首页|Reports on Robotics Findings from Department of ECE Provide New Insights (Deploy able Reconfigurable Antenna With Intrinsic Strain Sensing Capabilities for Stret chable Soft Robotic Applications)

Reports on Robotics Findings from Department of ECE Provide New Insights (Deploy able Reconfigurable Antenna With Intrinsic Strain Sensing Capabilities for Stret chable Soft Robotic Applications)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Robotic s. According to news reporting originating from Chennai, India, by NewsRx corres pondents, research stated, “Frequency reconfigurable antennas play a prominent r ole in telecommunication technologies. This paper presents a reconfigurable ante nna that could define the stretch properties in association with the intrinsic s train sensing capabilities.” Our news editors obtained a quote from the research from the Department of ECE, “The stretchable resin is synthesized using Magnesium Nitrate Hexahydrate, Alumi nium Nitrate Nanohydrate, the mixture was reduced and polymerized and finally ma de conductive stretchable resin with the help of CNT’s (Carbon Nano Tubes). The solution is characterized by the help of SEM and EDAX measurements. The conducti ng stretchable polymer resin could elongate upto 100% along with t he fabric dielectric, Lycra. The electrical conductivity of the resin is 8 S/m. The precise dimension of the antenna was done with the help of a micro-cutter. T he inverted S shape of the antenna helps to achieve bandwidth. The fabricated an tenna operates within 4GHz and 8GHz with a gain of up to 3.3dB, Front to the Bac k ratio of 7.42. It is experimented by varying the strain to achieve frequency r econfiguration ranging from 0% to 75%. The fabricatio n and characterization of extremely efficient stretchable and reconfigurable ant enna for C band frequency applications are described.”

ChennaiIndiaAsiaEmerging Technolog iesMachine LearningRoboticsRobotsDepartment of ECE

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.7)