Vehicle seat vibration strongly affects ride comfort. Its control becomes more challenging when biodynamic parameters of the human body, such as stiffness and damping, vary with posture and alter the vibration transmission characteristics of the seat-occupant system. To address this dynamic uncertainty, this paper proposes an artificial intelligence-based control framework for active seat vibration control, in which a multi-agent reinforcement learning tuned proportional-integral-derivative (PID) controller is developed based on the deep deterministic policy gradient (DDPG) algorithm. The multi-agent framework decomposes the coupled PID gain tuning task into three coordinated subtasks, thereby improving learning efficiency and training stability. To further improve comfort-oriented control performance, a finite impulse response (FIR) based reward formulation is introduced to emphasize the biodynamically sensitive low-frequency band relevant to human body vibration response. This design guides the controller to place greater emphasis on suppressing vibrations in the comfort-relevant frequency range while preserving adaptability to posture-induced parameter variation. Simulation results show that, compared with the RL-tuned PID controller without FIR-based reward formulation, the proposed framework reduces the root mean square (RMS) head displacement by 12% under sinusoidal excitation and by 16.4% and 20.3% under narrowband and wideband random excitations, respectively. Under impulse excitation, it also achieves a 98.9% further magnitude reduction relative to conventional PID control. When the biodynamic parameters vary with posture, the performance degradation remains below 1%, indicating strong robustness of the learned controller to parameter variation. Overall, the proposed framework improves vibration suppression performance and maintains stable control under biodynamic parameter variation, indicating the potential of artificial intelligence-based active seat vibration control.

SRv6 (Segment Routing over IPv6) serves as the cornerstone for next-generation internet solutions. In SRv6 networks, SDN architectures address critical challenges like multi-cloud SRv6 traffic engineering through deep integration with SRH parsing and Kubernetes CNI. Existing SDN implementations face two key limitations: 1) Inability to concurrently configure hybrid data planes combining VPP and Linux nodes, which diminishes SRv6’s cross-plane advantages; 2) Operational complexity from maintaining synchronization between disparate controllers, hindering unified southbound management of SRv6 nodes. To resolve these issues, we propose a redesigned SDN architecture for cloud-native networks featuring two innovations: 1) A Linux SR Plugin (LSP) southbound interface enabling coordinated SRv6 support across heterogeneous nodes; 2) Enhanced northbound API capabilities through key-value store integration with cloud platforms. The implemented LSP plugin allows transparent management of hybrid data planes via business-focused API operations. Our experimental validation demonstrates that the LSP successfully verifies all SRv6 configurations supported by both the Linux and VPP data planes while achieving ≥400 configurations/second throughput. These results confirm the LSP’s capability to deliver unified southbound control for SRv6 across heterogeneous network environments.

Abstract The original online fountain codes discard a large number of symbols that do not meet the requirements at the decoder. To improve channel utilization, this article proposes a new online fountain code. In the completion phase, the proposed code improves the receiving rules of encoded symbols, that is, the encoded symbols discarded in the original online fountain codes are selectively cached. Moreover, an optimal degree selection strategy of encoded symbols is obtained in the proposed scheme. The valid degree range of the proposed strategy is also analyzed, leading to an upper bound of cached events which eventually limits the number of feedbacks. The theoretical analysis and simulation results reveal that the proposed scheme outperforms two state‐of‐the‐art online fountain codes in terms of overhead factors, number of feedback transmissions, and encoding/decoding efficiency.

In this article, we present a coding method called expanding-window zigzag decodable fountain code with unequal error protection property (EWF-ZD UEP code) to achieve scalable multimedia transmission. The key idea of the EWF-ZD UEP code is to utilize bit-shift operation and expanding-window strategy to improve the decoding performance of the high-priority data without performance deterioration of the low-priority data. To provide more protection for the high-priority data, we precode the different importance level using LDPC codes of varying code rates. The generalized variable nodes of different importance levels are further grouped into several windows. Each window is associated with a selection probability and a bit-shift distribution. The combination of bit-shift and symbol exclusive-or operations is used to generate an encoded symbol. Theoretical and simulation results on input symbols of two importance levels reveal that the proposed EWF-ZD UEP code exhibits UEP property. With a small bit shift, the decoding delay for recovering high-priority input symbols is decreased without degrading the decoding performance of the low-priority input symbols. Moreover, according to the simulation results on scalable video coding, our scheme provides better basic video quality at a lower proportion of received symbols compared to three state-of-art UEP fountain codes.

Abstract By combining bit‐shift and exclusive‐or operations, a weighted zigzag decodable fountain code is proposed to achieve an unequal error protection property. In the proposed scheme, the input symbols of different importance levels are first pre‐coded into variable nodes using low‐density parity‐check codes. Then, bit‐shift operations prior to exclusive‐or are performed on the non‐uniformly selected variable nodes to generate encoded symbols. An analysis of the erasure probabilities based on the and‐or tree is further introduced. Simulation results show that by appropriately choosing the maximum bit‐shift amount, the proposed weighted zigzag decodable fountain codes can successfully recover the more important input symbols prior to the less important ones.

Traditional Canny edge detection algorithm has been widely used for the extraction of hyperbolic edges in ground penetrating radar (GPR). However, the Canny edge detection algorithm cannot adaptively determine the segmentation thresholds. Moreover, multiple hyperbolic edges are usually detected for each target, which increases the complexity of subsequent processing. In this letter, we propose a novel method that obtains only one hyperbolic edge for each target and automatically determines the segmentation threshold. Both simulation and real data results show that the proposed method is able to extract the target hyperbolic edges precisely and significantly improves the efficiency of target detection combined with the Hough transform in impulse GPR systems.

Online fountain codes have been proven to require lower overhead and fewer feedbacks than growth codes for successful decoding. In an attempt to improve the intermediate symbol recovery rate, the authors propose sending a number of degree‐1 input symbols prior to the build‐up phase based on a simple application of probability theory. In addition, during the completion phase, received encoded symbols with three neighbouring white (un‐decoded) symbols are retained for decoding and updating the decoding graph later. The performance and characteristics of the proposed improved online fountain codes are compared to those of the original online fountain codes over an erasure channel. Simulation results reveal that the improved online fountain codes outperform the original fountain codes in terms of intermediate symbol recovery rate, average encoded symbols required to be generated by the sender, average feedback transmissions, and encoding/decoding efficiency.

In this study, we describe a system-level multiple refactoring algorithm, which can identify the move method, move field, and extract class refactoring opportunities automatically according to the principle of “high cohesion and low coupling.” The algorithm works by merging and splitting related classes to obtain the optimal functionality distribution from the system-level. Furthermore, we present a weighted clustering algorithm for regrouping the entities in a system based on merged method-level networks. Using a series of preprocessing steps and preconditions, the “bad smells” introduced by cohesion and coupling problems can be removed from both the non-inheritance and inheritance hierarchies without changing the code behaviors. We rank the refactoring suggestions based on the anticipated benefits that they bring to the system. Based on comparisons with related research and assessing the refactoring results using quality metrics and empirical evaluation, we show that the proposed approach performs well in different systems and is beneficial from the perspective of the original developers. Finally, an open source tool is implemented to support the proposed approach.

Power consumption is a key issue for wireless network design. Cooperative routing, an efficient scheme for saving transmission power in wireless networks, combines the advantage of the cooperative communication in the physical layer and the routing technology in the network layer. In this paper, we propose a distributed minimum power cooperative routing algorithm based on optimal power allocation, namely the OPAMPCR algorithm that realizes minimum total transmission power for each source-destination pair. Each cooperative route from a source to a destination is a concatenation of cooperative transmission links and direct transmission links. For each type of link mode, the minimum transmission power required for a target BER is calculated. Especially, the proposed algorithm first finds the relay node which consumes the minimum total transmission power for assisting the data transmission. If such a relay does not exist, the direct transmission mode is applied in current link; Otherwise, we adopt the cooperative transmission mode. Then, the proposed OPAMPCR algorithm applies the distributed Bellman-Ford shortest-path routing algorithm to find the route with least transmission power required. Simulation results reveal that the OPAMPCR algorithm outperforms the SNCP algorithm, the CASNCP algorithm and the MPCR algorithm with respect to the average total transmission power per route and the power saving. Moreover, the proposed OPAMPCR algorithm achieve less hops per route than the MPCR algorithm.