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In this paper we exploit graphite's thermal absorption properties to drive the process of analyte sampling in paper-based biosensors. Graphite structures can be easily patterned or drawn on paper using a standard pencil and selective heating of the patterned layers can be remotely achieved using a light source. The resulting thermal gradient manifests itself as concentration gradients across the paper substrate which then triggers the flow of analyte to the selective areas. In this paper we have validated this hypothesis using a prototype made out of a low-cost filter paper substrate and a 300mW 808nm remote infrared laser source. Compared to a control paper substrate, we show an increase in temperature by more than 70°C (from 50°C to 120°C) in areas where the graphite is patterned. As a result the proposed prototype is also shown to demonstrate a higher sample-flow rate compared to the control. We anticipate that the proposed remote triggering of sample acquisition would be useful for different variants of paper-based biosensors that need to be integrated inside the food-package.

Oil-paper insulation is the main insulation material in power transformers, and its insulation performance directly determines the operating life and reliability of the transformers. The lack of understanding of the electrical characteristics of oilpaper insulation materials leads to the misunderstanding of operating condition of the transformers. Extensive studies have found that the characteristics of generation, recombination, dissipation, trap formation of electric charge in oil-paper systems are the main factors affecting their insulation properties. In this paper, thermal stress coupling by AC and DC electrical stresses are found to have significant impact on ions migration properties. Under these stresses, transformer oil molecules are decomposed into carriers that upon recombining, various gases are generated. Cellulose-based paper in oil-paper systems adsorbs electric charge to form electric double layer at the oilpaper interface. The electric double layer are physically interfacial traps in nature, which leads to the superb dielectric and breakdown characteristics of the oil-paper, especially under DC conditions. As for insulating oil, the recombination rate of ions affects the concentration of conductive ions in oil under different electric field types. Further studies are still needed to confirm the correlation between the characteristics of charges and the insulation properties of oil-paper, which is expected to improve the accuracy of transformer condition monitoring to prolong its operating life.

The amount of academic articles in digital libraries is increasing exponentially. This growth of scientific papers’ growth made it difficult for researchers to obtain related papers from their queries. Recommendation systems can help them resolve the problem of information overload. However, existing paper recommender methods generally rely on the simple citation network, which ignores the semantic of papers and has the problem of cold start. In this paper, a hybrid paper recommendation approach AMHG is proposed which is based on a multi-level citation heterogeneous graph. Unlike existing works which only use the reference relationship, we consider the same or similar authors’ papers to alleviate the cold start problem of zero-citation and newly published papers. Besides, the metadata information of papers is also incorporated into a representation model to generate better recommender results to alleviate the cold start problem. We use the authors’ influence factors to reorder the candidate list outputting by MLP to obtain high-quality articles. Through experiments, we compare our model with several methods on the DBLP-REC dataset to demonstrate that AMHG outperforms state-of-the-art performance and the effectiveness of recommender.

Smart devices are quickly becoming ubiquitous with the rise of portable biosensors and the internet of things. There exists particular interest in enhancing common objects to have smart capabilities and finding inexpensive solutions for diagnostic tools. One such example is transforming paper items into interactive devices and point-of-care analytic products. Due to the lightweight, flexible, and cost-efficient qualities of paper, unobtrusively powering these devices remains an outstanding problem. In this paper, we demonstrate an electrostatic human-touch powered energy harvesting system, integrated with flexible painted conductive electrodes on paper. This system harvests 8.5 nJ of energy and reaches a voltage of 1.3 V on a 10 nF energy storage capacitor. This technology not only provides a method of powering paper-based products with routine human gestures but can also detect human touch for input communication to sensors.

Water inside a transformer tank is undesirable because this water causes the following problems. Firstly, on overload bubbles of water may form in the oil weakening its dielectric strength and increasing the risk of catastrophic flashovers across the oil. Secondly, it shortens the life expectancy of the Kraft paper insulation, which is one of the main factors limiting the functional life of transformers. It is therefore necessary to measure the water content of paper insulation so that a utility can take corrective action when required. Cellulose adsorption isotherms have been used to determine the water content of paper using online water activity probes. A disadvantage however is that the application of this method requires that the insulation media is in thermodynamic equilibrium, which due to load cycling usually is not the case. Practically, this problem is resolved by taking a long-term average of the measurements. This solution is not optimal because actual changes in water content will also be smoothed out. The migration of water between paper and oil is a very slow process. Therefore, a low-pass mathematical filter was developed which would retain this process but reject higher frequency components. This filter was also investigated using measurement from operating power transformers.

Prior work on texture analysis of historic, photographic papers has focused primarily on measures of texture similarity. However, automated grouping or clustering of photographic paper textures in a way that is meaningful to art conservators remains an open problem. In this work a deep learning approach to automated classification is presented, for clusters derived from a human sorting experiment conducted by 19 art conservators and paper experts and subsequently extended through crowd-sourcing. The proposed approach uses a deep convolutional neural network, and results are presented on the performance in automatically classifying images when compared to human experts.

Using THz spectroscopy, we show how to uniquely determine parameters for industrial quality control of paper sheets. We carry out measurements within the industrial setting: no averaging, fast acquisition in ambient conditions, and quasi-real-time analysis. Paper parameters are extracted using a combination of statistical evaluation and physical interpretation of the light-matter interaction. We show that THz spectroscopy offers additional value as compared to currently used industrial sensors.

Under the HVDC condition, the formation of the space charge in the oil paper system could affect the electric field distribution for converter transformers. Therefore, it is important to analyze the factors that determine the space charge behavior such as the temperature, moisture, electric field, oil paper thickness ratio and multilayers such as (Oil+Paper+Oil+Paper) structure. This paper focuses on the space charge distribution affected by the multilayers (O+P+O+P) oil paper structure and different oil paper thickness ratios. The pulsed electroacoustic method (PEA) was used to measure space charge distributions. It is found that increasing oil thickness could facilitate charge injection. However, the increase of pressboard thickness could prevent the charge injection. It is also found that the same oil paper thickness ratio could lead to similar space charge injection within the oil paper sample under the same 10 kV/mm electric field. Therefore, based on the same oil paper thickness ratio, space charge measured from the thinner thickness sample were further applied to the thicker thickness sample to estimate electric field distribution using COMSOL software, which cannot be measured by the PEA system directly.

The thermal aging is one of the main factors leading to degradation of oil-paper insulation. Carbon oxides dissolved in insulation oil mainly come from cellulose pyrolysis, which indicates that the carbon oxides in oil can be used to predict the thermal aging degree of oil-paper insulation in power transformer. In this paper, accelerated thermal aging test for oilpaper samples composed of conventional 25# mineral oil and ordinary Kraft paper were performed at 130°C. The changing laws of degree of polymerization (DP) of insulation paper and content of carbon oxides in oil with thermal aging time were analyzed. The relationships between DP of insulation paper and the content of CO, CO 2 , CO+CO 2 and CO 2 /CO were also analyzed. The results show that DP of insulation paper decreases fast with time in early thermal aging stage and decreases slowly with time in late thermal aging stage. There is no significant quantitative relationship between the content of CO and DP, also CO 2 /CO and DP. The content of CO 2 and CO 2 /CO both are significant linear correlation with DP, which are good indexes used to judge the thermal degree of impregnated insulation paper.

In order to assess the moisture content of insulating pressboard in oil-immersed transformer, and thus providing reliable guidance for insulation condition and life estimation of oil-immersed transformer, in this paper, we made four groups of oil-paper insulation samples with different moisture content under laboratory conditions and studied the relationship between pressboard and moisture content by using polarization and depolarization current (PDC) technique. The experiment result shows that PDC curve is very sensitive to the change of moisture content, and the change of moisture content mainly affects the tail of depolarization current curve; the extended Debye model parameters can be obtained from depolarization current curve, and as the moisture content increases the number of branches in Debye model increases, the largest time constant becomes large; the peak of time domain spectroscopy turn up at 1000s., the time constant branch which is longer than 1000s presents the pressboard branches of Debye model; the feature quantity Q paper can show the internal polarization of insulating paper and has a linear fitting relationship with moisture content, so Q paper can be used to assess the moisture content of insulating pressboard.

Based on the sequential batch reactor (SBR) simulation model of the wastewater treatment process for a secondary fiber paper mill that we have developed, two control strategies, a neural network (BP-PID controller) and a fuzzy logic system (Fuzzy-PID controller), were separately combined with PID to control the dissolved oxygen (DO) concentration in the SBR process of paper mill. Two cases, without and with the presence of noise, have been studied and evaluated using the criteria considered in BSM1. For the case without noise, both two-control strategies demonstrated better control indices than that of the traditional PID control; for the case with noise, the Fuzzy-PID controller possessed the good control indices at almost the same expense of aeration energy. The results under two cases showed that the Fuzzy-PID controller demonstrated better performance than that of BP-PID controller, in the DO control of SBR process in the paper mill, Fuzzy-PID controller improved the control performance without increasing the aeration energy consumption.

A modified version of Costa's dirty paper channel is studied, in which both the input signal and the state experience stationary and ergodic time-varying fading. The fading coefficients are assumed to be known exclusively at the receiver. An inner bound of the achievable rates using lattice codes is derived and compared to an outer bound of the capacity. For a wide range of fading distributions, the gap to capacity is within a constant value that does not depend on either the power of the input signal or the state. The results presented in this paper are applied to a class of ergodic fading broadcast channels with receive channel state information, where the achievable rate region is shown to be close to capacity under certain configurations.

The insulating paper in the transformer undergoes degradation during operation. Condition monitoring and assessment for insulating paper is of great significance as the life of insulating paper mainly determines that of transformers. Accelerated thermal aging experiments were performed for insulating papers at 120 °C and 130 °C, respectively. The crystallinity index of insulating paper was calculated with the X-ray diffraction pattern and the refractive index of insulating paper was measured. The results showed that the crystallinity index and refractive index of the insulating paper increase with the decrease of degree of the polymerization in general. The above work is a fundamental attempt in investigating the correlation between paper degradation and its crystalline behavior, which indicates that the crystallinity index and refractive index of the cellulose fibers in insulating oil may be a feasible index to diagnose the paper degradation.

This paper conducted thermal aging experiments under 120°C to study the effects of passivator on insulating paper (board). Experiment group (oil containing passivator and typical materials inside transformer) and control group (oil containing typical materials inside transformer but no passivator) were prepared for thermal aging experiments, and the degree of polymerization of insulating paper, furfural products in oil and the breakdown voltage, insulation resistance and dielectric dissipation factor of insulating board were tested, respectively. The results show that the furfural content in oil, the degree of polymerization of insulating paper and the breakdown voltage, insulation resistance and dielectric dissipation factor of insulating board of the experiment group were very similar to that of the control group under the same aging conditions, and passivator did not bring damage to the insulating paper (board).

Many failures of high-voltage transformers and reactors occurred because of copper sulfide formation and deposition, this study aims to explore the influence of the insulating paper and atmosphere on the deposition of copper sulfide on the insulating windings as a result of the reaction of dibenzyl disulfide (DBDS) with copper in oil-immersed insulation. The accelerated thermal aging experiments under atmosphere (Nitrogen and air) conditions at 130°C were conducted by adding 1000mg/L dibenzyl disulfide (DBDS) and the insulation windings which half of copper strip were wrapped insulating paper to the mineral insulation oil. The deposition of copper sulfide were studied by observing the surface morphology of windings and analyzing the surface components of the windings by EDX. The results indicate that copper sulfide is prone to deposition on the copper strip surface which is wrapped by the insulating paper. Few copper sulfide is deposited on the bare copper strip under nitrogen, but copper sulfide is deposited on the copper strip under air. Meanwhile, the sediments of copper sulfide deposition on the copper strip under air are much higher than that under nitrogen, and more sediments of copper sulfide are deposited on the insulating paper under air than on the insulating paper under nitrogen. Moreover, the effect of wrapped paper and atmosphere on the mechanism of copper sulfide deposition will be discussed.

Deep neural networks (DNNs) have been successfully applied to a great variety of applications, ranging from small IoT devices to large scale services in a data center. In order to improve the efficiency of processing these DNN models, dedicated hardware accelerators are required for all these scenarios. Theoretically, there exists an optimized acceleration architecture for each application. However, considering the cost of chip design and corresponding tool-chain development, researchers need to trade off between efficiency and generality. In this work, we demonstrate that it is practical to use a unified architecture, called Ascend, to support those applications, ranging from IoT devices to data-center services. We provide a lot of design details to explain that the success of Ascend relies on contributions from different levels. First, heterogeneous computing units are employed to support various DNN models. And the datapath is adapted according to the requirement of computing and data access. Second, when scaling the Ascend architecture from a single core to a cluster containing thousands of cores, it involves design efforts, such as memory hierarchy and system level integration. Third, a multi-tier compiler, which provides flexible choices for developers, is the last critical piece. Experimental results show that using accelerators based on the Ascend architecture can achieve comparable or even better performance in different applications. In addition, various chips based on the Ascend architecture have been successfully commercialized. More than 100 million chips have been used in real products.

Physical synthesis has emerged as a core component in a modern circuit design flow. Large-scale optimization problem is often involved in the process, which requires substantial efforts to solve and no optimality is guaranteed. Reinforcement learning provides one direction to deal with the above issue by automatically acquiring knowledge through experience, which has shown great success in various applications. In this paper, we introduce the foundation of and the progress in reinforcement learning, and review some recent approaches in applying reinforcement learning to physical synthesis. We hope to inspire more work and to see more talented ideas in this field.

Edge environments are generating an increasingly large amount of data due to the proliferation of edge devices. Accommodating this large influx of data at edge servers is a challenging issue. While some data can be processed as it is generated, others must be stored for later access. This paper proposes the features that a new edge-native storage system must possess including support for user mobility and node fluctuation. To motivate this, we first describe several emerging edge applications and their data needs. We then describe the challenges in meeting these needs. We then evaluate an out-of-the-box cloud storage system, Cassandra, to assess it’s suitability as an edge storage system due to many edge-friendly features. We determined that while a cloud-based storage system can be ported to the edge meeting some of the challenges, other challenges require new solutions. Based on the challenges and the results of Cassandra case study, we propose a set of design principles for a new edge-native storage system.

Distributed smart and connected community applications of the present and future will continuously sense perishable data on which they make decisions. We say the data are perishable when they represent information sensed at a past point in time, possibly at a distant part of the distributed application, and for which the ground truth could have since changed. This paper discusses actions the sensing source, the network, and the controller can take to make the best possible decisions given that we know only a delayed version of the truth. Some of these actions to preserve freshness of data are based on known real-time techniques such as Time Sensitive Networking. To these we add Information Theory applied on a message-by-message basis and cross-layer coordination between content and network handling. Both the unexpectedness (surprisal) and timeliness (freshness) properties of perishable information can be encoded using a Shannon information entropy framework. Examples of smart community systems of the future for which these concepts are relevant are given.

Identifying the phase or stage of children’s spelling development is a regular activity in literacy classes. Usually, teachers assume some developmental theory and perform paper-and-pencil based tests. Existing digital tools often do not consider any of these known theories, nor do they capture the child’s handwriting. Therefore, some teachers prefer to continue applying the tests manually. Accordingly, this study’s research question is how to promote the performance of spelling tests on mobile devices, simulating the interaction between manual tests and the theoretical models already used by teachers. Through the Design Science Research Methodology (DSRM), we propose a method to apply child spelling tests in an automated way. In this work, we present the results of the child’s interface usability evaluation in the developed computer artifact, using guidelines of Touchscreen Interaction Design Recommendations for Children (TIDRC). The results indicate adequacy to the recommendations of 88% of items in visual and audio features (cognitive dimension), 75% in the physical dimension, and 47% in socio-emotional dimension. These results are promising and relevant compared to previous studies that evaluated apps using the TIDRC framework.

This paper deals with the observer design of nonlinear tubular chemical reactor models. The analysis is performed on a linearized version of the model around a steady-state profile, in which some coefficients are a function of the spatial variable. The study starts from a general model of chemical tubular reactors that will serve as a benchmark model for the formulation in the infinite dimensional Hilbert state space. The optimal output injection operator associated to the proposed observer is computed via the solution of the infinite-dimensional Riccati equation in the space variable. The performance of the observer is illustrated through numerical experiments of an industrial pulp bleaching tubular reactor model.

In this paper, we consider the problem of transmit signal and receive filter design for a phase modulated continuous wave (PMCW) monostatic single-input single-output (SISO) radar system when low-resolution analog-to-digital-convertor (ADC) is used at the receiver. By applying Bussgang decomposition model, the effect of low-resolution ADC is considered in the model, and two cases of colored and white noise are investigated. In the white noise case, it is concluded that the matched filter (MF) is the best filter maximizing the signal-to-interference-plus-noise-ratio (SINR) of the receive filter output. In the presence of colored noise, we propose a cyclic design procedure for maximizing the output SINR. Numerical examples illustrate the superiority of the proposed method in comparison with the well-known m- sequence probing signal with matched or designed receive filter. The effect of different parameters is also verified.

In order to study the nonuniform aging of the transformer main insulation, this paper designs an experiment and carries out frequency domain dielectric spectroscopy (FDS) testing and conductivity measurement to obtain material performance parameters required for simulation. Using the Comsol simulation software, a two-dimensional simulation model of the main insulation inside the transformer was established, and the finite element analysis in the frequency domain was carried out. The differences of field strength distribution, current density mode and space charge density between the models under uniform aging conditions and those under nonuniform aging conditions were compared and analyzed. Compared with the uniform aging, at low frequencies, nonuniform aging causes that high electric field strength is concentrated on the outermost insulating pressboard; the current flow path changes, and most of it flows through the oil gap-barrier; the accumulation position of free carriers changes.

Biodegradable ester-based electro insulating fluids have been tested for many dielectric properties and have been proposed and already used in electric devices as the substituent for mineral oil. Some properties are advantageous as high flash and fire point (> 250°), low impact of the water content on dielectric properties (H 2 O saturation hundreds ppm), positive impact on cellulose aging compared to mineral oil, readily biodegradability (OECD 301), etc. However, some properties are contradictory as high viscosity, relatively low oxidation stability, high pour point (eg. −17 ‐ ‐ 23°C rapeseed oil), etc. When employing such as fluid in the e.g. power transformer one needs to face also to the problems with material compatibility. Interactions of some materials in the transformer, such as structural elements, seals, inner part of bushings, separating plane, internal corrosion protection, etc. can interact with the organic ester to produce undesirable chemical reactions that can negatively affect the electrical and other properties of this liquids and oil leaks, failures etc. The paper describes the compatibility problems of such rapeseed based insulating liquid with other materials used in the transformer.

Facial Expressions play a vital role in the process of recognizing emotions and also to have non-verbal communication as well as in identifying people. Emotional Recognition turns out to be very important in everyday life just next to the tone of voice. Novel applications in Human-Computer Interaction (HMI) have been enabled by this and in many other areas. Inevitably most of the recent research on this area focuses on Convolutional Neural Networks (CNN) for extraction of features and inference from those features. In this paper we used CNN algorithm along with Histogram of gradients (HOG) features for higher accuracy. We perform emotional analysis of lecturers from Impartus videos. By detecting their emotions from a sequence of videos during their coursework, we evaluate the feedback they are expected to receive at the end of the duration of their course.

In order to improve the security of medical data, this paper uses a robust and secure dual watermarking technique in RDWT-SVD domain. Dual watermarking is achieved by embedding text watermark in medical watermark image using DWT, generating the final watermark. Before embedding, turbo code is used to encode the text watermark. Further, trade-off between imperceptibility and robustness is controlled in our scheme through firefly optimization. Experimental results indicate the proposed method offers high imperceptibility and robustness, and have superior overall performance.

Diabetic Retinopathy (DR) is a polygenic disorder issue that affects human eyes. Bruise to the blood vessels of the photosensitive tissue of the retina causes this complication. It’s most frequent in patients who had diabetes for more than ten years. This downside is going on in several individuals worldwide. However, the number of medical practitioners and also the tools needed for the detection of DR are very less for serving the mass population. In this paper, we have proposed DRDNet (Diabetic Retinopathy Diagnosis Network), a neural network framework based on capsule networks (CapsNets) for DR diagnosis. Experiments on a dataset with 1,265 images demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-ofthe-art techniques. The proposed DRDNet performs with an overall accuracy of 80.59% for five class, as compared to the closest competitor with an accuracy of 75.83%. We performed a study on a mixed dataset for two class and found that testing accuracy was 80.59%. We have also done training on a two class model and testing on other unseen datasets. Moreover, we observed that DRDNet has much higher confidence for the predicted probabilities as compared to other state-of-the-art techniques.

Speech Emotion Recognition (SER) is the technique for determining underlying emotions from speech samples. Usually text transcripts supplement vocal cues and contains additional information that boosts the SER process. An unsupervised Fuzzy Inference System (FIS) is developed for SER, in this paper, that incorporates audio and text features. The extracted features are: pitch, energy and textual sentiment score. The proposed system is based on Mamdani Fuzzy Inference model and is capable of determining four emotions: happy, sad, angry and neutral. Our FIS has three variants based on the sentiment lexicon- AFINN, SentiWordNet and VADER selected for computing textual sentiment score as the text feature. The main highlights of this work are: i) formulation of eleven novel fuzzy rules based on audio and text cues for SER ii) comparative analysis of all variants of our proposed unsupervised FIS with six state-of-the-art supervised machine learning approaches for SER iii) both speaker independent SER and speaker dependent SER are executed iv) investigations reveal that for few speakers in speaker-dependent SER have received higher accuracies than the others and v) the proposed unsupervised FIS can handle multiple datasets without any training while the supervised machine learning algorithms fail for cross-dataset evaluation. The experiments conducted on speech datasets: SAVEE and RAVDESS, indicate that our FIS has achieved higher accuracy and f1-scores as compared to the other state-of-the-art-methods.

In this letter, a novel design concept for circularly-polarized (CP) performance improvement of a single microstrip patch antenna (MPA) is studied and summarized via multiresonant modes. By investigating the internal E-field, it demonstrates that the direction of the equivalent magnetic currents (EMCs) at the patch edge are significantly changed under different feeding positions. Hence, two types of CP antennas are simulated and designed. Firstly, by reallocating three orthogonal modes in proximity to each other without sequential rotation, an extremely small frequency-ratio of about 1.04 is generated for the dual-polarized CP MPA. Secondly, by reallocating three orthogonal modes closely to each other with sequential rotation, the dual working bands of the MPA with same CP-polarization are merged to extend its CP bandwidth. In final, these MPAs are fabricated and measured. The measured results verify these predicted performances and the antenna keeps several advantages, i.e., low-profile, single-layer, single-element, wide AR-bandwidth, small frequency-ratio, and high efficiency.

Emerging smart services, such as indoor smart parking or patient monitoring and tracking in hospitals, incur a significant technical roadblock stemming primarily from a lack of cost-effective and easily deployable localization framework that impedes their widespread deployment. To address this concern, in this paper we present a low-cost, indoor localization and navigation system, which performs continuous and real-time processing of Bluetooth Low Energy (BLE) and IEEE 802.15.4a compliant Ultra-wideband (UWB) sensor data to localize and navigate the concerned entity to its desired location. Our approach depends upon fusing the two feature sets, using the UWB to calibrate the BLE localization mechanism.

Consider a two-user MIMO Gaussian Broadcast Channel with two antennas at the transmitter and two single-antenna receivers. While Dirty Paper Coding (DPC) achieves capacity, its practical adoption needs a precise comparison with the popular method of Zero Forcing Beam Forming (ZFBF). In this work, we obtain a simplified expression for the exact difference in sum rate between DPC and ZFBF at all SNRs. Using this expression, we obtain the distribution of the difference under block fading, and show a significant improvement in rate in wideband OFDM transmission by using DPC for only a small fraction of subcarriers.

European Rail Traffic Management System(ERTMS) efficiently manages and schedules the trainsguaranteeing passengers safety and service constraints andmaximizing railways capacity. The management system isresponsible of the elaboration needed to control the trainscirculation and to derive the necessary commands for trainsand trackside actuators such as railroad switches. Theseoperations need heavy computational resources and require anexchange of information between trains, trackside equipmentsand elaboration components. Currently the managementsystem is hosted in the Central Control Rooms that centrallymanage and supervise trains traffic of different geographicalareas. This paper proposes a new paradigm about the executionof the ERTMS management activities taking advantage fromcloud networks. It suggests to distribute among cloud resourcesthe computational load due to the elaboration of the collecteddata and the derivation of required commands for trains andactuators. This can make possible a proactive approach tothe trains management, suitable not only to consider the datasent by the trackside equipments, but also the informationdirectly collected onboard of the train. Despite trackside dataare considered by the management system, actually they arenot integrated with onboard real-time data that are sent tothe ground system for diagnosis purposes. These two sourcesof information can be jointly used to simulate in advance thetrain transit, deduce possible delays and anomalies and moreefficiently manage the trains circulation.

Qualitative Communication has been proposed to reduce the granularity of packet dropping due to congestion, from packet level to chunk level, which can significantly improve the user experienced latency and reduce the number of packet retransmissions under the reliable transmissions. This paper proposes an approach to achieve Qualitative Communication by applying random linear network coding to chunks in packet payload. With the help of New IP framework, the network can efficiently perform packet wash in order to retain as many chunks in the packets as possible when congestion happens. The paper proposes several advanced features to further boost the benefits of this approach.

In many fields, a mass of algorithms with completely different hyperparameters have been developed to address the same type of problems. Choosing the algorithm and hyperparameter setting correctly can promote the overall performance greatly, but users often fail to do so due to the absence of knowledge. How to help users to effectively and quickly select the suitable algorithm and hyperparameter settings for the given task instance is an important research topic nowadays, which is known as the CASH problem. In this paper, we design the Auto-Model approach, which makes full use of known information in the related research paper and introduces hyperparameter optimization techniques, to solve the CASH problem effectively. Auto-Model tremendously reduces the cost of algorithm implementations and hyperparameter configuration space, and thus capable of dealing with the CASH problem efficiently and easily. To demonstrate the benefit of Auto-Model, we compare it with classical Auto-Weka approach. The experimental results show that our proposed approach can provide superior results and achieves better performance in a short time., 12 pages, 3 figures

Mobile augmented reality (AR) has been attracting considerable attention from industry and academia due to its potential to provide vibrant immersive experiences that seamlessly blend physical and virtual worlds. In this paper we focus on creating contextual and personalized AR experiences via edge-based on-demand provisioning of holographic content most appropriate for the conditions and/or most matching user interests. We present edge-based hologram provisioning and pre-provisioning frameworks we developed for Google ARCore and Magic Leap One AR experiences, and describe open challenges and research directions associated with this approach to holographic content storage and transfer. The code we have developed for this paper is available online.

In this paper, a printed graphene compact, low-cost, disposal, flexible and conformal, coplanar waveguide (cpw) fed, linear array antenna is proposed. It was designed for 5.8GHz radars, portable electronic devices, and commercial wireless LAN applications and manufactured by screen printing formulated highly conductive graphene ink on paper substrate. The array achieves 73% total radiation efficiency and a peak gain value of 4.5dBi at 5.8GHz, with its bandwidth ranges from 4.6GHz to 7.9GHz (52.8%). Over the operating frequency, the radiation of the antenna was proved as a typical radiation pattern of a patch antenna array.

In this paper we present a novel method to print paper and cardboard without ink. The method is based on the carbonization of paper by a combination of lasers working on different wavelengths. A prove of concept system is created based on combining existing commercial systems to demonstrate the viability of the method. The results show that no debris is generated and the quality of the mark is superior in terms of contrast and resolution with previously known methods.

Sentiment Analysis is done by gathering the data, analyzing the text and aggregating the words in the text. The main focus of this survey is on the evaluation methods used and their respective core solutions. In this paper we are going to study the aspect based sentiment classification, which is classified into three. Those are Implicit, Explicit and Coreferential aspect based sentiment classification. In this paper we are also going to elaborate the polarity of the reviews into positive and negative sentiments. Aspect Based Classification not only promises to suppress the noisy data but also provides high efficiency in identifying the aspects with an addition of performing classification task with higher accuracy. This accuracy can be achieved by using Neural Network algorithms.

By integrating both energy harvesting and backscatter communication technologies, so-called ‘battery-free tag’ emerges as a promising solution to the energy related issues in IoT. However, such tags present new challenges due to the unstable energy supply and excitation signals, which are critical to realize successful backscatter communications. In this paper, we present a novel system, denoted as ecUWB, which enables robust communication for battery-free tags where unstable WiFi signals act as the unified source for both energy supply and excitation signals.At tag side, we propose a charging-transmission division scheme to achieve better signal utilization, and introduce a re-transmission mechanism for possible excitation signal interruption. At receiver side, we implement a simply method which is based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to predict the uncontrollable signals, and propose an energy-centric tag scheduling method according to the tag energy estimation results. Extensive experiments are carried out on customized tags and NI USRP platform. The results show that ecUWB outperforms the existing ones in terms of performance and efficiency under the unstable WiFi signals.

With the increasing popularity of optimization algorithms in electromagnetic engineering, it is clear that mixed-variable design problems prevail. This paper shows that the Cross-Entropy (CE) optimization method is intrinsically versatile to handle these and other types of problems. We provide implementation details of two antenna examples optimized by the CE method to demonstrate its elegance and efficiency.

High resolution wide bandwidth applications require power-efficient high-accuracy data converters. Multi-Stage Noise-Shaping (MASH) is a useful technique for the design of stable high-order ΔΣ modulators. In this paper, we propose a two-step MASH incremental ADC (IADC). In the first step it performs a third-order coarse quantization. Re-using the same hardware, in the second step the circuit performs fine quantization as a second-order IADC. Thus, it achieves fifth-order noise shaping with only three amplifiers. For a low oversampling ratio OSR = 32, the signal-to-noise ratio can be boosted by about 30 dB. The scheme is suitable for wide bandwidth applications.

This paper aims at studying the partial discharge (PD) characteristics of the LN 2 /PPLP insulation system under DC superimposed AC voltage for high temperature superconducting (HTS) cables. The pulse current method was adopted to measure the PD characteristics under the superimposed voltage with different DC components. The voltage virtual value (V rms ) of AC-DC superimposed voltage is set as 7 kV to analyze the effects of the DC component on the phase distribution of discharges times and amplitude. The experimental results show that: the number of discharges and the discharge amplitude decreases with the increases of DC component. According to the distribution characteristics of the phase spectrum of discharges times, the discharges with different ratio of DC components can be divided into three stages. The partial discharge pulse repetition rate is highest when AC voltage was applied. With the increase of DC components of the superimposed voltage, the total discharge times and the maximum discharge amplitude decrease, together with the widening of the PD phase distribution in stage I and II. When the DC component is up to 6 kV, the discharge times is minimized, and the PD is concentrated in the first quadrant in stage III. As a result, with the increase of the proportion of positive DC in the AC-DC superimposed voltage, the total discharge times and discharge amplitude decrease throughout the process of the PD. As the positive DC proportion increases, the discharge pulse in the positive period gradually exceeds that in the negative one.

Prof. Mojgan Daneshmand made seminal contributions to the advancements of substrate integrated waveguide (SIW) technology together with her students and colleagues. In this paper, we present our recent work on miniaturized SIW diplexer in memory of Mojgan and her family. The proposed diplexer is centered at 3.45 and 4.9 GHz for 5G sub-6 GHz communications, which is enabled by a common dual half-mode substrate integrated rectangular cavity (HMSIRC) coupled with singlemode cavities in each channel. Besides the elimination of T-junction, HMSIRCs are also introduced for the miniaturization of sub-6 GHz circuits. Additionally, by employing the first two modes in the dual-HMSIRC, a large frequency ratio of 1.42 between 3.45 and 4.9 GHz can be realized, which is difficult to implement with the original whole cavity scheme because of spurious passband problems. A 2nd-order example with ripple fractional bandwidths of 2% and 1.6% for the two channels is synthesized, designed, fabricated, and tested for demonstration.

The objective of the paper is to analyze the performance of a proposed control technique for VSC based HVDC transmission system and study the behavior of the quantites over DC faults and disturbances. The study is based on a 2-level VSC converter to which a deadbeat control scheme is applied. The analysis is carried on in the $d-q$ reference frame. The advantages of this technique over the conventional PI controller are fast tracking ability, precise, robust and fast dynamic response to the grid transient disturbances. The system is subjected to DC faults, and the response of the system is validated using MATLAB/SIMULINK platform.

The main interest of this paper is concept of Compressive Sensing and its application in 1D biomedical signals. The accent is on analyzing several types of biomedical signals and finding domain in which they are sparse. Therefore, we use gradient-based algorithm to reconstruct these biomedical signals with different number of samples. By working with gradient-based algorithm, we presented reconstruction of Electrocardiogram, Electroencephalogram and Electromyogram biomedical signals and drew a conclusion about differences between them. Several sparsifying basis are tested in order to find the most suitable one, for specific signal type, and we verified the whole theory by experimental results.

A systematic review of literature is a crucial aspect in pursuing any research problem. With the galloping growth in the number of researchers and scientific publications available online in digital libraries, there is information overload which makes the search of relevant papers cumbersome. An efficient academic paper recommendation process could be a promising maneuver for this purpose. The state-of-the-art methods in this domain primarily employ link-based approaches on citation network and do not consider the semantics (context) of papers being recommended, although the later one largely accounts for the relevance of recommendations. Also achieving online response time (scalability) is a perennial desire for any recommendation system. In this context we propose a hybrid approach “RecCite” that blends the popularity of papers with semantic similarity to acquire relevance. Further, the approach follows a top-down methodology for filtering papers from a more generic and larger network to a community pertaining to a SIG (Special Interest Group) that inherently addresses the scalability aspect. The proposed approach has been systematically evaluated with state-of-the-art baseline method using the publicly available Arnet-Miner data set. Satisfyingly, “RecCite” outperforms the baseline method with up to 37.66%, 20.14%, and 97.24% improvement in precision, recall, and response time, respectively.

As a tool for human technological advancement, the peer-review system acts as a gateway for ensuring academic paper qualities. However, the system has proven to be slow and expensive. Also, biasedness remains an unsolved problem. Such issues could become a major bottleneck, which can adversely impact research progress and dissemination of knowledge. This paper aims to propose a double-blind paper review system to preserve the authors and reviewers anonymity. This system also addresses issues concerning the reviewers payment, inconsistent review metrics, and biased reviews. The proposed solution utilizes the Hyperledger Fabric blockchain with the InterPlanetary File System (IPFS). The blockchain smart contracts provide a base for financial transactions between paper publishers and the reviewers. Hence, we introduce AcadCoin, a novel cryptocurrency used for supporting said financial transactions. Also, the Hyperledger blockchain provides user access control to achieve double-blindness in reviews. Along with the Hyperledger blockchain, the IPFS is used to store the paper documents, review documents and open metrics documents to reduce the storage requirement of the blockchain. A broad system architecture is constructed to combine the blockchain and the file storage system. This system architecture distributes nodes of the system to related parties. Finally, the blockchain network is implemented and tested using the Hyperledger Composer Playground environment.

With the great technological advances in colour printing, duplicating and scanning, counterfeit notes haves posed an increasing threat to currency markets all over the world. Over the past few years, the Yemeni currency market has suffered extensively from this problem. Although the Yemeni Paper Counterfeit- Currency Detection System (YPCDS) has made steps to counter this problem, it is limited to the 1000 denomination, leaving a considerable chance for counterfeiting other dominations. This paper offers a technical remedial solution to this problem for all denominations using image processing and machine learning techniques. The main aim of this system is to create an automatic system robot to recognize and detect Yemeni paper currency genuine or counterfeit. The system framework consists of five phases: acquisition of the currency image, preprocessing, feature extraction, classification, and verification. MATLAB is the main program for creating this system. The efficacy of the system was tested experimentally using four Yemeni currency categories and the system was found to be able to detect whether currency is genuine or counterfeit with high accuracy and fast processing time.

Searching for relevant literature is a fundamental part of academic research. The search for relevant literature is becoming a more difficult and time-consuming task as millions of articles are published each year. As a solution, recommendation systems for academic papers attempt to help researchers find relevant papers quickly. This paper focuses on graph-based recommendation systems for academic papers using citation networks. This type of paper recommendation system leverages a graph of papers linked by citations to create a list of relevant papers. In this study, we explore recommendation systems for academic papers using citation networks incorporating citation relations. We define citation relation based on the number of times the origin paper cites the reference paper, and use this citation relation to measure the strength of the relation between the papers. We created a weighted network using citation relation as citation weight on edges. We evaluate our proposed method on a real-world publication data set, and conduct an extensive comparison with three state-of-the-art baseline methods. Our results show that citation network-based recommendation systems using citation weights perform better than the current methods.

BBoxDB is a distributed key-bounding-box-value store which is capable of handling large scale $n-$dimensional data. Unlike existing key-value stores, each value is stored together with a bounding box which describes the location of the value in an $n-$dimensional space. BBoxDB splits large datasets automatically and spreads them across a cluster of nodes. The software works primarily on the bounding boxes of the stored data; operations like range queries or joins take only the bounding boxes of the data into consideration. In version 0.9.1, we implemented support for user defined filters (UDF) in BBoxDB; UDFs operate on the real values of the data. In this paper, we describe this novel feature for the first time. As an example, a UDF is developed in this paper which is capable of handling GeoJSON encoded data. The UDF consists only of a few lines of code and turns the generic distributed datastore BBoxDB into a specialized system that is capable of processing large scale GeoJSON encoded datasets. Operations like spatial joins or range queries on the real geometries of the data become possible. During our demonstration, we show the implementation of the UDF and perform range queries and spatial joins. The used dataset contains spatial data of the whole world and is obtained from the Open Street Map project. For visualization, the GUI of BBoxDB was extended in a way that spatial queries can easily be executed. Using the GUI, query results can be interactively explored; they are shown as an overlay on a map dynamically fetched from the Open Street Map project.