Publikační činnost Katedry telekomunikačních technologií / Publications of Department of Telecommunications (440)

Permanent URI for this collectionhttp://hdl.handle.net/10084/64796

Kolekce obsahuje bibliografické záznamy publikační činnosti (článků) akademických pracovníků Katedry telekomunikačních technologií (440) v časopisech registrovaných ve Web of Science od roku 2003 po současnost.
Do kolekce jsou zařazeny:
a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská-Technická univerzita Ostrava (VŠB-TUO),
b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

Bibliografické záznamy byly původně vytvořeny v kolekci Publikační činnost akademických pracovníků VŠB-TUO, která sleduje publikování akademických pracovníků od roku 1990.

Browse

Now showing 1 - 20 out of 388 results

Distributed sensing with nanoparticle-doped fibers in standard OTDR systems: Validation and performance analysis
(Elsevier, 2026) Leal-Junior, Arnaldo; Kepák, Stanislav; Pedruzzi, Eduarda; Nedoma, Jan; Martinek, Radek; Blanc, Wilfried
This paper presents the implementation of enhanced backscattering optical fibers in optical time-domain reflec tometry (OTDR). The increase in the backscattering is achieved by doping the fiber core with nanoparticles, resulting in the so-called nanoparticle-doped optical fibers (NPFs). The so-called NPF-OTDR sensor system takes advantage of the higher scattering of the NPFs to increase the spatial resolution and strain sensitivity of the distributed sensor system. The wavelength-dependent optical attenuation is measured over a wide range of wave lengths using the cutback method to evaluate the operational wavelength for the OTDR. In this case, the results indicated smaller attenuation in 1310 nm than at the other operational wavelengths of the OTDR system (i.e., 1550 nm and 1625 nm). For this reason, the OTDR is applied at the 1310 nm wavelength for different strains applied along the optical fiber (with 1 m separation between them). The results for strain estimation showed relative errors of around 3%, whereas the strain position estimation is around 0.24 m. These results indicate the feasibility of the proposed system with a potential spatial resolution of around 0.5 m (which can be even lower depending on the OTDR setup). Thus, the advantages of the OTDR using the NPF result in a better sensor system performance when compared with the standard optical fibers, significantly enhancing the conventional OTDR capabilities in terms of resolution with even comparable performance to specialty OTDR systems at a lower cost and with a simpler design.
Symbiotic communication systems in the Internet of Things: A framework for double adaptive performance analysis
(IEEE, 2026) Vu, Thai-Hoc; Nguyen, Tien-Tung; Nguyen, Tan N; Tu, Lam-Thanh; Vozňák, Miroslav
This letter studies the performance enhancement of symbiotic systems, which begins by formulating a closed-form adaptive mutualism symbiotic strategy for the backscatter coefficient to achieve minimal decoding errors for both primary and secondary signals. Then, we analyze two scenarios: First, the primary source adapts the modulation scheme based on the channel conditions of the primary signal to meet the target bit error rate (BER), evaluating metrics: mode selection probability, outage mode probability (OMP), BER, and spectral efficiency. Second, the primary source adapts its transmission rate and/or power according to three channel policies: constant power with optimal rate adaptation, optimal simultaneous power and rate adaptation, and truncated channel inversion with a fixed rate. Results show that for the first scenario, our proposed approach significantly improves the OMP and BER of the adaptive symbiotic system in the moderate and high signal-to-noise ratio (SNR) regimes compared to the fixed one, while the second scenario shows a promising choice for balancing capacity between backscatter and cellular rates in the low SNR regime.
A novel sensing and symbiotic communication approach
(IEEE, 2026) Vu, Thai-Hoc; Le, Anh-Tu; Tu, Lam-Thanh; Luong, Nguyen Cong; Vozňák, Miroslav
As next-generation technologies promise seamless integration of sensing and communication within unified systems by leveraging shared frequencies, signaling, and hardware, it is of interest to explore new communication approaches in a reciprocal and symbiotic manner. This letter proposes a sensing and symbiotic communication (SaSC) approach for uplink scenarios, where the base station simultaneously performs radar sensing, user communication, and backscatter information collection. To enforce symbiotic communications efficiently, adaptive reflective coefficients for backscatter nodes and diversity techniques for user communication receptions are jointly established. Upon this, closed-form expressions are derived to quantify outage probability for symbiotic communication and mutual information for radar sensing. Besides, deep quantitative analyses of high transmit power regimes and low rates are also analyzed to attain insights into system designs and solve a non-convex problem of the radar transmit power optimization to maximize radar mutual information under throughput constraints of symbiotic communication. Moreover, to achieve mutual interference cancellation between radar and symbiotic communication signals, a model of reversed radar symbols over consecutive periods has been outlined as a benchmark scheme. Simulation results affirm the potential and effectiveness of the proposed SaSC approach.
Latent diffusion for spectrum sensing of coexisting radar and communication signals
(IEEE, 2026) Huynh-The, Thien; Huynh, Phuoc-Long; Phan, Van-Ca; Vu, Thai-Hoc; da Costa, Daniel Benevides
The growing demand for spectrum efficiency in next-generation wireless networks, especially in vehicular environments, necessitates effective spectrum sensing (SS) techniques capable of managing the coexistence of technologies like fifth generation new radio (NR) and radar systems. This letter introduces SpecDiff, an innovative framework based on latent diffusion models for spectrogram segmentation, designed to identify and differentiate these coexisting signals in dynamic, noisy environments. SpecDiff leverages a generative diffusion model in a compact latent space, using an attention-based denoising process to enhance segmentation performance under low signal-to-noise ratios and complex channel conditions. The model achieves state-of-the-art performance, with a mean accuracy of 98.68% and mean intersection-over-union (IoU) of 96.30%, effectively identifying the occupied bandwidth in spectrograms. Furthermore, SpecDiff surpasses existing deep learning models in both accuracy and efficiency, offering a promising solution for spectrum sharing in future wireless networks.
Enhancing transparency and efficiency in blockchain harvest: Empowering farmers and consumers through transparent trading in agricultural applications
(Elsevier, 2025) Lakhan, Abdullah; Mohammed, Mazin Abed; Al-Budair, Lilian Qasim Alwan; Memon, Sajida; Slaný, Vlastimil; Deveci, Muhammet; Martinek, Radek
With the development of edge-cutting technologies, digital agriculture farming, product selling, and purchasing have been increasing progressively. On the other hand, transparency in digital agricultural agrochemicals (pesticides or herbicides) substances of products needs to be monitored carefully between production and selling to customers in a transparent form. Recently, blockchain has emerged as a decentralized technology, which is the most potent decentralized technology. It connects many nodes and validates their data transparency during application sharing. Therefore, it is a motivation to use blockchain technology for digital agriculture applications to meet data transparency, security, and privacy requirements. In this paper, we present enhancing transparency and efficiency in blockchain harvest: empowering farmers and consumers through transparent trading in agricultural application tasks. The application tasks are a mixture of agricultural things (IoT) sensors, products, monitoring, fertilizers, transport tracking, farmers, consumers, and institutional data for processing in digital healthcare applications. Our objective is to process the agricultural tasks during production and trading in an immutable, transparent, secure, and private form. To meet constraints such as processing time, blockchain validation, access control, and cyber-attacks, we suggest enhancing transparency and efficiency in blockchain harvesting and empowering farmers and consumers to trade securely and transparently. In proposed blockchain agriculture, all nodes are heterogeneous and connected; therefore, to avoid any time failure, cyberattacks, or block failure, we establish them in a stable form. This paper presents the agriculture blockchain harvest multi-tasking scheduling (ABHMTS) algorithm methodology, which amalgamates different methods: smart contracts, local offloading, multi-tasking, and blockchain validation with malware detection in blocks during data transfer among other nodes. The experimental results in simulation show that ABHMTS improved the transparency of agricultural task data by 98% and executed them with the minimum processing time by 20%, and reduced the risk of resource failure and resource consumption by 10% with higher malware detection in the framework as compared to baseline approaches.
POF-based biosensors for cortisol detection in seawater as a tool for aquaculture systems
(Springer Nature, 2024) Arcadio, Francesco; Soares, Simone; Nedoma, Jan; Aguiar, Dayana; Pereira, Ana Cristina; Zeni, Luigi; Cennamo, Nunzio; Marques, Carlos
A surface plasmon resonance (SPR) phenomenon implemented via D-shaped polymer optical fiber (POF) is exploited to realize cortisol biosensors. In this work, two immonosensors are designed and developed for the qualitative as well as quantitative measurement of cortisol in artificial and real samples. The performances of the POF-based biosensors in cortisol recognition are achieved using different functionalization protocols to make the same antibody receptor layer over the SPR surface via cysteamine and lipoic acid, achieving a limit of detection (LOD) of 0.8 pg/mL and 0.2 pg/mL, respectively. More specifically, the use of cysteamine or lipoic acid changes the distance between the receptor layer and the SPR surface, improving the sensitivity at low concentrations of about one order of magnitude in the configuration based on lipoic acid. The LODs of both cortisol biosensors are achieved well competitively with other sensor systems but without the need for amplification or sample treatments. In order to obtain the selectivity tests, cholesterol and testosterone were used as interfering substances. Moreover, tests in simulated seawater were performed for the same cortisol concentration range achieved in buffer solution to assess the immunosensor response to the complex matrix. Finally, the developed cortisol biosensor was used in a real seawater sample to estimate the cortisol concentration value. The gold standard method has confirmed the estimated cortisol concentration value in real seawater samples. Liquid-liquid extraction was implemented to maximize the response of cortisol in liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) analysis.
The role of photonic sensing technologies in Healthcare 5.0: A comprehensive review of future perspectives and applications
(IEEE, 2025) Leal-Junior, Arnaldo; Nedoma, Jan; Martinek, Radek
This article presents a review of Healthcare 5.0 and its historical perspective, considering the industrial revolutions. The main aspects and challenges of the next generation of healthcare systems are discussed. Due to the inherent advantages of optical fiber sensor (OFS) systems, such technology is comprehensively reviewed and discussed in terms of healthcare applications. Thereafter, an analytical correlation is performed between OFS technologies and the requirements of Healthcare 5.0, and discussed in detail. The key parameters in Healthcare 5.0 indicated that OFS technology aligns well with the requirements and challenges of Healthcare 5.0. Thus, photonic technologies play a key role in the future of healthcare, especially in innovative approaches for integrated sensing and communication (ISAC), as such approaches are readily available for use with optical fibers that combine excellent signal transmission and distributed sensing capabilities. Thus, the contributions of this work are related to the first review of Healthcare 5.0, focused on the role of photonic technologies in such new healthcare evolution, as well as the analysis concerning the key aspects of such applications.
Advanced fiber optic sensors for quantitative nitrite detection: Comparative analysis of plasmonic tilted fiber Bragg gratings and fiber optic tips with ion-imprinted polymers
(Elsevier, 2024) Liu, Xuecheng; Singh, Ragini; Zhang, Bingyuan; Caucheteur, Christophe; Santos, Nuno; Kumar, Santosh; Nedoma, Jan; Marques, Carlos
The presence of nitrite, a prevalent contaminant in natural environments, presents a significant environmental and human health concern. Hence, it is imperative to develop a sensor with the ability to quantitatively detect nitrite. This study focuses on the design and development of i) probe 1: tilted fiber Bragg gratings (TFBGs) and ii) probe 2: fiber optic tip-based plasmonic sensors utilizing ion-imprinted polymers. The concentration of nitrite was assessed at various levels using both sensing configurations. The outcomes indicated that the TFBGs-based sensor exhibited a sensitivity and limit of detection (LOD) of 0.469 nm/ln(mu g/mL) and 0.142 mu g/mL in the linear detection range of 0.5-50 mu g/mL. The fiber optic tip-based sensor exhibited a sensitivity and LOD of 1.16 nm/ln (mu g/mL) and 0.176 mu g/mL within the 1-50 mu g/mL linear detection range. The obtained sensing results reveal that the sensors presented in this study are able to accurately detect nitrite at various concentrations in a quantitative manner. Moreover, an assessment was conducted to examine the selectivity and reusability of the sensor individually, yielding satisfactory results.
Statistical analysis of the sum of double random variables for security applications in RIS-assisted NOMA networks with a direct link
(MDPI, 2025) Nguyen, Sang-Quang; Tran, Phuong T.; Minh, Bui Vu; Duy, Tran Trung; Le, Anh-Tu; Rejfek, Luboš; Tu, Lam-Thanh
Next- generation wireless communications are projected to integrate reconfigurable intelligent surfaces (RISs) to perpetrate enhanced spectral and energy efficiencies. To quantify the performance of RIS-aided wireless networks, the statistics of a single random variable plus the sum of double random variables becomes a core approach to reflect how communication links from RISs improve wireless-based systems versus direct ones. With this in mind, the work applies the statistics of a single random variable plus the sum of double random variables in the secure performance of RIS-based non-orthogonal multi-access (NOMA) systems with the presence of untrusted users. We propose a new communication strategy by jointly considering NOMA encoding and RIS's phase shift design to enhance the communication of legitimate nodes while degrading the channel capacity of untrusted elements but with sufficient power resources for signal recovery. Following that, we analyze and derive the closed-form expressions of the secrecy effective capacity (SEC) and secrecy outage probability (SOP). All analyses are supported by extensive Monte Carlo simulation outcomes, which facilitate an understanding of system communication behavior, such as the transmit signal-to-noise ratio, the number of RIS elements, the power allocation coefficients, the target data rate of the communication channels, and secure data rate. Finally, the results demonstrate that our proposed communication can be improved significantly with an increase in the number of RIS elements, irrespective of the presence of untrusted proximate or distant users.
A hierarchical set-enumeration tree enabling high occupancy item set mining and the use of an adaptive occupancy threshold
(Springer Nature, 2025) Tran, Thanh-Nam; Hoang, Vinh Truong; Truong, Thanh-Cong; Vozňák, Miroslav
The highly efficient HEP algorithm is a useful tool for mining High Occupancy (HO) item sets. Occupancy is an important measure that describes the interestingness of frequent item sets. The current study examines the efficiency problems in mining HO item sets and proposes an improved HEP algorithm, named advanced HEP (A-HEP), based on set theory rules which eliminate a large number of redundant iterations. The study also proposes a novel adaptive-and-modified HEP (NAM-HEP) algorithm that uses HO Set-Enumeration (SE) trees to store HO item sets. The study proposes definitions for adaptive thresholds such as support threshold and occupancy threshold based on the attributes of the transaction database for efficient pruning of the HO-SE tree. Two pseudo-code blocks are presented in addition to a detailed description of the A-HEP and NAM-HEP algorithms and their advantages. Using the A-HEP and NAM-HEP algorithms, HO item sets are investigated from the practical transaction databases named mushroom and retail. The results indicate that the proposed A-HEP and NAM-HEP algorithms enhance mining performance and runtime benchmarks.
Emulation of quantum key distribution networks
(IEEE, 2025) Mehic, Miralem; Dervisevic, Emir; Burdiak, Patrik; Lipovac, Vlatko; Fazio, Peppino; Vozňák, Miroslav
Network emulators play an important role in testing network systems, applications, and protocols. Emulators bridge the gap between simulation setups that lack realism in results and real-world trials that are accurate but often expensive, non-reproducible, and uncontrollable. This article presents an extended model of the Quantum Key Distribution Network Simulation Module (QKDNetSim) with a model catalog of QKD components and functionalities. We explore emulations of point-to-point connections in QKD networks and the interaction of essential components within QKD nodes. The presented tool will undoubtedly spur future development and teaching, and it is critical for testing novel applications and protocols applied to QKD networks.
On the effect of coverage range extent on next-cell prediction error for vehicular mobility in 5G/6G networks: a novel theoretic model
(IEEE, 2025) Fazio, Peppino
Over the last decade, 5G and forthcoming 6G archi tectures have undergone extensive standardization and prepara tions for the future. The literature in this field is saturated with studies on predicting mobile trajectories in cellular systems and guaranteeing quality of service and an adequate user experience in these environments. The current study aims to bridge mobility prediction and 5G/6Gpredictive approaches and demonstrate that the intrinsic paradigm of femto-cell and nano-cell deployment (based on very small radio coverage radii) for 5G provides the means to obtain more accurate time series data on user mobility and thus enable predictive models (e.g., machine learning) as suit able technologies for integration with 6G standards. This field is therefore an important avenue of research.
Next-cell and mobility prediction in new generation cellular systems based on convolutional neural networks and encoding mobility data as images
(Elsevier, 2024) Fazio, Peppino; Mehić, Miralem; Vozňák, Miroslav
Mobility prediction has been a popular research topic for many decades. With the advent of new generation technologies (5G and beyond) and smaller coverage cells, hand-over operations have become more frequent. Cellular system companies are therefore taking increasing interest in using the available predictive information on node movements to optimize and manage their bandwidth resources. In particular, the main challenging scope of our contribution consists in solving the issue of reliable next-cell prediction, aimed to call dropping probability minimization. In addition, our proposal is based on the innovative concept of mobility data to image encoding. The scheme is able to a-priori determine the next visited cells during host movements by applying a convolutional neural approach to mobility images. The power of machine learning is used to advantage, and highly accurate image classification is achieved for mobility prediction. We performed numerous simulation campaigns related to next-cell prediction in mobile cellular environments, obtaining very satisfactory results by the application of convolutional neural networks, which have an impressive history of effectiveness with image classification problems. The trained network has been associated to each coverage cell and the prediction accuracy has been evaluated.
Emerging technologies for 6G non-terrestrial-networks: From academia to industrial applications
(IEEE, 2024) Nguyen, Cong T.; Saputra, Yuris Mulya; Huynh, Nguyen Van; Nguyen, Tan N.; Hoang, Dinh Thai; Nguyen, Diep N.; Pham, Van-Quan; Vozňák, Miroslav; Chatzinotas, Symeon; Tran, Dinh-Hieu
Terrestrial networks form the fundamental infrastructure of modern communication systems, serving more than 4 billion users globally. However, terrestrial networks are facing a wide range of challenges, from coverage and reliability to interference and congestion. As the demands of the 6G era are expected to be much higher, it is crucial to address these challenges to ensure a robust and efficient communication infrastructure for the future. To address these problems, Non-terrestrial Network (NTN) has emerged to be a promising solution. NTNs are communication networks that leverage airborne (e.g., unmanned aerial vehicles) and spaceborne vehicles (e.g., satellites) to facilitate ultra-reliable communications and connectivity with high data rates and low latency over expansive regions. This article aims to provide a comprehensive survey on the utilization of network slicing, Artificial Intelligence/Machine Learning (AI/ML), and Open Radio Access Network (ORAN) to address diverse challenges of NTNs from the perspectives of both academia and industry. Particularly, we first provide an in-depth tutorial on NTN and the key enabling technologies including network slicing, AI/ML, and ORAN. Then, we provide a comprehensive survey on how network slicing and AI/ML have been leveraged to overcome the challenges that NTNs are facing. Moreover, we present how ORAN can be utilized for NTNs. Finally, we highlight important challenges, open issues, and future research directions of NTN in the 6G era.
DT-LSMAS: Digital Twin-Assisted Large-Scale Multiagent System for Healthcare Workflows
(IEEE, 2024) Lakhan, Abdullah; Mohammed, Mazin Abed; Zebar, Dilovan Asaad; Abdulkareem, Karrar Hameed; Deveci, Muhammet; Marhoon, Haydar Abdulameer; Nedoma, Jan; Martinek, Radek
Digital healthcare has garnered much attention from academia and industry for health and well-being. Many digital healthcare architectures based on large-scale edge and cloud multiagent systems (LSMASs) have recently been presented. The LSMAS allows agents from different institutions to work together to achieve healthcare processing goals for users. This article presents a digital twin large-scale multiagent strategy (DT-LSMAS) comprising mobile, edge, and cloud agents. The DT-LSMAS comprised different schemes for healthcare workflows, such as added healthcare workflows, application partitioning, and scheduling. We consider healthcare workflows with different biosensor data such as heartbeat, blood pressure, glucose monitoring, and other healthcare tasks. We partitioned workflows into mobile, edge, and cloud agents to meet the deadline, total time, and security of workflows in large-scale edge and cloud nodes. To handle the large-scale resource for real-time sensor data, we suggested digital twin-enabled edge nodes, where delay-sensitive workflow tasks are scheduled and executed under their quality of service requirements. Simulation results show that the DT-LSMAS outperformed in terms of total time by 50%, minimizing the risk of resource leakage and deadline missing during scheduling on heterogeneous nodes. In conclusion, the DT-LSMAS obtained optimal results for workflow applications.
Efficient handling of ACL policy change in SDN using reactive and proactive flow rule installation
(Springer Nature, 2024) Hussain, Mudassar; Amin, Rashid; Gantassi, Rahma, rahma; Alshehri, Asma Hassan; Frnda, Jaroslav; Raza, Syed Mohsan, Syed Mohsan
Software-defined networking (SDN) is a pioneering network paradigm that strategically decouples the control plane from the data and management planes, thereby streamlining network administration. SDN's centralized network management makes configuring access control list (ACL) policies easier, which is important as these policies frequently change due to network application needs and topology modifications. Consequently, this action may trigger modifications at the SDN controller. In response, the controller performs computational tasks to generate updated flow rules in accordance with modified ACL policies and installs flow rules at the data plane. Existing research has investigated reactive flow rules installation that changes in ACL policies result in packet violations and network inefficiencies. Network management becomes difficult due to deleting inconsistent flow rules and computing new flow rules per modified ACL policies. The proposed solution efficiently handles ACL policy change phenomena by automatically detecting ACL policy change and accordingly detecting and deleting inconsistent flow rules along with the caching at the controller and adding new flow rules at the data plane. A comprehensive analysis of both proactive and reactive mechanisms in SDN is carried out to achieve this. To facilitate the evaluation of these mechanisms, the ACL policies are modeled using a 5-tuple structure comprising Source, Destination, Protocol, Ports, and Action. The resulting policies are then translated into a policy implementation file and transmitted to the controller. Subsequently, the controller utilizes the network topology and the ACL policies to calculate the necessary flow rules and caches these flow rules in hash table in addition to installing them at the switches. The proposed solution is simulated in Mininet Emulator using a set of ACL policies, hosts, and switches. The results are presented by varying the ACL policy at different time instances, inter-packet delay and flow timeout value. The simulation results show that the reactive flow rule installation performs better than the proactive mechanism with respect to network throughput, packet violations, successful packet delivery, normalized overhead, policy change detection time and end-to-end delay. The proposed solution, designed to be directly used on SDN controllers that support the Pyretic language, provides a flexible and efficient approach for flow rule installation. The proposed mechanism can be employed to facilitate network administrators in implementing ACL policies. It may also be integrated with network monitoring and debugging tools to analyze the effectiveness of the policy change mechanism.
Multi-agent reinforcement learning framework based on information fusion biometric ticketing data in different public transport modes
(Elsevier, 2024) Lakhan, Abdullah; Rashid, Ahmed N.; Mohammed, Mazin Abed; Zebari, Dilovan Asaad; Deveci, Muhammet; Wang, Limin, limin; Abdulkareem, Karrar Hameed; Nedoma, Jan; Martinek, Radek
In smart cities, biometric technologies have become extensively used for ticket authentication on public transport. Information fusion plays a key role in biometric ticketing, allowing ticket validation with more data source validation in different public transport modes. This paper proposes a novel biometric technology -based mobile ticket application -based system. We formulate the problem as a multi -agent reinforcement learning framework for biometric ticketing in multi -transport environments. Specifically, we propose the Asynchronous Advantage Critic Biometric Ticketing Framework (A3CBTF) algorithm, which consists of different schemes based on the proposed system. The proposed algorithm framework operates in hybrid transport modes using a parallel reinforcement learning scheme. A key advantage of A3CBTF is that it enables passengers to use a single ticket for various public transport modes. Additionally, even when a passenger's mobile device is stolen, lost, or has a dead battery, they can still validate their tickets through different information fusion sources, such as fingerprint and face recognition. A3CBTF is a multi -agent system that integrates mobile, transport, edge, and cloud servers to facilitate ticket validation in a distributed environment. By optimizing both convex and concave optimizations, A3CBTF ensures efficient ticket validation with minimal processing time and maximizes validation rewards across different biometric technologies. Experimental results demonstrate that A3CBTF outperforms mobile off with other options such as fingerprint and face recognition in public transport as compared to other ticketing systems.
Renewable energy resource management using an integrated robust decision making model under entropy and similarity measures of fuzzy hypersoft set
(Elsevier, 2024) Saeed, Muhammad Haris; Saeed, Muhammad; Rahman, Atiqe Ur; Ahsan, Muhammad; Mohammed, Mazin Abed; Marhoon, Haydar Abdulameer; Nedoma, Jan; Martinek, Radek
The demand for renewable energy has significantly increased over the last decade with increased attention to the preservation of the environment and sustainable, optimal resource management. As traditional sources of energy production are depleting at an alarming rate and causing longlasting environmental damage, it is essential to explore green and cost-effective methodologies for meeting energy demand. With each country having different geographical, political, social, and natural factors, the problem arises of which renewable energy should be utilized for optimal resource management. This multi -criteria decision making (MCDM) challenge is tackled by applying a dynamic fuzzy hypersoft set -based Method for the evaluation of currently deployed Renewable Energy systems and providing a decision support system for the installation of new ones based on the factors mentioned above for Turkey. As the installation of new renewable energy projects and the evaluation of old ones is significantly influenced by human judgment, it leaves great room for uncertainty primarily because of the psychological factors of the expert. The novel concept of Fuzzy Hypersoft Sets (FHSs) and their Entropy (EN) and TOPSIS-based operations are first discussed with reference to the problem at hand. The presented structure is superior to the ones in the literature by allowing access to data parameters as sub -parametric values while utilizing the versatility of Fuzzy structures to deal with uncertainty. The technique has great potential to serve as a potential decision support system in any setting. For now, hypothetical expert ratings are used to illustrate the working of the dynamic structure along with a sensitivity analysis to investigate the primary criterion weights in sorting. The evaluation of currently deployed renewable energy systems using our methodology revealed an average improvement in system performance compared to traditional methods. Furthermore, the decision support system for the installation of new projects based on geographical, political, social, and natural factors exhibited a potential increase in overall system efficiency. These numeric outcomes highlight the effectiveness and practical applicability of our approach in optimizing resource management and fostering sustainable energy practices.
Load monitoring and appliance recognition using an inexpensive, low-frequency, data-to-image, neural network, and network mobility approach for domestic IoT systems
(IEEE, 2024) Fazio, Peppino; Mehić, Miralem; Vozňák, Miroslav
With the low integration costs and quick development cycle of all-IP-based 5G+ technologies, it is not surprising that the proliferation of IP devices for residential or industrial purposes is ubiquitous. Energy scheduling/management and automated device recognition are popular research areas in the engineering community, and much time and work have been invested in producing the systems required for smart city networks. However, most proposed approaches involve expensive and invasive equipment that produces huge volumes of data (high-frequency complexity) for analysis by supervised learning algorithms. In contrast to other studies in the literature, we propose an approach based on encoding consumption data into vehicular mobility and imaging systems to apply a simple convolutional neural network to recognize certain scenarios (devices powered on) in real time and based on the nonintrusive load monitoring paradigm. Our idea is based on a very cheap device and can be adapted at a very low cost for any real scenario. We have also created our own data set, taken from a real domestic environment, contrary to most existing works based on synthetic data. The results of the study's simulation demonstrate the effectiveness of this innovative and low-cost approach and its scalability in function of the number of considered appliances.
Construction of a high-temperature sensor for industry based on optical fibers and ruby crystal
(MDPI, 2024) Hercík, Radim; Mikolajek, Martin; Byrtus, Radek; Hejduk, Stanislav; Látal, Jan; Vanderka, Aleš; Macháček, Zdeněk; Koziorek, Jiří
This paper presents the construction of an innovative high-temperature sensor based on the optical principle. The sensor is designed especially for the measurement of exhaust gases with a temperature range of up to +850 degrees C. The methodology is based on two principles-luminescence and dark body radiation. The core of this study is the description of sensing element construction together with electronics and the system of photodiode dark current compensation. An advantage of this optical-based system is its immunity to strong magnetic fields. This study also discusses results achieved and further steps. The solution is covered by a European Patent.

Browse

Recent Submissions