The transition from 5 G to beyond 5 G (B5G) networks is accelerating the development of ubiquitous and intelligent IoT applications. Semantic communication (SemCom) has emerged as a transformative paradigm that prioritizes task-relevant meaning over bit-level accuracy. However, deploying computationally intensive semantic models on resource-constrained end devices presents a major challenge. To address this issue, we propose FedSC, a new task-oriented semantic communication framework that enables end-edge collaborative learning by integrating federated learning with knowledge distillation. FedSC distributes model training between edge servers and end devices, where lightweight terminal models are collaboratively optimized with the assistance of more capable edge nodes, thus ensuring efficient and privacy-preserving semantic communication across heterogeneous environments. The framework adopts a hierarchical learning architecture in which the end and edge models cooperate via dynamic feature alignment, guided by the information bottleneck principle to balance semantic fidelity and compression. To further enhance efficiency and robustness, FedSC incorporates Gumbel-Softmax discretization for bandwidth-efficient semantic representation and introduces an SNR-adaptive inference mechanism to adapt to fluctuating wireless conditions. Experiments on Raspberry Pi clusters show that FedSC achieves improved performance in image classification and reconstruction tasks while significantly reducing communication overhead compared to existing baseline approaches.

In order to improve performance and avoid overheating on mobile devices, precise thermal control with low overhead is crucial. To achieve this, we propose incorporating a performance monitoring counter (PMC)-based power model into thermal control, which enables a more accurate evaluation of the CPU’s power consumption. We demonstrate the plausibility of this approach using polynomial regression based on Moore’s Law. Additionally, we introduce a lightweight PMC sampling method that can collect multiple PMCs at once in the kernel space, reducing sampling overhead. By replacing the utilization-based model in the original the intelligent power allocation (IPA) with a PMC-based power model, we realize the PMC-based IPA governor can be ported to real mobile devices. After updating the thermal control governor in the Linux kernel, we perform tests on our PMC-based IPA using a mobile phone device. We compare it with Stepwise and IPA, which are commonly used in current mobile phone systems. We choose the CPU-intensive workbench, I/O-intensive workbench, and CPU and I/O-intensive hybrid workbench as workloads. The results show that PMC-based IPA effectively reduces energy consumption while improving performance. In particular, during the CPU and I/O-intensive hybrid experiment, where CPU-intensive and I/O-intensive tasks are executed alternately, PMC-based IPA reduces the running time by 10.0% and energy consumption by 16.6% compared to the original IPA. In order to verify the benefits of PMC-based IPA, mobile phone testing software AI Bench and Antutu are utilized. The results show that our scheme is able to control temperature more precisely than IPA and achieves a better score while consuming less energy, particularly during AI computing. These experiment results suggest that PMC-based IPA is valuable for practical use.

In this paper, we investigate the stability and performance of the interval type-2 (IT2) polynomial fuzzy-model-based tracking control system, formed by an IT2 polynomial fuzzy model and an IT2 polynomial fuzzy controller, based on the output feedback and sampled-data structure. IT2 fuzzy sets are employed to capture the uncertainties of the nonlinear plant. Furthermore, considering the digital implementation of control strategy and only the system outputs are available, the IT2 polynomial fuzzy controller is of discrete time and output-feedback type. Both membership-function-independent and membership-function-dependent stability analysis, with the consideration of H∞performance index, are conducted to develop stability conditions in terms of sum-of-square based on Lyapunov stability theory. The information of membership functions, system states, and sampling process are included in the stability analysis for the relaxation of stability conditions. Simulation examples are presented to verify the effectiveness of the proposed tracking control approach.

This paper investigates the coupled disturbance reconstruction, state estimation and trajectory tracking problems for a class of nonlinear systems. The sliding mode observer and the state feedback approaches are utilized to solve these problems. Considering only a partial of the system states are measured but the disturbances couple with time-varying parameters that the observer is designed by transforming the original system into a descriptor one. Hence, from the estimated values of system states and the decoupled disturbance, one can reconstruct the coupled disturbance. Upon these estimates, a state feedback based fault tolerant controller is designed such that the system states converge to the desired trajectory. Finally, to verify the validity of our scheme, a numerical simulation together with an experiment of 3-DOF robot are offered.

In this paper, the problems of stabilization for interval type-2 fuzzy systems with time-varying delay and parameter uncertainties are investigated. The objective is to design an interval type-2 fuzzy controller such that the closed-loop control system is asymptotically stable. The conditions for the existence of such a controller are delay dependent and membership function dependent in terms of linear matrix inequalities (LMIs). Based on a basic lemma, we formulate and solve the problem with more flexibility due to imperfect premise matching that the number of rules and premise membership functions are not necessary the same between the interval type-2 fuzzy model and interval type-2 fuzzy controller. A systematic approach making use of the information embedded in the lower and upper membership functions is employed to facilitate the stability analysis. A numerical example indicates the effectiveness of the derived results.

In this paper, the stabilization problems for a class of Takagi-Sugeno (T-S) fuzzy neutral systems are explored. Utilizing Pólya's theorem and some homogeneous polynomials techniques, the delay-dependent stabilization condition for T-S fuzzy neutral systems are proposed in terms of a linear matrix inequality (LMI) to guarantee the asymptotic stabilization of T-S fuzzy neutral systems. Lastly, an example is illustrated to demonstrate the effectiveness and applicability of the proposed method.