Redirected Walking (RDW) enables continuous locomotion in virtual environments (VEs) within limited physical spaces. However, classic RDW methods rely on static settings with tight boundaries, which can reduce their applicability in large shared physical spaces where boundary constraints are not dominant, especially under dynamic and multi-user conditions. To overcome this, we introduce a new task: Redirected Walking in Open Physical Spaces (OPSRDW), which allows users to navigate expansive VEs safely and naturally despite dynamic obstacles and without requiring a fixed boundary model. We further propose Dynamic Control and Redirection with Safety Constraints (DyCoRe), which formulates OPS-RDW as a constrained optimization problem. DyCoRe uses Dynamic Control Barrier Functions to model real-time collision avoidance constraints and solves an online Quadratic Programming problem to compute optimal velocities that minimize path deviation while ensuring safety. These velocities are mapped into real-time redirection gains, guiding users along natural paths while reducing collision risk. A relaxation mechanism is incorporated to handle infeasible scenarios. Extensive simulations suggest consistent improvements in safety and obstacle clearance over state-of-theart methods. User studies demonstrate that DyCoRe significantly improves navigation continuity, reduces the number of resets, and tends to reduce perceived discomfort compared to classic RDW strategies. DyCoRe provides an efficient and learning-free solution for safe and natural VR locomotion in open physical spaces with dynamic obstacles and multiple users.

In this work, we propose a privacy-preserving face recognition scheme based on secure similarity comparison on encrypted data. We innovatively split sensitive face embeddings into two shares and encrypt them to guarantee data privacy. We present a novel matrix blinding method to conduct face similarity computation on encrypted face embeddings. Furthermore, we design an effective re-encryption method to achieve non-local secure data update, which reduces the risk of data leakage. Simulation experiments demonstrate that the proposed scheme completes face recognition tasks securely and efficiently. With different size of datasets, scale of embeddings, and number of queries, our scheme accomplishes face recognition tasks at low costs without obvious accuracy penalty.

Saber, as one of the candidates of the NIST Post Quantum Cryptography (PQC) Standardization, is a post-quantum Key Encapsulation Mechanism (KEM) that provides strong security guarantees against quantum adversaries. Due to the high computational complexity, enhancing its efficiency is essential for real-world deployment. In practical applications, the high computational cost of polynomial multiplication remains a major bottleneck to its performance. To address this, we propose a novel optimization method for Saber by introducing a Fermat number transform (FNT) as a key algorithmic improvement. By leveraging a Fermat prime as one of the moduli, the modular multiplications are efficiently implemented by bitshift operations, thereby reducing the arithmetic complexity in negacyclic convolutions. We integrate FNT with a K-reductionbased modular reduction scheme which eliminates the need for precomputation or multiplications and enables reduction to be completed using only shifts and additions. To fully exploit the Intel hardware architecture, we design a high-performance AVX2-based vectorized implementation by utilizing 256-bit SIMD instructions. Specifically, FNT is optimized by using shift-based butterfly operations tailored to the structure of Fermat primes, while the modular reduction is accelerated by bitwise and shift instructions, ensuring high throughput and memory efficiency. Our experimental evaluation shows that the proposed polynomial multiplication scheme achieves a speedup of 14.7 % and reduces cycle counts by up to 8 % compared to previous state-of-the-art implementations. These improvements highlight the efficacy of our method, making it highly suitable for high-performance cryptographic applications.

With the remarkable technological development in cyber-physical systems, industry 4.0 has evolved by use of a significant concept named digital twin (DT). However, it is still difficult to construct a relationship between twin simulation and a real scenario considering dynamic variations, especially when dealing with small surface defect detection tasks with high performance and computation resource requirements. In this article, we aim to construct cyber-manufacturing systems to achieve a DT solution for small surface defect detection task. Focusing on DT-based solution, the proposed system consists of an Edge–Cloud architecture and a surface defect detection algorithm. Considering dynamic characteristics and real-time response requirement, Edge–Cloud architecture is built to achieve smart manufacturing by efficiently collecting, processing, analyzing, and storing data produced by factory. A deep learning–based algorithm is then constructed to detect surface defeats based on multi-modal data, i.e., imaging and depth data. Experiments show the proposed algorithm could achieve high accuracy and recall in small defeat detection task, thus constructing DT in cyber-manufacturing.

Elliptic curve cryptosystem has been widely applied in a lot of fields, such as finance, E-commerce and E-government. In this paper, we propose an optimized FPGA implementation of elliptic curve cryptosystem over 256-bit prime fields, which has high computational performance and low resource consumption. Specifically, we design a novel modular multiplier supporting four-level pipelining, which only needs 7 clock cycles to complete a single modular multiplication. It can process 4 modular multiplication operations (4 MMPO) simultaneously. We further design, on the basis of 4MMPO, a parallel architecture to efficiently implement point doubling and point addition operation. Finally, we testify the validity of our ECC processor on Xilinx's Virtex-7 FPGA platform. The result shows that it takes only 0.15ms for an elliptic curve point multiplication, the maximum frequency of the processor is about 123.27Mhz and the resource only needs 22938 look-up tables (LUTs).

Coral resources in global are facing a huge threat under climate change and increasing human activities. In this paper, at first, combined with Support Vector Machine (SVM), WorldView-3 satellite data (October 10, 2014) with high spatial resolution (1.2 m) and eight spectral bands are used to research classification of Zhao Shu island coral reef substrate. Coral reef, coral bleaching, coral sand and seawater are identified. Then training and checking samples are built artificially and label types from in situ data and finished classification result accuracy test. The results show that the total classification accuracy and Kappa coefficient are 95.28% and 0.90 respectively. These results demonstrate that high resolution images can provide more detailed information and are suitable to monitor the health of coral reefs. High resolution remote sensing classification results of coral reef can provide the important information for environment protection.