Search

This research proposes an inkjet printed dual-band dual-sense circularly polarized antenna using CPW-feeding on PET substrate. The antenna is designed and optimized using ANSYS HFSS, which operates at 4.01 GHz - 5.05 GHz (22.96%) and 6.23 GHz - 7.58 GHz (19.55%) with a return loss of $0.44\lambda $ x $0.52\lambda $ x $0.002\lambda $ , where $\lambda $ is the free-space wavelength at the lowest circular polarization frequency. Measurement of the fabricated version shows good agreement with the simulated version. To the best of author’s knowledge, this proposed design is the first circularly polarized antenna integrating flexibility with dual-band and dual-sensing characteristics.

Natural esters have become of interest for the industry in recent years as dielectric liquids for power transformers, and many studies are focused on their dielectric and chemical properties and on their influence in the degradation of the solid insulation due to aging. However, very little is known about their impact on the evolution of the mechanical properties of the paper insulation, which are acknowledged to have a considerable influence in their overall performance and reliability during the operating life of power transformers. This work studies the effects of thermal aging with vegetable oil in some commercial components, which are commonly used in power transformers, such as an insulated continuously transposed conductor (CTC) and samples of thermally upgraded crepe insulation. The changes in the properties of the crepe paper insulation are characterized through the degree of polymerization and tensile testing. Failure initiation and propagation in the insulation of the CTC is analyzed macroscopically. The results are compared with those obtained when using mineral oil, showing that the use of vegetable oil has a protective effect over mechanical properties of the studied types of paper insulation. [ABSTRACT FROM AUTHOR]

The article presents the discussion on theme of ISTAS 2024 inviting participation from practitioners in academia, industry, and government.

An introduction is presented in which the editor discusses articles in the issue on the topics including demonstrates exciting results based on self-imitation learning within a cross-modal setting; and providing advances in controllable generation and interpolation across attributes.

Mineral oil has traditionally been used as a cooling fluid in power transformers, but its low biodegradability and low fire point have motivated the search of alternatives. In this work, six different dielectric fluids have been studied, including four vegetable liquids, from sunflower, rapeseed, soybean, and palm, one synthetic ester and a mineral oil used for comparison. These oils were subjected to an accelerated thermal ageing in glass vessels at 150°C for four weeks (672 hours) in presence of Kraft insulating paper. Different oils parameters were measured during the ageing, i.e. breakdown voltage, dielectric dissipation factor, permittivity, DC resistivity, density, kinematic viscosity, flash and fire points, interfacial tension, acidity, and dissolved gases; additionally, the degree of polymerisation (DP) of the paper was measured. Results showed that the changes of the natural esters properties, except for the palm oil, were similar along the ageing time. Palm oil results were similar to those of the mineral oil, whereas synthetic ester showed a behaviour similar to natural esters. The kraft paper degradation was higher in the mineral oil, followed by the synthetic ester and the palm oil. No significant differences were found in the ageing with the natural esters.

Welcome to Volume 3 of IEEE Journal of Microwaves! In this issue we combine a complete set of regular contributions with Part 1 of a Microwave Theory and Technology Society 70th Anniversary commemoration, consisting of some twenty-one specially invited papers embodying this major milestone in the history of the organization. Several additional articles, including some major historic overviews, are to be released in our spring issue of JMW which contains Part 2 of this 70th Anniversary celebration. In order to make it possible to bring up all the anniversary issue papers contained in both parts one and two using a single database search, every special issue paper contains the index term: “MTT 70th Anniversary Special Issue.” The full set (covering two issues) of 70th anniversary papers can be accessed using a simple parenthesis-delineated search of the five-word phrase. Plans are also in place to combine all the on-line papers into a full-color print issue for distribution upon request. In addition to this very nice collection of quality papers, we celebrate six of our Outstanding Reviewers of 2021 and 2022 and announce our first IEEE Journal of Microwaves Best Paper prize.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

Discovering promising research themes in a scientific domain by evaluating semantic information extracted from bibliometric databases represents a challenging task for Natural Language Processing (NLP). While existing NLP methods generally characterize the research topics using unique key terms, we take a step further by more accurately modeling the research themes as finite sets of key terms. The proposed approach involves two stages: identifying the research themes from paper metadata using LDA topic modeling; and, evaluation of research theme trends by employing a version of the Mann-Kendall test that is able to cope with multivariate time series of term occurrences. The results obtained by applying this general methodology to Information Security domain confirm its viability.

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]

A list of periodical staff is presented including the editor-in-chief, the industrial and commercial power system department, and the industrial power conversion systems department.

The Last decade saw significant growth in the digital terrestrial television (DTT) subscribers. The High peak-to-average power ratio (PAPR) of the transmitted signal is implied by the large fluctuations in its amplitude, which is a serious issue in the contemporary and next generation DTT broadcasting systems. These large fluctuations prevent feeding the high power amplifier (HPA) at an operating point near its non-linear (NL) saturation region, thereby lowering its power efficiency. For a better future, energy-efficient green communications is the need of hour. This paper focuses on digital predistortion (DPD), which is among the widely used techniques for HPA linearization in order to minimize the NL effects. DPD technique requires a feedback signal. This paper proposes a novel adding signal-based digital predistortion, where the output of the HPA is gradually linearized by adding a signal at the input in an iterative manner. By Taylor series approximation, the DPD problem can be posed as a convex one. We prove that a tighter upper bound exists for the convergence factor and instead of directly solving the convex problem, we pick a convergence factor value within its bounds that offers a good trade-off between convergence speed and residual error value. The simulation results show that the proposed technique can quickly linearize the HPA with good convergence. [ABSTRACT FROM AUTHOR]

The 2022 IEEE World Congress on Services will be held in a hybrid mode with physical presence in Barcelona, Spain. SERVICES is the premier international forum for presenting and discussing the most recent and significant technical research contributions in the field of services computing. Centered around web-based services, SERVICES 2022 covers various systems and networking research pertaining to cloud, edge and IoT, as well as technologies for intelligent computing, learning, Big Data, blockchain, and digital healthcare applications, addressing critical issues such as knowledge network, high performance, security, privacy, dependability, trustworthiness, and cost-effectiveness. Particularly, the 2022 Congress will welcome papers on the aftermath and the impact of COVID-19 on services and the world infrastructure. In addition to co-located theme-topic conferences, the Congress will also include symposia supporting deep-dive discussions on emerging topics, and complement the SERVICES 2022 program with industry and application presentations and panels. Authors are invited to prepare early and submit original and unpublished papers to any of these conferences at www.easychair.org. All submitted manuscripts will be peer- reviewed by at least three reviewers. Accepted and presented papers will appear in the conference proceedings published by the IEEE Computer Society Press. SERVICES 2022 is the only premier professional event for the services computing field offered by IEEE, under the auspice of the Technical Committee on Services Computing (TCSVC). [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

The code base of system software is growing fast, which results in a large number of vulnerabilities: for example, 296 CVEs have been found in Xen hypervisor and 2195 CVEs in Linux kernel. To reduce the reliance on the trust of system software, many researchers try to provide trusted execution environments (TEEs), which can be categorized into two types: non-privileged TEEs and privileged TEEs. Non-privileged TEEs (e.g., Intel SGX) are extensible, but cannot protect security services like virtual machine introspection (VMI) due to the lack of system-level semantics. On the contrary, privileged TEEs (e.g., the secure world of ARM TrustZone) have system-level semantics, but any additional service implemented in the privileged TEE directly increases the TCB of the entire system. In this article, we propose a new design of TEE to support system-level security services and achieve better extensibility with a small TCB. Each TEE instance of the proposed design is named a Colony. Specifically, we introduce a secure monitor for isolation and capability management. Each Colony is assigned capabilities to access only necessary system-level semantics. We use the new TEE to build four security services, including secure device accessing, VMI tools, a system call tracer, and a much more complex service to virtualize ARM TrustZone with multiple Colonies. We have implemented the system on ARMv7 and ARMv8 platforms, in Xen hypervisor and Linux kernel, and perform a detailed evaluation to show its efficiency. 1. This paper is an extended version of the conference paper published in USENIX Security’17: vTZ: Virtualizing ARM TrustZone. A brief summary of differences is in Section. [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]

Presents the best paper awards for 2019-2021 and includes the list of reviewers who contributed to this publication in 2021. [ABSTRACT FROM AUTHOR]

The citation intent extraction and classification has long been studied as it is a good measure of relevancy. Different approaches have classified the citations into different classes; including weak and strong, positive and negative, important and unimportant. Others have gone further from binary classification to multi-classes, including extension, use, background, or comparison. Researchers have utilized various elements of the information, including both meta and contents of the paper. The actual context of any referred article lies within the citation context where a paper is referred. Various attempts have been made to study the citation context to capture the citation intent, but very few have encoded the words to their contextual representations. For automated classification, we need to train deep learning models, which take the citation context as input and provides the reason for citing a paper. Deep neural models work on numeric data, and therefore, we must convert the text information to its numeric representation. Natural languages are much complex than computer languages. Computer languages have a pre-defined fixed syntax where each word has a unique meaning. In contrast, every word in natural language may have a different meaning and may well be understood by understanding the position, previous discussion, and neighboring words. The extra information provides the context of a word within a sentence. We have, therefore, used contextual word representation, which is trained through deep neural networks. Deep models require massive data for generalizing the model, however, the existing state-of-the-art datasets don’t provide much information for the training models to get generalized. Therefore, we have developed our own scholarly dataset, Citation Context Dataset with Intent (C2D-I), an extension of the C2D dataset. We used a transformers based model for capturing the contextual representation of words. Our proposed method outperformed the existing benchmark methods with F1 score of 89%.

Tracking the trends and taking early steps accordingly is important in academia, as well as in other domains such as technology and finance. In this work, we focus on the problem of predicting the trending research topics from a collection of academic papers. Previous efforts model the problem in different ways and mostly apply classical approaches such as correlation analysis and clustering. There are also several recent neural model based solutions, however they rely on feature vectors and additional information for the trend prediction. In this work, given a collection of publications within the observation time window, we predict whether the use of a keyword will increase, decrease or be steady for the future time window (prediction window). As the solution, we propose a family of deep neural architectures that focus on generating summary representations for paper collections under the query keyword. Due to the sequence based nature of the data, Long Short-Term Memory (LSTM) module plays a core role, but it is combined with different layers in a novel way. The first group of proposed neural architectures consider each paper as a sequence of keywords and use word embeddings to construct paper collection representations. In this group, the proposed architectures differ from each other in the way year based and overall summary representations are constructed. In the second group, each paper is directly represented as a vector and the use of different paper embedding techniques are explored. The analyses of the models are performed on a variety of paper collections belonging to different academic venues, obtained from Microsoft Academic Graph data set. The experiments conducted against baseline methods show that proposed deep neural based models achieve higher trend prediction performance than the baseline models on the overall. Among the proposed models, paper embedding based models provide better results for most of the cases.

The next-generation wireless technology that can fulfill such a demand, namely the fifth-generation (5G) technology, should provide 1000 times larger capacity. Moreover, sixth-generation (6G) communication, which represents a significant upgrade from the fifth-generation (5G) network and is anticipated to operate from 100 GHz to 3 THz band, will be required in the years after 2030 due to newly developed data-hungry applications and the greatly expanded wireless network. To meet the ever-growing demands of wireless carriers, an efficient wireless access method that can improve wireless area throughput without expanding bandwidth or cell size is required. Radio Frequency (RF) Orbital Angular Momentum vortex waves (which is now on referred to as OAM waves) to address the concerns mentioned above have attracted much attention in recent years. Due to their orthogonality, different OAM waves of different modes can be multiplexed in the same frequency channel, which can greatly increase the channel capacity. Using the orthogonal modes, a new type of multiple access scheme known as Mode Domain Multiple Access (MDMA) can be used by multiple users using the same frequency channel without additional resources such as frequency and time. As a result, the channel capacity for the next generation wireless communication systems can be enhanced as well as the overall spectrum efficiency can be improved. This review paper begins with an overview of the next generation communication such as 5G communication technology and beyond. This paper first briefly discusses the theory of OAM waves and several methods to generate OAM waves. Various different designs have also been analyzed for their ability to generate OAM waves and discussion on several restrictions and solutions to resolve. Open concerns and development trends are discussed for possible future RF OAM antenna upgrades. This study also proposes that for next generation wireless communication employing OAM, the typically used Uniform Circular Array (UCA) could be paired with the Multiple-Input-Multiple-Output (MIMO) system to improve performance in dense or urban areas for multiusers. In addition, the purity of OAM-modes needs to be considered for efficient utilization of the OAM system for future communications at the radio domain.

Several important advancements in the integration of energy storage into microgrids have fueled a lot of research and development over the last ten years to achieve the global decarbonization goal by 2050. The effective integration of the energy storage system in the microgrid is essential to ensure a safe, reliable, and resilient operation. Nevertheless, the utilization of energy storage in microgrids brings several issues, including poor power quality and intermittence characteristics. To address these concerns, appropriate energy storage controllers and optimization schemes are required to manage and optimize the power efficiently and securely. Although various research works have been performed and published over the years, the analytical assessment of energy storage controllers and optimization schemes integration into microgrids has not been carried out yet. Thus, this paper presents a comprehensive analytical evaluation of energy storage controllers and optimization schemes in Microgrid by recognizing and evaluating the highly influential 110 manuscripts using the Scopus database within the year 2010-2021. The analytical analysis emphasizes the current research trends, keyword evaluation, research classification, country analysis, authorship, and research collaboration. The paper also discusses and compares 24 controllers and 21 optimization schemes in the highly cited 110 manuscripts. Besides, critical discussion and assessment are conducted over 15 emerging subject areas. The constructive analysis identifies the existing limitations and research gaps in the selected 110 papers. By analyzing the existing issues, this manuscript provides several guidelines and suggestions for future improvement. This survey will help to deepen the development concepts to achieve improved power quality, economic prosperity, energy savings, and increased efficiency towards sustainable operation and management in the microgrid.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

As this Special Section goes to publication, semiconductor manufacturing in the United Status, and globally, continues to expand at a seemingly torrid pace. Assisted by government funding and driven in part by artificial intelligence workloads that gobble up increasing amounts of data center computing capacity, Intel and TSMC fabs are going up in Arizona, TI and Samsung fabs are coming to Texas, and Micron has big plans in New York. Unfortunately, just like those flying cars we were once promised, AI has not yet eliminated the need for the skilled trades and engineers required to build and successfully operate a fab. As a result, workforce development has become an important part of the increasingly complex semiconductor manufacturing process: Where are the thousands of engineers the semiconductor industry needs to staff these new fabs going to come from? How can we make more students excited about science and engineering? While the Guest Editors don’t have all the answers, we are happy that ASMC contributes to the solution by actively supporting student presentations and posters and annually recognizing the best student paper of the conference. And, maybe some day, the artificial intelligence systems that semiconductor manufacturing has enabled will give us those Star Wars or Star Trek robots that can build fabs and make chips too. [ABSTRACT FROM AUTHOR]

The article offers information on the procedure for submission of a research paper in the fields of electrical and electronics engineering.

Name disambiguation is a key and also a very tough problem in many online systems such as social search and academic search. Despite considerable research, a critical issue that has not been systematically studied is disambiguation on the fly — to complete the disambiguation in the real-time. This is very challenging, as the disambiguation algorithm must be accurate, efficient, and error tolerance. In this paper, we propose a novel framework — CONNA — to train a matching component and a decision component jointly via reinforcement learning. The matching component is responsible for finding the top matched candidate for the given paper, and the decision component is responsible for deciding on assigning the top matched person or creating a new person. The two components are intertwined and can be bootstrapped via jointly training. Empirically, we evaluate CONNA on two name disambiguation datasets. Experimental results show that the proposed framework can achieve a 1.21-19.84 percent improvement on F1-score using joint training of the matching and the decision components. The proposed CONNA has been successfully deployed on AMiner — a large online academic search system. [ABSTRACT FROM AUTHOR]