Modeling and Optimization of Directly Modulated Piezoelectric Micromachined Ultrasonic Transducers.

The present work details a novel approach to increase the transmitting sensitivity of piezoelectric micromachined ultrasonic transducer arrays and performing the direct modulation of digital information on the same device. The direct modulation system can reach 3× higher signal-to-noise ratio level and 3× higher communication range (from 6.2 cm boosted to 18.

Design and Implementation of a SiC-Based VRFB Power Conditioning System.

An energy storage system using secondary batteries combined with advanced power control schemes is considered the key technology for the sustainable development of renewable energy-based power generation and smart micro-grids. The performance of energy storage systems in practical application mainly depends on their power conditioning systems. This paper proposes a silicon carbide-based multifunctional power conditioning system for the vanadium redox flow battery.

High performance complementary WS devices with hybrid Gr/Ni contacts.

Two-dimensional (2D) transition metal dichalcogenides have attracted vibrant interest for future solid-state device applications due to their unique properties. However, it is challenging to realize 2D material based high performance complementary devices due to the stubborn Fermi level pinning effect and the lack of facile doping techniques. In this paper, we reported a hybrid Gr/Ni contact to WS2, which can switch carrier types from n-type to p-type in WS2.

Artificial Intelligence-Based Bolt Loosening Diagnosis Using Deep Learning Algorithms for Laser Ultrasonic Wave Propagation Data.

The application of deep learning (DL) algorithms to non-destructive evaluation (NDE) is now becoming one of the most attractive topics in this field. As a contribution to such research, this study aims to investigate the application of DL algorithms for detecting and estimating the looseness in bolted joints using a laser ultrasonic technique. This research was conducted based on a hypothesis regarding the relationship between the true contact area of the bolt head-plate and the guided wave energy lost while the ultrasonic waves pass through it.

Graphdiyne Saturable Absorber for Passively Q-Switched Ho-Doped Laser.

High-quality all-carbon nanostructure graphdiyne (GDY) saturable absorber was successfully fabricated and saturable absorption properties in the 2 μm region were characterized using a commercial mode-locked laser as a pulsed source. The fabricated GDY was first used as an optical switcher in a passively Q-switched Ho laser. Under absorbed pump power of 2.

Recent Advances of Spatial Self-Phase Modulation in 2D Materials and Passive Photonic Device Applications.

Optical nonlinearity in 2D materials excited by spatial Gaussian laser beam is a novel and peculiar optical phenomenon, which exhibits many novel and interesting applications in optical nonlinear devices. Passive photonic devices, such as optical switches, optical logical gates, photonic diodes, and optical modulators, are the key compositions in the future all-optical signal-processing technologies. Passive photonic devices using 2D materials to achieve the device functionality have attracted widespread concern in the past decade.

A Multitasking-Oriented Robot Arm Motion Planning Scheme Based on Deep Reinforcement Learning and Twin Synchro-Control.

Humanoid robots are equipped with humanoid arms to make them more acceptable to the general public. Humanoid robots are a great challenge in robotics. The concept of digital twin technology complies with the guiding ideology of not only Industry 4.

Efficient Traffic Engineering in an NFV Enabled IoT System.

The Internet of Things (IoT) is increasingly creating new market possibilities in several industries' sectors such as smart homes, smart manufacturing, and smart cities, to link the digital and physical worlds. A key challenge in an IoT system is to ensure network performance and cost-efficiency when a plethora of data is generated and proliferated. The adoption of Network Function Virtualization (NFV) technologies within an IoT environment enables a new approach of providing services in a more agile and cost-efficient way.

Nonvolatile ferroelectric field-effect transistors.

Future data-intensive applications will have integrated circuit architectures combining energy-efficient transistors, high-density data storage and electro-optic sensing arrays in a single chip to perform in situ processing of captured data. The costly dense wire connections in 3D integrated circuits and in conventional packaging and chip-stacking solutions could affect data communication bandwidths, data storage densities, and optical transmission efficiency. Here we investigated all-ferroelectric nonvolatile LiNbO transistors to function through redirection of conducting domain walls between the drain, gate and source electrodes.

Computational design of anti-CRISPR proteins with improved inhibition potency.

Anti-CRISPR (Acr) proteins are powerful tools to control CRISPR-Cas technologies. However, the available Acr repertoire is limited to naturally occurring variants. Here, we applied structure-based design on AcrIIC1, a broad-spectrum CRISPR-Cas9 inhibitor, to improve its efficacy on different targets.

A Memristive Element Based on an Electrically Controlled Single-Molecule Reaction.

The exponential proliferation of data during the information age has required the continuous exploration of novel storage paradigms, materials, and devices with increasing data density. As a step toward the ultimate limits in data density, the development of an electrically controllable single-molecule memristive element is reported. In this device, digital information is encoded through switching between two isomer states by applying a voltage signal to the molecular junction, and the information is read out by monitoring the electrical conductance of each isomer.

Harmonic Distortion Optimization for Sigma-Delta Modulators Interface Circuit of TMR Sensors.

The tunneling magnetoresistance micro-sensors (TMR) developed by magnetic multilayer material has many advantages, such as high sensitivity, high frequency response, and good reliability. It is widely used in military and civil fields. This work presents a high-performance interface circuit for TMR sensors.

Design and Implementation of a GaN-Based Three-Phase Active Power Filter.

Renewable energy (RE)-based power generation systems and modern manufacturing facilities utilize a wide variety of power converters based on high-frequency power electronic devices and complex switching technologies. This has resulted in a noticeable degradation in the power quality (PQ) of power systems. To solve the aforementioned problem, advanced active power filters (APFs) with improved system performance and properly designed switching devices and control algorithms can provide a promising solution because an APF can compensate for voltage sag, harmonic currents, current imbalance, and active and reactive powers individually or simultaneously.

Robust Ag/ZrO/WS/Pt Memristor for Neuromorphic Computing.

The development of the information age has made resistive random access memory (RRAM) a critical nanoscale memristor device (MD). However, due to the randomness of the area formed by the conductive filaments (CFs), the RRAM MD still suffers from a problem of insufficient reliability. In this study, the memristor of Ag/ZrO/WS/Pt structure is proposed for the first time, and a layer of two-dimensional (2D) WS nanosheets was inserted into the MD to form 2D material and oxide double-layer MD (2DOMD) to improve the reliability of single-layer devices.

A new JPEG image steganalysis technique combining rich model features and convolutional neural networks.

The best traditional steganalysis methods aiming at adaptive steganography are the combination of rich models and ensemble classifier. In this study, a new steganalysis method for JPEG images based on convolutional neural networks is proposed to solve the high dimension problem in steganalysis from another aspect. On the basis of the original rich model, the algorithm adds different sizes of discrete cosine transform (DCT) basis functions to extract different detection features.

Application of CT radiomics in prediction of early recurrence in hepatocellular carcinoma.

Purpose: To appraise the ability of the computed tomography (CT) radiomics signature for prediction of early recurrence (ER) in patients with hepatocellular carcinoma (HCC).

Methods: A set of 325 HCC patients were enrolled in this retrospective study and the whole dataset was divided into 2 cohorts, including "training set" (225 patients) and "test set" (100 patients). All patients who underwent partial hepatectomy were followed up at least within 1 year.

Controllable digital resistive switching for artificial synapses and pavlovian learning algorithm.

The fundamental unit of the nervous system is a synapse, which is involved in transmitting information between neurons as well as learning, memory, and forgetting processes. Two-terminal memristors can fulfil most of these requirements; however, their poor dynamic changes in resistance to input electric stimuli remain an obstacle, which must be improved for accurate and quick information processing. Herein, we demonstrate the synaptic properties of ZnO-based memristors, which were significantly enhanced (∼340 times) by geometrical modulation due to the localized electric field enhancement.

Flexible Smart Noncontact Control Systems with Ultrasensitive Humidity Sensors.

The development of noncontact humidity sensors with high sensitivity, rapid response, and a facile fabrication process is urgently desired for advanced noncontact human-machine interaction (HMI) applications. Here, a flexible and transparent humidity sensor based on MoO nanosheets is developed with a low-cost and easily manufactured process. The designed humidity sensor exhibits ultrahigh sensitivity, fast response, great stability, and high selectivity, exceeding the state-of-the-art humidity sensors.

Impact of using the new American College of Radiology digital mammography phantom on quality survey in modern digital mammography systems: Evidence from nationwide surveys in Taiwan.

Objective: The purpose of this study was to investigate the impact of the new American College of Radiology (ACR) digital mammography (DM) phantom in evaluating phantom image quality (IQ) and average glandular dose (AGD) in a nationwide survey on DM systems.

Methods: On-site surveys of 239 DM units were conducted in 2017 and 2018, and comparisons were made between ACR screen-film mammography (SFM) phantom and DM phantom for accessing phantom IQ and AGD. The phantom IQ was assessed using the weighted phantom score, considering the size of each detail.

A visual encoding model based on deep neural networks and transfer learning for brain activity measured by functional magnetic resonance imaging.

Background: Building visual encoding models to accurately predict visual responses is a central challenge for current vision-based brain-machine interface techniques. To achieve high prediction accuracy on neural signals, visual encoding models should include precise visual features and appropriate prediction algorithms. Most existing visual encoding models employ hand-craft visual features (e.

A New Memristor with 2D Ti C T MXene Flakes as an Artificial Bio-Synapse.

Two-dimensional (2D) materials have attracted extensive research interest in academia due to their excellent electrochemical properties and broad application prospects. Among them, 2D transition metal carbides (Ti C T ) show semiconductor characteristics and are studied widely. However, there are few academic reports on the use of 2D MXene materials as memristors.

Image domain dual material decomposition for dual-energy CT using butterfly network.

Purpose: Dual-energy CT (DECT) has been increasingly used in imaging applications because of its capability for material differentiation. However, material decomposition suffers from magnified noise from two CT images of independent scans, leading to severe degradation of image quality. Existing algorithms exhibit suboptimal decomposition performance because they fail to fully depict the mapping relationship between DECT images and basis materials under noisy conditions.

Efficient solving algorithm for determining the exact sampling condition of limited-angle computed tomography reconstruction.

Total variation (TV) regularization-based iterative reconstruction algorithms have an impressive potential to solve limited-angle computed tomography with insufficient sampling projections. The analysis of exact reconstruction sampling conditions for a TV-minimization reconstruction model can determine the minimum number of scanning angle and minimize the scanning range. However, the large-scale matrix operations caused by increased testing phantom size are the computation bottleneck in determining the exact reconstruction sampling conditions in practice.

DOA Estimation and Self-Calibration under Unknown Mutual Coupling.

In practical applications, the assumption of omnidirectional elements is not effective in general, which leads to the direction-dependent mutual coupling (MC). Under this condition, the performance of traditional calibration algorithms suffers. This paper proposes a new self-calibration method based on the time-frequency distributions (TFDs) in the presence of direction-dependent MC.