan
| | - Tomoaki Kajiwara
- Graduate School of Biostudies, Kyoto University, Yoshida-Konoecho, Sakyo-ku, Kyoto, 606-8502 Japan
| | - Zendai Kashino
- Research Center for Advanced Science and Technology, The University of Tokyo, Komaba 4-6-1, Meguro-ku, Tokyo, 153-8904 Japan
| | - Masahiko Inami
- Research Center for Advanced Science and Technology, The University of Tokyo, Komaba 4-6-1, Meguro-ku, Tokyo, 153-8904 Japan
| | - Shizuka Koshimizu
- School of Agriculture, Meiji University, Higashimita 1-1-1, Tama-ku, Kawasaki, 214-8571 Japan
- Research Center for Advanced Science and Technology, The University of Tokyo, Komaba 4-6-1, Meguro-ku, Tokyo, 153-8904 Japan
| | - Hidehiro Fukaki
- Department of Biology, Graduate School of Science, Kobe University, Rokkodaicho 1-1, Nada-ku, Kobe, 657-8501 Japan
| | - Masaaki K Watahiki
- Faculty of Science and Graduate School of Life Science, Hokkaido University, Kita 10 Nishi 8, Kita-ku, Sapporo, 060-0810 Japan
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Saito H, Horie A, Maekawa A, Matsubara S, Wakisaka S, Kashino Z, Kasahara S, Inami M. Transparency in Human-Machine Mutual Action. JRM 2021. [DOI: 10.20965/jrm.2021.p0987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Recent advances in human-computer integration (HInt) have focused on the development of human-machine systems, where both human and machine autonomously act upon each other. However, a key challenge in designing such systems is augmenting the user’s physical abilities while maintaining their sense of self-attribution. This challenge is particularly prevalent when both human and machine are capable of acting upon each other, thereby creating a human-machine mutual action (HMMA) system. To address this challenge, we present a design framework that is based on the concept of transparency. We define transparency in HInt as the degree to which users can self-attribute an experience when machines intervene in the users’ action. Using this framework, we form a set of design guidelines and an approach for designing HMMA systems. By using transparency as our focus, we aim to provide a design approach for not only achieving human-machine fusion into a single agent, but also controlling the degrees of fusion at will. This study also highlights the effectiveness of our design approach through an analysis of existing studies that developed HMMA systems. Further development of our design approach is discussed, and future prospects for HInt and HMMA system designs are presented.
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Eshaghi K, Li Y, Kashino Z, Nejat G, Benhabib B. mROBerTO 2.0 – An Autonomous Millirobot With Enhanced Locomotion for Swarm Robotics. IEEE Robot Autom Lett 2020. [DOI: 10.1109/lra.2020.2966411] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Abstract
Locating a mobile target, untrackable in real-time, is pertinent to numerous time-critical applications, such as wilderness search and rescue. This paper proposes a hybrid approach to this dynamic problem, where both static and mobile sensors are utilized for the goal of detecting a target. The approach is novel in that a team of robots utilized to deploy a static-sensor network also actively searches for the target via on-board sensors. Synergy is achieved through: 1) optimal deployment planning of static-sensor networks and 2) optimal routing and motion planning of the robots for the deployment of the network and target search. The static-sensor network is planned first to maximize the likelihood of target detection while ensuring (temporal and spatial) unbiasedness in target motion. Robot motions are, subsequently, planned in two stages: 1) route planning and 2) trajectory planning. In the first stage, given a static-sensor network configuration, robot routes are planned to maximize the amount of spare time available to the mobile agents/sensors, for target search in between (just-in-time) static-sensor deployments. In the second stage, given robot routes (i.e., optimal sequences of sensor delivery locations and times), the corresponding robot trajectories are planned to make effective use of any spare time the mobile agents may have to search for the target. The proposed search strategy was validated through extensive simulations, some of which are given in detail here. An analysis of the method's performance in terms of target-search success is also included.
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Niroui F, Zhang K, Kashino Z, Nejat G. Deep Reinforcement Learning Robot for Search and Rescue Applications: Exploration in Unknown Cluttered Environments. IEEE Robot Autom Lett 2019. [DOI: 10.1109/lra.2019.2891991] [Citation(s) in RCA: 120] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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