One of the challenges in fine-grained classification is that subcategories with significant similarity are hard to be distinguished due to the equal treatment of all subcategories in existing algorithms. In order to solve this problem, a fine-grained...
The development of underground artificial cavities plays an important role in the exploitation of urban spatial resources. As the rapidly growing number of underground artificial cavities with different depths and scales increases, the detection and...
Compared with ordinary image classification tasks, fine-grained image classification is closer to real-life scenes. Its key point is how to find the local areas with sufficient discrimination and perform effective feature learning. Based on a bilinea...
The detection of crowd density levels and anomalies is a hot topic in video surveillance. Especially in human-centric action and activity-based movements. In some respects, the density level variation is considered an anomaly in the event. Crowd beha...
As the number of elderly individuals living alone increases, the risk of fall-related accidents correspondingly rises, underscoring the need for rapid fall detection systems. Because falls are difficult to predict in terms of location, detection syst...
Soil organic matter (SOM) content is a key indicator for assessing soil health, carbon cycling, and soil degradation. Traditional SOM detection methods are complex and time-consuming and do not meet the modern agricultural demand for rapid, non-destr...
Routine examination of entire histological slides at cellular resolution poses a significant if not insurmountable challenge to human observers. However, high-resolution data such as the cellular distribution of proteins in tissues, e.g., those obtai...
In sensor-based human activity recognition, many methods based on convolutional neural networks (CNNs) have been proposed. In the typical CNN-based activity recognition model, each class is treated independently of others. However, actual activity cl...
The world’s oceans are under stress from climate change, acidification and other human activities, and the UN has declared 2021–2030 as the decade for marine science. To monitor the marine waters, with the purpose of detecting discharges of tracers f...
A Bayesian convolutional neural network (BCNN) quantifies prediction uncertainty by introducing randomness into weights or activations, which is important for safety-critical applications such as medical diagnosis and autonomous driving. However, BCN...
This paper presents a disaster detection framework based on aerial imagery, utilizing a Branch Convolutional Neural Network (B-CNN) to enhance feature learning efficiency. The B-CNN architecture incorporates branch training, enabling effective traini...
Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power cardiac arrhythmia classifiers owing to their high weight compression rate. However, multi-class classification of ECG signals based on bCNNs is chal...
A binary convolutional neural network (BCNN) is a neural network promising to realize analysis of visual imagery in low-cost resource-limited devices. This study presents an efficient inference processor for BCNNs, named TORRES. TORRES performs infer...
Conventional GPS acquisition methods, such as Max selection and threshold crossing (MAX/TC), estimate GPS code/Doppler by its correlation peak. Different from MAX/TC, a multi-layer binarized convolution neural network (BCNN) is proposed to recognize...
The success of deep machine learning (DML) models in gaming and robotics has increased its trial in clinical and public healthcare solutions. In applying DML to healthcare problems, a special challenge of inadequate electrical energy and computing re...
Binary convolutional neural networks (BCNN) have shown good accuracy for small to medium neural network models. Their extreme quantization of weights and activations reduces off-chip data transfer and greatly reduces the computational complexity of c...
This work focuses on automatic gender and age prediction tasks from handwritten documents. This problem is of interest in a variety of fields, such as historical document analysis and forensic investigations. The challenge for automatic gender and ag...
Artificial intelligence is one of the most popular topics in computer science. Convolutional neural network (CNN), which is an important artificial intelligence deep learning model, has been widely used in many fields. However, training a CNN require...
We have developed a platform, termed PathoFusion, which is an integrated system for marking, training, and recognition of pathological features in whole-slide tissue sections. The platform uses a bifocal convolutional neural network (BCNN) which is d...
Computerized methods have been developed that allow quantitative morphological analyses of whole slide images (WSIs), e.g., of immunohistochemical stains. The latter are attractive because they can provide high-resolution data on the distribution of...
The accurate classification of Amanita is helpful to its research on biological control and medical value, and it can also prevent mushroom poisoning incidents. In this paper, we constructed the Bilinear convolutional neural networks (B-CNN) with att...
Background: The dynamic development of information technologies, particularly in the fields of computer image analysis and artificial intelligence (AI) algorithms, plays an increasingly important role in orthodontic diagnostics. Cephalometric images...
As an active microwave sensor, synthetic aperture radar (SAR) has the characteristic of all-day and all-weather earth observation, which has become one of the most important means for high-resolution earth observation and global resource management....
Thus far, few studies have been conducted on fine-grained classification tasks for the latest convolutional neural network ConvNeXt, and no effective optimization method has been made available. To achieve more accurate fine-grained classification, t...
RNA modification is vital to various cellular and biological processes. Among the existing RNA modifications, N6-methyladenosine (m6A) is considered the most important modification owing to its involvement in many biological processes. The prediction...
Recognition of surface targets has a vital influence on the development of military and civilian applications such as maritime rescue patrols, illegal-vessel screening, and maritime operation monitoring. However, owing to the interference of visual s...
In this paper, we propose a multi-feature fusion network (MFF-Net) for a modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring scheme. The constellation map data used in this work comes from five modulation format...
In brain–machine interface (BMI) systems, the performance of trained Convolutional Neural Networks (CNNs) is significantly influenced by the quality of the training data. Another issue is the training time of CNNs. This paper introduces a novel...
A primary problem faced during previous research was the gap in limited and unbalanced quantity of prior samples between computer classification tasks and targeted remote sensing applications. This paper presents the fusion method to overcome this li...
Flying ad hoc networks (FANETs) or drone technologies have attracted great focus recently because of their crucial implementations. Hence, diverse research has been performed on establishing FANET implementations in disparate disciplines. Indeed, civ...
Biometric identification systems are a fundamental building block of modern security. However, conventional biometric methods cannot easily cope with their intrinsic security liabilities, as they can be affected by environmental factors, can be easil...
This article proposes a new method of identity recognition in sanitary facilities based on electrocardiography (ECG) signals. Our team previously proposed a novel approach of invisible ECG at the thighs using polymeric electrodes, leading to the crea...
In this paper, a feedback training approach for efficiently dealing with distribution shift in synthetic aperture radar target detection using a Bayesian convolutional neural network is proposed. After training the network on in-distribution data, it...