Scan matching by cross-correlation and differential evolution

Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85

Automatic epilepsy detection using fractal dimensions segmentation and GP-SVM classification

Objective: The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and classification quality. An ideal set of features would make the classification problem trivial. This article presents novel methods of feature extraction processing and automatic epilepsy seizure classification combining machine learning methods with genetic evolution algorithms. Methods: Classification is performed on EEG data that represent electric brain activity. At first, the signal is preprocessed with digital filtration and adaptive segmentation using fractal dimensions as the only segmentation measure. In the next step, a novel method using genetic programming (GP) combined with support vector machine (SVM) confusion matrix as fitness function weight is used to extract feature vectors compressed into lower dimension space and classify the final result into ictal or interictal epochs. Results: The final application of GP SVM method improves the discriminatory performance of a classifier by reducing feature dimensionality at the same time. Members of the GP tree structure represent the features themselves and their number is automatically decided by the compression function introduced in this paper. This novel method improves the overall performance of the SVM classification by dramatically reducing the size of input feature vector. Conclusion: According to results, the accuracy of this algorithm is very high and comparable, or even superior to other automatic detection algorithms. In combination with the great efficiency, this algorithm can be used in real-time epilepsy detection applications. From the results of the algorithm's classification, we can observe high sensitivity, specificity results, except for the Generalized Tonic Clonic Seizure (GTCS). As the next step, the optimization of the compression stage and final SVM evaluation stage is in place. More data need to be obtained on GTCS to improve the overall classification score for GTCS.Web of Science142449243

Novel point-to-point scan matching algorithm based on cross-correlation

The localization of mobile robots in outdoor and indoor environments is a complex issue. Many sophisticated approaches, based on various types of sensory inputs and different computational concepts, are used to accomplish this task. However, many of the most efficient methods for mobile robot localization suffer from high computational costs and/or the need for high resolution sensory inputs. Scan cross-correlation is a traditional approach that can be, in special cases, used to match temporally aligned scans of robot environment. This work proposes a set of novel modifications to the cross-correlation method that extend its capability beyond these special cases to general scan matching and mitigate its computational costs so that it is usable in practical settings. The properties and validity of the proposed approach are in this study illustrated on a number of computational experiments.Web of Scienceart. ID 646394

Analysis of LoRaWAN transactions for TEG-powered environment-monitoring devices

Long-Range (LoRa) transmission technology is potentially a suitable solution in abundant applications such as smart cities, smart industries, smart health, and others, although it is challenging and complex to implement. LoRa is a non-cellular modulation technology for Long-Range Wide-Area Networks (LoRaWAN) and is suitable for Internet of Things (IoT) solutions through its long-range and low-power consumption characteristics. The present paper provides a comprehensive analysis of LoRa wireless transactions through several measurements, which differ in LoRa parameter configuration. The results showed dependency of the power consumed by the transaction on the selected Effective Isotropic Radiated Power (EIRP). The quantity of energy consumed by the transaction also significantly depends on the selected data rate (combination of the spread factor and bandwidth) and payload.Web of Science283363

Powering batteryless embedded platforms by piezoelectric transducers: A pilot study

This contribution presents a pilot study on powering battery-less embedded systems. First, a piezoelectric transducer principle and low-power techniques are reviewed in the background section. In the experimental part, the authors describe a testing setup consisting of piezoelectric transducer, DC/DC converter with energy storage, and evaluation microcontroller platform FRDM KL25Z). Three types of experiments have been conducted for two voltage configurations including charging speed, continuous operation and discharge test. Results presented in this article concentrate on power supply voltage 1.8 V and 3.3 V), total efficiency 67.16 % and 76.75 %) and operation times 24.28 s and 15 s) of the embedded system.Web of Science252353

A novel seismocardiogram mathematical model for simplified adjustment of adaptive filter

Nonclinical measurements of a seismocardiogram (SCG) can diagnose cardiovascular disease (CVD) at an early stage, when a critical condition has not been reached, and prevents unplanned hospitalization. However, researchers are restricted when it comes to investigating the benefits of SCG signals for moving patients, because the public database does not contain such SCG signals. The analysis of a mathematical model of the seismocardiogram allows the simulation of the heart with cardiovascular disease. Additionally, the developed mathematical model of SCG does not totally replace the real cardio mechanical vibration of the heart. As a result, a seismocardiogram signal of 60 beats per min (bpm) was generated based on the main values of the main artefacts, their duration and acceleration. The resulting signal was processed by finite impulse response (FIR), infinitive impulse response (IRR), and four adaptive filters to obtain optimal signal processing settings. Meanwhile, the optimal filter settings were used to manage the real SCG signals of slowly moving or resting. Therefore, it is possible to validate measured SCG signals and perform advanced scientific research of seismocardiogram. Furthermore, the proposed mathematical model could enable electronic systems to measure the seismocardiogram with more accurate and reliable signal processing, allowing the extraction of more useful artefacts from the SCG signal during any activity.Web of Science1115art. no. 244

An optical-based sensor for automotive exhaust gas temperature measurement

The article introduces the design of an optical-based sensor that measures automotive exhaust gas temperatures (EGTs) over a wide temperature range. To measure temperature, we combined the luminescence method and the blackbody radiation (BBR) principle. We also developed our own measurement hardware that includes the means to process and evaluate the signals obtained for temperature conversion using optical methods for application in the target temperature range (-40 degrees C to 820 degrees C). This temperature range is specified by the automotive industry according to current combustion engine designs and emission requirements, which stipulate accurate measurement of operating temperature for optimal functioning. Current measurement solutions are based on the thermocouple principle. This approach is problematic, especially with regard to electromagnetic interference and self-diagnostics, and problems also exist with the gradual penetration of moisture into the temperature probe under extreme thermal stress. The case study confirmed the full functionality of the new optical sensor concept. The benefit of the proposed concept is full compatibility with existing conceptual solutions while maintaining the advantages of optical-based sensors. The results indicated that a combination of the BBR and luminescence methods with a ruby crystal in the proposed solution produced an average absolute error of 2.32 degrees C in the temperature range -40 degrees C to 820 degrees C over a measurement cycle time of 0.25 s.Web of Science71art. no. 700571

Klasifikace typologie metabolismu na základě analýzy dat z energometrickýzch testů

Import 02/12/2011Práce se zabývá klasifikací typologie metabolismu na základě analýzy dat z energometrických testů. V práci jsou použity tři základní zdroje dat získané od pacientů, jedná se jednak o měření respiračního kvocientu a následně vypočtené hodnoty utilizace potravy. Dále jsou to data biochemických rozborů krve a analýzy insulínu. A v poslední řadě data z měření impedance tkáně a popis pacienta. Pacienti jsou rozdělení na dvě skupiny, analytickou a testovací skupinu. Data pro analýzu jsou zpracována analýzou hlavních komponent, redukována a upravena pomocí normalizačních algoritmů. Práce rovněž zahrnuje metodiku pro klasifikaci a stanovení klasifikačních parametrů. Po vytvoření klasifikačního prostoru je algoritmus ověřen pomocí testovací skupiny a zhodnocen testováním statistických hypotéz. Výsledkem práce je algoritmus pro automatický diagnostický systém, který na základě získaného vzoru určí zařazení pacienta do určité skupiny souhrnných vlastností.This work deals with the classification of metabolism typology based on analysis of the energometry tests. Three data sources from patient are used in this work. The sources mean data from respiratory quotient measurement and calculated indicators of food utilization. Further the data from biochemical analysis of blood and insulin test are also used in analysis. At last, the measurement of bio-impedance and patient description are used in the work. The patients are divided into two groups, analytical and test group. The analysis group is processed by principal component analysis and it is normalized. The work also includes methods for fuzzy classification and estimation of classification parameters. After the classification space estimation the algorithm is tested by the testing group and evaluated by statistical hypothesis testing. The aim of the thesis is design algorithm for an automatic diagnostic system which insert patient to the group of features based on the previous model.Prezenční450 - Katedra kybernetiky a biomedicínského inženýrstvívyhově

Návrh metodiky měření EKG s redukovaným počtem elektrod

Prezenční455 - Katedra měřicí a řídicí technikyNeuveden

Metody řízení pro energeticky nezávislé vestavěné měřící systémy

Prezenční450 - Katedra kybernetiky a biomedicínského inženýrstvíNeuveden