Outsourcing encrypted data to the cloud demands efficient and confidentiality-preserving search techniques, yet existing Searchable Symmetric Encryption (SSE) schemes suffer from severe access and search pattern leakage, and the problem is more intractable in multi-data-owner scenarios where cross-dataset privacy-preserving search remains unsolved. This paper proposes PSSE-MDO (Privacy-Preserving SSE with Pattern Obfuscation for Multiple Data Owners), the first SSE scheme that achieves robust access and search pattern hiding for multi-data-owner settings with constant client-side overhead. Our core innovation is a cryptographic protocol enabling users to issue a single constant-sized search token (trapdoor) for searching across all data owners' encrypted indexes. The scheme integrates Inner-Product Predicate Encryption (IPE) and Somewhat Homomorphic Encryption (SHE) to probabilistically obfuscate search results for each query. A key experimental result shows that the client-side cost approximately 120 ms for search token generation—remains constant regardless of the number of data owners, in contrast to existing pattern-hiding schemes with linearly scaling costs. This validates PSSE-MDO's practicality for real-world large-scale collaborative cloud systems, marking a significant step toward practical privacy-preserving search in federated cloud environments.

Linear-to-circular polarization converters find applications in modern 5G and 6G communication systems, satellite communications, and the Internet of Things paradigm. A cost-effective three-dimensional frequency-selective surface that uses water as the active material and silicone rubber as the container is proposed. The operation of the design in the 0.1–1 GHz frequency range is assessed through full-wave simulations. Field distribution maps are employed to elucidate the underlying resonance mechanisms. The stability of the proposed structure with respect to the angle of incidence and frequency is evaluated. A quasi-linear variation of the operating frequency with temperature is demonstrated, enabling temperature tunability. The proposed solution is suitable for prototyping, demonstration, and educational purposes in an eco-friendly context.

DGA (Domain Generation Algorithm) is a sophisticated technique widely adopted by botnets to dynamically generate domain names for their command-and-control servers, thereby evading detection and mitigation efforts. Unlike character-based DGA botnets, word-based DGA botnets usually generate domain names by concatenating meaningful words drawn from English dictionaries. This approach significantly increases the difficulty of distinguishing algorithmically generated domain names from legitimate ones. Conventional detection methods have demonstrated strong performance against character-based DGAs. However, they are generally ineffective or even unable to detect word-based DGAs. To address this challenge, we propose a novel ensemble model that integrates eXtreme Gradient Boosting (XGBoost) with Bidirectional Encoder Representations from Transformers (BERT) for the detection of word-based DGA domain names. Comprehensive experiments conducted on datasets encompassing 13 botnet families demonstrate that the proposed ensemble framework consistently outperforms individual classifiers and existing state-of-the-art approaches, achieving a detection rate of 99.88%.

In order to achieve speed sensorless control and enhance observation accuracy for a Linear Flux-Switching Permanent Magnet (LFSPM) motor in rail transit applications, a sensorless control system is proposed in this paper, utilizing a non-singular fast terminal sliding mode observer (NFTSMO) with a developed control law, and an improved phase-locked loop (PLL). First of all, the mathematical model of the LFSPM is established, and a conventional sliding mode observer (SMO) control strategy is designed. Subsequently, a non-singular fast terminal sliding mode surface is selected to replace the traditional linear sliding mode surface, and a control law tailored for the NFTSMO is developed. The improved observer effectively mitigates chattering and enhances observation precision. Furthermore, the super-twisting sliding mode control (STSMC) algorithm is integrated into the traditional PLL to refine observation accuracy further. Finally, the results of the simulation and hardware-in-the-loop (HIL) experiments show that the proposed control strategy significantly improves the system’s robustness, suppresses chattering, and validates effectiveness and feasibility.

Workflow detection is a challenge issue in the process of Industry 4.0, which plays a crucial role in intelligent production. However, it faces the problem of inaccurate phase classification and unclear boundary positioning, which are not well resolved in previous works. To solve them, this paper develops a temporal-aware workflow detection framework (TransGAN) which takes advantage of the complementarity between Transformer and graph attention network to improve phase discriminability. Specifically, temporal self-attention is firstly designed to learn the relationship between different positions of feature sequence. Then, multi-scale Transformer is introduced to encode pyramid features, which fuses multiple context cues for discriminative feature representation. At last, contextual and surrounding relations are learned in graph attention network for refined phase classification and boundary localization. Comprehensive experiments are performed to verify the effectiveness of our method. Compared to the advanced AFSD, the accuracy is improved by 2.3 % and 2.1 % when tIoU=0.5 on POTFD and THUMOS-14 dataset, respectively. Empirical study of running speed indicates that the proposed TransGAN can be deployed to real-world industrial environment for workflow detection.

This paper presents a novel Sparse Kogge-Stone adder architecture with a sparsity factor of 2, offering a compelling solution to the challenges faced by parallel prefix adders. The superior performance is achieved by including the hybrid carry prefix generator adder (HCPGA), which leads to the elimination of redundant components, and improvements in power consumption and circuit area without compromising computation speed. The proposed hybrid architecture efficiently generates carry prefixes that negates the need for the conventional generate and propagate block, resulting in reduced computational complexity. The effectiveness of the proposed architecture has been extensively validated using Cadence Virtuoso in the 45nm technology node. In addition to evaluating standard performance parameters such as power, delay, and area, comprehensive Monte Carlo simulations and process corner analyses have been performed to ensure the robustness and reliability of the design. Furthermore, the practical application of the proposed architecture has been demonstrated by integrating it into a digital multiplier architecture, showcasing its potential to enhance the computational capabilities of complex arithmetic circuits. This research contributes to the advancement of efficient adder designs for high-performance computing applications, making it highly beneficial and relevant for modern digital circuit designs.

Due to increased information security concerns, biometric recognition technology has become more important. Unimodal biometrics still work effectively, but they struggle with noise sensitivity and spoof attack susceptibility since they rely on a single data source. This paper uses advances in deep learning and machine learning to propose new unimodal systems for the palm, face, and iris. These models use deep wavelet transform networks (WTN) for face and iris identification and deep convolutional neural networks (CNNs) for palmprint identification. In addition, we introduce a novel multimodal biometric system based on unimodal systems. We get 98.29% for face, 98.86% for palmprint, and 95.59% for iris in individual unimodal systems with Support Vector Machines (SVM). This is done by using the new property MULB dataset, which has many biometric features. The multimodal system achieves 99.88% accuracy and a 0.0186 equal error rate, underscoring the relevance of several biometric features and the superior performance of the identification system.

Under continuous lightning strikes, the impulse dispersion characteristics of grounding devices more effectively indicate the effectiveness of tower grounding for lightning protection. These characteristics exhibit distinct behaviors under different lightning current pulse intervals. To investigate these characteristics, the present study employed ATP-EMTP simulation software to develop a dual-pulse model that accounted for the time-varying properties of soil and spark effects. The combined simulation and experimental approach was used to examine, the continuous impulse characteristics of grounding devices. The results reveal that the impulse characteristics under continuous strikes are significantly different from those under single strikes, primarily due to variations in the soil environment surrounding the grounding devices. The spark and recovery effects exert different impacts on soil impulse dispersion performance. Shorter pulse intervals lead to a superposition of first and second impulse currents, which increases current amplitude and spark effect, temporarily reducing soil resistivity and making the recovery effect of soil less pronounced. This results in lower impulse grounding resistance compared to longer intervals. Furthermore, with the increase in soil resistivity, the ratio of the second to the first impulse grounding resistance decreases, indicating that soil resistivity also influences the intensity of the spark effect on soil.

Block matching methods are one of the most widely used methods in motion estimation and compensation. In this work, we propose a new hybrid block matching motion estimation algorithm based on the Lucas and Kanade method as a distortion criterion to improve the accuracy of estimated motion. The proposed algorithm proceeds in three steps. In the first step, a small hexagonal pattern is used, in order to find the smaller motion vectors and thus fewer searching points. In the second step, the modified large hexagonal pattern is used to identify the direction of motion vectors. In the third step, the small hexagonal search pattern is used to refine the solution search. The proposed algorithm is tested on several both synthetic and real images sequences. The experimental results show that our proposal could achieve good performances in terms of amplitude and angular errors, prediction quality, and computational complexity, compared to some related works.

A new hybrid nine level multilevel inverter (H9MLI) topology is proposed and developed with the combination of switched capacitor (SC) and H-bridge structure. The main features of the proposed inverter are voltage boosting capability, reduced switch count, reduced switching control and extended vertical and horizontal topologies. In this paper, the operation modes, modulation strategy, power loss analysis and the simulation results for H9MLI and its extended topology (13 level and 17 level) are presented. The MATLAB/Simulink based simulation of the proposed inverter and its extended topologies for static and dynamic loads are analyzed and verified. The quantitative comparison indicating the merits of proposed topology over various prior-art multilevel inverter topologies were evaluated and tabulated in an inclusive way. The level shifted PWM technique is used for generating control pulses for the proposed inverter.