Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension–diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.

Deployment of UHF RFID technology in the industrial environment is not easy to perform because of the heterogeneity of the environment, the propagation of the high frequency radio signal, the interference, and the associated timeconsuming implementation. The paper deals with deployment and evaluation of developed autonomous system for measurement of UHF RFID signals in industrial environment. Such a system allows, in defined 3D real-world locations, to obtain autonomously information about the actual Received Signal Strength Indicator of the moving RFID tag (Etalon) that is registered while investigated with each antenna of RFID reader. A corridor was chosen as an experimental area, and Received Signal Strength Indicator was evaluated by both, simulation and measurement methods. The results show a statistically significant relationship between measured and modeled Received Signal Strength Indicator in dBm at the 99% confidence level. The presented autonomous system can contribute to a more comprehensive analysis of measuring the electromagnetic field emitted by the UHF RFID antennas in various industrial environments.

As a part of the supply chain, inventory management includes, among other things, maintaining the storage of stock, controlling the amount of product for sale and order fulfilment. In business terms, inventory management means the right stock, at the right levels, in the right place, at the right time. In the case of large outdoor warehouses, common identification methods are lengthy and inappropriate. One way to determine inventory easily and quickly is to deploy UAV’s (unmanned aerial vehicle) for product identification purposes. In this case, however, there is a problem in determining where the goods are located. A drone moves at higher altitudes, which can lead to a situation where we will not be able to determine the exact location of the goods. This article deals with a method of determining the correct flight level suitable to distinguish the identified items located at least 2 m apart. The evaluation is performed based on an RSSI (received signal strength indicator) value. The experiment proved that even at maximum reading distance of selected passive UHF RFID tags the two objects can be distinguished.

Every company and institution faces the necessity to manage its inventory. For high accuracy, security and easy operation of inventory management system it is necessary to have an easily recognizable identifier on all managed objects. The aim of our present project is to modernize the methods of automatic inventory management and asset protection with the help of the application of radio frequency identification technology (RFlD hereinafter) to automatically identify the occurrence of marked objects. During the solutions we have to deal with many obstacles such as the bindings of data flowing from RFID system with internal information systems of asset owner companies.Appropriate choice of tags for individual subjects, their testing and readability experiments also have to be done. Another important part of the solution, that has to be developed in the project, is the system of reading gates. The gates are integrated in the door frames to allow automated identification of the objects brought in and out of the selected room. By modifying of mentioned solution we also introduce the design of a gatehouse that is able to prevent theft or unauthorized take away of material and equipment. In contrast, when the permitted items are taken away the system can easily generate the necessary forms to manage status of selected objects, decreasing the administrative burden. The result of the project is a solution consisting of information system and described hardware components for inventory management. The information system is possible to integrate with corporate ERP information systems.The pilot project was implemented and tested at VSB laboratories and we suppose deployment of the system in close future in cooperation with TINT Ltd. Company.

No abstract