As semiconductor manufacturing advances into ultra-scaled technology nodes, Static Random Access Memory (SRAM) verification faces critical challenges stemming from increasingly stringent geometries, heightened process variability, and the sheer density of contemporary designs. Existing methodologies, notably Design Rule Checking and conventional pattern matching, often fall short in capturing the intricate multilayer interactions and subtle geometric deviations characteristic of advanced SRAM layouts. These limitations can lead to missed layout anomalies and potential functional failures, resulting in diminished yield. This paper introduces a specialized pattern-matching-based algorithm designed to address these challenges in SRAM verification. The algorithm integrates novel localization techniques, efficient spatial indexing, and an overlap detection procedure that together achieve exact verification while delivering substantial improvements in runtime performance. Experimental results confirm that this approach not only maintains 100% detection accuracy on complex SRAM benchmarks but also achieves speedups ranging from threefold to over twentyfold compared to state-of-the-art methods, and from twofold to several thousandfold on classic single-layer pattern matching datasets.

This paper introduces a novel speed control method for Permanent Magnet Synchronous Motors (PMSM) utilizing Linear Active Disturbance Rejection Control (LADRC). The initial step involves collecting data on the motor’s d-axis and q-axis currents, speed, and load torque during stable operation. A small-scale neural network, characterized by low computational demands, is then trained to provide a non-exact load torque estimation. Subsequently, this estimated value of the load torque is incorporated into the LADRC, resulting in a reduction of the pressure calculated by the Extended State Observer (ESO). The discrepancy between the actual and estimated load torque can be viewed as the total disturbance experienced by the system. Finally, a LADRC speed controller that utilizes an imprecise load torque estimation is constructed. To enhance the flexibility and adaptability of the method, a weight coefficient is added to the estimated load torque. The efficacy of this speed control strategy is confirmed through Matlab/Simulink simulation, demonstrating superior control performance when faced with sudden changes in load torque.

Private data alignment, as the prerequisite for multiparty collaborative computation, attracts more attention in recent years, and some existing researches achieve the intersection sharing through two-party private set intersection (PSI) protocol based on various cryptographic techniques. However, they focus on the correctness and confidentiality of the protocol in the two-party scenario, while ignoring the efficiency and scalability in multiparty scenario. Additionally, the multiparty PSI protocol is difficult to be compatible with two parties simultaneously. To this end, we propose an oblivious pseudorandom function-based PSI scheme to achieve the data alignment, which is suitable for two parties and multiple parties. Specifically, to avoid frequent interactions among multiple parties, an efficient filtering algorithm is designed with the assistance of a server. The security proof for semi-honest and corrupted parties is provided, meanwhile, the computation and communication overhead analysis is given in detail. To evaluate the performance, we deploy the proposed scheme in two-party and multiparty scenario, and compare it with the existing protocols to discuss the execution complexity and overhead, which shows the efficiency and scalability of the proposed scheme in the multiparty scenario.

Private set intersection (PSI) has emerged as a key cryptographic protocol, enabling secure data sharing and facilitating collaborative computing among distributed data providers in recent years. However, it remains challenging to achieve efficient multiparty private set intersection (MPSI) for large-scale data and numerous participants in an open environment. To this end, we propose EL-MPSI, an Efficient and Lightweight MPSI scheme based on Vector Oblivious Linear Evaluation (VOLE) and Oblivious Key-Value Store (OKVS), which enables secure data sharing in settings with millions of datasets and dozens of participants. By simplifying the interaction process among multiple participants, the proposed scheme achieves constant-level round complexity and provides resistance against malicious adversaries, as well as collusion attack. Through theoretical analysis and experiments, we demonstrate that the security, efficiency and scalability of our scheme perform better than existing state-of-the-art (SOTA) works. For millions of datasets and dozens of participants, EL-MPSI achieves second-level latency while keeping client communication overhead to approximately 10 MB. Moreover, in scenarios of malicious adversary setting, the extra execution overhead is negligible, which effectively facilitates large-scale data sharing.

Location retrieval based on visual information is to retrieve the location of an agent (e.g.human, robot) or the area they see by comparing their observations with a certain representation of the environment.Existing methods generally treat the problem as a content-based image retrieval problem and have demonstrated promising results in terms of localization accuracy.However, these methods are challenging to scale up due to the volume of reference data involved; and the image descriptions might not be easily understandable/communicable for humans to describe surroundings.Considering that humans often use less precise but easily produced qualitative spatial language and high-level semantic landmarks when describing an environment, a coarseto-fine qualitative location retrieval method is proposed in this work to quickly narrow down the initial location of an agent by exploiting the available information in large-scale open data.This approach describes and indexes a location/place using the perceived qualitative spatial relations between ordered pairs of covisible landmarks from the perspective of viewers, termed as 'qualitative place signatures' (QPS).The usability and effectiveness of the proposed method were evaluated using openly available datasets, together with simulated observations by considering different types perception errors.

Driven by the rapid development of the Internet of Things (IoT) technology, the issue of trust has become increasingly apparent and received considerable scholarly attention in recent years. With the occurrence of various security incidents, data leakage accidents, and service fraud, which seriously affects the quality of service and system efficiency of IoT, trust is fast becoming a key issue in IoT system. In addition to the security and efficiency, trust further contains the reliability, attack resistance, fairness, flexibility, and incentive, resulting in a wide range of new researches from the trust management framework to the quantification method. In this work, various dimensions of trust, including definition, composition, aggregation, and computation are introduced and analyzed. We focus on comprehensive comparison of the state-of-the-art trust management researches and the related applications. Besides, we explore and discuss the important challenges, including performance bottleneck, bidirectional trust, dynamic changes of context, privacy preserving, and cross-domain issue. Some potential enabling technologies for trust management are further analyzed. The objective of this article is to comprehend the trust issue and the composition of trust management in IoT, and illustrate the difference of existing work, thereby motivating further research interest in this field.

Vehicular ad-hoc network enhances driving safety and enables various intelligent transportation applications by adopting the revolutionary vehicular wireless communication technology. This has attracted a lot of attentions from both academia and industry in recent years. Given the sophistication of vehicular manufacturing and the heterogeneity of intelligent transport terminals, performing vehicular authentication is of great importance. The existing schemes have largely considered vehicle security and authentication within a single administrative domain, which lacks supervision of the authority and entity in the intelligent transportation system. In this article, we propose a multidomain vehicular authentication architecture by introducing blockchain technique to build distributed trust and share cross-domain information among multiple administrative domains. To guarantee the anonymity and traceability, a pseudonym-based privacy-preserving authentication method is proposed. Specifically, considering the supervision of authority and the resilience to key escrow, we design a two-phase pseudonym distribution mechanism with the assistance of a roadside unit (RSU) proxy. We conduct in-depth security analysis by comparing with existing works and deploy experiments to show the efficiency and feasibility of the proposed scheme in the multidomain scenario.

Social Internet of Things (SIoT) an emerging Internet-of-Things (IoT) service infrastructure, which integrates the social concept into IoT systems for enhancing service efficiency. By establishing a social relationship among objects, IoT devices can autonomously interact with each other free from human intervention. One critical issue in the development of SIoT is the trust issue, which is essential in fostering cooperation among objects. The existing approaches largely fall short in trust quantification, and also suffer from the bias of trustworthiness evaluation without properly considering inherently dynamic context changes and potential malicious behaviors. In this work, we combine social trust theory and incorporate the unique characteristics of IoT devices to address the trust issue in SIoT. We establish a general trust model which comprehensively captures the competence, willingness, and social relationship in SIoT. Specifically, we define two functions in terms of the Degree of Importance (DoI) and the Degree of Contribution (DoC) to compute the competence and willingness, and we present a formal quantitative trust model that is robust in the dynamic environment and against common malicious attacks. The effectiveness of our proposed trust model is verified through the security analysis and a series of simulation experiments. The results demonstrate that the proposed trust model is reliable and efficient in promoting the success rate of services, as well as improving the efficiency and security of services in SIoT.

Urban infrastructure assets (e.g. roads, water pipes) perform critical functions to the health and well-being of society. Although it has been widely recognised that different infrastructure assets are highly interconnected, infrastructure management in practice such as planning, installation and maintenance are often undertaken by different stakeholders without considering these dependencies due to the lack of relevant data and cross-domain knowledge, which may cause unexpected cascading social, economic and environmental effects. In this paper, we present a knowledge based decision support system for urban infrastructure inter-asset management. By considering various infrastructure assets (e.g. road, ground, cable), triggers (e.g. pipe leaking) and potential consequences (e.g. traffic disruption) as a holistic system, we model each sub-domain using a modular ontology and encapsulate the interdependence between them using a set of rules. Moreover, qualitative likelihood is assigned to each rule by domain experts (e.g. civil engineers) to encode the uncertainty of knowledge, and an inference engine is applied to predict the potential consequences of a given trigger with location specific data and the encoded rules. A web-based prototype system has been developed based on the above concept and demonstrated to a wide range of stakeholders. The system can assist in the process of decision making by aiding data collation and integration, as well as presenting potential consequences of possible triggers, advising on whether additional information is needed or suggesting ways of obtaining such information. The work shows an intelligent approach to integrate and process multi-source data to pioneer a novel way to aid a complex decision process with a high social impact.

In this paper, a new branch-and-price-and-cut algorithm is proposed to solve the one-dimensional bin-packing problem (1D-BPP). The 1D-BPP is one of the most fundamental problems in combinatorial optimization and has been extensively studied for decades. Recently, a set of new 500 test instances were proposed for the 1D-BPP, and the best exact algorithm proposed in the literature can optimally solve 167 of these new instances, with a time limit of 1 hour imposed on each execution of the algorithm. The exact algorithm proposed in this paper is based on the classical set-partitioning model for the 1DBPPs and the subset row inequalities. We describe an ad hoc label-setting algorithm to solve the pricing problem, dominance, and fathoming rules to speed up its computation and a new primal heuristic. The exact algorithm can easily handle some practical constraints, such as the incompatibility between the items, and therefore, we also apply it to solve the one-dimensional bin-packing problem with conflicts (1D-BPPC). The proposed method is tested on a large family of 1D-BPP and 1D-BPPC classes of instances. For the 1D-BPP, the proposed method can optimally solve 237 instances of the new set of difficult instances; the largest instance involves 1,003 items and bins of capacity 80,000. For the 1D-BPPC, the experiments show that the method is highly competitive with state-of-the-art methods and that it successfully closed several open 1D-BPPC instances.

The growth of individualized product demands drives high flexibility of manufacturing processes, which requires large-scale deployment of Industrial Internet of Things (IIoT). Since centralized control of IIoT suffers from poor flexibility in coping with disturbances and changes, a decentralized organization structure is a better choice, in which a permissioned blockchain-driven IIoT can enable partially decentralized self-organization and thus offload and accelerate the optimization of upper-level manufacturing planning. A novel iterative bi-level hybrid intelligence model named ManuChain is proposed to get rid of unbalance/inconsistency between holistic planning and local execution in individualized manufacturing systems. Lower-level blockchain-driven smart contracts proactively decentralize fine-grained and individualized task execution among machine tools via Raspberry Pi-based smart gateways and make the results available on an upper-level digital twin model for iterative coarse-grained holistic optimization. A prototype ManuChain based on a permissioned blockchain network is presented to realize both lower-level crowd self-organizing intelligence and upper-level holistic optimization intelligence.

A vaccine is a biological production that is related to people's lives. Currently, vaccine production supervision is very rough. The vaccine production records are completely controlled by the enterprise. Enterprises only submit production records to review agency for review when they need to sell vaccines. Production records are easy to forge and modify. In order to solve the shortcomings of traditional centralized management. We propose a supervision method for vaccine production based on double-level blockchain. We have designed a double-level blockchain structure. The first level is private data of vaccine production enterprise, including production records and corresponding hash. The next level is public data, including production records hash and vaccine information. In this way, we make vaccine enterprise to submit production records in a timely manner without fear of privacy leaks. We avoid enterprise tampering or falsification of production records through the non-tampering features and time stamps of the blockchain. Through these methods, we have realized efficiency supervision of vaccine production.

We present an interactive decision support system for assisting city infrastructure inter-asset management. It combines real-time site specific data retrieval, a knowledge base co-created with domain experts and an inference engine capable of predicting potential consequences and risks resulting from the available data and knowledge. The system can give explanations of each consequence, cope with incomplete and uncertain data by making assumptions about what might be the worst case scenario, and making suggestions for further investigation. This demo presents multiple real-world scenarios, and demonstrates how modifying assumptions (parameter values) can lead to different consequences.

The problem of automatically recognizing and fitting hyperbolae from ground-penetrating radar (GPR) images is addressed, and a novel technique computationally suitable for real-time on-site application is proposed. After preprocessing of the input GPR images, a novel thresholding method is applied to separate the regions of interest from background. A novel column-connection clustering (C3) algorithm is then applied to separate the regions of interest from each other. Subsequently, a machine learnt model is applied to identify hyperbolic signatures from outputs of the C3 algorithm, and a hyperbola is fitted to each such signature with an orthogonal-distance hyperbola fitting algorithm. The novel clustering algorithm C3 is a central component of the proposed system, which enables the identification of hyperbolic signatures and hyperbola fitting. Only two features are used in the machine learning algorithm, which is easy to train using a small set of training data. An orthogonal-distance hyperbola fitting algorithm for “south-opening” hyperbolae is introduced in this work, which is more robust and accurate than algebraic hyperbola fitting algorithms. The proposed method can successfully recognize and fit hyperbolic signatures with intersections with others, hyperbolic signatures with distortions, and incomplete hyperbolic signatures with one leg fully or largely missed. As an additional novel contribution, formulas to compute an initial “south-opening” hyperbola directly from a set of given points are derived, which make the system more efficient. The parameters obtained by fitting hyperbolae to hyperbolic signatures are very important features; they can be used to estimate the location and size of the related target objects and the average propagation velocity of the electromagnetic wave in the medium. The effectiveness of the proposed system is tested on both synthetic and real GPR data.

This paper presents a binary search heuristic algorithm for the rectangular strip-packing problem. The problem is to pack a number of rectangles into a sheet of given width and infinite height so as to minimize the required height. We first transform this optimization problem into a decision problem. A least-waste-first strategy and a minimal-inflexion-first strategy are proposed to solve the related decision problem. Lastly, we develop a binary search heuristic algorithm based on randomized local search to solve the original optimization problem. The computational results on six classes of benchmark problems have shown that the presented algorithm can find better solutions within a reasonable time than the published best heuristic algorithms for most zero-waste instances. In particular, the presented algorithm is proved to be the dominant algorithm for large zero-waste instances.

In this paper, we report DRM receiver synchronization methods at the tracking stage. The DRM synchronization tracking processes mainly include symbol timing offset estimation, fine fractional carrier frequency offset estimation, and sampling clock frequency offset estimation. All these estimation methods exploit the characteristics of DRM signal structure and gain reference cells inserted into the signal. Their performance is assessed by simulation with DRM full link-level simulator over different shortwave channel models and verified with DRM hardware platform based on FPGA. Both results demonstrate the robustness of the proposed tracking methods.

In this paper, we report DRM receiver synchronization methods at the acquisition stage. Synchronization process consists of robustness mode detection, coarse symbol timing estimation, coarse fractional carrier frequency offset estimation, integer carrier frequency offset estimation, and frame timing estimation. These detection and estimation methods exploit the characteristics of DRM signal structure and pilot cells. Their performance is assessed by simulation with DRM full link simulator and verified with DRM hardware platform based on FPGA.