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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]

In recent years, the joint classification approach of hyperspectral image (HSI) and light detection and ranging (LiDAR) data based on deep learning (DL) has received increasing attention. However, existing methods either lack interaction between heterogeneous features during feature extraction or treat them equally during interaction, inevitably resulting in redundant information stacking and reaching the performance bottleneck. To this end, we propose a novel discrepant bi-directional interaction fusion network (DBIFNet) for the collaborative classification of HSI and LiDAR data. First, a discrepant bi-directional interaction module (DBDIM) is designed to establish correlations between heterogeneous features to enhance the respective feature learning. Furthermore, a cross-modal attention fusion module (CAFM) is developed to dynamically fuse multimodal features, which can further improve classification performance. Extensive experiments on the Houston and Trento datasets demonstrate that the proposed DBIFNet can achieve competitive classification performance. [ABSTRACT FROM AUTHOR]

The performance of Network-on-Chip depends a lot on the routers and interconnect circuits used in them. The gradual movement of technology towards nanometer scales has increased the core abilities, leading to an increase in delays and interconnect power consumption. As a result, %60 of the total current chip power consumption is related to the interconnect circuits. This paper has presented a simultaneous Bi-directional current-mode transceiver (SBCT-NoC) with two modes of operation, transmitter, and receiver. Two transceivers connected at both ends of an interconnect can use two-way communication through the same link. The proposed transceiver can significantly reduce the delay and power consumption of Network-on-Chip interconnects and increase the reliability using differential logic. This paper has simulated the circuits using 32-nanometer technology while considering the crosstalk noise effect. The results show the considerable advantage of the proposed method compared to previous interconnected circuit methods. [ABSTRACT FROM AUTHOR]

A reconfigurable gain circuit is proposed for an LCLC resonant converter with bidirectional capability considering wide varying redox flow battery source. A suitable hybrid control scheme for LCLC resonant converter with secondary synchronous rectifier is proposed in this work. The proposed reconfigurable circuit enables to configure secondary bridge as an active voltage doubler, full-bridge circuit during forward power transfer mode and reverse power transfer mode, respectively. The reconfigurability helps significantly to design a transformer with lower secondary turns and achieve desired high gain. The reduced transformer secondary turns result in reduced transformer parasitics. The presence of LCLC resonant tank helps to provide the additional gain; the hybrid control scheme ensures zero-voltage switching turn-on for the primary, and secondary synchronous mosfets throughout the operating range. The proposed converter is analysed using fundamental harmonic approximation. The proposed reconfigurable gain circuit and hybrid control scheme is verified experimentally for an 800 V/1 kW hardware prototype fed from 24 to 54 V input. [ABSTRACT FROM AUTHOR]

In battery equalization systems, equalization speed, control complexity, and length of energy flow path are the important figures of merits. This article presents a novel bidirectional L2C3 resonant converter in a unique equalizer architecture with constant balancing current and fixed frequency control. The constant balancing current can be customized to achieve a stable balancing speed. The common equalizer unit transfers power bidirectionally between the entire string and any single cell. Design considerations of L2C3-based bidirectional equalizer are analyzed in detail, which ensures zero-voltage switching among all mosfets during the equalization process. The circuit is designed with synchronous rectification using the first harmonics approximation. The key contributions of this article include: a new bidirectional resonant topology for battery equalization; bidirectional constant current balancing with open-loop control; simplified synchronous rectification. An equalizer prototype with four lithium-ion battery cells at a balancing current of 500 mA is built and tested. The L2C3 circuit operates at 200 kHz with a peak efficiency of 89.4% and 90.1% under two balancing modes. Experimental results show the proposed scheme exhibits outstanding balancing performance. [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]

The dynamic uncertain relationship among each brain region is a necessary factor that limits EEG-based emotion recognition. It is a thought-provoking problem to availably employ time-varying spatial and temporal characteristics from multi-channel electroencephalogram (EEG) signals. Although deep learning has made remarkable achievements in emotion recognition, the biological topological information among brain regions does not fully exploit, which is vital for EEG-based emotion recognition. In response to this problem, we design a hybrid model called ST-GCLSTM, which comprises a spatial-graph convolutional network (SGCN) module and an attention-enhanced bi-directional Long Short-Term Memory (LSTM) module. The main advantage of ST-GCLSTM is that it can consider the biological topology information of each brain region to extract representative spatial-temporal features from multiple EEG channels. Specifically, we construct two layers SGCN by introducing adjacency matrices to adaptively learn the intrinsic connection among different EEG channels. Moreover, an attention-enhanced mechanism is placed into a bi-directional LSTM module to extract the crucial spatial-temporal features from sequential EEG data, and then these features serve as the input layer of the classifier to learn discriminative emotion-related features. Extensive experiments on the DEAP, SEED, and SEED-IV datasets demonstrate the effectiveness of the proposed ST-GCLSTM model, revealing that our model had an absolute performance improvement over state-of-the-art strategies. [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]

Recent spatial-temporal graph-based deep learning methods for Traffic Flow Prediction (TFP) problems have shown superior performance in modeling higher-level spatial interactions and temporal correlations. However, most of these methods suffer from post-fusion efficiency difficulty caused by separate explorations of the spatial communications and the temporal dependencies, which could result in delayed and biased predictions. To address that, we propose a Traffic Gated Graph Neural Networks (Traffic-GGNN) for real-time-fused spatial-temporal representation modeling. Firstly, we adopt bidirectional message passing to capture the location-wise spatial interactions. Secondly, we apply a GRU-based module to explore and aggregate the spatial interactions with the temporal correlations in a real-time fusion way. Lastly, we introduce a self-attention mechanism to reweight the location-based importance and produce the final prediction. Moreover, our proposed model allows end-to-end training thus it is easy to scale to diverse types of traffic datasets and yield better efficiency and effectiveness on three real-world datasets (SZ-taxi, Los-loop, and PEMS-BAY). [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]

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Cross-Cloud Federation (CCF) enables resource exchange among multiple, heterogeneous Cloud Service Providers (CSPs) to support the composition of services (workflow) hosted by different providers. CCF participation can either be fixed, or the types of services that can be used are limited to reduce the potential risk of service failure or secure access. Although many service selection approaches have been proposed in literature for cloud computing, their applicability to CCF i.e., cloud-to-cloud interaction, has not been adequately investigated. A key component of this cloud-to-cloud paradigm involves assessing the combined capability of contributing participants within a federation and their connectivity. A novel Aggregated Capability Assessment (AgCA) approach based on using the Consensus Assessment Initiative Questionnaire from Cloud Security Alliance is proposed for CCF. The proposed mechanism is implemented as a component of a centralized broker to enhance the quality of the selection process for participants within a federation. Our experimental results show that AgCA is a useful tool for partner selection in a dynamic, heterogeneous and multilevel cloud federation. [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]

This article introduces a four-element 300-GHz-band bi-directional phased-array transceiver (TRX). The TRX utilizes the same antenna, signal path, and local oscillator (LO) circuitry to operate either in transmitter (TX) mode or receiver (RX) mode. The TX mode adopts the outphasing technique to increase the average output power for higher order modulation schemes by utilizing the two mixers that are connected directly to the antenna in a mixer-last fashion. The two signal paths also enable the canceling of the LO feed-through (LOFT). The RX mode also benefits from the LOFT cancellation technique to suppress the LO emission, which is a common issue of the mixer-first RXs. The RX has a separate Hartley operation mode to reject the image signal coming from the TX. The TRX chip was implemented using CMOS 65-nm process, and a four-element phased array was implemented by stacking liquid crystal polymer (LCP) flexible printed circuit boards (PCBs). The stacked structure provides the required narrow antenna pitch at the 300-GHz band. The measured beam angle range is from −18° to 18°. The single-element power consumption is 750 mW for both TX mode and RX mode. [ABSTRACT FROM AUTHOR]

The issue of mining influential nodes in complex networks is a topic of immense interest. Recently, many methods have been proposed, but they suffer from certain limitations. In this article, a novel centrality measure based on local fuzzy information centrality (LFIC) is proposed. LFIC puts forward the concept that the inner structure of a node’s box contains information about the node’s importance. LFIC uses the amount of information contained in the node’s box as a measure of its importance. In LFIC, the uncertainty of information contained in nodes’ boxes is measured by the improved Shannon entropy. Most importantly, fuzzy logic is applied to deal with the uncertainty of neighbor nodes’ contributions to the center node’s importance, which is neglected by most existing methods. To verify the effectiveness of our proposed method, six existing methods are used for comparison and five experiments are conducted using six real-world complex networks. The experimental results indicate that the influential nodes identified by LFIC can cause a wider scope of infection in networks and have a larger effect on the network connectivity, thereby proving the effectiveness and accuracy of LFIC. The correlation between nodes’ LFIC values and their real infection ability is highly positive according to Kendall’s tau coefficient, proving LFIC’s credibility and superiority. The extension of LFIC, namely the bi-directional local fuzzy information centrality, is also proposed to explore its feasibility in weighted directed complex networks. [ABSTRACT FROM AUTHOR]

This article presents a multipolar neural stimulation and mixed-signal neural data acquisition (DAQ) chipset for fully implantable bi-directional brain–computer interfaces (BD-BCIs). The stimulation system employs four 40 V compliant current-stimulators, each capable of sourcing/sinking a maximum 12.75 mA stimulation current, connected to 16 output channels through a high-voltage (HV) switch fabric. A novel time-based charge balancing (TBCB) technique is introduced to reduce the residual voltage on the electrode-electrolyte interface during the inter-pulse time interval, achieving 2 mV charge balancing precision. Additionally, an analytical study of the charge balancing accuracy for the proposed technique is provided. The recording system incorporates a dual-mode DAQ architecture that consists of a 32-element front-end array and a mixed-signal back-end including analog-to-digital converters (ADCs) for both training (i.e., full-band) and decoding (i.e., baseband) operations. Leveraging the flexibility of the multipolar operation, stimulation-side contour shaping (SSCS) artifact cancellation is adopted to significantly suppress stimulation artifacts by up to 45 dB. SSCS method prevents the recording front-ends from saturation and greatly relaxes the dynamic range requirement of the recording system, enabling a truly bi-directional operation. The prototype chipset is fabricated in an HV 180-nm CMOS process and demonstrates a significant performance improvement compared to the prior art. [ABSTRACT FROM AUTHOR]

Road traffic accidents, especially those accidents with multiple-vehicle collision usually cause injuries and mortalities. Currently, studies on collision avoidance mainly focus on the control strategies for adjacent two vehicles or multiple vehicles in a single platoon direction. This paper proposes a bi-directional collision avoidance system for multiple vehicles to minimize the collision risk under the model predictive control (MPC) framework through switching the vehicle-following mode based on the inter-vehicular states. A hierarchical structure with an upper layer and a lower layer is designed. A dual-operational mode switching strategy and the vehicle-following model are formulated in the upper layer, together with the development of the acceleration redistribution cooperative strategy for vehicle platoon. While the lower layer is designed to track the desired acceleration received from the upper layer by considering the practical situation of the control commands. To tackle complex transitional operation, a dynamic weighted tuning strategy is proposed and integrated it with the MPC. The numerical results show that the proposed system outperforms the conventional collision avoidance system and is effective to avoid a collision or minimize the total impact of the vehicle platoon when the collision is unavoidable. [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]

This letter presents an ultra-compact two-stage 60-GHz differential bi-directional amplifier (DBA) design in a 65-nm CMOS process. To satisfy the stability and gain requirements, a differential neutralized bi-directional common-source gain cell combined with the cross-coupled gm-boosting technique is proposed. In addition, a layout-symmetrical coupled line transformer is used as the inter-stage matching network to achieve broadband operation, reducing insertion loss and ensuring identical responses in both forward/backward modes. The proposed DBA achieves a peak gain of 16.1 dB with a 3-dB bandwidth of 15 GHz (52–67 GHz), maximum OP $_{\mathrm {1 \,\,dB}}$ of 5.1 dBm, $P_{\mathrm {SAT}}$ of 9.6 dBm, a peak PAE of 11.5% at 62 GHz, respectively, consuming 70 mW from a power supply of 1.2 V. The circuit core occupies an ultra-compact area of only 0.0675 mm2. [ABSTRACT FROM AUTHOR]

Due to the discarding of downlink channel state information (CSI) amplitude and the employing of iteration reconstruction algorithms, 1-bit compressed sensing (CS)-based superimposed CSI feedback is challenged by low recovery accuracy and large processing delay. To overcome these drawbacks, this letter proposes a fusion learning scheme by exploiting the bi-directional channel reciprocity. Specifically, a simplified version of the conventional downlink CSI reconstruction is utilized to extract the initial feature of downlink CSI, and a single hidden layer-based amplitude-learning network (AMPL-NET) is designed to learn the auxiliary feature of the downlink CSI amplitude. Then, based on the extracted and learned amplitude features, a simple but effective amplitude-fusion network (AMPF-NET) is developed to perform the amplitude fusion of downlink CSI and thus improves the reconstruction accuracy for 1-bit CS-based superimposed CSI feedback while reducing the processing delay. Simulation results show the effectiveness of the proposed feedback scheme and the robustness against parameter variations. [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 article studies the integrated fault-tolerant control (FTC) and fault estimation (FE) problem using polynomial fuzzy model (PFM) based on the sum of squares (SOS) approach. The integrated approach not only considers the bidirectional interaction between fault diagnosis and control units but also ensures an optimal response. The transient management in the fault occurrence situation and the alleviation of bidirectional interaction between FE and FTC are the motivational challenges. However, the integrated approach increases the complexity and problem's dimension. In this situation, PFM can reduce the problem's dimensions and increase modeling accuracy. The additive actuator faults, input disturbance, and structured uncertainty are considered to bring the problem closer to practical applications. A polynomial fuzzy observer with unknown input is designed to estimate the time-varying faults that is used to remove the effects of faults on the control input. Thus, the proposed approach benefits from PFM in FE and FTC synthesis to reduce the design dimensions, have a more precise model, and make the synthesis less conservative. Moreover, the PFM is more robust against intense model uncertainties and faults. To evaluate the proposed approach, the FTC of an inverted-pendulum system is simulated. The fault-tolerant and fault estimation performances of the proposed approach are compared with those of the linear matrix inequality (LMI) approach. The simulated results show that the proposed SOS approach outperforms the LMI approach. [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]

Machine vision based vehicle counting and traffic flow estimation are challenging problems especially for dense traffic scenarios. Previous line of interest (LOI) counting methods rarely focus on dense scenarios and their performance largely relies on the accuracy of tracking. Avoiding the use of complex tracking methods, an LOI counting framework is proposed to address the bi-directional LOI counting problem in dense scenarios. There are three main contributions. Firstly, instead of treating the LOI vehicle counting problem as a combination of detecting and tracking of individual vehicles, the bi-directional traffic flow is taken as a whole and a novel spatio-temporal counting feature (STCF) is proposed for extracting bi-directional traffic flow features in dense traffic scenarios. Secondly, without relying on a multi-target tracking process for tracking and counting each vehicle, a counting network is proposed, called the counting Long Short-Term Memory (cLSTM) network, to do analysis of the bi-directional STCF features and vehicle counting in successive video frames. Lastly, an estimation model is designed for estimating traffic flow parameters including speed, volume and density. Experiments performed on the UA-DETRAC dataset and the captured videos show that the proposed vehicle counting method outperforms the tested representative LOI counting methods in both accuracy and speed, and that the proposed framework can efficiently estimate traffic flow parameters including speed, volume and density in real time. [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]

Recent studies have shown that the self-coherent systems, including the bi-directional, the Stokes-vector-modulation direct-detection (SVM-DD), and the Kramers-Kronig (KK) schemes, have the potential to reduce the cost of short-reach networks. One of the most attractive features of the self-coherent systems is that they may eliminate the necessity of a narrow-linewidth laser, which is mandatory in the conventional full coherent systems employing high-order quadrature-amplitude-modulation (QAM) formats. On the other hand, it is also recognized that the laser phase noise may have a significant impact on the system performance if there exists a large length mismatch between the signal and the CW tone in the transmission paths. In this paper, we analyze the impact of laser phase noise on the self-coherent systems numerically and experimentally by considering the bi-directional system as an example case. As a result, we show that the performance of the self-coherent system can be described efficiently by using a limited number of normalized parameters. We then reveal the existence of a distinctive threshold on the path length mismatch that influences the bit-error-rate (BER) performance; the penalty increases in a stepwise manner as the length mismatch exceeds the threshold and converges to a constant value. Based on the results, we suggest the feasibility of employing high-order QAM formats to transmit >800 Gb/s signals per polarization using a 10-MHz-linewidth laser. Finally, proof-of-concept experimental results are presented to verify the theoretical and numerical analyses. [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]

Bi-directional optogenetics at single-cell level requires localized, bright, and multi-color light sources to activate both excitatory and inhibitory opsins. To this end, here we report a simple fabrication method of high-density, two-color, quantum dot (QD) based micron-sized light emitting diode (micro-LED) arrays. In particular, we micro-patterned InP/ZnS QDs on top of GaN-based micro-LED pixels via a simple brushing method, and coated them with a spectral filter. The resulting array featured sub- $20~\mu \text{m}$ sized light spots near both 462 nm and 623 nm, with their optical power density being ca. 0.1 – 1.0 mW/mm2. Combined with its low crosstalk and fast response, our two-color QD-LED array may hold promise for bi-directional optogenetics ultimately at the cellular level. [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]

This article proposes a simple yet reliable no-reference image contrast evaluator (NICE) by generating bidirectional pseudoreferences (BPR). Different from the existing no-reference metrics that only operate on the contrast distorted image (CDI) itself, our proposed NICE-BPR measures the deviations of a CDI to its corresponding aggravated and enhanced counterparts (i.e., BPRs) in a hybrid feature space. Given a CDI, we first perform contrast aggravation and contrast enhancement using gamma correction and histogram equalization, respectively. Then, hybrid contrast-aware features are, respectively, extracted from the CDI and its corresponding BPRs via the analysis of histogram, entropy, and structure. The features obtained from the CDI are one-by-one compared with those from the BPRs to derive the bidirectional feature deviation vector. Finally, a quality predictor is built by learning a regression model to fuse the feature vector into a continuous quality score. Extensive experiments on several databases well-demonstrate the superiority of NICE-BPR. [ABSTRACT FROM AUTHOR]