Geo2SigMap: High-Fidelity RF Signal Mapping Using Geographic Databases

Radio frequency (RF) signal mapping, which is the process of analyzing and predicting the RF signal strength and distribution across specific areas, is crucial for cellular network planning and deployment. Traditional approaches to RF signal mapping rely on statistical models constructed based on measurement data, which offer low complexity but often lack accuracy, or ray tracing tools, which provide enhanced precision for the target area but suffer from increased computational complexity. Recently, machine learning (ML) has emerged as a data-driven method for modeling RF signal propagation, which leverages models trained on synthetic datasets to perform RF signal mapping in "unseen" areas. In this paper, we present Geo2SigMap, an ML-based framework for efficient and high-fidelity RF signal mapping using geographic databases. First, we develop an automated framework that seamlessly integrates three open-source tools: OpenStreetMap (geographic databases), Blender (computer graphics), and Sionna (ray tracing), enabling the efficient generation of large-scale 3D building maps and ray tracing models. Second, we propose a cascaded U-Net model, which is pre-trained on synthetic datasets and employed to generate detailed RF signal maps, leveraging environmental information and sparse measurement data. Finally, we evaluate the performance of Geo2SigMap via a real-world measurement campaign, where three types of user equipment (UE) collect over 45,000 data points related to cellular information from six LTE cells operating in the citizens broadband radio service (CBRS) band. Our results show that Geo2SigMap achieves an average root-mean-square-error (RMSE) of 6.04 dB for predicting the reference signal received power (RSRP) at the UE, representing an average RMSE improvement of 3.59 dB compared to existing methods

Impact Vibration Attenuation for a Flexible Robotic Manipulator through Transfer and Dissipation of Energy

Due to the presence of system flexibility, impact can excite severe large amplitude vibration responses of the flexible robotic manipulator. This impact vibration exhibits characteristics of remarkable nonlinearity and strong energy. The main goal of this study is to put forward an energy-based control method to absorb and attenuate large amplitude impact vibration of the flexible robotic manipulator. The method takes advantage of internal resonance and is implemented through a vibration absorber based on the transfer and dissipation of energy. The addition of the vibration absorber to the flexible arm generates a coupling effect between vibration modes of the system. By means of analysis on 2:1 internal resonance, the exchange of energy is proven to be existent. The impact vibrational energy can be transferred from the arm to the absorber and dissipated through the damping of the absorber. The results of numerical simulations are promising and preliminarily verify that the method is feasible and can be used to combat large amplitude impact vibration of the flexible manipulator undergoing rigid motion

Construction of retrovirus vector taking MDR1/ACBC1 and its transfection into human placenta derived mesenchymal stem cells

In the study, we used both the methods of perfusion and density gradient centrifugation to isolate and purify mesenchymal stem cells (MSCS) from placenta tissue, and constructed a retroviral vector with multiple drug resistant genes, and the green fluorescent protein (GFP) has been used as an indicative mark. The 293T cell was transfected by the retroviral vector PMX-flag-MDR1-GFP together with its peripheral membrane protein gene. After the infective and replication–defective retrovirus were acquired, we transfected them into human placenta-derived mesenchymal stem cells (HPMSCs). We successfully observed the expression of the reporter gene-GFP by using the green light fluorescence microscope and the p-glycoprotein (P-gp) expressed by exogenous gene MDR1 by Western Blotting. All these facts indicated that the retroviral vector PMX-flag-MDR1-GFP had successfully been transfected into HPMSCs and the exogenous gene multidrug resistance (MDR)1 was detected as normally expressed. The daunorubicin (DNR) pump experiment proved that P-gp of HPMSCs transfected with PMX-flag-MDR1-GFP was of biological activity. The result indicates that MDR1 retroviral vector can transfect the HPMSCs. Not only can the exogenous gene be expressed, but also the expression protein had the biological activity. The conclusion lays a solid foundation of the clinical application of MDR1 genetic therapy.Keywords: Transfect, human placenta-derived mesenchymal stem cells, multidrug resistance (MDR)1 gene

Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement

Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims at learning modality-invariant features from unlabeled cross-modality dataset, which is crucial for practical applications in video surveillance systems. The key to essentially address the USL-VI-ReID task is to solve the cross-modality data association problem for further heterogeneous joint learning. To address this issue, we propose a Dual Optimal Transport Label Assignment (DOTLA) framework to simultaneously assign the generated labels from one modality to its counterpart modality. The proposed DOTLA mechanism formulates a mutual reinforcement and efficient solution to cross-modality data association, which could effectively reduce the side-effects of some insufficient and noisy label associations. Besides, we further propose a cross-modality neighbor consistency guided label refinement and regularization module, to eliminate the negative effects brought by the inaccurate supervised signals, under the assumption that the prediction or label distribution of each example should be similar to its nearest neighbors. Extensive experimental results on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method, surpassing existing state-of-the-art approach by a large margin of 7.76% mAP on average, which even surpasses some supervised VI-ReID methods