Moisture Sensors on Conductive Substrates

A moisture sensor includes a first electrode (a conductive substrate) having a first sensor portion and a first terminal portion as well as a second electrode having a second sensor portion and a second terminal portion. The moisture sensor also includes a layer of porous dielectric material sandwiched between the first sensor portion and the second sensor portion. Further the moisture sensor includes a layer of dense insulating material sandwiched between the first terminal portion and the second terminal portion. Leads are then connected to the two terminal portions

Analysis of Fuzzy Logic based Textual Meaning Inference Approach for Comment Content Estimation in Social Networks

In recent years, social networking has become a very popular communication tool among internet users connected by one or more relationships. Thousands or even millions of users share their experiences and opinions on different aspects of life everyday through social networking communities. The positive or negative content of the comments posted by the members of the social network can arouse great interest among the members of the social network group. Understanding social networks requires the analysis of structural relationships and interaction patterns between users. In this paper, an analysis of fuzzy logic based textual meaning inference analysis was performed for the estimation of content in social networks. The positive comments made by the members on the social networks have the positive effect for the users to read comments. In this context, our semantic inference approach is analyzed with the help of fuzzy logic where the content of comment can be positive or negative. According to the input values in the fuzzy logic system, the relevant interpretation can be positive or negative. Considering that the results of the obtained system yields highly accurate results, we think that our fuzzy logic based semantic inference approach can be used in many social networks.WOS:00054527870001

Hand Gesture Recognition from 2D Images by Using Convolutional Capsule Neural Networks

Object classification and recognition are an important research area widely used in computer vision and machine learning. With the use of deep learning methods in the field of object recognition, there have been important developments in recent years. Object recognition and its sub-branches face recognition, motion recognition, and hand gesture recognition are now used effectively in devices used in daily life. Hand sign classification and recognition are an area that researchers are working on and trying to develop for human-computer interaction. In this study, a hybrid model was created by using a capsule network algorithm with a convolutional neural network for object classification. A dataset, named HG14, containing 14 different hand gestures was created. To measure the success of the proposed model in object recognition, training was carried out on HG14, FashionMnist, and Cifar-10 datasets. Also, VGG16, ResNet50, DenseNet, and CapsNet models were used to classify the images in HG14, FashionMnist, and Cifar-10 datasets. The results of the training were compared and evaluated. The proposed hybrid model achieved the highest accuracy rates with 90% in the HG14 dataset, 93.88% in the FashionMnist dataset, and 81.42% in the Cifar-10 dataset. The proposed model was found to be successful in all studies compared to other models.WOS:0006669375000112-s2.0-8510878717

DETERMINATION OF MAIN ELECTRICAL PARAMETERS OF Au-4H-n-SiC (MS) AND Au-Al2O3-4H-n-SiC (MIS) DEVICES

In this study, Au-4H-n-SiC metal-semiconductor (MS) and Au-Al2O3-4H-n-SiC metal-insulator-semiconductor (MIS) devices were fabricated to examine the effects on the performance of electronic devices of interfacial insulating materials. In order to determine the dielectric properties, capacitance/conductance-voltage (C/G-V) measurements were realized in a wide range of voltages (-3.0 V)-(11.0 V). Current-voltage (I-V) measurements to obtain the electric properties were realized at +/- 2:5V. Moreover, both the energy distributions of surface states (N-ss) and series resistance (R-s) were obtained from the C/G-V data. Obtained results provided that series resistance originating from interfacial layer (Al2O3) was more effective on the I-V and C/G-V characteristics which must be taken into account in the calculation of main electrical parameters. The rectification ratio (RR) and shunt resistance (R-sh) of the MIS device were almost 10(3) times greater than those of the MS structure. Using Al2O3 between Au and 4H-n-SiC also led to an increase in the value of barrier height (BH) and a decrease in the value of ideality factor (n). These results confirmed that Al2O3 layer leads to an increase in the performance of MS device with respect to low values of N-ss, reverse saturation current (I-0) and n and high values of RR, R-sh and BH.WOS:0006525878000022-s2.0-8510197897

A non-invasive continuous cuffless blood pressure estimation using dynamic Recurrent Neural Networks

Cardiovascular diseases (CVD) have become the most important health problem of our time. High blood pressure, which is cardiovascular disease, is a risk factor for death, stroke, and heart attack. Blood pressure measurement is commonly used to limit blood flow in the arm or wrist, with the cuff. Since blood pressure cannot be measured continuously in this method, the dynamics underlying blood pressure cannot be determined and are inefficient in capturing symptoms. This paper aims to perform blood pressure estimation using Photoplethysmography (PPG) and Electrocardiography (ECG) signals that do not obstruct the vascular access. These signals were filtered and segmented synchronously from the R interval of the ECG signal, and chaotic, time, and frequency domain features were subtracted, and estimation methods were applied. Different methods of machine learning in blood pressure estimation are compared. Dynamic learning methods such as Recurrent Neural Network (RNN), Nonlinear Autoregressive Network with Exogenous Inputs Neural Networks NARX-NN and Long-Short Term Memory Neural Network (LSTM-NN) used. Estimation results have been evaluated with performance criteria. Systolic Blood Pressure (SBP) error mean +/- standard deviation = 0.0224 +/- (2.211), Diastolic Blood Pressure (DBP) error mean +/- standard deviation = 0.0417 +/- (1.2193) values have been detected in NARX artificial neural network. The blood pressure estimation results are evaluated by the British Hypertension Society (BHS) and American National Standard for Medical Instrumentation ANSI/AAMI SP10: 2002. Finding the most accurate and easy method in blood pressure measurement will contribute to minimizing the errors. (C) 2020 Elsevier Ltd. All rights reserved.Duzce University, Scientific Research and Projects UnitDuzce University [2018.07.02.878]This paper has been supported by Duzce University, Scientific Research and Projects Unit with the Project number (2018.07.02.878).WOS:0005653740000442-s2.0-8508865975

Investigation of effects on dielectric properties of different doping concentrations of Au/Gr-PVA/p-Si structures at 0.1 and 1 MHz at room temperature

In order to improve and detailedly investigate the dielectric properties of polymer interfaces of Metal-Polymer-Semiconductor (MPS) structures, three types of MPS were fabricated by doping 1, 3 and 5% graphene (Gr) into the polyvinyl alcohol (PVA) interface material. Capacitance-Voltage (C-V) and Conductance-Voltage (G/omega-V) measurements were used to analyze the dielectric properties of three types of MPS. UsingC-Vand G/omega-V data, series resistance (R-s) affecting device performance and interface properties besides basic dielectric parameters of each structure such as both the real and imaginary components of complex dielectric constant (epsilon'and epsilon''), complex electrical modulus (M' and M''), loss tangent (tan delta), and ac electrical conductivity (sigma(ac)) were also calculated. The effect of graphene doping was examined for each parameter and obtained results were compared at both low (0.1 MHz) and high (1 MHz) frequencies. It was observed that epsilon and epsilon'' decreased with increasing graphene doping at both 0.1 and 1 MHz, while M' and M'' increased under same conditions. Moreover, both the M' and M'' vs V plots have two distinctive peaks between -2.0 V and 0.0 V due to a special density distribution of surface states between (Gr-PVA) and p-Si. The tan delta gradually increased with increasing graphene doping at only 0.1 MHz. As the doping ratio of graphene increases, the charge carriers in the structure generate more dipoles and create an earlier relaxation process. In other words, increasing the doping ratio helps to improve the series resistance effects in MPS structures. As a result, it was seen that the interfacial properties of MPS structures were improved by increasing the rate of graphene doping.Gazi University Scientific Research Center [GU-BAP.05/2019-26]All authors would like to thank Gazi University Scientific Research Center for the supports and contributions (Project No: GU-BAP.05/2019-26).WOS:0005593755000092-s2.0-8508936624

Developing and modeling of voice control system for prosthetic robot arm in medical systems

In parallel with the development of technology, various control methods are also developed. Voice control system is one of these control methods. In this study, an effective modelling upon mathematical models used in the literature is performed, and a voice control system is developed in order to control prosthetic robot arms. The developed control system has been applied on four-jointed RRRR robot arm. Implementation tests were performed on the designed system. As a result of the tests; it has been observed that the technique utilized in our system achieves about 11% more efficient voice recognition than currently used techniques in the literature. With the improved mathematical modelling, it has been shown that voice commands could be effectively used for controlling the prosthetic robot arm. Keywords: Voice recognition model, Voice control, Prosthetic robot arm, Robotic control, Forward kinemati

A Novel Blood Pressure Estimation Method with the Combination of Long Short Term Memory Neural Network and Principal Component Analysis Based on PPG Signals

International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME) -- APR 20-22, 2019 -- Antalya, TURKEYThe worldwide high blood pressure-related mortality rate is increasing. Alternative measurement methods are required for blood pressure measurement. There are similarities between blood pressure and photoplethysmography (PPG) signals. In this study, a novel blood pressure estimation methods based on the feature extracted from the PPG signals have been proposed. First of all, 12-time domain features have extracted from the raw PPG signal. Secondly, raw PPG signals have been applied to Principal Component Analysis (PCA) to obtain 10 new features. The resulting features have been combined to form a hybrid feature set consisting of 22 features. After features extraction, blood pressure values have automatically been predicted by using the Long Short Term Memory Neural Network (LSTM-NN) model. The prediction performance measures including MAE, MAPE, RMSE, and IA values have been used. While the combination of 12-time domain features from PPG signals and LSTM has obtained the MAPE values of 0,0547 in the prediction of blood pressures, the combination of 10-PCA coefficients and LSTM has achieved the MAPE value of 0,0559. The combination model of all features (22) and LSTM has obtained the MAPE values of 0,0488 in the prediction of blood pressures. The achieved results have shown that the proposed hybrid model based on combining PCA and LSTM is very promising in the prediction of blood pressure from PPG signals. In the future, the proposed hybrid method can be used as a wearable device in the measurement of blood pressure without any calibration.Duzce University Scientific Research and Projects UnitDuzce University [2018.07.02.878]This paper has been supported by Duzce University Scientific Research and Projects Unit with the Project number 2018.07.02.878.WOS:0006787710000752-s2.0-8508346427

A novel mathematical model including the wetness parameter as a variable for prevention of pressure ulcers

Pressure ulcers are injuries caused by external conditions such as pressure, friction, shear, and humidity resulting from staying in the same position for a long time in bedridden patients. It is a serious problem worldwide when assessed in terms of hospital capacity, nursing staff employment and treatment costs. In this study, we developed a novel mathematical model based on one of our previous models to prevent pressure ulcers or delay injuries. The proposed model uses a human thermal model that includes skin temperature, hypothalamus temperature, regional perspiration coefficient, and unconsciously loss of water amount. Moreover, in our model, we defined a variable wetness parameter in addition to the parameters, pressure, temperature, and humidity. The proposed model is mathematically defined in detail and tested for a wide range of parameters to show the model's effectiveness in determining the pressure ulcer formation risk. The model is also compared with a model from the literature that based on only the general parameters, pressure, temperature, and humidity. The obtained results showed that the model determines the risk of the occurrence of the pressure ulcer more precisely than the compared one.Duzce University Research Fund ProjectDuzce University [2015.07.02.385, 2016.07.02.506]The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is supported by Duzce University Research Fund Project both (No: 2015.07.02.385) and (No: 2016.07.02.506).WOS:0007115044000012-s2.0-85118272666PubMed: 3469665

Investigation of photo-induced effect on electrical properties of Au/PPy/n-Si (MPS) type schottky barrier diodes

WOS: 000399709300008Au/PPy/n-Si (MPS) type Schottky barrier diodes (SBDs) were produced and their current-voltage (I-V) characteristics were measured in the positive and negative bias regions at 300 K. The basic electronic quantities such as reverse-saturation current (I-o ), ideality factor (n), zero-bias barrier height (Phi(B0) ), series (R-s) and shunt resistances (R-sh) were obtained by using I-V data in total darkness and illumination (100 W/m(2)). The values of these parameters were found as 7.79 x 10(-9) A, 5.41, 0.75 eV, 1 k Omega and 130 M Omega in dark) and 4 x 10(-9) A, 4.89, 0.77 eV, 0.9 k Omega and 1.02 M Omega under illumination), respectively. Also the energy density distribution behaviors of surface states (N-ss ) have been acquired by calculation of effective barrier height (Phi(e) ) and ideality factor n (V) depending on voltage in total darkness and illumination. The values of N (ss) show an exponentially increase from the mid-gap of Si to the lower part of conduction band (E-c ) for two conditions. The possible current conduction mechanisms were determined by plotting of the double logarithmic I-V plots in the positive voltage zone and the value of current was found proportional to voltage (I similar to V (m) ). The high values of n and R-s were ascribed to the certain density distribution of N-ss localized at semiconductor /PPy interface, surface conditions, barrier inequality, the thickness of PPy interlayer and its roughness. The open-circuit voltage of the photodiode was found as 0.36 V under 100 W/m(2) illumination level. This is evidence that the fabricated sample is very sensitive to illumination. Therefore, it can be put into practice in optoelectronic industries as a photodiode or solar cells