The use of Convolutional Neural Network (CNN) has been rising in the field of image recognition and classification. Different kinds of CNN architectures were implemented in various skin disease classification systems. In this paper, a skin disease classification for differentiating Atopic Dermatitis and Psoriasis Chronic Plaque using MobileNet architecture of Convolutional Neural Network was implemented in a Raspberry Pi. The MobileNet model was trained using the images selected from AtlasDerm, Danderm, DermIs, DermNet, DermQuest and DermatoWeb as the dataset. The network was tested using 30 captured images from patients using a Raspberry Pi camera which were verified by the Dermatologist. Confusion matrix was utilized to compute for the accuracy of the model between the two skin diseases. As a result, the system achieved an accuracy of 88% and 90% in classifying Atopic Dermatitis and Psoriasis Chronic Plaque, respectively. The use of MobileNet architecture of CNN allowed the high accuracy classification in differentiating skin diseases.

Visually impaired suffers from having a hard time to navigate to their destination with just the use of canes. Many researches have been created to provide the visually impaired the ability to navigate freely but they usually have comfortability problem as their system requires many devices to be worn. In this paper, the researchers provides a navigation system that allows the visually impaired to navigate indoor and get to their destination by only wearing a Bluetooth headset. The Bluetooth headset is used for two way communication between the visually impaired and the system. The system uses Continuously Adaptive Mean-Shift (CAMShift) algorithm to detect the visually impaired, obstacles and destination with image processing and D* algorithm to determine the shortest path required to get into the destination. The system also uses voice recognition for the input of the visually impaired. The system has been able to provide an effective and more comfortable way of navigation system for the visually impaired.

In recent years, activity recognition gained popularity in the field of wearable applications. One field that benefitted from the development of activity recognition is medical health and monitoring. In the Philippines, FNRI stated that Non-Communicable Diseases (NCDs) caused 67% of the total death in the country. As such, companies started producing wearable devices that can measure burned calories based on the inputted exercise of the user which can help especially the health conscious. Although diet and exercise cannot be considered as cure, it can be an assistance and prevention. In this paper, the researchers aimed to recognize the activity of the user using a wrist worn device. The device is composed of a raspberry pi and accelerometer. In order to recognize the activity, the vibration signals from the accelerometer were analyse and processed based on neural network analysis which was used to predict the activity done by the user. Once the activity is predicted it will compute the corresponding burned calories per activity. The researchers conducted the experiment composed of 50 persons for training and 30 persons for testing of the prediction model wherein the subjects were asked to perform an exercise within 20 seconds equivalent to 1 trial. The subjects performed each activity good for 60 seconds which is equivalent to 3 trials for each activity, 12 trials in total for 1 person. The test resulted with a 91.11% accuracy based on a 360-trial experiment.

Metropolitan Manila, Philippines is one of the regions at high risk from flooding. Pandacan, is one of the districts in the city of Manila, located at the south of Pasig River which is prone to flooding. The purpose of this project is to provide a standalone flood water level monitoring system for the community in Kahilom Street Pandacan, Manila. The system is constructed through the use of Arduino Uno, GSM shield and sensors that will be powered by a solar panel with generator. The early warning device will be the three LED that is mounted to a PVC pipe and then the system will send an SMS notification to the people in the community. The functionality of the system was tested by the simulation of flooding. The results provided that the objectives of the design satisfied the needs of the client.

Real-time monitoring of temperature and humidity of a computer server room became crucial in order to assure that the performance of the server is not affected due to excessive room temperature and humidity. Unregulated and unmonitored increase in temperature and humidity of server room may also cause damage to equipment. Color mapping algorithm transforms the temperature colors of the image captured by the IP camera while the Bicubic algorithm interpolates data points on a two-dimensional grid. The aim of the system is to provide alert mechanism to the administrator when the server room's temperature and humidity goes beyond the critical value. The creation of the ZigBee based device capable of monitoring temperature and humidity of server rooms using thermal imaging can be used for remote administration and give a primary warning to the person who is in charge of the locality. The device can be used to effectively notify the administrator to avoid server equipment to fail.