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• Mental fatigue did not alter perceived effort during low-intensity cycling. • Wavelet analysis reveals EEG changes from mental fatigue during cycling. • Increased beta desynchronization and resynchronization at Cz due to mental fatigue. • Low-intensity cycling reduced perceived mental fatigue. This study explored how mental fatigue affects brain activity during a low-intensity bike task utilising a continuous wavelet transformation in electroencephalography (EEG) analysis. The aim was to examine changes in brain activity potentially linked to central motor commands and to investigate their relationship with ratings of perceived exertion (RPE). In this study, sixteen participants (age: 21 ± 6 y, 7 females, 9 males) underwent one familiarization and two experimental trials in a randomised, blinded, cross-over study design. Participants executed a low-intensity bike task (9 min; 45 rpm; intensity (W): 10 % below aerobic threshold) after performing a mentally fatiguing (individualized 60-min Stroop task) or a control (documentary) task. Physiological (heart rate, EEG) and subjective measures (self-reported feeling of mental fatigue, RPE, cognitive load, motivation) were assessed prior, during and after the bike task. Post-Stroop, self-reported feeling of mental fatigue was higher in the intervention group (EXP) (74 ± 16) than in the control group (CON) (37 ± 17; p < 0.001). No significant differences in RPE during the bike task were observed between conditions. EEG analysis revealed significant differences (p < 0.05) in beta frequency (13–30 Hz) during the bike task, with EXP exhibiting more desynchronization during the pedal push phase and synchronization during the pedal release phase. These results suggest that mental fatigue, confirmed by both subjective and neurophysiological markers, did not significantly impact RPE during the bike task, possibly due to the use of the CR100 scale or absence of a performance outcome. However, EEG data did reveal significant beta band alterations during the task, indicating increased neural effort under mental fatigue. These findings reveal, for the first time, how motor-related brain activity at the motor cortex is impacted during a low-intensity bike task when mentally fatigued. [ABSTRACT FROM AUTHOR]

• Novel method for the condition monitoring of gas turbines using acoustic emissions. • Wavelet analysis used to transform acoustic emission signals into images. • Deep convolutional neural networks used to classify turbine condition. • Network trained on experimental data with two healthy and two faulty conditions. Compared to vibration monitoring, acoustic emission (AE) monitoring in gas turbines is highly sensitive to changes that do not involve whole-body motion, such as wear, rubbing, and fluid-induced faults. AE signals captured by suitably mounted sensors can potentially provide early indications of abnormal turbine operation before such abnormalities manifest in structural vibration or emitted airborne noise. However, developing an online fault detection system requires extensive real-time data treatment to extract appropriate features and indicators from raw AE records. To build such a system for industrial turbines, researchers need to understand the AE-generating mechanisms associated with turbine operation and the sources of background noise. In this study, we aim to develop such an understanding using a small-scale turbine whose operational conditions can be modified safely to reflect both normal and faulty conditions. Our signal processing approach involves first extracting a time-series envelope using an averaging time selected to enhance major features and eliminate irrelevant noise. We then generate time–frequency features using a continuous wavelet transform, which are used to train a deep convolutional neural network to classify gas turbine conditions. The resulting model demonstrates high accuracy in classifying two normal running conditions and two faulty conditions at various turbine speeds. Overall, the proposed methodology offers a powerful tool for gas turbine condition monitoring, and we make all associated data available in open-source format to facilitate further research in this field.4 [ABSTRACT FROM AUTHOR]

Despite the imitability of the signatures due to presence of numerous image processing programs, online verification systems could provide sufficient security for e-signatures. Recent developments in touchscreen technology and android programming also lead to utilization of hidden interfaces stealthily collecting the unique characteristics and storing the key features aside from geometrics. Therefore, we initially designed a signing interface for touchscreens which stealthily collects the precise coordinates while an individual is signing on the screen by fingertips. Even if the coordinate data is extracted as a matrix consisting of x and y values with corresponding time, the speed array is consequently calculated to investigate the higher frequency regions. The speed data processed by continuous wavelet transformations (CWT) to reveal the frequency information of the signing speed with respect to time information. The grayscale spectrograms created by wavelet transforms are converted into arrays for subsequent training session performed by support vector machines (SVM). The trained network successfully classified further attempts of the real and fake signatures with 1.67% false negative (FNR), 3.33% false positive rates (FPR) and 3.41% equal error rate (EER) for 120 signatures, even though the signature is totally public. For understanding the validity of the CWT and SVM running consecutively, the experiments are re-conducted for the signatures taken from SVC2004 and SUSIG public databases. [ABSTRACT FROM AUTHOR]

Streamflow is an essential component of the hydrologic cycle in the regional and global scale and the main source of fresh water supply. It is highly associated with natural disasters, such as droughts and floods. Therefore, accurate streamflow forecasting is essential. Forecasting streamflow in general and monthly streamflow in particular is a complex process that cannot be handled by data-driven models (DDMs) only and requires pre-processing. Wavelet transformation is a pre-processing technique; however, application of continuous wavelet transformation (CWT) produces many scales that cause deterioration in the performance of any DDM because of the high number of redundant variables. This study proposes multigene genetic programming (MGGP) as a selection tool. After the CWT analysis, it selects important scales to be imposed into the artificial neural network (ANN). A basin located in the southeast of Turkey is selected as case study to prove the forecasting ability of the proposed model. One month ahead downstream flow is used as output, and downstream flow, upstream, rainfall, temperature, and potential evapotranspiration with associated lags are used as inputs. Before modeling, wavelet coherence transformation (WCT) analysis was conducted to analyze the relationship between variables in the time-frequency domain. Several combinations were developed to investigate the effect of the variables on streamflow forecasting. The results indicated a high localized correlation between the streamflow and other variables, especially the upstream. In the models of the standalone layout where the data were entered to ANN and MGGP without CWT, the performance is found poor. In the best-scale layout, where the best scale of the CWT identified as the highest correlated scale is chosen and enters to ANN and MGGP, the performance increased slightly. Using the proposed model, the performance improved dramatically particularly in forecasting the peak values because of the inclusion of several scales in which seasonality and irregularity can be captured. Using hydrological and meteorological variables also improved the ability to forecast the streamflow. [ABSTRACT FROM AUTHOR]

Abstract: In order to improve equipment throughput and device yield, chamber leaks needs to be strictly monitored. A new technique for leak detection is presented and this was accomplished by combining backpropagation neural network, discrete wavelet transformation (DWT), and continuous transformation (CWT). Different types of BPNN models were constructed with raw, DWT, and CWT data and these are referred to as raw, DWT, and CWT models, respectively. Constructed models were validated with a total of 47 data sets for normal and leaky chamber conditions. The experimental data were in-situ collected by using an optical emission spectroscopy. Both raw and DWT models could detect all abnormal data sets. Worst detection by CWT model was noted. Wider detection margin provided by DWT model was attributed to enhanced sensitivity of model to leaky condition. A modified cumulative control chart was applied to the statistical mean of raw OES spectra as well as to DWT and CWT data. The statistical mean-based CUSUM control chart was unable to detect chamber leaks. In contrast, chamber leaks could be identified by all model-based CUSUM control charts. Of the proposed models, DWT model is identified to be the most appropriate to chamber leak detection. [ABSTRACT FROM AUTHOR]