Search Results (443)

The measurement and assessment of indoor air quality in terms of respirable particulate constituents is relevant, especially in light of the COVID-19 pandemic and associated infection events. To analyze indoor infectious potential and to develop customized hygiene concepts, the measurement monitoring of the anthropogenic aerosol spreading is necessary. For indoor aerosol measurements usually standard lab equipment is used. However, these devices are time-consuming, expensive and unwieldy. The idea is to replace this standard laboratory equipment with low-cost sensors widely used for monitoring fine dust (particulate matter—PM). Due to the low acquisition costs, many sensors can be used to determine the aerosol load, even in large rooms. Thus, the aim of this work is to verify the measurement capability of low-cost sensors. For this purpose, two different models of low-cost sensors are compared with established laboratory measuring instruments. The study was performed with artificially prepared NaCl aerosols with a well-defined size and morphology. In addition, the influence of the relative humidity, which can vary significantly indoors, on the measurement capability of the low-cost sensors is investigated. For this purpose, a heating stage was developed and tested. The results show a discrepancy in measurement capability between low-cost sensors and laboratory measuring instruments. This difference can be attributed to the partially different measuring method, as well as the different measuring particle size ranges. The determined measurement accuracy is nevertheless good, considering the compactness and the acquisition price of the low-cost sensors. Full article

In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view, including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of their different reflection patterns. The proposed framework consists of four processes: signal processing, cloud point clustering, target tracking, and target recognition. Signal processing translates the raw collected signals into spare cloud points. These points are merged into several clusters, each representing a single target in three-dimensional space. Target tracking estimates the new location of each detected target. A novel convolutional neural network model was designed to extract and recognize the features of drone and non-drone targets. For the performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments was used for training and testing the convolutional neural network. The proposed recognition model achieved accuracies of 98.4% and 98.1% for one and two targets, respectively. Full article

Security remains a top priority for those users in the hotel, even with the advent of innovative technological advances. This is because many tragic incidents, such as theft and crime, have occurred with unrestricted access. This paper proposes an intelligent door access system that would allow hotel guests to authenticate into their rooms without resorting to traditional closeness access methods. Therefore, research was conducted to solidify the understanding and refine the capabilities of the proposed system. This project aims to promote high-security aspects access system technology, which is Near-Field Communication with the use of application that have the function of simulated smart keys for explicit validation access. A Host-Card Emulator opens the opportunities for efficient financial benefit and the launch of a protective mechanism in the post-pandemic period. The suggested method was statistically and analytically accessed on hotel guests and staff from various hotels in Malaysia. The proposed system is a contactless NFC access control system that employs smartphone Host Card Emulation application technology to reduce the need for appropriate physical access, enhance security, and publicize the use of mobile access systems in the hospitality industry. Full article

Owing to the expansion of non-face-to-face activities, security issues in video conferencing systems are becoming more critical. In this paper, we focus on the end-to-end encryption (E2EE) function among the security services of video conferencing systems. First, the E2EE-related protocols of Zoom and Secure Frame (SFrame), which are representative video conferencing systems, are thoroughly investigated, and the two systems are compared and analyzed from the overall viewpoint. Next, the E2EE protocol in a Government Public Key Infrastructure (GPKI)-based video conferencing system, in which the user authentication mechanism is fundamentally different from those used in commercial sector systems such as Zoom and SFrame, is considered. In particular, among E2EE-related protocols, we propose a detailed mechanism in which the post-quantum cryptography (PQC) key encapsulation mechanism (KEM) is applied to the user key exchange process. Since the session key is not disclosed to the central server, even in futuristic quantum computers, the proposed mechanism, which includes the PQC KEM, still satisfies the E2EE security requirements in the quantum environment. Moreover, our GPKI-based mechanism induces the effect of enhancing the security level of the next-generation video conferencing systems up to a quantum-safe level. Full article

Electricity is widely regarded as the most adaptable form of energy and a major secondary energy source. However, electricity is not economically storable; therefore, the power system requires a continuous balance of electricity production and consumption to be stable. The accurate and reliable assessment of electrical energy consumption enables planning prospective power-producing systems to satisfy the expanding demand for electrical energy. Since Saudi Arabia is one of the top electricity consumers worldwide, this paper proposed an electricity consumption prediction model in Saudia Arabia. In this work, the authors obtained a never-before-seen dataset of Saudi Arabia’s electricity consumption for a span of ten years. The dataset was acquired solely by the authors from the Saudi Electrical Company (SEC), and it has further research potential that far exceeds this work. The research closely examined the performance of ensemble models and the K* model as novel models to predict the monthly electricity consumption for eighteen service offices from the Saudi Electrical Company dataset, providing experiments on a new electricity consumption dataset. The global blend parameters for the K* algorithm were tuned to achieve the best performance for predicting electricity consumption. The K* model achieved a high accuracy, and the results of the correlation coefficient (CC), mean absolute percentage error (MAPE), root mean squared percentage error (RMSPE), mean absolute error (MAE), and root mean squared error (RMSE) were 0.9373, 0.1569, 0.5636, 0.016, and 0.0488, respectively. The obtained results showed that the bagging ensemble model outperformed the standalone K* model. It used the original full dataset with K* as the base classifier, which produced a 0.9383 CC, 0.1511 MAPE, 0.5333 RMSPE, 0.0158 MAE, and 0.0484 RMSE. The outcomes of this work were compared with a previous study on the same dataset using an artificial neural network (ANN), and the comparison showed that the K* model used in this study performed better than the ANN model when compared with the standalone models and the bagging ensemble. Full article

The design and implementation of a measuring device for the determination of pigment content in plant leaves is a topic of essential importance in plant biology, agriculture, and environmental research. The timely and sufficiently accurate determination of the content of these molecules provides valuable insight into the health, photosynthetic activity, and physiological state of plants. This paper presents the key aspects and results of the development and implementation of such a measuring device. It makes it possible to measure a larger number of pigments per type compared with the devices for commercial use that are currently known to us, and the accuracy of measurements depends mostly on the specific type of plant that is being tracked. The developed device presents a measurement accuracy ranging between 72% and 97% compared with a reference method and between 87% and 90% compared with a reference technique. Also, by using the device, a significant reduction in time and required resources can be achieved in measuring the content of pigments and nitrogen in plant leaves. This is a prerequisite for the more effective monitoring of the growth and health of plants, as well as optimizing the process of growing and caring for them. The work will be continued with the focus of the research aimed at generalizing the models for determining pigments and nitrogen in plants. Full article

The emergence of Industry 5.0 took place in the mid-2010s, presenting a novel vision for the future of an industry that places emphasis on human involvement in the production process. Following the outbreak of the COVID-19 pandemic, there has been a substantial surge in the popularity of this concept, gaining traction not only in the business realm but also within academic circles. This increased attention can be attributed to a heightened focus on crucial aspects such as sustainability and resilience. The objective of this study is to present an updated overview of key bibliometric trends in Industry 5.0 research. The findings indicate a remarkable expansion of research activities in the field of Industry 5.0, as evidenced by a substantial increase in the number of publications and citations. Concurrently, the growth of Industry 5.0 research has led to the emergence of diverse perspectives and the exploration of related research themes such as artificial intelligence, big data, and human factors. In summary, this study enhances our understanding of the Industry 5.0 concept by providing an updated overview of the current state of research in this area and suggesting potential avenues for future investigations. Full article

In the current era of technology, the internet of things (IoT) plays a vital role in smart drug delivery systems. It is an emerging field that offers promising solutions for improving the efficacy, safety, and patient compliance of drug therapies. IoT-based drug delivery systems leverage advanced devices, sophisticated sensors, and smart tools to monitor and analyse the health matrices of the patient in real-time, allowing for personalised and targeted drug delivery. This technology is implemented through various types of devices, including wearable and implantable devices such as infusion pumps, smart pens, inhalers, and auto-injectors. However, the development and implementation of IoT-based drug delivery systems pose several challenges, such as ensuring data security and privacy, regulatory compliance, compatibility, and reliability. In this paper, the latest research on smart wearable devices and its analysis are addressed. It also focuses on the challenges of ensuring the safe and efficient use of this technology in healthcare applications. Full article

Automated classification of satellite images is a challenging task that enables the use of remote sensing data for environmental modeling of Earth’s landscapes. In this document, we implement a GRASS GIS-based framework for discriminating land cover types to identify changes in the endorheic basins of the ephemeral salt lakes Chott Melrhir and Chott Merouane, Algeria; we employ embedded algorithms for image processing. This study presents a dataset of the nine Landsat 8–9 OLI/TIRS satellite images obtained from the USGS for a 9-year period, from 2014 to 2022. The images were analyzed to detect changes in water levels in ephemeral lakes that experience temporal fluctuations; these lakes are dry most of the time and are fed with water during rainy periods. The unsupervised classification of images was performed using GRASS GIS algorithms through several modules: ‘i.cluster’ was used to generate image classes; ‘i.maxlik’ was used for classification using the maximal likelihood discriminant analysis, and auxiliary modules, such as ‘i.group’, ‘r.support’, ‘r.import’, etc., were used. This document includes technical descriptions of the scripts used for image processing with detailed comments on the functionalities of the GRASS GIS modules. The results include the identified variations in the ephemeral salt lakes within the Algerian part of the Sahara over a 9-year period (2014–2022), using a time series of Landsat OLI/TIRS multispectral images that were classified using GRASS GIS. The main strengths of the GRASS GIS framework are the high speed, accuracy, and effectiveness of the programming codes for image processing in environmental monitoring. The presented GitHub repository, which contains scripts used for the satellite image analysis, serves as a reference for the interpretation of remote sensing data for the environmental monitoring of arid and semi-arid areas of Africa. Full article

Opportunistic networks allow for communication between nearby mobile devices through a radio connection, avoiding the need for cellular data coverage or a Wi-Fi connection. The limited spatial range of this type of communication can be overcome by using nodes in a mesh network. The purpose of this research was to examine a commercial application of electronic mesh communication without a mobile data plan, Wi-Fi, or satellite. A mixed study, with qualitative and quantitative strategies, was designed. An experimental session, in which participants tested opportunistic networks developing different tasks for performance, was carried out to examine the system. Different complementary approaches were adopted: a survey, a focus group, and an analysis of participants’ performance. We found that the main advantage of this type of communication is the lack of a need to use data networks for one-to-one and group communications. Opportunistic networks can be integrated into professional communication workflows. They can be used in situations where traditional telephones and the Internet are compromised, such as at mass events, emergency situations, or in the presence of frequency inhibitors. Full article

The e-Archeo project, commissioned from ALES S.p.A.by the Ministry of Culture (MIC), aims to valorise the multimedia experience of eight Italian archaeological sites. This paper discusses the University of Verona’s contribution to this project, which focuses on the virtual reconstruction of two Roman villas located in Sirmione and Desenzano (Lombardy). This paper outlines the 3D survey methodologies and scientific back-end approach using Extended Matrix. The architectural and decorative reconstruction process for each site is elucidated, providing a comprehensive understanding of the process followed. Furthermore, the University developed a narrative to accompany virtual visits. One of the main project outputs was e-Archeo 3d, a virtual reality web app that allows remote and on-site use. Full article

In recent years, the adoption of innovative technologies in maritime transport and logistics systems has become a key aspect towards their development and growth, especially due to the complex and heterogeneous nature of the maritime environment. On the other hand, Internet of Things (IoT) solutions are gaining importance in the shipping industry thanks to the huge number of distributed cameras and sensors in modern ships, cargoes and sea ports, which can be exploited to improve safety, costs and productivity. This paper presents an experimental evaluation of a maritime platform, which enables a wide range of 5G-based services in the context of logistics and maritime transportation. Its core is a Narrow Band (NB)-IoT framework used to run massive IoT services on top of a hybrid terrestrial–satellite network and feed a OneM2M platform with significant data on maritime transport to develop high-level and value-added logistic applications on top. Among the many different services that could be provided by the maritime platform, we focus on the cargo-ship container tracking use case through the Global Tracking System, which allows for continuous container monitoring all over the seas in a port-to-port service scenario. The results of the experimental tests illustrate the capacity of the platform in managing the high number of messages transmitted by the container tracking devices (i.e., more than 3000) and its efficiency in limiting the average maximum latency and packet loss below $5.5$ s and $0.9$%, respectively. Full article

Ambient temperature significantly affects the electrical and light parameters of LEDs, such as forward and reverse current, voltage drop LEDs and luminous flux. With an increase in temperature, the decrease in the intensity of LED radiation is explained by physical processes, including the phenomena of non-radiative recombination due to impurity levels, recombination on the surface, losses carriers in the barrier layers of heterostructures, etc. The increase in temperature is also significantly reduces the useful life of LEDs and the LED device in general. Drivers, which allows to stabilize the operating current with a change in the supply voltage of the device and, as the result is light flux. But in LEDs of various types, current stabilization does not lead to the stabilization of the light flux when the temperature regime of their operation changes. When changing ambient temperature in the range of +40…+60 °C, the luminous flux of LEDs is significant decreases even in the case when their current is kept constant, as we can see from documentation for most of LED types. An article analyzes the effect of temperature on electrical and light parameters LEDs with different types of drivers as part of LED lighting devices, such as LED lamps and LED spotlights, in order to offer possible constructive solutions for partial reduction or elimination of the decline problem luminous flux of LED devices under conditions of their operation at high temperatures. Full article

Over the last years, capabilities of robotic systems have quantitatively and qualitatively improved. But going beyond isolated robotic systems, the integration and interoperability of robotic capabilities in complex work processes remains a major challenge. This lack of tools to integrate robots needs to be addressed on technical, semantic and organizational level. In the ROBxTASK research project, we developed an approach to support cooperation between different types of users in order to enable domain experts, with no robotic know-how, to work with robot-assisted workflows. By engineering robotic skills at a useful and usable level of abstraction for experts in different domains, we aim to increase re-usability of these skills on two different levels, (robotic) device level, and on level of application specific workflows. The researched prototype consists of a web platform, which allows (a) engineers to register (robotic) devices and the implemented skills of the devices, (b) domain experts to use a graphical task design environment to create workflows across multiple robotic devices and lastly (c) robot co-workers to download and execute the workflow code in a local environment with digital twins or real robots. Additionally skills and workflows can be shared across organisations. Initial user studies have shown that the visual programming environment is accessible and the defined skill-set is easy to understand even for domain experts that are inexperienced in the field of robotics. Full article

Many modern vehicles collect inter-vehicle distance data from radar sensors as input to driver assistance systems. However, vehicle manufacturers often use proprietary algorithms to conceal the collected data, making them inaccessible to external individuals, such as researchers. Aftermarket sensors may circumvent this issue. This study investigated the use of low-cost radar sensors to determine inter-vehicle distances during real-world semi-automated truck platooning on two-way, two-lane rural roads. Radar data from the two follower trucks in a three-truck platoon were collected, synchronized and filtered. The sensors measured distance, relative velocity and signal-to-noise ratio. Dashboard camera footage was collected, coded and synchronized to the radar data, providing context about the driving situation, such as oncoming trucks, roundabouts and tunnels. The sensors had different configuration parameters, suggested by the supplier, to avoid signal interference. With parameters as chosen, sensor ranges, inferred from maximum distance measurements, were approximately 74 and 71 m. These values were almost on par with theoretical calculations. The sensors captured the preceding truck for 83–85% of the time where they had the preceding truck within range, and 95–96% of the time in tunnels. While roundabouts are problematic, the sensors are feasible for collecting inter-vehicle distance data during truck platooning. Full article