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The brain plays a pivotal role in locomotion by coordinating muscles through interconnections that get established by the peripheral nervous system. To date, many attempts have been made to reveal the underlying mechanisms of humans' gait. However, decoding cortical processes associated with different walking conditions using EEG signals for gait-pattern classification is a less-explored research area. In this paper, we design an EEG-based experiment with four walking conditions (i.e., free walking, and exoskeleton-assisted walking at zero, low, and high assistive forces by the use of a unilateral exoskeleton to right lower limb). We proposed spatio-spectral representation learning (SSRL), a deep neural network topology with shared weights to learn the spatial and spectral representations of multi-channel EEG signals during walking. Adoption of weight sharing reduces the number of free parameters, while learning spatial and spectral equivariant features. SSRL outperformed state-of-the-art methods in decoding gait patterns, achieving a classification accuracy of 77.8%. Moreover, the features extracted in the intermediate layer of SSRL were observed to be more discriminative than the hand-crafted features. When analyzing the weights of the proposed model, we found an intriguing spatial distribution that is consistent with the distribution found in well-known motor-activated cortical regions. Our results show that SSRL advances the ability to decode human locomotion and it could have important implications for exoskeleton design, rehabilitation processes, and clinical diagnosis.

Worldwide, Air Navigation Service Providers (ANSP) are striving to exceed the desired safety levels. The Terminal Manoeuvre Area (TMA) is one of the most safetycritical areas in ATM as it encompasses the most critical phase of flight, i.e., departure and landing. An aircraft, during the final approach phase, is required to remain in a stable configuration and prevent any undesired state such as an unstable approach, which may subsequently lead to incidents/accidents such as Go-Around, Runway Excursions, etc. In this paper, we propose a data-driven framework to model the aircraft 4D trajectories in the final approach phase by adopting sparse variational Gaussian process (SVGP) model. The model is trained to learn the aircraft landing dynamics from Advanced Surface Movement Guidance and Control System (A-SMGCS) data, during the final approach phase. We experimentally demonstrate that SVGP provides an interpretable probabilistic bound of aircraft parameters that can quantify deviation and perform real-time anomaly detection. The findings of this work can increase situational awareness of the air traffic controller and has implications for the design of a new approach procedure in complex runway configurations such as parallel approach.

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The increasing demand in air transportation is pushing the current air traffic management (ATM) system to its limits in the airspace capacity and workload of air traffic controllers (ATCOs). ATCOs are in an urgent need of assistant tools to aid them in dealing with increased traffic, specifically in resolving potential conflict. Since current automated conflict resolutions are not in conformance with the thinking or preferences of individual ATCOs, consequently, they are unlikely accepted by the ATCOs. In this work, we build an artificial intelligence system as a digital assistant to support ATCOs in resolving potential conflicts. Our system consists of two core components: an intelligent interactive conflict solver (iCS) to acquire ATCOs' demonstrations, and an AI agent. The AI Agent is based on reinforcement learning to suggest conflict resolutions. We observe that providing the AI agent with the human resolutions, which are acquired and characterized by our intelligent interactive conflicts solver, not only improves the agent's performance but also gives it the capability to suggest more human-like resolutions.That could help to increase the ATCOs' acceptance rate of the agent's suggested resolutions.Our system could be further developed as personalized digital assistants of ACTOs to maintain their workloads manageable when they have to deal with sectors with increased traffic.