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

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

Optimizing of Q-learning day/night energy strategy for solar harvesting environmental wireless sensor networks nodes

This research article presents the application of the Q-learning algorithm in the operational duty cycle control of solar-powered environmental wireless sensor network (EWSN) nodes. Those nodes are commonly implemented as embedded devices using low-power and low-cost microcontrollers. Therefore, there is a significant need for an effective and easy way to implement a machine learning (ML) algorithm in terms of computer performance. This approach uses a Q-learning-based policy implementing a sleep/run switching algorithm driven by the state of charge. The presented algorithm is based on two modes: daylight and nighttime, which is a suitable solution for solar-powered systems. The study includes the complete process of design EWSN node strategy with an optimal reward policy. The presented algorithm was tested and verified on an EWSN node model and a 5-year data set of solar irradiance values was used for the learning process and its validation. As part of the study, we are also presenting the validation in terms of Q-learning parameters, which include the learning rate and discount factor. The result section shows that the overall performance of the presented solution is more suitable for solar-powered EWSN then state-of-the-art studies. Both day/night experiments reached 828 203 measurement/transmission cycles, which is 12.7 % more than in the previous studies using the strategy defined by the state of energy storage.Web of Science27

A hardware approach of a low-power IoT communication interface by NXP FlexIO module

This article describes implementation possibilities of specialized microcontroller peripherals, as hardware solution for Internet of Things (IoT) low-power communication, interfaces. In this contribution, authors use the NXP FlexIO periphery. Meanwhile, RFC1662 is used as a reference communication standard. Implementation of RFC1662 is performed by software and hardware approaches. The total power consumption is measured during experiments. In the result section, authors evaluate a time-consumption trade-off between the software approach running in Central Processing Unit (CPU) and hardware implementation using NXP FlexIO periphery. The results confirm that the hardware-based approach is effective in terms of power consumption. This method is applicable in IoT embedded devices.Web of Science256393

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

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

A phonocardiographic-based fiber-optic sensor and adaptive filtering system for noninvasive continuous fetal heart rate monitoring

This paper focuses on the design, realization, and verification of a novel phonocardiographic-based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio-SNR, Root Mean Square Error-RMSE, Sensitivity-S+, and Positive Predictive Value-PPV.Web of Science174art. no. 89

Non-invasive fetal monitoring: a maternal surface ECG electrode placement-based novel approach for optimization of adaptive filter control parameters using the LMS and RLS algorithms

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size mu and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.Web of Science175art. no. 115

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