High quality image reconstruction from undersampled k -space data is key to accelerating MR scanning. Current deep learning methods are limited by the small receptive fields in reconstruction networks, which restrict the exploitation of long-range information, and impede the mitigation of full-image artifacts, particularly in 3D reconstruction tasks. Additionally, the substantial computational demands of 3D reconstruction considerably hinder advancements in related fields. To tackle these challenges, we propose the following: 1) A novel convolution operator named Faster Fourier Convolution (FasterFC), aims at providing an adaptable broad receptive field for spatial domain reconstruction networks with fast computational speed. 2) A split-slice strategy that substantially reduces the computational load of 3D reconstruction, enabling high-resolution, multi-coil, 3D MR image reconstruction while fully utilizing inter-layer and intra-layer information. 3) A single-to-group algorithm that efficiently utilizes scan-specific and data-driven priors to enhance k -space interpolation effects. 4) A multi-stage, multi-coil, 3D fast MRI method, called the faster Fourier convolution based single-to-group network (FAS-Net), comprising a single-to-group k -space interpolation algorithm and a FasterFC-based image domain reconstruction module, significantly minimizes the computational demands of 3D reconstruction through split-slice strategy. Experimental evaluations conducted on the NYU fastMRI and Stanford MRI Data datasets reveal that the FasterFC significantly enhances the quality of both 2D and 3D reconstruction results. Moreover, FAS-Net, characterized as a method that can achieve high-resolution (320, 320, 256), multi-coil, (8 coils), 3D fast MRI, exhibits superior reconstruction performance compared to other state-of-the-art 2D and 3D methods.

Recent advancements in Magnetic Resonance Imaging (MRI) reconstruction techniques aim to accelerate the imaging process. However, these methods still face two key limitations. Firstly, although the same location consistently provides anatomical information across different modalities, such as organs, tissues, or lesions, previous studies have predominantly relied on single-modality information, overlooking the potential advantages of incorporating complementary data from other modalities. Secondly, while adjacent MRI slices often capture the same location or organ with similar anatomical structures, only a few methods consider the information from neighboring slices during the reconstruction process. To address these challenges, we propose aMulti-modalMulti-slice cooperativeDual-domain cascaded de-alising network for MR imagingReconstruction (MMDR). Specifically, we design a multi-slice and multi-modal feature fusion network based on 3D convolution and swin transformer that efficiently extracts multi-modal features from MRI. Then, a dual domain cascaded recurrent network through dense-blocks with large receptive fields for fast MRI reconstruction is explored. Extensive experiments on the IXI datasets were carried out to evaluate the proposed method's robustness across varying network structures, under-sampling rates, and sampling patterns. MMDR demonstrates promising performance across both qualitative and quantitative metrics, particularly with a competitive PSNE of 42.15 and an SSIM of 0.984 for T2 reconstruction using 30% T2WI and PDWI, as well as achieving a PSNE of 39.92 and an SSIM of 0.966 for PDWI reconstruction with 30% PDWI and T2WI.

LiDAR sensors are almost indispensable for autonomous robots to perceive the surrounding environment. However, the transmission of large-scale LiDAR point clouds is highly bandwidth-intensive, which can easily lead to transmission problems, especially for unstable communication networks. Meanwhile, existing LiDAR data compression is mainly based on rate-distortion optimization, which ignores the semantic information of ordered point clouds and the task requirements of autonomous robots. To address these challenges, this article presents a task-driven Scene-Aware LiDAR Point Clouds Coding (SA-LPCC) framework for autonomous vehicles. Specifically, a semantic segmentation model is developed based on multidimension information, in which both 2-D texture and 3-D topology information are fully utilized to segment movable objects. Furthermore, a prediction-based deep network is explored to remove the spatial–temporal redundancy. The experimental results on the benchmark semantic KITTI dataset validate that our SA-LPCC achieves state-of-the-art performance in terms of the reconstruction quality and storage space for downstream tasks. We believe that SA-LPCC jointly considers the scene-aware characteristics of movable objects and removes the spatial–temporal redundancy from an end-to-end learning mechanism, which will boost the related applications from algorithm optimization to industrial products.

Due to the huge volume of point cloud data, storing and transmitting it is currently difficult and expensive in autonomous driving. Learning from the high-efficiency video coding (HEVC) framework, we propose a novel compression scheme for large-scale point cloud sequences, in which several techniques have been developed to remove the spatial and temporal redundancy. The proposed strategy consists mainly of three parts: intracoding, intercoding, and residual data coding. For intracoding, inspired by the depth modeling modes (DMMs), in 3-D HEVC (3-D-HEVC), a cluster-based prediction method is proposed to remove the spatial redundancy. For intercoding, a point cloud registration algorithm is utilized to transform two adjacent point clouds into the same coordinate system. By calculating the residual map of their corresponding depth image, the temporal redundancy can be removed. Finally, the residual data are compressed either by lossless or lossy methods. Our approach can deal with multiple types of point cloud data, from simple to more complex. The lossless method can compress the point cloud data to 3.63% of its original size by intracoding and 2.99% by intercoding without distance distortion. Experiments on the KITTI dataset also demonstrate that our method yields better performance compared with recent well-known methods.Note to Practitioners—This article deals with the problem of efficient compression of point cloud sequences that come from light detection and ranging (LiDARs) mounted on autonomous mobile robots. The vast amount of point cloud data could be an important bottleneck for transmission and storage. Inspired by the HEVC algorithm, we develop a novel coding architecture for the point cloud sequence. The scans are divided into intraframe and interframe, which are encoded separately using different techniques. Our method can be used for the compression of LiDAR point cloud sequences or dense LiDAR point cloud map and will significantly reduce the transmission bandwidth and storage spaces. We have to admit that although our method is less effective for real-time solutions, it can be highly efficient for off-line applications. Future studies will concentrate on further optimizing the coding algorithm to reduce the computational complexity and trying to find a balance between them.

Light detection and ranging (LiDAR) plays an indispensable role in autonomous driving technologies, such as localization, map building, navigation and object avoidance. However, due to the vast amount of data, transmission and storage could become an important bottleneck. In this article, we propose a novel compression architecture for multi-line LiDAR point cloud sequences based on clustering and convolutional long short-term memory (LSTM) networks. LiDAR point clouds are structured, which provides an opportunity to convert the 3D data to 2D array, represented as range images. Thus, we cast the 3D point clouds compression as a range image sequence compression problem. Inspired by the high efficiency video coding (HEVC) algorithm, we design a novel compression framework for LiDAR data that includes two main techniques: intra-prediction and inter-prediction. For intra-frames, inspired by the depth modeling modes (DMM) adopted in 3D-HEVC, we develop a clustering-based intra-prediction technique, which can utilize the spatial structure characteristics of point clouds to remove the spatial redundancy. For inter-frames, we design a prediction network model using convolutional LSTM cells. The network model is capable of predicting future inter-frames using the encoded intra-frames. As a result, temporal redundancy can be removed. Experiments on the KITTI dataset demonstrate that the proposed method achieves an impressive compression ratio (CR), with 4.10% at millimeter precision, which means the point clouds can compress to nearly 1/25 of their original size. Additionally, compared with the well-known octree, Google Draco, and MPEG TMC13 methods, our algorithm yields better performance in compression ratio.

Millimeter wave (mmWave) has been claimed as the viable solution for high-bandwidth vehicular communications in 5G and beyond. To realize applications in future vehicular communications, it is important to take a robust mmWave vehicular network into consideration. However, one challenge in such a network is that mmWave should provide an ultra-fast and high-rate data exchange among vehicles or vehicle-to-infrastructure (V2I). Moreover, traditional real-time channel estimation strategies are unavailable because vehicle mobility leads to a fast variation mmWave channel. To overcome these issues, a channel estimation approach for mmWave V2I communications is proposed in this paper. Specifically, by considering a fast-moving vehicle secnario, a corresponding mathematical model for a fast time-varying channel is first established. Then, the temporal variation rule between the base station and each mobile user and the determined direction-of-arrival are used to predict the time-varying channel prior information (PI). Finally, by exploiting the PI and the characteristics of the channel, the time-varying channel is estimated. The simulation results show that the scheme in this paper outperforms traditional ones in both normalized mean square error and sum-rate performance in the mmWave time-varying vehicular system.

Due to the huge volume of point cloud data, storing or transmitting it is currently difficult and expensive in autonomous driving. Learning from the high efficiency video coding (HEVC) coding framework, we propose an advanced coding scheme for large-scale LiDAR point cloud sequences, in which several techniques have been developed to remove the spatial and temporal redundancy. The proposed strategy consists mainly of intra-coding and inter-coding. For intra-coding, we utilize a cluster-based prediction method to remove the spatial redundancy. For inter-coding, a predictive recurrent network is designed, which is capable of generating future frames according to the previously encoded frames. By calculating the residual error between the predicted and real point cloud data, the temporal redundancy can be removed. Finally, the residual data is quantized and encoded by lossless coding schemes. Experiments are conducted on the KITTI data set with four different scenes to verify the effectiveness and efficiency of the proposed method. Our approach can deal with multiple types of point cloud data from the simple to more complex, and yields better performance in terms of compression ratio compared with octree, Google Draco, MPEG TMC13 and other recently proposed methods.

Hybrid precoding, combining the digital and analogue precoding, is a key enabler to reach a compromise between system performance and hardware complexity in millimetre wave (mmWave) massive multiple‐input multiple‐output (MIMO) systems. Most previous studies on hybrid precoding considered narrowband channels. However, mmWave systems are expected to operate on wideband channels with frequency selectivity. In this study, the authors focus on the hybrid precoding design in mmWave massive MIMO systems using orthogonal frequency division multiplexing (OFDM). By transforming the hybrid precoding problem into a series of sub‐problems, they propose a low‐complexity algorithm to design the digital precoder and analogue precoder alternately. In particular, a phase pursuit method is introduced to seek this solution of analogue precoding matrix with the constant amplitude constraint. Simulation results and complexity evaluations reveal that the proposed algorithm can obtain better performance and lower complexity than some existing solutions in mmWave OFDM systems.

As the latest video coding standard, high‐efficiency video coding (HEVC) achieves better performance and supports higher resolution compared with the predecessor standard, H.264/advanced video coding (AVC). Intra‐coding is an important feature in HEVC standard, which reduces the spatial redundancy significantly, due to the flexible coding structure, and high density of angular prediction modes. However, the improvement on coding efficiency is obtained at the expense of the extraordinary computation complexity. This study presents a novel coding unit (CU) partitioning technique for HEVC. By using a fast texture complexity detection method, which is based on two‐dimensional Haar wavelet transform, texture complexity for each CU can be extracted. According to the Haar wavelet coefficients obtained, an early CU splitting termination is proposed to decide whether a CU should be decomposed into four lower dimensions CUs or not. Experimental results demonstrate that the fast CU partition strategy achieves better trade‐off between rate‐distortion performance and complexity reduction than the previous algorithms. Compared with the reference software HM16.7, the proposed algorithm can lessen the encoding time up to 46.22% on average, with a negligible bit rate increase of 0.45%, and quality losses lower than 0.04 dB, respectively.

Abstract Machine‐to‐machine (M2M) techniques have significant application potential in the emerging internet of things, which may cover many fields from intelligence to ubiquitous environment. However, because of the data exposure when transmitted via cable, wireless mobile devices, and other technologies, its security vulnerability has become a great concern during its further extending development. This problem may even get worse if the user privacy and property are considered. Therefore, the authentication process of communicating entities has attracted wide investigation. Meanwhile, the data confidentiality also becomes an important issue in M2M, especially when the data are transmitted in a public and thereby insecure channel. In this paper, we propose a promising M2M application model that connects a mobile user with the home network using the existing popular Time Division‐Synchronous Code Division Multiple Access (TD‐SCDMA) network. Subsequently, a password‐based authentication and key establishment protocol is designed to identify the communicating parties and hence establish a secure channel for data transmissions. The final analysis shows the reliability of our proposed protocol. Copyright © 2012 John Wiley & Sons, Ltd.

In this article, we suggest an efficient beam switching technique for the emerging 60GHz wireless personal area networks. Given the pre-specified beam codebooks, the beam switching process, aiming to identify the best beam-pair for data transmissions, is formulated as a global optimization problem in a two-dimension plane that is formed by the potential beam pattern index. As the analytical gradient information of the objective reward function is practically unavailable, Rosenbrock numerical algorithm is properly adopted to implement beam searching, by implicitly approaching and exploiting the gradient descent direction through the numerical pattern-search mechanic. In order to enhance search performance, furthermore, a novel initialization process is presented to provide the feasible initial solution for Rosenbrock search. Inspired by the appealing conception of small-region dividing and conquering, this pre-search algorithm can efficiently reduce the search scope and hence improve the success probability. The developed beam switching technique, i.e. an initialization process followed by Rosenbrock search, exhibits a much lower complexity than the current state-of-the-art strategies. It is demonstrated from both theoretical analysis and numerical experiments that, compared with the existing popular methods, the required protocol overhead of the new beam-training procedure can be significantly reduced, accompanying the power consumption of 60GHz devices.