Future Vehicle-to-Everything (V2X) scenarios require high-speed, low-latency, and ultra-reliable communication services, particularly for applications such as autonomous driving and in-vehicle infotainment. Dense heterogeneous cellular networks, which incorporate both macro and micro base stations, can effectively address these demands. However, they introduce more frequent handovers and higher energy consumption. Proactive handover (PHO) mechanisms can significantly reduce handover delays and failure rates caused by frequent handovers, especially with the mobility prediction capability enhanced by artificial intelligence and machine learning (AI/ML) technologies. Nonetheless, the energy-efficient joint optimization of PHO and resource allocation (RA) remains underexplored. In this paper, we propose an AI/ML-based energy-efficient PHO and RA (AEPHORA) framework, which leverages AI/ML-based predictions of vehicular mobility to jointly optimize PHO and RA decisions. AEPHORA aims to minimize the time-averaged system transmit power while satisfying quality of service (QoS) constraints on communication delay and reliability. Simulation results demonstrate the effectiveness of the AEPHORA framework in balancing energy efficiency with QoS requirements in high-demand V2X environments.

Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and communication capabilities. We consider a base station (BS) coordinating nearby ICVs to train a neural network in a collaborative yet distributed manner, in order to limit data traffic and privacy leakage. However, due to the mobility of vehicles, the connections between the BS and ICVs are short-lived, which affects the resource utilization of ICVs, and thus, the convergence speed of the training process. In this paper, we propose an accelerated FL-ICV framework, by optimizing the duration of each training round and the number of local iterations, for better convergence performance of FL. We propose a mobility-aware optimization algorithm called MOB-FL, which aims at maximizing the resource utilization of ICVs under short-lived wireless connections, so as to increase the convergence speed. Simulation results based on the beam selection and the trajectory prediction tasks verify the effectiveness of the proposed solution.

We propose a new generative model-based predictive display for robotic teleoperation over high-latency communication links. Our method is capable of rendering photo-realistic images of the scene to the human operator in real time from RGB-D images acquired by the remote robot. A preliminary exploration stage is used to build a coarse 3D map of the remote environment and to train a generative model, both of which are then used to generate photo-realistic images for the human operator based on the commanded pose of the robot. Data captured by the remote robot is used to dynamically update the 3D map, enabling teleoperation in the presence of new and relocated objects. Various experiments validate our proposed method’s performance and benefits over alternative methods.

Motion planning is a largely solved problem for robot arms with joint state feedback, but remains an area of research for sensorless manipulators such as toy robot arms and heavy equipment such as excavators and cranes. A promising approach to this problem is deep learning, which employs a pre-trained convolutional neural network to identify manipulator links and estimate joint states from a monocular camera video feed. Whereas manual labeling of training image sets is tedious and non-transferable, a simulation environment can automatically generate labeled training image sets of any size. The issue is the gap between simulated and real-world images. This paper solves this problem by implementing a Generative Adversarial Network. The complete joint state estimation pipeline is implemented and tested in hardware experiments to validate our proposed approach.

The demands of an increasingly knowledge based society and the advances in mobile phone technology are combining to spur the growth of mobile learning. However, for mobile learning to attain its full potential, it is essential to develop more advanced technologies that are tailored to the needs of this new learning environment. We present a non-invasive emotion-aware m-learning model based on multi-modal emotion detection, to deliver learning services that correspond to the students' needs, and to adapt a learning system according to a student's status, thereby enhancing the student's learning experience. The research presented in this paper is to augment our m-learning system in daily use at the School of Continuing Education of Shanghai Jiao Tong University, a blended-learning institution with 35.000 students.

In this paper, we present a wearable ECG recorder which senses real-time ECG signal, undertakes preprocessing and extracts ECG waveform and fiducial points. The ECG signal then is sent through ULP Bluetooth to the paired smartphone, in which the application classifies the ECG signal using classifiers updated by the intervene of cardiologist in a novel interactive method.