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Automatic liver segmentation not only plays an important role in the analysis of liver disease, but also reduces the cost and humanity’s impact in segmentation. In addition, liver segmentation is a very challenging task due to countless anatomical variations and technical difficulties. Many methods have been designed to overcome these challenges, but these methods still need to be improved to obtain the desired segmentation precision. In this paper, a fast algorithm is proposed for liver extraction from CT images with single-block linear detection. The proposed method does not require iteration; thus, the computational time and complexity are decreased enormously. In addition, the initialization is not crucial in the algorithm, so the algorithm’s robustness and specificity are improved. The experimental evaluation of the proposed method revealed effective segmentation in normal and abnormal (liver hemangioma and liver cancer) abdominal CT images. The average sensitivity, accuracy, and specificity for liver cancer are 96.59%, 98.65%, and 99.03%, respectively. The results of image segmentation approximate the manual segmentation results by the technical doctor. Moreover, our method shows superior flexibility to newly published method with comparable performance. The advantage of our method is verified with experimental results, which is described in detail. [ABSTRACT FROM AUTHOR]

Cellular vehicular networks constitute an important part of future vehicular communication systems. However, they pose challenges to fulfill vehicles with demands for the transmission rates of various network services while achieving satisfactory costs and energy consumption. The cloud radio access network architecture is considered as a green wireless access network framework that reduces the deployment costs of base stations while achieving resource sharing and dynamic scheduling to lower energy consumption, and thus will greatly improve spectrum efficiency. in this article, we first propose a cloud vehicular radio access network (C-VRAN) architecture to enable rapid network innovation for green vehicular communications. Specifically, we propose a vehicular network framework consisting of centralized processing, collaborative radio, real-time cloud computing, and data compression functions. Then, the design and the potential benefits of C-VRAN are presented in detail. Moreover, an effective data compression approach for the proposed architecture is proposed to meet low-distortion compression for cellular vehicular network data, thereby improving fiber resource utilization. Finally, we briefly discuss key challenges and solutions for future green vehicular communications. [ABSTRACT FROM AUTHOR]

In Intelligent Transport Systems (ITSs), Internet of Vehicles (IoV) communications and computation offloading technology have been introduced to assist with the burdensome sensing task processing, thus prompting a new design paradigm called mobile sensing–communication–computation (MSCC) synergy. Most researchers have focused on offloading strategy design to reduce energy consumption or execution costs, but ignore the intrinsic characteristics of tasks, which may lead to poor performance. This paper studies the offloading strategy of vehicle MSCC tasks represented by a Directed Acyclic Graph (DAG) structure. According to the DAG dependency of the subtasks, this paper proposes a computation offloading strategy to optimize energy consumption under time constraints. An energy consumption model for task execution is established. Then, the Simulated Annealing and Tabu Search hybrid optimization algorithm (ST-HO) is designed to solve the problem of minimizing the energy consumption. Crucially, this research integrates the concept of symmetry into the typical DAG structure of MSCC tasks, ensuring the integrity and efficiency of task execution in ITS. The simulation results show that ST-HO reduces energy consumption by at least 5.58% compared to the conventional algorithm. Particularly, the convergence speed of ST-HO is improved by 52.63% when the replication strategy of symmetric task is considered. [ABSTRACT FROM AUTHOR]

Next-generation (5G) vehicular networks will support various network applications, leading to specific requirements and challenges for wireless access technologies. This trend has motivated the development of the long-term evolution-vehicle (LTE-V) network, a 5G cellular-based vehicular technology. Due to the limited bandwidth for vehicular communications, it is important to efficiently utilize slim spectrum resources in vehicular networks. In this paper, we introduce a cloud radio access network (C-RAN)-based vehicular network architecture, named C-VRAN, which facilitates efficient management and centralized processing of vehicular networks. Furthermore, we propose a discrete cosine transform (DCT)-based data compression scheme for C-VRAN to enhance the effective data rate of the fronthaul network. This scheme first uses DCT to perform time-frequency conversion of LTE-V I/Q data and then utilizes the Lloyd-Max algorithm to quantify data in the frequency domain before finally selecting an appropriate coding scheme to achieve better performance. Simulation results show that the proposed scheme can achieve 3 times compression ratio within 1% error vector amplitude distortion, and it also has strong independence and versatility, allowing it to be used as a standalone module for the current LTE-V system. [ABSTRACT FROM AUTHOR]

The performance of orthogonal frequency division multiplexing (OFDM) based wireless vehicular communication systems is faced with the great challenge of impulsive noise (IN), which could limit the application of OFDM in ultra-reliable low-latency communication scenarios. In this paper, the challenge of IN elimination for OFDM-based wireless systems is efficiently overcome by the proposed sparse learning algorithms and probabilistic framework inspired by the emerging machine learning theories. For the first time, the sparse machine learning theory is introduced to IN recovery and elimination. Exploiting the measurement vector of IN observed from the reserved null sub-carriers as the input, a novel sparse machine learning based algorithm of sparse cross-entropy minimization is proposed, in which the probability distribution of the IN support is iteratively updated by minimizing the loss function, i.e. the cross-entropy. The proposed algorithm is able to effectively and efficiently learn the sparse pattern and converge to the accurate distribution of IN support. To facilitate an accelerated and even more efficient learning process, regularization is imposed on the loss function by adding a weighting parameter in favor of the accurate distribution. The computer simulation results confirm that the proposed scheme outperforms conventional methods while utilizing fewer spectrum resources over wireless vehicular channels. [ABSTRACT FROM AUTHOR]

The future vehicle‐to‐everything (V2X) scenarios greatly call for reliable and efficient communication links between roadside units (RSUs) and vehicles. Integrated sensing and communication (ISAC) represents one of the key candidate technologies for 6G, which provides new solutions for this problem. In this paper, the beam tracking design for V2X communication is investigated by exploiting the ISAC technique, where the RSU uses the echoes of the ISAC signals to estimate the motion parameters of the vehicles. Compared with traditional feedback‐based beam tracking, the proposed method can reduce the overall signaling costs and improve communication robustness. Furthermore, to achieve high accuracy and low latency for beam alignment, a Trajectory Adaptive Unscented Kalman Filter (TA‐UKF) beam tracking algorithm is proposed, which obtains the optimal state transition model following the change of the vehicle state. Considering practical application scenarios, an ISAC‐based adaptive beam coverage (ISAC‐AC) scheme is further developed for the beam tracking of extended targets, which can achieve the beam misalignment of vehicles in practical scenarios. Numerical results demonstrate that the proposed beam tracking and coverage strategies outperform baseline approaches. [ABSTRACT FROM AUTHOR]

A large number of new data consuming applications are emerging in the daily routines of mobile users. Device-to-Device (D2D) communication as a new paradigm is introduced to reduce the increasing traffic and offload it to the user equipment (UE). With the development of UE multi-radio interface, we first develop a new hybrid architecture concept for D2D communication. The architecture combines ISM 2.4G spectrum as the Out-Band mode using Bluetooth and Wifi-Direct with the cellular spectrum as the In-Band mode. Secondly, we design a scheme that forms the Out-Band cluster and makes the following periodic signaling interaction via the Bluetooth interface. Traffic is transferred via the Wifi-Direct interface inside the cluster but carried on the cellular spectrum among the clusters. Simulation results show that our proposal increases the system throughput, saves power consumption and prolongs the clusters lifetime. [ABSTRACT FROM AUTHOR]

Artificial Intelligence of Things (AIoT) is an emerging field within the Internet of Things (IoT) that caters to the growing demand for intelligent computing and communications. Vehicular fog computing (VFC) with AIoT enhance the processing speed of intelligent services. However, this integration also brings about significant energy wastage and high latency problems due to the large amount of data generated by intelligent tasks. To address these issues, our paper introduces EALSO, a novel approach for energy-aware and latency-sensitive task offloading in VFC. EALSO takes into account the quality of service requirements of tasks by mapping them to specific latency constraints and adjustable task priorities. To optimize the energy consumption of VFC networks while ensuring the quality of task offloading, we propose a two-stage hybrid heuristic energy consumption optimization (HHECO) algorithm. In the first stage, a stable matching algorithm is employed for fog vehicle selection. In the second stage, the heuristic algorithm is guided to reduce interference using decision tree techniques and intelligently allocate transmission power to minimize energy consumption and total network latency. We further validate our approach through real-world scenario simulations, demonstrating its superiority over state-of-the-art methods in terms of both energy consumption and latency. [ABSTRACT FROM AUTHOR]

The arrival of big data and the Internet of Things (IoT) era greatly promotes innovative in-network computing techniques, where the edge-cloud continuum becomes a feasible paradigm in handling multi-dimensional resources such as computing, storage, and communication. In this article, an energy constrained unmanned aerial vehicle (UAV)-aided mobile edge-cloud continuum framework is introduced, where the offloaded tasks from ground IoT devices can be cooperatively executed by UAVs acts as an edge server and cloud server connected to a ground base station (GBS), which can be seen as an access point. Specifically, a UAV is powered by the laser beam transmitted from a GBS, and can further charge IoT devices wirelessly. Here, an interesting task offloading and energy allocation problem is investigated by maximizing the long-term reward subject to executed task size and execution delay, under constraints such as energy causality, task causality, and cache causality. A federated deep reinforcement learning (FDRL) framework is proposed to learn the joint task offloading and energy allocation decision while reducing the training cost and preventing privacy leakage of DRL training. Numerical simulations are conducted to verify the effectiveness of our proposed scheme as compared to three baseline schemes. [ABSTRACT FROM AUTHOR]

In millimeter wave (mmWave) communication, the utilization of multi-hop relay technology has been regarded as a promising approach to overcome the significant path loss encountered during signal transmission. However, the traditional active relay network suffers from low energy efficiency (EE) and uneven resource distribution. To address these challenges, we introduce Reconfigurable Intelligent Surface (RIS) as a passive relay to the mmWave communication system and create a hybrid relay system that combines passive and active relays, which aims to improve EE through the multi-hop relay. Additionally, with the development of Artificial Intelligence, deep Q-learning (DQN) is applied to optimize the hybrid relay system in this paper, where every transmitted signal of the base station (BS) is considered an agent. In this approach, the network is trained based on the interaction between the collected environment information and the users’ relay allocation strategy. Considering competition-cooperation relationships of multiple users, we propose a multi-agent DQN (MADQN) algorithm to allocate the relay resource where the primary goal is maximizing EE. Simulation results demonstrate that our proposed scheme can effectively converge to the optimal relay link, further improving EE and reducing energy consumption in comparison with conventional schemes. [ABSTRACT FROM AUTHOR]

Recently, research intergrading medicine and Artificial Intelligence has attracted extensive attention. Mobile health has emerged as a promising paradigm for improving people's work and life in the future. However, high mobility of mobile devices and limited resources pose challenges for users to deal with the applications in mobile health that require large amount of computational resources. In this paper, a novel computation offloading mechanism is proposed in the environments combining of the Internet of Vehicles and Multi-Access Edge Computing. Through the proposed mechanism, mobile health applications are divided into several parts and can be offloaded to appropriate nearby vehicles while meeting the requirements of application completion time, energy consumption, and resource utilization. A particle swarm optimization based approach is proposed to optimize the the aforementioned computation offloading problem in a specific medical application. Evaluations of the proposed algorithms against local computing method serves as baseline method are conducted via extensive simulations. The average task completion time saved by our proposed task allocation scheme increases continually compared with the local solution. Specially, the global resource utilization rate increased from 71.8% to 94.5% compared with the local execution time. [ABSTRACT FROM AUTHOR]

To improve the accuracy of assessment, many previous works take into account the video content. However, these previous works just only consider the video content, but do not consider the location and importance of the degraded content. Thus, this paper takes into account not only the video content, but also the location and importance of the degraded content, and proposes a hierarchical content importance-based video quality assessment. Firstly, we propose to use the hierarchical content importance-based frame degradation rate (HFDR) metric to quantify the importance of degraded content hierarchically. Secondly, we propose to use the intra random access point (IRAP) loss rate (ILR) metric to quantify the impact of IRAP. Finally, the proposed HFDR metric and ILR metric are subsequently used to develop an objective video quality assessment model. The experimental results show that the predicted mean opinion score (MOS) of the proposed method highly correlates with the actual MOS. [ABSTRACT FROM AUTHOR]