This paper proposes a computational method for the reliability of 2-separable networks. Based on graph theory and probability theory, this method simplifies the calculation process by constructing a network equivalent model and designing correspondin...
Considering the increasing scale and severity of damage from recent cybersecurity incidents, the need for fundamental solutions to external security threats has increased. Hence, network separation technology has been designed to stop the leakage of...
In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications...
Remote-sensing time-series data are significant for global environmental change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environme...
A dual autoencoder employing separable convolutional layers for image denoising and deblurring is represented. Combining two autoencoders is presented to gain higher accuracy and simultaneously reduce the complexity of neural network parameters by us...
Bird sounds have obvious characteristics per species, and they are an important way for birds to communicate and transmit information. However, the recorded bird sounds in the field are usually mixed, which making it challenging to identify different...
To solve the challenge of single-channel blind image separation (BIS) caused by unknown prior knowledge during the separation process, we propose a BIS method based on cascaded generative adversarial networks (GANs). To ensure that the proposed metho...
Poly[N-9′-hepta-decanyl-2,7-carbazole-alt-5-5-(4′,7′-di-2-thienyl-2′,1′,3′-benzothiadiazole)] (PCDTBT) is a stable semiconducting polymer with high rigidity in its molecular chains, which makes it difficult to organize into an ordered structure and a...
Speech separation, sometimes known as the “cocktail party problem”, is the process of separating individual speech signals from an audio mixture that includes ambient noises and several speakers. The goal is to extract the target speech i...
The Gaussian-radial-basis function neural network (GRBFNN) has been a popular choice for interpolation and classification. However, it is computationally intensive when the dimension of the input vector is high. To address this issue, we propose a ne...
Attackers’ intrusion into the Enterprise LAN is increasing every year, and the method is becoming more intelligent and crafty. Various security measures against external network intrusions, such as firewalls, are being studied and applied to protect...
Railways are crucial in economic development and improving people’s livelihoods. Therefore, defect detection and maintenance of rails are particularly important. In order to accurately separate and identify the rail defect from the mixed signal...
Ship-radiated noise separation is critical in both military and economic domains. However, due to the complex underwater environments with multiple noise sources and reverberation, separating ship-radiated noise poses a significant challenge. Traditi...
Speech separation, as an important research direction in audio signal processing, has been widely studied by the academic community since its emergence in the mid-1990s. In recent years, with the rapid development of deep neural network technology, s...
Ceramic-coated polyolefin separator technology is considered a simple and effective method for the improvement of lithium-ion battery (LIB) safety. However, the characteristics of ceramic powder can adversely affect the surface structure and ion cond...
Speech separation is a hot topic in multi-speaker speech recognition. The long-term autocorrelation of speech signal sequences is an essential task for speech separation. The keys are effective intra-autocorrelation learning for the speaker’s s...
The performance of an Unmanned Underwater Vehicle (UUV) is significantly influenced by the magnitude of self-generated noise, making it a crucial factor in advancing acoustic load technologies. Effective noise management, through the identification a...
The mesoscopic phase separation in two- and three-dimensional gels has been studied by computer simulation of a bead-spring model of Lennard-Jones particles. The formation of complex networks of high-density phase (HDP) has been investigated and part...
As a promising material for CO2/N2 separation, PolyActiveTM can be used as a separation layer in thin-film composite membranes (TFCM). Prior studies focused on the modification of PolyActiveTM using low-molecular-weight additives. In this study, the...
The ambitious climate targets of the European Union emphasise the necessity to reduce carbon dioxide emissions in the building sector. Therefore, various sustainable heat sources should be used in existing district heating systems to cover the heat d...
The 6FDA-based network PI has attracted significant attention for gas separation. A facile strategy to tailor the micropore structure within the network PI membrane prepared by the in situ crosslinking method is extremely significant for achieving an...
Image inpainting is an active area of research in image processing that focuses on reconstructing damaged or missing parts of an image. The advent of deep learning has greatly advanced the field of image restoration in recent years. While there are m...
Reducing energy consumption of mobile communication networks has gained significant attentions since it takes a major part of the total energy consumption of information and communication technology (ICT). In this paper, we consider 5G networks with...
Ship-radiated noise is the main basis for ship detection in underwater acoustic environments. Due to the increasing human activity in the ocean, the captured ship noise is usually mixed with or covered by other signals or noise. On the other hand, du...
In this paper, we apply a machine-learning approach to learn traveling solitary waves across various physical systems that are described by families of partial differential equations (PDEs). Our approach integrates a novel interpretable neural networ...
In upcoming smart urban environments, various things can be interconnected, and the Internet of Things (IoT) can be used to construct a safer and more convenient urban environment. Things in the IoT need an addressing system that can uniquely identif...
Accurate target speech mask estimation is the key to single-channel speech separation. Masks generated by conventional mask networks are easily corrupted by interfering speech and background noise, which degrades separation performance. To solve this...
Although hyperspectral data provide rich feature information and are widely used in other fields, the data are still scarce. Training small sample data classification is still a major challenge for HSI classification based on deep learning. Recently,...
Municipal household solid waste (MHSW) has become a serious problem in China over the course of the last two decades, resulting in significant side effects to the environment. Therefore, effective management of MHSW has attracted wide attention from...
Over the past few years, deep learning algorithms have held immense promise for better multi-spectral (MS) optical remote sensing image (RSI) analysis. Most of the proposed models, based on convolutional neural network (CNN) and fully convolutional n...
The super-resolution generative adversarial network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied by unpleasant artifacts. To f...
Hyperspectral images (HSIs) are pivotal in various fields due to their rich spectral–spatial information. While convolutional neural networks (CNNs) have notably enhanced HSI classification, they often generate redundant spatial features. To ad...
Underwater acoustic signals (UAS) in marine environments are often contaminated by noise from biological activity, ocean waves, and equipment. Under low signal-to-noise ratio (SNR) conditions, target ship-radiated components and background noise are...
Fault tolerance has long been a major concern for sensor communications in fault-tolerant cyber physical systems (CPSs). Network failure problems often occur in wireless sensor networks (WSNs) due to various factors such as the insufficient power of...
In this work, we propose a new physics-informed neural network framework based on the method of separation of variables (SVPINN) to solve the distributed-order time-fractional advection–diffusion equation. We develop a new method for calculatin...
Automatic Dependent Surveillance-Broadcast (ADS-B) signals are very vital in air traffic control. However, the space-based ADS-B signals are easily overlapped and their message cannot be correctly received. It is challenge to separate overlapped sign...
Speech separation is a well-known problem, especially when there is only one sound mixture available. Estimating the Ideal Binary Mask (IBM) is one solution to this problem. Recent research has focused on the supervised classification approach. The c...
The integrated electronic nose (e-nose) design, which integrates sensor arrays and recognition algorithms, has been widely used in different fields. However, the current integrated e-nose system usually suffers from the problem of low accuracy with s...
We propose a method for the blind separation of sounds of musical instruments in audio signals. We describe the individual tones via a parametric model, training a dictionary to capture the relative amplitudes of the harmonics. The model parameters a...
The COVID-19 epidemic has created highly unprocessed emotions that trigger stress, anxiety, or panic attacks. These attacks exhibit physical symptoms that may easily lead to misdiagnosis. Deep-learning (DL)-based classification approaches for emotion...
The neural network-based hyperspectral images (HSI) classification model has a deep structure, which leads to the increase of training parameters, long training time, and excessive computational cost. The deepened network models are likely to cause t...
Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share t...
With the rapid development of electronic technology, the electromagnetic interference encountered by airborne synthetic aperture radar (SAR) is no longer satisfied with a single type of interference, and it often encounters both suppressive and decep...
Convergence speed and steady-state source separation performance are crucial for enable engineering applications of blind source separation methods. The modification of the loss function of the blind source separation algorithm and optimization of th...
This paper presents a hardware implementation of a one-dimensional convolutional neural network using depthwise separable convolution (DSC) on the VC707 FPGA development board. The design processes the one-dimensional rolling bearing current signal d...
Urban separate sewer systems face significant challenges from rainfall-derived infiltration and inflow (RDII) during the wet season. To achieve the integrated optimization of operational safety, energy consumption, and carbon emissions, this study pr...
As an active microwave imaging sensor for the high-resolution earth observation, synthetic aperture radar (SAR) has been extensively applied in military, agriculture, geology, ecology, oceanography, etc., due to its prominent advantages of all-weathe...
In recent years, convolutional neural networks (CNNs) have been increasingly leveraged for the classification of hyperspectral imagery, displaying notable advancements. To address the issues of insufficient spectral and spatial information extraction...
Traditional hyperspectral image semantic segmentation algorithms can not fully utilize the spatial information or realize efficient segmentation with less sample data. In order to solve the above problems, a U-shaped hyperspectral semantic segmentati...
Oil/water mixtures from industrial and domestic wastewater adversely affect the environment and human beings. In this context, the development of a facile and improved separation method is crucial. Herein, dopamine was used as a bioadhesive to bind t...
of 147