Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) build a communication path between human brain and external devices. Among EEG-based BCI paradigms, the most commonly used one is motor imagery (MI). As a hot research topic, MI EEG-based BCI has largely contributed to medical fields and smart home industry. However, because of the low signal-to-noise ratio (SNR) and the non-stationary characteristic of EEG data, it is difficult to correctly classify different types of MI-EEG signals. Recently, the advances in Deep Learning (DL) significantly facilitate the development of MI EEG-based BCIs. In this paper, we provide a systematic survey of DL-based MI-EEG classification methods. Specifically, we first comprehensively discuss several important aspects of DL-based MI-EEG classification, covering input formulations, network architectures, public datasets, etc. Then, we summarize problems in model performance comparison and give guidelines to future studies for fair performance comparison. Next, we fairly evaluate the representative DL-based models using source code released by the authors and meticulously analyse the evaluation results. By performing ablation study on the network architecture, we found that (1) effective feature fusion is indispensable for multi-stream CNN-based models. (2) LSTM should be combined with spatial feature extraction techniques to obtain good classification performance. (3) the use of dropout contributes little to improving the model performance, and that (4) adding fully connected layers to the models significantly increases their parameters but it might not improve their performance. Finally, we raise several open issues in MI-EEG classification and provide possible future research directions.

This paper presents a novel solution to address the challenges in achieving energy efficiency and cooperation for collision avoidance in UAV swarms. The proposed method combines Artificial Potential Field (APF) and Particle Swarm Optimization (PSO) techniques. APF provides environmental awareness and implicit coordination to UAVs, while PSO searches for collision-free and energy-efficient trajectories for each UAV in a decentralized manner under the implicit coordination. This decentralized approach is achieved by minimizing a novel cost function that leverages the advantages of the active contour model from image processing. Additionally, future trajectories are predicted by approximating the minima of the novel cost function using calculus of variation, which enables proactive actions and defines the initial conditions for PSO. We propose a two-branch trajectory planning framework that ensures UAVs only change altitudes when necessary for energy considerations. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method in various situations.

Optical network-on-chip (ONoC) is an emerging chip-scale optical interconnection technology to realize high-performance and power-efficient intercore communication for many-core processors. Multicast communication is popularly used in parallel applications on chip. However, existing researches for multicast in ONoC mainly focus on the optimization of one multicast. This limits the practical applications of the research outcomes because we often face the dynamic formation of multiple multicast groups in real network systems. In this article, we define the problem of routing and wavelength assignment for multiple multicasts in ONoC with the objective of minimizing the number of wavelengths required. To solve the problem, we first formulate it as an integer programming model for general topologies. Then we design routing policies for special instances that optimally use only one wavelength on mesh topology. For general instances, we design a group-partitioning routing algorithm for multiple multicasts (GPRMM). GPRMM decouples a group of multicasts into a number of subgroups, each of which matching one of the special instances. Theoretical results show that the number of wavelengths required by GPRMM is no more than the Destination Density $\sigma _{d}$ , i.e., the maximum number of multicasts with destinations in the same row or column. Moreover, we find the upper bound and the lower bound on the number of wavelengths required for GPRMM. The wavelength requirement is also upper bounded by the network size $n$ for an $n\times n$ mesh network. Simulation results show that GPRMM can reduce the number of wavelengths by 26.7% compared with previous methods. GPRMM has the advantages of low routing complexity, low wavelength requirement, low power consumption, and good scalability.

Manycore processor becomes the mainstream platform for cloud computing applications. However, the design of highperformance and sustainable inter-core communication network is still a challenging problem. Optical Network on Chip (ONoC) is a promising chip-scale optical communication technology with high bandwidth capacity and energy efficiency. In this paper, we present a Wavelength Reused Hierarchical ONoC architecture, WRH-ONoC. It leverages the nonblocking wavelength-routed $\lambda$-router and the hierarchical networking to reuse the limited number of available wavelengths. In WRH-ONoC, all the cores are grouped to multiple subsystems, and the cores in the same subsystem are directly interconnected using a single $\lambda$-router for nonblocking communication. For inter-subsystem communication, all subsystems are further connected through multiple $\lambda$-routers and gateways in a hierarchical manner. Thus, the available wavelengths can be reused in different $\lambda$-routers. Furthermore, WRHm-ONoC, an efficient extension with multicast ability is also proposed. Given the numbers of cores and available wavelengths, we derive the minimum hardware requirement, the expected endto- end delay, and the maximum data rate. Theoretical analysis and simulation results indicate WRH-ONoC achieves prominent improvement on communication performance and sustainability, e.g., 46:0% of reduction on zero-load delay and 72:7% of improvement on throughput for 400 cores with modest hardware cost and energy overhead.