Grey wolf optimization (GWO) is one of the recently proposed heuristic algorithms imitating the leadership hierarchy and hunting mechanism of grey wolves in nature. The aim of these algorithms is to perform global optimization. This paper presents a...
Reliable high-fidelity video transmission over noisy quantum channels remains challenging, especially due to temporal dependencies introduced by modern video compression standards. These codecs, such as versatile video coding (VVC), employ inter-fram...
The greater cane rat algorithm (GCRA) draws inspiration from the seasonal behavioral patterns of the greater cane rats: extensive roaming during the non-breeding period for global exploration, and aggregative foraging during the reproductive period f...
Efficient and reliable transmission of compressed images over noisy channels remains a significant challenge due to the high sensitivity to noise. Quantum communication offers a promising solution by encoding classical information into quantum states...
Specific emitter identification (SEI) is a highly active research area in physical layer security. In this paper, we propose a SEI scheme based on time-frequency domain channel, spatial, and self-attention mechanisms (TF-CSS) for deep networks with f...
Photonic neural network chips have been widely studied because of their low power consumption, high speed and large bandwidth. Using amplitude and phase to encode, photonic chips can accelerate complex-valued neural network computations. In this arti...
Sentiment analysis aims to study, analyse and identify the sentiment polarity contained in subjective documents. In the realm of natural language processing (NLP), the study of sentiment analysis and its subtask research is a hot topic, which has ver...
In recent years, U-Net-based architectures have been widely applied to polarimetric synthetic aperture radar (PolSAR) semantic segmentation. However, successive downsampling may lead to the loss of fine spatial details, while conventional U-Net-style...
Most of the existing quantum-inspired models are based on amplitude-phase embedding to model natural language, which maps words into Hilbert space. In quantum-computing theory, the vectors corresponding to quantum states are all complex values, so th...
Multimodal sentiment analysis is challenging because textual, visual, and acoustic evidence is heterogeneous and oft en weakly aligned. Here, we present QRSAN, a quantum-inspired residual self-attention network that integrates an LSTM text encoder, m...
Interrupted sampling repeater jamming (ISRJ) is characterized by its coherent processing gains and flexible modulation techniques. ISRJ generates spurious targets along the range, which presents significant challenges to the radar systems. However, e...
Polarimetric synthetic aperture radar (PolSAR) image classification is a pixel-wise issue, which has become increasingly prevalent in recent years. As a variant of the Convolutional Neural Network (CNN), the Fully Convolutional Network (FCN), which i...
This work is devoted to the application of complex-valued neural networks based on the multilayer neural network with multi-valued neurons (MLMVN) for short-term electrical load forecasting in smart grid energy systems. Accurate forecasting is a crit...
Algebraic methodologies are developed for addressing the inverse problem of recovering a collection of bivariate observations from selected subsets of their marginal and joint integer moments. Particular attention is devoted to the case of samples co...
Recently, deep learning models, such as autoencoder, deep belief network and convolutional autoencoder (CAE), have been widely applied on polarimetric synthetic aperture radar (PolSAR) image classification task. These algorithms, however, only consid...
The multimodal fusion of hyperspectral images (HSI) and LiDAR data for land cover classification encounters difficulties in modeling heterogeneous data characteristics and cross-modal dependencies, leading to the loss of complementary information due...
Aspect-Based Sentiment Analysis (ABSA) has gained significant popularity in recent years, which emphasizes the aspect-level sentiment representation of sentences. Current methods for ABSA often use pre-trained models and graph convolution to represen...
With the maturation of digital radio frequency memory (DRFM) technology, various intra-pulse retransmission interference methods have emerged. These flexible and changeable retransmission interference methods pose significant challenges to radar dete...
Translating Synthetic aperture radar (SAR) images into optical images is intrinsically ill-posed because microwave backscatter and optical reflectance describe different physical properties of the observed scene. Although frequency-domain modeling ha...
One major challenge faced by deep learning-based methods that detect target objects in the form of bounding boxes is object occlusion. High degrees of occlusion significantly diminish the accuracy of instance segmentation. Nonetheless, complex-valued...
High-order phase defocus occurs in ISAR imaging of maneuvering targets. Traditional methods are limited by motion model mismatches, whereas existing deep imaging relies on complex-valued convolutions or self-attention mechanisms, whose excessive comp...
This paper presents a proposal of an efficient binaural room-acoustics auralization method, an essential goal of room-acoustics modeling. The method uses a massively parallel wave-based room-acoustics solver based on a dispersion-optimized explicit t...
Current management strategies for sorghum aphids heavily rely on indiscriminate chemical application, leading to severe environmental consequences and impacting food safety. While precision spraying offers a viable remediation for pesticide overuse,...
We propose an exploratory framework for a Bohmian model of quantum matter propagating on a classical curved spacetime background. The gravitational sector is governed by classical Einstein field equations throughout; no quantisation of spacetime is a...
Categorical data is a fundamental element of real-world tabular classification problems. In typical machine learning workflows, encoding strategies are used to convert categorical data into numerical representations. These encoding strategies vary in...
Absolute moment block truncated coding (AMBTC) is a lossy image compression technique aiming at low computational cost, and has been widely studied. Previous studies have investigated the performance improvement of AMBTC; however, they often over des...
Real-time video streaming over vehicular ad-hoc networks (VANETs) has been considered as a critical challenge for road safety applications. The purpose of this paper is to reduce the computation complexity of high efficiency video coding (HEVC) encod...
This paper presents a novel efficient encoding method and a high-throughput low-complexity encoder architecture for quasi-cyclic low-density parity-check (QC-LDPC) codes for the 5th-generation (5G) New Radio (NR) standard. By storing the quantized va...
A quantum measurement can be regarded as a communication channel, in which the parameters of the state are expressed only in the probabilities of the outcomes of the measurement. We begin this paper by considering, in a non-quantum-mechanical setting...
The satisfiability problem (SAT) in propositional logic determines if there is an assignment of values that makes a given propositional formula true. Recently, fast SAT solvers have been developed, and SAT encoding research has gained attention. This...
In an era where artificial intelligence is rapidly expanding into creative domains, the challenge of modeling human-like cognition and emotion in generative processes becomes increasingly central. The present study was made in connection with the exh...
Adaptive bitrate streaming over HTTP (DASH) requires content-aware bitrate ladders to balance bandwidth utilisation and quality of experience, particularly for computationally demanding codecs such as H.266/Versatile Video Coding (VVC). This paper in...
Image steganography is a multipurpose-serving key emerging technology that is used for covertly transferring, storing, and governing various digital data, including intellectual properties and copyrights, social media data, multimedia data, and secre...
In video encoding rate control, adaptive selection of the initial quantization parameter (QP) is a critical factor affecting both encoding quality and rate control precision. Due to the diversity of video content and the dynamic nature of network con...
The constant increase in multimedia Internet traffic in the form of video streaming requires new solutions for efficient video coding to save bandwidth and network resources. HTTP adaptive streaming (HAS), the most widely used solution for video stre...
In this study, we developed a single leg knee joint assistance robot. Commonly used exoskeletons have a left-right pair, but when only one leg of the wearer is uncomfortable, it is effective to wear the exoskeleton on only the uncomfortable leg. The...
This research introduces a quantum machine learning (QML) approach for predicting heart disease (HD). The method combines preprocessing of data with quantum feature map (QFM) and quantum classification techniques. In the method, clinical data of HD a...
Data privacy is a major concern when accessing and processing sensitive medical data. A promising approach among privacy-preserving techniques is homomorphic encryption (HE), which allows for computations to be performed on encrypted data. Currently,...
Accurate forecasting of stock market indices is crucial for investors, financial analysts, and policymakers. The integration of encoder and decoder architectures, coupled with an attention mechanism, has emerged as a powerful approach to enhance pred...
Scene text recognition has significant application value in autonomous driving, smart retail, and assistive devices. However, due to challenges such as multi-scale variations, distortions, and complex backgrounds, existing methods such as CRNN, ViT,...
Multivariate time series prediction models perform the required operation on a specific window length of a given input. However, capturing complex and nonlinear interdependencies in each temporal window remains challenging. The typical attention mech...
In recent years, geographic knowledge graphs (GeoKGs) have shown great promise in representing spatio-temporal and event-driven knowledge. However, existing knowledge graph embedding approaches mainly focus on structural patterns and often overlook t...
This study introduces an innovative deep learning model, Residual-EnDecode-Feedforward Attention Mechanism-Long Short-Term Memory (REDF-LSTM), designed to overcome the high uncertainty challenges faced by traditional soil moisture prediction methods....
Visual–Inertial Odometry (VIO) is a foundational technology for autonomous navigation and robotics. However, existing deep learning-based methods face key challenges in temporal modeling and computational efficiency. Conventional approaches, su...
Although Lempel–Ziv complexity (LZC) can reflect changes in object characteristics by measuring changes in independent patterns in the signal, it can only be applied to one-dimensional time series and cannot be directly applied to two-dimension...
Continuous advancements in digital image transmission technology within network environments have heightened the necessity for secure, convenient, and well-suited image encryption systems. Base64 encoding possesses the ability to convert raw data int...
This work introduces a quantum K-Nearest Neighbor (K-NN) classifier algorithm. The algorithm utilizes angle encoding through a Quantum Random Access Memory (QRAM) using n number of qubit addresses with
The event sensor provides high temporal resolution and generates large amounts of raw event data. Efficient low-complexity coding solutions are required for integration into low-power event-processing chips with limited memory. In this paper, a novel...
Community detection in complex networks plays a pivotal role in modern scientific research, including in social network analysis and protein structure analysis. Traditional community detection methods face challenges in integrating heterogeneous mult...
The rapid growth of geographic information technologies in the field of processing and analysis of spatial data has led to a significant increase in the role of geographic information systems in various fields of human activity. However, solving comp...
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