Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain–computer interface (BCI). Researchers initially developed simple linear models and machine learning algorithms to classify and recognize brain activities. With the great success of deep learning on image recognition and generation, deep neural networks (DNN) have been engaged in reconstructing visual stimuli from human brain activity via functional magnetic resonance imaging (fMRI). In this paper, we reviewed the brain activity decoding models based on machine learning and deep learning algorithms. Specifically, we focused on current brain activity decoding models with high attention: variational auto-encoder (VAE), generative confrontation network (GAN), and the graph convolutional network (GCN). Furthermore, brain neural-activity-decoding-enabled fMRI-based BCI applications in mental and psychological disease treatment are presented to illustrate the positive correlation between brain decoding and BCI. Finally, existing challenges and future research directions are addressed.

To address the problem of a serious Internet of Things (IoT) talent shortage, the Chinese government is encouraging universities and institutions to establish a major in IoT Engineering to train high-level IoT talent. Since 2011, 700 Chinese universities and institutions have established IoT Engineering undergraduate programs. We surveyed the history, status, curriculum, and problems of IoT undergraduate degree programs in China. Based on data collected from faculties, students, and graduates, we found that the IoT Engineering curriculum was an unsystematic patchwork and was deficient in practical platforms. We then provide suggestions for further development and exploration of IoT education by presenting a Technical Knowledge Map of IoT Engineering. This survey work on Chinese IoT education can prompt Chinese educators to reflect on its achievements and problems, which will contribute to IoT talent cultivation worldwide.

The aeronautical spectrum band for air-to-ground (A/G) communications is severely underutilized, known less than 12.5%, yet anticipating scarcity in the near future because of the explosive growth in aeronautical service data. Air-to-air (A/A) communications allow aircraft in close proximity to communicate with each other directly without ground stations or satellites, and are regarded as an effective complement for A/G communications in terms of the spectrum utilization efficiency. In this paper, to enable such spectral efficiency that A/A links reuse the spectrum of A/G communications, we propose a cognitive solution that A/A communications are regarded as secondary users which sense idle primary A/G communication channels and transmit data on these channels wherever possible, which brings two major issues: spectrum sensing and sharing between A/A and A/G communications. A coalitional manner is devised that some A/A links are grouping together to perform cooperative spectrum sensing and sharing while an interference graph is used to address the interference that arises from spectrum sharing within a group. We then formulate the cooperative spectrum sensing and sharing into a coalitional graph game played by all the A/A links involved. Finally, a distributed coalitional graph game algorithm is developed to maximize the utility so as to optimize the schedule of the A/A transmissions on the available A/G channels. Extensive simulations show that the achievable sum rate approaches to the results of the optimal exhausted searching with a relatively low complexity.

With the development of modern communication and the Internet of Things (IoT) in the need to provide seamless interconnection between heterogeneous devices, we design an adjustable-distance resonant antenna suitable for 5G communication. The antenna meets the multiangle radiation requirements of the antenna for flexible positioning of IoT devices and conforms to the concept of green Internet basic hardware, with low energy and small size. In our design, three parasitic elements will couple with the higher order modes through the slot-hole excitation of a higher order mode dielectric resonator antenna with a dielectric constant of 10. By controlling the distance between the three parasitic elements and changing the capacitor at their terminals, radiation variation in multiple directions can be achieved. The proposed model focuses on the relationships among the three element distances and the effect of the third parasitic element on the radiation angle. Good results were obtained for gain, bandwidth, and radiation angle. Through simulation, the dielectric resonator antenna works successfully in the 15-GHz frequency band. Thus, the antenna array can be from −34° to 34° in the horizontal direction and from 0° to −36° in the vertical direction. At the same time, the gains are kept at a good level and the bandwidths are greater than 2 GHz. Compared with other dielectric resonant antennas, the parameters of this antenna are not reduced, and the flexibility of the radiation direction angles is increased. These evaluation parameters are considered ideal conditions for device-to-device communication in 5G IoT applications.

With the growing interest in providing internet access and cellular connectivity in the commercial aircraft, the compatibility between Air-to-Air (A2A) communications and current Air-to-Ground (A2G) macro-cellular communications is necessary to form an Aeronautical Ad hoc Network (AANet). Due to the typical features of airborne communications, such as high mobility, long transmission range and three-dimensional macro-cells, AANet is affected by intermittent links, which results in variant delayed data transmissions. To this end, we exploit a dynamic graph approach to model such an AANet with rapidly changing topology under a realistic airborne scenario. Targeting the problem of intermittent transmissions, by employing A2A, an AANet can bear three transmission modes: direct transmission, connected relay transmission and opportunistic transmission. To investigate the potential gain of A2A transmissions in terms of the end-to-end data flow transmission delay and the data traffic amount, we formulate an integer non-linear programming problem that minimizes the average end-to-end delay from Internet Gateways (IGWs) on shore to the aircraft receivers through multiple options of the transmission modes. Through solving this minimization problem based on the realistic dynamic graph model, the tradeoffs between the end-to-end transmission delay and other key network factors related to AANet formation and resource allocation can be obtained without much compromise on the transmission delay. The results show that the average end-to-end delay of the AANet is comparable to the direct transmission delay when having a tradeoff with the data traffic amount, buffer size and the spectrum sharing.