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Process-monitoring laboratories in the pulp and paper industry generally use a combination of wet chemical analyses and physical measurements to certify the fitness of a production pulp for a specific end-use. These laboratory tests require time and the effort of trained personnel, limiting their utility for real-time process control. Here we show that Raman probes of unrefined cellulosic pulps, well-suited to the online measurement of in-process materials, can predict the quality attributes of manufactured papers. The accuracy of prediction improves when the covariance is modelled in a reduced measurement space selected by a data-driven, feature-selection technique referred to as a Template Oriented Genetic Algorithm (TOGA). TOGA, combined with discrete wavelet transform (DWT), isolates functional-group features that correlate best with mechanical properties paper derived from refined pulp. Paper makers refine market pulps to build sheet strength using a beating process that decreases freeness as it increases fibre-fibre bonding. Methods demonstrated here predict manufactured sheet properties obtainable after any specified degree of refining from the Raman spectrum of an unrefined pulp. This analysis capacity will enable both vendors of market pulp and makers of sheet paper to specify in advance the amount of beating required to produce a desired product, thereby saving cost and conserving resources.

This study explored the relationship between syntactic complexity and rhetorical functions and the relationship between syntactic complexity and level of move/step realization in learner-generated research paper introductions. We created a corpus of 79 introductions written by undergraduate L2 students at intermediate to high-intermediate levels. We annotated the introductions for moves and steps, measured their syntactic complexity at the global, clausal, and phrasal levels, and rated their level of move/step realization across four levels (no, low, intermediate, and high). We conducted one-way MANOVA, one-way ANOVA, Pearson correlation, and stepwise multiple linear regression analyses. We found sentences realizing Reviewing items of previous research function to be significantly longer than other steps in Move1 and to contain significantly more clausal and phrasal complexity features. Sentences performing Claiming centrality function also demonstrated significantly more phrasal complexity features as measured by complex nominals per clause. We also found a higher level of phrasal complexity in introductions with a higher level of move/step realization, which can be attributed to introductions including Step1 and Step3, rather than containing all steps. Our findings imply that there is value in developing explicit instructions on how to use complex structures in performing rhetorical steps in research paper introductions. © 2021 Elsevier Ltd

The dirty paper wiretap channel, also referred to as the Gaussian wiretap channel with noncausal channel state at the transmitter, plays an important role in wireless physical layer security. In this paper, the dirty paper wiretap channel is revisited by considering actions on the channel state, i.e., the transmitter generates an action sequence that affects the formation of the channel state. Lower and upper bounds on the secrecy capacity of this model are proposed, and numerical results indicate that the actions on the state help to increase the achievable secrecy rate of the dirty paper wiretap channel. The study of this paper offers a new option for enhancing the secrecy rates of the state-dependent wiretap channel models.

Photographs are taken as valid evidences in various scenarios of our day to day life. Because of the developments in the field of Image Processing, altering images according to ones need is not a difficult task. Techniques of Image Forensics play its crucial role at this juncture. One of the mostly found types of image tampering is Copy-Move forgery. A copy-move forgery is performed by copying a region in an image and pasting it on another region in the same image, mostly after some form of post-processing like rotation, scaling, blurring, noise addition, JPEG compression etc. Two types of copy-move forgery detection techniques exist in literature. They are the Block based methods and Key-point based methods. Both the methods have their own advantages and limitations. This paper presents a survey on the recent developments in block based methods. As forgeries have become popular, the importance of forgery detection is much increased. Copy-move forgery, one of the most commonly used methods, copies a part of the image and pastes it into another part of the image. In this paper, we propose a detection method of copy-move forgery that localizes duplicated regions using Zernike moments. Since the magnitude of Zernike moments is algebraically invariant against rotation, the proposed method can detect a forged region even though it is rotated. Our scheme is also resilient to the intentional distortions such as additive white Gaussian noise, JPEG compression, and blurring. Experimental results demonstrate that the proposed scheme is appropriate to identify the forged region by copy-rotate-move forgery.

The intelligent task of semantically assigning a paper to a reviewer with respect to his knowledge domain remains a challenging task in academic conferences. From literature, a number of automated reviewer assignment systems have been presented which are based on distributional semantic models such as Term Frequency-Inverse Document Frequency (TF-IDF), Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) have been used to capture semantics. Thus, this study presents the comparative study of the three models based on their derived suitability scores between a paper meant for review and a reviewer’s representation papers. From the experimental results obtained, it shows that TF-IDF outperformed the accuracy level of the other two models by a substantial margin.

In this paper we propose to implements blockchain technology for Internet of Things (IoT) networks that use Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. To eliminate the reliance on a single leader and improve performance, we propose to allow multiple leaders to propose request batches independently and, possibly, concurrently, by using different overlay networks. Nodes participate in parallel consensus rounds without contention, and they only contend with others to reserve the next available spot for the atomic insertion of a new transaction batch or block into the replicated blockchain ledger. We develop an analytical model for the multiple leader PBFT ordering service by using a Discrete-time Markov chain. Our evaluations show that our model outperforms the original multiple entry point PBFT protocol in a wide range of parameter values, and that it scales well with the number of orderer nodes in the PBFT committee and block arrival rate.

Automation in every domain is today’s need, so in question paper setting also. The proposed system is automation of paper setting and identification of difficulty level of questions and question papers, which uses blooms taxonomy categorization to classify the questions in easy, medium and high categories. By using blooms taxonomy, the difficulty level of question can be identified, which is helpful in generating qualitative question paper. While categorizing questions, system uses natural language pre-processing techniques, which are understood by computer and also uses Latent Dirichlet Allocation algorithm. Based on paper setter’s requirement, paper setting is done; also, if user wants to check difficulty level of any question paper then this is also done by proposed system using NLP. The paper setting using difficulty measure is right benchmark for evaluating student’s efficiency. Automation of paper setting improves the speed, and difficulty level module improves the quality.

People re-identification is a fundamental stage in video surveillance systems. This stage is used for determining the labels of the people’s images usually based on their visual similarities. The appearance salience caused by wearing the clothes and carrying objects, with specific colors can be used for improving the performance of re-identification systems. To address the mentioned point, in this paper, we propose a color-based re-ranking process. In the proposed approach first, the input images are semantically segmented as: person’s body, possible carried objects, and background. Then the most frequent colors of person’s body and possible carried objects are used to re-order the ranked results obtained from two commonly used re-identification approaches i.e., Gaussian of Gaussian (GOG) and Hierarchical Gaussian Descriptors (HGDs). Experimental results on various people re-identification datasets show that our proposed re-ranking approach can improve the performance of re-identification systems.

In this paper, we present a plug and play Rock-Paper-Scissors game set that utilizes CNN-based static hand gesture recognition and a Markov chain-based bot. Each game round, the camera output is fed to the neural network and the hand gesture result is compared to the current Markov chain state. We utilize a CNN trained with a dataset containing rock-paperscissors hand gestures and unwanted inputs, and a multi-state Markov chain. Automated tests confirm the bot prediction capabilities and practical tests verify the precision of static hand gesture recognition. Tests against a verification dataset reveal a recognition accuracy of 97.4%.

Paper illustrates the process of topic modeling and text classification. Specifically, the dataset used is a corpus consisting of scientific publications published by Neural Information Systems Processing Conference. Topic modeling itself is performed using Latent Dirichlet Allocation model. It is followed by optimization of a number of topics on the basis of topic coherence, a quality measure of human interpretability. Results of topic modeling are used for labeling data prior to text classification. Labels are determined based on the distribution of assigned papers' topics over time. Specifically, peak changes used for differentiating between time periods dominated by specific topics are calculated as a Kullback-Leibler divergence. Finally, transforming data into the feature vectors, several different text classification approaches are evaluated. As observed, the greatest accuracy score is recorded for the use of extreme gradient boosting classifier being 77.1%.

The Uplink Wavefront Corrector System (UWCS) is a pathfinder instrument to demonstrate the uplink correction by Adaptive Optics techniques; this novel application can be directly usable in two fields: Free-Space Optical Communications and the generation of Laser Guide Stars. A Rayleigh LGS is propagated to the sky while the atmospheric wavefront aberrations are measured by a Shack-Hartmann WFS with 12 x 12 sub-apertures using a Natural Guide Star as a reference. The laser upwards propagation path is then pre-compensated by a 97-actuator deformable mirror. A scoring camera is attached to the finder telescope, next to the main aperture, in order to show the overall result, which is assessed in terms of beam power concentration. Present paper described the design process of the UWCS and its integration and testing in the Optical Ground Station telescope, at Teide Observatory (Spain).

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

Micro aerial vehicle (MAV) gradually gains plentiful attention in recent years. However, it is still a full challenge to design MAVs with high aerodynamic performance because aerodynamic efficiency of wings deteriorates sharply at low Reynolds numbers. Under this circumstance, we focus on one unconventional configuration with annular wing, which can be folded from a square paper. The annular paperplane configuration is described with geometric parameters, and the longitudinal and lateral aerodynamic characteristics are studied with RANS method.

Paper cooperation network embodies expert topic similarity in an extent, thus, a novel method is proposed for expert topic analysis and extraction by combining paper cooperation network and topic model. In the method, we extract each paper’ author information and construct an expert cooperation network. At the same time, by means of LDA model, a probabilistic topic model is also built to analyze papers’ latent topics. Then, by making full use of the feature that adjacent nodes in the expert cooperation network share similar themes distribution, we makes a constraint on expert topic distribution in Gibbs sampling process of solving the probabilistic topic model. Experimental results on NIPS dataset show that the proposed method can effectively extract expert topics, and the expert paper cooperation network plays a very good supporting role on the extracting task.

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

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

\(\mathcal {PSVR}\) is a novel routing algorithm for pub/sub systems in ad-hoc networks focusing on scenarios where communications links are unstable and nodes frequently change subscriptions. It is a compromise of size and maintenance effort for routing tables due to sub- and unsubscriptions and the length of routing paths. Designed in a self-stabilizing manner it scales well with network size. The evaluation with real world deployment reveals that \(\mathcal {PSVR}\) only needs slightly more messages than a close to optimal routing structure for publication delivery, and creates shorter routing paths than an existing self-stabilizing algorithm.

Electrocardiogram (ECG) paper records are used commonly for diagnosing heart abnormalities. The stored paper ECG records may be blurred or affected by noise. Enhancement of such blurred paper ECG records is done using a low-pass Wiener filter and background grid removal using adaptive thresholding, signal extraction, and parameter extraction. In this work, clinically important parameters such as heart rate, R-peak, RR-interval, and S-peak are extracted from the enhanced digitized ECG signal, and these extracted parameters are then compared with printed parameters on original paper ECG records. The average absolute error between extracted parameters and original paper ECG parameters is 0.02 with an average accuracy of 97.66%. The extracted digitized signal from stored ECG records may be useful for retrospective analysis of cardiac abnormalities, using automated diagnosis software.

The term “Microservice Architecture” has sprung up in recent years as a new style of software design that gains popularity as cloud computing prospers. In microservice-based applications, different microservices collaborate with one another via interface calls, but they may also compete for resources when an increase of users’ need renders the resources insufficient. This poses new challenges for allocating resources efficiently during runtime. To tackle the problem, we propose a novel approach based on Congestion Game in this paper. Firstly, we use a weighted directed acyclic graph to model the inter-relationship of the microservices that compose an application. Then we use M/G/1 Queue in Queue Theory to describe the arrival process of access requests, and combine it with the above graph to calculate the arrival rate of access requests to each microservice, which in turn is used to estimate response time in a newly-designed microservice revenue function. Finally, we define resources competing problem as a congestion game where each microservice is a player aiming to maximize its revenue, and propose an algorithm to find Nash equilibrium in polynomial time. Experiment results show that our approach can effectively improve the overall performance of the system with limited resources, and outperform Binpack and Spread, two scheduling strategies used in Docker Swarm.

As computational needs increase in relation to the growing fields of Internet of Things and Deep Learning, energy-efficient, computational units are needed to bypass DSP units within Von Neumann architectures. A charge-mode vector matrix multiplier (VMM) with compute-in memory capabilities was fabricated in the Global Foundries 55nm LP process. The array is comprised of a 156 row by 512 column crossbar where each row computes a 512 element binary dot product in the charge domain. This normalized analog multiply and accumulate (MAC) is carried out by charge-injection devices who compute a 1-bit multiplication in the charge domain. Preliminary test results show successful, linear output computation in the analog domain to various input vectors that are both digital and multi-level analog. The 156 × 512 compute-in memory, CID array has been simulated to achieve an efficiency of 1.8 TOPs per mW.

The aim of this research is to classify the front side (observe) and back side (reserve) of Myanmar currencies (kyats) in three denominations. The system consists of binary image conversion, noise removal, feature extraction, and recognition. Zernike moment is used for feature extraction. The features from the top left, top right, bottom left and bottom right of the currency are extracted. The classification of the correct currency using the k -nearest neighbor algorithm based on the data set generated by Zernike Moment. The accuracy is measured based on the confusion matrix. The experimental results suggested that the approach is sufficiently effective and efficient in classifying the paper currencies.

Boolean Satisfiability (SAT), the first problem proven to be NP-complete, is intractable on digital computers based on the von Neumann architecture. An efficient SAT solver can benefit many applications such as artificial intelligence, circuit design, and functional verification. Recently, a SAT solver approach based on a deterministic, continuous-time dynamical system (CTDS) was introduced [1]. This approach shows polynomial analog time-complexity on even the hardest k-SAT (k ≥ 3) problem instances, but at an energy cost dependent on exponentially growing auxiliary variables. This paper reports a novel analog hardware SAT solver, AC-SAT, implementing the CTDS via incorporating novel, analog circuit design ideas. AC-SAT is intended to be used as a co-processor and is programmable for handling different problem specifications. Furthermore, with its modular design, AC-SAT can be readily extended to solve larger size problems. SPICE simulation results show that AC-SAT can indeed solve the SAT problems, and it has speedup factors of ∼104 on even the hardest 3-SAT problems, when compared with a state-of-the-art SAT solver on digital computers.

Anyone who has written one knows how frustratingly difficult it can be to perfect a computer program. Some of the founding fathers of computing set out ideas for reasoning about software — one would say today ‘techniques for proving that a program satisfies its specification’. Alan Turing presented a paper entitled Checking a Large Routine that laid out a workable method for reasoning about programs. Sadly his paper had little impact. Understanding the problem faced, Turing’s proposal and what followed provides insight into how ideas evolve. Comparing three contributions from the 1940s with the current state of the art clarifies a problem that still costs society a fortune each year.

Joint pushing and proactive caching holds the promise of significantly increasing the throughput and decreasing the latency of content-centric wireless networks. In this work, we import the pushing and caching mechanism into a traditional packet-oriented network for decreasing the average queueing delay. Since the decreasing relies on extra costs paid for the pushing-caching mechanism, such as the cost for the cache buffer's capacity, extra transmission power for pushing and so on, we mainly derive the average queueing delay under given cost in order to study the relationship between the average queueing delay and the extra cost. In order to do so, a metric named System State is defined to describe the transmitter and user's current buffer states. Based on the System State, a two-dimensional Markov Chain is built to derive the average queueing delay and the average hitting ratio. The results show us how much the average queueing delay can achieve when the cache buffer's capacity which usually has a high cost is given, and then provide us a method to make a tradeoff between the average queueing delay and the cost of the user's buffer size. Simulation results validate our conclusion.

We study a dirty paper channel model where the input is constrained to belong to a PAM constellation. In particular, we provide lower bounds on the capacity as well as explicit coding schemes for the binary-input dirty- paper channel. We examine the case of causal as well as non-causal side information.

We introduce a new method for evaluation of sparkle of the combination of display and AG-layer by application of a weighting of the Fourier amplitudes of the random luminance modulations that are perceived as sparkle, according to the contrast sensitivity function of the human visual system. This new approach provides improved sensitivity to small sparkle variations, better control of parasitic low frequency components caused by non-uniformities of display illumination and an improved match to visual ratings with subtle differences in the low sparkle region.

Proactively pushing content to users has emerged as a promising way of coping with the explosively growing traffic demand of next-generation mobile networks. However, it is still unclear whether content pushing can improve the energy efficiency of hard delay-constrained communications over additive white Gaussian noise (AWGN) channels. With pushing, the energy consumption can be reduced by increasing the available transmission time. But if the user never needs the pushed content, pushing may result in wasted energy. Based on the random content request delay, this paper derives the content request probability threshold that determines whether a content file should be pushed under the hard delay constraint. Moreover, an optimal strategy to allocate transmission power in content pushing and on-demand delivery phases is also proposed. It is shown that the energy efficiency of systems with pushing can be significantly improved as the content request probability increases. Numerical results validate the effectiveness of the proposed power allocation strategy, compared with the on-demand scheme.

Understanding relationships among financial entities can provide insight into the behavior of complex financial eco-systems. In this demonstration paper, we consider datasets of financial documents that describe the activity or role played by a financial institution (FI), typically with respect to a financial product or another financial entity. We develop community models based on financial institutions (FI) and their behavior or activity described by their roles (Role). Our models are based on an intuitive assumption that FIs will form communities, and FIs within a community are more likely to collaborate with other FIs in that community, and to play the same role, in other communities. Inspired by the Latent Dirichlet Allocation (LDA) and topic models, we develop several probabilistic financial community models and we use those models to identify interesting financial communities in two datasets.

Hidden Markov model HMM (1) is one of the important approaches for information extraction. In this paper, a model of the improved first-order hidden Markov HMM (2) is proposed. In the HMM (2), the output probability of the observation is not only dependent on the current state of the model, but also dependent on the previous state of the current state of the model. The algorithm of the ML and the algorithm of the Viterbi are analyzed. At last, experiments show that the HMM (2) is more precise than the HMM (1).

We determine a duality between broadcast and multiple access channels that can be used to obtain the capacity region and optimal transmission strategy for one channel based on the capacity-achieving transmission strategy and region for the dual channel. This duality result in applicable to additive Gaussian noise and fading channels for several different notions of fading channel capacity, including ergodic capacity, outage capacity, and minimum rate capacity. We show that duality can be used to obtain any of these capacities for the fading broadcast channel from the same capacity on the dual MAC channel, and vice versa. We then apply this general result to obtain the minimum rate capacity of the fading multiple access channel, which also yields the ergodic and outage capacity as special cases. Next we turn our attention to broadcast channels with multiple antennas at the transmitter and receiver (the MIMO channel). Since this channel is in general non-degraded, its capacity region remains an unsolved problem. We establish a duality between the achievable region of the MIMO broadcast channel using Costa’s “dirty-paper” coding and the capacity region of the MIMO multiple-access channel, which is easy to compute. We also show that the dirty paper achievable region yields the sum-rate capacity of the MIMO broadcast channel.

We consider a set of parallel, two-user scalar Gaussian broadcast channels, where the transmitter wishes to send independent information to each of the receivers and common information to both receivers. The capacity region of this channel is implicitly characterized in [A. El Gamal, (1980)]. Here, we provide an explicit characterization of the power and rate allocation schemes that achieve the boundary of the three-dimensional rate region. We also propose a dirty-paper coding achievable region for MIMO broadcast channels with common information.

A paper currency recognition system has a wide range of applications such as self receiver machines for automated teller machines and automatic good-selling machines. In this paper a new paper currency recognition system based on Fourier-Mellin transform, Markovian characteristics and Support Vector Machine (SVM) is presented. In the first, a pre-processing algorithm by Fourier-Mellin transform is performed. The key feature of Fourier-Mellin transform is that it is invariant in rotation, translation and scale of the input image. Then, obtained image is segmented and markovian characteristics of each segment have been utilized to construct a feature vectors. These vectors are then fed into SVM classifier for paper currency recognition. In order to evaluate the effectiveness of the system several experiments are carried out. Experimental result indicates that the proposed method achieved high accuracy rate in paper currency recognition.

Predicting the most impactful (big-hit) paper among a researcher’s publications so it can be well disseminated in advance not only has a large impact on individual academic success, but also provides useful guidance to the research community. In this work, we tackle the problem of given the corpus of a researcher’s publications in previous few years, how to effectively predict which paper will become the big-hit in the future. We explore a series of features that can drive a paper to become the big-hit, and design a novel Bayesian ranking algorithm AdaWIRL (Adaptive Weighted Impact Ranking Learning) that leverages a weighted training schema and an adaptive timely false correction strategy to predict big-hit papers. Experimental results on the large ArnetMiner dataset with over 1.7 million authors and 2 million papers demonstrate the effectiveness of AdaWIRL. Specifically, it correctly predicts over 78.3 % of all researchers’ big-hit papers and outperforms the compared regression and ranking algorithms, with an average of \(5.8\,\%\) and \(2.9\,\%\) improvement respectively. Further analysis shows that temporal features are the best indicator for personal big-hit papers, while authorship and social features are less relevant. We also demonstrate that there is a high correlation between the impact of a researcher’s future works and their similarity to the predicted big-hit paper.

A simple approach to calculate the amplitude component of a wave front propagating in space from a hologram is proposed. It is able to calculate the amplitude distribution on a plane at any distance rapidly using a standard GPU. This is useful for analyzing and reconstructing the 3D image encoded on a hologram.

In this paper, we study how to cluster Remote Radio Heads (RRHs) into Virtual Base-Stations (VBSs) in a Cloud-based Radio Access Network to optimally manage the tradeoff between improving the performance of cell-edge users and maintaining high spatial reuse for the overall system. We develop Gibbs-sampling based algorithms that can find the desirable global VBS configuration from an arbitrarily given set of allowable VBS configurations. While Gibbs-sampling has been used to solve other wireless control problems, its application to VBS clustering faces new challenges both due to the difficulty in estimating the quality of a VBS configuration under rapid channel variations, and due to a new global coupling effect. We leverage Random Matrix Theory to develop a method that can quickly estimate the quality of a VBS configuration based only on average channel statistics. Further, we use perturbation analysis to develop a distributed approximation of the Gibbs sampler to circumvent the global coupling effect, which then allows different parts of the network to search for better VBS configurations in parallel. Our numerical results demonstrate how the proposed algorithm can be used as a general tool to evaluate the system performance under a variety of clustering constraints.

IR monitoring is a basic tool for quality control and predictive maintenance in the paper industry. There are also other possible interests, e.g. measurements of temperature profiles of high speed production lines. The first possibility to use IR for the above mentioned use appears to be applications of glass heat treatment in on line production. At this time there is a lot of interest to be able to measure temperature profiles from a fast moving surface (e.g. in paper machines) with velocities like 10 - 20 m/s. Normal low speed line scanners typically give scanning speed of 50 Hz which in this case means 20 m/s/50 Hz equals 40 cm resolution. The obtained resolution is not normally high enough. To have more accurate vision from the studied surface IR camera line scanning was proposed to be a solution with line scanning frequencies of 8000 Hz which would provide resolution of 20 m/s/8000 Hz equals 0.25 cm between surface points. It is a well-known fact that all undesired frequencies in paper transfer systems disturb the uniform paper drying process which in some cases is the basic reason for low quality coating or even broken paper line. The possibility to detect these drying problems with IR frequency analysis will give a new way to control the paper drying process. With IR analysis it is possible to get information about temperature distributions along the paper track. Using a well established frequency analysis as a tool, the error estimations are obtained. In most cases an open-roll image from paper track is very desirable because there are no existing wide area measurement systems for this purpose. The careful analysis of this new method is given in the manuscript.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Survey of research papers is not an easy task for novice researchers, because they are not always good at finding all appropriate keywords for the survey. Moreover, it is not easy for them to understand positions of papers in their research fields instantly, even when they use famous search engines like Google Scholar, it may often take a long time for them to find scholarly literature. On the other hand, many researchers have presented citation visualization techniques for surveying research papers. However, it is still often difficult to observe the complicated relations across multiple research fields or traverse the entire relations in their interest. In this paper, we proposed a visualization technique for citation networks applying topic-based paper clustering. Our technique categorizes papers applying LDA (Latent Dirichlet Allocation), and constructs clustered networks consisting of the papers.

We compare Fresco’s analysis of the Turing machine-based notion of computation with that of others, in particular with functional programming and with the reversible computing paradigm of Toffoli and others. We conclude that, although much useful philosophical work can be done by the sort of analysis that Fresco proposes, there is, nevertheless, always likely to be a number of individually viable but different accounts of computation.

Dr. Hugh Loebner in his 1994 article in Communications [1] makes the statement regarding future LP competitions, "the winner of the Loebner Grand Prize must develop a computer with associated hardware that can respond 'intelligently' to audio visual input in the manner that a human would. ... Turing wrote, 'The question and answer method seems suitable for introducing any one of the fields of human endeavor that we wish to include.' Well, I would like to ask questions about images and pattern recognition. If the computer answers appropriately it is intelligent."

SVM is a new general learning method based on the statistic learning system which can be used as an effective means to process small sample, nonlinear and high dimensional pattern recognition. This paper did research on the learning algorithm of support vector machine, extracted characteristic data of banknote which is on account of PCA according to the characteristics of the support vector machine (SVM), and proposed to put support vector machine (SVM) into banknotes denomination recognition by combining SMO training algorithm with one-to-many multi-value classification algorithm. Besides, this article used genetic algorithm in parameters optimization such as the punishment coefficient C of soft margin SVM and the width parameter of Gaussian kernel function. The ultimate purpose is to recognize the denomination of banknote efficiently and accurately. The experimental results verified that this kind of recognition method increases the recognition accuracy up to 90% or more.

Radio-wave propagation through wireless channel is a complicated phenomenon characterized by fading which is the result of multipath propagation. In wireless communication system, random process associated with fading channels can usually characterized by their PDF (Probability Density Function) and CDF (Cumulative Distribution Function). Signal fading can drastically affect the performance of terrestrial communication systems. Several statistical models are available for describing the fading envelope of the received signal in which Rayleigh, Rician and Nakagami are the most frequently applied models. Higher-order statistics such as Level Crossing Rate (LCR) & Average Fade Duration render (AFD) insight into signals which is not always available at lower orders.

In this paper, the Two-Way Channel (TWC) with noncausal Channel State Information (CSI) is investigated. For the discrete memoryless channel an achievable rate region is derived. Then by extending the result to the Gaussian TWC with additive interference noise, it is shown that the capacity region of the latter channel is the same as the capacity when there is no interference, i.e., a two-way version of Costa's writing on dirty paper problem is established.

ECG signals recorded on paper are transferred to the digital time database with the help of an automated data extraction system developed here. A flatbed scanner is used to form an image database of each 12-lead ECG signal. Those images are then fed into a Pentium PC having a system to extract pixel-to-pixel co-ordinate information to form a raw database with the help of some image processing techniques. These raw data are then ported to the regeneration domain of the system to check the captured pattern with the original wave shape. The sampling period of each ECG signal is computed after detection of QRS complex. Finally, discrete Fourier transform of the generated database is performed to observe the frequency response properties of every ECG signal. Some interesting amplitude properties of monopolar chest lead V4 and V6 are noticed which are stated.

The explosion of big data applications imposes severe challenges of data processing speed and scalability on traditional computer systems. However, the performance of the von Neumann machine is greatly hindered by the increasing performance gap between CPU and memory, motivating the active research on new or alternative computing architectures. As one important instance, neuromorphic computing systems inspired by the working mechanism of human brains have gained considerable attention. In this work, we propose a heterogeneous computing system with neuromorphic computing accelerators (NCAs) that are built with emerging memristor technology. In the proposed system, NCA is designed to speed up the artificial neural network (ANN) executions in many high-performance applications by leveraging the extremely efficient mixed-signal computation capability of nanoscale memristor-based crossbar (MBC) arrays. The hierarchical MBC arrays of the NCA can be flexibly configured to different ANN topologies through the help of an analog Network-on-Chip (A-NoC). A general approach which translates the target codes within a program to the corresponding NCA instructions is also developed to facilitate the utilization of the NCA. Our simulation results show that compared to the baseline general purpose processor, the proposed heterogeneous system can achieve on average 18.2x performance speedup and 20.1x energy reduction over nine representative applications while constraining the computation accuracy degradation within an acceptable range.

The academic Abstract genre is one of the most important parts of a research paper because it is viewed at first by readers. When well written, it enables them to quickly and accurately identify the main ideas and contributions of the work presented. Hence writing an abstract is the most difficult part. Poorly written abstracts can affect negatively the significance and understanding of the paper. In order to investigate the rhetorical moves (or communicative stages) creating the meaning of abstracts, this study analyzes a corpus of 1,500 international conference papers (ICALT) using Corpus Linguistic tools and LDA (Latent Dirichlet Allocation) algorithm. This study shows that abstracts are written focusing on research purpose and methods rather than describing the background and commenting results. LDA algorithm provides a list of topics within the corpus of abstracts based on research areas. Future works on LDA algorithm should strengthen its semantic approach based on the rhetorical moves.

The author presents a history of his efforts to design sustainable and economical circuit construction on paper, which he finds more akin to craft than industry. He focuses on a collection of modules called Rollz-5, which creates organic rhythms out of geometrical forms. A future application of this work will be to create radio devices based on the Platonic solids.

In order to segment paper web defections effectively, texture features, based on the Gauss-Markov random field model, were used in this paper. By introducing the characteristics of paper web texture features, the maximum difference of the local texture parameters w 1 , w 2 , w 3 and w 4 of the Gauss-Markov random field model was used as a judgment index for web defection segmentation. A dirty spot defection was segmented by this method and its result shows that the paper web defections can be effectively segmented by the judgment index. The max difference of the local texture parameters of the Gauss-Markov random field model can be used as the judgment index to segment the defections in the paper web, which has the characteristics of nature texture.