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The Piergiorgio L. E. Uslenghi Letters Prize Paper Award was established in 2009 to recognize the best contribution published in IEEE Antennas and Wireless Propagation Letters during the previous year. [ABSTRACT FROM AUTHOR]

The Harold A. Wheeler Applications Prize Paper Award is presented to the authors of the best applications article published in the IEEE Transactions on Antennas and Propagation during the previous year. [ABSTRACT FROM AUTHOR]

The R. W. P. King Award is presented to an author under 36 years of age for the best paper published in the IEEE Transactions on Antennas and Propagation during the previous year. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

The IEEE Communications Magazine (ComMag) is soliciting creative Feature Topic (FT) proposals to complement its Series and Open Call publication tracks. A Feature Topic (FT) is a group of papers focusing on a timely narrow-scope topic of interest to the readership of the magazine. They must not duplicate other publication tracks and must offer opportunities for manuscript streams not traditionally submitted to Series and Open Calls. Potential Guest Editors (GEs) interested in organizing an FT can submit a proposal to the Editor-in-Chief (EiC) of the IEEE Communications Magazine. Proposers must read and fully understand the IEEE Communications Magazine guidelines for authors, reviewers, and editors (https://www.comsoc.org/publications/magazines/ieee-communications-magazine), and must pay special attention the Policy for Proposing and Organizing a Feature Topic (https://www.comsoc. org/publications/magazines/ieee-communications-magazine/policy-for-proposing-and-organizing-feature-topics). Proposals not taking all the magazine guidelines into full consideration cannot be accepted. It is highly recommended that the lead GE communicate his topic, plan, proposed GE list to ComMag (EiC or AEiC) to discuss them before taking the time to prepare a complete proposal and/or finalizing a committed list of GEs for this matter. We are interested in all topics within the magazine scope and as described in the above mentioned guidelines. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

The aspects of sum rate and fairness for dirty paper coding (DPC) based MIMO downlink communications are investigated in this paper. We first apply the $\ell _{1}$ -norm fairness measure to formulate the problem of fairness maximization for a given sum rate as an optimization problem. The problem is unfortunately nonconvex and cannot be efficiently solved. To overcome the difficulty, we invoke the uplink-downlink duality to transform the problem back and forth between uplink and downlink communications. An efficient, iterative waterfilling based algorithm is then proposed to yield achievable rates with the best possible fairness values. Simulation results show that the proposed approach offers an enormous gain in the achievable sum rates for a wide range of fairness values, when compared to the popular successive zero-forcing DPC-based and block diagonalization based coding schemes. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

The article offers information on the journal's call for papers on topics like military communications and networks, internet of things, and artificial intelligence and data science for communications.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

We are pleased to announce that the June 2022 issue of the IEEE Transactions on Dielectrics and Electrical Insulation (TDEI) will be a special issue on electrets and related phenomena. This issue is open to all authors. Presenters of papers at the IEEE 18th International Symposium on Electrets (ISE18) to be held during September 24–28, 2021 in Shanghai (China) are especially encouraged to submit their work to this special issue. For more information on ISE18 please go to https://ise18.tongji.edu.cn/. [ABSTRACT FROM AUTHOR]

Under temperature and moisture combined, bubbles will be formed in oil-impregnated paper/pressboard (OIP/OIPB). Although many studies have been done to understand the thermo-induced bubble phenomenon in OIPB, the formation mechanism is not, let alone its effect on discharge characteristics and insulation performance of OIPB. In this article, the phenomena and mechanisms of thermally induced bubbles, their effects and mechanisms on the partial discharge (PD), and breakdown characteristics of OIPB are investigated. The results show that the bubble inception temperature (BIT) decreases significantly with the increase of the moisture of OIPB. There is a growth process of bubbles from small to large, and the heating temperature and moisture content of OIPB affect the gas component inside the bubble. Thermal bubbles significantly reduce the PD inception voltage (PDIV) and breakdown voltage (BV), and the PD evolution and dissolved gas components in the oil show significant differences in the presence/absence of bubbles. Interestingly, PDIV and BV start to decrease in a temperature interval before BIT, and we infer that many microbubbles and gaseous channels exist inside OIPB before observable bubbles are formed on the surface. Considering the fibrous porous structure of OIPB and combining it with the bubble nucleation theory, we propose a new model for interpreting bubble formation and insulation degradation. The model suggests that bubbles (vapors) form first in microcavities and holes inside the OIPB and then spill over to the surface to form observable bubbles. The microcavities and pores in the OIPB are filled with gas (vapor) as the main cause of PD inception and insulation degradation. This study contributes to understanding the mechanisms of bubble formation in oil–paper insulation as well as provides a reference for risk assessment during overload operation and diagnosis of bubble-induced related faults of transformers. [ABSTRACT FROM AUTHOR]

The IEEE International Conference on Plasma Science (ICOPS) is held annually, sponsored by the Plasma Science and Applications Committee (PSAC) of the IEEE Nuclear and Plasma Science Society (NPSS). The 47th IEEE NPSS ICOPS was organized ONLINE from December 06 to 10, 2020, by the National Institute of Education, Nanyang Technological University, Singapore. The ICOPS2020 was co-organized together with the 2nd Asia-Pacific Conference on Plasma and Terahertz Science (APCOPTS) which is envisioned as the future Asian version of the ICOPS series of conferences. The ICOPS2020 was initially planned as an onsite physical conference from May 24 to 28, 2020, at Marina Bay Sands, Singapore. However, due to the COVID-19 pandemic, it was shifted to online virtual conference. The conference’s technical program included a wide range of plasma science and technology topics, which ranged from basic and fundamental research to traditional and emerging technology applications. Since the ICOPS2020 was co-organized with APCOPTS, it also had a very strong coverage of fields related to terahertz science and applications. A total of 627 presentations from scientists and participants from over 41 countries were made at ICOPS2020 with a record total registration of 754. All presentations were recorded and were available to the registered participants for 90 days after the conference was over. The program also included a women-in-engineering talk by Jackie Y. Ying from A* Star Singapore and a young professionals networking symposium on “Plasma Entrepreneurship” with the theme “Plasma entrepreneurship—Active translation of cutting-edge research to commercially viable product.” [ABSTRACT FROM AUTHOR]

The objectives of this tutorial are as follows: 1) to help students and researchers develop a basic understanding of how pulsed-power systems are used to create high-energy-density (HED) matter; 2) to develop a basic understanding of a new, compact, and efficient pulsed-power technology called linear transformer drivers (LTDs); 3) to understand why LTDs are an attractive technology for driving HED physics (HEDP) experiments; 4) to contrast LTDs with the more traditional Marxgenerator/ pulse-forming-line approach to driving HEDP experiments; and 5) to briefly review the history of LTD technology as well as some of the LTD-driven HEDP research presently underway at universities and research laboratories across the globe. This invited tutorial is part of the Mini-Course on Charged Particle Beams and High-Powered Pulsed Sources, held in conjunction with the 44th International Conference on Plasma Science in May of 2017. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

The article offers information on the journal's call for feature topic (FT) proposals to complement its Series and Open Call publication tracks.

Efficient point cloud compression is essential for applications like virtual and mixed reality, autonomous driving, and cultural heritage. This paper proposes a deep learning-based inter-frame encoding scheme for dynamic point cloud geometry compression. We propose a lossy geometry compression scheme that predicts the latent representation of the current frame using the previous frame by employing a novel feature space inter-prediction network. The proposed network utilizes sparse convolutions with hierarchical multiscale 3D feature learning to encode the current frame using the previous frame. The proposed method introduces a novel predictor network for motion compensation in the feature domain to map the latent representation of the previous frame to the coordinates of the current frame to predict the current frame’s feature embedding. The framework transmits the residual of the predicted features and the actual features by compressing them using a learned probabilistic factorized entropy model. At the receiver, the decoder hierarchically reconstructs the current frame by progressively rescaling the feature embedding. The proposed framework is compared to the state-of-the-art Video-based Point Cloud Compression (V-PCC) and Geometry-based Point Cloud Compression (G-PCC) schemes standardized by the Moving Picture Experts Group (MPEG). The proposed method achieves more than 88% BD-Rate (Bjøntegaard Delta Rate) reduction against G-PCCv20 Octree, more than 56% BD-Rate savings against G-PCCv20 Trisoup, more than 62% BD-Rate reduction against V-PCC intra-frame encoding mode, and more than 52% BD-Rate savings against V-PCC P-frame-based inter-frame encoding mode using HEVC. These significant performance gains are cross-checked and verified in the MPEG working group. [ABSTRACT FROM AUTHOR]

How to model the effect of reflection is crucial for single image reflection removal (SIRR) task. Modern SIRR methods usually simplify the reflection formulation with the assumption of linear combination of a transmission layer and a reflection layer. However, the large variations in image content and the real-world picture-taking conditions often result in far more complex reflection. In this paper, we introduce a new screen-blur combination based on two important factors, namely the intensity and the blurriness of reflection, to better characterize the reflection formulation in SIRR. Specifically, we present Screen-blur Reflection Networks (SRNet), which executes the screen-blur formulation in its network design and adapts to the complex reflection on real scenes. Technically, SRNet consists of three components: a blended image generator, a reflection estimator and a reflection removal module. The image generator exploits the screen-blur combination to synthesize the training blended images. The reflection estimator learns the reflection layer and a blur degree that measures the level of blurriness for reflection. The reflection removal module further uses the blended image, blur degree and reflection layer to filter out the transmission layer in a cascaded manner. Superior results on three different SIRR methods are reported when generating the training data on the principle of the screen-blur combination. Moreover, extensive experiments on six datasets quantitatively and qualitatively demonstrate the efficacy of SRNet over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recent developments in the field of non-local attention (NLA) have led to a renewed interest in self-similarity-based single image super-resolution (SISR). Researchers usually use the NLA to explore non-local self-similarity (NSS) in SISR and achieve satisfactory reconstruction results. However, a surprising phenomenon that the reconstruction performance of the standard NLA is similar to that of the NLA with randomly selected regions prompted us to revisit NLA. In this paper, we first analyzed the attention map of the standard NLA from different perspectives and discovered that the resulting probability distribution always has full support for every local feature, which implies a statistical waste of assigning values to irrelevant non-local features, especially for SISR which needs to model long-range dependence with a large number of redundant non-local features. Based on these findings, we introduced a concise yet effective soft thresholding operation to obtain high-similarity-pass attention (HSPA), which is beneficial for generating a more compact and interpretable distribution. Furthermore, we derived some key properties of the soft thresholding operation that enable training our HSPA in an end-to-end manner. The HSPA can be integrated into existing deep SISR models as an efficient general building block. In addition, to demonstrate the effectiveness of the HSPA, we constructed a deep high-similarity-pass attention network (HSPAN) by integrating a few HSPAs in a simple backbone. Extensive experimental results demonstrate that HSPAN outperforms state-of-the-art approaches on both quantitative and qualitative evaluations. Our code and a pre-trained model were uploaded to GitHub (https://github.com/laoyangui/HSPAN) for validation. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable positioning. However, due to limited satellite coverage or communication disruptions, UAVs may lose signals for positioning. In such situations, vision-based techniques can serve as an alternative, ensuring the self-positioning capability of UAVs. However, most of the existing datasets are developed for the geo-localization task of the objects captured by UAVs, rather than UAV self-positioning. Furthermore, the existing UAV datasets apply discrete sampling to synthetic data, such as Google Maps, neglecting the crucial aspects of dense sampling and the uncertainties commonly experienced in practical scenarios. To address these issues, this paper presents a new dataset, DenseUAV, that is the first publicly available dataset tailored for the UAV self-positioning task. DenseUAV adopts dense sampling on UAV images obtained in low-altitude urban areas. In total, over 27K UAV- and satellite-view images of 14 university campuses are collected and annotated. In terms of methodology, we first verify the superiority of Transformers over CNNs for the proposed task. Then we incorporate metric learning into representation learning to enhance the model’s discriminative capacity and to reduce the modality discrepancy. Besides, to facilitate joint learning from both the satellite and UAV views, we introduce a mutually supervised learning approach. Last, we enhance the Recall@K metric and introduce a new measurement, SDM@K, to evaluate both the retrieval and localization performance for the proposed task. As a result, the proposed baseline method achieves a remarkable Recall@1 score of 83.01% and an SDM@1 score of 86.50% on DenseUAV. The dataset and code have been made publicly available on https://github.com/Dmmm1997/DenseUAV. [ABSTRACT FROM AUTHOR]

Effectively evaluating the perceptual quality of dehazed images remains an under-explored research issue. In this paper, we propose a no-reference complex-valued convolutional neural network (CV-CNN) model to conduct automatic dehazed image quality evaluation. Specifically, a novel CV-CNN is employed that exploits the advantages of complex-valued representations, achieving better generalization capability on perceptual feature learning than real-valued ones. To learn more discriminative features to analyze the perceptual quality of dehazed images, we design a dual-stream CV-CNN architecture. The dual-stream model comprises a distortion-sensitive stream that operates on the dehazed RGB image, and a haze-aware stream on a novel dark channel difference image. The distortion-sensitive stream accounts for perceptual distortion artifacts, while the haze-aware stream addresses the possible presence of residual haze. Experimental results on three publicly available dehazed image quality assessment (DQA) databases demonstrate the effectiveness and generalization of our proposed CV-CNN DQA model as compared to state-of-the-art no-reference image quality assessment algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on the facial micro-expression (FME) generation task, which has potential application in enlarging digital FME datasets, thereby alleviating the lack of training data with labels in existing micro-expression datasets. Despite obvious progress in the image animation task, FME generation remains challenging because existing image animation methods can hardly encode subtle and short-term facial motion information. To this end, we present a facial-prior-guided FME generation framework that takes advantage of facial priors for facial motion generation. Specifically, we first estimate the geometric locations of action units (AUs) with detected facial landmarks. We further calculate an adaptive weighted prior (AWP) map, which alleviates the estimation error of AUs while efficiently capturing subtle facial motion patterns. To achieve smooth and realistic synthesis results, we use our proposed facial prior module to guide motion representation and generation modules in mainstream image animation frameworks. Extensive experiments on three benchmark datasets consistently show that our proposed facial prior module can be adopted in image animation frameworks and significantly improve their performance on micro-expression generation. Moreover, we use the generation technique to enlarge existing datasets, thereby improving the performance of general action recognition backbones on the FME recognition task. Our code is available at https://github.com/sysu19351158/FPB-FOMM. [ABSTRACT FROM AUTHOR]

Small moving objects at far distance always occupy only one or a few pixels in image and exhibit extremely limited visual features, which bring great challenges to motion detection. Highly evolved visual systems endow flying insects with remarkable ability to pursue tiny mates and prey, providing a good template to develop image processing method for small target motion detection. The insects’ excellent sensitivity to small moving objects is believed to come from a class of specific neurons called small target motion detectors (STMDs). However, existing STMD-based methods often experience performance degradation when coping with complex natural scenes. In this paper, we propose a bio-inspired visual system with spatio-temporal feedback mechanism (called Spatio-Temporal Feedback STMD) to suppress false positive background movement while enhancing system responses to small targets. Specifically, the proposed visual system is composed of two complementary subnetworks and a feedback loop. The first subnetwork is designed to extract spatial and temporal movement patterns of cluttered background by neuronal ensemble coding. The second subnetwork is developed to capture small target motion information where its output and signal from the first subnetwork are integrated together via the feedback loop to filter out background false positives in a recurrent manner. Experimental results demonstrate that the proposed spatio-temporal feedback visual system is more competitive than existing methods in discriminating small moving targets from complex natural environments. [ABSTRACT FROM AUTHOR]

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]

For the 3D NAND memory, the higher oxide/nitride (ON) stacked dielectric is preferred to enhance the storage capacity, and multi-layer dielectric requirements, such as thickness uniformity and interfacial smoothness between films, gathers more interest for the performance of 3D NAND flash memory. Unsatisfactory thickness uniformity between layers is a challenge not only for the device performance but also the following etch process steps. The thickness uniformity can get worse with a little facility degradation. The degradation of the vacuum system, such as the throttle valve position, has the potential to cause process drift. This can have an impact on the thickness repeatability of each layer in a multiple dielectric stack. To reduce the process variation in multi-layer dielectric deposition for 3D NAND fabrication, process monitoring, and equipment diagnostic study is suggested in this paper. Optical emission spectroscopy (OES) is employed for plasma process monitoring and equipment state variable identification (SVID) data are investigated to find the source of the process variation. From the comparison experiments of 5 and 30 paired oxide/nitride stack deposition, we found equipment and/or facility degradation may induce the minute process drift. Among them, we suggest the potential of process drift due to the throttle valve position. [ABSTRACT FROM AUTHOR]

This paper studies the problem of scheduling machines in the photolithography area of a semiconductor manufacturing facility. The scheduling problem is characterized as an unrelated parallel machine scheduling problem with machine eligibilities, sequence- and machine-dependent setup times, auxiliary resources and transfer times for the auxiliary resources. Each job requires two auxiliary resources: a reticle and a pod. Reticles are handled in pods and a pod contains multiple reticles. Both reticles and pods are used on multiple machines and a transfer time is required if transferred from one machine to another. A novel constraint programming (CP) approach is proposed and is benchmarked against a mixed-integer programming (MIP) method. The results of the study, consisting of a real-world case study at a global semiconductor manufacturer, demonstrate that the CP approach significantly outperforms the MIP method and produces high-quality solutions for multiple real-world instances, although optimality cannot be guaranteed. [ABSTRACT FROM AUTHOR]