EMV 3-D Secure is an authentication service mainly to identify and verify cardholders for card-not-present (CNP) transactions over the Internet. EMV 3-D Secure services are provided by international credit card brands such as Visa, Mastercard and American Express, and its protocol is specified by EMVCo. There are known existing works on evaluating security of several versions of 3-D Secure, such as a formal verification using Casper/FDR2 for the old specification (3-D Secure 1.0) and a spoofing attack using reverse engineering on risk assessment indicators for the current specification, EMV 3-D Secure (3-D Secure 2.0). However, there is no security verification of EMV 3-D Secure based on its protocol specification. Formal methods are known as methods that can verify security with high fidelity to the protocol specification and have been actively researched in recent years. In this paper, we verify the security of EMV 3-D Secure using ProVerif, an automated security verification tool for cryptographic protocols. First, one of the difficulties we faced is to correctly extract the detailed protocol structure from the entire specification that is written by natural language over 400 pages. Based on the extracted protocol structure, we formalize Challenge Flow for authentication by secret information under three environments (App-based (default-sdk), App-based (split-sdk), and Browser-based) in the latest version 2.3.1.1, which are specified for the purpose of identity verification in CNP transactions. We then verify the confidentiality and resistance to off-line dictionary attacks of secret information, the authenticity and the resistance to replay attacks against both man-in-the-middle attacks and colluding attacks with relay servers. As verification results, we show that Challenge Flow satisfies all of the above security requirements. Furthermore, we discuss the necessity of the unilateral authenticated channel between the cardholder and the card issuer assumed in the EMV 3-D Secure specification, and show that if we use a public channel instead of a unilateral authenticated channel, Challenge Flow still satisfies security requirements. It indicates that the protocol can be more efficient than the specification without reducing security.

We propose a new method to compute the joint reliability importance which is a useful index for reliability design. The key idea is to apply a special matrix to the computation of the marginal reliability importance. The computational complexity of the existing algorithm for computing the joint reliability importance in terms of each pair of components is the product of the square of the number of components and the computational complexity of computing the reliability of the system. However, we found that a reduction in order to the product of the number of components and the computational complexity of computing the reliability of the system is possible if the system can be represented by a special type of directed graph or any combinatorial model when the sum of disjoint products method is used to compute the reliability of the system.

In conventional fault diagnostic methods, supervised learning-based approaches may not be applicable to practical systems because of the extensive requirements for labeled data. Moreover, conventional approaches have not adequately addressed the challenges posed by sparsely labeled and imbalanced datasets. To address these limitations, we propose a semi-supervised fault diagnostic method based on graph convolutional networks with generative adversarial networks. Distinct from conventional methods, the proposed method instructs a discriminator to extract features from labeled and unlabeled data. The discriminator is employed to construct a similarity matrix to enhance the efficacy of graph-based methods. A graph-based classifier with a discriminator can efficiently perform fault diagnosis without requiring data augmentation. The fault diagnostic methods were evaluated in terms of their classification accuracy to validate the superiority of the proposed method. The simulation results confirm that the proposed method can improve classification accuracy by up to 66% compared with conventional methods.

We investigate the computational complexity of a simple one-dimensional origami problem. We are given a paper strip P of length n + 1 and fold it into unit length by creasing at unit intervals. Consequently, we have several paper layers at each crease in general. The number of paper layers at each crease is called the crease width at the crease. For a given mountain-valley assignment of P, in general, there are exponentially many ways of folding the paper into unit length consistent with the assignment. It is known that the problem of finding a way of folding P to minimize the maximum crease width of the folded state is NP-complete. In this study, we investigate a related paper-folding problem. For any given folded state of P, each crease has its mountain-valley assignment and crease-width assignment. Then, can we retrieve the folded state uniquely when only partial information about these assignments is given? We introduce this natural problem as the crease-retrieve problem, for which there are a number of variants depending on the information given about the assignments. In this paper, we show that some cases are polynomial-time solvable and that some cases are strongly NP-complete.

In this paper we first define (t, s)-completely independent spanning trees, which is a generalization of completely independent spanning trees. A set of t spanning trees of a graph is (t, s)-completely independent if, for any pair of vertices u and v, among the set of t paths from u to v in the t spanning trees, at least s ≤ t paths are internally disjoint. By (t, s)-completely independent spanning trees, one can ensure any pair of vertices can communicate each other even if at most s - 1 vertices break down. We prove that every maximal planar graph has a set of (3, 2)-completely independent spanning trees, every tri-connected planar graph has a set of (3, 2)-completely independent spanning trees, and every 3D grid graph has a set of (3, 2)-completely independent spanning trees. Also one can compute them in linear time.

This work introduces the dueling dice problem, which is a variant of the multi-armed dueling bandit problem. A die is a set of m arms in this problem, and the goal is to find the best set of m arms from n arms (m ≤ n) by an iteration of dueling dice. In a round, the learner arbitrarily chooses two dice α ⊆ [n] and β ⊆ [n] and lets them duel, where she roles dice α and β, observes a pair of arms i ∈ α and j ∈ β, and receives a probabilistic result X_i,j ∈ {0, 1}. This paper investigates the sample complexity of an identification of the Condorcet winner die, and gives an upper bound O(nh^-2(log log h^-1 + log nm²γ^-1)m log m) where h is a gap parameter and γ is an error parameter. Our problem is closely related to the dueling teams problem by Cohen et al. 2021. We assume a total order of the strength over arms similarly to Cohen et al. 2021, which ensures the existence of the Condorcet winner die, but we do not assume a total order of the strength over dice unlike Cohen et al. 2021.

The performance of phase-aware speech enhancement has improved dramatically in recent years. Combined with complex convolutions, deep complex U-Net and deep complex convolution recurrent network (DCCRN) have achieved superior performance in monaural phase-aware speech enhancement. However, these methods optimize the models with loss only in the time domain and ignore the global correlations along the frequency axis that capture the harmonic information between frequency bands. Also, the algorithms based on self-attention exhibit high computational complexity. To strike the balance between performance and computational cost, we propose a new monaural phase-aware method in the time-frequency domain on the deep complex U-Net structure. Specifically, this proposed method incorporates a dual-path recurrent neural network (DPRNN) block in the bottleneck to model both frequency-domain correlation and time-domain correlation. Additionally, attention modules are implemented between the complex encoder and decoder layers. This introduces a more effective way of enhancing the representation of the model, rather than directly concatenating their outputs. Finally, a post-processing module is introduced to mitigate the over-suppression of speech and residual noise. We conduct ablation studies to validate the effectiveness of the dual-path method and the post-processing module. Also, compared to several recent speech enhancement models, the proposed algorithm demonstrates remarkable improvements in terms of objective metrics.

In this paper, a hybrid active and passive (HAP) multiple-input multiple-output (MIMO) radar network is considered, where target returns from both active radar transmitters and illuminators of opportunity (IOs) are employed to complete target detection. With consideration for the active radar power limitation and the total available number constraint for the IOs, the joint discrete power allocation and antenna selection for target detection in HAP MIMO radar is studied. A game-theoretic framework is proposed to solve the problem where the target probability of detection (PD) of the HAP MIMO radar is utilized to build a common utility. The formulated discrete game is proven to be a potential game that possesses at least one pure strategy Nash equilibrium (NE) and an optimal strategy profile that maximizes the PD of HAP radar. The properties of the formulated game, including the feasibility, existence and optimality of NE, are also analyzed. The proposed game's pure strategy NE is determined to be an optimal scheme under certain conditions. An iterative algorithm is then designed to achieve the pure strategy NE. The designed algorithm's convergence and complexity are discussed. It is demonstrated that the designed algorithm can achieve almost optimal target detection performance while maintaining low complexity. Under certain conditions, the designed algorithm can obtain optimal performance.

If scattered X-rays carry information that is independent of that is carried by primary X-rays, the accuracy of attenuation coefficients estimated using both primary and scattered X-rays is expected to be better than that estimated using only primary X-rays. However, because scattered X-rays cannot be easily introduced into conventional X-ray computed tomography (CT), the issue has gained scant attention. This study demonstrates theoretically that the measurement of scattered X-rays improves the accuracy of reconstruction in CT, even in a photoelectric absorption scenario. Here, the CT geometry was simplified for a system that targeted a homogeneous thin cylinder, retaining the necessary configuration. Furthermore, we constructed a mathematical model termed the π-junction model. This model is an extension of the T-junction model used in one of our previous studies. It addresses the photoelectric effect, which was not considered in the T-junction model. The variance in the estimation of the attenuation coefficients of this model from the measurements of both primary and scattered photons was evaluated as the Cramer-Rao lower bound. Both the theory and numerical experiments using Monte Carlo simulation showed that the accuracy of estimating the attenuation coefficient could be improved by measuring the scattered X-rays together with the primary X-rays, even in the presence of photoelectric absorption. This result provides a basis for the superiority of using scattered X-rays.

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.

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.

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 0.14dB as well as save average number of iterations by 3.2%.

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.

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 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.

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.

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.

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.

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 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 CKKSscheme. A major limitation in homomorphic encryption is the inability to efficiently handle non-linear activation functions, such as ReLU, due to their nonpolynomial 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.

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.

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.

This paper proposes standard cell layout style to reduce the block area in double-diffusion break FinFET process. The first generation of FinFET process technology requires a double-diffusion break to shutdown the leakage current under the dummy gate. Double-diffusion break at the edge of the standard cell requires two additional unit cells for the dummy gates and it results in a large block area. We propose a FinFET cell layout style which VDD/VSS diffusions can be shared with adjacent cells. The proposed layout structure places the VDD/VSS-diffusions at the cell edge to place these nodes adjacently, and it eliminates the use of a double diffusion break. We also propose a diffusion reorder algorithm to improve the use of common potential node sharing. Experimental results show that the proposed cell library with a new layout style and reordering algorithm achieves an 8.39% area reduction in on average.

Reliability-based belief propagation (RBP) decoding algorithms are used to decode low-density parity-check (LDPC) codes. However, due to the reliability of comparing and sorting in traditional algorithms, conventional RBP decoders significantly lose in resource consumption. This letter presents an enhanced reliability list-based check-belief propagation (RL-CBP) algorithm. The RL-CBP algorithm reduces computational complexity by scheduling a concise list of check-beliefs. Moreover, the list is applied for comparisons and selections of check-beliefs. The selected checkbelief transforms the decoding message between edges; all check-beliefs are iteratively enlarged according to the reliabilities, and high-performance decoding will be achieved. The simulation results and analyses show that the proposed method achieves a reliability-list gain compared with the checkbelief propagation (CBP) algorithm but consumes much fewer calculations than the traditional RBP algorithm.

This paper studies ΔΣ modulators for discretetime closed loop systems. ΔΣ modulators have been originally developed as efficient analog-to-digital converters (ADCs). Recently, ΔΣ modulators are designed based on the characteristics of the system that uses the ΔΣ modulator. For example in a control system, quantization may degrade control performance due to quantization errors, while the input to any practical system is limited to a range. Then, the saturation of the control input may cause windup phenomena such as overshoots of the system outputs and instability of the control system. In this paper, we propose a design of ΔΣ modulators to mitigate the effects of quantization and saturation in a discrete-time closed loop system. We design the ΔΣ modulator to minimize the norm of the quantization error at the system output to reduce the effects of the quantization error under a stability condition to avoid the saturation of the input on the closed-loop system. Numerical examples are provided to see the effectiveness of our proposed design.

In recent years, chaos maps and opaque predicates have received widespread attention in the field of code obfuscation. Chaos map is proved opaque in n-state predicates code obfuscation. We use n-state opaque predicates to improve the control flow flattening technique and flatten the control structure of the code. Finally, we demonstrate that Henon map scheme outperforms other obfuscation schemes.

Agile Earth observation satellite (AEOS) mission planning (AEOSMP) problem aims to optimize observation efficiency by selecting and scheduling tasks from the Earth's surface, subject to complex resource constraints. Increased flexibility of AEOS presents challenges for autonomous mission planning and scheduling. Deep reinforcement learning (DRL) and clustering tasks are two approaches to enhance the autonomy and observation efficiency of AEOSMP. This letter introduces two innovative algorithms to tackle the AEOSMP problem: the Sequential Clique Clustering and PPO Planning algorithm (SCC-PPO) and the Simultaneous Clustering and Planning PPO Algorithm (SCP-PPO). SCC-PPO initially partitions the mission tasks into cliques, followed by planning. In contrast, SCP-PPO combines clustering and planning into a single, concurrent process. Numerical simulations reveal that SCP-PPO enhances the observation reward by 1.01% to 11.43% compared to SCC-PPO.

Reconfigurable intelligent surface (RIS) is treated as a promising technology for future wireless communications. In this letter, we investigate the active RIS-assisted MISO systems, which can overcome the multiplicative fading effect introduced by the passive RIS. In particular, the active sub-connection architecture of RIS is used to overcome the high power consumption of the existing fully-connected architecture, where each component independently controls its phase shift but shares the same power amplifier. In order to reduce the power loss of each component, we propose to add a switcher device in the power amplifier to select the most appropriate component. As the component is selected, the active number of components is significantly reduced along with less power consumption. Our analysis shows that the introduced switcher brings less performance loss, indicating that higher energy efficiency (EE) can be achieved. Furthermore, EE maximization problems with power constraints are considered in the active RIS assisted system for the architecture with switcher, and the corresponding joint beamforming design is derived. Simulation results show that the proposed structure and method with switcher can effectively improve EE compared with the schemes without switcher.

With the increasing number of tasks undertaken by unmanned aerial vehicle (UAV) clusters, the corresponding flight ad hoc networks must process an increasing amount of service data, and different service types have different transmission requirements. According to these requirements, existing methods often prioritize high-priority packets at the expense of low-priority packets. To overcome this limitation, this paper proposes a dynamic time-division multiple access (TDMA) protocol based on service priority called the SPD-TDMA protocol. To meet the requirements of packet transmission with different priorities, a queue-scheduling algorithm based on maximum waiting time was designed. In this algorithm, packets received from upper-layer applications are allocated to different queues according to their priority, and a corresponding waiting time step is allocated to each queue. Packets with higher priority have shorter waiting times. When the transmission time slot arrives, the packets with the shortest remaining waiting time are sent preferentially, which not only meets the transmission requirements of high-priority packets but also effectively avoids the starvation of low-priority packets. On this basis, to optimize the usage of time-slot resources further, the SPD-TDMA protocol adopts a priority predictive time-slot scheduling mechanism to avoid overbooking time slots or wasting idle time-slot resources to improve the overall efficiency and throughput of the network. Simulation results based on OMNeT++ indicated that compared with the IEEE 802.11DCF, TDMA, and P-TDMA protocols, the proposed method increases the packet delivery rate by 80%, 58%, and 27%, and throughput by 85%, 58%, and 29%, respectively.

A load voltage estimation and regulation system for wireless power transfer circuits exploiting only primary-side control is proposed. The proposed system provides regulated load voltage independent of load and coupling conditions, using neither a secondary-side controller nor an external coil for intercommunication, as required in conventional systems. Adaptive frequency control based on a phase-locked loop technique enables the estimation of load voltage using only the electric parameters measurable on the primary side. Some formulas for voltage estimation and for designing a two-port matching circuit on the secondary side to maximize power transfer efficiency are derived. The versatile and explicit design formulas allow designers to easily determine circuit parameters to theoretically maximize efficiency corresponding to the specifications of each application. It is confirmed that a prototype system implemented on an one-coin-sized printed circuit board regulates the load voltage for several values of load and coupling coefficient.

As low-latency designs tend to have a small number of rounds to decrease latency, the differential-type cryptanalysis can become a significant threat to them. In particular, since a multiple-branch-based design, such as Orthros can have the strong clustering effect on differential attacks due to its large internal state, it is crucial to investigate the impact of the clustering effect in such a design. In this paper, we present a new SAT-based automatic search method for evaluating the clustering effect in the multiple-branch-based design. By exploiting an inherent trait of multiple-branch-based designs, our method enables highly efficient evaluations of clustering effects on this-type designs. We apply our method to the low-latency PRF Orthros, and show a best differential distinguisher reaching up to 7 rounds of Orthros with 2^116.806 time/data complexity and 9-round distinguisher for each underlying permutation which is 2 more rounds than known longest distinguishers. Besides, we update the designer's security bound for differential attacks based on the lower bounds for the number of active S-boxes, and obtain the optimal differential characteristic of Orthros, Branch 1, and Branch 2 for the first time. Consequently, we improve the designer's security bound from 9/12/12 to 7/10/10 rounds for Orthros/Branch 1/Branch 2 based on a single differential characteristic. Moreover, we define Orthros-like three-branch-based PRF in order to investigate the impact of the clustering effect when increasing the number of branches. Based on the results of our evaluation, we show that adding one more branch makes the clustering effect easy to happen, but is promising to enhance the security against differential cryptanalysis.

Double-layer coils (DLCs) have been extensively investigated for compact inductive power transfer (IPT) systems operating in the MHz frequency range. Different from previous studies which focus mainly on realizing self-resonance and enhancing the Q factor, this paper enhances both the Q factor and the self inductance to achieve high efficiency and guarantee target output voltage when deployed in IPT applications. By using a lumped-element model derived from transmission line concept, this paper shows that the self inductance when the two layers are serially connected is approximately more than 3 times of that when the two layers are open-ended. Motivated by this feature, we focus on the series-connected DLC and investigate a resonance scheme using two external capacitors: one inserted between the two layers and the other inserted outside the coil. In this resonance scheme, parameters of the capacitors are chosen not only to enhance the Q factor but also to maintain the self inductance. Our aircored sample coils of 100 mm outer diameter exhibit self inductance of 7.69 μH, Q of 308 at 6.78 MHz, and 94% power transfer efficiency at 50 mm transmission distance. These results are attractive when compared to recent self-resonant open-ended DLCs having similar dimensions.

With the rapid rise of the electric vehicle industry, the gap between electric vehicle ownership and available charging pile is becoming increasingly large. In order to ensure the automatic charging efficiency of electric vehicles, it becomes crucial to achieve identification and location of electric vehicle charging ports efficiently and accurately. However, existing technologies face numerous challenges, such as noise interference, large data volumes, and low registration efficiency, which lead to suboptimal performance in charging port identification and positioning. Existing point cloud data noise reduction, feature point extraction and registration techniques for charging port identification and location have problems such as low noise reduction accuracy, poor quality of extracted points and low registration efficiency. Therefore, this paper proposes an optimization strategy for electric vehicle charging port identification and location based on improved point cloud registration. Firstly, the adaptive K-dimensional tree (K-D Tree) method is used to reduce the noise for point cloud data by dynamically selecting the optimal splitting dimension and value. Next, using the geometric feature information of the point cloud data, high quality feature key points are extracted by clustering analysis. Then, a feedback updating mechanism based on the registration loss function is proposed, which updates the K-D Tree model in real-time by the calculation results of the loss function to improve the registration efficiency as well as the charging port identification accuracy. Finally, simulation experiments are conducted to verify the performance of the proposed method in the identification and location of electric vehicle charging ports. The simulation results indicate that, compared with baseline 1 and baseline 2, the intersection over union (IOU) of proposed algorithm is increased by 43.54% and 55.46%, respectively.

We introduce ”GNN-Opt,”a method that adapts ”DNN-Opt's”machine learning approach for analog circuit design, particularly op-amp sizing. By utilizing Graph Neural Networks, GNN-Opt demonstrates superior efficiency in transferring design knowledge across similar topologies, accelerating to achieve higher FoM with same simulation times.

This paper proposes the continuous-time modeling and analysis methods for a hysteretic buck DC-DC converter to examine its frequency characteristics (f-characteristics) and transient response. Determining the small-signal transfer function to derive the fcharacteristics has been challenging due to the hysteretic behavior of the comparator. In this paper, the hysteretic comparator is modeled as an amplifier with a delay time between the input exceeding the hysteresis window boundary and the output state change, and with a voltage gain defined as the ratio between the comparator's output voltage change and the voltage difference of both input terminals during transition. The delay time is calculated based on the f-characteristics of the designed comparator circuits, and the voltage gain is determined using this delay time and the slew rate of the input signal. Subsequently, the overall small-signal transfer function and frequency characteristics are determined. It is also demonstrated that the proposed model accurately predicts the transient response when the load current changes. To verify the proposed modeling and analysis methods, a hysteretic buck DC-DC converter was designed and circuit-simulated using device parameters from a 0.18μm CMOS process. The simulation results closely matched the calculated frequency characteristics and the transient response.

We consider a zero-order-hold (ZOH) triggered control problem for a chain of integrators under measurement feedback. Two challenging aspects of our control problems are (i) the feedback information is distorted by uncertain noises, (ii) the control input is only updated discretely. With our ZOH triggered controller, we carry out the the system analysis to show that the control performance and ultimate bounds can be adjusted by control parameters and interexecution time. We provide simulation results to confirm the validity of our control method.

This letter emphasizes memory-based computing's potential in enhancing efficiency for wearable devices with limited resources, focusing on active noise control (ANC) in wireless earbuds. Our proposed memory-based selective fixed-filter ANC (MSFANC) scheme reduces power consumption and delay through lookup table (LUT) computation and simplified filter matching. Theoretical analysis and simulations demonstrate MSFANC's effectiveness, offering a new paradigm for resource-constrained wearable systems with non-volatile memories' (NVM) support.

A low-cost super-resolution frequency modulated continuous wave (FMCW) phased array radar employing a single radio-frequency (RF) channel sampling scheme is proposed. Since the conventional phased array radar lacks element-level array signals, it cannot achieve angle super-resolution. By exploiting time sequence phase weighting (TSPW) technology, the proposed radar can obtain the element-level array signals without connecting additional transmit/receive (T/R) components to each antenna element, as that in element-level digital arrays. Furthermore, the freedom of the array has been further enhanced by exploiting the nested array structure. Numerical simulation results are presented to demonstrate the effectiveness of the proposed system.

In this paper, an area-selective deep reinforcement learning scheme is proposed to achieve high-quality wireless localization. The conventional localization schemes based on deep learning face several challenges that are labor-intensive for data collection and labeling, lack of adaptability to large-scale, etc. To address these issues, the proposed scheme incorporates deep reinforcement learning (DRL) with a reward-setting mechanism The localization problem is modeled as a dynamic decision process to leverage the capabilities of DRL. The device location is determined by an iterative decision-making procedure, which is an area-selective process. The proposed scheme consists of two distinct modes, namely train and deployment modes. During the train mode, an agent learns the optimal actions for a given environment. The learned agent estimates the position of device in deployment mode. Simulations were conducted to demonstrate the advantages of the proposed scheme and the results showed that it offers better localization performance, adaptability, and time complexity than conventional schemes.

The application of deep reinforcement learning (DRL) has become a hot research topic in unmanned aerial vehicle (UAV) path planning and resource allocation. However, current DRL methods do not consider coordination among spectrum, path and power, leading to a waste of spectrum resources. A coordinated routing and resource allocation Q network (CRRQN) algorithm with low computing complexity in multiple UAVs scenarios is proposed, and a co-optimization module is proposed to enhance coordination among path planning, spectrum and power allocation in CRRQN by designing their reward functions. Moreover, double deep Q network (DDQN) is employed to guarantee its stability. The simulation shows that the CRRQN algorithm reduces the flight time by about 4% and improves the channel capacity by about 15% compared to the existing algorithms. The running time per test epoch of CRRQN reduces by about 35%.

Cyber Mimic Defense (CMD) is an active defense theory emerging in recent years, and CMD improves system robustness and security by inherent uncertainty, heterogeneity, redundancy, and other characteristics. Among them, scheduling methods, which are the key technologies of CMD, directly affect the ability of mimic systems to resist vulnerabilities and backdoor attacks. However, most of the existing scheduling methods lack a careful study of executor similarity and highorder heterogeneity. Based on this, a fine-grained heterogeneity metric method that considers high-order common vulnerabilities is proposed. Then, an adaptive scheduling method that combines actuator heterogeneity and historical confidence is proposed, and the dynamics and reliability of this scheduling method are verified by simulation experiments. Specifically, under the experimental conditions of 4 and 5 executor redundancy, the experimental experiments were compared with the CRS, TIRTS and RSMHS methods. Through 80 tests, 80 scheduling cycles and the average failure probability of the system were obtained. Experimental results show that compared with the RSMHS scheduling method, the average scheduling cycle of the HCVCS scheduling method proposed in this paper increases by 42.8% and 45.3%, and the average failure probability of the system decreases by 30.4% and 24.8%.

SPECK is a family of lightweight block ciphers. While Sun et al.'s evaluation of SPECK's differential characteristics was the most effective, it left room for further analysis over longer rounds. In this paper, we use a SAT-based search algorithm to analyze SPECK48, SPECK96, and SPECK128, presenting optimal differential characteristics up to 19, 15, and 11 rounds, respectively. We also explore the best characteristics up to 20 rounds for SPECK48, 18 rounds for SPECK96, and 20 rounds for SPECK128.

In this paper we present a simple while comprehensive analytical design procedure for distributed amplifiers. Distributed amplifiers are attractive for designers due to their wideband capability. When designing a distributed amplifier, the first question that comes to mind is how wide the bandwidth can be. This paper answers this question by using the self-matching and low-pass properties of a distributed amplifier. Self-matching property of a distributed power amplifier is an interesting point that distinguishes it from other types of power amplifiers that are usually based on input and output matching networks. Here the estimation of the bandwidth of a distributed amplifier structure is discussed. The equations that are used in this paper can bring good insight and they can assist designers. Furthermore, we have explained the frequency behavior of a tapered distributed amplifier analytically for the first time. In order to validate the approach presented here, we have used published designs including our previously published design as practical examples. The flowchart of the design procedure is also provided.

Elderly action recognition is a more challenging task due to the fact that elderly individuals move with small amplitude and long duration of actions. There are two pivotal issues that warrant further exploration including the development of enhanced temporal feature representations and the expansion of convolutional models' capacity to capture long-range temporal features. In this paper, we propose a novel Skeleton Temporal Fusion Graph Convolutional Network (STF-GCN) for skeleton-based elderly action recognition, which effectively models advanced temporal feature representations. More specifically, the STF-GCN employs three encoding strategies to integrate two types of temporal feature representations. These strategies are designed to capture the intricacies of motion dynamics and the subtleties in action variations, enabling a more accurate and robust recognition of elderly actions. Furthermore, we propose a Skeleton Temporal Fusion (STF) module to highlight the temporal feature representations, employing a structure that alternates between large and small kernel convolutions to achieve various effective receptive fields. The integration of large kernel convolutions allows our model to perceive an expanded temporal context, enhancing its ability to deeply understand action dynamics. Our evaluation demonstrates that the STF-GCN achieves state-of-the-art performance on the largest elderly dataset, ETRI-Activity3D. Additionally, more extensive experimental results show that the STF-GCN is also comparable to other methods in general action recognition tasks.

The deep learning method has been proven to be perfect in the field of multi-ship formation (MSF) recognition for high-frequency surface wave radar (HFSWR). However, the range-Doppler (RD) images of MSF are not always available in large quantities for training. And there is diversification in formation styles. In this paper, we propose a signal processing method for HFSWR formation recognition, which performs RD imaging through coherent accumulation and motion compensation. In the Doppler profile, the peaks are equal to sub-targets. The experiments based on actual RD background verify the feasibility and robustness of the proposed method.

Considering the problem that the vector speed control system of permanent magnet synchronous motor (PMSM) is susceptible to uncertainties such as load disturbances, a model-free control strategy based on terminal second-order sliding mode (TSOSM-MFC) is proposed. First, the mathematical model of PMSM is analysed and processed, and the corresponding ultra-local model of the system is summarised; then, the model-free terminal sliding mode speed controller is designed according to the ultra-local model of the system, and the chattering phenomenon in the system is suppressed by designing the second-order sliding mode reaching law (SOSMRL). Then, the sliding mode disturbance observer (SMDO) is designed to estimate the unknown part of the ultra-local model and compensate the speed loop to improve the robust performance of the system. Finally, the feasibility and effectiveness of the proposed control method are verified by simulation.

In this letter, we prove that binary cycle codes constructed from simple connected graphs have optimal stopping redundancy. For such a code, we also obtain a full-rank parity-check matrix whose number of minimum-size stopping sets is equal to the number of minimum-weight codewords of the code.

This paper analyzed and verified the condition for obtaining the maximum output power with an 85kHz class-D half-bridge zero-voltage-switching (ZVS) inverter. The shunt capacitance in the class-D half-bridge ZVS inverter is formed by nonlinear parasitic capacitance and linear external capacitors. The design equation of the shunt capacitance is derived. For verification, two class-D half-bridge inverters are designed with Si-MOSFETs and SiC-MOSFETs in four specifications. The simulated and experimental waveform verified the validity of the design procedure for achieving the ZVS operation, and the measured result verified the analysis of output power characteristics with good consistency. Furthermore, the relationship between the maximum output power and parasitic capacitance is analyzed. It is clarified that the power MOSFET with smaller parasitic capacitance can obtain higher maximum output power. By comparing the maximum output power between the Si-MOSFET and SiC-MOSFET, it is indicated that the SiC-MOSFET with smaller parasitic capacitance can obtain higher maximum output power than Si-MOSFET, verifying the condition for obtaining the maximum output power.

The quality detection of copper alloys plays a crucial role in enhancing the factory's economic and production efficiency, particularly in addressing surface defects and ensuring component size and specification accuracy. This paper proposes a deep learning-based quality detection method for detecting the defect on the surfaces of copper alloy components, encompassing both surface defect detection and external dimensional quality assessment. For defect detection, the method achieves an accuracy of 94% with an average detection time of 29ms. In dimensional quality detection, the accuracy reaches 96%, with an average detection time of 3 seconds. Validation confirms that this deep learning-based method significantly improves the factory's detection efficiency.

Large-scale image pre-training models have recently demonstrated strong representation capabilities in spatial information contexts. Prior works apply these models to video action recognition through fully fine-tuning, which is expensive and resource-intensive. To reduce computational costs, some studies have shifted their focus to efficient parameter fine-tuning methods. However, existing efficient fine-tuning methods lack exploration of multi-scale information in videos. In this work, the Multi-scale spatio-temporal Adapter (MST-Adapter) is proposed for parameter-efficient Image-to-Video transfer learning. By freezing the pretrained models and adding the lightweight adapters, we only need to update few parameters, which is highly efficient. In addition, extensive experiments on two video action recognition benchmarks show that our method can learn high-quality video spatio-temporal representations and achieve competitive or even better performance than prior works.