The problem of finding the optimal deployment of sensors is becoming increasingly important with the growing expansion of the Internet of Things paradigm and increased usage of sensor networks in different applications. During the installation of sensor networks, sensor placement directly affects the performance of the system. The general problem of determining the position and orientation of the sensors with the goal of optimal coverage of a given environment is NP-hard. In this manuscript, an effective stochastic method for the placement of sensors in arbitrarily given two-dimensional and three-dimensional environments is proposed. The method uses models of generic isotropic and directional sensors with the defined probabilistic coverage. The optimization function combining the environment and sensor models based on the area coverage metric is proposed. Three optimization algorithms are compared with regard to obtained coverage score, execution time, and reliability, and the results are presented and discussed.
Maritime industry is one of the most globally connected industries that include transportation of numerous types of goods and documents across the world. With that said, it is safe to say that abundance of financial and paper-trail transactions are made every day in order for goods to be transported from one place to another. The scope of this paper is to show that by implementing blockchain technology savings in time and money could be generated. This paper presents costs of container freights and rates in the last few years and assumes possible future costs of container freights and rates if blockchain based technology is implemented. Additionally, by using comparative method economical and time value of “traditional” bill of lading is compared with a blockchain bill of lading solution. It is also important to mention the potential impact of the blockchain technology on the world environment and ecology by reducing global paper consumption and emissions from vehicles that are used in the transportation process. This paper also gives a descriptive and comprehensive overview of current and future applications of blockchain technology in maritime industry.
Each individual describes unique patterns during their gait cycles. This information can be extracted from the live video stream and used for subject identification. In appearance based recognition methods, this is done by tracking silhouettes of persons across gait cycles. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. When such sensors are used for gait recognition, existing RGB appearance based methods can be extended to get a substantial gain in recognition accuracy. In this paper, this is accomplished using information fusion techniques that combine features from extracted silhouettes, used in traditional appearance based methods, and the height feature that can now be estimated using depth data. The latter is estimated during the silhouette extraction step with minimal additional computational cost. Two approaches are proposed that can be implemented easily as an extension to existing appearance based methods. An extensive experimental evaluation was performed to provide insights into how much the recognition accuracy can be improved. The results are presented and discussed considering different types of subjects and populations of different height distributions.
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