In this letter, we propose a single frame based method to remove the stripe noise, meanwhile preserving the vertical details. The key idea is to employ the side-window filter to performedge-preserving smoothing, and then accurately separate the stripe noise via a 1D column guided filter. Experimental results demonstrate the effectiveness and efficiency of our method.

The performance of target detection and tracking is primarily limited by ionospheric interference in High Frequency Surface Wave Radar (HFSWR). Joint Domain Localised (JDL) has been proved to be an effective algorithm for ionospheric clutter suppression in HFSWR. However, the implementation of JDL in the traditional CPU platform cannot afford the real-time requirement in HFSWR. With the help of the tremendous parallel computational horsepower in GPU, in this paper we investigate the real-time implementation of JDL algorithm for HFSWR using Graphics Processing Unit (GPU). We also perform a comparative analysis in terms of the performance using the CPU-based implementation and the GPU-based implementation. Experimental result shows that the GPU-based implementation accelerates the computation by over 24.72 times as compared to the CPU-based implementation which meets the real-time requirement of HFSWR.

In this paper, we propose a class of 1-generator quasi-twisted codes with special structures and investigate their application to construct ternary quantum codes. We discuss the algebraic structure of these 1-generator quasi-twisted codes and their dual codes. Moreover, sufficient conditions for these quasi-twisted codes to satisfy Hermitian self-orthogonality are given. Then, some ternary quantum codes exceeding the Gilbert-Varshamov bound are derived from such Hermitian self-orthogonal 1-generator quasi-twisted codes. In particular, sixteen quantum codes are new or have better parameters than those in the literatures, eight of which are obtained by the progapation rules.

In this paper, we present a framework for composing discrete-event simulation models from a large amount of airspace traffic data without using any specific waypoints. The framework consists of two parts. In the first part, abstracted route graphs that indicate representative routes in the airspace are composed. We propose two methods for extracting important routes in the form of graphs based on combination of various technologies such as space partition, trajectory clustering, and skeleton extraction. In the second part, discrete-event simulation models are composed based on statistical information on flight time along each edge of the abstracted route graph. The composed simulation models have intermediate granularity between micro models, such as multi-agent simulation, and macro models, such as queuing models, and therefore they should be classified as mesoscopic models. Finally, we show numerical results to evaluate the accuracy of the simulation model.

Toffoli gates are an important primitive in reversible Boolean logic. In quantum computation, these Toffoli gates are composed using other elementary gates, most notably the Clifford+T basis. However, in fault-tolerant implementations of quantum circuits, the T-gate incurs extra cost relative to Clifford gates like the S-gate and CNOT gate. Relative-phase Toffoli Gates (RTOFs) have been proposed as a way to reduce this T-count at the cost of incurring a relative phase that could skew the final quantum states. In this paper, we utilize an observation that the relative phase which RTOFs introduce can be canceled by the appropriate application of less expensive S-gates instead of T-gates. It leverages alternate forms of the RTOF including incorporating S-gates into it or moving around its input bits in order to simplify the logic to erase the relative phase. We find experimentally that our method has a clear advantage in most cases, and identify several types of circuits that it could be synergistic with.

To super-resolve low-resolution (LR) face image suffering from strong noise and fuzzy interference, we present a novel approach for noisy face super-resolution (SR) that is based on three-level information representation constraints. To begin with, we develop a feature distillation network that focuses on extracting pertinent face information, which incorporates both statistical anti-interference models and latent contrast algorithms. Subsequently, we incorporate a face identity embedding model and a discrete wavelet transform model, which serve as additional supervision mechanisms for the reconstruction process. The face identity embedding model ensures the reconstruction of identity information in hypersphere identity metric space, while the discrete wavelet transform model operates in the wavelet domain to supervise the restoration of spatial structures. The experimental results clearly demonstrate the efficacy of our proposed method, which is evident through the lower Learned Perceptual Image Patch Similarity (LPIPS) score and Fréchet Inception Distances (FID), and overall practicability of the reconstructed images.

Radio Frequency Identification (RFID) is one of the key technologies of the Internet of Things. However, during its application, it faces a huge challenge of co-frequency interference cancellation, that is, the tag collision problem. The multi-tag anti-collision problem is modeled as a Blind Source Separation (BSS) problem from the perspective of system communication transmission layer signal processing. In order to reduce the cost of the reader antenna, this paper uses the boundedness of the tag communication signal to propose an underdetermined RFID tag anti-collision method based on Bounded Component Analysis (BCA). This algorithm converts the underdetermined tag into the signal collision model is combined with the BCA mechanism. Verification analysis was conducted using simulation data. The experimental results show that compared with the nonnegative matrix factorization (NMF) algorithm based on minimum correlation and minimum volume constraints, the bounded component analysis method proposed in this article can perform better. Solving the underdetermined collision problem greatly improves the effect of eliminating co-channel interference of tag signals, improves the system bit error rate performance, and reduces the complexity of the underdetermined model system.

In 2004, Ryoh Fuji-Hara et al. IEEE Trans. Inf. Theory. 50(10):2408-2420, 2004) proposed an open problem of finding a maximum multiplicative subgroup G in ℤ_n satisfying two conditions: (1) the sum of any two distinct elements in G is nonzero; (2) any difference from G is still a unit in ℤ_n. The subgroups satisfying Condition (2) is called difference unit group. Difference unit group is related to difference packing, zero-difference balanced function and partitioned difference family, and thus have many applications in coding and communication.

Suppose the canonical factorization of n is $\prod_{i=1}^{k}{p_i^{e_i}}$. In this letter, we mainly answer the open problem with the result that the maximum cardinality of such a subgroup G is $\frac{d}{2^m}$, where d = gcd(p₁ - 1,p₂ - 1,…,p_k - 1) and m = v₂(d). Also an explicit construction of such a subgroup is introduced.

The Sparkle permutation family is used as an underlying building block of the authenticated encryption scheme Schwaemm, and the hash function Esch which are a part of one of finalists in the National Institute of Standards and Technology (NIST) lightweight cryptography standardization process. In this paper, we present distinguishing attacks on 6-round Sparkle384 and 7-round Sparkle512. We used divide-and-conquer approach and the fact that Sparkle permutations are keyless, as a different approach from designers' long trail strategy. Our attack on Sparkle384 requires much lower time complexity than existing best one; our attack on Sparkle512 is best in terms of the number of attacked rounds, as far as we know. However, our results do not controvert the security claim of Sparkle designers.

Radio Frequency Identification (RFID) is crucial for the Internet of Things, with a key challenge being the efficient prevention of tag collisions for quick identification. This paper presents a novel approach for rapid tag recognition in small to medium-sized warehouses, combining a tag optimization feature set with a tail code recognition mechanism. To minimize the frequency of scanning for duplicate tags and reduce the occurrence of collisions, we construct an optimization feature set based on the reader's position. This set helps in assessing the likelihood of tag repetition through its linear variation. It also incorporates a tail code mechanism that recognizes only the last 22 digits of the tag's EPC code, significantly speeding up identification. The tail code length is dynamically adjusted based on the number of tags to maintain uniqueness. Simulation results indicate that our approach significantly reduces the identification of duplicate tags and minimizes the instances of collisions.

Astate-space approach for adaptive second-order IIR notch digital filters is explored. A simplified iterative algorithm is derived from the gradient-descent method to minimize the mean-squared output of an adaptive notch digital filter. The stability and parameter-estimation bias are then analyzed by employing a first-order linear dynamical system. As a consequence, it is clarified that the resulting parameter estimate is unbiased. Finally, a numerical example is presented to demonstrate the validity and effectiveness of the adaptive state-space notch digital filter and bias analysis of parameter estimation.

In this paper, we propose a novel communication scheme that combines reconfigurable intelligent surface with transmitted adaptive space shift keying (RIS-TASSK), where the number of active antennas is not fixed. In each time slot, the desired candidate antenna or antenna combination will be selected from all available antenna combinations for conveying information bits. Besides, an antenna selection method based on channel gains is proposed for RIS-TASSK to improve the bit error rate (BER) performance and decrease the complexity, respectively. By comparing with the RIS-aided transmitted space shift keying and RIS-aided transmitted generalized space shift keying schemes, the simulation and theoretical results showthat the proposed scheme has better BER performance and appropriate complexity.

We consider the Doppler ambiguity compensation problem for weak moving target detection in passive bistatic radar. Detecting an unknown high-speed weak target has a high probability of the presence of Doppler ambiguity, which will decrease the integration performance and accordingly make the target detection difficult under low signal-to-noise ratio (SNR) environments. Resorting to the well-known keystone transform (KT) method, an approach to compensate for the Doppler ambiguity within the batch is proposed for the first time. The proposed approach establishes a good coupling between the reference and echo signals by adding a frequency shift related to the Doppler frequency in the procedure of computing the cross ambiguity function (CAF). Simulation results show that the coherent integration gain of our approach is close to the theoretical upper bound even in the presence of Doppler ambiguity.

Differential factors, introduced by Tezcan and Özbudak at LightSec 2014, are properties of the S-boxes that equalize the counters of some guessed keys, thereby reducing the key space for the key guess process. Differential factors have been used to reduce the key space for the attacks on SERPENT, PRESENT, PRIDE, and RECTANGLE. In this paper, we demonstrate that some differential factors do not actually reduce the key space for the differential-linear attack on SERPENT and the relatedkey differential attack on RECTANGLE. Moreover, by comparing these instances with the differential attack on PRESENT, where differential factors do have an effect, we identify a sufficient condition for the practical use of differential factors. This condition enables preemptive identification of differential factors that could impact the key space for attacks on other ciphers.

The task of Speech Emotion Detection (SED) aims at judging positive class and negetive class when the speaker expresses emotions. The SED performances are heavily dependent on the diversity and prominence of emotional features extracted from the speech. However, most of the existing related research focuses on investigating the effects of single feature source and hand-crafted features. Thus, we propose a SED approach using multi-source low-level information based recurrent branches. The fusion multi-source low-level information obtain variety and discriminative representations from speech emotion signals. In addition, focal-loss function benifit for imbalance classes, resulting in reducing the proportion of well-classified samples and increasing the weights for difficult samples on SED tasks. Experiments on IEMOCAP corpus demonstrate the effectiveness of the proposed method. Compared with the baselines, MSIR achieve the significant performance improvements in terms of Unweighted Average Recall and F1-score.

In this letter, we propose a method to introduce tabu search into Edge Assembly Crossover (EAX), which is an effective crossover method in solving the traveling salesman problem (TSP) using genetic algorithms. The proposed method, called EAX-tabu, archives the edges that have been exchanged over the past few generations into the tabu list for each individual and excludes them from the candidate edges to be exchanged when generating offspring by the crossover, thereby increasing the diversity of edges in the offspring. The effectiveness of the proposed method is demonstrated through numerical experiments on medium-sized instances of TSPLIB and VLSI TSP.

To fully exploit the attribute information in graphs and dynamically fuse the features from different modalities, this letter proposes the Attributed Graph Clustering Network with Adaptive Feature Fusion (AGC-AFF) for graph clustering, where an Attribute Reconstruction Graph Autoencoder (ARGAE) with masking operation learns to reconstruct the node attributes and adjacency matrix simultaneously, and an Adaptive Feature Fusion (AFF) mechanism dynamically fuses the features from different modules based on node attention. Extensive experiments on various benchmark datasets demonstrate the effectiveness of the proposed method.

With the emphasis on personal information privacy protection in wireless communications, the new dimension low-interception covert transmission technology represented by the vortex wave with Orbital Angular Momentum (OAM) has received attention from both academia and industry. However, the current OAM low-interception transmission techniques all assume that the eavesdropper can only receive plane wave signals, which is a very ideal situation. Once the eavesdropper is configured with an OAM sensor, the so-called mode covert channel will be completely exposed. To solve this problem, this paper proposes a vortex microwave photon low-interception transmission method. The proposed method utilizes the differential operation between plane and vortex microwave photons signals to construct the covert differential channel, which can hide the user data in the mode domain. Compared with the traditional spread spectrum transmission, our proposed covert differential channel schemes need less transmitted power to achieve reliable transmission, which means less possibility of being intercepted by the eavesdropper.

In this paper, we consider the spectra of Boolean functions with respect to the nega-Hadamard transform. Based on the properties of the nega-Hadamard transform and the solutions of the Diophantine equations, we investigate all possibilities of the nega-Hadamard transform of Boolean functions with exactly two distinct nega-Hadamard coefficients.

Automated tongue segmentation plays a crucial role in the realm of computer-aided tongue diagnosis. The challenge lies in developing algorithms that achieve higher segmentation accuracy and maintain less memory space and swift inference capabilities. To relieve this issue, we propose a novel Pool-unet integrating Pool-former and Multi-task mask learning for tongue image segmentation. First of all, we collected 756 tongue images taken in various shooting environments and from different angles and accurately labeled the tongue under the guidance of a medical professional. Second, we propose the Pool-unet model, combining a hierarchical Pool-former module and a U-shaped symmetric encoder-decoder with skip-connections, which utilizes a patch expanding layer for up-sampling and a patch embedding layer for down-sampling to maintain spatial resolution, to effectively capture global and local information using fewer parameters and faster inference. Finally, a Multi-task mask learning strategy is designed, which improves the generalization and anti-interference ability of the model through the Multi-task pre-training and self-supervised fine-tuning stages. Experimental results on the tongue dataset show that compared to the state-of-the-art method (OET-NET), our method has 25% fewer model parameters, achieves 22% faster inference times, and exhibits 0.91% and 0.55% improvements in Mean Intersection Over Union (MIOU), and Mean Pixel Accuracy (MPA), respectively.

Join is an important but data-intensive and compute-intensive operation in database systems. Moreover, there are multiple types of join operations according to different join conditions and data relationships with diverse complexities. Because most existing solutions for accelerating the join operation on field programmable gate arrays (FPGAs) focus only on the easiest join application, this study presents a novel architecture that is suitable for multiple types of join operation. This architecture has a modular design and consists of three components that are executed sequentially and in pipeline. Specifically, the top-K sorter is used instead of the full sorter to reduce resource utilization and advance the merge processing. Further, the architecture is perfectly compatible with both N-to-1 and N-to-M join relationships, and can also adapt well to both equi-join and band-join. Experimental results show that this design, which is implemented on an FPGA, achieved a high join throughput of 242.1 million tuples per second, which is better than other reported FPGA implementations.

To address the challenge of target signals being completely submerged by ionospheric clutter during typhoon passages, this letter proposes a chaotic detection method for target signals in the background of ionospheric noise under typhoon excitation. Experimental results demonstrate the effectiveness of the proposed method in detecting target signals with harmonic characteristics from strong ionospheric clutter during typhoon passages.

Constructing new families of APN functions is an important and challenging topic. Up to now, only six infinite families of APN monomials have been found on finite fields of even characteristic. To study APN functions, partially APN functions have attracted plenty of researchers' particular interests recently. In this paper, we propose several new infinite classes of 0-APN power functions over 𝔽_2ⁿ by using the multivariate method and resultant elimination. Furthermore, we use Magma soft to show that these 0-APN power functions are CCZ-inequivalent to the known 0-APN power functions.

Recently, trace representation of a class of balanced quaternary sequences of period p from the classical cyclotomic classes was given by Yang et al. (Cryptogr. Commun.,15 (2023): 921-940). In this letter, based on the generalized cyclotomic classes, we define a class of balanced quaternary sequences of period pⁿ, where p = ef + 1 is an odd prime number and satisfies e ≡ 0 (mod 4). Furthermore, we calculate the defining polynomial of these sequences and obtain the formula for determining their trace representations over ℤ₄, by which the linear complexity of these sequences over ℤ₄ can be determined.

This paper serves as an introductory overview of quantum deletion error-correction codes, a burgeoning field within quantum coding theory. Covering foundational concepts, existing research, and open questions, it aims to be the first accessible resource on the subject. This paper contains basic definitions of terms so that readers can read it regardless of their background. This paper invites readers to explore this primer and take their initial steps into the realm of quantum deletion error-correcting codes research.

Radar emitter identification (REI) is a crucial function of electronic radar warfare support systems. The challenge emphasizes identifying and locating unique transmitters, avoiding potential threats, and preparing countermeasures. Due to the remarkable effectiveness of deep learning (DL) in uncovering latent features within data and performing classifications, deep neural networks (DNNs) have seen widespread application in radar emitter identification (REI). In many real-world scenarios, obtaining a large number of annotated radar transmitter samples for training identification models is essential yet challenging. Given the issues of insufficient labeled datasets and abundant unlabeled training datasets, we propose a novel REI method based on a semi-supervised learning (SSL) framework with virtual adversarial training (VAT). Specifically, two objective functions are designed to extract the semantic features of radar signals: computing cross-entropy loss for labeled samples and virtual adversarial training loss for all samples. Additionally, a pseudo-labeling approach is employed for unlabeled samples. The proposed VAT-based SS-REI method is evaluated on a radar dataset. Simulation results indicate that the proposed VAT-based SS-REI method outperforms the latest SS-REI method in recognition performance.

This study explores adaptive output feedback leader-following in networks of linear systems utilizing switching logic. A local state observer is employed to estimate the true state of each agent within the network. The proposed protocol is based on the estimated states obtained from neighboring agents and employs a switching logic to tune its adaptive gain by utilizing only local neighboring information. The proposed leader-following protocol is fully distributed because it has a distributed adaptive gain and relies on only local information from its neighbors. Consequently, compared to conventional adaptive protocols, the proposed design method provides the advantages of a very simple adaptive law and dynamics with a low dimension.

The rapid advancement of cloud-edge-end collaboration offers a feasible solution to realize low-delay and low-energy-consumption data processing for internet of things (IoT)-based smart distribution grid. The major concern of cloud-edge-end collaboration lies on resource management. However, the joint optimization of heterogeneous resources involves multiple timescales, and the optimization decisions of different timescales are intertwined. In addition, burst electromagnetic interference will affect the channel environment of the distribution grid, leading to inaccuracies in optimization decisions, which can result in negative influences such as slow convergence and strong fluctuations. Hence, we propose a cloud-edge-end collaborative multi-timescale multi-service resource management algorithm. Large-timescale device scheduling is optimized by sliding window pricing matching, which enables accurate matching estimation and effective conflict elimination. Small-timescale compression level selection and power control are jointly optimized by disturbance-robust upper confidence bound (UCB), which perceives the presence of electromagnetic interference and adjusts exploration tendency for convergence improvement. Simulation outcomes illustrate the excellent performance of the proposed algorithm.

In this letter, we give a characterization for a generic construction of bent functions. This characterization enables us to obtain another efficient construction of bent functions and to give a positive answer on a problem of bent functions.

We propose a pre-T event-triggered controller (ETC) for the stabilization of a chain of integrators. Our per-T event-triggered controller is a modified event-triggered controller by adding a pre-defined positive constant T to the event-triggering condition. With this pre-T, the immediate advantages are (i) the often complicated additional analysis regarding the Zeno behavior is no longer needed, (ii) the positive lower bound of interexecution times can be specified, (iii) the number of control input updates can be further reduced. We carry out the rigorous system analysis and simulations to illustrate the advantages of our proposed method over the traditional event-triggered control method.

Multi-object tracking (MOT) algorithms are typically classified as one-shot or two-step algorithms. The one-shot MOT algorithm is widely studied and applied due to its fast inference speed. However, one-shot algorithms include two sub-tasks of detection and re-ID, which have conflicting directions for model optimization, thus limiting tracking performance. Additionally, MOT algorithms often suffer from serious ID switching issues, which can negatively affect the tracking effect. To address these challenges, this study proposes the DETrack algorithm, which consists of feature decomposition and feature enhancement modules. The feature decomposition module can effectively exploit the differences and correlations of different tasks to solve the conflict problem. Moreover, it can effectively mitigate the competition between the detection and re-ID tasks, while simultaneously enhancing their cooperation. The feature enhancement module can improve feature quality and alleviate the problem of target ID switching. Experimental results demonstrate that DETrack has achieved improvements in multi-object tracking performance, while reducing the number of ID switching. The designed method of feature decomposition and feature enhancement can significantly enhance target tracking effectiveness.

Images with rich color information are an important source of information that people obtain from the objective world. Occasionally, it is difficult for people with red-green color vision deficiencies to obtain color information from color images. We propose a method of color correction for dichromats based on the physiological characteristics of dichromats, considering hue information. First, the hue loss of color pairs under normal color vision was defined, an objective function was constructed on its basis, and the resultant image was obtained by minimizing it. Finally, the effectiveness of the proposed method is verified through comparison tests. Red-green color vision deficient people fail to distinguish between partial red and green colors. When the red and green connecting lines are parallel to the a^* axis of CIE L^*a^*b^*, red and green perception defectives cannot distinguish the color pair, but can distinguish the color pair parallel to the b^* axis. Therefore, when two colors are parallel to the a^* axis, their color correction yields good results. When color correction is performed on a color, the hue loss between the two colors under normal color vision is supplemented with b^* so that red-green color vision-deficient individuals can distinguish the color difference between the color pairs. The magnitude of the correction is greatest when the connecting lines of the color pairs are parallel to the a^* axis, and no color correction is applied when the connecting lines are parallel to the b^* axis. The objective evaluation results show that the method achieves a higher score, indicating that the proposed method can maintain the naturalness of the image while reducing confusing colors.

A novel temporal convolution network-gated recurrent unit (NTCN-GRU) algorithm is proposed for the greatest of constant false alarm rate (GO-CFAR) frequency hopping (FH) prediction, integrating GRU and Bayesian optimization (BO). GRU efficiently captures the semantic associations among long FH sequences, and mitigates the phenomenon of gradient vanishing or explosion. BO improves extracting data features by optimizing hyperparameters besides. Simulations demonstrate that the proposed algorithm effectively reduces the loss in the training process, greatly improves the FH prediction effect, and outperforms the existing FH sequence prediction model. The model runtime is also reduced by three-quarters compared with others FH sequence prediction models.

This paper reports a spatial extrapolation of the sound field with a physics-informed neural network. We investigate the spatial extrapolation of the room impulse responses with physics-informed SIREN architecture. Furthermore, we proposed a noise-robust extrapolation method by introducing a tolerance term to the loss function.

This letter proposes a scheme for the backward transmission of the propagation-delay based three-user X channel, which is reciprocal to the forward transmission. The given scheme successfully delivers 10 expected messages in 6 time-slots by cyclic interference alignment without loss of degrees of freedom, which supports efficient bidirectional transmission between the two ends of the three-user X channel.

Minutiae pattern extraction plays a crucial role in finger print registration and identification for electronic applications. However, the extraction accuracy is seriously compromised by the presence of contaminated ridge lines and complex background scenarios. General image processing-based methods, which rely on many prior hypotheses, fail to effectively handle minutiae extraction in complex scenarios. Previous works have shown that CNN-based methods can perform well in object detection tasks. However, the deep neural networks (DNNs)-based methods are restricted by the limitation of public labeled datasets due to legitimate privacy concerns. To address these challenges comprehensively, this paper presents a fully automated minutiae extraction method leveraging DNNs. Firstly, we create a fingerprint minutiae dataset using a semi-automated minutiae annotation algorithm. Subsequently, we propose a minutiae extraction model based on Residual Networks (Resnet) that enables end-to-end prediction of minutiae. Moreover, we introduce a novel non-maximal suppression (NMS) procedure, guided by the Generalized Intersection over Union (GIoU) metric, during the inference phase to effectively handle outliers. Experimental evaluations conducted on the NIST SD4 and FVC 2004 databases demonstrate the superiority of the proposed method over existing state-of-the-art minutiae extraction approaches.

We consider Feistel ciphers instantiated with tweakable block ciphers (TBCs) and ideal ciphers (ICs). The indistinguishability security of the TBC-based Feistel cipher is known, and the indifferentiability security of the IC-based Feistel cipher is also known, where independently keyed TBCs and independent ICs are assumed. In this paper, we analyze the security of a single-keyed TBC-based Feistel cipher and a single IC-based Feistel cipher. We characterize the security depending on the number of rounds. More precisely, we cover the case of contracting Feistel ciphers that have d ≥ 2 lines, and the results on Feistel ciphers are obtained as a special case by setting d = 2. Our indistinguishability security analysis shows that it is provably secure with d + 1 rounds. Our indifferentiability result shows that, regardless of the number of rounds, it cannot be secure. Our attacks are a type of a slide attack, and we consider a structure that uses a round constant, which is a well-known counter measure against slide attacks. We show an indifferentiability attack for the case d = 2 and 3 rounds.

Boolean functions play an important role in symmetric ciphers. One of important open problems on Boolean functions is determining the maximum possible resiliency order of n-variable Boolean functions with optimal algebraic immunity. In this letter, we search Boolean functions in the rotation symmetric class, and determine the maximum possible resiliency order of 9-variable Boolean functions with optimal algebraic immunity. Moreover, the maximum possible nonlinearity of 9-variable rotation symmetric Boolean functions with optimal algebraic immunity-resiliency trade-off is determined to be 224.

Direct-current biased optical orthogonal frequency division multiplexing (DCO-OFDM) exhibits a high peak-to-average power ratio (PAPR), which leads to nonlinear distortion in the system. In response to the above, the study proposes a scheme that combines direct-current biased optical orthogonal frequency division multiplexing with index modulation (DCO-OFDM-IM) and convex optimization algorithms. The proposed scheme utilizes partially activated subcarriers of the system to transmit constellation modulated symbol information, and transmits additional symbol information of the system through the combination of activated carrier index. Additionally, a dither signal is added to the system's idle subcarriers, and the convex optimization algorithm is applied to solve for the optimal values of this dither signal. Therefore, by ensuring the system's peak power remains unchanged, the scheme enhances the system's average transmission power and thus achieves a reduction in the PAPR. Experimental results indicate that at a system's complementary cumulative distribution function (CCDF) of 10^-4, the proposed scheme reduces the PAPR by approximately 3.5 dB compared to the conventional DCO-OFDM system. Moreover, at a bit error rate (BER) of 10^-3, the proposed scheme can lower the signal-to-noise ratio (SNR) by about 1 dB relative to the traditional DCO-OFDM system. Therefore, the proposed scheme enables a more substantial reduction in PAPR and improvement in BER performance compared to the conventional DCO-OFDM approach.

In this paper, we first recall the concept of 2-tuples distribution matrix, and further study its properties. Based on these properties, we find four special classes of 2-tuples distribution matrices. Then, we provide a new sufficient and necessary condition for n-variable rotation symmetric Boolean functions to be 2-correlation immune. Finally, we give a new method for constructing such functions when n = 4t - 1 is prime, and we show an illustrative example.

In this letter, a novel projection algorithm is proposed in which projection onto a triangle consisting of the three even-vertices closest to the vector to be projected replaces check polytope projection, achieving the same FER performance as exact projection algorithm in both high-iteration and low-iteration regime. Simulation results show that compared with the sparse affine projection algorithm (SAPA), it can improve the FER performance by 0.2 dB as well as save average number of iterations by 4.3%.

In face super-resolution reconstruction, the interference caused by the texture and color of the hair region on the details and contours of the face region can negatively affect the reconstruction results. This paper proposes a semantic-based, dual-branch face super-resolution algorithm to address the issue of varying reconstruction complexities and mutual interference among different pixel semantics in face images. The algorithm clusters pixel semantic data to create a hierarchical representation, distinguishing between facial pixel regions and hair pixel regions. Subsequently, independent image enhancement is applied to these distinct pixel regions to mitigate their interference, resulting in a vivid, super-resolution face image.

The concept of dual function radar-communication (DFRC) provides solution to the problem of spectrum scarcity. This paper examines a multiple-input multiple-output (MIMO) DFRC system with the assistance of a reconfigurable intelligent surface (RIS). The system is capable of sensing multiple spatial directions while serving multiple users via orthogonal frequency division multiplexing (OFDM). The objective of this study is to design the radiated waveforms and receive filters utilized by both the radar and users. The mutual information (MI) is used as an objective function, on average transmit power, for multiple targets while adhering to constraints on power leakage in specific directions and maintaining each user's error rate. To address this problem, we propose an optimal solution based on a computational genetic algorithm (GA) using bisection method. The performance of the solution is demonstrated by numerical examples and it is shown that, our proposed algorithm can achieve optimum MI and the use of RIS with the MIMO DFRC system improving the system performance.

In this study, we focus on evaluating the false-positive probability of the Demirci-Selçuk meet-in-the-middle attack, particularly within the context of configuring precomputed tables with multisets. During the attack, the adversary effectively reduces the size of the key space by filtering out the wrong keys, subsequently recovering the master key from the reduced key space. The false-positive probability is defined as the probability that a wrong key will pass through the filtering process. Due to its direct impact on the post-filtering key space size, the false-positive probability is an important factor that influences the complexity and feasibility of the attack. However, despite its significance, the false-positive probability of the multiset-based Demirci-Selçuk meet-in-the-middle attack has not been thoroughly discussed, to the best of our knowledge. We generalize the Demirci-Selçuk meet-in-the-middle attack and present a sophisticated method for accurately calculating the false-positive probability. We validate our methodology through toy experiments, demonstrating its high precision. Additionally, we propose a method to optimize an attack by determining the optimal format of precomputed data, which requires the precise false-positive probability. Applying our approach to previous attacks on AES and ARIA,we have achieved modest improvements. Specifically, we enhance the memory complexity and time complexity of the offline phase of previous attacks on 7-round AES-128/192/256, 7-round ARIA-192/256, and 8-round ARIA-256 by factors ranging from 2^0.56 to 2³. Additionally, we have improved the overall time complexity of attacks on 7-round ARIA-192/256 by factors of 2^0.13 and 2^0.42, respectively.

The dispersion problem is a variant of facility location problems, that has been extensively studied. Given a polygon with n edges on a plane we want to find k points in the polygon so that the minimum pairwise Euclidean distance of the k points is maximized. We call the problem the k-dispersion problem in a polygon. Intuitively, for an island, we want to locate k drone bases far away from each other in flying distance to avoid congestion in the sky. In this paper, we give a polynomial-time approximation scheme (PTAS) for this problem when k is a constant and ε < 1 (where ε is a positive real number). Our proposed algorithm runs in O(((1/ε)² + n/ε)^k) time with 1/(1 + ε) approximation, the first PTAS developed for this problem. Additionally, we consider three variations of the dispersion problem and design a PTAS for each of them.

Diagnosis at an early stage is clinically important for the cure of skin cancer. However, since some skin cancers have similar intuitive characteristics, and dermatologists rely on subjective experience to distinguish skin cancer types, the accuracy is often suboptimal. Recently, the introduction of computer methods in the medical field has better assisted physicians to improve the recognition rate but some challenges still exist. In the face of massive dermoscopic image data, residual network (ResNet) is more suitable for learning feature relationships inside big data because of its deeper network depth. Aiming at the deficiency of ResNet, this paper proposes a multi-region feature extraction and raising dimension matching method, which further improves the utilization rate of medical image features. This method firstly extracted rich and diverse features from multiple regions of the feature map, avoiding the deficiency of traditional residual modules repeatedly extracting features in a few fixed regions. Then, the fused features are strengthened by up-dimensioning the branch path information and stacking it with the main path, which solves the problem that the information of two paths is not ideal after fusion due to different dimensionality. The proposed method is experimented on the International Skin Imaging Collaboration (ISIC) Archive dataset, which contains more than 40,000 images. The results of this work on this dataset and other datasets are evaluated to be improved over networks containing traditional residual modules and some popular networks.

With the increase of the number of Mobile User Equipments (MUEs), numerous tasks that with high requirements of resources are generated. However, the MUEs have limited computational resources, computing power and storage space. In this paper, a joint coverage constrained task offloading and resource allocation method based on deep reinforcement learning is proposed. The aim is offload the tasks that cannot be processed locally to the edge servers to alleviate the conflict between the resource constraints of MUEs and the high performance task processing. The studied problem considers the dynamic variability and complexity of the system model, coverage, offloading decisions, communication relationships and resource constraints. An entropy weight method is used to optimize the resource allocation process and balance the energy consumption and execution time. The results of the study show that the number of tasks and MUEs affects the execution time and energy consumption of the task offloading and resource allocation processes in the interest of the service provider, and enhances the user experience.

Point cloud video contains not only color information but also spatial position information and usually has large volume of data. Typical rate distortion optimization algorithms based on Human Visual System only consider the color information, which limit the coding performance. In this paper, a Coding Tree Unit (CTU) level quantization parameter (QP) adjustment algorithm based on JND and spatial complexity is proposed to improve the subjective and objective quality of Video-Based Point Cloud Compression (V-PCC). Firstly, it is found that the JND model is degraded at CTU level for attribute video due to the pixel filling strategy of V-PCC, and an improved JND model is designed using the occupancy map. Secondly, a spatial complexity detection metric is designed to measure the visual importance of each CTU. Finally, a CTU-level QP adjustment scheme based on both JND levels and visual importance is proposed for geometry and attribute video. The experimental results show that, compared with the latest V-PCC (TMC2-18.0) anchors, the BD-rate is reduced by - 2.8% and -3.2% for D1 and D2 metrics, respectively, and the subjective quality is improved significantly.

Multi-access edge computing (MEC) is an emerging technology of 5G and beyond mobile networks which deploys computation services at edge servers for reducing service delay. However, edge servers may have not enough computation capabilities to satisfy the delay requirement of services. Thus, heavy computation tasks need to be offloaded to other MEC servers. In this paper, we propose an offloading solution, called optimal delay offloading (ODO) solution, that can guarantee service delay requirements. Specificially, this method exploits an estimation of queuing delay among MEC servers to find a proper offloading server with the lowest service delay to offload the computation task. Simulation results have proved that the proposed ODO method outperforms the conventional methods, i.e., the non-offloading and the energy-efficient offloading [10] methods (up to 1.6 times) in terms of guaranteeing the service delay under a threshold.

In 2005, Nandi introduced a class of double-block-length compression functions h^π (x) := (h(x) , h( π(x) ) ), where h is a random oracle with an n-bit output and π is a non-cryptographic public permutation. Nandi demonstrated that the collision resistance of h^π is optimal if π has no fixed point in the classical setting. Our study explores the collision resistance of h^π and the Merkle-Damåard hash function using h^π in the quantum random oracle model. Firstly, we reveal that the quantum collision resistance of h^π may not be optimal even if π has no fixed point. If π is an involution, then a colliding pair of inputs can be found for h^π with only O(2^n/2) queries by the Grover search. Secondly, we present a sufficient condition on π for the optimal quantum collision resistance of h^π. This condition states that any collision attack needs Ω(2^2n/3) queries to find a colliding pair of inputs. The proof uses the recent technique of Zhandry's compressed oracle. Thirdly, we show that the quantum collision resistance of the Merkle-Damgård hash function using h^π can be optimal even if π is an involution. Finally, we discuss the quantum collision resistance of double-block-length compression functions using a block cipher.