bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study and Analysis of Electronic Stethoscope Signal Using MATLAB & LabVIEW as Effective Tools
Authors: Oktivasari P. , R. Amy H. S. , Freddy H. , Suprijadi  
Published: June 2020

Abstract
This paper trades effectively with the study and analysis of heart sound signal processing from Electronic Stethoscope by using the Matlab tool. Examination of heart sound signal includes generation and simulation of heart sound signal, acquisition of real-time heart sound data, heart sound signal filtering and processing, extraction of feature, comparison of different techniques for cardiac signal processing. Heart sound signal analysis used FFT and Wavelet transform, identification of normal heart sound, calculating Beat Per Minute (BPM), and so on using the most familiar and multipurpose Matlab functionality with user interface LabVIEW. The reasonable need for Matlab functions can contribute us to work in high accuracy and convenience with heart sound signals for processing and analyzing both in realtime and through simulation.
Epileptic Seizure Detection in EEGs by Using Random Tree Forest, Naïve Bayes and KNN Classification
Authors: Lestari Fauzia P. , Haekal Mohammad , Edmi Edison Rizki , Ravi Fauzy Fikry , Nurul Khotimah Siti , Haryanto Freddy 
Published: June 2020

Abstract
Epilepsy is a disease that attacks the nerves. To detect epilepsy, it is necessary to analyze the results of an EEG test. In this study, we compared the naive bayes, random tree forest and K-nearest neighbor (KNN) classification algorithms to detect epilepsy. The raw EEG data were pre-processed before doing feature extraction. Then, we have done the training in three algorithms: KNN Classification, naïve bayes classification and random tree forest. The last step was validation of the trained machine learning. Comparing those three classifiers, we calculated accuracy, sensitivity, specificity, and precision. The best trained classifier is KNN classifier (accuracy: 92.7%), rather than random tree forest (accuracy: 86.6%) and naïve bayes classifier (accuracy: 55.6%). Seen from precision performance, KNN Classification also gives the best precision (82.5%) rather than Naïve Bayes classification (25.3%) and random tree forest (68.2%). But, for the sensitivity, Naïve Bayes classification is the best with 80.3% sensitivity, compare to KNN 73.2% and random tree forest (42.2%). For specificity, KNN classification gives 96.7% specificity, then random tree forest 95.9% and Naïve bayes 50.4%. The training time of naïve bayes was 0.166030 sec, while training time of random tree forest was 2.4094sec and KNN was the slower in training that was 4.789 sec. Therefore, KNN Classification gives better performance than naïve bayes and random tree forest classification.
Vibration Sensor Based On Self-Mixing Interferometry To Identify Human Heart Rate
Authors: Yaqin M A , Yunifar R I , Ariyanti N D , Zulfikar A Z , Syamsudin S , Buka O , Nasori N , Endarko E , Rubiyanto A 
Published: June 2020

Abstract
The monitoring heart rate by vibration sensor based on self-mixing interferometry (SMI) had been done. Experimental schemes and the performance of vibration sensor based on SMI as a function of frequency and amplitude of human heart rate signals had been examined. The vibration sensor consists of a laser diode as a light source (λ = 785 nm) with integrated detector, a mirror as a vibrating target, and a simulated heart signal from ECG generator. ECG generator signals and audio amplifier are adjusted in frequencies of 1-100 kHz. The result showed that the feedback parameter of SMI signal could be identified at frequency 50 Hz. The heart signal was shown clearly at frequency 100 kHz.
Effect of ozone addition on virgin coconut oil using changes in light transmission polarization
Authors: Ainurrofik N , Azam M , Sugito H , Richardina V , Firdausi K S 
Published: June 2020

Abstract
Today, the investigation of ozonized cooking oil becomes a very interesting research development topic, in addition to the study of ozone interactions in liquids and its application to the treatment of skin cancer. This research was conducted to determine the change in the transmission polarization angle in Virgin Coconut Oil (VCO) that was interacted with ozone. Laser pointers with a wavelength of 650 nm were used as light sources. The polarization changes are proportional to the duration of ozonation, and the longer duration is accompanied by more accumulative numbers of free fatty acids. The interaction is considered to be quite effective, with supposedly one ozone molecule interacting with one symmetric triglyceride molecule to become a new asymmetric triglyceride molecule in the interaction. This interaction increases the active optical properties of VCO samples. In contrast to the latest sophisticated spectroscopic methods, the polarization method seems simpler by simply measuring the change in polarization of light. With the ability of the polarization method that can distinguish the characteristics of cooking oil before and after experiencing treatment, this method has a large chance to be developed in subsequent studies relating to polarization as an indicator of the interaction of light with the material in the field of biophysics and other related fields.
Simulation of single particle flowing in a microfluidic device using molecular dynamics method
Authors: Viridi Sparisoma , Haryanto Freddy , Anshori Isa , Haekal Mohammad 
Published: June 2020

Abstract
Blood cells are modeled as spherical particles that flow through a microfluidic device with one inlet and two outlet channels, which is designed as a separator of blood particles. Molecular dynamics (MD) method was used intuitively in the simulation with the help of Semi-Circle Segmented Path Generator (SCSPG) as an approximation in creating fluid profile along the device channel. The trajectories generated from SCSPG was advanced using a fully developed Poiseuille flow with maximum fluid speed on the trajectories and the tails of speed distribution which was extended to the size of the channel with speed of zero at the channel walls. It has been observed that for a single particle trajectory the outlet channel was chosen by the particle depends on the axial position of the particles. Mass of particle determines how hard the particle deflects due to fluid profile. A better design is proposed in this work for separating two groups of particles with different size.
Pulmonary Disease Pattern Recognition on X-Ray Radiography Image Using Artificial Neural Network (ANN) Method
Authors: Fitriyah N , Kurniawati L D , Purwanti E , Astuti S D 
Published: June 2020

Abstract
This research aims to recognize the pattern of pulmonary disease on x-ray radiography image using artificial neural network (ANN) method. The images, which were used such as images of healthy pulmonary, pulmonary tuberculosis, and pulmonary tumour. Pattern recognition was using an extraction feature of GLCM (Gray Level Co-occurrence Matrix) and back propagation method. Before being identified, the images were processed by median filter and adaptive histogram equalization. The GLCM features that used were homogeneity, energy, contrast, variance and correlation. The parameters were learning rate and hidden layer. Learning rate was 0.3 and hidden layer was 25. Back propagation training showed 100% accuracy, which all of 44 images were used had been successfully identified. From the result, the healthy pulmonary showed 60% accuracy, 83.3% for pulmonary tuberculosis and 100% for pulmonary tumor. Hence, the overall result showed 81.25% accuracy, which 13 of 16 images had been successfully identified. From these result, extraction feature of GLCM using back propagation method was capable to recognize the pattern of pulmonary disease. However, due to narrow range of the feature, this application may not be used optimally for comparing features in every category of images. Therefore, the further research is needed to determine the best features and parameters of training back propagation.
Signal Comparison of Developed EEG Device and Emotiv Insight Based on Brainwave Characteristics Analysis
Authors: Harke Pratama Sra , Rahmadhani Andri , Bramana Andrian , Oktivasari Prihatin , Handayani Nita , Haryanto Freddy , Suprijadi  , Nurul Khotimah Siti 
Published: June 2020

Abstract
The usage of wireless system and dry electrode on electroencephalography (EEG) device becomes widely demanding, particularly in commercial purposes. While the wireless system is needed for lesser cable interference and practical function for mobility, the dry electrode is very important for signal consistency in longer period of brainwave acquisition. Previously, a wireless EEG device was developed in our laboratory; however, the evaluation of the acquired brainwave is needed for further usage and development. This research aimed to compare the signal acquired by the developed EEG device using Emotiv Insight device as a benchmark, which is already an established wireless and dry electrode-based EEG on the market. The brainwave acquisitions were conducted on the subject while resting with eyes closed. AF3 and AF4 of frontal lobe channels were used as the electrode placements. The results were then characterized using frequency band analysis, SNR comparison, and general signal inspection. The result showed that the signal patterns on both devices were visually similar. A minor difference on the amplitude scale can be adjusted by normalization method. The result of alpha band calculation, which is normally detected in resting activity, found similar on both devices. Furthermore, the SNR result from developed device was considered fairly close to the benchmarking device. This study showed that developed EEG device was considered comparable to Emotiv Insight in detecting alpha band extracted from resting frontal lobe, as well as in the brainwave filtering process and accuracy.
Preliminary Studies Comparing Heart Rate Measurement Using Optical Sensor Based on Self Mixing Interferometry and Photoplethysmography (PPG)
Authors: Yunifar R I , Yaqin M A , Ariyanti N D , Zulfikar A Z , Syamsudin S , Buka O , Nasori N , Endarko E , Rubiyanto A 
Published: June 2020

Abstract
Heart Rate is important information about the physiological and psychological condition of human. The pulse rate is directly related to the heart activity because of the impact of the volume changing of blood correlated to the heart which pumps blood throughout the body periodically. Heart rate measurement is used generally ECG (Electrocardiogram) or Blood Pressure Measurement in the medical world. In this paper the ratio of the heart rate is analyzed by using a technique photoplethysmography and optical sensor based on self-mixing interferometry. A typical self-mixing configuration consist of a laser diode as a light source (LD, 785 nm) and the photodetector which integrated in the package. Photoplethysmography consist of infrared (IR, 940 nm) acts as source of light, photodiode as a photo-detector, PPG circuit, arduino and PC. Photoplethysmography is non-invasive optical technique that can used to detect blood volume changes in the microvascular bed of tissue. The result show that human heart rate signals can be detected using the SMI method with a frequency of 100 kHz and input current 50 mA, but using PPG method the human heart signal was detected in the amplitude range of 2,070 to 2,080 mV.
Evaluation of radiation dose from coronary CT angiography (CCTA) examination associated with prospective ECG-triggering technique in multidetector 640-slice scanner
Authors: Shamsul Shahril , Sabarudin Akmal , Abdul Hamid Hamzaini , Abu Bakar Norzailin , Maskon Oteh , Khalis Abdul Karim Muhammad 
Published: June 2020

Abstract
In this study we evaluate radiation dose from a prospective ECG-triggered coronary CT angiography (CCTA) examination multidetector 640-slice CT and compares with other establish references. A total of 98 patient subjects were fall into inclusion criteria and related parameters has been recorded and analysed. It was estimated that the effective dose was at 5.23 ± 4.38 mSv (range 1.34 to 20.35 mSv) and it was significantly lower than that stated in the national dose reference level (NDRL) with 57% difference. It is highly recommended that the practice of using prospective ECG-triggering CCTA with low scanning range provides an optimum amount of radiation dose when compared to previous practice.
Design and Construction of Information-based Teaching Cloud Space in Colleges and Universities under the Framework of TPACK
Authors: Gan Baiqiang , Wang Rongxia , Zhang Chi , Lv Peide 
Published: June 2020

Abstract
In order to improve the quality of information-based teaching in colleges and universities, promote the penetration of information technology into the classroom, and improve the teaching effect, based on the TPACK framework, this paper designs the information-based teaching cloud space in colleges and universities, and constructs the system framework by combining the learning theory and teaching needs with the characteristics of the cloud space. Teaching cloud space is to carry out research from four dimensions: technology dimension, teaching dimension, discipline dimension and deep learning dimension, to realize the integration of technology and education and teaching ideas, and to build a personalized teaching cloud space. The research results show that the information-based teaching cloud space based on TPACK theory can improve the teaching of practical courses and stimulate students’ curiosity and enthusiasm for learning.
The Design and Implementation of the Smart Trash Can based on the Internet of Things
Authors: Wang Yujie , Xu Yanzhu , Zhang Bingchang , Zhang Junhong , Su Xinpu 
Published: June 2020

Abstract
Choosing MSP430F149 as the main control chip and using the human body sensor module, infrared ranging module, motor, ultrasonic module, GSM module as peripheral components, the Smart trash can proposed in this paper can realize automatic garbage classification and garbage overflow detection. The human body infrared sensor module senses the approach of the human body in real time, triggers the Single-Chip Microcomputer to work, and opens the lid of the trash can; The entry of garbage is monitored in real time, responding to the Single-Chip Microcomputer driving the LDC1000 metal sensor for metal detection; The purpose of garbage classification could be achieved through the forward and reverse rotation of the motor; After the motor returns to its original position, the ultrasonic module installed in the middle of the trash can is used to detect whether the garbage is overflowed. The GSM module determines whether to send a message according to the ultrasonic detection. The test of the device shows that the control effect is good and the expected design goal is achieved.
Innovating Computerized Social Insurance Management Model
Authors: Li QingPing 
Published: June 2020

Abstract
At this stage, the industry background of China’s social insurance management mechanism is facing reforms. In the process of reform, in accordance with relevant issues, through analysis and discussion, focus on properly handling issues, enhance the effectiveness of the organization’s work, take an information processing method, rely on computers Technology makes social insurance management tend to be automated, intelligent and efficient. By promoting the informationization of social insurance management, simplifying the steps of handling social insurance business, integrating the data, file, and teller system models, implementing streamlined fund payment business management and control procedures, maximizing the use of computer applications, and strengthening social insurance institutions work results, social insurance agency business model to achieve innovation.
A Modified Mean Shift Method for Fault Region Extraction in Infrared Image
Authors: Ni Hui , Xu Jinxia , Wang Ruoyue , Bi Jiangang , Wang Feng , Xu Xiaolu 
Published: June 2020

Abstract
Aiming to detect the fault of electrical equipment with infrared thermography, this paper presents a modified mean shift clustering method for finding the region of fault. At the beginning, the whole image is separated by the highest threshold, as to find the coarse fault region. Then the mean shift clustering method is modified by introducing the weight factor associated with the neighboring pixels, in order to cluster the region with similarity. Meanwhile, the original way of mean shift clustering method to iterate in the image is abandoned, and we propose a threshold segmentation mechanism to solve. It thereby promotes the speed of clustering, and extracts the region of fault effectively. Finally, the experiments on real world infrared images show that our method has higher performance than some existing methods, including original mean shift clustering method.
A fast calibration method of the robot’s 3D vision system
Authors: Qin Lei , Zheng Zhenxing , Diao Shipu 
Published: June 2020

Abstract
Calibration accuracy and convenience are very important for the rapid production of vision-based robots. Although there are many calibration methods for the 3D vision system, the convenience of calibration cannot meet the needs of industrial sites. In this paper, we propose a fast calibration method based on a 3D calibration block. Its key point is to complete the calibration process through multiple space point constraints, and the space points used are jointly solved by multiple planes obtained by local point cloud fitting. The experimental results show that the proposed method can quickly complete the calibration of the robot’s 3D vision system, and the calibration accuracy can meet low precision occasions.
BDS RTK High-precision Positioning Algorithm for Deformation Solution
Authors: Zhou Bo , Wang Shouhua , Feng Siyu , Tang Donghai , Weng Chenghao , Dong Hang , Chang Xiaochi 
Published: June 2020

Abstract
In view of the increasing scope, scale and quantity of landslide, debris flow and other geological disasters and without achieving long-term stable high-precision monitoring in the exist, a solution algorithm on RTK high-precision differential positioning deformation is presented in this paper. The algorithm uses high-precision board receiver as the satellite positioning reference station, the u-blox receiver as the monitoring station. The carrier phase double difference relative positioning model is based on the carrier phase observation and pseudo range observation, combined with the RTK unscented Kalman filter model and smooth filtering algorithm, and then more accurate position results are obtained. This method can be used to calculate the position of deformation monitoring body with high precision, which is simple, easy to realize and practical. The positioning accuracy of deformation monitoring is higher. After smoothing, the horizontal precision can reach 3mm, and the elevation precision can reach 5mm.
Design of Home Service Robot for Pet Caring
Authors: Deng Weilun , Shi Yongtao , Li Chuanqing , Zou Tian , Tian Chunlai 
Published: June 2020

Abstract
Pet dogs are good friends in people’s home living. Some people sometimes are inconvenient to take care of pet dogs. A smart robot for caring pets is designed in this paper. The robot can actively interact with the pet dog in the way of serve and relieve the separation anxiety of the pet. During the serve process, the multi-stage gears and rack meshing transmissions are used to achieve near, medium and long-range launch modes with stable convenience performance. The structure of this companion pet robot is displayed. It is mainly composed of feeding system, serve system, grasp system, driving system and real-time monitoring system. It is demonstrated here with the feasibility of the machine function and the rationality of the structure design is analysed. The robot will support a good convenience help to pet caring in home living.
Home Position and Tool Coordinates Calibration for 6-DOF Robot Based on Fixed-point Constraint
Authors: Zhao Honghua , Zhao Jian , Lin Yifei , Han Qifei , Cao Baolu , chen Mianpeng 
Published: June 2020

Abstract
In the application process of industrial robots, it is necessary to ensure that the home position is correct when the robot changes the end tool, so that the coordinate values of tool can be calculated more accurately and the accuracy of the robot’s trajectory can be satisfied. A comprehensive calibration method of tool coordinates and home position of serial industrial robots based on fixed-point constraints is presented in this paper. Based on Denavit-Hartenberg (D-H) kinematics theory, the error models of tool coordinates and home position errors of 6-DOF serial industrial robots are established. By constraining the tool center point (TCP) of the robot with fixed points, the joint data of calibration points with different postures at the same position are collected, and the error value of the controlled object is identified by the least square algorithm. The calibration experiment results are basically consistent with the factory calibration parameters, which proves the feasibility of the calibration algorithm.
Research on Fault Diagnosis of Complex Equipment Based on Artificial Intelligence
Authors: Zhou Quan , Liu Lan 
Published: June 2020

Abstract
According to the requirement of fault diagnosis for complex equipment, the fault diagnosis signal is reconstructed by wavelet analysis technology and the multi-information fusion method of fault diagnosis based on evidence theory is established. The intelligent fault diagnosis method and fault reasoning method are established by using Bayesian network technology which provides the theory and method support for fault diagnosis of complex equipment.
Skin Reflectance Model for Multispectral System
Authors: Feng Sui , Zheng Jinjin 
Published: June 2020

Abstract
This is a pre-study on the non-invasive detection and contrast observation of pigmented lesions. We obtained a proper skin reflectance model of a multispectral imaging system that generates images perpendicular exposure of skin perpendicularly exposed reflectance in the wavelength ranging from 470 to 630 nm at an internal of 2 nm, altogether 81 different wavelengths in the visible range. The reflectance data was collected form 27 different skin lesions each 500*500 pixels in various conditions from 8 persons. Since the spectrum is mixture of layers of skin tissue distribution, independent component analysis (ICA) and pure components analysis were proposed to fit the experimental data, and the layered-dependent skin parameters melanin content, blood volume fraction, and haemoglobin content were extracted from the skin reflectance model. The skin reflectance model fitted spectrum curves show 4% average (12% maximum) difference from the experimental data, which were acceptable. The skin model is accepted for the multispectral imaging system in further studies.
Research on Monopulse Angle Measurement Algorithm in Pseudolite Local Positioning System
Authors: Yan Enqi , Yang Jun , Meng Zhijun , Guo Xiye , Zhang Yufeng 
Published: June 2020

Abstract
The pseudo-satellite local positioning system can solve the problem of blind spots in satellite navigation systems, such as tunnels, underground parking lots, and indoor positioning. The method of direction-finding positioning can be combined with distance-finding positioning to improve positioning accuracy, and it can also independently realize positioning functions. The basis of direction-finding positioning is to accurately estimate the direction of the signal source. The sum-difference patterns of amplitude monopulse angle measurement algorithm has mature applications in the field of tracking radar, and can achieve accurate angle estimation. The sum-difference patterns of amplitude monopulse angle measurement algorithm is applied to a pseudo-satellite local positioning system. Through simulation experiments, the angle measurement performance of the algorithm under different conditions is analyzed.