Miracle-dz/UniFAR-FTUs huggingface dataset:
Linear theory for filtering nonlinear multiscale systems with model error

In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuous-time noisy observations of all components of the slow variables. Mathematically, this paper presents new results on higher order asymptotic expansion of the first two moments of a conditional measure. In particular, we are interested in the application of filtering multiscale problems in which the conditional distribution is defined over the slow variables, given noisy observation of the slow variables alone. From the mathematical analysis, we learn that for a continuous time linear model with Gaussian noise, there exists a unique choice of parameters in a linear reduced model for the slow variables which gives the optimal filtering when only the slow variables are observed. Moreover, these parameters simultaneously give the optimal equilibrium statistical estimates of the underlying system, and as a consequence they can be estimated offline from the equilibrium statistics of the true signal. By examining a nonlinear test model, we show that the linear theory extends in this non-Gaussian, nonlinear configuration as long as we know the optimal stochastic parametrization and the correct observation model. However, when the stochastic parametrization model is inappropriate, parameters chosen for good filter performance may give poor equilibrium statistical estimates and vice versa; this finding is based on analytical and numerical results on our nonlinear test model and the two-layer Lorenz-96 model. Finally, even when the correct stochastic ansatz is given, it is imperative to estimate the parameters simultaneously and to account for the nonlinear feedback of the stochastic parameters into the reduced filter estimates. In numerical experiments on the two-layer Lorenz-96 model, we find that the parameters estimated online, as part of a filtering procedure, simultaneously produce accurate filtering and equilibrium statistical prediction. In contrast, an offline estimation technique based on a linear regression, which fits the parameters to a training dataset without using the filter, yields filter estimates which are worse than the observations or even divergent when the slow variables are not fully observed. This finding does not imply that all offline methods are inherently inferior to the online method for nonlinear estimation problems, it only suggests that an ideal estimation technique should estimate all parameters simultaneously whether it is online or offline.
Designing and Deploying Service-Centric Systems : The SeCSE Way

In spite of recent research, designing and deploying largescale service-centric systems remains a challenge. The SeCSE project tries to tackle this challenge by creating new methods, tools and techniques for requirements analysts, system integrators and service providers that support the costeffective development and use of dependable services and service-centric applications. The results achieved in the project are being experimented in the European automotive and telecommunication sectors. In this paper we presents the demo that we have been developing for the last two project years. The goal is to show the tools available to a service integrator that creates a new service relying on others that are already existing and are offered either by the project partners or by other players in the Internet. The paper also describes how this resulting service behaves at runtime.
Global Contrast Factor - a New Approach to Image Contrast

Contrast in image processing is usually defined as a ratio between the darkest and the brightest spots of an image. In this paper we introduce a different contrast definition. The newly introduced Global Contrast Factor (GCF) corresponds closer to the human perception of contrast. GCF uses contrasts at various resolution levels in order to compute overall contrast. Experiments were conducted in order to find weight factors needed to calculate GCF. GCF measures richness of detail as perceived by a human observer, and as such can be used in various application areas like rendering, tone mapping, volume visualization, and lighting design.
Introduction of novel dispatching rules for grid scheduling algorithms

Grid scheduling problems are dynamic as the jobs and resources in the system vary overtime. The Rule-based scheduling algorithms are a new trend in grid scheduling which are applicable in dynamic grid environments; The arriving jobs waiting for execution is ordered according to a certain rule and they are dispatched for processing according to that order. Grid scheduling algorithms usually involve more than one objective function. In particular, it is not enough to minimize the Maximum completion time (Makespan) objective function without taking into consideration the lateness of the jobs (Tardiness). In this paper two novel dispatching rules are proposed that aims to minimize the maximum completion time of jobs (Makespan) and at the same time reduce the average lateness of these jobs (Tardiness). Furthermore, the proposed dispatching rules are combined with a local Tabu search algorithm that can be programmed to further optimize various grid scheduling objective functions. A complete set of comparison between the various combinations of techniques is presented based on simulation results obtained using Grid Sim.
Secrecy capacity in large cooperative networks in presence of eavesdroppers with unknown locations

In this paper, an extended large wireless network, with a single transmitter-receiver pair, under the secrecy constraint is considered. In contrast to works which use idealized assumptions, a more realistic network situation with unknown eavesdroppers locations is investigated: the legitimate users only know their own Channel State Information (CSI), not the eavesdroppers CSI. Also, the network is analyzed by taking in to account the effects of both fading and path loss. Under these assumptions, a power efficient cooperative scheme, named stochastic virtual beamforming, is proposed. Applying this scheme, any desired pair of secure rate and outage level denoted by (Rs, ε) will be achievable given that the density of the legitimate users tends to infinity. Although, the derived constraints limit the density of eavesdroppers for any fixed density of legitimate users, but by tending the legitimate users density to the infinity, the tolerable density of eavesdroppers will become unbounded too.
Hadoop-Based Genome Comparisons

Due to the ever increasing amounts of data in application areas relevant both for business and research, the requirements for data handling have increased significantly over the last years, often exceeding the capabilities of standard software, which has been used in specific application areas. To face this challenges software needs to be adapted or rewritten to integrate novel big data handling techniques. This paper focuses on the implementation of a genome sequence comparison application from the domain of bioinformatics running on top of Hadoop while relying on HBase for data management and MapReduce jobs for computation.
Analysis of multidomain features for ECG classification

In this work we studied the classification performance of models based on intervals, angles and amplitudes. These features were extracted from both ECG leads and different scales of the wavelet decomposition. The MIT-BIH Arrhythmia database was used, following AAMI recommendations for class labeling and results presentation. The training and testing set and any cross-validation division of the database was made patient-oriented. A floating feature selection algorithm was used to obtain best performing models in the training set. This model was evaluated in the test set obtaining a global accuracy of 90%; for normal beats, sensitivity (Se) 92%, positive predictive value (+P) 85%; for supraventricular beats, Se 88%, +P 93%; for ventricular beats Se 90%, +P 92%. This classifier model based on multidomain features performs better than other state of the art methods, with a fraction of the features.
Poisson and Gaussian Approximations for Multi-Server Queues with Batch Arrivals and Batch Abandonment

In this paper, we consider nonstationary multi-server queues and multiserver loss queues with batch arrivals and where we allow customer abandonment to also occur as a Markovian batch process. To estimate the performance of the system, we develop two new approximations that take advantage of the functional Kolmogorov forward equations. The first approximation we develop uses the Poisson distribution, which is motivated from the infinite server queue offered load process. The second approximation uses a Gaussian distribution, which is motivated by the many server heavy traffic limit theorems of Mandelbaum et al [13]. We compare our results with Monte Carlo simulation and show that our approximations provide accurate estimates of the mean, variance, probability of delay, and the probabilty of blocking for the queueing process.
A New Colorimetrically-Calibrated Automated Video-Imaging Protocol for Day-Night Fish Counting at the OBSEA Coastal Cabled Observatory

Field measurements of the swimming activity rhythms of fishes are scant due to the difficulty of counting individuals at a high frequency over a long period of time. Cabled observatory video monitoring allows such a sampling at a high frequency over unlimited periods of time. Unfortunately, automation for the extraction of biological information (i.e., animals' visual counts per unit of time) is still a major bottleneck. In this study, we describe a new automated video-imaging protocol for the 24-h continuous counting of fishes in colorimetrically calibrated time-lapse photographic outputs, taken by a shallow water (20 m depth) cabled video-platform, the OBSEA. The spectral reflectance value for each patch was measured between 400 to 700 nm and then converted into standard RGB, used as a reference for all subsequent calibrations. All the images were acquired within a standardized Region Of Interest (ROI), represented by a 2 × 2 m methacrylate panel, endowed with a 9-colour calibration chart, and calibrated using the recently implemented “3D Thin-Plate Spline” warping approach in order to numerically define color by its coordinates in n-dimensional space. That operation was repeated on a subset of images, 500 images as a training set, manually selected since acquired under optimum visibility conditions. All images plus those for the training set were ordered together through Principal Component Analysis allowing the selection of 614 images (67.6%) out of 908 as a total corresponding to 18 days (at 30 min frequency). The Roberts operator (used in image processing and computer vision for edge detection) was used to highlights regions of high spatial colour gradient corresponding to fishes' bodies. Time series in manual and visual counts were compared together for efficiency evaluation. Periodogram and waveform analysis outputs provided very similar results, although quantified parameters in relation to the strength of respective rhythms were different. Results indicate that automation efficiency is limited by optimum visibility conditions. Data sets from manual counting present the larger day-night fluctuations in comparison to those derived from automation. This comparison indicates that the automation protocol subestimate fish numbers but it is anyway suitable for the study of community activity rhythms.
Visualization and Image Processing of Aircraft Shock Wave Structures

A schlieren imaging system which uses the sun as a light source was developed to obtain direct flow-field images of shock waves of aircraft in flight. This system was used to study how shock waves evolve to form sonic booms. The image quality obtained was limited by several optical and mechanical factors. Converting the photographs to digital images, and applying digital image processing techniques greatly improved the final quality of the images, and more clearly showed the shock structures.
Identification of Nutrition Elements in Orange Leaves by Laser Induced Breakdown Spectroscopy

Laser induced breakdown spectroscopy (LIBS) has been used for the determination of the species and concentration of elements in industries. However, there is less research for LIBS analysis of agricultural and environmental samples. In this work, a LIBS system has been evaluated for the determination of nutrition elements in orange leaves. The experimental setup was designed by using a Nd:YAG laser operating at 1064 nm (200 mJ per pulse and 5HZ repetition rate) and an Avantes spectrometer (200-1100nm wavelength range)with ICCD detector. The species and relative contents were identified by LIBS spectra, which belonged to qualitative analysis. The quantitive analysis will be carried combining with routine analysis such as ICP-OES.
Leakage current suppression in virtual DC bus inverter using improved auxiliary circuit

Transformerless photovoltaic inverter is characterized by low loss and high efficiency features. But the topology is burdened with leakage current generation problem. In order to minimize the problem, virtual DC bus concept inverter could be a good solution which is proposed in the literature. But some problem still exists with this inverter. Although it suppresses the leakage current through PV panel to ground capacitor but it cannot eliminate the leakage current through one of the inverter phase to ground capacitor. A very conventional LCL filter is used in previous literature to solve the problem but they all offers substantial amount of leakage current in the system. In order to minimize this problem, we have proposed different designed auxiliary circuit for virtual DC bus inverter. The proposed circuit can suppress the leakage current comprehensively and improve the output voltage quality of the inverter. To verify the advantage of the proposed circuit, simulations are carried out which demonstrates a very promising result.
On the Best Rank-1 and Rank-(

In this paper we discuss a multilinear generalization of the best rank-R approximation problem for matrices, namely, the approximation of a given higher-order tensor, in an optimal leastsquares sense, by a tensor that has prespecified column rank value, row rank value, etc. For matrices, the solution is conceptually obtained by truncation of the singular value decomposition (SVD); however, this approach does not have a straightforward multilinear counterpart. We discuss higherorder generalizations of the power method and the orthogonal iteration method.
ABACUS: A novel array multiplier-accumulator architecture for low energy applications

Design and verification of a novel array multiplier-accumulator architecture, named ABACUS, is introduced in this paper. The design priority in this architecture is low energy operation instead of the traditional ‘performance-first’ approach. ABACUS uses a threshold function to implement multiple fast carry operations in parallel through a cellular array, and therefore significantly deviates from the conventional approaches based on half/full adder or counter building blocks. The ABACUS architecture was formally verified for correct functionality of any unsigned m × n multiplication. Hardware implementation has been validated on FPGAs. The energy-delay advantages were architecturally analyzed over the traditional carry-save array, and circuit implementation focus areas have been accordingly developed.
υ-structured support vector machines

This paper considers a υ-structured support vector machine (υ-SSVM) which is a structured support vector machine (SSVM) incorporating an intuitive balance parameter υ. In the absence of the parameter υ, cumbersome validation would be required in choosing the balance parameter. We theoretically prove that the parameter υ asymptotically converges to both the empirical risk of margin errors and the empirical risk of support vectors. The stochastic subgradient descent is used to solve the optimization problem of the υ-SSVM in the primal domain, since it is simple, memory efficient, and fast to converge. We verify the properties of the υ-SSVM experimentally in the task of sequential labeling handwritten characters.
Visual feature tracking based on PHD filter for vehicle detection

Vehicle detection is one of the classical application among the Advance Driver Assistance Systems (ADAS). Applications like emergency braking or adaptive cruise control (ACC) require accurate and reliable vehicle detection. In latest years the improvements in vision detection have lead to the introduction of computer vision to detect vehicles by means of these more economical sensors, with high reliability. In the present paper, a novel algorithm for vehicle detection and tracking based on a probability hypothesis density (PHD) filter is presented. The first detection is based on a fast machine learning algorithm (Adaboost) and Haar-Like features. Later, the tracking is performed, by means features detected within the bounding box provided by the vehicle detection. The features, are tracked by a PHD filter. The results of the features being tracked are combined together in the last step, based on several different methods. Test provided show the performance of the PHD filter in public sequences using the different methods proposed.
Migrate or not? exploiting dynamic task migration in mobile cloud computing systems

Contemporary mobile devices generate heavy loads of computationally intensive tasks, which cannot be executed locally due to the limited processing and energy capabilities of each device. Cloud facilities enable mobile devices-clients to offload their tasks to remote cloud servers, giving birth to Mobile Cloud Computing (MCC). The challenge for the cloud is to minimize the task execution and data transfer time to the user, whose location changes due to mobility. However, providing quality of service guarantees is particularly challenging in the dynamic MCC environment, due to the time-varying bandwidth of the access links, the ever changing available processing capacity at each server and the timevarying data volume of each virtual machine. In this article, we advocate the need for novel cloud architectures and migration mechanisms that effectively bring the computing power of the cloud closer to the mobile user. We consider a cloud computing architecture that consists of a back-end cloud and a local cloud, which is attached to wireless access infrastructure (e.g. LTE base stations). We outline different classes of task migration policies, spanning fully uncoordinated ones, in which each user or server autonomously makes its migration decisions, up to the cloud-wide migration strategy of a cloud provider. We conclude with a discussion of open research problems in the area.
A novel particle swarm optimization for image fusion

Spatial resolutions of IKONOS high-resolution panchromatic (PAN) and low-resolution multispectral (MS) satellite images are 1 m and 4 m, respectively. Image fusion method based on particle swarm optimization is developed for color distortion and blocking artifacts in fused images. A novel particle spatially registered multi-focus images. In order to get the optimal image, the size of block is defined as particle. Two evaluation criteria such as information entropy and cross entropy are used on the analysis and effect evaluation of different fused images. The performance of this method is better than the performance of the existed fusion method. It can eliminate the blocking artifacts of the fusion image and save the time of fusion. The experiment results show that the method is an effective method.
An actively switched pulsed induction accelerator

A coaxial accelerator which will launch a 45 mm diameter, 225 g-mass to 2000 m/s is described. The launcher is a true induction device, as no current feed to the armature is provided. The armature is a multiturn design, which forces a uniform current density and prevents excessive heating at the rear of the armature. To meet the 450 kJ launch goal, the accelerator is composed of 47 separate stages. Each stage has its own capacitor power supply, which is discharged on the arrival of the armature. The system uses a sense and switch approach to ensure correct timing of the power supply discharges. In-bore armature position is detected using fiber optics; the necessary signals are fed into a programmable controller, which determines the velocity. Using the calculation, the controller determines the ideal time to fire the next stage and initiates the discharge at the appointed time. The accelerator described is roughly 38% efficient (kinetic energy/stored energy) with a bore diameter of 45 mm. Simulations indicate efficiencies over 55% are possible with al 60 mm bore launcher and continue to increase with larger bore sizes. The launcher and armature designs, power supply, and controls are discussed. Predicted performance of a five-stage launcher currently being built is presented. Experimental results from single-stage tests are presented and compared to simulated results. Solid (monolithic) and multiturn (wound) armature tests are also described. >
eMonitoring for eHealth: Research Projects for Assisted Living

Education and research exist hand-in-hand, and todays' students are likely to rely heavily on tomorrows' technologies for assisted living when they reach the later stages of their lives. That makes them primary stakeholders in terms of current eHealth research, but below PhD level student project time is limited. Therefore, involving them to a greater extent in their own futures requires care in considering how smaller projects can be created that facilitate progression of overarching eHealth work. Therefore, this paper considers the future need for eHealth monitoring and describes some projects that, while allowing independent learning over short timescales, can add significant value to related academic research.
Graphics-Processor-Unit-Based Parallelization of Optimized Baseline Wander Filtering Algorithms for Long-Term Electrocardiography

Long-term electrocardiogram (ECG) often suffers from relevant noise. Baseline wander in particular is pronounced in ECG recordings using dry or esophageal electrodes, which are dedicated for prolonged registration. While analog high-pass filters introduce phase distortions, reliable offline filtering of the baseline wander implies a computational burden that has to be put in relation to the increase in signal-to-baseline ratio (SBR). Here, we present a graphics processor unit (GPU)-based parallelization method to speed up offline baseline wander filter algorithms, namely the wavelet, finite, and infinite impulse response, moving mean, and moving median filter. Individual filter parameters were optimized with respect to the SBR increase based on ECGs from the Physionet database superimposed to autoregressive modeled, real baseline wander. A Monte-Carlo simulation showed that for low input SBR the moving median filter outperforms any other method but negatively affects ECG wave detection. In contrast, the infinite impulse response filter is preferred in case of high input SBR. However, the parallelized wavelet filter is processed 500 and four times faster than these two algorithms on the GPU, respectively, and offers superior baseline wander suppression in low SBR situations. Using a signal segment of 64 mega samples that is filtered as entire unit, wavelet filtering of a seven-day high-resolution ECG is computed within less than 3 s. Taking the high filtering speed into account, the GPU wavelet filter is the most efficient method to remove baseline wander present in long-term ECGs, with which computational burden can be strongly reduced.