The use of occupational exoskeletons has grown fast in manufacturing industries in recent years. One major scenario of exoskeleton use in manufacturing is to assist overhead, power hand tool operations. This preliminary work aimed to determine the effects of arm-supporting exoskeletons on shoulder muscle activity and human-hand tool coupling in simulated overhead tasks with axially applied vibration. An electromagnetic shaker capable of producing the random vibration spectrum specified in ISO 10819 was hung overhead to deliver vibrations. Two passive, arm-supporting exoskeletons, with one (ExoVest) transferring load to both the shoulder and pelvic region while the second one (ExoStrap) transferring load primarily to the pelvic region, were used in testing. Testing was also done with the shaker placed in front of the body to better understand the posture and exoskeleton engagement effects. The results collected from 6 healthy male subjects demonstrate the dominating effects of the overhead working posture on increased shoulder muscle activities. Vibration led to higher muscle activities in both agonist and antagonist shoulder muscles to a less extent. Exoskeleton use reduced the anterior deltoid and serratus anterior activities by 27% to 43%. However, wearing the ExoStrap increased the upper trapezius activities by 23% to 38% in the overhead posture. Furthermore, an increased human-shaker handle coupling was observed in the OH posture when wearing the ExoVest, indicating a more demanding neuromuscular control.
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Sci Rep
December 2024
College of Electronic Engineering, National University of Defense Technology, Hefei, 230000, China.
Spectrum sensing is a key technology and prerequisite for Transform Domain Communication Systems (TDCS). The traditional approach typically involves selecting a working sub-band and maintaining it without further changes, with spectrum sensing being conducted periodically. However, this approach presents two main issues: on the one hand, if the selected working band has few idle channels, TDCS devices are unable to flexibly switch sub-bands, leading to reduced performance; on the other hand, periodic sensing consumes time and energy, limiting TDCS's transmission efficiency.
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December 2024
Department of Computer Science, College of Education for Pure Sciences, University of Basrah, Basrah, Iraq.
Vehicular Ad-hoc Networks (VANETs) are growing into more desirable targets for malicious individuals due to the quick rise in the number of automated vehicles around the roadside. Secure data transfer is necessary for VANETs to preserve the integrity of the entire network. Federated learning (FL) is often suggested as a safe technique for exchanging data among VANETs, however, its capacity to protect private information is constrained.
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November 2024
NIMS Institute of Engineering and Technology (NIET), NIMS University, Jaipur, Rajasthan, 303121, India.
Field Programmable Gate Arrays are extensively used in space, military, and commercial sectors due to their reprogrammable nature. In high-safety environments, ensuring fault tolerance is crucial to improving the performance of electronic and computational systems. Common fault-tolerant methods include time redundancy, double modular redundancy, triple modular redundancy, hardware redundancy, self-checking, self-repairing, and Operand Width Aware Hardware Reuse.
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November 2024
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China. Electronic address:
Sensors (Basel)
November 2024
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Caoan Rd, Shanghai 201804, China.
This research introduces a cutting-edge approach to traffic infrastructure digitization, integrating UAV oblique photography with LiDAR point clouds for high-precision, lightweight 3D road modeling. The proposed method addresses the challenge of accurately capturing the current state of infrastructure while minimizing redundancy and optimizing computational efficiency. A key innovation is the development of the TJYRoad-Net model, which achieves over 85% mIoU segmentation accuracy by including a traffic feature computing (TFC) module composed of three critical components: the Regional Coordinate Encoder (RCE), the Context-Aware Aggregation Unit (CAU), and the Hierarchical Expansion Block.
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