Convolutional neural network-based model observer for signal known statistically task in breast tomosynthesis images.

Background: Since human observer studies are resource-intensive, mathematical model observers are frequently used to assess task-based image quality. The most common implementation of these model observers assume that the signal information is exactly known. However, these tasks cannot thoroughly represent situations where the signal information is not exactly known in terms of size and shape.

Deep reinforcement learning-based propofol infusion control for anesthesia: A feasibility study with a 3000-subject dataset.

In this work, we present a deep reinforcement learning-based approach as a baseline system for autonomous propofol infusion control. Specifically, design an environment for simulating the possible conditions of a target patient based on input demographic data and design our reinforcement learning model-based system so that it effectively makes predictions on the proper level of propofol infusion to maintain stable anesthesia even under dynamic conditions that can affect the decision-making process, such as the manual control of remifentanil by anesthesiologists and the varying patient conditions under anesthesia. Through an extensive set of evaluations using patient data from 3000 subjects, we show that the proposed method results in stabilization in the anesthesia state, by managing the bispectral index (BIS) and effect-site concentration for a patient showing varying conditions.

A Deep Learning-Based Semantic Segmentation Model Using MCNN and Attention Layer for Human Activity Recognition.

With the development of wearable devices such as smartwatches, several studies have been conducted on the recognition of various human activities. Various types of data are used, e.g.

Evaluation of Growth Responses of Lettuce and Energy Efficiency of the Substrate and Smart Hydroponics Cropping System.

Smart sensing devices enabled hydroponics, a concept of vertical farming that involves soilless technology that increases green area. Although the cultivation medium is water, hydroponic cultivation uses 13 ± 10 times less water and gives 10 ± 5 times better quality products compared with those obtained through the substrate cultivation medium. The use of smart sensing devices helps in continuous real-time monitoring of the nutrient requirements and the environmental conditions required by the crop selected for cultivation.

BattleSound: A Game Sound Benchmark for the Sound-Specific Feedback Generation in a Battle Game.

A haptic sensor coupled to a gamepad or headset is frequently used to enhance the sense of immersion for game players. However, providing haptic feedback for appropriate sound effects involves specialized audio engineering techniques to identify target sounds that vary according to the game. We propose a deep learning-based method for sound event detection (SED) to determine the optimal timing of haptic feedback in extremely noisy environments.

Treatment of Ischemic Stroke by Atorvastatin-Loaded PEGylated Liposome.

There is insufficient evidence on the effect of nanoparticles, particularly liposomes loaded with a statin, on acute ischemic stroke. We investigated the impact of atorvastatin-loaded PEG (polyethylene glycol) conjugated liposomes (LipoStatin) on the outcomes in rats with cerebral ischemia-reperfusion. PEGylated liposome loaded with atorvastatin was developed as a nanoparticle to specifically accumulate in an ischemic region and release the drug to ameliorate the harmful effects of the stroke.

Deep Learning-Based ADHD and ADHD-RISK Classification Technology through the Recognition of Children's Abnormal Behaviors during the Robot-Led ADHD Screening Game.

Although attention deficit hyperactivity disorder (ADHD) in children is rising worldwide, fewer studies have focused on screening than on the treatment of ADHD. Most previous similar ADHD classification studies classified only ADHD and normal classes. However, medical professionals believe that better distinguishing the ADHD-RISK class will assist them socially and medically.

Deep-Learning-Based ADHD Classification Using Children's Skeleton Data Acquired through the ADHD Screening Game.

The identification of attention deficit hyperactivity disorder (ADHD) in children, which is increasing every year worldwide, is very important for early diagnosis and treatment. However, since ADHD is not a simple disease that can be diagnosed with a simple test, doctors require a large period of time and substantial effort for accurate diagnosis and treatment. Currently, ADHD classification studies using various datasets and machine learning or deep learning algorithms are actively being conducted for the screening diagnosis of ADHD.

Silicone engineered anisotropic lithography for ultrahigh-density OLEDs.

Ultrahigh-resolution patterning with high-throughput and high-fidelity is highly in demand for expanding the potential of organic light-emitting diodes (OLEDs) from mobile and TV displays into near-to-eye microdisplays. However, current patterning techniques so far suffer from low resolution, consecutive pattern for RGB pixelation, low pattern fidelity, and throughput issue. Here, we present a silicone engineered anisotropic lithography of the organic light-emitting semiconductor (OLES) that in-situ forms a non-volatile etch-blocking layer during reactive ion etching.

A Low-Cost Foot-Placed UWB and IMU Fusion-Based Indoor Pedestrian Tracking System for IoT Applications.

Among existing wireless and wearable indoor pedestrian tracking solutions, the ultra-wideband (UWB) and inertial measurement unit (IMU) sensors are the popular options due to their accurate and globally referenced positioning, and low-cost and compact size, respectively. However, the UWB position accuracy is compromised by the indoor non-line of sight (NLOS) and the IMU estimation suffers from orientation drift as well as requiring position initialization. To overcome these limitations, this paper proposes a low-cost foot-placed UWB and IMU fusion-based indoor pedestrian tracking system.

Noise-Robust Multimodal Audio-Visual Speech Recognition System for Speech-Based Interaction Applications.

Speech is a commonly used interaction-recognition technique in edutainment-based systems and is a key technology for smooth educational learning and user-system interaction. However, its application to real environments is limited owing to the various noise disruptions in real environments. In this study, an audio and visual information-based multimode interaction system is proposed that enables virtual aquarium systems that use speech to interact to be robust to ambient noise.

Deep Learning-Based Subtask Segmentation of Timed Up-and-Go Test Using RGB-D Cameras.

The timed up-and-go (TUG) test is an efficient way to evaluate an individual's basic functional mobility, such as standing up, walking, turning around, and sitting back. The total completion time of the TUG test is a metric indicating an individual's overall mobility. Moreover, the fine-grained consumption time of the individual subtasks in the TUG test may provide important clinical information, such as elapsed time and speed of each TUG subtask, which may not only assist professionals in clinical interventions but also distinguish the functional recovery of patients.

Circularly polarized light-sensitive, hot electron transistor with chiral plasmonic nanoparticles.

The quantitative detection of circularly polarized light (CPL) is necessary in next-generation optical communication carrying high-density information and in phase-controlled displays exhibiting volumetric imaging. In the current technology, multiple pixels of different wavelengths and polarizers are required, inevitably resulting in high loss and low detection efficiency. Here, we demonstrate a highly efficient CPL-detecting transistor composed of chiral plasmonic nanoparticles with a high Khun's dissymmetry (g-factor) of 0.

Convolutional neural network-based metal and streak artifacts reduction in dental CT images with sparse-view sampling scheme.

Purpose: Sparse-view sampling has attracted attention for reducing the scan time and radiation dose of dental cone-beam computed tomography (CBCT). Recently, various deep learning-based image reconstruction techniques for sparse-view CT have been employed to produce high-quality image while effectively reducing streak artifacts caused by the lack of projection views. However, most of these methods do not fully consider the effects of metal implants.

A streak artifact reduction algorithm in sparse-view CT using a self-supervised neural representation.

Purpose: Sparse-view computed tomography (CT) has been attracting attention for its reduced radiation dose and scanning time. However, analytical image reconstruction methods suffer from streak artifacts due to insufficient projection views. Recently, various deep learning-based methods have been developed to solve this ill-posed inverse problem.

In-Cabin Monitoring System for Autonomous Vehicles.

In this paper, we have demonstrated a robust in-cabin monitoring system (IMS) for safety, security, surveillance, and monitoring, including privacy concerns for personal and shared autonomous vehicles (AVs). It consists of a set of monitoring cameras and an onboard device (OBD) equipped with artificial intelligence (AI). Hereafter, this combination of a camera and an OBD is referred to as the AI camera.

Smartphone-Based SARS-CoV-2 and Variants Detection System using Colorimetric DNAzyme Reaction Triggered by Loop-Mediated Isothermal Amplification (LAMP) with Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR).

Coronavirus disease (COVID-19) has affected people for over two years. Moreover, the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants has raised concerns regarding its accurate diagnosis. Here, we report a colorimetric DNAzyme reaction triggered by loop-mediated isothermal amplification (LAMP) with clustered regularly interspaced short palindromic repeats (CRISPR), referred to as DAMPR assay for detecting SARS-CoV-2 and variants genes with attomolar sensitivity within an hour.

Review of ML and AutoML Solutions to Forecast Time-Series Data.

Time-series forecasting is a significant discipline of data modeling where past observations of the same variable are analyzed to predict the future values of the time series. Its prominence lies in different use cases where it is required, including economic, weather, stock price, business development, and other use cases. In this work, a review was conducted on the methods of analyzing time series starting from the traditional linear modeling techniques until the automated machine learning (AutoML) frameworks, including deep learning models.

Single Neuron for Solving XOR like Nonlinear Problems.

XOR is a special nonlinear problem in artificial intelligence (AI) that resembles multiple real-world nonlinear data distributions. A multiplicative neuron model can solve these problems. However, the multiplicative model has the indigenous problem of backpropagation for densely distributed XOR problems and higher dimensional parity problems.

End-to-End Sentence-Level Multi-View Lipreading Architecture with Spatial Attention Module Integrated Multiple CNNs and Cascaded Local Self-Attention-CTC.

Concomitant with the recent advances in deep learning, automatic speech recognition and visual speech recognition (VSR) have received considerable attention. However, although VSR systems must identify speech from both frontal and profile faces in real-world scenarios, most VSR studies have focused solely on frontal face pictures. To address this issue, we propose an end-to-end sentence-level multi-view VSR architecture for faces captured from four different perspectives (frontal, 30°, 45°, and 60°).

Markerless 3D Skeleton Tracking Algorithm by Merging Multiple Inaccurate Skeleton Data from Multiple RGB-D Sensors.

Skeleton data, which is often used in the HCI field, is a data structure that can efficiently express human poses and gestures because it consists of 3D positions of joints. The advancement of RGB-D sensors, such as Kinect sensors, enabled the easy capture of skeleton data from depth or RGB images. However, when tracking a target with a single sensor, there is an occlusion problem causing the quality of invisible joints to be randomly degraded.

Effect of optical blurring of X-ray source on breast tomosynthesis image quality: Modulation transfer function, anatomical noise power spectrum, and signal detectability perspectives.

We investigated the effect of the optical blurring of X-ray source on digital breast tomosynthesis (DBT) image quality using well-designed DBT simulator and table-top experimental systems. To measure the in-plane modulation transfer function (MTF), we used simulated sphere phantom and Teflon sphere phantom and generated their projection data using two acquisition modes (i.e.

From Low to High Saturation Magnetization in Magnetite Nanoparticles: The Crucial Role of the Molar Ratios Between the Chemicals.

In this study, a comprehensive characterization of iron oxide nanoparticles synthesized by using a simple one-pot thermal decomposition route is presented. In order to obtain monodisperse magnetite nanoparticles with high saturation magnetization, close to the bulk material, the molar ratios between the starting materials (solvents, reducing agents, and surfactants) were varied. Two out of nine conditions investigated in this study resulted in monodisperse iron oxide nanoparticles with high saturation magnetization (90 and 93% of bulk magnetite).