Miracle-dz/UniFAR-FTUs huggingface dataset:
Model reference adaptive control for multi-input multi-output nonlinear systems using neural networks

This paper presents a method of MRAC (model reference adaptive control) for multi-input multi-output (MIMO) nonlinear systems using NNs (neural networks). The control input is given by the sum of the output of the NN (neural network). The NN is used to compensate the nonlinearity of plant dynamics that is not taken into consideration in the usual MRAC. The role of the NN is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems.
Implementation of Intelligent Home Network and u-Healthcare System based on Smart-Grid

In this paper, we established ZIGBEE home network and combined smart-grid and u-Healthcare system. We assisted for amount of electricity management of household by interlocking home devices of wireless sensor, PLC modem, DCU and realized smart grid and u-Healthcare at the same time by verifying body heat, pulse, blood pressure change and proceeded living body signal by using SVM algorithm and variety of ZIGBEE network channel and enabled it to check real-time through IHD which is developed by user interface. In addition, we minimized the rate of energy consumption of each sensor node when living body signal is processed and realized Query Processor which is able to optimize accuracy and speed of query. We were able to check the result that is accuracy of classification 0.848 which is less accounting for average 17.9% of storage more than the real input data by using Mjoin, multiple query process and SVM algorithm.
Visual analytics for massive urban public transport data

Public transport systems (PTSs) play an important role in modern cities. From the perspective of commuters, PTS provides shared and rapid transport services that are essential for the general public in a city. From the perspective of city management and urban planning, PTS has significant economical, social and environmental impacts to an entire city. Hence, studying PTS is highly beneficial to both individuals as well as a city as a whole, and it has long been a hot topic in transport research. However, many conventional transport researches have long been relying on simulation and survey data, making the results less of conviction. Thanks to recent advances in sensing technologies, such as RFID cards, laser scanners and GPS devices, movements acquisition has become convenient. Vast amount of urban public transport data has been collected automatically and pervasively, promoting more research focus on analyzing and exploring public transport data when studying PTS. However, analyzing massive urban public transport data is a challenging task due to its high-complex, large-size, multi-mode and spatio-temporal characteristics. To get over these challenges, visual analytics show great potential as they can make the way of processing public transport data transparent: Visual analytics can provide interactive means for transport researchers to examine the actual processes of analyzing data instead of just the results. This thesis investigates advanced visualization technologies for analyzing and exploring massive urban public transport data that consists of commuter RFID card data, transport network and transit schedule in Singapore. To address various analytical tasks raised by transport researchers, a family of novel visual analytics systems have been developed. Specifically, three aspects of high-level information, which are essential in transport modeling and analysis processes, have been extracted from the input dataset for visualization and exploration: ii • Interchange Pattern, which describes how moving objects redistribute when entering and passing through a junction node in a traffic network. A novel visual representation, namely the interchange circos diagram, has been proposed to present interchange pattern emerged from the public transport data. • Waypoints-Constrained OD Pattern, which restricts origin-destination (OD) pattern with commuter trajectories passing through user-specified entry and exit waypoints in a transport network. A novel unified visual representation, namely the waypoints-constrained OD view, has been proposed to explore waypoints-constrained OD pattern. • Mobility, which can be considered as the travel efficiencies of commuters through PTS. An integrated visualization with three modules: isochrone map view, isotime flow map view and OD-pair journey view, has been proposed to address a family of analytical tasks based on inputs from transport researchers.
Floquet scattering theory for current and heat noise in large amplitude adiabatic pumps

We discuss the statistical correlation properties of currents and energy flows generated by an adiabatic quantum pump. Our approach emphasizes the important role of quantized energy exchange between the sea of electrons and the oscillating scatterer. The pump-frequency introduces a natural energy scale. In the low temperature limit, thermal energy much smaller than a modulation quantum, the pump generates a shot-like noise which manifests itself in photon-assisted quantum mechanical exchange amplitudes. In the high temperature limit, thermal energy much larger than a modulation quantum, the pump producesa thermal-like noise due to ac-currents generated by the pump. We predict that with increasing temperature the frequency dependence of the noise changes. The current noise is linear in pump-frequency at low temperatures, is quadratic at intermediate temperatures, and is linear again at high temperatures. Similarly, in the same temperature regions, the heat flow noise is proportional to the third, second and first power of the pump-frequency.
Hemophagocytic Syndrome: Experience in a tertiary care centre

Introduction: Hemophagocytic syndrome also known as Hemophagocytic Lymphohistiocytosis (HLH) is a potentially fatal hyperinflammatory condition. It can be primary or a secondary disorder associated with a variety of underlying conditions. These disorders feature severe cytopenias due to uncontrolled hemophagocytosis and clinical symptoms result from disordered immune regulation and cytokine storm. Materials and Methods: The present study was done at the tertiary care centre attached to University Medical College in western India. Forty five patients with cytopenias who were suspected to have secondary HLH were included in this study. Clinical details and routine investigations, tests necessary to diagnose HLH i.e. Ferritin, Triglyceride, LDH, Fibrinogen levels were done. Bone marrow aspirate in these patients showed evidence of hemophagocytosis. Statistical analysis was done using the appropriate software and statistical tests. Results: Infection was the commonest cause associated with secondary HLH in our study. 95.5% patients had anaemia while 42.2% patients had fever along with other clinical manifestations. Elevated Serum Ferritin levels were found in 55.5% patients. 82.1% and 63.0% patients had elevated Lactate Dehydrogenase and elevated triglycerides levels respectively while only 35.6% patients had low fibrinogen levels. Higher levels of ferritin were associated with severe cytopenias and with poor outcome of the patients. PT and APTT were also deranged in a significant proportion of patients. We have also observed raised APTT values correlating with abnormal ferritin levels and cytopenias in our study. This could be because most of these cases were associated with sepsis. We found significant association of elevated serum Ferritin, Triglyceride and LDH levels with HLH. Conclusion: Hemophagocytic syndromes should be considered in unexplained cytopenias. A bone marrow aspiration or organ biopsy revealing hemophagocytosis along with Serum Ferritin, Triglycer
A Discriminative Model Approach to Basin Extreme Precipitation Forecasting using a Rich Feature Set

A discriminative learning model with a rich feature set was used for extreme precipitation forecasting of a river basin to effectively use available historical observation data. Discrimination models use decision functions that are suitable for forecasting in cases where factors are complex or even unknown. In this study, we used the neural network technique that belongs to the empirical risk minimization method, and a support vector machine that belongs to the structural risk minimization method to be constructed on rich feature sets of rainfall in the Dongjiang basin. The results of forecast experiments show that our method was more effective compared with four types of traditional time series methods and the Naive Bayes method that belongs to generative models. The support vector machine yielded the maximum F1 value.
A linearly constrained adaptive algorithm for constant modulus signal processing

This dissertation introduces an adaptive technique that is suitable for signal processing tasks in severe co-channel interference and multipath environments. The new approach is based on the minimization of complex envelope variations at the processor output subject to a set of linear constraints on the processor coefficients. As a result, it does not require a training, reference or desired signal for adaptation. This Linearly Constrained Constant Modulus (LCCM) method combines two existing untrained adaptive techniques: linearly constrained minimization of output power and unconstrained minimization of complex envelope or modulus variations. LCCM addresses the signal cancellation problem that occurs when untrained adaptive processors are used in hostile signal environments; minimization of output power can lead to signal cancellation in multipath scenarios and unconstrained minimization of envelope variations can lead to signal cancellation in applications that include constant envelope co-channel interference. Analysis of a narrowband array application shows that the LCCM approach will eliminate the signal cancellation problem when appropriate a priori information is incorporated into the constraint set. An additional result is that the signal of interest does not necessarily need to have the constant envelope property. Adaptive implementation of the LCCM method is accomplished using the common technique of Stochastic Gradient Descent (SGD). Stable adaptive recursions are derived via comparison with other SGD recursions that are used for existing adaptive approaches. In addition, the roles of convergence parameters are described and practical values are specified. For special cases, convergence in the mean is shown. In a typical signal processing scenario, LCCM is used for initial acquisition of a signal and then adaptive processing is switched to a decision directed mode. Simulation results for LCCM in the start-up or acquisition mode are presented for both single and multi-channel reception of a digitally modulated signal. The single channel experiment consists of a fractionally spaced LCCM equalizer with a signal subspace based linear constraint. The multi-channel experiment consists of a narrowband array with a look direction constraint. In both cases, the LCCM processor rejects the co-channel interference and compensates for the multipath distortion. (Copies available exclusively from Micrographics Department, Doheny Library, USC, Los Angeles, CA 90089-0182.)
Information Literacy in Virtual Environments: Changing Needs of P-12 Learners

This chapter provides an overview of information literacy needs of P-12 learners in virtual environments. As more of life is spent in global digital participatory culture, information literacy skills have moved from primarily print-based to include digital content evaluation, content curation, and a personal responsibility for digital citizenship. Using standards for 21st century learning from the American Library Association, examples highlight information literacy elements embedded in immersive learning environments, such as the Anne Frank simulation (a 3D replica of Amsterdam and the annex where Anne’s family hid) and a digital citizenship game built by students in the game of Minecraft. Creators of immersive learning environments in virtual worlds must accurately depict historical eras and cite sources for authority and accuracy of information. These 3D simulations provide opportunities to teach information literacy in virtual spaces with a shared “sense of presence”. Learners in virtual environments have digital citizenship responsibilities from a very young age.
Content based Image Retrieval using Local Patterns: A Survey

Paper presents a survey on the technical achievements of Content Based Image Retrieval (CBIR), this area is been active from past two decades. The survey includes the information of local patterns covering the research aspects of image feature extraction which is the most fundamental and prominent base of CBIR. Furthermore, from current technology available and demand from the real world applications such as face recognition, medical diagnosis etc., and some research directions are identified and suggested. Keywords— CBIR(Content Based Image Retrieval);Local
A Linux Kernel Module for Locking Down Applications on Linux Clients

Preventing the installation and execution of unauthorized software should be a high priority for any organization. Allowing users to install and execute unauthorized software can expose an organization to a variety of security risks. In this paper we present a graylisting solution to control application execution on Linux clients using a loadable kernel module. Our developed kernel based solution, Locking Applications on Linux Clients or LALC is a new Linux subsystem which adds a graylisting application lockdown capability to Linux kernel. The restriction policy applied by LALC to specific client is based on the preconfigured security level of the client's group and on the application the client desire to execute or to install. LALC is flexible enough to support the business needs as well as new applications and new versions of existing applications. And it is so secure that no end user can circumvent its configuration.
The Millennium Arecibo 21 Centimeter Absorption-Line Survey. IV. Statistics of Magnetic Field, Column Density, and Turbulence

We discuss observations of the magnetic field, column density, and turbulence in the cold neutral medium (CNM). The observed quantities are only indirectly related to the intrinsic astronomical ones. We relate the observed and intrinsic quantities by relating their univariate and bivariate probability distribution functions (pdf's). We find that observations of the line-of-sight component of a magnetic field do not constrain the pdf of the total field Btot very well but do constrain the median value of Btot. In the CNM, we find a well-defined median magnetic field 6.0 ± 1.8 μG. The CNM magnetic field dominates thermal motions. Turbulence and magnetism are in approximate equipartition. We find that the probability distribution of column density N⊥(H ) in the sheets closely follows N⊥(H )-1 over a range of 2 orders of magnitude, 0.026 ≲ N⊥(H ) ≲ 2.6 × 1020 cm-2. The bivariate distributions are not well enough determined to constrain structural models of CNM sheets.
Physics lessons taught by pre-service teachers and in-service teachers – didactic case report and comparison

The aim of this paper is to present the outcomes of the project that is solved at the Faculty of Science in Olomouc, Czech Republic. One of the key activities of the project is to interconnect more closely the pre-graduate teacher training with the practice at schools and the cooperation with teachers of various secondary schools. Video hospitalizations of lessons are provided. Lessons realized by experienced teachers are compared with lessons realized by university students (pre-service teachers). The lessons are analyzed and reflected. Particular examples of the comparison of various physics lessons will be presented, crucial problems in these two types of lessons will be discussed.
Improving Machined Surface Shape Prediction by Integrating Multi-Task Learning With Cutting Force Variation Modeling

The shapes of machined surfaces play a critical role affecting powertrain performance, and therefore, it is necessary to characterize the shapes with high resolution. State-ofthe-art approaches for surface shape characterization are mostly data-driven by interpolating and extrapolating the spatial data but its precision is limited by the density of measurements. This paper explores the new opportunity of improving surface shape prediction through considering the similarity of multiple similar manufacturing processes. It is a common scenario when the process of interest lacks sufficient data whereas rich data could be available from other similar-but-not-identical processes. It is reasonable to transfer the insights gained from other relevant processes into the surface shape prediction. This paper develops an engineering-guided multitask learning (EG-MTL) surface model by fusing surface cutting physics in engineering processes and the spatial data from a number of similar-but-not-identical processes. An iterative multitask Gaussian process learning algorithm is developed to learn the model parameters. Compared with the conventional multitask learning, the proposed method has the advantages in incorporating the insights on cutting force variation during machining and is potentially able to improve the prediction performance given limited measurement data. The methodology is demonstrated based on the data from real-world machining processes in an engine plant. [DOI: 10.1115/1.4034592]
Adaptive neural dynamic surface sliding mode control for uncertain nonlinear systems with unknown input saturation

In this article, an adaptive neural dynamic surface sliding mode control scheme is proposed for uncertain nonlinear systems with unknown input saturation. The non-smooth input saturation nonlinearity is firstly approximated by a smooth non-affine function, which can be further transformed into an affine form according to the mean value theorem. Then, one simple sigmoid neural network is employed to approximate the uncertain nonlinearity including the input saturation, and the approximation error is estimated using an adaptive learning law. Virtual controls are designed in each step by combing the dynamic surface control and integral sliding mode technique, and thus the problem of complexity explosion inherent in the conventional backstepping method is avoided. With the proposed control scheme, no prior knowledge is required on the bound of input saturation, and comparative simulations are given to illustrate the effectiveness and superior performance.
APPLICATION OF THE DOMINANCE-BASED ROUGH SET APPROACH TO RANKING AND SIMILARITY-BASED CLASSIFICATION PROBLEMS

The thesis concerns decision aiding methods for multicriteria ranking problem and similarity-based classification problem (case-based reasoning, CBR). For both problems, we present methods that adapt the Dominance-based Rough Set Approach (DRSA). In the multicriteria ranking problem, there is given a finite set of objects (also called alternatives, actions, solutions, etc.), described by a set of criteria (cardinal and/or ordinal ones). The aim of decision aiding is to recommend to a decision maker (DM) a ranking of the given objects that reflects her/his preferences. This ranking is supposed to be a complete preorder or a partial preorder. The only conclusion that stems from the formulation of a multicriteria decision problem is the dominance relation over the set of objects, however, leaving many objects incomparable, it is too poor to make a useful recommendation. Therefore, in Multicriteria Decision Aiding (MCDA), it is necessary to acquire from the DM an additional information, called preference information. In our approach, this information consists of decision examples in the form of pairwise comparisons of some reference objects. It is used to build a preference model of the DM. This model induces a preference structure on the set of objects to be ranked. A proper exploitation of this structure yields a ranking of objects that is presented to the DM. Acceptance of this ranking by the DM depends, on one hand, on the accuracy of prediction of pairwise comparisons of new objects, and on the other hand, on the DM’s conviction that the preference model represents her/his preferences in a comprehensible and accurate way. Existing decision aiding methods for multicriteria ranking differ by the type of acquired preference information, and by the type of preference model built from this information. A preference model can take the form of a function (e.g., additive value function), binary relation (e.g., outranking relation), or a set of if-then decision rules of the type “if a conjunction of elementary conditions on chosen criteria holds, then it is suggested to take a given decision”. Traditional preference models applied in MCDA, i.e., value functions and outranking relations, are hard to understand by the DM, require complex preference information, and are based on rather strong assumptions, that are often not met in practice. Therefore, it is currently strongly postulated that the preference model should be induced (learned) from decision examples supplied by the DM. Expression of preferences by making some exemplary decisions does not require from the DM the specification of
Service Function Chaining (SFC) General Use Cases

The delivery of value-added services relies on the invocation of advanced Service Functions in a sequential order. This mechanism is called Service Function Chaining (SFC). The set of involved Service Functions and their order depends on the service context and other deployment-specific considerations. Having a single use case document eases the effort of deriving requirements that are to be met by SFC solution(s). Moreover, it allows to identify commonalities between the various use cases that are of interest. This document presents a set of general use cases of Service Function Chaining (SFC).
A low-distortion BiCMOS 7th-order Bessel filter operating at 2.5 V supply

This paper presents the design of a 7th-order continuous-time Bessel filter using a new low-voltage and highly-linear BiCMOS OTA. A high-gain and parasitic-insensitive integrator is obtained by using an active capacitor scheme. The filter has been fabricated in a 1 /spl mu/m, double-poly 6-GHz BiCMOS process. The filter is operated at 2.5 V supply and has a nominal -3 dB cut-off frequency set at 600 kHz. The inband group delay variation is less than 10 ns. The THD measured with a 100 kHz input signal is less than -49 dB for a 2 V/sub pp/ amplitude. The filter can be frequency tuned with an external resistor over almost a decade with a gain variation less than 0.2 dB. The dynamic range is about 77 dB. A CMRR of 53 dB in the passband is observed, thanks to a careful common-mode control strategy.
Super-Resolution Restoration Method Based on Low-Resolution Image Fusion

This paper offers a Super-Resolution(SR) image restoration algorithm based on Low-Resolution(LR) image fusion. The algorithm fuses a set of LR images to form an image with same resolution as the High-Resolution(HR) image, and interposes a LR image as the initialization guess for the iterative restoration algorithm. Both the above images are used to find the original HR image by the Tikhonov regularization algorithm. Analysis and simulation results confirm that the algorithm has good robustness and faster computation.
Application of Augmented Reality (AR)Technique to the Evaluation of Projects

The technique of AR is to place the simulation model of an object (from a computer) in real surroundings (from a digital pick up camera). The authors apply the technique to the evaluation of the schemes of large projects for the first time. In the paper is stated the method of AR technique based on affine structure in detail.Two examples are presented,and their visual results are very well.AR technique can make the real scene of a project to be built present itself before our eyes. The application of AR technique supplies means for the visualization, evaluation and comparison of design schemes of projects. It increases the reliability of the design schemes of the projects and reduces the design cost of them.