scispace - formally typeset
Search or ask a question

Showing papers presented at "Biomedical Engineering International Conference in 2019"


Proceedings ArticleDOI
01 Nov 2019
TL;DR: Experimental results show the methods can identify and evaluate quickly respiratory frequencies of two persons through the wood wall based on Doppler frequency and variance statistic of the respiratory signal.
Abstract: Multiple human detection through the wall has become an interesting topic for security, rescue, life detecting under earthquake rubble, etc. This paper presents a UWB radar at 3 GHz for detecting multiple humans through the wall based on Doppler frequency and variance statistic of the respiratory signal. Technically, we have referred to efficiently simple methods are FFT and variance statistic for identifying the respiratory frequency of multiple persons quickly. Experimental results show the methods can identify and evaluate quickly respiratory frequencies of two persons through the wood wall.

12 citations


Proceedings ArticleDOI
01 Nov 2019
TL;DR: An automatic classification technique for microscopic white blood cell images focusing on images from fresh blood smears using deep convolutional neural network (DCNN) is proposed in this paper.
Abstract: White blood cells are the one of immune system that are involved in protecting the body against infection disease and foreign invaders. There are difference category of white blood cell and each category can indicate about the irregularity of body. Nowadays, White blood cell diagnosis is usually examined manually by doctor. This process consumes a lot time, cost and susceptible to error compare with automatic computerize process. An automatic classification technique for microscopic white blood cell images focusing on images from fresh blood smears[1] is proposed in this paper. The classification is conducted using a proposed method that consist of deep convolutional neural network (DCNN). 10,000 Microscopic blood images were tested and the classification method obtain 93%

11 citations


Proceedings ArticleDOI
01 Nov 2019
TL;DR: SA-SF hydrogel has great potential as bioink for use in bioprinter and was easy to control mechanical properties by adjusting SA and SF concentrations which could be a tool for studies integrity, printability and mass transport of hydrogels.
Abstract: Bioink design is a big challenge in bioprinting of functional tissues. Bioink serves as extracellular matrix providing structural support and guiding growth and development of tissue. Alginate (SA) and silk fibroin (SF) have been used to create hydrogels due to their excellent biocompatibility and biodegradability. In this study, we investigated printability and cell compatibility of alginate-silk fibroin hydrogels for bioprinting. Different concentrations of SA-SF solution were tested for printability by pushing the mixture through needle and syringe. For Cell compatibility study, osteosarcoma were encapsulated in SA-SF hydrogel, determined cell viability using Live/dead and PrestoBlue™ assay. SA-SF hydrogel containing 1%(w/v) sodium alginate and 2% (w/v) silk fibroin successfully formed uniform strands and supported cell viability. The hydrogel was easy to control mechanical properties by adjusting SA and SF concentrations which could be a tool for studies integrity, printability and mass transport of hydrogels. We demonstrated SA-SF hydrogels has great potential as bioink for use in bioprinter.

10 citations


Proceedings ArticleDOI
01 Nov 2019
TL;DR: This research created a hand glove for deaf and mute people to provide better communication between disabling and normal people and tested the accuracy of the glove, it has accuracy about 70 – 100% which depends on another factor.
Abstract: Communication is very important in daily life while many people in Thailand cannot communicate by talking. On the other hand, they can communicate only sign language. The sign language used in a specific group of people, but other people cannot understand. Therefore, we decided to do a project about glove for sign language communication. This research created a hand glove for deaf and mute people to provide better communication between disabling and normal people. We applied computer programming by used the flex sensor and GY-521 Module. The flex sensor has measured the bent of fingers and generated an analog output. GY-521 Module is use for measured the direction and movement of the hand. After got data from both sensors, it will send to Arduino IDE converted all the data to alphabet and text before converted to speech. Then we tested the accuracy of the glove, it has accuracy about 70 – 100% which depends on another factor. From this process, we got a hand glove for sign language communication. This glove will help deaf and mute people to communicate more efficiently.

8 citations


Proceedings ArticleDOI
01 Nov 2019
TL;DR: A biomedical electrode patch for detecting an electrocardiogram signal which is provoked by electrical activity through the heart has been investigated in this article, where the electrode patch was made of chemical derived graphene as an electrically conductive layer and polyhydroxyalkanoate (PHA) as a flexible substrate.
Abstract: A biomedical electrode patch for detecting an electrocardiogram signal which is provoked by electrical activity through the heart has been investigated. The electrode patch was made of chemical derived graphene as an electrically conductive layer and polyhydroxyalkanoate (PHA) as a flexible substrate. This gra-phene/PHA patch has been promised to be completely biocom-patible and biodegradable by microbes in terrestrial environments. According to the fabrication of the electrode patch, gra-phene was synthesized using Hummers’ method and reduction with hydrazine hydrate. The PHA membrane, an aliphatic polyester bioplastic, was accumulated by Ralstonia eutropha and then was casted using electrospinning technique to serve nanofiber scaffold for an abundance of graphene to be addressed. The gra-phene/PHA electrodes were interfaced to 3-lead electrocardiogram (ECG) sensor and amplifier modules controlling by computing microcontroller. Real-time in vitro monitoring of the simulated ECG signals were observed, including normal condition at 60 BPM and abnormal heart rhythms, through the interfaces of graphene/PHA electrodes. The signals have been shown obviously and no degradation over time, however, the signal attenuation might increase due to in vivo measurement of human skin-electrode impedance.

7 citations


Proceedings ArticleDOI
01 Nov 2019
TL;DR: The aim of this paper is to bring the new approach to classify the species of bacteria from Digital Image of Bacterial Species (DIBaS) by using Image Processing and Deep Convolutional Neural Network model to reduce the time consumption and increase the classification accuracy in traditional way.
Abstract: Bacteria is one of the reasons that cause many diseases, and the diagnosis is hard to be done because of its shape and complexity. Thus, regularly the examination needs to be done by experts or specialists to classify the species of them and it also takes long time with the possibility on incorrect recognition. In Thailand there is still not enough experts to deal with it so the aim of this paper is to bring the new approach to classify the species of bacteria from Digital Image of Bacterial Species (DIBaS) by using Image Processing and Deep Convolutional Neural Network (DCNN) model to reduce the time consumption and increase the classification accuracy in traditional way. The result of the model gave the average accuracy at 95.09%.

7 citations


Proceedings ArticleDOI
01 Nov 2019
TL;DR: A prototype for verification of the temperature and relative humidity inside the infant incubator and to improve the accuracy of the sensor, as well as allowing the prototype to monitor temperature and Relative humidity via IoT for teaching in medical instrumentation calibration topic.
Abstract: This research aims to design a prototype for verification of the temperature and relative humidity inside the infant incubator and to improve the accuracy of the sensor, as well as allowing the prototype to monitor temperature and relative humidity via IoT for teaching in medical instrumentation calibration topic. The designed device composed of 3 main parts: 1) Input consists of 5 DS18B20 temperature sensors and 1 DHT22 humidity sensor, 2) The processor part used NodeMCU (ESP8266) board which programming with Arduino IDE and 3) The display was divided into two parts: IoT and data logger to save data into external memory before published data to thingspeak.com. The designed device was set in the water bath together with OM-CP Data Logger tested on 5 to 40° C to find temperature error for each sensor at each temperature value. The prototype was reprogrammed with compensation the error equations of each sensor in the program and then retest with the infant incubator temperature 32- 40°C. The result found that the error values were highly decreased. Performance testing of the designed device in the infant incubator was compared to the Incubator Analyzer Brand Fluke Biomedical INCU II based on IEC 60601-2-19 test on 32° C and 36° C. The pattern of the temperature changes inside infant incubator of the prototype device compared with the standard device was closely same for both temperatures. The average temperatures and relative humidity measured from the prototype device on Steady Temperature Condition (STC) were had approximate acceptable values compared to mean values obtained from the standard instrument. All temperatures reading from the prototype were different from the standard device temperature less than 0.5°C, and the relative humidity reading from the prototype was different from relative humidity reading from the standard device of less than 5 %.

6 citations


Proceedings ArticleDOI
01 Nov 2019
TL;DR: The experimental results show the characteristic of the average RSSI level when the human stands at each distance within the wireless network, and real-time RSSI signals affected by human movement with different patterns are demonstrated.
Abstract: Device-free localization (DFL) is the technology that monitors and analyzes the change of the received signal strength indicator (RSSI) level caused by human movement to locate the human position in a wireless network. Since human presence and movement in the communication link influences the RSSI level, most of the DFL techniques consider such effects as a major factor for position estimation. In this paper, the effect of human presence on the RSSI level at the different distances within/and nearby the wireless link is studied first. Then, the effect of human movement with different movement patterns on the RSSI level is investigated. Our experiments have been carried out in an indoor environment, a laboratory room, where a wireless network with 2.4 GHz, IEEE 802.15.4 technology has been employed. The experimental results show the characteristic of the average RSSI level when the human stands at each distance within the wireless network. In addition, real-time RSSI signals affected by human movement with different patterns are also demonstrated. Our findings are useful for understanding RSSI signal patterns, and designing more efficient DFL algorithms.

5 citations


Proceedings ArticleDOI
01 Nov 2019
TL;DR: This paper proposes a technique called Adaptive Complicated Chromosome Image Enhancement (ACCIE) using the determining threshold value method, and the dynamically determining intensity adjustment method that helps to generate a proper chromosome skeletonization and yields 86.81% for complicated chromosome number determination.
Abstract: The light microscopic image of the chromosome is one of the sources to diagnose genetic disorders. Chromosome counting is the first step for diagnosing genetic abnormalities. However, in the case of complicated chromosome pattern in images, it still needs improvement due to its complication and poor image quality. Moreover, every chromosome images are different in contrast and brightness. Therefore, in order to achieve a better performance in chromosome counting, the complicated chromosome images need to be specially enhanced. This paper proposes a technique called Adaptive Complicated Chromosome Image Enhancement (ACCIE) using the determining threshold value method, and the dynamically determining intensity adjustment method. The proposed method helps to generate a proper chromosome skeletonization and yields 86.81% for complicated chromosome number determination.

5 citations


Proceedings ArticleDOI
01 Nov 2019
TL;DR: The preliminary system for home-use uroflowmetry with Narrowband Internet of Things (NBIoT) implementation is introduced, which will help physician to closely monitor his patient symptoms and help with treatment adjustment.
Abstract: Uroflowmetry is one of the most useful tools for patient with lower urinary tract symptoms (LUTS). While there are more and more patients suffered from LUTS, the accessibility of the device is still very low Also stress and unfamiliar environment at physician offices may affects the test result. This study introduces the preliminary system for home-use uroflowmetry with Narrowband Internet of Things (NBIoT) implementation. Patient can get tested from their home and the data will be automatically send to physician’s office. This will help physician to closely monitor his patient symptoms and help with treatment adjustment. The system includes prototype home uroflowmetry device, based on weight transducer method. connected with NB-IoT module, real time clock, SD card for backup memory, the data will send to server using user datagram protocol (UDP). The device user interface designed to be simplest and easy to understand by patient. Physician will access the result from hospital by log in to desktop application that download data from server and process and visualize uroflowmetry test result. This study also includes with preliminary tests of the system among research group members to prototype user experience and system flow.

5 citations


Proceedings ArticleDOI
01 Nov 2019
TL;DR: This research is presented the bio-signal activities of arm movements by using deep learning for classification between right-arm and left-arm by using the classification method of EEG signals data to develop a BCI in the future.
Abstract: This research is presented the bio-signal activities of arm movements by using deep learning for classification between right-arm and left-arm. It’s well-known that Electroencephalography (EEG) shows neural oscillation behaviors in electrical voltage form. Also, Brain-Computer Interface (BCI) is direct communication between neural oscillation and computer to control machines without physical movements. So, this paper aims to present the classification method of EEG signals data to develop a BCI in the future. By using deep learning to classification data is classified into raise the right arm, raise the left arm. And decrease EEG signal data by using Principal Component Analysis (PCA). PCA can reduce the data size of EEG signal from 1000x28 to 28x28. Experimental result of classification has accuracy 90.86% and 94.71%

Proceedings ArticleDOI
01 Nov 2019
TL;DR: 2-stage thresholding technique for automatic avian RBC counting is proposed and provided the count with error rate much less than the clinically acceptable value.
Abstract: The chicken industry ranks tenth in the world in term of output in the world meat market. Red blood cell (RBC) count is one of the basic health screening protocol required in an exported meat industry. However, the human’s automated blood analyzer cannot be applied to avian RBC, because the shape of avian blood (ellipse) is different from the mammal one (circle). In this paper, we propose 2-stage thresholding technique for automatic avian RBC counting. First, the blood smear slide is binarized into RBC and non-RBC area by applying Otsu thresholding. Then, image morphology and Otsu thresholding are reapplied to detect the blood nucleus. After that, the connected component analysis is applied to count the number of RBC. The experiment demonstrated that the proposed technique was simple and provided the count with error rate (2.23%) much less than the clinically acceptable value (5%).

Proceedings ArticleDOI
01 Nov 2019
TL;DR: It is found that addition of CMC into polymer solution could modulate scaffold architecture and swelling abilities and suggested that PVA/CMC porous scaffold could be used in cartilage tissue repair.
Abstract: Cartilage has limited intrinsic capacity for self-repair after injury due to a lack of blood supply and low cell density. Tissue engineering holds promise for building cartilage grafts that withstand the stresses in joint. Major challenges of functional cartilage tissue development are scaffolding materials and structure of scaffold to support cartilage tissue formation. Scaffolds for engineered cartilage have been involved with the use of synthetic and natural polymers. Synthetic polymers provide well-control mechanical properties, while they are relatively inert to cell adhesion and tissue formation. Instead, natural polymers allow inherent cellular interaction and are present in abundance. In this study, polyvinyl alcohol (PVA) and carboxymethyl cellulose (CMC) were combined to form copolymer solution used in porous scaffold fabrication. Our goal was to investigate effects of PVA/CMC complex network on pore formation in scaffold and on cartilage tissue development. We found that addition of CMC into polymer solution could modulate scaffold architecture and swelling abilities. Fourier transform Infrared Spectroscopy (FTIR) of PVA/CMC scaffold showed the peak at 1599 cm−1 of C=O group, indicating the incorporation of CMC into the scaffold. Chondrocyte viability was observed up to 14 days post-cell seeding. These data suggested that PVA/CMC porous scaffold could be used in cartilage tissue repair.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: This platform was a preliminary step of a bedside ECG monitoring platform that can visualize the ECG signal with a probability of normal, abnormal and noise with an accuracy of 99.1%, which can be adapted for a reduction of the false alarm rate.
Abstract: The real-time wireless 6-lead electrocardiogram (ECG) monitoring platform with the application of a convolutional neural network (CNN) in Arrhythmia classification is proposed in this study. The platform consists of two main parts. The mainboard with ADS1292R: a 24-bit ECG analog frontend integrated circuit and a low-cost low-power microcontroller: ESP32 which received and resample to 250 Hz before sending through Wi-Fi. This device is being powered by a 5V 16,000mAh Li-ion battery power supply. The 2 channel signal from the analog frontend was transferred to a computer server for data collection. Lead II data were then stored as a 6-seconds ECG epoch (1536 samples) in order to visualize and classified with a pre-trained CNN. There were two CNN models with a residual network architecture (ResNet) conducted in this study: SmallNet and BigNet. SmallNet classified an ECG signal into 3 classes which are nor-mal, abnormal, and noise, then reported them in a probability form on the displaying screen. BigNet can be used to identify 10 different types of arrhythmia which are normal, left bundle branch block beat, right bundle branch block beat, atrial premature beat, premature ventricular contraction, ventricular escape beat, a fusion of ventricular and normal beat, paced beat, other abnormal beat and noise. This platform was a preliminary step of a bedside ECG monitoring platform that can visualize the ECG signal with a probability of normal, abnormal and noise with an accuracy of 99.1%, which can be adapted for a reduction of the false alarm rate. Moreover, the stored data can also call back later to determine the type of arrhythmia with an ac-curacy of 98.5% if any abnormality occurred.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: The aim is to develop an automated method for ADPKD patient kidney segmentation and quantifying TKV, which is evaluated against clinical reference standard TKKV measurements.
Abstract: Autosomal dominant polycystic kidney disease (ADPKD) is characterized by progressive bilateral renal cyst formation, leading to severe increases in kidney volume and loss of function. Total kidney volume (TKV) is the only established biomarker for tracking ADPKD. This is measured multiple times per year from each patient to examine the extent of renal enlargement and overall cyst load. Currently this is conducted by planimetry tracing, which involves manually delineating kidneys from surrounding tissues in the abdominal cavity using a digital drawing tool. By performing this on every image in a magnetic resonance scan, TKV is estimated. This is a time-consuming and laborious process for radiologists. Our aim is to develop an automated method for ADPKD patient kidney segmentation and quantifying TKV. Thirteen MRI scans of kidneys ranging across the spectrum from normal to severe cyst load were analyzed. Images were separated into two halves, each made up of 200 square regions. Features were extracted from grayscale values of each region, and these data were combined in a supervised decision tree algorithm to classify between kidney and non-kidney regions. Filtering and dilation were applied to the classified 400x400 matrix in order to roughly segment the kidneys. Contrast enhancement and k-means clustering was performed before applying an active contour function to determine kidney edges. Eccentricity analysis confirmed appropriate relative sphericity for segmented kidney shapes, before combining their areas with linear extrapolation to estimate TKV. This protocol is evaluated against clinical reference standard TKV measurements.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: This paper presents a visualization of the PSG study, explicates the design of features and provides a comparison between the approaches of learning from the computed features versus the signal directly.
Abstract: Polysomnography (PSG) is a sleep study where multiple parameters of a subject are continuously monitored to detect sleep disorders that can have adverse health effects. This study uses Machine Learning techniques to automatically detect and classify apneas and hypopneas in PSG data. By incorporating features that sleep analysts look for in PSG data we present a machine learning based approach to automatically detect the presence of hypopnea or apnea and classify the type of pathology. This paper presents a visualization of the PSG study, explicates the design of features and provides a comparison between the approaches of learning from the computed features versus the signal directly. Our results demonstrate that a hierarchical SVM model trained on a small set of features yields an accuracy of 82.6% with a high precision of 86%.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: An investigation of crying signal spectra is used to classify categories of infant cries and shows that CNN based deep learning achieves high performance.
Abstract: In this paper, an investigation of crying signal spectra is used to classify categories of infant cries. Three different types of crying considered in this work are hungry, sleepy and burping need. These cries are preprocessed and converted for calculation of Mel-Frequency Cepstral Coefficients (MFCC) before being classified by Convolutional Neural Network (CNN). Experimental results show that CNN based deep learning achieves high performance of 84%.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: This paper employs different machine learning algorithms to perform a regression study to predict systolic blood pressure (SBP) levels using blood pressure dataset of Dr. Raymond Lam, GlaxoSmithKline, Toronto, Ontario, Canada as a guide.
Abstract: This paper employs different machine learning algorithms to perform a regression study to predict systolic blood pressure (SBP) levels. We used blood pressure dataset of Dr. Raymond Lam, GlaxoSmithKline, Toronto, Ontario, Canada in this study. There are 500 patients in the dataset, 250 have normal blood pressure level and the other 250 have hypertension. There are 500 predictors in the dataset. 17 predictors are patients’ non-genomic information and the rest are 483 genetic markers. In this paper, we have selected only the following 13 factors as predictors in this study to reduce the complexity of the problem. The predictors included in this study are ’gender’, ’married’, ’smoke’, ’exercise level’, ’age’, ’weight’, ’height’, ’alcohol consumption’, ’treatment for hypertension’, ’stress level’, ’salt intake level’, ’income’ and ’education level’. The regression model that gave the lowest root mean square error in SBP of 25.68 for the 13 predictors is Gaussian Process Regression using the squared exponential function in the regression model. Although the RMS value was quite high, it was sufficient to draw some conclusions and identify the factors that do affect the SBP level.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: In this paper, size-controlled nonporous SiO 2 -NPs were synthesized through the sol-gel process using calcination process at 680 °C for 3 hours.
Abstract: Silica nanoparticle (SiO 2 -NPs) are widely used in biomedical applications. Herein, size-controlled nonporous SiO 2 -NPs were synthesized through the sol-gel process. Calcium (Ca), Zinc (Zn), Nickel (Ni), and Iron (Fe) were successfully incorporated into the SiO 2 -NPs through calcination process at 680 °C for 3 hours. SEM results show high homogeneity of spherical SiO 2 -NPs with a diameter of 120-140 nm. The surface morphology of doped SiO 2 -NPs was altered due to the formation of some deposits. EDS spectra confirmed that Ca, Zn, Fe, and Ni were successfully incorporated into SiO 2 -NPs with not all of the nominal ratio. Atomic % of doped ions increased as the nominal ratio increased from one to two.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: The results demonstrate clue evidence to discover novel target sites of action of antipsychotics that might play a pivotal role in cognitive deficits in schizophrenia.
Abstract: Cognitive function is the intellectual activity of mental processes, such as, attention, processing speed, learning and memory, executive function, verbal fluency, and working memory. Cognitive deficits may contribute to functional disability in psychiatric disorders. Therefore, improving cognitive function has the potential to enhance the quality of life and occupational capacity, and reduce disease problems and societal costs. Recent evidence emphasized that conventional antipsychotics effective in treating positive symptoms but have almost no therapeutic benefit on cognitive impairment and produce a poor functional outcome. It has been hypothesized that antipsychotics might induce cognitive impairment or decline in psychiatric disorders. Therefore, this study aims to elucidate the network of proteins that may involve in antipsychotic drug-induced cognitive impairment. The results of GeneCards analysis showed 213 target genes of commonly use antipsychotics in schizophrenia, 122 genes associated with cognitive function in schizophrenia, and 28 genes related to antipsychotics and cognitive function in schizophrenia. Protein-protein interaction (PPI) network of 12 significantly interconnected proteins was generated by the PPI network analysis and clustering method. The 16 significant pathways related to antipsychotic drugs and cognitive function were identified using pathway grouped network analysis. These results demonstrate clue evidence to discover novel target sites of action of antipsychotics that might play a pivotal role in cognitive deficits in schizophrenia.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: In this article, a temperature controlled blood bank transport cooler is proposed to maintain the properties of blood for transportation within the hospital. But, the authors only used a microcontroller with Proportional-Integral-Derivative (PID) controller to control the temperature in the blood bank cooler.
Abstract: The transportation of blood is one of the most important issues in the hospital and healthcare sector since the blood bags have to be kept within a certain range of the temperatures for the entire travel time in order to maintain blood quality during transportation. Therefore, the objective of this work is to design, develop and construct a temperature controlled blood bank transport cooler to maintain the properties of blood for transportation within the hospital. The developed blood bank transport cooler uses thermoelectric peltier cooler to create coolness and a microcontroller with Proportional-Integral-Derivative (PID) controller is used to control the temperature in the blood bank cooler within the range between 2°C and 8°C. The current temperature in the cooler is monitored on the LCD display and the notification as light and sound will be activated when the current temperature is out of the desired range. To verify the performance of the developed blood bank cooler, the temperature measured from the temperature sensor of the developed blood bank is compared with the temperature measured from the temperature calibrator. The results show that the measurement errors are within ±10.984%, which is in the acceptable range according to the standards of the World Health Organization (WHO).

Proceedings ArticleDOI
01 Nov 2019
TL;DR: This model provides a clear explanation of the interaction behavior between ICP, CCP, IOP, ABP and BF of healthy individuals.
Abstract: In case study, the dynamical behavior of various systems including intracranial pressure (ICP), cerebral perfusion pressure (CPP), intraocular pressure (IOP), arterial blood pressure (ABP), and blood flow (BF) are studied based on the equivalent electrical model. The healthy people from clinical data are used for study those behaviors. Resistor-Capacitance network is constructed to simulate ICP inside the skull, IOP of the retinal vessel, CPP in the skull. Moreover, ABP from the heart (85 - 120 mmHg) and Intraspinal Pressure (ISP) (50 - 60 mmHg) are applied as inputs to this model. The results show the value of ICP of normal state, IOP, and CPP in the skull are 5-15 mmHg, 20-35 mmHg, and 65-90 mmHg respectively. For the phase relationship among ABP, CPP, IOP, and ICP are synchronized. The differential phase between ABP and BF is 0.25 to 0.5 second where ABP waveform was leaded BF waveform. Our model is verified by clinical data from noninvasive measuring method. This model provides a clear explanation of the interaction behavior between ICP, CCP, IOP, ABP and BF of healthy individuals.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: The proposed method provided less than 5% counting error for the staining that did not have the dark RBC's boundary, and Otsu's multiple thresholding is used to extract the nucleus of RBC.
Abstract: Broiler is among Thai major industries. Red blood cell (RBC) count is one of the basic health screening protocols. The counting is often done manually. The mammalian blood analyzer cannot be used, because the shapes of mammalia and avian RBCs are different. RBC's color also varies among staining methods. Furthermore, RBCs can be grouped together. In this paper, we propose the automatic method for counting avian RBC. Otsu's multiple thresholding is used to extract the nucleus of RBC. The problems of the varying color of and the conglomerated RBC are tackled by applying iterative process. Only one parameter needs to be set manually. The experiment on five different staining types demonstrated that our proposed method provided less than 5% counting error for the staining that did not have the dark RBC's boundary.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: The bone color scale, which varied from black, through gray-blue, to white, outperformed other color scales, followed by autumn color scale which the important benefit is that the micro-calcium spots were illustrated shinier and clearer.
Abstract: In this study, we aimed to find an effective method for improving visibility of microcalcifications on breast mammograms by using pseudo-color image processing techniques. We had considered twelve pseudo-coloring algorithms or color scales including autumn, bone, cool, copper, hot, rainbow, pink, spring, summer, winter, springtime-1, and springtime-2 color scales. Experiments were performed on 20 mammograms including 10 benign images, and 10 malignant images. The resultant images of using pseudo-color image processing with the twelve color scales were evaluated by radiologists. They determined that at what degree the pseudo-color images are helpful for improving visibility of micro-calcium spots on the mammograms by using 5-level score (5=very high, 4=high, 3=neutral, 2=low, 1=very low). The results using Ian McDowell’s rating scale showed that the performances of bone and autumn color scales are very good. The performances of copper, winter, summer, springtime-1, pink, and hot color scales are good, and the performances of springtime-2, spring, cool, and rainbow color scales are fair. The bone color scale, which varied from black, through gray-blue, to white, outperformed other color scales, followed by autumn color scale which the important benefit is that the micro-calcium spots were illustrated shinier and clearer.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: The two-stage circular Hough transform is proposed as the alternative automatic method for canine red blood cell counting and provided the counting with the average error of less than 5% with the best result of 1.439% error.
Abstract: Red blood cell counting is one of the most requested blood tests and can be effectively done by the dedicated equipment. In this paper, we proposed the two-stage circular Hough transform as the alternative automatic method for canine red blood cell counting. The possible blood cell area in the input slide image is first extracted. In the first stage, the Circular Hough transform with low sensitivity is applied to detect the non-overlapped cells. The detected cells are removed. The Circular Hough transform with high sensitivity is re-applied to the remaining region in order to detect the overlapped cells. The proposed method can be easily implemented into personal computers. The proposed algorithm was tested with many slides of canine red blood cell. It provided the counting with the average error of less than 5% with the best result of 1.439% error.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: In this paper, the authors analyze brainwave, electroencephalogram signal (EEG), of healthy people while they perform cognitive tasks to identify when a patient achieves an implicit learning event.
Abstract: Attention Deficit Hyperactivity Disorder (ADHD) is a neurological disorder that affects many people in the world. Many researchers had speculated that behavior therapy could be used to treat people with ADHD. One of the promising behavioral therapy to help patients train their brains to focus, impulse control and executive functions is neurofeedback therapy. Using neurofeedback, therapists can learn how to influence patients’ brain activity by adjusting the training regimen according to the feedback brainwave. According to past research, it was speculated that implicit learning and children with ADHD could have an atypical relationship. The aim of this paper is to analyze brainwave, electroencephalogram signal (EEG), of healthy people while they perform cognitive tasks to identify when a patient achieves an implicit learning event. Once the event is temporally identified, a pattern in the brainwave could be extracted which can potentially help in designing the neurofeedback treatment to help people with ADHD by training the brain to regular evoking the implicit learning brain state.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: In this paper, a 3D model of microwave balloon antenna for treatment urethral stricture from BPH was presented, and the 3D modeling heating pattern's ability of thin slot antenna of microwave balloon in the research was also demonstrated.
Abstract: In this paper, the 3D model of Microwave balloon antenna for treatment urethral stricture from Benign Prostatic Hyperplasia (BPH) was presented. The 3D modeling heating pattern’s ability of thin slot antenna of microwave balloon in the research was also demonstrated. As the results showed that the distribution of temperature and surrounding temperature of modeling of open microwave system. Microwave system at 2.45 GHz. Power Operation are 40, 60 and 80 W. Time operation are 5 - 600s. A design microwave balloon antenna has protection of critical tissue structure.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: In this article, a planetary centrifugal mixer was developed to increase the homogeneity of the self-setting calcium phosphate cement (CPC) mixtures in batch production, and the preliminary study of characteristics of the mixer, the revolution speed, centrifugal acceleration (1-66G), and their relations were reported.
Abstract: Self-setting calcium phosphate cement (CPC), used in orthopedic surgery for bone replacement, usually consists of the powder mixture and setting liquid (aqueous) or carrier liquid (non-aqueous). After mixing two-phase compounds, the injectable viscous solution was formed and able to mold into the desired shape of the bone. The heterogeneous mixture, however, affects the dissolution-precipitation reaction ending in the unwanted chemical products, the deformed shape of solidified materials, and the undesired mechanical properties. In addition, the debris of heterogeneity increased the clogged needle probability when extruding with the cartridge of the 3D printer. In order to overcome these sort of problems and refine the homogeneity of CPC, the blade-free planetary mixer was used. Herein, the planetary centrifugal mixer was developed to increase the homogeneity of the CPC mixtures in batch production. The preliminary study of characteristics of the mixer, the revolution speed, centrifugal acceleration (1-66G), and their relations, was reported. The homogeneity of a CPC mixture was tested into two parts: the mixings of the powder-powder compound, and the powder-liquid paste. ImageJ, Fiji bundles, was used to evaluate the homogeneity of the mixture. The centrifugal acceleration of 16G and 66G homogeneously mixed a 1.2g of CaCO 3 /Orange Red 735 and a 1.5g of αTCP/Glycerine respectively within 3 minutes. This study intentionally contributed to the fields of bio-printing and biofabrication, especially artificial bone, by improving the mixing process of the bone cement formula and demonstrating the idea for industrializing the bone cement into the commercial product.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: In this paper, the analysis of double antenna variable setting and microwave system designing for appropriated heat to eliminate hepatic cancer by 3D finite element analysis in vitro experiment pattern was presented.
Abstract: In our country, many people need appropriate treatment from hepatic cancer. Most of them still get the traditional method treatment which is high cost and too risky. Microwave ablation treatment is used microwave heat to eliminate cancer which is one of the other choices. However, it still needs to limit the area of heat dissipation. To prevent microwave heat destroy other organs. So, this research intends to present the analysis of double antenna variable setting and microwave system designing for appropriated heat to eliminate hepatic cancer by 3D finite element analysis in vitro experiment pattern. In the experiment, the open-tip antennas will be used and placed in parallel. The experiment will study the appropriate determination of each antenna switching time and power transmitting value. Moreover, there is an experiment with other two different types of antenna, slot, and slot with insulating jacket. The result shows that double antennas with different operating time and power cause heat to destroy hepatic cancer cells which can determine the size and has a geometric shape as desired. This technology can be applied to the treatment of hepatic cancer that has an asymmetrical shape.

Proceedings ArticleDOI
01 Nov 2019
TL;DR: An automatic segmentation algorithm in surface using U-shape inverted residuals to achieve end-to-end learning network that avoids handcraft feature extraction and is automatically learning.
Abstract: Surface-Defect segmentation plays an important role. It is very necessary to detect on products during production process. Though there are several previous works in surface-defect segmentation, it needs high handcraft skill. We address automatic segmentation algorithm in surface using U-shape inverted residuals to achieve end-to-end learning network. A proposed method is data acquisition, surface-defect segmentation network creation and training. First, the experimental image is augmented by image processing technique, such as rotation, flip, translation, skew and zoom in, which is randomly augmented. Second, U-shape inverted residuals segmentation network is created by changing backbone of encoder and reconstructs decoder by inverted of encoder in order to improve performance of segmentation network. In the final step, the training step of the proposed network is set. To evaluate the performance of the proposed network, each plastic hose tip and dental caries 10,000 image are used to compare between proposed network and Unet [15]. From the experiment, Dice score and IoU are 77.11% and 62.75% in plastic hose tip, respectively. In dental caries problem, Dice score and IoU are 84.16% and 72.65%, respectively. The results show that the proposed network is satisfactory and able to be improved for higher performance. Advantages of the method are that it avoids handcraft feature extraction and is automatically learning.