Dependent Quantization (DQ) is a new quantization tool introduced in the Versatile Video Coding (VVC) standard. While it provides better rate-distortion calculation accuracy, it also increases the computational complexity and hardware cost compared to the widely used scalar quantization. To address this issue, this paper proposes a parallel-dependent quantization hardware architecture using Verilog HDL language. The architecture preprocesses the coefficients with a scalar quantizer and a high-frequency filter, and then further segments and processes the coefficients in parallel using the Viterbi algorithm. Additionally, the weight bit width of the rate-distortion calculation is reduced to decrease the quantization cycle and computational complexity. Finally, the final quantization of the TU is determined through sequential scanning and judging of the rate-distortion cost. Experimental results show that the proposed algorithm reduces the quantization cycle by an average of 56.96% compared to VVC’s reference platform VTM, with a Bjøntegaard delta bit rate (BDBR) loss of 1.03% and 1.05% under the Low-delay P and Random Access configurations, respectively. Verification on the AMD FPGA development platform demonstrates that the hardware implementation meets the quantization requirements for 1080P@60Hz video hardware encoding.

This paper proposes a determination method of the cascaded number for lumped parameter models (LPMs) of the transmission lines. The LPM is used to simulate long-distance transmission lines, and the cascaded number significantly impacts the simulation results. Currently, there is a lack of a system-level determination method of the cascaded number for LPMs. Based on the theoretical analysis and eigenvalue decomposition of network matrix, this paper discusses the error in resonance characteristics between distributed parameter model and LPMs. Moreover, it is deduced that optimal cascaded numbers of the cascaded π-type and T-type LPMs are the same, and the Γ-type LPM has a lowest analog accuracy. The principle that the maximum simulation frequency is less than the first resonance frequency of each segment is presented. According to the principle, optimal cascaded numbers of cascaded π-type, T-type, and Γ-type LPMs are obtained. The effectiveness of the proposed determination method is verified by simulation.

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.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a novel scheme where the information bits are conveyed through the subcarrier activation pattern (SAP) and the symbols on the active subcarriers. Specifically, the subcarriers are partitioned into many subblocks and the subcarriers in each subblock can have two states, active or idle. Unfortunately, OFDM-IM inherits the high peak-to-average power ratio (PAPR) problem from the classical OFDM. The OFDM-IM signal with high PAPR induces in-band distortion and out-of-band radiation when it passes through high power amplifier (HPA). Recently, there are attempts to reduce PAPR by exploiting the unique structure of OFDM-IM, which is adding dither signals in the idle subcarriers. The most recent work dealing with the dither signals is using dithers signals with various amplitude constraints according to the characteristic of the corresponding OFDM-IM subblock. This is reasonable because OFDM subblocks have distinct levels of robustness against noise. However, the amplitude constraint in the recent work is efficient for only additive white Gaussian noise (AWGN) channels and cannot be used for maximum likelihood (ML) detection. Therefore, in this paper, based on pairwise error probability (PEP) analysis, a specific constraint for the dither signals is derived over a Rayleigh fading channel.

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.

Accurate segmentation of fundus vessel structure can effectively assist doctors in diagnosing eye diseases. In this paper, we propose a fundus blood vessel segmentation network combined with cross-modal features and verify our method on the public data set OCTA-500. Experimental results show that our method has high accuracy and robustness.

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.

In future deep space communication missions, VLBI (Very Long Baseline Interferometry) based on antenna array technology remains a critical detection method, which urgently requires the improvement of synthesis performance for antenna array signals. Considering this, focusing on optimizing the traditional antenna grouping method applied in the phase estimation algorithm, this letter proposes a “L/2 to L/2” antenna grouping method based on the maximum correlation signal-to-noise ratio (SNR). Following this idea, a phase difference estimation algorithm named “Couple” is presented. Theoretical analysis and simulation verification illustrate that: when ρ ＜ -10dB, the proposed “Couple” has the highest performance; increasing the number of antennas can significantly improve its synthetic loss performance and robustness. The research of this letter indicates a promising potential in supporting the rising deep space exploration and communication missions.

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.

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.

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.

Spatial modulation (SM) is a type of multiple-input multiple-output (MIMO) technology that provides several benefits over traditional MIMO systems. SM-MIMO is characterized by its unique transmission principle, which results in lower costs, enhanced spectrum utilization, and reduced inter-channel interference. To optimize channel estimation performance over frequency-selective channels in the spatial modulation system, cross Z-complementary pairs (CZCPs) have been proposed as training sequences. The zero correlation zone (ZCZ) properties of CZCPs for auto-correlation sums and cross-correlation sums enable them to achieve optimal channel estimation performance. In this paper, we systematically construct CZCPs based on binary Golay complementary pairs and binary Golay complementary pairs via Turyn’s method. We employ a special matrix operation and concatenation method to obtain CZCPs with new lengths 2M + N and 2(M + L), where M and L are the lengths of binary GCP, and N is the length of binary GCP via Turyn’s method. Further, we obtain the perfect CZCP with new length 4N and extend the lengths of CZCPs.

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.

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.