Miracle-dz/UniFAR-FTUs huggingface dataset:
IEEE 802.11 and Bluetooth coexistence analysis methodology

As unlicensed (UL) band utilization for daily office functions increases, an understanding of how different wireless services, operating in the same band, may impact each other becomes an important issue. Both Bluetooth wireless personal area networks and IEEE 802.11 wireless local area networks share the same 2.4 GHz UL band and provide complementary wireless solutions for connectivity. A method was developed for examining wireless services coexistence in order to evaluate the impact interference may have on network performance. The methodology for the analysis was centered on deriving a closed form solution for the probability of collision in terms of the network and radio propagation parameters. The approach is illustrated by examining the coexistence between 802.11b and Bluetooth within typical operational ranges for both network traffic and RF environments.
A Comparative Study of Image Segmentation Based on the Improved Meanshift Software with Edison

Image segmentation is the technique and the process to separate the image into regions which have different characteristics and extract the interested objects from the image. Meanwhile, image segmentation is a vital important issue in many fields such as image processing, pattern recognition and artificial intelligence and it has wide application in various fields. This paper performs a great deal of contrastive analysis experiments on a series of images by using improved meanshift software and Edison software. The results show that improved meanshift software is easier to segment clearly than Edison in terms of similar color; the improved meanshift software segmentation is smoother than Edison in image shadow, the segmentation results hold favorable consistency in terms of human perception; the improved meanshift software segmentation is clearer than Edison in texture segmentation such as vegetation. The improved meanshift software has a better effect on the segmentation of boundary, road, etc. Both of them can remove the noise points effectively, but improved meanshift software is more sensitive to brightness; while the Edison software has a faster speed compared to the improved meanshift software.
Identification of coastal wetland using rule inferring

The main purpose of this paper was to explore the potential of decision tree classifier in the identification and change monitoring of coastal wetland. A part of the coastal wetland in the Northern Jiangsu Province was taken as test area. Decision tree classifiers derived using different ways were applied to the classification of coastal wetland and the results were compared by independent sampling points. It was shown that the post-classification improvements by using knowledge rules could achieve higher accuracy than automatic machine learning method. In the post-classification improvements, the selection of feature vectors was crucial to the improvement of accuracy of classification.
Fault trees and sequence dependencies

One of the frequency cited shortcomings of fault-tree models, their inability to model so-called sequence dependencies, is discussed. Several sources of such sequence dependencies are discussed, and new fault-tree gates to capture this behavior are defined. These complex behaviors can be included in present fault-tree models because they utilize a Markov solution. The utility of the new gates is demonstrated by presenting several models of the FTPP (fault-tolerant parallel processor), which include both hot and cold spares. <<ETX>>
High-speed bus arbiter for multiprocessors

Shared-bus interconnection schemes normally suffer from insufficient capacity. Increasing their bandwidth reduces the problem but makes bus arbitration somewhat difficult. This paper presents a fair bus-arbiter design, its implementation and simulation results. Although the techniques originated from the particular constraints of the architecture considered, it is generally applicable to high-speed arbitration problems and has a low hardware cost.
A two-level classifier for fingerprint recognition

A PC-based two-level classifier for fingerprint recognition is presented. Input fingerprint images are preprocessed via Laplacian edge detector and a skeletonization algorithm. The thinned binary image is then classified into one of the four categories of fingerprint via a curve tracing algorithm and a scoring method. Local density of the designated area is then computed for the second-level classification (i.e. within category classification). The proposed method is found to be reliable for a small set of fingerprints. <<ETX>>
Linear prediction with a variable analysis frame size

This paper describes a speech analysis-synthesis system based on stationary linear prediction formulation. This system uses a variable analysis frame size concept. The k-parameters are used to represent the spectral information in the speech. The statistical and quantization properties of k-parameters are studied in detail. A method for calculating the analysis frame size based on energy and pitch period variations within a speech waveform has been developed. The speech analysis-synthesis system has been implemented on the computing facility of the Signal Processing Laboratory at Case Western Reserve University. Average data rates of 4800, 3600, and 2400 bits/s have been achieved on a limited speech data base of male speakers.
Automatic Summarization Based on Mutual Information

Many existing automatic summarization methods often process a text based on the paragraphs. These methods ignore the correlation degree among paragraphs, which lead to the inaccuracy of the extracted summary. To solve the problem, we present a method based on mutual information. Firstly, we divide the whole text into several smaller blocks which relevant to each topic by measuring the degree of association among words, sentences and paragraphs. Then we compute the weight of the sentences by using the improved formula which aims to evaluate the importance of one sentence to a topic. Finally, the summary can be generated by combining the sentences selected from the different topics. The experiment result shows the validity of the method.
Crowdsourcing of Medical Data

ABSTRACT Right from open innovation to creativity, content generation to software development, marketing to crowdfunding, the tremendous growth of crowdsourcing has touched almost every field. The realm has entered even in medical sector now. This paper deals with this new concept of crowdsourcing medical data covering the reasons behind this revolution, prevailing examples of medical crowdsourcing, and the probable issues that can be encountered.
Virtual Clustered-based Multiprocessor Scheduling in Linux Kernel

Recent advancements of multiprocessor architectures have led to increasing use of multiprocessors in real-time embedded systems. The two most popular real-time scheduling approaches in multiprocessors are global and partitioned scheduling. Cluster based multiprocessor scheduling can be seen as a hybrid approach combining benefits of both partitioned and global scheduling. Virtual clustering further enhances it by providing dynamic cluster resource allocation duringrun-time and applying hierarchical scheduling to ensure temporal isolation between different software components. Over the years, the study of virtual clustered-based multiprocessor scheduling has been limited to theoretical analysis. In this thesis, we implemented a Virtual-Clustered Hierarchical Scheduling Framework (VC-HSF) in Linux without modifying the base Linux kernel. This work includes complete design, implementation and experimentation of this framework in a multiprocessor platform. Our main contributions are twofold: (i) to the best of our knowledge, our work is the first implementation of any virtual-clustered real-time multiprocessor scheduling in an operating system, (ii) our design and implementation gives practical insights about challenges of implementing any virtual-clustered algorithms for real-time scheduling.
An unsupervised classification method for polarimetric SAR images with a projection approach

Two unsupervised training algorithms are presented based on a projection method for polarimetric SAR images. For this, polarimetric features, including the entropy H, angle /spl alpha/, anisotropy A, the largest eigenvalue of the coherence matrix /spl lambda//sub 1/ and coherence /spl gamma/, are computed. Based on these features three noncorrelated and independent variates, which are the first three principle components are extracted using the principle component analysis. Then, a three-dimensional Cartesian coordinate is constructed using those three resultant variates. In order to reasonably define the initial cluster centers for the unsupervised classification, a projection method is proposed. As a result, a sophisticated two-dimensional feature space is obtained. Two initial cluster center determination algorithms, the histogram and histogram-quadrant methods, based on the above two-dimensional feature space are presented. Unsupervised classifications (with a minimum distance decision rule) are conducted based on the defined initial cluster centers. The classification results show that these approaches have high performance in convergence, speed, data volume and accuracy of training and classifying.
Estimating 3D camera motion for rendering audio in virtual scenes

In the production phase of digital cinema content it is important to allow the director not only to preview the final rendered scene early in the shooting process, but also to prehear the 5.1 surround and HRTF-based binaural versions, thus enabling visual and auditive artistic decisions to be taken at the shooting stage. In many cases camera tracking data is not available for all cameras on the set (e.g. handheld ones) and thus the motion of the camera needs to be estimated. In this paper we describe an approach for estimation of 3D camera motion and its use for real-time audio rendering. (1 page)
Cramer-Rao bound for joint direction of arrival, time delay estimation in DS-CDMA systems

In direct-sequence code-division multiple access (DS-CDMA) radiolocation systems, the estimation of propagation time delay and direction of arrival (DOA) is a crucial task. It can also be used for acquisition and tracking. To evaluate the accuracy of the estimated parameters, this paper derived the conditional Cramer-Rao bound (CRB) for the joint estimator of propagation time delay and DOA in asynchronous DS-CDMA systems. No knowledge of the transmitted bits is required. Simple asymptotic expressions of the CRB are presented. In the numerical study, the CRB and the simulation results of a subspace-based joint estimator are presented. Both CRB and the estimator are shown to be robust against near-far effect in CDMA systems with multiple users.
Robust Pronominal Resolution within Chinese Text

Anaphora Resolution is playing more and more important role in Natural Language Processing. There is an increasing need for the development of effective and robust strategies of anaphora resolution to meet the demands of practical applications. However, traditional approaches to anaphora resolution rely heavily on multilevel linguistic knowledge, such as syntactic, semantic, contextual and domain knowledge. It is undoubtedly difficult to acquire such knowledge at present. This paper presents a two-step approach with limited knowledge to resolve pronominal anaphora within Chinese text, which only uses number features, gender features and the features of grammatical roles. In this approach, a filter is firstly used to eliminate those expressions whose features are inconsistent with the pronoun, and thus form a set of potential antecedent candidates; then, a scoring algorithm is employed to calculate score of the candidates, and the candidate with the highest score is selected as the resultant antecedent. The algorithm does not examine each candidate in the set, but automatically determine whether to end the calculation or not by dynamically testing a termination condition, therefore the computational complexity is low. In addition, the approach does not need a deep analysis of the text, and can easily be implemented. Experiment shows the result is satisfactory.
Goodness-of-Fit-Techniques

"Overview, Ralph B. D'Agostino and Michael A. Stephens Graphical Analysis, Ralph B. D'Agostino Tests of Chi-Squared Type, David S. Moore Tests Based on EDF Statistics, Michael A. Stephens Tests Based on Regression and Correlation, Michael A. Stephens Some Transformation Methods in Goodness-of-Fit, Charles P. Quesenberry Moment (Ob1, b2 ) Techniques, K. O. Bowman and L. R. Shenton Tests for the Uniform Distribution, Michael A. Stephens Tests for the Normal Distribution, Ralph B. D,Agostino Tests for the Exponential Disrtibution, Michael A. Stephens Analysis of Data from Censored Samples, John R. Michael and William R. Schucany The Analysis and Detection of Outliers, Gary L. Tietjen Appendix Table 1: Cumulative Distribution Function of the Standard Normal Distribution Table 2: Critical Values of the Chi-Square Distribution Simulated Data Sets Real Data Sets "
High Speed Reprojection And Its Applications

Reprojection is the process by which projections are produced from an image such that, if these projections are filtered and backprojected, they yield the original image. Because of the computational expense of reprojection, algorithms that employ this process have never been widely used. A method is presented that enables an unmodified backprojector to be used as a reprojector. Because backprojectors are designed to exploit the parallelism in the backprojection algorithm, the time required to obtain reprojections is significantly reduced. Another method, based on the Fourier Slice Theorem, is presented that enables a general purpose array processor to be used as a high speed reprojector. It is also shown that the parameters of the reprojection algorithm can be adjusted to decrease significantly the time required to perform an application that uses reprojection. Finally, two applications of reprojection in computed tomography are discussed.
Opinion Summarization: Automatically Creating Useful Representations of the Opinions Expressed in Text

Opinion analysis is concerned with extracting information about attitudes, beliefs, emotions, opinions, evaluations and sentiment expressed in texts. To date, research in the area of opinion analysis has focused on developing methods for the automatic extraction of opinions and their attributes. While this opinion information is useful, its true potential can be realized only after it is consolidated (summarized) in a meaningful way: the raw information contained in individual opinions is often incomplete and their number is overwhelming. Until now, the task of domain-independent opinion summarization has received little research attention. We address this void by proposing methods for opinion summarization. Toward that end, we formulate new approaches for the problems of determining what opinions should be attributed to the same source ( source coreference resolution) and whether opinions are on the same topic (topic identification/coreference resolution). Additionally, we introduce novel evaluation metrics for the quantitative evaluation of the quality of complete opinion summaries. Finally, we describe and evaluate OASIS, the first opinion summarization system known to us that produces domain-independent non-extract based summaries. Results for the individual components are encouraging and the overall summaries produced by OASIS outperform a competitive baseline by a large margin when we put more emphasis on computing an aggregate summary during evaluation.
Performance of a novel XCP-OFDM system

This paper proposes an OFDM system that uses circularly polarized waves for improving the system performance. The circular polarization system has the characteristic that it cannot receive waves that are reflected an odd number of times. Using this characteristic, a modified OFDM system that uses cross-handed circular polarization (XCP-OFDM) is proposed. XCP-OFDM gives a much improved system performance due to a reduction of interference caused by reflected waves and interchannel interference. Both theoretical analysis and system simulation results are described.
A Localization Algorithm of Multi-Hop Three Dimensional AOA with Space-Based Angle Transmission and its Application

Wireless sensor node's localization is a funda-mental technology in Wireless Sensor Networks. There are only quite a few study on three-dimensional (3D) localization which is suffered in slow progress, actually, is one of the main difficulties in WSN localization. Based on the study of the existing two-dimensional positioning algorithm and the application of terrain modeling, localization algorithm for sensor nodes in (3D) condition has been focus on as well as the application of terrain model. Using the idea proposed by representative algorithm--APS multi-hop AOA (Angle of Arrival), this paper proposed a new algorithm named Multi-hop Three Dimensional AOA With Space-based Angle Trans-mission (MSAT3D AOA). Using this technology, target nodes can use information of anchor nodes which are more than one hop away form. This paper also combined MSAT3D AOA algorithm with Delaunay triangulation algorithm for terrain modeling.
The intellectual development of management information systems, 1972-1982: a co-citation analysis

Researchers in all academic disciplines benefit from an understanding of the intellectual development of their field. This understanding is essential for conducting studies which build systematically on prior research. The purpose of this study is to document the intellectual development of the ideas represented by published research in Management Information Systems MIS based on an author co-citation analysis. The resulting mapping is intended to serve as a benchmark for future assessments of MIS as a field as well as a means for documenting the emergence of new research specialties. The study sought to identify 1 the subfields which constitute MIS research, 2 the reference disciplines of these subfields, 3 the diffusion of the ideas represented by these subfields to other disciplines, and 4 which of these subfields represent active areas of current MIS research. Nine invisible colleges, or informal clusters of research were uncovered. These nine empirically defined conceptual groupings collectively define the intellectual foundations of MIS as well as the forces currently shaping MIS research. Four of the clusters represent early MIS research themes which are still popular, based on subsequent citation patterns. Despite the centrality of the concept of the organization to widely-accepted definitions of MIS, the results suggest that MIS research is not well-grounded in organization theory nor have MIS research results been widely diffused in the organizational literature. Suggestions for developing a better link between MIS and organizational theory are presented based on the concept of organizational effectiveness.
Fast algorithm for digital retinal image alignment

An algorithm based on an automatic multistage, multiple-featured-based registration strategy has been developed to decrease the amount of computation and to increase the speed of alignment for digital retinal images. The retinal image is digitized and processed to detect the location of the optical disk as a coarse feature. The branch point of a retinal vessel is automatically set up as a template by multilevel vessel tracing with adaptive-thresholding, cavity-filling, neighborhood-averaging, and stem-thinning techniques. Flowcharts for the automatic template setup and image alignment are given. <<ETX>>
Applying a hybrid data-mining approach to prediction problems: a case of preferred suppliers prediction

Data mining has emerged as powerful tools to automate the decision-making processes. The deficiency of the traditional approach of data mining for prediction is that the low-accuracy decision rules that cannot be used and the quality rules, with high accuracy, do not guarantee that they will be used since the premise (condition) part of the rule should match with the input domain of the testing data sets. Furthermore, negative information/data, which are defined as information/data, misclassify the outcomes from the testing data set and are normally discarded. It is possible to reuse the information/data for training purposes and they contain a positive impact on prediction accuracy. This paper presents a data-mining-based hybrid approach that consists of a novel rough-set algorithm for feature selection and an enhanced multi-class support vector machines (SVMs) method for accurate prediction. The approach can simultaneously derive decision rules, identify the most significant features and generate a well-tuned prediction model with high accuracy. A case study of supplier selection of a video game system is validated by historical data and the results show the practical viability of the hybrid approach for predicting preferred suppliers.
A user study comparing head-mounted and stationary displays

Head-mounted displays, as popularized by virtual reality systems, offer the opportunity to immerse a user in a synthetically generated environment. While there is much anecdotal evidence that this is a qualitative jump in the user interface, there is little quantitative data to establish that emersion improves task performance. The authors present the results of a user study: users performing a generic search task decrease task performance time by roughly half (42% reduction) when they change from a stationary display to a head-mounted display with identical properties (resolution, field-of-view, etc.). A second result is that users who practice with the head-mounted display reduce task completion time by 23% in later trials with the stationary display, suggesting a transfer effect. <<ETX>>
A type-I hybrid ARQ protocol over optimal-sequence CDMA link

It has been shown that the capacity of a CDMA link can be significantly enhanced by optimally selecting or adapting the set of spreading sequences employed under the constraint that a prescribed level of SNR performance is achieved. We propose a type-I hybrid ARQ protocol which makes use of the optimally selected spreading sequences in the physical layer to allow simultaneous transmission of multiple packets over a CDMA link. Packets are divided into two classes, namely retransmission packets and newly arrived packets. Packets in each class are assigned optimally selected spreading sequences and a target SNR. With proper choices of the number of packets to be transmitted and the SNR targets in each time slot, the proposed type-I hybrid ARQ protocol provides significant improvement on the link throughput, particularly when the transmission power is abundant.
Labeled graph notations for graphical models Extended Report

We introduce new diagrammatic notations for probabilistic independence networks (including Bayes nets and graphical models). These notations include new node and link types that allow for natural representation of a wide range of probabilistic data models including complex hierarchical models. The diagrammatic notations also support models defined on variable numbers of complex objects and relationships. Node types include random variable nodes, index nodes, constraint nodes, and an object supernode. Link types include conditional dependency, indexing and index limitation, variable value limitation, and gating a dependency between nodes or objects by an arbitrary graph. Examples are shown for clustering problems, information retrieval, unknown graph structures in biological regulation, and other scientific domains. The diagrams may be taken as a shorthand notation for a more detailed syntactic representation by an algebraic expression for factored probability distributions, which in turn may be specified by stochastic parameterized grammar or graph grammar models. We illustrate these ideas with previously described applications and potential new ones.