As one of the most basic specifications for many types of sensors, sensor resolution has been largely overlooked in a multitude of state estimation studies. Under the binary-encoding mechanism, this article deals with the outlier-resilient state estimation problem of Markov jump neural networks (MJNNs) with sensor resolution. An improved binary-encoding procedure, capable of assigning distinct bit lengths to different MJNN modes, is proposed to accommodate diverse physical constraints and limited network resources. Building on this procedure, a mode-dependent state estimation scheme embedded with a saturation function is put forward to alleviate the by-effects of external disturbances, decoding errors, and measurement outliers. Sufficient conditions are derived to guarantee the exponential ultimately boundedness of estimation errors. Lastly, simulation experiments are carried out to demonstrate the applicability of the proposed method.

This article investigates the predictor-based stabilization by noise for nonlinear systems. A novel concept, the noise-compensated auxiliary ordinary differential equation (ODE), is introduced to simulate system behavior, predict system state, and compensate for delay effects in the corresponding stochastic differential equation (SDE). Utilizing the auxiliary ODE, a predictor-based stabilizing noise is designed. Unlike conventional predictor-based schemes for stochastic systems, the proposed approach fully accounts for stochastic influences while generating state values that can be directly utilized for control. The proposed scheme is further applied to networked control systems (NCSs) under dual-channel packet loss, where the number of consecutive packet losses is allowed to be unbounded. In this way, the conventional assumption of a finite upper bound on packet loss is removed, and the system stability is guaranteed even under arbitrarily high-packet loss rates. To showcase the superiority of the proposed methodology, numerical simulations are conducted.

As the demand for higher integration density and performance efficiency continues to grow, 3D stacking has emerged as a promising solution. In 3D ICs, the complexity of physical design and the optimization space is significantly increasing. Therefore, researching high-quality 3D native instead of pesudo 3D physical design has become even more important. This paper reviews recent advancements and persistent challenges in 3D physical design, focusing on F2F bonding technologies. Then, this paper discusses several issues that still require further research and some overlooked problems, with the hope of helping researchers develop higher-quality 3D native physical design tools in the future.

Cross-lingual and cross-domain knowledge alignment without sufficient external resources is a fundamental and crucial task for fusing irregular message. Aiming to discover equivalent objects from different knowledge graphs (KGs), embedding-based entity alignment (EA) has been attracting great interest from industry and academic research recently. Most of related methods usually explore the correlation between entities and relations through neighbor nodes, structural information and external resources. However, the complex intrinsic interactions among triple elements and role information are rarely modeled, which leads to the inadequate illustration. In addition, external resources are unavailable in some scenarios especially cross-lingual and cross-domain applications, which reflects the weak scalability. To tackle the above insufficiency, a novel universal EA framework (OTIEA) based on ontology pair and role enhancement mechanism via triple-aware attention is proposed in this paper without introducing external resources. Specifically, an ontology-enhanced triple encoder is designed via mining intrinsic correlations and ontology pair information instead of independent elements. In addition, the EA-oriented representations can be obtained in triple-aware entity decoder by fusing role diversity. Finally, a bidirectional iterative alignment strategy is deployed to expand seed entity pairs. The experimental results on three real-world datasets show that our framework achieves a competitive performance compared with baselines.

In this paper, the stability analysis and H∞controller design problem are investigated for a class of randomly sampled-data systems with uniformly bounded sampling errors and random denial-of-service (DoS) attacks, with an application to a fourth-order interleaved flyback module-integrated converter (IFMIC). First, a discrete-time stochastic model is formulated for the considered system affected by random sampling errors and DoS attacks. A stability condition that incorporates the expectation of a stochastic, nonlinear, and attack-dependent coupling matrix is then derived through a (reduced-order) Vandermonde matrix method. Subsequently, the existence of this expectation is established, followed by the application of the Kronecker product operation to effectively decompose the resulting matrix expectation. On this basis, an H∞synthesis algorithm is designed to guarantee the exponential mean-square stability and H∞performance of the discrete-time system. Furthermore, a special case considering only random sampling errors is analyzed, along with its corresponding H∞controller. Compared with existing studies, the proposed method yields a fixed-dimensional linear matrix inequality (LMI) condition that remains unaffected by the upper bound of consecutive DoS attacks and does not require reformulation when network conditions vary, thus enhancing both computational efficiency and implementation flexibility. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation studies on a fourth-order IFMIC under different operations.

This paper proposes a data-driven self-triggered model predictive control (MPC) scheme for constrained linear time-invariant (LTI) systems that are unknown and affected by bounded process and measurement noise. The proposed scheme relies solely on initial input-output data and accounts for noise in both offline and online measurements. A self-triggered control policy is developed based on optimal input-output trajectories obtained by solving the data-driven MPC optimization problem. Recursive feasibility of the control scheme is guaranteed and practical exponential stability of the closed-loop system is established under sufficiently small noise level and certain technical conditions. Two simulation examples are provided to validate the effectiveness of the theoretical results.

In this article, the stability analysis and synthesis issues of networked control systems with asynchronous sensors and controllers are studied, where the stochastic variable obeying a certain probability distribution is introduced to characterize the clock offset between the sensor and the controller. First, a continuous-time framework covering random clock offsets and consecutively lost packets is established. An appropriate discrete-time stochastic augmented model is then constructed to investigate the analysis and synthesis problems of resulting continuous-time framework. Therefore, we prove that the stochastic stability of the discrete-time model implies the stochastic stability of resulting continuous-time system. Based on the discrete-time stochastic augmented model, the random and highly nonlinear terms are decoupled with the help of the law of total expectation, Kronecker product operation, and reduced-order confluent Vandermonde matrix. Subsequently, we present the stability condition in the form of linear matrix inequality and design the desired gain matrix such that the original continuous-time system is stochastically stable. Finally, two numerical examples and a practical example are utilized to clarify the practicability of the designed strategy.

This article focuses on the mean-square exponential stability of networked stochastic systems with time delays (NSSTDs) under nonlinear coupling and deception attacks, employing a sampled-data control strategy. A generalized Halanay inequality for NSSTDs is proposed to investigate the stability of the closed-loop system, where multiple time delays with different bounds are considered, with an incorporation of the graph theory. By the comparison of the continuous control system and the sample-data control system, the equivalence condition on the stabilities of the two systems is studied. Meanwhile, estimates for the maximum tolerable attack probability and the corresponding largest sampling period are obtained. Moreover, a qualitative analysis of various indicators for the deception attacks and the sampling period is revealed. Following this, the theorized results are applied to linear systems with multiple time delays under nonlinear coupling, and matrix inequalities for identifying the appropriate value of the control gain are provided by the generalized Halanay inequality. To show the correctness of the results, computational simulations are conducted.

This article addresses the stabilization of neutral stochastic delay systems (NSDSs) employing aperiodically intermittent controllers (APIC) based on delay feedback and asynchronous switching. To tackle issues arising from the neutral term, we introduce a special auxiliary system (AS) that is not a neutral system, and is distinct from existing literature 41. Utilizing the Lyapunov–Krasovskii functional approach and the iterative method, the stability criterion for the AS is given, which consists of the bound of three delay functions and the duty-cycle. If the stability criterion is satisfied, the AS will achieve mean-square exponentially stability, offering a viable APIC design scheme for non-NSDSs. Additionally, employing the equivalence technique (ET), this article obtains an additional bound for the system delay function, denoted by τ*. When the system delay function$\tau(t)<\tau^{*}$, we demonstrate that the NSDS with intermittent feedback is mean-square exponentially stable if the non-neutral AS is stable. This method is called as AS method based on non-neutral type (ASMbNT). With one comparison, this article reveals that the ASMbNT proposed in this article not only addresses the problem considered in 41, but also yields improved results. Lastly, to demonstrate the effectiveness and validity of the proposed approach, a numerical example is presented.

Open-source electronic design automation (EDA) shows promising potential in unleashing EDA innovation and lowering the cost of chip design. The open-source EDA toolchain is a comprehensive set of software tools designed to facilitate the design, analysis, and verification of electronic circuits and systems. We developed a physical design EDA toolchain (named iPD) from netlist to GDS-II, including design, analysis, and verification. iPD now covers the whole flow of physical design (including floorplan, placement, clock tree synthesis, routing, timing optimization etc.), part of the analysis tools (timing analysis and power analysis), and part of the verification tools (design rule check). For more friendly support EDA research and development and chip design, we design a reliability, extendibility, ease-of-use, and feature richness physical design toolchain. This paper introduces the software structure, functions, and metrics of the iPD toolchain.

Multi-view stereo (MVS) aims to reconstruct the dense 3D geometry of a scene by processing and relating images captured from different viewpoints. Despite impressive successes, most existing techniques simply supervise cost volumes or depth maps through conventional classification or regression methods, thereby inadequately exploring the depth representation’s full potential. Moreover, reconstructing areas with occlusions or weak textures continues to be a long-standing challenge within MVS. Another critical issue, frequently neglected, is the potential inaccuracy of ground truth depths, as evidenced in datasets like DTU. To address these problems, we introduce EA-MVSNet, an innovative error-aware MVS framework designed to enhance depth prediction. The key contributions of this work include three parts: (1) We present a novel error-aware depth representation that enhances depth prediction accuracy through error-aware learning, thereby improving reconstruction quality. (2) We develop a Deformable Feature Pyramid Network (DFPN), meticulously designed to augment reconstruction details in occluded and texture-deficient areas. (3) We introduce a cross-view consistency guidance module into the learning process, effectively mitigating the detrimental effects of ground truth depth inaccuracies and fostering faster convergence. Comprehensive experiments on the DTU dataset and Tanks and Temples dataset validate the superiority of our EA-MVSNet. Compared to the preceding UniMVSNet, EA-MVSNet achieves a notable 7.6% decrease in overall reconstruction error on the DTU dataset, and boosts the mean F-score by 3.0% and 4.1% in the intermediate and advanced groups of the Tanks and Temples dataset, respectively, surpassing most recent state-of-the-art methods.

Die-to-die (D2D) placement is a more challenging stage in achieving higher performance with complex constraints, critically impacting timing, power, yield, cost, etc. Existing placers often rely on indirect objectives (e.g., considering cut sizes in tier assignment), which can lead to a loss of the overall solution space utilization and may even deviate from the actual objective. To address this issue, this paper leverages the natural dominance relationship between decision variables to transform the original problem into a bilevel programming problem equivalently. Additionally, an alternating optimization framework is introduced to enhance the exploration of the overall solution space. On the one hand, we propose two tier optimization operators for simultaneous optimization of wirelength and #terminal in global and detailed perspectives; On the other hand, we present a near-optimal terminal legalization algorithm following an efficient multi-tier co-placement. Compared with the top three winners of the ICCAD'22 contest, our placer achieves 4.33%, 4.42%, and 5.88% smaller wire-length, 79.61 %, 16.74%, and 15.76% fewer #terminal and competitive runtime. Moreover, our placer always uses the fewest #terminal and achieves amazing wirelength reduction when the terminal size changes.

The safety, stability and reliability of the modern complex processes have always been the focus of the industry. An abnormity can lead to failures in the production and manufacturing processes or even dramatic accidents. The fault diagnosis techniques aim to enhance the aforementioned aspects by detecting the system's deviations from the normal operating conditions and providing early warnings. By mining the hidden system features in the historical data, complex physical modeling procedures and the dependence on large amounts of prior knowledge can be avoided. In many practical scenarios, data-driven fault diagnosis algorithms are more suitable for modern industrial diagnosis. In this paper, a novel approach is proposed which integrates both multivariate statistical analysis and deep neural network to deal with the nonlinearities in the complex systems. Based on the theory of traditional data-driven methods, deep learning methods and the newly proposed method, a MATLAB-based fault diagnosis toolbox is developed and published online. Plentiful function libraries are provided to the researchers to analyze those algorithms and satisfy the need of practical industrial applications. By applying the developed toolbox, the characteristics of those algorithms are also compared, especially on the time-consumption feature and the fault discrimination feature.