The electrification of the transportation sector is an important step to ensure a sustainable system. It offers a promising solution to reduce oil consumption, decrease Greenhouse Gas (GHG) emissions, improve air quality, increase transportation sector efficiency, and it can play an important role in the integration of renewable energy resources into the electric grid. However, one of the biggest bottlenecks of the electrification of vehicles is the requirement for various charging infrastructure options. Three technologies have emerged as game-changers: Dynamic Wireless Charging (DWC), Battery Swapping Stations, and Fast Charging Stations (FCSs). These advancements have the potential to increase the adoption of Electric Vehicles (EVs) by addressing critical challenges such as range anxiety and charging downtime issues of EV users. The primary focus of this work is on FCS, which currently represents the most readily available option in the market. To ensure the optimal utilization of this charging infrastructure, it is important to address the challenges related to routing EVs to the nearest FCS that can meet the EV charging requirements, such as wait time, charging duration and charging cost. Vehicle-to-Everything (V2X) and the Internet of Vehicles (IoV) will play a critical role in enhancing EV routing to the FCS. Additionally, the coordination of EV charging demand is essential to achieve grid load balancing and prevent grid overload. The main focus of this paper is to present the vision and future trends of Smart Green IoV in the era of electric vehicles for sustainable V2X connectivity, grid integration, vehicular networking, data security, and services.

Block partition structure is a critical module in video coding scheme to achieve significant gap of compression performance. Under the exploration of the future video coding standard, named Versatile Video Coding (VVC), a new Quad Tree Binary Tree (QTBT) block partition structure has been introduced. In addition to the QT block partitioning defined in High Efficiency Video Coding (HEVC) standard, new horizontal and vertical BT partitions are enabled, which drastically increases the encoding time compared to HEVC. In this paper, we propose a lightweight and tunable QTBT partitioning scheme based on a Machine Learning (ML) approach. The proposed solution uses Random Forest classifiers to determine for each coding block the most probable partition modes. To minimize the encoding loss induced by misclassification, risk intervals for classifier decisions are introduced in the proposed solution. By varying the size of risk intervals, tunable trade-off between encoding complexity reduction and coding loss is achieved. The proposed solution implemented in the JEM-7.0 software offers encoding complexity reductions ranging from 30average for only 0.7% to 3.0% Bjxntegaard Delta Rate (BDBR) increase in Random Access (RA) coding configuration, with very slight overhead induced by Random Forest. The proposed solution based on Random Forest classifiers is also efficient to reduce the complexity of the Multi-Type Tree (MTT) partitioning scheme under the VTM-5.0 software, with complexity reductions ranging from 25% to 61% in average for only 0.4% to 2.2% BD-BR increase.

Uncertainty is inherent in machine learning methods, especially those for camouflaged object detection aiming to finely segment the objects concealed in background. The strong “center bias” of the training dataset leads to models of poor generalization ability as the models learn to find camouflaged objects around image center, which we define as “model bias”. Further, due to the similar appearance of camouflaged object and its surroundings, it is difficult to label the accurate scope of the camouflaged object, especially along object boundaries, which we term as “data bias”. To effectively model the two types of biases, we resort to uncertainty estimation and introduce predictive uncertainty estimation technique, which is the sum of model uncertainty and data uncertainty, to estimate the two types of biases simultaneously. Specifically, we present a predictive uncertainty estimation network ( PUENet ) that consists of a Bayesian conditional variational auto-encoder (BCVAE) to achieve predictive uncertainty estimation, and a predictive uncertainty approximation (PUA) module to avoid the expensive sampling process at test-time. Experimental results show that our PUENet achieves both highly accurate prediction, and reliable uncertainty estimation representing the biases within both model parameters and the datasets.

Given their outstanding performance, the DNN models have been deployed in many real-world applications. However, recent studies have demonstrated that they are vulnerable to small carefully crafted perturbations, i.e., adversarial examples, which considerably decrease their performance and can lead to devastating consequences, especially for safety-critical applications, such as autonomous vehicles, healthcare and face recognition. Therefore, it is of paramount importance to offer defense solutions that increase the robustness of DNN against adversarial attacks. In this paper, we propose a novel defense solution based on a DDSA. The proposed method is performed as a pre-processing step, where the adversarial noise of the input samples is removed before feeding the classifier. The pre-processing defense block can be associated with any classifier, without any change to their architecture or training procedure. In addition, the proposed method is a universal defense, since it does not require any knowledge about the attack, making it usable against any type of attack. The experimental results on MNIST and CIFAR-10 datasets have shown that the proposed DDSA defense provides a high robustness against a set of prominent attacks under white-, gray-and black-box settings, and outperforms state-of-the-art defense methods.

The amount of video content and the number of applications based on multimedia information increase each day. The development of new video coding standards is a challenge to increase the compression rate and other important features with a reasonable increase in the computational load. The Joint Video Experts Team of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group (MPEG) have worked together to develop the versatile video coding standard, finalized in July 2020 as the International Standard 23090-3 (MPEG-I Part 3). This article overviews some interesting consumer electronic use cases, the compression tools described in the standard, the current available real-time implementations, and the first industrial trials done with this standard.