nils-herrmann/clean_scirepeval huggingface dataset:
Biological microdevice with fluidic acoustic streaming for measuring uric acid in human saliva

The healthcare system requires new devices for a rapid monitoring of a patient in order to improve the diagnosis and treatment of various diseases. Accordingly, new biomedical devices are being developed. In this paper, a fully-integrated biological microdevice for uric acid analysis in human saliva is presented. It is based on optical spectrophotometric measurements and incorporates a mixture system based on acoustic streaming, that enhances the fluids reaction due to both heating and agitation generated by this effect. Acoustic streaming is provided by a piezoelectric β-PVDF film deposited underneath the microfluidic die of the device. Further, it incorporates the electronics for the detection, readout, data processing and signal actuation. Experimental results proved that acoustic streaming based on this piezoelectric polymer is advantageous and reduces in 55% the time required to obtain the analysis results.
Design and implementation of a safety communication network in railways with intelligent fault diagnosis

This paper presents a network that connects various safety sensors located on level crossings and in stations. These sensors are used to detect obstacles on the railway line and proximity between trains. The information is centralised in the operations and control centre(OCC). The network has been designed in sections, each of which consists of a dual bus structure, with the particular feature that if one of the buses fails, the packets are routed to the other. Fault detection on the network is performed using intelligent diagnostic techniques, applying the IEEE 1232-2002 standard. By examining the result of the diagnosis, it is possible to ascertain the optimal route from each sensor to the OCC. Monitoring is performed using active network techniques. The diagnostic system sends packets containing code that is executed at each node.
Using co-design techniques to increase the reliability of the Electronic control system for a Multilevel Power Converter

This paper explain the application of hardware-software co-design methodology to the distribution of the controller task between the processors of a electronic control systems for a multilevel power converter, in order to increase the controller reliability. This framework operates with a component library that provides multiple alternatives for a given task, each of which is potentially different from the others in terms of reliability, performance, and area metrics. The experimental evaluation demonstrates the importance of including co-design techniques to achieve better system reliability
Refactoring Meets Model-Driven Spreadsheet Evolution

Software refactoring is a well-known technique that provides transformations on software artifacts with the aim of improving their overall quality. In this paper we present a set of refactoring for Class Sheets, a modelling language that allows to specify the business logic of a spreadsheet in an object-oriented fashion. The set of refactoring that we propose allows us to improve the quality of these spreadsheet models. Moreover, it is implemented in a setting that guarantees that all model refactoring are automatically carried to all the corresponding (spreadsheet) instances, thus providing an automatic evolution of the data so it is always synchronized with the model.
Quadratic gain converter with output voltage ripple mitigation

This paper introduces a DC-DC converter topology with the following features: (i) quadratic voltage gain, which allows the converter to work with wide voltage line variations in a wide operation voltage range with a minimal variation of duty cycle and without the use of extreme duty cycles; (ii) output voltage ripple mitigation, this feature can be accomplished in a specific duty cycle by proper selection of components and PWM signals. The proposed converter can be designed to have zero output voltage ripple for the nominal gain (duty cycle), this allows the use of small values of capacitance. The operating principles and steady-state equations, for the discussed topology in CCM are presented in detail. Experimental tests are presented to validate the propositions.
An immersive Virtual Reality interface for Civil Engineering dissemination amongst pre-university students

Game-based interfaces and Virtual Reality (VR) environments have shown encouraging results regarding Engineering and educational applications. In the Architecture, Engineering and Construction (AEC) sector, several developments have been made regarding the application of games and VR experiences. Simultaneously, Engineering Education is gradually adapting new practises for improvising transferability of knowledge amongst students, which also entail gaming and VR-based applications. This paper includes a description of an immersive VR interface developed under the scope of an ongoing educational programme. The developed VR interface allows participants to explore a 3D model of one of the campus' buildings, simultaneously discovering the different disciplines of Civil Engineering.
Bayesian Constrained Local Models Revisited

This paper presents a novel Bayesian formulation for aligning faces in unseen images. Our approach revisits the Constrained Local Models (CLM) formulation where an ensemble of local feature detectors are constrained to lie within the subspace spanned by a Point Distribution Model (PDM). Fitting such a model to an image typically involves two main steps: a local search using a detector, obtaining response maps for each landmark (likelihood term) and a global optimization that finds the PDM parameters that jointly maximize all the detections at once. The so-called global optimization can be posed as a Bayesian inference problem, where the posterior distribution of the shape (and pose) parameters can be inferred in a maximum a posteriori (MAP) sense. This work introduces an extended Bayesian global optimization strategy that includes two novel additions: (1) to perform second order updates of the PDM parameters (accounting for their covariance) and (2) to model the underlying dynamics of the shape variations, encoded in the prior term, by using recursive Bayesian estimation. Extensive evaluations were performed against state-of-the-art methods on several standard datasets (IMM, BioID, XM2VTS, LFW and FGNET Talking Face). Results show that the proposed approach significantly increases the fitting performance.
On the Role of Interpolation in the Normalization of Non-ideal Visible Wavelength Iris Images

The growth in practical applications for iris biometrics has been accompanied by relevant developments in the underlying algorithms and techniques. Along with the research focused on near-infrared (NIR) cooperatively captured images, efforts are being made to minimize the trade-off between the quality of the captured data and the recognition accuracy on less constrained environments, where images are obtained at the visible wavelength, at increased distances, over simplified protocols and adverse lightning. This paper addresses the effect of the interpolation method, used in the iris normalization stage, in the overall recognition error rates. This effect is stressed for systems operating under less constrained image acquisition setups and protocols, due to higher variations in the amounts of captured data. Our experiments led us to conclude that the utility of the image interpolating methods is directly corresponding to the levels of noise that images contain.
High-Speed Tracking with Kernelized Correlation Filters

The core component of most modern trackers is a discriminative classifier, tasked with distinguishing between the target and the surrounding environment. To cope with natural image changes, this classifier is typically trained with translated and scaled sample patches. Such sets of samples are riddled with redundancies—any overlapping pixels are constrained to be the same. Based on this simple observation, we propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude. Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers. For kernel regression, however, we derive a new kernelized correlation filter (KCF), that unlike other kernel algorithms has the exact same complexity as its linear counterpart. Building on it, we also propose a fast multi-channel extension of linear correlation filters, via a linear kernel, which we call dual correlation filter (DCF). Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source.
Improving OpenID Connect Federation’s Interoperability with Web Semantics

Companies adhere to federate environments in order to add value to their businesses by aggregating third parties services. Cloud computing comes in hand in this process speeding up the development and deployment of new service providers (SPs) and identity providers (IdPs) to be used into a federated environment. However, IdPs and SPs of OpenID Connect federations have a strong dependency on each other, they have to use the very same attribute names in order to exchange data. Our proposal lowers the dependency between IdPs and SPs using web semantics; instead of an agreement at a lower level such as attributes names, we propose that IdPs and SPs establish an agreement on the ontology to be used. Therefore, the name of attributes would not matter, but the semantic concept that attributes are associated with. Further, our proposal was added and tested into the OpenId Connect Implementation called MITREid Connect.
How to build a timely computing base using real-time Linux

We recently introduced a new model to deal with the problem of handling application timeliness requirements in environments with loose real-time guarantees. This model, called the timely computing base (TCB), is partially synchronised. From an engineering point of view it requires systems to be constructed with a small control part, a TCB module, to protect vital resources with respect to timeliness and to provide basic time related services to applications. Although many different instantiations of systems with a TCB can be envisaged we have chosen to implement a TCB using PC hardware running the real-time Linux operating system over a fast Ethernet network. The paper describes the experience gained during the implementation process and shows that it is possible to construct a TCB without the need for special software or hardware components. The problem of achieving real-time communication under RT-Linux is also discussed: we describe the porting of a Linux network driver to RT-Linux, explaining the required modifications to allow predictability.
Use of functional principal components analysis in CW subTHz spectroscopy for hydrocarbon emulsified water assessment

Functional Principal Component Analysis is applied to study the variability of spectra from a set of samples constituted by different types of hydrocarbons with low levels of water contents. The spectra were measured with a spectrometer working in the (sub-)terahertz band from 75 GHz to 110 GHz. The results of our first assessments indicate that the functional principal components allow for simultaneous discrimination between different types of hydrocarbons and emulsified water.
A novel two switches based DC-DC multilevel voltage multiplier

This paper proposes a novel DC-DC converter: the DC-DC multilevel voltage multiplier (MVM). An Nx MVM can be built with two switches, 2N-2 diodes and 2N-2 capacitors, free of magnetic components. It is based on the multilevel converters principle and designed for unidirectional power transfer applications. Each device blocks one voltage level, then high voltage converters can be synthesized with low voltage devices. The main advantage of this topology is the reduced number of transistors and gate drives. Possible applications have a wide range from low power silicon-based voltage multipliers, implemented inside microchips, to medium power level DC links for multilevel inverters based distributed generation (DG) systems, where a capacitorpsilas voltage balancing is a challenge for more than three levels. Experimental results prove the principle of such converters.
A methodology for optimizing the FPGA implementation of industrial control systems

When used for concurrent hardware implementation, field-programmable gate arrays (FPGAs) allows the use of numerous operational units such as multipliers and adders in order to implement control systems. However, when short sampling periods are utilized, scheduling with resource constraints and sharing aimed at reducing the required resources can be necessary. This is especially true in systems where the used sampling period is greater than the computation time delay. The aim of this paper is to present a methodology based on FPGA implementation with resource constraints applied to the implementation of Second Order Generalized Integrators (SOGIs) used in power converters. A systematic way of converting a Simulink specification into VHDL code is described which mainly involves customized fixed-point hardware definition, Data Flow Graph (DFG) extraction, Algorithm State Machine with Data path (ASMD) representation and VHDL specification of the system, inter alia.
Fabrication and Characterization of High-Performance Polymer-Based Magnetoelectric DC Magnetic Field Sensors Devices

The development of a DC magnetic field sensor based on a magnetoelectric (ME) PVDF/Metglas composite is reported. The ME sensing composite has an electromechanical resonance frequency close to 25.4 kHz, a linear response (r<sup>2 </sup> = 0.997) in the 0–2 Oe DC magnetic field range, and a maximum output voltage of 112 mV (ME voltage coefficient α<sub>33</sub> of ≈30 V·cm<sup>−1</sup>·Oe<sup>−1</sup>). By incorporating a charge amplifier, an AC-RMS converter and a microcontroller with an on-chip analog-to-digital converter, the ME voltage response is not distorted, the linearity is maintained, and the ME output voltage increases to 3.3 V (α<sub>33effective</sub> = 1000 V·cm<sup>−1</sup>·Oe<sup>−1</sup>). The sensing device, including the readout electronics, has a maximum drift of 0.12 Oe with an average total drift of 0.04 Oe, with a sensitivity of 1.5 V·Oe<sup>−1</sup> (15 kV·T<sup>−1</sup>), and a 70 nT resolution. This feature is for the first time reported on a polymer-based ME device and compares favourably with a reference Hall sensor that showed a maximum drift of 0.07 Oe and an average error of 0.16 Oe, 5 V·T<sup> −1</sup> sensitivity, and 2 μT resolution. Such properties allied to the accurate measurement of the DC magnetic field (H<sub>DC</sub>) in the 0–2 Oe range make this polymer-based device very attractive for applications, such as Earth magnetic field sensing, digital compasses, navigation, and magnetic field anomaly detectors, among others.
Probabilistic midterm transmission planning in a liberalized market

This paper shows a midterm transmission planning methodology for liberalized electricity markets. This methodology evaluates expansions and reinforcements using a transmission adequacy linear programming model. This type of modeling solves efficiently, taking into account power exchange deviations, n-1 network preventive adequacy level, and nonsupply demand. Statistical results are obtained sampling power exchange scenarios and computing transmission investment sensitivities. After each sample of generation and consumption bidding and generator and circuit failures, means, ranges, and confidence intervals of transmission investment sensitivities are updated. These sensitivities are computed using dual variables and reduced costs of the transmission adequacy model. This statistical sensitivity information and additional information are evaluated jointly using multicriteria decision theory. An extended Garver's six-bus and the Spanish system cases are analyzed.
Configuration Interface for Industrial Wireless Sensor Networks

Wireless sensor networks (WSNs) are deployed to sense, monitor and act on the environment. Some applications of these networks, especially in industrial sense and react scenarios, require high performance and guaranties. Our work is focused on building a system that allows configuring/reconfiguring alarms, actions or closed-loop techniques in the context of GINSENG project -- wireless sensor networks with performance control guarantees. We propose an approach for interaction with real-world devices through a web services interface, allowing users to configure and apply various operations, including complex closed-loop techniques that monitor and act over any actuator in the WSN. To allow the interaction between a client application and the motes we implemented an API to access services of the motes.
Development of virtual reality game-based interfaces for civil engineering education

Virtual Reality (VR) is gradually becoming a part of the life of the average person as hard-ware prices become more affordable for the end user. A growing variety of hardware allows users to engage in a series of immersive experiences for ludic, educational or even professional activities. In this regard, the diversity of affordable solutions could provide an alternative to highly immersive, but expensive environments such as, CAVE experiences . The development of VR technologies is starting to spread its influence to the AECO (Architecture, Engineering, Construction and Operations) sector through the creation of new work methodologies and techniques, as well as original interfaces for communication. Creating new tools for the discussion of topics and providing collaborative work among participants with different backgrounds provides opportunities not only for the industry, but also for applications in education. The development of VR applications and its outcomes, regarding the acceptance (or lack of it) from the students will be the focus of this paper. The description of three case studies will be fully detailed, providing data analysis from each one of the tests conducted.
Absolute-Frequency high-resolution real-time terahertz dual-comb spectrometer

An absolute-frequency Terahertz dual frequency comb spectrometer for tunable, high-resolution and real-time rapid acquisition is presented. Two combs (probe and local oscillator) are electro-optically generated from the same line of a master optical frequency comb selected by optical injection locking. Photomixing each of these two initial optical combs with a coherent mode from the same master comb generates a narrow-linewidth Terahertz dual comb through difference frequency generation. The resulting dual-comb teeth are absolutely referenced to a RF clock standard.
Algorithmic strategies for optimizing the parallel reduction primitive in CUDA

Many general-purpose applications exploit Graphics Processing Units (GPUs) by executing a set of well-known dataparallel primitives. Those primitives are usually invoked from the host many times, so their throughput has a great impact on the performance of the overall system. Thus, the study of novel algorithmic strategies to optimize their implementation on current devices is an interesting topic to the GPU community. In this paper we focus on optimizing the reduction primitive, which merely reduces a data sequence into a single value using a binary associative operator. Although tree-based and sequential-based algorithms have been already implemented on GPUs, a comparison of both algorithm performance had not been carried out yet. Thus, our first contribution is to present an experimental study of state-of-the-art reduction algorithms on CUDA. Next we introduce two algorithmic optimizations that are integrated into the fastest solution (a sequential-based algorithm), improving its throughput even more. Finally, we replicate this methodology to the segmented version of the primitive, which applies when the input is composed of several independent segments. In this case, it is not clear which algorithm exhibits the best performance, since throughput deeply depends on the distribution of segments along the input. According to our results, tree-based algorithms run faster for small segments, while sequential methods are better for medium and large ones.
Web application for the training of the correct pronunciation of words in Portuguese for people with speech and language disorders — preliminary usability study

In this paper, a Web application that allows people with speech impairments to train and amend pronunciation of words by repeating words presented in a interface with visual and auditory stimuli, is presented. With this app, we aim at present a training assistive tool to be used by the user, in a daily situation, where the main asset is the feedback about the rate of correct pronunciation, thus allowing users to train, autonomously, at home. Additionally, we present a preliminary user study to assess usability of the proposed application. For that, we invited a group of people with speech impairments to perform training tasks with the application. In the assessment, we register the following parameters: effectiveness (successful conclusion of the training tasks and percentage of pronouncing success); efficiency (number of errors made, number and type of difficulties found); and, satisfaction (the acceptance and comfort to complete the training tasks). Overall, the preliminary results showed that participants achieved an efficiency rate of 53.4% in the accomplishment of the proposed tasks. On average, for each image, participants missed at least 2 times, where the most registered difficulty was the pronunciation of words with accents. Regarding satisfaction, we noticed that they like it as they ask not only to repeat the test but also the addition of more pictures to perform more tests with the proposed web application.
State machine model-based middleware for control and processing in industrial wireless sensor and actuator networks

The advancements in communications and embedded systems have led to the proliferation of wireless sensor and actuator networks (WSANs) in a wide variety of application domains. One important key of many such WSAN applications is how to configure/program functionalities after deployment. Some application domains even require that sensor nodes be deployed in harsh environments (e.g., refineries), where they need to be configured over-the-air. Over-the-air programming takes more than 4 second to transmit an image code to a node where, typically, a single image has 40kB. With the proposed model, small code blocks (states and transitions) are generated (about 4kB) to upload to nodes. This methodology allows reducing the programming time to 5 times less, release bandwidth and keep battery on nodes. To support our approach we propose an architecture based on state machines model which provides an easy user-readable high-level representation of states and transitions. The communication time is reduced through the reduction of the code image size that it is needed to send to each node. We develop an architecture that allows users to define and program applications based on Markov techniques, to simultaneously facilitate the application design and take into account important requirements such as reliability. The experimental section shows a working deployment of this concept in an industrial refinery setting.