Abstract This review paper is about Implementation of “Automatic Attendance Reporting System”. The purpose of this review is to facilitate students and administration, using a more reliable and efficient attendance system. The problem with our current system is inefficiency; as it is affected by considerable human errors and maximum chances of proxy. The old paperwork system for marking attendance is also... This review paper is about Implementation of “Automatic Attendance Reporting System”. The purpose of this review is to facilitate students and administration, using a more reliable and efficient attendance system. The problem with our current system is inefficiency; as it is affected by considerable human errors and maximum chances of proxy. The old paperwork system for marking attendance is also time-consuming. Implementation of automatic attendance reporting system will not only make the system reliable but it will also reduce the time and proxy chances. The student can mark his attendance and can get its acknowledgement within a fraction of a second. Moreover, in this review we have added a database and a server to control the class schedules and to record the attendance data. Every student has its own module which contains a biometric component to make a proxy-free attendance. Nodes will collect the data of all students and communicate it to the server. Server will record the attendance by entering it to its database. We are moving towards a highly service-oriented sensor-node network where we can apply highly interactive embedded applications. Our designed infrastructure will not only appreciate the attendance reporting application but also provide compatible services for applications which are generally used with a sensor-node network. Read More Read Less

Abstract In this paper, we have proposed some parallel filtration techniques of point clouds. These techniques are logically based on existing ones present in the open-source Point Cloud Library (PCL). We have used the MapReduce framework provided by Big Data platforms such as Apache Hadoop to address the problem of scalability as well as the completion of processing intensive tasks with... In this paper, we have proposed some parallel filtration techniques of point clouds. These techniques are logically based on existing ones present in the open-source Point Cloud Library (PCL). We have used the MapReduce framework provided by Big Data platforms such as Apache Hadoop to address the problem of scalability as well as the completion of processing intensive tasks with relatively cheaper hardware. Read More Read Less

Abstract An improved Constriction Factor Particle Swarm Optimization (CFPSO) algorithm is proposed and used to solve the optimization problem through establishing multi-objective decision-making model, which is applied to choose the best coalition partners association in food traceability system. The results of simulation by standard CFPSO and improved CFPSO shows that: in comparison with those of the standard CFPSO, the improved CFPSO... An improved Constriction Factor Particle Swarm Optimization (CFPSO) algorithm is proposed and used to solve the optimization problem through establishing multi-objective decision-making model, which is applied to choose the best coalition partners association in food traceability system. The results of simulation by standard CFPSO and improved CFPSO shows that: in comparison with those of the standard CFPSO, the improved CFPSO can be well used to find the decision of multi-objective decision-making model, and it avoids trapping into the local optimal solution prematurely, increases search speed and enlarges the probability of converging to a global optimal solution. Simulation by improved CFPSO is better to explain the running logic of traceability system in theory, and provide theoretical basis for executive decision making in food traceability system. Read More Read Less