Search

Abstract For bidirectional grid‐connected DC/AC converters, the power flow variation substantially alters the system stability, and especially severs under heavy load. Towards this problem, this paper proposes a novel impedance uniform control method to eliminate the stability degradation under power flow variations, which is achieved by reshaping the converter negative incremental impedance to resistive in the stability fragile mode. The d‐axis voltage perturbation is extracted to dynamically modify the active power current reference. Hence, the system bidirectional damping performance can be simultaneously improved by this dedicated design. Without switching control modes or sacrificing dynamic performance, the grid‐connected converter system can maintain satisfied stability under bidirectional power flow variations. Finally, the effectiveness of the proposed control method has been validated by both simulations and experiments.

For bidirectional grid‐connected DC/AC converters, the power flow variation substantially alters the system stability, and especially severs under heavy load. Towards this problem, this paper proposes a novel impedance uniform control method to eliminate the stability degradation under power flow variations, which is achieved by reshaping the converter negative incremental impedance to resistive in the stability fragile mode. The d‐axis voltage perturbation is extracted to dynamically modify the active power current reference. Hence, the system bidirectional damping performance can be simultaneously improved by this dedicated design. Without switching control modes or sacrificing dynamic performance, the grid‐connected converter system can maintain satisfied stability under bidirectional power flow variations. Finally, the effectiveness of the proposed control method has been validated by both simulations and experiments. [ABSTRACT FROM AUTHOR]

Neurofeedback (NF) shows promise in enhancing memory, but its application to the medial temporal lobe (MTL) still needs to be studied. Therefore, we aimed to develop an NF system for the memory function of the MTL and examine neural activity changes and memory task score changes through NF training. We created a memory NF system using intracranial electrodes to acquire and visualise the neural activity of the MTL during memory encoding. Twenty trials of a tug-of-war game per session were employed for NF and designed to control neural activity bidirectionally (Up/Down condition). NF training was conducted with three patients with drug-resistant epilepsy, and we observed an increasing difference in NF signal between conditions (Up–Down) as NF training progressed. Similarities and negative correlation tendencies between the transition of neural activity and the transition of memory function were also observed. Our findings demonstrate NF's potential to modulate MTL activity and memory encoding. Future research needs further improvements to the NF system to validate its effects on memory functions. Nonetheless, this study represents a crucial step in understanding NF's application to memory and provides valuable insights into developing more efficient memory enhancement strategies. [ABSTRACT FROM AUTHOR]

The non-implantation bi-directional brain-computer interface (BCI) is a neural interface technology that enables direct two-way communication between the brain and the external world by both “reading” neural signals and “writing” stimulation patterns to the brain. This technology has vast potential applications, such as improving the quality of life for individuals with neurological and mental illnesses and even expanding the boundaries of human capabilities. Nonetheless, non-implantation bi-directional BCIs face challenges in generating real-time feedback and achieving compatibility between stimulation and recording. These issues arise due to the considerable overlap between electrical stimulation frequencies and electrophysiological recording frequencies, as well as the impediment caused by the skull to the interaction of external and internal currents. To address those challenges, this work proposes a novel solution that combines the temporal interference stimulation paradigm and minimally invasive skull modification. A longitudinal animal experiment has preliminarily validated the feasibility of the proposed method. In signal recording experiments, the average impedance of our scheme decreased by $4.59~{k}\Omega $ , about 67%, compared to the conventional technique at 18 points. The peak-to-peak value of the Somatosensory Evoked Potential increased by 8%. Meanwhile, the signal-to-noise ratio of Steady-State Visual Evoked Potential increased by 5.13 dB, and its classification accuracy increased by 44%. The maximum bandwidth of the resting state rose by 63%. In electrical stimulation experiments, the signal-to-noise ratio of the low-frequency response evoked by our scheme rose by 8.04 dB, and no stimulation artifacts were generated. The experimental results show that signal quality in acquisition has significantly improved, and frequency-band isolation eliminates stimulation artifacts at the source. The acquisition and stimulation pathways are real-time compatible in this non-implantation bi-directional BCI solution, which can provide technical support and theoretical guidance for creating closed-loop adaptive systems coupled with particular application scenarios in the future. [ABSTRACT FROM AUTHOR]

Bidirectional capacitive power transfer technology makes it possible for energy sharing among multiple electronic devices. This article proposes an asymmetrical bidirectional power conversion topology to satisfy different input and output characteristics’ requirements and increase load variation tolerance. A π–T (CLC–LCL) resonant topology is designed for this bidirectional conversion mode with constant output voltage characteristics in both directions. A hybrid power flow regulation strategy is proposed by integrating multiple zero-voltage switching soft-switching operating points switching and phase-shifted mode. This method overcomes the problem of mixing two different type topologies (push–pull and full bridge) with two different power regulation modes (switch soft-switching operating points and phase shift), which provide a way to make two different chargers compatible. Simulation and experimental results verified the proposed method. [ABSTRACT FROM AUTHOR]

Considering the bidirectional three-phase DC/AC converter, it presents different impedance characteristics on AC side under different power flow directions, resulting in different stability margin. This may cause the system instability at high-power level. This directionally oriented stability difference has not been paid enough attention in the grid-connected converter control. To mitigate the stability variations under bidirectional power flow, a dynamic grid voltage-based impedance reshaping control is proposed in this paper. The proposed method extracts the grid voltage dynamic component, and correspondingly compensates the power output, which is capable of regulating the power output coordinated with voltage dynamics in both power flow directions, neutralizing the stability difference and enhancing the bidirectional power flow stability. Unlike the conventional unidirectional damping optimization control in the current loop, the proposed control method can maintain satisfied stability under bidirectional power flow through the grid side voltage, which can avoid increasing the current loop delay, and thus endow the converter flexible bidirectional power process capability. The effectiveness of the proposed method has been verified by both simulations and experiments. [ABSTRACT FROM AUTHOR]

Neurofeedback (NF) shows promise in enhancing memory, but its application to the medial temporal lobe (MTL) still needs to be studied. Therefore, we aimed to develop an NF system for the memory function of the MTL and examine neural activity changes and memory task score changes through NF training. We created a memory NF system using intracranial electrodes to acquire and visualise the neural activity of the MTL during memory encoding. Twenty trials of a tug-of-war game per session were employed for NF and designed to control neural activity bidirectionally (Up/Down condition). NF training was conducted with three patients with drug-resistant epilepsy, and we observed an increasing difference in NF signal between conditions (Up–Down) as NF training progressed. Similarities and negative correlation tendencies between the transition of neural activity and the transition of memory function were also observed. Our findings demonstrate NF’s potential to modulate MTL activity and memory encoding. Future research needs further improvements to the NF system to validate its effects on memory functions. Nonetheless, this study represents a crucial step in understanding NF’s application to memory and provides valuable insights into developing more efficient memory enhancement strategies.

This paper presents a sigma-modified adaptive control algorithm to enhance the charging profile in a multi-objective electric vehicle (EV) charging installation. The present algorithm takes care of multiple parametric uncertainties and grid non-idealities to provide an instantaneous control updation in order to achieve well-regulated charging dynamics. With the support of renewable energy and battery energy storage (BES), the present algorithm also ensures an uninterrupted charging profile with controller robustness and stability for bi-directional EV charging. The sigma-mod adaptive controller provides an iterative error convergence at each clock interval of supply voltage dynamics to guarantee improved power quality operation in presence of grid distortions. To further improve the reliability of EV charging opportunities, a solar photovoltaic (PV) array in conjunction with the battery energy storage supports the ancillary services through maximum power point operation. Multivariable sliding mode control and rule-based phase-shift adaptation at different stages of power transformation assure faster convergence, parameter uncertainty and controller stability for the bi-directional EV charging operation. A 3.3 kW PV-integrated off-board charging facility is designed and developed as a laboratory prototype to validate the multi-mode charging architecture with minimal grid dependency. [ABSTRACT FROM AUTHOR]

Most existing RGB-D salient detection models pay more attention to the quality of the depth images, while in some special cases, the quality of RGB images may even have greater impacts on saliency detection, which has long been ignored and underestimated. To address this problem, in this paper, we present a Bi-directional Progressive Guidance Network (BPGNet) for RGB-D salient object detection, where the qualities of both RGB and depth images are involved. Since it is usually difficult to determine which modality data have low quality in advance, a bi-directional framework based on progressive guidance (PG) strategy is employed to extract and enhance the unimodal features with the aid of another modality data via the alternative interactions between the saliency prediction results and the extracted features from the multi-modality input data. Specifically, the proposed PG strategy is achieved by using the proposed Global Context Awareness (GCA), Auxiliary Feature Extraction (AFE) and Cross-modality Feature Enhancement (CFE) modules. Benefiting from the proposed PG strategy, the disturbing information within the input RGB and depth images can be well suppressed, while the discriminative information within the input images gets enhanced. On top of that, a Fusion Prediction Module (FPM) is further designed to adaptively select those features with higher discriminability as well as enhancing the common information for the final saliency prediction. Experimental results demonstrate that our proposed model is comparable to those of state-of-the-art RGB-D SOD models. [ABSTRACT FROM AUTHOR]

A directional airborne network consisting of nodes equipped with multibeam antennas is considered. These nodes use the frequency division duplex mode of communication. This allows formation of multiple routes between a pair of source and destination nodes, where every forward and reverse route completely overlaps. Each of these routes supports the bidirectional traffic. These routes are formed using the bidirectional ad hoc on-demand multipath distance vector (BAOMDV) routing protocol. In this article, we propose a local route repair scheme, called BAOMDV-LR, when these routes break due to node mobility. The proposed route repair scheme preserves the overlapping and link-disjoint (or node-disjoint) characteristics of the routes, while reducing the need for expensive route rediscovery. The proposed scheme significantly improves the overall network performance for bidirectional traffic (in terms of packet delivery ratio, end-to-end delay, and routing overhead) as compared to the BAOMDV and other existing routing schemes, especially at higher node speeds and data rates. [ABSTRACT FROM AUTHOR]

In order to solve the problem of high voltage spikes on the secondary side of the high-frequency transformer when the three-phase high-frequency isolated matrix inverter (HFIMI) operates under the conventional modulation strategies, a new modulation strategy is proposed without introducing an auxiliary circuit. In the proposed scheme, the H-bridge inverter adopts phase-shift control. The matrix converter (MC) adopts the voltage-type de- and recoupling idea, and the decoupled positive and negative group inverters are, respectively, applied with a modified Space vector pulse width modulation (SVPWM) strategy to operate synchronously with the H-bridge inverter. When the H-bridge inverter is in dead-zone mode, the switching tubes of MC are all turned on to provide continuous flow paths for leakage inductance and output filter inductance current and suppress high voltage spikes. The operating mode of the three-phase HFIMI under the proposed modulation strategy is analyzed in detail, the realization conditions for soft switching are designed, and the soft-switching ranges are also discussed. The feasibility and validity of the voltage spike suppression strategy are verified by building a 3-kW principle prototype. The experimental results show that the voltage spikes are effectively suppressed, all switches achieve zero-voltage soft-switching, and the peak efficiency of the three-phase HFIMI can reach 95.2%. [ABSTRACT FROM AUTHOR]

The stochastic cloud cover on photovoltaic (PV) panels affects the solar power outputs, producing high instability in the integrated power systems. It is an effective approach to track the cloud motion during short-term PV power forecasting based on data sources of satellite images. However, since temporal variations of these images are noisy and non-stationary, pixel-sensitive prediction methods are critically needed in order to seek a balance between the forecast precision and the huge computation burden due to a large image size. Hence, a graphical learning framework is proposed in this study for intra-hour PV power prediction. By simulating the cloud motion using bi-directional extrapolation, a directed graph is generated representing the pixel values from multiple frames of historical images. The nodes and edges in the graph denote the shapes and motion directions of the regions of interest (ROIs) in satellite images. A spatial-temporal graph neural network (GNN) is then proposed to deal with the graph. Comparing with conventional deep-learning-based models, GNN is more flexible for varying sizes of input, in order to be able to handle dynamic ROIs. Referring to the comparative studies, the proposed method greatly reduces the redundancy of image inputs without sacrificing the visual scope, and slightly improves the prediction accuracy. [ABSTRACT FROM AUTHOR]

The use of complementary information, namely depth or thermal information, has shown its benefits to salient object detection (SOD) during recent years. However, the RGB-D or RGB-T SOD problems are currently only solved independently, and most of them directly extract and fuse raw features from backbones. Such methods can be easily restricted by low-quality modality data and redundant cross-modal features. In this work, a unified end-to-end framework is designed to simultaneously analyze RGB-D and RGB-T SOD tasks. Specifically, to effectively tackle multi-modal features, we propose a novel multi-stage and multi-scale fusion network (MMNet), which consists of a cross-modal multi-stage fusion module (CMFM) and a bi-directional multi-scale decoder (BMD). Similar to the visual color stage doctrine in the human visual system (HVS), the proposed CMFM aims to explore important feature representations in feature response stage, and integrate them into cross-modal features in adversarial combination stage. Moreover, the proposed BMD learns the combination of multi-level cross-modal fused features to capture both local and global information of salient objects, and can further boost the multi-modal SOD performance. The proposed unified cross-modality feature analysis framework based on two-stage and multi-scale information fusion can be used for diverse multi-modal SOD tasks. Comprehensive experiments ($\sim 92\text{K}$ image-pairs) demonstrate that the proposed method consistently outperforms the other 21 state-of-the-art methods on nine benchmark datasets. This validates that our proposed method can work well on diverse multi-modal SOD tasks with good generalization and robustness, and provides a good multi-modal SOD benchmark. [ABSTRACT FROM AUTHOR]

Cross-modal remote sensing text-image retrieval (RSCTIR) has recently become an urgent research hotspot due to its ability of enabling fast and flexible information extraction on remote sensing (RS) images. However, current RSCTIR methods mainly focus on global features of RS images, which leads to the neglect of local features that reflect target relationships and saliency. In this article, we first propose a novel RSCTIR framework based on global and local information (GaLR), and design a multi-level information dynamic fusion (MIDF) module to efficaciously integrate features of different levels. MIDF leverages local information to correct global information, utilizes global information to supplement local information, and uses the dynamic addition of the two to generate prominent visual representation. To alleviate the pressure of the redundant targets on the graph convolution network (GCN) and to improve the model’s attention on salient instances during modeling local features, the denoised representation matrix and the enhanced adjacency matrix (DREA) are devised to assist GCN in producing superior local representations. DREA not only filters out redundant features with high similarity, but also obtains more powerful local features by enhancing the features of prominent objects. Finally, to make full use of the information in the similarity matrix during inference, we come up with a plug-and-play multivariate rerank (MR) algorithm. The algorithm utilizes the $k$ nearest neighbors of the retrieval results to perform a reverse search, and improves the performance by combining multiple components of bidirectional retrieval. Extensive experiments on public datasets strongly demonstrate the state-of-the-art performance of GaLR methods on the RSCTIR task. The code of GaLR method, MR algorithm, and corresponding files have been made available at: https://github.com/xiaoyuan1996/GaLR. [ABSTRACT FROM AUTHOR]

Remote sensing image registration is the basis of change detection, environmental monitoring, and image fusion. Under severe appearance differences, feature-based methods have difficulty in finding sufficient feature matches to solve the global transformation and tackling the local deformation caused by height undulations and building shadows. By contrast, nonrigid registration methods are more flexible than feature-based matching methods, while often ignoring the reversibility between images, resulting in misalignment and inconsistency. To this end, this article proposes a nonrigid bidirectional registration network (NBR-Net) to estimate the flow-based dense correspondence for remote sensing images. We first propose an external cyclic registration network to strengthen the registration reversibility and geometric consistency by registering Image A to Image B and then reversely registering back to Image A. Second, we design an internal iterative refinement strategy to optimize the rough predicted flow caused by large distortion and viewpoint difference. Extensive experiments demonstrate that our method shows a performance superior to the state-of-the-art models on the multitemporal satellite image dataset. Furthermore, we attempt to extend our method to heterogeneous remote sensing image registration, which is more common in the real world. Therefore, we test our pretrained model in a satellite and unmanned aerial vehicle (UAV) image registration task. Due to the cyclic registration mechanism and coarse-to-fine refinement strategy, the proposed approach obtains the best performance on two GPS-denied UAV image datasets. Our code will be released at https://github.com/xuyingxiao/ NBR-Net. [ABSTRACT FROM AUTHOR]

In conventional electromagnetic energy harvesting systems, a two-port ac–dc rectifier is utilized to process the harvested power. However, it is difficult to achieve voltage regulation and maximum-power-point-tracking (MPPT) simultaneously. To resolve this issue, this article proposes a three-port power-electronic-interface (PEI) dedicated to this application. A battery port is introduced to buffer the redundant energy. A bidirectional dc–dc converter is utilized to tightly regulate the load side voltage. To capture the maximum power for the harvester, a simple MPPT method is proposed for this PEI. The power tracking of ac source can be simplified by dealing with the power of the battery port. The regulations of power and output voltage are fully decoupled with different control degree-of-freedoms. Moreover, when the harvester is in idle mode, the load can be powered by the battery via the bidirectional dc–dc converter independently. A 24-mW rated laboratory prototype, which processes the power flow among a 0.6 V, 100 Hz ac source, a 1.2 V battery, and a 3.3 V constant voltage load is developed and tested. The proposed concept is validated by experimental results. [ABSTRACT FROM AUTHOR]

Three-phase CLLC resonant converters provide higher power conversion capability as compared to half-bridge and full-bridge structures. In addition to the unique features of CLLC converters for bidirectional applications, the three-phase structure provides significantly reduced output current ripple (smaller output capacitor), parallel power processing (reduced components size and current peak stress), and better thermal distribution (smaller heatsinks). However, with practical, i.e., nonzero, resonant component tolerances, these benefits are normally less, and sometimes significantly less than expected in the ideal case. In this article, the unbalanced behavior of the converter with 15 unknown resonant components is identified and analyzed. A new analysis methodology is proposed to investigate the worst-cases of current-sharing among above 32 000 possible scenarios in three-phase CLLC resonant converters. In addition, this article shows that phase-shifting techniques can be effective to mitigate the unbalanced behavior of the converter. The proposed analysis in this article helps to determine the highest admissible tolerance in the components to keep the converter working within a certain range of unbalanced behavior without requiring any balancing techniques. The proposed analytical framework is verified with experimental and simulation results of a 3-kW bidirectional three-phase CLLC experimental prototype. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to solve the problem of internal and string stability of decentralized large-scale bi-directional vehicular platoons (DLBVPs) under uncertain dynamics with constant spacing strategy. It is assumed that each following vehicle can only measure the relative position with respect to its predecessor and subsequent vehicles. To estimate the uncertain dynamics of each following vehicle and unknown leader acceleration, a new decentralized robust adaptive consensus protocol is introduced which by using only relative position measurement, guarantees internal and string stability. It will be shown that the control gains are tuned without requiring any knowledge about the platoon size (number of following vehicles) and consequently, the controller is robust against size changing due to common maneuvers. By using Lyapunov theorem, we will prove that under this approach, the position and velocity tracking errors of each vehicle converge to zero asymptotically. Furthermore, it is shown that the adaptation laws have the main role in assuring string stability of DLBVPs with constant spacing strategy. Several simulation and practical results will depict the merits of this algorithm. [ABSTRACT FROM AUTHOR]

This article presents a single-objective modulated model predictive control for a bidirectional dc–dc flying-capacitor (FC) converter in a microgrid. The presence of an FC facilitates the converter to integrate a low-voltage battery to a high-voltage dc bus at reduced voltage stress on its power switches. The converter in such a configuration demands a multiobjective controller to accomplish dc bus and FC voltage regulations and bidirectional power flow. The proposed controller realizes these multiple control objectives by determining the optimum duty ratio for the power switches using a single-objective cost function based on the battery current. In doing so, the converter realizes its multiple control objectives without weighting factors in the cost function and operates its power switches at a fixed switching frequency. The proposed controller also eliminates an additional control loop by utilizing an improved dynamic reference model to generate an appropriate battery current reference for the dc bus voltage regulation and bidirectional power flow. Finally, the proposed system is validated experimentally under step response of the dc bus voltage, load, PV power, and system parameter variations, and compared with a finite control set model predictive control to prove its effectiveness. [ABSTRACT FROM AUTHOR]

Exploiting multi-scale representations is critical to improve edge detection for objects at different scales. To extract edges at dramatically different scales, we propose a bi-directional cascade network (BDCN) architecture, where an individual layer is supervised by labeled edges at its specific scale, rather than directly applying the same supervision to different layers. Furthermore, to enrich multi-scale representations learned by each layer of BDCN, we introduce a scale enhancement module (SEM), which utilizes dilated convolution to generate multi-scale features, instead of using deeper CNNs. These new approaches encourage the learning of multi-scale representations in different layers and detect edges that are well delineated by their scales. Learning scale dedicated layers also results in a compact network with a fraction of parameters. We evaluate our method on three datasets, i.e., BSDS500, NYUDv2, and Multicue, and achieve ODS F-measure of 0.832, 2.7 percent higher than current state-of-the-art on the BSDS500 dataset. We also applied our edge detection result to other vision tasks. Experimental results show that, our method further boosts the performance of image segmentation, optical flow estimation, and object proposal generation. [ABSTRACT FROM AUTHOR]

Fifth Generation District Heating and Cooling (5GDHC) networks, in which low temperature water is distributed to water-source heat pumps (WSHPs) in order to meet thermal demands, are expected to have a significant impact on the decarbonisation of energy supply. Thermal storage installed in these networks offers operational flexibility that can be leveraged to integrate renewable electrical and thermal energy sources. Thus, when considered as part of a smart multi-energy district, 5GDHC substation devices (e.g., WSHPs, storage) may be optimally operated using Model Predictive Control (MPC) in order to match demand with low-cost supply of electricity. However, the application of MPC requires the ability to model 5GDHC networks within the context of a multi-energy system. Hence, this paper extends an existing, generalised control-oriented modelling framework for multi-energy systems to accommodate 5GDHC networks. Additions include the ability to represent hydraulic pumps, thermodynamic cycle devices (such as WSHPs) and multi-energy networks within the framework. Furthermore, an economic MPC (eMPC) scheme is proposed for energy management of 5GDHC-based smart districts. Finally, a case study is presented in which the proposed eMPC controller is compared with rule-based control for economic operation of a smart district. [ABSTRACT FROM AUTHOR]

The development of medical imaging techniques has greatly supported clinical decision making. However, poor imaging quality, such as non-uniform illumination or imbalanced intensity, brings challenges for automated screening, analysis and diagnosis of diseases. Previously, bi-directional GANs (e.g., CycleGAN), have been proposed to improve the quality of input images without the requirement of paired images. However, these methods focus on global appearance, without imposing constraints on structure or illumination, which are essential features for medical image interpretation. In this paper, we propose a novel and versatile bi-directional GAN, named Structure and illumination constrained GAN (StillGAN), for medical image quality enhancement. Our StillGAN treats low- and high-quality images as two distinct domains, and introduces local structure and illumination constraints for learning both overall characteristics and local details. Extensive experiments on three medical image datasets (e.g., corneal confocal microscopy, retinal color fundus and endoscopy images) demonstrate that our method performs better than both conventional methods and other deep learning-based methods. In addition, we have investigated the impact of the proposed method on different medical image analysis and clinical tasks such as nerve segmentation, tortuosity grading, fovea localization and disease classification. [ABSTRACT FROM AUTHOR]

As a new energy saving approach for green communications, energy harvesting (EH) could be suitable technique to facilitate massive connections for large number of devices in such networks. The spectrum shortage occurs in huge number of devices which access with small-cell and macro-cell networks. To tackle these challenges, we develop a tractable framework relying on prominent techniques such as non-orthogonal multiple access (NOMA), antenna selection and energy harvesting. In this paper, we aim at practical scenarios of small cell networks by jointly evaluating capable of interference management and EH. We benefit from transmission approaches including full duplex (FD) and bi-directional transmission to improve the main performance system metrics such as outage probability and throughput. Three useful schemes are explored by considering EH and inter-cell interference. We derive the closed-form and asymptotic expressions for system metrics. We then perform extensive simulations with different system configurations to confirm the effectiveness of the proposed small-cell NOMA systems. [ABSTRACT FROM AUTHOR]

The characteristics of external electromagnetic fields (EEMFs) produced by underground cables in the lossy earth are further investigated and studied. This paper develops an excitation current in the cables with the bi-directional propagation for the calculations of the vectors of EEMFs. By adopting the newly proposed method in this paper, it allows the considerations of the terminating conditions and the lengths of the cable on the calculations of the vectors of EEMFs. Moreover, the vectors of EEMFs are calculated, compared and discussed using various load/source impedances. The effects of lengths of underground cables on the vectors of EEMFs are studied. Also, the frequency-dependent earth models are included into the investigations of the EEMFs. The results presented in this paper are compared and verified with the Numerical Electromagnetics Code. [ABSTRACT FROM AUTHOR]

This paper investigates the error bounds for distance estimation in visible light communication (VLC) systems. More precisely, we study the effect of signal-dependent shot noise (SDSN) on the estimation error bounds for both synchronous and asynchronous systems. Moreover, a bi-directional synchronization protocol is exploited, which can mitigate the effects of clock-biasing between the transmitter and receiver. The results demonstrate that SDSN negatively affects the distance estimation bounds for all considered scenarios. Furthermore, the bi-directional distance estimation protocol outperforms the directional estimation techniques even in the case of perfect synchronization between the transmitter and receiver. [ABSTRACT FROM AUTHOR]

The emergence of the 5G network has brought broad prospects for the massive terminal access and ubiquitous Internet of Things (IoTs). Potential attacking opportunities triggered by this progress are severely impacting the security fortress of current networks, especially in the edge access part. However, due to the unitary protection and inferior isolation, available security schemes are incapable of effectively eliminating these hidden perils. Motivated by these facts, we propose a Multi-dimensional Fine-grained Control (MFC) framework to strengthen safety and reliability in Radio Access Networks (RANs). First, we comprehensively survey and summarize the existing security schemes to grasp respective effects and limitations. Second, the MFC framework is established to describe the model structure and implementation processes. An identifier mapping mechanism is designed to achieve network isolation. We perform the security analysis of MFC by theoretically comparing diversified policies. Third, an integrated set of the authentication prototype system is created with wireless environment parameters settings. Specific verification scenarios are illustrated. Finally, we test the performances of the MFC framework. Validation results demonstrate that the proposed scheme can accomplish reliable security control at the access side. Comparing to multiple schemes, the performances, in terms of time and concurrency, are optimized. Therefore, the MFC framework is feasible for applications in 5G or IoT.

Recurrent neural network (RNN) has become popular for human motion prediction thanks to its ability to capture temporal dependencies. However, it has limited capacity in modeling the complex spatial relationship in the human skeletal structure. In this work, we present a novel diffusion convolutional recurrent predictor for spatial and temporal movement forecasting, with multi-step random walks traversing bidirectionally along an adaptive graph to model interdependency among body joints. In the temporal domain, existing methods rely on a single forward predictor with the produced motion deflecting to the drift route, which leads to error accumulations over time. We propose to supplement the forward predictor with a forward discriminator to alleviate such motion drift in the long term under adversarial training. The solution is further enhanced by a backward predictor and a backward discriminator to effectively reduce the error, such that the system can also look into the past to improve the prediction at early frames. The two-way spatial diffusion convolutions and two-way temporal predictors together form a quadruple network. Furthermore, we train our framework by modeling the velocity from observed motion dynamics instead of static poses to predict future movements that effectively reduces the discontinuity problem at early prediction. Our method outperforms the state of the arts on both 3D and 2D datasets, including the Human3.6M, CMU Motion Capture and Penn Action datasets. The results also show that our method correctly predicts both high-dynamic and low-dynamic moving trends with less motion drift. [ABSTRACT FROM AUTHOR]

This paper describes systems, devices, and methods to implement a bi-directional integrated inter-tower wireless communications network (IITWCN). The described technology can be implemented in combination with the Broadcast Core Network (BCN) in next generation broadcast eco-system and, therefore, support new business cases for broadcast operators such as the delivery of flexible datacasting services or support broadcast or point-to-point Internet services. The introduced bi-directional inter-tower communications network (ITCN) extends the previous unidirectional in-band distribution links (IDL) and adopts the on-channel repeater (OCR) as a simplified backhaul solution in single frequency networks (SFN). The concept of the coordinated ITCN is also presented, aiming at future broadcast Internet services. The ITCN provides a scalable and configurable network solution embedded in a broadcast system, which becomes independent from any non-broadcasting telecommunication infrastructure. The described technology partially relies on the infrastructure of the underlying broadcast/multicast network, using the allocated service channels without requiring additional frequency bands or a separate frequency band. The bi-directional inter-transmitter communication links are therefore referred to as integrated transmission links and the corresponding network as an integrated network. [ABSTRACT FROM AUTHOR]

There are various ways to derive multilevel converter topologies as pointed out by the author's previous work. This letter proposes a voltage-source generalized multilevel converter topology that unifies several generalized multilevel topologies and also incorporates bidirectional switches. Most of the known multilevel topologies can be derived from this topology and new topologies can also be derived from it. Examples have been given in this letter. This generalized topology helps to understand complicated converter topologies, especially those with bidirectional switches and it can also be used to determine the voltage requirements/ratings of the devices in various topologies. This letter has summarized the rules to derive topologies from this generalized topology and provides experimental results for one of the derived multilevel converter examples. It is expected that new topologies can be inspired or derived from this letter and used for existing and emerging applications, with this letter as a milestone in the research of multilevel converter topologies. [ABSTRACT FROM AUTHOR]

An optimized configuration of bidirectional Z-source circuit breaker using a coupled inductor is introduced for the fault isolation in low-voltage dc microgrid. The proposed circuit breaker is designed to operate with a single coupled inductor for bidirectional power flow. Use of the single coupled inductor for the power flow in both the directions reduces the component count. In this article, the operating principle of the breaker is explained for a short-circuit fault condition and breaker design criteria are derived. The performance of the circuit breaker is evaluated by carrying out a MATLAB simulation study. The functional prototype is developed to validate the breaker capabilities. Experimental results show that the fault is cleared within few microseconds. Additionally, the breaker is tested under the step load change condition for a current five times the steady-state rated current of the breaker and results are satisfactory. [ABSTRACT FROM AUTHOR]

We propose a bidirectional flowmeter based on specialty fiber interferometry by controlled mode excitation for measuring the frequency of the vortices induced by a bluff body. The proposed arrangement of the cylindrical bluff body with a slit and the interferometric system based on modal excitation allows the bidirectional flowmeter to determine the flow velocities in either direction precisely and effectively. We have encapsulated the specialty fiber interferometer in a protective metal casing, which not only allows the interferometer to respond to the vortex frequency but also provides mechanical stability to the whole flowmeter so at to operate in a harsh environment. Experiments are conducted by introducing the modal interferometer in the slit of the cylindrical bluff body at various flow rates ranging from Reynolds number (Re) = 7200 to 56 600. A linear response is observed between the flow velocity and the vortex frequency from both directions. Thus, this work provides a reconfigurable bidirectional flowmeter, which is sensitive even at low flow rates and demonstrates a large operating range. [ABSTRACT FROM AUTHOR]

This article presents a double integral sliding mode control (DSMC) and a variable phase-shift algorithm for bidirectional electric vehicle (BEV) charger based on front-end bridgeless converter. An interleaved phase-shifted bridgeless converter is widely preferred for ac–dc bidirectional power conversion due to its reduced switching loss and a significant decrease in passive filter components. However, the converter commutation process during phase-reversal in the presence of grid distortion often adds complexity to achieve zero voltage switching. Estimation of grid frequency and closer zero-crossing detection (ZCD) at the point of common coupling during abnormal grid conditions are the key challenges for the successive operation of bridgeless converter topology. This article describes an adaptive grid-frequency identification technique with noise uncertainty to identify a closer phase-reversal occurrence for ZCD. The DSMC, in addition, offers flexibility toward parametric variation with a gain adaptive control. A modification in the phase-shift algorithm of dc−dc power converter is also explained with the inclusion of charging dynamics of battery operation. The DSMC and phase-shift control with improved ZCD are implemented in Opal-RT platform to verify the charging performance with severe distorted grid conditions. A laboratory prototype for a 3.3-kW EV charger is also developed to validate the controller implementation for BEV charging operation. [ABSTRACT FROM AUTHOR]

The fusion of multi-modal data (e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET)) has been prevalent for accurate identification of Alzheimer’s disease (AD) by providing complementary structural and functional information. However, most of the existing methods simply concatenate multi-modal features in the original space and ignore their underlying associations which may provide more discriminative characteristics for AD identification. Meanwhile, how to overcome the overfitting issue caused by high-dimensional multi-modal data remains appealing. To this end, we propose a relation-induced multi-modal shared representation learning method for AD diagnosis. The proposed method integrates representation learning, dimension reduction, and classifier modeling into a unified framework. Specifically, the framework first obtains multi-modal shared representations by learning a bi-directional mapping between original space and shared space. Within this shared space, we utilize several relational regularizers (including feature-feature, feature-label, and sample-sample regularizers) and auxiliary regularizers to encourage learning underlying associations inherent in multi-modal data and alleviate overfitting, respectively. Next, we project the shared representations into the target space for AD diagnosis. To validate the effectiveness of our proposed approach, we conduct extensive experiments on two independent datasets (i.e., ADNI-1 and ADNI-2), and the experimental results demonstrate that our proposed method outperforms several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

For the application of wireless power transfer (WPT) technology in robot arm, it is necessary to provide flexible power supply for each arm device across multiple arm joints. However, rotation of the joints and the variation of the loads will lead to the fluctuation of the output voltage at each stage. To guarantee stable output voltage, a bidirectional multistage WPT (BM-WPT) method is proposed to provide energy for multiple loads. A bowl-coupling structure and three-dimensional orthogonal ring coil arrangement are proposed to reduce coupling variation against joint rotation. With a special resonant topology design for each stage, energy can be injected or output freely on each stage regardless of energy transfer direction. This topology can also reduce the sensitivity of the output voltages with the variation of the loads. The simulation and experimental results have verified the effectiveness of the proposed method and the coupling mechanism. [ABSTRACT FROM AUTHOR]

This brief presents an advanced proportional-derivative (PD) voltage control mechanism for DC/DC boost converters suffering from the parameter and load variations. The proposed controller eliminates the requirement for current feedback by designing the first-order observer estimating the voltage derivative without the use of converter parameters, which corresponds to the first contribution. As another contribution, a specific feedback gain structure for PD-loop and the active damping component guarantees the first-order closed-loop transfer function from the reference to the output voltage by the stable pole-zero cancellation. A prototype bi-directional 3-kW boost converter experimentally validates the closed-loop performance of the proposed technique. [ABSTRACT FROM AUTHOR]

In a power packet dispatching system, the electricity is converted to pulse power with an information tag attached to its voltage waveform physically. The packetized power is forwarded toward objective loads by the router according to tag information attached to the power packet. In this brief, a bi-directional power packet router is shown, which enables us to dispatch and receive the power at the same port. By using the bi-directional router, the feasibility of driving DC motor and management of its regenerative power is verified in the power packet dispatching system. [ABSTRACT FROM AUTHOR]

This article proposes a new multipolarization antenna, and its multiple states of polarization are realized by scanning the operating frequency. The proposed antenna is based on a fixed configuration and fed by a single port. This is extremely simple and attractive in many applications, such as frequency-agile radar systems and multipolarization target identification systems, especially for those operating at higher microwave and millimeter-wave frequencies. The multipolarization antenna is realized by combining a bidirectional planar antenna with a carefully designed polarization rotating reflector. The reflector reflects and rotates the wave radiated from the bidirectional antenna. Then, two orthogonal waves of the same magnitude, including the reflected wave and the wave radiated directly from the bidirectional antenna, are obtained. Furthermore, the designed reflector helps to tune the phase difference between two orthogonal waves ranging from 90° to −180° with a monotonic continuous variation. The conceptual design of the proposed antenna is analyzed in detail, and a prototype is designed, fabricated, and experimentally tested to validate the design procedure. [ABSTRACT FROM AUTHOR]

The lack of an envelope detector (ED) hinders the realization of bidirectional communication between radio frequency identification (RFID)/near field communication (NFC) tags and readers in metal-oxide thin film transistor (TFT) technology. To solve this, we present a novel ED implementation based on unipolar metal-oxide TFTs. The ED is fully integrated by n-type TFTs and has a novel topology. It can detect an amplitude shift keying (ASK) signal with a minimum modulation depth of 15% and a maximum envelope rate of 8kbit/s, and its minimum supply voltage reaches 2.5 V. These characteristics are greatly improved compared with those of previous works based on complementary organic or hybrid organic/metal-oxide TFTs. [ABSTRACT FROM AUTHOR]

This article proposes a scheme to achieve improved efficiencies for a standalone photovoltaic (PV) system. Specifically, a combined three-port LLC structure is utilized to adaptively regulate the power sharing between the PV and the energy storage battery. This is fulfilled by adjusting the equivalent resonant conditions of two phase-controlled resonant tanks through a shared resonant capacitor. Thus, the overall efficiency is improved by simultaneously reducing the power conversion stages and optimizing LLC converters with fixed-frequency operation. Because an extra power flow path is added to discharge the battery, the nonisolated converter used for battery charging does not need to be bidirectional. Also, since each resonant tank can share part of the load, the new circuit does not add any extra costs of power components. In addition to detailed theoretical analysis, experimental results verify the effectiveness of the proposed concept. [ABSTRACT FROM AUTHOR]

This article introduces a new lossless passive snubber for the bidirectional buck/boost converter. The proposed snubber comprises a low number of passive components which jointly contribute to achieve soft switching condition at both the buck and boost operations of the converter. Without using the auxiliary switch, soft switching condition is ensured over a wide load range with a relatively low circulating current. Also, there is no need for any complex control method to diminish circulating current at light loads. Consequently, by using a simple auxiliary circuit and the conventional control methods, excellent efficiency is acquired over a wide load range. The proposed topology is analyzed in detail and to confirm the theoretical analysis, the experimental results of a 500 W–100 kHz prototype for both the boost and buck modes in full-load and 20% of full-load are presented. [ABSTRACT FROM AUTHOR]

The objective of this brief is to propose a hot-swappable grid-connected multilevel converter (HGM-converter) for the battery energy storage system (BESS). The proposed HGM-converter is composed of series-connected bi-directional H-bridge sub-converters and a LCL filter. Advantages of the proposed HGM-converter include: hot-swappable capability, single-stage power conversion, low dc-bus voltage, and individual power control for each battery module. Therefore, the equalization, lifetime extension, and capacity flexibility of the BESS can be achieved. Based on the equivalent model, the power flow of the battery module can be estimated without the need of input current sensor. Also, with the interleaved operation between the sub-converters, the current ripple of the output inductor can be reduced too. The computer simulations and hardware experimental results are shown to verify the performance of the proposed HGM-converter. [ABSTRACT FROM AUTHOR]

This brief presents a switchless bidirectional amplifier (BDA) with 7-to-30 GHz of bandwidth (BW) and 11.2 dB of peak gain. Common impedance matching networks (IMN) are utilized for both directions, to form a low power and compact amplification stage. It achieves +0.58 dB/BW of a gain slope, which reaches its peak value at 21 GHz. Its output-referred 1-dB compression point (OP1dB) and group delay (GD) are measured as −2.5 dBm and 41 psec. while having ± 1.5 dB and ± 9.1 psec. variation over the defined BW. Moreover, the BDA dissipates 14 mW of power in 0.42 mm2 of area (including pads). The measurement results demonstrate that the BDA is compatible with the wideband transceiver systems. To the best of the authors’ knowledge, the presented design has the best operating bandwidth with a positively sloped gain without sacrificing from the area, power consumption and other remaining RF features such as noise figure (NF). [ABSTRACT FROM AUTHOR]

Wireless power transfer systems using coupled magnetic resonances are susceptible to the transfer distance variation between the transmitter (Tx) and receiver (Rx) since the coupling between Tx and Rx is highly position dependent. Once the transfer distance deviates from the optimum one, the coupling will be either excessive or weak, which results in power transfer efficiency (PTE) degradation. This article presents a Tx structure consisting of multiple subcoils oriented in opposite directions to keep the coupling relatively constant over an extensive range of transfer distances. The proposed design can achieve a PTE of 88–70% with a transfer distance varying from 0 to 70 mm and a PTE of 85–60% with a misalignment of 0–80 mm at a 40 mm transfer distance. The radius of the Tx and Rx is 84.6 and 45.1 mm, respectively. The measured PTE of the proposed design is better than 70% with a 0–50 mm transfer distance while the misalignment changing from 0 to 50 mm. The proposed system is much less sensitive to the transfer distance variation than the convention system and has a great potential for wireless charging applications. [ABSTRACT FROM AUTHOR]

This article introduces a zero voltage switching (ZVS) interleaved buck/boost bidirectional converter using the active-clamp technique. In the proposed converter, by utilizing a simple auxiliary circuit comprised of an auxiliary switch, an auxiliary inductor, and a clamp capacitor, ZVS condition is secured for both the boost and buck operation modes, regardless of the converter operating duty cycle or load condition. Furthermore, the converter can operate with the conventional fixed-frequency pulsewidth modulation (PWM) control procedure. The boost and buck modes of the proposed converter are analyzed in both conditions of D > 0.5 and D < 0.5. To confirm the theoretical analysis, the experimental results of a 350 W–100 kHz prototype are presented. [ABSTRACT FROM AUTHOR]