Age of information (AoI)-sensitive bidirectional computation tasks quality of service (QoS) for unmanned surface vehicles (USVs) is a critical issue in realizing ship-shore cooperative systems. In this paper, a reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV)-USV mobile edge computing (MEC) network architecture is proposed, where one RIS-carried tethered UAV (TUAV) and rotary-wing UAVs (RUAVs) are cooperatively dispatched to serve USVs bidirectional data computation with average AoI (AAoI) constraint. The minimization of weighted sum USVs AAoI and RUAVs flight energy is formulated by jointly considering RUAVs service duration indicators, TUAV-mounted RIS phase shift, TUAV hovering altitude, and RUAVs' trajectories. A heursitic solution is proposed to address this minimized issue. In particular, a novel mixed linear quadratic Lyapunov framework is utilized to transform the original long-term stochastic problem into a list of deterministic single-slot problems. Then, each single-slot problem is divided into two subproblems. First, the subproblem of RUAVs' trajectories is tackled by an enhanced whale optimization algorithm. Second, the subproblem of RUAVs service duration indicators, TUAV-mounted RIS phase shift and TUAV hovering altitude is addressed by an enhanced alternating optimization algorithm. The results demonstrate that the proposed heuristic solution reduces long-term RUAVs flight energy consumption by approximately 50% while maintaining satisfactory USVs AAoI.

As the first satellite specifically designed to serve Sustainable Development Goals (SDGs), the Sustainable Development Science Satellite 1 (SDGSAT-1) has attracted numerous attention by providing panchromatic glimmer imagery with a unprecedented clarity of 10 meters. However, unsatisfactory positional accuracy of glimmer imagery hinder further fine-grained studies. This study identifies the cause of these positional errors as misalignments generated during the data production process, and thus proposes a correction method based on deep learning object detection and cross-correlation. The method achieves accurate measurements of the position, direction, and distance of the misalignment, which are used to correct the glimmer imagery and to effectively improve the positional accuracy. In this study, we (1) propose a simulated misalignment dataset which demonstrated with excellent performance during the training of multiple deep learning object detection models, reaching up to 99.44% average precision (AP); (2) propose pixel brightness correlation coefficient based on cross-correlation and validate its effectiveness to characterize misalignments; (3) improve detection efficiency by the threshold design of window average brightness and object detection confidence; (4) explore the reasons for the misalignment suggests that it is the difference between the satellite’s designed operating time and the actual operating time that leads to the Charge-coupled Device(CCD) stitching flaw. Our proposed method can contribute to evaluating and improving the positional accuracy of SDGSAT-1 panchromatic glimmer imagery, providing a way to obtain datasets with good spatial consistency and to conduct related research.

A protograph low-density parity-check (P-LDPC) code-based lossy coding system is proposed for compressing the Gaussian mixture model source, as a particular case of shipping transportation systems. The compression performance is benchmarked against the rate-distortion bounds for this source. Some effective methods are developed to improve both the encoder and the decoder to reduce the compression distortion. Experimental results demonstrate that the proposed methods achieve good performance while maintaining low complexity.

A lossy source coding system based on the protograph low-density parity-check (P-LDPC) code is proposed for Gaussian source compression. In the proposed system, the conventional belief propagation (BP) algorithm is modified to be a concatenated BP-inverse BP (BP-$i$BP) for encoding and decoding, where the$i$BP is constructed by a fully-connected layer of a neural network. Compared to the existing approximate message passing algorithm, the proposed BP-$i$BP realizes a float-to-bit compression with low complexity for arbitrary Gaussian sources. The BP-$i$BP is implemented based on the linking relation of the protograph; therefore, it is necessary to optimally design the protograph to obtain better rate-distortion function (RDF) performance. Regarding the coding optimal procedure, a mutual information iteration convergence (MIIC) algorithm is designed as the optimal criterion to determine the source P-LDPC code with minimum distortion. Inspired by the plane construction of quantum stabilizer code, a lattice topological splicing (LTS) algorithm is proposed for regularly building the protograph to reduce the code searching complexity. By using the MIIC and the LTS algorithms, the BP-$i$BP based on the designed P-LDPC code maintains good distortion performance close to the RDF limit.

For the double protograph low-density parity-check (DP-LDPC) code pair, indicating the source and channel codes utilized in the joint source channel coding, the coding optimization is generally implemented by the differential evolution searching with high complexity. In this paper, a new structured design of the DP-LDPC code pair is proposed based on Graph theory to reduce the searching complexity, namely mesh model-based merging (M3) method. Firstly, four basic mesh models are designed to merge the protographs at any dimension. According to the different structures of the source and channel codes, the merging boundary constraint is statistically established to limit the using number of basic models. Then, a prior sorting algorithm is proposed to arrange the selection priorities of basic models for source and channel codes, respectively, which greatly refines the merging accuracy. Since most of the degrees are symmetric and predetermined in the merged protographs, the searching complexity is significantly reduced when the merged protographs are used as the initial input. Finally, simulation results demonstrate that the merging DP-LDPC code pairs show better performances compared to the existing code pairs. Furthermore, the proposed M3 method is hardware-friendly in Internet of Things because of its simple structure.

A CM2-S3 transmission link in wireless body area networks (WBAN) is implemented with a joint source channel coding (JSCC) system based on double protograph low-density parity-check (DP-LDPC) code pairs with high reliability and low power consumption. For the practicality and suitability considerations, the$M$-ary differential chaos shift keying scheme is introduced to modulate the CM2-S3 channel with better performance than the standard modulations in WBAN. Due to the non-Gaussian-like distribution of the CM2-S3 channel model, the initial joint protograph extrinsic information transfer (JPEXIT) algorithm does not work as a coding analysis tool. To solve this problem, a numerical Gaussian approximation is employed to modify the iterative mutual information function of the JPEXIT for achieving more precise output. Then, a joint code rate compatible (JCRC) method, designed with compatibility in resisting variable channel variances and adapting different source statistics, is proposed for optimizing the DP-LDPC code pairs. Based on the JCRC method, the code searching algorithm is simplified with lower complexity because of the reduced cycle-index. The simulation results show that optimized code pairs have better bit-error ratio performances compared to existing codes, which provides a strong technical support to promote high-quality data transmission in eHealthcare on the physical layer.

The joint source channel coding (JSCC) system is sensitive to the changing channel characteristics, because the optimally designed source-channel code pair always needs to be redesigned when the transmission environment is updated. A solution is to seek a common code pair for variable channels. Channel model 3 (CM3) is a variable on-body channel following the Weibull distribution, which is analyzed in four conditions due to varying shape factors, namely with exponential, Rayleigh, log-normal and normal distributions. The JSCC system is considered in this paper based on double protograph low-density parity-check (DP-LDPC) code pair over the variable CM3 channel. Firstly, four DP-LDPC code pairs are searched by the conventional differential evolution (DE) algorithm with four distributions of the Weibull model, respectively. However, these four code pairs only present good bit-error ratio performance in their own distribution, and have error floors in the other three. To resolve the problem, an artificial intelligence method based on principle component analysis (PCA) algorithm is designed to extract the common code pair. It is demonstrated that the common code pair performs well in noise-varying and practical-parameter transmissions. Instead of four DE code pairs, the designed one PCA code pair provides a strong technical support to low-complexity manufacturing such as eHealthcare.