The 2019 coronavirus disease (COVID-19) has rapidly spread across the globe. It is crucial to identify positive cases as rapidly as humanely possible to provide appropriate treatment for patients and prevent the pandemic from spreading further. Both chest X-ray and computed tomography (CT) images are capable of accurately diagnosing COVID-19. To distinguish lung illnesses (i.e., COVID-19 and pneumonia) from normal cases using chest X-ray and CT images, we combined convolutional neural network (CNN) and recurrent neural network (RNN) models by replacing the fully connected layers of CNN with a version of RNN. In this framework, the attributes of CNNs were utilized to extract features and those of RNNs to calculate dependencies and classification base on extracted features. CNN models VGG19, ResNet152V2, and DenseNet121 were combined with long short-term memory (LSTM) and gated recurrent unit (GRU) RNN models, which are convenient to develop because these networks are all available as features on many platforms. The proposed method is evaluated using a large dataset totaling 16,210 X-ray and CT images (5252 COVID-19 images, 6154 pneumonia images, and 4804 normal images) were taken from several databases, which had various image sizes, brightness levels, and viewing angles. Their image quality was enhanced via normalization, gamma correction, and contrast-limited adaptive histogram equalization. The ResNet152V2 with GRU model achieved the best architecture with an accuracy of 93.37%, an F1 score of 93.54%, a precision of 93.73%, and a recall of 93.47%. From the experimental results, the proposed method is highly effective in distinguishing lung diseases. Furthermore, both CT and X-ray images can be used as input for classification, allowing for the rapid and easy detection of COVID-19.

One of the common causes of a heart attack is fibrillation, a condition that causes an irregular and often abnormally fast heart rate. There is scientific evidence that the survival rate of sudden cardiac arrest patients who are rescued with cardiopulmonary resuscitation (CPR) and with the use of an automated external defibrillator (AED) is significantly increased. Despite the recommendation that automated external defibrillators should be installed in the workplace, along with a proper management system and training for employees on how to use the device, less than 70% of non-residential areas have an AED installed. The situation is even worse in residential areas, with less than 30% having an AED installed. This research concerns the development of a medical drone managing system that can deliver an AED in case of emergency. An application was developed that can be installed on the mobile phone and/or tablet of the patient or the accompanying person. In the event of a heart attack, the patient or the accompanying person can call a medical drone by sending coordinates to the drone station and a notification to medical staff. The drone station administrator can respond by sending the drone, which automatically lands at the patient’s location. After being tested in a simulation situation, the operational field test yielded satisfactory results. The medical drone can land within 1.5 m of the destination. The designed AED drone can be used not only to deliver AEDs, but also first aid kits and prescribed drugs suitable for medical care. Such a system is especially useful in the current context of the COVID-19 pandemic.

This paper presents an egg-counting system method that uses a morphological operation and shows the results in real time on a website. The method begins with image acquisition from a Raspberry Pi camera and converts it to greyscale. A Gaussian blur filter is applied to reduce noise, and the blurred image is then converted to a binary image. Next, an opening operation is utilized to delete small objects from the foreground. Finally, the eggs in the image are counted using a contoured matrix, and the number of eggs is shown on a website. The system was able to count 100% of the eggs and showed the number of eggs in real time.

This paper presents the comparison of Faster R-CNN and Atrous Faster R-CNN, which detection model, in the different distance and light condition. Also, the dataset for model training is COCO, and the classification model is residual network. The parameter for decision the performance of the model is Mean Average Precision (mAP). The results from an object resolution at 1024x768 of Faster R-CNN at 3 meters in the evening achieved mAP 1.000. Besides, the mAP at 5 meters and 8 meters were 0.798 and 0.760, respectively. The same resolution as previous, the results of Atrous Faster R-CNN at 3 meters in the evening presented mAP 1.000. Also, the mAP at 5 meters and 8 meters were 1.000 and 0.960, respectively. In addition, Atrous Faster R-CNN had better accuracy than Faster R-CNN with appropriate range and brightness from the period of the day for real-life usage.

This paper presents methods of vascular extraction and optic disk localization in the retinal images. Our approach begins with preprocessing to improve the quality of blood vessels. In the next step, a matrix filter was applied to express blood vessels. Finally, the blood vessel structure was used to estimate the location of the optic disk. The proposed method was tested on all different forty retinal images from the DRIVE database, which public retinal image dataset. The results of vessel extraction were compared with the ground truth image. The error of vascular extraction showed that the average sensitivity and accuracy were 79.81% and 94.98%, respectively. The optic disk localization achieved 97.5%.

This paper presents blood pressure waveform simulation using the Bernstein polynomial model, Bézier-Bernstein model, and Rational Bézier-Bernstein model. All mathematical models can generate the blood pressure waveform which is similar to the normal blood pressure waveform. Moreover, all mathematical models can simulate a normal blood pressure waveform as well. As the results, the Rational Bezier-Bernstein model is a simple form, low order, easy to implement in the microcontroller.