Are you a System Integrator wanting to find a suitable IoT platform? Are you an IoT hardware vendor ready to sell your products? Are you a software outsourcing company that wishes to work on an IoT project? If you want to become part of FAVORIOT IoT Preferred Ecosystem Partners, check out the following steps:

1. Subscribe to any package (Beginner, Startup or Business or customize to your needs/number of devices) – https://www.favoriot.com/home/iotplatform/pricing
2. Test your hardware integration or show your application development skills using our FAVORIOT IoT middleware
3. Show screenshots of your dashboards, streams of data, sample codes, visualization dashboards or any photos as a proof of your work. Send to info@favoriot.com
4. A better output of your work (or complexity of your project) will indicate the level of your readiness to undertake bigger and complex projects.
5. Once approved, FAVORIOT will request for Quotation from Preferred Partners when FAVORIOT participating in IoT projects.

This project presents the design of pocket-sized halal sensor used to detect lard adulteration in oil using dielectric spectroscopy method. This system will feature the application of Interdigitated Electrode (IDE) connected to Analog Devices’ AD5933 Evaluation Board to measure impedance and a mobile app that acts as the User Interface.

The whole system will be connected using Arduino, specifically Arduino Uno and an HC-06 Bluetooth module. The main motivation of the design is to provide a portable, real-time and accurate lard measurement device which is quite scarce in the market.

Besides that, this project will also focus on the performance of dielectric spectroscopy in detecting lard adulteration. Lard adulteration will be measured using the system designed and the sensor’s performance is measured via FAVORIOT IoT Platform.

Physically, the system consists of the following components:

Figure 1: System components

Figure 2: Mobile app connected to the sensor using Bluetooth. [1] Select device page. [2] Homepage when no device is connected. [3] Homepage when the device is connected.

[Note: This project is being developed by UPM, our FAVORIOT’s University’s collaborator. Article was written by Nurhani Amirah Adenan]

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.