Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Catheter Segmentation & Localization in 3D Cardiac Ultrasound Using Direction-Fused Fcn

Fast and accurate catheter detection in cardiac catheterization using harmless 3D ultrasound (US) can improve the efficiency and outcome of the intervention. However, the low image quality of US requires extra training for sonographers to localize the catheter. In this paper, we propose a catheter detection method based on a pre-trained VGG network, which exploits 3D information through re-organized cross-sections to segment the catheter by a shared fully convolutional network (FCN), which is called a Direction-Fused FCN (DF-FCN). Based on the segmented image of DF-FCN, the catheter can be localized by model fitting. Our experiments show that the proposed method can successfully detect an ablation catheter in a challenging ex-vivo 3D US dataset, which was collected on the porcine heart. Extensive analysis shows that the proposed method achieves a Dice score of 57.7%, which offers at least an 11.8% improvement when compared to state-of the-art instrument detection methods. Due to the improved segmentation performance by the DF-FCN, the catheter can be localized with an error of only 1.4 mm.
The design and implementation of the active RFID location system

With the data services and multimedia services rapidly increasing,the demand for positioning and navigation are also increasing.But a perfect positioning technology is still not used,because of the positioning time,positioning accuracy and the complex environment conditions.To address these issues,the features and issues pertinent to several current typical positioning technology were analyzed.Finally,an active RFID location system was proposed,which is based on RFID technology.Analysis shows that this system has hige positioning accuracy,strong anti-jamming capability,wide positioning range and fits for the scenario when the tags number is large.
Pedagogical Applications and Social Effects of Mobile Technology Integration

Book details here: In recent years, the rapid growth and development in mobile technology devices such as mobile phones and tablets has had a significant impact on society, and has brought amazing changes various fields, including education. For example, the popularity of mobile technologies that support collaborative learning has been ex-using technology, but computer devices are now relatively universal in schools of mobile technologies has enabled educators to send instructional messages in flexible ways (Kim, Rueckert, Kim, Seo, 2013). From the view point of Educational Sciences, mobile devices have remarkable features which can create more effective learning environments for traditional classes (Kert, 2013). It is worth noting that different learners can try to use different learning materials and facilitate their learning processes (Uzunboylu & Ozdaml, 2011). Therefore, learners should make active use of electronically-supported learning tools to provide real benefits in their learning processes (Sánchez-Franco, Peral-Peral & Villarejo-Ramos, 2014).
Selection Oriented Database Data Distribution Strategy for Cloud Computing

Many methods have been proposed to satisfy the needs of massive data processing,among which cloud computing is an outstanding one.The main thought of Cloud computing is using large number of PCs to compose a huge cluster as a server.With the development of Cloud Computing technology,more and more applications will turn into Cloud,including the DBMS.As Database system requires ACID,when it comes to data distribution,some operations' performance may decline,such as joins.In this paper we proposed a Selection Oriented data Distribution strategy(SOD) to improve the performance of DBMS in Cloud Computing,which works well through the experiments.
Legible Robot Motion Planning

The goal of this thesis is to enable robots to produce motion that is suitable for human-robot collaboration and co-existence. Most motion in robotics is purely functional: industrial robots move to package parts, vacuuming robots move to suck dust, and personal robots move to clean up a dirty table. This type of motion is ideal when the robot is performing a task in isolation. Collaboration, however, does not happen in isolation. In collaboration, the robot’s motion has an observer, watching and interpreting the motion. In this work, we move beyond functional motion, and introduce the notion of an observer into motion planning, so that robots can generate motion that is mindful of how it will be interpreted by a human collaborator. We formalize predictability and legibility as properties of motion that naturally arise from the inferences that the observer makes, drawing on action interpretation theory in psychology. Predictable motion stems from a goal-to-action inference and matches the observer’s expectation, given the robot’s goal. Legible motion stems from an action-to-goal inference: the robot is clearly conveying its goal with its ongoing motion. We propose models for these inferences based on the principle of rational action, Bayesian inference, and the principle of maximum entropy. We then use a combination of constrained trajectory optimization and machine learning techniques to enable robots to plan motion that is predictable or legible. Finally, we verify that the generated motions are more predictable and legible, and evaluate the impact of such motion on a physical human-robot collaboration task. Our results suggest that predictability and legibility do not only increase task performance, but also make the collaboration process more fluent, increasing subjective metrics such as trust or comfort. We also show generalizations of the legibility formalism to deception, gestures, and assistive teleoperation.
Research on quantum evolutionary algorithm based on immune theory and its convergence

A quantum evolutionary algorithm based on immune operator is proposed. Optimal solution is obtained by the mechanism in which antibodies are clonally selected, immune cells accomplish cross-mutation, memory cells are produced and similar antibodies are suppressed. It not only maintains better population diversity than the classical quantum evolutionary algorithm, but also converges to the global optimal solution rapidly.Simulation experiments show the convergence and the superiority of the MQEA .
Statistical Performance Effect of Feature Selection Techniques on Eye State Prediction Using EEG

Several recent studies have demonstrated that electrical waves recorded by electroencephalogram (EEG) can be used to Predict eye state (Open or Closed) and all the studies in the literatures used 14 electrodes for data recording. To reduce the number of electrodes without affecting the statistical performance of an EEG device, it is not an easy task. Hence, the focus of this paper is on reducing the number of EEG electrodes by means of feature selection techniques without any consequences on the statistical performance measures of the earlier EEG devices. In this study, we compared different attribute evaluators and classifiers. The results of the experiments have shown that ReliefF attribute evaluator was the best to identify the two least important features (P7, P8) with 96.3% accuracy. The overall results show that two data-recording electrodes could be removed from the EEG devices and still perform well for eye state prediction. The accuracy achieved was equal to 96.3% with KStar (K*) classifier which was also the best classifier among the 21 tested classifiers in this study.
Data-Intensive Science and Engineering:Requirements and Challenges

Scientific exploration after experimental science,theoretical science and computational science phases,into data-intensive science phase,are accompanied by the arrival of the big data era.Generally,big data refers to a data set with a size of hundreds of TB,or several PB or even above,and it is often distributed,heterogeneous and in low-quality.It is critical to devise novel methods to manage big data since traditional database management techniques are unfeasible to manage big data efficiently and effectively,though such techniques,especially the commercial relational DBMSs,have achieved great success in the past decades.This paper discusses concrete requirements and realistic challenges of Data-Intensive Science and Engineering(DISE),ranging from data storage and organization,computational method,data analysis,to user interfaces.Meanwhile,data quality,data security and data curation should be paid more attentions.In this paper,we attempt to describe the architecture of DISE,review the recent progress,and discuss the challenges and future work briefly.
FEASIBILITY RESEARCH ON ROV DYNAMIC POSITIONING WITH USBL

The repetition positioning precision from USBL system produced by Sonardyne Co. is investigated by pool and lake experiments. The aim of the experiments is to verify whether the USBL system can be used in the process of ROV dynamic positioning. The experiments results have shown that the USBL system has given an acceptable repetition precision with gesture dynamic compensation even if the pitch and roll of the support vessel actually exist. And the USBL system can be used for ROV dynamic positioning.
Control de aplicaciones y wearables en smartphones Android mediante agentes conversacionales

English) This Bachelor Thesis is divided in 2 parts, each part describes the design and the implementation of an application for different and complementary devices: 1. Mobile application: Propose a different methodology for developing applications with user interfaces based in virtual agents with the ability of recognizing and synthetize voice. Based on this methodology we have developed an application that incorporates this elements and enable the interaction with other applications, providing them the functionality mentioned. Because of that, the application has been called “Gateway Application”. With it, you do not need a great knowledge of the technologies involved (virtual assistant, speech recognition and speech synthesis). This application has the following features: a. Connection with cloud service (virtual assistant service, recognition and voice synthesis service), where is located the intelligence of the virtual assistant. This is used to maintain a natural dialogue with the user, aimed at obtaining information or at navigating in third-party applications, which is wanted to incorporate this functionality. b. Dynamic and flexible configuration, by means of a text file that contains configuration parameters. Due to this file, the appearance and the functionality (semantic domain of the application, in other words, the knowledge root of the virtual assistant) of the application are flexible. c. Interaction with applications, both, applications with connection interface and applications part of Google android operating system. d. Ability to connect and exchange data with external devices such smartwatches, by using bluetooth. 2. Wearable (smartwatch) application: Create a model application based on a new methodology for smartwatches focused on access and view data. Until now, the focus was always on the mobile, while the smartwatch performed the additional functionalities, receiving notifications. With this new structure we have got that the use of the application starts and ends in the smartwatch, getting one new role much more relevant that the previous one. Palabras clave (castellano) Asistente Virtual, Aplicaciones, Android, Smartwatch, API, Fragmentos, Gateway, AIML, La nube, Fichero de configuración, Comandos de voz, Iteración, Integración, Mapas, Reconocedor de voz.
Structures of three ependymin-related proteins suggest their function as a hydrophobic molecule binder

Ependymin-related proteins fold into a dimer using a subunit formed by stacking antiparallel β-sheets and contain a hydrophobic pocket that is found in the similar folds of bacterial VioE and LolA/LolB. As in VioE and LolA/LolB, this hydrophobic pocket may be used to bind hydrophobic molecules.
Herramienta Fuzzy de Inferencia basada en Web para la evaluación del riesgo vascular

1Bioinnova Group, School of Health Universidad Nacional Abierta y a Distancia UNAD, Bogotá, Colombia 2Department of Electrical Engineering and Computer Science, Massachussets Institute of Technology 3Sigma Biomedical 4Department of Electrical Engineering and Computer Science, Massachussets Institute of Technology Correspondencia: leyunda@unad.edu. co Leonardo Yunda ORCID: 0000-0003-0166-9999, Jorge Millan: ORCID 0000-0002-3517-1083 Recibido: 11/03/2015 Aceptado: 29/05/2015 Abstract Objective. Developing a Web-based Fuzzy Inference Tool for cardiovascular risk assessment. The tool uses evidence-based medicine inference rules for membership classification. Methods. The system framework allows adding variables such as gender, age, weight, height, medication intake, and blood pressure, with various types of membership functions based on classification rules. Results. By inputting patient clinical data, the tool allows health professionals to obtain a prediction of cardiovascular risk. The tool can also be later used to predict other types of risks including cognitive and physical disease conditions.
Modern Subunit Vaccines: Development, Components, and Research Opportunities

Traditional vaccines, based on the administration of killed or attenuated microorganisms, have proven to be among the most effective methods for disease prevention. Safety issues related to administering these complex mixtures, however, prevent their universal application. Through identification of the microbial components responsible for protective immunity, vaccine formulations can be simplified, enabling molecular‐level vaccine characterization, improved safety profiles, prospects to develop new high‐priority vaccines (e.g. for HIV, tuberculosis, and malaria), and the opportunity for extensive vaccine component optimization. This subunit approach, however, comes at the expense of decreased immunity, requiring the addition of immunostimulatory agents (adjuvants). As few adjuvants are currently used in licensed vaccines, adjuvant development represents an exciting area for medicinal chemists to play a role in the future of vaccine development. In addition, immune responses can be further customized though optimization of delivery systems, tuning the size of particulate vaccines, targeting specific cells of the immune system (e.g. dendritic cells), and adding components to aid vaccine efficacy in whole immunized populations (e.g. promiscuous T‐helper epitopes). Herein we review the current state of the art and future direction in subunit vaccine development, with a focus on the described components and their potential to steer the immune response toward a desired response.
“Tense” /æ/ is still lax: A phonotactics study

The production of the lax vowel /ae/ is widely studied as a sociolinguistic variable in American English, as several dialects differ in the allophonic distribution of lax and tense variants of the vowel. Using a forced-choice well-formedness judgment task, this paper tests whether the /ae/ used by Northern Cities Shift (NCS) speakers, which is described as a raised and tensed [ɛə] in all environments, is still represented as a lax vowel in speakers’ grammars. Participants who were native speakers of California English, which only tenses preceding nasals, and NCS English, which tenses everywhere, were asked to choose which of a pair of nonce words, constructed to include a lax-only environment /Vsk, Vsp/, as in gasp, risk , sounds more like a possible English word. California English speakers significantly prefer frames containing lax vowels, including [ae], over those containing tense vowels. NCS speakers respond in the same way: they favor [ɛə] in trials like [bɛəsp] vs. [bisp] just as California English speakers favor [ae] in [baesp] vs. [bisp]. This suggests they keep the vowel in the phonologically active class usually considered to be lax vowels, providing evidence that speakers generalize features based on lexical distributions rather than phonetic properties.
Structured Electronic Design Negative Feedback Amplifiers

Thank you for downloading structured electronic design negative feedback amplifiers. Maybe you have knowledge that, people have search hundreds times for their favorite books like this structured electronic design negative feedback amplifiers, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some malicious bugs inside their laptop.
The non-linearity of speech production

The traditional linear conception of speech production shared by the majority of formalists and functionalists alike envisages a step-by-step succession of stages on the way from conceptual intention to speech chain product. However, the alternative conception of parallel distributed processing has for some time been gaining favour in such quarters. This contribution presents facts from a number of languages, including English, that support the idea that speech processing must be in large part non-linear, including at the lower levels involved in lexical access and phonological spell-out. Top-down contextual processesmust constrain bottom-up generation, even when the relevant contextual trigger appears to lie further ahead in the linear speech chain. A Whiteheadian perspective is adopted to help gain insight into the non-linear processes involved in speech production and comprehension.
High Helicities of Lys-Containing, Ala-Rich Peptides Are Primarily Attributable to a Large, Context-Dependent Lys Stabilization

Peptides 1K, YKGGGAAAAAAAAKAAAAAAAAAGGGK-NH2; 2K, YKGGGAAAAAKAAAAAKAAAAAAGGGK-NH2; and 3K, YKGGGAAAAKAAAAKAAAAKAAAGGGK-NH2 have been prepared by solid-phase synthesis, purified, and characterized by amino acid analysis, MALDI mass spectrometry, and ultracentrifugation. Their circular dichroism (CD) spectra of unaggregated solutions are reported for measurements in 0.01 M NaCl at 2, 25, and 60 °C and at 2 °C in aqueous guanidinium hydrochloride (0−3 M) and aqueous trifluoroethanol (TFE, 0−15 mol %). The CD spectra exhibit a helical signature in 0.01 M NaCl or in water−TFE at 2 °C, and the intensities of the mean residue ellipticities at the minimums of 222 nm in 0.01 M NaCl are (1K) −9100, (2K) −18 100, and (3K) −19 900 deg cm-1 dmol-1. These ellipticities are accurately modeled using a Lifson−Roig algorithm by the helical propensities previously reported by Renold et al. (Renold, P.; Tsang, K.-Y.; Shimizu, L. S.; Kemp, D. S. J. Am. Chem. Soc. 1996, 118, 12234−12235.) but not by those of Doig and Baldwin (.
Logistic regression for circular data

This paper considers the relationship between a binary response and a circular predictor. It develops the logistic regression model by employing the linear-circular regression approach. The maximum likelihood method is used to estimate the parameters. The Newton-Raphson numerical method is used to find the estimated values of the parameters. A data set from weather records of Toowoomba city is analysed by the proposed methods. Moreover, a simulation study is considered. The R software is used for all computations and simulations.
Visual tracking of a moving object of a robot head with 3 DOF

Visual tracking of a moving object is one of the fundamental functions of a robot head. In order to implement this task, it is necessary to process visual images and control the head motion. Firstly, this paper proposes the computer architecture with duplex computers and dual operating systems. One computer with the windows operating system is in charge of image processing, the other computer with the RT-Linux operating system is used to control the motion of the robot head. Then, we discuss the mechanism of the robot head with three degrees of freedom, and derive the image processing algorithm. The developed robot head has the height of 0.17 [m], width of 0.15 [m], depth of 0.12 [m] and the weight of 2.8 kg. Finally, experimental results show the robot head can track a moving object precisely in 3D space.
Graph Theory as a Tool for teaching Mathematical Processes

Graph theory is mathematics strands. It is a interesting subject matter. Our claim is that this subject can serve as a tool for learning mathematical processes . Notional council of teachers on mathematics of America (NCTM) proposed five standards that students should learn in K-12. In this paper we showed how can use Graph theory to teach three standards of NCTM. It is a suitable context to teach problem solving and there is good connection between Graph theory and other science. Finally it can be used for representing models and physical situations.
Dark encounter computations : (and musings on biological networks) (extended abstract)

Two models of distributed computation are described in which the agents are anonymous finite-state sensors interacting through a communication network whose dynamics (in case the agents are mobile) and/or topology they do not control or even know about. These models were recently introduced in a series of papers by Angluin et al. [3,4,2,5]. In one model, the agents are stationary but form a network of unknown topology. Furthermore, the pair-wise communication links in the network become available in a random and unpredictable infinite sequence. The sensors would like to compute a function of their inputs. While doing so they are, by design, or inherent limitation, not endowed with unique identities (hence the term “dark encounter computations” in the title of this note). Angluin et al. developed a theory about such networks that, among other results, showed that their computational power is dependent on topology. Perhaps contrary to expectation, the power is inversely proportional to the connectivity, with a straight-line configuration being the most powerful and the complete graph configuration being the weakest. This observation leads naturally to a number of questions related to the structure of the network. What topological properties of the network can be computed? (e.g., is the network a ring? ). What properties can individual sensor nodes compute? (e.g., am I in a cycle? ). These and related questions are answered in the affirmative in [2]. In the second model, agents are randomly mobile and communicate via pairwise interactions. As with the first model, the agents are anonymous and have no control of the sequence of interactions. A stochastic assumption ensures that every pair of agents eventually encounter each other.1 Angluin et al. show that this model of computation turns out to be surprisingly powerful. In a type of computation called “population protocol” the networks are able to compute predicates such as parity and majority. Population protocols can also perform modular arithmetic (for details, see [4]). There are, of course, a number of possible definitions of what it means for these networks to “compute” something. In the work of Angluin et al. the network is
Second Occurrence Focus and theAcoustics of Prominence

Partee (1991) challenged the significance of the observation that certain adverbs (e.g., only) reliably associate with phonologically prominent words to truth‐conditional effect, noting second occurrence (i.e., repeated or given) focus (SOF) appears to lack a phonological realization. Rooth (1996), Bartels (2004), Beaver et al. (2004), Jaeger (2004), and Fry and Ishihara (2005) argued that, while not intonationally prominent, an SOF word can be marked by increased duration and/or increased rms intensity. An acoustic study of verb‐noun homophone pairs is reported. Three sophisticated speakers uttered five repetitions of the targets, embedded in discourses, in first occurrence (FOF), SOF, and unfocused (NF) contexts, both pre‐ and postnuclearly. Syntagmatic comparison shows a durational effect for focus and ordering in the FOF/NF case, yet only an effect of ordering in SOF/NF; similarly, an effect of spectral balance (H1‐H3) was found for FOF/NF but none for SOF/NF. Results suggest, contrary to previous ass.