Search

Introduction: Exercise physiology investigates the complex and multifaceted human body responses to physical activity (PA). The integration of electrical bioimpedance (EBI) has emerged as a valuable tool for deepening our understanding of muscle activity during exercise. Method: In this study, we investigate the potential of using the EBI technique for human motion recognition. We analyze EBI signals from the quadriceps muscle and extensor digitorum longus muscle acquired when healthy participants in the range 20–30 years of age performed four lower body PAs, namely squats, lunges, balance walk, and short jumps. Results: The characteristics of EBI signals are promising for analyzing PAs. Each evaluated PA exhibited unique EBI signal characteristics. Discussion: The variability in how PAs are executed leads to variations in the EBI signal characteristics, which, in turn, can provide insights into individual differences in how a person executes a specific PA. [ABSTRACT FROM AUTHOR]

IntroductionExercise physiology investigates the complex and multifaceted human body responses to physical activity (PA). The integration of electrical bioimpedance (EBI) has emerged as a valuable tool for deepening our understanding of muscle activity during exercise.MethodIn this study, we investigate the potential of using the EBI technique for human motion recognition. We analyze EBI signals from the quadriceps muscle and extensor digitorum longus muscle acquired when healthy participants in the range 20–30 years of age performed four lower body PAs, namely squats, lunges, balance walk, and short jumps.ResultsThe characteristics of EBI signals are promising for analyzing PAs. Each evaluated PA exhibited unique EBI signal characteristics.DiscussionThe variability in how PAs are executed leads to variations in the EBI signal characteristics, which, in turn, can provide insights into individual differences in how a person executes a specific PA.

Fracture disease of fasteners and elastic strips is one of the risk sources of train operation safety. The fastener sling status detection is mainly based on manual inspection, which is difficult to detect in a timely manner, and there is a lack of effective non-destructive testing methods. The acoustic emission technology was applied to the damage detection of fastener sling, and a fastener acoustic emission signal acquisition system was developed; type B sling static load compression experiment system was built; the complete acoustic emission signal during the fracture and damage of the type B strip was acquired. Using the acoustic emission parameter analysis method to analyze the three damage stages of the slug fracture process, the crack extension stage acoustic emission events are more active and the amplitude is higher. The characteristics of the acoustic emission signal at the different damage stages were obtained by acoustic emission waveform analysis method, and the energy of the acoustic emission signal from the crack initiation to the extension to the fracture damage stage increased in the frequency band, and the later stage of damage, the higher frequency component of the acoustic emission signal. [ABSTRACT FROM AUTHOR]

Natural disasters, such as wildfires, can cause widespread devastation. Future-proofing infrastructure, such as buildings and bridges, through technological advancements is crucial to minimize their impact. Fires in disasters often stem from damaged fuel lines and electrical equipment, such as the 2018 California wildfire caused by a power line fault. To enhance safety, IoT applications can continuously monitor the health of emergency personnel. Using Bluetooth 5.0 and wearables in mesh networks, these apps can alert others about an individual's location during emergencies. However, fire can disrupt wireless networks. This study assesses Bluetooth 5.0's performance in transmitting signals in fire conditions. It examined received signal strength indicator (RSSI) values in a front open-fire chamber using both Peer-to-Peer (P2P) and mesh networks. The experiment considered three transmission heights of 0.61, 1.22, and 1.83 m and two distances of 11.13 m and 1.52 m. The study demonstrated successful signal transmission with a maximum loss of only 2 dB when transmitting through the fire. This research underscores the potential for reliable communication in fire-prone environments, improving safety during natural disasters. [ABSTRACT FROM AUTHOR]

Natural fractures play a crucial role in both exploration and development phases in oil and gas industry, as their network and orientation significantly affect flow and recovery strategies. This study highlights the importance of identifying opening-mode fracture systems within a mature Deveçatağı oil field that has been actively producing for nearly half a century. In order to investigate the most effective rock physics parameter to locate oil saturated zones in the field, this study applies detailed rock physics analysis and 2D forward modeling to pinpoint the principal factors affecting production in the Deveçatağı oil field, addressing the challenge posed by the limited shear wave wireline data acquired from only one well within this highly heterogeneous area. Analysis show that acoustic impedance is a crucial elastic property for detecting oil-saturated zones especially in areas with natural fractures. This study successfully integrates drilling-induced fracture (DIF) data, ant tracking maps, and seismic inversion outputs to understand on the issues leading to dry wells and advances in developing more effective exploration and recovery methods in fractured reservoirs. After almost 50 years of operational activities, well stimulation processes, and natural fractures that are present in this field, understanding oil drainage mechanisms in such environments is complex, emphasizing the need for comprehensive analysis to optimize production strategies. • Integrated fracture analysis to identify opening-mode fractures and their relationship with hydrocarbon presence in carbonates. • Mapping fractures by correlating drilling-induced fractures with seismic ant-tracking for precise subsurface interpretation. • 2D forward modeling and seismic inversion to effectively highlight oil-saturated zones in fractured reefal carbonates. • Emphasis on a multidisciplinary approach to analyze geomechanical behavior and fluid dynamics in complex geological settings. [ABSTRACT FROM AUTHOR]

There is a clear trend in the aircraft industry to use more electrical systems in propulsion and electromechanical and electrohydraulic actuators to comply with environmental sustainability and to improve reliability and maintenance processes. The increase of electrical consumption requires an increase of the rated voltages to supply power to these so called more electrical aircraft (MEA). Unfortunately, this voltage rise, currently up to 540 V$_{dc}$, can lead to ionization processes within the electric wiring due to the lower air density at high altitudes. As a consequence, a degraded insulation can create extremely hazardous events in flight such as arcs between wires (serial) or between wires and fuselage (parallel). Serial arcs in dc bus circuits are specially dangerous for the aircraft operation, so its detection within fractions of seconds is required to ensure a reliable operation. However, during a sustained serial arc the rated current can be passing through air deceiving the protections and avoiding their tripping. In this article, it has been found that arcing introduces high frequency current pulses superimposed to the dc component that can be identified to detect the occurrence of an arc. The frequency components of these pulses depend on the line characteristics and can be detected with inductive sensors. This manuscript also designs and tests a light and inexpensive sensor for arc detection in aircraft applications. [ABSTRACT FROM AUTHOR]

Due to the proliferation of brain and neurological disorders (World Health Organization 2006), EEG (Blinowska and Durka 2006) is gaining attention as a support for decision making in the fields of neurology, psychology, and psychiatry. But EEG data are not always easy to understand. Therefore, extracting the desired information from EEG data in different contexts is an important requirement. This article analyses state-of-the-art EEG signal processing tools and proposes a new one: Signaleeg. This addresses the limitations of previous tools. It has been designed with the aim of helping users to build predictive models from EEG signals in a process that is called signal-data mining (DM). Moreover, Signaleeg is user friendly and multi-threaded, with optimisation facilities for finding the best predictive model. It has been implemented and tested in three scenarios: schizophrenia diagnosis, alcoholism detection, and emotion recognition. The tool provided good results in each case, thus demonstrating its versatility. [ABSTRACT FROM AUTHOR]

Insulated power cables are becoming increasingly popular in today’s developing distribution and transportion networks. However, due to aging, deterioration, and various operational and environmental stresses, insulation defects may appear and so the cable needs to be monitored in a timely manner to avoid unexpected failures. Many of these defects are responsible for partial discharge (PD) activity. The localization of the sources of these discharges is a highly decisive facet in the condition-based monitoring of power cables. The techniques for the localization of single-PD defects in insulated power cables are well presented in the current bibliography. However, when several simultaneous PD sources are active, the localization of the sources becomes quite complex. This paper develops an efficient technique for the separation and localization of multiple PD sources in a medium voltage cable. The experimental results are obtained with single-end-based measurements using a high-frequency current transformer in a laboratory environment. The data processing based on the spectral characteristics of the signals is carried out by using the power ratios technique in order to determine the presence of different types of PD. Once the signals are separated, the PD sources can be localized with an individualized analysis of each source through time-domain reflectometry. The proposed methodology can be very valuable to improve the location diagnostic capability of the condition-based monitoring solutions, especially for underground cables. [ABSTRACT FROM AUTHOR]

Postpartum hemorrhage (PPH) is one of the major causes of maternal mortality and morbidity worldwide, with uterine atony being the most common origin. Currently there are no obstetrical techniques available for monitoring postpartum uterine dynamics, as tocodynamometry is not able to detect weak uterine contractions. In this study, we explored the feasibility of monitoring postpartum uterine activity by non-invasive electrohysterography (EHG), which has been proven to outperform tocodynamometry in detecting uterine contractions during pregnancy. A comparison was made of the temporal, spectral, and non-linear parameters of postpartum EHG characteristics of vaginal deliveries and elective cesareans. In the vaginal delivery group, EHG obtained a significantly higher amplitude and lower kurtosis of the Hilbert envelope, and spectral content was shifted toward higher frequencies than in the cesarean group. In the non-linear parameters, higher values were found for the fractal dimension and lower values for Lempel-Ziv, sample entropy and spectral entropy in vaginal deliveries suggesting that the postpartum EHG signal is extremely non-linear but more regular and predictable than in a cesarean. The results obtained indicate that postpartum EHG recording could be a helpful tool for earlier detection of uterine atony and contribute to better management of prophylactic uterotonic treatment for PPH prevention.

A two channel 65-nm CMOS RF-waveform characterizer is presented that enables multi-harmonic adaptive matching networks or adaptive digital pre-distortion in RF-power amplifiers. The characterizer measures the dc component and the first 3 harmonics of RF signals by applying a DFT to 8 (ideally) equally spaced quasi-dc output voltages. Conventionally in these types of systems accuracy is limited by sample timing accuracies, which in our case are mainly due to delay cell mismatch. We introduce a novel way to cancel delay cell mismatch, that significantly increases measurement accuracy at the cost of only a small power and area increase. The RF-waveform characterizer achieves 6.8-bit measurement linearity together with a (clock feedthrough limited) 24 dB SFDR. The measured power consumption for our proof-of-principle demonstrator is 18.6 mW at a maximum input signal frequency of 1.1 GHz under continuous operation. [ABSTRACT FROM AUTHOR]

The measurement of partial discharge (PD) signals in the radio frequency (RF) range has gained popularity among utilities and specialized monitoring companies in recent years. Unfortunately, in most of the occasions the data are hidden by noise and coupled interferences that hinder their interpretation and renders them useless especially in acquisition systems in the ultra high frequency (UHF) band where the signals of interest are weak. This paper is focused on a method that uses a selective spectral signal characterization to feature each signal, type of partial discharge or interferences/noise, with the power contained in the most representative frequency bands. The technique can be considered as a dimensionality reduction problem where all the energy information contained in the frequency components is condensed in a reduced number of UHF or high frequency (HF) and very high frequency (VHF) bands. In general, dimensionality reduction methods make the interpretation of results a difficult task because the inherent physical nature of the signal is lost in the process. The proposed selective spectral characterization is a preprocessing tool that facilitates further main processing. The starting point is a clustering of signals that could form the core of a PD monitoring system. Therefore, the dimensionality reduction technique should discover the best frequency bands to enhance the affinity between signals in the same cluster and the differences between signals in different clusters. This is done maximizing the minimum Mahalanobis distance between clusters using particle swarm optimization (PSO). The tool is tested with three sets of experimental signals to demonstrate its capabilities in separating noise and PDs with low signal-to-noise ratio and separating different types of partial discharges measured in the UHF and HF/VHF bands. [ABSTRACT FROM AUTHOR]

Spatial localization of emitting sources is especially interesting in different fields of application. The focus of an earthquake, the determination of cracks in solid structures, or the position of bones inside a body are some examples of the use of multilateration techniques applied to acoustic and vibratory signals. Radar, GPS and wireless sensors networks location are based on radiofrequency emissions and the techniques are the same as in the case of acoustic emissions. This paper is focused on the determination of the position of sources of partial discharges in electrical insulation for maintenance based on the condition of the electrical equipment. The use of this phenomenon is a mere example of the capabilities of the proposed method but it is very representative because the emission can be electromagnetic in the VHF and UHF ranges or acoustic. This paper presents a method to locate more than one source in space with only two receivers, one of them in a fixed position and the other describing a circumference around the first one. The signals arriving from the different sources to the antennas are first separated using a classification technique based on their spectral components. Then, the individualized time differences of arrival (TDOA) from the sources collected at different angles describe a function, angle versus TDOA, that has all the geometric information needed to locate the source. The paper will show how to derive these functions for any source analytically with the position of the source as unknown parameters. Then, it will be demonstrated that it is possible to fit the curve with experimental measurements of the TDOA to obtain the parameters of the position of each source. Finally, the technique is extended to the localization of the emitter in three dimensions.

A classification methodology based on Support Vector Machines (SVM) is proposed to locate the faulted zone in power distribution networks. The goal is to reduce the multiple-estimation problem inherent in those methods that use single end measures (in the substation) to estimate the fault location in radial systems. A selection of features or descriptors obtained from voltages and currents measured in the substation are analyzed and used as input of the SVM classifier. Performance of the fault locator having several combinations of these features has been evaluated according to its capability to discriminate between faults in different zones but located at similar distance. An application example illustrates the precision, to locate the faulted zone, obtained with the proposed methodology in simulated framework. The proposal provides appropriate information for the prevention and opportune attention of faults,requires minimum investment and overcomes the multiple-estimation problem of the classic impedance based methods.

Spatial localization of emitting sources is especially interesting in different fields of application. The focus of an earthquake, the determination of cracks in solid structures or the position of bones inside a body are some examples of the use of multilateration techniques applied to acoustic and vibratory signals. Radar, GPS and wireless sensors networks location are based on radiofrequency emissions and the techniques are the same as in the case of acoustic emissions. This paper is focused on the determination of the position of sources of partial discharges inside electrical insulation for maintenance based on the condition of the electrical machine. The use of this phenomenon is a mere example of the capabilities of the proposed method because its emission can be electromagnetic in the UHF range or acoustic when the insulation is immersed in oil. Generally, when a pulse is radiated from a source, the wave will arrive to two receivers at different times. One of the advantages of measuring these time differences of arrival or TDOA is that it is not required a common clock as in other localization techniques based on the time of arrival (TOA) of the pulse to the receiver. With only two sensors, all the possible points in the plane that would give the same TDOA describe a hyperbola. Using an independent third receiver and calculating the intersection of the three hyperbolas will give the position of the source. Therefore, planar localization of emitters using multilateration techniques can be solved at least with three receivers. This paper presents a method to locate sources in a plane with only two receivers, one of them in a fixed position and the other is placed describing a circumference around the first one. The TDOA are measured at different angles completing a total turn and obtaining a function, angle versus TDOA, that has all the geometric information needed to locate the source. The paper will show how to derive this function analytically with two unknown parameters: the distance and bearing angle from the fixed receiver to the source. Then, it will be demonstrated that it is possible to fit the curve with experimental measurements of the TDOA to obtain the parameters of the position of the source.

A classification methodology based on Support Vector Machines (SVM) is proposed to locate the faulted zone in power distribution networks. The goal is to reduce the multiple-estimation problem inherent in those methods that use single end measures (in the substation) to estimate the fault location in radial systems. A selection of features or descriptors obtained from voltages and currents measured in the substation are analyzed and used as input of the SVM classifier. Performance of the fault locator having several combinations of these features has been evaluated according to its capability to discriminate between faults in different zones but located at similar distance. An application example illustrates the precision, to locate the faulted zone, obtained with the proposed methodology in simulated framework. The proposal provides appropriate information for the prevention and opportune attention of faults, requires minimum investment and overcomes the multiple-estimation problem of the classic impedance based methods.

In this paper, an approach for characterizing event databases composed of voltage and current signals measured at a distribution substation during faults is presented. Voltage and current features or descriptors obtained from such signals and knowledge based methods are proposed for locating faults in power distribution systems. As it is shown, this is a simple, efficient and economical option that takes advantage of all the information directly related to the fault location contained in the signal measured at the distribution substation. The characterization proposed here constitutes a fundamental step towards the development of knowledge based methods. These are mixed with model based methods to obtain efficient and fast hybrid fault location algorithms to be applied in power distribution systems.

Fractal Dimensions (FD) are one of the popular measures used for characterizing signals. They have been used as complexity measures of signals in various fields including speech and biomedical applications. However, proper interpretation of such analyses has not been thoroughly addressed. In this paper, we study the effect of various signal properties on FD and interpret results in terms of classical signal processing concepts such as amplitude, frequency, number of harmonics, noise power and signal bandwidth. We have used Higuchi's method for estimating FDs. This study may help in gaining a better understanding of the FD complexity measure itself, and for interpreting changing structural complexity of signals in terms of FD. Our results indicate that FD is a useful measure in quantifying structural changes in signal properties. [ABSTRACT FROM AUTHOR]

Noise suppression in acoustic emission data was attempted by developing and using artificial neural networks (ANN) and with the long-term objective of in-flight monitoring. In-flight experiments conducted earlier and the noise characteristics outlined therein were taken as basis for their simulation in the laboratory. Simulated noise sources were classified through both supervised and a combination of un-supervised and supervised training of ANN. AE signals were generated by fatigue spectrum load tests on CFRP specimens and their failure modes were characterized. Finally, simulated noise and the actual signals were mixed and re-classified into their respective classes. The results obtained are encouraging and the methods and procedures adopted confirm the feasibility of the approach for field applications. [Copyright &y& Elsevier]

Considering the high requirements for omnidirectional video compression, we propose an objective quality evaluation method to assess quality loss in encoding omnidirectional videos. According to characteristics of 360° videos, we consider multi-space signal characterization (MSSC) to fully characterize the distortions of video signals from spatial/image domains to frequency domains and from image content to motion information, and further consider multi-channel information aggregation (MCIA) to fuse scores from multiple projection planes and temporal divided groups. The main innovation of our method is to establish a universal framework in bridging the connection between typical quality assessment and 360° quality assessment to measure 360° video quality effectively and efficiently. Experimental results show that our method outperforms state-of-the-art 2D quality metrics and quality metrics for omnidirectional images. [ABSTRACT FROM AUTHOR]

Postpartum hemorrhage (PPH) is one of the major causes of maternal mortality and morbidity worldwide, with uterine atony being the most common origin. Currently there are no obstetrical techniques available for monitoring postpartum uterine dynamics, as tocodynamometry is not able to detect weak uterine contractions. In this study, we explored the feasibility of monitoring postpartum uterine activity by non-invasive electrohysterography (EHG), which has been proven to outperform tocodynamometry in detecting uterine contractions during pregnancy. A comparison was made of the temporal, spectral, and non-linear parameters of postpartum EHG characteristics of vaginal deliveries and elective cesareans. In the vaginal delivery group, EHG obtained a significantly higher amplitude and lower kurtosis of the Hilbert envelope, and spectral content was shifted toward higher frequencies than in the cesarean group. In the non-linear parameters, higher values were found for the fractal dimension and lower values for Lempel-Ziv, sample entropy and spectral entropy in vaginal deliveries suggesting that the postpartum EHG signal is extremely non-linear but more regular and predictable than in a cesarean. The results obtained indicate that postpartum EHG recording could be a helpful tool for earlier detection of uterine atony and contribute to better management of prophylactic uterotonic treatment for PPH prevention. [ABSTRACT FROM AUTHOR]

Magnetic particle imaging (MPI) is a novel technology, which opens new possibilities for promising biomedical applications. MPI uses magnetic fields to generate a specific response from magnetic nanoparticles (MNPs), to determine their spatial location non-invasively and without using ionizing radiation. One open challenge of MPI is to achieve further improvements in terms of sensitivity to translate the currently preclinical performed research into clinical applications. In this work, we study the noise and background signals of our preclinical MPI system, to identify and characterize disturbing signal contributions. The current limit of detection achieved with our device was determined previously to be20ng of iron. Based on the results presented in this work, we describe possible hardware and software improvements and estimate that the limit of detection could be lowered to about 1-2 ng. Additionally, a long-term analysis of the scanner performance over the last 3 years is presented, which proved to be an easy and effective way to monitor possible changes or damage of hardware components. All the presented results were obtained by analysing empty scanner measurements and the presented methodology can easily be adapted for different scanner types, to compare their performances., (Creative Commons Attribution license.)

Spatial localization of emitting sources is especially interesting in different fields of application. The focus of an earthquake, the determination of cracks in solid structures, or the position of bones inside a body are some examples of the use of multilateration techniques applied to acoustic and vibratory signals. Radar, GPS and wireless sensors networks location are based on radiofrequency emissions and the techniques are the same as in the case of acoustic emissions. This paper is focused on the determination of the position of sources of partial discharges in electrical insulation for maintenance based on the condition of the electrical equipment. The use of this phenomenon is a mere example of the capabilities of the proposed method but it is very representative because the emission can be electromagnetic in the VHF and UHF ranges or acoustic. This paper presents a method to locate more than one source in space with only two receivers, one of them in a fixed position and the other describing a circumference around the first one. The signals arriving from the different sources to the antennas are first separated using a classification technique based on their spectral components. Then, the individualized time differences of arrival (TDOA) from the sources collected at different angles describe a function, angle versus TDOA, that has all the geometric information needed to locate the source. The paper will show how to derive these functions for any source analytically with the position of the source as unknown parameters. Then, it will be demonstrated that it is possible to fit the curve with experimental measurements of the TDOA to obtain the parameters of the position of each source. Finally, the technique is extended to the localization of the emitter in three dimensions. [ABSTRACT FROM AUTHOR]