Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning. The paper first reviews the main achievements and research of the robot, which were mainly based on the breakthrough of automatic control and hardware in mechanics. With the evolution of artificial intelligence, many pieces of research have made further progresses in adaptive and robust control. The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. Finally, major achievements based on these methods are summarized and analyzed thoroughly, and future research challenges are proposed.

Urban intelligence is an emerging concept which guides a series of infrastructure developments in modern smart cities. Human-computer interaction(HCI) is the interface between residents and the smart cities, it plays a key role in bridging the gap in applicating information technologies in modern cities. Hand gestures have been widely acknowledged as a promising HCI method, recognition human hand gestures using surface electromyogram (sEMG) is an important research topic in the application of sEMG. However, state-of-the-art signal processing technologies are not robust in feature extraction and pattern recognition with sEMG signals, several technical problems are still yet to be solved. For example, how to maintain the availability of myoelectric control in intermittent use, since pattern recognition qualities are greatly affected by time variability, but it is unavoidable during the daily use. How to ensure the reliability and effectiveness of myoelectric control system also important in developing a good human-machine interface. In this paper, linear discriminant analysis (LDA) and extreme learning machine (ELM) are implemented in hand gesture recognition system, which is able to reduce the redundant information in sEMG signals and improve recognition efficiency and accuracy. The characteristic map slope (CMS) is extracted by using the feature re-extraction method, because CMS can strengthen the relationship of features cross time domain and enhance the feasibility of cross-time identification. This study is focusing on optimizing the time differences in sEMG pattern recognitions, the experimental results are beneficial to reducing the time differences in gesture recognition based on sEMG. The recognition framework proposed in this paper can enhance the generalization ability of HCI in the long term use and it also simplifies the data collection stage before training the device ready for daily use, which is of great significance to improve the time generalization performance of a HCI system.

By means of critical coupling and impedance matching theory, we have numerically simulated the perfect absorption of monolayer graphene. Through the critical coupling effect and impedance matching, we studied a perfect single-band absorption of the monolayer graphene and obtained high quality factor (Q-factor = 664.2) absorption spectrum which has an absorbance close to 100% in the near infrared region. The position of the absorption spectrum can be adjusted by changing the ratio between the radii of the elliptic cylinder air hole and the structural period. The sensitivity of the absorber can be achieved S = 342.7 nm/RIU (RIU is the per refractive index unit) and FOM = 199.2 (FOM is the figure of merit), which has great potential for development on biosensors. We believe that our research will have good application prospects in graphene photonic devices and optoelectronic devices.

When surface plasmon resonance (SPR) occurs, the incident light is absorbed by the surface of the SPR structure, thus minimizing the intensity of the reflected light. Therefore, the SPR method is adopted in this paper to achieve perfect absorption of the absorbent. In this paper, we first propose a multi-frequency broadband absorber structure based on graphene SPR, which uses the continuous resonance of patterned graphene surface plasmon in the frequency spectrum to form a multi-frequency broadband absorption. In this simulation, a sandwich-stack structure was adopted, whereby the patterned graphene is situated on top of the SiO₂ layer and the metal layer. The broad-band absorption bands of the absorber were obtained as 4.14–4.38 THz, 5.78–6.36 THz, and 7.87–8.66 THz through the analog simulation of finite-difference time-domain method (FDTD) solutions. Then, based on the multi-layer resonant unit structure, through the superposition and combination of absorbing units responding to different frequency bands, the perfect absorption of ultra-wideband is achieved. The data results illustrate that the total absorption bandwidth of the absorber is 2.26 THz, and the relative absorption bandwidth B_w is equal to 28.93%. The electric field in X-Y direction of the absorber in the perfect absorption band is analyzed, respectively, and the dynamic tunability of the absorber is studied. Finally, we studied whether the absorbing structure still has efficient absorption characteristics for the two polarization modes when the incident angle is changed from 0° to 70°. The structure model proposed has potential value for application in terahertz photoelectric detection, filtering, and electromagnetic shielding.