Sirindhorn International Institute of Technology

About: Sirindhorn International Institute of Technology is a based out in . It is known for research contribution in the topics: Supply chain & Combustion. The organization has 1048 authors who have published 1678 publications receiving 30067 citations.

Passenger estimation system using Wi-Fi probe request

Abstract: Public transportation is a popular means of travel for many urban residents. However, users may not know the location and number of people on a vehicle, which results in uncertainty and wasted time while waiting. To help users manage their time more efficiently, a passenger estimation system that can provide the location and occupancy of each vehicle would be useful. The location can be determined accurately by using GPS data, while it is noted that most passengers on a vehicle typically use smartphones, which are equipped with wireless systems that can be monitored to estimate the number of users in that area. Smartphones with Wi-Fi enabled always send out probe requests that contain unique MAC addresses. By monitoring these probe requests, the number of passengers on the vehicle can be estimated. The time period and signal strength from each probe request, along with the location of the vehicle, is used to categorize a group of smartphone users as passengers or non-passengers. The device is implemented and evaluated on the shuttle bus system of Thammasat University, Thailand.

Understanding the unobservable population in call detail records through analysis of mobile phone user calling behavior: A case study of Greater Dhaka in Bangladesh

Abstract: The understanding of mass population movements has greatly advanced with the rapid spread of ubiquitous devices. Anonymized call detail records (CDRs) for mobile phones have enabled us to not only trace individual trajectories but also approximate activity patterns, including significant locations such as homes and workplaces. The majority of studies analyzing CDRs attempt to utilize the mobility patterns of anonymized crowds to improve transportation and public health. This is quite reasonable because CDRs can capture the movements of people at given times and places, whereas general statistics usually account for a population based on their locations of residence. However, it has also been pointed out that there are discrepancies between the movements of people as observed through CDRs and those of an entire population in a given area. This is because CDRs only represent device users. In fact, we can never learn about the population that is unobservable through CDRs only by analyzing CDRs. Therefore, this study attempts to provide clues to help us understand the whereabouts of the unobservable population by analyzing two months of the CDRs for 58 volunteers with mobile device service from a major telecommunications company in combination with field survey data from Dhaka. We surveyed the personal and household attributes of mobile users in relation to their calling behavior. The analysis results show that per mobile user observed in CDRs, there is an average of roughly 2.4 to 2.8 unobservable people. Their age groups and gender composition are also provided. We find that male and female users exhibit opposite trends in call locations according to the presence of children within the household. In addition, based on field observations, we find that the location and time distributions of small children follow some specific routines. Our findings contribute to the understanding of the whereabouts of the unobservable population, the majority of whom are children and are considered to be vulnerable to disasters or infectious diseases but are difficult to locate through CDRs alone.

Human identification using skeletal gait and silhouette data extracted by Microsoft Kinect

Abstract: Since the war on terrorists was declared, human identification area of research has gain its popularity throughout the world. Gait, a biometrie information obtained by one's walk, is used to identify a human widely because it can be done unobtrusively. Moreover, it is nearly impossible to alter gait features continuously. In this study, we propose a technique to identify a human using gait data extracted by Microsoft Kinect. We construct a distance function between two walking sequences using combinations of skeletal static features, skeletal kinematic features from movements and silhouette feature (mass vector). The proposed distance function is then used in the classification process along with fc-nearest neighbor technique. Our technique yields accuracy of 92.56% which outperforms those techniques proposed by Hong et. al., Cheewakidakarn et. al., Saitong-in et al., Preis et al., Milovanovic et al. and Boulgouris et al. Furthermore, we discover that skeletal kinematic features reveal the unique characteristic of human subjects better than skeletal static and silhouette features.

Biodegradation of organics in landfill leachate by immobilized white rot fungi, Trametes versicolor BCC 8725.

Abstract: Immobilized Trametes versicolor BCC 8725 was evaluated for the biodegradation of the organic components of four different types of landfill leachate collected at different time periods and locations from the Nonthaburi landfill site of Thailand in batch treatment. The effects of carbon source, ammonia and organic loading on colour, biochemical oxygen demand (BOD) and chemical oxygen demand (COD) removal, and the reuse of immobilized fungi were investigated. It was found that fungi can remove 78% of colour, reduce BOD by 68% and reduce COD by 57% in leachate within 15 days at optimum conditions. Organic loading and ammonia were the factors that affected the biodegradation. When immobilized T. versicolor on polyurethane foam (PUF) was subjected to repeated use for treatment over the course of three cycles, the decolourization efficiency of the first and the second cycle was very similar, whereas the third cycle was about 20% lower than the first cycle under similar conditions. The obtained removal of colour...