This work focuses on training dataset enhancement of informative relational triplets for Scene Graph Generation (SGG). Due to the lack of effective supervision, the current SGG model predictions perform poorly for informative relational triplets with inadequate training samples. Therefore, we propose two novel training dataset enhancement modules: Feature Space Triplet Augmentation (FSTA) and Soft Transfer. FSTA leverages a feature generator trained to generate representations of an object in relational triplets. The biased prediction based sampling in FSTA efficiently augments artificial triplets focusing on the challenging ones. In addition, we introduce Soft Transfer, which assigns soft predicate labels to general relational triplets to make more supervisions for informative predicate classes effectively. Experimental results show that integrating FSTA and Soft Transfer achieve high levels of both Recall and mean Recall in Visual Genome dataset. The mean of Recall and mean Recall is the highest among all the existing model-agnostic methods.

For flight simulators, it is crucial to create three-dimensional terrain using clear remote sensing images. However, due to haze and other contributing variables, the obtained remote sensing images typically have low contrast and blurry features. In order to build a flight simulator visual system, we propose a deep learning-based dehaze model for remote sensing images dehazing. An encoder-decoder architecture is proposed that consists of a multiscale fusion module and a gated large kernel convolutional attention module. This architecture can fuse multi-resolution global and local semantic features and can adaptively extract image features under complex terrain. The experimental results demonstrate that, with good generality and application, the model outperforms existing comparison techniques and achieves high-confidence dehazing in remote sensing images with a variety of haze concentrations, multi-complex terrains, and multi-spatial resolutions.

Watermarking methods require robustness against various attacks. Conventional watermarking methods use error-correcting codes or spread spectrum to correct watermarking errors. Errors can also be reduced by embedding the watermark into the frequency domain and by using SIFT feature points. If the type and strength of the attack can be estimated, the errors can be further reduced. There are several types of attacks, such as scaling, rotation, and cropping, and it is necessary to aim for robustness against all of them. Focusing on the scaling tolerance of watermarks, we propose a watermarking method using SIFT feature points and DFT, and introduce a pilot signal. The proposed method estimates the scaling rate using the pilot signal in the form of a grid. When a stego-image is scaled, the grid interval of the pilot signal also changes, and the scaling rate can be estimated from the amount of change. The accuracy of estimating the scaling rate by the proposed method was evaluated in terms of the relative error of the scaling rate. The results show that the proposed method could reduce errors in the watermark by using the estimated scaling rate.

Using a limited number of probes has always been a focus in interface-level network topology probing to discover complete network topologies. Stop-set-based network topology probing methods significantly reduce the number of probes sent but suffer from the side effect of incomplete topology information discovery. This study proposes an optimized probing method based on stop probabilities (SPs) that builds on existing stop-set-based network topology discovery methods to address the issue of incomplete topology information owing to multipath routing. The statistics of repeat nodes (RNs) and multipath routing on the Internet are analyzed and combined with the principles of stop-set-based probing methods, highlighting that stopping probing at the first RN compromises the completeness of topology discovery. To address this issue, SPs are introduced to adjust the stopping strategy upon encountering RNs during probing. A method is designed for generating SPs that achieves high completeness and low cost based on the distribution of the number of RNs. Simulation experiments demonstrate that the proposed stop-probability-based probing method almost completely discovers network nodes and links across different regions and times over a two-year period, while significantly reducing probing redundancy. In addition, the proposed approach balances and optimizes the trade-off between complete topology discovery and reduced probing costs compared with existing topology probing methods. Building on this, the factors influencing the probing cost of the proposed method and methods to further reduce the number of probes while ensuring completeness are analyzed. The proposed method yields universally applicable SPs in the current Internet environment.

This paper proposes a scatterer information estimation method using numerical data for the response waveform of a backward transient scattering field for both E- and H-polarizations when a two-dimensional (2-D) coated metal cylinder is selected as a scatterer. It is assumed that a line source and an observation point are placed at different locations. The four types of scatterer information covered in this paper are the relative permittivity of a surrounding medium, the relative permittivity of a coating medium layer and its thickness, and the radius of a coated metal cylinder. Specifically, a time-domain saddle-point technique (TD-SPT) is used to derive scatterer information estimation formulae from the amplitude intensity ratios (AIRs) of adjacent backward transient scattering field components. The estimates are obtained by substituting the numerical data of the response waveforms of the backward transient scattering field components into the estimation formulae and performing iterative calculations. Furthermore, a minimum thickness of a coating medium layer for which the estimation method is valid is derived, and two kinds of applicable conditions for the estimation method are proposed. The effectiveness of the scatterer information estimation method is verified by comparing the estimates with the set values. The noise tolerance and convergence characteristics of the estimation method and the method of controlling the estimation accuracy are also discussed.

To enhance fuel efficiency and lower manufacturing and maintenance costs, in-vehicle wireless networks can facilitate the weight reduction of vehicle wire harnesses. In this paper, we utilize the Impulse Radio-Ultra Wideband (IR-UWB) of IEEE 802.15.4a/z for in-vehicle wireless networks because of its excellent signal penetration and robustness in multipath environments. Since clear channel assessment is optional in this standard, we employ polling control as a multiple access control to prevent interference within the system. Therein, the preamble overhead is large in IR-UWB of IEEE 802.15.4a/z. Hence, aggregating as much sensor data as possible within each frame is more efficient. In this paper, we assume that reading out data from sensors and sending data to actuators is periodical and that their respective phases can be adjusted. Therefore, this paper proposes an integer linear programming-based scheduling algorithm that minimizes the number of transmitted frames by adjusting the read and write phases. Furthermore, we provide a heuristic algorithm that computes a sub-optimal but acceptable solution in a shorter time. Experimental validation shows that the data aggregation of the proposed algorithms is robust against interference.

In this letter, we study the dynamic antenna grouping and the hybrid beamforming for high altitude platform (HAP) massive multiple-input multiple-output (MIMO) systems. We first exploit the fact that the ergodic sum rate is only related to statistical channel state information (SCSI) in the large-scale array regime, and then we utilize it to perform the dynamic antenna grouping and design the RF beamformer. By applying the Gershgorin Circle Theorem, the dynamic antenna grouping is realized based on the novel statistical distance metric instead of the value of the instantaneous channels. The RF beamformer is designed according to the singular value decomposition of the statistical correlation matrix according to the obtained dynamic antenna group. Dynamic subarrays mean each RF chain is linked with a dynamic antenna sub-set. The baseband beamformer is derived by utilizing the zero forcing (ZF). Numerical results demonstrate the performance enhancement of our proposed dynamic hybrid precoding (DHP) algorithm.

This paper discusses the synchronization of two identical canard-generating oscillators. First, we investigate a canard explosion generated in a system containing a Bonhoeffer-van der Pol (BVP) oscillator using the actual parameter values obtained experimentally. We find that it is possible to numerically observe a canard explosion using this dynamic oscillator. Second, we analyze the complete and in-phase synchronizations of identical canard-generating coupled oscillators via experimental and numerical methods. However, we experimentally determine that a small decrease in the coupling strength of the system induces the collapse of the complete synchronization and the occurrence of a complex synchronization; this finding could not be explained considering four-dimensional autonomous coupled BVP oscillators in our numerical work. To numerically investigate the experimental results, we construct a model containing coupled BVP oscillators that are subjected to two weak periodic perturbations having the same frequency. Further, we find that this model can efficiently numerically reproduce experimentally observed synchronization.

This paper proposes a determination method of the cascaded number for lumped parameter models (LPMs) of the transmission lines. The LPM is used to simulate long-distance transmission lines, and the cascaded number significantly impacts the simulation results. Currently, there is a lack of a system-level determination method of the cascaded number for LPMs. Based on the theoretical analysis and eigenvalue decomposition of network matrix, this paper discusses the error in resonance characteristics between distributed parameter model and LPMs. Moreover, it is deduced that optimal cascaded numbers of the cascaded π-type and T-type LPMs are the same, and the Γ-type LPM has a lowest analog accuracy. The principle that the maximum simulation frequency is less than the first resonance frequency of each segment is presented. According to the principle, optimal cascaded numbers of cascaded π-type, T-type, and Γ-type LPMs are obtained. The effectiveness of the proposed determination method is verified by simulation.

Convolutional neural network (CNN) is widely used in acoustic scene classification (ASC) tasks. In most cases, local convolution is utilized to gather time-frequency information between spectrum nodes. It is challenging to adequately express the non-local link between frequency domains in a finite convolution region. In this paper, we propose a dual-path convolutional neural network based on band interaction block (DCNN-bi) for ASC, with mel-spectrogram as the model’s input. We build two parallel CNN paths to learn the high-frequency and low-frequency components of the input feature. Additionally, we have created three band interaction blocks (bi-blocks) to explore the pertinent nodes between various frequency bands, which are connected between two paths. Combining the time-frequency information from two paths, the bi-blocks with three distinct designs acquire non-local information and send it back to the respective paths. The experimental results indicate that the utilization of the bi-block has the potential to improve the initial performance of the CNN substantially. Specifically, when applied to the DCASE 2018 and DCASE 2020 datasets, the CNN exhibited performance improvements of 1.79% and 3.06%, respectively.

Emerging data-intensive services in distribution grid impose requirements of high-concurrency access for massive internet of things (IoT) devices. However, the lack of effective high-concurrency access management results in severe performance degradation. To address this challenge, we propose a cloud-edge-device collaborative high-concurrency access management algorithm based on multi-timescale joint optimization of channel pre-allocation and load balancing degree. We formulate an optimization problem to minimize the weighted sum of edge-cloud load balancing degree and queuing delay under the constraint of access success rate. The problem is decomposed into a large-timescale channel pre-allocation subproblem solved by the device-edge collaborative access priority scoring mechanism, and a small-timescale data access control subproblem solved by the discounted empirical matching mechanism (DEM) with the perception of high-concurrency number and queue backlog. Particularly, information uncertainty caused by externalities is tackled by exploiting discounted empirical performance which accurately captures the performance influence of historical time points on present preference value. Simulation results demonstrate the effectiveness of the proposed algorithm in reducing edge-cloud load balancing degree and queuing delay.

In recent years, measuring biomagnetic fields in the Earth’s field by differential measurements of scalar-mode OPMs have been actively attempted. In this study, the sensitivity of the scalar-mode OPMs under the geomagnetic environment in the laboratory was studied by numerical simulation. Although the noise level of the scalar-mode OPM in the laboratory environment was calculated to be 104 pT/$\sqrt{\mathrm{Hz}}$, the noise levels using the first-order and the second-order differential configurations were found to be 529 fT/cm/$\sqrt{\mathrm{Hz}}$ and 17.2 fT/cm2/$\sqrt{\mathrm{Hz}}$, respectively. This result indicated that scalar-mode OPMs can measure very weak magnetic fields such as MEG without high-performance magnetic shield roomns. We also studied the operating conditions by varying repetition frequency and temperature. We found that scalar-mode OPMs have an upper limit of repetition frequency and temperature, and that the repetition frequency should be set below 4 kHz and the temperature should be set below 120°C.

Use case descriptions describe features consisting of multiple concepts with following a procedural flow. Because existing feature location techniques lack a relation between concepts in such features, it is difficult to identify the concepts in the source code with high accuracy. This paper presents a technique to locate concepts in a feature described in a use case description consisting of multiple use case steps using dependency between them. We regard each use case step as a description of a concept and apply an existing concept location technique to the descriptions of concepts and obtain lists of modules. Also, three types of dependencies: time, call, and data dependencies among use case steps are extracted based on their textual description. Modules in the obtained lists failing to match the dependency between concepts are filtered out. Thus, we can obtain more precise lists of modules. We have applied our technique to use case descriptions in a benchmark. Results show that our technique outperformed baseline setting without applying the filtering.

Previously a method was reported to determine the mathematical representation of the microwave oscillator admittance by using numerical calculation. When analyzing the load characteristics and synchronization phenomena by using this formula, the analysis results meet with the experimental results. This paper describes a method to determine the mathematical representation manually.

Automatic detection of prohibited items is vital in helping security staff be more efficient while improving the public safety index. However, prohibited item detection within X-ray security inspection images is limited by various factors, including the imbalance distribution of categories, diversity of prohibited item scales, and overlap between items. In this paper, we propose to leverage the Poisson blending algorithm with the Canny edge operator to alleviate the imbalance distribution of categories maximally in the X-ray images dataset. Based on this, we improve the cascade network to deal with the other two difficulties. To address the prohibited scale diversity problem, we propose the Re-BiFPN feature fusion method, which includes a coordinate attention atrous spatial pyramid pooling (CA-ASPP) module and a recursive connection. The CA-ASPP module can implicitly extract direction-aware and position-aware information from the feature map. The recursive connection feeds the CA-ASPP module processed multi-scale feature map to the bottom-up backbone layer for further multi-scale feature extraction. In addition, a Rep-CIoU loss function is designed to address the overlapping problem in X-ray images. Extensive experimental results demonstrate that our method can successfully identify ten types of prohibited items, such as Knives, Scissors, Pressure, etc. and achieves 83.4% of mAP, which is 3.8% superior to the original cascade network. Moreover, our method outperforms other mainstream methods by a significant margin.

In interdependent systems, such as electric power systems, entities or components mutually depend on each other. Due to these interdependencies, a small number of initial failures can propagate throughout the system, resulting in catastrophic system failures. This paper addresses the problem of finding the set of entities whose failures will have the worst effects on the system. To this end, a two-phase algorithm is developed. In the first phase, the tight bound on failure propagation steps is computed using a Boolean Satisfiablility (SAT) solver. In the second phase, the problem is formulated as an Integer Linear Programming (ILP) problem using the obtained step bound and solved with an ILP solver. Experimental results show that the algorithm scales to large problem instances and outperforms a single-phase algorithm that uses a loose step bound.

A 150 GHz fundamental oscillator employing an inter-stage matching network based on a transmission line is presented in this letter. The proposed oscillator consists of a two-stage common-emitter amplifier loop, whose inter-stage connections are optimized to meet the oscillation condition. The oscillator is designed in a 130-nm SiGe BiCMOS process that offers fT and fMAX of 350 GHz and 450 GHz. According to simulation results, an output power of 3.17 dBm is achieved at 147.6 GHz with phase noise of -115 dBc/Hz at 10 MHz offset and figure-of-merit (FoM) of -180 dBc/Hz.

A wireless sensor terminal module of 5cc size (2.5 cm × 2.5 cm × 0.8 cm) that does not require a battery is proposed by integrating three kinds of circuit technologies. (i) a low-power sensor interface: an FM modulation type CMOS sensor interface circuit that can operate with a typical power consumption of 24.5 μW was fabricated by the 0.7-μm CMOS process technology. (ii) power supply to the sensor interface circuit: a wireless power transmission characteristic to a small-sized PCB spiral coil antenna was clarified and applied to the module. (iii) wireless sensing from the module: backscatter communication technology that modulates the signal from the base terminal equipment with sensor information and reflects it, which is used for the low-power sensing operation. The module fabricated includes a rectifier circuit with the PCB spiral coil antenna that receives wireless power transmitted from base terminal equipment by electromagnetic resonance coupling and converts it into DC power and a sensor interface circuit that operates using the power. The interface circuit modulates the received signal with the sensor information and reflects it back to the base terminal. The module could achieve 100 mm communication distance when 0.4 mW power is feeding to the sensor terminal.

Compared to subword based Neural Machine Translation (NMT), character based NMT eschews linguistic-motivated segmentation which performs directly on the raw character sequence, following a more absolute end-to-end manner. This property is more fascinating for machine translation (MT) between Japanese and Chinese, both of which use consecutive logographic characters without explicit word boundaries. However, there is still one disadvantage which should be addressed, that is, character is a less meaning-bearing unit than the subword, which requires the character models to be capable of sense discrimination. Specifically, there are two types of sense ambiguities existing in the source and target language, separately. With the former, it has been partially solved by the deep encoder and several existing works. But with the later, interestingly, the ambiguity in the target side is rarely discussed. To address this problem, we propose two simple yet effective methods, including a non-parametric pre-clustering for sense induction and a joint model to perform sense discrimination and NMT training simultaneously. Extensive experiments on Japanese⟷Chinese MT show that our proposed methods consistently outperform the strong baselines, and verify the effectiveness of using sense-discriminated representation for character based NMT.

Video text detection (VTD) aims to localize text instances in videos, which has wide applications for downstream tasks. To deal with the variances of different scenes and text instances, multiple models and feature fusion strategies were typically integrated in existing VTD methods. A VTD method consisting of sophisticated components can efficiently improve detection accuracy, but may suffer from a limitation for real-time applications. This paper aims to achieve real-time VTD with an adaptive lightweight end-to-end framework. Different from previous methods that represent text in a spatial domain, we model text instances in the Fourier domain. Specifically, we propose a scale-aware Fourier Contour Embedding method, which not only models arbitrary shaped text contours of videos as compact signatures, but also adaptively select proper scales for features in a backbone in the training stage. Then, we construct VTD-FCENet to achieve real-time VTD, which encodes temporal correlations of adjacent frames with scale-aware FCE in a lightweight and adaptive manner. Quantitative evaluations were conducted on ICDAR2013 Video, Minetto and YVT benchmark datasets, and the results show that our VTD-FCENet not only obtains the state-of-the-arts or competitive detection accuracy, but also allows real-time text detection on HD videos simultaneously.