In this quickly developing world, automatic currency identification and recognition are crucial tasks. Several financial institutions, such as banks and hardware-based devices such as vending machines and slot machines, play an essential role in all monetary unification fields. Accurate coin recognition is essential in various contexts, including vending machines, currency exchange, and archaeological research. However, the distinctive visual characteristics of Brazilian coins, including variations in size, color, and design, pose significant challenges for automated classification. Most of the existing currency recognition systems are based on the physical properties of the currencies, such as length, breadth, and mass. At the same time, image-based methods rely on other properties like color, shape, and edge. This paper presents a novel deep-learning framework tailored to classify Brazilian coins. Our proposed deep learning framework leverages state-of-the-art convolutional neural networks (CNNs) to address these challenges. We introduce a Repetitive Feature Extractor Convolution Neural Network (RFE-CNN) model to recognize the currency faster and accurately. Our framework employs a multi-stage approach for coin classification. First, a pre-processing module handles coin localization and image enhancement to mitigate variations in lighting and background. Next, an RFE-CNN-based feature extractor extracts discriminative features from the coin images. We explore transfer learning from pre-trained models to enhance the model’s generalization capability, given limited data availability. We used a comprehensive dataset of Brazilian coins, comprising various denominations, minting years, and conditions, to facilitate model training and evaluation. The dataset includes high-resolution images captured under diverse lighting and environmental conditions, ensuring robust model performance in real-world scenarios. In conclusion, our proposed deep learning framework offers a powerful and efficient solution for classifying Brazilian coins. The framework’s adaptability makes it a valuable tool for recognizing coins from other regions with similar visual diversity and variability challenges. The proposed model has achieved a classification accuracy of 98.34% for the classification of Brazilian coins.

The swift advancement of electric vehicle (EV) technology enhances the focus on sustainable energy storage and underscores the crucial significance of lithium-ion batteries. This research primarily presents the techniques of forecasting the Remaining Useful Life (RUL) of lithium-ion battery using advanced Machine Learning (ML) methods such as Random Forest (RF) and Support Vector Machine (SVM). This research centres around the thorough preprocessing of a detailed dataset received from the NASA Ames Prognostics Center of Excellence. The One-way ANOVA method is employed to find the optimum set of features. The exhaustive hyperparameter-tuning (HPT) was performed to boost the performance of the ML models. An important component of this study is its pragmatic methodology, which considered real-time variables such as temperature changes and usage cycles to analyses the effect on battery capacity (cap). The proposed system helped to understand the behaviors of battery deterioration trends more comprehensively. The effectiveness of the system is decided based on the R2 score and Mean Squared Error (MSE). The RF model has shown R2 score of 0.83 and MSE of 1.67. The result enhances lithium-ion battery safety and efficiency by establishing new predictive models. Thus, it provides a better battery management system for electric vehicles. As a result, it promotes the development of more sustainable and economical energy solutions.

Nitric acid is one of the most important products in the chemical industry, ranking third globally in terms of acid production. Although nitric acid has many industrial applications, its primary function is the production of ammonium nitrate, which is used in the fertilizer industry. In this report, we propose a plan for an Ostwald process plant that will produce 1000 metric tons of nitric acid per day. Based on an effective energy analysis, we have concluded that using a single pressure method provides optimal results. First, ammonia is vaporized using process heat at 1000 kPa and 35 °C before being superheated using steam to a temperature of 80 °C. Filtered air is compressed by an axial compressor to a discharge pressure of approximately 740 kPa and a temperature of 155 °C. After conducting a site evaluation, three existing manufacturing sites are being considered for the acid production plant: Ogun State (Nigeria), Gwadar Seaport (Pakistan), and Ras-Alkhair Seaport (Saudi Arabia). Based on the results of the site assessment, Ras-Alkhair has been selected as the most suitable location for the nitric acid plant. About 65 % of all nitric acid produced worldwide is used in the production of ammonium nitrate, which is in turn used in the fertilizer and explosives industries. The synthetic nitric acid that will be produced from this plant will be used in the production of fertilizers.

The study highlights the primary Industrial AI trends in research and implementation of modern technologies and application domains for AI based on real-time data analytics. It recognizes, identifies, and explains the fundamental designing principles that play a role in the integration of AI into various applications.

The purpose of this article is to explain a new technology for monitoring floods. This article describes a cost-effective and easy-to-use system for timely warning people about impending floods to save lives and minimize damage during disasters. The suggested system will help emergency management teams monitor floods and predict future water levels to provide ample information to communities likely to be hit by floods. Based on the assessment of current information-gathering techniques and public policies used to manage floods, the authors established that a smart flood monitoring system is more effective in warning individuals of impending floods. As an example, we illustrate that synthetic artificial neural networks can significantly improve the ability to spot floods as soon as they occur. Our method significantly reduces the risk posed by floods by accurately predicting their occurrence using computer forecasts based on meteorological data. The new method for effectiveness evaluation is confirmed by the increasing number of developing countries using related systems to fight floods because of their low costs and reliability. New results about the usefulness of the system will improve its use in managing floods and can be the basis for changes in the methods for emergency preparation in various regions. This study is novel because the proposed flood monitoring system is based on existing technologies and can be used to record and disseminate data on floods with minimal interference. Keywords: Flood, Climate, Temperature Sensor, Internet of Things DOI： https://doi.org/10.35741/issn.0258-2724.58.4.13

One of the deadliest diseases in this day and age is cancer. In order to find the best treatment, numerous researchers from various professions are working together. Magnetic nanoparticle hyperthermia is one of the therapeutic options (MNPH). In order to do this, magnetic nanoparticles (MNPs) are inserted into the tissues of cancerous tumors, and an external coil carrying current is used to produce an alternating magnetic field (AMF) that activates the MNPs inside the tumor tissues. In exchange, activation of the MNPs would provide concentrated heat at the sites of the tumors, which has the potential to be potent enough to kill tumor tissues. Eddy currents are produced at nearby healthy tissues as a result of alternating magnetic and electric fields, which is also known as a magnetic field reversal. Particularly when large currents (up to several hundred Amps) offer the necessary AMF intensity to treat deep tissue malignancies, the associated eddy currents cause unwelcome heat and harm the nearby healthy tissues. A complete computational and graphical electromagnetic examination of a genuine virtual human model is presented in this research. With various coil designs, specific absorption rates (SARs) are computed and displayed for various bodily tissues. The ideal coil arrangement with the lowest eddy currents on the surrounding healthy tissues and the highest AMF at the MNPs injected into the prostatic tumor was determined.

Applications of Cyber-Physical Systems (CPSs) greatly influenceseveral industrial sectors. Treat-ing security-related concerns with utmost seriousness is necessary for the CPS to work correctly. Although CPS supervises the manufacturing process, the type and volume of cyberattacks that try to obtain data from CPS are significantly increasing. Since attacks on CPS can disrupt production, cause financial losses, and endanger national security, they must be prevented and detected. The general operation of the physical process can nevertheless be affected, and system failure is caused by specific traditional measures designed to anticipate CPS cyber-attacks. Also, as the system appears to be extremely complicated and no pertinent information about the item under investigation is available, the productive prediction of cyber-attacks in CPS remains a complex problem. This work will handle these issues using the proposed framework using Transfer Learning with the VGG16 model. The proposed TL-VGG16 achieves 96% accuracy, higher than existing CPS intrusion detection techniques.

Recently, the focus of international conflicts has shifted from military to scientific and technological with the rapid development of electronic devices, which also generate electromagnetic pollution. To overcome and/or mitigate this pollution, the fabrication of lightweight and high-efficiency microwave-absorbing materials (MAMs) has been an urgent task for the scientific community. MAMs possess significant potential across domains such as information security, 5 G communication, energy harvesting, diminished radar cross-section, and advancements in military stealth technology. Two-phase LaFeO3/CoS2 nanocomposite was fabricated to be expected as promising MAMs. Four different LaFeO3/CoS2-based MAMs showed different microwave absorption performances, where low filler loading samples showed poor features and high filler loading samples showed excellent features. MAMs with 30 and 40 wt% filler loading (named LFC-30 and LFC-40) could reach excellent values for both reflection loss (RL) and effective absorption bandwidth (EAB). The RL values were -34 and -46 dB, and the EAB values were 3.1 and 4.0 GHz for LFC-30 and LFC-40, respectively. The RL and EAB values of LFC-40 could absorb similar to 99.99% of incident microwave and mostly cover the whole X band with a thin thickness of 1.6 mm. The excellent microwave absorption performance of LFC-40 could also be confirmed by the free-space method. Superior dielectric and magnetic loss tangents, rich relaxation polarization processes, good impedance matching, and a high attenuation constant could be the origins of high-efficiency MAMs in LFC-40.

This paper investigates electromagnetic field interaction with human tissue during magnetic nano-particles hyperthermia (MNPH). Namely, the specific absorption rates (SARs) are computed and presented across different tissues in the body with different coil configurations for deep seated cancers, such as pancreatic cancers. Special attention is given to the coil placement in front and at the back of the human model centered at the pancreas. The studies have showed that the from coil produces less unwanted eddy currents in the normal tissues than back coil.

Cancer is one of the deadliest diseases in this era. Currently many researchers from different fields are collaborating to find an optimum cure. One of the treatment methods involves the use of Magnetic Nanoparticles Hyperthermia (MNPH). This involves the insertion of Magnetic Nanoparticles (MNPs) at cancerous tumors locations and using an external current carrying coil to apply an alternating magnetic field (AMF) that would activate the MNPs located inside the tumor tissues. In return, the activation of the MNPs would result in concentrated heat at tumor locations, which can be intense enough to destroy tumor tissues. Alternating magnetic fields means also the introduction of electric field, which results in eddy currents at surrounding healthy tissues. The resulted eddy currents produce unwanted heat and damage the surrounding healthy tissues especially when high currents (up to several hundred Amps) is used to deliver the required AMF intensity for deep tissues tumors. This paper presents a full computational and graphical electromagnetic study of a real human model. Specific absorption rates (SARs) are computed and presented across different tissues in the body with different coil configurations. The optimum coil configuration with the maximum AMF at the pancreatic tumor injected MNPs and minimum eddy currents on surrounding healthy tissues was deduced.

Cloud architectures provide abundant resources, albeit with prolonged link delays. Meanwhile, fog architectures provide limited resources at the expense of limited resources. Hence, hybrid fog-cloud solutions have been proposed to combine the benefits of both architectures. However, users demand low latency and high processing still cannot be accommodated using the hybrid model. Therefore, this paper presents a heterogeneous single-tier fog architecture suitable for data-intensive and delay-sensitive requests, which represents the worst-case scenario for the type of incoming requests from the network perspective. Moreover, an efficient service function chain provisioning scheme is implemented here on the proposed architecture. The scheme leverages coordinated generalized pattern search algorithm and yields reduced network delays and energy consumption as opposed to the hybrid solution.

A tapered fork-shaped antenna having small ground for ultra-wide band (UWB) antenna is proposed in this paper. Finite element method has been successfully employed to simulate and optimize the feed line, ground, and tapered fork-shaped diameter to enhance the performance of the antenna in terms of bandwidth obviously for the ultra-wideband purposes. An acceptable impedance matching performance has been achieved, which is a band wider than the UWB band that is defined by the Federal Communications Commission (FCC). In this paper, the detailed design parameters including the key elements in bandwidth enhancement is presented. The results of the finite element simulations have been presented as well indicating the return loss and radiation pattern of the proposed antenna.

Antennas are essential elements in development of wireless communication systems in different sectors such as, medical, communications, industrial, urban planning and even the oil and gas sector. Designing an antenna for global positioning systems needs to meet a certain criteria like the right bandwidth, the impedance matching at the operating frequency and an omnidirectional radiation pattern. In this paper, a printed antenna is proposed to be used for smart tracking and positioning of vehicles and equipment or the oil and gas fields, by providing the ability to easily track work from site to site. The antenna has been simulated by a fill wave finite element based solver and the geometry is optimized for the best performance at 2 main operating frequencies reserved for GPS. The optimized antenna has been fabricated on the results of the measurement are presented as well.

As the majority of the radio localization applications are adopting the 5.8 GHz band, in this paper, an elliptical slotted patch antenna with a defected ground structure is proposed for wideband Applications. The -10dB S11 bandwidth is 7.8GHz (4.1 GHz to 11.9 GHz).. The effective directional radiation patterns and the large impedance bandwidth has been witnessed from simulation results. The proposed antenna is simple in design and compact in size, while providing broadband impedance matching.

The new 3-D proposed structure has a stop band at 2.45 GHz. This stop band is the operating frequency for common microwave heating devices. The structure is periodic. The infrared emissions are passed through a large enough opening inside the microwave cavity in order to allow the temperature of the object inside to be read by the thermopile installed outside the chamber. Using the FE Eigen analysis it was possible to design the unit cell of the structure in order to locate the stop bands. After locating the stop bands the structure was analyzed deterministically using an FE solver, then three unit cells was fabricated. Through the three unit cells fabrication and measurements satisfactory results have been achieved when the proposed structure was tested in a commercial microwave acid digestion system.

A Brain Computer Interface (BCI) is a communication system that receives neurological signals from the brain and translates them into control commands for electrical (e.g., computer mouse) and electromechanical (e.g., Wheelchair) devices. The development of such systems was intended originally to aid individuals with a condition called locked-in syndrome. Individuals with this condition have lost all their voluntary muscle control but remain cognitively intact (i.e., mentally aware of their surroundings- can feel emotions, recognize objects/people but are unable to move). This means that they are trapped in their own bodies. The use of BCI may one day improve the independence and quality of life of people with this disability.

This paper presents a comparison between various feeding tesingle feed aperture coupledchniques of a circularly polarized micro-strip antenna design intended to operate at the Global Navigation Satellite Systems (GNSS) frequencies. The proposed antenna is composed of a micro-strip slotted patch antenna printed on a Rogers RO3006 substrate, a foam layer of 2 mm thick. The single feed aperture coupled antenna design consisted of a single microstrip line placed on a Rogers RO3006 substrate and two ground plane slots. The dual feed aperture coupled antenna design consisted of two microstrip lines placed on a Rogers RO3006 substrate, two ground plane slots and a wideband commercial 3-dB SMT coupler. The dual feed coaxial cables consisted of two coaxial cables that are connected to 3-dB SMT coupler. The aperture coupling feed antenna design and especially the single feed design showed remarkable antenna matching and total efficiency. The combined full-wave antenna results with the measured S-Parameters of the coupler showed very good performances in terms of antenna matching and axial ratio on larger bandwidths.

ABSTRACTThis paper presents a novel design for an optimized circularly polarized microstrip antenna. Circular polarized microstrip antennas have many uses due to its low profile design. However, one of the microstrip antenna's main limitations arises from its narrow frequency bandwidth. Therefore, it is necessary to use special design techniques in order to increase the operating frequency bandwidth while maintaining all of the other respectable optimum radiation characteristics. The proposed design involves using a dual‐feed square patch, which is gap coupled with two parasitic patches in a mirrored L‐shaped configuration. This design resulted in an axial ratio bandwidth of 11%, S11 bandwidth of 14% and a gain of 9 dB at the 2.15 GHz center frequency. The proposed design was built, and the measured results were in agreement with the simulation results. Additionally, this antenna design was used in an array structure in order to increase the gain, while maintaining a good axial ratio. These enhanced radiation results allow the use of the proposed antenna design in many domains such as wireless and military applications without facing the limitations of the narrow frequency bandwidth. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:597–603, 2016

This paper presents a design of a micro-strip circularly polarized antenna intended for the Global Navigation Satellite Systems (GNSS). The presented device is composed of a micro-strip slotted patch antenna printed on a Rogers RO3006 substrate, a foam layer of 2 mm thick and a wideband commercial 3-dB SMT coupler. The combined fullwave antenna results with the measured S-Parameters of the coupler shows very good performances in terms of antenna matching and axial ratio on larger bandwidths.