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Secondary control of the inductive-power-transfer (IPT) system can achieve robust output regulation by eliminating the long propagation delay of wireless communication in the feedback control between the transmitter and receiver. Recently, the multilevel switched-capacitor converter has been employed for rectification in the receiver, but experiences high charge sharing loss. In this letter, a resonant modular multilevel rectifier (RMMR) is proposed to eliminate the charge sharing loss in the conventional multilevel rectifier. The proposed RMMR is applied in a series–series compensated IPT system to implement independent secondary control for output voltage regulation with low harmonic distortion and reduced conduction loss. The operating principle is analyzed in detail and a generic analytical expression of the system voltage gain is provided. In addition, the operation of the proposed topology is validated experimentally, and a peak dc–dc efficiency of 96.6% is measured in the experiments. [ABSTRACT FROM AUTHOR]

Wireless rechargeable sensor networks (WRSNs) employ wireless charging transfer technology to provide dynamic energy replenishment for sensors. Previous related research considers enhancing the flexibility of WRSNs with the use of wireless charging vehicles (WCVs). Although this research prolongs network lifetime to some extent, there still exist two essential drawbacks: off-road limitation and traveling speed limitation. Benefiting from the intrinsic flexibility, high flight speed, low cost and small size, wireless charging drones (drones) have great potentials to be used to charge sensors in WRSNs. However, suffering from limited battery capacities, it is impossible to use drones for charging sensors in large WRSNs independently. To conquer the drawbacks of WCVs and drones, we propose a novel WRSN model which integrates WCVs, WCV-carried drones and independent drones to construct an efficient novel charging system. The proposed system, which aims to extend network lifetime and to minimize the time of charging scheduling on premise of satisfying charging requests with the cooperation of WCVs and drones. Thus, we develop a novel charging scheduling scheme, which divides the sensing region into three circular sub-regions for scheduling the WCVs or drones collaboratively. Sensor nodes located in the inner ring are charged by independent drones directly from the base station. Nodes located in the middle ring are charged by WCVs, while the outer ring charged by WCV-carried drones. Simulation results show that our scheduling scheme outperforms multiple WCVs which either employ temporal or distantial priority scheme and earliest-deadline first scheme with respect to the lifetime and network charging efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a gaze correction and animation method for high-resolution, unconstrained portrait images, which can be trained without the gaze angle and the head pose annotations. Common gaze-correction methods usually require annotating training data with precise gaze, and head pose information. Solving this problem using an unsupervised method remains an open problem, especially for high-resolution face images in the wild, which are not easy to annotate with gaze and head pose labels. To address this issue, we first create two new portrait datasets: CelebGaze ($256 \times 256$) and high-resolution CelebHQGaze ($512 \times 512$). Second, we formulate the gaze correction task as an image inpainting problem, addressed using a Gaze Correction Module (GCM) and a Gaze Animation Module (GAM). Moreover, we propose an unsupervised training strategy, i.e., Synthesis-As-Training, to learn the correlation between the eye region features and the gaze angle. As a result, we can use the learned latent space for gaze animation with semantic interpolation in this space. Moreover, to alleviate both the memory and the computational costs in the training and the inference stage, we propose a Coarse-to-Fine Module (CFM) integrated with GCM and GAM. Extensive experiments validate the effectiveness of our method for both the gaze correction and the gaze animation tasks in both low and high-resolution face datasets in the wild and demonstrate the superiority of our method with respect to the state of the art. [ABSTRACT FROM AUTHOR]

The vanilla Few-shot Learning (FSL) learns to build a classifier for a new concept from one or very few target examples, with the general assumption that source and target classes are sampled from the same domain. Recently, the task of Cross-Domain Few-Shot Learning (CD-FSL) aims at tackling the FSL where there is a huge domain shift between the source and target datasets. Extensive efforts on CD-FSL have been made via either directly extending the meta-learning paradigm of vanilla FSL methods, or employing massive unlabeled target data to help learn models. In this paper, we notice that in the CD-FSL task, the few labeled target images have never been explicitly leveraged to inform the model in the training stage. However, such a labeled target example set is very important to bridge the huge domain gap. Critically, this paper advocates a more practical training scenario for CD-FSL. And our key insight is to utilize a few labeled target data to guide the learning of the CD-FSL model. Technically, we propose a novel Generalized Meta-learning based Feature-Disentangled Mixup network, namely GMeta-FDMixup. We make three key contributions of utilizing GMeta-FDMixup to address CD-FSL. Firstly, we present two mixup modules – mixup-P and mixup-M that help facilitate utilizing the unbalanced and disjoint source and target datasets. These two novel modules enable diverse image generation for training the model on the source domain. Secondly, to narrow the domain gap explicitly, we contribute a novel feature disentanglement module that learns to decouple the domain-irrelevant and domain-specific features. By stripping the domain-specific features, we alleviate the negative effects caused by the domain inductive bias. Finally, we repurpose a new contrastive learning module, dubbed ConL. ConL prevents the model from only capturing category-related features via introducing contrastive loss. Thus, the generalization ability on novel categories is improved. Extensive experimental results on two benchmarks show the superiority of our setting and the effectiveness of our method. Code and models will be released. [ABSTRACT FROM AUTHOR]

The assessment of personality is crucial not only for scientific inquiries but also for real-world applications such as personnel selection. In this article, we propose and validate a novel implicit measure to predict an individual's levels in the Big Five personality traits from 5 minutes of electroencephalography (EEG) recordings. Participants viewed Chinese words with positive, negative, and neutral emotions. The multi-channel event-related potentials elicited by these emotional words were used to train a sparse regression model for personality prediction. Results from a large test sample of 196 participants indicated that the personality scores derived from the proposed measure reached significant correlations with a commonly used questionnaire (r =.50,.60,.49,.55, and.49 for agreeableness, conscientiousness, neuroticism, openness, and extraversion, respectively). The EEG-based personality scores showed good external validity as well, capable of predicting behavioral indices and psychological adjustment similar to self-reported scores. Besides, the EEG-based scores were relatively stable across time, as reflected by the test-retest reliability of.5 ∼.7 for the five personality traits within a cohort of 33 participants 19-78 days later. These evaluations suggest that the proposed measure can serve as a viable alternative to conventional personality questionnaires in practice. [ABSTRACT FROM AUTHOR]

Since the luminance of Micro-LEDs is highly dependent on the injected current density, inevitable voltage fluctuations will lead to luminance fluctuations and weak luminance robustness. In this work, Micro-LED chips working in non-electrical contact and non-carrier injection (NEC and NCI) mode is proposed, which is totally different from the current-injected mode. The luminance of NEC and NCI Micro-LEDs first increases with the increase of the voltage, and the saturation luminance can be obtained during a relatively large voltage range, which is defined as an operating voltage window. Then the luminance decreases when the voltage is further increased. It is demonstrated that the operating voltage windows can enhance luminance robustness. Finally, the insulating layer that is crucial for the operating voltage windows is optimized. And the working mechanisms related to the existence of operating voltage windows are discussed. [ABSTRACT FROM AUTHOR]

Conventional visual BCIs, in which control channels are tagged with stimulation patterns to elicit distinguishable brain patterns, has made impressive progress in terms of the information transfer rates (ITRs). However, less development has been seen with respect to user experience and complexity of the technical setup. The requirement to tag each of targets by a unique stimulus substantially limits the flexibility of conventional visual BCI systems. A method for decoding the targets in the environment flexibly was therefore proposed in the present study. A BCI speller with thirteen symbols drawn on paper was developed. The symbols were interspersed with four flickers with distinct frequencies, but the user did not have to gaze at flickers. Rather, subjects could spell a sequence by looking at the symbols on the paper. In a cue-guided spelling task, the average offline and online accuracies reached 89.3± 7.3% and 90.3± 6.9% for 13 subjects, corresponding to ITRs of 43.0± 7.4 bit/min and 43.8± 6.8 bit/min. In an additional free-spelling task for seven out of thirteen subjects, an accuracy of 92.3± 3.1% and an ITR of 45.6± 3.3 bit/min were achieved. Analysis of a simulated online system showed the possibility to reach an average ITR of 105.8 bit/min by reducing the epoch duration from 4 to 1 second. Reliable BCI control is possible by gazing at targets in the environment instead of dedicated stimuli which encode control channels. The proposed method can drastically reduce the technical effort for visual BCIs and thereby advance their applications outside the laboratory. [ABSTRACT FROM AUTHOR]

Objective: We present a new type of brain-computer interface (BCI) that utilizes the retinotopic mapping of motion-onset visual evoked potentials (mVEP) to accomplish four control channels using a single motion stimulus. Methods: Participants selected a BCI command by fixating one of four target locations around a centrally presented visual motion stimulus. A template-matching method was employed to recognize the users’ intention by decoding the position of the motion stimulus in the peripheral visual field, and classification performances were evaluated in an offline manner. The motion stimulus eccentricity between the targets and the visual motion stimulus varied among 5.1°, 6.7°, 9.8°, and 13.0°. Results: Distinct N200 spatial patterns were elicited when participants directed attention overtly to the target locations. A four-class classification accuracy of 72.2 ± 5.05% was achieved with a distance of 5.1° visual angle between the targets and the visual motion stimulus. The classification accuracies decreased with increasing motion stimulus eccentricities but remained separable well above the chance level at 13.0° (47.3 ± 8.54%). Conclusion: Our results support the feasibility of a single-stimulus, multitarget mVEP BCI. Significance: The proposed system can simplify the visual stimulation of mVEP BCIs, improve user experience and pave the way for simple yet efficient BCI communication. [ABSTRACT FROM AUTHOR]