The 17th International Conference Interdisciplinarity in Engineering

In this paper, we investigated numerically the thermal and electrical performances of a cylindrical thermophotovoltaic (TPV) system cavity composed of a SiC emitter, four GaSb photovoltaic cells and four heat–reflecting mirrors. Using a Multiphysics coupling between the heat transfer and surface–to–surface radiation domains, we emphasized the importance of the effect of the conductive–convective heat transfer on the photocell surface cooling. SiC emissivity values of 0.8, 0.7, 0.6 and 0.5 were considered during simulations, with heat transfer coefficients hc of the cooling surface between 100 and 500 W/m2K. An abrupt energy conversion efficiency decrease under a value of 19% was observed when the cell temperature was rising over 375 K under TPV working conditions with radiator emissivity of 0.8 and hc of 200 W/m2K. By maintaining the radiator temperature at low values (around 1000 K) with a low spectral emissivity of 0.5, an air cooling system with forced convection working at hc = 200 W/m2K can mitigate the thermal losses inside the PV cells.

Compressed air represents a total energy consumption of approximately 20% of the total industrial energy consumption, being usually considered one of the most expensive utilities in the industry. A condition for increasing energy efficiency is the optimal management of the equipment used, of which industrial compressors for the production of compressed air are some of the most important components that can generate energy savings. In this work, a new method for monitoring the energy consumption of industrial compressors for the production of compressed air is developed with the help of Janitza-type intelligent meters through which all electrical parameters of the compressors are measured. The statistical data necessary for the development of the new method were measured on 3 industrial compressors from an industrial hall for a period of 365 days. A total consumption of approximately 500,000 kWh was recorded. The data collected after monitoring the power parameters and energy consumption on the system consisting of 3 industrial compressors that supply compressed air to an industrial production hall, show us that the most used compressor in operation is compressor number 3, having an annual energy consumption of approximately W = 288 169.70 kWh, representing approximately half of the total energy consumption of the compressors. The total energy consumption of the compressor system is approximately 494,358.30 kWh. Referring to an approximate price of 1.3 Ron/kWh, the total cost required to obtain compressed air is approximately 642,668.79 Ron which can bring improvements to energy efficiency. The compressed air production system can also be dimensioned more correctly in order to develop a particular energy-saving solution.

Prediction of small-scale and even large-scale solar energy is a hot research field. In fact, the intermittent nature of solar energy can disrupt the stability and management of electrical energy. Therefore, photovoltaic output power forecasts are recommended to solve this problem. In this paper, we will present a neural network-based photovoltaic (PV) power prediction method, in particular the long-short-term memory (LSTM) method. We are interested in this work in very short-term predictions because of its lack of presence in the literature. The proposed LSTM method is trained for this prediction category ranging from 5 min to 60 min (1 h) ahead. Then, the LSTM approach is trained and tested on real data measured from the PV power plant installed in northern Morocco. Based on the evaluation indices of the prediction models, i.e. R2, MAE, RMSE and MAE, the results found show that the optimized LSTM model showed its robustness and prediction efficiency on all validated prediction horizons. Moreover, the proposed method exhibits more accuracy in terms of RMSE errors compared to other well-known methods in the literature, including RNN, CNN and ELM.

Solar energy has a higher degree of volatility due to climatic constraints and scenarios. The efficient and successful deployment of solar energy necessitates an accurate and robust prediction method for predicting solar irradiance (GHI: Global Horizontal Irradiance). Inappropriate selection of Extreme Learning Machine network (ELMN) parameters creates the generalization issue, computational burden and unnecessary complexity. To address the issue of optimizing ELMN parameters. This research work addresses the issue with the development of a hybrid prediction approach (CSA-ELMN) combination of the Crow Search Algorithm (CSA) and Extreme Learning Machine Network (ELMN). The novel aspect of this investigation is using a crow search algorithm during the extreme learning machine training phase to optimize synaptic connection weights, bias and hidden layer neurons, which have been successfully evaluated in the Solar Irradiance (GHI) predictions application. Four statistical indices, including the mean square error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and mean relative error (MRE), were computed to assess the proposed hybrid prediction model (CSA-ELMN). The findings of the CSA-ELM approach shows that it improves GHI prediction precision compared to other traditional and hybrid approaches.

Solar photovoltaic systems are gaining popularity as a clean energy technology. However, accurately identifying the parameters of solar cells remains a challenging task. Researchers are actively working on modeling and simulating photovoltaic systems to overcome this challenge, despite the numerous methods proposed in scientific literature. This article presents a new method for extracting parameters from photovoltaic solar cells, addressing the lack of information provided by manufacturers. The proposed approach utilizes the Differential Evolution algorithm, outperforming existing techniques based on the same algorithm. By improving parameter extraction accuracy, this method enhances the modeling and simulation of solar cells, leading to optimized design and performance of solar energy systems. Overall, the study introduces an effective solution for parameter extraction, contributing to advancements in solar energy technology.

In this paper will be analyzed the carbon dioxide (CO2) emissions of battery electric vehicles (BEVs) during charging. The charging will be performed at a fast direct current (DC) charging station. In this case a high amount of power is required in a short amount of time. The CO2 emissions for the power supplied to the charging station will be calculated considering the power mix in Romania at several selected hours and days. Different BEVs from various car manufacturers are considered in the paper. These BEVs represent the models with the highest sales in Europe in 2022, for which the charging is usually performed under one hour. Fuel consumption for each selected BEV is considered for cold and warm weather, compared to other researchers’ work where the average fuel consumption was taken into account. The results from the MATLAB software demonstrate that the increased fuel consumption of BEVs in cold weather causes higher CO2 emissions.

Electric vehicles (EVs) are becoming increasingly popular due to their capacity to reduce reliance on oil and reduce greenhouse gas emissions. Among the electric components of an electric vehicle (EV), the battery is considered as the biggest bottleneck. Among the several battery alternatives, lithium-ion batteries are widely used to power electric vehicles. The condition of a battery encompasses several aspects, including the state-of-charge (SoC), state-of-health (SoH), state-of-power (SoP), and state-of-life (SoL). A battery’s state-of-charge (SoC) refers to the proportion of its capacity that is still useable. Changes in the operating circumstances of the electric vehicle (EV) in which the battery is placed have the greatest impact on the SoC. The output voltage of a battery indicates its state of charge. When a battery’s output voltage goes below a set cut-off value, the state-of-charge (SoC) is deemed to be zero. In other words, the SoC is decided by whether the output voltage of the battery exceeds or falls below a certain threshold. In this paper, the neural network (NN) time series analysis is applied to predict the output voltage of a battery is introduced. The proposed approach is base in the past values to predict the actual measurement using Levenberg-Marquardt algorithm to optimize the input values. The objective is to estimate and forecast the output voltage of a battery during the operation of an electric vehicle (EV). The results show that utilizing the NN based Levenberg-Marquardt algorithm for forecasting yields a lower individual absolute error (IAE).

The safety and reliability of battery storage systems are essential for the widespread adoption of electrified transportation and new energy generation. One of the crucial parameters for the safe management and effective control of batteries is the state of charge (SOC). In recent years, there has been a great deal of interest in machine-learning-based SOC estimation methods for lithium-ion batteries. However, a common issue with these models is that they frequently exhibit unstable estimation performances, which makes it challenging to use them in real-world scenarios. To address this problem, a framework based on convolutional neural networks (CNNs) uses measurements of the voltage, current, and temperature while the battery is charging to directly estimate SOC. The CNN is trained using randomized data. To increase accuracy, training data was enhanced with noise and error that included multiple layers and neurons. Additionally, the algorithm was examined for various temperature distributions, which would be common for many applications. With the aid of statistical indicator metrics, the proposed model’s accuracy and generalizability are demonstrated in the experiments using data gathered under various working conditions. The experiment’s findings show that the proposed model’s maximum error is less than 1.9%.

Addressing sustainable methods to enhance the thermal performance of building envelopes becomes essential considering that buildings account for 38% of all energy-related CO2 emissions worldwide. This paper focuses on investigating the thermal performance of a wall constructed with hollow bricks made of lightweight concrete and eco-friendly materials. Three case solutions are analyzed: lightweight concrete with recycled PET, lightweight concrete with polystyrene waste, and lightweight concrete with sawdust waste. Simulations are conducted in Autodesk CFD Simulation Software, comparing empty cavities to those filled with insulation material. The results demonstrated that the volumetric contribution of voids, which constituted a mere 10.52% of the total wall volume, had a negligible impact on the overall thermal conductivity of the wall. Analyzing the effectiveness of the PET, polystyrene, and sawdust composite within the concrete blocks revealed that incorporating polystyrene yielded the most efficient block configuration. Notably, optimal efficiency was achieved by integrating polystyrene within the concrete matrix and insulating the voids, while the least effective scenario involved incorporating sawdust into the concrete block without void insulation. The aim is to assess the thermal behavior and efficiency of the wall, emphasizing the potential of eco-friendly materials for achieving sustainable building design. These findings underscore the importance of carefully selecting materials and insulation strategies to optimize the thermal performance of building envelopes.

This research looks at the unstable squeezing of MHD nanofluid flow and heat transfer between two parallel plates while taking into account the impact of thermal radiation and the Cattaneo-Christov heat flux model rather than the traditional Fourier's equation of heat conduction. The controlling momentum and energy equations are converted into non-linear ordinary differential equations with the necessary boundary conditions via a similarity transformation. Rung-Kutta (RK4) solves the obtained non-linear ordinary differential equations. The effects on the temperature and velocity profiles of various active factors, including the radiation parameter, the heat source parameter, the magnetic parameter, the squeeze number, the volume fraction of nanofluid, and the thermal relaxation parameter, are investigated. Furthermore, the Nusselt number's value is computed and shown in figures. The results show that, in comparison to Fourier's law, there is a smaller temperature distribution in the Cattaneo–Christov heat flux model. Moreover, Nusselt number decreases with increasing heat source parameter and increases with decreasing thermal relaxation parameter.

The paper discusses the addition of bentonite and titanium dioxide nanoparticles in the cement mortar mix in terms of thermal conductivity. Calcined bentonite nanoparticles replaced cement particles in the mix by 1 wt%, 1.5 wt%, and 2 wt% per weight of cement. Titanium dioxide, being a non-reactive material, replaced the aggregates in the mix by 0.5 wt% and 0.75 wt% by weight of cement. A comparison between the individual impact of each type of nanoparticle, at 28 and 56 days of curing, on the thermal conductivity of the cement mortar was assessed. For evaluating the thermal performance, the density, surface moisture and thermal conductivity were recorded using laboratory equipment. The specific heat capacity value for each specimen was computed based on the volumetric heat capacity results given by the ISOMET 2114 device. The water content influence on the thermal performance was pointed out, as the 56 days samples registered a lower thermal conductivity and a higher specific heat capacity value. The higher hydrophilicity of bentonite was pointed out by the higher thermal conductivity at 28 days of curing. After water evaporation, at 56 days, the smallest thermal conductivity value was obtained for 0.5 wt% TiO2 – modified cement mortar specimen. The highest specific heat capacity at 56 days of curing was registered for 0.75 wt% TiO2 – modified samples.

The paper focuses on the study of the onset of thermal instability in a horizontal porous layer saturated by power-law fluid and undergoing a throughflow. The lower impermeable wall is supposed to have a uniform constant temperature while the upper free surface is held at uniform heat flux. The saturating fluid in a solid matrix flows according to the Forchheimer model where the non-Newtonian fluid density fulfills the theory of the Oberbeck-Boussinesq approximation. The normal modes approach is applied to obtain the eigenvalues problem. An analytical solution is developed to solve the resulting ordinary differential equations. The finding shows the effect of the drag number on the stability of the power law fluid in both cases of weak and strong flow rates.

The development of transformer-based language models brings a paradigm shift in the world of smart applications. The ChatGPT model opened new horizons in the field of natural language understanding and generation. This paper presents a survey on the history of transformer models, on the basic architecture and application areas. The last section is devoted to two use cases experiments on the application of ChatGPT. The first domain relates to Human-Level Programming and the second focuses on the semantic functional parsing of text sentences. The performed analysis demonstrates the big potential in the transformer language models.

In this paper, a comparison of communication solutions between a programmable controller and industrial controller devices is presented. The study was conducted through a project in which is investigated and developed methods for Open Platform Communication (OPC) client to programmable controller efficient, reliable, real-time communication. The proposed for investigation approaches rely on C# OPC client implementation and the Node-RED platform, maintaining goal in achieving the same functionalities. The first method involves using libraries to create functions for establishing a connection with the server and managing the obtained data. The second method involves using an API and several associated tools.

The article deals with the development, structure, design and control of elevator transportation processes using PLCs and their testing for their application in the automation of such processes, addressing the case of automatic control of an extraction machine was addressed. The main functional and system requirements and specifications for the extraction machine are presented, also describing the design and the simulation of the control algorithm. The coding using the Ladder program and the off-line testing of the system, respectively its on-line testing, are presented. Based on experimentation in laboratory conditions, we tested and demonstrated the capabilities and usefulness of using the command-and-control computer system with PLC for vertical extraction. The practical implications are those stemming from the shift of paradigm, from hardware-oriented to software oriented, thus driving significant cost efficiency through lower installation and maintenance costs, faster installation and great robustness, leading to a reduced total cost of ownership over other solutions.

The purpose of this study is to perform the monitoring of the automated belt conveyor transportation process using SCADA technology as well as to obtain the necessary skills for applying SCADA in case of other processes. Modern modelling and simulation methods were used, in order to analyse the results of the control simulation of multi-motor drives. This was done simultaneously with the optimization of the parameters of the automation model, then resorting to the analysis and synthesis of the Ladder program for command and control, correlated with the SCADA software. The method of modelling, simulation and software development for the middle layer of a belt transport flow is described. The practical application of modelling, simulation and software development and testing on the experimental plat-form is presented. The originality of the study results from the fact that it is carried out on the principal structure of the process, the automation strategy, respectively the specific requirements of the automatic control. The experimental test platform is hardware-in-the-loop. Monitoring is done using a series of software monitors written using SCADA software for remote monitoring and control. Practical implications of this work are that, based on this template, additional implementations would more easily be found, for instance by using future versions of SCADA with newer hardware and with additional monitoring system, for instance to develop applications such as transport belts and passenger transport bands in airports.

Classical control theory offers a range of control solutions for industrial processes, which provides different conventional controller structures (PID) and empirical, analytical, and optimization-based tuning criteria. This paper presents the principles of obtaining the transfer functions of a system with two coupled tanks, followed by a proposal for a two-loop control scheme (P and PI). For tuning the controllers’ parameters, the pole placement method in the complex plane is used. Additionally, root locus analysis is performed to determine the range of variation for a parameter to maintain the desired performance. The calculations and the closed-loop system simulation were performed using the Matlab/Simulink environment.

A light EV (Electric Vehicle) with two frontal in-wheel permanent magnet synchronous motors (PMSM) is analyzed from the perspective of steering angle integration in parallel with a speed cycle. Considering the given vehicle dynamics requirements, an analysis of the powertrain operating points compared to the operational area of the vehicle is performed. Under specified constraints depending on speed, a steering angle profile is generated and integrated in the analysis. Lateral forces resulting from the steering angle profile implementation are investigated from the sideslip risk point of view. The analysis implies rigorous speed control for each in-wheel motor implemented with a dedicated individual speed controller. Custom models are analyzed and verified by simulations, thereafter validation is performed using a physical testbench. The motor shaft speeds and currents absorbed from the DC source are supervised during the analysis. The powertrain energy consumption is determined after performing the testing cycle without and in parallel with the steering angle cycle.

Robotics relies on the integration of perception and action, with Artificial Intelligence (AI) playing a central role in achieving true intelligence. The success of AI has captivated scientific communities and the public, enabling humans to maximize their potential and approach human-like cognitive capabilities. One significant influence of AI on robotics is in perception. Through computer vision and sensor technologies, robots can analyze visual and sensory information, enabling them to understand and interpret their surroundings. AI algorithms enhance object recognition, enabling robots to identify and classify objects, people, and obstacles in real-time. AI technologies find applications in healthcare, manufacturing, transportation, energy, finance, advertising, consulting, and government. The global AI market was valued at USD 93.5 billion in 2021, projected to reach USD 1,011.7 billion by 2030, with a 38.1% estimated compound annual growth rate (CAGR). As technology giants continue to innovate, advanced AI technologies are being adopted across industries. This paper aims to comprehensively describe the current state and future directions of AI, providing insights into the impact it will have on the field of robotics.

This study investigates the natural convection phenomenon in a closed square cavity filled with copper-water nanofluid under differential heating. The numerical modeling approach utilized is the lattice Boltzmann method. The cavity is composed of two stiff walls on the right and left sides, while the remaining two walls are adiabatic. Rectangular obstructions of varying heights (20% to 40% of the cavity side length) and fixed widths (10% of the side length) are present on the horizontal walls. Laminar flow of an incompressible fluid is considered in the simulations. A custom Fortran code is developed to solve the governing equations for flow and heat transfer, enabling the calculation of the Nusselt number. The investigation focuses on studying the influence of the volume fraction of the nanofluid (ranging from 0% to 10%) and the length of the fins on heat transfer. The findings reveal enhanced heat transfer when employing nanofluids, highlighting their potential benefits. The lattice Boltzmann method is employed as the numerical approach, facilitating accurate modeling and analysis of natural convection within the investigated cavity configuration.

Carbon dioxide is a suffocating gas resulting either from industrial activities or from combustion or explosion. There may also be carbon dioxide deposits under pressure, quartered in porous geological formations. This gas can show slow or violent releases with accumulation at ground level. The presence of this gas in closed or semi-closed spaces can seriously affect the human body and when the concentrations are close to 12% Vol. Death can occur. To establish preventive measures, it is very important to know how carbon dioxide affects the human body as well as how it disperses into the air. Also, in order to establish the necessary measures, it is necessary to know the dispersion dynamics of carbon monoxide both horizontally and vertically. The paper presents the CFD analysis on determining the dynamics of dispersion in the stages of accumulation and evacuation of carbon dioxide in a closed enclosure.

In everyday life, the production lines of companies are required to be flexible, rapidly adopting new processes and methods in order to ensure their competitiveness in the market. The main objective of this study was to analyze the impact of automation on a workstation at an industrial company which paints accessories. By means of simulation, one was able to identify several aspects that negatively affect the company’s overall capacity, namely reduced productivity and long cycle times. The digital tools developed through Visual Basic for Applications constituted the starting point for the automation of several repetitive and bureaucratic tasks which support decision-making, initiating the process of Digital Transformation at the organization. In economic terms, this improvement in the workplace can potentially reduce costs in the order of thousands of euros annually, in addition to increasing productivity thus improving the company’s general performance.

Vehicle architectures are rapidly changing from mechanical to electronic and electrical systems as the automobile industry experiences a period of enormous upheaval. Today’s cars can include up to 100 distinct electronic control units (ECUs), each of which is responsible for a different function like air conditioning, power steering, or cruise. However, several automakers are working on developing technologies that allow better data flow, reduced latency, and lower weight to reduce complexity and boost cost efficiency as a result of the expansion in vehicle complexity and consumer demands for more advanced, intelligent, and customizable vehicles. There is an increasing need for clever software to enable these sophisticated features now that autonomous driving is within reach thanks to this fundamental shift in car architecture. This study addresses the issue of concurrent software and hardware development, which calls for more effective methods of testing and validating vehicle systems and reducing the time to market.

This paper deals with the simulation of a new type of funicular with hybrid drive (diesel –electric), which follows the global trend of limiting the use of fossil fuel-based engines and replacing them with electric drive systems. A very powerful Multiphysics simulation software called Amesim is used for the simulation. Amesim provides many tools for electrical, mechanical, and hydraulic parts, which means that the entire system can be simulated with the same software. To guarantee the accuracy of the simulation results for the hybrid electric funicular’s mode of operation, the model built using the Simcenter Amesim simulation tool was improved. By using the nominal data of the hydraulic parts discovered during project activities, the simulation blocks for the hydraulic system were parameterized in this manner. One of them is driven by an electric motor, while the other is propelled by a thermal engine, depending on the situation. Hydraulic fluid is delivered through pipes to the pump driving the funicular drum. The energy flow is reversed in scenarios involving braking and energy recovery, with the drum’s kinetic energy driving the hydraulic pump/motor, which then powers the motor/hydraulic pump coupled to the electric machine’s shaft and functions in generator mode.

Identifying the driver using artificial intelligence can help in many situations, like saving the profile and loading the configurations preferred or helping insurance companies with their plan for each individual driver. Also determining if the person behind the wheel is not the owner/regular driver can help law enforcement agencies to find out faster the theft of a car and it may help them to catch the culprit more efficient. The present work investigates the applicability of neural networks for driver identification in various scenarios: learning all but one driver and see if it classify the left one correctly; learning only one driver and the rest as thieves. The paper analyses the accuracy of neural networks in determining the driver in each scenario, and also checks how the network would react in a stolen vehicle scenario. The data set used for this experiment was collected in 2016, having 94380 records, split between 10 drivers. We obtained promising results in the second and second scenario, the best threshold recorded (99.71%) belonging to the fifth driver, using the configuration with 14 most impactful features and two hidden layers.

This paper tries to find another approach for heart rate variability and its frequency domain related parameters through estimating the instantaneous frequency of electrocardiogram signals. The proposed procedure estimates the instantaneous frequency through different methods using spectrogram and time-frequency distributions. This work tries to find a correlation between instantaneous frequency of ECG signals and heart rate variability comparing the instantaneous frequency variation with the heart rate variability spectrum. The presented procedures try to find correlation between mathematically defined parameters and treats signals only from a digital processing view.

Due to the current high levels of traffic in the city of Oporto (Portugal), a Decision Support System, based on a simulation modelling approach, has been developed to allow the users to obtain different results by changing several parameters related to the Urban Traffic Network. The system also allows the achievement of the desired scenarios: the speed of the agents involved, acceleration, the number of vehicles circulating on the road, the possibility of choosing routes to follow, and the signal times of traffic lights. The goal is to make the tool available to any ordinary user interested in this study, who will then be able to change the different variables, according to what the user intends to investigate. Accordingly, the decision-maker will know whether the changes carried out during simulation have met the expected objectives. Simulation is increasingly applied worldwide to a wide range of sectors, with the support of a growing number of programs and applications available to ordinary users.

Outlier detection in continuous nonlinear systems is essential as the presence of outliers might be indicators of faults, diseases, cyberattacks, or system malfunctions. However, the complex nature of such systems significantly increases the difficulty of developing outlier detection techniques. Due to the high complexity of such systems, accurate model based approaches are often difficult to design. While supervised outlier detection techniques yield great performance, the scarcity of labeled data motivates the necessity of unsupervised approaches. This paper introduces a data-driven approach for unsupervised outlier detection, which utilizes a hybrid combination of Autoencoders and One-Class Support Vector Machines. Experimental assessment was performed on the Tennessee Eastman Process dataset, and the performance of the proposed solution was measured using nine independent metrics, including detection delay and, true and false positive rates. Furthermore, a comparison with other recent techniques was performed, with notable results in terms of false alert rates and detection delay.

The pharmaceutical industry operates in an era of vast amounts of data generated from various sources, presenting opportunities and challenges. This article explores the issues faced by the pharmaceutical industry in effectively managing data and highlights the potential of federated learning as a solution. Data management challenges discussed include data privacy, security, and scalability. The article emphasizes the transformative potential of federated learning, a decentralized machine learning approach, in addressing these issues. By enabling training on decentralized data sources while ensuring privacy and security, federated learning offers a promising avenue for revolutionizing data management in the pharmaceutical industry. The adoption of federated learning can enhance drug development processes, enabling faster and more efficient analysis while preserving the confidentiality of sensitive information. This article presents a comprehensive analysis of the data challenges faced by the pharmaceutical industry. It provides insights into how federated learning can mitigate these issues, paving the way for improved data management practices in pursuing innovative therapies and advancements in healthcare.

The brain-computer interface (BCI) research field provides advanced biomedical technology based unique assistive or rehabilitative solutions aimed at helping people with neuromotor disabilities. The purpose of this paper is to develop a new brain-computer interface for controlling both a virtual simulation and a real experimental system of a vending machine or an automated machine for delivering various juices by transmitting commands based on detecting the P300 evoked biopotentials detected from the GTEC Unicorn headset embedding 8 EEG sensors. Therefore, after using the GTEC Unicorn Recorder official software to check the quality of the acquired raw EEG signals, the BCI commands are generated by triggering the P300 evoked biopotentials in the GTEC Unicorn Speller official board. The resulted EEG based P300 packets are sent in the LabVIEW instrument by the UDP communication protocol. The original LabVIEW application comprises two State-Machine programming sequences both for pictures-based simulation of delivering juices and the real experimentation of a physical Vending Machine controlled by the Arduino Mega 2560 development board. Also, the proposed LabVIEW instrument includes a programming code sequence facilitating the Bluetooth communication between the computer and the Arduino to enable the transmission of commands for controlling the DC pump air motors. The presented experimental prototype should be employed as a training or educational tool both for people with neuromotor disabilities and novice researchers into the brain-computer interface scientific field.

Sequence prediction problems are prevalent in various domains, including natural language processing, time series analysis, bioinformatics, and condition-based maintenance. Evaluating the performance of sequence prediction models is crucial to assess their accuracy, robustness, and generalization capabilities. This paper presents an overview of evaluation methods used for sequence prediction problems. Throughout the paper, we emphasize the importance of selecting suitable evaluation methods that align with the specific characteristics and goals of typical sequence prediction problems. We also provide insights into the considerations associated with each evaluation method. The paper discusses the fundamental metrics commonly employed, such as accuracy, precision, recall, and F1-score, which provide insights into the overall performance of sequence prediction models. Additionally, some more specialized metrics tailored to sequence prediction, are presented. These metrics account for the unique characteristics and challenges of sequence data. In the paper evaluation techniques specific to distinct types of sequence prediction problems are evaluate such as, perplexity, BLEU score, and ROUGE score which are widely used to evaluate language models and machine translation systems. In time series analysis, metrics such as mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) are commonly employed.

Automotive system faced in the past decade an abundance of security services proposed by the scientific literature to strengthen their system security. The solutions solve problems in terms of key distribution, data authentication, or system monitoring. While the volume of research done brings in consequence novel ideas, strong validation and extensive experimentation is a must to prove their viability and correctness. Consequently, the work at hand offers a formal analysis of two existing security services for automotive systems, namely for a Key Distribution Service (KDS) and for a data authentication and aggregation method titled Mixed data authentication for Controller Area Network (MixCAN). While the KDS aims to distribute long-term and short-term cryptographic keys, MixCAN envisions a lightweight authentication protocol through Encrypted Bloom Filters (EBFs). The objective of the formal analysis is to prove the correctness of the mentioned security solutions through a Burrows-Abadi-Needham (BAN) logic analysis.

In this paper, a low-profile wideband substrate-integrated waveguide (SIW) horn antenna is proposed. The antenna structure with a thicker substrate is designed to produce a smoother transition with lower reflection and radiation loss. The SIW horn is designed by gradually detaching the upper and lower metallic plane to enhance bandwidth and realize a smooth transition from the dielectric horn to free space. Additionally, air vias are arranged in a specific pattern in the flare part of the antenna to improve radiation performance and gain. The proposed SIW horn antenna demonstrates a wide operating bandwidth of 15 GHz to 25 GHz and achieves a high gain of up to 9.3 dBi.

In this paper, a self-oscillating voltage tuning antenna using varactor diode is presented. While Self-Oscillating Active Antenna (SOAA) can be realized in different ways, in this work we made direct integration of the transistor and the C-shaped ring radiator (feedback circuit). Specifically, we study two parts of the configuration, the first for the design of a self-oscillating antenna that oscillates exactly at 5.432 GHz. The second goal is to design the active SOA to oscillate in the interval [5.16–5.8] GHz; this is the objectif of writing this communication. The oscillator is designed on a Rogers R04003C substrate. The adopted substrate height is ℎ = 0.508 mm, the dielectric constant $$\varepsilon_{r}$$ ε r  = 3.38, and the loss tangent is 0.0027. A harmonic balance simulation is used to get the out-put voltage spectrum. The obtained results are based on the simulations carried out with Advanced Design System (ADS) tool. The resonant frequency is 5.432 GHz, with an output power of 8.23 dBm. Finally, we managed to obtain a SOA whose resonance frequency sweeps at the proposed interval (5 GHz WIFI WLAN band).

An X-band GaAs Monolithic Microwave Integrated phase shifter for phased array antenna is designed. The phase shifter (PS) Monolithic Microwave Integrated Circuits (MMIC) provides 16 phase shift states between 0° and 360° in increments of 22.5°. The design includes high-pass/low-pass networks for the 180° and 90° phase, and switched filter networks for the 45° and 22.5° phase. The GaAs HEMTs are used as key components for controlling the phase shifter. All drain and source terminals are DC grounded while the gates are applied to 0 V or −3 V.The phase shifter obtains 5 ± 2.1 dB insertion loss and the input and output return losses are higher than 6.5 dB over the 8–12 GHz bandwidth of interest. The chip area is very compact, just about 3.5 mm x 1.6 mm, including all pads.

Plasmonic chemical and biological sensors have many advantages such as compact sizes and extremely high sensitivity. Biosensors based on plasmonic waveguides and resonators are one of the most attractive candidates for industrial applications of nanotechnology. A Metal-Insulator-Metal (MIM) waveguide structure consisting of Split Square Ring Resonator (SSRR) was proposed and numerically investigated by using the Finite Element Method (FEM). After a detailed analysis of the transmission characteristics and magnetic field strength of the structure, the structure can generate two Lorentzian sharp resonances where it was found that resonance wavelength and transmittance can be achieved by adjusting the width of the split. In addition, after optimizing the geometric parameters, the refractive index sensing sensitivity (S) and figure of merit (FOM) of the structure can be optimal, which are $$1320\,nm/RIU$$ 1320 n m / R I U and $$\mathrm{50,6 }\,{RIU}^{-1}$$ 50 , 6 RIU - 1 , respectively. On this basis, the structure could have a great potential in optical refractive index sensing in the biological, micro and nano fields.

In this paper the fractal iteration technique has been applied to obtain the second iteration of folded slot antenna. This fractal antenna has been designed on substrate Roger RT6002 with dielectric constant $${\varepsilon }_{r}=2.94$$ ε r = 2.94 and substrate thickness $$h=0.76$$ h = 0.76 mm. The results show that there is a relationship between the iteration number and the resonance frequency. With an increase in the number of iterations, the resonance frequency decreases with a constant ratio. The use of fractal structures to design antennas makes them more miniaturized.The simulation and optimization are performed using CST Studio suite. The result shows that the operating frequency decreased from 8.24GHz to 7.33GHz.

The purpose of this investigation is to assess various properties of the antenna at 28 GHz, such as return loss, Voltage Standing Wave Ratio (VSWR), gain, bandwidth, surface current, and radiation pattern values. The antenna properties were developed through simulations using CST software. Rogers 5880 substrates with a dielectric constant (εr) of 2.2 and a substrate thickness (h) of 0.508 mm were utilized, along with copper patches having a thickness of 0.035 mm. Initially, single antennas were designed to achieve the desired parameters, followed by the creation of antenna arrays such as 1 × 2, 1 × 4, and 1 × 8. The objective of planning the array antenna is to enhance both the antenna gain and directivity. The simulation results demonstrate incremental values for single antennas, 1 × 4 antenna arrays, and 1 × 8 antenna arrays. The return loss values achieved are –58 dB, –38 dB, and –34 dB respectively. Additionally, the gain values obtained are 7.3 dB, 12.5 dB, and 15.3 dB respectively. Furthermore, the antenna bandwidth is measured at 0.7 GHz, 0.82 GHz, and 1.5 GHz respectively.

Substrate integrated waveguide represent a promising solution of designing performing circuits with a low-cost, high-quality factor and it is easy to be integrated with other planar circuits. This paper presents the design of a microstrip diplexer through a substrate-integrated waveguide. The diplexer consists of two independently designed SIW circular cavity filters combined together using a T-junction with center frequencies of 9 and 10 GHz, respectively.