Imaging technology has revolutionized the printing industry, enhancing efficiency, quality, and versatility in various printing processes, including graphic applications on 2D and 3D objects, as well as additive manufacturing (AM) or 3D printing - directly building 3D objects layer-by-layer from digital model. This paper exemplifies the applications of imaging technologies in various printing processes of 2D and 3D printing applications. In the realm of packaging printing on 2D substrates, the roles of imaging technologies are highlighted through applications in quantifying paper topography (surface roughness), ink absorption, and dynamic interactions between the print plate, the ink, and the substrate. Regarding AM or 3D printing, imaging technology is of fundamental importance enabling the entire process from digitalization of the object with 3D scanning, CAD design and visualization, to in-situ (real time) monitoring of manufacturing process, and post-production quality inspection, e.g. revealing the relationship between the variation of mechanical strength of 3D printed objects with its pore characteristics have been provided.

This paper presents a novel reduced-reference image quality assessment (RR IQA) method from monocular dynamic scene images for neural radiance fields (NeRF). Despite recent advancement in NeRF, evaluating the performance of NeRF models remains challenging due to the difficulty associated with obtaining ground truth viewpoint images for dynamic scenes. Collecting such ground truth images for NeRF model evaluation typically requires capturing the target scene from multiple synchronized cameras, which is labor-intensive. To address this issue, we propose a novel RR IQA metric called amplitude-dissimilarity (AMDIS), which focuses on evaluating NeRF models without requiring ground truth viewpoint images. The key idea behind AMDIS is that the differences between two near-viewpoint images are mainly absorbed in the phase components. Thus, AMDIS evaluates NeRF models by measuring the dissimilarity between the Fourier amplitude components of the training and synthesized images. Because AMDIS only uses the training and synthesized images, the corresponding ground truth viewpoint images are not required for the evaluation. The experimental results demonstrate that the proposed AMDIS is strongly correlated with major full-reference IQA methods that directly use ground truth viewpoint images.

Recently, the research on traffic accident prediction models via deep learning has attracted significant attention. Many recent high-accuracy accident prediction models rely on bounding boxes obtained from object detection, which cannot predict single-vehicle accidents with a high fatality rate because of their structure. This paper proposes a model that predicts single-vehicle accidents by estimating the probability of accident occurrence at the frame level. The proposed model integrates depth and segmentation information along with RGB images and optical flow information to enhance prediction accuracy. To validate the effectiveness of the proposed model in single-vehicle accident scenarios, this study constructed a CARLA Accident Dataset using a driving simulator and a dataset containing only single-vehicle accident scenes selected from the Detection of Traffic Anomaly dataset. The proposed model demonstrated high accuracy in the investigated datasets, indicating its effectiveness in predicting single-vehicle accidents.

In recent years, the construction industry has been advancing construction DX and ICT construction. These efforts assume the availability of a communication environment, which may be challenging in certain terrains, such as canyons or underground. Self-position estimation, indispensable for the autonomous operation of construction machinery, is also a critical topic. In this study, multiple airborne markers with local coordinates measured by surveying instruments were detected in images acquired by a 360-degree camera mounted on a construction machine to estimate the machine’s self-position.

This study addresses the issue of designing false target waveforms for radar tracking and proposes an intelligent radar-tracking interference waveform design method, the MMISCKF algorithm. The study introduces the residual constraint function of the square root cubature Kalman filter, distance-velocity coupling constraint function, and improved genetic algorithm and designs interference effectiveness evaluation indicators. Comparative experiments show that the MMISCKF interference algorithm can effectively avoid suppression by radar anti-interference methods and achieve radar-tracking loss faster than uniform acceleration towing. The theoretical analysis and experimental results demonstrate that the proposed MMISCKF interference algorithm is a fast and effective radar-tracking false target deception method holding theoretical and engineering significance.

In this study, we propose the systematic offset voltage reduction method considering the channel length modulation effect for the two-stage CMOS operational amplifiers (Op-Amps) and comparators. The proposed method employs the half-circuit of the input stage in two-stage Op-Amps as the output stage. Using the proposed method, each terminal voltage of the MOS transistors in the input and output stages is aligned, and the channel length modulation effect can be ignored. To generalize the proposed method, we applied the proposed method to Op-Amps with the cascode active load and differential difference amplifier. The systematic offset voltage was evaluated and compared by simulation using HSPICE with TSMC 0.18 μm model parameters. Consequently, we confirmed that the proposed method can reduce the systematic offset voltage by 95% or more.

The CAP (Consistence, Availability, Network partitioning) theorem of distributed system determines that the blockchain system cannot guarantee the high availability of the system under the premise of data consistency and partition fault tolerance, which makes it difficult for blockchain-based decentralized application (DApp) systems to deliver as high performance as the classical centralized systems can offer. The performance bottleneck of blockchain applications is an important factor that hinders the implementation of blockchain applications, and how to improve the performance of blockchain system is an important part to improve the blockchain ecology nowadays. However, most of the current optimization algorithms for business flow focus on the business flow itself, and rarely pay attention to the role of the system on business optimization, but the emergence of business flow based on distributed system (blockchain) gives more room for business flow optimization. This paper proposes a performance optimization scheme based on Petri Net combined with blockchain business flow (PNBOA), and simulation experiments show that the scheme can effectively improve the reliability of the system and increase the throughput of the blockchain by 21.2% on the consortium chain system.

Recently, the practical applications of advanced cryptographic protocols, such as Multi-Party Computation (MPC), Fully Homomorphic Encryption (FHE), and Zero Knowledge Proofs (ZKP), have spurred the development of a series of new symmetric encryption primitives. These novel symmetric encryption primitives, referred to as Arithmetization-Oriented (AO) ciphers, aim to minimize the number of field multiplications in large finite fields, including 𝔽_2ⁿ or 𝔽_p. In order to evaluate the algebraic degrees of AO ciphers over 𝔽_2ⁿ, the general monomial prediction technique was proposed by Cui et al. at ASIACRYPT 2022. However, when using their searching tool to evaluate the algebraic degrees of AO ciphers with complex affine layers, the efficiency is low, preventing solutions within a predetermined timeframe. In this study, we extend the propagation rules of monomials for field-based operations and present an automatic searching tool based on Mixed Integer Linear Programming (MILP) and Boolean Satisfiability (SAT) Problem for evaluating the upper bound of the algebraic degrees. Moreover, to accurately calculate the algebraic degrees of monomials in the SAT model, we improve the sequence encoding method, enabling it to accurately determine whether the monomials of degree d exist in the output. We apply our new searching tool to various AO ciphers, including Chaghri, MiMC, and Ciminion. For Chaghri, we compare our results with the Coefficient Grouping technique proposed by Liu et al. at EUROCRYPT 2023, and our results yield tighter upper bounds compared to Liu et al.’s findings. Additionally, we evaluate the algebraic degrees of Chaghri and MiMC with arbitrary complex affine layers and obtain tighter bounds compared to the results from Liu et al. at CRYPTO 2023. Regarding Ciminion, we have observed that starting from the 4-th round, the upper bound on the algebraic degrees for each round actually 1 degree lower than the previous bound. Our searching tool enables a more precise evaluation of the algebraic degrees of AO ciphers, contributing to a deeper understanding of the design and analysis of such primitives.

Single image dehazing is a notoriously challenging task in image processing due to the numerous unknown factors involved. Most existing dehazing methods are based on physical models of the haze formation process. However, real-world haze scenes cannot be accurately mathematically modeled due to the existence of various unquantifiable factors in the scene. Therefore, the dehazing methods based on physical models often perform poorly in complex haze scenes. In this paper, we propose a novel black-box dehazing equation. In this equation, the haze is modelled as an additional image interference layer, without explicitly reasoning about the physical model of haze formation. The dehazing process is modelled as removing this image interference layer. Based on this equation, we propose a novel network architecture called the Black-box Dehazing Network (BDN). Moreover, we propose a joint loss function for training this network. The joint loss function not only evaluates pixel-level differences between the dehazed image and the free image, but also measures differences in texture, color, and structure between the hazy image and its corresponding dehazed version as well as those between the hazy image and its haze-free version. In training, BDNet is only fed pairs of free images and their corresponding hazy images. The corresponding hazy patches are generated on-the-fly during network training. Experimental results demonstrate that the proposed method has the advantage of universality and outperforms existing state-of-the-art dehazing methods.

This letter presents an investigation into the synchronization of an autonomous system comprising two nearly identical canard-generating Bonhoeffer-van der Pol (BVP) oscillators coupled via a resistor and a capacitor in parallel. We first demonstrate via numerical simulations that this system exhibits butterfly synchronization, i.e., a phase shift between the canards in a weakly coupled system of nearly identical canard-generating BVP oscillators. Furthermore, the butterfly synchronization in the coupled system is observed experimentally.

In this paper, we introduce a novel approach to improve secure neural network inference by addressing the challenges posed by homomorphic encryption, specifically within the context of the CKKS scheme. A major limitation in homomorphic encryption is the inability to efficiently handle non-linear activation functions, such as ReLU, due to their non-polynomial nature. We propose an innovative 7th-degree polynomial approximation of the ReLU function, generated using the Remez algorithm, which closely mimics ReLU’s behavior while being fully compatible with encrypted operations. To further optimize performance, we introduce dynamic domain extension techniques, which allow for efficient scaling of inputs during polynomial evaluation, significantly reducing computational overhead. Our method is validated using the MNIST dataset, demonstrating secure inference on encrypted data with 97.93% accuracy, while achieving near-plaintext performance. This work represents a significant step forward in the practical application of homomorphic encryption for neural network inference, providing a more efficient and accurate approach to approximating non-linear functions under encryption.

Many researchers have proposed optimization methods to reduce the computational complexity of the Euclidean projection onto check polytope in the alternating direction method of multipliers (ADMM) decoding for Low-Density Parity-Check (LDPC) codes. Existing the sparse affine projection algorithm (SAPA) projects the vector to be projected onto an χ-dimension affine hull and the dimension χ is fixed, resulting in deteriorating decoding performance. In this letter, bisection method assisted affine projection algorithm is proposed to determine the correct projection dimension for each the vector to be projected with the bisection method iterative algorithm. Simulation results show that the proposed algorithm can improve the accuracy of projection results by 68.2%. The FER performance of the proposed algorithm is almost the same as that of the exact projection algorithm, and compared with the sparse affine projection algorithm (SAPA), it can improve the FER performance by 0.14 dB as well as save average number of iterations by 3.2%.

The channel estimation approaches for indoor multiple-input multiple-output (MIMO) visible light communication (VLC) systems based on compressed sensing (CS) theory, can effectively reduce the pilot overhead for channel training and the computational cost. However, conventional CS reconstruction techniques, such as the sparse adaptive matching pursuit (SAMP), are unable to achieve a satisfactory balance between accuracy and efficiency. To address this issue, we propose an algorithm that combines the fuzzy control strategy with SAMP, namely FC-SAMP. This algorithm utilizes fuzzy rules to simulate human decision-making processes. It dynamically adapts the step size by considering the iterative residuals and their variation rate, to achieve an efficient and accurate estimate for sparse channel state information. The simulation results show that the proposed FC-SAMP outperforms the orthogonal matching pursuit (OMP), the SAMP, and other variable step size algorithms in indoor MIMO VLC systems, in terms of the convergence rate and estimation error.

Metaphase chromosome classifications based on the positional relationship between sister chromatids are used to evaluate the function of the cohesin complex, which tethers sister chromatids until cell division. Currently, classification is manually performed by researchers, which is time consuming and biased. This study aims to automate the analysis using multiple convolutional neural network (CNN)-trained models. By improving our prototype model with a 73.1% concordance rate, one of the proposed new models achieved a maximum concordance rate of 93.33% after applying a fine-tuning method and ensemble learning method. The results suggest that CNN-based models can automatically classify chromosome shapes.