Posts in SMARTCITY

Remote Optical Sensor for Ammonia Detection in Water

February 17th, 2018 Posted by HOW-TO, IOT PLATFORM, SMARTCITY 0 thoughts on “Remote Optical Sensor for Ammonia Detection in Water”

Ammonia is a chemical compound that made up of two gases which is nitrogen and hydrogen. Ammonia can be found in several advantages and disadvantages toward human and environment. The advantages of ammonia can be seen from it is very useful that can be used in many industries such as in agriculture, food processing, petroleum, rubber, and other industries. For example, we can see that from agriculture, it is used for livestock feeds, fertilizer, and crop protection. In petroleum, it is used for the protection of equipment to avoid corrosion.

However, during the manufacturing process from industry, there will produce wastewater that contains a concentration of ammonia. When ammonia reacts to water, there are two chemical species exist in water which is in un-ionized form, NH3 and ionized form, NH4. The unionized form is called ammonia whereas ionized form is called ammonium. These two forms which together are called total ammonia nitrogen or in short forms TAN. At the same time, the weak base is formed which means the pH value is greater than 7. It is very important to know that the unionized form is toxic. Therefore, this toxicity will bring negative effect to the environment and may harm aquatic life.

In this project, remote optical sensor towards ammoniacal nitrogen in water will be developed to give an alert if ammoniacal nitrogen is detected in water. For the cloud including the server, database, and the interconnection between it, FAVORIOT IoT platform will be used.

[Note: This project is being developed by UPM, our FAVORIOT’s University’s collaborator. Article was written by Ng Foo Guan]

You can check out the whole LIST of IOT PROJECTS by our University Collaborators.

Robust Vision-Based Human Detection in a Dynamically Varying Environment

February 16th, 2018 Posted by HOW-TO, IOT PLATFORM, SMARTCITY 0 thoughts on “Robust Vision-Based Human Detection in a Dynamically Varying Environment”

Regardless of various research endeavors, the performance of current human detection system is still a long way from what could be utilized dependably under varying realistic environment. This is expected to some extent to the inborn troubles related to the human body and nature in which it is found. The non-unbending nature of the human body offers to ascend to varieties in the stances that it can accept and when this is combined with movement a few displaying issues are exhibited. Because of some position and angle of the camera, the view and size varieties represent some technical difficulties to the models that can be implemented. It is different with other objects that normally show up in one shape, people can be dressed in any form of changing shading and surface. The location of the human object in the environment is an essential part of the appearance. For example, the environment illumination could upgrade or corrupt the appearance relying on the direction and nature of the light. Most of the challenges with complex background are mostly experienced in the open area. Occlusion is always a challenge for robustness with several human and interactivities. This might be the parts of body cover with another part of a body which create inter-object occlusion which happens when one human walk in front and another human is behind.

As a result, we propose to develop the vision-based human detection system with Artificial Intelligence method: Deep Neural Network and Internet-of-Things (IoT) technology to strengthen the robustness of the system. FAVORIOT IoT Cloud platformact as a middleware will be fully utilized to store images to prevent data loss through the internet.

[Note: This project is being done by UPM, our FAVORIOT’s University’s collaborator]

You can check out the whole LIST of IOT PROJECTS by our University Collaborators.

Optical Flow Tracking for Real-Time Object Detection System

February 15th, 2018 Posted by HOW-TO, SMARTCITY 0 thoughts on “Optical Flow Tracking for Real-Time Object Detection System”

Human detection and tracking system are one of the most fundamental tasks in computer vision field where motion detection and tracking are coupled to locate human through the video captured by an observer (installed camera). Optical flow algorithm is one of the methods that used to analyze the path of pixels and reflects and the apparent changes of the targeted moving object between previous and current locations. It is frequently applied in human detection and tracking system since it is easier to be applied to input sources and variations-effective. However, detecting humans in sequences of frames or even in a real-time video is still very challenging due to their appearance variations and different poses adoptions.

Security is becoming the main concern of society in urban cities since the scarcity of jobs raise, which indicates that property of residents is being threatened by thefts and destruction. Traditional surveillance systems need human operators to differentiate all activities and behaviors of objects for forensic investigation purpose and require significant judgment and decision for an event happening, there is a probability of mistake due to a long period of passive watching which results in negligence. Besides, a huge amount of data received is impossible to be handled and extracted due to time-consuming and high expenses. Therefore, this project is proposed to develop a computer vision system for real-time human detection and tracking using optical flow algorithm.

Human will be identified and tracked with classifier with the features extracted by the human descriptor. Human behaviors will be analyzed and interpreted based on video frames obtained from the observer (camera). Raspberry Pi 3 board is selected to develop and implement detection and tracking and FAVORIOT middleware will be used as a communication media that allows all data from IoT end device to be uploaded and used as administration references.

[Note: This project is being done by UPM, our FAVORIOT’s University’s collaborator]

You can check out the whole LIST of IOT PROJECTS by our University Collaborators.

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