A healthy and independent life requires skeletal muscles to maintain optimal function throughout the lifespan, which is in turn dependent on efficient activation of processes that regulate muscle development, homeostasis, and metabolism. Thus, identifying mechanisms that modulate these processes is of crucial priority. Noncoding RNAs (ncRNAs), including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), have emerged as a class of previously unrecognized transcripts whose importance in a wide range of biological processes and human disease is only starting to be appreciated. In this review, we summarize the roles of recently identified miRNAs and lncRNAs during skeletal muscle development and pathophysiology. We also discuss several molecular mechanisms of these noncoding RNAs. Undoubtedly, further systematic understanding of these noncoding RNAs’ functions and mechanisms will not only greatly expand our knowledge of basic skeletal muscle biology, but also significantly facilitate the development of therapies for various muscle diseases, such as muscular dystrophies, cachexia, and sarcopenia.

Considering the installation cost and coverage, the received signal strength indicator (RSSI)-based indoor positioning system is widely used across the world. However, the indoor positioning performance, due to the interference of wireless signals that are caused by the complex indoor environment that includes a crowded population, cannot achieve the demands of indoor location-based services. In this paper, we focus on increasing the signal strength estimation accuracy considering the population density, which is different to the other RSSI-based indoor positioning methods. Therefore, we propose a new wireless signal compensation model considering the population density, distance, and frequency. First of all, the number of individuals in an indoor crowded scenario can be calculated by our convolutional neural network (CNN)-based human detection approach. Then, the relationship between the population density and the signal attenuation is described in our model. Finally, we use the trilateral positioning principle to realize the pedestrian location. According to the simulation and tests in the crowded scenarios, the proposed model increases the accuracy of the signal strength estimation by 1.53 times compared to that without considering the human body. Therefore, the localization accuracy is less than 1.37 m, which indicates that our algorithm can improve the indoor positioning performance and is superior to other RSSI models.

Indoor positioning and navigation is an essential field of location-based service. Non-line of sight (NLOS) error restricts the accuracy of indoor positioning. Many researchers have studied the localization problem in indoor NLOS environments, but there is still a problem that NLOS error cannot be mitigated in unknown areas. To solve the above problems, this paper proposes a method of constructing virtual base stations in unknown areas (UA-VBS), and presents the corresponding positioning algorithm to calculate the location of the user equipment. Firstly, the base stations are selected and the initial positioning is carried out. Then, multiple virtual base stations are constructed according to the user equipment positions in the first three steps. The LOS base stations and virtual base stations participate in the TDOA calculation together, and calculate the base stations’ combination with the minimum residual and the corresponding positioning result. Finally, the pedestrian dead reckoning fusion weight is updated by the residual value, and the accurate positioning result in NLOS environment is obtained. Simulation and experimental results show that the proposed algorithm has high positioning accuracy and stability in NLOS environment.

3D reconstruction of indoor scenes is a hot research topic in computer vision. Reconstructing fast, low-cost, and accurate dense 3D maps of indoor scenes have applications in indoor robot positioning, navigation, and semantic mapping. In other studies, the Microsoft Kinect for Windows v2 (Kinect v2) is utilized to complete this task, however, the accuracy and precision of depth information and the accuracy of correspondence between the RGB and depth (RGB-D) images still remain to be improved. In this paper, we propose a post-rectification approach of the depth images to improve the accuracy and precision of depth information. Firstly, we calibrate the Kinect v2 with a planar checkerboard pattern. Secondly, we propose a post-rectification approach of the depth images according to the reflectivity-related depth error. Finally, we conduct tests to evaluate this post-rectification approach from the perspectives of accuracy and precision. In order to validate the effect of our post-rectification approach, we apply it to RGB-D simultaneous localization and mapping (SLAM) in an indoor environment. Experimental results show that once our post-rectification approach is employed, the RGB-D SLAM system can perform a more accurate and better visual effect 3D reconstruction of indoor scenes than other state-of-the-art methods.

The research on reconfigurable antennas has some disadvantages such as low working frequency and large size. This paper presents a Ka-band patch antenna with pattern reconfigurability using RF MEMS switches. The antenna contains one main patch, two sub-patches, two parasitic patches, and two RF MEMS switches. By controlling the states of the RF MEMS switches, the antenna can achieve three different radiation patterns (−8°, 0°, and +8°) at 35 GHz. The pattern analysis was based on the electric field vector addition method. The analytical, simulated, and measured results agree well with each other. Due to its compact and thin structure of 3.7 mm × 5.2 mm × 0.4 mm, this antenna can be applied in fields such as satellites, smartphones, etc.

The fifth generation (5G) cellular communication system is designed to support Time Difference of Arrival (TDOA), Round-Trip Time (RTT), and Direction of Arrival (DOA) measurements for indoor positioning. To mitigate the positioning error caused by non-line-of-sight (NLOS), existing base station selection methods identify channel conditions and only use line-of-sight (LOS) signals for positioning. However, different selected base station combination would lead to a different geometric dilution of precision (GDOP), base station selection based only on channel condition is not fully applicable for the hybrid positioning. This paper derives the GDOP for the hybrid TDOA, RTT, and DOA positioning, and proposes a GDOP-assisted base station selection method, which is based on both channel conditions and GDOP value changes. The simulation shows that using the proposed base station selection method could lead to higher positioning accuracy than base station selection based only on channel condition. In the simulation, in the side region of the scenario, where the change of selected base station combination causes a notable increment in GDOP value, the positioning accuracy improvement caused by the proposed method is greater than that in the center region.