The paper aims to present the concepts of an innovative, integrated visitor support system for distributed entertainment parks, based on Internet of Things (IoT) technology and Big Data analysis. The proposed system will include logistical functions to streamline the customer service process in the centers, offering a profiled tourist product based on unique natural or thematic value. Basing on the modular structure, implementation and integration of the modern IT network, tracking and monitoring technologies, Fog Computing, decision support system and Big Data concepts, it is planned to create a flexible, scalable, reliable, fault tolerant and high security product, corresponding to the expectations of the potential recipients.
Clustering is one of the main task of data mining, where groups of similar objects are discovered and grouping of similar data as well as outliers detection are performed. Processing of huge datasets requires scalable models of computations and distributed computing environments, therefore efficient parallel clustering methods are required for this purpose. Usually for parallel data analytics the MapReduce processing model is used. But growing computer power of heterogeneous platforms based on graphic processors and FPGA accelerators causes that CUDA and OpenCL models may be interesting alternative to MapReduce. This paper presents comparative analysis of effectiveness of applying MapReduce and CUDA/OpenCL processing models for clustering. We compare different methods of clustering in terms of their possibilities of parallelization using both models of computation. The conclusions indicate directions for further work in this area.
The article presents comparative analysis of solutions utilized in real time location systems (RTLS). Particular focus is paid to feasibility of implementing the described systems for the purpose of theme park management. Selected aspects from systems such as: RFID, Infrared, Bluetooth, Wi-Fi, UWB or optical systems are considered. The discussion aims to address the question, which real time location system posses the widest capabilities in the context of their applications in servicing traffic for the tourism industry.
Proc. SPIE. 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018
KEYWORDS: Mobile devices, Internet, Analytics, Data modeling, Visualization, Sensors, Computer simulations, Sensor networks, Data acquisition, Smart sensors, Systems modeling, Instrument modeling, Data fusion, Data analysis
Major modern theme parks, consisting of tens or even close to one hundred of tourist attractions, are growing continually more complex. The operation of such parks is becoming increasingly more difficult which makes developing their management systems a very challenging endeavor. More so when many different smart objects like cameras, mobile terminals and various sensors (e.g. RFIDs) are deployed throughout the environment which must be integrated into the system. Moreover, in order for the system to be conformable with the concept of Internet of Things and with the idea of Smart City, it should make an intelligent use of a distributed sensor network and provide smart capabilities, i.e. improving the process of tourists management within a smart territory, e.g. automatic congestion avoidance. Implementing such algorithms which involve Big Data analytics can be a very demanding task, especially when the system is required to be scalable to support a huge number of smart devices. Therefore the ability to accurately simulate theme park and test many different scenarios (e.g. distinct hardware configurations, varying positions of attractions, contrasting tourists’ actions) becomes imperative. In this paper we describe a method for simulating IoT-based theme parks. Presented methodology integrates several models: tourists behavior model, tourist attractions model, theme park model and simulation model. Our goal is to create a computer simulation which is able to efficiently model smart theme park.
The article presents characteristics of popular systems of CCTV and includes a description of basic groups of devices appearing in these systems. The research part contains analysis of interferences that may appear in HD-TVI technology which is one of the most modern technologies introduced in CCTV systems. There were examined video signal interferences caused by: impact of power supply, influence of the parameters of the transmission path and impact of external devices. There are discussed relevant methods of eliminating the examined irregularities.
Proc. SPIE. 10445, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017
KEYWORDS: Modeling, Digital signal processing, Modulation, Scanners, Particles, Signal processing, Microcontrollers, Action potentials, Signal generators, Medical equipment, Analog electronics, Medical device development, Heat therapy
The system of signal processing for electrotherapeutic applications is proposed in the paper. The system makes it possible to model the curve of threshold human sensitivity to current (Dalziel’s curve) in full medium frequency range (1kHz-100kHz). The tests based on the proposed solution were conducted and their results were compared with those obtained according to the assumptions of High Tone Power Therapy method and referred to optimum values. Proposed system has high dynamics and precision of mapping the curve of threshold human sensitivity to current and can be used in all methods where threshold curves are modelled.
In this paper, generation methods of sinusoidal medium frequency electrotherapeutic signals have been studied. Signals of this type are increasingly used in electrotherapy owing to the development of both physical medicine and engineering sciences. The article presents analysis and comparison of analogue and digital methods of generation therapeutic signals. Analysis presented in the paper attempts to answer the question which technique of medium frequency signal generation can be most broadly applied in electrotherapy methods.