Category: Nevin Manimala

Sensors (Basel). 2023 Feb 3;23(3):1737. doi: 10.3390/s23031737.

ABSTRACT

Capacitive touch panels (CTPs) have the merits of being waterproof, antifouling, scratch resistant, and capable of rapid response, making them more popular in various touch electronic products. However, the CTP has a multilayer structure, and the background is a directional texture. The inspection work is more difficult when the defect area is small and occurs in the textured background. This study focused mainly on the automated defect inspection of CTPs with structural texture on the surface, using the spectral attributes of the discrete cosine transform (DCT) with the proposed three-way double-band Gaussian filtering (3W-DBGF) method. With consideration to the bandwidth and angle of the high-energy region combined with the characteristics of band filtering, threshold filtering, and Gaussian distribution filtering, the frequency values with higher energy are removed, and after reversal to the spatial space, the textured background can be weakened and the defects enhanced. Finally, we use simple statistics to set binarization threshold limits that can accurately separate defects from the background. The detection outcomes showed that the flaw detection rate of the DCT-based 3W-DBGF approach was 94.21%, the false-positive rate of the normal area was 1.97%, and the correct classification rate was 98.04%.

PMID:36772777 | DOI:10.3390/s23031737

Sensors (Basel). 2023 Feb 3;23(3):1683. doi: 10.3390/s23031683.

ABSTRACT

The secure operation of smart grids is closely linked to state estimates that accurately reflect the physical characteristics of the grid. However, well-designed false data injection attacks (FDIAs) can manipulate the process of state estimation by injecting malicious data into the measurement data while bypassing the detection of the security system, ultimately causing the results of state estimation to deviate from secure values. Since FDIAs tampering with the measurement data of some buses will lead to error offset, this paper proposes an attack-detection algorithm based on statistical learning according to the different characteristic parameters of measurement error before and after tampering. In order to detect and classify false data from the measurement data, in this paper, we report the model establishment and estimation of error parameters for the tampered measurement data by combining the the k-means++ algorithm with the expectation maximization (EM) algorithm. At the same time, we located and recorded the bus that the attacker attempted to tamper with. In order to verify the feasibility of the algorithm proposed in this paper, the IEEE 5-bus standard test system and the IEEE 14-bus standard test system were used for simulation analysis. Numerical examples demonstrate that the combined use of the two algorithms can decrease the detection time to less than 0.011883 s and correctly locate the false data with a probability of more than 95%.

PMID:36772723 | DOI:10.3390/s23031683

Sensors (Basel). 2023 Feb 2;23(3):1659. doi: 10.3390/s23031659.

ABSTRACT

Game playing is an accessible leisure activity. Recently, the World Health Organization officially included gaming disorder in the ICD-11, and studies using several bio-signals were conducted to quantitatively determine this. However, most EEG studies regarding internet gaming disorder (IGD) were conducted in the resting state, and the outcomes appeared to be too inconsistent to identify a general trend. Therefore, this study aimed to use a series of statistical processes with all the existing EEG parameters until the most effective ones to identify the difference between IGD subjects IGD and healthy subjects was determined. Thirty subjects were grouped into IGD (n = 15) and healthy (n = 15) subjects by using the Young’s internet addition test (IAT) and the compulsive internet use scale (CIUS). EEG data for 16 channels were collected while the subjects played League of Legends. For the exhaustive search of parameters, 240 parameters were tested in terms of t-test, factor analysis, Pearson correlation, and finally logistic regression analysis. After a series of statistical processes, the parameters from Alpha, sensory motor rhythm (SMR), and MidBeta ranging from the Fp1, C3, C4, and O1 channels were found to be best indicators of IGD symptoms. The accuracy of diagnosis was computed as 63.5-73.1% before cross-validation. The most interesting finding of the study was the dynamics of EEG relative power in the 10-20 Hz band. This EEG crossing phenomenon between IGD and healthy subjects may explain why previous research showed inconsistent outcomes. The outcome of this study could be the referential guide for further investigation to quantitatively assess IGD symptoms.

PMID:36772696 | DOI:10.3390/s23031659

Sensors (Basel). 2023 Feb 2;23(3):1645. doi: 10.3390/s23031645.

ABSTRACT

The article presents a method for diagnosing cycloidal gear damage on a laboratory stand. The damage was simulated by removing the sliding sleeves from two adjacent external pins of the cycloidal gearbox. Damage to the sliding sleeves may occur under operating conditions and can lead to the destruction of the gear unit. Hence, early detection is essential. Signals from torque sensors, rotational speed sensors and vibration acceleration sensors of input and output shafts for various rotational speeds and transmission loads were recorded. The frequency analysis of these signals was carried out. Due to the fluctuation of the rotational speed, the frequency spectrum gives an approximate picture and is not useful in detecting this type of damage. The statistical characteristics of the signal were determined. However, only statistical moments of higher orders, such as kurtosis, are sensitive to the tested damage. Therefore, the use of multifractal analysis of the vibration signal using the wavelet leader method (WLMF) was considered. Then log-cumulants of the multifractal spectrum were selected as the new signal features.

PMID:36772684 | DOI:10.3390/s23031645

Sensors (Basel). 2023 Feb 2;23(3):1642. doi: 10.3390/s23031642.

ABSTRACT

Engine fault detection is conducive to improving equipment reliability and reducing maintenance costs. In practical scenarios, high-quality data is difficult to obtain. Usually, only single-sensor data is available. This paper proposes a fault detection method combining Variational Mode Decomposition (VMD) and Random Forest (RF). At first, the spectral energy distribution is obtained by decomposing and statistic the engine data of multiple working conditions. Based on the spectral energy distribution, the overall optimal mode number was identified, and the quadratic penalty term was optimized using SNR. The improved VMD (IVMD) improves mode aliasing and iterative efficiency and unifies feature dimensions. Decomposition of real signals demonstrates the effectiveness. The paper designs a feature vector composed of seven types of attributes, including unit bandwidth energy, center frequency, maximum singular value and so on. The feature vector is then fed to RF for classification. Features are selected in order of importance to classification to improve the training efficiency. By comparing with various algorithms, the proposed method has higher accuracy and faster training efficiency in single-speed, multi-speed and cross-speed single-sensor data diagnosis. The results show that the method has application prospects with little training data and low hardware requirements.

PMID:36772682 | DOI:10.3390/s23031642

Sensors (Basel). 2023 Feb 2;23(3):1615. doi: 10.3390/s23031615.

ABSTRACT

Heart rate variability (HRV) indexes are becoming useful in various applications, from better diagnosis and prevention of diseases to predicting stress levels. Typically, HRV indexes are retrieved from the heart’s electrical activity collected with an electrocardiographic signal (ECG). Heart-induced mechanical signals recorded from the body’s surface can be utilized to record the mechanical activity of the heart and, in turn, extract HRV indexes from interbeat intervals (IBIs). Among others, accelerometers and gyroscopes can be used to register IBIs from precordial accelerations and chest wall angular velocities. However, unlike electrical signals, the morphology of mechanical ones is strongly affected by body posture. In this paper, we investigated the feasibility of estimating the most common linear and non-linear HRV indexes from accelerometer and gyroscope data collected with a wearable skin-interfaced Inertial Measurement Unit (IMU) positioned at the xiphoid level. Data were collected from 21 healthy volunteers assuming two common postures (i.e., seated and lying). Results show that using the gyroscope signal in the lying posture allows accurate results in estimating IBIs, thus allowing extracting of linear and non-linear HRV parameters that are not statistically significantly different from those extracted from reference ECG.

PMID:36772656 | DOI:10.3390/s23031615

Sensors (Basel). 2023 Feb 1;23(3):1603. doi: 10.3390/s23031603.

ABSTRACT

The study of brain connectivity plays an important role in understanding the functional organizations of the brain. It also helps to identify connectivity signatures that can be used for evaluating neural disorders and monitoring treatment efficacy. In this work, age-related changes in brain connectivity are studied to obtain aging signatures based on various modeling techniques. These include an energy-based machine learning technique to identify brain network interaction differences between two age groups with a large (30 years) age gap between them. Disconnectivity graphs and activation maps of the seven prominent resting-state networks (RSN) were obtained from functional MRI data of old and young adult subjects. Two-sample t-tests were performed on the local minimums with Bonferroni correction to control the family-wise error rate. These local minimums are connectivity states showing not only which brain regions but also how strong they are working together. They work as aging signatures that can be used to differentiate young and old groups. We found that the attention network’s connectivity signature is a state with all the regions working together and young subjects have a stronger average connectivity among these regions. We have also found a common pattern between young and old subjects where the left and right brain regions of the frontal network are sometimes working separately instead of together. In summary, in this work, we combined machine learning and statistical approaches to extract connectivity signatures, which can be utilized to distinguish aging brains and monitor possible treatment efficacy.

PMID:36772649 | DOI:10.3390/s23031603

Sensors (Basel). 2023 Feb 1;23(3):1566. doi: 10.3390/s23031566.

ABSTRACT

Signal acquisition is a crucial step in Global Navigation Satellite System (GNSS) receivers, which is typically solved by maximizing the so-called Cross-Ambiguity Function (CAF) as a hypothesis testing problem. This article proposes to use deep learning models to perform such acquisition, whereby the CAF is fed to a data-driven classifier that outputs binary class posteriors. The class posteriors are used to compute a Bayesian hypothesis test to statistically decide the presence or absence of a GNSS signal. The versatility and computational affordability of the proposed method are addressed by splitting the CAF into smaller overlapping sections, which are fed to a bank of parallel classifiers whose probabilistic results are optimally fused to provide a so-called probability ratio map from which acquisition is decided. Additionally, the article shows how noncoherent integration schemes are enabled through optimal data fusion, with the goal of increasing the resulting classifier accuracy. The article provides simulation results showing that the proposed data-driven method outperforms current CAF maximization strategies, enabling enhanced acquisition at medium-to-high carrier-to-noise density ratios.

PMID:36772605 | DOI:10.3390/s23031566

BMC Public Health. 2023 Feb 10;23(1):303. doi: 10.1186/s12889-023-15185-x.

ABSTRACT

BACKGROUND: Suicide among the elderly has become a global public health concern. This study was carried out to determine the trend of completed suicide rates according to age, sex, and ethnicity and the suicidal methods among the elderly in Malaysia.

METHODS: All suicide-related deaths in elderly aged 60 years and above from the Year 1995 to 2020 reported to the National Registration Department (NRD) were analyzed. Causes of death for suicide were coded based on the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10). The completed suicide rate was calculated by dividing the completed suicide number by the total elderly population for the respective year.

RESULTS: Overall, the analysis of 1,600 suicide-related deaths was investigated over 26 years. Male was seen to be correlated with higher suicidal risk, with a male-female relative risk (RR) = 1.89 (95%CI:1.46,2.45). The risk of suicide was also found to be significantly higher for those aged 60 to 74 years old and Chinese, with RR = 4.26 (95%CI:2.94, 6.18) and RR = 5.81 (95%CI: 3.70, 9.12), respectively. Hanging was found to be a statistically significant suicide method (IRR:4.76, 95%CI:2.34,9.65) as compared to pesticide poisoning. The completed suicide rate was fluctuating over the years.

CONCLUSIONS: In conclusion, it is believed that Malaysia’s elderly suicide rate has reached an alarmingly high incidence. By identifying the crucial criteria of sociodemographic factors, the government and responsible agencies have the essential and additional information to put together a more effective strategy and approach to overcome the issue in the future.

PMID:36765292 | DOI:10.1186/s12889-023-15185-x

BMC Musculoskelet Disord. 2023 Feb 10;24(1):113. doi: 10.1186/s12891-023-06202-6.

ABSTRACT

BACKGROUND: Bone mineral density (BMD) alterations in response to multivitamin exposure were rarely studied. Our study assessed the association of coexposure to six types of vitamins (i.e., vitamins B12, B9, C, D, A and E) with BMD measurements in adults in the US.

METHODS: Data were collected from participants aged ≥ 20 years (n = 2757) in the U.S. National Health and Nutrition Examination Surveys (NHANES) from 2005 to 2006. Multiple linear regression, restricted cubic splines, principal component analysis (PCA) and weighted quantile sum (WQS) regression were performed for statistical analysis.

RESULTS: The circulating levels of vitamins B12 and C were positively associated with BMDs, and an inverted L-shaped exposure relationship was observed between serum vitamin C and BMDs. PCA identified two principal components: one for ‘water-soluble vitamins’, including vitamins B12, B9 and C, and one for ‘fat-soluble vitamins’, including vitamins A, D and E. The former was positively associated with total femur (β = 0.009, 95%CI: 0.004, 0.015) and femoral neck (β = 0.007, 95%CI: 0.002, 0.013) BMDs, and the latter was negatively associated with BMDs with non-statistical significance. The WQS index constructed for the six vitamins was significantly related to total femur (β = 0.010, 95%CI: 0.001, 0.018) and femoral neck (β = 0.008, 95%CI: 0.001, 0.015) BMDs, and vitamins B12 and C weighted the most. The WQS index was inversely related to BMDs with non-statistical significance, and vitamins E and A weighted the most.

CONCLUSION: Our findings suggested a positive association between water-soluble vitamin coexposure and BMD, and the association was mainly driven by vitamins B12 and C. Negative association between fat-soluble vitamin coexposure and BMD was indicated, mainly driven by vitamins E and A. An inverted L-shaped exposure relationship was found between vitamin C and BMD.

PMID:36765290 | DOI:10.1186/s12891-023-06202-6