In modern radar systems, the Generalized compound distribution model is more suitable for describing the amplitude distribution characteristics of radar sea clutter. Accurately and efficiently simulating sea clutter has important practical significance for radar signal processing and sea surface target detection. However, in traditional zero memory nonlinearity (ZMNL) method, the correlated Generalized compound distribution model cannot deal with non-integral or non-semi-integral parameter. In order to overcome this shortcoming, a new method of generating correlated Generalized compound distributed clutter is proposed, which changes the generation method of Generalized Gamma distributed random sequences in traditional Generalized compound distribution models. Firstly, by combining with the Gamma distribution and using the additivity of the Gamma distribution, the Probability Density Function (PDF) of Gamma function is transformed into a second-order nonlinear ordinary differential equation, and the Gamma distributed sequence under arbitrary parameter is solved. Then the Generalized Gamma distributed sequence with arbitrary parameter can be obtained through the nonlinear transformation relationship between the Generalized Gamma distribution and the Gamma distribution, so that the shape parameters of the Generalized compound distributed sea clutter are extended to general real numbers. Simulation results show that the proposed method is not only suitable for clutter simulation with non-integral or non-semi-integral shape parameter values, but also further improves the fitting degree.

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.

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.

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.

An unmanned aerial vehicle (UAV) can be used for wireless communication and localization, among many other things. When terrestrial networks are either damaged or non-existent, and the area is GPS-denied, the UAV can be quickly deployed to provide communication and localization services to ground terminals in a specific target area. In this study, we propose an UAV operation model for unified communication and localization using reinforcement learning (UCL-RL) in a suburban environment which has no cellular communication and GPS connectivity. First, the UAV flies to the target area, moves in a circular fashion with a constant turning radius and sends navigation signals from different positions to the ground terminals. This provides a dynamic environment that includes the turning radius, the navigation signal transmission points, and the height of the unmanned aerial vehicle as well as the location of the ground terminals. The proposed model applies a reinforcement learning algorithm where the UAV continuously interacts with the environment and learns the optimal height that provides the best communication and localization services to the ground terminals. To evaluate the terminal position accuracy, position dilution of precision (PDOP) is measured, whereas the maximum allowable path loss (MAPL) is measured to evaluate the communication service. The simulation result shows that the proposed model improves the localization of the ground terminals while guaranteeing the communication service.

This paper presents a comprehensive design approach to load-independent radio frequency (RF) power amplifiers. We project the zero-voltage-switching (ZVS) and zero-voltage-derivative-switching (ZVDS) load impedances onto a Smith chart, and find that their loci exhibit geodesic arcs. We exploit a two-port reactive network to convert the geodesic locus into another geodesic. This is named geodesic-to-geodesic (G2G) impedance conversion, and the power amplifier that employs G2G conversion is called class-G2G amplifier. We comprehensively explore the possible circuit topologies, and find that there are twenty G2G networks to create class-G2G amplifiers. We also find out that the class-G2G amplifier behaves like a transformer or a gyrator converting from dc to RF. The G2G design theory is verified via a circuit simulation. We also verified the theory through an experiment employing a prototype 100 W amplifier at 6.78 MHz. We conclude that the presented design approach is quite comprehensive and useful for the future development of high-efficiency RF power amplifiers.

The load-independent zero-voltage switching class-E inverter has garnered considerable interest as an essential component in wireless power transfer systems. This inverter achieves high efficiency across a broad spectrum of load conditions by incorporating a load adjustment circuit (LAC) subsequent to the resonant filter. Nevertheless, the presence of the LAC influences the output impedance of the inverter, thereby inducing a divergence between the targeted and observed output power, even in ideal lossless simulations. Consequently, iterative adjustments to component values are required via an LC element implementation. We introduce a novel design methodology that incorporates an external quality factor on the side of the resonant filter, inclusive of the LAC. Thus, the optimized circuit achieves the intended output power without necessitating alterations in component values.

Recent years have seen a general resurgence of interest in analog signal processing and computing architectures. In addition, extensive theoretical and experimental literature on chaos and analog chaotic oscillators exists. One peculiarity of these circuits is the ability to generate, despite their structural simplicity, complex spatiotemporal patterns when several of them are brought towards synchronization via coupling mechanisms. While by no means a systematic survey, this paper provides a personal perspective on this area. After briefly covering design aspects and the synchronization phenomena that can arise, a selection of results exemplifying potential applications is presented, including in robot control, distributed sensing, reservoir computing, and data augmentation. Despite their interesting properties, the industrial applications of these circuits remain largely to be realized, seemingly due to a variety of technical and organizational factors including a paucity of design and optimization techniques. Some reflections are given regarding this situation, the potential relevance to discontinuous innovation in analog circuit design of chaotic oscillators taken both individually and as synchronized networks, and the factors holding back the transition to higher levels of technology readiness.

Unmanned aerial vehicle (UAV)-assisted systems have attracted a lot of attention due to its high probability of line-of-sight (LoS) connections and flexible deployment. In this paper, we aim to minimize the upload time required for the UAV to collect information from the sensor nodes in disaster scenario, while optimizing the deployment position of UAV. In order to get the deployment solution quickly, a data-driven approach is proposed in which an optimization strategy acts as the expert. Considering that images could capture the spatial configurations well, we use a convolutional neural network (CNN) to learn how to place the UAV. In the end, the simulation results demonstrate the effectiveness and generalization of the proposed method. After training, our CNN can generate UAV configuration faster than the general optimization-based algorithm.

In 2005, Nandi introduced a class of double-block-length compression functions hπ(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 hπ is optimal if π has no fixed point in the classical setting. Our study explores the collision resistance of hπ and the Merkle-Damgård hash function using hπ in the quantum random oracle model. Firstly, we reveal that the quantum collision resistance of hπ 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 hπ with only O(2n/2) queries by the Grover search. Secondly, we present a sufficient condition on π for the optimal quantum collision resistance of hπ. This condition states that any collision attack needs Ω(22n/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 hπ 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.

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 recent years, blockchain has been widely applied in the Internet of Things (IoT). Blockchain oracle, as a bridge for data communication between blockchain and off-chain, has also received significant attention. However, the numerous and heterogeneous devices in the IoT pose great challenges to the efficiency and security of data acquisition for oracles. We find that the matching relationship between data sources and oracle nodes greatly affects the efficiency and service quality of the entire oracle system. To address these issues, this paper proposes a distributed and efficient oracle solution tailored for the IoT, enabling fast acquisition of real-time off-chain data. Specifically, we first design a distributed oracle architecture that combines both Trusted Execution Environment (TEE) devices and ordinary devices to improve system scalability, considering the heterogeneity of IoT devices. Secondly, based on the trusted node information provided by TEE, we determine the matching relationship between nodes and data sources, assigning appropriate nodes for tasks to enhance system efficiency. Through simulation experiments, our proposed solution has been shown to effectively improve the efficiency and service quality of the system, reducing the average response time by approximately 9.92% compared to conventional approaches.

In this paper, a cluster-based three-dimensional (3D) non-stationary vehicle-to-vehicle (V2V) channel model with circular arc motions and antenna rotates is proposed. The channel model simulates the complex urban communication scenario where clusters move with arbitrary velocities and directions. A novel cluster evolution algorithm with time-array consistency is developed to capture the non-stationarity. For time evolution, the birth-and-death (BD) property of clusters including birth, death, and rebirth are taken into account. Additionally, a visibility region (VR) method is proposed for array evolution, which is verified to be applicable to circular motions. Based on the Taylor expansion formula, a detailed derivation of space-time correlation function (ST-CF) with circular arc motions is shown. Statistical properties including ST-CF, Doppler power spectrum density (PSD), quasi-stationary interval, instantaneous Doppler frequency, root mean square delay spread (RMS-DS), delay PSD, and angular PSD are derived and analyzed. According to the simulated results, the non-stationarity in time, space, delay, and angular domains is captured. The presented results show that motion modes including linear motions as well as circular motions, the dynamic property of the scattering environment, and the velocity of the vehicle all have significant impacts on the statistical properties.

To improve the recognition rate of the end-to-end modulation recognition method based on deep learning, a modulation recognition method of communication signals based on a cascade network is proposed, which is composed of two networks: Stacked Denoising Auto Encoder (SDAE) network and DCELDNN (Dilated Convolution, ECA Mechanism, Long Short-Term Memory, Deep Neural Networks) network. SDAE network is used to denoise the data, reconstruct the input data through encoding and decoding, and extract deep information from the data. DCELDNN network is constructed based on the CLDNN (Convolutional, Long Short-Term Memory, Fully Connected Deep Neural Networks) network. In the DCELDNN network, dilated convolution is used instead of normal convolution to enlarge the receptive field and extract signal features, the Efficient Channel Attention (ECA) mechanism is introduced to enhance the expression ability of the features, the feature vector information is integrated by a Global Average Pooling (GAP) layer, and signal features are extracted by the DCELDNN network efficiently. Finally, end-to-end classification recognition of communication signals is realized. The test results on the RadioML2018.01a dataset show that the average recognition accuracy of the proposed method reaches 63.1% at SNR of -10 to 15 dB, compared with CNN, LSTM, and CLDNN models, the recognition accuracy is improved by 25.8%, 12.3%, and 4.8% respectively at 10 dB SNR.

Software refactoring is an important process in software development. During software refactoring, code smell is a popular research topic that refers to design or implementation flaws in the software. Large class is one of the most concerning code smells in software refactoring. Detecting and refactoring such problem has a profound impact on software quality. In past years, software metrics and clustering techniques have commonly been used for the large class detection. However, deep-learning-based approaches have also received considerable attention in recent studies. In this study, we apply graph neural networks (GNNs), an important division of deep learning, to address the problem of large class detection. First, to support the extensive data requirements of the deep learning task, we apply a semiautomatic approach to generate a substantial number of data samples. Next, we design a new type of directed heterogeneous graph (DHG) as an input graph using the methods similarity matrix and software metrics. We construct an input graph for each class sample and make the graph classification with GNNs to identify the smelly classes. In our experiments, we apply three typical GNN model architectures for large class detection and compare the results with those of previous studies. The results show that the proposed approach can achieve more accurate and stable detection performance.

This paper proposes an analytical model of maximum operating frequency of class-D zero-voltage-switching (ZVS) inverter. The model includes linearized drain-source parasitic capacitance and any duty ratio. The nonlinear drain-source parasitic capacitance is equally linearized through a charge-related equation. The model expresses the relationship among frequency, shunt capacitance, duty ratio, load impedance, output current phase, and DC input voltage under the ZVS condition. The analytical result shows that the maximum operating frequency under the ZVS condition can be obtained when the duty ratio, the output current phase, and the DC input voltage are set to optimal values. A 650 V/30 A SiC-MOSFET is utilized for both simulated and experimental verification, resulting in good consistency.

During the COVID-19 pandemic, a robust system for masked face recognition has been required. Most existing solutions used many samples per identity for the model to recognize, but the processes involved are very laborious in a real-life scenario. Therefore, we propose “CPNet” as a suitable and reliable way of recognizing masked faces from only a few samples per identity. The prototype classifier uses a few-shot learning paradigm to perform the recognition process. To handle complex and occluded facial features, we incorporated the covariance structure of the classes to refine the class distance calculation. We also used sharpness-aware minimization (SAM) to improve the classifier. Extensive in-depth experiments on a variety of datasets show that our method achieves remarkable results with accuracy as high as 95.3%, which is 3.4% higher than that of the baseline prototype network used for comparison.

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.

Existing weakly-supervised segmentation approaches based on image-level annotations may focus on the most activated region in the image and tend to identify only part of the target object. Intuitively, high-level semantics among objects of the same category in different images could help to recognize corresponding activated regions of the query. In this study, a scheme called Cycle-Consistency of Semantics Network (CyCSNet) is proposed, which can enhance the activation of the potential inactive regions of the target object by utilizing the cycle-consistent semantics from images of the same category in the training set. Moreover, a Dynamic Correlation Feature Selection (DCFS) algorithm is derived to reduce the noise from pixel-wise samples of low relevance for better training. Experiments on the PASCAL VOC 2012 dataset show that the proposed CyCSNet achieves competitive results compared with state-of-the-art weakly-supervised segmentation approaches.

High Frequency Surface Wave Radar holds significant potential in sea detection. However, the target signals are often surpassed by substantial sea clutter and ionospheric clutter, making it crucial to address clutter suppression and extract weak target signals amidst the strong noise background.This study proposes a novel method for separating weak harmonic target signals based on local tangent space, leveraging the chaotic feature of ionospheric clutter.The effectiveness of this approach is demonstrated through the analysis of measured data, thereby validating its practicality and potential for real-world applications.