Search results

Discovering and recommending points of interest are drawing more attention to meet the increasing demand from personalized tours. This paper aims to propose and evaluate two fuzzy-based systems for decision of sightseeing spots considering different conditions.

In the system, the authors considered four input parameters as follows: ambient temperature (AT), air quality (AQ), noise level (NL) and the current number of people (CNP) to decide the sightseeing spots visit or not visit (VNV). The authors call the proposed system: fuzzy-based decision visiting systems (FBDVSs). The authors implemented two systems as follows: FBDVS1 (three input parameters) and FBDVS2 (four input parameters). The authors make a comparison study between FBDVS1 and FBDVS2. The authors evaluate the proposed systems by computer simulations.

From the simulations results, the authors conclude that when CNP is increased, the VNV is increased. However, when AQ and NL are increased, the VNV is decreased. Also, when the AT is around 18°C-26°C, the VNV is the best. Comparing the complexity, the FBDVS2 is more complex than FBDVS1. However, FBDVS2 considers also the AT, which makes the system more reliable.

In the future, the authors would like to make extensive simulations to evaluate the proposed systems and compare the performance of the proposed systems with other systems.

By simulation results, the authors have shown that the proposed system has a good performance and can choose good sightseeing spots.

The purpose of this work is to implement an ambient intelligence (AmI) testbed to improve human sleeping conditions.

The implemented testbed is composed of the sensor node, sink node and actor node. As sensor node, the authors use a microwave sensor module (MSM) called DC6M4JN3000, which emits microwaves in the direction of a human or animal subject. These microwaves reflect back off the surface of the subject and change slightly in accordance with movements of the subject’s heart and lungs. As sink node, the authors use Raspberry Pi 3 Model B computers. In the sink node, the data are processed and then clustered by the k-means clustering algorithm. Then, the result is sent to the actor node (Reidan Shiki PAD module), which can be used for cooling and heating the bed.

The authors carried out simulations and experiments. Based on the simulation results, it was found that the room lighting, humidity and temperature have different effects on humans during sleeping. The best performance is shown when LIG parameter is 10 units, HUM parameter is 50 and TEM parameter is 25. Based on experimental results, it was found that the implemented AmI testbed has a good effect on humans during sleeping.

For simulations, three input parameters were considered. However, new parameters that affect human sleeping conditions also need to be investigated. Further, the experiments were carried out for one person. More extensive experiments with multiple people are needed to have a better evaluation.

In this research work, a new fuzzy-based system was implemented to improve human sleeping conditions. The authors presented three new input parameters to evaluate the output (sleeping condition). The authors implemented and evaluated a testbed and showed that the implemented AmI testbed has a good effect on humans during sleeping, thus improving their quality of life (QoL).