Search

A 3D modeling system with all-inclusive functionality is too demanding for a casual 3D modeler to learn. There has been a shift towards more approachable systems, with easy-to-learn, intuitive interfaces. However, most modeling systems still employ mouse and keyboard interfaces, despite the ubiquity of tablet devices and the benefits of multi-touch interfaces. We introduce an alternative 3D modeling and fabrication paradigm using developable surfaces, inspired by traditional papercrafting, and we implement it as a complete system designed for a multi-touch tablet, allowing a user to fabricate 3D scenes. We demonstrate the modeling and fabrication process of assembling complex 3D scenes from a collection of simpler models, in turn shaped through operations applied to virtual paper. Our fabrication method facilitates the assembly of the scene with real paper by automatically converting scenes into a series of cutouts with appropriately added fiducial markers and supporting structures. Our system assists users in creating occluded supporting structures to help maintain the spatial and rigid properties of a scene without compromising its aesthetic qualities. We demonstrate several 3D scenes modeled and fabricated in our system, and evaluate the faithfulness of our fabrications relative to their virtual counterparts and 3D-printed fabrications.

Coater sections are examples of multi-span web transporting systems, with dynamic characteristics related to the sheet properties and control schemes. This paper first describes and analyzes a set of field records of a production 2-coater section paper machine. Then, extending the 1-coater section model developed in a related paper, a precise simulation model of a 2-coater section is implemented. This model is used to evaluate the beginning of the coating process and the effect on the section responses of different bandwidth settings in the tension and speed controllers. Results show agreement with the field records and demonstrate that the developed model is a powerful tool to analyze and understand the behavior of coater sections during both, transient and steady state conditions.

This letter presented a pencil-on-paper-based ecofriendly capacitive touchpad fabricated through a low-cost lab- and solvent-free fabrication process. Economical, readily available, and biodegradable materials, such as pencil and paper, have been used for sensor fabrication. A touchpad based on five such sensors has been developed and evaluated by using it as a user control interface to on/off LEDs and displaying results on LCD and mobile app. The graphite sensors in the user interface have been shielded from the direct interaction of humans and the environment using changeable cling film to make the interface reliable and reusable. The interface can be used in different conditions and users by changing the shielding film. Simultaneously, the system's functionality will remain consistent and functioned similarly to the conventional touch sensors and buttons. These recyclable and easily arrayed touch sensor-based keypads can contribute to future developments in disposable and lightweight interfaces for human–machine interface, medical and healthcare applications, smart packaging, security monitoring, and consumer products.

In the last decade, much effort has been invested in using methanol as an oil-soluble chemical marker for assessing the condition of insulating paper. The use of this marker as paper life index presents many advantages particularly with new transformers insulated with thermally upgraded papers. However, until now, no interpretation model has been available for its extensive use by the transformer community. In this paper, a methanol-based interpretation model is presented for the first time using postmortem paper analysis on core-type transformers. This model allows the evaluation of the average degree of polymerization of a transformer's cellulose winding. Furthermore, threshold values based on the methanol concentration are given using this approach. Finally, model validation was performed on a limited number of papers taken from transformers being assessed in the repair shop.

Paper surfaces under the microscopic view are observed to be formed by intertwisted wood fibers. Such structures of paper surfaces are unique from one location to another and are almost impossible to duplicate. Previous work used microscopic surface normals to characterize such intrinsic structures as a “fingerprint” of paper for security and forensic applications. In this work, we examine several key research questions of feature extraction in both scientific and engineering aspects to facilitate the deployment of paper surface-based authentication when flatbed scanners are used as the acquisition device. We analytically show that, under the unique optical setup of flatbed scanners, the specular reflection does not play a role in norm map estimation. We verify, using a larger dataset than prior work, that the scanner-acquired norm maps, although blurred, are consistent with those measured by confocal microscopes. We confirm that, when choosing an authentication feature, high spatial-frequency subbands of the heightmap are more powerful than the norm map. Finally, we show that it is possible to empirically calculate the physical dimensions of the paper patch needed to achieve a certain authentication performance in equal error rate (EER). We analytically show that log(EER) is decreasing linearly in the edge length of a paper patch.

Finding relevant papers is a non-trivial problem for scholars due to the tremendous amount of academic information in the era of scholarly big data. Scientific paper recommendation systems have been developed to solve such problem by recommending relevant papers to scholars. However, previous paper recommendations calculate paper similarity based on hand-engineered features which are inflexible. To address this problem, we develop a scientific paper recommendation system, namely VOPRec, by vector representation learning of paper in citation networks. VOPRec takes advantages of recent research in both text and network representation learning for unsupervised feature design. In VOPRec, the text information is represented with word embedding to find papers of similar research interest. Then, the structural identity is converted into vectors to find papers of similar network topology. After bridging text information and structural identity with the citation network, vector representation of paper can be learned with network embedding. Finally, top- $Q$ Q recommendation list is generated based on the similarity calculated with paper vectors. Through the APS data set, we show that VOPRec outperforms state-of-the-art paper recommendation baselines measured by precision, recall, F1, and NDCG.

The existing plethora of document classification techniques exploits different data sources either from the content or metadata of research articles. Various journal publishers like Springer, Elsevier, IEEE, etc., do not provide open access to the content of research articles, whereas metadata is freely available there. Metadata like title, keyword, and abstract can serve as a better alternative to the content in various scenarios. In the current literature, researchers have assessed the role of some of the metadata individually. We believe that the collective contribution of metadata parameters can play a significant role in classifying research papers. This paper presents a comprehensive evaluation of the role of metadata, individually as well as in combinations to achieve the objective of research paper classification. Moreover, we have classified the research articles into ACM hierarchy root categories (e.g. general literature, hardware, software, etc.). In this comprehensive evaluation, we have assessed all the possible combinations of metadata features against different classifiers such as Random Forest, K Nearest Neighbor, and Decision Tree. The results of this research reveal that the title & keywords combination outperforms other combinations with an F-measure score of 0.88.

This paper studies the secure transmission in a multiuser broadcast channel where only the statistical channel state information of the eavesdropper is available. We propose to apply secret dirty-paper coding (S-DPC) in this scenario to support the secure transmission of one user and the normal (unclassified) transmission of the other users. By adopting the S-DPC and encoding the secret message in the first place, all the information-bearing signals of the normal transmission are treated as noise by potential eavesdroppers and thus provide secrecy for the secure transmission. In this way, the proposed approach exploits the intrinsic secrecy of multiuser broadcasting and can serve as an energy-efficient alternative to the traditional artificial noise (AN) scheme. To evaluate the secrecy performance of this approach and compare it with the AN scheme, we propose two S-DPC-based secure transmission schemes for maximizing the secrecy rate under constraints on the secrecy outage probability (SOP) and the normal transmission rates. The first scheme directly optimizes the covariance matrices of the transmit signals, and a novel approximation of the intractable SOP constraint is derived to facilitate the optimization. The second scheme combines zero-forcing dirty-paper coding and AN, and the optimization involves only power allocation. We establish efficient numerical algorithms to solve the optimization problems for both schemes. Theoretical and simulation results confirm that, in addition to supporting the normal transmission, the achievable secrecy rates of the proposed schemes can be close to that of the traditional AN scheme, which supports only the secure transmission of one user.

Paper-based digital microfluidic biochips (or P-DMFBs) are becoming highly impelling due to its low-cost and in-place fabrication of electrodes and control wiring on a single piece of paper having an inkjet printer and conductive ink. Despite enormous advantages, several complex design rules also subsist, such as avoidance of induced control interference, minimum separation among the control lines, and congestion-free wiring on a single layer, which is to be correlated leading toward overall feasibility of the design. Several cost raising issues, such as schedule length, control pin count, and wire length, must be considered for attaining a successful fluid-control codesign. Moreover, design gaps exist among the subtasks of the fluid level, control level, and fluid-control design as a whole, which undeniably impose expensive design cycles increasing overall cost. This article builds a machine learning-based model for the pin-constrained P-DMFBs to predict violation in control design beforehand and accordingly guides the fluid-control codesign to tackle important cost-driving issues while attaining congestion- and conflict-free wiring. This model effectively eliminates the design cycles producing a low-cost platform. The predictive model has been evaluated over a balanced data set. Several benchmarks for assessing the performance are studied.

Objective: Early stage diagnosis of sepsis without overburdening health services is essential to improving patient outcomes. Methods: A fast and simple-to-use platform that combines an integrated circuit with paper microfluidics for simultaneous detection of multiple-metabolites appropriate for diagnostics was presented. Paper based sensors are a primary candidate for widespread deployment of diagnostic or test devices. However, the majority of devices today use a simple paper strip to detect a single marker using the reflectance of light. However, for many diseases such as sepsis, one biomarker is not sufficient to make a unique diagnosis. In this work multiple measurements are made on patterned paper simultaneously. Using laser ablation to fabricate microfluidic channels on paper provides a flexible and direct approach for mass manufacture of disposable paper strips. A reusable photodiode array on a complementary metal oxide semiconductor chip is used as the transducer. Results: The system measures changes in optical absorbance in the paper to achieve a cost-effective and easily implemented system that is capable of multiple simultaneous assays. Potential sepsis metabolite biomarkers glucose and lactate have been studied and quantified with the platform, achieving sensitivity within the physiological range in human serum. Conclusion: We have detailed a disposable paper-based CMOS photodiode sensor platform for real-time simultaneous detection of metabolites for diseases such as sepsis. Significance: A combination of a low-cost paper strip with microfluidic channels and a sensitive CMOS photodiode sensor array makes our platform a robust portable and inexpensive biosensing device for multiple diagnostic tests in many different applications.

Several miniaturized viscometers, or microviscometers, have been developed exploiting numerous rapid prototyping techniques. Among them, paper microstrips, famously known as microfluidic paper-based analytical devices ( $\mu $ PADs), have become popular due to their cost-efficacy, simple fabrication, fast response, and easily disposable. Many fabrication methods are existing to develop paper microstrips. Herein, an alternative fabrication method is proposed where fused deposition modeling (FDM)-based 3-D printer (3DP) has been employed using polycaprolactone (PCL) filament. F, image processing has been utilized to measure viscosity in such microfluidic domain. Viscosity was calculated by measuring the time taken by the fluid to cover a fixed length between two spots in the microchannel based on the programed and color-coded regions-of-interest. The image processing program was developed considering the change in the gray scale in the virtual region of interests (ROIs) in the microchannel during the fluid flow in the paper microstrips. A 3-D printed handheld platform, containing raspberry pi with on-board camera and display, was developed to execute the image processing and automate the entire work flow. In the proposed device, the accuracy was measured to be >92%.

This paper presents a wireless, self-powered, affinity-based biosensor based on the integration of paper-based microfluidics with our previously reported method for self-assembling radio-frequency (RF) antennas. At the core of the proposed approach is a silver-enhancement technique that grows portions of a RF antenna in regions where target antigens hybridize with target specific affinity probes. The hybridization regions are defined by a network of nitrocellulose based microfluidic channels which implement a self-powered approach to sample the reagent and control its flow and mixing. The integration substrate for the biosensor has been constructed using polyethylene and the patterning of the antenna on the substrate has been achieved using a low-cost ink-jet printing technique. The substrate has been integrated with passive radio-frequency identification (RFID) tags to demonstrate that the resulting sensor-tag can be used for continuous monitoring in a food supply-chain where direct measurement of analytes is typically considered to be impractical. We validate the proof-of-concept operation of the proposed sensor-tag using IgG as a model analyte and using a 915 MHz Ultra-high-frequency (UHF) RFID tagging technology.

Mobile and wireless communications are playing an irreplaceable role in our daily life. 5G is highly expected and can be considered as the convergence of mobile and wireless communications. This topic becomes one of the hottest research topics recently, and papers on this topic have been paid much attention. Table I lists the top ten papers published in the IEEE Transactions on Microwave Theory and Techniques (TMTT) in 2019 with the highest numbers of Full Text View since published. Seven papers are about millimeter-waves (mm-waves), and because mm-waves multiple-input–multiple-output (MIMO) will be adopted globally, the 24.25–27.5-GHz band and 27–29.5-GHz bands are now approved worldwide as the standard 5G communication bands. The most popular paper is published by Asbeck et al. [item 1) in the Appendix] for mm-wave power amplifiers.

Due to the increasing size of s (s), their design and optimization phases (i.e., ) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some trade-off between design quality and computational overhead. ML can build powerful models that have successfully been employed in related domains. In this survey, we categorize how () may be used and is used for design-time and run-time optimization and exploration strategies of s. A meta-study of published techniques unveils areas in that are well-explored and underexplored with, as well as trends in the employed algorithms. We present a comprehensive categorization and summary of the state of the art on for. Finally, we summarize remaining challenges and promising open research directions.

In this article, we put forward the substantial challenges in cyber resilience in the domain of autonomous systems and outline foundational solutions to address these challenges. These solutions fall into two broad themes: resilience-by-design and resilience-by-reaction . We use several application drivers from autonomous systems to motivate the challenges in cyber resilience and to demonstrate the benefit of the solutions. We focus on some autonomous systems in the near horizon (autonomous ground and aerial vehicles) and also a little more distant (autonomous rescue and relief). For resilience-by-design , we focus on design methods in software that are needed for our cyber systems to be resilient. In contrast, for resilience-by-reaction , we discuss how to make systems resilient by responding, reconfiguring, or recovering at runtime when failures happen. We also discuss the notion of adaptive execution to improve resilience, execution transparently and adaptively among available execution platforms (mobile/embedded, edge, and cloud). For each of the two themes, we survey the current state, and the desired state and ways to get there. We conclude the paper by looking at the research challenges we will have to solve in the short and the mid-term to make the vision of resilient autonomous systems a reality. This article came out of discussions that started at the NSF-sponsored Grand Challenges in Resilience Workshop held at Purdue in 2019 with the co-authors contributing to going into the depth of the issues and then this article.

Early paper manufacturing used sieve-like molds through which paper pulp was drained. Two pieces of paper are called moldmates if they were made using the same mold. When a large body of one artist?s work on paper exists, the identification of moldmates can help in establishing chronology, suggest paper preferences, and indicate periods of intense activity of the artist. Rembrandt is an especially good example. With several thousand prints in existence today, the study of Rembrandt?s prints has occupied scholars for over two centuries, and the study of his printing papers occupies a prominent place within this scholarship [1]. This article examines the feasibility of moldmate identification via chain-line pattern matching and conducts a series of experiments that demonstrate how accurately the measurements can be made, how straight and parallel the lines may be, and provides a rule of thumb for the number of chain lines required for accurate moldmate identification using a simplified model. The problem of identifying moldmates among Rembrandt?s prints is presented as a pair of image/signal processing tasks; our strategy is to provide basic solutions to these tasks and to then reveal the shortcomings of these solutions in the hopes of encouraging future work in the signal processing community. With the support of the Morgan Library & Museum and the Metropolitan Museum of Art, both of which are in New York, we have made high-resolution data available [2] to facilitate this quest.

A Midwestern pulp and paper mill had arc-flash ratings around 100 cal/cm2 on 50 480-V unit substations. New relays were installed to replace the existing protection and add light detection to improve arc-flash detection times. During a couple of one-day outages, sample installations took place. During the cold mill outage, 44 installations were completed over 12 days by working around the clock. Old relays were removed, new plates were mounted and wired in, and fiber sensors were routed through the buswork and into the breaker cubicles. Updated arc-flash studies show an average arc-flash rating reduction of 90%.

Wireless sensors and networks are used only occasionally in current control loops in the process industry. With rapid developments in embedded and highperformance computing, wireless communication, and cloud technology, drastic changes in the architecture and operation of industrial automation systems seem more likely than ever. These changes are driven by ever-growing demands on production quality and flexibility. However, as discussed in "Summary," there are several research obstacles to overcome. The radio communication environment in the process industry is often troublesome, as the environment is frequently cluttered with large metal objects, moving machines and vehicles, and processes emitting radio disturbances [1], [2]. The successful deployment of a wireless control system in such an environment requires careful design of communication links and network protocols as well as robust and reconfigurable control algorithms.

In this paper, we present a new scheme for implementing virtual keyboards, which uses only two to four motion-recognition rings per hand and a two-dimensional keyboard template (e.g., an A4 size paper with printed key positions). It has the benefit of portability, customizability, and low-cost when compared with existing approaches. Essentially, we have shown that wearing two wireless IoT rings on the middle phalanges of two fingers of each hand, users can input the alphabetic characters into a computing device by typing on a flat paper on a desk, and potentially in mid-air. We have demonstrated that two rings are sufficient in capturing the gestures and motions of all fingers in a typing hand for keystrokes recognition. A single wireless IoT ring, which weighs 7.8 grams, consists of a Bluetooth low energy (BLE) unit, a micro inertial measurement unit (mIMU), and a cell battery. The 3-axes attitude angles and the Z-axis acceleration of each ring are adopted as the features for keystroke recognition. The overall keystroke recognition accuracy rate can reach as high as 94.8% when two IoT rings are worn by a user on each hand; this accuracy rate increases to 98.6%, when four rings are worn on each typing hand.

Screen printing is a stencil process where conductive inks are patterned onto substrates through a fine mesh of threads. Nowadays, screen printing can be used to print radio frequency identification (RFID) antenna structures onto flexible and ultra-low-cost substrates such as paper. In this paper, we present a high frequency RFID (HF RFID) reader antenna system, operating at 13.56 MHz, using screen printed Ag particle ink as conductor and using HP photo paper as substrate. The proposed antenna system comprises four loop antenna elements, matched to $50~ \Omega $ , and one I2C addressed SP4T multiplexer circuitry, controlled through an exterior embedded system. The geometries, designs and characterizations of the antenna system are described in the manuscript in details. Measurement results show that the antenna system has low power reflections and a suitable ${Q}$ factor. It has a maximum 11.1 cm RFID tag read range at an antenna system input power of 33 dBm. 2-D RFID tag positioning can be enabled by utilizing the RFID tag interrogation zones formed by the four loop antenna elements. In addition, a parametrical study is carried out to investigate the effect of loop antenna element dc resistance on the antenna element performance. It can be concluded that the proposed method can be used to create low-cost and large-area HF RFID reader antenna systems.

Citation network is often used for academic recommendation. However, it is difficult to achieve high recommendation accuracy and low time complexity because it is often very large and sparse and different citations have different purposes. What's more, some citations include unreasonable information, such as in case of intentional self-citation. To improve the accuracy of citation network-based academic recommendation and reduce the time complexity, we propose an academic recommendation method for recommending authors and papers. In which, an author-paper bilayer citation network is built, then an enhanced topic model, Author Community Topic Time Model (ACTTM) is proposed to detect high quality author communities in the author layer, and a set of attributes are proposed to comprehensively depict the author/paper nodes in the bilayer citation network. Experimental results prove that the proposed ACTTM can detect high quality author communities and facilitate low time complexity, and the proposed academic recommendation method can effectively improve the recommendation accuracy.

The papers in this special section were presented at the 2019 IEEE International Symposium on Circuits and Systems (ISCAS 2019), that was held in Sapporo, Hokkaido, Japan from May 26 to 29, 2019.

Continuous monitoring of breathing flow is an essential but often poorly utilized predictor that is causing poor patient outcome. A low-cost, light-weight, easy-to-use, reliable, and disposable breathing sensor is required to bypass the limitations of existing conventional sensors, which are bulky, expensive, and often require experts to handle. In this article, a low-cost, inkjet-printed graphene sensor on a disposable glossy photo-paper substrate is presented for breathing-rate monitoring. The sensor architecture consists of a graphene nanoparticle based thin functional layer on top of a silver nanoparticle based interdigitated conductive pattern. A standard office inkjet-printer was configured with nanoparticle inks and a printed circuit board design software was utilized for the layouts. The sensor was tested in a laboratory environment, and its data were analyzed for different breathing patterns. An empirical model of the sensor was developed using the Cole–Cole impedance model. Test results showed successful detection of breathing rates for different breathing patterns. The prototype sensor provides a low-cost, disposable, and practical solution for frequent breathing pattern recognition.

Introduction: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form. Objectives: We present a MATLAB-based tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal. Methods: 30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results. Results: The validation demonstrates very high correlation values of several standard ECG parameters: PR interval 0.984 +/−0.021 (p-value < 0.001), QRS interval 1+/− SD (p-value < 0.001), QT interval 0.981 +/− 0.023 p-value < 0.001, and RR interval 1 +/− 0.001 p-value < 0.001. Conclusion: Digitized ECG signals from existing paper or scanned ECGs can be obtained with more than 95% of precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnostic and prognostic evaluation of patients with cardiovascular disease., We present a MATLAB-based tool and algorithm that converts a printed or scanned ECG into a digitized ECG signal. Validation on 30 ECG scanned curves more than 95% precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnosis and evaluation of patients with cardiovascular disease.

This paper focuses on the common mistakes made by students at the early stages of research when writing scientific papers, thesis and reports. It points out mistakes to be avoided and the basic rules that can be inferred by reading a few scientific documents but are not usually clearly written and that we, as professors, end up teaching over and over again. The outcome of this paper will be, hopefully, that we will not need to correct the same common mistakes again and you, as a student, will have a faster lane to publishing. Starting with the abstract: it is composed of a single paragraph, does not contain acronyms or references and describes in short the work, main highlights and points out the results or main conclusions obtained from the work being presented. The abstract is an independent part of the paper and commonly has a character or word limit that you need to respect. It can be read as a ``stand-alone'' and the paper starts in the introduction, meaning that the introduction is not the sequence of the abstract and it can have some text in common if needed.

The interaction between technology and healthcare has a long history. However, recent years have witnessed the rapid growth and adoption of the Internet of Things (IoT) paradigm, the advent of miniature wearable biosensors, and research advances in big data techniques for effective manipulation of large, multiscale, multimodal, distributed, and heterogeneous data sets. These advances have generated new opportunities for personalized precision eHealth and mHealth services. IoT heralds a paradigm shift in the healthcare horizon by providing many advantages, including availability and accessibility, ability to personalize and tailor content, and cost-effective delivery. Although IoT eHealth has vastly expanded the possibilities to fulfill a number of existing healthcare needs, many challenges must still be addressed in order to develop consistent, suitable, safe, flexible, and power-efficient systems that are suitable fit for medical needs. To enable this transformation, it is necessary for a large number of significant technological advancements in the hardware and software communities to come together. This keynote paper addresses all these important aspects of novel IoT technologies for smart healthcare-wearable sensors, body area sensors, advanced pervasive healthcare systems, and big data analytics. It identifies new perspectives and highlights compelling research issues and challenges, such as scalability, interoperability, device-network-human interfaces, and security, with various case studies. In addition, with the help of examples, we show how knowledge from CAD areas, such as large scale analysis and optimization techniques can be applied to the important problems of eHealth.

Designing clean, reusable, and repeatable experiments for a research paper does not have to be difficult. We report on our efforts to create an integrated toolchain for running, processing, and including the results of computer experiments in scientific publications.

In this paper, we present a practical application of dirty paper coding (DPC) for the Gaussian cognitive Z-interference channel. A two stage transmission scheme is proposed in which the cognitive transmitter first obtains the interference signal from the primary transmitter and then uses DPC to improve the performance of the cognitive link. Numerical results show that causal knowledge of the interference provides more than 3 dB improvement in performance in certain scenarios over a scheme that does not use interference cancellation. Results are also shown when the cognitive transmitter operates in both half-duplex and full-duplex modes.

Transmission of video traffic over the Internet has grown exponentially in the past few years with no sign of waning. This increasing demand for video services has changed user expectation of quality. Various mechanisms have been proposed to optimise the Quality of Experience (QoE) of end users video. Studying these approaches are necessary for new methods to be proposed or combination of existing ones to be tailored. We discuss challenges facing the optimisation of QoE for video traffic in this paper. It surveys and classifies these mechanisms based on their functions. The limitation of each of them is identified and future directions are highlighted., 18 pages, 7 figures

THE YEAR IS 2028. IT'S 8 P.M. ON A Wednesday night and you're famished. You're staring wistfully at the only remaining item in your refrigerator: a package of sausages with an unappetizing grayish hue. Ugh. Did they always look like that? Are they still safe to eat? In 2018, you'd have to rely on your sense of smell and take a gamble. But in 2028, you might simply wave your smartphone over the package. The smartphone interrogates the package's embedded sensor, which measures the concentration of gases associated with meat decomposition. The smartphone displays the message "Safe to eat within the next 20 hours," and then offers a list of recipes for cooking with sausages. Too hungry to bother with the recipes, you tear open the package, toss the sausages into a frying pan, and discard the package-along with its sensor technology.

A long-term vision of the power electronics community is to develop highly integrated power electronic converters that are specialized for their particular applications. The motivation for a migration from silicon (Si) to silicon carbide (SiC) power electronics is driven by the need for those power converters to have much greater power density, reliability, and overall system performance without costly devices or designs. The benefit of moving to SiC is that, for power electronics, it is inevitable that there will be a rapid transition from Si to SiC technology.

Our community publishes peer-reviewed journal, magazine, and conference papers and articles sharing a commitment to originality and to quality content and writing. These papers vary in their characteristics and style, depending on their objectives and their targeted readership. The IEEE Publication Services and Products Board (PSPB) Operations Manual (OM) also provides guidelines differentiating these paper types. There is confusion sometimes, however, concerning the features of magazine papers in particular. Some authors, especially early-career authors, do not fully appreciate certain differences between magazine papers and their journal, transaction, and letter counterparts. This can create a challenge for magazine editors and may result in delay, or rejection, of papers that could have been accepted if written with a better understanding of what magazines are about. We focus our attention here on this issue as it applies to IEEE Communications Magazine (ComMag) in particular. Our goal is to help our new and/or young authors who want to publish in ComMag to customize their writing to the nature of this magazine and enable them to author papers with higher chances of acceptance.

The electrophoretic display (EPD) has been widely applied on electronic readers (E-Readers) for displaying image/text contents. With a touch panel, the user can put annotations on the contents at will. However, none of the available E-Readers support the function of paintbrush, which allows the user to plot gray tone figures. This is because the EPD is a bistable display whose driving method is quite complicated. In this paper, we first discuss the issues of realizing paintbrush function on the EPD display controller and then propose a new signal processing engine for the paintbrush function. The engine contains four stages: 1) screening the detected pixel coordinates; 2) interpolating pixels; 3) performing image halftoning to generate more visual gray levels; and 4) driving the pixels to the target states. The experimental results show that the engine meets the real-time requirement to complete the pixel interpolation as well as produce the gray-tone traces.

The eminent growth in connected devices technologies, like the Internet of Things (IoT), 5 th generation (5G) and beyond communication systems, lead to innovations for human beings. The uninterrupted data communication services play diverse roles in the mobile user's routine life. The various changing user group patterns are unpredictable due to the day-by-day up-gradation of user devices. Even though the existing telecommunication services have upgraded up to 5G with high performance distributed computing (HPDC) in the distributed data networks (DDN) for ultra-reliable and low latency communication (URLLC) services, yet the backend distributed database systems (DDS) are facing serious security and privacy issues due to lack of federated intelligence in the data networks. Recently, we have seen an increased focus and effort by users and policymakers toward enhancing security and privacy related to the collection and usage of the data in DDNs. When it comes to the intelligence using machine/deep learning (ML/DL) of the HPDC systems for security and privacy, enough dataset is required, which often includes personal user information to train ML/DL models. As data privacy and security represents a growing critical concern, given the above-mentioned new areas of legislation and policies, novel ML methodologies like federated learning (FL) have been developed in part to address these concerns.

This paper studies the authentication problem of specific pieces of paper using mobile imaging devices. Prior work showing high matching accuracy has used the normal vector field , which serves as a unique, microscopic, physically unclonable feature of paper surfaces, estimated by consumer grade scanners. Industrial cameras were also used to capture the appearance of the surface rendered after the normal vector field based on the laws of optics under a semi-controlled lighting condition. In comparison, past explorations based on mobile cameras were very limited and have not had substantial success in obtaining consistent appearance images due to the uncontrolled nature of the ambient light. We show in this paper that images captured by mobile cameras can be used for authentication when the camera flashlight is exploited for creating a semi-controlled lighting condition. We have proposed new algorithms to demonstrate that the normal vector field of paper surface can be estimated by using multiple camera-captured images of different viewpoints. Perturbation analysis shows that the proposed method is robust to inaccurate estimates of camera locations, and a matching accuracy of $10^{-\textrm {4}}$ in equal error rate can be achieved using 6 to 8 images under a lab-controlled ambient light environment. Our findings can relax the restricted imaging setups and enable paper authentication under a more casual, ubiquitous setting with a mobile imaging device, which may facilitate duplicate detection of paper documents and counterfeit mitigation of merchandise packaging.

EditorsThis January 2018 issue of the IEEE Transactions on Visualization and Computer Graphics contains the proceedings of IEEE VIS 2017, held during 1-6 October 2017. In 2017, IEEE VIS returns to the city of Phoenix, AZ, USA, for the conference's 28th year. The conference will be held at the Hyatt Regency Phoenix hotel. VIS consists of three conferences, held concurrently: the IEEE Visual Analytics Science and Technology Conference (VAST 2017), the IEEE Information Visualization Conference (InfoVis 2017), and the IEEE Scientific Visualization Conference (SciVis 2017). Information on the paper review process is provided along with an overview of each conference.

We derive the capacity region of the two-user dirty-paper Gaussian multiaccess channel (MAC) with conferencing encoders. In this MAC, prior to each transmission block, the transmitters can hold a conference in which they can communicate with each other over error-free bit pipes of given capacities. The received signal suffers not only from additive Gaussian noise but also from additive interference, which is known noncausally to the transmitters but not to the receiver. The additive interference is modeled as Gaussian or uniform over a sphere. We show that the interference can be perfectly mitigated, i.e., that the capacity region without interference can also be achieved in its presence. This holds irrespective of whether the transmitters learn the interference before or after the conference. It follows as a corollary that also for the MAC with degraded message sets, the interference can be perfectly mitigated if it is known noncausally to the transmitters. To derive our results, we generalize Costa's single-user writing-on-dirty-paper achievability result to channels with dependent interference and not-necessarily Gaussian noise.

Application-oriented papers provide an important way to invigorate and cross-pollinate the visualization field, but the exact criteria for judging an application paper's merit remain an open question. This article builds on a panel at the 2016 IEEE Visualization Conference entitled "Application Papers: What Are They, and How Should They Be Evaluated?" that sought to gain a better understanding of prevalent views in the visualization community. This article surveys current trends that favor application papers, reviews the benefits and contributions of this paper type, and discusses their assessment in the review process. It concludes with recommendations to ensure that the visualization community is more inclusive to application papers.

With the advent of Network Function Virtualization (NFV) techniques, a subset of the Internet traffic will be treated by a chain of virtual network functions (VNFs) during their journeys while the rest of the background traffic will still be carried based on traditional routing protocols. Under such a multi-service network environment, we consider the co-existence of heterogeneous traffic control mechanisms, including flexible, dynamic service function chaining (SFC) traffic control and static, dummy IP routing for the aforementioned two types of traffic that share common network resources. Depending on the traffic patterns of the background traffic which is statically routed through the traditional IP routing platform, we aim to perform dynamic service function chaining for the foreground traffic requiring VNF treatments, so that both the end-to-end SFC performance and the overall network resource utilization can be optimized. Towards this end, we propose a deep reinforcement learning based scheme to enable intelligent SFC routing decision-making in dynamic network conditions. The proposed scheme is ready to be deployed on both hybrid SDN/IP platforms and future advanced IP environments. Based on the real GEANT network topology and its one-week traffic traces, our experiments show that the proposed scheme is able to significantly improve from the traditional routing paradigm and achieve close-to-optimal performances very fast while satisfying the end-to-end SFC requirements.

Digital pen and paper technologies are proving increasingly popular in vertical markets such as health care and manufacturing, which make heavy use of standardized forms that can easily be produced on specialized paper. But the still-untapped frontier is the horizontal zone of everyday consumers. Within this vast and tempting territory resides the commercial digital pen's most-often targeted subgroup: notetakers - whether they're journalists, lawyers, or the many students who sit daily with their heads bobbing between their paper or computer notebooks and the lecturer at the podium. In the past year, several new digital pens have targeted this group in the hope of uniting the broader market's paper and digital worlds. And one digital pen - Livescribe's Pulse smartpen - is working out its grand vision of a paper-based mobile computing platform that it hopes will radically alter how we view pens and their possibilities.

As a part of each graduate program study, graduate students need to conduct research in a specific field and, most importantly, publish the results of their work in journals and conference publications. While this might sound easy to some graduate students, it may be the most challenging task for many, specifically those in the early years of their studies. Particularly, students have questions regarding transferring the results of their work from a "report" into a research paper, writing high-quality research papers, and how to choose among the various journals and conference publications in their fields. Very often, many students have good results but they have a difficult time publishing them.

The Editorial Board of the IEEE Open Access Journal of Power and Energy (OAJPE) would like to recognize the following best papers published between October 2017 and September 2020 in OAJPE and its preceding journal, IEEE Power and Energy Technology Systems Journal (PETS-J). 1) X. Zhao, Y. Xue, and X.-P. Zhang, “Fast frequency support from wind turbine systems by arresting frequency nadir close to settling frequency,” IEEE Open Access J. Power Energy , vol. 7, pp. 191–202, 2020. 2) P. Kotsampopoulos et al. , “A benchmark system for hardware-in-the-loop testing of distributed energy resources,” IEEE Power Energy Technol. Syst. J. , vol. 5, no. 3, pp. 94–103, Sep. 2018.

Knowledge of the condition of power transformer winding insulation paper is fundamental to making optimum asset replacement decisions in the power industry. The ability to assess the aged condition of Kraft paper quickly and non-destructively using portable instrumentation would significantly increase the opportunities for gaining this knowledge. Insulation paper degrades over time in-service and its degree of polymerization (DP) reduces, eventually affecting its mechanical strength. At low DP levels the insulation may start to disintegrate and the risk of electrical breakdown increases. Currently-used methods of estimating DP are either approximate or destructive. The use of spectroscopy together with multivariate statistical analysis (MVSA) provides a powerful non-destructive evaluation of the condition of paper. From initial feasibility studies, we have developed a simple, portable system (TRANSPEC) using fiber-optics and broad-band spectroscopy that can measure the degree of polymerization of various aged transformer papers to a precision of approximately 30 DP units with a spatial resolution of 14 mm. The system can also measure the chemical composition and condition of the insulating mineral oil. MVSA regression models were constructed from library spectral data, and these models are used to predict the DP of other papers with parameters that fall within the range spanned by the set of calibration samples. Separating oil and moisture information from wetted paper is possible and will be reported in a separate publication. With a single TRANSPEC system, non-destructive in-situ analysis of the DP of insulating paper is possible, providing a rapid cost-effective method for transformer insulation condition assessment and monitoring, which correlates well with current destructive methods.

We consider transmission over the ergodic fading multiple-antenna broadcast (MIMO-BC) channel with partial channel state information at the transmitter and full information at the receiver. Over the equivalent non-fading channel, capacity has recently been shown to be achievable using transmission schemes that were designed for the "dirty paper" channel. We focus on a similar "fading paper" model. The evaluation of the fading paper capacity is difficult to obtain. We confine ourselves to the linear-assignment capacity, which we define, and use convex analysis methods to prove that its maximizing distribution is Gaussian. We compare our fading-paper transmission to an application of dirty paper coding that ignores the partial state information and assumes the channel is fixed at the average fade. We show that a gain is easily achieved by appropriately exploiting the information. We also consider a cooperative upper bound on the sum-rate capacity as suggested by Sato. We present a numeric example that indicates that our scheme is capable of realizing much of this upper bound.

Recently, the vector dirty paper coding (DPC) achievable rate region has been shown to be the capacity region of a multiple-input multiple-output Gaussian broadcast channel (MIMO GBC). With DPC, the multiuser interference noncausally known at the transmitter can be completely removed. In this paper, we present a vector DPC structure for MIMO GBC. It is a generalization of the single antenna superposition dirty paper coding for the scalar Gaussian dirty paper problem proposed by Bennatan et al. In a theoretical random code setting, this construction is shown to be able to achieve the promised rate performance of the MIMO GBC. We also implement it with existing vector quantizer and capacity-achieving channel coding. Combined with iterative decoding, a design example validates the effectiveness of our methods.

A generalization of the problem of writing on dirty paper is considered in which one transmitter sends a common message to multiple receivers. Each receiver experiences on its link an additive interference (in addition to the additive noise), which is known noncausally to the transmitter but not to any of the receivers. Applications range from wireless multi-antenna multicasting to robust dirty paper coding. We develop results for memoryless channels in Gaussian and binary special cases. In most cases, we observe that the availability of side information at the transmitter increases capacity relative to systems without such side information, and that the lack of side information at the receivers decreases capacity relative to systems with such side information. For the noiseless binary case, we establish the capacity when there are two receivers. When there are many receivers, we show that the transmitter side information provides a vanishingly small benefit. When the interference is large and independent across the users, we show that time sharing is optimal. For the Gaussian case we present a coding scheme and establish its optimality in the high signal-to-interference-plus-noise limit when there are two receivers. When the interference is large and independent across users we show that time-sharing is again optimal. Connections to the problem of robust dirty paper coding are also discussed., Comment: To appear, IT. Trans. on Information Theory. Modified to incorporate reviewer's comments. Also includes some additional results for the Gaussian case

Critical sections of a paper machine such as the wet end presses, size press, on-machine coater, and reel may be required to undergo large and fast load changes in normal operation. These changes must result in minimum disturbance to the ingoing sheet tension or nip roll speed in order to avoid sheet breaks or wrinkling. This paper evaluates two disturbance-rejection control techniques, one using disturbance feedforward (or disturbance input decoupling) based on a nip-pressure disturbance observer and the other using a two-degrees-of-freedom controller. Evaluations were done in both a press section and a size press section of a lightweight paper machine, and the results showed that both methods are very effective in reducing the maximum speed deviation to about 10% of the values without compensation and can operate combined with torsional oscillation compensation without interference between them