Search results for: mobile tracking applications

Authors: Mahsa Shafaei Bajestani, Mahmoud Yazdani, Aliakbar Golshani

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Artificial lightweight aggregates have a wide range of applications in industry and engineering. Nowadays, the usage of this material in geotechnical activities, especially as backfill in retaining walls has been growing due to the specific characteristics which make it a competent alternative to the conventional geotechnical materials. In practice, a material with lower weight but higher shear strength parameters would be ideal as backfill behind retaining walls because of the important roles that these parameters play in decreasing the overall active lateral earth pressure. In this study, two types of Light Expanded Clay Aggregates (LECA) produced in the Leca factory are investigated. LECA is made in a rotary kiln by heating natural clay at different temperatures up to 1200 °C making quasi-spherical aggregates with different sizes ranged from 0 to 25 mm. The loose bulk density of these aggregates is between 300 and 700 kN/m³. The purpose of this research is to determine the stress-strain behavior, shear strength parameters, and the energy absorption of LECA materials. Direct shear tests were conducted at five normal stresses of 25, 50, 75, 100, and 200 kPa. In addition, conventional triaxial compression tests were operated at confining pressures of 50, 100, and 200 kPa to examine stress-strain behavior. The experimental results show a high internal angle of friction and even a considerable amount of nominal cohesion despite the granular structure of LECA. These desirable properties along with the intrinsic low density of these aggregates make LECA as a very proper material in geotechnical applications. Furthermore, the results demonstrate that lightweight aggregates may have high energy absorption that is excellent alternative material in seismic isolations.

Keywords: expanded clay, direct shear test, triaxial test, shear properties, energy absorption

Procedia PDF Downloads 169

Authors: A. N. Ghafar, O. A. Chaudhari, W. Oettel, P. Fontana

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This study is a part of the EU project GEOCOND-Advanced materials and processes to improve performance and cost-efficiency of shallow geothermal systems and underground thermal storage. In heat exchange boreholes, to improve the heat transfer between the pipes and the surrounding ground, the space between the pipes and the borehole wall is normally filled with geothermal grout. Traditionally, bentonite has been a crucial component in most commercially available geothermal grouts to assure the required stability and impermeability. The investigations conducted in the early stage of this project during the benchmarking tests on some commercial grouts showed considerable sensitivity of the rheological properties of the tested grouts to the mixing parameters, i.e., mixing time and velocity. Further studies on this matter showed that bentonite, which has been one of the important constituents in most grout mixes, was probably responsible for such behavior. Apparently, proper amount of shear should be applied during the mixing process to sufficiently activate the bentonite. The higher the amount of applied shear the more the activation of bentonite, resulting in change in the grout rheology. This explains why, occasionally in the field applications, the flow properties of the commercially available geothermal grouts using different mixing conditions (mixer type, mixing time, mixing velocity) are completely different than expected. A series of tests were conducted on the grout mixes, with and without bentonite, using different mixing protocols. The aim was to eliminate/reduce the sensitivity of the rheological properties of the geothermal grouts to the mixing parameters by replacing bentonite with polymeric (non-clay) stabilizers. The results showed that by replacing bentonite with a proper polymeric stabilizer, the sensitivity of the grout mix on mixing time and velocity was to a great extent diminished. This can be considered as an alternative for the developers/producers of geothermal grouts to provide enhanced materials with less uncertainty in obtained results in the field applications.

Keywords: flow properties, geothermal grout, mixing time, mixing velocity, rheological properties

Procedia PDF Downloads 130

Authors: Yuzhen Hu, Sirui Wang

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As a training base for intellectual talents, universities have a large number of students. Teaching is a primary activity in universities, and during the teaching process, a large number of people gather both inside and outside the teaching buildings, posing a strong risk of close contact. The class schedule is the fundamental basis for teaching activities in universities and plays a crucial role in the management of teaching order. Different class schedules can lead to varying degrees of indoor gatherings and trajectories of class attendees. In recent years, highly contagious diseases have frequently occurred worldwide, and how to reduce the risk of infection has always been a hot issue related to public safety. "Reducing gatherings" is one of the core measures in epidemic prevention and control, and it can be controlled through scientific scheduling in specific environments. Therefore, the scientific prevention and control goal can be achieved by considering the reduction of the risk of excessive gathering of people during the course schedule arrangement. Firstly, we address the issue of personnel gathering in various pathways on campus, with the goal of minimizing congestion and maximizing teaching effectiveness, establishing a nonlinear mathematical model. Next, we design an improved genetic algorithm, incorporating real-time evacuation operations based on tracking search and multidimensional positive gradient cross-mutation operations, considering the characteristics of outdoor crowd evacuation. Finally, we apply undergraduate course data from a university in Harbin to conduct a case study. It compares and analyzes the effects of algorithm improvement and optimization of gathering situations and explores the impact of path blocking on the degree of gathering of individuals on other pathways.

Keywords: the university timetabling problem, risk prevention, genetic algorithm, risk control

Procedia PDF Downloads 98

Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 76

Authors: Martin Doran, Roy Sterritt, George Wilkie

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NASA, ESA, and NSSC space agencies have plans to put planetary rovers on Mars in 2020. For these future planetary rovers to succeed, they will heavily depend on sensors to detect obstacles. This will also become of vital importance in the future, if rovers become less dependent on commands received from earth-based control and more dependent on self-configuration and self-decision making. These planetary rovers will face harsh environments and the possibility of hardware failure is high, as seen in missions from the past. In this paper, we focus on using Autonomic principles where self-healing, self-optimization, and self-adaption are explored using the MAPE-K model and expanding this model to encapsulate the attributes such as Awareness, Analysis, and Adjustment (AAA-3). In the experimentation, a Pioneer P3-DX research robot is used to simulate a planetary rover. The sonar sensors on the P3-DX robot are used to simulate the sensors on a planetary rover (even though in reality, sonar sensors cannot operate in a vacuum). Experiments using the P3-DX robot focus on how our software system can be adapted with the loss of sonar sensor functionality. The autonomic manager system is responsible for the decision making on how to make use of remaining ‘enabled’ sonars sensors to compensate for those sonar sensors that are ‘disabled’. The key to this research is that the robot can still detect objects even with reduced sonar sensor capability.

Keywords: autonomic, self-adaption, self-healing, self-optimization

Procedia PDF Downloads 353

Authors: Sebastian Leal-Arenas

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TikTok’s influence on contemporary society is undeniable. The impact of the mobile app transcends entertainment, as shown by the growing presence of specialized accounts dedicated to providing educational content, particularly as it pertains to language learning. However, the prevailing trend on the platform is vocabulary and grammar acquisition, neglecting a critical component: pronunciation. This study examines English pronunciation materials available on TikTok by taking a comprehensive approach that incorporates established assessment tools, such as the Learning Object Review Instrument and the Framework for Language Learning App Evaluation. Furthermore, novel evaluation categories are introduced to provide a more holistic assessment of these educational resources. 60 English pronunciation videos were part of the analysis. The findings reveal that these audio-visual materials present clear audio bolstered by high-quality video content and automatically generated closed captions. These three components enhance the comprehensibility of the input, making these concise videos valuable assets for language learners. Nevertheless, certain deficiencies are observed, such as the lack of emphasis on specific segments and their relationship with articulators. Improvements and refinements are discussed, as well as their potential utility within the language classroom. This study contributes to the ongoing investigation of multimedia materials used for language teaching and emphasizes the need to adapt pronunciation instruction methods to today’s technology.

Keywords: pronunciation, segments, teaching materials, technology

Procedia PDF Downloads 90

Authors: Carlos Gordon, Patricio Encalada, Henry Lema, Diego Leon, Dennis Chicaiza

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The following work presents a proposal of autonomous navigation of mobile robots implemented in an omnidirectional robot Kuka Youbot. We have been able to perform the integration of robotic operative system (ROS) and machine learning algorithms. ROS mainly provides two distributions; ROS hydro and ROS Kinect. ROS hydro allows managing the nodes of odometry, kinematics, and path planning with statistical and probabilistic, global and local algorithms based on Adaptive Monte Carlo Localization (AMCL) and Dijkstra. Meanwhile, ROS Kinect is responsible for the detection block of dynamic objects which can be in the points of the planned trajectory obstructing the path of Kuka Youbot. The detection is managed by artificial vision module under a trained neural network based on the single shot multibox detector system (SSD), where the main dynamic objects for detection are human beings and domestic animals among other objects. When the objects are detected, the system modifies the trajectory or wait for the decision of the dynamic obstacle. Finally, the obstacles are skipped from the planned trajectory, and the Kuka Youbot can reach its goal thanks to the machine learning algorithms.

Keywords: autonomous navigation, machine learning, path planning, robotic operative system, open source computer vision library

Procedia PDF Downloads 181

Authors: A. Monfared, S. Faghihi

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Metallic glasses (MGs) are newcomers in the field of metals that show great potential for soft and bone tissue engineering due to the amorphous structure that endows unique properties. Up to now, various MGs based on Ti, Zr, Mg, Zn, Fe, Ca, and Sr in the form of a ribbon, bulk, thin-film, and powder have been investigated for biomedical purposes. This article reviews the compositions and biomedical properties of MGs as well as analyzes results in order to guide new approaches and future development of MGs.

Keywords: metallic glasses, biomaterials, biocompatibility, biocorrosion

Procedia PDF Downloads 218

Authors: Antonio Brigido, Maria Meireles, Francisco Barros, Gaspar Mota, Fernanda Terra, Lidia Melo, Marcelo Reis, Camilo Souza

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As the globalization of the software process advances, companies are increasingly committed to improving software development technologies across multiple locations. On the other hand, working with teams distributed in different locations also raises new challenges. In this sense, automated processes can help to improve the quality of process execution. Therefore, this work presents the development of a tool called TSS Script that automates the sample preparation process for carrier requirements validation tests. The objective of the work is to obtain significant gains in execution time and reducing errors in scenario preparation. To estimate the gains over time, the executions performed in an automated and manual way were timed. In addition, a questionnaire-based survey was developed to discover new requirements and improvements to include in this automated support. The results show an average gain of 46.67% of the total hours worked, referring to sample preparation. The use of the tool avoids human errors, and for this reason, it adds greater quality and speed to the process. Another relevant factor is the fact that the tester can perform other activities in parallel with sample preparation.

Keywords: Android, GSD, automated testing tool, mobile products

Procedia PDF Downloads 326

Authors: Emna Hlel, Salma Jamousi, Abdelmajid Ben Hamadou

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Every model for knowledge representation to model real-world applications must be able to cope with the effects of uncertain phenomena. One of main defects of classical ontology is its inability to represent and reason with uncertainty. To remedy this defect, we try to propose a method to construct probabilistic ontology for integrating uncertain information in an ontology modeling a set of basic publications DBLP (Digital Bibliography & Library Project) using a probabilistic model.

Keywords: classical ontology, probabilistic ontology, uncertainty, Bayesian network

Procedia PDF Downloads 350

Authors: Sherif Keddis, Mohamed Azzam, Norbert Schwesinger

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Harvesting mechanical energy from footsteps or other impacts is a possibility to enable wireless autonomous sensor nodes. These can be used for a highly efficient control of connected devices such as lights, security systems, air conditioning systems or other smart home applications. They can also be used for accurate location or occupancy monitoring. Converting the mechanical energy into useful electrical energy can be achieved using the piezoelectric effect offering simple harvesting setups and low deflections. The challenge facing piezoelectric transducers is the achievable amount of energy per impact in the lower mJ range and the management of such low energies. Simple setups for energy extraction such as a full wave bridge connected directly to a capacitor are problematic due to the mismatch between high impedance sources and low impedance storage elements. Efficient energy circuits for piezoelectric harvesters are commonly designed for vibration harvesters and require periodic input energies with predictable frequencies. Due to the sporadic nature of impact harvesters, such circuits are not well suited. This paper presents a self-powered circuit that avoids the impedance mismatch during energy extraction by disconnecting the load until the source reaches its charge peak. The switch is implemented with passive components and works independent from the input frequency. Therefore, this circuit is suited for impact harvesting and sporadic inputs. For the same input energy, this circuit stores 150% of the energy in comparison to a directly connected capacitor to a bridge rectifier. The total efficiency, defined as the ratio of stored energy on a capacitor to available energy measured across a matched resistive load, is 63%. Although the resulting energy is already sufficient to power certain autonomous applications, further optimization of the circuit are still under investigation in order to improve the overall efficiency.

Keywords: autonomous sensors, circuit design, energy harvesting, energy management, impact harvester, piezoelectricity

Procedia PDF Downloads 157

Authors: Naveen Kumar Perumalla, Devadas Kuna, Mohammed Akhter Ali

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Satellite Navigation System such as the United States Global Positioning System (GPS) plays a significant role in determining the user position. Similar to that of GPS, Indian Regional Navigation Satellite System (IRNSS) is a Satellite Navigation System indigenously developed by Indian Space Research Organization (ISRO), India, to meet the country’s navigation applications. This system is also known as Navigation with Indian Constellation (NavIC). The NavIC system’s main objective, is to offer Positioning, Navigation and Timing (PNT) services to users in its two service areas i.e., covering the Indian landmass and the Indian Ocean. Six NavIC satellites are already deployed in the space and their receivers are in the performance evaluation stage. Four NavIC dual frequency receivers are installed in the ‘Advanced GNSS Research Laboratory’ (AGRL) in the Department of Electronics and Communication Engineering, University College of Engineering, Osmania University, India. The NavIC receivers can be operated in two positioning modes: Stand-alone IRNSS and Hybrid (IRNSS+GPS) modes. In this paper, analysis of various parameters such as Dilution of Precision (DoP), three Dimension (3D) Root Mean Square (RMS) Position Error and Horizontal Position Error with respect to Visibility of Satellites is being carried out using the real-time IRNSS data, obtained by operating the receiver in both positioning modes. Two typical days (6th July 2017 and 7th July 2017) are considered for Hyderabad (Latitude-17°24'28.07’N, Longitude-78°31'4.26’E) station are analyzed. It is found that with respect to the considered parameters, the Hybrid mode operation of NavIC receiver is giving better results than that of the standalone positioning mode. This work finds application in development of NavIC receivers for civilian navigation applications.

Keywords: DoP, GPS, IRNSS, GNSS, position error, satellite visibility

Procedia PDF Downloads 218

Authors: Mohammed Tahir, Jonas Lagergren

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Vancron 40, a nitrided powder metallurgical tool Steel, is used in cold work applications where the predominant failure mechanisms are adhesive wear or galling. Typical applications of Vancron 40 are among others fine blanking, cold extrusion, deep drawing and cold work rolls for cluster mills. Vancron 40 positive results for cold work rolls for cluster mills and as a tool for some severe metal forming process makes it competitive compared to other type of work rolls that require higher precision, among others in cold rolling of thin stainless steel, which required high surface finish quality. In this project, three roll materials for cold rolling of stainless steel strip was examined, Vancron 40, Narva 12B (a high-carbon, high-chromium tool steel alloyed with tungsten) and Supra 3 (a Chromium-molybdenum tungsten-vanadium alloyed high speed steel). The purpose of this project was to study the depth profiles of the ironed stainless steel strips, emergence of galling and to study the lubrication performance used by steel industries. Laboratory experiments were conducted to examine scratch of the strip, galling and surface roughness of the roll materials under severe tribological conditions. The critical sliding length for onset of galling was estimated for stainless steel with four different lubricants. Laboratory experiments result of performance evaluation of resistance capability of rolls toward adhesive wear under severe conditions for low and high reductions. Vancron 40 in combination with cold rolling lubricant gave good surface quality, prevents galling of metal surfaces and good bearing capacity.

Keywords: Vancron 40, cold rolling, adhesive wear, galling, surface finish, lubricant, stainless steel

Procedia PDF Downloads 531

Authors: Doru Anastasiu Popescu, Raducanu Dragos Ionut

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In this paper the presentation of a tool which measures the similarity between two websites is made. The websites are compound only from webpages created with HTML. The tool uses three ways of calculating the similarity between two websites based on certain results already published. The first way compares all the webpages within a website, the second way compares a webpage with all the pages within the second website and the third way compares two webpages. Java programming language and technologies such as spring, Jsoup, log4j were used for the implementation of the tool.

Keywords: Java, Jsoup, HTM, spring

Procedia PDF Downloads 392

Authors: Weihao Wang, Zhulin Zong

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Two-dimensional ordered statistical constant false alarm detection is a widely used method for detecting weak target signals in radar signal processing applications. The method is based on analyzing the statistical characteristics of the noise and clutter present in the radar signal and then using this information to set an appropriate detection threshold. In this approach, the reference cell of the unit to be detected is divided into several reference subunits. These subunits are used to estimate the noise level and adjust the detection threshold, with the aim of minimizing the false alarm rate. By using an ordered statistical approach, the method is able to effectively suppress the influence of clutter and noise, resulting in a low false alarm rate. The detection process involves a number of steps, including filtering the input radar signal to remove any noise or clutter, estimating the noise level based on the statistical characteristics of the reference subunits, and finally, setting the detection threshold based on the estimated noise level. One of the main advantages of two-dimensional ordered statistical constant false alarm detection is its ability to detect weak target signals in the presence of strong clutter and noise. This is achieved by carefully analyzing the statistical properties of the signal and using an ordered statistical approach to estimate the noise level and adjust the detection threshold. In conclusion, two-dimensional ordered statistical constant false alarm detection is a powerful technique for detecting weak target signals in radar signal processing applications. By dividing the reference cell into several subunits and using an ordered statistical approach to estimate the noise level and adjust the detection threshold, this method is able to effectively suppress the influence of clutter and noise and maintain a low false alarm rate.

Keywords: two-dimensional, ordered statistical, constant false alarm, detection, weak target signals

Procedia PDF Downloads 84

Authors: B. S. Akhmetov, S. T. Akhmetova, A. I. Ivanov, T. S. Kartbayev, A. Y. Malygin

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The problem of training of a network of artificial neurons in biometric appendices is that this process has to be completely automatic, i.e. the person operator should not participate in it. Therefore, this article discusses the issues of training the network of artificial neurons and the description of the non-iterative learning algorithm of artificial neuron.

Keywords: artificial neuron, biometrics, biometrical applications, learning of neuron, non-iterative algorithm

Procedia PDF Downloads 499

Authors: Angelis P. Barlampas

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Learning Objectives: The purpose of this study is to inform briefly the reader about the applications of AI in chest radiology. Background: Currently, there are 190 FDA-approved radiology AI applications, with 42 (22%) pertaining specifically to thoracic radiology. Imaging findings OR Procedure details Aids of AI in chest radiology1: Detects and segments pulmonary nodules. Subtracts bone to provide an unobstructed view of the underlying lung parenchyma and provides further information on nodule characteristics, such as nodule location, nodule two-dimensional size or three dimensional (3D) volume, change in nodule size over time, attenuation data (i.e., mean, minimum, and/or maximum Hounsfield units [HU]), morphological assessments, or combinations of the above. Reclassifies indeterminate pulmonary nodules into low or high risk with higher accuracy than conventional risk models. Detects pleural effusion . Differentiates tension pneumothorax from nontension pneumothorax. Detects cardiomegaly, calcification, consolidation, mediastinal widening, atelectasis, fibrosis and pneumoperitoneum. Localises automatically vertebrae segments, labels ribs and detects rib fractures. Measures the distance from the tube tip to the carina and localizes both endotracheal tubes and central vascular lines. Detects consolidation and progression of parenchymal diseases such as pulmonary fibrosis or chronic obstructive pulmonary disease (COPD).Can evaluate lobar volumes. Identifies and labels pulmonary bronchi and vasculature and quantifies air-trapping. Offers emphysema evaluation. Provides functional respiratory imaging, whereby high-resolution CT images are post-processed to quantify airflow by lung region and may be used to quantify key biomarkers such as airway resistance, air-trapping, ventilation mapping, lung and lobar volume, and blood vessel and airway volume. Assesses the lung parenchyma by way of density evaluation. Provides percentages of tissues within defined attenuation (HU) ranges besides furnishing automated lung segmentation and lung volume information. Improves image quality for noisy images with built-in denoising function. Detects emphysema, a common condition seen in patients with history of smoking and hyperdense or opacified regions, thereby aiding in the diagnosis of certain pathologies, such as COVID-19 pneumonia. It aids in cardiac segmentation and calcium detection, aorta segmentation and diameter measurements, and vertebral body segmentation and density measurements. Conclusion: The future is yet to come, but AI already is a helpful tool for the daily practice in radiology. It is assumed, that the continuing progression of the computerized systems and the improvements in software algorithms , will redder AI into the second hand of the radiologist.

Keywords: artificial intelligence, chest imaging, nodule detection, automated diagnoses

Procedia PDF Downloads 75

Authors: Hossam E. Emam

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Metal–organic frameworks (MOFs) are new hybrid materials investigated from 15 years ago; they synthesized from metals as inorganic center joined with multidentate organic linkers to form a 1D, 2D or 3D network structure. MOFs have unique properties such as pore crystalline structure, large surface area, chemical tenability and luminescent characters. These significant properties enable MOFs to be applied in many fields such like gas storage, adsorption/separation, drug delivery/biomedicine, catalysis, polymerization, magnetism and luminescence applications. Recently, many of published reports interested in superiority of MOFs for functionalization of textiles to exploit the unique properties of MOFs. Incorporation of MOFs is found to acquire the textiles some additional formidable functions to be used in considerable fields such like water treatment and fuel purification. Modification of textiles with MOFs could be easily performed by two main techniques; Ex-situ (preparation of MOFs then applied onto textiles) and in-situ (ingrowth of MOFs within textiles networks). Uniqueness of MOFs could be assimilated in acquirement of decorative color, antimicrobial character, anti-mosquitos character, ultraviolet radiation protective, self-clean, photo-luminescent and sensor character. Additionally, textiles treatment with MOFs make it applicable as filter in the adsorption of toxic gases, hazardous materials (such as pesticides, dyes and aromatics molecules) and fuel purification (such as removal of oxygenated, nitrogenated and sulfur compounds). Also, the porous structure of MOFs make it mostly utilized in control release of insecticides from the surface of the textile. Moreover, MOF@textiles as recyclable materials lead it applicable as photo-catalyst composites for photo-degradation of different dyes in the day light. Therefore, MOFs is extensively considered for imparting textiles with formidable properties as ingeniousness way for textile functionalization.

Keywords: MOF, functional textiles, water treatment, fuel purification, environmental applications

Procedia PDF Downloads 151

Authors: H. Hajimolahoseini, J. Hashemi, D. Redfearn

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Background: Catheter ablation is an effective therapy for symptomatic atrial fibrillation (AF). The intracardiac electrocardiogram (IEGM) collected during this procedure contains precious information that has not been explored to its full capacity. Novel processing techniques allow looking at these recordings from different perspectives which can lead to improved therapeutic approaches. In our previous study, we showed that variation in amplitude measured through Shannon Entropy could be used as an AF recurrence risk stratification factor in patients who received Ibutilide before the electrograms were recorded. The aim of this study is to further investigate the effect of Ibutilide on characteristics of the recorded signals from the left atrium (LA) of a patient with persistent AF before and after administration of the drug. Methods: The IEGMs collected from different intra-atrial sites of 12 patients were studied and compared before and after Ibutilide administration. First, the before and after Ibutilide IEGMs that were recorded within a Euclidian distance of 3 mm in LA were selected as pairs for comparison. For every selected pair of IEGMs, the Probability Distribution Function (PDF) of the amplitude in time domain and magnitude in frequency domain was estimated using the regression analysis. The PDF represents the relative likelihood of a variable falling within a specific range of values. Results: Our observations showed that in time domain, the PDF of amplitudes was fitted to a Gaussian distribution while in frequency domain, it was fitted to a Rayleigh distribution. Our observations also revealed that after Ibutilide administration, the IEGMs would have significantly narrower short-tailed PDFs both in time and frequency domains. Conclusion: This study shows that the PDFs of the IEGMs before and after administration of Ibutilide represents significantly different properties, both in time and frequency domains. Hence, by fitting the PDF of IEGMs in time domain to a Gaussian distribution or in frequency domain to a Rayleigh distribution, the effect of Ibutilide can easily be tracked using the statistics of their PDF (e.g., standard deviation) while this is difficult through the waveform of IEGMs itself.

Keywords: atrial fibrillation, catheter ablation, probability distribution function, time-frequency characteristics

Procedia PDF Downloads 162

Authors: Hafizah Che Hasan, Fateen Nazwa Yusof, Maslina Daud

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Internet of Things (IoT) is a powerful industry system, which end-devices are interconnected and automated, allowing the devices to analyze data and execute actions based on the analysis. The IoT technology leverages the technology of Radio-Frequency Identification (RFID) and Wireless Sensor Network (WSN), including mobile and sensor. These technologies contribute to the evolution of IoT. However, due to more devices are connected each other in the Internet, and data from various sources exchanged between things, confidentiality of the data becomes a major concern. This paper focuses on one of the major challenges in IoT; authentication, in order to preserve data integrity and confidentiality are in place. A few solutions are reviewed based on papers from the last few years. One of the proposed solutions is securing the communication between IoT devices and cloud servers with Elliptic Curve Cryptograhpy (ECC) based mutual authentication protocol. This solution focuses on Hyper Text Transfer Protocol (HTTP) cookies as security parameter.  Next proposed solution is using keyed-hash scheme protocol to enable IoT devices to authenticate each other without the presence of a central control server. Another proposed solution uses Physical Unclonable Function (PUF) based mutual authentication protocol. It emphasizes on tamper resistant and resource-efficient technology, which equals a 3-way handshake security protocol.

Keywords: Internet of Things (IoT), authentication, PUF ECC, keyed-hash scheme protocol

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Authors: Adedayo Ajayi, Patrick Luk, Liyun Lao

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This paper studies the supportive needs for the electrical infrastructure of the green airport. In particular, the core objective revolves around the choice of electric grid configuration required to meet the expected electrified loads, i.e., the taxiing and charging loads of hybrid /pure electric aircraft in the airport. Further, reliability and resilience are critical aspects of a newly proposed grid; the concept of mobile energy storage as energy as a service (EAAS) for grid support in the proposed green airport is investigated using an autonomous electric taxiing robot (A-ETR) at a case study (Cranfield Airport). The performance of the model is verified and validated through DigSILENT power factory simulation software to compare the networks in terms of power quality, short circuit fault levels, system voltage profile, and power losses. Contingency and reliability index analysis are further carried out to show the potential of EAAS on the grid. The results demonstrate that the low voltage a.c network ( LVAC) architecture gives better performance with adequate compensation than the low voltage d.c (LVDC) microgrid architecture for future green airport electrification integration. And A-ETR can deliver energy as a service (EaaS) to improve the airport's electrical power system resilience and energy supply.

Keywords: reliability, voltage profile, flightpath 2050, green airport

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Authors: Kafuko M. Maria, Namisango Fatuma, Byomire Gorretti

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Mobile learning (m-learning) is steadily growing and has undoubtedly derived benefits to learners and tutors in different learning environments. This paper investigates the variation in benefits derived from enhanced classroom learning through use of m-learning platforms in the context of a developing country owing to the fact that it is still in its initial stages. The study focused on how basic technology-enhanced pedagogic innovation like cell phone-based learning is enhancing classroom learning from the learners’ perspective. The paper explicitly indicates the opportunities presented by enhanced learning to a conventional learning environment like a physical classroom. The findings were obtained through a survey of two universities in Uganda in which data was quantitatively collected, analyzed and presented in a three banded diagram depicting the variation in the obtainable benefits. Learners indicated that a smartphone is the most commonly used device. Learners also indicate that straight lectures, student to student plus student to lecturer communication, accessing learning material and assignments are core activities. In a TEL environment support by smartphones, learners indicated that they conveniently achieve the prior activities plus discussions and group work. Learners seemed not attracted to the possibility of using TEL environment to take lectures, as well as make class presentations. The less attractiveness of these two factors may be due to the teacher centered approach commonly applied in the country’s education system.

Keywords: technology enhanced learning, m-learning, classroom learning, perceived benefits

Procedia PDF Downloads 233

Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 80

Authors: Ertuğ Yıldırım, Dile Kara, Salih Zeki Yıldız

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Metal containing polymers (MCPs) are macro molecules usually containing metal-ligand coordination units and are a multidisciplinary research field mainly based at the interface between coordination chemistry and polymer science. The progress of this area has also been reinforced by the growth of several other closely related disciplines including macro molecular engineering, crystal engineering, organic synthesis, supra molecular chemistry and colloidal and material science. Schiff base ligands are very effective in constructing supra molecular architectures such as coordination polymers, double helical and triple helical complexes. In addition, Schiff base derivatives incorporating a fluorescent moiety are appealing tools for optical sensing of metal ions. MCPs are well-known systems in which the combinations of local parameters are possible by means of fluoro metric techniques. Generally, without incorporation of the fluorescent groups with polymers is unspecific, and it is not useful to analyze their fluorescent properties. Therefore, it is necessary to prepare a new type epoxy polymers with fluorescent groups in terms of metal sensing prop and the other photo chemical applications. In the present study metal containing polymers were prepared via poly functional monomeric Schiff base metal chelate complexes in the presence of dis functional monomers such as diglycidyl ether Bisphenol A (DGEBA). The synthesized complexes and polymers were characterized by FTIR, UV-VIS and mass spectroscopies. The preparations of epoxy polymers have been carried out at 185 °C. The prepared composites having sharp and narrow excitation/emission properties are expected to be applicable in various systems such as heat-resistant polymers and photo voltaic devices. The prepared composite is also ideal for various applications, easily prepared, safe, and maintain good fluorescence properties.

Keywords: Schiff base ligands, crystal engineering, fluorescence properties, Metal Containing Polymers (MCPs)

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Authors: Getaneh Berie Tarekegn, Li-Chia Tai

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With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarms, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Bahieddine. Bouabdellah, Benameur. Amiri, Abdelkader.nouri

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Europium-doped gallium nitride (EuGaN) is a promising material for optoelectronic and thermoelectric devices. This study investigates its optical properties using density functional theory (DFT) with the FP-LAPW method and MBJ+U correction. The simulation substitutes a gallium atom with europium in a hexagonal GaN lattice (6% doping). Distinct absorption peaks are observed in the optical analysis. These results highlight EuGaN's potential for various applications and pave the way for further research on rare earth-doped materials.

Keywords: eugan, fp-lapw, dft, wien2k, mbj hubbard

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Authors: Khalid Ali, Manar Jammal

Abstract:

In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity

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Authors: Jaroslava Sucha, Martin Dolejs, Helena Pipova, Panajotis Cakirpaloglu

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The paper introduces the research project which is focused on evaluating the level of pathological relation towards computer or video games playing (including any games played by using a screen such as a mobile or a tablet). The study involves representative sample of the Czech adolescents between ages 11 and 19. This poster presents the psychometric indicators of the new psychologic assessment method (mean, standard deviation, reliability, validity) which will be able to detect an acceptable level of games’ playing and at the same time will detect and describe the level of gaming which might be potentially risky. The prevalence of risky computer game playing at Czech adolescents in age 11 to 19 will be mentioned. The research study also aims to describe the personality profile of the problematic players with respect to the digital games. The research area will encompass risky behaviour, aggression, the level of self-esteem, impulsivity, anxiety and depression. The contribution will introduce a new test method for the assessment of pathological playing computer games. The research will give the first screening information of playing computer games in the Czech Republic by adolescents between 11-19 years. The results clarify what relationship exists between playing computer games and selected personality characteristics (it will describe personality of the gamer, who is in the category of ‘pathological playing computer games’).

Keywords: adolescence, computer games, personality traits, risk behaviour

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Authors: Tempe S. F. Adams, Michael J. Chase, Tracey L. Rogers, Keith E. A. Leggett

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Transfrontier wildlife corridors can be successful conservation tools, connecting protected areas and reducing the impact of habitat fragmentation on mobile species. Urban wildlife corridors have been proposed as a potential mitigation tool to facilitate the passage of elephants through towns without causing conflict with urban communities. However, because such corridors are typically narrow and close to human development, wildlife (particularly large mammals) may be less likely to use them. We used remote-sensor camera traps and global positioning system collars to identify the movement patterns of African elephants Loxondonta africana through narrow, urban corridors in Botswana. The corridors were in three types of human-dominated land-use designations with varying levels of human activity: agricultural, industrial and open space recreational land. We found that elephants used the corridors within all three land-use designations and we identified, using a model selection approach, that season, time of day and rainfall were important factors in determining the presence of elephants in the corridors. Elephants moved more slowly through the narrow corridors compared with their movement patterns through broader, wide-ranging corridors. Our results indicate that urban wildlife corridors are useful for facilitating elephants to pass through urban areas.

Keywords: camera traps, conservation, human-elephant coexistence, land management, urban wildlife corridors

Procedia PDF Downloads 307

Authors: Sogand Barghi

Abstract:

The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

Procedia PDF Downloads 77