Deep Learning-based Limited Feedback Designs for MIMO Systems

We study a deep learning (DL) based limited feedback methods for multi-antenna systems. Deep neural networks (DNNs) are introduced to replace an end-to-end limited feedback procedure including pilot-aided channel training process, channel codebook design, and beamforming vector selection. The DNNs are trained to yield binary feedback information as well as an efficient beamforming vector which maximizes the effective channel gain. Compared to conventional limited feedback schemes, the proposed DL method shows an 1 dB symbol error rate (SER) gain with reduced computational complexity.Comment: to appear in IEEE Wireless Commun. Let

Optimizing Base Placement of Surgical Robot: Kinematics Data-Driven Approach by Analyzing Working Pattern

In robot-assisted minimally invasive surgery (RAMIS), optimal placement of the surgical robot base is crucial for successful surgery. Improper placement can hinder performance because of manipulator limitations and inaccessible workspaces. Conventional base placement relies on the experience of trained medical staff. This study proposes a novel method for determining the optimal base pose based on the surgeon's working pattern. The proposed method analyzes recorded end-effector poses using a machine learning-based clustering technique to identify key positions and orientations preferred by the surgeon. We introduce two scoring metrics to address the joint limit and singularity issues: joint margin and manipulability scores. We then train a multi-layer perceptron regressor to predict the optimal base pose based on these scores. Evaluation in a simulated environment using the da Vinci Research Kit shows unique base pose score maps for four volunteers, highlighting the individuality of the working patterns. Results comparing with 20,000 randomly selected base poses suggest that the score obtained using the proposed method is 28.2% higher than that obtained by random base placement. These results emphasize the need for operator-specific optimization during base placement in RAMIS.Comment: 8 pages, 7 figures, 2 table

A transfer method for embedding conductive fillers on the surface of multi-scale structures for 3D flexible conductors

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Amine-Functionalized Lignin as an Eco-Friendly Antioxidant for Rubber Compounds

Although the typical antioxidant, N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD), ensures high durability and long lifespan for rubber compounds, it generates a highly toxic quinone in water, causing serious environmental pollution. Herein, as an alternative material of 6PPD, we newly introduce eco-friendly amine-functionalized lignin (AL) to be incorporated in rubber, which can provide excellent combinatorial antiaging properties of thermal stability and ozone/fatigue resistances through radical scavenging effect. The heterolytic ring-opening reaction of AL and sulfur can accelerate curing and improve the cross-link density by 28% (v, 4.107 × 10–4 mol/cm3), consequently reducing the ozone vulnerable areas of the matrix and further improving the aging resistance. Notably, AL allows its rubber compound to exhibit superior anti-ozone performance after ozone aging, with the arithmetic surface roughness (Sa) of 2.077 μm, which should be compared to that of 6PPD (4.737 μm). The developed chemically modified lignin and the methodology have enormous potential as a promising additive for future eco-friendly rubber compounds. The eco-friendly lignin-based antioxidant manufactured by amination reaction has the potential to reduce environmental pollution for the future rubber industry