Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Considerations of Efficiency Enhanced LLC PEV Charger Using Reconfigurable Transformer

LLC topology demonstrates optimal efficiency near its resonant frequency, where its normalized voltage gain is close to unity. However, in plug-in electric vehicle (PEV) charging applications, the voltage gain of the LLC converter needs to vary in a wide range. This makes the switching frequency deviate away from the resonant frequency. In this paper, an improved LLC-based PEV charger is proposed to cope with this challenge. The proposed converter adopts a reconfigurable two-level turns-ratio transformer. With the increase of output voltage, the transformer secondary side reconfigures from low turns to high turns. Thus, the switching frequency range can be effectively squeezed around the resonant frequency in constant current charging stage. In constant voltage charging stage, the switching frequency of the LLC converter is exactly tuned to the resonant frequency. Therefore, the overall efficiency can be enhanced over the entire charging process. The design considerations of the proposed PEV charger are addressed in detail. A 1.35-kW prototype to charge a 250–450 V battery pack is designed and tested both in steady state and dynamically. The prototype demonstrates 97.47% peak efficiency and enhanced efficiency performance over the wide output range.
chronVAL/chronSIM: A Tool Suite for Timing Verification of Auto-motive Applications

Automotive software systems become more and more complex presenting tougher safety requirements and tighter timing constraints. On the other hand, today, timing verification of automotive systems is considered late in the development process, mainly at the implementation stage. Consequently, this leads generally to late detection of design mistakes involving extra costs. In this paper, we present a tool suite dedicated for timing analysis of automotive systems, chronSIM/chronVAL. We propose to evaluate the capabilities of this tool suite against the timing analysis needs of automotive applications.
Multiclassifier for severity‐level categorization of glioma tumors using multimodal magnetic resonance imaging brain images

Brain tumor segmentation and classification is a crucial challenge in diagnosing, planning, and treating brain tumors. This article proposes an automatic method that categorizes the severity level of the tumors to render an effective diagnosis. The proposed fractional Jaya optimizer‐deep convolutional neural network undergoes the severity classification based on the features obtained from the segments of the magnetic resonance imaging (MRI) images. The segments are obtained using the particle swarm optimization that ensures the optimal selection of the segments from the MRI image and yields the core tumor and the edema tumor regions. The experimentation using the BRATS database reveals that the proposed method acquired a maximal accuracy, specificity, and sensitivity of 0.9414, 0.9429, and 0.9708, respectively.
3D Object Pose Estimation Using Multi-Objective Quaternion Learning

In this paper, a framework is proposed for object recognition and pose estimation from color images using convolutional neural networks (CNNs). 3D object pose estimation along with object recognition has numerous applications, such as robot positioning versus a target object and robotic object grasping. Previous methods addressing this problem relied on both color and depth (RGB-D) images to learn low-dimensional viewpoint descriptors for object pose retrieval. In the proposed method, a novel quaternion-based multi-objective loss function is used, which combines manifold learning and regression to learn 3D pose descriptors and direct 3D object pose estimation, using only color (RGB) images. The 3D object pose can then be obtained either by using the learned descriptors in the nearest neighbor (NN) search or by direct neural network regression. An extensive experimental evaluation has proven that such descriptors provide greater pose estimation accuracy than the state-of-the-art methods. In addition, the learned 3D pose descriptors are almost object-independent and, thus, generalizable to unseen objects. Finally, when the object identity is not of interest, the 3D object pose can be regressed directly from the network, by overriding the NN search, thus significantly reducing the object pose inference time.
Exploring influencing factors of technology use for active and healthy ageing support in older adults

ABSTRACT Aim of this study is to investigate the influence of technological and social cognitive factors for the use of sensor-based technologies for active and healthy ageing (AHA) support by older adults. In a mixed methods approach, data was initially obtained from an online questionnaire completed by older health technology users and used in a regression analysis, where factors from the Technology Acceptance Model (TAM) and the Social Cognitive Theory (SCT) served as predictors for health technology use (HTU). Further, in-depth interviews were conducted with older adults to gain insights into technology use and physical activity behaviour of older adults. The regression analysis showed that the TAM and SCT factors accounted for a significant proportion of variance (39.5%) in HTU. Significant predictors of HTU were physical activity (.399**), social support (.287*), and expectations regarding individual health (.440*) and physical appearance (−.470**), indicating physical activity as mediator for HTU. The qualitative analysis indicated the conflation of technology support with social environments as key for physical activity behaviour in older adults. The findings indicate physical activity as a mediator in HTU by older adults and suggest that the consideration of social factors in health technology design may facilitate the uptake of AHA technologies.
HIERARCHICAL SYSTEM FOR EVALUATING TESTS IN AN E-LEARNING SYSTEM USING AN EXPERT SYSTEM

The article deals with a hierarchical system and an approach for evaluating tests in e-learning systems using an expert system. Tests in e-learning system can be created in various modifications. In the proposed approach, the test in e-learning system is divided into categories of questions which represent the set of related questions. Then, we identify the criteria to evaluate questions and categories of questions. Then, we propose a hierarchical system to evaluate each question or a category of questions including continuous visualisation of the results. The hierarchical system uses an expert system for the evaluation. Based on visualised results, the creator of the test can recognise results for each question and the category of questions. The final evaluation of the e-learning test is also visualised and shown to the test creator. In the paper, individual steps of the proposed hierarchical systems are described. The proposed expert system is experimentally verified on test results of several students.
Adaptive Spatial Modulation MIMO Based on Machine Learning

In this paper, we propose a novel framework of low-cost link adaptation for spatial modulation multiple-input multiple-output (SM-MIMO) systems-based upon the machine learning paradigm. Specifically, we first convert the problems of transmit antenna selection (TAS) and power allocation (PA) in SM-MIMO to ones-based upon data-driven prediction rather than conventional optimization-driven decisions. Then, supervised-learning classifiers (SLC), such as the $K$ -nearest neighbors (KNN) and support vector machine (SVM) algorithms, are developed to obtain their statistically-consistent solutions. Moreover, for further comparison we integrate deep neural networks (DNN) with these adaptive SM-MIMO schemes, and propose a novel DNN-based multi-label classifier for TAS and PA parameter evaluation. Furthermore, we investigate the design of feature vectors for the SLC and DNN approaches and propose a novel feature vector generator to match the specific transmission mode of SM. As a further advance, our proposed approaches are extended to other adaptive index modulation (IM) schemes, e.g., adaptive modulation (AM) aided orthogonal frequency division multiplexing with IM (OFDM-IM). Our simulation results show that the SLC and DNN-based adaptive SM-MIMO systems outperform many conventional optimization-driven designs and are capable of achieving a near-optimal performance with a significantly lower complexity.
Analysis of Genetic Algorithm for synthesis digital systems modeled in finite state machine

— To achieve smaller digital systems, like microprocessors, controller, etc., it is require design them with a small area and the treats the power dissipation. These issues are important because can prolong the time of use of the equipment and reduce the manufacturing costs. To do so, digital circuits can be modeled as finite state machines with a large amount of states for most practical problems. To achieve a minimum result, you need to optimize a state assignment. Find a solution that meets these characteristics, i
Organic Selection and Social Heredity: The Original Baldwin Effect Revisited

The so-called “Baldwin Effect” has been studied for years in the fields of Artificial Life, Cognitive Science, and Evolutionary Theory across disciplines. This idea is often conflated with genetic assimilation, and has raised controversy in trans-disciplinary scientific discourse due to the many interpretations it has. This paper revisits the “Baldwin Effect” in Baldwin’s original spirit from a joint historical, theoretical and experimental approach. Social Heredity – the inheritance of cultural knowledge via non-genetic means in Baldwin’s term – is also taken into account. I shall argue that the Baldwin Effect can occur via social heredity without necessity for genetic assimilation, instead the Baldwin Effect can promote more plasticity to facilitate future intelligence when the fidelity of social heredity is high. Computational experiments are then carried out to support the hypothesis of interest. The role of mind and intelligence in evolution and its implications in an extended synthesis of evolution are briefly discussed.
Snow cover as a morphogenic agent determining ground climate, landforms and runoff in the Valdecebollas massif, Cantabrian Mountains

Snowfalls are important meteorological events affecting the physical environment of the Cantabrian Mountains. This work analyzes the effects of snow on several elements such as relief, landforms, ground climate and snowmelt waters. The ground thermal regime and associated parameters were studied using temperature data loggers and satellite images and were described in combination with observed geomorphological processes and landforms. A geomorphological map was drawn up and trends in climate patterns and runoff were calculated. Ground temperature monitoring in warm years is not optimal, though allow to know the limit conditions for developing cold processes. Results show that geomorphological processes are not significant and that solifluction deriving from snowmelt, is the only active process in years without freeze or with thick snow cover. Snowfall evolution in recent decades in correlation with flow water and climate features provide the certainty that snow distribution also affects efficacy in runoff generation and moves the flow peak in rivers due to early snowmelt.
VAPOR: A Visualization Package Tailored to Analyze Simulation Data in Earth System Science

Visualization is an essential tool for analysis of data and communication of findings in the sciences, and the Earth System Sciences (ESS) are no exception. However, within ESS, specialized visualization requirements and data models, particularly for those data arising from numerical models, often make general purpose visualization packages difficult, if not impossible, to use effectively. This paper presents VAPOR: a domain-specific visualization package that targets the specialized needs of ESS modelers, particularly those working in research settings where highly-interactive exploratory visualization is beneficial. We specifically describe VAPOR’s ability to handle ESS simulation data from a wide variety of numerical models, as well as a multi-resolution representation that enables interactive visualization on very large data while using only commodity computing resources. We also describe VAPOR’s visualization capabilities, paying particular attention to features for geo-referenced data and advanced rendering algorithms suitable for time-varying, 3D data. Finally, we illustrate VAPOR’s utility in the study of a numerically- simulated tornado. Our results demonstrate both ease-of-use and the rich capabilities of VAPOR in such a use case.
Block Meshing TLM Based Approach for Low Frequency Antennas Characterization

Modeling and characterizing low frequency antennas in realistic environments is considered as one of the most difficult computational problems. This complexity arises mainly from the multiscale nature of low-frequency scenarios. In this article, we present a block meshing technique with TLM to accurately represent the geometry of thin-wire antennas. This enables us to accurately compute its input impedance and radiated power. Numerical simulations are presented to show the efficiency and the accuracy of the proposed approach, with comparisons with regular fine meshing and MoM.
LVCUGEN (TSP-based solution) and first porting feedback

In a context of complexification of embedded systems within satellites, CNES is an actor in the will to promote standardization and reuse from a mission to another. By definition, in the frame of satellites payloads, the flight software is mission specific and therefore developed from scratch for each instrument. This is due to the fact that most of the payload developments in the scope of scientific missions are contracted to national institutes (each time a different one), those have generally poor background in embedded real time software engineering. But even in this case, a certain number of subsets can be considered as generic and would benefit in not being redeveloped for each mission.
Distributions of Multivariate Equal-Correlated Nakagami-$m$ Fading Employing MRC

Maximal ratio combiner output distributions of multivariate equal-correlated Nakagami-<inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> fading in their single-integral form are derived in this paper. The new distributions provide better alternatives to those obtained under selection combing (SC) diversity. Cross verification is mathematically performed to validate the proposed findings, which exactly reduce to existing results under SC deployment when the diversity order is neutralized. To show the effectiveness of the new findings, ergodic channel capacity is derived. Monte Carlo simulation is shown to match theoretical predictions. The new results diversify theoretical developments under equal-correlated Nakagami-<inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> fading, and initiate new directions for further progress.
Design of Supply Chain Network Model for Perishable Products with Stochastic Demand: An Optimized Model

Supply chain network design in perishable product has become a challenging task due to its short life time, spoilage of product in degradation nature and stochastic market demand. This paper focused on designing and optimizing model for perishable product in stochastic demand, which comprises multiple levels from producer, local collector, wholesaler and retailers. The ultimate goal is to optimize availability and net profit of all members in supply chain network model for avocado fruit under stochastic demand. The network model has considered the quality deterioration rate of the product with increased order of transportation time. The validity of developed model was tested with data collected from avocado supply chain network in Ethiopian market.
The New Combined Closed-Solution for 3D Reconstruction of Environment Based on Iterative Closest Point Algorithm

This The scientific problem at solving which the present project is directed consists in the development of accurate methods for reconstruction of a three-dimensional map of the accessible of environment with requied accuracy of reconstruction. The problem of consistent aligning of 3D point data is known registration task. The most popular registration algorithm is the Iterative Closest Point algorithm. Three basic problems are characteristic for the ICP algorithm: first, the convergence of the algorithm depends strongly on the choice of the initial approximation; second, the algorithm does not take into account the local shape of the surface around each point; and, third, the search for the nearest points is of high computational complexity. In this paper a new close solutions to 3D total variation regularization will be obtained and effective algorithms for restoring 3D data will be designed. The proposed approach improves the accuracy and convergence of reconstruction methods for complex and large-scale scenes. The performance and computational complexity of the proposed RGB-D Mapping algorithm in real indoor environments is discussed. Keywords—simultaneous localization and mapping; a threedimensional map; iterative closest point algorithm; orthogonal transformations; variational problem of the point-to-point
Urinary Stone Detection on CT Images Using Deep Convolutional Neural Networks: Evaluation of Model Performance and Generalization.

Purpose To investigate the diagnostic accuracy of cascading convolutional neural network (CNN) for urinary stone detection on unenhanced CT images and to evaluate the performance of pretrained models enriched with labeled CT images across different scanners. Materials and Methods This HIPAA-compliant, institutional review board-approved, retrospective clinical study used unenhanced abdominopelvic CT scans from 535 adults suspected of having urolithiasis. The scans were obtained on two scanners (scanner 1 [hereafter S1] and scanner 2 [hereafter S2]). A radiologist reviewed clinical reports and labeled cases for determination of reference standard. Stones were present on 279 (S1, 131; S2, 148) and absent on 256 (S1, 158; S2, 98) scans. One hundred scans (50 from each scanner) were randomly reserved as the test dataset, and the rest were used for developing a cascade of two CNNs: The first CNN identified the extent of the urinary tract, and the second CNN detected presence of stone. Nine variations of models were developed through the combination of different training data sources (S1, S2, or both [hereafter SB]) with (ImageNet, GrayNet) and without (Random) pretrained CNNs. First, models were compared for generalizability at the section level. Second, models were assessed by using area under the receiver operating characteristic curve (AUC) and accuracy at the patient level with test dataset from both scanners (n = 100). Results The GrayNet-pretrained model showed higher classifier exactness than did ImageNet-pretrained or Random-initialized models when tested by using data from the same or different scanners at section level. At the patient level, the AUC for stone detection was 0.92-0.95, depending on the model. Accuracy of GrayNet-SB (95%) was higher than that of ImageNet-SB (91%) and Random-SB (88%). For stones larger than 4 mm, all models showed similar performance (false-negative results: two of 34). For stones smaller than 4 mm, the number of false-negative results for GrayNet-SB, ImageNet-SB, and Random-SB were one of 16, three of 16, and five of 16, respectively. GrayNet-SB identified stones in all 22 test cases that had obstructive uropathy. Conclusion A cascading model of CNNs can detect urinary tract stones on unenhanced CT scans with a high accuracy (AUC, 0.954). Performance and generalization of CNNs across scanners can be enhanced by using transfer learning with datasets enriched with labeled medical images. © RSNA, 2019Supplemental material is available for this article. : An earlier incorrect version appeared online. This article was corrected on August 6, 2019.
Analysis and Design of Soft-Switching Active-Clamping Half-Bridge Boost Inverter for Inductive Wireless Charging Applications

This paper presents an accurate analysis and a design methodology for a fixed-frequency-controlled active-clamping half-bridge boost inverter (HBBI)-based series–series compensated inductive power transfer (SS-IPT) charging system. First, the operation principles of the active-clamping HBBI in the charging system are analyzed. Consequently, both the steady-state model and the small-signal model are correctly derived by using the extended describing function (EDF) method and are used to design the system. The derived steady-state model is employed to develop a new design approach to achieve zero-voltage switching (ZVS) for the inverter. The dynamic behavior of the system is investigated, and a digital controller for charging current regulation is designed based on the derived small-signal model. The proposed methodology enables not only reducing switching losses but also avoiding bifurcation. Finally, a 1-kW laboratory prototype is implemented to verify the accuracy of the theoretical analyses. Simulation and experimental results demonstrate that the control method can effectively regulate the charging current with a fast response and no steady-state errors and can enable the inverter to achieve ZVS over a wide variation of the charging current and the battery voltage. The hardware prototype achieves a peak dc-to-dc efficiency of 93.4% at a 170-mm air gap, which is comparable to other IPT systems at similar power levels in the literature.
Lexicalized Grammar Specialization for Restricted Applicative Languages

In the context of a spoken interfaces, we present a methodology and an implemented workbench called EGAL (Lexicalized Tree Grammar Extraction) dedicated to design and test restricted languages used in specific task-oriented applications. A complementary methodology is proposed to process the extraction of these applicative languages from a general LTAG grammar and a training corpus. Additional results allow us to estimate the representativeness of the training corpus. An application of the system is presented for the tuning of a LTAG grammar dedicated to a spoken interface on the basis of a Wizard of Oz corpus. || Nous presentons une methododologie pratique et une plate-forme appelee EGAL (Extraction de Grammaire d'Arbres Lexicalises) dedies a la conception et au test d'un sous-langage d'application pour un systeme de dialogue oral homme-machine. Une methodologie c
Effects of the Particle Swarm Optimization parameters for structural dynamic monitoring of cantilever beam

Nowadays, particle swarm optimization (PSO) algorithm has become a widespread optimization method. However, it is well known that its main parameters (inertia weight, two learning factors, velocity constraint and population size) have a critical effect on its performance. Currently the effects of PSO parameters on structural health monitoring have not been comprehensively studied. Therefore, in this paper, the PSO algorithm is used for damage detection assessment of a cantilever beam, and the simulation results are used to analyze the effects of PSO parameters. There are five levels for each parameter in our experiment, mean fitness value and success rate for each level are used as criteria to measure the convergence and stability of the PSO algorithm. Considering the effect of population size on CPU time, a trade-off strategy is presented to further determine the selection of population size.