Time Domain Research Articles

Time–Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks

Existing deep learning-based seismic signal denoising methods primarily operate in the time domain. Those methods are ineffective when noise overlaps with the seismic signal in the time domain. Time–frequency domain-based deep learning methods are relatively rare and usually employ single loss function, resulting in suboptimal performance on low SNR signals and potential damage to P wave. This paper proposes a method based on generative adversarial networks (GANs). Compared to convolutional neural networks, the discriminator in GANs helps retain more true signal details by judging denoising performance. Additionally, an attention mechanism is introduced to fully extract signal features, and a perceptual loss is employed to evaluate the difference between the denoised result and the target’s high-level features. Experimental results show that this method can effectively improve SNR and ensure that the denoised result is close to the true signal. Furthermore, by comparing DeepDenoiser and ARDU, it is proven that the proposed method achieves better denoising performance, especially for low SNR signals, while causing less damage to the seismic signals.

ECG screening and prevention of sudden cardiac death in endurance athletes over 40

Abstract Introduction CV diseases increase with age. There are many athletes aged >40 years who practise in certain endurance sports (marathon, ultramarathon and cycling), that involve very high intensity physical exertion and prolonged efforts. This population, like others athletes, undergo to annual examination with a medical history, spirometry, urine test and exercise test. CVdisease and sudden cardiac death (SCD) are the main cause of mortality in athletes during sport and this specific population group are at increased CV risk due to their multiple comorbidities, so this kind of screening remains controversial. Aims The primary aim of the study is to evaluate whether a 5-minute 'mini Holter ECG', associated with the conventional examination, can have an additional role for early screening and prevention of CV diseases in older endurance sport population. Materials The population were selected as part of the larger 'Heart And Run' project, an ECG collection of endurance athletes of the main sports clubs in Rome - Italy. A control group of healty individiuals was collected for comparison. The ECG traces were recorded at rest for 5 minutes with the Kardia Mobile device 6L™. HRV indices were analysed using the software Kubios. The study group were compared for an ECG morphological analysis and HRV parameters in the time and frequency domain. Results A total of 353 subjects (182 athletes, 171 controls) were analysed. The athlets show a significantly higher incidence of sinus arrhythmias, LAFB, 1°degree AVB, Jpoint elevation and STsegment abnormalities. We found also a significantly shorter QTc in athlets compared to controls (395.95 ± 29.51 ms vs. 411.6 ± 29.01 ms), suggestive of greater protection from malignant ventricular arrhythmias. In line with the ECG findings, the HRV PNS (-0.25 ± 1.25 vs -1.05 ± 1.45) and SNS (0.69 ± 1.44 vs 2.38 ± 1.89) indices were respectively higher and lower in the athletes group than in the controls group. Differences between the two groups were also found in the time and frequency HRV domain analysis (SDNN, RMSSD, NN50 and pNN50). Due to the absence of normality range, we considered values lower of the 5th or higher of the 95th percentile range, for the main HRV parameters, as abnormals. We identified 29/182 virtually abnormal HRV individuals. Those patients could be at increased CV risk, worthy of further investigation. In total, out-of-range HRV values or morphological abnormalities were detected in 54 athletes (29%), a population that will need further attention through follow-up and additional examinations. Conclusions Despite competitive endurance sports are associated with increased vagal tone, which is virtually protective against CV diseases, even in athletes over 40, the addition of the 5-minute cardiac 'mini Holter' could increase the sensitivity in detection of CV disease, in a specific high-risk group of athletes, suggesting additional clinical and instrumental investigations in such patients.

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

AbstractFault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning‐based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation‐based augmentation) may lead to distortion of multi‐view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real‐world datasets validate the effectiveness of the proposed method.

Evaluation of the single-frequency variometric approach based on low-cost GNSS observations and different satellite combinations for detecting short-term dynamic behaviors

This study presents the capability of the single-frequency (SF) variometric approach (VA) technique with low-cost GNSS observations to detect short-term dynamic behaviors. Harmonic oscillations with amplitudes between 5 and 20 mm and frequencies between 0.3 and 5.0 Hz were generated employing a single-axis shake table to investigate the performance of the SF-VA technique in the structural health monitoring (SHM) system. Besides, a Mw 6.9 Kobe, 1995 earthquake simulation was generated using the shake table to analyze the SF-VA performance for the earthquake early warning (EEW) system. A low-cost u-blox ZED-F9P GNSS receiver and ANN-MB-00 patch antenna were used to collect GNSS observations at a 20 Hz sampling rate during the experiments. The observations were processed using the MATLAB-based open-source PPPH-VA software in real-time (RT) mode, considering eight different satellite combinations. The capability of the SF-VA technique to detect horizontal dynamic behaviors in RT mode was investigated in the frequency and time domains, accepting the displacements from the linear variable differential transformer sensor as a reference. The results in the frequency domain demonstrate that the SF-VA technique with low-cost GNSS observations can successfully detect the peak frequency value of short-term harmonic oscillations up to 5 Hz. Moreover, time domain findings emphasize that the short-time dynamic oscillations can be determined with the SF-VA technique with an accuracy ranging from 0.8 to 6.4 mm. Earthquake simulation experiment results demonstrate that the strong ground motions caused by mega earthquakes can be determined at mm-level by the SF-VA method. The results of both experiments show that multi-GNSS observations contribute to the SF-VA technique considerably. Overall, the findings reveal that the SHM and EEW systems can be operated with low-cost GNSS receivers, and the natural frequency of the man-made structures and accurate displacement values of seismic waveforms can be determined in RT with the SF-VA technique.

Application of multi-objective optimization genetic algorithm to design terahertz metamaterials with fano resonances

In the prosperous development of terahertz (THz) metamaterials, Fano resonances have gained attention due to their potential applications in ultrasensitive systems. The performance of Fano resonance is directly influenced by the geometrical parameters of the element structure. However, the traditional design rules for Fano resonances in metamaterials rely on an empirical trial-and-error strategy, necessitating significant effort to achieve optimal results. To address this issue, we propose a design method in this study that utilizes the finite integration technique in time domain (FITD) along with a multi-objective optimization genetic algorithm for the intelligent design of metamaterial structures exhibiting the Fano resonance phenomenon. The FITD method is primarily used to calculate the Fano resonance with different metamaterial geometric structure parameters, while the genetic algorithm efficiently selects the optimal solution. Our method, characterized by high efficiency and complete independence from prior knowledge, could offer a new design technique for metamaterials with specific functions, thereby contributing to the development of THz applications.

Detection and Analysis of Abnormal High-Current Discharge of Cylindrical Lithium-Ion Battery Based on Acoustic Characteristics Research

The improvement of battery management systems (BMSs) requires the incorporation of advanced battery status detection technologies to facilitate early warnings of abnormal conditions. In this study, acoustic data from batteries under two discharge rates, 0.5 C and 3 C, were collected using a specially designed battery acoustic test system. By analyzing selected acoustic parameters in the time domain, the acoustic signals exhibited noticeable differences with the change in discharge current, highlighting the potential of acoustic signals for current anomaly detection. In the frequency domain analysis, distinct variations in the frequency domain parameters of the acoustic response signal were observed at different discharge currents. The identification of acoustic characteristic parameters demonstrates a robust capability to detect short-term high-current discharges, which reflects the sensitivity of the battery’s internal structure to varying operational stresses. Acoustic emission (AE) technology, coupled with electrode measurements, effectively tracks unusually high discharge currents. The acoustic signals show a clear correlation with discharge currents, indicating that selecting key acoustic parameters can reveal the battery structure’s response to high currents. This approach could serve as a crucial diagnostic tool for identifying battery abnormalities.

ECG screening and Sudden Cardiac Death prevention in endurance athletes

Abstract Introduction Sudden cardiac death (SCD) is the leading cause of mortality in athletes during sport activity. Identifying non-invasive and repeatable risk markers during annual athletes screening remains a crucial goal for the scientific community for the prevention of cardiovascular disease. The ECG, exercise test and spirometry are part of the annual screening program in the sport population over 40 years of age. The Heart Rate Variability (HRV) has been shown to be an independent predictor of mortality in populations with CV disease, but its relationship with SCD has been little explored in those at low risk, including athletes. This study aims to use a 5 minute ECG screening in a specific population of athletes practicing endurance sports and evaluate its clinical repercussions. Aims The primary aim of the study is to assess whether morphological and spectral ECG analysis in endurance athletes by the 5-minute 'mini Holter ECG' can add information to the annual screening for the prevention of CV diseases and SCD. Materials The study population were selected from Endurance Marathon athletes. A control group was also collected for comparison. The 5 minutes ECG was recorded at rest with the Kardia Mobile 6L™ device that use three electrodes, which allows 6 leads recording. HRV indices were obtained using the Kubios software. A morphological analysis was performed and the HRV parameters were analyzed in the time and frequency domains. Results A total of 554 subjects (220 athletes-334 controls, 325 male-229 women) were analyzed. A higher incidence of sinus arrhythmias, LAFB, 1stdegree AVB, J point/ST-segment abnormalities in athletes compared to controls was observed. Athletes had a significantly shorter QTc than the control group (394.87±30.55 ms vs. 407.01±29.09 ms), suggestive of greater protection from malignant ventricular arrhythmias. The HRV PNS (-0.73±1.25 vs -0.21±1.45) and SNS (1.71±1.91 vs 0.58 ± 1.43) indices were also higher and lower in the athlete group than in the control group, suggestive for higher vagal tone. Given the absence of a normality range of standard HRV parameters in the athlete population, values in the normal range of the 5th to 95th percentile were extrapolated in the whole population for the main HRV parameters. The project, therefore, is to evaluate patients with HRV values higher or lower than 5-95 percentile to identify those deserving further clinical investigations and closer follow-up for suspected increased risk of CV disease and SCD. Conclusions The 5-minute cardiac Holter, combining a more accurate analysis in terms of morphology and arrhythmias than the 12-lead ECG, allows a greater number of static and dynamic alterations to be detected; moreover, the analysis of HRV could provide even more information, such that it could be considered as a complementary tool during the examination and FUP of athletes. Further long-term prospective studies are needed to correlate alterations in HRV and clinical events.

Optimal Scale-Invariant Wavelet Representation and Filtering of Human Otoacoustic Emissions.

Otoacoustic emissions (OAEs) are generated in the cochlea and recorded in the ear canal either as a time domain waveform or as a collection of complex responses to tones in the frequency domain (Probst et al. J Account Soc Am 89:2027-2067, 1991). They are typically represented either in their original acquisition domain or in its Fourier-conjugated domain. Round-trip excursions to the conjugated domain are often used to perform filtering operations in the computationally simplest way, exploiting the convolution theorem. OAE signals consist of the superposition of backward waves generated in different cochlear regions by different generation mechanisms, over a wide frequency range. The cochlear scaling symmetry (cochlear physics is the same at all frequency scales), which approximately holds in the human cochlea, leaves its fingerprints in the mathematical properties of OAE signals. According to a generally accepted taxonomy (Sher and Guinan Jr,J Acoust Soc Am 105:782-798, 1999), OAEs are generated either by wave-fixed sources, moving with frequency according with the cochlear scaling (as in nonlinear distortion) or by place-fixed sources (as in coherent reflection by roughness). If scaling symmetry holds, the two generation mechanisms yield OAEs with different phase gradient delay: almost null for wave-fixed sources, and long (and scaling as 1/f) for place-fixed sources. Thus, the most effective representation of OAE signals is often that respecting the cochlear scale-invariance, such as the time-frequency domain representation provided by the wavelet transform. In the time-frequency domain, the elaborate spectra or waveforms yielded by the superposition of OAE components from different generation mechanisms assume a much clearer 2-D pattern, with each component localized in a specific and predictable region. The wavelet representation of OAE signals is optimal both for visualization purposes and for designing filters that effectively separate different OAE components, improving both the specificity and the sensitivity of OAE-based applications. Indeed, different OAE components have different physiological meanings, and filtering dramatically improves the signal-to-noise ratio.

Abnormal Detection and Fault Diagnosis of Adjustment Hydraulic Servomotor Based on Genetic Algorithm to Optimize Support Vector Data Description with Negative Samples and One-Dimensional Convolutional Neural Network

Because of the difficulty in fault detection for and diagnosing the adjustment hydraulic servomotor, this paper uses feature extraction technology to extract the time domain and frequency domain features of the pressure signal of the adjustment hydraulic servomotor and splice the features of multiple pressure signals through the Multi-source Information Fusion (MSIF) method. The comprehensive expression of device status information is obtained. After that, this paper proposes a fault detection Algorithm GA-SVDD-neg, which uses Genetic Algorithm (GA) to optimize Support Vector Data Description with negative examples (SVDD-neg). Through joint optimization with the Mutual Information (MI) feature selection algorithm, the features that are most sensitive to the state deterioration of the adjustment hydraulic servomotor are selected. Experiments show that the MI algorithm has a better performance than other feature dimensionality reduction algorithms in the field of the abnormal detection of adjustment hydraulic servomotors, and the GA-SVDD-neg algorithm has a stronger robustness and generality than other anomaly detection algorithms. In addition, to make full use of the advantages of deep learning in automatic feature extraction and classification, this paper realizes the fault diagnosis of the adjustment hydraulic servomotor based on 1D Convolutional Neural Network (1DCNN). The experimental results show that this algorithm has the same superior performance as the traditional algorithm in feature extraction and can accurately diagnose the known faults of the adjustment hydraulic servomotor. This research is of great significance for the intelligent transformation of adjustment hydraulic servomotors and can also provide a reference for the fault warning and diagnosis of the Electro-Hydraulic (EH) system of the same type of steam turbine.

Frequency multiplexed dual-pulse fiber-optic Φ-OTDR for a fading-free distributed fiber sound sensor

Distributed fiber-optic sensor utilizing optical interference signal detection of the Rayleigh backscattering (RBS) light along the fiber under test (FUT) has greatly progressed over the past 20 years. It can now realize real-time sound signal recovery along the FUT with a good fidelity and high sensitivity. However, the performance of the practical distributed fiber-optic sound sensor is still limited by the fading problem, as invalid spatial channels at unknown positions arise due to the low signal intensity at these positions and the resulting near-zero signal-to-noise ratio (SNR). In this paper, we propose a novel, to the best of our knowledge, fading-free fiber sensor structure by incorporating an acousto-optic frequency shift (AOFS) loop into the light source in a dual-pulse phase optical time domain reflectometry (Φ-OTDR). As a result of stable frequency shifting and feedback within the loop, the loop source can output a high-repetition-rate narrow pulse train consisting of multiple separated frequencies. This pulse train is further shaped into two pulse bursts by an acousto-optic modulator (AOM) and an in-balanced fiber-optic Mach–Zehnder interferometer (MZI). The pulse bursts are then sent into the sensing fiber, while the RBS light is detected using an Avalanche photodiode after an erbium-doped fiber amplifier. The experimental results show that the proposed structure is capable of vibration signal recovery with a good SNR all along the FUT. The fading-free performance is evaluated under different AOFS loop gains, showing significant potential in practical fiber sensor network applications.

Novel transient-state analysis approach for distributed temperature sensing based on spontaneous Raman scattering

The light scattering phenomenon in optical fibres has been implemented in several applications for years. Nowadays, commercial distributed sensing systems for temperature measurements – distributed temperature sensing (DTS) – are gaining more attention and are being applied in many fields. The DTS systems are commonly based on optical time domain reflectometry or optical frequency domain reflectometry analysis approaches. This paper presents the simulation model and primary experimental results of a novel approach for data analysis for DTS systems based on the measurement of the backscattered Raman anti-Stokes radiation: the optical transient-state domain reflectometry. The novelty of this approach is the fast and accurate proposed analysis algorithm, as well as the cost-effective, secure and reliable set-up, whose main aspect is the implementation of long, low-power input laser pulses.

Quantitative differentiation of normal and wound healed mice skin by terahertz time domain spectroscopy

Quantitative differentiation of normal and wound healed mice skin by terahertz time domain spectroscopy

Response of Semicircular Canyons and Movable Cylindrical Cavities to SH Waves in Anisotropic Half-space Geology

The development of tunnels or the laying of underground pipelines are essential engineering projects in modern society, and in canyon tunnels and underground pipeline projects, the surface motion and cavity edge motion have been topics of concern in ground vibration problems. In this paper, we investigate the wave scattering problem in an elastic half-space anisotropic medium containing a semicircular canyon and a subsurface movable cylindrical cavity by using the wave function expansion method, the complex function method and the mirror method. By deriving the governing equation and transforming it into the standard form of the Helmholtz equation satisfying the zero-stress boundary condition, we solve the corresponding displacement functions. Introducing a position correction coefficient, the scattered wave field in a half-space anisotropic medium is constructed by the mirror method, which improves the problem of scattered wave source singularity in anisotropic half-space medium. Then, combining the free boundary conditions with a Fourier series expansion method we solve for the unknown coefficients in the equations. The correctness of the method is verified by degenerating it to a classical analytic solution. Finally, using frequency and time domain analysis, we investigate the effects of the relevant parameters on the surface motion| w1|( w), the dynamic stress concentration factor (DSCF) and the displacement amplitude| w2|. The results show that rock anisotropy and the presence of semicircular canyons have a significant effect on the dynamic response of subsurface structures. This not only provides a theoretical basis for practical unlined tunnels or pipeline projects, but can also provide a basis for seismic design of underground structures.

Assessing heart rate fragmentation to predict atrial fibrillation in the general population aged 65

Abstract Rationale Screening the general population aged 65 for atrial fibrillation (AF) has been proposed as a preventive measure against potential complications. The dysfunction of the autonomic nervous system (ANS) plays an important role in atrial fibrillation (AF). Short-term HRV is conceptually attributed to physiologic vagal tone modulation. But recent data suggest that a component of short-term HRV, called heart rate fragmentation (HRF), is related to ANS breakdown and may be a predictor of AF occurrence. Metrics derived from heart rate variability (HRV) that depict heart rate fragmentation (HRF) have been suggested to reflect autonomic nervous system (ANS) dysfunction. Objectives To assess the predictive capacity of HRF markers for AF occurrence over an 18-year follow-up in individuals aged 65 at the study’s inspection and to develop a predictive score for AF onset. Methods A cohort study was designed to prospectively assess the predictive value of ANS activity level among general population regarding cardiovascular events and mortality. 1011 subjects aged 65 were enrolled in the study from September 2000 to December 2002. The study population had no history of AF and a low cardiovascular risk. A median follow-up of 17.8 years was managed. HRV data were acquired by 24-h Holter electrcardiogram (ECG) monitoring at baseline and assessed by frequency domain and time domain methods. Heart rate fragmentation using the percentage of inflection points (PIP) and alpha 1-index (using fractal evaluation of NN intervals) were then calculated. The study outcome was the development of AF during follow-up, defined as a standard 12-lead ECG recording or Holter recording with ≥ 30 s of AF. Results AF was diagnosed in 123 patients during the 18-year follow-up, representing a cumulative incidence of 13.4%. Using multivariate analysis, the occurrence of AF was independently predicted by the male gender (HR=1.74, 95%CI:1.16-2.63, p<0.01), a heart rate below 70bpm (HR=1.85, 95%CI:1.18-2.90, p<0.01), an increased percentage of NN intervals that differ by more than 50 ms from the previous interval (pnn50) (HR=1.69, 95%CI:1.06-2.70, p=0.03), an increased percentage of inflection points (HR=2.80, 95%CI:1.66-4.72,p<0.01), and a reduced alpha 1 index (HR=1.61, 95%CI:1.03-2.53, p=0.04). Given these results, a predictive score was built up based on the 4 independent predictors, ranging from 0 to 6 (Graphical Abstract). The high-risk group, defined as having a PROOF-AF score of 4 or higher, exhibited a 3.3-fold higher risk of developing AF (Figure). The PROOF-AF score demonstrated superior predictive performance compared to other available risk scores for incident AF such as CHARGE-AF score, C2HEST score, FHS score, and JMC score. Conclusion HRF parameters, included in the PROOF-AF score, may be used to identify individuals aged 65 without pre-existing health conditions who are at high-risk of developing AF and assist population screening.Graphical abstractFigure

Life prediction method of rolling bearing based on CNN-LSTM-AM

Bearing is the key component to determine the health of machinery, and it is of great significance to monitor its working status in real time and predict its remaining useful life. In recent years, the RUL prediction method based on deep learning has been widely used and achieved good prediction results. Here, a bearing life prediction method based on convolution neural network (CNN), long short term memory (LSTM) and attention mechanism (AM) is proposed. First of all, the time domain and frequency domain features of the original vibration signals of rolling bearings are extracted, and the extracted feature set is normalized as the input of CNN. The main function of CNN is to extract spatial features and reduce the dimension of the data. Then, using LSTM to extract the information that may be ignored by CNN, the feature information extracted by CNN-LSTM is input to the attention mechanism for weighting, and the key information is screened. And then more accurately represent the degradation characteristics of the equipment, and finally get the bearing remaining life. The performance of the model is verified by two sets of public data sets, and the experimental results show that it is compared with the CNN-LSTM method. The root mean square error (RMSE) index based on CNN-LSTM-AM method is reduced by 14.6 % and 13.8 % respectively, and the score index is increased by 2.0 % and 2.4 % respectively. The results show that the proposed method has higher accuracy in bearing RUL prediction.

Dynamic Clustering of Wind Turbines Using SCADA Signal Analysis

This work explores the ability to dynamically group the Wind Turbine (WT) of a Wind Farm (WF) based on the behavior of some of their Supervisory Control And Data Acquisition (SCADA) signals to detect the turbines that exhibit abnormal behavior. This study is centered on a small WF of five WTs and uses the observation that the same signals from different WTs in the same WF coherently evolve temporally in a time domain, describing very similar waveforms. In this contribution, averaged signals from the SCADA system are used and omit maximums, minimums and standard deviations, focusing mainly on velocities and other slowly varying signals. For the temporal analysis, sliding windows of different temporal durations are explored. The signals are encoded using the Discrete Cosine Transform, which reduces the problem’s dimensions. A hierarchical tree is built in each time window. Clusters are formed by pruning the tree using a threshold interpretable in terms of distance. It is unnecessary to work with an a priori known number of clusters. A protocol for enumerating the clusters based on the tree’s shape is then established, making it easier to follow the evolution of the clusters over time. The capability to automatically identify WTs whose signals differ from the group’s behavior can alert and program preventive maintenance operations on such WTs before a major breakdown occurs.

Modelling Li-V2O5 Batteries Using Galvanostatic Intermittent Titration Technique and Electrochemical Impedance Spectroscopy: Towards Final Applications

Given the relevance of lithium and post-lithium batteries as electrochemical energy storage systems, the peculiar crystalline structure of V2O5 and its doping capacity play key roles in lithium-ion battery technology. To integrate them in high-efficiency modules, systematic methodologies are required to estimate the state of charge in a reliable way and predict the Li-V2O5 battery’s performance according to their electrochemical phenomena, including two plateaus in the galvanostatic cycling curves and the dynamic behavior governed by the energy demand. Most state of charge estimation and battery modeling procedures are focused on conventional Li-batteries that show a unique plateau. In this work, we propose a systematic methodology based on the galvanostatic intermittent titration technique and electrochemical impedance spectroscopy to study battery performance in the time and frequency domains, respectively. The proposed methodology, with a time–frequency correlation, promotes a deeper understanding of the electrochemical phenomena and general behavior of Li-V2O5 batteries, allowing for its subsequent extrapolation to more complex and higher-capacity lithium and post-lithium batteries used in high-power applications with a minimum error.

Wide-range resistivity characterization of semiconductors with terahertz time-domain spectroscopy

Resistivity is one of the most important characteristics in the semiconductor industry. The most common way to measure resistivity is the four-point probe method, which requires physical contact with the material under test. Terahertz time domain spectroscopy, a fast and non-destructive measurement method, is already well established in the characterization of dielectrics. In this work, we demonstrate the potential of two Drude model-based approaches to extract resistivity values from terahertz time-domain spectroscopy measurements of silicon in a wide range from about 10−3 Ωcm to 102 Ωcm. One method is an analytical approach and the other is an optimization approach. Four-point probe measurements are used as a reference. In addition, the spatial resistivity distribution is imaged by X-Y scanning of the samples to detect inhomogeneities in the doping distribution.

Band Gap Modulation in Zn2TiO4 Spinels for Efficient UV-A Persistent Luminescence

Spinels are important materials for an application in bioimaging. The key advantage with spinel-type hosts is the presence of antisite defects, which act as charge reservoirs for trapping electrons and holes at complementary defect sites. This makes them a host system similar to a molecular system. Herein, we present a systematic approach to modulating the band gap of an inverse Zn2TiO4 spinel. With a change in ZnO concentration, the absorption band at 375 nm diminishes and disappears at a ZnO:TiO2 concentration of 1.40:1.00. The band gap of the material is modified from 3.30 to 4.40 eV. The crystal structure of the sample does not change drastically as determined using X-ray diffraction and Rietveld refinement. The Zn2TiO4 emits in the UV-A region with a lifetime in the time domain of `ns’. The sample also shows persistent luminescence of at least 15 min upon excitation with 254 nm with prominent emission in the UV-A region (300–390 nm). The present results open a new avenue for the synthesis of spinel hosts where the band gap can be modified with ease. The UV emission thus observed is expected to find usage in interesting applications like photocatalysis, anti-counterfeiting, water disinfecting, etc.

Investigation of Nerve Fiber Network in THz Spectrum Range

The behavior of the nerve fiber system of the spinal cord of animals in the THz wavelength range (0.1-3 THz) was experimentally studied using a time domain spectrometer. Terahertz wave transmittance of nerve tissue is investigated, when DC voltage is applied to the sample. It has been shown that when a constant voltage is applied along the nerve fibers (also in the absence of voltage), no noticeable changes occur in the transmitted THz wave, regardless of the orientation of the sample relative to the THz E-field vector. Significant changes in the electrodynamic properties of the sample were observed when the nerve fibers were parallel to the THz E-field vector and the applied voltage was perpendicular to the nerve fibers, particularly, resonant absorption was observed at frequencies of 0.6 THz and 2 THz.