In recent years, semantic communication has received a lot of attention due to its ability to solve the challenges faced by traditional communication systems. However, little attention has been paid to the fact that during data compression and transmission, the lost data can be reconstructed by neural networks to improve transmission efficiency. In order to solve the impact of the loss of semantic information on the transmission performance in vehicular networks, this paper proposes a goal-oriented communication based on semantic reconstruction (GOCSR). By designing a semantic reconstruction network at the receiver, the lost semantic information is predicted and reconstructed, and then the complete semantic information is used to perform downstream tasks. To evaluate the efficiency of GOCSR, extensive simulation experiments are conducted using the Cityscapes dataset. Simulation results show that GOCSR can achieve higher target execution performance than the existing semantic communication schemes.

Task-oriented semantic communication has gained notable interest for its capacity to minimize transmitted data volume without sacrificing task performance. Previous research has mainly concentrated on random masking, which may obscure critical features and hinder the model's ability to learn transferable representations. In this paper, a robust semantic communication system based on semantic-aware masking and discrete codebook (SAMDC) is proposed. Specifically, we develop a semantic-aware sampling strategy, which can selectively mask image patches with low semantic importance instead of random masking, to enhance the model's capacity to learn semantic information and boost training efficiency. Moreover, we also apply an improved robust discrete codebook, shared between the transmitter and receiver. This codebook comprises orthogonal and trainable basis vectors that symbolize the encoded features, thereby enhancing the system's robustness. Experimental results demonstrate that our proposed robust SAMDC significantly enhances the processing efficiency of semantic information. This improvement leads to better performance in communication tasks, particularly in challenging low signal-to-noise ratio (SNR) situations.

The integration of semantic communication technology into intelligent vehicular networks represents a promising research direction, as it significantly reduces data transmission volume and spectrum usage, addressing the high demands of transmitting large-scale visual information between vehicles. Existing studies typically assume that communicating parties share a common database. However, this assumption poses significant privacy risks, particularly in inter-vehicle scenarios. To address these challenges, we propose a federated learning-based semantic-aware communication for vehicular networks. In this approach, each intelligent vehicle locally trains a semantic communication model and uploads its model parameters to the edge server for aggregation. To further enhance data transmission efficiency, we introduce a semantic-aware vector quantized variational autoencoder (SVQ-VAE) architecture as the local semantic communication model. This architecture optimizes transmission by selectively compressing and quantizing only the most relevant semantic information for the task. Additionally, to address data heterogeneity among vehicles, we propose a hypernetwork-based personalized federated learning (HPFL) scheme. This approach enhances the model’s scalability and generalization by training a hypernetwork at the edge server to generate specific weight parameters for each vehicle’s semantic communication model. Simulation experiments on the CIFAR-10 and BDD100K datasets demonstrate that our proposed federated learning-based semantic-aware communication achieves superior task completion rates, semantic transmission efficiency and transmission delay compared to existing federated semantic communication architectures.

In recent years, semantic communication has garnered significant attention for its potential to address challenges in traditional communication systems. However, in complex communication environments, semantic communication still faces challenges such as semantic information loss, low transmission efficiency, and poor adaptability. This paper proposes a novel Semantic Communication with Adaptive Semantic Reconstruction (SCASR) scheme to enhance transmission efficiency and adaptability in complex communication environments. First, a compression mechanism based on semantic importance is designed to achieve flexible and efficient semantic compression. Then, we develop an adaptive semantic reconstruction network to predict and reconstruct lost semantic information. Finally, we integrate an attention mechanism into the reconstruction network, dynamically adjusting parameter weights based on Signal-to-Noise Ratio (SNR), Semantic Compression Rate (SCR), and Packet Loss Rate (PLR) to improve reconstruction quality and adaptability. To evaluate the efficiency of SCASR, we conduct extensive simulation experiments on semantic segmentation tasks using the Cityscapes dataset. Results demonstrate that SCASR outperforms existing semantic communication and traditional schemes, offering higher Mean Intersection over Union (mIoU), and enhanced Semantic Transmission Benefit (STB).

With the development of intelligent transportation systems (ITS), edge cooperative caching (ECC) technology has been introduced into vehicular ad hoc networks (VANETs) to reduce the transmission delay of vehicle access data and improve network performance. ECC technology achieves faster and more efficient data retrieval by storing frequently accessed data at the network edge, especially for popular or time-sensitive content in VANETs. However, due to the limited caching resources, allocating storage resources for caching and deciding which data to cache becomes a challenge. This paper first proposes a vehicle-edge network model, and then based on this network model, proposes an optimization problem that minimizes the average transmission delay. To solve this optimization problem, we propose a centralized edge cooperative caching (CECC) strategy, which converts it into a multiple-choice knapsack (M CK) problem. Finally, we propose a greedy algorithm to obtain an approximate optimal solution to the MCK problem. Simulation results show that compared with existing ECC strategies, the CECC strategy can effectively improve caching performance.

Edge caching has emerged as an effective solution to the challenges posed by massive content delivery in the vehicular network. In vehicular networks, vehicles and roadside units (RSUs) can serve as intermediate relays with caching capabilities. However, due to the mobility of vehicles, the topology of the edge network changes frequently, which leads to frequent link interruptions and increases the transmission delay. This paper proposes an adaptive cooperative caching (ACC) strategy to adapt the frequent changes in the vehicular edge network topology and describes an optimization problem to minimize the average transmission delay. Then, the optimization problem is transformed into two sub-optimization problems: multiplechoice knapsack (MCK) problem and multiple minimum-weight dominating set (MMWDS) problem. Finally, two greedy algorithms with low complexity are designed to solve the above two optimization problems and obtain approximate solutions to the optimal caching decision. Simulation results show that ACC can effectively improve the cache hit rate and reduce the average transmission delay and the communication overhead compared with other caching strategies.

High-performance multicast-enabled packet forwarding engines (PFEs), as an essential component of high-end switches, use a polynomial-time membership query algorithm to determine which port(s) the data packet should be forwarded. The currently widely used query algorithm is Bloom Filter (BF), which has been proven to have many fatal flaws. Another error-free membership query algorithm includes Scalar-pair Vectors Routing Forwarding (SVRF), Fractional-NScalar-pair Vectors Routing Forwarding (Frac-NSVRF), and the Per-Port Prime Filter Array (P3FA) also have some shortcomings in space and time efficiencies. In this paper, we proposed a hybrid strategy: Divaricate Heterogeneous SVRF (DH-SVRF) scheme, which based on the P3FA and Frac-NSVRF, which randomly divides all member ships intoNgroups, and each group has the same structure and is independent of each other to obtain higher time efficiency and space utilization. Finally, we also discussed the selection of the optimal egress-diversity threshold. Through mathematical modeling and simulation, we validate that the proposed DH-SVRF scheme is superior to the SVRF/Frac-NSVRF and traditional BF in terms of scalability, space utilization, and time efficiency in specific conditions such as appropriate egress-diversity thresholds.

High-performance multicast-enabled switches and routers are being constantly developed, which use a polynomial-time group membership query algorithm within the Packet Forwarding Engines (PFEs) to determine whether or not a packet is forwarded through a unique or multiple egress ports. Among these, Bloom filter (BF) and Scalar-pair Vectors Routing and Forwarding (SVRF) are being considered as two representations of the membership query algorithms. However, both approaches suffer from some fatal weaknesses such as space and time inefficiencies, especially for a carrier-grade PFE with high port-density feature. In order to solve these imperfections of SVRF, we propose an improved Fractional-N Scalar-matrix and Vectors Routing and Forwarding (Fractional-N SVRF) scheme based on re-examining the idea of the original SVRF. The Fractional-N SVRF preprocesses a scalar-matrix by dividing an n-element group into N-columns (sub-blocks), thus element' keys belonging to distinct sub-blocks and sub-scalars are allowed to reuse relatively smaller identical prime keys. Based on Fractional-N SVRF, membership queries can be partitioned to leverage task parallelism, therefore better performance is achieved in terms of memory consumption and computational complexity.

Considering that the traffic characteristics of certain applications feature small-data patterns especially in typical IoT control scenarios such as robot control on the downlink. Aggregation data delivery system has good performance in handling multi-source, multi-destination, and massive small-data delivery with characteristics of low-overhead, high-throughput, and ultra-low-latency for a resource-constrained wireless transmission system. In order to rapidly deliver multiple small-data to multiple receivers efficiently, based on P4 switches, we attempt to develop a novel arithmetic aggregation coding scheme based on RNS, which would meet the need to aggregately encode multiple small-data messages, as an indispensable complementary component to the resource-constrained wireless transmission system.

Electrospinning has exhibited excellent benefits to treat the trauma for tissue engineering due to its produced micro/nano fibrous structure. It can effectively adhere to the tissue surface for long-term continuous therapy. This paper develops a robotic electrospinning platform for endoluminal therapy. The platform consists of a continuum manipulator, the electrospinning device, and the actuation unit. The continuum manipulator has two bending sections to facilitate the steering of the tip needle for a controllable spinning direction. Non-circular joint profile is carefully designed to enable a constant length of the centreline of a continuum manipulator for stable fluid transmission inside it. Experiments are performed on a bronchus phantom, and the steering ability and bending limitation in each direction are also investigated. The endoluminal electrospinning is also fulfilled by a trajectory following and points targeting experiments. The effective adhesive area of the produced fibre is also illustrated. The proposed robotic electrospinning shows its feasibility to precisely spread more therapeutic drug to construct fibrous structure for potential endoluminal treatments.

Accurate force sensing is important for endoluminal intervention in terms of both safety and lesion targeting. This paper develops an FBG-based force sensor for robotic bronchoscopy by configuring three FBG sensors at the lateral side of a conical substrate. It allows a large and eccentric inner lumen for the interventional instrument, enabling a flexible imaging probe inside to perform optical biopsy. The force sensor is embodied with a laser-profiled continuum robot and thermo drift is fully compensated by three temperature sensors integrated on the circumference surface of the sensor substrate. Different decoupling approaches are investigated, and nonlinear decoupling is adopted based on the cross-validation SVM and a Gaussian kernel function, achieving an accuracy of 10.58 mN, 14.57 mN and 26.32 mN along X, Y and Z axis, respectively. The tissue test is also investigated to further demonstrate the feasibility of the developed triaxial force sensor.

Time series data is collected in almost every industrial field; mining knowledge from it has been attracting extensive attention in the data mining community. In this paper, we focus on temporal association rule mining from real-valued time series. Early work employs symbolization-based methods, but the symbolized representation misses out similarity of the original series, resulting in mining invalid rules. Although state-of-the-art work directly manipulates the original series, it may still find false rules due to the lack of correlation analysis. In our work, we present a hybrid approach combining the idea of direct manipulation and symbolization, which not only preserves the information about the raw data but also realizes the correlation analysis. Specifically, we leverage the similarity-preserving property of motifs, i.e. frequent occurring subsequences in time series, to partially symbolize the raw data. Then, for each rule candidate as a pair of motifs, we propose a rule searching framework to investigate the underlying relationships between them. To evaluate rule candidates, we accommodate the shape similarity by utilizing the relative information gain based on Minimum Description Length principle, and further develop a novel rule interestingness measure R_cos, which generalizes the classical measure cosine for association rules. We perform comprehensive experiments on both artificial and real world datasets, and the results show that the proposed rule searching framework and the rule interestingness measure are effective for mining valid temporal association rules from real-valued time series.

This paper presents a novel time series classification algorithm. It exploits time-delay embedding to transform time series into a set of points as a distribution, and attempt to classify time series by classifying corresponding distributions. It proposes a novel geometrical feature, i.e. characteristic subspace, from embedding points for classification, and leverages class-weighted support vector machine (SVM) to learn for it. An efficient boosting strategy is also developed to enable a linear time training. The experiments show great potentials of this novel algorithm on accuracy, efficiency and interpretability.