Special Issue on Innovation in Computing, Engineering Science & Technology 2022

Technologies designed for digital provenance, especially the Internet of Things (IoT) and blockchain, may allow for security, transparency, and traceability in the global supply chain. However, upstream nodes in the supply chain that work for large-scale production suppliers are not considered. In addition, most IoT blockchain systems adopt an ID-based signature scheme that may affect the efficiency of IoT devices. We propose using aggregate verification to improve the security and efficiency of ID-based verification, reduce network traffic on the blockchain, and transfer computing overhead to aggregator nodes. This paper implements a multi-layer blockchain for Agriculture 4.0 supply chain management that has higher efficiency, effectiveness, and security in comparison to conventional blockchains. We design a Multi-Layer Aggregate Verification (MLAV) solution to improve supply chain management with IoT Blockchain for Agriculture 4.0 through the following methods. First, we use a multi-layer IoT blockchain system to reduce Ethereum gas fee. Second, we design an ID-based Aggregate Verification scheme, thereby eliminating the certificate management cost in the traditional Public Key Infrastructure (PKI) and reducing bandwidth and computation time requirements. Third, we implement a three-layer blockchain infrastructure. In Layer 1, IoT devices sense and upload data to the system’s database; in Layer 2, smart contracts execute aggregate ID-based signature verification from IoT devices and upload the transactions to the private blockchain; in Layer 3, a batch converts the layer 2 data and uploads its Merkle root to Ethereum, thereby reducing the required gas fee.

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The NFC wireless charging feature is an extension of the NFC technology that can be implanted on wearables. The purpose of this paper is to show how to increase power transfer efficiency on both transmitter and receiver antenna systems. To demonstrate this problematic, firstly this paper gives an overview of how this NFC feature is implemented (architecture, power transfer, carrier frequency, communication bandwidth…), and can be complementary to Qi technology. Then, it provides a study on how to improve the power transfer efficiency on the antennas. To perform this result, the designer can adapt some antenna parameters as coupling coefficient, quality factor, matching method, and the antenna size. If these recommendations are respected, the power transfer efficiency between the antennas could reach between 70% and 80% with the NFC charging technology.

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In this paper, we propose a highly accurate indoor position and direction estimation system using a simple fully connected deep neural network (DNN) model on Bluetooth Low Energy (BLE) Received Signal Strength Indicators (RSSIs). Since the system’s ultimate goal is to function as an indoor navigation system, the system estimates the indoor position simultaneously as the direction of movement using BLE RSSI fingerprints recorded indoors. To identify the direction of movement along with the position, we decided to use multiple time instances of RSSI measurements and fed them to a fully-connected DNN. The DNN is configured to output the direction with the location simultaneously. RSSIs are known to be affected by various fluctuating factors in the environment and thus tend to vary widely. To achieve stable positioning, we examine and compare the effects of temporal interpolation and extrapolation as preprocessing of multiple RSSI sequences on the accuracy of the estimated coordinates and direction. We will also examine the number of beacons and their placement patterns required for satisfactory estimation accuracy. These experiments show that the RSSI preprocessing method optimum for practical use is interpolation and that the number and placement of beacons to be installed does affect the estimation accuracy significantly. We showed that there is a minimum number of beacons required to cover the room in which to detect the location if the estimation error is to be minimized, in terms of both location and direction of movement. We were able to achieve location estimation with an estimation error of about 0.33 m, and a movement estimation error of about 10 degrees in our experimental setup, which proves the feasibility of our proposed system. We believe this level of accuracy is one of the highest, even among methods that use RSSI fingerprints.

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In this paper, we propose a highly accurate indoor position and direction estimation system using a simple fully connected deep neural network (DNN) model on Bluetooth Low Energy (BLE) Received Signal Strength Indicators (RSSIs). Since the system’s ultimate goal is to function as an indoor navigation system, the system estimates the indoor position simultaneously as the direction of movement using BLE RSSI fingerprints recorded indoors. To identify the direction of movement along with the position, we decided to use multiple time instances of RSSI measurements and fed them to a fully-connected DNN. The DNN is configured to output the direction with the location simultaneously. RSSIs are known to be affected by various fluctuating factors in the environment and thus tend to vary widely. To achieve stable positioning, we examine and compare the effects of temporal interpolation and extrapolation as preprocessing of multiple RSSI sequences on the accuracy of the estimated coordinates and direction. We will also examine the number of beacons and their placement patterns required for satisfactory estimation accuracy. These experiments show that the RSSI preprocessing method optimum for practical use is interpolation and that the number and placement of beacons to be installed does affect the estimation accuracy significantly. We showed that there is a minimum number of beacons required to cover the room in which to detect the location if the estimation error is to be minimized, in terms of both location and direction of movement. We were able to achieve location estimation with an estimation error of about 0.33 m, and a movement estimation error of about 10 degrees in our experimental setup, which proves the feasibility of our proposed system. We believe this level of accuracy is one of the highest, even among methods that use RSSI fingerprints.

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Owing to the recent development of AI technology, various studies on computer-aided diagnosis systems for CT image interpretation are being conducted. In particular, studies on the detection of lung cancer which is leading the death rate are being conducted in image processing and artificial intelligence fields. In this study, to improve the anatomical interpretation ability of CT images, the lung, soft tissue, and bone were set as regions of interest and configured in each channel. The purpose of this study is to select a detector with optimal performance by improving the quality of CT images to detect lung cancer tumors. Considering the dataset construction phase, pixel arrays with Hounsfield units applied to the regions of interest (lung, soft tissue, and bone region) were configured as three-channeled, and a histogram processing the technique was applied to create a dataset with an enhanced contrast. Regarding the deep learning phase, the one-stage detector (RetinaNet) performs deep learning on the dataset created in the previous phase, and the detector with the best performance is used in the CAD system. In the evaluation stage, the original dataset without any processing was used as the reference dataset, and a two-stage detector (Faster R-CNN) was used as the reference detector. Because of the performance evaluation of the developed detector, a sensitivity, precision, and F1-score rates of 94.90%, 96.70%, and 95.56%, respectively, were achieved. The experiment reveals that an image with improved anatomical interpretation ability improves the detection performance of deep learning and human vision.

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This paper is an extended version of the journal presented at ICECCME2021. In ICECCME2021, the authors presented that we have developed a solitary wave track circuit (SW-TC), and it is energy-saving compared to existing track circuits. Furthermore, we also explained that it can realize advanced train control at a low cost, equivalent to digital automatic train control. After that, we have conducted research to improve preventive maintenance, which is a problem of existing track circuits, by using SW-TC. In this extended paper, we explain that we can further expand the functions of SW-TC, added new functions such as insulation deterioration diagnosis of the track circuit. With these new functions, the SW-TC can improve reliability, availability, maintainability, and safety. Especially, because of the effect of the insulation deterioration diagnosis function, so railway operators can significantly reduce the time required to identify the cause, when a track circuit failure occurs.

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In this paper, we investigate the utilization of three effective detection methods to identify potential threats of insects in date palm trees. The detection techniques presented here are the application of infrared radiation, microwave antennas and metamaterials based sensors. Experimental trials using IR radiation took place in a local farm. Moreover, the second sensing system is based on microwave antennas that are designed and numerically simulated at the 2.45 GHz-band. Lastly, the third detection method focuses on the design and development of low-power microwave sensor based on metamaterials concept. Based on the processed and analyzed results, the aforementioned sensing techniques are able to predict existence of red palm weevils within date palms.

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Mobility is a fundamental human desire. All societies aspire to safe and efficient mobility at low ecological and economic costs. ADAS systems (Advanced Driver Assistance Systems) are safety systems designed to eliminate human error in driving vehicles of all types. ADAS systems such as Radars use advanced technologies to assist the driver while driving and thus improve their performance. Radar uses a combination of sensor technologies to perceive the world around the vehicle and then provide information to the driver or take safety action when necessary. Conventional radars based on the emission of electromagnetic and ultrasonic waves have been consumed in the face of the challenges of the constraints of modern autonomous driving, and have not been generalized on all roads. For this reason, we studied the design and construction of a computer vision radar to reproduce human behavior, with a road line lane detection approach based on the histogram of the grayscale image that gives good estimates in real-time, and make a comparison of this method with other computer vision methods performed in the literature: Hough, RANSAC, and Radon.

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Various interruptions to the daily lives of researchers have necessitated the usage of simulations in projects which may not have initially relied on anything other than physical inquiry and experiments. The programs and algorithms introduced in this paper, which is an extended version of research initially published in ARAIG And Minecraft: A COVID-19 Workaround, create an optimized search space and egress path to the initial starting point of a user’s route using a modification (“mod”) of the digital game Minecraft. We initially utilize two approaches for creating a search space with which to find edges in the resulting graph of the user’s movement: a naive approach with the time complexity of O(n2) and an octree approach, with the time complexity of O(nlogn). We introduce a basic A* algorithm to search through the resulting graph for the most efficient egress path. We then integrate our mod with the visualization tool for the “As Real As It Gets” (ARAIG) haptic suit, which provides a visual representation of the physical feedback the user would receive if he were to wear it. We finish this paper by asking a group of four users to test this program and their feedback is collected.

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This article presents a roadmap for distribution grid µPMU hardware and software design consideration and implantation to ensure high performance within limited computational time of sampling frequency 512 samples/cycle. A proposed 12 channels, multi-voltage level µPMU hardware and rules of voltage and current transducer, analog filter, analog-to-digital converter, sampling rate definition, and PCB design and selection are presented. From the software view, software minimization procedures are implemented to reduce the estimation time of the proposed µPMU to 18 µsec under high sampling frequency operation. Additionally, error estimation and compensation are used to ensure robust performance, while the computational burden of the error compensation stage is reduced by Taylor series linearization. The proposed µPMU is designed to provide traditional phasor, frequency and harmonics measurements besides a point-on-wave under dynamic operation mode. The proposed device is tested under IEEE Std C37. 118.1 and 118.2 and showed accurate phasor estimation up to 0.03% for the magnitude and angle accuracy up to 0.0036o, while the frequency is estimated with maximum variation of 0.032% under dynamic operation.

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Applying black-box ML models in high-stakes fields like criminology, healthcare and real-time operating systems might create issues because of poor interpretability and complexity. Also, model building methods that include interpretability is now one of the growing research topics due to the absence of interpretability metrics that are both model-agnostic and quantitative. This paper introduces model selection methods with trade off between interpretability and accuracy of a model. Our results show 97% improvement in interpretability with 2.5% drop in accuracy in AutoMPG dataset using MLP model (65% improvement in interpretability with 1.5% drop in accuracy in MNIST dataset).

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Estimation of energy demand has important implications for economic and social stability leading to a more secure energy future. One-year-ahead energy demand estimation for Turkey has been proposed in this paper, using the metaheuristics method with GDP, the total population, and the quantities of imports and exports, as inputs variables. The records obtained from historical data were bifurcated into training and test datasets, where the training dataset is used by the algorithm in the process of generating models, while the test dataset was used to evaluate the performance of the algorithm. Here, two particular approaches have been proposed: Grammatical Evolution alone, and an ensemble of Grammatical Evolution with Differential Evolution. Under these four different forms are developed, viz, Grammatical Evolution with a recursive grammar (M1), an ensemble of Grammatical evolution executed on a linear grammar and Differential Evolution (M2), an ensemble of Grammatical evolution executed on a quadratic grammar and Differential Evolution (M3), and, Grammatical Evolution with a recursive grammar and Differential Evolution (M4). Moreover, the present approaches were also compared for estimation accuracy against the previously published DE models. It was substantiated that the M4 proposal exhibited the best performance towards estimation. It is therefore established that the current approach exhibits a better estimation capability (with RMSE of 2.2002), compared to the models previously available in the literature. M4 approach is then employed to predict the future energy demand using the same set of socio-economic inputs and the results demonstrated high prediction accuracy with an RMSE of 2.2278.

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In this paper, Compact bandpass filters have been designed. A single bandpass filter was designed using novel triple concentric complementary split-ring resonators placed along the microstrip line in the ground plane. Gaps and via were placed on the microstrip line to control electromagnetic characteristics, resulting in a single bandpass filter. In turn, spiral resonators were attached to the microstrip transmission line at the gaps in the transmission line to obtain a compact dual passband filter. Stepped impedance microstrip line and T-shaped stubs were attached to the microstrip line in between spiral resonators. The structure designed resulted in a Triple bandpass filter. A fractional bandwidth of 3% was achieved at the center frequency of 3GHz. The filter had a 1.5dB insertion loss which is the minimum value in the operating frequency band. The filter resonance frequency was 1.32 GHz and 2.47GHz which have a fractional bandwidth of 7.5% and 4.85% respectively and the corresponding insertion loss was 1.3dB and 1.8dB respectively. The triple bandpass filter had a fractional bandwidth of 1.16%, 11.4%, and 1.86%, centered at 1.29 GHz, 2.27 GHz, and 3.21GHz with 1.6dB, 1.3dB, and 1.8 dB insertion loss at the respective frequencies. The proposed bandpass filters are useful for GPS, WLAN, WiMAX, and radar applications.

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There are different factors which contribute to the amount of output power which can be delivered by solar photovoltaic (PV) module at any time of the year. The factors include but not limited to solar irradiation, ambient temperature, relative humidity, wind velocity, position of sun in the sky, geographical position of installed solar PV module and others. Apparent position of the sun in the sky contribute to the amount of electromagnetic radiation from the sun reaching the module’s surface area. With the sun further away from the module and irradiance reaching the module surface area at an angle non perpendicular to the surface leads to low output power delivered by the module. In southern hemisphere the PV module experience high output power around November/December which are summer months and low output power around June/July which are winter months. This paper develops performance adjustment factor of fixed solar PV module to adjust PV module output power such that the PV system can deliver required amount of power during winter season. The results show that the value of performance adjustment factor for fixed solar PV module or system was established and can be used to adjust performance or output power for winter periods.

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The aim of the article is to analyze the annual reports on cyber security of Central European countries, i.e. the Czech Republic, Slovakia, Poland, Germany, and Austria. The article focuses on the development of the state of cyber security, actors of threats in cyberspace, cyber threats, and the most common types of attacks. The article evaluates the objectives of cyber-attacks from the point of view of state institutions, organizations, and state and private companies, and they have listed the follow-up measures here. The method used is a critical verbal evaluation with comments and comparative analysis to find the strengths and weaknesses of the evaluated cyber security strategies and learn from them. The experiment of the cyber defense against phishing attacks is mentioned as an example of the cyber defense of individuals. The rules in Microsoft Outlook were used by filtering incoming email messages. The result is promising by stopping 88 % of phishing emails. The discussion and conclusion state that COVID-19 played a big role in the cyber security situation in countries to the analyzed documents.

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The false nearest neighbors (FNN) method estimates the variables of a system by sequentially embedding a time series into a higher-dimensional delay coordinate system and finding an embedding dimension in which the neighborhood of the delay coordinate vector in the lower dimension does not extend into the higher, that is, a dimension in which no false neighbors or neighborhoods exist. However, the FNN method requires an arbitrary threshold value to distinguish false neighborhoods, which must be considered each time for each time series to be analyzed. In this study, we propose a robust method to estimate the minimum embedding dimension, which eliminates the arbitrariness of threshold selection. We applied the proposed approach to the van der Pol and Lorenz equations as representative examples of chaotic time series. The results verified the accuracy of the proposed variable estimation method, which showed a lower error rate compared to the minimum dimension estimates for most of the thresholding intervals set by the FNN method.

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Radon transform is a mathematical tool widely applied in various domains, including biophysics and computer tomography. Previously, it was discovered that applying the Radon transform to a binary image comprising circle forms resulted in discontinuity. As a result, the line detection approach based on it became discontinued. The d-Radon transform is a modified version of the Radon transform that is presented as a solution to this problem. The properties of the circle cause the Radon transform to be discontinuous. This work extends this finding by looking into the Radon transform’s regularity property and a proposed modification to a convex shape. We discovered that regularity in the Radon space is determined by the regularity of the shape’s point. This leads to the continuity condition for the line detection method.

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A nested Mach-Zehnder interferometer (MZI) configuration whose phase shifters are placed in parallel is suitable for silicon-silica hybrid structure to realize a high-speed optical switch. Even when the signal wavelength deviates from an optimal wavelength, the crosstalk of the nested MZI optical switch can be suppressed by employing phase generating couplers (PGCs) in place of directional couplers. We calculate the characteristics of a 4-stage nested MZI switch with PGCs, and show that crosstalk is lower than −40 dB over a wavelength range of as wide as 200 nm from 1450 to 1650 nm in six output ports. We also examine the scalability of the multi-stage nested MZI switch, and deduce the required number of switch stages for given output port counts with low crosstalk.

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In this paper, an improved model has been proposed for investigating the impact of cyber-attacks on power systems regarding frequency disturbances and voltage disruption while changing the load called ICAPS. The proposed ICAPS model is formulated by five different controllers, such as LFC, AGC, AGC-PID, AVR, and AVR-PID, implemented in two sets of the system model. Specifically, a stable limit of the speed regulation of LFC, integral controller gain of AGC, and amplifier gain of AVR are determined from their characteristic equations derived from the Routh-Hurwitz array. In contrast, the Proportional-Integral-Derivative (PID) controller gains for AGC-PID and AVR-PID are determined using the tuning process. The key aspect of this paper is to obtain the impact of cyber-attacks on the power system in terms of frequency disturbances and voltage disruptions while changing the load. According to our knowledge, no one has considered these issues at a time. In order to evaluate the proposed ICAPS model and how it works, a series of experiments was conducted using the MATLAB Simulink tool. The simulation results are presented in this paper in terms of frequency deviation and voltage disruption (i.e., positive and negative biased cyber-attack) and system oscillations. Finally, the simulation results successfully identified the most severe attack in this model to prevent the power system from unstable conditions.

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The impact of previous energy crisis gives an insight into what happens when the oil price crashes. Μany countries were affected due the slow reaction in boosting the economic growth in key sectors such as oil-importing activities and the economic restructuring progress to face the challenges. But the present energy crisis resulting from the COVID-19 pandemic period and closure of many borders, has encouraged power distributors and generators to have recourse to renewable energy for grid integration. Countries such as USA, Germany, Italy, Spain and India are moving towards increasing the share of renewable energy on the grid. The increasing use of solar power systems over the past few years is being favoured for the decarbonization process. The percentage growth in integrating solar PV energy is forecasted to reach 23% in the future. This widespread application of renewable power energy sources (RES) such as wind and solar power comprise of many challenges namely power quality and stability. With this consequent increase in RES, synchronous generators are being displaced and replaced by power electronics grid interface, which reduces the overall rotating masses, hence the system inertia. Stability study in the presence of renewable energy is therefore an important aspect to be considered to meet the required power quality of the grid. This paper brings forward the use of equal area criterion (EAC) method for assessing the stability of the power system network with presence of renewable energy such as solar energy system in the generation portfolio. EAC provides an effective visual and analytical approach for transient stability analysis. The investigations have been performed within the steady and transient conditions in form of a 3-phase fault. The modelling and analysis were carried out using MATLAB/Simulink. Calculation of the critical clearing time (CCT) for stability assessment is the main contribution to knowledge. The increase in the CCT confirms that the Solar PV penetration to the grid will improve the transient characteristics of the national grid network.

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In current societies, web application systems take central roles in computer systems. Thus, web client programming using JavaScript has increased values to add dynamic features and functions in web pages by well working with HTML and CSS. In this paper, as a new type of exercise problem for its self-study, we propose a code modification problem (CMP). In CMP, a source code with HTML/CSS elements and JavaScript functions for study, and the screenshots of both the original and the slightly altered web pages are provided to the students, who will need to edit the source code to generate the modified page. The goal of CMP is for students to carefully read the source code and comprehend how to use the components and functions through modifying parameters, values, or messages there. String matching is used to check the correctness of any answer. Through solving CMP instances, the students are expected to master the basic concepts of web client programming. To evaluate the proposal, we generated and assigned 25 CMP instances to 37 students in Okayama University. In addition, we offered project assignments of freely implementing source codes by referring to solved CMP instances to evaluate heir learning effects. With the solution results, the validity of the proposal has been confirmed.

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This paper looks for the merging of solar PV power plant with thermal power generation and interlinked through tie-lines approaching into an interlinked PV-Thermal model for the design and analysis of load frequency controllers. Moreover, the different models of controllers are also proposed and tested to regulate the frequency of PV-Thermal interlinked system for a certain disturbance in one of the regions of an interlinked system. The choice of auxiliary controllers is assessed utilizing assorted tuning strategies and the comparative examination of all models are shown via graphical response and via calculating numerical values to bring back the frequency and tie-line power deviations back to the standard level and to analyze the viable control activity. In addition, an HVDC link is also used between linked PV-Thermal areas to stabilize the frequency and tie-power quickly as well as to stabilize the system operation for alteration in changing loading conditions of the interlinked system.

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In different areas of industry (mining, food processing, pharmaceutical, manufacturing…), the problem of grain, aggregate and clusters flow arises during the handling activities especially loading and unloading. Thus, the study and control of the parameters that govern the flow of the granular medium and its interaction with its environment are key parameters to achieve the desired operational excellence and performance of these activities. The adhesion of granular materials on various surfaces of equipments (trucks surfaces, hoppers, silos…) is one of the major problems facing mining companies. In this paper we presented the Calibration of the Discrete Element Method (DEM) parameters for modeling phosphate ore built on the identification of the repose angle. This will help us to specify the correct inputs parameters that will be introduced for the modeling of adhesion phenomena. First we introduced the model contact which allow us to well define contact between phosphate particles-particles (or clusters-clusters) and phosphate particles (clusters) with tipper surface taking into consideration the cohesive and plastic nature of the contact. Secondly, we presented a calibration method based on the determination of the repose angle of the phosphate ore. This method allowed us, using fractional factorial designs and Box Behnken designs, to determine the optimal parameters for a more accurate simulation. Thirdly, we studied the impact of particle velocities on the tipper surface during charging and discharging of phosphate ore. This study allowed us to predict the areas most affected by the abrasion-erosion phenomenon caused by the impact of particles on the tipper surface. This calibration method allowed us to identify the optimal values for the key parameters that will be used later in the modelling of the phosphate clogging phenomenon on the surfaces of transport truck tippers in the mines.

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Given the strict Quality of Experience (QoE) and Quality of Service (QoS) criteria for video transmission, such as delivery ratio, transmission delay, and mean opinion Score (MOS), video streaming is one of the hardest challenges in Vehicular Ad-Hoc Networks (VANETs). Additionally, VANET attributes, including environmental impediments, fluctuating vehicle density, and highly dynamic topology, have an impact on video streaming. Creating efficient visual communications in these networks will give drivers access to business and entertainment applications, greater support, safer navigation, and better traffic management. This work particularly investigates the mobility characteristics of autos and the causes of link failure in order to precisely anticipate the dependability of connections between vehicles and build a reliable routing service protocol to satisfy various QoS application demands. Then, a link-time duration model is suggested. Link dependability is assessed and taken into consideration when creating a new self-adaptative routing protocol for video transmission. Due to the quick changes in topology, finding and maintaining the optimum end-to-end route is quite challenging. However, the heuristic Q-Learning algorithm could continually alter the routing path via interactions with the surrounding. This study suggests an algorithm for reliable self-adaptive routing (RSAR). Through changing the heuristic function and integrating the reliability parameter, RSAR works well with VANET. The Network Simulator NS-2 is used to illustrate how well RSAR performs. According to the findings, RSAR is particularly beneficial for many VANET applications since it efficiently addresses the issues brought on by changes in topology.

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In recent years, people-counting problems have increased in popularity, especially in crowded indoor spaces, e.g., public transport. In peak hours, trains move significant numbers of passengers, producing delays and inconveniences for their users. Therefore, analysing how people use public transport is essential to solving this problem. The current analysis estimates how many people are inside a train station by using the number of people entering and leaving the ticket gates or estimating the train occupancy based on conventional CCTV cameras. However, this information is insufficient for knowing the train occupancy. The required data includes vehicle usage: how many people enter or leave a vehicle or which door is the most used. This paper presents a solution to the stated problem based on a multi-object tracker with a sequential visual appearance predictor and a line-based counting strategy to analyse each passenger’s trajectory using an overhead fisheye camera. The camera selection inside the train was made after profoundly studying the railway environment. The method proposes a module to compute the total train occupancy. The solution is robust against occlusions thanks to the selected tracker and the fisheye camera field of view. This work shows a proof of concept dataset containing pseudo-real case scenarios of people’s affluence in train doors recorded by fisheye cameras. Its purpose is to prove the system’s functionality in these scenarios. The proposed approach achieved an overall accuracy of counting people getting on and off of 90.78% in the pseudo-real dataset, proving that this approach is valid.

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Renewable energy is an essential source of green growth for countries facing a shortage of fossil fuels. They offer a sustainable, inexhaustible, carbon-free solution to the future energy dependency of nations. Morocco, which has no traditional energy resources, depends entirely on the international primary energy market to meet its growing demand. For this reason, Morocco launched the National Energy Strategy in 2009 to reach 42% renewable production by 2020. This strategy has been renewed to 52% by 2050. Thanks to this policy, the country has been able to address most of its energy challenges. This study analyzes the energy mix of Morocco from 2010 to 2050. The methodology adopted is to simulate Morocco’s electricity mix for this period. We assumed we were at the beginning of deploying the country’s energy policy to assess the adopted strategic decisions. The analysis shows that the different technological solutions for electricity production chosen at the beginning of Morocco’s energy transition could be better. Indeed, the decision to develop concentrated solar power as the leading renewable source and coal as a backup option, for example, appears to be contested. However, according to the third scenario of our study, renewables have the potential to become the main source of energy for the Moroccan power grid.

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We present in this contribution the distribution of a global multi-input-multi-output system in a sensor and actuator network. Based on controllability and observability, the global system is decentralized and the system properties are preserved as a result. This results in multiple decentralized local single-input-single-output systems with the same system order as the global system. As these local systems are implemented on decentralized CPUs in the network, the computational effort of the nodes has to be minimized. This is achieved by approximating the input and output behavior and reducing the system order of the decentralized local systems. For this purpose, the two most common techniques, Balanced Truncation and Krylov subspace methods, are presented. Kalman filters are used for state reconstruction. To approximate the input/output behavior of the global system, information from all decentralized reduced local systems is necessary, thus a fully interconnected network is used for communication. By decentralized fusion algorithms in the network nodes, the Kalman filter algorithm is separated and distributed in the network.

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Electroencephalography (EEG) is one of the most common and benign methods for analyzing and identifying abnormalities in the human brain. EEG is an incessant measure of the activities of the human brain. In contrast, when the measurement of EEG is bounded by time and the EEG is synchronized to an exterior stimulus, is known as Event-Related Potential (ERP). ERP has the capability to perceive and explore the human brain’s responses to specific sensitive, cognitive, or motor events in real time with high temporal resolution. Among the various techniques, the oddball paradigm is very famous in EEG studies. In an oddball paradigm experiment, brain responses to frequent and infrequent stimuli are measured. However, the success of ERP research is very much dependent on the analysis of clean data sets and unfortunately, EEG is a combination of both neural and non-neural activities which introduce significant sources of noise that are not related to the brain’s response to the external stimulus. These unrelated non-EEG components are acknowledged as artifacts and due to these, the quality of the EEG may damage by decreasing SNR (signal-to-noise ratio). In addition, these artifacts may mislead the actual information in the study. Addressing this problem, the purpose of this research is to introduce a machine learning algorithm (ML) that can screen EEG/ERP data to remove data epochs that are disrupted by artifacts and thus produce a clean data set. Overall, three unsupervised ML algorithms are applied to identify noisy epochs and it is found that the DBScan method performs best with 93.43% accuracy. Finally, the success of this study will allow the ERP study to have a cleaner ERP data set in normal laboratory conditions with less complexity in the ERP studies.

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The emergence of blockchain technology has facilitated the digital transformation of many businesses and thereby increased the competitiveness of China in the global market. As a cutting-edge technology, Blockchain has significantly influenced the practices across all business sectors. Focusing on the retail and insurance sectors, this paper analyzes the blockchain technology applications in two companies – Suning and PingAn. Success factors in blockchain implementation were discussed by using TOE framework. Similarities and differences in the implementation and development processes are compared. Features from the two corresponding industries – retail and insurance are also summarized. The major results revealed that retail and insurance industries are two of the most important application sectors of blockchain technology, and Suning and PingAn are both pioneers in blockchain technology development and implementation in their specific sectors. However, there are still distances between Chinese blockchain providers and world leading providers of blockchain, even though China has the largest number of blockchain patents in the world. The research results provide meaningful insights and practical implications to the blockchain application fields.

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Owing to technological development, the internet has become the world’s largest platform where an unaccountable amount of e-news information is freely available to use. Most of the time, e-newspaper readers have to examine the massive collection of e-news articles to locate necessary information relevant to them. Massive semi-structured and unstructured texts usually mislead the readers when they search and understand data for some knowledge. Furthermore, manually reading a collection of e-news articles for some knowledge is tedious and unproductive. The literature related to Knowledge Discovery from text documents has had a substantial improvement in this regard and Association Rule Extraction using text documents, in particular, has become a more frequent and imperative research approach to finding out the most significant information, patterns, and features in the text documents while diminishing the time for reading all the documents. This study provides a comprehensive review of Association Rule extraction using textual data covering the essential topics; Pre-processing, steps in Association Rule Mining, and rule mining algorithms. Out of the various existing association rule mining algorithms, the two most important algorithms, Apriori and FP Growth, are chosen for the experiment using e-news articles. Based on the experimental results, this study discusses the performance, significant bottlenecks, recent breakthroughs of rule mining algorithms, and finally the perspective directions to facilitate future research.

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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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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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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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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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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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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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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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The increase of servers in data centers has become a significant problem in recent years that leads to a rise in energy consumption. The problem of high energy consumed by data centers is always related to the active hardware especially the servers that use virtualization to create a cloud workspace for the users. For this reason, workload placement such as virtual machines or containers in servers is an essential operation that requires the adoption of techniques that offer practical and best solutions for the workload placement that guarantees an optimization in the use of material resources and energy consumption in the cloud. In this article, we propose an approach that uses heuristics and meta-heuristics to predict cloud container placement and power consumption in data centers using a Genetic Algorithm (GA) and First Fit Decreasing (FFD). Our algorithms have been tested on CloudSim and the results showed that our methods gave better and more efficient solutions, especially the Genetic Algorithm after comparing them with Ant Colony Optimization (ACO) and Simulated Annealing (SA).

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Edge detection-based image steganography schemes usually embed data in edge pixels only. However, some schemes embed data in non-edge pixels as well. In that case, the schemes embed more bits in the edges than in the smoothed areas. In all cases, the schemes perform large changes in a tiny area of the image during small data embedding. Detecting such local modifications is comparatively easier for a steganalyzer. As a result, it is preferable to distribute bits evenly across the image. Again, the schemes struggle to hide large messages in a cover image due to the algorithmic approach of hiding a fixed number of bits per pixel. In this research, we have overcome that problem by employing multiple edge detectors in generating a resultant edge image. Depending on the embedding needs, we have checked whether a single edge detector is sufficient to help in conceiving all bits or not. If it is not possible for a single-edge detector, we have hybridized them. Hybridization of edge images is performed either by logical AND, OR or OR with dilation. When the message size is very small, we have generated the resultant edge image by doing a logical AND operation among the edge images. That strategy have reduced the number of edge pixels as well as helped in distributing the to-be-embedded bits over the image in a more evenly manner. Similarly, to meet a larger embedding demand, we have performed a logical OR operation among the same edge images to increase the number of edge pixels. Even, to meet more embedding demand, we have dilated the OR-resultant image. These processes were carried out dynamically in the research according to an embedding demand. The experimental results deduce that this scheme embeds 92.37%, 73.92%, 74.78%, and 9.60% more bits than four competing methods. Similarly, for small embedding demand, the proposed scheme demonstrates 37.45%, 46.87%, 44.21%, and 55.56% higher PSNR values than the others. Moreover, statistical analyses state that this scheme demonstrates stronger security against attacks.

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The “Electronic Nose” approach, which is exclusive to gas measurement systems, uses gas sensors as odor detectors. Design faults exist in the existing electronic nose (e-nose) chamber, such as its large volume, difficult construction, etc. In order to obtain measurements in a satisfactory state, we want to create a gas chamber that can provide favorable conditions for the sensor array, taking into account the ideal gas flow morphology and detector placement. To describe and identify the design capable of offering the best performance for a genuine idea, the e-nose chamber was created using ParaVIEW simulation and FreeCAD conception. According to the results, the spherical sensing container with connections from both pipes in a tangential arc style gives the highest performance in terms of turbulence reduction, in that case, we are printing this chamber and put it in a gas flow prototype to see the performance of the quality measurement of the sensors inside it, and the result shows that these sensors have good acquisition responses by testing the homogeneity distribution inside the chamber.

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Corn is one of the most important agricultural products in the world. However, climate change greatly threatens corn yield, further increasing already prevalent diseases. Northern corn leaf blight (NLB) and Gray Leaf Spot are two major corn diseases with lesion symptoms that look very similar to each other, and can lead to devastating loss if not treated early. While early detection can mitigate the amount of fungicides used, manually inspecting maize leaves one by one is time consuming and may result in missing infected areas or misdiagnosis. To address these issues, a novel deep learning method is introduced based on the low latency YOLOv3 object detection algorithm, Dense blocks, and Convolutional Block Attention Modules, i.e., CBAM, which can provide valuable insight into the location of each disease symptom and help farmers differentiate the two diseases. Datasets for each disease were hand labeled, and when combined, the base YOLOv3, Dense, and Dense-attention had AP_0.5 NLB lesions/AP_0.5 Gray leaf spot lesions value pairs of 0.769/0.459, 0.763/0.448, and 0.785/0.483 respectively.

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Globally, road transportation has become one of the most reliable means of moving goods and services from one place to the other. It has contributed immensely to the standard of living and modern civilization. However, this means of transportation is characterised by some issues which are poised to be harmful to the human population if not properly addressed. One of such issues is the presence of potholes, bumps, and other road anomalies. Unfortunately, the late identification of road anomalies (Speedbumps and Potholes) and the inability of drivers to detect and slow down while approaching such road anomalies has also been a big challenge faced by many nations. Therefore, there is a need for an automatic and intelligent approaches to be built into vehicles to mitigate the number of road accidents caused by these anomalies. In this work, the development of an intelligent road anomaly identification and manoeuvring system for autonomous vehicle is presented. The developed system focuses on the detection of road anomalies specifically speedbumps and potholes; and the regulation of the vehicular speed when these anomalies are detected. A modified Histogram Oriented Gradient (HOG) and Fuzzy Logic Control (FLC) have been proposed in this work. Furthermore, promising results have been obtained and presented which depicts the proposed HOG algorithm outweigh other techniques in the identification and detection of speedbumps and potholes. In addition, the developed FLC was able to regulate the speed of the vehicle in the presence of speedbumps as well as navigate the vehicle accordingly in the presence of potholes.

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This article focuses on risk analysis using a multi-criteria decision-making method. Due to many performed risk analyses for soft targets, we are constantly trying to find new methods for objective risk assessment. Many risk analyses are subjective, which is a problem when planning security measures and comparing results (different events, objects, places, etc.). In this text, we present our case study, which deals with the use of fuzzy TOPSIS. As a reference object, we have chosen one of the specific categories of soft targets – cultural events. The goal was to find the location most at risk of violent attacks on a selected cultural event – a music concert. We then established cooperation with three experts. The completed data in the risk analysis was then compared with practice. The selected fuzzy TOPSIS method was chosen as presumably more objective. Our hypothesis was confirmed. The results were objective and consistent with practical experiences.

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