Miracle-dz/UniFAR-FTUs huggingface dataset:
The dynamics of unsteady supersonic combustion

The dynamic behavior of an unsteady, three-dimensional supersonic flowfield with nonequilibrium chemistry was numerically analyzed. The effects of turbulence were accounted for using the concepts of large eddy simulation (LES) and implemented in an existing threedimensional solver of the compressible Navier-Stokes equations with chemical reactions called ACHIEVE. The code was applied to a model supersonic combustion chamber prediction. The time accurate computation was accelerated by an implicit method and implemented on a massively parallel computer. The results of the present three-dimensional simulation are analyzed with respect to their timewise behaviour and compared, where applicable, to two-dimensional predictions and experimental investigations.
Fast finite-energy planes in symplectizations and applications

We define the notion of fast finite-energy planes in the symplectization of a closed 3-dimensional energy level $M$ of contact type. We use them to construct special open book decompositions of $M$ when the contact structure is tight and induced by a (non-degenerate) dynamically convex contact form. The obtained open books have disk-like pages that are global surfaces of section for the Hamiltonian dynamics. Let $S \subset \R^4$ be the boundary of a smooth, strictly convex, non-degenerate and bounded domain. We show that a necessary and sufficient condition for a closed Hamiltonian orbit $P\subset S$ to be the boundary of a disk-like global surface of section for the Hamiltonian dynamics is that $P$ is unknotted and has self-linking number -1.
Trajectory-based Optimal Control Techniques the Basics of Learning Control

I n a not too distant future, robots will be a natural part of daily life in human society, providing assistance in many areas ranging from clinical applications, education and care giving, to normal household environments [1]. It is hard to imagine that all possible tasks can be preprogrammed in such robots. Robots need to be able to learn, either by themselves or with the help of human supervision. Additionally, wear and tear on robots in daily use needs to be automatically compensated for, which requires a form of continuous self-calibration, another form of learning. Finally, robots need to react to sto-chastic and dynamic environments, i.e., they need to learn how to optimally adapt to uncertainty and unforeseen changes. Robot learning is going to be a key ingredient for the future of autonomous robots. While robot learning covers a rather large field, from learning to perceive, to plan, to make decisions, etc., we will focus this review on topics of learning control, in particular, as it is concerned with learning control in simulated or actual physical robots. In general, learning control refers to the process of acquiring a control strategy for a particular control system and a particular task by trial and error. Learning control is usually distinguished from adaptive control [2] in that the learning system can have rather general optimization objectives—not just, e.g., minimal tracking error—and is permitted to fail during the process of learning, while adaptive control emphasizes fast convergence without failure. Thus, learning control resembles the way that humans and animals acquire new movement strategies, while adaptive control is a special case of learning control that fulfills stringent performance constraints, e.g., as needed in life-critical systems like airplanes. Learning control has been an active topic of research for at least three decades. However, given the lack of working robots that actually use learning components, more work needs to be done before robot learning will make it beyond the laboratory environment. This article will survey some ongoing and past activities in robot learning to assess where the field stands and where it is going. We will largely focus on nonwheeled robots and less on topics of state estimation, as typically explored in wheeled robots [3]–6], and we emphasize learning in continuous state-action spaces rather than discrete state-action spaces [7], [8]. We will illustrate the different topics of robot learning with examples from our own research with anthropomorphic …
Performance analysis and optimal parameter selection for 300-based brain-computer interface

The paper is dedicated to parameter adjustment and performance analysis of the brain-computer interface based on P300 paradigm. The aim of the paper is to present the construction of the brain-computer interface and the study of optimal parameter selection as well as the data analysis process. The BCI with different parameters was run and tested under the case study. The aim of the study was to find the most suitable parameter of P300-based BCI.
Topics in Optical Communications toward Efficient Failure Management for Reliable Transparent Optical Networks Failure Management Issues in Transparent Optical Networks Failure Management Optical Performance Monitoring Structural Properties of Self-organizing Complex Systems Small Worlds Scale-free 

INTRODUCTION The rapid growth of data traffic, primarily Inter-net traffic, in the past several years is driving the demand for high-speed communication networks. Optical networks based on wavelength-division multiplexing (WDM) have been established as the most promising solution for satisfying the ever increasing capacity requirements in telecommunication networks. WDM is a technology that can exploit the large potential bandwidth of optical fibers by dividing it among different wavelengths. Transparent optical networks (TONs) are dynamically reconfigurable WDM networks that establish and tear down all-optical data connections, called lightpaths, between pairs of nodes. These connections can traverse multiple links in the physical topology, and yet transmission via a lightpath is entirely in the optical domain. The reliability of such networks is critical since a single failure can cause tremendous data loss. Although transparency has many attractive features, such as speed and insensitivity to data rate and protocol format, it introduces several vulnerabilities to security. Optical performance monitoring is much more difficult since it must be performed in the optical domain. A failure management system is used to deal with failures in the TON, which could be due to either component faults or deliberate attacks that aim to disrupt the proper functioning of the network. Due to the transparency inherent in TONs, nodes do not have access to service-bearing wavelengths except where data lightpaths terminate. Thus, management and control information is carried over a separate supervisory wavelength that is optoelectronically processed at each node [1]. We refer to this interconnec-tion of supervisory channels as the supervisory plane. In case of failure, failure management receives alarms from the monitoring equipment available (via the supervisory plane), and then attempts to locate and isolate the source. Meanwhile , the source and destination nodes of failed lightpaths are notified of the failure, after which they launch their restoration mechanisms. In this article we propose creating a hybrid supervisory plane whose structure is such that it can speed up and improve critical security information exchange, and thus improve the network's ability to reconfigure and reestablish communication in the presence of failures. We propose adding a set of long-range supervisory lightpaths in addition to the point-to-point channels between physically neighboring nodes, aimed at creating a small-world scale-free topology. The small-world and scale-free properties are structural properties that have been observed in many self-organizing complex systems. Self-organizing systems are those in which local low-level interactions and processes between individual entities …
Florida Atlantic University

A5.1 (a) Find the magnetic field B and the magnetic vector potential A at point P, a distance s from an infinite straight wire carrying a current I. (b) A steady current I flows down an infinite straight wire of radius R. Find the magnetic field B  and vector potential A  inside and outside the wire. (c) A long solenoid consisting of n turns per unit length wrapped around a cylinder tube of radius R caries current I in the direction as shown in the figure. Find the magnetic field B  and vector potential A  in the regions s < R and s > R.
Reed-solomon Decoder

The diagram below shows the high level architecture of the Reed-Solomon decoder. It also indicates communication between modules using FIFOs. The blocks highlighted in orange are the primary modules of the algorithm, and are thus also the most complex. The primary modules receive t, and the error corrector received k from the iteration control module through FIFO decoupled paths. This detail is not shown in the diagram below for clarity.
Physiological mechanisms of yield improvement in historical U.S. soybean germplasm

Soybean (Glycine max Merr.) is the world's most widely grown leguminous crop and an important source of protein and oil for food and feed. Soybean yields have increased substantially throughout the past century with yield gains widely attributed to genetic advances and improved cultivars, as well as advances in farming technology and practice. Although soybean yields do not appear to be stagnating, the current rate of gain is insufficient to meet the United Nations target of doubling crop yields by 2050. While soybean yields have been increased through traditional breeding efforts, the physiological mechanisms underlying past yield gains in the U.S. are largely unknown. Therefore, the aims of this thesis research are to gain a better understanding of the physiological basis of past improvements in soybean yield in order to help identify strategies for increasing future production. First, in a two year experiment, twenty-four soybean cultivars released between 1923 and 2007 were grown in field trials. Physiological improvements in the efficiencies by which soybean canopies intercepted light (í µí¼ í µí±), converted light energy into biomass (í µí¼ í µí±), and partitioned biomass into seed (í µí¼ í µí±) were examined. Seed yield increased on average by 26.5 kg ha-1 yr-1 , and the increase in seed yield was driven by improvements in all three efficiencies. Although the time to canopy closure did not change in historical soybean cultivars, extended growing seasons and decreased lodging in more modern lines drove improvements in í µí¼ í µí±. Greater biomass production per unit of absorbed light resulted in improvements in í µí¼ í µí±. Soybean seed biomass increased at a rate greater than total above-ground biomass, resulting in an increase in í µí¼ í µí±. It is thought that there is little room for further improvements in í µí¼ í µí± and í µí¼ í µí± as 84 years of traditional breeding has driven these efficiencies close to theoretical maxima. í µí¼ í µí± is still well below its theoretical maxima and is, therefore, a target for future yield gains. Next, in order to investigate the potential mechanisms underlying the increase in í µí¼ í µí± with cultivar year of release (YOR), photosynthetic (A) and respiratory capacity was measured within this set of historical germplasm over 3 growing seasons. Traditional soybean breeding has improved í µí¼ í µí± through greater rates of A with no change in respiratory capacity. The gains in …
THREE-DIMENSION HAMILTONIAN BROADCAST WORMHOLE-ROUTING

Broadcast is one of the most important approach in distributed memory parallel computers that is used to find a routing approach from a one source to all nodes in the mesh. Broadcasting is a data communication task in which corresponds to one-to-all communication. Routing schema is the approach that used to determine the road that is used to send a message from a source node to destination nodes. In this paper, we propose an efficient two algorithms for broadcasting on an all-port wormhole-routed 3D mesh with arbitrary size. In wormhole routing large network packets are broken into small pieces called FLITs (flow control digits). The destination address is kept in the first flit which is called the header flit and sets up the routing behavior for all subsequent flits associated with the packet. If the packets of the message can’t deliver to their destination and there is a cyclic dependence over the channels in the network, then the deadlock even is occurred. In this paper we introduce an efficient two algorithms, Three-Dimension Hamiltonian Broadcast (3-DHB) and Three-Dimension Six Ports Hamiltonian Broadcast (3-DSPHB) which used broadcast communication facility with deadlock-free wormhole routing in general threedimensional networks. In this paper the behaviors of these algorithms were compared using simulation. The results presented in this paper indicate that the advantage of the proposed algorithms.
ASYMPTOTIC BEHAVIOR OF ATTRACTORS FOR INHOMOGENEOUS ALLEN-CAHN EQUATIONS (Mathematical Analysis of Pattern Formation Arising in Nonlinear Phenomena)

We consider front propagation problems for forced mean curvature flows with a transport term and their phase field variants that take place in stratified media, i.e., heterogeneous media whose characteristics do not vary in one direction. We provide a convergence result relating asymptotic in time front propagation in the diffuse interface case to that in the sharp interface case, for suitably balanced nonlinearities of Allen-Cahn type. Our results generalize previous results for forced Allen-Cahn equations.
MEDICAL IMAGE AUTHENTICATION WITH ENHANCED WATERMARKING TECHHNIQUE THROUGH VISUAL CRYPTOGRAPHY

Technological evolution has increased the insecurity of the biomedical images produced day to day. Avoidance of the database hacking and preserving the privacy of digital biometric data stored in the central database has become of paramount importance. In order to preserve the security of the database, an enhanced visual cryptographic technique is proposed in this paper. Cryptographic technique allows information to be encrypted in such a manner that decryption cannot be handled by the non-legitimate user. The proposed method allows a secret sharing scheme, where an image is broken up into two shares and then overlaid in such a way that an unauthenticated person cannot reveal this secret image. The partitioned image is compared with all the images in the target using Hausdorff Distance metric algorithm. When both the images match, the authentication is provided. This paper also focuses on outsourcing data to an untrusted client to predict the category of the image without getting any information about the image itself and the final result of the classification. The experimental results show that our proposed method is efficient over the existing methods.
Hardware - software hybrid packet processing for intrusion detection systems

Security is a major issue in today’s communication networks. Designing network intrusion detection systems (NIDS) calls for high performance circuits in order to keep up with the rising data rates. Offloading software processing to hardware realizations is not an economically viable solution and hence hardware-software based hybrid solutions for the NIDS scenario are discussed in literature. By deploying processing on both hardware and software cores simultaneously by using an intelligent rule parsing algorithm, we aim to minimize the number of packets whose waiting time is greater than a predefined threshold. This fairness criterion implicitly ensures in obtaining a higher throughput as depicted by our results.
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Magnifying lenses : how the spectral analysis of the voice (human and animal) can be used to strengthen the connection between text and music

This commentary describes how my compositions in the last four years have been based upon models of human and animal vocal utterances, including both textual and paralinguistic elements, and how they strengthen the connection between text and music especially via synthetic relationships, based on the reproduction of the microscopic sound features of the voices which utter text. The textual elements are projected into the speech domain, thus acquiring uniqueness and including in the composition the aura of the being whose voice is analysed and later re-synthesised by the instruments involved. Varied repetition is used to feature text at multiple levels of enlargement, or filtering stages. All these occurrences of the same textual material have been regarded as nodes of a rhizome, since I conceived them as windows on to plateaux with different temporal scales or filtering processes, all intersecting a clearly defined socio-political plateau. The window form proved very useful for the inclusion of improvisation-based blocks and for the incorporation of elements from previously existing compositions into new ones. The Deleuzian concept of the rhizome created a suitable frame for the main aspects of my research and, since the change of analytical methodology in the course of the portfolio reflects my growing interest in perception, it is my intention to proceed along the path shown by Deleuze and Bergson to investigate the concepts of repetition and memory. Within the portfolio, the socio-political layer is at first determined by the content of the text, but later also by the identity of the person uttering the text and the techniques (s)he uses, so that in one of my compositions I successfully embedded music belonging to a non-Western culture. These trends show a future path for my research – namely, the identification of social differences among individuals and the detection of the traces of trauma through the analysis of their voices.
EFFICIENT MCMC-BASED IMAGE DEBLURRING WITH NEUMANN BOUNDARY CONDITIONS

The problem of uncertainty quanti(cid:2)cation (UQ) for inverse problems has become of signi(cid:2)cant recent interest. However, UQ requires more than the classical methods for computing solutions of inverse problems. In this paper, we take a Bayesian approach for the solution of ill-posed deconvolution problems with a symmetric convolution kernel and Neumann boundary conditions. The prior is modeled as a Gaussian Markov random (cid:2)eld (GMRF) with the same boundary conditions and symmetry assumptions. These assumptions yield better results in certain instances and also allow for the use of the discrete cosine transform for fast computations. Moreover, we use a hierarchical model for the noise precision (inverse-variance) and prior precision parameters. This leads to a posterior density function from which we can compute samples using a basic Markov Chain Monte Carlo (MCMC) method. The resulting samples can then be used for both estimation (using, e.g., the sample mean) and uncertainty quanti(cid:2)cation (using, e.g., histograms, the sample variance, or a movie created from the image samples). We provide a numerical experiment showing that the method is effective, computationally ef(cid:2)cient, and that for certain problems, the boundary conditions can yield signi(cid:2)cantly better results than if a periodic boundary is assumed. The novelty in the work lies in the combination of the MCMC method, Neumann boundary conditions, GMRF priors, and in the use of a movie to visualize uncertainty in the unknown image.
Previous and Ongoing Work

Computer systems are a pervasive aspect of daily life. From the smart phones we use to access our favorite websites, to the services those websites provide, to the computer systems that manage core infrastructure services (e.g., air traffic control, the power grid, or the stock market), there are very few aspects of a normal day not mediated by computer systems. Two byproducts of this integration are increased frustration when these systems don’t work properly and corresponding appreciation for the value of dependable systems. The goal of my research is to build robust distributed systems that simply work despite the best, and worst, efforts of the environment. Building robust systems is challenging for multiple reasons. On the one hand, the definition of “works” is system dependent and frequently invokes a tension between performance and some notion of correct behavior. On the other hand, there are many environmental factors that can get in the way: crashed components, corrupted hardware, transient electromagnetic interference, delayed and corrupted messages, misconfigured servers, malicious attackers, disaffected sysadmins, selfish users, benign concurrency, changes in workload, . My approach is based on a synergy between system building and theoretical work. To be confident that my systems work as advertised, I base my design and implementation efforts on solid theoretical foundations. To ensure that my theoretical foundations are relevant, I leverage the system building experience to develop new theoretical models and correctness conditions that better match the system deployment requirements. I have applied this methodology to a variety of applications in domains ranging from peer-topeer backup to live video streaming to scalable cluster file systems. In the process I have developed new fault models and built systems that are both more practical and more robust than previously thought possible. In the following I briefly overview of my previous work and future plans.1