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Prediction and detection of skin diseases have always been a challenging and critical task for health care professionals. There are many clinics and skin practitioners who are offering their services at exorbitant costs. At the same time, skin condition prevails in our country in every part. In the current scenario majority of the skincare practitioners are using traditional methods to diagnose the disease which may take a considerable amount of time. Skin Diseases are serious problems in recent times as it is a matter of environmental factors, socioeconomic factors, lack of full diet, etc. This paper is a comparative study about understanding various skin diseases related to normal skin issues as well as cosmetology. A comparison between oily, dry, and normal skin is also projected in this study. A survey of different papers is done on basis of technologies used, results with accuracy, ethical conduct, number of diseases diagnosed, datasets, etc are done.

This paper describes approaches to the use of wearables and related monitoring and diagnostic tools to glean and present data to patients and their caregivers in the form of Personal Health Dashboard. Raw data from devices such as fitness watches, blood glucose and blood pressure monitors can be analyzed by machine learning algorithms ensembles over the cloud or at the edge, and the resulting patterns can be presented to allow for actionable responses. The advent of low-cost computational, sensing, monitoring and diagnostic tools allow for cost-effective solutions that can have wide adaptability. The paper draws from prior and ongoing research conducted by the team to present these positions in the advancement of healthcare.

In power transformers in which is dominated the oil-paper insulation system, it is the effort to use innovative insulating materials to achieve greater quality, reliability and longer service life of the transformer. In this paper, we present the results of research into the insulating properties of an oil-paper insulation system in a DC and AC electric field. In the experiments MOL TO 40A transformer oil and two types of paper used in transformers, KREMPEL DPP and insulating paper used in BEZ transformers, were used. The results from the experiments show that KREMPEL insulating paper has a higher electrical strength and the electrical strength of both oil-impregnated insulating papers is greater when applying DC voltage.

The assessment of the aging condition is becoming increasingly important for operators of high voltage assets. Power transformers are especially one of the most important units in the electrical power grid. Due to the fact that oil-paper insulation is still the most economical form of insulation, and is therefore found in the majority of transformers, the condition assessment of the paper insulation is decisive for estimating the remaining service life. Since the determination of the condition via furans is associated with certain fluctuations, several other aging parameters are investigated. Therefore, this study intends to determine how those aging markers of the insulating liquid behave in the absence of insulating paper and how the values change with insulating paper; thus, from the difference between important parameters, indicating the paper aging might be possible. For the investigation, a non-inhibited and an inhibited insulating oil as well as a further inhibited oil with a different composition, are tested as the most common insulating liquids. For this purpose, an open system is reproduced, which has permanent contact with the environment air. Accordingly, the study is carried out for long-term thermal stress to obtain samples with a wide spectrum of aging status. The entire study extended over a period of 14–15 weeks at a temperature of 130 °C with performing the measurements of the parameters at various aging times. The study reveals that the results can be used for an improved condition assessment of transformers.

This paper presents a novel, low cost flexible paper substrate suitable for biosensing applications. A microstrip patch antenna consisting of copper tape foil and a cellulose filter paper as a substrate with thickness of 180 μm is fabricated and tested. The substrate has measured dielectric constant (e r ) of 1.9 and losses (tan δ ) of 0.025. For 3.8 GHz resonance, the antenna has size of 29.5 mm x 38 mm on a ground plane of 50 mm x 60 mm. Measurements of the final design are in excellent agreement with simulations. Moreover, measurements of the patch antenna on the proposed substrate (cellulose filter paper) collected at different bending radii and folding angles demonstrate performance consistency.

The degree of polymerization is a useful technique to monitor insulation capabilities of oil impregnated paper. In present work, accelerated thermal ageing experiments have been conducted by taking Kraft and thermal upgraded papers along with two types of dielectric oil (mineral oil and vegetable oil) at different temperatures $(90^{\circ}C, 110^{\circ}C$ and $130^{\circ}C)$. An improved 2nd order kinetic model has been developed based on experimental results which utilized to develop a relationship between temperature and final degree of polymerization. The proposed model can be used to analyze and monitor thermal deterioration of oil paper insulation at any temperature. Moreover, the proposed novel mathematical modelling can be employed to predict premature failure as well as end of life criteria of power transformers.

Basic repair and maintenance tasks of industrial machines are usually performed by local service personnel at the location of the component to be maintained. Complex maintenance tasks require an expert who travels to the customer to solve a specific problem. This process is time and cost intensive. Augmented Reality (AR) Remote Assistance offers means to support newcomers on site by guiding personnel through the maintenance process with novel visualizations. This paper describes an Augmented Reality based remote assistance tool for industrial purposes. We present the technology and our implementation approach, as well as the challenges and limitations we have encountered. The potential of the tool was evaluated and analyzed in an industry oriented use case for repairing an industrial PC. The results were compared with a maintenance procedure that was carried out using standard paper repair instructions. Our approach enabled the users to perform complex tasks faster and with fewer errors. Overall, the error rate decreased significantly due to the AR-based remote support. We also found that paper-based instructions are still more efficient in solving simple tasks.

Paper-based digital microfluidic biochip (PB-DMFB) technology provides a promising solution to many biochemical applications. However, PB-DMFB manufacturing process may suffer from potential security threats. For example, both Trojan insertion and man-in-the-middle attack may affect the functionality of PB-DMFBs. To ensure the correct functionality of PB-DMFBs, we propose a watermarking scheme to hides information in the PB-DMFB layout, which allows users to check design integrity and authenticate the source of the PB-DMFB design. As a result, it serves as a first countermeasure against both Trojan insertion and man-in-the-middle attacks for PB-DMFBs.

Machine learning (ML) has increasingly been adopted in safety-critical systems such as autonomous vehicles (AVs) and industrial robotics. In these domains, reliability and safety are important considerations, and hence it is critical to ensure the resilience of ML systems to faults and errors. On the other hand, soft errors are becoming more frequent in commodity computer systems due to the effects of technology scaling and reduced supply voltages. Further, traditional solutions for masking hardware faults such as Triple-Modular Redundancy (TMR) are prohibitively expensive in terms of their energy and performance overheads. Therefore, there is a compelling need to ensure the resilience of ML applications to soft errors on commodity hardware platforms.We first experimentally assess the resilience of safety-critical ML applications to soft errors. We demonstrate through fault injection experiments that even a single bit flip due to a soft error can lead to misclassification in Deep Neural Network (DNN) applications deployed in AVs, leading to safety violations. However, not all the errors in an DNN will result in serve consequences such as safety violations, and hence it is sufficient to protect the DNN from the ones that do. Unfortunately, finding all possible errors that result in safety violations is a very compute intensive task. We propose BinFI, a fault injection approach that efficiently injects critical faults that are highly likely to result in safety violations, based on the unique properties of DNNs. Finally, we propose Ranger, an approach to protect DNNs from critical faults with minimal performance overheads and no accuracy loss. We will conclude by presenting some of our ongoing work, and the future challenges in this area.

The exchange of academic marks between HEIs (Higher Education Institutions) is mandatory in every student mobility programs (i.e. the EU Erasmus Program) but that process remains to present date with insufficient technological support and the absence of a comprehensive reference model that allows the integration of potential technological solutions for the exchange of academic data with existing Academic Information Systems seems to limit greatly the possibility of adopting solutions of this type referred to in the existing literature. This work addresses this issue, conducting an initial bibliographic review aimed at the identification of the fundamental requirements of such an architecture as well as explores some of the technologies that are showing potential for usage in the safe exchange of academic results between partner HEIs, with particular interest in blockchain technology applied in an educational context. info:eu-repo/semantics/publishedVersion

Even today, oil-paper insulation is one of the most important insulation systems for high-voltage components, which is mainly used in transformers. Due to an increase of regulations and obligations for transformer operators, alternative insulating liquids are becoming more and more important, especially esters, as these liquids are characterized as an environmentally friendly and low flammability substitute. So far there is only little experience on the ageing behaviour of ester-paper insulations and on the formation of ageing products with this mixed dielectric. The aim of this study is to investigate the influence of a synthetic based ester, a rapeseed-based and a soya-based natural ester on the ageing behaviour of a Kraft paper insulation. In this context, the correlation of ageing markers, such as the acid value, the moisture content etc., is measured in the liquid and the current condition of the paper insulation is investigated. It could be observed that the ageing behaviour of studied samples is different to mineral oil based liquids and also differences between synthetic and natural esters could be seen.

In the power transformer, the oil-impregnated paper is used for providing insulation between live conducting parts, which comprises of transformer oil and insulating paper (either Kraft paper/Pressboard). These insulating papers are made up of cellulose fibers and it gets easily degraded at higher hot-spot temperature with increase in aging periods. So this affects the lifetime of solid insulation and further transformer life. In this research work, the latest advanced solid insulation named “Nomex paper” made up of aramid polymers is considered as a solid insulating paper material for analyzing the aging behavior. Furthermore, the electrical properties such as breakdown voltage of both oil and paper, flash point and fire point, viscosity have been measured to study the change in characteristics during aging. Also, the level of degradation of solid insulation has been identified by measuring Breakdown voltage of paper under all aging times. On looking standard IEEE C57.91, all these samples were analyzed at the maximum hot-spot temperature of about $130^{\mathbf {\circ }}$C. The results clearly show that the solid insulation gets affected due to deterioration and decomposition.

In this paper, we study the state-dependent Gaussian wiretap channel with action encoder and noncausal state at the transmitter, which is also called the action-dependent dirty paper wiretap channel. Two lower bounds on the secrecy capacity of this model are provided, and we find that the two bounds have their advantages and disadvantages, i.e., none of the two always performs better than the other. In addition, we find that taking action on the state helps to increase the achievable secrecy rate of the dirty paper wiretap channel.

Due to the shortcomings of electrical properties and furfural in characterizing the aging characteristics of paper insulation and the limitations of field application, this paper focuses on the feasibility of evaluating the polymerization degree of insulating paper by the ethanol (EtOH) content in transformer oil. The accelerated thermal aging test is conducted, during which ethanol is produced by degradation of insulating paper. For each oil-paper samples, with different ethanol concentrations are prepared by controlling the aging time. In this paper, a gas chromatograph equipped with a mass spectrometer (GC-MS) measurement method with headspace injection is applied to obtain a fitting curve between ethanol content in oil and average degree of polymerization (DPv), which is a method for judging the degree of paper insulation aging condition. Then, showing the influence of temperature on ethanol generated in this experiment. At last, it is indicated that it is feasible to evaluate the polymerization degree of insulating paper by using the ethanol content in transformer oil.

Moisture in transformer paper insulation is one of the most important parameters to be monitored in order to assess the useful life of the transformer. Time domain spectroscopy based on polarization and depolarization current measurements is one of the methods that can be used for detection of moisture content in the transformer paper insulation. However, these measurements could be influenced by thermal transient during the application of voltage, which may lead to an inaccurate interpretation of the insulation condition. In this paper, a novel method is proposed in order to avoid this thermal transient effect by using the high frequency component of the of depolarization current acquired during the measurement. Preliminary results show a correlation between the high frequency component of the depolarization current waveform and the moisture content in the paper insulation within a moisture content range of about 3 %. At higher moisture content, a resistive leakage current seems to have a negative effect on the measured depolarization current., 2019 IEEE 20th International Conference on Dielectric Liquids (ICDL), June 23-27, 2019, Roma, Italy

This paper describes a user identification method conducted across online social networks (OSNs) using information regarding friends’ locations, in contrast to a conventional method based on the similarity of two display names. This latter method encounters the problem of decreased identification accuracy if a user registers different display names across OSNs. Our proposed method aims at addressing this issue by utilizing information on friends’ locations. We convert location information extracted from an OSN to that of such geographic administrative units as country, state and city, and calculate the weighted occurrence frequency of a location pair on the basis of distance between the pair. This is based on the hypothesis that a friend list relatively rarely includes an account pair whose locations are extremely distant from one another. We calculate a similarity score between two accounts’ weighted occurrence frequencies for each geographic administrative unit. Finally, we identify a user on the basis of individual similarity scores or weighted average scores for a given geographic administrative unit. Evaluation experiments show that our proposed method improves performance over that of a conventional method (accuracy: 83.1%$\rightarrow$91.2%).

International audience; Internet of Things (IoT) is a network where several and various interconnected, smart objects continuously generate alarge amount of data transmitted over Internet. Several efforts and research works in IoT focused on hardware constraints, lowcost, application design, as well as good communication technologies. However, due to the intrinsic characteristics of diversity,heterogeneity, large-scale, dynamic and large volume of data generated and various IoT applications needs, using traditionaldatabase management mechanisms and analytics architectures is not generally suitable. Then, it is challenging to provideefficient IoT data storage and query processing mechanisms for satisfying IoT application needs. This paper identifies the mainspecifications and mechanisms of data and query management for IoT and classifies them. Moreover, this work presentsadvances on data and query management mechanisms on IoT, shows their advantages and limits and discusses the challengingopen research issues that need to be addressed for providing guidelines for further contributions.

Several applications can benefit from recording information about the places a mobile entity visits and the length of time it spends there (e.g., shoppers, employees, buses, movable equipment, autonomous robots, etc.). In this paper, we present our approach to recording spatio-temporal information in a secure and inviolable way using a Distributed Ledger Technology. So far in this work, we observe that, although sending data to the blockchain is a heavy process, there are ways that this transaction time does not disrupt the flow of new data such as adding these data to a queue. However, this makes the sending process overall longer. We intend to continue this study by testing different blockchain services and comparing their performances.

We report on THz metamaterials for chemical sensing fabricated on low-loss, plastic, flexible substrates. The metamaterial is bonded to a paper sheet, which is saturated with the liquid to be analysed. Liquids in contact with the metamaterial surface with different refractive indices result in a shift of the metamaterial resonant frequency. This shift in frequency can be detected by measuring its complex transmission using a THz vector network analyser, in the frequency range 0.75–1.1 THz. The sensitivity of the metamaterial is 0.0015 refractive index units for a spectral resolution of 1 GHz which is well achievable with the VNA system.

The problem of joint information transmission and input signalling estimation over a state-dependent channel is considered. The general single-letter upper and lower bounds of the optimal capacity-distortion trade-offs are provided. For the Gaussian dirty paper channel, we calculate the bounds by choosing parameters carefully, and obtain a computable form eventually. In such a way, we obtain a complete characterization for this problem setting.

Blockchains (BCs) received significant attentions from FinTeh in recent years. Many proof-of-concepts (POCs) projects and experiments have been carried out to explore the strengths and weaknesses of constructing financial market infrastructures upon BCs. The experiments showed that considerable improvements are still necessary to meet financial requirements such as safety, efficiency, reliability, scalability, security and transparency. The Principles for Financial Market Infrastructure (PFMI), issued by the Committee on Payment and Settlement System (CPSS) and International Organization of Securities Commissions (IOSCO), is a key standard for designing Financial Market Infrastructures (FMIs) so that global financial markets could be more robust and stable, especially, could withstand financial shocks. It is thus necessary to design BC-based FMIs according to PFMI. This position paper analyzes financial system requirements and the challenges, and discusses the design issues of BCs in meeting PFMI.

Virtual Reality (VR) and 360 film have caught the attention of audiences and content creators and emerged as a new media, however, the market penetration of VR and head mounted hardware has remained slow despite the availability of more affordable mobile options. This has resulted in some audiences turning to VR cinemas, festivals and out-of-home exhibitions. Creating affordable, scalable VR cinemas presents a number of challenges and many of the decisions taken in both developing and facilitating these curated exhibitions directly impact audience's reception of spatial audio soundtracks. This workshop position paper looks to discuss the potential issues and future solutions in the use of current synchronous exhibition applications, the competing formats, standardised Head Related Transfer Functions, headphone build/colourisation, and the on-boarding process.

Our proposed method focuses on offline friends who are acquainted with a target user in the real world. We use Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to distinguish between online friends of a target user who are also offline friends and online friends who are acquainted with the target user only in cyberspace. By utilizing the probability of friendship as a function of the geographical distance between users’ home locations, our proposed method achieves an estimation accuracy 1.7 percentage point better than that of a conventional method. We have also been able to verify the effectiveness of an optimization method that narrows down candidate areas for a target user’s home location.

The present work presents a hybrid identification of recursive least squares with a metaheuristic called radial movement optimization (RMO) applied to the joints of a cylindrical robotic manipulator. The main contribution of the research is to consider the covariance matrix with RMO. At the end a comparative analysis will be made with some classic methods. The results of the identifications were tested with the RMSE. A covariance matrix has also been generated from manipulator identifications.

In HVDC converter transformers, the potential DC component in electric stresses imposed on valve windings would unfavorably cause space charge accumulation inside oil-paper insulation. Insulation degradation and aging may have great effects on space charge behaviors in oil-paper insulation. This paper focuses on the effect of the low molecular weight (LMA) and the moisture on space charge phenomenon in oil impregnated-paper insulation. Pulsed electro-acoustic (PEA) technique was used in this study to measure space charge behaviors in 400 $\mu$ m thick, dry and moist single-layer paper sample impregnated in mineral oil prepared with different amounts of formic acid. The measurements were carried out at $30 ^ { \circ } \mathrm { C }$ under a field strength of 20 kV/mm. It was found that LMA greatly strengthens the homo charge accumulation. The combination of LMA and moisture results in even more active space charge accumulations. In dry samples, the existence of LMA causes slower charge decaying rate. However, the introduction of the moisture accelerates the charge dissipation.

A paper based simple, flexible, and perfect resonant electromagnetic wave absorber is proposed in this article. The absorber consists of the conducting grid patterns where unit cell of the pattern is a two-dimensional grid based structure and the bottom is a conducting ground plane. The grid based top conducting patterns are created by inkjet printing over a thin paper having conducting layer at the bottom. The proposed absorber undoubtedly attains a cost-effective and eco-friendly solution for designing a flexible electromagnetic absorber. The experimental and simulation results are found to be in excellent agreement. The proposed absorber exhibits perfect absorption of 99.99% at 10.84 GHz and possesses an overall thickness of 0.531 mm.

Paper-based digital microfluidic biochips (PB-DMFBs) provide a promising solution for microfluidic bioassays. Due to the low-cost substrate material and low demand for complicated manufacturing equipment, PB-DMFBs can be fabricated without foundry. On the flip side, convenience of fabrication allows PB-DMFBs to be fabricated everywhere, which makes it is difficult to manage production and distribution of IP (bioassays). As a result, PB-DMFBs are vulnerable to security threats. IP and its creator, the biocoders, may suffer from infringement. To ensure IP protection, in this paper, we proposed the first Digital Rights Management (DRM) scheme to protect IPs of PB-DMFBs from security threats. A chip-level synthesis algorithm is also presented to realize not only complex biochemical operations but also the demand of DRM.

The insertion of mica barrier between coaxial electrodes results in a larger reduction of leakage current than the use of kraft paper insertion. The reduction is influenced by the barrier position and the testing voltage level. When using the kraft paper, there is a certain voltage level beyond which the characteristic of leakage current as a function of barrier position is relatively the image of that under the voltage level. At this level, the leakage current is almost the same wherever the barrier position. The use of mica insertion tends to shift the symmetry axis of the characteristic curves, meaning higher leakage current level.

Converter transformer is the important part of high voltage direct current transmission system (HVDC), whose safety reliability is related to the safety of HVDC. Compared to the traditional AC transformer, there is a complex AC-DC electric field inside the converter transformer, which is more likely to cause a decline in insulation performance, after the internal oil-paper insulation is dampened. In this paper, a test platform for insulation breakdown strength of oil-paper insulation was set up according to the standard in IEC60243. This paper analyzed the change regulation of moisture content of oil-paper insulation. The difference of breakdown strength of oil-paper insulation, which contain different percentage of moisture, were studied under AC, DC, different ratio of AC/DC voltage and polarity reversal voltage, and combined with double-layer dielectric theory, the effect of moisture content and voltage type on breakdown characteristics of oil-paper insulation was analyzed. The analysis showed that most of the moisture was concentrated in the insulating paper, with the increase of moisture content, the increasing amplitude of electrical conductivity of insulating paper is larger than that of insulating oil. As a result, the electric field strength on the insulation paper decreases and the electric field strength on the insulation oil increases, which made the insulation gap easier to breakdown

This paper focuses on the potential and practice of alternative forms of assessment to the conventional written exam. Readers will be interested in issues such as why would a staff member want to replace the written exam and how can I be sure that the replacement is equally effective at capturing student attainment. The paper summarises a trial at the University of Sheffield where the written exam was replaced by a computer quiz which was marked automatically.

International audience; The design of a dipole antenna printed on a paper substrate is presented in this paper. The antenna which integrates a compact balun is devoted for dual-band 2.4/5 GHz WLAN applications. The antenna is based on a double-side printed multilayer paper substrate and is fed with a coaxial cable for the testing. The simulated results of the whole structure are also presented in detail and compared with the measured performances.

Even though AI/ML is of strategic importance for many companies, it is not easy to come up with an AI life cycle in a multi-cloud world that is being increasingly embraced. In this paper, we present our position on an AI strategy that is future proof and does not force vendor lock in.

Optimal linear transmitter beamformers in multi-antenna multi-user systems are of the Minimum Mean Squared Error (MMSE) type (dual uplink MMSE receivers). MMSE designs make an optimal compromise between noise enhancement and interference suppression and reduce to matched filters at low SNR and zero-forcing at high SNR. We consider a realistic scenario of user channels of varying attenuation and constrain the beamformers to either zero-force or ignore each interference term. This leads to a reduced-order zero-forcing (RO-ZF) design in which the number of interference sources being zero-forced increases with SNR. We apply a simple large systems analysis (applicable to Massive MIMO) to determine the asymptotic performance of RO-ZF designs, determine the optimal ZF orders, and compare to optimal and ZF linear and Dirty Paper Coding (DPC) designs. RO-ZF designs lead to variable reductions of computational complexity and channel state information (CSI) requirements (esp. in future multi-cell extensions), both important considerations in Massive MIMO systems.

Bitcoin blockchain is growing big, yet its transaction capacity hit its limits in 2017. One solution heavily discussed within the bitcoin community is to let it grow faster by increasing size of the blocks. However, making transactions smaller instead of blocks bigger is more sustainable and also brings other additional benefits. In this paper, we focus on space wasted by non-cryptographic use of hashes and uncompressed numbers within transaction inputs. In our analyses, we show that transactions can be made approximately 16% smaller, depending on their complexity.

Machine learning (ML) components are increasingly adopted in many automated systems. Their ability to learn and work with novel input/incomplete knowledge and their generalization capabilities make them highly desirable solutions for complex problems. This has motivated the inclusion of ML techniques/components in products for many industrial domains including automotive systems. Such systems are safety-critical systems since their failure may cause death or injury to humans. Therefore, their safety must be ensured before they are used in their operational environment. However, existing safety standards and Verification and Validation (V&V) techniques do not properly address the special characteristics of ML-based components such as non-determinism, non-transparency, instability. This position paper presents the authors' view on the safety of automotive systems incorporating ML-based components, and it is intended to motivate and sketch a research agenda for extending a safety standard, namely ISO 26262, to address challenges posed by incorporating ML-based components in automotive systems.

In the era of emerging Smart Built Environments (SBEs), a smart house, unlike regular houses with static "components", consists of numerous interconnected and often actuated devices, capable of executing tasks independent of user supervision. Living in such a SBE, where for example, the furniture can rearrange itself, and the doors open and close of their own volition, may be difficult and unpredictable. Furthermore, cyber-security attacks and intrusion could allow attackers to assume control of the SBE, damage its components and to potentially harm its inhabitants. Such novel characteristics of SBEs present developers with several unique challenges with regards to implementing the needed safety and security measures and protocols that go along with them. With such environments, therefore, there is a need for a system that is capable of monitoring user activities in real-time, identifying the safety and security hazards to users in their immediate local context, warning users of these hazards, and perhaps even taking preventative and mitigative action against the hazards that it identified. In this paper, we survey some of these challenges and explore the design and implementation of a system designed around the safety and security of SBE inhabitants. We propose an approach to modeling SBE safety that combines the three laws of robotics and the swarm behavior model. We also present a preliminary prototype and discuss a case study.

Cyber security and data science are two of the fastest growing fields in Computer Science and more recently they are being integrated for various applications. This position paper will review the developments in applying Data science for cyber security and cyber security for data science and then discuss the applications in Social Media.

In this position paper, we propose a decentralized architecture for publishing RDF. Unlike traditional client-server architectures, we propose a peer-to-peer (P2P) architecture for SPARQL query processing and RDF knowledge sharing. We present a general architecture and its key problems. Further, we discuss its potential with respect to solving the availability and performance issues that for a long time have been prevailing on existing infrastructures.

This effort presents an analysis of a system intended for wireless crack sensing and based on zero-power harmonic transponders. The sensors, which are fabricated by using a paper substrate, are suitable for both structural health and supply-chain monitoring. In the text, different techniques to recover information from the proposed tags are firstly explored, with a particular focus on the potentialities and trade-offs of each approach. Then, the impact of the presence of multiple-nodes on the performance of the system is considered and, finally, some proposals for crack localization in presence of multiple tags are explored and experimentally evaluated.

In order to study on transformer oil — paper insulation aging based on multi-characteristic parameters, especially the acid in oil, this paper set up four groups of contrast experiments with # 25 transformer oil and Kraft paper. And then this paper puts forward evaluation methods and standards for the degree of insulation aging based on acid value assessment. The results show that the acid value of oil has little correlation with the degree of aging, but its mutation point can be used as the warning threshold for aging failure of insulating paper. The degree of polymerization, furfural, CO and total hydrocarbons are strongly related to the aging of oil and paper insulation. Furfural, CO and total hydrocarbons are mostly produced by aging of insulating paper and can be used as evaluation parameters for insulating paper aging. This paper has great practical significance to guide the work for transformer state monitoring, the early warning of defects and fault handling.

Paper influence analysis is essential technology of the literature management system. In recent years, research on influence analysis has received crucial attention from the industry and academia. In this field, there are many new challenges in how to evaluate valuable papers in the paper citation network. Therefore, in this paper, we have proposed an influence model in paper citation networks with integration features of PageRank and Hyperlink-Induced Topic Search (HITS). First, three ranking features are extracted with the combination of PageRank value, hub, and authority. Second, more features are added to our ranking classification method. Third, the ranking-based classification with $P$ ageRank and HITS is proposed to analyze the paper propagation. An influence score is computed to evaluate the importance of a paper. The experiments show that our ranking-based influence model is effective for paper propagation.

In modern academic social network, it is very difficult for scholars to find academic papers consistent with their research direction. Time is a critical factor in paper recommendation. As time goes on, the impact of an academic paper would gradually fade. Likewise, the research interests of users may also change. Therefore, we propose a temporal perceptual neural collaborative filtering model that integrates the multi-dimensional features of papers. We conducted our experiments on the dataset from CiteULike, comparing the recommended results by using four time-decay functions and evaluating our model with multiple evaluation indicators. The satisfactory results show that our model is effective in filtering out the expired papers by considering the characteristics of papers and the changes of scholars' interests.

The paper mills are one of the main industries on which the economy of India is based. The paper production process is very complex and comprised of several stages. One of them is the head box which is the key sub process of this entire system. It distributes the stock uniformly over the wire section of the paper mill. The automatic control system design is the focus of this paper. The considered process is very challenging for control system design due the presence of dead time which makes the system unstable. So, a very popular technique, IMC (internal model control), is attempted. Its performance is analyzed. Also, a modern approach, ANFIS (adaptive neuro fuzzy inference system) is adopted for the control system design for the same process. Finally, the control performance of both the approaches has been compared. For simulations, MATLAB is used in this work.

Focusing on the automatic production scheduling of the ratio of waste paper in a paper mill, the research target was minimizing the purchase cost of waste paper under multiple constraints. Having divided, the field data (mixing ratios of waste paper and pulp properties) into the training and validation data sets, the scheduling ratios of waste paper were optimized. Firstly, from the point of view of the average pulp properties and the variances, the predicted results proved that the BP-NN predicted model accuracies of the pulp properties with the mixing ratio of waste paper were better than those of SVM and GA-SVM. Secondly, the minimization of the purchasing costs of waste paper under some constraints were obtained with the BP-NN predicted model and the non-dominated sorting genetic algorithm (NSGAII), Comparing with the general GA in the previous study, the scheduling results improved the pulp brightness by 9.24%, and reduced the purchasing costs of waste paper by 2.16%.

In this paper the experimental results of a compact low-power rectenna in paper substrate, designed to operate in the Wi-Fi band, are presented. The complete prototype, based on an annular slot antenna and a single-diode rectifier, features a weight of 1.5 grams and shows an RF-to-dc conversion efficiency in the design band of about 40 % for a -10 dBm available input power, of about 28 % at -15 dBm, and in the range [10, 22] % at -20 dBm, corresponding to an output DC voltage in the order of 320, 240 and 60 mV respectively. Additionally, the rectenna features an efficiency higher then 7 % in the whole band 1.8-2.7 dBm for a power density estimated around 3 μW/cm2.

Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the image and multi-layer perceptron network (MLP) to create the actual decision boundaries. On one hand, QML models can be useful in both of these tasks. Convolution with parameterized quantum circuits (Quanvolution) can extract rich features from the images. On the other hand, quantum neural network (QNN) models can create complex decision boundaries. Therefore, Quanvolution and QNN can be used to create an end-to-end QML model for image classification. Alternatively, we can extract image features separately using classical dimension reduction techniques such as, Principal Components Analysis (PCA) or Convolutional Autoencoder (CAE) and use the extracted features to train a QNN. We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN. Particularly, we make a case for trainable filters in Quanvolution and CAE-based feature extraction for image datasets (instead of dimension reduction using linear transformations such as, PCA). We discuss various design choices, potential opportunities, and drawbacks of these models. We also release a Python-based framework to create and explore these hybrid models with a variety of design choices.

Low-cost inkjet printing of a Frequency Selective Surface (FSS) paper poster to reduce the drop of 5G signals within an indoor environment is introduced in this paper. An array of square loop elements is employed for this application. This low-cost FSS operates at a central frequency of 25.2 GHz while covering the 24 GHz band used in 5G systems. CST Microwave Studio™ was employed to simulate the FSS. The array structure was fabricated using a household Inkjet Printer on a photo paper using nanoparticle silver ink filled in refillable ink cartridges. Simulation transmission responses compared well with measurements. Inkjet printing could print tracks with fine details required for the mm-wave applications and offered reliable conductivity for the filtering performance. Printing of low-cost posters can help in improving the strength of 5G signals.

As Machine Learning (ML) becomes pervasive in the era of artificial intelligence, ML specific tools and frameworks are required for architectural research. This paper introduces NeuroMeter, an integrated power, area, and timing modeling framework for ML accelerators. NeuroMeter models the detailed architecture of ML accelerators and generates a fast and accurate estimation on power, area, and chip timing. Meanwhile, it also enables the runtime analysis of system-level performance and efficiency when the runtime activity factors are provided. NeuroMeter’s micro-architecture model includes fundamental components of ML accelerators, including systolic array based tensor units (TU), reduction trees (RT), and 1D vector units (VU). NeuroMeter has accurate modeling results, with the average power and area estimation errors below 10% and 17% respectively when validated against TPU-v1, TPU-v2, and Eyeriss.Leveraging the NeuroMeter’s new capabilities on architecting manycore ML accelerators, this paper presents the first in-depth study on the design space and tradeoffs of “Brawny and Wimpy” inference accelerators in datacenter scenarios with the insights that are otherwise difficult to discover without NeuroMeter. Our study shows that brawny designs with 64x64 systolic arrays are the most performant and efficient for inference tasks in the 28nm datacenter architectural space with a 500mm2 die area budget. Our study also reveals important tradeoffs between performance and efficiency. For datacenter accelerators with low batch inference, a small $(\sim 16$%) sacrifice of system performance (in achieved Tera OPerations per Second, aka TOPS) can lead to more than a 2x efficiency improvement (in achieved TOPS/TCO). To showcase NeuroMeter’s capability to model a wide range of diverse ML accelerator architectures, we also conduct a followon mini-case study on implications of sparsity on different ML accelerators, demonstrating wimpier accelerator architectures benefit more readily from sparsity processing despite their lower achievable raw energy efficiency.

The multi-user multiple-input multiple-output (MU-MIMO) scheme is one of the main methods for improving the frequency-efficiency in wireless communication systems. In MU-MIMO systems, a base station equipped with multiple antennas and a large number of receivers with multiple antennas communicate simultaneously. Conventionally, we adopted the principle of chaos communications to MU-MIMO and proposed a multi-user chaos MIMO (MU-C-MIMO) scheme that has physical layer security between each receiver. In the previous study, we utilized block diagonalization (BD) — a liner precoding method — to remove interference components in MU-C-MIMO. On the one hand, channel gain for each user is not maximized in BD. On the other hand, the gain maximization can be realized using nonlinear precoding, and further improvement of transmission quality is expected in MU-C-MIMO. Therefore, in this paper, we propose a MU-C-MIMO scheme using dirty paper coding (DPC), a nonlinear precoding method, in order to realize the most ideal precoding characteristics. The effectiveness of this scheme is evaluated through numerical simulations with comparisons to a conventional MU-C-MIMO system with BD.

This paper presents a study on partial discharge (PD) characteristics of aged oil impregnated paper under high humidity level. The presence of the void in the impregnated paper with exposure to high relative humidity would induce partial discharge occurrences which eventually degrade the insulation performance of the paper. In this work, an artificial cylindrical void was created and the oil impregnated paper was aged up to 16 hours at 90% relative humidity. Moreover, partial discharge inception voltage (PDIV) and partial discharge extinction voltage (PDEV) were recorded. Based on the results, the longer aging time of kraft paper, the lower the PDIV and the PD magnitude. It is implied that the longer exposure to a high relative humidity, the more moisture being absorbed by the paper causes high repetition of PD at corona discharge level.