Brain-inspired Predictive Coding Improves the Performance of Machine Challenging Tasks.

Backpropagation has been regarded as the most favorable algorithm for training artificial neural networks. However, it has been criticized for its biological implausibility because its learning mechanism contradicts the human brain. Although backpropagation has achieved super-human performance in various machine learning applications, it often shows limited performance in specific tasks.

Effect of radiofrequency exposure on body temperature: Real-time monitoring in normal rats.

Radiofrequency radiation (RFR) can generate heat in living organisms. In this study, we monitored the body temperature of healthy animals during RFR exposure in real time using an implantable iButton data logger. A reverberation chamber system for small animals was used for this radiofrequency (RF) exposure in vivo study.

Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features.

In the machine learning and data science pipelines, feature extraction is considered the most crucial component according to researchers, where generating a discriminative feature matrix is the utmost challenging task to achieve high classification accuracy. Generally, the classical feature extraction techniques are sensitive to the noisy component of the signal and need more time for training. To deal with these issues, a comparatively new feature extraction technique, referred to as a wavelet scattering transform (WST) is utilized, and incorporated with ML classifiers to design a framework for bearing fault classification in this paper.

Proposed Safety Guidelines for Patient Assistants in an Open MRI Environment.

The wide-open side of an open magnetic resonance imaging (MRI) system allows a patient to easily contact the patient assistant during MRI scans. A wide-open-shaped magnet is highly effective when interventional procedures are necessary. Patient assistants can provide comfort by holding a part of the patient's body.

Analysis of Operational Characteristics of AlGaN/GaN High-Electron-Mobility Transistor with Various Slant-Gate-Based Structures: A Simulation Study.

This study investigates the operational characteristics of AlGaN/GaN high-electron-mobility transistors (HEMTs) by applying a slant-gate structure and drain-side extended field-plate (FP) for improved breakdown voltage. Prior to the analysis of slant-gate-based HEMT, simulation parameters were extracted from the measured data of fabricated basic T-gate HEMTs to secure the reliability of the results. We suggest three different types of slant-gate structures that connect the basic T-gate electrode boundary to the 1st and 2nd SiN passivation layers obliquely.

Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study.

Acute coronary syndrome (ACS) has been one of the most important issues in global public health. The high recurrence risk of patients with coronary heart disease (CHD) has led to the importance of post-discharge care and secondary prevention of CHD. Previous studies provided binary results of ACS recurrence risk; however, studies providing the recurrence risk of an individual patient are rare.

Video Super-Resolution Method Using Deformable Convolution-Based Alignment Network.

With the advancement of sensors, image and video processing have developed for use in the visual sensing area. Among them, video super-resolution (VSR) aims to reconstruct high-resolution sequences from low-resolution sequences. To use consecutive contexts within a low-resolution sequence, VSR learns the spatial and temporal characteristics of multiple frames of the low-resolution sequence.

Dual-Functional Self-Attachable and Stretchable Interface for Universal Three-Dimensional Modular Electronics.

Stretchable electronics have become essential for custom-built electronics, self-assembling robotics, and wearable devices. Although many stretchable electronics contain integrated systems, they still limit bulky connection systems. We introduce a new dual-functioned self-attachable and stretchable interface (SASI), allowing a direct and instant interconnection between rigid and soft electronics.

A Method of Deep Learning Model Optimization for Image Classification on Edge Device.

Due to the recent increasing utilization of deep learning models on edge devices, the industry demand for Deep Learning Model Optimization (DLMO) is also increasing. This paper derives a usage strategy of DLMO based on the performance evaluation through light convolution, quantization, pruning techniques and knowledge distillation, known to be excellent in reducing memory size and operation delay with a minimal accuracy drop. Through experiments regarding image classification, we derive possible and optimal strategies to apply deep learning into Internet of Things (IoT) or tiny embedded devices.

Safety and Potential Usability of Immersive Virtual Reality for Brain Rehabilitation: A Pilot Study.

This study was conducted to demonstrate the safety and usability of an immersive virtual reality (VR) game as a rehabilitative training by assessing adverse events (AEs), adherence, and satisfaction in patients with brain injury who had free optional opportunities. The results were analyzed retrospectively. Seventy-eight patients with brain injury, undergoing rehabilitation treatment for motor impairment, were recruited.

Silver-Reduced Poly(Ethylene Glycol) Diacrylate Composites with Microline Arrays for Directional Bending Sensors.

Herein, a soft and flexible polymer composite sensor with a surface structure is manufactured that is sensitive to a wide range of mechanical stimuli, including small actions and large motions. A polymer sensor performing with a piezoresistive mechanism is proposed by synthesizing a new conductive polymer composite to fabricate a microline structure by itself, named Ag-reduced poly(ethylene glycol) diacrylate (PEGDA) composite directional bending sensor (ACBS). A simple but effective process of forming nanoparticles (NPs) and surface structures is a notable characteristic.

PUFTAP-IoT: PUF-Based Three-Factor Authentication Protocol in IoT Environment Focused on Sensing Devices.

In IoT-based environments, smart services can be provided to users under various environments, such as smart homes, smart factories, smart cities, smart transportation, and healthcare, by utilizing sensing devices. Nevertheless, a series of security problems may arise because of the nature of the wireless channel in the Wireless Sensor Network (WSN) for utilizing IoT services. Authentication and key agreements are essential elements for providing secure services in WSNs.

Multichannel Acoustic Spectroscopy of the Human Body for Inviolable Biometric Authentication.

Specific features of the human body, such as fingerprint, iris, and face, are extensively used in biometric authentication. Conversely, the internal structure and material features of the body have not been explored extensively in biometrics. Bioacoustics technology is suitable for extracting information about the internal structure and biological and material characteristics of the human body.

Occluded Pedestrian-Attribute Recognition for Video Sensors Using Group Sparsity.

Pedestrians are often obstructed by other objects or people in real-world vision sensors. These obstacles make pedestrian-attribute recognition (PAR) difficult; hence, occlusion processing for visual sensing is a key issue in PAR. To address this problem, we first formulate the identification of non-occluded frames as temporal attention based on the sparsity of a crowded video.

A Thermo-Mechanically Robust Compliant Electrode Based on Surface Modification of Twisted and Coiled Nylon-6 Fiber for Artificial Muscle with Highly Durable Contractile Stroke.

In this paper, we propose a novel and facile methodology to chemically construct a thin and highly compliant metallic electrode onto a twisted and coiled nylon-6 fiber (TCN) with a three-dimensional structure via surface modification of the TCN eliciting gold-sulfur (Au-S) interaction for enabling durable electro-thermally-induced actuation performance of a TCN actuator (TCNA). The surface of the TCN exposed to UV/Ozone plasma was modified to (3-mercaptopropyl)trimethoxysilane (MPTMS) molecules with thiol groups through a hydrolysis-condensation reaction. Thanks to the surface modification inducing strong interaction between gold and sulfur as a formation of covalent bonds, the Au electrode on the MPTMS-TCN exhibited excellent mechanical robustness against adhesion test, simultaneously could allow overall surface of the TCN to be evenly heated without any significant physical damages during repetitive electro-thermal heating tests.

In vivo Preclinical Tumor-Specific Imaging of Superparamagnetic Iron Oxide Nanoparticles Using Magnetic Particle Imaging for Cancer Diagnosis.

Purpose: Magnetic particle imaging (MPI) is an emerging radiation-free, non-invasive three-dimensional tomographic technology that can visualize the concentrations of superparamagnetic iron oxide nanoparticles (SPIONs). To verify the applicability of the previously proposed point-of-care testing MPI (PoCT-MPI) in medical diagnosis and therapeutics, we imaged SPIONs in animal tumor models.

Methods: CT26 or MC38 mouse colon carcinoma cells (2 × 10 cells) were subcutaneously injected into the right flank of BALB/c mice.

MFA-net: Object detection for complex X-ray cargo and baggage security imagery.

Deep convolutional networks have been developed to detect prohibited items for automated inspection of X-ray screening systems in the transport security system. To our knowledge, the existing frameworks were developed to recognize threats using only baggage security X-ray scans. Therefore, the detection accuracy in other domains of security X-ray scans, such as cargo X-ray scans, cannot be ensured.

iAKA-CIoT: An Improved Authentication and Key Agreement Scheme for Cloud Enabled Internet of Things Using Physical Unclonable Function.

The Internet of Things (IoT) with cloud services are important functionalities in the latest IoT systems for providing various convenient services. These cloud-enabled IoT environments collect, analyze, and monitor surrounding data, resulting in the most effective handling of large amounts of heterogeneous data. In these environments, secure authentication with a key agreement mechanism is essential to ensure user and data privacy when transmitting data between the cloud server and IoT nodes.

Multilayer Perceptron-Based Real-Time Intradialytic Hypotension Prediction Using Patient Baseline Information and Heart-Rate Variation.

Intradialytic hypotension (IDH) is a common side effect that occurs during hemodialysis and poses a great risk for dialysis patients. Many studies have been conducted so far to predict IDH, but most of these could not be applied in real-time because they used only underlying patient information or static patient disease information. In this study, we propose a multilayer perceptron (MP)-based IDH prediction model using heart rate (HR) information corresponding to time-series information and static data of patients.

A Work-Related Musculoskeletal Disorders (WMSDs) Risk-Assessment System Using a Single-View Pose Estimation Model.

Musculoskeletal disorders are an unavoidable occupational health problem. In particular, workers who perform repetitive tasks onsite in the manufacturing industry suffer from musculoskeletal problems. In this paper, we propose a system that evaluates the posture of workers in the manufacturing industry with single-view 3D human pose-estimation that can estimate the posture in 3D using an RGB camera that can easily acquire the posture of a worker in a complex workplace.

High density integration of stretchable inorganic thin film transistors with excellent performance and reliability.

Transistors with inorganic semiconductors have superior performance and reliability compared to organic transistors. However, they are unfavorable for building stretchable electronic products due to their brittle nature. Because of this drawback, they have mostly been placed on non-stretchable parts to avoid mechanical strain, burdening the deformable interconnects, which link these rigid parts, with the strain of the entire system.

Exploiting Missing Value Patterns for a Backdoor Attack on Machine Learning Models of Electronic Health Records: Development and Validation Study.

Background: A backdoor attack controls the output of a machine learning model in 2 stages. First, the attacker poisons the training data set, introducing a back door into the victim's trained model. Second, during test time, the attacker adds an imperceptible pattern called a trigger to the input values, which forces the victim's model to output the attacker's intended values instead of true predictions or decisions.

Entropy-Aware Model Initialization for Effective Exploration in Deep Reinforcement Learning.

Effective exploration is one of the critical factors affecting performance in deep reinforcement learning. Agents acquire data to learn the optimal policy through exploration, and if it is not guaranteed, the data quality deteriorates, which leads to performance degradation. This study investigates the effect of initial entropy, which significantly influences exploration, especially in the early learning stage.

Semi-Supervised Domain Adaptation for Multi-Label Classification on Nonintrusive Load Monitoring.

Nonintrusive load monitoring (NILM) is a technology that analyzes the load consumption and usage of an appliance from the total load. NILM is becoming increasingly important because residential and commercial power consumption account for about 60% of global energy consumption. Deep neural network-based NILM studies have increased rapidly as hardware computation costs have decreased.