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A precoding technique based on polar codes and probabilistic shaping is introduced for dirty paper coding. Two variants of the precoding use multi-level shaping and sign-bit shaping in one dimension. The decoder uses multi-stage successive-cancellation list decoding with list-passing across the bit levels. The approach achieves approximately the same frame error rates as polar codes with multi-level shaping over standard additive white Gaussian noise channels at a block length of 256 symbols and with different amplitude shift keying (ASK) constellations.

We consider the problem of communicating over a channel that randomly “tears” the message block into small pieces of different sizes and shuffles them. For the binary torn-paper channel with block length $n$ and pieces of length ${\mathrm{ Geometric}}(p_{n})$ , we characterize the capacity as $C = e^{-\alpha }$ , where $\alpha = \lim _{n\to \infty } p_{n} \log n$ . Our results show that the case of ${\mathrm{ Geometric}}(p_{n})$ -length fragments and the case of deterministic length- $(1/p_{n})$ fragments are qualitatively different and, surprisingly, the capacity of the former is larger. Intuitively, this is due to the fact that, in the random fragments case, large fragments are sometimes observed, which boosts the capacity.

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.

This paper presents the design and analysis of a paper-based chipless RFID sensor for wetness content measurement in plant leaves. The sensor is designed using a passive microwave resonator to measure the ambient physical parameter. The resonator is designed with a rectangular loop inductively coupled with an interdigital capacitor. The experimental analysis of the designed sensor is illustrated here to prove its efficacy. The resonator provides adequate backscattered signal for wireless measurement in the microwave frequency band. When interrogated with a couple of UWB antennas and a VNA, it yields accurate leaf wetness response of the plant. The commercial competence of this sensing device is validated through its experimental analysis on three different types of leaf samples. This sensor is suitable for plant health monitoring in any small-scale gardening applications such as nurseries and greenhouses.

This paper investigates the impact of institutes and papers over time based on the heterogeneous institution-citation network. A new model, IPRank, is introduced to measure the impact of institution and paper simultaneously. This model utilises the heterogeneous structural measure method to unveil the impact of institution and paper, reflecting the effects of citation, institution, and structural measure. To evaluate the performance, the model first constructs a heterogeneous institution-citation network based on the American Physical Society (APS) dataset. Subsequently, PageRank is used to quantify the impact of institution and paper. Finally, impacts of same institution are merged, and the ranking of institutions and papers is calculated. Experimental results show that the IPRank model better identifies universities that host Nobel Prize laureates, demonstrating that the proposed technique well reflects impactful research. Refereed/Peer-reviewed

Unplanned outages within the paper industry lead to significant costs to operators. To reduce unplanned outages and losses, condition monitoring (CM) techniques can be implemented to determine asset health and help target maintenance to where it is required most. The challenges faced by the paper industry to implement condition-based maintenance (CBM) on their medium-voltage (MV) and high-voltage (HV) networks and solutions for online partial discharge (OLPD) monitoring are due to the range and number of assets present and the geographical spread coupled with problems of retrofitting the technology on pre-existing plants. The implementation of OLPD testing or monitoring can vary based on several factors: network topology, connected assets, switchboard type, and the owner's budget and desired local engineer involvement. This article discusses different approaches to implementing an OLPD test program, ranging from simple handheld survey units to complete network monitoring. Its focus is on case studies of the deployment of OLPD testing and monitoring solutions at several U.S. paper sites. The case studies present diagnostic testing of unshielded MV cable at one paper mill up to full permanent OLPD monitoring systems at two others.

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.

Traction transformer is vital equipment in high-speed railway. Insulation status decides its safety and reliability, and the frequency-domain dielectric spectrum (FDS) test is one of the most effective methods reflecting the changing of insulation status. For field applications, the following problems should be addressed: 1) how to obtain the result of paper insulation from the combined result of the oil–paper insulation system and 2) how to discriminate the defects of insulation paper between aging and damp. In this article, the first problem was transferred to a nonlinear equation set, and a cuckoo search algorithm optimized by the differential evolution algorithm and the quadratic interpolation (QI) method (CS-DQ algorithm) was proposed to solve it. Then, the insulation states were discriminated by establishing a multiclass least-squares support vector machine (LS-SVM) model, in which the CS-DQ algorithm was also used. Finally, a diagnostic approach for the insulation paper in the traction transformer was proposed. The results in the laboratory show that the pure result of insulation paper can be obtained, and the insulation states can be discriminated effectively by using the proposed approach. Meanwhile, the proposed CS-DQ algorithm has a better performance than the conventional CS algorithm. The results of field testing also verify the proposed approach.

Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.

Moisture accumulates with the growing aging progress of oil-paper insulation and further shortens the remaining life of the transformer. The frequency-domain spectroscopy (FDS) technique can be used to realize the moisture estimation. However, the moisture estimation results would be unreliable once the aging effect on FDS was ignored. Given this issue, an alternative model including the aging effect is thus proposed using FDS and intelligent algorithm. In this work, the feature parameters of FDS data are used to build the databases for characterizing the aging degree and moisture. Then, the moisture estimation models are developed using the weighted K-nearest neighbor (K-NN) algorithm. The accuracy and applicability of the proposed models are finally discussed in laboratory and field conditions. In that respect, the findings reveal that the reported model is available for moisture estimation of transformer oil-paper insulation under various aging degrees and test temperatures.

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.

Reseachers have investigated numerous factors influencing citation counts of cited papers. One factor investigated has been the number of gained citations, as this could increase the visibility of cited papers and subsequently induce further citations. In this paper, aiming to identify a particular kind of citation that could trigger a rapid growth in the citation counts of cited papers, a concept of "citation promoter" was proposed. We defined citation promoters based on the annual citation rates of the cited papers and the co-citation counts received by the pair of cited and citing papers. The comparative results showed that papers would obtain a sharp rise in citation counts shortly after they were cited by citation promoters. Papers that received citation promoters at an early age outperformed other papers in long-term citation counts. In addition, we developed a classification model for predicting whether a citing paper would be a citation promoter for its cited paper. Since it was a class imbalanced problem (4% positive instances), and there was a lack of content and author features in our dataset, our preliminary models achieved moderate performance with an $F_1$ score slightly higher than 0.5, while the $F_1$ score obtained by random guessing was 0.07. National Research Foundation (NRF) Accepted version This research is supported by the National Research Foundation, Prime Ministers Office, Singapore under its Science of Research, Innovation and Enterprise programme (SRIE Award No. NRF2014-NRF-SRIE001-019).

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.

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.

This paper presents the design, fabrication, and characterization results of a paper-based, low-cost, easy-to-use, comfortable and wearable tactile sensor array for human health monitoring applications. Paper substrate and spray-deposited metallic electrodes and traces are employed to achieve a low fabrication cost. Capacitive sensing scheme employing a deformable triangular PDMS sensing membrane is chosen due to its high sensitivity and zero DC power dissipation. Trade-offs among different sensor array designs are investigated to achieve an optimal design, which consists of a $1\times 5$ sensor array occupying an area of 10 mm $\times10$ mm. Each sensor in the array achieves a nominal capacitance value and sensitivity of approximately 1 pF and 30 fF/mmHg, respectively. Fabricated sensor array can be comfortably attached to an individual’s temporal region and ankle area to monitor real-time blood pressure (BP) pulse waveform with high fidelity, from which heart rate and heart rate variability information can be accurately obtained. In addition, the prototype sensor array demonstrates its capability of detecting atrial fibrillation (AFib) from BP pulse waveform measured from an AFib patient. [2020-0179]

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%.

Oil-paper insulation ageing of transformer has characteristics of spatial locality due to uneven temperature distribution. The transformer is sealed, it is difficult to obtain the local ageing state of oil-paper insulation by sampling directly. Currently, there are no effective detection method to detect the insulation state of local regions. The oil-immersed paper resistivity inversion method can nondestructively assess the local state of oil-paper insulation. However, the current inversion methods still have shortcomings and need to be improved. In this paper, combining the back propagation neural network (BPNN) and Newton downhill method (NDM), an improved inversion method is proposed to optimize the current inversion method, so as to reduce the effect of initial value on it and make calculated results converge better. Finally, a 3D 10 kV transformer model is established to verify the effectiveness of the proposed method. The resistivity calculated by the improved method show that the method can provide initial resistivity values for technicians and improve the convergence and accuracy of current inversion method, which is more suitable for extending to the engineering practice.

This paper presents an augmented reality-based Chinese character composition game (ARC) that employs augmented papers to engage lower primary school students in collaborative Chinese character learning. A design research approach was used to gain a holistic view of designing, enacting, and evaluating the ARC. The participants included four teachers and five classes of students from two primary schools. The school-based research findings suggested that: 1) the ARC was an effective system for improving beginning learners’ orthographic knowledge of Chinese characters, particularly for those students with low Chinese language proficiency; 2) the tangible paper interface engaged students in group work and increased the game playfulness; and 3) students’ language proficiency had a significant effect on their perceptions of ease of use and usefulness of the augmented paper game. This paper not only substantiated the potential of the augmented paper as a carrier of educational content, but also instantiated how to implement it to promote social and cognitive supports for interaction and collaboration.

Paper-based digital microfluidic biochips (P-DMFBs) have recently emerged as a promising low-cost and fast-responsive platform for biochemical assays. In P-DMFBs, electrodes and control lines are printed on a piece of photograph paper using an inkjet printer and carbon nanotubes (CNTs) conductive ink. Compared with traditional digital microfluidic biochips (DMFBs), P-DMFBs enjoy significant advantages, such as faster in-place fabrication with printer and ink, lower costs, and better disposability. Since electrodes and CNT control lines are printed on the same side of this paper, a critical design challenge for P-DMFB is to prevent control interference between moving droplets and the voltages on CNT control lines. Control interference may result in unexpected droplet movements and thus incorrect assay outputs. To address this design challenge, a control-fluidic codesign methodology is proposed in this paper, along with two demonstrative design flows integrating both fluidic design and control design, i.e., the droplet-oriented codesign flow and the electrode-oriented codesign flow. The droplet-oriented flow is suitable for designing biochips with sparse electrodes and relatively larger number of droplets, whereas the electrode-oriented flow is suitable for biochips with dense electrodes and smaller number of droplets. The computational simulation results of real-life bioassays demonstrate the effectiveness of the proposed codesign flows.

In this note, it is shown that the results claimed in the paper [1] —as well as the examples presented there—are incorrect.

Special Feature that lists the names of people involved in the review of technical papers for the IEEE Transactions on Industry Applications and Industry Applications Magazine.

The volume of research articles in digital repositories is increasing. This spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries. The problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories. In the traditional recommendation approaches, the results of the query miss many high-quality papers, in the related work section, which are either published recently or have low citation count. To overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles. Many research paper recommendation approaches have been implemented which includes collaborative filtering-based, content-based, and citation analysis-based techniques. The collaborative filtering-based approaches primarily use paper-citation matrix for recommendations, whereas the content-based approaches only consider the content of the paper. The citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest. It is therefore very difficult for a recommender system to recommend high-quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information. The proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors' collaboration network finds key authors from those papers. The papers selected by this hybrid approach are then recommended to the user. The results have shown that our proposed method performs exceedingly well as compared with the state-of-the-art existing systems, such as Google scholar and multilevel simultaneous citation network.

Current transformers (CTs) are extensively used in electrical switchyards and substations for metering and protection purposes [1] and are expected to operate properly for at least 30 years [2]. Normally, aging of CTs under service conditions is relatively slow, but they also may fail prematurely due to various reasons, such as design faults, inadequate quality during manufacture, lightning strikes, operation beyond rated conditions and inadequate maintenance [3]. Compared with other high voltage apparatus, failure of a CT can be violent or even catastrophic, causing damages to the CT itself as well as to nearby located assets and humans [4–9]. Build-up of internal pressure during a failure of a CT results in an instant fire due to burning of oil and explosion of porcelain housing, where porcelain pieces can spread to a distance up to 100 m [4], [6].

Dirty paper coding is an interference pre-cancellation method for known interference at the transmitter and serves as a basic building block in the digital watermarking system. In this letter, we investigate the dirty paper model in the simplest digital communication system, where both the codeword and the interference are binary. For watermark embedment, we derive the relevant coding, the constant coding, and the symmetric relevant coding when the encoder focuses on the binary codeword and interference. The channel capacity is analyzed and the optimal parameter is discussed in the case.

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.

Evolutionary game dynamics is a combination of game theory and dynamical systems. Using dynamical theory, we investigate chaotic behavior in asymmetric Rock-Paper-Scissors games under imitative dynamics with two different populations. Our theoretical analysis and numerical simulations demonstrate that the dynamical system can give rise to chaotic behavior in zero-sum and positive-sum asymmetric games. However, chaos cannot occur in a negative-sum asymmetric game. In particular, the numerical simulations show that there is a limit cycle in a negative-sum asymmetric game.

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.

We present a novel implementation of a Rock-Paper-Scissors (RPS) game interaction with a social robot. The framework is tailored to be computationally lightweight, as well as entertaining and visually appealing through collaboration with designers and animators. The fundamental gesture recognition pipeline employs a Leap motion device and two separate machine learning architectures to evaluate kinematic hand data on-the-fly. The first architecture is used to recognize and segment human motion activity in order to initialize the RPS play, and the second architecture is used to classify hand gestures into rock, paper or scissors. The employed tabletop robot interacts in the RPS play through unique animated gestural movements and vocalizations designed by animators which communicate the robot's choices as well as cognitive reflection on winning, losing and draw states. Performance of both learning architectures is carefully evaluated with respect to accuracy, reliability and run time performance under different feature and classifier types. Moreover, we assess our system during an interactive RPS play between robot and human. Experimental results show that the proposed system is robust to user variations and play style in real environment conditions. As such, it offers a powerful application for the subsequent exploration of social human-machine interaction.

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.

Interdisciplinary teams and studies need new approaches to design prototypes using tools indistinguishable from the ones they are used to. We utilize a digital pen and physical paper, as the everyday life tools, to build a smart interface for laser-cutters, giving non-technical experienced people the possibility to rapidly, seamlessly, and collaboratively fabricate creative prototypes.

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.

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.

The benefits of using a low-voltage insulation monitoring system on loads grounded with the three most common grounding methods are reviewed in this article. Premature motor failures caused by moisture will be discussed. An introduction to the concept of continuous offline monitoring will be provided as well as its safety benefits, and the advantage of using a low-voltage monitoring signal will be explored.

Dirty paper coding (DPC) offers an information-theoretic result for pre-cancellation of known interference at the transmitter. In this letter, we propose practical DPC schemes that use only one error correction code. Our designs focus on practical use from the viewpoint of complexity. For fair comparison with previous schemes, we compute the complexity of proposed schemes by the number of operations used. Simulation results show that compared to previous DPC schemes, the proposed schemes require lower transmission power to maintain the bit error rate to be within $10^{-5}$ .

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.

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.

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.

Over the decades, the immense growth has been reported in research publications due to continuous developments in science. To date, various approaches have been proposed that find similarity between research papers by applying different similarity measures collectively or individually based on the content of research papers. However, the contemporary schemes are not conceptualized enough to find related research papers in a coherent manner. This paper is aimed at finding related research papers by proposing a comprehensive and conceptualized model via building ontology named COReS: Content-based Ontology for Research Paper Similarity. The ontology is built by finding the explicit relationships (i.e., super-type sub-type, disjointedness, and overlapping) between state-of-the-art similarity techniques. This paper presents the applications of the COReS model in the form of a case study followed by an experiment. The case study uses InText citation-based and vector space-based similarity measures and relationships between these measures as defined in COReS. The experiment focuses on the computation of comprehensive similarity and other content-based similarity measures and rankings of research papers according to these measures. The obtained Spearman correlation coefficient results between ranks of research papers for different similarity measures and user study-based measure, justify the application of COReS for the computation of document similarity. The COReS is in the process of evaluation for ontological errors. In the future, COReS will be enriched to provide more knowledge to improve the process of comprehensive research paper similarity computation.

Pancreatic disorders, originating from alcoholism or other sources, have grown into an epidemic in modern society. It is now well known that regular monitoring of -amylase and lipase activities in the blood serum is crucial to judge the condition of the pancreas and assess progress in treatment. However, there is hardly any gadget available at the user's site which can provide guidance on pancreatic health. The present spectroscopic techniques used in pathological laboratories are not only costly but also involve significant time and expertise for testing and analysis. In view of the above, we report the development of a paper-based chemiresistive sensor for the quantitative detection of lipase concentration in human serum. The sensor can be integrated with a point-of-care-testing (POCT) device to provide affordable primary healthcare related to the pancreatic disorders. The hydrolysis of olive oil with lipase present in human blood serum results in the generation of H ions on a reduced graphene oxide-coated paper substrate. A linear variation in electrical resistance with lipase loading in serum configured the working principle of the sensor. The sensors are characterized and calibrated with known concentrations of lipase followed by successful validation with real-life human serum samples. The lower cost, simpler operation, smaller response time, and easy disposability of the sensors ensure their suitability to evolve into a POCT kit for the regular monitoring of pancreatic health at the user's end.

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.

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.

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