Proposed System Research Articles

Risk, Sensitive Analysis, and Optimization of a New Multi-generation System Activated by Solar and Biomass Energies

In recent years, the use of clean energies has become much more important due to the increase of environmental pollutants and the reduction of fossil fuel resources. Another important parameter in the establishment of energy systems is the assessment of the risks caused by the construction of the system and the provision of operational solutions to manage and reduce these risks. In this research, a new multi-generation system that is activated using solar and biomass energies has been evaluated thermodynamically and thermo-risky. This system consists of a Brayton cycle, a steam Rankine cycle, a transcritical CO2 cycle, a proton membrane electrolyzer (PEME) unit, and an internal combustion engine (ICE). A notable characteristic of the proposed system is its capability to recover waste heat from the gas turbine cycle, which is subsequently utilized to drive both a steam Rankine cycle and a transcritical CO2 cycle. The sensitivity analysis has been performed to investigate the effects of the input parameters on the system performance and finally, an optimization for the effective parameters of the system has been conducted. The results of the calculations show that the proposed system has energy and exergy efficiencies of 22.05% and 15.93%, respectively. The risk analysis was conducted by considering the potential hazards such as jet fires, jet of combusted gases, and system overpressure. The analysis indicates that the system poses a low level of risk. The overall risk, along with the risk per unit of system exergy, has been estimated at 4.247 ×10-8 injuries/year and 3.73 ×10-11 injuries/year.kW, respectively. In optimal conditions, the energy and exergy efficiency of the system reaches 25.79% and 19.08%.

On the Need to Develop a Federal Program to Optimize the Economic Base of Public Control in Russia

Annotation. This article is devoted to the analysis of the prospects for the development and adoption of a federal program aimed at optimizing the economic basis of public control in the Russian Federation. The authors substantiate the position that one of the main problems hindering the development of public control in the country is its weak economic base. Due to the fact that public authorities play an important role in the process of formation and functioning of subjects of public control in Russia, the basis for financing the organization and activities of public control are funds from state and municipal budgets. In this regard, the authors substantiate the need for the development and adoption by the Government of the Russian Federation of a comprehensive federal program dedicated to strengthening organizational, technological, material and property independence and independence of subjects of public control. The work substantiates the need to include in the draft of the above-mentioned federal program sections devoted to: ensuring the material and technical independence of subjects of public control from public authorities; ensuring their financing primarily from protected items of public budgets; development of processes for the use of modern information and communication technologies by these entities; consolidation of a system of criteria for assessing the effectiveness and efficiency of the work of subjects of public control as the main condition for increasing the volume of their funding; consolidating a system of control on the part of public authorities regarding budget financing of the activities of these entities; systems for organizing public-private partnerships in the field of public control. In addition, the work substantiates the system of proposals for participation in the development of the above-mentioned federal program of the country's Public Chamber.

RFID BASED BUS TICKETING SYSTEM

The main objective of this paper is to generate passenger tickets using RFID technology for the fare of a particular destination. The appropriate amount is deducted from the RFID card. In addition to that, in proposal system RFID has proven to be one of the most promising technologies in recent years and can be effectively employed in various applications since it is economical and widely used tool for tracking and locating purposes.Radio-frequency identification (RFID) is a technology that uses electromagnetic fields to automatically identify and track tags attached to objects .A small radio transponder, a radio receiver, and a radio transmitter make up an RFID system The RFID application, on the other hand, has become a popular tool for both tracking transit vehicles and the public ticketing system describes a design of IoT enabled real time bus tracking system There is an mobile app to recharge the RFID card.The message of money transaction is send to the users registered mobile number.The main aim of this project is to reduce the fraud in ticket transaction.The bus door will open only after scanning the RFID card.In this project we use IR sensor to detect the passengers entry and exit from the bus.When the passenger scan the RFID card then the LCD display shows the passengers information like their name,RFID card number,account balance.The ticket price is based on the distance travelled in the bus.This system also used to prevent the unfair ticket price for transportation.If the RFID card is missing then the passenger can able to block the RFID card to prevent unauthorized access.Public transportation is a significant role for methods among individuals. Key words—Radio Frequency Identification System (RFID), Infrared sensor (IR sensor).

An FPGA-Accelerated Quartz Crystal Measurement System Based on a Negative Feedback Loop With Quadrature Demodulation

Rapid measurement of the resonant frequency of quartz crystal (QC) devices plays a key role in applying QC-based dynamic monitoring, such as respiration monitoring and kinetic process in electrochemical reactions. This work develops an FPGA-accelerated QC measurement system without software-based curve fitting. A negative feedback loop based on the susceptance response collected by the quadrature demodulation circuit is constructed. Moreover, a proportional-integral-differential control and tracking strategy is applied to improve the measurement speed significantly. The test results show that the implemented system can track a frequency shift of 5 kHz bandwidth within 50 ms. The standard deviation of the measured frequency of a QC device under test is only 0.17 Hz, which indicates that the proposed system is high stability and high precision. The frequency measurement results have a relative error of 0.8 ppm compared with the Agilent 4294A impedance analyzer. It is demonstrated that the proposed QC measurement system has great potential for QC-based dynamic detection.

Secure deep learning-based energy efficient routing with intrusion detection system for wireless sensor networks

In general, wireless sensor networks are used in various industries, including environmental monitoring, military applications, and queue tracking. To support vital applications, it is crucial to ensure effectiveness and security. To prolong the network lifetime, most current works either introduce energy-preserving and dynamic clustering strategies to maintain the optimal energy level or attempt to address intrusion detection to fix attacks. In addition, some strategies use routing algorithms to secure the network from one or two attacks to meet this requirement, but many fewer solutions can withstand multiple types of attacks. So, this paper proposes a secure deep learning-based energy-efficient routing (SDLEER) mechanism for WSNs that comes with an intrusion detection system for detecting attacks in the network. The proposed system overcomes the existing solutions’ drawbacks by including energy-efficient intrusion detection and prevention mechanisms in a single network. The system transfers the network’s data in an energy-aware manner and detects various kinds of network attacks in WSNs. The proposed system mainly comprises two phases, such as optimal cluster-based energy-aware routing and deep learning-based intrusion detection system. Initially, the cluster of sensor nodes is formed using the density peak k-mean clustering algorithm. After that, the proposed system applies an improved pelican optimization approach to select the cluster heads optimally. The data are transmitted to the base station via the chosen optimal cluster heads. Next, in the attack detection phase, the preprocessing operations, such as missing value imputation and normalization, are done on the gathered dataset. Next, the proposed system applies principal component analysis to reduce the dimensionality of the dataset. Finally, intrusion classification is performed by Smish activation included recurrent neural networks. The proposed system uses the NSL-KDD dataset to train and test it. The proposed one consumes a minimum energy of 49.67 mJ, achieves a better delivery rate of 99.92%, takes less lifetime of 5902 rounds, 0.057 s delay, and achieves a higher throughput of 0.99 Mbps when considering a maximum of 500 nodes in the network. Also, the proposed one achieves 99.76% accuracy for the intrusion detection. Thus, the simulation outcomes prove the superiority of the proposed SDLEER system over the existing schemes for routing and attack detection.

Hybrid classification of XGBoost-based ADAM optimization for coronary artery disease diagnosis

A common cardiovascular illness with high fatality rates is coronary artery disease (CAD). Researchers have been exploring alternative methods to diagnose and assess the severity of CAD that are less invasive, cost-effective, and utilize noninvasive clinical data. Machine learning algorithms have shown promising and potential results. Accordingly, this study focuses on assisting medical practitioners with CAD detection by using a hybrid classification system combining XGBoost and Adam optimization. The primary approach incorporates One-Hot encoding to transform categorical attributes within the dataset, enhancing the precision of predictions. The secondary approach constitutes a hybrid classification model integrating XGBoost and employing Adam optimizations for CAD detections. The efficacy of the recommended method is assessed using the cleveland, Hungarian, and Statlog heart-disease data sets. The proposed system and the standard Grid and Random Search classifiers are compared. The experimental outcomes indicate that the suggested model achieves a notable prediction accuracy of 94.19%. This represents an improvement of 7 to 8% over the existing grid search algorithm and 2 to 3% improvement over the random search algorithm for the above all datasets. Hence, the proposed system can be a valuable tool for identifying CAD patients, offering enhanced prediction accuracy.

Authorization of Aadhar data using Diffie Helman key with enhanced security concerns

In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time.

Reducing shallow foundation rotation due to reverse faulting using predefined shear planes

Permanent ground displacement due to faulting during strong earthquakes threatens the stability of civil structures. To protect surface foundations from faulting, several mitigation techniques have been developed. Owing to the limitations of earlier mitigation strategies for unidentified reverse faulting conditions, this research presents a novel mitigation system, including the installation of sliders beneath the wings of a V-shaped concrete element, to reduce footing rotation. To evaluate the effectiveness of the suggested mitigation strategy, concrete strength tests, a physical centrifuge test and a comprehensive numerical study for different fault locations and dip angles were conducted for the first time. Results demonstrated that the placement of the sliders beneath the V-shaped wings had a significant effect on dissipating the fault dislocation. Compared to the previous mitigation techniques, the proposed system decreased the foundation rotation from a high value (11°) to a low value (3·2°) for the worst-case scenarios. Furthermore, the friction coefficients among sliders and concrete, the concrete wing inclination angle, length and thickness were critical parameters to design the optimum proposed system. In contrast, soil layer thickness and relative density did not significantly affect the footing rotation.

Fuzzy logic controller-based Luo converter for light electric vehicles

<p>A brushless DC motor (BLDC) drive fed by a Luo converter is presented in this paper for the use in electric vehicle (EV) applications. The proposed Luo converter provides stable and ripple free output for EV. An approach for getting the stable output voltage is by using a fuzzy logic controller to control the Luo converter. It helps to generate the appropriate pulse with respect to the feedback voltage. This proposed system has the advantages like voltage increase, high-gain output with low ripples and high efficiency. The performance of this proposed drive is tested through hardware prototype at varying line voltage levels and results are demonstrated. A comparative analysis is presented to justify the effectiveness of the proposed Luo converter fed EV motor.</p>

DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework.

The global prevalence of childhood and adolescent obesity is a major concern due to its association with chronic diseases and long-term health risks. Artificial intelligence technology has been identified as a potential solution to accurately predict obesity rates and provide personalized feedback to adolescents. This study highlights the importance of early identification and prevention of obesity-related health issues. To develop effective algorithms for the prediction of obesity rates and provide personalized feedback, factors such as height, weight, waist circumference, calorie intake, physical activity levels, and other relevant health information must be taken into account. Therefore, by collecting health datasets from 321 adolescents who participated in Would You Do It! application, we proposed an adolescent obesity prediction system that provides personalized predictions and assists individuals in making informed health decisions. Our proposed deep learning framework, DeepHealthNet, effectively trains the model using data augmentation techniques, even when daily health data are limited, resulting in improved prediction accuracy (acc: 0.8842). Additionally, the study revealed variations in the prediction of the obesity rate between boys (acc: 0.9320) and girls (acc: 0.9163), allowing the identification of disparities and the determination of the optimal time to provide feedback. Statistical analysis revealed that the performance of the proposed deep learning framework was more statistically significant (p 0.001) compared to the other general models. The proposed system has the potential to effectively address childhood and adolescent obesity.

Cross-Chain Digital Asset System for Secure Trading and Payment

Blockchain as a ledger technology is attractive without the need for central servers. There are many types of blockchains in different fields, such as digital asset trading and payment, which have business interactions. In the fields, the digital asset and payment information on their blockchains need to be securely cooperative with each other. However, some business activities are on different blockchains, which have different consensus algorithms and network architectures, thus limiting the interoperability among these activities and making each blockchain an island. Cross-chain technology can connect different blockchains and realize the interoperability and sharing of information among them. This work designs a cross-chain digital asset system for secure trading and payment. It builds two parallel chains, i.e., digital asset chain (DAC) and payment chain (PC), and their functions are analyzed and designed. The cross-chain, i.e., relay chain (RC), is used to realize cross-chain interoperability, where the cross-chain message format and authority setting are designed to endow parallel chains with the ability to recognize. The decentralized characteristic of the RC allows cross-chain messages to be safely transmitted to ensure secure trading and payment. Through testing and analysis, the proposed system can provide more secure trading and payment than its peers.

Image quality evaluation: evaluation of the image quality of actual images by using machine learning models

Evaluating image features is a significant step in image processing in applications like number plate detection, vehicle tracking and many image processing-based applications. Image processing-based applications need accurate parts to get the best outcomes. Feature detection is done based on various feature detection techniques. The proposed system aims to get the best feature detector based on the input images by evaluating the image features. For assessing the image features, the proposed system worked on various descriptors like oriented FAST and rotated brief (ORB), learned arrangements of three patch codes (LATCH), binary robust independent elementary features (BRIEF), and binary robust invariant scalable keypoints (BRISK) to extract and evaluate the features using K-nearest neighbor (KNN)-matching and retrieve the inliers of the matching. Each descriptor produces different matching features and inliers; with the matchings and inliers, the inlier ratio calculates to show the analysis. To increase performance, we also examine adding depth information to descriptors.

Effective privacy preserving in cloud computing using position aware Merkle tree model

In this research manuscript, a new protocol is proposed for predicting the available space in the cloud and verifying the security of stored data. The protocol is utilized for learning the available data, and based on this learning, the available storage space is identified, after which the cloud service providers allow for data storage. The Integrity verification separates the private and the public data, which avoids privacy issues. The integration of the private data is done with the help of cloud service providers with respect to the third-party auditing (TPA). Earlier, public key cryptography and bilinear map technologies have been combined by the researchers, but the computation time and costs were high. To secure the integrity of the data storage, the client should execute several computations. Therefore, this research suggests a reliable and effective method called position-aware Merkle tree (PMT), which is implemented for ensuring data integrity. The proposed system uses a PMT that enables the TPA to perform multiple auditing tasks with high efficiency, less computational cost and computation time. Simulation results clearly shows that the developed PMT method consumed 0.00459 milliseconds of computation time, which is limited when compared to the existing models.

Portable smart attendance system on Jetson Nano

The masked face recognition-based attendance management system is an important biometric-based attendance tracking solution, especially in light of the COVID-19 pandemic. Despite the use of various methods and techniques for face detection and recognition, there currently needs to be a system that can accurately recognize individuals while they are wearing a mask. This system has been designed to overcome the challenges of widespread mask use, impacting the effectiveness of traditional face recognition-based attendance systems. The proposed system uses an innovative method that recognizes individuals even while wearing a mask without the need for removal. With its high compatibility and real-time operation, it can be easily integrated into schools and workplaces through an embedded system like the Jetson Nano or conventional computers executing attendance applications. This innovative approach and its compatibility make it a desirable solution for organizations looking to improve their attendance-tracking process. The Experimental results indicates using maximum resources possible the execution time needed on Jetson Nano is 15 to 22 seconds and 14 to 18 seconds respectively and the average frame capture if there are at least one face detected on Jetson Nano is 3-4 frames.

A Sinusoidal Doubly Salient Electromagnetic Machine Drive Fed by Third-Harmonic Injection Two-Stage Matrix Converter With Integrated Injection Inductor

This manuscript studies a sinusoidal doubly salient electromagnetic machine (SDSEM) drive fed by third-harmonic injection two-stage matrix converter (3TSMC) with integrated injection inductor to improve the power density of the whole system. First, compared with the conventional matrix converter (MC), the 3TSMC suffers from larger volume due to the inductor in the harmonic current injection circuit. In the proposed system, the harmonic injection current is synthesized by the field current of SDSEM and the injection inductor is removed. Then, the control strategy is proposed to decouple the current injection control and field current control, which means that the harmonic current and the field current can be regulated flexibly. Finally, a prototype is built to evaluate the proposed system and control strategy. Furthermore, the proposed 3TSMC-fed drive with the corresponding control strategy is also suitable for the other DC-excited three-phase sinusoidal AC machines, such as wound rotor synchronous machines.

A Wearable All-Printed Textile-Based 6.78 MHz 15 W-Output Wireless Power Transfer System and Its Screen-Printed Joule Heater Application

While research in passive flexible circuits for Wireless Power Transfer (WPT) such as coils and resonators continues to advance, limitations in their power handling and low efficiency have hindered the realization of efficient all-printed high-power wearable WPT receivers. Here, we propose a screen-printed textile-based 6.78 MHz resonant inductive WPT system using planar inductors with concealed metal-insulator-metal (MIM) tuning capacitors. A printed voltage doubler rectifier based on Silicon Carbide (SiC) diodes is designed and integrated with the coils, showing a power conversion efficiency of 80-90% for 2-40 W inputs over a wide load range. Compared to prior wearable WPT receivers, it offers an order of magnitude improvement in power handling along with higher efficiency (approaching 60%), while using all-printed passives and a compact rectifier. The coils exhibit a simulated Specific Absorption Rate (SAR) under 0.4 W/kg for 25 W received power, and under 21 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> C increase in the coils' temperature for a 15 W DC output. Additional fabric shielding is investigated, reducing harmonics emissions by up to 17 dB. We finally demonstrate a wirelessly-powered textile-based carbon-silver Joule heater, capable of reaching up to 60 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> C at 2 cm separation from the transmitter, as a wearable application which can only be wireless-powered using the proposed system.

An automated system for classifying types of cerebral hemorrhage based on image processing techniques

The brain is one of the most important vital organs in the human body. It is responsible for most of the body’s basic activities, such as breathing, heartbeat, thinking, remembering, speaking, and others. It also controls the central nervous system. Cerebral hemorrhage is considered one of the most dangerous diseases that a person may be exposed to during his life. Therefore, the correct and rapid diagnosis of the hemorrhage type is an important medical issue. The innovation in this work lies in extracting a huge number of effective features from computed tomography (CT) images of the brain using the Orange3 data mining technique, as the number of features extracted from each CT image reached (1,000). The proposed system then uses the extracted features in the classification process through logistic regression (LR), support vector machine (SVM), k-nearest neighbor algorithm (KNN), and convolutional neural networks (CNN), which classify cerebral hemorrhage into four main types: epidural hemorrhage, subdural hemorrhage, intraventricular hemorrhage, and intraparenchymal hemorrhage. A total of (1,156) CT images were tested to verify the validity of the proposed model, and the results showed that the accuracy reached the required success level with an average of (97.1%).

Multifunctional Optical Tomography System With High-Fidelity Surface Extraction Based on a Single Programmable Scanner and Unified Pinhole Modeling.

Macroscopic optical tomography is a non-invasive method that can visualize the 3D distribution of intrinsic optical properties or exogenous fluorophores, making it highly attractive for small animal imaging. However, reconstructing the images requires prior knowledge of surface information. To address this, existing systems often use additional hardware components or integrate multimodal information, which is expensive and introduces new issues such as image registration. Our goal is to develop a multifunctional optical tomography system that can extract surface information using a concise hardware design. Our proposed system uses a single programmable scanner to implement both surface extraction and optical tomography functions. A unified pinhole model is used to describe both the illumination and detection procedures for capturing 3D point cloud. Line-shaped scanning is adopted to improve both spatial resolution and speed of surface extraction. Finally, we integrate the extracted surface information into the optical tomographic reconstruction to more accurately map the fluorescence distribution. Comprehensive phantom experiments with different levels of complexity were designed to evaluate the performance of surface extraction and fluorescence tomography. We also imaged the axillary lymph nodes in living mice after injection of fluorophore, demonstrating the proposed system facilitates more reliable fluorescence tomography. We have successfully developed a versatile optical tomography system by leveraging concise hardware design and unified pinhole modeling. Phantom validation demonstrates that our system provides high-precision surface information with a maximum error of 0.1 mm, while the surface-guided FMT reconstruction is more reliable than the blind reconstruction using simplified surface geometry, elevating several quantitative metrics including RMSE, CNR, and Dice. Our work explores the feasibility of obtaining additional surface information using existing components of standalone optical tomography. This makes the optical tomographic technique more accurate and more accessible to biomedical researchers.

Hand LightWeightNet: an optimized hand pose estimation for interactive mobile interfaces

In this paper, a hand pose estimation method is introduced that combines MobileNetV3 and CrossInfoNet into a single pipeline. The proposed approach is tailored for mobile phone processors through optimizations, modifications, and enhancements made to both architectures, resulting in a lightweight solution. MobileNetV3 provides the bottleneck for feature extraction and refinements while CrossInfoNet benefits the proposed system through a multitask information sharing mechanism. In the feature extraction stage, we utilized an inverted residual block that achieves a balance between accuracy and efficiency in limited parameters. Additionally, in the feature refinement stage, we incorporated a new best-performing activation function called “activate or not” ACON, which demonstrated stability and superior performance in learning linearly and non-linearly gates of the whole activation area of the network by setting hyperparameters to switch between active and inactive states. As a result, our network operated with 65% reduced parameters, but improved speed by 39% which is suitable for running in a mobile device processor. During experiment, we conducted test evaluation on three hand pose datasets to assess the generalization capacity of our system. On all the tested datasets, the proposed approach demonstrates consistently higher performance while using significantly fewer parameters than existing methods. This indicates that the proposed system has the potential to enable new hand pose estimation applications such as virtual reality, augmented reality and sign language recognition on mobile devices.

A Clinically Explainable AI-Based Grading System for Age-Related Macular Degeneration Using Optical Coherence Tomography.

We propose an automated, explainable artificial intelligence (xAI) system for age-related macular degeneration (AMD) diagnosis. Mimicking the physician's perceptions, the proposed xAI system is capable of deriving clinically meaningful features from optical coherence tomography (OCT) B-scan images to differentiate between a normal retina, different grades of AMD (early, intermediate, geographic atrophy (GA), inactive wet or active neovascular disease [exudative or wet AMD]), and non-AMD diseases. Particularly, we extract retinal OCT-based clinical imaging markers that are correlated with the progression of AMD, which include: (i) subretinal tissue, sub-retinal pigment epithelial tissue, intraretinal fluid, subretinal fluid, and choroidal hypertransmission detection using a DeepLabV3+ network; (ii) detection of merged retina layers using a novel convolutional neural network model; (iii) drusen detection based on 2D curvature analysis; (iv) estimation of retinal layers' thickness, and first-order and higher-order reflectivity features. Those clinical features are used to grade a retinal OCT in a hierarchical decision tree process. The first step looks for severe disruption of retinal layers' indicative of advanced AMD. These cases are analyzed further to diagnose GA, inactive wet AMD, active wet AMD, and non-AMD diseases. Less severe cases are analyzed using a different pipeline to identify OCT with AMD-specific pathology, which is graded as intermediate-stage or early-stage AMD. The remainder is classified as either being a normal retina or having other non-AMD pathology. The proposed system in the multi-way classification task, evaluated on 1285 OCT images, achieved 90.82% accuracy. These promising results demonstrated the capability to automatically distinguish between normal eyes and all AMD grades in addition to non-AMD diseases.