Volume 7, Issue 6

Before succumbing to the 2019 Coronavirus pandemic, information and communication technologies (ICTs) have sustained a ubiquitous presence in human lives and society. ICTs have changed the standards and dynamics of educational practices (EPs). Many academic institutions had already integrated technological-based pedagogical instructions into their educational practices but, in various cases, faced challenges of failing to consider the perceptions of chief users, students, teachers, and subjective norms. This paper is an extension of work originally presented at the 2022 11th International Conference on Education and Information Technology (ICEIT). This paper aims to demonstrate and provide future directions regarding the effects of ICTs and how such usage proliferates and disharmonizes learning and teaching experiences and academic achievement. The expanded version of the technology acceptance model (TAM2) is the theoretical foundation for this research. TAM2 provides insight into how the perceptions of students and teachers matter when adopting and using ICTs in educational practices. Depending on these perceptions of perceived ease of use and usefulness, using ICTs in educational practices can impact intentional and behavioral use, currently and futuristically. Subjective norms also influence individuals’ perceptions and willingness to use ICTs for educational practices. Limitations, strengths, and future recommendations and directions are discussed.

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Hackers on the Internet often send attacking commands through compromised hosts, called stepping-stones, for the purpose to be hidden behind a long interactive communication session. In a stepping-stone attack, an intruder uses a chain of stepping-stones as relay machines and remotely login these machines using a remote login program such as SSH (secure shell). A great number of detection methods for SSI have been proposed since 1995. Many of these existing detection approaches are either not easy to implement, or not efficient as a great number of packets have to be monitored and analyzed. Some of these detection methods for SSI are even not effective as their capabilities to detect SSI are very limited. In this paper, we propose an effective detection method for SSI by using packet crossover. Packet crossover ratios can be easily computed, and thus our proposed detection method for SSI cannot only be easily implemented, but also efficient. Well-designed network experiments are conducted and the effectiveness of the developed SSID algorithm is verified through the experiments.

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The efficient processing of database applications on computing systems with multi-tiered persistent storage devices needs specialized algorithms to create optimal persistent storage management plans. A correct allocation and deallocation of multi-tiered persistent storage may significantly improve the overall performance of data processing. This paper describes the new algorithms that create allocation and deallocation plans for computing systems with multi-tiered persistent storage devices. One of the main contributions of this paper is an extension and application of a notation of Petri nets to describe the data flows in multi-tiered persistent storage. This work assumes a pipelined data processing model and uses a formalism of extended Petri nets to describe the data flows between the tiers of persistent storage. The algorithms presented in the paper perform linearization of the extended Petri nets to generate the optimal persistent storage allocation/deallocation plans. The paper describes the experiments that validate the data allocation/deallocation plans for multi-tiered persistent storage and shows the improvements in performance compared with the random data allocation/deallocation plans.

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In complex swarm robotic applications that perform different tasks such as transportation and observation, robot swarms should construct and maintain a formation to adapt and move as a single large-scale robot. For example, transportation and observation tasks require unique robot swarms with either high densities to support the weight of the transported objects or low densities to avoid overlapping field of views and avoid obstructions. Previous literature has not focused on structure optimization because swarming provides a large-collective capability. This paper proposes a leader-follower-controlled collective movement method by calculating direction and distance potentials between robots based on geometric constraints, constricting robot positioning along radial gradients around the leader robot according to these potentials. This paper demonstrates a robot swarm applying the proposed method while maintaining formations with different densities while moving and evaluates the robot swarm structure-maintaining performance.

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This paper presents a new software for stereometry training. Its name is StereoMV (Stereo Math Vision). It is results from a dissertation work on the topic “Stereoscopic Training System”. It introduces virtual reality systems that are both immersive and non-immersive. The difference between them is in the equipment and the stereo effect they offer. A comparative analysis is performed between the virtual environments that are part of the software. 3D visualization is of four types: traditional, 3D active, 3D passive, and through immersive systems such as CAVE (Cave Automatic Virtual Environment) and HMD (Head Mounted Display). There are also four visualization modes in Java3D. Mixed Immediate Mode is used to implement stereoscopic projection using 3D active technology. Passive technology uses anaglyph glasses with a red and blue filter. Traditional visualization can be implemented from any computer. The system’s most important function is exporting the objects in a file with the extension .obj. All this is thanks to a Java 3D library and a new boundary method involved in generating geometric objects, which sets the program apart from existing software solutions for training in the discipline of stereometry. The software can be used for stereo visualization of random 3D models from various scientific fields. Unlike planimetry in stereometry, a three-dimensional drawing would be much more difficult to understand, and that’s why 3D technology comes to the rescue. This way, the student’s interest will be strengthened, from where their spatial thinking will be further developed.

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The rapid advancement of the Internet of Things (IoT) is distinguished by heterogeneous technologies that provide cutting-edge services across a range of application domains. However, by eavesdropping on encrypted WiFi network traflc, attackers can infer private information such as the types and working status of IoT devices in a business or residential home. Moreover, since attackers do not need to join a WiFi network, such a privacy attack is very easy for attackers to conduct while at the same time invisible and leaving no trace to the network owner. In this paper, we extend our preliminary work originally presented at the CCNC’22 conference by using a new set of time series monitored WiFi data frames with extended machine learning algorithms. We instrument a testbed of 10 IoT devices and conduct a detailed evaluation using multiple machine learning techniques for fingerprinting, achieving high accuracy up to 95% in identifying what IoT devices exist and their working status. Compared with our previous work in , the new approach could achieve IoT device profiling much quicker while maintaining the same level of classification accuracy. Moreover, the experimental results show that outside intruders can significantly harm the IoT devices without joining a WiFi network and can launch the attack within a minimum time without leaving any detectable footprints.

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In recent years, unmanned aerial vehicles (UAVs), especially small and light multicopters driven by electrical motors and batteries, have experienced a boom in applications. The electrical drive system is a central component of these UAVs. This paper introduces the basics of these drives and presents control methods for them using permanent magnet synchronous motors (PMSMs). Control of these drives is based on field-oriented control (FOC) optimised for high speed (for instance, 200 el. krpm). For the multicopter drives, sensorless control is preferred, i.e., no position or speed sensor on the motors is necessary. Therefore, in this paper, the rotor position is estimated by a sensorless method based on a back electromotive force (back emf) observer combined with a start-up process. The parametrisation methods of the observer and the start-up process are described as well. The observer and the integration of it in multicopter drives are the major innovative parts of this paper. These introduced methods are verified by simulation and experiments. In experiments two motors are considered. One is applied to operate at the maximal speed up to more than 200 el. krpm. The other is a special UAV drive motor and applied for experiments with propeller, under similar operating conditions as UAVs. The results prove the performance and effectiveness of the introduced methods.

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Emotion is the most important component of being human, and very essential for everyday activities, such as the interaction between people, decision making, and learning. In order to adapt to the COVID-19 pandemic situation, most of the academic institutions relied on online video conferencing platforms to continue educational activities. Due to low bandwidth in many developing countries, educational activities are being mostly carried out through audio interaction. Recognizing an emotion from audio interaction is important when video interaction is limited or unavailable. The literature has documented several studies on detection of emotion in Bangla text and audio speech data. In this paper, ensemble machine learning methods are used to improve the performance of emotion detection from speech data extracted from audio data. The ensemble learning system consists of several base classifiers, each of which is trained with both spontaneous emotional speech and acted emotional speech data. Several trials with different ensemble learning methods are compared to show how these methods can yield an improvement over traditional machine learning method. The experimental results show the accuracy of ensemble learning methods; 84.37% accuracy was achieved using the ensemble learning with bootstrap aggregation and voting method.

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The knowledge and understanding of the technology of quantum computers and their superiority over classical computers are still insufficient or uncertain for many communities of researchers, manufacturers, investors and the general public. For this reason, we try in this article to present and explain some of the basic concepts of quantum computers. We explain how the quantum phenomena may be employed to conceive a quantum computer by defining the qubit that will represent the data entity corresponding to the bit in the classical computer and how this computer can effectively be powerful. We address the issue of strengthening the information system security through a simulation of a spy hunter and the importance of image processing security using the quantum computer, which will minimize the data processing time regardless of the amount of data to be processed. The security of the images will lead us to introduce the new prospects of using multilevel systems instead of binary systems, which will exponentially increase the gain in the size of the memory used.

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The paper aims to describe a new technological and organizational approach in order to manage teleconsultation and telemonitoring processes involving a Physician, who remotely interacts with one or more Specialists, in order to evaluate and discuss the specific clinical conditions of a patient, based primarily on the sharing of digital clinical data, reports and diagnostic images. In the HINT project (Healthcare INtegration in Telemedicine), a teleconsultation and telemonitoring cloud platform has been developed using a Hub and Spoke architecture, based on a Business Process Management System (BPMS). The specialized clinical centres (Hubs) operate in connection with the territorial hospital centres (Spokes), which receive specific diagnostic consultations and telemonitoring data from the appropriate Specialist, supported by advanced AI systems. The developed platform overcomes the concepts of a traditional and fragmented teleconsultation and consequently the static organization of Hubs and Spokes, evolving towards an integrated clinical workflow management. The project platform adopts international healthcare standards, such as HL7 FHIR, IHE (XDS and XDW) and DICOM for the acquisition and management of healthcare data and diagnostic images. A Workflow Management System implemented in the platform allows to manage multiple and contemporaneous processes through a single platform, correctly associating the tasks to the Physicians responsible for their execution, monitoring the status of the health activities and managing possible clinical issues.

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Having reasonable inventory control is a priority for any company because a lack of inventory could incur economic losses. Not having the necessary stock for the timely production of your orders could generate dissatisfaction in your customers and possibly cause them to lose them. Likewise, reasonable inventory control allows quick decision-making for the company’s benefit. The study’s objective was to improve the management of the request process, and quality control of the raw material of a textile supplier exporter of Peruvian alpaca yarn and an exporter of the textile company focused on transforming alpaca fiber and other natural fibers into high-value-added products. It was redesigned under the Process Management approach, developing and implementing software to control raw materials and requests following the Scrum framework and using the Oracle Apex tool. The results obtained with the new system were very positive, increasing workers’ productivity, eliminating manual tasks and calculations, avoiding confusion in the allocation of material to scheduled orders, and the response time of the delivery date of orders and, therefore, customer satisfaction. Concluding that, it was possible to lower from an initial 30% to 0.08% of the requests answered in a period greater than 48 hours with the application of the developed system that allows rapid decision-making.

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Many consequences can be resulted in mismanagement of crowd, which might get people injured or even lose their lives. Thus, crowd management helps in controlling overcrowded areas during events, and allowing authorities to monitor, manage and reduce incidents. Space science and technology have made huge leap in crowd management, let alone when this technology integrated with Artificial intelligent. Hence, space-based systems like CubeSats are seen as the best approach to help monitoring and providing various reasons. For instance, wide coverage footprint, collect specific area data simultaneously, assist in aerial photography, as well as providing wide range of wireless communications services that can integrate appropriately with AI and wearable devices. This work aims to design a CubeSat vehicle to manage crowds during short-term, large-scale events. The proposed system is, also, coupled with AI framework along with the camera and wearable devices to monitor the crowds’ performance continuously by relying on two aspects: Firstly, aerial imaging (e.g., photos and videos). Secondly, using wearable devices that can be worn to monitor vital signs of crowds. Moreover, the proposed system can independently relieve congestion, as well as notify the ground controller of all problems to take further actions. The obtained results confirm that the proposed innovative solution is for crowd management with accuracy reached 95%, and MSE equal to 0.049.

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This paper is an extension of the work originally presented in the 26th International Con- ference on Automation and Computing. This study regarding hardware prefetching aims at concealing cache misses, leading to maximizing the performance of modern processors. This paper leverages prefetch coverage improvement as a way to achieve the goal. Original work proposes two different storage buffers to enhance prefetch coverage; block offset buffer and block address buffer. The block offset buffer updates its contents with the offsets of a cache block accessed, while the block address buffer contains the address of a cache block prefetch- issued. The offset buffer is utilized to speculate a local optimum offset per page. The offset buffer is proposed to adopt multiple lengths of delta history in observing offset patterns from completely trained table. This paper advances to employ incompletely trained table as well, while in other prefetching methods including original work, only completely trained candidates are utilized. Furthermore, we construct the table on the fly. Rather than using only completely built tables, we offer utilizing and updating table concurrently. This paper also proposes a re- fined metric from existing prefetch accuracy metric, to measure net contribution of a prefetcher. Compared to the original work, we have 2.5% and 3.8% IPC speedup increment with single- and 4-core configuration, respectively, in SPEC CPU 2006. In SPEC CPU 2017, our work achieves 4.5% and 5.5% IPC speedup improvement with single- and 4-core configuration, re- spectively, over the original work. Our work outperforms the 2nd best prefetcher, PPF, by 2.9% and 2.7% IPC speedup with single- and 4-core configuration, respectively, in SPEC CPU 2006. In SPEC CPU 2017, our work surpasses both Berti by 1% and SPP by 2.1% IPC speedup with 4-core configuration in SPEC CPU 2017.

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The constant evolution of Information and Communication Technologies, Internet, access to different free software, among others; they generate problems in the management of information security in companies; to mitigate risks, vulnerabilities, and information threats, an alternative was presented considering that information security systems are the basis for decision-making at the government, strategic, tactical, and operational levels. The objective is to design a security prototype applied to business management to mitigate risks, vulnerabilities and threats to information. The deductive method and exploratory research were used for the analysis of the information. Turned out prototypes that allow mitigating risks, vulnerabilities and threats in information management for data control and integrity. It was concluded that the security prototype proposed for a commercial information system; it is security system suitable for public and private companies. In the simulation carried out, it was determined that if the number of risks and threats is high, there will be a greater probability that a problem will arise in the security of the system.

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Process mining is an innovative technique through which inefficiencies in production systems can be eliminated. This technique has therefore become very important internationally in recent years and is also useful for pursuing the improvement of production systems by extrapolating process knowledge from event logs recorded by information systems. Process mining has easy application in production systems as current business processes are integrated with information systems and this makes data available immediately. This makes the complex nature of industrial operations understandable and, for this reason, process mining could also be used in the healthcare field where cost containment and the quality of service increasing offered to the community has become paramount. The problem is that in this sector, it is much more complex to identify useful data in order to extrapolate the relevant log file. The article aim is to examine the state of the art of the application of process mining in the healthcare sector in order to understand the level of diffusion of these techniques. In the light of this analysis, a case study will then be analysed on the application of process mining techniques in the healthcare sector, and in particular in medical teleconsultation in the field of neuroradiology.

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Seto Inland Sea is situated between Japanese islands Honshu, Kyushu, and Shikoku. It is separated from the ocean by the Bungo Channel, Kii Channel, and other narrow straits around Shikoku Island. The objective of the article is to draw the attention to the question of how well the coastal population and infrastructure of those locations are protected against a tsunami wave appearing in the area of Nankai through offshore Japan and consequently make informed decision about strengthening the protections if needed. This question is critical because strong underwater earthquakes are expected according to the major earthquake repeatability in this subduction zone. Hence, the influence of strait width and tsunami wave period to the wave amplitude change after passing a strait was studied through the application of numerical modelling. Both models of geometry of computation domain with the flat bottom and the real bathymetry of the area under study were used. Numerical modelling was based on nonlinear shallow water system, commonly used in tsunami related studies. All calculations were made at a personal computer equipped with the hardware code accelerator – FPGA (Field Programmable Gates Array) Calculator, which had been recently introduced by the authors. A series of computational experiments, both with model straits and in the water area around southern part of Japan, have shown that when a tsunami wave passes through the strait, its height is significantly reduced, providing better tsunami safety regulations of the population of the inner seas separated from the ocean by narrow straits. At the same time, longer tsunami waves retain a larger amplitude after passing through the strait compared to shorter ones. Result of the paper consists of numerical study of the influence of narrow straits on the maximal heights of tsunami wave. Qualitative corollaries between length of tsunami and reduction rate of the wave amplitude after passing a strait should help warning services to properly evaluate tsunami danger in water areas separated from tsunami source by a narrow strait.

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With the significant spread of 5G telecommunication systems, the demand for high quality of service and better coverage is growing rapidly. Multiple-input–multiple-output is planned to be among the key technologies in 5G telecommunication’s field. In this article, a new fully digital 2×2 MIMO testbed is implemented, using USRP B210 and 2 pairs of microstrip antennas with 4 radiation elements. In this implementation we cover the reconfigurability of the USRP combined with MATLAB, providing flexible integration between real-time and software host processing.

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The South African government has embarked on the implementation of the National Health Insurance (NHI), to increase access to healthcare and improve service delivery. However, the project has being encountering various challenges, which are not empirically known. The aim of the study is to identify the factors that influence the implementation of the NHI from ICT readiness assessment perspective. Qualitative data was collected from communities and healthcare service providers including government as the legislative body. The duality of structure of structuration theory was used as lens to guide the analysis of the study, from ICT readiness assessment and perspective. From the analysis, four factors, current state, areas of coverage, role of ICT, role of human are found to influence the implementation of the NHI. The originality of the findings lies on the empirical nature of the study.

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Bird watchers and people obsessed with raising and taming birds make a kind of motivation about our subject. It consists of the creation of an Android application called ”Birds Images Predictor” which helps users to recognize nearly 210 endemic bird species in the world. The proposed solution compares the performance of the python script, which realizes a convolutional neural network (CNN), and the performance of the cloud-bound mobile application using IBM’s visual recognition service to choose the platform one. android form. In the first solution we presented an architecture of a CNN model to predict bird class. While the other solution, which shows its effectiveness, is based on IBM’s visual recognition service, we connect the IBM project that contains the training images with our Android Studio project using an API key , and the IBM process classifies the image captured or downloaded from the application and returns the prediction result which indicates the type of bird. Our study highlights three major advantages of the solution using IBM’s visual recognition service compared to that of CNN, the first appears in the number of images used in training which is higher compared to the other and the strong distinction between bird types where images and bird positions come together in color. The second advantage is to create a trained model saved on the cloud in order to use it with each prediction the most difficult thing to do locally due to the low processing capacity of smartphones. The last advantage is reflected in the correct prediction with a certainty of 99% unlike the other solution due to the instability of the CNN model.

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This paper presents the results of a documentary research on the use of information technology in emergency remote teaching in 66 higher educational institutions in Brazil. The theoretical background of this study is based on the works of Feenberg, Bagglaey, Veloso & Mill, Castañeda & Selwyn and Hodges. The methodological approach consisted of analyzing reports published by YDUQS, an educational holding responsible for managing all the 66 institutions examined in this research. Such analysis aimed at identifying data concerning investments in information technology and its use throughout the Covid-19 pandemic. Results have revealed that investment in information systems as well as technological mediation of academic routines and pedagogical practices paved the way for a rapid response to the crisis triggered by the pandemic and the maintenance of student satisfaction. Nevertheless, the data available within the reports was not enough to draw conclusions on learning management neither on other pedagogical aspects of emergency remote teaching.

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This study aims to develop and validate a biological food preference task that simultaneously evaluates biological responses to visual stimuli of various food states and subjective evaluations of foods and to examine how these biological responses are related to food preference behavior. We recruited seventeen healthy male and female subjects to observe changes in cerebral blood flow related to salivation and the prefrontal cortex region while performing a food preference task related to visual stimuli of various food states. We also examined the relationship between these changes and the subjects’ subjective evaluations. The results showed that subjective evaluations of the various states of visual stimuli differed from subjective evaluations of the different food states. Furthermore, comparing the hemodynamic response function of cerebral blood flow to each visual stimulus, we observed a trend of activation of brain activity in the prefrontal and parotid regions during task execution. In addition, correlations were calculated between the subject’s subjective evaluation and cerebral blood flow in the prefrontal and parotid regions and between cerebral blood flow in the prefrontal and parotid regions, and significant differences were observed. Our findings demonstrate the potential of combining the evaluation of food in different states with cerebral blood flow indices in biological responses to visual cues of food.

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Alzheimer’s disease has proven to be the major cause of dementia in adults, making its early detection an important research goal. We have used Ensemble ELMs (Extreme Learning Models) on the OASIS (Open Access Series of Imaging Studies) data set for Alzheimer’s detection. We have explored various single layered light-weight ELM networks. This is an extension of the conference paper submitted on implementation of various ELMs to study the difference in the timing of execution for classification of Alzheimer’s Disease (AD) Data. We have implemented various ensemble ELMs like Ridge, Bagging, Boosting and Negative correlation ELMs and a comparison of their performance on the same data set is provided.

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While it is commonly accepted that climate change needs to be addressed to protect both human and environmental health, it is not widely understood what steps need to be taken to accomplish this daunting task. Additionally, there is currently no formal definition of what constitutes a ‘green’ company or ‘green’ best practice, despite the rising usage of the term. We found that companies that are considered ‘green’ have well-documented, quantifiable improvements in their sustainability plans and initiatives. These plans are published yearly in publicly available progress reports. Multi-year goals, with progress mapped from year to year, follow trends in the following areas: reduction in carbon emissions, energy obtained through renewable energy sources, amount of waste diverted from landfills, third-party certifications for buildings, water conservation, increasing ‘green’ requirements from suppliers, and sustainable fleet management. To address the gap between industry and government practices, and to capitalize on recent interest and investment in ‘green’, we recommend that all U.S. government agencies formalize and publicly release sustainability policies with quantifiable goals, identify practices to be implemented, and define metrics to measure progress. To effectively develop and implement these plans, we recommend: (1) each agency evaluate their current organization to develop a baseline, (2) define milestones and targets using the baseline as a starting point such that industry standards can be reached, and (3) release a finalized, publicly available sustainability plan.

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This study investigated the vulnerabilities of three operating systems: Windows 10, macOS, and Ubuntu. The analysis of secondary data obtained from the CVE and NVD databases for the study period demonstrates varying OS vulnerability. Quantitative assessment of the vulnerability (using the vulnerability score) for the investigated operating systems found consistent results in the security vulnerability of these OS. The correlation of the disclosed vulnerabilities data and the average weighted vulnerability yielded coefficients of -0.3674, -0.4081, and 0.3473 for macOS, Windows 10, and Ubuntu Linux. These results demonstrate windows 10 as having the highest security vulnerability, followed by macOS. Ubuntu Linux had the lowest vulnerability scores. These results were validated by the CVSS distribution of the vulnerability score. The results point to the impact of the popularity of OS on the number of attacks in a given period. OS used by many people tend to attract significant attacks testing their integrity, security, and safety.

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Massive integration of inverter dominated renewable energy systems (RESs), i.e., wind turbines (WTs), reduces the reliance on conventional alternator-based power stations. The system inertia and damping aspects of the power system were significantly decreased by this extensive integration of inverter-based WT system, which impacts on the fault ride-through (FRT) competence and thus precipitates the frequency instability. Modern grid code instructed to operate the WT system similar like conventional power plants. However, most of the conventional inverter controller failed to fulfil the requirement. To compensate for the issues, an advanced control method of a VSG for variable speed wind turbines with a permanent magnet synchronous generator (VSWT-PMSG) is proposed by this work. The proposed control scheme mimics the behavior of a conventional alternator and includes an active-power frequency control scheme with a governor model accompanied by an automatic voltage regulator (AVR) model, along with a current feedback loop system which enhance the system inertia and consider damping aspects of the system during serious fault conditions, i.e., three line to ground (3LG) fault. The suggested VSG-based inverter controller’s functionality has been verified using the simulation model.

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We are interested in taking into account ambiguity in the analysis phase of data warehouses, using fuzzy logic. We want to offer decision makers the possibility of using natural language in this phase. We created in a previous work the Baccalaureate fuzzy data warehouse which we were able to query with seven natural language terms to which we created seven membership functions. In this work, we present a fuzzy data warehouse for server failures that we created and for which we used the same terms to which we associated seven membership functions too. And, we carried out a comparison at the end of which we concluded that the definition of the values of the membership function differs according to the context of analysis. Our solution is extensible and can be enriched with new natural terms language. The next step is to design a conversational interface that enables a natural language conversation between the decision maker and the fuzzy data warehouse.

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In Senegal, agriculture has always been seen as the foundation on which the socioeconomic development of the country rests. However, in the rural world, agriculture remains traditional at a time when the challenges of food self-sufficiency to accompany emergence are launched. In the southern part of Senegal commonly called Casamance, the abundance of rain makes it possible to practice rice cultivation and market gardening research must therefore play a leading role in the introduction of technological innovations, techniques, and decision-support tools to promote productive, competitive, and sustainable agriculture. Therefore, smart agriculture must focus on new solutions for water irrigation, soil quality, and culture monitoring. The emergence of the Internet of Things (IoT) is perceived as a very important lever for successful high-end intelligent agriculture. Indeed, the appearance of increasingly specialized monitoring sensors combined with new wireless communication technologies constitutes good decision-making tools.
The proposal of this paper consists of a new network architecture that can cover a large cultivation area to carry out water irrigation techniques in Casamance. It is, therefore, a question of identifying the best communication technology among new Low-Power, Wide Area Networks (LPWANs) such as Long-Range (LoRa), SigFox, etc which is suited to the environment considered. Also, the choice of the best deployment of sensors for better coverage. The choice of technology must be motivated by the financial costs and the range of transmission. The deployment must fix the optimal distance between the sensors minimizing the interferences according to some parameters specific to the environment. An analytical study is used on the deployment to determine the optimal distance between two gateway nodes to reduce induced interference.

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To reduce the time necessary for determination of tsunami source parameters it is proposed to optimize the location of sensors system and to use only a part of the measured wave profile. Based on computation, it is possible to balance the number of sensors in use and the time period after the earthquake needed to obtain tsunami wave parameters. Numerical experiments show that even a part of the wave period (compared to ¼ of the entire period) provides enough information to get the wave amplitude within the well-known concept of calculation in advance. This is due to the application of Fourier theory in the form of orthogonal decomposition of the measured wave profile. It is important that the proposed algorithm requires only a few seconds using regular personal computer. Using the real depth profile offshore Japan we compute the time required to calculate the wave amplitude (with 10 percent accuracy) in case of one, two and three sensors. Optimization could be performed in terms of minimal time required to get the wave profile. It is also possible to calculate the sensor network design, which provides the maximal time between wave parameters determination and the wave approaching nearest coast. The new feature here observed is that optimal positions of sensors are different if one needs minimizing time to detect tsunami wave or maximazing time it takes the wave to approach the nearest cost after recovering the wave parameters at source. This may require to rearrange decision making at tsunami warning centers.

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Aging is considered to be a complex process in almost every species’ life, which can be studied at a variety of levels of abstraction as well as in different organs. Not surprisingly, biometric characteristics from facial images play a significant role in predicting human’s age. Specifically, automatic age estimation in real-time situation has begun to affirm its position as an essential process in a vast variety of applications. In this paper, two approaches are addressed as solutions for such application: prediction of accurate age and age group by using the two most fundamental techniques in the domain of deep learning – convolutional neural networks (CNNs) and deep neural networks (DNNs). In summary, this work can be split into two main key contributions. By applying a novel hierarchical aggregation built on the base of neural network developed from the training dataset, in the first stage, features extraction, the convolutional activation features are extracted from the captured facial image. As soon as this part is done, the features classification step is performed, in which Softmax Regression (SR) and majority vote classifiers are applied to predict accurate age and age group respectively. The effectiveness of the designed model was showed satisfactorily in the experimental results, which emphasizes the promising of the solution and indicates another direction for future development of algorithms and models in the field of machine learning.

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Knowledge of pedestrian and vehicle movement patterns can provide valuable insights for city planning. Such knowledge can be acquired via passive outdoor localization of WiFi-enabled devices using measurements of Received Signal Strength Indicator (RSSI) from WiFi probe requests. In this paper, which is an extension of the work initially presented in WiMob 2021, we continue the work on the mobility intelligence system (MobIntel) and study two broad approaches to tackle the problem of RSSI-based passive outdoor localization. One approach concerns multilateration and fingerprinting, both adapted from traditional active localization methods. For fingerprinting, we also show flaws in the previously reported area-under-the-curve method. The second approach uses machine learning, including machine learning-boosted multilateration, reference point classification, and coordinate regression. The localization performance of the two approaches is compared, and the machine learning methods consistently outperform the adapted traditional methods. This indicates that machine learning methods are promising tools for RSSI-based passive outdoor localization.

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In recent years the development of autonomous vehicles has increased tremendously and a variety of methodologies had been applied to make them more safe and secure. This work shows a multilevel approach combining Failure Mode, Effects and Criticality Analysis of an autonomous railway system with sociological and technical aspects to support safe operations and human-machine interactions in the field of autonomous railway systems. This approach includes all relevant technical components, as well as the assessment of measures for a safety process based on the Failure Mode, Effects and Criticality Analysis. We applied the Persona- Roberta model to assess safety aspects at the interface between humans and machines and applied both results to establish training materials. The results provide answers to questions about the avoidance of technical errors, discussions on security and safety aspects and shows organizational development tools for accident prevention. In the future the created knowledge will be used to improve trust in digital solutions and Cyber-Physical Systems.

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