Proposed System Research Articles

A Novel Hypothesis for Migraine Disease Mechanism: The Creation of a New Attractor Responsible for Migraine Disease Symptoms

Migraine Disease (MD) is one of the common primary headaches that can prevent patients from their everyday life. Despite the high prevalence, the pathophysiology of the disease has not been clearly understood yet. Here, the brain is considered as a dynamical system. The Chua’s circuit with a chaotic attractor is the proposed model. This attractor has a one-scroll mode representing a healthy brain and a double-scroll mode representing a migraine sufferer brain. We believe that MD and Chua’s systems have certain behavioral similarities. The boundaries of the attractor are the sensitive brain areas in which any small trigger can start the ictal phase of the migraine. The transition from the inter-ictal phase to the ictal phase in migraine patients occurs due to a decrease in serotonin levels when the brain is within the boundaries of the first attractor. Here, this is the results of the increase of system parameters. In addition, the transition from the ictal phase to the inter-ictal phase in a migraine sufferer brain is caused by a disruption of coordination in the brain’s structures and this lasts for a certain period for every migraine patient. The structures which are the result of the Migraine Generator Network (MGN) and Cortical Spreading Depression (CSD). This explanation may propose newer methods for preventing or curing MD. To better understand MD to control it and shrink the areas involved in this disease, it is better to know the dynamic systems better. It may help prevent the formation of migraine ictal attractor or even make the migraine ictal phase attractor smaller even after it has been formed.

A Comparative Study of 9-Level Inverter and 15-Level Multi Inverter for a Grid Connected System

Abstract: Recently, the emergence of single phase multilevel inverter has been increased due to its advantages over traditional one. Multilevel Inverters [MLI] are the ones which can give out a stepwise output waveform with the use of power electronic switches, power diodes and some DC voltage source which might be a series/parallel connected PV cells, a renewable source or a battery. Multilevel inverters not only produce low harmonic distortion but also decrease the dv/dt stresses on the equipment Multilevel inverters is a good option as the output voltage of a Multilevel inverter is a stepped waveform which approaches a sinusoid providing lesser harmonic distortion. As the number of levels are increased the harmonic distortion reduces but at the same time the number of switching devices and the DC voltage sources required increases, thus increasing the complexity of the system design and control. Multilevel inverter proposed in this paper uses only 7 switches to provide 9 levels and 15 levels of output voltage. A symmetrical Cascaded H bridge Multi level inverter (SCHBMLI) and Asymmetrical Cascaded H bridge Multilevel Inverter (ASCHBMLI) using PD-PWM technique have been analysed in this paper for grid connection. The Asymmetric Cascade H Bridge [ASCHMLI] implemented in real time system using PIC16F877A microcontroller with RL load. This type of proposed system is used for high power applications in photovoltaic and it’s reduce overall cost as well as size of the system.

«Внутренний клиент» в работе центров «Мои документы»

The article presents a study devoted to identifying the degree of satisfaction of an internal client (employees of the My Documents offices in new regions). The degree of their satisfaction with their own professional activities, vision of professional prospects, interaction with managers and applicants of the MFC is analyzed. Based on the conducted survey and the analyzed results, a system of proposals has been formulated, the implementation of which, according to the authors, will increase the level of clientcentricity in working with an internal client, and therefore increase the effectiveness of interaction between MFC employees and citizens in need of state assistance.

An Intelligent E-Pharmacopoeia Retrieval System Using Responsive Web Design

To develop an e-pharmacopeia retrieval mobile system aimed at assisting medical personnel in efficiently assessing precise drug information and ensuring patient safety, this study utilized responsive web design. The research framework incorporated the convergence of the technology acceptance model (TAM) and the information systems success model (ISSM) to investigate user satisfaction and continuance intention. A survey was administered to 151 pharmacy students who had utilized the system for over 30 minutes. The findings revealed that the TAM-ISSM model significantly influenced user satisfaction, surpassing the individual impacts of TAM and ISSM. Furthermore, user satisfaction exhibited a positive and significant impact on continuance intention, which, in turn, demonstrated a positive and significant influence on both individual and organizational performance. This suggests that the proposed system is proficient in accurately identifying and processing drugs, thereby mitigating the pain induced by diseases in patients.

Large-Area Thickness Measurement of Transparent Films Based on a Multichannel Spectral Interference Sensor

Thickness measurement of thin films is essential for quality control in the manufacturing process of the semiconductor and display industries. Real-time monitoring of film thickness during production is an urgent technical problem to be solved. In this study, a method for large-area thickness measurement of transparent films based on a multichannel spectral interference sensor is proposed. The sensor simultaneously acquires multichannel spectral interference signals through a combination of fan-out fiber optic bundles, detection probes, and an imaging spectrometer. The spectral data are calibrated and transformed into the wavenumber dimension, and then the power spectral density estimation method is used to demodulate the data frequency to swiftly derive the film thickness. The thickness measurement capacity of the proposed system is successfully validated on two standard film samples with a relative deviation of less than 0.38% and a relative standard deviation of less than 0.044%. The total spectral acquisition and calculation time for a single multichannel measurement was approximately 7.5 ms. The experimental results on polyimide films show that the measurement efficiency of the system is at least 4 times higher than that of the traditional system, indicating the potential of the multichannel spectral interference sensor for online monitoring in film production.

Optical fiber weak magnetic sensing system based on strip-ring sensing structure and PGC-Improved algorithm

Optical fiber weak magnetic sensing system based on strip-ring sensing structure and PGC-Improved algorithm

Scheme for a WDM-PON with colorless optical network units based on a flat optical frequency comb.

A new, to the best of our knowledge, scheme for a wavelength division multiplexing passive optical network (WDM-PON) based on a flat optical frequency comb (OFC) is proposed. Using an OFC as the optical source of the system can realize the colorlessness of optical network units (ONUs), and the direct detection of the downlink data further simplifies the ONU structure. The coherent demodulation of the uplink data improves the system performance due to the coherence of the comb lines. In this research, the proposed system is studied for its performance and power budgeting. The results show the flexibility, effectiveness, and practicability of the proposed scheme, which can be applied to future high-capacity optical access networks.

DexFuncGrasp: A Robotic Dexterous Functional Grasp Dataset Constructed from a Cost-Effective Real-Simulation Annotation System

Robot grasp dataset is the basis of designing the robot's grasp generation model. Compared with the building grasp dataset for Low-DOF grippers, it is harder for High-DOF dexterous robot hand. Most current datasets meet the needs of generating stable grasps, but they are not suitable for dexterous hands to complete human-like functional grasp, such as grasp the handle of a cup or pressing the button of a flashlight, so as to enable robots to complete subsequent functional manipulation action autonomously, and there is no dataset with functional grasp pose annotations at present. This paper develops a unique Cost-Effective Real-Simulation Annotation System by leveraging natural hand's actions. The system is able to capture a functional grasp of a dexterous hand in a simulated environment assisted by human demonstration in real world. By using this system, dexterous grasp data can be collected efficiently as well as cost-effective. Finally, we construct the first dexterous functional grasp dataset with rich pose annotations. A Functional Grasp Synthesis Model is also provided to validate the effectiveness of the proposed system and dataset. Our project page is: https://hjlllll.github.io/DFG/.

Effective classification of alzheimer disease based on image tractography framework utilizing GM-ABC-NN

Effective classification of alzheimer disease based on image tractography framework utilizing GM-ABC-NN

Cost-effective reliability enhancement for video stitching applications based on error-tolerance

Cost-effective reliability enhancement for video stitching applications based on error-tolerance

An Apparatus for Measuring Combined Shear-Tensile Loading in Fibrous Tissues Ex Vivo.

Soft tissues such as tendon and ligament undergo a combination of shear and tensile loading in vivo due to their boundary conditions at muscle and/or bone. Current experimental protocols are limited to pure tensile loading, biaxial loading, or simple shear, and thus may not fully characterize the mechanics of these tissues under physiological loading scenarios. Our objective was to create an experimental protocol to determine the shear modulus of fibrous tissues at different tensile loads. We assembled a four-actuator experimental system that facilitated shear deformation to be superimposed on a tissue subjected to an axial preload. We measured shear modulus in axially loaded electrospun nanofiber scaffolds with either randomly oriented or aligned fibers. We found that shear modulus in the nanofiber phantoms was shear-strain stiffening and dependent on both the axial load (p < 0.001) and fiber alignment (p < 0.001) of the scaffold. The proposed system can enhance our understanding of microstructure and functional mechanics in soft tissues, while also providing a platform to investigate the behavior of electrospun scaffolds for tissue regeneration. Our experimental protocol for determining loaded shear modulus would be further useful as a method to gauge tissue mechanics under loading conditions that are more representative of physiological loads applied to tendon and ligament.

A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time

Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients’ lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively.

An improved custom convolutional neural network based hand sign recognition using machine learning algorithm

Abstract The biggest challenge the deaf and dumb group faces is that individuals around them do not understand sign language, which they use to communicate with one another. Written communication is slower than face‐to‐face contact, despite the fact that it can be used. Many sign languages have been developed around the world because they are more effective in emergency situations than text‐based communication. India in‐spite of having the large deaf population of almost 18 million and having only around 250 trained/untrained; skilled interpreters. The proposed system can utilize a custom convolution neural networks (CCNNs) model to identify hand motions in order to resolve this issue. This system uses a filter to process the hand before sending it through a classifier to identify the type of hand movements. CCNN strategy employs two levels of algorithm to predict and evaluate symbols that are increasingly similar to one another in order to get as close to precisely recognizing the symbol presented as possible. Convolutional neural networks (CNNs) are able to precisely identify a variety of gestures after being trained on large datasets of hand sign photographs. As a result of their frequent usage of many layers of filters and pooling to extract relevant information from the input images, these networks can recognize hand signs with an accuracy rate of 99.95%, which is much greater than previously built models like SIGNGRAPH, SVM, KNN, CNN + Bi‐LSTM, 3D‐CNN and 2D CNN network and 1D CNN skeleton network. The simulation result shows that a suggested CCNN‐based learning approach is useful for hand sign detection and future usage research when compared with existing machine learning models.

Augmenting cybersecurity in smart urban energy systems through IoT and blockchain technology within the Digital Twin framework

Augmenting cybersecurity in smart urban energy systems through IoT and blockchain technology within the Digital Twin framework

IoT BIOMETRIC ATTENDANCE SYSTEM NEXUS WITH AUTOMATED DATA STREAMLINING AND SOFTWARE ORCHESTRATION

Biometric technology involves the recognition and authentication of individuals by examining unique characteristics of the human body. In modern times, attendance is taken traditionally in educational institutions using paper and pen, it increases the workload for teachers and also takes time. The purpose of this design is to make a biometric system for students. As the world becomes modern and digitalized, so we need to make a portable system for attendance with an embedded software application. By this proposal, authorized access will be given to the teacher to activate the biometric for each period. After student attendance is recognized by a fingerprint sensor the required messages will be sent to their parents for every period and the data will be stored in the application. In addition, the proposed system could increase productivity and reduce the manual process, truancy, and lateness in educational institutions. Keywords-- IoT, Attendance, Fingerprint, Biometrics, Firebase, Android Application

CLOUD BASED DUPLICATION REMOVAL SYSTEM

Deduplication involves eliminating duplicate or redundant data to reduce stored data volume, commonly used in data backup, network optimization, and storage management. However, traditional deduplication methods have limitations with encrypted data and security. The primary objective of this project is to develop new distributed deduplication systems that offer increased reliability. In these systems, data chunks are distributed across the Hadoop Distributed File System (HDFS), and a robust key management system is utilized to ensure secure deduplication with slave nodes. Instead of having multiple copies of the same content, deduplication removes redundant data by retaining only one physical copy and referring other instances to that copy. The granularity of deduplication can vary, ranging from an entire file to a data block. The MD5 and 3DES algorithms are used to enhance the deduplication process. The proposed approach in this project is the Proof of Ownership (POF) of the file. With this method, deduplication can effectively address the issues of reliability and label consistency in HDFS storage systems. The proposed system has successfully reduced the cost and time associated with uploading and downloading data, while also optimizing storage space. Key Words: Cloud computing, data storage, file checksum algorithms, computational infrastructure, duplication.

Implementation of a Wireless Sensor Network for Environmental Measurements

Nowadays, the need to monitor different physical variables is constantly increasing and can be used in different applications, from humidity monitoring to disease detection in living beings, using a local or wireless sensor network (WSN). The Internet of Things has become a valuable approach to climate monitoring, daily parcel monitoring, early disease detection, crop plant counting, and risk assessment. Herein, an autonomous energy wireless sensor network for monitoring environmental variables is proposed. The network’s tree topology configuration, which involves master and slave modules, is managed by microcontrollers embedded with sensors, constituting a key part of the WSN architecture. The system’s slave modules are equipped with sensors for temperature, humidity, gas, and light detection, along with a photovoltaic cell to energize the system, and a WiFi module for data transmission. The receiver incorporates a user interface and the necessary computing components for efficient data handling. In an open-field configuration, the transceiver range of the proposed system reaches up to 750 m per module. The advantages of this approach are its scalability, energy efficiency, and the system’s ability to provide real-time environmental monitoring over a large area, which is particularly beneficial for applications in precision agriculture and environmental management.

Designing and Developing an Advanced Drone-Based Pollution Surveillance System for River Waterways, Streams, and Canals Using Machine Learning Algorithms: Case Study in Shatt al-Arab, South East Iraq

This study explores pollution detection and classification in the Shatt al-Arab River using advanced image processing techniques. Our proposed system integrates Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms. The Shatt al-Arab River in Basra, Iraq, faces increasing pollution from human activities, including oil spills, debris, and wastewater. We conducted extensive surveys of the river and its tributaries using a DJI Mavic drone, amassing over 1000 images to train machine learning models. The results indicate that RF excels with 94% accuracy for oil spills, 92% for wastewater, and 95% for debris. SVM also performs well, achieving 92%, 88%, and 94% accuracy for the respective pollutants. KNN, though insightful, lags with 85%, 89%, and 86% accuracy. Trained on this novel image dataset, these models show promising accuracy in detecting various pollution types from drone footage.

Online 3D behavioral tracking of aquatic model organism with a dual-camera system

Online 3D behavioral tracking of aquatic model organism with a dual-camera system

From Dissonance to Dialogue: A Token-Based Approach to Bridge the Gap Between Manufacturers and Customers

This article presents a novel token-based recall communication system, which integrates Enterprise Resource Planning (ERP) systems and blockchain technology to enhance recall communication and cooperation between manufacturers and customers. We employed a design science research methodology to develop a set of design principles and features that support the interoperable system. Our findings demonstrate that we can significantly improve recall coordination, traceability, and co-value creation between involved parties. By focusing on the integration potential of traditional technologies like ERP systems with blockchain and token techniques, we reveal innovative synergies for both social and technical subsystems. The study explores the implications of the proposed system for both theory and practice, offering insights into the advantages and challenges of such integration. The evaluation conducted with industry experts demonstrates high reusability of the design principles.