As well as the DT adaptations from the ED course of action determined by the SLC in MIMO-OFDM systems.Author Contributions: Conceptualization, J.L.; methodology, J.L.; software program, I.R.; validation, J.L., and D.B.; formal evaluation, J.L. and I.R.; investigation, I.R.; writing–original draft preparation, J.L. and I.R.; writing–review and editing, J.L.; visualization, J.L. and I.R..; supervision, J.L. and D.B.; All authors have study and agreed to the published version in the manuscript. Funding: This investigation received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.AbbreviationsThe following abbreviations are made use of within this manuscript: AWGN BS CFAR CLT CP CR CRN CSI CSS DSA DT ED EGC IoT ISI MIMO MISO MRC NU OFDM PU RF ROC SISO SIMO SL SLC SLS SNR SS STBC SU Additive white Gaussian noise Base station Constant false alarm price Central limit theorem Cyclic prefix Cognitive radio Cognitive radio networks Channel state data Cooperative Spectrum sensing Dynamic spectrum access Dynamic threshold Energy detection Equal Achieve Combining Internet of Factors Inter-symbol interference Multiple-input multiple-output Various input-single output Guretolimod MedChemExpress Maximal Ratio Combining Noise uncertainty Orthogonal frequency-division multiplexing Main user Radio frequency GNE-371 Cancer Receiver operating characteristic Single-input single-output Single-input multiple-output Square-law Square-law combining Square-Law Choice Signal-to-noise ratio Spectrum sensing Space ime block codes Secondary usersSensors 2021, 21,27 of
sensorsArticlePoint Cloud Resampling by Simulating Electric Charges on Metallic SurfacesKyoungmin Han 1 , Kyujin Jung 1 , Jaeho Yoon 2 and Minsik Lee 1, Division of Electrical and Electronic Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Korea; [email protected] (K.H.); [email protected] (K.J.) School of Electrical Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Korea; [email protected] Correspondence: [email protected]; Tel.: 82-31-400-Citation: Han, K.; Jung, K.; Yoon, J.; Lee, M. Point Cloud Resampling by Simulating Electric Charges on Metallic Surfaces. Sensors 2021, 21, 7768. https://doi.org/10.3390/ s21227768 Academic Editor: Kourosh Khoshelham Received: 13 October 2021 Accepted: 16 November 2021 Published: 22 NovemberAbstract: 3D point cloud resampling according to computational geometry continues to be a challenging dilemma. In this paper, we propose a point cloud resampling algorithm inspired by the physical traits on the repulsion forces involving point electrons. The points in the point cloud are regarded as electrons that reside on a virtual metallic surface. We iteratively update the positions in the points by simulating the electromagnetic forces among them. Intuitively, the input point cloud becomes evenly distributed by the repulsive forces. We further adopt an acceleration and damping terms in our simulation. This technique could be viewed as a momentum strategy in mathematical optimization and thus increases the convergence stability and uniformity efficiency. The net force on the repulsion forces may well include a normal directional force with respect towards the nearby surface, which can make the point diverge from the surface. To stop this, we introduce a simple restriction method that limits the repulsion forces amongst th.
