Showing papers by "Brno University of Technology published in 2017"

EvoApproxSb: Library of approximate adders and multipliers for circuit design and benchmarking of approximation methods

Abstract: Approximate circuits and approximate circuit design methodologies attracted a significant attention of researchers as well as industry in recent years. In order to accelerate the approximate circuit and system design process and to support a fair benchmarking of circuit approximation methods, we propose a library of approximate adders and multipliers called EvoApprox8b. This library contains 430 non-dominated 8-bit approximate adders created from 13 conventional adders and 471 non-dominated 8-bit approximate multipliers created from 6 conventional multipliers. These implementations were evolved by a multi-objective Cartesian genetic programming. The EvoApprox8b library provides Verilog, Matlab and C models of all approximate circuits. In addition to standard circuit parameters, the error is given for seven different error metrics. The EvoApprox8b library is available at: www.fit.vutbr.cz/research/groups/ehw/approxlib

Brightening of dark excitons in monolayers of semiconducting transition metal dichalcogenides

Abstract: We present low-temperature magneto-photoluminescence experiments which demonstrate the brightening of dark excitons by an in-plane magnetic field B applied to monolayers of different semiconducting transition metal dichalcogenides. For WSe2 and WS2 monolayers, the dark exciton emission is observed at??~50 meV below the bright exciton peak and displays a characteristic doublet structure whose intensity grows with B 2, while no magnetic field induced emission peaks appear for MoSe2 monolayer. Our experiments also show that the MoS2 monolayer has a dark exciton ground state with a dark-bright exciton splitting energy of??~100 meV.

Mechanisms and kinetics of CO2 hydrogenation to value-added products: A detailed review on current status and future trends

Abstract: Global warming, fossil fuel depletion and energy security are driving scientists to investigate the mechanism of hydrocarbons production from CO 2 hydrogenation. The need for more comprehensive understanding on mechanism of CO 2 hydrogenation to hydrocarbons remains controversial because of the complex reaction mechanism and a large number of involved species. The micro mechanism of CO 2 hydrogenation to hydrocarbons has been considered as a possible remedy to fulfill the requirements. This review comprehensively discusses two processes: reverse water gas shift (RWGS) and CO 2 hydrogenation to hydrocarbons. The review includes reaction mechanisms and catalyst effects on yields and rates. In addition, the review outlines each of the Fischer-Tropsch (FT) micro mechanisms. The review infers some mechanisms from existing work and proposes a new mechanism that improves several predictions. These mechanisms form the design basis for optimal reactor design.

Speech disorders in Parkinson's disease: early diagnostics and effects of medication and brain stimulation.

Abstract: Hypokinetic dysarthria (HD) occurs in 90% of Parkinson’s disease (PD) patients. It manifests specifically in the areas of articulation, phonation, prosody, speech fluency, and faciokinesis. We aimed to systematically review papers on HD in PD with a special focus on (1) early PD diagnosis and monitoring of the disease progression using acoustic voice and speech analysis, and (2) functional imaging studies exploring neural correlates of HD in PD, and (3) clinical studies using acoustic analysis to evaluate effects of dopaminergic medication and brain stimulation. A systematic literature search of articles written in English before March 2016 was conducted in the Web of Science, PubMed, SpringerLink, and IEEE Xplore databases using and combining specific relevant keywords. Articles were categorized into three groups: (1) articles focused on neural correlates of HD in PD using functional imaging (n = 13); (2) articles dealing with the acoustic analysis of HD in PD (n = 52); and (3) articles concerning specifically dopaminergic and brain stimulation-related effects as assessed by acoustic analysis (n = 31); the groups were then reviewed. We identified 14 combinations of speech tasks and acoustic features that can be recommended for use in describing the main features of HD in PD. While only a few acoustic parameters correlate with limb motor symptoms and can be partially relieved by dopaminergic medication, HD in PD seems to be mainly related to non-dopaminergic deficits and associated particularly with non-motor symptoms. Future studies should combine non-invasive brain stimulation with voice behavior approaches to achieve the best treatment effects by enhancing auditory-motor integration.

Timing Is Important: Unmanned Aircraft vs. Satellite Imagery in Plant Invasion Monitoring.

Abstract: The rapid spread of invasive plants makes their management increasingly difficult Remote sensing offers a means of fast and efficient monitoring, but still the optimal methodologies remain to be defined The seasonal dynamics and spectral characteristics of the target invasive species are important, since, at certain time of the vegetation season (eg at flowering or senescing), plants are often more distinct (or more visible beneath the canopy) Our aim was to establish fast, repeatable and a cost-efficient, computer-assisted method applicable over larger areas, to reduce the costs of extensive field campaigns To achieve this goal, we examined how the timing of monitoring affects the detection of noxious plant invaders in Central Europe, using two model herbaceous species with markedly different phenological, structural and spectral characteristics They are giant hogweed (Heracleum mantegazzianum), a species with very distinct flowering phase, and the less distinct knotweeds (Fallopia japonica, F sachalinensis and their hybrid F ×bohemica) The variety of data generated, such as imagery from purposely-designed, unmanned aircraft vehicle (UAV) and VHR satellite, and aerial colour orthophotos enabled us to assess the effects of spectral, spatial and temporal resolution (ie, the target species’ phenological state) for successful recognition The demands for both spatial and spectral resolution depended largely on the target plant species In the case that a species was sampled at the most distinct phenological phase, high accuracy was achieved even with lower spectral resolution of our low-cost UAV This demonstrates that proper timing can to some extent compensate for the lower spectral resolution The results of our study could serve as a basis for identifying priorities for management, targeted at localities with the greatest risk of invasive species’ spread and, once eradicated, to monitor over time any return The best mapping strategy should reflect morphological and structural features of the target plant and choose appropriate spatial, spectral and temporal resolution The UAV enables flexible data acquisition for required time periods at low cost and is, therefore, well suited for targeted monitoring; while satellite imagery provides the best solution for larger areas Nonetheless, users must be aware of their limits

Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics.

Abstract: One of main steps in a study of microbial communities is resolving their composition, diversity and function. In the past, these issues were mostly addressed by the use of amplicon sequencing of a target gene because of reasonable price and easier computational postprocessing of the bioinformatic data. With the advancement of sequencing techniques, the main focus shifted to the whole metagenome shotgun sequencing, which allows much more detailed analysis of the metagenomic data, including reconstruction of novel microbial genomes and to gain knowledge about genetic potential and metabolic capacities of whole environments. On the other hand, the output of whole metagenomic shotgun sequencing is mixture of short DNA fragments belonging to various genomes, therefore this approach requires more sophisticated computational algorithms for clustering of related sequences, commonly referred to as sequence binning. There are currently two types of binning methods: taxonomy dependent and taxonomy independent. The first type classifies the DNA fragments by performing a standard homology inference against a reference database, while the latter performs the reference-free binning by applying clustering techniques on features extracted from the sequences. In this review, we describe the strategies within the second approach. Although these strategies do not require prior knowledge, they have higher demands on the length of sequences. Besides their basic principle, an overview of particular methods and tools is provided. Furthermore, the review covers the utilization of the methods in context with the length of sequences and discusses the needs for metagenomic data preprocessing in form of initial assembly prior to binning.

Speaker-Aware Neural Network Based Beamformer for Speaker Extraction in Speech Mixtures.

Abstract: In this work, we address the problem of extracting one target speaker from a multichannel mixture of speech. We use a neural network to estimate masks to extract the target speaker and derive beamformer filters using these masks, in a similar way as the recently proposed approach for extraction of speech in presence of noise. To overcome the permutation ambiguity of neural network mask estimation, which arises in presence of multiple speakers, we propose to inform the neural network about the target speaker so that it learns to follow the speaker characteristics through the utterance. We investigate and compare different methods of passing the speaker information to the network such as making one layer of the network dependent on speaker characteristics. Experiments on mixture of two speakers demonstrate that the proposed scheme can track and extract a target speaker for both closed and open speaker set cases.

Advanced diamond-reinforced metal matrix composites via cold spray: properties and deposition mechanism

Abstract: Diamond-reinforced metal matrix composites (DMMC) have great potential for wear-resistance applications due to the superior hardness of the diamond component. Cold spray as an emerging coating technique is able to fabricate coatings or bulk materials without exceeding the material melting point, thereby significantly lowering the risk of oxidation, phase transformation, and excessive thermal residual stress. In this paper, thick DMMC coatings were deposited onto aluminum alloy substrate via cold spray of three feedstock powders: copper-clad diamond and pure copper, and their mixtures. It was found that, due to its low processing temperature, cold spray is able to prevent graphitization of the diamond in the DMMC coatings. Further to that, the original diamond phase was almost completely retained in the DMMC coatings. In case of the coatings fabricated from copper-clad diamond powders only, its mass fraction reached 43 wt.%, i.e. value higher than in any previous studies using conventional pre-mixed powders. Furthermore, it was found that the added copper content powders acted as a buffer, effectively preventing the fracture of the diamond particles in the coating. Finally, the wear test on the coatings showed that the cold sprayed DMMC coatings had excellent wear-resistance properties due to the diamond reinforcement.

Accurate simulation of transcranial ultrasound propagation for ultrasonic neuromodulation and stimulation.

Abstract: Non-invasive, focal neurostimulation with ultrasound is a potentially powerful neuroscientific tool that requires effective transcranial focusing of ultrasound to develop. Time-reversal (TR) focusing using numerical simulations of transcranial ultrasound propagation can correct for the effect of the skull, but relies on accurate simulations. Here, focusing requirements for ultrasonic neurostimulation are established through a review of previously employed ultrasonic parameters, and consideration of deep brain targets. The specific limitations of finite-difference time domain (FDTD) and k-space corrected pseudospectral time domain (PSTD) schemes are tested numerically to establish the spatial points per wavelength and temporal points per period needed to achieve the desired accuracy while minimizing the computational burden. These criteria are confirmed through convergence testing of a fully simulated TR protocol using a virtual skull. The k-space PSTD scheme performed as well as, or better than, the widely used FDTD scheme across all individual error tests and in the convergence of large scale models, recommending it for use in simulated TR. Staircasing was shown to be the most serious source of error. Convergence testing indicated that higher sampling is required to achieve fine control of the pressure amplitude at the target than is needed for accurate spatial targeting.

Analysis of Score Normalization in Multilingual Speaker Recognition.

Abstract: NIST Speaker Recognition Evaluation 2016 has revealed the importance of score normalization for mismatched data conditions. This paper analyzes several score normalization techniques for test conditions with multiple languages. The best performing one for a PLDA classifier is an adaptive s-norm with 30% relative improvement over the system without any score normalization. The analysis shows that the adaptive score normalization (using top scoring files per trial) selects cohorts that in 68% contain recordings from the same language and in 92% of the same gender as the enrollment and test recordings. Our results suggest that the data to select score normalization cohorts should be a pool of several languages and channels and if possible, its subset should contain data from the target domain.

pqsfinder: an exhaustive and imperfection-tolerant search tool for potential quadruplex-forming sequences in R.

Abstract: Motivation: G-quadruplexes (G4s) are one of the non-B DNA structures easily observed in vitro and assumed to form in vivo. The latest experiments with G4-specific antibodies and G4-unwinding helicase mutants confirm this conjecture. These four-stranded structures have also been shown to influence a range of molecular processes in cells. As G4s are intensively studied, it is often desirable to screen DNA sequences and pinpoint the precise locations where they might form. Results: We describe and have tested a newly-developed Bioconductor package for identifying potential quadruplex-forming sequences (PQS). The package is easy-to-use, flexible and customizable. It allows for sequence searches that accommodate possible divergences from the optimal G4 base composition. A novel aspect of our research was the creation and training (parametrization) of an advanced scoring model which resulted in increased precision compared to similar tools. We demonstrate that the algorithm behind the searches has a 96% accuracy on 392 currently known and experimentally observed G4 structures. We also carried out searches against the recent G4-seq data to verify how well we can identify the structures detected by that technology. The correlation with pqsfinder predictionswas 0.622, higher than the correlation 0.491 obtained with the second best G4Hunter. Availability:http://bioconductor.org/packages/pqsfinder/ This paper is based on pqsfinder-1.4.1.

Mechanical and microstructural characterization of powder metallurgy CoCrNi medium entropy alloy

Abstract: The present study is focused on synthesis and mechanical properties characterization of equiatomic CoCrNi medium entropy alloy (MEA). Powder metallurgy processes of mechanical alloying (MA) with subsequent spark plasma sintering (SPS) for bulk alloy densification have been utilized. As opposed to the single-phase alloys of identical composition fabricated via casting routes, the alloy after SPS compaction consisted of a major FCC solid solution phase (94.4%), minor fraction of secondary BCC phase (5.6%, precipitated at the FCC grains boundaries), and negligible amount of oxide inclusions. The alloy exhibited high ultimate tensile strength of 1024 MPa and a elongation to fracture of 26%. Elastic modulus of the alloy reached 222 GPa and the thermal expansion coefficient (CTE) was measured as 17.4 × 10−6 K−1 The plastic deformation in the alloy is carried out by a combination of dislocation glide and mechanical nano-twinning at room temperature.

Nanostructured Polypyrrole-Based Ammonia and Volatile Organic Compound Sensors.

Abstract: The aim of this review is to summarize the recent progress in the fabrication of efficient nanostructured polymer-based sensors with special focus on polypyrrole. The correlation between physico-chemical parameters, mainly morphology of various polypyrrole nanostructures, and their sensitivity towards selected gas and volatile organic compounds (VOC) is provided. The different approaches of polypyrrole modification with other functional materials are also discussed. With respect to possible sensors application in medicine, namely in the diagnosis of diseases via the detection of volatile biomarkers from human breath, the sensor interaction with humidity is described as well. The major attention is paid to analytes such as ammonia and various alcohols.

Holistic recognition of low quality license plates by CNN using track annotated data

Abstract: This work is focused on recognition of license plates in low resolution and low quality images. We present a methodology for collection of real world (non-synthetic) dataset of low quality license plate images with ground truth transcriptions. Our approach to the license plate recognition is based on a Convolutional Neural Network which holistically processes the whole image, avoiding segmentation of the license plate characters. Evaluation results on multiple datasets show that our method significantly outperforms other free and commercial solutions to license plate recognition on the low quality data. To enable further research of low quality license plate recognition, we make the datasets publicly available.

FireProt: web server for automated design of thermostable proteins

Abstract: There is a continuous interest in increasing proteins stability to enhance their usability in numerous biomedical and biotechnological applications. A number of in silico tools for the prediction of the effect of mutations on protein stability have been developed recently. However, only single-point mutations with a small effect on protein stability are typically predicted with the existing tools and have to be followed by laborious protein expression, purification, and characterization. Here, we present FireProt, a web server for the automated design of multiple-point thermostable mutant proteins that combines structural and evolutionary information in its calculation core. FireProt utilizes sixteen tools and three protein engineering strategies for making reliable protein designs. The server is complemented with interactive, easy-to-use interface that allows users to directly analyze and optionally modify designed thermostable mutants. FireProt is freely available at http://loschmidt.chemi.muni.cz/fireprot.

Ouroboros: early identification of at-risk students without models based on legacy data

Abstract: This paper focuses on the problem of identifying students, who are at risk of failing their course. The presented method proposes a solution in the absence of data from previous courses, which are usually used for training machine learning models. This situation typically occurs in new courses. We present the concept of a "self-learner" that builds the machine learning models from the data generated during the current course. The approach utilises information about already submitted assessments, which introduces the problem of imbalanced data for training and testing the classification models. There are three main contributions of this paper: (1) the concept of training the models for identifying at-risk students using data from the current course, (2) specifying the problem as a classification task, and (3) tackling the challenge of imbalanced data, which appears both in training and testing data. The results show the comparison with the traditional approach of learning the models from the legacy course data, validating the proposed concept.

Oxidative Stress and Heavy Metals in Plants.

Abstract: Oxidative stress is a pathological process related to not only animal kingdom but also plants. Regarding oxidative stress in plants, heavy metals are frequently discussed as causative stimuli with relevance to ecology. Because heavy metals have broad technological importance, they can easily contaminate the environment. Much of previous effort regarding the harmful impact of the heavy metals was given to their toxicology in the animals and humans. Their implication in plant pathogeneses is less known and remains underestimated.

SLAM++1-A highly efficient and temporally scalable incremental SLAM framework

Abstract: The most common way to deal with the uncertainty present in noisy sensorial perception and action is to model the problem with a probabilistic framework. Maximum likelihood estimation is a well-kno...

Identification and Rating of Developmental Dysgraphia by Handwriting Analysis

Abstract: Developmental dysgraphia, being observed among 10–30% of school-aged children, is a disturbance or difficulty in the production of written language that has to do with the mechanics of writing. The objective of this study is to propose a method that can be used for automated diagnosis of this disorder, as well as for estimation of difficulty level as determined by the handwriting proficiency screening questionnaire. We used a digitizing tablet to acquire handwriting and consequently employed a complex parameterization in order to quantify its kinematic aspects and hidden complexities. We also introduced a simple intrawriter normalization that increased dysgraphia discrimination and HPSQ estimation accuracies. Using a random forest classifier, we reached 96% sensitivity and specificity, while in the case of automated rating by the HPSQ total score, we reached 10% estimation error. This study proves that digital parameterization of pressure and altitude/tilt patterns in children with dysgraphia can be used for preliminary diagnosis of this writing disorder.

Influence of scanning rate on quality of AFM image: Study of surface statistical metrics

Abstract: The purpose of this work is to study the dependence of AFM-data reliability on scanning rate. The three-dimensional (3D) surface topography of the samples with different micro-motifs is investigated. The analysis of surface metrics for estimation of artifacts from inappropriate scanning rate is presented. Fractal analysis was done by cube counting method and evaluation of statistical metrics was carrying out on the basis of AFM-data. Combination of quantitate parameters is also presented in graphs for every measurement. The results indicate that the sensitivity to scanning rate growths with fractal dimension of the sample. This approach allows describing the distortion of the images against scanning rate and could be applied for dependences on the other measurement parameters. The article explains the relevance and comparison of fractal and statistical surface parameters for characterization of data distortion caused by inappropriate choice of scanning rate.

Resistive Random Access Memory Cells with a Bilayer TiO2/SiOX Insulating Stack for Simultaneous Filamentary and Distributed Resistive Switching

Abstract: In order to fulfill the information storage needs of modern societies, the performance of electronic nonvolatile memories (NVMs) should be continuously improved. In the past few years, resistive random access memories (RRAM) have raised as one of the most promising technologies for future information storage due to their excellent performance and easy fabrication. In this work, a novel strategy is presented to further extend the performance of RRAMs. By using only cheap and industry friendly materials (Ti, TiO2, SiOX, and n++Si), memory cells are developed that show both filamentary and distributed resistive switching simultaneously (i.e., in the same I–V curve). The devices exhibit unprecedented hysteretic I–V characteristics, high current on/off ratios up to ≈5 orders of magnitude, ultra low currents in high resistive state and low resistive state (100 pA and 125 nA at –0.1 V, respectively), sharp switching transitions, good cycle-to-cycle endurance (>1000 cycles), and low device-to-device variability. We are not aware of any other resistive switching memory exhibiting such characteristics, which may open the door for the development of advanced NVMs combining the advantages of filamentary and distributed resistive switching mechanisms.

Nutritional Potential of Selected Insect Species Reared on the Island of Sumatra

Abstract: Inhabitants of the Indonesian island of Sumatra are faced with the problem of insufficient food supplies and the consequent risk of undernourishment and health issues. Edible insects as a traditional and readily available food source could be part of the solution. The nutritional value of insects depends on many factors, e.g., species, developmental stage, sex, diet, and climatic conditions. However, edible insects bred in Sumatra for human consumption have never before been assessed with regard to their nutritional value. Our study involved analyses of crude protein, chitin, fat and selected fatty acid contents of giant mealworm larvae (Zophobas morio), larvae of the common mealworm (Tenebrio molitor) and nymphs of the field cricket (Gryllus assimilis). Crude protein content in the samples ranged from 46% to 56%. Highest (35%) and lowest (31%) amounts of fat were recorded in giant mealworm larvae and larvae of the common mealworm, respectively. Chitin amounts ranged from 6% to 13%. Based on these values, which are comparable to those known from other food insects reared in different regions of the world, the edible species bred in Sumatra could become food sources with a potential to help stave off hunger and undernourishment.

Impact of Laser-Induced Breakdown Spectroscopy data normalization on multivariate classification accuracy

Abstract: Multivariate data analysis (MVDA) is getting popular across the spectroscopic community. To assess accurate results, the obtained data should be preprocessed prior to utilization of any MVDA algorithm. The process of data normalization or “internal standardization” is widely used across a broad range of applications. In this manuscript we investigate the utilization of Laser-Induced Breakdown Spectroscopy (LIBS) coupled with MVDA. However, many articles regarding the use of MVDA on data from LIBS do not provide any information about the data pretreatment. This work describes the impact of LIBS data normalization approaches on MVDA classification accuracy. Also, the impact of classical data preprocessing (mean centering and scaling) exploiting the prior utilization of MVDA was studied. This issue was investigated exploiting simple soft independent modelling of class analogies algorithm. The findings were generalized for three sample matrices (steel, Al alloys, and sedimentary ores). Furthermore, the selection of an appropriate normalization algorithm is not trivial since the spectrum of each sample matrix is composed of a different number of elements and corresponding elemental lines.