IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

This paper forms part I of two-part series that present the full path for constructing a Lean-Agile platform using Simulink and test the different criteria of such manufacturing systems. management of manufacturing systems is one of the main rules in engineering in order to reduce the waste (lean) and to fulfill the changing requirements of the market (agile). This paper describes the process of construction of the basic waterfall platform using Matlab/Simulink, and to develop the Matlab program that facilitates the input process of constants and variables into the model and then to show the results. this platform will mimic the manufacturing system of motorcycle engine that takes six parts as input and outputs four different models, Matlab/Simulink is used to construct the platform, and the library of Simscape used to model the discrete (not continuous) behavior of the system. Waterfall platform has been constructed. and simulated with the specific constants and variables from an excel file. Showing the results of workers-usage, over-processing, machine-usage, queue-usage. It shows the orders done with time of simulation, illustrating and indication of lean and agility of the proposed system. Different figures were generated and introduced to show various measuring criteria that evaluate the performance of the proposed system. The different measurement criteria drawn using a second Matlab program developed specially for this purpose.

Traditional authentication methods such as passwords are susceptible to easy hacking, and as technology progresses, the demand is on the rise for more reliable and secure recognition systems, which can be used in surveillance and biometrics applications etc. A biometric recognition system of individuals is established on the unique features of the individual. Multi modal biometric systems represent a significant research field with widespread applications of recognition systems. Unimodal biometric systems have a variety of issues, including nonuniversality and noisy data. Certain limitations and difficulties can be partially addressed with the use of multi modal biometric systems. In this paper, a survey on multi-biometric systems is presented to highlight the challenges, strengths and weaknesses of some of the research discussed in this study which contributes to enhancing the understanding and development of robust and reliable biometric authentication solutions, essential for ensuring security in various domains as well as suggest directions for future work.

In this article, a modified self-recurrent wavelet neural network (MSRWNN) structure is used as a feedforward controller in the direct inverse control (DIC) method. Nonlinear dynamical systems are controlled with the help of this intelligent control strategy. The particle swam optimization (PSO) is used as an efficient optimization tool to determine the ideal MSRWNN parameter settings. A nonlinear dynamical system is considered to demonstrate the efficacy of the suggested control strategy. In addition, the ability of the MSRWNN to effectively regulate the nonlinear system under consideration is specifically assessed in terms of control accuracy and resilience to external disturbances via the execution of many assessment tests. The outcomes of each of these tests have shown the control scheme's effectiveness, with significant improvements in performance metrics. Specifically, the proposed method achieved a 25% reduction in Integral Squared Error (ISE) compared to traditional neural network controllers, and improved disturbance rejection capability by 30%. Furthermore, from a comparative study, the MSRWNN has demonstrated superior control accuracy and performance reliability over other related controllers.

Computer vision has demonstrated itself to be a key component of recent technological advancements. Among computer vision applications, the self-driving automobile. The self-driving cars is one of the most significant developments in the automotive sector. Also Currently, artificial intelligence, especially neural networks, has contributed greatly to the field of automated driving. This paper presents an overview of the use of artificial neural networks for lane detection and demonstrates how this technology has become a key component of vision-controlled autonomous vehicles. Convolutional Neural Networks (CNN) are used for lane detection, which contributes to the car\'s autonomous operation. Also, the convolutional Neural Networks model gives the vehicle the ability to learn from various road conditions and weather conditions weather rainy, sunny, night or day, granting it the capability of direction prediction so that it can handle any scenario arise which will help the driver to make the right decision and avoid accidents.

Kinship verification is the process of using observable characteristics, including facial features or other biometric data, to ascertain whether two people are familial related. Current approaches to kinship verification encounter multiple challenges, despite their significance in fields such as biometric security, family reunification, and forensic investigations. The most accurate way to confirm kinship is by DNA analysis, which is expensive, time-consuming, and invasive; in contrast, the traditional methods using manual comparison and facial recognition are more prone to human mistake. Visual verification of kinship among family members can be difficult to reliably rely on due to genetic variability, aging, and environmental influences. Automated methods that can reliably and efficiently confirm kinship using non-invasive methods are required, especially in situations where immediate identification is essential, like in the investigation of missing persons cases and disaster response. This study explores the use of different verification kinship techniques, ranging from various machine learning techniques reaching to up-to-date deep learning techniques. Finally, this study will discuss the datasets, preprocessing, methodology, advantages, disadvantages, and performance measures of selected state-of-the-art studies of kinship verification, and present the best technique used in kinship verification with the most related dataset.

Nowadays, with the development of correspondence via the Internet and the crises that occurred after 2019, many institutions in various fields, whether public or private, and banks and financial transactions, began to carry out most of them via the Internet and needed to sign various documents electronically. There are many problems with the signature images, documents, transactions, or files that make the verification process difficult or require preprocessing for signature. One important part of biometric authentication is signature verification, which tries to separate genuine signatures from fraud. Both online and offline signature verification techniques are examined in this survey. In offline systems, properties like size, shape, and texture are extracted for comparison from static representations of signatures. On the other hand, online systems collect dynamic data such as pen pressure, acceleration, and velocity. This paper gives a comprehensive assessment of the current studies and outcomes in the last 8 years in the subject of online and offline handwritten signature verification. It is possible to improve verification accuracy by creating a hybrid system that combines the advantages of both methods. This study investigates cutting-edge strategies to enhance the functionality of signature verification systems, such as machine learning algorithms and feature extraction techniques. The ultimate objective is to create dependable and strong systems that can successfully thwart intricate forgery efforts. Whereas the accuracy in most offline research may reach 100%, and this may be mostly due to the fact that the matching is of one-to-one type, while in online methods is one-to-many.

Because block chain technology can improve distributed systems' security, dependability, and resilience, it has been gaining popularity. Research based on this technique has helped a number of fields, including data analysis, finance, remote sensing, and healthcare. The primary characteristics that make block chain technology appealing include distributed ledgers, decentralization, privacy, transparency, and data immutability. However, because there is a chance of a privacy breach, medical records that hold private patient information make this system extremely complex. The purpose of this project is to investigate block chain applications in the healthcare sector. We also include articles that touch on other topics, like the Internet of Things, information management, medicine supply chain tracking, and privacy and security issues. Lastly, we aim to investigate block chain concepts in the medical field by evaluating their advantages and disadvantages and providing direction to other studies in the field. We also provide a summary of the Block chain's techniques.

Simulation software is considered as an important step in the development of internet of things (IoT) scenarios and has been the subject of intense research in the past decade. While most previous efforts in simulating these devices have focused on the communication protocols and the sensor node layers issues, a significant aspect remains in precisely measuring some health metrics of football players. Some of the IoT technologies are allowed to be used during the real match or training practice, while most of them were forbidden by the Fédération Internationale de Football Association (FIFA). One promising aspect, is to simulate the health metrics of the players during the training session using the concept of wireless sensor nodes with integrated sensors. The available simulation programs do not support all the required features in the football field. For that, in this paper the TrainPlayer is presented, a health level sensor network simulator which collects the required metrics for the players nodes and can send the data payload using the Message Queuing Telemetry Transport (MQTT) protocol. It developed by utilizing the object-oriented programming features and the graphical user interface in python libraries. The work simulates the movement of the player on the pitch and how the metrics are generated, collected and sent to the server. The collected data is varied during the exercises. This monitoring system helps the coaches to fix the training workload by the team trainer or coach to reach the optimal fitness level. The results show the reliability of the MQTT protocol in message delivery and the acceptable delay time. TrainPlayer's ability to measure detailed metrics can shed new light on design proper training workload for each player. In the future, the software can be enhanced with additional features to become a complete tool for simulating football players.