Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical Instrumentation Application And Design

Thank you for reading medical instrumentation application and design. Maybe you have knowledge that, people have search hundreds times for their favorite books like this medical instrumentation application and design, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some infectious bugs inside their laptop. medical instrumentation application and design is available in our digital library an online access to it is set as public so you can get it instantly. Our digital library hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the medical instrumentation application and design is universally compatible with any devices to read.
The Practice of Clustering in CRM and Discussion on Characteristic Distill

With the development of Data Mining and the growth of information,many enterprises and cooperates begin to use the Data Mining technique to analyze and process the large amount of data which is accumulated in the business activities in order to find out important rules for marketing strategy.Customer clustering is a crucial issue.According to customer clustering,similar customer groups cluster on the basis of their personal and behavioral attributes.Then,the enterprises can work out different marketing strategies and apply them to each customer group respectively.The thesis focuses on two questions about clustering formation.First,several typical clustering algorithms and their complexities are introduced.Meanwhile,the application and efficiency of the clustering algorithm are discussed in it,and then the effectual arithmetic on how to carry through characteristic distill in the clustering formation is given out.
Teaching Sports Concepts And Skills A Tactical Games Approach

Thank you very much for downloading teaching sports concepts and skills a tactical games approach. Maybe you have knowledge that, people have look hundreds times for their chosen books like this teaching sports concepts and skills a tactical games approach, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some malicious bugs inside their desktop computer.
Are supermassive black holes shrouded by 'super-Oort' clouds of comets and asteroids?

The last decade has seen a dramatic confirmation that an in situ star formation is possible inside the inner parsec of the Milky Way. Here we suggest that giant planets, solid terrestrial-like planets, comets and asteroids may also form in these environments, and that this may have observational implications for Active Galactic Nuclei (AGN). Like in debris discs around main sequence stars, collisions of large solid objects should initiate strong fragmentation cascades. The smallest particles in such a cascade - the microscopic dust - may provide a significant opacity. We put a number of observational and physical constraints on AGN obscuring torii resulting from such fragmentation cascades. We find that torii fed by fragmenting asteroids disappear at both low and high AGN luminosities. At high luminosities, $L \sim L_{\rm Edd}$, where $L_{\rm Edd}$ is the Eddington limit, the AGN radiation pressure blows out the microscopic dust too rapidly. At low luminosities, on the other hand, the AGN discs may avoid gravitational fragmentation into stars and solids. We also note that these fragmentation cascades may be responsible for astrophysically "large" dust particles of approximately micrometer sizes that were postulated by some authors to explain unusual absorption properties of the AGN torii.
Object Detection Method Based on"What"and"Where"Information

Inspired by the theory of two visual pathways,a novel model for object detection is proposed based on"what"and"where"information.Context-centered"where"information is used to control top-down attention,and guide bottom-up attention which is driven by"what"information.The procedure of top-down attention can be divided into two stages:pre-attention and focus attention.In the stage of pre-attention,"where"information can be used to provide the prior knowledge of presence or absence of objects which decides whether search operation is followed.By integrating the result of focus attention with"what"information,at- tention is directed to the region that is most likely to contain the object and series of salient regions for samples are detected.Experi- mental results with natural images demonstrate its effectiveness.
UCONF Model Construction and Analgsis Based on Fuzzy Integrated Control

The paper proposes a UCONF(usage control fuzzy) model based on fuzzy comprehensive control,and gives reference monitors of the model.The model uses fuzzy integrated logic to compute credibility,by which the authorization policy of UCON is simplified.As a result,the rights authorized can be changed dynamically,which may facilitate the granting or revoking of rights.The manageability and excutability of the model can be also enhanced therein.Analysis shows that this model fully reflects trusting relationship between subjects in the open-network environment,resolves the access control issues of combining static and dynamic methods,and ensures the security control of information system.
Improved Method of Transfer Close Package for Cloud Resource Clustering in Cloud Computing

A cloud computing resource clustering framework based on fuzzy theory is constructed. There are five steps: characteristics and requirements set, data standardization process, data normalization process, fuzzy similarity matrix, cluster partition. In the clustering partition, the transitive closure method is improved, and the special elements in the fuzzy similarity matrix are used as the control points to simplify the optimization process and improve the optimization accuracy. The experimental results show that the cloud computing resource based on the improved transitive closure method is fast and the resource utilization rate is high after clustering.
Trend Analysis of Data Mining Research Using Topic Network Analysis

In this paper, we propose a topic network analysis approach which integrates topic modeling and social network analysis. We collected 2,039 scientific papers from five top journals in the field of data mining published from 1996 to 2015, and analyzed them with the proposed approach. To identify topic trends, time-series analysis of topic network is performed based on 4 intervals. Our experimental results show centralization of the topic network has the highest score from 1996 to 2000, and decreases for next 5 years and increases again. For last 5 years, centralization of the degree centrality increases, while centralization of the betweenness centrality and closeness centrality decreases again. Also, clustering is identified as the most interrelated topic among other topics. Topics with the highest degree centrality evolves clustering, web applications, clustering and dimensionality reduction according to time. Our approach extracts the interrelationships of topics, which cannot be detected with conventional topic modeling approaches, and provides topical trends of data mining research fields.
A Comparison of Summarization Techniques for Small Sets of Micro Blogs.

KAKARADDI, SHISHIR MAHESH. A Comparison of Summarization Techniques for Small Sets of Micro Blogs. (Under the direction of Dr. Robert St. Amant.) Microblogs are a popular way to keep abreast with important information about different topics. Half a billion people across the world use Twitter, and 340 million tweets are posted each day. With so much available data, summarization is critical. This thesis examines the problem of summarizing tweets from a single Twitter user over the course of a few weeks to a month; for typically active Twitter users, this is between 35 and 60 tweets. The summarization takes the form of a subset of those tweets. We have developed and compared algorithms that can be used to summarize tweets in this way. We describe an unsupervised algorithm that clusters tweets by topic, using information retrieval techniques, and then selects a specific tweet from each cluster, relying on sentiment. An experimental evaluation shows that this approach produces results that are not as good as those of human summarizers, but are better than an existing baseline for such summaries. © Copyright 2012 by Shishir Mahesh Kakaraddi
Cognitive Foundations Of Musical Pitch

This book addresses the central problem of music cognition: how listeners' responses move beyond mere registration of auditory events to include the organization, interpretation, and remembrance of these events in terms of their function in a musical context of pitch and rhythm. Equally important, the work offers an analysis of the relationship between the psychological organization of music and its internal structure. Combining over a decade of original research on music cognition with an overview of the available literature, the work will be of interest to cognitive and physiological psychologists, psychobiologists, musicians, music researchers, and music educators.
Modeling Big Data Variety with Graph Mining Techniques

Graphs are ubiquitous and have become increasingly important in modeling diverse kinds of objects. In many real-world applications, instances are not represented as feature vectors, but as graphs with complex structures, e.g., chemical compounds, program flows, XML web documents and brain networks. One central issue in graph mining research is graph classification, which has a wide variety of real world applications, e.g., drug activity predictions, toxicology tests and kinase inhibitions. There are some major challenges in real-world graph classification problems as follows: 1) Learning from graphs with multiple labels:} For example, a chemical compound can inhibit the activities of multiple types of kinases, e.g., ATPase and MEK kinase; One drug molecular can have anti-cancer efficacies on multiple types of cancers. 2) Learning from a small number of labeled graphs: In many real world applications, the labels of graph data are very expensive or difficult to obtain. Creating a large training dataset can be too expensive, time-consuming or even infeasible. For example, in molecular medicine, it requires time, efforts and excessive resources to test drugs' anti-cancer efficacies by pre-clinical studies and clinical trials, while there are often copious amounts of unlabeled drugs or molecules available from various sources. 3) Learning from uncertain graphs: For example, in neuroimaging, the functional connectivities among different brain regions are highly uncertain. In such applications, each human brain can be represented as an uncertain graph, instead of a certain graph. In this thesis, we explore four different settings of graph classification: multi-label setting, semi-supervised setting, active learning setting, and uncertain graph setting. In the multi-label setting, each graph object can be assigned with multiple labels. In semi-supervise setting and active learning setting, we explore two different settings to reduce the labeling costs in graph classification problems. In uncertain graph setting, we explore how to incorporate the uncertainty information in the graph structure for graph classification problems.
Study on Performance of Gray System Model for the Satellite Clock Error Secular Prediction

Gray system model is used in secular predicting GPS Cs clock error and Rb clock error, compared with quadratic polynomial model. Calculating results show that in the secular term(210 days) predicting GPS Cs clock error precision of gray system model is up to ns, and GPS Rb clock error precision of gray system model is up to 10ns,which exceeds quadratic polynomial model obviously and satisfies precision requirements in the real application.
Method for improvement of soft input decryption performances

In this work, the strategy for correction of bits using Soft Input Decryption is analyzed. Soft Input Decryption is used for correction of cryptographic check values, which are very fragile when they are transmitted over noisy channels: only one wrong bit at input of the decryptor causes about 50% of errors at output of the decryptor. Soft Input Decryption corrects wrong bits of cryptographic check values in most of cases using combination of SISO channel decoding and decrypting. Soft Input Decryption is an iterative process and therefore it is needed to minimize duration of iterations. This paper suggests an algorithm for optimization of Soft Input Decryption, which enables restriction of needed time to, theoretically, about 15% of it. Suggested optimization is performed for ECDSA digital signatures.
Data literacy in the smart university approach

Equipping classrooms with inexpensive sensors for data collection can provide students and teachers with the opportunity to interact with the classroom in a smart way. In this paper two approaches to acquiring contextual data from a classroom environment are presented. We further present our approach to analysing the collected room usage data on site, using low cost single board computer, such as a Raspberry Pi and Arduino units, performing a significant part of the data analysis on-site. We demonstrate how the usage data was used to model specifcic room usage situation as cases in a Case-based reasoning (CBR) system. The room usage data was then integrated in a room recommender system, reasoning on the formalised usage data, allowing for a convenient and intuitive end user experience based on the collected raw sensor data. Having implemented and tested our approaches we are currently investigating the possibility of using (XML)Schema-informed compression to enhance the security and efficiency of the transmission of a large number of sensor reports generated by interpreting the raw data on-site, to our central data sink. We are investigating this new approach to usage data transmission as we are aiming to integrate our on-going work into our vision of the Smart University to ensure and enhance the Smart University's data literacy.
BOKHARI-SEPFL routing protocol based on fuzzy logic for WSNs

In this paper a new protocol using fuzzy logic control called BOKHARI-SEPFL has been proposed. Fuzzy logic control based on three variables, distance of nodes form base station, density of nodes and the battery level of nodes along with the traditional threshold values used in SEP are used to enhance the process of cluster head election in the existing SEP protocol and improve the lifetime and throughput of the Wireless Sensor Network. The result of the simulation which has been done in MATLAB simulator indicates that Stable Election Protocol based on fuzzy logic is more energy efficient and improves the lifetime and throughput of the network by 73.2 and 68.54 % respectively comparing with the existing SEP protocol.
E-VOTING SYSTEM USING MOBILE SMS

E-Electronic voting system have the potential to improve percentage of the voting. In the traditional voting, such as, the electronics voting and paper based voting, percentage of voting is decreasing. Now a day's most of voters are busy in their work and most of the voters are living far away from voting centre. some voters don't like to wait in queues. Thus, due to this, voters don't visit to the polling booth. Thus, percentage of voting is decreasing, which is main and serious drawback of traditional voting scheme. Now a day's some improvement is needed in this field. Thus, in this paper we are introducing such a system, which will eliminate drawback of traditional voting scheme. This new voting scheme is based on SMS. SMS is a key future of the second generation(2G) mobile. In the second generation , GSM (global system for mobile communications) is very famous technology. In next generation of mobile, such as 2.5G, 2.75G, 3G & 4G, SMS is one of the prime features. So we implement a new voting system, based on SMS. mobile telephony is widely used, there are more than 6,800,000,000 mobile users worldwide and population of the voter in world is 7,012,000,000 that is 96.97% voter use mobile worldwide. In these paper an electronic voting system using SMS is presented but to care of the remaining 212,000,000 voter that is 3.03% of the voter will use the traditional electronic voting machine as it is. Considering the major crowd of mobile users we developed modified voting machine which support both electronic voting machine and SMS based voting. we are able to exploit existing mobile authentication mechanism and provide enhance voter authentication with mobility while maintaining voter privacy. now days additional problem is of declaring the result of voting, So to declare result normally 8-10 days will be taken. in this day security of voting machine is prime question and very large cost paid for this. in our modified voting machine we can declare result within 1hr of voting and also the voting result send to all registered mobile number. Keyword: SMS, Electronic voting machine, E-electronic voting machine, SMS based voting.
Face Recognition Based on Local Feature Analysis

A face recognition method based on feature analysis is presented here. The local features are first located by the face structure knowledge and gray level distribution information, rather than searching the whole image as it does in Elastic Bunch Graph Matching. The coarse positions of the features are located, and then we use a data structure named Face Bunch Graph to adjust the feature positions. After the accurate positions of the features are located, the face is represented by the Gabor jets, which calculated on these local feature positions. Several distance metrics are tested and the results are given. In our experiment, this method works better than the Eigenface, and consumes less time than the traditional Elastic Bunch Graph method.
Explicit Trusted Proxy in HTTP/2.0

The purpose of this Internet Draft is to continue the discussion on explicit and trusted proxy as intermediary of HTTP2 traffic. The httpbis wg has agreed on the HTTP2 usage with HTTP URIs, with or without TLS, without any constraints from the standard (see: issue 314). To distinguish between an HTTP2 connection meant to transport "https" URIs resources and an HTTP2 connection meant to transport "http" URIs resource, the draft proposes to register a new value in the Application Layer Protocol negotiation (ALPN) Protocol IDs registry specific to signal the usage of HTTP2 to transport "http" URIs resources: h2clr. This document describes two alternative methods for an user-agent to automatically discover and for an user to provide consent for a Trusted Proxy to be securely involved when he or she is requesting an HTTP URI resource over HTTP2 with TLS. The consent is supposed to be per network access. The draft also describes the role of the Trusted Proxy in helping the user to fetch HTTP URIs resource when the user has provided consent to the Trusted Proxy to be involved.
A Text Classification Method for Chinese Pornographic Web Recognition

A text classification method for Chinese pornographic web recognition is proposed.It consists of four key components:automatic Chinese word segmentation,stop-word-list establishment,feature selection,text classification, etc.respectively discussed.To enrich the dictionary of Chinese word segmentation system,a new word identification algorithm is proposed,which is mainly based on word frequency statistics,and supplemented by artificial decision as well as Chinese part of tagging.On the basis of the Chinese stop-word-list selection method proposed,a stop-word-list containing 300 stop words is established.Subsequently,using the CHI square method,a 400-dimension feature vector is decided.In addition,by analyzing Naive Bayes classifier and CHI square method,two influencing factors are added.One is the ratio of included features number and selected feature number;the other is the ratio of included feature number and included unique words.Given the concept of different people' s subjective understanding of pornographic is quit different,the pornographic value of a web page is used as the output of the classifier.The experimental results show that the proposed method can achieve better classification performance,compared with the existing text classification method.
Analyzing databases using data analytics

There are many public and private databases of oil field properties the analysis of which could lead to insights in several areas. The recent trend of Big Data has given rise to novel analytic methods to effectively handle multidimensional data, and to visualize them to discover new patterns. The main objective of this research is to apply some of the methods used in data analytics to datasets with reservoir data. Abstract Using a commercial reservoir properties database, we created and tested three data analytic models to predict ultimate oil and gas recovery efficiencies, using the following methods borrowed from data analytics: linear regression, linear regression with feature selection, and Bayesian network. We also adopted similarity ranking with principal component analysis to create a reservoir analog recommender system, which recognizes and ranks reservoir analogs from the database. Among the models designed to estimate recovery factors, the linear regression models created with variables selected with sequential feature selection method performed the best, showing strong positive correlations between actual and predicted values of reservoir recovery efficiencies. Compared to this model, Bayesian network model, and simple linear regression model performed poorly. For the reservoir analog recommender system, an arbitrary reservoir is selected, and different distance metrics were used to rank analog reservoirs. Because no one distance metric (and hence the given reservoir analog list) is superior to the other, the reservoirs given in the recommended list are compared along with the characteristics of distance metrics.
Mathematical Substantiation of the Rational Package (Bag) of Fully-Formed Fur ArticlesWith Content of Polymer Composition

Improving the mechanisms of shaping and molding in order to achieve a high quality of manufacturing garments by increasing their form stability is the need to evaluate the technological conditions for the formation of the package [1-3]. Insufficient stability of the form of parts of clothing after the technological process can be the result of a number of reasons. Some of them are: incomplete consideration of the strain relaxation in the material and the package; when determining the technological conditions for shaping; the failure to comply with technological regimes; ignoring the location of materials or applying the polymer composition in a package of parts; disturbing the mechanism of formation; and the lack of mechanisms for the formation of a rational package [4-7].