Fully connected (FC) layers are used in almost all neural network architectures ranging from multilayer perceptrons to deep neural networks. FC layers allow any kind of symmetric/asymmetric interaction between features without making any assumption a...
As a key ingredient of deep neural networks (DNNs), fully-connected (FC) layers are widely used in various artificial intelligence applications. However, there are many parameters in FC layers, so the efficient process of FC layers is restricted by m...
Learning efficient image representations is at the core of the scene classification task of remote sensing imagery. The existing methods for solving the scene classification task, based on either feature coding approaches with low-level hand-engineer...
Recently, there have been rapid advances in high-resolution remote sensing image retrieval, which plays an important role in remote sensing data management and utilization. For content-based remote sensing image retrieval, low-dimensional, representa...
We report a single-shot phase-detection method using deep learning in a holographic data-storage system. The error rate was experimentally confirmed to be reduced by up to three orders of magnitude compared with that in the conventional phase-determi...
Chest X-ray evaluation is challenging due to its high demand and the complexity of diagnoses. In this study, we propose an optimized deep learning model for the multi-label classification of chest X-ray images. We leverage pretrained convolutional ne...
This study proposes a sliding-mode-based adaptive control framework for symmetric quad-rotor altitude and attitude tracking under parametric uncertainties and mismatched disturbances. To address mismatched disturbances, a finite-time disturbance obse...
Obtaining accurate sound speed profiles (SSPs) in near-real-time is of great significance for ocean exploration, underwater communication and improving the performance of sonar systems. In response to the problem that traditional sound speed estimati...
In this paper, a novel smooth group
Medical named entity recognition (NER) focuses on extracting and classifying key entities from medical texts. Through automated medical information extraction, NER can effectively improve the efficiency of electronic medical record analysis, medical...
Despite the success of deep learning models, it remains challenging for the over-parameterized model to learn good representation under small-sample-size settings. In this paper, motivated by previous work on out-of-distribution (OoD) generalization,...
The number of vehicle accidents has increased in recent years due to overloaded goods carriers. Off-road driving, mountain roads, and sharp edges on a road are the main causes of an imbalance in overloaded trucks. In rural areas, where smaller roads...
This work proposes and describes a deep learning-based approach utilizing recurrent neural networks (RNNs) for beam pattern synthesis considering uniform linear arrays. In this particular case, the deep neural network (DNN) learns from previously opt...
Many traditional fruit vendors still rely on manual sorting to pick out high-quality apples. This process is not only time-consuming but can also damage the apples. Meanwhile, automated detection technology is still in its early stage and lacks full...
In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas ne...
In regular convolutional neural networks (CNN), fully-connected layers act as classifiers to estimate the probabilities for each instance in classification tasks. The accuracy of CNNs can be improved by replacing fully connected layers with gradient...
One of the most important parts of a text-independent speaker verification system is speaker embedding generation. Previous studies demonstrated that shortcut connections-based multi-layer aggregation improves the representational power of a speaker...
Given the ubiquity of mobile devices, understanding the context of human activity with non-intrusive solutions is of great value. A novel deep neural network model is proposed, which combines feature extraction and convolutional layers, able to recog...
Human activity recognition (HAR) can effectively improve the safety of the elderly at home. However, non-contact millimeter-wave radar data on the activities of the elderly is often challenging to collect, making it difficult to effectively improve t...
The traditional algorithms for recognizing handwritten alphanumeric characters are dependent on hand-designed features. In recent days, deep learning techniques have brought about new breakthrough technology for pattern recognition applications, espe...
Developing a spiking neural network architecture that could prospectively be trained on energy-efficient neuromorphic hardware to solve various data analysis tasks requires satisfying the limitations of prospective analog or digital hardware, i.e., l...
In this paper, a new nonsmooth optimization-based algorithm for solving large-scale regression problems is introduced. The regression problem is modeled as fully-connected feedforward neural networks with one hidden layer, piecewise linear activation...
Network science can offer fundamental insights into the structural and functional properties of complex systems. For example, it is widely known that neuronal circuits tend to organize into basic functional topological modules, called network motifs....
This paper presents a complete system for the automatic identification of pathological Diabetic Macular Edema (DME) cases using Optical Coherence Tomography (OCT) images as source of information. To do so, the system extracts a set of deep features u...
Limited-angle iterative reconstruction (LAIR) reduces the radiation dose required for computed tomography (CT) imaging by decreasing the range of the projection angle. We developed an image-quality-based stopping-criteria method with a flexible and i...
Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detectio...
Colonoscopy, which refers to the endoscopic examination of colon using a camera, is considered as the most effective method for diagnosis of colorectal cancer. Colonoscopy is performed by a medical doctor who visually inspects one’s colon to fi...
Since leaf temperature (LT) is not a trivial measurement, deep-neural networks (DNN) and machine learning (ML) models were evaluated in this study as tools for estimating foliage temperature. Two DNN methods were used. The first DNN used convolutiona...
The present study focuses on the use of Convolutional Neural Networks (CNN or ConvNet) to classify a multi-seasonal dataset of Sentinel-2 images to discriminate four grassland habitats in the “Murgia Alta” protected site. To this end, we compared two...
In modern industries, high precision dimensional measurement plays a pivotal role in product inspection and sub-pixel edge detection is the core algorithm. Traditional interpolation and moment methods have achieved some success. However, those method...
In this paper, we present a new algorithm for cross-domain classification in aerial vehicle images based on generative adversarial networks (GANs). The proposed method, called Siamese-GAN, learns invariant feature representations for both labeled and...
Accurate hyperspectral image classification has been an important yet challenging task for years. With the recent success of deep learning in various tasks, 2-dimensional (2D)/3-dimensional (3D) convolutional neural networks (CNNs) have been exploite...
In this paper, we present an interaction force estimation method that uses visual information rather than that of a force sensor. Specifically, we propose a novel deep learning-based method utilizing only sequential images for estimating the interact...
Deep learning techniques are being widely applied to Human Activity Recognition (HAR). This paper describes the implementation and evaluation of a HAR system for daily life activities using the accelerometer of an iPhone 6S. This system is based on a...
The presented paper proposes a hybrid neural architecture that enables intelligent data analysis efficacy to be boosted in smart sensor devices, which are typically resource-constrained and application-specific. The postulated concept integrates prio...
Representation invariance plays a significant role in the performance of deep convolutional neural networks (CNNs) and human visual information processing in various complicated image-based tasks. However, there has been abounding confusion concernin...
In recent years, the number of deaths and injuries resulting from traffic accidents has been increasing dramatically all over the world due to distracted drivers. Thus, a key element in developing intelligent vehicles and safe roads is monitoring dri...
Data-driven methods with multi-sensor time series data are the most promising approaches for monitoring machine health. Extracting fault-sensitive features from multi-sensor time series is a daunting task for both traditional data-driven methods and...
Predicting internet user demographics based on traffic behavior analysis can provide effective clues for the decision making of network administrators. Nonetheless, most of the existing researches overly rely on hand-crafted features, and they also s...
One of the main principles of Deep Convolutional Neural Networks (CNNs) is the extraction of useful features through a hierarchy of kernels operations. The kernels are not explicitly tailored to address specific target classes but are rather optimize...
In a multi-agent system, multi-job assignment is an optimization problem that seeks to minimize total cost. This can be generalized as a complex problem in which several variations of vehicle routing problems are combined, and as an NP-hard problem....
This paper deals with the influence of the rolling shear deformation on the flexural behavior of CLT (Cross-Laminated Timber) panels. The morphological configuration of the panels, which consist of orthogonal overlapped layers of boards, led to a par...
Recently, digital pathology is an essential application for clinical practice and medical research. Due to the lack of large annotated datasets, the deep transfer learning technique is often used to classify histopathology images. A softmax classifie...
This paper develops a reliable deep-learning framework to extract latent features from spatial properties and investigates adaptive surrogate estimation to sequester CO2 into heterogeneous deep saline aquifers. Our deep-learning architecture includes...
Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the r...
A lost-in-space star identification algorithm based on a one-dimensional Convolutional Neural Network (1D CNN) is proposed. The lost-in-space star identification aims to identify stars observed with corresponding catalog stars when there is no prior...
Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good p...
Levodopa administration is currently the most common treatment to alleviate Parkinson’s Disease (PD) symptoms. Nevertheless, prolonged use of Levodopa leads to a wearing-off (WO) phenomenon, causing symptoms to reappear. To build a personalized...
Speech emotion recognition (SER) is one of the most exciting topics many researchers have recently been involved in. Although much research has been conducted recently on this topic, emotion recognition via non-verbal speech (known as the vocal burst...
This paper presents several different Neural Network based DPD architectures for hybrid beamforming (HBF) mMIMO applications. They are formulated, tested and compared based on their ability to compensate nonlinear distortion of power amplifiers in a...
of 22