Technical Review of Radio Frequency Identification and Internet of Things Technologies in Business Operations and Automated Indoor Location
Author(s): Andreea-Alina Cornea,Alexandru Marius Obretin / Language(s): English
/ Issue: 3/2023
Keywords: Radio Frequency Identification; Internet of Things; indoor location; commerce; technology
The technological diversity applied in practical examples entails the identification and the analysis of different implementation options, thus opening the way to a world of technology. The goal of this paper is to spot novel prospects for incorporating sensors and Big Data techniques for better property administration strategies, through an indoor localization and navigation architecture. The hereby analysis uses an approach that combines Radio Frequency Identification technology, performance efficient algorithms for calculating routes in very large datasets, methodologies for optimizing spatial representations and Internet of Things specific hardware distribution techniques to properly address challenging logistics issues and complex facilities management. The proposed solution is tailored for a large underground parking area in a commercial center, enabling easy car location identification, automatic parking time calculation and dynamically determined occupancy levels, along with descriptive statistics about busy time intervals or visiting patterns.
Accurate determination of chromosome centromere location is of high importance in cytogenetics, particularly in karyotyping, chromosome classification and determination of exposure to genotoxic environmental effects. This study investigates the ability of CNN to accurately predict the human chromosome centromere location and the effect centering chromosomes in images, by predicted centromere location, has on classification accuracy. Dataset, used to train and test CNN models, contained 8283 annotated individual chromosome images. Prior to performing centromere detection, followed by chromosome classification, the individual chromosome images are preprocessed using sequence of filtering algorithms. The CNN model achieved an average error of 0.5586 and 0.4543 in predicting x and y coordinates of centromere location, respectively. The achieved classification accuracy of randomly oriented and centered chromosomes in images, is 71.10 and 96.73%, respectively. Achieved increase in chromosome classification accuracy of 25.63% highlights importance of chromosome centromere detection, importance of positional variation removal, and high performance of CNN in prediction of centromere location and chromosome classification.
Convolutional neural networks are special types of artificial neural networks that can solve various tasks in computer vision, such as image classification, object detection, and general recognition. The paper presents the basic building blocks of convolutional neural networks and their architecture, and compares their recognition accuracy with other character recognition techniques using the example of character recognition from vehicle registration plates. The purpose of the experiments was to determine the optimal configuration of the convolutional neural network and the influence of the size and design method of the training set on the recognition rate. The study shows that although convolutional neural networks have recently gained attention, traditional recognition methods are still relevant, and the choice of the right classifier and its configuration depends on the type of recognition task.
Nonlinear Auto-Regressive with Exogenous Inputs (NARX) Modelling of Hydroponics Water pH Level in response to Acid and Alkaline Solutions
Author(s): Mohammad Farid Saaid,Mohd Khairi Nordin,Ihsan Mohd Yassin,Nooritawati Md Tahir / Language(s): English
/ Issue: 3/2023
Keywords: NARX; pH level; acid and alkaline; hydroponic; system identification
Nutrients are essential to optimising plant growth. However, the introduction of fertiliser in a hydroponics setup influences the pH level of the nutrient solution. This, in turn, could affect plants' growth as many types of plants require a specific pH range to grow optimally. Conventional hydroponics cultivation performs pH adjustment manually – a meticulous and error-prone process. Manual adjustment of pH solutions is prone to estimation errors, particularly when the pH levels change drastically due to the slow response of the solution to the addition of alkaline or acidic mixtures and sensitivity to minute errors in mixture delivery. For these reasons, a model to estimate the solution's pH would help improve the delivery accuracy of the alkaline and acidic mixtures. Past research offers minimal study to optimally construct the model from a System Identification (SI) perspective. This study represents a pH water neutralisation behaviour using the Nonlinear Autoregressive model with Exogeneous Inputs (NARX). The project begins with input and output data acquisition, leading to the development of the NARX model. Model performance was then evaluated by analysing the model fit and residual distribution.
Managing Environmental Noise with Mobile Noise Barriers - a Case Study of a Dolomite Quarry in Slovakia
Author(s): Martin Rovnak,Miroslav Mindas,Jana Chovancová,Emília Huttmanová,Róbert Štefko,Richard Fedorko,Sylvia Jencova,Viktória Ali-Taha,Rastislav Kotulic / Language(s): English
/ Issue: 3/2023
Keywords: Environmental management; noise barriers; environmental noise; permissible values; quarry production line
This paper addresses the issue of environmental noise from mining operations, focusing on the specific case of a quarry located in the municipality of Trebejov, Slovakia. The study aims to demonstrate an effective approach to reducing noise pollution through the implementation of mobile noise barriers. The research was motivated by the negative impact of noise from the processing line on the well-being of the population residing in the vicinity of the facility. The study involved conducting extensive noise measurements at various locations surrounding the quarry. The collected data were analyzed to assess the existing noise levels and identify the areas of highest noise exposure. Based on the findings, the implementation of mobile noise barriers was proposed as a solution. The results of the study demonstrated that the mobile noise barriers successfully reduced the noise levels below the legal limits, significantly improving the well-being of the population living near the quarry. The case study opens up the possibility of using mobile noise barriers in other areas and operations with high noise exposure.
There are a significant number of scientific publications that use epidemic models to propagate malicious objects in a computer network. These models are based on Markovian models with a constant intensity of transitions, which does not correspond to real conditions, since the intensity of transitions changes due to the fact that after some time antivirus systems start to recognize malware. This paper proposes an original approach based on an epidemic model with variable infection intensity of hosts in a computer network. In the beginning, when the threat is not recognized, the malware spreads rapidly. After a certain period of time, the antivirus system recognizes the malicious code, which leads to a decrease in the infection intensity. Simulations have been done for different infection intensity and threat recognition. Demonstrated models account for the infection time of hosts in the computer network, latency phase, malware detection, and clearance from the system.
An Overview and Comparison of Selected State-of-the-Art Algorithms Inspired by Nature
Author(s): Marko Gulić,Martina Žuškin,Vilim Kvaternik / Language(s): English
/ Issue: 3/2023
Keywords: Optimization; nature-inspired metaheuristics; comparison of metaheuristics; optimization functions
Optimization is essential in various fields such as finance, transportation, energy, and health care. However, solving real optimization problems, especially nondeterministic polynomial, requires considerable computational resources. Metaheuristics provide fast and cost-effective solutions to these problems. In this paper, eight state-of-the-art nature-inspired metaheuristic algorithms that have demonstrated excellent performance are compared in detail. In addition, a novel tournament procedure has been proposed to produce a quality ranking of selected metaheuristic algorithms, which are compared based on their optimization results, even if they were not originally tested with the same set of test functions, but only partially. The selected algorithms are evaluated using thirty-two test functions, which is a representative sample size. The evaluation also showed that while one algorithm produced the best overall results, this does not mean that this algorithm is the best for solving each function. This also highlights the need for further research in metaheuristic algorithms.
Solving engineering design tasks requires the use of analytical formulas and dependencies. The direct inclusion of mathematical expressions in the artificial neural network (ANN) is not possible. This research studies the possibility of applying the neural networks method for designing of single or double-side reinforced concrete sections. A Visual Basic for Applications (VBA) macro was developed in the MS Excel environment to solve the task of determining the required area of the reinforcement by given geometric dimensions and bending moment and applying classical analytical formulas for reinforced concrete sections design. The training of the pre-configured neural network is performed by approximately 34000 sets of matching input parameters. The presented results from the trained ANN are compared and analysed against the exact analytical solutions. The study presents an approach to the application of structural design calculations. The results suggest that the approach is applicable to more complicated structural design problems.
Entrepreneurship Education of University Students - Examples of Good Practice
Author(s): Petr Adamec,Roman HRMO / Language(s): English
/ Issue: 3/2023
Keywords: Entrepreneurship; education; university students; motivation
ntrepreneurship education is offered at all levels of education. This theme resonates in many strategic documents at the national and European levels. The first aim of this paper is to present two educational activities aimed at developing the entrepreneurial skills of university students in the Czech Republic. The second objective is to present the results of surveys conducted among students of a university-wide elective course focused on the above-mentioned area. Survey was focused on motivation and expectations from participating in the course. The most substantive responses are presented in the paper. These activities are also inspiring for contemporary engineering pedagogy.
Keywords: Spectrum disorder (ADS); picture exchange communication system (PECS); mobile application
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that causes challenges in communication and social interaction. Since there is no single treatment for autism, autistic children need extra attention from their parents or caregivers to overcome their linguistic deficiency. Scientific studies have shown that technology-based educational methods are effective and can lead to a significant improvement, especially for autistic children. In recent years, an increasing number of mobile and multimedia applications have been developed to enhance autistic children's verbal communication, emotions, social behavior, and interaction skills. However, many of these systems are either in English or in local autistic children’s native languages. Others are designed to promote specific knowledge, i.e., emotions, or are limited in their features. This paper designs and implements a prototype for Arabic mobile application, called Aawn, to help and improve Arab autistic children's, in their own language, communication and emotions, as well as educational and organizational skills. Aawn is integrated with various supporting technologies based on the Picture Exchange Communication System (PECS) and augmented with graphical features. The system is built and developed using Android Studio and various cloud-based tools. The system can be extended by artificial intelligence (AI) technologies.
Real-Time Monitoring System Using IoT for Photovoltaic Parameters
Author(s): Asnil Asnil,Krismadinata Krismadinata,Irma Husnaini,Hanif Hazman,Erita Astrid / Language(s): English
/ Issue: 3/2023
Keywords: Photovoltaic monitoring; Internet of thing; Thinger.io; photovoltaic parameters; solar radiation
The monitoring performance of the photovoltaic system in real time is required for estimation and optimization purposes. This monitoring system has been the subject of extensive research, but in general it employs costly commercial software and as well as other license-based software. This paper discusses the design of a photovoltaic parameter monitoring system that uses Internet of Things technology to monitor in real time. This software is intended to be as interactive as possible in order to display all measurement data in the form of graphical according to a user-specified time interval. The constructed system is tested to display the measurement parameters of the fixed and dual axis solar tracker. The test results indicate that the system can display data in real time. Every data shown is accessible from any location and at any time, so long as the device being used is connected to the Internet. This system obviously can be applied and developed for an internet of thing-based photovoltaic parameter monitoring system to facilitate system estimation and optimization.
This paper presents the development and usage of a web application for troubleshooting logical circuits. The application is designed to support the teaching and learning of troubleshooting logical circuits by providing students with a platform to practice troubleshooting and allowing teachers to monitor students' progress. The web application features an interactive interface that allows students to simulate and troubleshoot logical circuits using the method of truth table. The application also provides students with immediate feedback on their troubleshooting attempts and highlights areas that need improvement. Furthermore, the application has a tracking system that allows instructors to monitor students' progress and identify areas where students may need additional support. Growing complexity and usage of logical combinational circuits require better methods for teaching and improving students’ engagement.
Forward scattering radar has been widely used to classify human movements. To reduce concern of prolonged radiation exposure, Wi-Fi based passive scattering radar with bistatic topology is proposed. The movements include walking, bending, sitting, and kneeling. The enveloping algorithm applied to Wi-Fi based passive forward scattering radar signals has produced a comparable pattern to the ones from the active configuration. The features extracted from the frequency spectrum are also similar for both radar topologies. These led to comparable performance when classifying using artificial neural network. Hence, Wi-Fi can effectively reduce the cost of radar sensors.
Supervised Transfer Learning for Multi Organs 3D Segmentation With Registration Tools for Metal Artifact Reduction in CT Images
Author(s): Hanaa M. Al Abboodi,Amera W. Al-funja,Najlaa Abd Hamza,Alaa H. Abdullah,Bashar H. Shami / Language(s): English
/ Issue: 3/2023
Keywords: MAR; supervised transfer learning; pretrained-model; 3D semantic segmentation
Radiological diagnostics are undeniably crucial in everyday healthcare. CT scans can sometimes provide misleading clues and delay successive treatment due to artifacts caused by reflected radiation from metallic implants. This work successfully segments multiple organs containing metal implants and discards artifacts using a combination of non-rigid transformations, Scribbles-based segmentation, and a pre-trained auto segmentation model (DynaUnet -Pretrained-Model). The presented transfer learning model combined the benefits of an interactive environment and reduced computational and processing-time costs. The transfer learning model proved high auto segmentation performance for multi-organs with metal implants' presence by decreasing metal artefact's impact on the segmentation process and the achieved segmentation accuracies between 0.9998 for the spleen and 0.9829 for the stomach.
Keywords: Energy harvesting; solar modules; single-axis solar tracker; IoT
This study examines the effectiveness of static and dynamic PV module models for solar energy gathering. The static design of the first solar panel is used, while the dynamic design of the second solar panel with a single-axis tracker is used. Finding the best model for capturing solar energy and turning it into electrical energy is the aim. Monitoring systems use IoT technologies. To detect variables including current, voltage, radiation, temperature, and humidity, the system has a number of sensors. The Thinger i.o program, coupled to the Arduino Uno used to control these sensors uses the Internet of Things (IoT) concept to evaluate and keep track of the outcomes of parameter measurements. As a result, the acquired measurement results can be viewed on the Thinger i.o application and checked remotely from any location. The three tests show that systems using dynamic ideas are better able to capture solar energy than static systems. The performance discrepancy is at its widest in the third test, when the dynamic system generates 14.4% more electrical energy than the static system.
Analysis of Technological Gaps for Agricultural Chains Through the Analytical Hierarchical Process – AHP
Author(s): Willian Alejandro Orjuela,Juan Manuel Andrade,German Dario Hembuz,Santiago Quintero / Language(s): English
/ Issue: 3/2023
Keywords: Technological gaps; research agendas; agricultural chains; analytical hierarchical process
The technological advances experienced by some countries can revolutionary change the productivity and competitiveness of a wide range of economic sectors due to the opening of possibilities of new ways of doing things better; however, the state of advancement of technology in the respective sectors and countries must first be determined. The aim of the study was to identify the technological gaps in the different productive links of the banana production chain in Colombia, in comparison with different leading countries in the world, to establish the best practices in each of these links. The study was quantitative in nature and the Hierarchical Analytical Process - AHP was used as a multi-criteria tool for information processing and analysis. The study population corresponded to the countries with a banana production chain, and the sample corresponded to the 13 countries with the highest volume of production. Among the multiple existing comparison criteria, the following were selected for analysis: basic research, production volume, exported value, exported quantity and unit value of exports. As a result, it was obtained that Nigeria presents the greatest advances and conditions in the productive chain analyzed according to the established criteria. Likewise, a methodology was established that can be extrapolated to be used in the evaluation of national and international agricultural production chains within the framework of technological gaps.
Keywords: Geographic information systems; land use change; remote sensing; Ulayat land
Ulayat land is traditional West Sumatra land frequently subject to competing demands from various stakeholders, including government agencies, private companies, and local communities. Those demands lead to the need to update geospatial data in the form of land use modeling for further land management purposes. This research used remote sensing technology to develop land use and land cover models based on cellular automata and socio-economic data. This research shows that there will be a significant increase in population in 2030, followed by changes in residential land use. The land use model shows that the use of customary land in most areas is not followed by a change in ownership which shows that tribe rules are still being followed and maintained correctly.
Content Analysis of the Subject of Technology at Basic Schools in Slovakia within the Context of the Upcoming Education Reform
Author(s): Alena Hašková,Danka Lukáčová / Language(s): English
/ Issue: 3/2023
Keywords: Education reform; curricula; primary and secondary schools (ISCED 1-2); technical education; content analysis; students' interest in technical study programs
Since 1989, various initiatives aimed at supporting humanity, linguistic, information, technical or polytechnical education in basic schools in Slovakia (i.e. at primary and lower secondary schools according to ISCED) were announced and implemented. At the end of 2020 the Ministry of Education, Science, Research and Sport of the Slovak Republic announced its intention to carry out a curricular reform on the level of ISCED 1 and ISCED 2. The article presents starting points and conceptions of both the previous as well as the announced curricular reforms, and subsequently the authors focus on the impact of these reforms on the content of the subject of technology, in frame of which technical education of students at lower level of secondary education is carried out. From the authors' point of view, the curricular reform of the subject of technology should increase students' interest in technically oriented fields of study. In this context, they present results of a research study, the purpose of which was to determine how students evaluate the current content (before the announced reform) of the technology subject.
Time Series Regression: Prediction of Electricity Consumption Based on Number of Consumers at National Electricity Supply Company
Author(s): Mohammad Idhom,Akhmad Fauzi,Trimono Trimono,Prismahardi Aji Riyantoko / Language(s): English
/ Issue: 3/2023
Keywords: Power electricity usage; number of consumers; time series regression; MAPE
Electrical energy is one of the components of Gross Domestic Product that is able to encourage the economy because it has become a basic need of the community. To meet the increasing demand for electrical energy, the Indonesia National Electricity Providers (PLN) need to predict the amount of electrical power required based on the customer numbers to meet the demand for adequate electricity supply. This study aims to predict electric power based on electricity user customers using a time series regression model. The data used in this study are secondary data which get from PLN annual report in 2021. This study resulted in a finding of the best prediction model based on the Akaike Information Criterion (AIC) value, namely the time series regression model with the error value modeled by the AR(1) model, while the forecasting accuracy measure used the value MAPE of 9.77%. This means that the result of model prediction is highly accurate.
Keywords: Knowledge management; KM process; KM infrastructure; smart university
There is a lack of extensive research on the development of smart universities (SU). The aim of this paper is to investigate the impact of knowledge management (KM) on the development of SU (DSU) in Iraq. According to the knowledge-based view (KBV), resource-based view (RBV), and KM capability model, this study suggests that the KM process (KMP) and KM infrastructure (KMI) have a positive impact on SUs development. Furthermore, the study suggests that KMI functions as a mediator. The study was conducted using a sample of five public universities in Iraq. A purposive sampling method was utilised to collect data from a total of 209 academic staff members. The study's results demonstrate that KMP (knowledge acquisition, knowledge sharing, and knowledge application) and KMI (IT infrastructure and human KM) have a significant impact on the DSU. Additionally, it was discovered that KMI plays a role in mediating the influence of KMP on the DSU. The findings indicate that successful KM can support the progress of SU. Further cross-national and variable-based studies are required to enhance the validity of this conclusion.
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