Visible-Infrared (RGB-IR) Unmanned Aerial Vehicle (UAV) object detection integrates complementary cues from visible and infrared sensors, offering broad application potential. However, due to sensor parallax, it still faces the challenge of weak spatial misalignment, which significantly limits its performance in UAV-based object detection. Existing methods emphasize strict alignment, overlooking spectral heterogeneity under varying illumination. To address these issues, we propose the Illumination Guided Implicit Alignment Network (IGIANet) to mitigate modality heterogeneity without explicit alignment. Specifically, we integrate three novel modules. First, we propose an illumination-guided frequency modulation module that adaptively allocates fusion weights to visible and infrared features based on global illumination estimation, effectively alleviating modality imbalance under varying lighting conditions. Second, we introduce a frequency-guided cross-modality differential enhancement module, which computes differential cues across frequency domains to enhance complementary information and highlight weakly aligned and low-contrast regions. Finally, we introduce an implicit alignment-driven dynamic fusion module that actively estimates offsets and generates dynamic, position-adaptive fusion kernels to align and fuse modalities. Extensive experiments demonstrate that IGIANet outperforms state-of-the-art models on various benchmarks, achieving 80.9% mAP on DroneVehicle, 57.1% mAP on VEDAI, and 49.4% mAP on FLIR.

As a fundamental primitive in privacy-preserving machine learning as a service, encrypted k-nearest neighbors ( $$\textit{k}\text {NN}$$ ) should ideally provide strong confidentiality guarantees while remaining practical for cloud deployment. However, existing schemes often face a trade-off between security and deployability: many single-server solutions do not achieve chosen-plaintext security, whereas stronger constructions typically rely on non-colluding multi-server assumptions. In addition, most existing designs are based on classical hardness assumptions and therefore do not offer post-quantum resilience. To address these limitations, we present $$\mathtt {MEHP\text {-}kNN}$$ , a single-server encrypted $$\textit{k}\text {NN}$$ framework built on secure sorting under $$\texttt{CKKS}$$ -based fully homomorphic encryption. To the best of our knowledge, this framework is among the first in the considered setting to jointly target single-server deployment, chosen-plaintext confidentiality, and post-quantum security based on RLWE assumptions. Since the direct construction incurs high computational and memory overhead, we further propose $$\mathtt {iMEHP\text {-}kNN}$$ , an optimized Top-k query framework that removes homomorphic operations unnecessary for full sorting and reduces the cost of indicator-function evaluation through a refined polynomial design. We implement both schemes using OpenFHE and evaluate them under practical parameter settings. Experimental results show that, for a 128-dimensional feature space and a database of up to 16,384 entries, $$\mathtt {iMEHP\text {-}kNN}$$ achieves a 4 $$\times $$ to 18 $$\times $$ runtime improvement and a 12% to 33% reduction in memory usage compared with the $$\mathtt {MEHP\text {-}kNN}$$ baseline. These results improve the practical feasibility of secure single-server encrypted $$\textit{k}\text {NN}$$ queries in outsourced cloud environments.

As CMOS technology continues to scale, the associated reduction in device reliability margins has made accurate reliability evaluation a critical component of digital circuit design. Traditional methods typically assess reliability based on the average behavior of multiple input vectors (MIVs), while neglecting the significant variation introduced by individual input vector (IIV). In practice, different IVs often exhibit heterogeneous reliability distributions, and in sequential circuits with temporal correlation, these differences may span several orders of magnitude. This paper proposes a probabilistic injection framework for reliability analysis that explicitly considers input correlation in a sequential circuit. The method enables both fine-grained evaluation for each IIV and global assessment across MIVs, thereby offering a comprehensive understanding of not only average circuit reliability but also reliability bounds under specific input conditions. Experimental results on ISCAS’89, ITC’99, IWLS’05 and reference benchmark circuits demonstrate that the proposed approach achieves higher accuracy and greater stability compared to traditional methods.

This paper investigates the problem of eco-friendly autonomous driving platoon control with integrated privacy preserving. A unified car-follower modeling framework is proposed to address single-lane, multi-lane, and mode transition platoon merging scenarios. To safeguard information privacy while maintaining control accuracy, the stochastic vector obfuscation mechanism is developed. Corresponding privacy-preserving control strategies and privacy multiplication protocols (PMP) are designed for each scenario. Theoretical analyses establish stability, consensus, and privacy guarantees. Numerical simulations further confirm the effectiveness in preserving privacy and stability. Moreover, comparative experiments show that the proposed approach outperforms existing methods in terms of privacy preservation, real-time performance, and control accuracy.

Session-based recommendation systems focus on capturing users’ evolving intents from short interaction sequences, yet they persistently face three key challenges: the difficulty in dynamically discriminating between short-term and long-term interests, the inherent tradeoff between sequential modeling and relational dependency learning, and the pervasive noise and sparsity in real-world session data. To tackle these issues, we propose Multivariate Relationship Graph Embedding (MRGE), a novel framework that synergizes enhanced recurrent modeling with graph-structured representations. Specifically, MRGE leverages a self-attention–enhanced RNN to concurrently model short-term intents and long-term preferences within sessions, while constructing a heterogeneous session graph that captures multi-relational item dependencies without compromising temporal fidelity. In addition, we introduce an auxiliary edge augmentation mechanism based on neighbor similarity to mitigate data sparsity and noise, thereby facilitating more robust information propagation. Extensive experiments on three public benchmarks— Delicious , Gowalla , and Foursquare —show that MRGE consistently surpasses state-of-the-art baselines and achieves significant improvements in top- \(K\) recommendation accuracy. Our implementation is available at: https://github.com/July-jz/MRGEcode .

Drug-target interaction (DTI) prediction is an important stage in drug research. Accurate prediction relies on collecting drug and target properties and understanding their complicated interactions. However, conventional approaches struggle to capture small structural differences in medicinal compounds and fail to extract long-range dependence in protein sequences. Moreover, drug and protein characteristics are often combined through simple concatenation, overlooking cross-modal complementarity. To address these constraints, we present a novel DTI prediction model, BPCI-DTI. The model presents a biomolecular graph extractor, BioGraphNet, which leverages multi-level graph isomorphism networks to learn local neighborhood and global topological properties of medicinal compounds, boosting sensitivity to structural alterations. It also blends convolutional neural networks and multi-head convolutional selfattention to capture both local and long-range relationships in protein sequences. Additionally, a cross-modal interaction fusion module with a gating mechanism successfully gathers drug-target interaction information for better prediction accuracy. BPCIDTI was examined on four DTI datasets and outperformed six baseline approaches in AUROC and AUPRC, exhibiting robust predictive performance.

The intelligent connected transportation system represents an inevitable trend, reshaping both traffic participants and control mechanisms. As the number of connected and autonomous vehicles increases, traffic flow is transitioning to a mixed environment of autonomous and manually driven vehicles. The transition to mixed traffic flows introduces critical privacy risks, notably real-time location tracking and unauthorized data collection. To address these challenges, we propose a privacy-aware micro-behavior coordination framework. It combines secure computation protocols and fine-grained control mechanisms for car-following and lane-changing behaviors. In this way, the approach simultaneously maintains traffic efficiency and data privacy. Theoretical proofs and numerical simulations demonstrate the effectiveness of the framework in safeguarding sensitive information while maintaining smooth traffic flow. Critically, our solution bridges the gap between collaborative driving and privacy preservation in mixed traffic.

This paper addresses the leader-following consensus problems in nonlinear multi-agent systems (MASs), focusing on handling malicious denial-of-service (DoS) attacks. Initially, we introduce an innovative dynamic event-triggered scheme to mitigate network congestion induced by DoS attacks. This scheme, utilizing sampled data, aims to minimize the harmful effects of attacks, and ensures the proficient allocation of limited network resources. Specifically, the sampling period automatically switches depending on the system’s attack status, while the threshold parameter within the predefined triggering condition can be adaptively tuned to match the dynamic characteristics of the controlled system. Building upon this, we propose an event-driven control protocol, along with the construction of a new switched MASs model. It is shown, both analytically and numerically, that our designed control strategy exhibits superiority over traditional co-design frameworks from the aspect of three pivotal metrics: average number of event triggers (ANET), convergence time, and control cost.

Intelligent Transportation System (ITS) is a critical component of smart cities, however, certain issues significantly limit the construction of ITS. On the one hand, as the core resource of ITS, traffic data often suffers from missing values due to sensor failure, communication interruption, and so on. On the other hand, traffic flow change is a complex dynamic process that resulted from periodic changes caused by social activities, which increases the difficulty of data completion. To address these issues, the Message Passing Period-Aware Imputation Network (MPPAIN) is proposed. Firstly, the spatial-temporal information is transmitted sequentially in time order by the Message Passing Block based on gated recurrent unit, and the missing values are preliminarily estimated. Then, the output is fed to the Period-Aware Block to find the main frequency components that represent the changes of the traffic flow in the frequency domain through the Fourier transform. Subsequently, the traffic flow is divided according to the major periods, and the second time estimation is completed by extracting the intra-periodic and inter-periodic features simultaneously through the convolutional neural network. Finally, a bi-directional structure is designed by reversing the input traffic flow in time order to further extract the spatial-temporal and periodic features from the future to the past. Experiments demonstrate that the proposed model has excellent data imputation capabilities in various simulated missing rates and missing scenarios on several real traffic datasets.

Spatiotemporal fusion strategies are a crucial direction in traffic flow prediction. However, studies often emphasize the learning of local dynamic spatiotemporal dependencies from historical data while neglecting the potential impacts of label sequence autocorrelation, nonstationary signals, and temporal pattern changes on spatiotemporal dependency modeling. For example, the delayed propagation of abnormal traffic conditions, such as sudden traffic congestion, and abnormal weather between nodes and within sequences may trigger signal shifts, which in turn lead to changes in local flow patterns. Such changes can produce locally dependent misleading learning, making it difficult for spatiotemporal fusion strategies to accurately reflect the true relationships between signals. We propose a framework for traffic flow prediction, which first enhances the original signals in a targeted manner using knowledge of the autocorrelation of sequences through a multiform feature enhancement module, to obtain a more representative and enriched feature representation for model training. The framework processes features by decoupling multi-granularity in temporal patterns, comprehensively identifying complex traffic patterns, and eliminating the impact of nonstationary noise. A dual-channel spatiotemporal fusion network models local spatiotemporal dependencies and global seasonal dependencies to reasonably predict traffic. Experimental results on four real-world datasets show that the original method improves the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics by an average of 5.47%, 4.27%, and 7.05%, respectively, compared to all the metrics of the baseline model over the last two years. We also evaluated the performance of each module through ablation studies.

The input vector-oriented reliability estimation of sequential circuits plays an important role in predicting their reliability boundaries and identifying their reliability-critical gates. This article presents an input vector-oriented programmable method based on fanout-source tracking and reduction for the reliability evaluation of sequential circuits. In the proposed method, fanout-source tracking is introduced to track fanout sources of a computing node to determine the fanout sources affecting the node output signals. An iterative reduction method is presented to eliminate duplicate calculations caused by fanout reconvergences without reducing the accuracy. A dynamic fanout relevance-keeping-based calculation method is used to approximate the trend probability vector of low-priority nodes outputting “0” and “1” to accelerate the calculations at a small accuracy loss. A complexity-accuracy tradeoff method based on a programmable fanout source length is designed to facilitate reasonable calculations as needed. Experimental results on large-scale circuits show that the average relative error of the proposed method is 1.01% with Monte Carlo (MC) as a reference. Moreover, the proposed method is 4,308.13 times faster than the MC on average, but its average memory cost is 3.40 higher than that of the MC model. Compared with similar methods, the proposed method not only is suitable for large-scale circuits and performs better in accuracy, but also enables programmable computation to dynamically balance the tradeoff between accuracy and speed as actual needed, resulting in better applicability and scalability.

A common paradigm is followed by several current click-through rate (CTR) prediction models based on user behavior sequences. They first apply embedding technology to map users’ past behavior to low-dimensional dense vectors and then utilize an attention technique to acquire user interest representation from behavior sequences, using current candidates as queries. However, these approaches overemphasize the role of items similar to the candidate items in the historical sequence and ignore the learning of other contextual features as well as the sequential behavior patterns of users. In this article, we present a deep click-through prediction model that incorporates a multigranularity interest activation and implicit feature interactions. Our model first incorporates the nonlinearly extended user representation in the user behavior sequence and uses multiple fully connected layers to obtain the global user interest representation, thereby improving the model’s memorization ability for users. Then, a multikernel convolutional network is employed to learn the behavior patterns of the user with different window sizes to solve the problem of pattern diversity and interest mutation noise in behavioral sequences. Finally, the model implements implicit second-order feature interactions across the user-side, item-side, and contextual features via a multihead self-attention network, which can maintain the model’s performance in the presence of scarce user behavior sequences. Compared with the benchmark model, deep interest network (DIN), our model achieved RelaImpr gains of 1.67%, 3.36%, and 3.04% on three publicly available datasets and 6.09%, 6.08%, and 10.22% with the elimination of user history behavior sequence information. Experiments and discussions on module ablation and parameters that have a significant impact on model performance are also presented.

In this article, we are devoted to addressing the state-feedback set stabilization of Boolean control networks with state-dependent random impulses by utilizing a hybrid index model. By comparison with the previous impulsive Boolean networks, this model can be used to describe the instantaneousness of various impulsive behaviors more clearly. In order to avoid the occurrence of Zeno phenomenon, we first introduce the basic concept of forward completeness and further establish the judging criterion. After that, an algorithm is presented to derive the largest control invariant subset of a given subset. Based on this, we derive a necessary and sufficient criterion for finite-time feedback set stabilizability. Similarly, the result is also obtained for the asymptotic case, and the asymptotic set stabilizers are designed by dividing the whole state space into several layers. Moreover, we also investigate the relationships between different stabilizabilities. Last, two illustrative examples are presented to demonstrate the efficiency of the theoretical results.

Vehicle intelligent terminals often require end-user applications (APPs) to continuously send information to external data aggregator for monitoring or control tasks. However, communication channels exposed to the open environment can lead to an undesirable loss of privacy for drivers, despite the benefits provided by these APPs. It is worth mentioning that different APPs of vehicle intelligent terminals may require different key information. Given these personalized application scenarios, the use of traditional privacy protection technology may encounter limitations. To address this problem, we propose a compressive privacy-preserving algorithm that employs a compression matrix to maintain the normal function of applications while ensuring privacy. Specifically, the algorithm utilizes sensors to linearly transform the original vectors of measurements at each time step into a lower-dimensional space. The compressed measurements are then transmitted to a fusion center. Optimization problems are formulated for the current time step as well as two time steps. The performance of the algorithm is evaluated using the Cramer-Rao bound. Simulation results and comparisons further validate the effectiveness of the compressive privacy algorithm.

This brief studies the distributed synchronization of time-delay coupled neural networks (NNs) with impulsive pinning control involving stabilizing delays. A novel differential inequality is proposed, where the state's past information at impulsive time is effectively extracted and used to handle the synchronization of coupled NNs. Based on this inequality, the restriction that the size of impulsive delay is always limited by the system delay is removed, and the upper bound on the impulsive delay is relaxed, which is improved the existing related results. By using the methods of average impulsive interval (AII) and impulsive delay, some relaxed criteria for distributed synchronization of time-delay coupled NNs are obtained. The proposed synchronization conditions do not impose on the upper bound of two consecutive impulsive signals, and the lower bound is more flexible. Moreover, our results reveal that the impulsive delays may contribute to the synchronization of time-delay systems. Finally, typical networks are presented to illustrate the advantage of our delayed impulsive control method.

Identifying the critical paths is crucial to reducing the complexity of performance analysis and reliability calculation for logic circuits. In this article, we propose a method for identifying the critical path in a combination circuit using a reinforcement learning framework to enhance its applicability and compatibility. Initially, we configured the learning environment of the model based on circuit structure information to provide valuable information for decision-making on time. Subsequently, the upper confidence bound applied to trees (UCT) algorithm is employed to construct the behavior decision strategy of the model, which avoids invalid traversal and reduces computing costs. Then, a goal-oriented reward and punishment function is constructed based on the distance from the circuit primary outputs. Finally, based on the parallel computing strategy, we construct an adaptive training method to improve the model’s prediction accuracy by using finite sampling, which speeds up the convergence speed and enhances the quality of the model. Experimental results on benchmark circuits show that, with the functional timing analysis method as the reference, the average accuracy of the proposed method is as high as 99.39% and the single average calculation speed is 18.07 times faster than that of the reference method. Compared with the Monte Carlo model, the proposed method has a higher critical path hit rate, and the average calculation speed is 928.75 times faster.

The flourishment of machine learning and deep learning methods has boosted the development of cheminformatics, especially regarding the application of drug discovery and new material exploration. Lower time and space expenses make it possible for scientists to search the enormous chemical space. Recently, some work combined reinforcement learning strategies with recurrent neural network (RNN)-based models to optimize the property of generated small molecules, which notably improved a batch of critical factors for these candidates. However, a common problem among these RNN-based methods is that several generated molecules have difficulty in synthesizing despite owning higher desired properties such as binding affinity. However, RNN-based framework better reproduces the molecule distribution among the training set than other categories of models during molecule exploration tasks. Thus, to optimize the whole exploration process and make it contribute to the optimization of specified molecules, we devised a light-weighted pipeline called Magicmol; this pipeline has a re-mastered RNN network and utilize SELFIES presentation instead of SMILES. Our backbone model achieved extraordinary performance while reducing the training cost; moreover, we devised reward truncate strategies to eliminate the model collapse problem. Additionally, adopting SELFIES presentation made it possible to combine STONED-SELFIES as a post-processing procedure for specified molecule optimization and quick chemical space exploration.

In this paper, an efficient image encryption scheme based on a novel mixed linear–nonlinear coupled map lattice (NMLNCML) system and DNA operations is presented. The proposed NMLNCML system strengthens the chaotic characteristics of the system, and is applicable for image encryption. The main advantages of the proposed method are embodied in its extensive key space; high sensitivity to secret keys; great resistance to chosen-plaintext attack, statistical attack, and differential attack; and good robustness to noise and data loss. Our image cryptosystem adopts the architecture of scrambling, compression, and diffusion. First, a plain image is transformed to a sparsity coefficient matrix by discrete wavelet transform, and plaintext-related Arnold scrambling is performed on the coefficient matrix. Then, semi-tensor product (STP) compressive sensing is employed to compress and encrypt the coefficient matrix. Finally, the compressed coefficient matrix is diffused by DNA random encoding, DNA addition, and bit XOR operation. The NMLNCML system is applied to generate chaotic elements in the STP measurement matrix of compressive sensing and the pseudo-random sequence in DNA operations. An SHA-384 function is used to produce plaintext secret keys and thus makes the proposed encryption algorithm highly sensitive to the original image. Simulation results and performance analyses verify the security and effectiveness of our scheme.

In many practice application, the cost for acquiring abnormal data is quite expensive, thus the one-class classification (OCC) problem attracts great attention. As one of the solutions, support vector data description (SVDD) gains a continuous focus in outlier detection since it is based on the data description. For the sphere obtained by SVDD, both the center and the volume (or radius) strongly depend on the support vectors, while the support vectors are sensitive to the tradeoff parameter C . Hence, how to select this parameter is a rather challenging problem. In order to address this problem, we define several distance metrics relative to the image region in Gaussian kernel space. With the distance metrics, two probability densities relative to the global region and the local region are designed, respectively. Then, the information quantity and the information entropy are developed for regularizing the tradeoff parameter. This novel SVDD is called global plus local jointly regularized support vector data description (GL-SVDD), in which both the global region information and the local image region information jointly penalize the images as possible outliers. Finally, we use the UCI dataset and the hyperspectral data of cherry fruit to evaluate the performance of several OCC approaches. Experimental results show that GL-SVDD is encouraging.

The adaptive neurofuzzy inference system (ANFIS) is a structured multioutput learning machine that has been successfully adopted in learning problems without noise or outliers. However, it does not work well for learning problems with noise or outliers. High-accuracy real-time forecasting of traffic flow is extremely difficult due to the effect of noise or outliers from complex traffic conditions. In this study, a novel probabilistic learning system, probabilistic regularized extreme learning machine combined with ANFIS (probabilistic R-ELANFIS), is proposed to capture the correlations among traffic flow data and, thereby, improve the accuracy of traffic flow forecasting. The new learning system adopts a fantastic objective function that minimizes both the mean and the variance of the model bias. The results from an experiment based on real-world traffic flow data showed that, compared with some kernel-based approaches, neural network approaches, and conventional ANFIS learning systems, the proposed probabilistic R-ELANFIS achieves competitive performance in terms of forecasting ability and generalizability.

In nanometric integrated circuits, to harden reliability-critical gates (RCGs) is an important step to improve overall circuit reliability at a low cost. To locate RCGs quickly and efficiently is a key prerequisite for selective hardening at the early stage of circuit design. This article develops a new approach for locating RCGs for multiple input vectors in combinational circuits, using an input vector-oriented pruning technology to identify RCGs, and a sensitivity-based algorithm to measure the criticality of gate reliability (CGR) for each identified RCG. To accelerate the location of RCGs, a feedback-based algorithm mines the accumulated simulation data for each RCG, and a grouping algorithm handles RCGs with similar CGR in the stage of convergence checking. Simulations on 74-series and ISCAS 85 benchmark circuits show that the average accuracy of the proposed method is 0.986 with Monte–Carlo (MC) as the reference and it is 7181 times faster than the MC model. Also, this method performs better than other approximate algorithms in terms of location accuracy and time overhead.

Redundancy-reliability calculation for deep neural networks is of great importance for their lightweight operation. This article proposes a method for calculating the redundancy-reliability of convolutional neural networks (CNNs) based on strip-median attributes. First, based on the common cause principle, an input-sample-oriented reliability calculation method with automatic boundary detection is constructed to measure the network reliability differences caused by normal classification boundary changes. Next, the adaptive clustering algorithm is used and combined with strip-median attributes to identify the redundant strips of each convolutional layer (CONV) in the networks. Then, based on the central limit theorem and the Pauta criterion, a fault injection method with adaptive convergence ability is constructed to effectively calculate the soft-error-oriented redundancy-reliability of CNNs. The tests of multiple widely used CNNs with multiple datasets showed that with the results of the Monte Carlo (MC) model as the references, the reliability calculation accuracy of the proposed method was approximately 0.9834 and was 3.0% higher than that of the logical flips at bit level (LFBL) method. Additionally, the identification accuracy of the proposed method for redundant nodes in CNNs was better than that of the HRank method and the$1 \times N$pruning method, by average factors of approximately 14.32 and$1.55\times $, respectively. Furthermore, the redundant protection and the adaptive layer geometric center searching mechanism designed by the proposed method were effective. Thus, the proposed method can effectively achieve a one-to-one correspondence between the redundancy and reliability of CNNs.

Reliable circuits help prevent artificial intelligence (AI) systems from being corrupted by the soft errors occurred in memories or combinational circuits, which promotes the development of AI security. However, it is a great challenge to measure the reliability of combinational circuits at register transfer level (RTL) rapidly and efficiently. In this paper, a new fast and accurate computational model based on stochastic computation (SC) is presented to meet these objectives. In the proposed approach, the circuit netlists at RTL are parsed to satisfy the requirements of SC on the bitstream structure of the circuits, and then a Sobol sequence-based algorithm for generating uniform non-Bernoulli sequences is built to reduce the random fluctuations occurred in probability calculations. After that, an adaptive algorithm based on a MAX–MIN ant system is constructed using graphics processing unit-based parallel schemes to greatly accelerate the calculation. The experimental results validate our proposed technique, showing that this approach was approximately 51 and 42 times faster than the traditional SC approach and the stochastic computational model (SCM), respectively; its required sequence length was approximately 1.66 times shorter than that of the traditional SC approach, and its relative error was two times smaller than that of the SCM.

In this study, we discuss how multi-agent systems (MASs) with a leader can achieve distributed bipartite tracking consensus using asynchronous impulsive control strategies. The proposed asynchronous impulsive control approach does not require the impulse to occur simultaneously for all agents. The communication links between neighboring nodes of MASs are antagonistic. When the leader’s control input is non-zero, sufficient conditions are obtained to achieve bipartite asynchronous impulsive tracking consensus in closed-loop MASs. More extensive ranges of asynchronous impulsive effects are discussed, and the designed controller’s feedback can effectively work against adverse impulsive permutation. Simple algebraic conditions for estimating the impulsive gain boundary and asynchronous impulsive interval are presented. Theoretical results are demonstrated with illustrative examples.

Accurate and effective localization of reliability-critical gates (RCGs) is one of the important prerequisites for low-cost circuit fault tolerance in the early stages of circuit design. This article introduces an accurate and effective approach for localizing RCGs in combinational logic blocks through a benchmarking technique. In the proposed approach, uniform non-Bernoulli sequences are used to produce a set of input vectors for driving circuits. A full-period linear congruential algorithm is employed to generate a sequence that provides the sampled order for the RCGs to be analyzed. This ensures that each gate in the circuit is treated as fairly as possible. To accelerate the localization process, an input-vector-based pruning technique combined with a counting method is also introduced to identify the specified number of RCGs. Then, the criticality of gate reliability for each RCG is measured through benchmarking. A clustering algorithm carries out the convergence checking for the proposed approach. The performance of the proposed approach was evaluated in terms of accuracy, stability, and time-space overhead by various simulations on 74-series circuits and ISCAS-85 benchmark circuits. The results show that its accuracy is close to that of the Monte Carlo model and its stability is better than that of other approximate methods. Moreover, compared with approximate methods, the time overhead of our approach is advantageous in the presence of similar memory overheads.

The identification of reliability-critical primary input leads (RCPIs) plays an important role in the testing and prediction of reliability boundaries of logic circuits. This article presents a gate-sensitive-attributes-based approach to estimate the criticality of the primary input leads in combinational circuits to their reliability. Oriented to the input vector, a subcircuit-based traversal method marks the critical input leads of each gate in a circuit. Gate-sensitive attributes and a reverse recursive algorithm quantify the effect of each RCPI on circuit reliability under the input vector. A parallel calculation method based on subcircuits with only one primary output reduces the computational complexity to accelerate the calculation process. Similarity-based clustering avoids unnecessary calculations, and a self-adaptive strategy is used to check convergence. Experimental results on benchmark circuits show that the average accuracy of this approach is 0.9634 with Monte Carlo (MC) as the reference and it is 3445 times faster than the MC on average while its average memory cost is 1.67 greater than the MC model. Although the fitness of the worst input vector obtained by other reference methods is 1.09 times better than that of this approach on average, this approach is approximately 21 times faster than that reference method on average.

Network security issues are significant for cyber-physical systems while existing wireless networks are vulnerable to cyber-attacks. This paper proposes a novel detector for stealthy attacks in distributed consensus-based filtering, which has fewer objects to detect and thus can reduce the time consumption of the detector. Based on the proposed detector, we can analyze the estimated performance of the distributed estimator under a stealthy attack. The purpose of this study is to analyze the properties and performance of the estimator equipped with the proposed detector depending on the adversary’s available resources. First, we study the sufficient condition for distributed state estimation to be resilient based on the assumption that the adversary has sufficient resources. With limited adversary resources, the optimal Kalman gain can be obtained by minimizing state estimation error covariance. Furthermore, a sufficient condition is presented to guarantee the convergence of the estimation error covariance in the estimator equipped with the proposed detector. Finally, a simulation example is provided to demonstrate the effect of the detector on the distributed estimator.

In the brief, delayed impulsive control is investigated for the synchronization of chaotic neural networks. In order to overcome the difficulty that the delays in impulsive control input can be flexible, we utilize the concept of average impulsive delay (AID). To be specific, we relax the restriction on the upper/lower bound of such delays, which is not well addressed in most existing results. Then, by using the methods of average impulsive interval (AII) and AID, we establish a Lyapunov-based relaxed condition for the synchronization of chaotic neural networks. It is shown that the time delay in impulsive control input may bring a synchronizing effect to the chaos synchronization. Furthermore, we use the method of linear matrix inequality (LMI) for designing average-delay impulsive control, in which the delays satisfy the AID condition. Finally, an illustrative example is given to show the validity of the derived results.

The stabilization problem of Boolean control networks (BCNs) under pinning control is investigated in this article, and the set of pinned nodes is minimized. A BCN is a Boolean network with Boolean control inputs in it. When the given BCNs cannot realize stabilization under existing Boolean control inputs, pinning control strategy is introduced to make the BCNs achieve stabilization. The Warshall algorithm is introduced to verify the stabilizability of BCNs, then novel computational feasible algorithms are developed to design the minimum number pinning controller for the system. By using our method, the minimum set of pinned nodes can be found with relatively low computational complexity. Finally, the theoretical result is validated using a biological example.

In this article, synchronization problem in an array of output-coupled Boolean control networks (BCNs) is studied by using event-triggered sampled feedback control. Algebraic forms of an array of output-coupled BCNs are presented via the semitensor product (STP) of matrices. Based on the algebraic forms, a necessary and sufficient condition is obtained for the synchronization of an array of output-coupled BCNs. Furthermore, an algorithm is proposed to design event-triggered sampled feedback controllers. Finally, the obtained results are well illustrated by numerical examples.

This article investigates the set stabilization of probabilistic Boolean control networks (PBCNs) under sampled-data (SD) state-feedback control within finite and infinite time, respectively. First, the algorithms are, respectively, proposed to find the sampled point set and the largest sampled point control invariant set (SPCIS) of PBCNs by SD state-feedback control. Based on this, a necessary and sufficient criterion is proposed for the global set stabilization of PBCNs by SD state-feedback control within finite time. Moreover, the time-optimal SD state-feedback controller is designed. It is interesting that if the sampled period (SP) is changed, the time of global set stabilization of PBCNs may also change or even the PBCNs cannot achieve set stabilization. Second, a criterion for the global set stabilization of PBCNs by SD state-feedback control within infinite time is obtained. Furthermore, all possible SD state-feedback controllers are obtained by using all the complete families of reachable sets. Finally, three examples are presented to illustrate the effectiveness of the obtained results.

In this brief, we investigate the robustness of outputs with respect to disturbances for Boolean control networks (BCNs) by semi-tensor product (STP) of matrices. First, BCNs are converted into the corresponding algebraic forms by STP, then two sufficient conditions for the robustness are derived. Moreover, the corresponding permutation system and permutation graph are constructed. It is proven that if there exist controllers such that the outputs of permutation systems are robust with respect to disturbances, then there must also exist controllers such that the outputs of the corresponding original systems achieve robustness with respect to disturbances. One effective method is proposed to construct controllers to achieve robustness. Examples are also provided to illustrate the correctness of the obtained results.

This paper studies the set stabilization of Boolean control networks (BCNs) under sampled-data state feedback control (SDSFC). The main research content is divided into two parts. First, the topological structure of BCNs under given SDSFC is investigated. The fixed point and sampled cycle are defined, respectively. It is found that sampled cycles allow elements to be repeated and not every element can be regarded as an initial state, and this is quite different from conventional cycles of BCNs. A theorem is presented to calculate the number of fixed points and an algorithm is given to find all fixed points and sampled cycles. Second, the set stabilization problem of BCNs by SDSFC is investigated based on the sampled point set and the sampled point control invariant set (SPCIS). A necessary and sufficient condition is derived for the global set stabilization of BCNs by SDSFC, and further sampled-data state feedback controllers are also designed. The interesting thing is that if a state enters the SPCIS as an unsampled point, then it may run out of the given set again, which is in sharp contrast to conventional BCNs. Finally, an example is given to illustrate the efficiency of the obtained results.

In this paper, we investigate the sampled-data state feedback control (SDSFC) for the synchronization of Boolean control networks (BCNs) under the configuration of drive-response coupling. Necessary and sufficient conditions for the complete synchronization of BCNs are obtained by the algebraic representations of logical dynamics. Based on the analysis of the sampling periods, we establish an algorithm to guarantee the synchronization of drive-response coupled BCNs by SDSFC. An example is given to illustrate the significance of the obtained results.

The sensitivity of circuit input vectors plays an important role in estimating circuit reliability bounds and identifying reliability-critical gates. Consequently, to effectively calculate the circuit sensitivity for the input vectors is becoming a necessity for nanocircuits, helping circuit designers to select the architecture that best optimizes the tradeoffs between reliability and area power delay. Combining probability signals and employing the iterative strategy presented in Monte Carlo method, this paper proposes an iterative algorithm based on a probabilistic transfer matrix to investigate the circuit sensitivity for the given input vectors, ensuring computational precision and speed. Simulation results on benchmark circuits show that the proposed algorithm is an efficient and accurate method to calculate the sensitivity of the applied input vectors, and that it can be used to improve circuit reliability at a small cost in the early stages of circuit design.

In this paper, we investigate the global stability of quaternion-valued neural networks (QVNNs) with time-varying delays. On one hand, in order to avoid the noncommutativity of quaternion multiplication, the QVNN is decomposed into four real-valued systems based on Hamilton rules: $ij=-ji=k,~jk=-kj=i$ , $ki=-ik=j$ , $i^{2}=j^{2}=k^{2}=ijk=-1$ . With the Lyapunov function method, some criteria are, respectively, presented to ensure the global $\mu $ -stability and power stability of the delayed QVNN. On the other hand, by considering the noncommutativity of quaternion multiplication and time-varying delays, the QVNN is investigated directly by the techniques of the Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) where quaternion self-conjugate matrices and quaternion positive definite matrices are used. Some new sufficient conditions in the form of quaternion-valued LMI are, respectively, established for the global $\mu $ -stability and exponential stability of the considered QVNN. Besides, some assumptions are presented for the two different methods, which can help to choose quaternion-valued activation functions. Finally, two numerical examples are given to show the feasibility and the effectiveness of the main results.

This paper investigates the controller designing for disturbance decoupling problem (DDP) of singular Boolean control networks (SBCNs). Using semi-tensor product (STP) of matrices and the Implicit Function Theorem, a SBCN is converted into the standard BCN. Based on the redundant variable separation technique, both state feedback and output feedback controllers are designed to solve the DDP of the SBCN. Sufficient conditions are also given to analyze the invariance of controllers concerning the DDP of the SBCN with function perturbation. Two illustrative examples are presented to support the effectiveness of these obtained results.

Non-parametric statistical methods are applied to verdict that early failure behavior of the testing process may have less impact on later failure process, so it happens in software failure time prediction that one does not have enough information to estimate the software failure process well but do have enough information to estimate the failure data at given instance. The prediction accuracy of software reliability prediction models based on recurrent neural network, feed-forward neural network, relevance vector machine, support vector machine and some nonhomogeneous Poisson process models is compared. Experimental results show that software failure time prediction models based on transduction inference theory could achieve higher prediction accuracy.