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.

The phenomena of iced line galloping in overhead transmission lines, caused by wind or asymmetric icing, can directly result in structural damage, windage yaw discharge of conductor, and metal damage, posing significant risks to the operation of power systems. However, the existing prediction methods for iced line galloping are difficult to achieve accurate predictions due to the lack of a large amount of iced line galloping data that matches real-world conditions. To address these issues, this paper studies the overhead iced transmission line galloping response prediction. First, the models of finite element, aerodynamic coefficient, and aerodynamic excitation for the iced conductor are constructed. The dynamic response of the conductor is simulated using finite element software to obtain a dataset of conductor galloping under different parameters. Secondly, a particle swarm optimization-conditional generative adversarial network (PSO-CGAN) based iced transmission line galloping prediction model is proposed, where the weight parameters of loss function in CGAN are optimized by PSO. The model takes initial wind attack angle, wind speed, and span as inputs to output prediction results of iced transmission line galloping. Then, based on the dynamics and galloping features of the conductor, the effects of different initial wind attack angles, wind speeds, and icing thickness on galloping are analyzed. Finally, the superior performance of the proposed model is verified through simulations.

The distribution station serves as a foundational component for managing the power system. However, there are missing data in the areas without collection devices due to the limitation of device deployment, leading to an adverse impact on the real-time and precise monitoring of distribution stations. The problem of missing data can be solved by the pseudo measurement data deduction method. Traditional pseudo measurement data deduction methods overlook the temporal and contextual correlations of distribution station data, resulting in a lower restoration accuracy. Motivated by the above challenges, this paper proposes a novel pseudo measurement data deduction model for minimal data collection requirements in distribution stations. Compared to the traditional GAN, the proposed enhanced GAN improves the architecture by decomposing the input tensor of the generator, allowing it to handle high-dimensional and intricate data. Furthermore, we enhance the loss function to accelerate the model’s convergence speed. Our proposed approach allows GAN to be trained within a supervised environment, effectively enhancing the accuracy of model training. The simulation result shows that the proposed algorithm achieves better performances compared with existing methods.

The globalization of the Integrated Circuit (IC) supply chain has introduced the risk of Hardware Trojan (HT) insertion. We propose an unsupervised Hardware Trojan detection method based on the Enhanced Local Outlier Factor (ELOF) algorithm to detect HT efficiently. This method extracts structural and testability features and employs the scoring mechanism of the ELOF algorithm to emphasize the deviation of suspicious HT nets from clusters. Experimental results on Hardware Trojan libraries show that the method achieves an average prediction accuracy (A) of 97.36%, a True Negative Rate (TNR) of 97.81%, a precision (P) of 40.94%, and an F-measure of 49.28%, all of which outperform the Local Outlier Factor (LOF) algorithm and Cluster-Based Local Outlier Factor (CBLOF) algorithm. Notably, the method exhibits superior performance in terms of True Positive Rate (TPR), reaching 70.86%, indicating its efficiency in identifying HT and reducing false negatives. The results demonstrate that the proposed algorithm and feature combination in the approach can significantly enhance the efficiency of Trojan detection.

Wingsuit flying search is a meta-heuristic algorithm that effectively searches for optimal solutions by narrowing down the search space iteratively. However, its performance is affected by the balance between exploration and exploitation. We propose a four-layered hierarchical population structure algorithm, multi-layered chaotic wingsuit flying search (MCWFS), to promote such balance in this paper. The proposed algorithm consists of memory, elite, sub-elite, and population layers. Communication between the memory and elite layers enhances exploration ability while maintaining population diversity. The information flow from the population layer to the elite layer ensures effective exploitation. We evaluate the performance of the proposed MCWFS algorithm by conducting comparative experiments on IEEE Congress on Evolutionary Computation (CEC) benchmark functions. Experimental results prove that MCWFS is superior to the original algorithm in terms of solution quality and search performance. Compared with other representative algorithms, MCWFS obtains more competitive results on composite problems and real-world problems.

The multiple chaos embedded gravitational search algorithm (CGSA-M) is an optimization algorithm that utilizes chaotic graphs and local search methods to find optimal solutions. Despite the enhancements introduced in the CGSA-M algorithm compared to the original GSA, it exhibits a pronounced vulnerability to local optima, impeding its capacity to converge to a globally optimal solution. To alleviate the susceptibility of the algorithm to local optima and achieve a more balanced integration of local and global search strategies, we introduce a novel algorithm derived from CGSA-M, denoted as CGSA-H. The algorithm alters the original population structure by introducing a multi-level information exchange mechanism. This modification aims to mitigate the algorithm’s sensitivity to local optima, consequently enhancing the overall stability of the algorithm. The effectiveness of the proposed CGSA-H algorithm is validated using the IEEE CEC2017 benchmark test set, consisting of 29 functions. The results demonstrate that CGSA-H outperforms other algorithms in terms of its capability to search for global optimal solutions.

The learning with errors (LWE) problem is one of the fundamental problems in cryptography and it has many applications in post-quantum cryptography. There are two variants of the problem, the decisional-LWE problem, and the search-LWE problem. LWE search-to-decision reduction shows that the hardness of the search-LWE problem can be reduced to the hardness of the decisional-LWE problem. The efficiency of the reduction can be regarded as the gap in difficulty between the problems. We initiate a study of quantum search-to-decision reduction for the LWE problem and propose a reduction that satisfies sample-preserving. In sample-preserving reduction, it preserves all parameters even the number of instances. Especially, our quantum reduction invokes the distinguisher only 2 times to solve the search-LWE problem, while classical reductions require a polynomial number of invocations. Furthermore, we give a way to amplify the success probability of the reduction algorithm. Our amplified reduction is incomparable to the classical reduction in terms of sample complexity and query complexity. Our reduction algorithm supports a wide class of error distributions and also provides a search-to-decision reduction for the learning parity with noise problem. In the process of constructing the search-to-decision reduction, we give a quantum Goldreich-Levin theorem over ℤ_q where q is a prime. In short, this theorem states that, if a hardcore predicate a・s (mod q) can be predicted with probability distinctly greater than (1/q) with respect to a uniformly random a ∈ ℤ_qⁿ, then it is possible to determine s ∈ ℤ_qⁿ.

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.

This paper investigates non-binary quantum codes correcting multiple insertion errors. This paper provides an insertion-correcting procedure of the deletion-correcting non-binary codes constructed by Matsumoto and Hagiwara. By giving the procedure, we present multiple-insertion-correcting non-binary quantum codes.

IoT devices, which possess limited battery capacity and computing capabilities, are unable to meet many applications’ demands. The integration of wireless power transfer and edge computing has emerged as a promising solution for this problem. Nevertheless, efficiently making offloading decisions and allocating resources pose significant challenges, particularly in the scenarios of multiple access points (APs). This paper focuses on optimizing the sum computation rate (SCR) in a wireless powered network having multiple APs. The devices work in binary offloading, operating under frequency-division multiple access (FDMA) and time-division multiple access (TDMA), respectively. To efficiently address these two mixed-integer nonlinear programming problems, a deep reinforcement learning based algorithm is employed to determine the near-optimal offloading decisions. Additionally, under the given offloading decision, we present an algorithm using the golden section search for FDMA to obtain the subsequent optimal time allocation, and apply convex optimization algorithm to obtain the optimal time allocation for TDMA. Our algorithms achieve over 95 percent of the maximum SCR with low complexity. In comparison to the baseline algorithms, our proposed algorithms exhibit advantages in terms of convergence speed and attained SCR.

RGB-D semantic segmentation has attracted increasing attention over the past few years. The depth feature encodes both the shape of a local geometry as well as the base (whereabout) of it in a larger context. RGB and depth images can be concatenated into one and inputted into a network model, reducing additional computation but resulting in some distractive information as they are multimodal. For the problem, we propose a Shape-aware Convolutional layer with Convolutional Kernel Attention (CKA-ShapeConv) for reducing the distractive information by leveraging each unique input feature to rectify the kernels. Instead of using a single convolution kernel, we aggregate N parallel convolution kernels based on input-dependent attention. Specifically, four sets of attention weights are firstly calculated from each input feature map, next N parallel convolution kernels are weighted and aggregated along different dimensions, which ensure that the generated convolution kernel is more capable of catching semantic information from the input feature map, reducing interference between RGB and depth features. Then the aggregated convolution kernel is decomposed into two components: base and shape, two new learnable weights are introduced to cooperate with them independently, and finally a convolution is applied on the re-weighted combination of these two components. These two components can capture semantic and shape information of regions effectively, respectively. Meanwhile, our CKA-ShapeConv layer can be easily integrated into most existing backbone models with only a small amount of additional computation. Our experiments on NYUDv2 and SUN RGB-D datasets show that the proposed CKA-ShapeConv layer can improve the performance of backbone models effectively.

Quantization is commonly used to reduce the inference time of convolutional neural networks (CNNs). To reduce the inference time without drastically reducing accuracy, optimal bit widths need to be allocated for each layer or filter of the CNN. In conventional methods, the optimal bit allocation is obtained by using the gradient descent algorithm while minimizing the model size. However, the model size has little to no correlation with the inference time. In this paper, we present a computational-complexity metric called MAC×bit that is strongly correlated with the inference time of quantized CNNs. We propose a gradient descent-based regularization method that uses this metric for optimal bit allocation of a quantized CNN to improve the recognition accuracy and reduce the inference time. In experiments, the proposed method reduced the inference time of a quantized ResNet-18 model by 21.0% compared with the conventional regularization method based on model size while maintaining comparable recognition accuracy.

This letter proposes a method that can track the movement of noise sources in fixed filter ANC and virtual sensing ANC systems by using source localization with multiple microphones. Since the optimal noise control filter depends on the location of the noise source, the proposed system prepares optimal noise control filters in advance for multiple locations where the noise is expected to move. The noise source location is then identified using the noise source localization method during the operation of the ANC system, and the appropriate noise control filter is selected according to the location. Simulation results using actual impulse responses show that a noise reduction of approximately 20 dB is possible even if the noise source moves.

Frequency hopping sequences (FHSs) play a significant role in modern frequency hopping spread spectrum communication and radar systems. In terms of application, the aperiodic Hamming correlation (HC) holds greater significance compared to the periodic HC as it directly impacts the communication performance. In addition, it is crucial for each user’s FHS to have a substantial wide-gap (WG) in order to prevent the received signals from interfering with each other. In this letter, we obtain a new bound by extending the aperiodic bound proposed by Peng-Fan and the WG FHS bound introduced by Li-Fan-Yang-Wang. The proposed bound is strict since they can be verified using specific parameters of aperiodic WG FHSs.

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 perform edge-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.