A review of recent studies on up-to-date IC shows that electromigration (EM) has become one of the major challenges for 2.5D and 3D chiplet heterogeneous integration (CHI) systems. However, most existing researches on EM are focusing on 2D power delivery network without taking Through Silicon Via (TSV) and non-uniformly thermal distribution condition between dies into consideration. To address this problem, this article proposes a novel EM simulation tool ChipletEM for 2.5D and 3D CHI systems. A finite volume method (FVM) based electrical-thermal co-simulation model is employed to get initial temperature and current density inside TSV. And a finite difference time domain (FDTD) solver is used for hydrostatic stress simulation for both nucleation and postvoiding phases. Thermal migration (TM) effect is also considered in the solver. An analytical TSV thermal solver is employed for temperature distribution simulation and thermal dependent current simulation. The FDTD EM solver and TSV thermal solver are coupled together at each time step so that the interaction among EM stress, thermal stress, void growth, resistance change, IR drop and Joule heating effects can be simulated within a single simulation framework. Simulation results show that compared with Finite Element Method (FEM) tool, average error is 0.61% in nucleation phase and 2.4% in growth phase. And the error of proposed method is reduced from 22.22% to 5.24% compared with state of art atomic flux divergence (AFD) method.

Federated Learning (FL) is an emerging distributed learning paradigm that enables collaborative model training among multiple clients without requiring access to their private local data. However, in practical settings, FL commonly encounters both model heterogeneity and data heterogeneity. Variations in network architectures and patterns of non-independent and non-identically distributed (non-IID) data across clients often lead to significant degradation in global model performance. Existing approaches that utilize knowledge distillation to address heterogeneity in FL typically rely on access to public datasets or partial sharing of private data. Such reliance increases communication overhead while introducing privacy concerns, which impede practical deployment. In this work, we propose a novel heterogeneous federated learning framework, referred to as FedPAG. Our method extracts statistical information from Batch Normalization layers to synthesize pseudo-data aligned with local distributions, enabling similarity-aware aggregation of student models on the server while circumventing the need for external or shared data. Meanwhile, each client preserves and transfers its local knowledge via local knowledge distillation, enabling personalized optimization. By eliminating the dependence on public datasets and reducing privacy risk, FedPAG provides a scalable and privacy-preserving solution for heterogeneous FL. Rigorous experiments on three real-world datasets under diverse non-IID settings validate FedPAG’s superior performance and robustness over baseline methods in heterogeneous FL environments.

Achieving excellent generalization on perceiving real traffic scenarios with diversity is the long-term goal for building robust autonomous driving systems. A recent theoretical study shows that the generalization on the worst-group of test samples is far more difficult than others. Therefore, we propose to discover potential shortness of certain perception module by analyzing its worst-scenario performance. However, with the benchmark datasets growing huge and tremendous, exhaustive searching for the worst perception scenario (WPS) seems to be time consuming and unnecessary. To address this, we present an automatic searching scheme empowered by reinforcement learning. In this case, worst scenario mining is formulated as the discrete search on the Visual Operation Design Domain (ODD), namely scenario representation, by optimizing LSTM-RNN controller with the worst-performance reward. Moreover, a time-efficient K-reciprocal re-ranking technique is utilized to match the predicted scenario parameters with existing test data. The proposed method has been validated by finding the most challenging scenarios for various vehicle detectors on KITTI, BDD100k and our own benchmark set EVB. Furthermore, searching performances w.r.t different Visual ODDs are investigated and it is found that visual representations through generative adversarial network contribute to a better performance.

Chiplet heterogeneous integration (CHI) is one of the important technology choices to continue Moore’s law. However, due to the characteristics of high power and low supply voltage in CHI systems, heavy currents need to flow through the power delivery network (PDN), and the Joule heating effect will result in the overall temperature increase of the CHI system. Meanwhile, the high temperature will cause the current as well as the performance of the system to degrade and a series of reliability problems will occur. In this article, an effective electrical-thermal coupling model is proposed to predict the steady-state temperature distribution of a 2.5-D CHI system considering the Joule heating effect and the temperature effect on the IR drop. The equivalent electrical conductivity model is also built up to describe the design features of the redistribution layer (RDL), bump, and through silicon via (TSV) structures based on the electrical-thermal duality. Furthermore, the governing equations for voltage distribution and temperature distribution are solved simultaneously by utilizing the finite volume method (FVM) with nonuniform mesh to realize the electrical-thermal co-simulation of the multiscale CHI system. The model application is further performed to investigate the influence of the model parameters on the voltage drop and temperature distribution of the CHI system. The verified systems and simulated results of the present investigation demonstrate the viability and accuracy of voltage and temperature field co-simulation and indicate that the new proposed electrical-thermal model is helpful in thermal and voltage drop analysis of packaging structures with the Joule heating effect and can be adopted to assist in the physical design optimization of 2.5-D CHI or 3-D heterogeneous stacked chips.

Considering the safety factors in the railway, this paper mainly focuses on the optimal speed of high-speed train or locomotive from the starting station to the ending station. Firstly, some concepts in the railway safety, which include the risk source, hidden danger, accident and safety warning point, are introduced to better understand the railway safety factors. Secondly, the mathematical model including objective function and the corresponding constraints is analyzed to solve the optimal train speed problem. Thirdly, the PSO algorithm is used to provide and design the optimal speed train from the starting station to the ending station. Simulation results show that PSO algorithm can handle with the optimal train speed problem and the obtained optimal speed are provided to ensure the train safety in the constant transportation time.

IoT based remote health monitoring systems play an important role to provide a technological solution for health monitoring and reach essential medical services to the patients. However, a review of the existing literature reveals that the existing remote health monitoring systems rarely consider difficulty of interoperability among devices in the system. In this paper, we propose a Named Data Networking (NDN) based remote health monitoring system, which involves the functions of measuring and combining patient’s health data, real-time named based data transmission between gateway and health center, data analysis at hospital side, and overall the realization of two-way communication between doctors and patients. The whole work is carried out from three aspects: simulation, hardware implementation, client platform building (App development). Simulation part focuses on the construction of the whole system in the virtual NDN network environment on the ndnSIM platform. The hardware part implements system construction and basic functions in reality. For the client platform, we build a mobile application, which achieves the functionality of online communication between doctors and patients.

In the case of particle swarm optimization, this paper mainly analyzes and discusses the mathematical model and the analysis on searching for the global optimum region. Firstly, the global optimum region Θ is defined and calculated in the convergence step and the divergence step. Furthermore, the rate μ of locating into the global optimum region is mathematically related to the number of particles, the number of generations, the fitness landscape, the ratio between exploration ability and exploitation ability, etc. Simulation results on Schaffer f6 function can help to understand the obtained results. Finally, those corresponding results and several remarks in the paper are helpful for the tradeoff between exploration ability and exploitation ability, together with the suitable searching strategy of particle swarm optimization algorithm.

This article explores the application of existing social media platforms for human–robot interaction. With the increasing popularity of social media platforms that connect humans, we propose to portray domestic robots as buddies on the contact list of family members and present a robot management system that employs complementary social media platforms for humans to interact with the vacuuming robot Roomba and a surveillance robot developed on top of iRobot Create. The social media platforms adopted include short message services (SMS), instant messenger (MSN), an online shared calendar (Google Calendar), and a social networking site (Facebook). Hence, we can provide a rich set of user-familiar, intuitive, and highly accessible interfaces, allowing users to flexibly choose their preferred tools in different situations. An in-lab experiment and a multiday field study are conducted to study the characteristics and strengths of each interface and to investigate users’ perception to the robots and behaviors in choosing the interfaces.

As an important approach to ensure safety and naturalness, compliant motion is already implemented in large-size robots, but it is still an undeveloped area in child-oriented robots as it calls for a lightweight and compact solution compared with large-size robots. In this paper, we proposed the design of a social robot which aims at conducting safe and playful Human-Robot Interaction (HRI), especially with children. We built a teddy bear robot prototype based on hybrid passive-active compliant system which consists of flexible joints as passive part and compliant motion controller as active part. The compliant controller detects external perturbation through motor state variables, therefore force and torque sensors could be omitted to keep overall system compact. Experiments conducted in typical HRI scenarios showed that the hybrid passive-active compliant system enabled our robot to conduct safer and more interactive physical interaction compared with robot under traditional control method.