We evaluated soft-error tolerance by heavy-ion irradiation test on three-types of flip-flops (FFs) named the standard FF (STDFF), the dual feedback recovery FF (DFRFF), and the DFRFF with long delay (DFRFFLD) in 22 and 65 nm fully-depleted silicon on insulator (FD-SOI) technologies. The guard-gate (GG) structure in DFRFF mitigates soft errors. A single event transient (SET) pulse is removed by the C-element with the signal delayed by the GG structure. DFRFFLD increases the GG delay by adding two more inverters as delay elements. We investigated the effectiveness of the GG structure in 22 and 65 nm. In 22 nm, Kr (40.3 MeV-cm2/mg) and Xe (67.2 MeV-cm2/mg) irradiation tests revealed that DFRFFLD has sufficient soft-error tolerance in outer space. In 65 nm, the relationship between GG delay and CS reveals the GG delay time which no error was observed under Kr irradiation.

Efficient multiplexing of ultra-reliable and low-latency communications (URLLC) and enhanced mobile broadband (eMBB) traffic, as well as ensuring the various reliability requirements of these traffic types in 5G wireless communications, is becoming increasingly important, particularly for vertical services. Interference management techniques, such as coordinated inter-cell scheduling, can enhance reliability in dense cell deployments. However, tight inter-cell coordination necessitates frequent information exchange between cells, which limits implementation. This paper introduces a novel RAN slicing framework based on centralized frequency-domain interference control per slice and link adaptation optimized for URLLC. The proposed framework does not require tight inter-cell coordination but can fulfill the requirements of both the decoding error probability and the delay violation probability of each packet flow. These controls are based on a power-law estimation of the lower tail distribution of a measured data set with a smaller number of discrete samples. As design guidelines, we derived a theoretical minimum radio resource size of a slice to guarantee the delay violation probability requirement. Simulation results demonstrate that the proposed RAN slicing framework can achieve the reliability targets of the URLLC slice while improving the spectrum efficiency of the eMBB slice in a well-balanced manner compared to other evaluated benchmarks.

As a hierarchical network framework, clustering aims to divide nodes with similar mobility characteristics into the same cluster to form a more structured hierarchical network, which can effectively solve the problem of high dynamics of the network topology caused by the high-speed movement of nodes in aeronautical ad hoc networks. Based on this goal, we propose a multi-hop distributed clustering algorithm based on link duration. The algorithm is based on the idea of multi-hop clustering, which ensures the coverage and stability of clustering. In the clustering phase, the link duration is used to accurately measure the degree of stability between nodes. At the same time, we also use the link duration threshold to filter out relatively stable links and use the gravity factor to let nodes set conditions for actively creating links based on neighbor distribution. When selecting the cluster head, we select the most stable node as the cluster head node based on the defined node stability weight. The node stability weight comprehensively considers the connectivity degree of nodes and the link duration between nodes. In order to verify the effectiveness of the proposed method, we compare them with the N-hop and K-means algorithms from four indicators: average cluster head duration, average cluster member duration, number of cluster head changes, and average number of intra-cluster link changes. Experiments show that the proposed method can effectively improve the stability of the topology.

Photovoltaic power is an important part of sustainable development. Accurate prediction of photovoltaic power can improve energy utilization and prevent resource waste. However, the volatility and uncertainty of photovoltaic power make power prediction difficult. Although Informer has achieved good prediction results in the field of time series prediction, it does not put forward a good solution for the volatility of series and the leakage of future information when stacking. Therefore, this paper proposes a photovoltaic power prediction model based on VMD-Informer-DCC. Firstly, Spearman’s feature selector was used to screen the sequence features. Then, the VMD layer was added to the encoder of Informer to decompose the feature sequence to reduce the volatility of the feature sequence. Finally, the dilated causal convolutional layer was used to replace the Self-attention distilling of Informer, which expanded the receptive field of Informer information extraction and ensured the causality of time series prediction. To verify the effectiveness of the model, this paper uses the dataset of a photovoltaic power plant in Jilin Province in 2021 to conduct a large number of experiments. The results show that the VMD-Informer-DCC model has high prediction accuracy and wide applicability.

This letter deals with joint carrier frequency offset (CFO) and direction of arrival (DOA) estimation based on the minimum variance distortionless response (MVDR) criterion for interleaved orthogonal frequency division multiple access (OFDMA)/space division multiple access (SDMA) uplink systems. In order to reduce the computational load of two-dimensional searching based methods, the proposed method includes only once polynomial CFO rooting and does not require DOA paring, hence it raises the searching efficiency. Several simulation results are provided to illustrate the effectiveness of the proposed method.

Space is becoming increasingly congested and contested, which calls for effective means to conduct effective monitoring of high-value space assets, especially in Space Situational Awareness (SSA) missions, while there are imperfections in existing methods and corresponding algorithms. To overcome such a problem, this letter proposes an algorithm for accurate Connected Element Interferometry (CEI) in SSA based on more interpolation information and iterations. Simulation results show that: (i) after iterations, the estimated asymptotic variance of the proposed method can basically achieve uniform convergence, and the ratio of it to ACRB is 1.00235 in δ0 ∈ [-0.5, 0.5], which is closer to 1 than the current best AM algorithms; (ii) In the interval of SNR ∈ [-14dB, 0dB], the estimation error of the proposed algorithm decreases significantly, which is basically comparable to CRLB (maintains at 1.236 times). The research of this letter could play a significant role in effective monitoring and high-precision tracking and measurement with significant space targets during futuristic SSA missions.

This letter presented several new constructions of complementary sets (CSs) with flexible sequence lengths using matrix transformations. The constructed CSs of size 4 have different lengths, namely N + L and 2N + L, where N and L are the lengths for which complementary pairs exist. Also, presented CSs of size 8 have lengths N + P, P + Q and 2P + Q, where N is length of complementary pairs, P and Q are lengths of CSs of size 4 exist. The achieved designs can be easily extended to a set size of 2n+2 by recursive method. The proposed constructions generalize some previously reported constructions along with generating CSs under fewer constraints.

Cyclic codes are a subclass of linear codes and have applications in consumer electronics, data storage systems, and communication systems as they have efficient encoding and decoding algorithms. Let C(t,e) denote the cyclic code with two nonzero αt and αe, where α is a generator of 𝔽*3m. In this letter, we investigate the ternary cyclic codes with parameters [3m - 1, 3m - 1 - 2m, 4] based on some results proposed by Ding and Helleseth in 2013. Two new classes of optimal ternary cyclic codes C(t,e) are presented by choosing the proper t and e and determining the solutions of certain equations over 𝔽3m.

Permutation polynomials have important applications in cryptography, coding theory and combinatorial designs. In this letter, we construct four classes of permutation polynomials over 𝔽2n × 𝔽2n, where 𝔽2n is the finite field with 2n elements.

Convolutional neural network (CNN) is widely used in acoustic scene classification (ASC) tasks. In most cases, local convolution is utilized to gather time-frequency information between spectrum nodes. It is challenging to adequately express the non-local link between frequency domains in a finite convolution region. In this paper, we propose a dual-path convolutional neural network based on band interaction block (DCNN-bi) for ASC, with mel-spectrogram as the model’s input. We build two parallel CNN paths to learn the high-frequency and low-frequency components of the input feature. Additionally, we have created three band interaction blocks (bi-blocks) to explore the pertinent nodes between various frequency bands, which are connected between two paths. Combining the time-frequency information from two paths, the bi-blocks with three distinct designs acquire non-local information and send it back to the respective paths. The experimental results indicate that the utilization of the bi-block has the potential to improve the initial performance of the CNN substantially. Specifically, when applied to the DCASE 2018 and DCASE 2020 datasets, the CNN exhibited performance improvements of 1.79% and 3.06%, respectively.

To achieve precise measurement without landing, the high-mobility vehicle-mounted theodolite needs to be leveled quickly with high precision and ensure sufficient support stability before work. After the measurement, it is also necessary to ensure that the high-mobility vehicle-mounted theodolite can be quickly withdrawn. Therefore, this paper proposes a hierarchical automatic leveling strategy and establishes a two-stage electromechanical automatic leveling mechanism model. Using coarse leveling of the first-stage automatic leveling mechanism and fine leveling of the second-stage automatic leveling mechanism, the model realizes high-precision and fast leveling of the vehicle-mounted theodolites. Then, the leveling control method based on repeated positioning is proposed for the first-stage automatic leveling mechanism. To realize the rapid withdrawal for high-mobility vehicle-mounted theodolites, the method ensures the coincidence of spatial movement paths when the structural parts are unfolded and withdrawn. Next, the leg static balance equation is constructed in the leveling state, and the support force detection method is discussed in realizing the stable support for vehicle-mounted theodolites. Furthermore, a mathematical model for “false leg” detection is established furtherly, and a “false leg” detection scheme based on the support force detection method is analyzed to significantly improve the support stability of vehicle-mounted theodolites. Finally, an experimental platform is constructed to perform the performance test for automatic leveling mechanisms. The experimental results show that the leveling accuracy of established two-stage electromechanical automatic leveling mechanism can reach 3.6″, and the leveling time is no more than 2 mins. The maximum support force error of the support force detection method is less than 15%, and the average support force error is less than 10%. In contrast, the maximum support force error of the drive motor torque detection method reaches 80.12%, and its leg support stability is much less than the support force detection method. The model and analysis method proposed in this paper can also be used for vehicle-mounted radar, vehicle-mounted laser measurement devices, vehicle-mounted artillery launchers and other types of vehicle-mounted equipment with high-precision and high-mobility working requirements.

Artificial intelligence and the introduction of Internet of Things technologies have benefited from technological advances and new automated computer system technologies. Eventually, it is now possible to integrate them into a single offline industrial system. This is accomplished through machine-to-machine communication, which eliminates the human factor. The purpose of this article is to examine security systems for machine-to-machine communication systems that rely on identification and authentication algorithms for real-time monitoring. The article investigates security methods for quickly resolving data processing issues by using the Security operations Center’s main machine to identify and authenticate devices from 19 different machines. The results indicate that when machines are running offline and performing various tasks, they can be exposed to data leaks and malware attacks by both the individual machine and the system as a whole. The study looks at the operation of 19 computers, 7 of which were subjected to data leakage and malware attacks. AnyLogic software is used to create visual representations of the results using wireless networks and algorithms based on previously processed methods. The W76S is used as a protective element within intelligent sensors due to its built-in memory protection. For 4 machines, the data leakage time with malware attacks was 70 s. For 10 machines, the duration was 150 s with 3 attacks. Machine 15 had the longest attack duration, lasting 190 s and involving 6 malware attacks, while machine 19 had the shortest attack duration, lasting 200 s and involving 7 malware attacks. The highest numbers indicated that attempting to hack a system increased the risk of damaging a device, potentially resulting in the entire system with connected devices failing. Thus, illegal attacks by attackers using malware may be identified over time, and data processing effects can be prevented by intelligent control. The results reveal that applying identification and authentication methods using a protocol increases cyber-physical system security while also allowing real-time monitoring of offline system security.

This paper proposes a scheme for reducing pilot interference in cell-free massive multiple-input multiple-output (MIMO) systems through scalable access point (AP) selection and efficient pilot allocation using the Grey Wolf Optimizer (GWO). Specifically, we introduce a bidirectional large-scale fading-based (B-LSFB) AP selection method that builds high-quality connections benefiting both APs and UEs. Then, we limit the number of UEs that each AP can serve and encourage competition among UEs to improve the scalability of this approach. Additionally, we propose a grey wolf optimization based pilot allocation (GWOPA) scheme to minimize pilot contamination. Specifically, we first define a fitness function to quantify the level of pilot interference between UEs, and then construct dynamic interference relationships between any UE and its serving AP sets using a weighted fitness function to minimize pilot interference. The simulation results shows that the B-LSFB strategy achieves scalability with performance similar to large-scale fading-based (LSFB) AP selection. Furthermore, the grey wolf optimization-based pilot allocation scheme significantly improves per-user net throughput with low complexity compared to four existing schemes.

Boolean functions with a few Walsh spectral values have important applications in sequence ciphers and coding theory. In this paper, we first construct a class of Boolean functions with at most five-valued Walsh spectra by using the secondary construction of Boolean functions, in particular, plateaued functions are included. Then, we construct three classes of Boolean functions with five-valued Walsh spectra using Kasami functions and investigate the Walsh spectrum distributions of the new functions. Finally, three classes of minimal linear codes with five-weights are obtained, which can be used to design secret sharing scheme with good access structures.

The existing discrete-logarithm-based two-round multi-signature schemes without using the idealized model, i.e., the Algebraic Group Model (AGM), have quite large reduction loss. This means that an implementation of these schemes requires an elliptic curve (EC) with a very large order for the standard 128-bit security when we consider concrete security. Indeed, the existing standardized ECs have orders too small to ensure 128-bit security of such schemes. Recently, Pan and Wagner proposed two two-round schemes based on the Decisional Diffie-Hellman (DDH) assumption (EUROCRYPT 2023). For 128-bit security in concrete security, the first scheme can use the NIST-standardized EC P-256 and the second can use P-384. However, with these parameter choices, they do not improve the signature size and the communication complexity over the existing non-tight schemes. Therefore, there is no two-round scheme that (i) can use a standardized EC for 128-bit security and (ii) has high efficiency. In this paper, we construct a two-round multi-signature scheme achieving both of them from the DDH assumption. We prove that an EC with at least a 321-bit order is sufficient for our scheme to ensure 128-bit security. Thus, we can use the NIST-standardized EC P-384 for 128-bit security. Moreover, the signature size and the communication complexity per one signer of our proposed scheme under P-384 are 1152 bits and 1535 bits, respectively. These are most efficient among the existing two-round schemes without using the AGM including Pan-Wagner’s schemes and non-tight schemes which do not use the AGM. Our experiment on an ordinary machine shows that for signing and verification, each can be completed in about 65 ms under 100 signers. This shows that our scheme has sufficiently reasonable running time in practice.

Emerging data-intensive services in distribution grid impose requirements of high-concurrency access for massive internet of things (IoT) devices. However, the lack of effective high-concurrency access management results in severe performance degradation. To address this challenge, we propose a cloud-edge-device collaborative high-concurrency access management algorithm based on multi-timescale joint optimization of channel pre-allocation and load balancing degree. We formulate an optimization problem to minimize the weighted sum of edge-cloud load balancing degree and queuing delay under the constraint of access success rate. The problem is decomposed into a large-timescale channel pre-allocation subproblem solved by the device-edge collaborative access priority scoring mechanism, and a small-timescale data access control subproblem solved by the discounted empirical matching mechanism (DEM) with the perception of high-concurrency number and queue backlog. Particularly, information uncertainty caused by externalities is tackled by exploiting discounted empirical performance which accurately captures the performance influence of historical time points on present preference value. Simulation results demonstrate the effectiveness of the proposed algorithm in reducing edge-cloud load balancing degree and queuing delay.

Real-time matting is a challenging research in deep learning. Conventional CNN (Convolutional Neural Networks) approaches are easy to misjudge the foreground and background semantic and have blurry matting edges, which result from CNN’s limited concentration on global context due to receptive field. We propose a real-time matting approach called RMViT (Real-time matting with Vision Transformer) with Transformer structure, attention and content-aware guidance to solve issues above. The semantic accuracy improves a lot due to the establishment of global context and long-range pixel information. The experiments show our approach exceeds a 30% reduction in error metrics compared with existing real-time matting approaches.

Federated learning is a distributed machine learning approach in which clients train models locally with their own data and upload them to a server so that their trained results are shared between them without uploading raw data to the server. There are some challenges in federated learning, such as communication size reduction and client heterogeneity. The former can mitigate the communication overheads, and the latter can allow the clients to choose proper models depending on their available compute resources. To address these challenges, in this paper, we utilize Neural ODE based models for federated learning. The proposed flexible federated learning approach can reduce the communication size while aggregating models with different iteration counts or depths. Our contribution is that we experimentally demonstrate that the proposed federated learning can aggregate models with different iteration counts or depths. It is compared with a different federated learning approach in terms of the accuracy. Furthermore, we show that our approach can reduce communication size by up to 89.4% compared with a baseline ResNet model using CIFAR-10 dataset.

The purpose of Facial Beauty Prediction (FBP) is to automatically assess facial attractiveness based on human aesthetics. Most neural network-based prediction methods do not consider the ranking information in the task. For scoring tasks like facial beauty prediction, there is abundant ranking information both between images and within images. Reasonable utilization of these information during training can greatly improve the performance of the model. In this paper, we propose a novel end-to-end Convolutional Neural Network (CNN) model based on ranking information of images, incorporating a Rank Module and an Adaptive Weight Module. We also design pairwise ranking loss functions to fully leverage the ranking information of images. Considering training efficiency and model inference capability, we choose ResNet-50 as the backbone network. We conduct experiments on the SCUT-FBP5500 dataset and the results show that our model achieves a new state-of-the-art performance. Furthermore, ablation experiments show that our approach greatly contributes to improving the model performance. Finally, the Rank Module with the corresponding ranking loss is plug-and-play and can be extended to any CNN model and any task with ranking information. Code is available at https://github.com/nehcoah/Rank-Info-Net.

In the software testing phase, software reliability growth models (SRGMs) are commonly used to evaluate the reliability of software systems. Traditional SRGMs are restricted by their assumption of a continuous growth pattern for the failure detection rate (FDR) throughout the testing phase. However, the assumption is compromised by Change-Point phenomena, where FDR fluctuations stem from variations in testing personnel or procedural modifications, leading to reduced prediction accuracy and compromised software reliability assessments. Therefore, the objective of this study is to improve software reliability prediction using a novel approach that combines genetic algorithm (GA) and deep learning-based SRGMs to account for the Change-point phenomenon. The proposed approach uses a GA to dynamically combine activation functions from various deep learning-based SRGMs into a new mutated SRGM called MuSRGM. The MuSRGM captures the advantages of both concave and S-shaped SRGMs and is better suited to capture the change-point phenomenon during testing and more accurately reflect actual testing situations. Additionally, failure data is treated as a time series and analyzed using a combination of Long Short-Term Memory (LSTM) and Attention mechanisms. To assess the performance of MuSRGM, we conducted experiments on three distinct failure datasets. The results indicate that MuSRGM outperformed the baseline method, exhibiting low prediction error (MSE) on all three datasets. Furthermore, MuSRGM demonstrated remarkable generalization ability on these datasets, remaining unaffected by uneven data distribution. Therefore, MuSRGM represents a highly promising advanced solution that can provide increased accuracy and applicability for software reliability assessment during the testing phase.