Miracle-dz/UniFAR-FTUs huggingface dataset:
A Harmonic Termination Technique for Single- and Multi-Band High-Efficiency Class-F MMIC Power Amplifiers

This paper presents a harmonic termination technique for single- and multi-band high-efficiency class-F monolithic microwave integrated circuit (MMIC) power amplifiers (PAs). The harmonic termination network (HTN), realized with the minimum possible number of elements, can be used to terminate an arbitrary number of harmonics in a single-band PA or harmonics of multiple frequencies in a concurrent multi-band PA. The drain and gate bias lines are embedded in the HTNs to obviate the need for RF chokes and reduce the chip area. A single- and a dual-band MMIC PA are designed using the proposed technique and implemented in a 0.25- μm AlGaAs-InGaAs pHEMT technology. The single-band 5.5-GHz PA provides 27.5 dBm of output power with 70% power-added efficiency (PAE). This PAE level is achieved by terminating the first five harmonics at the output as well as the first three harmonics at the input of PA. The dual-band PA, operating at 5 and 12 GHz, delivers the output power of 28.0 and 26.7 dBm with PAE of 58% and 51%, at the two frequency bands, respectively.
UNIVERSITY OF MIAMI A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Electrical and Computer Engineering EDGE PRESERVING IMAGE COMPRESSION FOR MAGNETIC RESONANCE IMAGES USING DANN-BASED NEURAL NETWORKS Tat Chee Wan Approved:

of a master’s thesis at the University of Miami. Thesis supervised by Professor Mansur R. Kabuka. No. of pages in text: 51. With the tremendous growth in imaging applications and the development of filmless radiology, the need for compression techniques which can achieve high compression ratios with user specified distortion rates become necessary. Boundaries and edges in the tissue structures are vital for detection of lesions and tumors, which in turn requires the preservation of edges in the image. Unlike existing lossy transform-based compression techniques such as FFT and DCT, edge preservation is addressed in this new compression scheme. The proposed Edge Preserving Image Compressor (EPIC) combines lossless compression of edges with neural network compression techniques based on Dynamic Associative Neural Networks (DANN), to provide high compression ratios with user specified distortion rates in an adaptive compression system well-suited to parallel implementations. Improvements to DANN-based training through the use of a variance classifier for controlling a bank of neural networks speed convergence and allow the use of higher compression ratios for “simple” patterns. The adaptation and generalization capabilities inherent in EPIC also facilitate progressive transmission of images through varying the number of quantization levels used to represent compressed patterns. EPIC was able to achieve average compression ratios of 7.26:1 with an averaged Average Mean Squared Error (AMSE) of 0.0147.
A resource prediction model for virtualization servers

Monitoring and predicting resource consumption is a fundamental need when running a virtualized system. Predicting resources is necessary because cloud infrastructures use virtual resources on demand. Current monitoring tools are insufficient to predict resource usage of virtualized systems so, without proper monitoring, virtualized systems can suffer down time, which can directly affect cloud infrastructure. We propose a new modelling approach to the problem of resource prediction. Models are based on historical data to forecast short-term resource usages. We present here in detail three of our prediction models to forecast and monitor resources. We also show experimental results by using real-life data and an overall evaluation of this approach.
Group-Structured and Independent Subspace Based Dictionary Learning

Thanks to the several successful applications, sparse signal representation has become one of the most actively studied research areas in mathematics. However, in the traditional sparse coding problem the dictionary used for representation is assumed to be known. In spite of the popularity of sparsity and its recently emerged structured sparse extension, interestingly, very few works focused on the learning problem of dictionaries to these codes. In the first part of the paper, we develop a dictionary learning method which is (i) online, (ii) enables overlapping group structures with (iii) non-convex sparsity-inducing regularization and (iv) handles the partially observable case. To the best of our knowledge, current methods can exhibit two of these four desirable properties at most. We also investigate several interesting special cases of our framework and demonstrate its applicability in inpainting of natural signals, structured sparse non-negative matrix factorization of faces and collaborative filtering. Complementing the sparse direction we formulate a novel component-wise acting, epsilon-sparse coding scheme in reproducing kernel Hilbert spaces and show its equivalence to a generalized class of support vector machines. Moreover, we embed support vector machines to multilayer perceptrons and show that for this novel kernel based approximation approach the backpropagation procedure of multilayer perceptrons can be generalized. In the second part of the paper, we focus on dictionary learning making use of independent subspace assumption instead of structured sparsity. The corresponding problem is called independent subspace analysis (ISA), or independent component analysis (ICA) if all the hidden, independent sources are one-dimensional. One of the most fundamental results of this research field is the ISA separation principle, which states that the ISA problem can be solved by traditional ICA up to permutation. This principle (i) forms the basis of the state-of-the-art ISA solvers and (ii) enables one to estimate the unknown number and the dimensions of the sources efficiently. We (i) extend the ISA problem to several new directions including the controlled, the partially observed, the complex valued and the nonparametric case and (ii) derive separation principle based solution techniques for the generalizations. This solution approach (i) makes it possible to apply state-of-the-art algorithms for the obtained subproblems (in the ISA example ICA and clustering) and (ii) handles the case of unknown dimensional sources. Our extensive numerical experiments demonstrate the robustness and efficiency of our approach.
Measurement of respiration rate in preterm infants by laser Doppler vibrometry

The continuous monitoring of patients recovered in intensive care units is a mandatory tasks for clinicians in order to check conditions and effects of therapies. Among the many signals and quantities observable, the ones related to the heart and particularly the lung's activity are of primary interest. Today the majority of the measurement methods used for the assessment of the respiration rate are based on invasive methods (spriometry) which are often included into mechanical ventilator to which the patients, in need of assisted breathing, are attached. In this paper an optical, non-contact measurement method for monitoring of the respiration rate in pre-term infants is reported. It is based on the assessment of abdominal wall movements detected by a laser Doppler vibrometer and can be operated at a distance from the patient and outside the incubator. A total of 9 preterm patients have been monitored; respiration rates measured by a reference instrument (BabyLog 8000 Draeger plus) and the proposed non-contact method have been measured for a period of time of 60s. Data have been compared using the Bland-Altman analysis and results demonstrate an optimal correlation (R=0.97) among data and an uncertainty of the proposed method of ± 0.13s (K=2).
Title Control of MEMS vibration modes with pulsed digital oscillators : part II — simulation and experimental results

This paper extends our previous work on the selective excitation of mechanical vibration modes in MEMS devices using Pulsed Digital Oscillators. It begins by pres enting extensive simulation results using the set of iterative map s that model the system and showing that it is possible to activate t wo or three spatial modes (resonances) of the mechanical struc ture with a Pulsed Digital Oscillator (PDO). The second part of this paper presents experimental results corroborating the the ory and simulation results. It is shown that it is possible to separa tely excite vibration modes of the device by setting a few paramet rs of the PDO structure such as the sampling frequency and the sign of the feedback loop.
On the strength of the nonlinearity in isotropic turbulence

Abstract Turbulence governed by the Navier–Stokes equations shows a tendency to evolve towards a state in which the nonlinearity is diminished. In fully developed turbulence, this tendency can be measured by comparing the variance of the nonlinear term to the variance of the same quantity measured in a Gaussian field with the same energy distribution. In order to study this phenomenon at high Reynolds numbers, a version of the direct interaction approximation is used to obtain a closed expression for the statistical average of the mean-square nonlinearity. The wavenumber spectrum of the mean-square nonlinear term is evaluated and its scaling in the inertial range is investigated as a function of the Reynolds number. Its scaling is dominated by the sweeping by the energetic scales, but this sweeping is weaker than predicted by a random sweeping estimate. At inertial range scales, the depletion of nonlinearity as a function of the wavenumber is observed to be constant. At large scales it is observed that the mean-square nonlinearity is larger than its Gaussian estimate, which is shown to be related to the non-Gaussianity of the Reynolds-stress fluctuations at these scales.
Parallel RBF-based Reconstruction from Contour Dataset

In this paper, a parallel algorithm for RBF-based surface reconstruction from contours is presented. The reconstruction process is divided into local reconstruction and global reconstruction phases. In the local reconstruction phase, surface patches are reconstructed from a set of adjacent slices of contours, these tasks are scheduled to be executed in parallel. In the global reconstruction phase, weighted function and local function values are evaluated in parallel. To balance the overload among CPUs or threads and get best parallel computing performance, the total sample points involved in task groups are distributed evenly. The experiment results show that the proposed schedule accelerate the reconstruction process greatly.
Low-Bit Rate Feedback Strategies for Iterative IA-Precoded MIMO-OFDM-Based Systems

Interference alignment (IA) is a promising technique that allows high-capacity gains in interference channels, but which requires the knowledge of the channel state information (CSI) for all the system links. We design low-complexity and low-bit rate feedback strategies where a quantized version of some CSI parameters is fed back from the user terminal (UT) to the base station (BS), which shares it with the other BSs through a limited-capacity backhaul network. This information is then used by BSs to perform the overall IA design. With the proposed strategies, we only need to send part of the CSI information, and this can even be sent only once for a set of data blocks transmitted over time-varying channels. These strategies are applied to iterative MMSE-based IA techniques for the downlink of broadband wireless OFDM systems with limited feedback. A new robust iterative IA technique, where channel quantization errors are taken into account in IA design, is also proposed and evaluated. With our proposed strategies, we need a small number of quantization bits to transmit and share the CSI, when comparing with the techniques used in previous works, while allowing performance close to the one obtained with perfect channel knowledge.
Cosserat fluids and the continuum mechanics of turbulence : a generalized Navier – Stokes-α equation with complete boundary conditions By

We here develop a continuum-mechanical formulation and generalization of the Navier– Stokes-α equation based on a general framework for fluid-dynamical theories involving gradient dependencies (Fried & Gurtin 2005). That generalization entails two additional material length scales: one of energetic origin, the other of dissipative origin. In contrast to Lagrangian averaging, our formulation delivers boundary conditions — involving yet another material length scale — and a complete framework based on thermodynamics applied to an isothermal system. As an application, we consider the classical problem of turbulent flow in a plane, rectangular channel with fixed, impermeable, slip-free walls and make comparisons with results obtained from direct numerical simulations. For this problem, only one of the material length scales involved in the flow equation enters the final solution. When the additional material length scale associated with the boundary conditions is signed to ensure satisfaction of the second law at the channel walls the theory delivers solutions that agree neither quantitatively nor qualitatively with observed features of plane channel flow. On the contrary, we find excellent agreement when the sign of the additional material parameter associated with the boundary conditions violates the second law. We discuss the implication of this result.
What are the best routes for us to use for driving home tonight in rush hour traffic?

We show that the capacity of a complex network that models a city street grid to support congested traffic can be optimized by using routes that collectively minimize the maximum ratio of betweenness to capacity in any link. Networks with a heterogeneous distribution of link capacities and with a heterogeneous transport load are considered. We find that overall traffic congestion and average travel times can be significantly reduced by a judicious use of slower, smaller capacity links.
Multiobjective Genetic Algorithms for Solving the Impairment-Aware Routing and Wavelength Assignment Problem

In future transparent (all-optical) WDM networks, the signal quality of transmission (QoT) will degrade due to physical layer impairments. In this paper, we propose two genetic algorithms for solving the static impairment-aware RWA (IA-RWA) problem by accounting for the impact of physical impairments in the optimization process when searching for the optimum routing path and wavelength channel. The first algorithm indirectly considers the physical impairments through the insertion of the path length and the number of common hops in the search process, using classical multiobjective optimization (MOO) strategies. The second algorithm is a single-objective genetic algorithm (GA) that uses the Q factor for the evaluation of the feasibility of the selected RWA solution. The Q factor is used in each iteration of the algorithm in a self-learning mode in order to evaluate the fitness of each solution to the RWA problem and trigger the evolution of the population. Performance results have shown that considering path length and number of common hops for indirectly handling impairments provide an efficient solution to the IA-RWA problem.
An Experimental Study of Software Metrics for Real-Time Software

The rising costs of software development and maintenance have naturally aroused intere5t in tools and measures to quantify and analyze software complexity. Many software metrics have been studied widely because of the potential usefulness in predicting the complexity and quality of software. Most of the work reported in this area has been related to nonreal-time software. In this paper we report and discuss the results of an experimental investigation of some important metrics and their relationship for a class of 202 Pascal programs used in a real-time distributed processing environment. While some of our observations confirm independent studies, we have noted significant differences. For instance the correlations between McCabe's control complexity measure and Halstead's metrics are low in comparison to a previous study. Studies of the type reported here are important for understanding the relationship between software metrics.
Event-Based Learning : Educational and Technological Perspectives

Teachers have traditionally introduced current events in the classroom to leverage news topics relevant to the curriculum; however, to my knowledge no studies have attempted to provide a comprehensive research view on these event-related teaching activities. This paper presents an introductory event-based learning analysis from educational and technological research perspectives. This research effort provides a broad characterization of event-based learning that aims to inform the design of an online event-based learning system. From the educational viewpoint, I identify the pedagogical characteristics of event-based learning archetypes elicited from a review of relevant learning activities. From the technological perspective, I propose a modeling framework to organize relevant research undergirding an idealized online event-based learning system. High-level requirements and new research questions flow from a conceptual walk-through of the archetypes using the proposed framework. These new research directions and requirements include in-situ teacher practice studies, formal educational impact analyses, contextual event filtering, novel educational metadata modeling, unified information indexing, experiential learning environments, and composite visualization displays.
Causal influence of age at first drink on alcohol involvement in adulthood and its moderation by familial context.

OBJECTIVE Previous research has established a connection between early age at drinking initiation and greater alcohol involvement in adulthood, but it has not yet been established whether this is a causal effect. The current study used a multilevel discordant twin design to examine individual and contextual effects, and an interaction between these effects, of the age at drinking initiation on the frequency and quantity of drinking in adulthood. METHOD Participants were 4,194 same-sex twins (2,264 monozygotic, 1,924 dizygotic; 2,270 women; Mage = 29.9 years) from the Australian Twin Registry who completed a telephone interview that included assessments of the age at alcohol use initiation and past-year frequency and quantity of alcohol use. Multilevel models were estimated using data from the full sample and using data from only monozygotic twins. Individual (within-twin-pair comparison) and family contextual (between-twin-pair comparison) effects were estimated. RESULTS The age at first drink was related to the past-year frequency (r = -.16) and quantity of drinking (r = -.12) in young adulthood. Individual (causal) and family context effects of age at drinking onset predicted later adult drinking frequency and quantity. There was also a significant cross-level interaction between individual and family contexts for frequency but not quantity of drinking. CONCLUSIONS Results of this study indicate a potential causal effect of age at drinking onset on adult alcohol involvement as well as the importance of examining both individual and contextual effects in discordant twin studies.
ATP Release From Activated Neutrophils Occurs via Connexin 43 and Modulates Adenosine-Dependent Endothelial Cell Function

Extracellular ATP liberated during hypoxia and inflammation can either signal directly on purinergic receptors or can activate adenosine receptors following phosphohydrolysis to adenosine. Given the association of polymorphonuclear leukocytes (PMNs) with adenine-nucleotide/nucleoside signaling in the inflammatory milieu, we hypothesized that PMNs are a source of extracellular ATP. Initial studies using high-performance liquid chromatography and luminometric ATP detection assays revealed that PMNs release ATP through activation-dependent pathways. In vitro models of endothelial barrier function and neutrophil/endothelial adhesion indicated that PMN-derived ATP signals through endothelial adenosine receptors, thereby promoting endothelial barrier function and attenuating PMN/endothelial adhesion. Metabolism of extracellular ATP to adenosine required PMNs, and studies addressing these metabolic steps revealed that PMN express surface ecto-apyrase (CD39). In fact, studies with PMNs derived from cd39−/− mice showed significantly increased levels of extracellular ATP and lack of ATP dissipation from their supernatants. After excluding lytic ATP release, we used pharmacological strategies to reveal a potential mechanism involved in PMN-dependent ATP release (eg, verapamil, dipyridamole, brefeldin A, 18-α-glycyrrhetinic acid, connexin-mimetic peptides). These studies showed that PMN ATP release occurs through connexin 43 (Cx43) hemichannels in a protein/phosphatase-A–dependent manner. Findings in human PMNs were confirmed in PMNs derived from induced Cx43−/− mice, whereby activated PMNs release less than 15% of ATP relative to littermate controls, whereas Cx43 heterozygote PMNs were intermediate in their capacity for ATP release (P<0.01). Taken together, our results identify a previously unappreciated role for Cx43 in activated PMN ATP release, therein contributing to the innate metabolic control of the inflammatory milieu.
Real-time screen image scaling and its GPU acceleration

In this paper, we propose a simple and effective scheme for screen image scaling, targeting at real-time applications like remote desktop and screen sharing. To balance visual quality and complexity, we build our scheme upon bilinear interpolation and devise a content adaptive post-processing method. Our scheme keeps the text/graphics regions of screen images as sharp as the original while avoiding magnifying noise and artifacts in the picture regions. Further, the involved operations are quite suitable for hardware acceleration. When implemented on commodity GPUs, our scheme achieves a frame rate more than 300 fps, which is fast enough for practical use.
Automated testing of IDS rules

As technology becomes ubiquitous, new vulnerabilities are being discovered at a rapid rate. Security experts continuously find ways to detect attempts to exploit those vulnerabilities. The outcome is an extremely large and complex rule set used by Intrusion Detection Systems (IDSs) to detect and prevent the vulnerabilities. The rule sets have become so large that it seems infeasible to verify their precision or identify overlapping rules. This work proposes a methodology consisting of a set of tools that will make rule management easier.
Designing Influence Metric at the Architectural Level for Improving the Reliability of a System

ABSTRACT Though some components play a major role for enhancing the quality of a system, but exactly identifying those components at the early stage is a big challenge. Metrics that are designed at the early stage guide both the test manager and the system analyst in decision making. In this paper, we propose an Influence Metric at the architectural level to get the influence of a component towards the system failures. First, we generate an intermediate graph called Sequence Diagram Graph (SDG) for a sequence diagram and compute the occurrence probability of each event within the sequence diagram based on operational profile of the system. Then, we propose an algorithm called Influence Computation Algorithm (ICA) to compute the influence of a component within a use case and within the whole system. The influence of a component c is decided by checking how many components are calling directly or indirectly the component c and the probabilities of their call to