Transformer has emerged as one of the modern neural networks that has been applied in numerous applications. However, transformers’ large and deep architecture makes them computationally and memory-intensive. In this paper, we propose the block...
After conducting deep research on all existing reversible data hiding (RDH) methods based on Alattar’s integer transform, we discover that the frequently-used method in obtaining the difference value list of an image block may lead to high embe...
This paper describes a current differential relay for transformer protection that operates in conjunction with a core saturation detection-based blocking algorithm. The differential current for the magnetic inrush or over-excitation has a point of in...
The instability of the winding-cushion structure is one of the primary causes of transformer failures. Insulation cushion compression and offset are the predominant forms leading to structural instability. Therefore, this paper, using the SFSZ7-31500...
The paper explores the possibilities of the structural and functional transformation of blocks in the historical center of Zagreb as a part of modernization after many years of neglect as well as earthquakes in 2020. The research aims to determine ho...
The goal of block matching (BM) is to locate small patches of an image that are similar to a given patch or template. This can be done either in the spatial domain or, more efficiently, in a transform domain. Full search (FS) BM is an accurate, but c...
In this paper, we proposed a high accurate and stable Legendre transform algorithm, which can reduce the potential instability for a very high order at a very small increase in the computational time. The error analysis of interpolative decomposition...
Fine-grained image classification remains an ongoing challenge in the computer vision field, which is particularly intended to identify objects within sub-categories. It is a difficult task since there is both minimal and substantial intra-class vari...
This work investigates the effectiveness of block transform coding (BTC) as a lightweight, training-free quantization strategy for compressing the weights of pretrained deep neural networks. The proposed method applies a rule-based block transform wi...
Multichannel images, i.e., images of the same object or scene taken in different spectral bands or with different imaging modalities/settings, are common in many applications. For example, multispectral images contain several wavelength bands and hen...
As a result of the rise in network technology, information security has become particularly important. Digital images play an important role in network transmission. To improve their security and efficiency, a new color image encryption algorithm is...
Recently, transformers have demonstrated notable improvements in natural advanced visual tasks. In the field of computer vision, transformer networks are beginning to supplant conventional convolutional neural networks (CNNs) due to their global rece...
Nowadays, the images are transferred through open channels that are subject to potential attacks, so the exchange of image data requires additional security in many fields, such as medical, military, banking, etc. The security factors are essential i...
Nowadays, HSI classification can reach a high classification accuracy when given sufficient labeled samples as training set. However, the performances of existing methods decrease sharply when trained on few labeled samples. Existing methods in few-s...
Multi-person pose estimation is the task of detecting and regressing the keypoint coordinates of multiple people in a single image. Significant progress has been achieved in recent years, especially with the introduction of transformer-based end-to-e...
This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN) environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of t...
The Transformer model has received significant attention in Human Activity Recognition (HAR) due to its self-attention mechanism that captures long dependencies in time series. However, for Inertial Measurement Unit (IMU) sensor time-series signals,...
As a nonverbal cue, gaze plays a critical role in communication, expressing emotions and reflecting mental activity. It has widespread applications in various fields. Recently, the appearance-based gaze estimation method, which utilizes CNN (convolut...
In mineral processing, visual-based online particle size analysis systems depend on high-precision image segmentation to accurately quantify ore particle size distribution, thereby optimizing crushing and sorting operations. However, due to multi-sca...
In this study, a Level III reliability design of an armor block of rubble mound breakwater was developed using the optimized probabilistic wave height model for the Korean marine environment and Van der Meer equation. To demonstrate what distinguishe...
In recent years, Transformer-based methods have demonstrated proficiency in capturing complex patterns for time series forecasting. However, their quadratic complexity relative to input sequence length poses a significant bottleneck for scalability a...
We propose a novel architecture, Transformer Dil-DenseUNet, designed to address the challenges of accurately segmenting stroke lesions in MRI images. Precise segmentation is essential for diagnosing and treating stroke patients, as it provides critic...
Underwater object detection and classification pose significant challenges due to environmental factors such as water turbidity and variable lighting conditions. This research proposes a novel approach that integrates advanced imaging techniques with...
Precise medical image segmentation plays a vital role in disease diagnosis and clinical treatment. Although U-Net-based architectures and their Transformer-enhanced variants have achieved remarkable progress in automatic segmentation tasks, they stil...
In this paper, an adaptive block compressive sensing (BCS) method is proposed for compression of synthetic aperture radar (SAR) images. The proposed method enhances the compression efficiency by dividing the magnitude of the entire SAR image into mul...
The normal operation of rolling bearings is crucial to the performance and reliability of rotating machinery. However, the collected vibration signals are often mixed with complex noise, and the transformer network cannot fully extract the characteri...
The investigation of properties of amphiphilic block copolymers as stabilizers for non-lamellar lyotropic liquid crystalline nanoparticles represents a fundamental issue for the formation, stability and upgraded functionality of these nanosystems. Th...
A symmetric block cipher employing a substitution–permutation duo is an effective technique for the provision of information security. For substitution, modern block ciphers use one or more substitution boxes (S-Boxes). Certain criteria and des...
The semantic segmentation of bone structures demands pixel-level classification accuracy to create reliable bone models for diagnosis. While Convolutional Neural Networks (CNNs) are commonly used for segmentation, they often struggle with complex sha...
With the evolution of the More Electric Aircraft (MEA) concept, high pulse converters have gained the attention of researchers due to their higher power quality. Among the high pulse converters, 18-pulse autotransformer rectifier unit (ATRU) offers b...
Metro stray currents flowing into neutral-point-grounded transformers can cause serious direct current (DC) bias. Affected by both metro train and urban power grid operations, transformer neutral-point DC caused by metro stray current is complex, dyn...
With the aim of solving the problem of coal congestion caused by big coal blocks in underground mine scraper conveyors, in this paper we proposed the use of a YOLO-BS (YOLO-Big Size) algorithm to detect the abnormal phenomenon of coal blocks on scrap...
Visual inspection of the workplace and timely reminders of unsafe behaviors (e.g, not wearing a helmet) are particularly significant for avoiding injuries to workers on the construction site. Video surveillance systems generate large amounts of non-s...
The quality of ISAR (Inverse Synthetic Aperture Radar) images has a significant impact on the detection and recognition of targets. Therefore, ISAR image quality assessment is a fundamental prerequisite and primary link in the utilization of ISAR ima...
Current deep learning-based change detection approaches mostly produce convincing results by introducing attention mechanisms to traditional convolutional networks. However, given the limitation of the receptive field, convolution-based methods fall...
Single-image super-resolution (SISR) techniques have become a vital tool for improving image quality and clarity in the rapidly evolving field of digital imaging. Convolutional neural network (CNN) and transformer-based SISR techniques are very popul...
Traditional manual inspection methods for tunnel lining leakage are subjective and inefficient, while existing models lack sufficient recognition accuracy in complex scenarios. An intelligent leakage identification model adaptable to complex backgrou...
Aerial optical cameras are the primary method for capturing high-resolution images to produce large-scale mapping products. To improve aerial photography efficiency, multiple cameras are often used in combination to generate large-format virtual cent...
Smiling has often been incorrectly interpreted as “happy” in the popular facial expression datasets (AffectNet, RAF-DB, FERPlus). Smiling is the most complex human expression, with positive, neutral, and negative smiles. We focused on fin...
Variations in the thickness of retinal layers serve as early diagnostic indicators for various fundus diseases, and precise segmentation of these layers is essential for accurately measuring their thickness. Optical Coherence Tomography (OCT) is an i...
Transformers have performed better than traditional convolutional neural networks (CNNs) for image super-resolution (SR) reconstruction in recent years. Currently, shifted window multi-head self-attention based on the swin transformer is a typical me...
Deep learning-based image classification techniques have been widely utilized in low-voltage AC series-type fault arc detection. However, the transformation of signals into images frequently leads to significant loss of current signal characteristics...
It is always an interesting research topic for digital receiver (DRX) designers to develop a DRX with (1) ultrawide instantaneous bandwidth (IBW), (2) high sensitivity, (3) fine time-of-arrival-measurement resolution (TMR), and (4) fine frequency-mea...
Brain tumors are one of the deadliest types of cancer. Rapid and accurate identification of brain tumors, followed by appropriate surgical intervention or chemotherapy, increases the probability of survival. Accurate determination of brain tumors in...
Bamboo is an important economic crop with up to a large number of species. The distribution of bamboo species is wide; therefore, it is difficult to collect images and make the recognition model of a bamboo species with few amount of images. In this...
Leukemia is a life-threatening blood cancer that affects a large cross-section of the population, which underscores the great need for timely, accurate, and efficient diagnostic solutions. Traditional methods are time-consuming, subject to human vuln...
Synthetic Aperture Radar (SAR) is renowned for its all-weather and all-time imaging capabilities, making it invaluable for ship target recognition. Despite the advancements in deep learning models, the efficiency of Convolutional Neural Networks (CNN...
Infrared and visible image fusion technologies are used to characterize the same scene using diverse modalities. However, most existing deep learning-based fusion methods are designed as symmetric networks, which ignore the differences between modal...
The trend towards the decentralization and decarbonization of the energy sector stimulates the adoption of generation facilities based on renewable energy sources (RES) and distributed generation (DG) facilities that utilize secondary energy resource...
Low-light image enhancement (LLIE) requires modeling spatially extensive and interdependent degradations across large pixel regions, while directly equipping diffusion-based LLIE with heavy global modules inside the iterative denoising backbone leads...
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