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It is known that the determination of the good-dice-in-bad-neighborhoods (GDBNs) has been regarded as an effective technique to reduce the value of the defect parts per million (DPPM) by identifying and rejecting the suspicious dice even though they are good in testing. Instead of examining eight immediate neighbors in a small-sized $3\times 3$ window or exploiting simple linear regression, a large-sized window can be used to recognize the broad-sighted neighborhoods and accurately infer the suspiciousness level for any given die. In this paper, the artificial neural networks (ANN)-based method can be proposed to solve the GDBN identification. Furthermore, two enhanced techniques can be further presented to improve the inference accuracy of the original ANN-based method by considering the variation of the time-dependent wafer patterns and the wafer-to-wafer relationship between two adjacent wafers. After applying the two enhanced techniques, the business profits can be improved in the new ANN-based method. Various experiments on two datasets clearly reveal the superiority of the proposed ANN-based method over the other existing methods. In addition to the reduction of the DPPM value, the new ANN-based method can achieve the 1.5X–2X better reduction in the cost of the return merchandise authorization (RMA). On the other hand, the experimental results show that the similar result can also be obtained in the other lower-yield products. By using the new ANN-based method, the relationships on bad dice cross wafers can be captured and the highly-accurate inference results can be simultaneously maintained. [ABSTRACT FROM AUTHOR]

The ultra-high voltage direct current (UHVDC) system is widely constructed due to its suitability for large-capacity long-distance transmission, and its losses have become an important part of the power grid losses. However, the overall and subcomponent losses of the power system are difficult to measure by measuring devices or accurately calculated by existing algorithms. In this paper, a convolutional neural network (CNN)-based digital twin model for UHVDC system loss measurement is proposed. The parameters and data of the physical system are fed into the digital space system through the data interaction. In the digital twin model of the digital space system, a CNN network with added topology information extraction layers and key equipment parameter adaptive perception layers excavates the deep multidimensional correlation features of the digital space mapping data. Then a multi-task joint processing layer fuses the extracted deep features and missing information to calculate the total and sub-component losses. The training and testing results based on actual UHVDC project data show that the mean absolute error (MAE) is 0.9, while R2 is 0.9999, which proves the accuracy of the proposed digital twin model is superior to mainstream deep learning models. This model is embedded in digital twin devices and applied in actual UHVDC converter stations.

Most researchers focus on designing accurate crowd counting models with heavy parameters and computations but ignore the resource burden during the model deployment. A real-world scenario demands an efficient counting model with low-latency and high-performance. Knowledge distillation provides an elegant way to transfer knowledge from a complicated teacher model to a compact student model while maintaining accuracy. However, the student model receives the wrong guidance with the supervision of the teacher model due to the inaccurate information understood by the teacher in some cases. In this paper, we propose a dual-knowledge distillation (DKD) framework, which aims to reduce the side effects of the teacher model and transfer hierarchical knowledge to obtain a more efficient counting model. First, the student model is initialized with global information transferred by the teacher model via adaptive perspectives. Then, the self-knowledge distillation forces the student model to learn the knowledge by itself, based on intermediate feature maps and target map. Specifically, the optimal transport distance is utilized to measure the difference of feature maps between the teacher and the student to perform the distribution alignment of the counting area. Extensive experiments are conducted on four challenging datasets, demonstrating the superiority of DKD. When there are only approximately 6% of the parameters and computations from the original models, the student model achieves a faster and more accurate counting performance as the teacher model even surpasses it. [ABSTRACT FROM AUTHOR]

The deep neural networks (DNNs) have freed the synthetic aperture radar automatic target recognition (SAR ATR) from expertise-based feature designing and demonstrated superiority over conventional solutions. There has been shown the unique deficiency of ground vehicle benchmarks in shapes of strong background correlation results in DNNs overfitting the clutter and being nonrobust to unfamiliar surroundings. However, the gap between fixed background model training and varying background application remains underexplored. This letter proposes a solution called contrastive feature alignment (CFA) aiming to learn invariant representation for robust recognition. The proposed method contributes a mixed clutter variants’ generation strategy and a new inference branch equipped with channel-weighted mean square error (CWMSE) loss for invariant representation learning. In specific, the generation strategy is delicately designed to better attract clutter-sensitive deviation in feature space. The CWMSE loss is further devised to better contrast this deviation and align the deep features activated by the original images and corresponding clutter variants. The proposed CFA combines both classification and CWMSE losses to train the model jointly, which allows for the progressive learning of invariant target representation. Extensive evaluations conducted on the MSTAR dataset and six DNN models prove the effectiveness of our proposal. The results demonstrate that the CFA-trained models are capable of recognizing targets among unfamiliar surroundings that are not included in the dataset and are robust to varying signal-to-clutter ratios (SCRs). [ABSTRACT FROM AUTHOR]

Deep learning (DL) has succeeded in the field of target detection and has been introduced into the research works of ground-moving target indication (GMTI) for synthetic aperture radar (SAR) recently. Due to the lack of labeled data in SAR/GMTI, simulated data are usually employed to support the training of networks, which has proved to be a feasible way in practice. Although some simulated data are very close to the real radar data, the fact is that distribution differences between them are inevitable and always lead to a performance loss of the network. Motivated by recent advances in transfer learning, this letter proposes a new method for ground-moving target detection of multichannel SAR systems, namely, subdomain adaptive residual network (SARN). It is built on the basis of ResNet18, and subdomain adaptation is introduced. During the network training, multikernel local maximum mean discrepancy (MK-LMMD) is minimized as well as classification error. Experiments on three-channel SAR data show that the proposed method significantly improves the detection performance as compared with CA-CFAR and the DL method. [ABSTRACT FROM AUTHOR]

In this letter, we introduce deep active learning (AL) for multi-label image classification (MLC) problems in remote sensing (RS). In particular, we investigate the effectiveness of several AL query functions for the MLC of RS images. Unlike the existing AL query functions (which are defined for single-label classification or semantic segmentation problems), each query function in this letter is based on the evaluation of two criteria: 1) multi-label uncertainty and 2) multi-label diversity. The multi-label uncertainty criterion is associated with the confidence of the deep neural networks (DNNs) in correctly assigning multi-labels to each image. To assess this criterion, we investigate three strategies: 1) learning multi-label loss ordering; 2) measuring temporal discrepancy of multi-label predictions; and 3) measuring the magnitude of approximated gradient embeddings. The multi-label diversity criterion is associated with the selection of a set of images that are as diverse as possible to each other which prevents redundancy among them. To assess this criterion, we exploit a clustering-based strategy. We combine each of the abovementioned uncertainty strategies with the clustering-based diversity strategy, resulting in three different query functions. All the considered query functions are introduced for the first time in the framework of MLC problems in RS. Experimental results obtained on two benchmark archives show that these query functions result in the selection of a highly informative set of samples at each iteration of the AL process. [ABSTRACT FROM AUTHOR]

The inadvertent incorporation of deviating samples into the measured indirect and direct path delays is generally unavoidable in the practical implementation of passive elliptic localization. These outlying observations, however, can do great harm to the positioning performance if left untreated. Here, a robust statistics-based method is put forward as the solution to such a problem. The non-outlier-resistant $\ell _{2}$ cost function in the traditional least squares (LS) formulation is replaced by a certain differentiable error measure that possesses resistance to the presence of abnormally large fitting errors. A globally optimized hybrid quasi-Newton and particle swarm optimization (PSO) algorithm is then developed for an efficient realization of the robust estimator. The strong capability of the presented approach to deal with outliers and its applicability to typical adverse localization environments are demonstrated via simulations. [ABSTRACT FROM AUTHOR]

With the maturity of deep learning algorithm in synthetic aperture radar (SAR) target recognition field, convolutional neural network (CNN) has become the most effective model. However, the interpretability and the separability of feature maps extracted from convolution layers have not been specially analyzed neither qualitatively nor quantitatively, which makes the traditional model work like a “black box.” To alleviate the problem, a novel model based on separability measure (SM)-CNN is proposed in this letter, which introduces the principle of maximal coding rate reduction (MCR $^{2}$) to the backbone module. SM-CNN quantitatively analyzes the separability of the feature maps and takes the value as a vital part of the loss function to guide the training process of the model. The calculation process of the SM values can be strictly derived mathematically, so it is more interpretable, turning the black box into a “gray box.” In addition, the proposed model can achieve comparable recognition performance of the backbone networks with reduced computational complexity. Comparative experiments based on moving and stationary target acquisition and recognition (MSTAR) and OpenSARShip datasets verify the effectiveness and practicability of the method proposed in this letter. [ABSTRACT FROM AUTHOR]

Arbitrary-oriented target detection is widely used in optical remote-sensing image processing, and there have been lots of anchor-based detectors using horizontal bounding boxes. However, the image targets of various scales and shapes make it difficult to tune optimal anchor parameters, whereas the complex background and nonmaximum suppression (NMS) require well-aligned bounding box to predict dense targets. In this letter, a scale-independent IoU (SIoU) loss is proposed for bounding box regression, which can adaptively adjust the shape of predicted boxes and speed up the convergence. Besides, the regression branch of the fully convolutional one-stage object detector (FCOS) is refined to implement the novel intersection over union (IoU) loss for rotated bounding box regression. Extensive experiments on HRSC2016 and a large-scale dataset for object detection in aerial images (DOTA) show that our method obtains 88.1% mean average precision (mAP) under an IoU threshold of 0.5 on HRSC2016, which is 1.1% higher than generalized IoU (GIoU) loss and 0.7% than complete IoU (CIoU) loss. [ABSTRACT FROM AUTHOR]

Ultra-high voltage direct current (UHVDC) transmission systems have multiple inputs and multiple outputs, nonlinearity and strong coupling, making it challenging to accurately measure component and system losses using traditional methods based on system mechanisms and simplified modeling of parameters. In this paper, a digital twin modeling method based on deep learning networks is proposed to solve the above problem. The proposed method extracts sequence features of loss data using LSTM based on improved Res-LSTM network modeling, fuses all features effectively by combining residual structure with convolution kernel, and establishes a digital twin model of the UHVDC transmission system to realize strong coupling and nonlinear mapping from multiple inputs to multiple outputs. The experimental results show that the Res-LSTM model is 37.57% lower than the LSTM model for MSE in the total loss calculation mode, and 11.8% lower than the LSTM model for MSE in the sub-loss sequence calculation mode. It is verified that the Res-LSTM model has high accuracy in constructing the digital twin model to measure the total loss of the UHVDC transmission system. The proposed digital twin model can help analyze and provide some useful suggestions for loss reduction of the UHVDC transmission system.

Recent work on hybrid switched-capacitor converters has demonstrated exceptionally high efficiencies and power densities through the use of multilayer ceramic capacitors (MLCCs). However, when used in such converters as the main energy transfer components, the capacitors experience high voltage and current ripple often under large dc voltage bias. Yet, capacitor characterization is typically done only with small signal excitation, and under low or no dc bias, yielding highly inaccurate loss models. This work presents a technique for obtaining detailed loss characterizations of MLCCs under more realistic operating conditions through a carefully designed calorimetric setup. Experimental results from several types of MLCCs are presented over a wide range of operating conditions. Finally, a linear model is presented to accurately estimate MLCC losses.

Power-electronic-converter-induced motor bearing currents are a widespread problem in the field of electrical drives. Parasitic capacitances between the electrical machine's parts provide a path for leakage currents, which finally harm metallic ball bearings. Remembering that end-windings have a significant contribution in building up stray capacitances, a countermeasure affecting both the lamination stack and end-winding areas is needed. This article focuses on a countermeasure against the noncirculating bearing currents in the cluster of solutions at the motor side. The electrostatic shielding approach, which is a known principle to reduce capacitive couplings, is applied in electrical machines every now and then. In this article, the principle of slot-embedded grounded electrodes is extended to cover also the end-windings. Thus, the electrodes provide a better shaft-to-ground voltage mitigating effect compared to the case where electrodes are applied only in the lamination stack area. The feasibility and effectiveness of such a countermeasure are investigated. Different options in terms of scaling of both the machine size and diameter of the electrodes were analyzed and corresponding conclusions were pointed out. [ABSTRACT FROM AUTHOR]

In addition to their main functionality of converting DC input power to AC output power, today’s photovoltaic inverters are generally required to be capable of providing reactive power. While there are well-established mathematical models that use the correlation between inverter losses and the transmitted active power to estimate inverter efficiency for any given active power operating point, the additional losses in the inverter due to the supply of reactive power are less well studied. In this work, the conversion efficiencies of three different photovoltaic inverters were measured for various active power and reactive power setpoints. Based on these measurements, two mathematical models are proposed to represent the conversion losses as a function of active and reactive output power. One model is of empirical nature and expands preexisting models to include terms that take the reactive power into consideration. The other model takes the dominant loss mechanisms in the conversion stage of inverters into account and considers the effect of reactive power provision on them. Both models were compared with a model variant proposed in literature. They are shown to perform with high accuracy over the entire operating range, while requiring only a small number of known efficiency values for parametrization. There are several fields of application for these new models: They allow photovoltaic park operators to precisely estimate the individual losses of a solar power plant that feeds reactive power into the grid. For grid operators, they enable a comparative loss analysis for different reactive power sources.

Electric Vehicles are a realistic and sustainable alternative to traditional vehicles. However, their autonomy is still perceived as a strong limitation. To overcome it, new and simpler methods for charging must be provided. Wireless charging is a feasible solution for this issue. In particular, the charge based on induction methods is the most mature wireless charging technology but it is still under study to improve its efficiency under any condition. To enhace this metric, a deep study about the losses of the charger must be performed. This paper presents a theoretical model for the losses in EV wireless charger so it can help to estimate the efficiency of this system before it is implemented. The main advantage of the proposed model relies on the fact that it can be used without relying on the voltage and current waveforms. Instead, datasheet parameters are used for this computation. The model, which is valid for multiple operational conditions, has been tested in a 1.8-kW prototype. The evaluation is completed with an exhaustive study of the efficiency when the chargers are not operating with nominal conditions, in which critical operating points are detected.

Magnetic induction tomography (MIT) has recently been accomplished using a single inductive sensor, but under the assumption that it behaves as an ideal inductor. When performing a scan with a sensing coil that typically consists of a collection of concentric circular printed circuit board (PCB) copper traces connected in series, small amounts of parasitic capacitance arise that contribute to measured tank circuit loss. Results of this work quantify the magnitude of parasitic capacitance and associated losses. For current inductive sensors, capacitance-related loss is shown to diminish considerably when the coil is positioned more than ~2 cm from a target boundary. Recognizing that single-coil MIT scans generally position the coil within 2 cm of a target boundary, a correction is proposed, which enables a more accurate measurement of true inductive loss. Previously published scan data over agarose phantoms are then reexamined to show the negative impact on imaging fidelity that results when capacitance-related losses are ignored. Image comparisons are made using full 3-D image reconstruction, demonstrating that failure to compensate for parasitic capacitance loss can degrade image fidelity. [ABSTRACT FROM AUTHOR]

High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imaging using deep learning (DL). This study conducts a comprehensive and insightful survey and analysis of recent developments in deep HDR imaging methodologies. We hierarchically and structurally group existing deep HDR imaging methods into five categories based on (1) number/domain of input exposures, (2) number of learning tasks, (3) novel sensor data, (4) novel learning strategies, and (5) applications. Importantly, we provide a constructive discussion on each category regarding its potential and challenges. Moreover, we review some crucial aspects of deep HDR imaging, such as datasets and evaluation metrics. Finally, we highlight some open problems and point out future research directions. [ABSTRACT FROM AUTHOR]

We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly provided and then incorporated into the training process. By using the differentiable properties of persistent homology, a concept used in topological data analysis, we can specify the desired topology of segmented objects in terms of their Betti numbers and then drive the proposed segmentations to contain the specified topological features. Importantly this process does not require any ground-truth labels, just prior knowledge of the topology of the structure being segmented. We demonstrate our approach in four experiments: one on MNIST image denoising and digit recognition, one on left ventricular myocardium segmentation from magnetic resonance imaging data from the UK Biobank, one on the ACDC public challenge dataset and one on placenta segmentation from 3-D ultrasound. We find that embedding explicit prior knowledge in neural network segmentation tasks is most beneficial when the segmentation task is especially challenging and that it can be used in either a semi-supervised or post-processing context to extract a useful training gradient from images without pixelwise labels. [ABSTRACT FROM AUTHOR]

This paper introduces experimental and numerical analysis of nozzle ablation in SF6 gas circuit breakers. Ablation and changes in the diameter of a nozzle were measured in the experiments. In addition, an ablation model was developed and simulations of nozzle ablation were carried out. The relationship between geometric changes to the nozzle due to ablation and the injection energy was investigated using both experimental measurements and data predicted by simulation. The predicted values for mass loss and geometric changes to the nozzles tested show similar tendencies to the experimental results. Also, the developed ablation model was applied to the mixture of fluoronitrile and CO2 gas. By predicting flow and pressure changes due to the ablation effect, the interruption performance of gas circuit breakers after repeated testing can be analyzed. [ABSTRACT FROM AUTHOR]

The problem of quickest detection of a change in the mean of a sequence of independent observations is studied. The pre-change observations are assumed to be stationary, while the post-change observations are allowed to be non-stationary. The case where the pre-change distribution is known is studied first, and then the extension where only the mean and variance of the pre-change distribution are known. No knowledge of the post-change distributions is assumed other than that the means of the observations are above some pre-specified threshold larger than the pre-change mean. For the case where the pre-change distribution is known, a test is derived that asymptotically minimizes the worst-case detection delay over all possible post-change distributions, as the false alarm rate goes to zero. Towards deriving this asymptotically optimal test, some new results are provided for the general problem of asymptotic minimax robust quickest change detection in non-stationary settings. Then, the limiting form of the optimal test is studied as the gap between the pre- and post-change means goes to zero, called the Mean-Change Test (MCT). It is shown that the MCT can be designed with only knowledge of the mean and variance of the pre-change distribution. The performance of the MCT is also characterized when the mean gap is moderate, under the additional assumption that the distributions of the observations have bounded support. The analysis is validated through numerical results for detecting a change in the mean of a beta distribution. The use of the MCT in monitoring pandemics is also demonstrated. [ABSTRACT FROM AUTHOR]

This article presents an implantable impulse-radio ultra-wideband (IR-UWB) wireless telemetry system for intracortical neural sensing interfaces. A 3-D hybrid impulse modulation that comprises phase shift keying (PSK), pulse position modulation (PPM), and pulse amplitude modulation (PAM) is proposed to increase modulation order without significantly increasing the demodulation requirement, thus leading to a high data rate of 1.66 Gbps and an increased air-transmission range. Operating in a 6–9-GHz UWB band, the presented transmitter (TX) supports the proposed hybrid modulation with a high energy efficiency of 5.8 pJ/bit and modulation quality [error vector magnitude (EVM) < −21 dB]. A low-noise injection-locked ring oscillator (ILRO) supports 8-PSK with a phase error of 2.6°. A calibration-free delay generator realizes a 4-PPM with only $115~\mu \text{W}$ and avoids potential cross-modulation between PPM and PSK. A switch-cap power amplifier (PA) with asynchronous pulse-shaping performs 4-PAM with high energy efficiency and linearity. The TX is implemented in 28-nm CMOS technology, occupying a 0.155 mm2 core area. The wireless module including a printed monopole antenna has a module area of only 1.05 cm2. The TX consumes in total 9.7 mW when transmitting −41.3-dBm/MHz output power. The wireless telemetry module has been validated ex-vivo with a 15-mm multi-layer porcine tissue, and achieves a communication (air) distance up to 15 cm, leading to at least $16\times $ improvement in distance-moralized energy efficiency of 45 pJ/bit/m compared to a state-of-the-art. [ABSTRACT FROM AUTHOR]

We propose and experimentally demonstrate a distributed temperature and curvature sensing method based on spontaneous Raman backscattered lights in a few-mode optical fiber. A loop structure is used to demodulate the distributed temperature information using the Raman anti-Stokes (AS) scattering lights. And the fiber curvature is estimated by the loss of Raman Stokes (S) light. This system can measure temperature and curvature at the same position of the fiber simultaneously. The experimental results show that the temperature uncertainty and spatial resolution can reach up to 0.24 °C and 1.8 m on 6 km sensing fiber. Besides, the maximum measurement error is 0.11 cm within the curvature measurement range of 0.5–3 cm. [ABSTRACT FROM AUTHOR]

This article investigates diagnosability and $T$ -diagnosability for discrete-event systems modeled by unambiguous max-plus automata (UMPAs). More precisely, diagnosability requires that the occurrence of any fault can be detected within a finite number of events after the fault has occurred. $T$ -diagnosability requires that the occurrence of any fault can be detected within a delay of at most $T$ time units after its occurrence. First, we propose a polynomial-time algorithm based on the construction of a nondeterministic finite automaton over a weighted alphabet for diagnosability verification of a UMPA. Second, we prove that $T$ -diagnosability of a UMPA can be studied by reducing it to the problem of diagnosability. Third, we introduce an approach to calculate the upper on the time needed for detecting fault occurrence for a diagnosable UMPA, and its complexity is of sixth order in the number of states of the UMPA. [ABSTRACT FROM AUTHOR]

This article presents an improved algorithm for identification of the excess loss statistical parameter to get more accurate prediction of multiple harmonic magnetic hysteresis and loss properties of grain-oriented silicon steel sheets. Magnetic hysteresis characteristics of a specimen are measured on a magnetic measurement platform. Static hysteresis property can be simulated accurately by the Preisach model. The excess loss field expression under multiple harmonic magnetizations is constructed based on an extraction algorithm for the statistical parameters of excess loss. According to the equivalence of loss separation and field separation, a new dynamic model is established. In the case of different multiple harmonic magnetizations, the magnetic hysteresis characteristics obtained by predictions are compared with the test results. Also, the influences of peaked flux density, harmonic order, and harmonic content upon the total iron loss are investigated. [ABSTRACT FROM AUTHOR]

The ac copper loss, which results from the skin effect, the proximity effect, and the bundle-level circulating current, is widely concerned in high-speed electrical machines since they grow rapidly with the frequency. While the skin effect can be easily suppressed by the utilization of thinner wires, the circulating current is generally restrained by a complicated transposition technique. Moreover, the transposition technique is better suited to the preformed winding other than the random-wound winding, hence, it is still difficult to suppress the circulating current ac loss in the random-wound winding. In this article, a practical transposition technique is proposed for the circulating current loss suppression for high-speed electrical machines with random-wound windings. To implement this technique, a specific winding former is designed and manufactured. The proposed transposition technique is verified in a 200 kW, 54 000 r/min high-speed permanent magnet machine, contributing to a reduction of 55.2% copper loss compared to the untransposed winding. The experiment result indicates that the proposed transposition technique is practical in engineering. [ABSTRACT FROM AUTHOR]

In the high-speed flux-switching permanent magnet (FSPM) machine, the ac copper loss issue in the armature winding conductor is very prominent, due to the high-frequency leakage magnetic field in stator slot, which seriously affects the machine performance. To model and calculate ac copper loss more accurately, a coupled electric circuit-magnetic field model is constructed based on 2D-finite element method, and consequently the influence of modeling methods and winding types on ac copper loss calculation of a 12-slot/10-pole (12/10) 54.7 kW/10 kr/min FSPM machine is investigated. First, ten simplified ac copper loss calculation models are established to deal with the problem of large consumption of calculation time and computer resources caused by the excessive number of wires, small size of winding conductors, high precision modeling, and mesh refinement. Second, the influences of voltage-/current-source and steady/rotating rotor are considered, and the ten models are evaluated in terms of computing time and accuracy. Third, based on the preferred model from the ten models, the effects of frequency, round/flat wires, and wire sizes on ac copper loss are further studied. Fourth, to investigate the influence of flux leakage at the end of winding on ac copper loss, a 3-D ac copper loss calculation model with different axial lengths of winding end space is established. Fifth, the change process of ac copper loss and transient temperature of winding under short time overload condition are obtained combined with the bidirectional coupling model between electromagnetic and thermal field method through multiple iterations of electromagnetic loss and temperature. Finally, the stator modules of round and flat copper wires are manufactured, respectively, and an experimental modular device is built to measure the ac copper loss, which confirms the improved accuracy of the predictions by the proposed model. [ABSTRACT FROM AUTHOR]

In this letter, a Jaumann-like rasorber is examined, which offers wideband absorption, angular stability, and polarization insensitivity. The structure is defined by three layers with two resistive layers placed on top of a lossless layer. Also, all three layers use capacitors to ensure compactness for the metasurface rasorber. These features provide broad angular stability up to 55 $^{\circ }$ while also maintaining low insertion losses of about 1 dB and only 0.6 dB for measured broadside incidence. The rasorber is also shown to work as a radome in the near field of a planar antenna array. [ABSTRACT FROM AUTHOR]

Recent work on using herbarium images for automatic plant identification, and in particular to do domain adaptation to field images, has been promising. A potential way to address such domain adaptation problem is generative: Hallucinate how a herbarium image would have looked like when it was in the field, and use such synthetic, fresh and green image for plant identification purposes. Such generative task, called senescence reversion, has been poorly explored. To our knowledge, it has been studied only in terms of paired data, meaning, with pairs of dry and fresh images of the same specimen. Such paired data is hard to produce, curate and find. In this work we explore herbarium senescence reversion via unpaired data. The lack of pairs at specimen level presents its own challenges, as capturing the intricacies of each species depends on images of different specimens from different domains. We explore learning a mapping from a herbarium image to leaf plant images and vice-versa, aligning two models, one for each herbarium and leaf domain. We experiment against the state-of-the-art paired baseline Pix2Pix which yields a SSIM of 0.8986, compared to our unpaired approach that yields 0.8865, showing very similar results on reconstruction metrics regardless of our approach not having the luxury of pairs by specimen. Additionally, we apply perceptual loss in order to improve the natural look of the synthetic images. The balance of how much perception is good to avoid reconstruction problems is also studied. Lastly, using a new unpaired dataset built by ourselves, our results show that using a low lambda value from 0.025 to 0.05 for perceptual loss, helps getting lower Frachet Inception Distances and higher Inception Scores. To our knowledge, no other work has focused on reverting senescence of herbarium sheet images based on unpaired data. [ABSTRACT FROM AUTHOR]

This brief presents a new wide stopband substrate integrated waveguide (SIW) filter with multimode suppression based on a multilayer structure. All spurious modes below TE404 of a multilayer SIW filter operating in TE101 could be effectively suppressed by moving the intercoupling slots away from the sidewall by about 1/6 of the cavity’s width. To verify it, two prototypes are designed, fabricated, and measured. The results show a small size and a wide stopband of up to 4.03 ${f} _{0}$ (${f} _{0}$ is the filter’s center frequency) with a simple design. The proposed technology could be efficient for developing wide stopband SIW filters in wireless/microwave circuits and systems. [ABSTRACT FROM AUTHOR]

A 28 GHz dual-mode phase-invariant wide-dynamic-range variable-gain amplifier (VGA) with adaptive phase compensators is presented for use as a gain control element in a beamforming IC, which must have a small chip size and low power consumption. It consists of a single-stage differential amplifier with main and auxiliary signal paths, where the auxiliary paths are introduced to achieve most-significant-bit (MSB) gain control. The wide-gain dynamic range is also covered through a least-significant-bit (LSB) gain-control scheme, which is implemented with bias control of common gate (CG) transistors in the main signal paths. In addition, the phase variations due to the gain controls are drastically reduced by introducing phase compensators for both gain controls. The proposed VGA shows a gain dynamic range of 24 dB, maximum gain of 13 dB and root-mean-square (RMS) phase error of 2.77 degrees at 28 GHz. The maximum DC power consumption is 24 mW at the maximum gain, the minimum is 4 mW at the minimum gain, and the chip size is 0.066 mm2. [ABSTRACT FROM AUTHOR]

This article proposes a novel unknown input functional observer design approach toward discrete-time interval type-2 Takagi–Sugeno fuzzy system models subject to measurable and unmeasurable premise variables. By constructing a new state vector that contains both the unknown inputs and the system states, functional observers are proposed for the cases with measurable and unmeasurable premise variables to estimate this new state vector for unknown input and/or state estimation. The observer design problem is converted into the solvability issue of a linear matrix equation involving observer gain matrices, and the existence conditions of the observers are explicitly obtained based on matrix rank analysis. Meanwhile, instead of solving the intricate Sylvester equation directly, the solution of the simplified matrix equation is employed to derive the observer gains. Moreover, the effectiveness and the superiority of the presented method are demonstrated via two illustrative examples. [ABSTRACT FROM AUTHOR]

The letter demonstrates a coaxial transmission line-to-substrate integrated waveguide (CT-SIW) transition using aperture-coupling approach. The method broadens the bandwidth (BW) and reduces the transition insertion loss (IL). Two coaxial line supports with apertures for coupling are attached at the ends of substrate integrated waveguide (SIW). The copper inlaid of the apertures increases coupling of the coaxial line to SIW and can be controlled by the aperture length and the length of the line wire put in the aperture. The transition was designed, fabricated, and experimentally evaluated. The transition provides the measured 10 dB return loss (RL) fractional BW (FBW) of 104.3%, and 15 dB RL (FBW) of 78.06%. The IL of 0.37 to 0.87 dB at 19.63–62.7 GHz frequency range was obtained. The measured results are well correlated with the simulated ones. [ABSTRACT FROM AUTHOR]

A power combined wideband power amplifier (PA) covering the $J$ -band (220–320 GHz) is presented in 130-nm BiCMOS technology. The input power is split by two cascaded 1-to-2 power splitters with amplification stages in-between. The four split signals drive four output stages, which have their outputs combined within a 4-way zero-degree combiner. The splitting and combining networks also incorporate impedance matching. After de-embedding the I/O pads and baluns of 2 dB loss at each side, the PA achieves a gain of 20 dB at the middle of the band and a minimum gain of 17 dB at 320 GHz with I/O return losses below −5 dB. The PA records a saturated output power ranging from 9.5 to 14.5 dBm across the $J$ -band. It consumes 710 mW from a 3 V supply which corresponds to a drain efficiency ($\eta _{d}$) of 3.15% at 270 GHz. The presented PA achieves twice better bandwidth with 1.5 times better $\eta _{d}$ than the state-of-the-art silicon-based amplifiers above 200 GHz. To the authors’ knowledge, this is the first PA covering the whole $J$ -band in silicon technologies. [ABSTRACT FROM AUTHOR]

An air-filled mode selective transmission line (AF-MSTL) consisting of an opened groove gap waveguide (GWG) and an inverted coplanar waveguide (CPW) is proposed in this letter. By using the electromagnetic band gap (EBG) structure, the electrical contacts of AF-MSTL are not required during the assembly process. To verify the design, the AF-MSTL prototype, connected to two designed grounded-CPW (GCPW) to AF-MSTL transitions to build a back-to-back structure, has been fabricated and measured. A measured insertion loss of 0.2857 dB/mm at 110 GHz is achieved, which is lower than the dielectric filled (DF) MSTL of 0.5012 dB/mm at 110 GHz. [ABSTRACT FROM AUTHOR]

This letter presents a four-way power combined $D$ -band power amplifier (PA) in 0.13- $\mu \text{m}$ SiGe technology. The conventional cascode topology is modified by adding an additional interstage network between the common-emitter (CE) and common-base (CB) devices. Further techniques, such as power combining and adaptive bias circuits, are implemented to boost the power generation and the efficiency of the amplifier. The realized PA exhibits a saturated output power of 19.6 dBm with a maximum power-added-efficiency (PAE) of 9.5% at 130 GHz, which is a leading-edge performance among the reported silicon (Si) $D$ -band PAs in similar technologies. The small-signal gain peaks at 16 dB and the PA has a 3-dB bandwidth of 18 GHz. [ABSTRACT FROM AUTHOR]

This article presents a radio propagation model for macrocell coverage predictions in built-up areas for sub-6-GHz frequency band. The proposed two-ray model is a combination of various individual propagation models available in the literature, namely the discrete mixed Fourier transform split-step parabolic-equation (DMFT-SSPE) to characterize reflection and diffraction phenomena due to the presence of buildings and terrain irregularities, the building-transmission model (BTM) to account for the signal attenuation through the nearby buildings and the International Telecommunication Union of Radio-communications (ITU-R) model for attenuation in vegetation to weight the excess attenuation caused by the surrounding vegetation. The performance of the proposed modeling approach was assessed against measurement results obtained from an extensive measurement campaign conducted in Rio de Janeiro, Brazil. The receiver (RX) antenna was assembled on an automotive van, and it was made to travel around the city recording the received signal level. Several drive tests were conducted, including different transmitter (TX) locations and scenarios, and at various frequencies foreseen for 5G systems, namely 750 MHz, 2.5 GHz, and 3.5 GHz. [ABSTRACT FROM AUTHOR]

This communication presents a millimeter-wave wideband dual-polarized low-temperature cofired ceramics (LTCC) antenna array based on metasurfaces (MSs) for beam-scanning applications. Benefiting from the wideband and large-freedom property of MS-based structures, a novel ±45° dual-polarized wideband MS-based antenna (MSA) element with high isolation is designed. The antenna element is fed by two orthogonal striplines on different layers, through a crosslike dog-bone slot and a narrow slot, respectively, to realize the two polarizations. The feeding structure consists of slot–stripline–slot–stripline from top to bottom and is specially designed to improve the polarization isolation of the antenna element. The proposed MSA element is compactly arranged with a small distance ($ < 0.5\lambda$) to construct a 4 $\times $ 4 array integrated in an LTCC package, where via walls with metal ring strips are loaded between adjacent elements to reduce the gain fluctuation. Consequently, the array can achieve a good beam-scanning capability over a wide band of 19.5% with a maximum scanning angle up to ±55° and a small gain variation of less than 3 dB. Meanwhile, the active $S$ parameters almost always remain below −10 dB, enabling good impedance matching when scanning at all angles. With these features, the proposed design has potential for application in 5G base-station systems. [ABSTRACT FROM AUTHOR]

The class of random features is one of the most popular techniques to speed up kernel methods in large-scale problems. Related works have been recognized by the NeurIPS Test-of-Time award in 2017 and the ICML Best Paper Finalist in 2019. The body of work on random features has grown rapidly, and hence it is desirable to have a comprehensive overview on this topic explaining the connections among various algorithms and theoretical results. In this survey, we systematically review the work on random features from the past ten years. First, the motivations, characteristics and contributions of representative random features based algorithms are summarized according to their sampling schemes, learning procedures, variance reduction properties and how they exploit training data. Second, we review theoretical results that center around the following key question: how many random features are needed to ensure a high approximation quality or no loss in the empirical/expected risks of the learned estimator. Third, we provide a comprehensive evaluation of popular random features based algorithms on several large-scale benchmark datasets and discuss their approximation quality and prediction performance for classification. Last, we discuss the relationship between random features and modern over-parameterized deep neural networks (DNNs), including the use of high dimensional random features in the analysis of DNNs as well as the gaps between current theoretical and empirical results. This survey may serve as a gentle introduction to this topic, and as a users’ guide for practitioners interested in applying the representative algorithms and understanding theoretical results under various technical assumptions. We hope that this survey will facilitate discussion on the open problems in this topic, and more importantly, shed light on future research directions. Due to the page limit, we suggest the readers refer to the full version of this survey https://arxiv.org/abs/2004.11154. [ABSTRACT FROM AUTHOR]

In this paper, we consider how to incorporate psychophysical measurements of human visual perception into the loss function of a deep neural network being trained for a recognition task, under the assumption that such information can reduce errors. As a case study to assess the viability of this approach, we look at the problem of handwritten document transcription. While good progress has been made towards automatically transcribing modern handwriting, significant challenges remain in transcribing historical documents. Here we describe a general enhancement strategy, underpinned by the new loss formulation, which can be applied to the training regime of any deep learning-based document transcription system. Through experimentation, reliable performance improvement is demonstrated for the standard IAM and RIMES datasets for three different network architectures. Further, we go on to show feasibility for our approach on a new dataset of digitized Latin manuscripts, originally produced by scribes in the Cloister of St. Gall in the the 9th century. [ABSTRACT FROM AUTHOR]

Cross-modal retrieval has recently attracted growing attention, which aims to match instances captured from different modalities. The performance of cross-modal retrieval methods heavily relies on the capability of metric learning to mine and weight the informative pairs. While various metric learning methods have been developed for unimodal retrieval tasks, the cross-modal retrieval tasks, however, have not been explored to its fullest extent. In this paper, we develop a universal weighting metric learning framework for cross-modal retrieval, which can effectively sample informative pairs and assign proper weight values to them based on their similarity scores so that different pairs favor different penalty strength. Based on this framework, we introduce two types of polynomial loss for cross-modal retrieval, self-similarity polynomial loss and relative-similarity polynomial loss. The former provides a polynomial function to associate the weight values with self-similarity scores, and the latter defines a polynomial function to associate the weight values with relative-similarity scores. Both self and relative-similarity polynomial loss can be freely applied to off-the-shelf methods and further improve their retrieval performance. Extensive experiments on two image-text retrieval datasets, three video-text retrieval datasets and one fine-grained image retrieval dataset demonstrate that our proposed method can achieve a noticeable boost in retrieval performance. [ABSTRACT FROM AUTHOR]

In conventional person re-identification (re-id), the images used for model training in the training probe set and training gallery set are all assumed to be instance-level samples that are manually labeled from raw surveillance video (likely with the assistance of detection) in a frame-by-frame manner. This labeling across multiple non-overlapping camera views from raw video surveillance is expensive and time consuming. To overcome these issues, we consider a weakly supervised person re-id modeling that aims to find the raw video clips where a given target person appears. In our weakly supervised setting, during training, given a sample of a person captured in one camera view, our weakly supervised approach aims to train a re-id model without further instance-level labeling for this person in another camera view. The weak setting refers to matching a target person with an untrimmed gallery video where we only know that the identity appears in the video without the requirement of annotating the identity in any frame of the video during the training procedure. The weakly supervised person re-id is challenging since it not only suffers from the difficulties occurring in conventional person re-id (e.g., visual ambiguity and appearance variations caused by occlusions, pose variations, background clutter, etc.), but more importantly, is also challenged by weakly supervised information because the instance-level labels and the ground-truth locations for person instances (i.e., the ground-truth bounding boxes of person instances) are absent. To solve the weakly supervised person re-id problem, we develop deep graph metric learning (DGML). On the one hand, DGML measures the consistency between intra-video spatial graphs of consecutive frames, where the spatial graph captures neighborhood relationship about the detected person instances in each frame. On the other hand, DGML distinguishes the inter-video spatial graphs captured from different camera views at different sites simultaneously. To further explicitly embed weak supervision into the DGML and solve the weakly supervised person re-id problem, we introduce weakly supervised regularization (WSR), which utilizes multiple weak video-level labels to learn discriminative features by means of a weak identity loss and a cross-video alignment loss. We conduct extensive experiments to demonstrate the feasibility of the weakly supervised person re-id approach and its special cases (e.g., its bag-to-bag extension) and show that the proposed DGML is effective. [ABSTRACT FROM AUTHOR]

In this article, a method for removing the return loss effect during shielding effectiveness (SE) measurements for a shielding enclosure is proposed. According to the IEEE 299.1 standard, a receiving antenna is located inside the enclosure to be measured, and this antenna measures the signal loss after passing through the enclosure. The receiving antenna experiences the return loss at its input, and this return loss increases the SE because the occurrence of the return loss is equivalent to the increasing loss when passing through the enclosure. The IEEE 299.1 standard provides a method for removing the return loss effect. The return-loss removal is only effective, however, when the receiving antenna has an input impedance that does not significantly change, remaining almost constant at the adjacent frequencies. The proposed method generalizes the return-loss removal, and thus the return loss effect can be removed irrespective of the input impedance condition of the receiving antenna. As the return loss is removed for any type of the receiving antenna, the SE can be accurately measured to prevent an overestimation of the SE. [ABSTRACT FROM AUTHOR]

Recent advancements in the silicon carbide (SiC) power semiconductor technology offer improvements for high-power converters, where today silicon (Si) devices are still dominant. Bipolar SiC devices feature particularly good conduction capability while blocking high voltages. With expected advances in SiC material quality and processing technology, resulting in higher charge carrier lifetimes, methods for tailoring will be required. In this article, three differently optimized 10-kV SiC p-i-n diodes are compared regarding their switching and conduction performance in a 50-kHz LCC converter with a high output voltage. The converter topology features capacitively assisted switching, resulting in reduced switching losses for diodes with short reverse recovery tails. One diode group was subjected to a novel carrier lifetime tailoring method, involving simultaneous annihilation and generation of carbon vacancies. Another group was tailored via proton irradiation. Tradeoffs for the optimization of the diodes are highlighted. The analysis is supported by circuit simulations, device simulations, static measurements, switching waveform measurements, and calorimetric loss measurements. The results show a total rectifier loss reduction of 37%, compared to a state-of-the-art implementation with eight 1-kV Si diodes. The switching losses account for 3%–19% of the total losses, indicating a much higher possible operation frequency. [ABSTRACT FROM AUTHOR]

Metal oxide varistors (MOVs) with steady decrease and continuous increase in power loss are generally distinguished as stable and instable, respectively. In this article, the power loss of modern stable MOVs were found to anomalously increase, when the aging voltage was even lowered. In other words, those MOVs were superficially stable in harsh conditions but instable in mild conditions, which was against common sense. In addition, a crossover phenomenon and an overall deterioration in the current–voltage characteristics were observed in the forward and reverse directions, respectively. The unique aging results were in well consistency with the numerical simulation, where redistribution of ions in depletion layers was involved. For modern stable MOVs with adamant interface states, the formation of “U”-shaped ion distribution in depletion layers resulted in increased barrier height at high voltages but decreased height at low voltages. For an arbitrary MOV, the evolution of power loss during aging was a result of competition between consumption of interface states and re-distribution of ions in depletion layers. Therefore, the power loss might not be sufficient to represent the degradation of MOVs. Quantitative studies deep into the dynamics of point defects of Schottky barriers of MOVs are urgent. [ABSTRACT FROM AUTHOR]

We consider a distributed function computation problem in which parties observing noisy versions of a remote source facilitate the computation of a function of their observations at a fusion center through public communication. The distributed function computation is subject to constraints, including not only reliability and storage but also secrecy and privacy. Specifically, 1) the function computed should remain secret from an eavesdropper observing the public communication and correlated observations, measured in terms of the information leaked about the arguments of the function, to ensure secrecy regardless of the exact function used; 2) the remote source should remain private from the eavesdropper and the fusion center, measured in terms of the information leaked about the remote source itself. We derive the exact rate regions for lossless and lossy single-function computation and illustrate the lossy single-function computation rate region for an information bottleneck example, in which the optimal auxiliary random variables are characterized for binary-input symmetric-output channels. We extend the approach to lossless and lossy asynchronous multiple-function computations with joint secrecy and privacy constraints, in which case inner and outer bounds for the rate regions that differ only in the Markov chain conditions imposed are characterized. [ABSTRACT FROM AUTHOR]

Label distribution learning (LDL) is the state-of-the-art approach to dealing with a number of real-world applications, such as chronological age estimation from a face image, where there is an inherent similarity among adjacent age labels. LDL takes into account the semantic similarity by assigning a label distribution to each instance. The well-known Kullback-Leibler (KL) divergence is the widely used loss function for the LDL framework. However, the KL divergence does not fully and effectively capture the semantic similarity among age labels, thus leading to suboptimal performance. In this article, we propose a novel loss function based on the optimal transport theory for the LDL-based age estimation. A ground metric function plays an important role in the optimal transport formulation. It should be carefully determined based on the underlying geometric structure of the label space of the application in-hand. The label space in the age estimation problem has a specific geometric structure, that is, closer ages have more inherent semantic relationships. Inspired by this, we devise a novel ground metric function, which enables the loss function to increase the influence of highly correlated ages; thus exploiting the semantic similarity among ages more effectively than the existing loss functions. We then use the proposed loss function, namely, $\gamma $ –Wasserstein loss, for training a deep neural network (DNN). This leads to a notoriously computationally expensive and nonconvex optimization problem. Following the standard methodology, we formulate the optimization function as a convex problem and then use an efficient iterative algorithm to update the parameters of the DNN. Extensive experiments in age estimation on different benchmark datasets validate the effectiveness of the proposed method, which consistently outperforms state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This article is concerned with a distributed filtering problem for Markov jump systems subject to the measurement loss with unknown probabilities. A centralized robust Kalman filter is designed by using variational Bayesian methods and a modified interacting multiple model method based on information theory (IT-IMM). Then, a distributed robust Kalman filter based on the centralized filter and a hybrid consensus method called hybrid consensus on measurement and information (HCMCI) is designed. Moreover, boundedness of the estimation errors and the estimation error covariances are studied for the distributed robust Kalman filter. [ABSTRACT FROM AUTHOR]

Theoretical and empirical foundations of how radio waves behave in practical wireless channels need to be fully revisited for millimeter-wave (mmWave) frequencies so that fifth-generation (5G) technologies may be successful. Kriging is an outstanding geostatistal interpolation technique that employs variography to understand the spatial variability of known samples at specific locations to predict unmeasured samples, based on the fact that there is an implied connection between the measured value and its location in space. The research we here report is aimed at validating the improvement in predictions when this tool is included for mmWave frequency path loss modeling in a long indoor corridor with break. In order to quantify the accuracy of the proposed methodology, it is compared with a well-established procedure described in the literature. Extensive path-loss measurements were collected through specialized narrowband sounders at 28 and 60 GHz. Spatially averaged power measurements using omnidirectional and directive antennas at different heights provided the empirical basis for the three-dimensional Kriging-aided model. It was found that this method significantly improves the accuracy as it considers all the singularities and site-associated features that are implicit in measured samples. This is important to obtain a reliable path loss model for planning and deployment of mmWave wireless communication systems in indoor scenarios. [ABSTRACT FROM AUTHOR]

This proposal examines the effect of grass vegetation elevation and density on path loss between sensors deployed in an IoT-enabled wireless sensor network (IoWSN) crop monitoring infrastructures. Observations via real-time measurement campaigns at different node heights and vegetation depths revealed that the sensor deployment made using free-space or tree vegetation based path loss model (PLM) experiences network disconnectivity due to variations in vegetation density in a cropping cycle. An empirical PLM is formulated to identify signal strength at different development phases of paddy and sugarcane medium grass vegetation. For this, the 2.4 GHz RF path loss coefficient (PLC) is estimated using data collected through measurement campaign over combinations of sensor height and vegetation density. Further, the formulated PLC is used to train multiple regression model to develop a generic PLM for all medium grass vegetation. Improvements in coverage and connectivity have been validated through proof of concept. [ABSTRACT FROM AUTHOR]

We discuss and extend two approaches to target detection and motion parameter estimation in a very low observable (VLO) underwater multipath environment. The maximum likelihood probabilistic data association (ML-PDA) algorithm considers all possible path–measurement hypotheses, whereas the maximum likelihood probabilistic multiple hypothesis tracker (ML-PMHT) assumes independence among the returns’ origins leading to a simpler likelihood formulation. In particular, we present a recursive form of the multipath ML-PDA algorithm for improved computational efficiency and a generative multipath ML-PMHT algorithm that accounts for the prior probability distribution of the number of received measurements. By simulation of an active sonar tracking scenario, we show that while the two algorithms perform similarly for few returns, the added fidelity of the ML-PDA outperforms the ML-PMHT in parameter estimation as returns accumulate over multiple scans and dimensions. However, the computational cost of the ML-PDA discourages its use beyond simple scenarios. This cost, coupled with the comparable detection performance of the two algorithms, suggests that the simpler ML-PMHT is the better choice for practical use. We also show how both the ML-PDA and ML-PMHT can be applied to realistic ocean environments using ocean acoustic modeling software. This article is an extension of previous work on comparing the standard versions of the ML-PDA and ML-PMHT algorithms. [ABSTRACT FROM AUTHOR]

Mobile crowd sensing (MCS) arises as a promising data collection paradigm that leverages the power of ubiquitous mobile devices to acquire rich information regarding their surrounding environment. In many location-based sensing tasks, workers are required to associate their sensing reports with corresponding geographic coordinates. Such information leaves a trail of worker's historical location record which thus poses a severe threat to their location privacy. On the other hand, individual workers may perceive location privacy differently. Instead of following conventional solutions that aim to perfectly hide user privacy, this paper adopts a novel alternative approach. A user-centric location privacy trading framework, called ULPT, is constructed to facilitate location privacy trading between workers and the platform. Each worker can decide how much location privacy to disclose to the platform in an MCS task based on its own location privacy leakage budget $\xi$ ξ . The higher $\xi$ ξ is, the more privacy its reported location discloses. Accordingly, it receives higher payment from the platform as compensation. Besides, ULPT enables the platform to select a suitable set of winning workers to achieve desirable MCS service accuracy while taking into account of its budget limit and worker privacy requirements. For this purpose, a heuristic algorithm is devised with a bounded optimality gap. As formally proved in this manuscript, ULPT guarantees a series of nice properties, including $\xi$ ξ -privacy, $(\alpha, \beta)$ (α , β) -accuracy, budget feasibility. Moreover, both rigorous theoretical analysis and extensive simulations are conducted to evaluate tradeoffs among these three. [ABSTRACT FROM AUTHOR]