Basic Signal Research Articles

An optimal deep learning based Islanding power quality detection technique for distributed generation systems

Power quality disturbance (PQD) defines the presence of inconsistencies that occur in the usual wave shapes of voltage and current signals. Power quality is considered the main challenge for power industry with the increase in dynamic load and highly subtle electronic devices. Besides, the islanding events, particularly unintended islanding, grasp significant challenges and it needs to be identified at the early stage. Islanding is an anomalousstate in the power system, where the distributed generators (DGs) are placed on supplying electrical energy to the local load even after the shortage of the major grid. Therefore, it is essential to identify and differentiate the PQ events and islanding events in ensuring pollution-free power, equipment, and labor safety. With this motivation, this paper presents an automated optimal deep learning based islanding detection (AODL-ID) technique. The proposed AODL-ID technique involves three major stages namely decomposition, classification, and hyperparameter tuning. Firstly, an empirical mode decomposition (EMD) approach is utilized to decompose the basic signals from the polluted signals. In addition, bidirectional gated recurrent neural network (BiGRNN) technique is employed for the classification of islanding and non-islanding PQ events in the wind energy penetrated DG systems by means of features (Voltage and current (RMS, half-cycle, peak and fundamental) Frequency. Power Factor / Cos Phi. Power and energy (active, reactive, harmonic, apparent)). Since the hyperparameters play a significant role in overall classification performance, the hyperparameter tuning of the BiGRNN model takes place using chaotic crow search algorithm (CCSA). To examine the enhanced classification outcome of the AODL-ID technique, a set of experimental analyses is carried out and the outcomes are investigated interms of various evaluation metrics. The simulation outcomes highlighted the supremacy of the AODL-ID technique over the compared techniques.

Multi-dimensional graph fractional Fourier transform and its application to data compression

Many multi-dimensional signals appear in the real world, such as digital images and data that has spatial and temporal dimensions. Designing a transform method to process these multi-dimensional signals in graph fractional domain is a key challenge in graph signal processing. This paper investigates the novel transform for multi-dimensional graph signals defined on Cartesian product graph and studies several related properties. Our work includes: (i) proposing the two-dimensional graph fractional Fourier transforms using two basic graph signal processing methods i.e. based on Laplacian matrix and adjacency matrix; (ii) extending the two-dimensional transforms to multi-dimensional graph fractional Fourier transforms (MGFRFT). MGFRFT provides an additional fractional analysis tool for multi-dimensional graph signal processing; (iii) exploring the advantages of MGFRFT in terms of spectrum, directional characteristics and computational time; (iv) applying the proposed transform to data compression to highlight its utility and effectiveness.

Hardware and software complex for the detection and identification of informative features in images of means of nonverbal communication

Subject of study. Techniques for identifying informative features in dynamic images (video) of means of nonverbal communication of operators during a joint constructive activity were the subject of this study. Aim of study. This study aimed to develop methods for detecting and identifying significant features of nonverbal communication in the received dynamic (video) images of faces of partners, i.e., key features in the joint activity of partners upon disabling the verbal communication channel. Method. A technique for interaction was developed, with a hardware and software complex designed for identifying communication markers in images of facial expressions and eye movements of partners during a joint play activity, in which nonverbal communication is a key aspect and the speech communication channel is disabled. The joint activity involved one partner searching for a hidden target in the perceived image using a nonverbal clue from the second partner, who views the target position on his screen. Partners were chosen among representatives of two geographically distant cultures—Russian and Chinese. The interactions of the partners during their work with images of the object of activity received via simultaneous video communication in two research centers located in western and eastern Eurasia were evaluated through simultaneously recorded physiological parameters. Main results. A new algorithm for investigating images as a means of nonverbal interaction for ensuring the achievement of a shared goal without the speech communication channel was designed. The effectiveness of eye movements of the partners, which are the key features of nonverbal communication, was demonstrated during the constructive joint activity. The task was presented to Chinese and Russian test subjects in the form of English text. Based on the performed measurements, nonverbal interaction in the task of looking for the target position functions similarly for representatives of both cultures and depends on the roles of the partners and the type of executed task. The correlation between the activities of different neural networks of the brain that ensure the recognition of images during the target search in nonverbal communication was identified. An analysis of the images of eye movement during the communication isolated a classic pattern of eye capture for guided and guiding partners. Constant switching occurs between the assessment of images of partners’ eyes and the target search in the entire image. The obtained results suggest that 1) the cognitive process of recognition of facial expressions is probably the most complex visual cognitive process, and 2) universal behavioral algorithms exist when a common joint activity understandable to both partners is performed. Practical significance. A hardware and software complex for detecting significant features of nonverbal communication in the images of faces was designed. The working capacity of the proposed method for detecting features was demonstrated using clear basic signals of nonverbal communication, thus enabling new methods of training artificial neural networks to ensure intuitive nonverbal communication between machines and humans via the recognition of hidden markers of nonverbal communication.

ECG Simulator Based on Microcontroller Equipped with Arrhythmia Signal

Electrocardiograph (ECG) is one of the diagnostic sciences that is often studied in modern medicine, used to detect damage to the components of the heart or disorders of the heart rhythm called arrhythmias. The purpose of this research is to develop an Electrocardiograph simulator that is equipped with arrhythmia. The main design consists of an Arduino Mega 2560 microcontroller, MCP4921 DAC (Digital to Analog Converter) circuit, a network resistor, and a sensitivity selection circuit. The MCP4921 type DAC converts the digital signal data into analog data which will then be forwarded to the resistor network circuit as a signal formation for each lead. The basic signal image data used for the formation of normal Electrocardiograph and arrhythmias were taken from the Electrocardiograph recorder using Phantom Electrocardiograph. Based on the readings on the Beat Per Minute setting of the module to the Beat Per Minute printout on the Electrocardiograph recorder, the error rate value for the Normal Sine Rhythm parameter is 0.790% for Beat Per Minute 30, 0.383% for Beat Per Minute 60, 0.535% for Beat Per Minute 120, 0.515% for Beat Per Minute 180 and 0.593% for Beat Per Minute 240. The error rate for the Arrhythmia parameter is 2.076% for ventricular tachycardia Beat Per Minute 160 and 0.494% for Supraventricular Tachycardia Beat Per Minute 200. The design of the Electrocardiograph simulator can simulate the signals of the human body and it can be used as a medium in the learning process in the world of health

Real-Life Application of a Wearable Device towards Injury Prevention in Tennis: A Single-Case Study.

The purpose of this article is to present the use of a previously validated wearable sensor device, Armbeep, in a real-life application, to enhance a tennis player’s training by monitoring and analysis of the time, physiological, movement, and tennis-specific workload and recovery indicators, based on fused sensor data acquired by the wearable sensor—a miniature wearable sensor device, designed to be worn on a wrist, that can detect and record movement and biometric information, where the basic signal processing is performed directly on the device, while the more complex signal analysis is performed in the cloud. The inertial measurements and pulse-rate detection of the wearable device were validated previously, showing acceptability for monitoring workload and recovery during tennis practice and matches. This study is one of the first attempts to monitor the daily workload and recovery of tennis players under real conditions. Based on these data, we can instruct the coach and the player to adjust the daily workload. This optimizes the level of an athlete’s training load, increases the effectiveness of training, enables an individual approach, and reduces the possibility of overuse or injuries. This study is a practical example of the use of modern technology in the return of injured athletes to normal training and competition. This information will help tennis coaches and players to objectify their workloads during training and competitions, as this is usually only an intuitive assessment.

Effects of Provenance, Transport Processes and Chemical Weathering on Heavy Mineral Composition: A Case Study From the Songhua River Drainage, NE China

Understanding the heavy mineral composition of the Songhua River basin in NE China and the influencing factors (e.g., provenance, transport processes and chemical weathering) is crucial for the study of both the source-to-sink processes and the drainage evolution in the region. To this end, a total of 43 samples were collected from the river bars and terraces of the main and tributary streams of the Songhua River, and analyzed for heavy minerals in different grain-size fractions based on the novel automated TESCAN Integrated Mineral Analyzer (TIMA) combined with standard optical method. The results show that the tributaries originating from different mountains have significantly different heavy mineral composition. The locally occurring basic source signal in the tributaries of the Nenjiang River (the largest tributary of the Songhua River) are not well preserved in the Nenjiang River and the Songhua River trunk streams, indicating that the control of sources on heavy mineral composition is influenced by fluvial processes. Additionally, significant differences in the heavy mineral composition of different reaches of the same river also indicate that the heavy mineral composition is significantly influenced by fluvial processes. Influenced by hydraulic sorting during river processes, heavy minerals are enriched in different size fractions with the low-density minerals systematically overestimated in a wide window, suggesting an advantage of multi-window policy over wide window policy. In contrast to modern river sediments, the original heavy mineral composition of river terrace sediments has been severely damaged due to chemical weathering, so the degree of chemical weathering of terrace sediments needs to be evaluated first in provenance tracing and paleo-drainage evolution studies. TIMA has an irreplaceable role in identifying mineral species, additional images and elemental composition, and however, it performs poorly in identifying polycrystalline minerals, thus the combination with traditional methods can obtain more complete and accurate information.

Modulator Coefficient Tracking Method of Resonant Integrated Optical Gyroscope Based on Novel Four-State Modulation

A modulator coefficient tracking system (MCTS) with the novel four-state modulation technique is proposed to improve the stability of scale factor for resonant integrated optical gyroscope (RIOG). Firstly, we analyze the influence mechanism of integrated optical phase modulator (IOPM) coefficient on the scale factor stability of RIOG. Then, the novel four-state modulation technique is proposed to synchronously detect angular velocity information and IOPM coefficient without additional optical devices, which consists of the basic triangular modulation signal and the triangular signal for demodulating modulator coefficient. Finally, MCTS based on the novel four-state modulation technique is designed to real-timely track the modulator coefficient and adjust loop-gain to be constant for improving the scale factor stability of RIOG. The experimental results show that the tracking error of MCTS is 0.23% from −40 °C to +60 °C and the scale factor stability of RIOG is optimized to 163.37 ppm. The proposed MCTS with the novel four-state modulation technique can significantly improve the performance of RIOG, which contributes to promoting the applications of RIOGs.

Simultaneously Transmitting and Reflecting (STAR) RIS Aided Wireless Communications

The novel concept of simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RISs) is investigated, where the incident wireless signal is divided into transmitted and reflected signals passing into both sides of the space surrounding the surface, thus facilitating a full-space manipulation of signal propagation. Based on the introduced basic signal model of `STAR', three practical operating protocols for STAR-RISs are proposed, namely energy splitting (ES), mode switching (MS), and time switching (TS). Moreover, a STAR-RIS aided downlink communication system is considered for both unicast and multicast transmission, where a multi-antenna base station (BS) sends information to two users, i.e., one on each side of the STAR-RIS. A power consumption minimization problem for the joint optimization of the active beamforming at the BS and the passive transmission and reflection beamforming at the STAR-RIS is formulated for each of the proposed operating protocols, subject to communication rate constraints of the users. For ES, the resulting highly-coupled non-convex optimization problem is solved by an iterative algorithm, which exploits the penalty method and successive convex approximation. Then, the proposed penalty-based iterative algorithm is extended to solve the mixed-integer non-convex optimization problem for MS. For TS, the optimization problem is decomposed into two subproblems, which can be consecutively solved using state-of-the-art algorithms and convex optimization techniques. Finally, our numerical results reveal that: 1) the TS and ES operating protocols are generally preferable for unicast and multicast transmission, respectively; and 2) the required power consumption for both scenarios is significantly reduced by employing the proposed STAR-RIS instead of conventional reflecting/transmiting-only RISs.

A reinforcement‐learning approach for individual pitch control

AbstractIndividual pitch control has shown great capability of alleviating the oscillating loads experienced by wind turbine blades due to wind shear, atmospheric turbulence, yaw misalignment, or wake impingement. This work presents a novel controller structure that relies on the separation of low‐level control tasks and high‐level ones. It is based on a neural network that modulates basic periodic pitch angle signals. This neural network is trained with reinforcement learning, a trial and error way of acquiring skills, in a low‐fidelity environment exempt from turbulence. The trained controller is further deployed in large eddy simulations to assess its performances in turbulent and waked flows. Results show that the method enables the neural network to learn how to reduce fatigue loads and to exploit that knowledge to complex turbulent flows. When compared to a state‐of‐the‐art individual pitch controller, the one introduced here presents similar load alleviation capacities at reasonable turbulence intensity levels, while displaying very smooth pitching commands by nature.

Metagenomics analysis of cultured mucosal bacteria from colorectal cancer and adjacent normal mucosal tissues.

Introduction. Colorectal cancer (CRC) is one of the most common cancers worldwide. Multiple risk factors are involved in CRC development, including age, genetics, lifestyle, diet and environment. Of these, the role of the gut microbiota in cancer biology is increasingly recognized.Hypothesis/Gap Statement. Micro-organisms have been widely detected in stool samples, but few mucosal samples have been detected and sequenced in depth.Aim. Analysis of cultured mucosal bacteria from colorectal cancer and adjacent normal mucosal tissues with metagenomics sequencing.Methodology. Twenty-eight paired tumour and non-tumour tissues from 14 patients undergoing surgery for CRC were analysed. We removed the influence of eukaryotic cells via culture. The composition of mucosal microbiota in intestinal mucosa were detected and analysed with metagenomic sequencing.Results. Compared with non-cultured mucosal sample, 80 % bacteria species could be detected after culture. Moreover, after culture, additional 30 % bacteria could be detected, compared with non-cultured samples. Since after culture it was difficult to estimate the original abundance of microbiome, we focused on the identification of the CRC tissue-specific species. There were 298 bacterial species, which could only be cultured and detected in CRC tissues. Myroides odoratimimus and Cellulophaga baltica could be isolated from all the tumour samples of 14 CRC patients, suggesting that these species may be related to tumour occurrence and development. Further functional analysis indicated that bacteria from CRC tissues showed more active functions, including basic metabolism, signal transduction and survival activities.Conclusion. We used a new method based on culture to implement information on prokaryotic taxa, and related functions, which samples were from colorectal tissues. This method is suitable for removing eukaryotic contamination and detecting micro-organisms from other tissues.

Intraventricular hemorrhage induces inflammatory brain damage with blood-brain barrier dysfunction in immature rats.

We aimed to characterize a preclinical model of intraventricular hemorrhage-induced brain damage (IVH-BD) in extremely low birth weight newborns (ELBWN), to identify potential therapeutic targets based on its pathophysiology. IVH was induced in 1-day-old (P1) Wistar rats by left periventricular injection of clostridium collagenase (PVCC). At P6, P14, and P45 IVH-BD (area of damage, motor and cognitive deficits, Lactate/N-acetylaspartate ratio), white matter injury (WMI: ipsilateral hemisphere and corpus callosum atrophy, oligodendroglial population and myelin basic protein signal reduction), blood-brain barrier (BBB) dysfunction (occludin and Mfsd2a expression, Gadolinium leakage) and inflammation (TNFα, TLR4, NFkB, and MMP9 expression; immune cell infiltration), excitotoxicity (Glutamate/N-acetylaspartate), and oxidative stress (protein nitrosylation) were assessed. Sham animals were similarly studied. IVH-BD leads to long-term WMI, resulting in motor and cognitive impairment, thus reproducing IVH-BD features in ELBWN. BBB dysfunction with increased permeability was observed at P6 and P14, coincident with an increased inflammatory response with TLR4 overexpression, increased TNFα production, and increased immune cell infiltration, as well as increased excitotoxicity and oxidative stress. This model reproduced some key hallmarks of IVH-BD in ELBWN. Inflammation associated with BBB dysfunction appears as relevant therapeutic target to prevent IVH-BD-induced WMI. Paraventricular injection of clostridium collagenase (PVCC) to 1-day-old Wistar rats uniquely reproduced the neuroimaging, histologic and functional characteristics of intraventricular hemorrhage-induced brain damage (IVH-BD) in extremely low birth weight newborns (ELBWN). PVCC-induced IVH triggered a prolonged inflammatory response associated with blood-brain barrier increased permeability, which in turn facilitates the infiltration of inflammatory cells. Thus, PVCC led to white matter injury (WMI) resulting in long-term motor and cognitive impairment. This model offers a valuable tool to obtain further insight into the mechanisms of IVH-BD in ELBWN and proposes some key therapeutic targets.

Dealing with Mathematically Difficult Topics in Electrical and Electronic Engineering

Due to insufficient mathematical background, electrical and electronic engineering students may have difficulties in handling essential concepts and processes related to their field. For example, it has been observed that students find it difficult to comprehend topics such as Fourier analysis, noise, Maxwell equations and wave propagation etc. The article describes an attempt to approach such concepts in a two-step manner, first the topics are addressed in an introductory way (by means of particular, yet representative, examples and simple mathematics) and, when the students have got the necessary background and confidence, the topics are addressed in a more formal manner. As a case-study, the proposed approach has been applied to topics such as basic signal quantities, Fourier analysis, noise and chromatic dispersion in optical fibers.

A Comparison Study for Cardiac Arrhythmias Noise Cancellation Techniques

The electrocardiogram (ECG), which represents the electrical activity of the heart, is always chosen as the basic signal for diagnosing cardiac abnormalities and detecting the patient's states. Generally, the various filters are applied (preprocessing) to denoise the artifacts from ECG signal. This step makes the decision making and diagnosis simpler and faster. The most common noise sources are power line interference and baseline wandering. This article discusses different filtering techniques used to denoise the ECG signals. Electrocardiogram signals were downloaded from the MIT-BIH Arrhythmia Database. The MIT-BIH Database was the first set of standard test materials that is generally available to evaluate arrhythmia detection. To remove baseline wander, 4 filters were designed. The filters are a fourth-order high-pass Butterworth filter, a second-order finite impulse response Hamming window, an finite impulse response rectangular window with length L, and an finite impulse response Hanning window. To extract the power line interference (60 Hz), 4 filters were designed: a second-order digital bandpass Butterworth filter, a second-order finite impulse response filter, a Chebyshev filter, and an finite impulse response notch filter with Hanning window. Finally, 5 parameters were used to evaluate filter performance; the parameters are frequency response, phase response, average filter delay (group delay), phase delay, and signal-to-noise ratio. The results show that, for baseline wander filters, a fourth-order high-pass Butterworth filter removes more noise and has no time delay. Power line interference filters have the same signal-to-noise ratio approximately.

Radar Emitter Classification Based on a Multiperspective Collaborative Clustering Method and Radar Characteristic Spectrum

Modern cognitive electronic reconnaissance methods for radar systems must contend with the complex electromagnetic environments arising from the deployment of multiple signal sources and radar countermeasures, which greatly limit access to the degree of prior information required to enable effective target recognition. The present work addresses this issue by proposing a multiperspective collaborative clustering method for sorting radiation sources based on the multiperspective information of radar signals. In contrast to conventional collaborative training approaches, which are suitable only for semisupervised learning, the proposed multiperspective collaborative clustering method performs unsupervised clustering, cluster label transfer, and dimensionality reduction by linear discriminant analysis iteratively based on the differences between the clustering results obtained from two signal perspectives radiation signal sorting can be conducted in a noncooperative context. The results of comparative experiments demonstrate that the proposed multiperspective sorting method can make full use of the difference information between basic signal characteristics and intrapulse features and thereby improve the accuracy of clustering-based radiation source sorting. Accordingly, the sorting ability of the proposed method is superior to those of other state-of-the-art clustering methods and that of the single-perspective clustering-based sorting method.

Characterization of resting functional MRI activity alterations across epileptic foci and networks.

Brain network alterations have been studied extensively in patients with mesial temporal lobe epilepsy (mTLE) and other focal epilepsies using resting-state functional magnetic resonance imaging (fMRI). However, little has been done to characterize the basic fMRI signal alterations caused by focal epilepsy. Here, we characterize how mTLE affects the fMRI signal in epileptic foci and networks. Resting-state fMRI and diffusion MRI were collected from 47 unilateral mTLE patients and 96 healthy controls. FMRI activity, quantified by amplitude of low-frequency fluctuations, was increased in the epileptic focus and connected regions in mTLE. Evidence for spread of this epileptic fMRI activity was found through linear relationships of regional activity across subjects, the association of these relationships with functional connectivity, and increased activity along white matter tracts. These fMRI activity increases were found to be dependent on the epileptic focus, where the activity was related to disease severity, suggesting the focus to be the origin of these pathological alterations. Furthermore, we found fMRI activity decreases in the default mode network of right mTLE patients with different properties than the activity increases found in the epileptic focus. This work provides insights into basic fMRI signal alterations and their potential spread across networks in focal epilepsy.

Study on body area network of smart clothing for physiological monitoring

With the popularity of the concept of big health and the important role of wearable devices in medical health, users pay more attention to the collection and acquisition of physiological data, but wearable devices attached to human users are independent, and the degree of data sharing is low. Improving the data sharing, accuracy, and reliability of wearable device monitoring is a problem that the article needs to study and solve. Specifically, the researcher summarizes the characteristics of the physiological monitoring smart clothing, and the basic physiological data parameters of human body, and analyzes the collection of three basic signals of electrocardiogram, body temperature, and human movement. This article summarizes the requirements and key technologies of body area network transmission of smart clothing, and studies the body area network node design, energy consumption optimization mode, and network architecture of physiological monitoring smart clothing. At the same time, based on the previous research, the multi-interaction process of smart clothing is formed, and then the standard evaluation system of smart clothing body domain network for evaluation is proposed. The results show that the optimized structure of the body area network of smart clothing proposed by the researchers is efficient, convenient, and mobile, and meets the characteristics of safety, reliability, low power consumption, and portability of smart clothing, especially in the field of physiological monitoring. The standard evaluation system of smart clothing body area network provides a practice-oriented theoretical reference for the current research of smart clothing body area network.

Research Progress of Physiologic Parameters Monitoring Technology for Critical Neonates

Physiological parameter monitoring is essential to medical staff to evaluate, diagnose and treat patients in neonatal intensive care unit (NICU). Monitoring in NICU includes basic vital signal monitoring and functional monitoring. Basic vital signal monitoring (including ECG, respiration, SpO2, blood pressure, temperature) is advanced and focus on study of usability, continuity and anti-interference. Functional monitoring (including respiratory function, circulatory function, cerebral function) still focus on study of monitoring precision and reliability. Meanwhile, video monitoring and artifact intelligence have presented well performance on improving monitoring precision and anti-interference. In this article, the main parameters and relevant measurement technology for monitoring critical neonates were described.

Recent Advances in Wearable Optical Sensor Automation Powered by Battery versus Skin-like Battery-Free Devices for Personal Healthcare-A Review.

Currently, old-style personal Medicare techniques rely mostly on traditional methods, such as cumbersome tools and complicated processes, which can be time consuming and inconvenient in some circumstances. Furthermore, such old methods need the use of heavy equipment, blood draws, and traditional bench-top testing procedures. Invasive ways of acquiring test samples can potentially cause patient discomfort and anguish. Wearable sensors, on the other hand, may be attached to numerous body areas to capture diverse biochemical and physiological characteristics as a developing analytical tool. Physical, chemical, and biological data transferred via the skin are used to monitor health in various circumstances. Wearable sensors can assess the aberrant conditions of the physical or chemical components of the human body in real time, exposing the body state in time, thanks to unintrusive sampling and high accuracy. Most commercially available wearable gadgets are mechanically hard components attached to bands and worn on the wrist, with form factors ultimately constrained by the size and weight of the batteries required for the power supply. Basic physiological signals comprise a lot of health-related data. The estimation of critical physiological characteristics, such as pulse inconstancy or variability using photoplethysmography (PPG) and oxygen saturation in arterial blood using pulse oximetry, is possible by utilizing an analysis of the pulsatile component of the bloodstream. Wearable gadgets with “skin-like” qualities are a new type of automation that is only starting to make its way out of research labs and into pre-commercial prototypes. Flexible skin-like sensing devices have accomplished several functionalities previously inaccessible for typical sensing devices due to their deformability, lightness, portability, and flexibility. In this paper, we studied the recent advancement in battery-powered wearable sensors established on optical phenomena and skin-like battery-free sensors, which brings a breakthrough in wearable sensing automation.

Inverse Filtering of Magnetic Prospection Data—A Gateway to the Social Structure of Cucuteni–Tripolye Settlements?

Magnetic mapping is a common method for investigating archaeological sites. Typically, the magnetic field data are treated with basic signal improving processing followed by image interpretation to derive the location and outline of archaeological objects. However, the magnetic maps can yield more information; we present a two-step automatized interpretation scheme that enables us to infer the social structure from the magnetic map. First, we derive the magnetization distribution via inverse filtering by assuming a constant depth range for the building remains. Second, we quantify the building remains in terms of their total magnetic moment. In our field example, we consider this quantity as a proxy of household prosperity and its distribution as a social indicator. The inverse filtering approach is tested on synthetic data and cross-checked with a least-squares inversion. An extensive modeling study highlights the influence of depth and thickness of the layer for the filter construction. We deduce the rule of thumb that by choosing a rather too deep and too thick a layer, errors are smaller than for layers too low and too thin. The interpretation scheme is applied to the magnetic gradiometry map of the Chalcolithic Cucuteni–Tripolye site Maidanetske (Ukraine) that comprises the anomalies of about 2300 burned clay buildings. The buildings are arranged along concentric ellipses around an inner vacant space and a vacant ring. Buildings along this ring corridor have increased total magnetic moments. The total magnetic moment indicates the remaining building material and therefore the architecture. Lastly, architecture can reflect economic or social status. Consequently, the increased magnetic moment of buildings along the ring corridor indicates a higher economic or social status. The example of Maidanetske provides convincing evidence that the inversion of magnetic data and the quantification of buildings in terms of their magnetic moments enables the investigation of the social structure within sites.

Nonlinear analysis of cylindrical gear dynamics under varying tooth breakage

Linear and nonlinear analyses served to detect the degree of tooth damage in a cylindrical gear. The gear in question is widely used in industrial applications and is often subject to gear tooth damage. Multiple-sourced vibrations affecting the test stand may distort or conceal the vibration components responsible for tooth failure. Given its construction, the test stand offers low stiffness, and additional control and measurement equipment further contribute to overall vibrations. Therefore, the vibration signal is likely to contain various disturbances, which further hinder correct diagnosis. Basic vibroacoustic signal analysis methods, which were employed at a preliminary stage, failed to produce unambiguous and satisfactory results. Hence, nonlinear recurrence analysis of gear operation dynamics was applied. It is assumed that the occurrence of damage to the gear will impede the regularity of drive transmission. The adopted research method addresses the limitations of a two-state diagnosis of gear condition (no damage/damage) by allowing the extent of gear tooth damage to be specified.