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This article proposes a high-frequency integrated gate driver for gallium nitride (GaN) synchronous buck converter. The proposed adaptive bang-bang dead-time control minimizes dead-time at any load condition. Thus, it mitigates the excessive power loss caused by GaN device reverse conduction in high-frequency applications. The proposed charge sharing bootstrap circuit ensures sufficient gate overdrive voltage with reduced chip area. Fabricated in a TSMC 0.25-μm BCD process, the driver integrated circuit enabled GaN-based buck converter to operate at 10 MHz switching frequency with minimal dead-time to as low as 0.4 ns under light load and heavy load conditions. Compared with fixed dead-time control, the proposed work improves around 5% efficiency under heavy load condition. The fully integrated bootstrap circuit with 100 and 60 pF (HV) capacitor obtained the lower than 0.6 V driving voltage drop with only 45% capacitance and around half of the voltage drop compared with a conventional bootstrap circuit. Besides, the proposed driver successfully tackled the parasitic ringing and dead-time overcharging issues in the GaN converter. [ABSTRACT FROM AUTHOR]

This brief proposes a driver integrated circuit (IC) for a GaN-based synchronous buck converter to minimized the dead-time loss of GaN devices and improve converter efficiency. The proposed adaptive control achieves minimized dead-time for any loading condition. A driver IC is fabricated in a 0.18 ${\mu }\text{m}$ BCD process to verify the concept. The measurement result achieves only 0.7 ns and 0.8 ns dead-time at the minimum and maximum load condition, respectively. The efficiency improves 2.1% at 2 A full load compared to the fixed dead-time control counterpart. [ABSTRACT FROM AUTHOR]

Mobile robot navigation is an essential problem in robotics. We propose a method for constructing and training fuzzy logic controllers (FLCs) to coordinate a robotic team performing collision-free navigation and arriving simultaneously at a target location in an unknown environment. Our FLCs are organized in a multilayered architecture to reduce the number of tunable parameters and improve the scalability of the solution. In addition, in contrast to simple traditional switching mechanisms between target seeking and obstacle avoidance, we develop a novel rule-embedded FLC to improve the navigation performance. Moreover, we design a grouping and merging mechanism to obtain transparent fuzzy sets and integrate this mechanism into the training process for all FLCs, thus increasing the interpretability of the fuzzy models. We train the proposed FLCs using a novel multiobjective hybrid approach combining a genetic algorithm and particle swarm optimization. Simulation results demonstrate the effectiveness of our algorithms in reliably solving the proposed navigation problem. [ABSTRACT FROM AUTHOR]

We study a novel task, Video Question-Answer Generation (VQAG), for challenging Video Question Answering (Video QA) task in multimedia. Due to expensive data annotation costs, many widely used, large-scale Video QA datasets such as Video-QA, MSVD-QA and MSRVTT-QA are automatically annotated using Caption Question Generation (CapQG) which inputs captions instead of the video itself. As captions neither fully represent a video, nor are they always practically available, it is crucial to generate question-answer pairs based on a video via Video Question-Answer Generation (VQAG). Existing video-to-text (V2T) approaches, despite taking a video as the input, only generate a question alone. In this work, we propose a novel model Generator-Pretester Network that focuses on two components: (1) The Joint Question-Answer Generator (JQAG) which generates a question with its corresponding answer to allow Video Question “Answering” training. (2) The Pretester (PT) verifies a generated question by trying to answer it and checks the pretested answer with both the model’s proposed answer and the ground truth answer. We evaluate our system with the only two available large-scale human-annotated Video QA datasets and achieves state-of-the-art question generation performances. Furthermore, using our generated QA pairs only on the Video QA task, we can surpass some supervised baselines. As a pre-training strategy, we outperform both CapQG and transfer learning approaches when employing semi-supervised (20%) or fully supervised learning with annotated data. These experimental results suggest the novel perspectives for Video QA training. [ABSTRACT FROM AUTHOR]

Human brain inherently exhibits latent mental processes which are likely to change rapidly over time. A framework that adopts a fuzzy inference system is proposed to model the dynamics of the human brain. The fuzzy inference system is used to encode real-world data to represent the salient features of the EEG signals. Then, an unsupervised clustering is conducted on the extracted feature space to identify the brain (external and covert) states that respond to different cognitive demands. To understand the human state change, a state transition diagram is introduced, allowing visualization of connectivity patterns between every pair of states. We compute the transition probability between every pair of states to represent the relationships between the states. This state transition diagram is named as the Fuzzy Covert State Transition Diagram (FCOSTD), which helps the understanding of human states and human performance. We then apply FCOSTD on distracted driving experiments. FCOSTD successfully discovers the external and covert states, faithfully reveals the transition of the brain between states, and the route of the state change when humans are distracted during a driving task. The experimental results demonstrate that different subjects have similar states and inter-state transition behaviour (establishing the consistency of the system) but different ways to allocate brain resources as different actions are being taken. [ABSTRACT FROM AUTHOR]

Brain-computer interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human brain and the external devices. Motor imagery-based brain-computer interfaces are popular because they avoid unnecessary external stimuli. Although feature extraction methods have been illustrated in several machine intelligent systems in motor imagery-based brain-computer interface studies, the performance remains unsatisfactory. There is increasing interest in the use of the fuzzy integrals, the Choquet and Sugeno integrals, that are appropriate for use in applications in which fusion of data must consider possible data interactions. To enhance the classification accuracy of brain-computer interfaces, we adopted fuzzy integrals, after employing the classification method of traditional brain-computer interfaces, to consider possible links between the data. Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system. Ten volunteers performed a motor imagery-based brain-computer interface experiment, and we acquired electroencephalography signals simultaneously. The multimodal fuzzy fusion-based brain-computer interface system enhanced performance compared with traditional brain-computer interface systems. Furthermore, when using the motor imagery-relevant electroencephalography frequency alpha and beta bands for the input features, the system achieved the highest accuracy, up to 78.81% and 78.45% with the Choquet and Sugeno integrals, respectively. Herein, we present a novel concept for enhancing brain-computer interface systems that adopts fuzzy integrals, especially in the fusion for classifying brain-computer interface commands. [ABSTRACT FROM AUTHOR]

This paper proposes a new multiobjective optimization approach to designing a fuzzy logic system (FLS) using process data and applies it to the wall-following control of a mobile robot. The objectives considered include both the interpretability and control performance of the FLS. It is assumed that no off-line training data are available in advance, and the rule base is initially empty. All rules are generated through an online clustering and fuzzy set merging (OCFM) algorithm using data generated online during the FLS evaluation process. The OCFM builds a reference rule base that flexibly partitions the input space with distinguishable fuzzy sets (FSs). Based on the reference rule base, a new multiobjective front-guided continuous ant-colony optimization (MO-FCACO) algorithm is proposed to optimize the FLS structure and parameters. In addition to the objective functions defined to evaluate the FLS control performance, a transparency-oriented objective function is defined with constraints imposed on the FS parameters to obtain an interpretable FLS with transparent FSs. The MO-FCACO solves the constrained multiobjective optimization problem by optimizing all of the free parameters in an FLS through ant-path selection, sampling operation, and front-guided optimization processes. The multiobjective FLS design approach is applied to control the orientation and moving speed of a mobile robot in performing the wall-following task. Optimization performance of the MO-FCACO is verified through comparisons with various multiobjective population-based optimization algorithms. Experimental results verify the effectiveness of the designed FLSs in controlling a real robot. [ABSTRACT FROM PUBLISHER]

In stereoscopic displays, crosstalk is typically observed when the views for both eyes are not clearly separated. Crosstalk affects the quality of 3D images and can cause visual fatigue in observers. According to the results of perception experimentation, different visual depth (disparity) and motion blur can also affect the perception of 3D image quality. Therefore, considering the effects of crosstalk, disparity, and motion blur, we propose an evaluation index to quantify 3D image performance for stereoscopic displays. This index assesses the optical quality of a 3D display as well as the effects of image content. According to human vision experimentation, the 3D image index (\Delta X3D) is validated to have a high positive correlation (0.8) with human perception. After combining the human visual mechanism and \Delta X{3D}, the proposed index can represent the visual quality of static and dynamic images on stereoscopic displays. [ABSTRACT FROM PUBLISHER]

In this paper, we present a locally controllable liquid crystal lens array for a partially switchable 2D/3D display, from which 2D images without resolution degradation and 3D images can simultaneously emerge. The liquid crystal (LC) lens has the advantages of low driving voltages (<\6\ Vrms) and a simple structure for which the fabrication only requires two photolithography processes. A partial lens array can be formed arbitrarily using a passive driving method. Finally, we demonstrate a 3.5-in partially switchable 2D/3D display that can present partial 3D images with 10.8% crosstalk and partial 2D images without resolution degradation. [ABSTRACT FROM PUBLISHER]

The optical emission spectrometer (OES) is an effective experimental tool for monitoring plasma states and the composition of gases during the growth of silicon thin films by plasma-enhanced chemical vapor deposition. In this paper, hydrogenated amorphous silicon (a-Si) (a-Si:H) and microcrystalline silicon (\mu\c-Si) thin films have been deposited in a parallel-plate radio frequency (RF) plasma reactor using silane and hydrogen gas mixtures. The plasma emission atmosphere was recorded using an OES system during the growth of the Si thin films. The plasma was simultaneously analyzed during the process using an OES method to study the correlation between growth rate and microstructure of the films. In the deposition, the emitted species (\SiH^\ast, \Si^\ast,\ \and\ \H^ \ast) were analyzed. The OES analysis supported a chemisorption-based deposition model of the growth mechanism. The effects of RF power, electron-to-substrate distance, and \H2 dilution of the emission intensities of excited SiH, Si, and H on the growth rate and microstructures of the film were studied. Finally, single-junction a-Si:H and \mu\c-Si solar cells were obtained with initial aperture area efficiencies of 9.71% and 6.36%, respectively. A tandem a-Si/\mu\c-Si cell was also realized with an efficiency of 12.3%. [ABSTRACT FROM PUBLISHER]

A second-order continuous-time delta–sigma modulator incorporating a proposed 4-bit asynchronous sequential quantizer and a digital excess-loop-delay (ELD) compensation technique is presented. The sequential operation of the proposed quantizer facilitates low power consumption while the hardware-efficient digital compensation technique allows the modulator to accommodate ELD. With a 1-MHz bandwidth and a 60-MHz sampling rate, the measured peak signal-to-noise-and-distortion ratio and dynamic range are 62 and 67 dB, respectively. Fabricated in a 90-nm CMOS, this chip consumes only 0.89 mW from a 1.2-V supply. [ABSTRACT FROM PUBLISHER]

This paper proposes a new learning approach for evolving dynamic gaits of a hexapod robot. The controller that coordinates the leg movements consists of fully connected recurrent neural networks (FCRNNs). To automate the FCRNN parameter design, a symbiotic species-based particle swarm optimization (SSPSO) algorithm is proposed. There are multiple swarms in the SSPSO, where a swarm only optimizes the relevant parameters to a single node. The number of swarms is equal to the number of nodes in an FCRNN. The symbiotic behavior of particles from different swarms corresponds to the symbiotic structure of different nodes in an FCRNN. For a particle update, particles in different swarms update independently using a local version of particle swarm optimization (PSO) based on speciation. In each swarm, species are formed adaptively in each iteration according to both particle distance and performance. The design of FCRNNs using the SSPSO for temporal sequence generation and hexapod robot dynamic gait evolution for forward movement is conducted. For the latter, a multiple-FCRNN controller is first designed using a simulated hexapod robot. The designed controller is then successfully applied to a real hexapod robot gait control. The SSPSO is compared with the genetic algorithm and different PSO algorithms to verify its efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes an evolutionary-group-based particle-swarm-optimization (EGPSO) algorithm for fuzzy-controller (FC) design. The EGPSO uses a group-based framework to incorporate crossover and mutation operations into particle-swarm optimization. The EGPSO dynamically forms different groups to select parents in crossover operations, particle updates, and replacements. An adaptive velocity-mutated operation (AVMO) is incorporated to improve search ability. The EGPSO is applied to design all of the free parameters in a zero-order Takagi–Sugeno–Kang (TSK)-type FC. The objective of EGPSO is to improve fuzzy-control accuracy and design efficiency. Comparisons with different population-based optimizations of fuzzy-control problems demonstrate the superiority of EGPSO performance. In particular, the EGPSO-designed FC is applied to mobile-robot navigation in unknown environments. In this application, the robot learns to follow object boundaries through an EGPSO-designed FC. A simple learning environment is created to build this behavior without an exhaustive collection of input–output training pairs in advance. A behavior supervisor is proposed to combine the boundary-following behavior and the target-seeking behavior for navigation, and the problem of dead cycles is considered. Successful mobile-robot navigation in simulation and real environments verifies the EGPSO-designed FC-navigation approach. [ABSTRACT FROM AUTHOR]

In this letter, the electric characteristics of the fabricated organic light-emitting diode (OLED) devices are analyzed after a bias stress. Experimental results demonstrate that when a constant voltage is applied to the OLED device, the OLED threshold voltage gradually increases, resulting in the driving current to decay. Therefore, a novel voltage driving scheme for active-matrix OLEDs using low-temperature polysilicon thin-film transistors (poly-Si TFTs) is proposed. This circuit uses three TFTs to increase the aperture ratio of panels and the driving current of the OLED device to ameliorate the luminance drop that is caused by OLED degradation. Simulation results indicate that the proposed pixel circuit has high immunity to VTH variations of the poly-Si TFTs and provides an extra compensation current against OLED degradation. [ABSTRACT FROM AUTHOR]