IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

The purpose of this review paper is to contribute to the body of
knowledge surrounding legged walking robots, specifically those utilizing
four legs. It aims to inform researchers, engineers, and enthusiasts about the
advancements, potential applications, and future prospects of these robots,
ultimately driving further research and development in this exciting field.
The review covered the most recent research articles in the field of designing
and manufacturing robots, as well as how to choose the mechanism of
actuator between hydraulic or electric systems and their impact on the
weight increase and energy consumption, or potential noise generation
during the operation like Bigdog robot. The payload was also assessed, with
a comparison of the speed chart for each quadruped robot. It has been found
that several critical technologies driving quadruped robots\\\' advancement
include new biomimetic structures, high power density actuators, techniques
for controlling in real time and integrated environmental perception,
depending on the application fields, such as industrial automation,
healthcare, and exploration. Each of these elements is essential for achieving
high-performance quadruped robots. Notably, the paper also delves
understanding the fundamental methods used by mobile robots to perceive
their surroundings, pay load (N), normalized speed (m/s), and engine
mechanism. This work is valuable for all researchers in the field of a five-bar
mechanism legged robot to keep up with the most recent advancements in
this field. Furthermore, this review paper can be considered as a guideline
for researchers who are intended to perform further research on mobile
robot.

More than 66% of the Earth's total surface area is covered by water. Clean water is one of the basic needs of everyday life. Consistent pollution of water bodies can have far-reaching effects on the lives of living organisms. The World Health Organization has reported that the provision of safe drinking water for human consumption is a challenge that has reached alarming levels. This is because nearly 70% of the total water withdrawals worldwide are used in agriculture. To determine the water's suitability for human consumption, Tests are typically conducted by examining the properties of water in terms of physical, biological, and chemical conditions. There are various methods to measure water quality. Recently, an ongoing process has been shown to improve water quality. To solve this problem, this paper is using a machine learning model to Contribution for creation a smart model capable of classifying potable and non-potable water that is released to the water share by the competent authorities and the ability of Machine Learning models to monitor and predict water, especially Managing and Planning Water Resources. datasets were utilized in training and evaluating machine learning models, which includes (3276) samples with nine attributes and two labels indicating water usability According to the in our work the (Random Forest) algorithm have the best Through the results that appeared by accuracy (0.954084, 0. 88 2484, and 0. 931849 for three sizes of data.) and then (Decision Tree, K-Nearest Neighbor, Logistic Regression, SVC) sequentially

This research explores the integration of machine learning into security systems, addressing the growing challenges posed by cyber threats. It traces the historical context of cybersecurity measures and the development of machine learning in intrusion detection, anomaly detection, and behavioral analysis. By examining traditional security approaches and their limitations, the study highlights the transformative potential of machine learning. Case studies provide concrete examples of successful applications, demonstrating how machine learning reduces cyber threats. Ethical considerations, implementation challenges, biases, and regulatory aspects are discussed, highlighting the complexities of integrating machine learning into security frameworks. Furthermore, the research explores emerging technologies in cybersecurity and offers insights into the future of machine learning in security. In conclusion, the importance of continuous research is emphasized, positioning machine learning as a dynamic force shaping the future of digital defense and overcoming various challenges in the cybersecurity landscape.

Unmanned aerial vehicles, or UAVs, are gaining significant attention because of the strategic and financial information and value involved in aerial applications, as well as the sensitive data collected through embedded sensors. UAVs are rapidly developing in various fields and find widespread utility in military applications for effective target tracking, battlefield surveillance, radiation monitoring, and sports. UAVs are useful and advantageous, but they can also be attacked by a variety of techniques, including jamming, fuzzing, false data injection, and zero attacks. Researchers were looking on robust security mechanisms to protect UAVs from attackers in order to combat such security risks. However, there is a large number of vulnerabilities in designed protocols that hackers could take advantage of. For the identification and addressing of those vulnerabilities and weaknesses, it is becoming highly important to study and analyze current security protocols that are used in the UAVs. In this study, a thorough survey will be introduced on authentication and cryptography that are used in the UAV protocols, as well as describing architecture, procedure, and security of Micro Aerial Vehicle Link (MavLink) protocol. We will explain as well the cryptography and authentication approach that has been developed by the use of the MavLink protocol.

In recent years, significant progress has been made in the field of diagnosis and classification of brain tumors, mainly attributed to advancements in artificial intelligence and medical imaging. The main objective of this study is to improve the detection and classification of brain tumors by applying and utilizing of artificial intelligence (AI) and recent advancements in medical imaging techniques. Automation of the process of tumor identification and then classification, in addition to tumor grading, will definitely improve all procedures of brain tumor treatment and enhance patient care. The proposed system combines convolutional neural networks (CNNs), which act as extract features, and the You Only Look Once Algorithm (YOLOv7) for effective object identification and accurate classification. The methodology described in this study involves employing a technique of multilayer classification, which integrates three distinct datasets. This comprehensive approach shows an exceptional levels of accuracy and precision. At the initial level, the model attains a 99.78% accuracy in distinguishing between tumor and nontumor cases. At the next level the system accurately sorts types of brain tumors (such, as glioma, meningioma and pituitary tumors) with an average accuracy of 99.35%. Moving on to the final stage it successfully distinguishes between low grade and high grade glioma tumors with a precision of 93.07%. Moreover the model shows accuracies ranging from 99.41%, to 99.61% when classifying types of brain tumors and nontumor cases. The proposed system has the ability to determine the boundaries of the tumor, and thus this has helped in calculating the sizes of tumors with high accuracy.

Outer surface integrity must be maintained throughout the machining process during the production of metal parts, which need the ability to monitor temperatures in real time. Changes in temperature during the machining process lead to changes in the behavior of the product after work is completed.To achieve quality and productivity criteria, the metal parts manufacturing process requires a continuous and precise temperature measuring and monitoring system by using IOT. An intelligent temperature measurement can be used during machining processes on conventional, non-conventional, and computer numerical control (CNC) machines. This paper presents a performance analysis of a temperature measuring system for machining process applications via Arduino IDE. In this proposal, a temperature monitoring system based on Arduino is used. The system would utilize an: MLX90614 infrared thermometer sensor” to measure the temperature of the workpiece during machining processes. The methodology's aim is to use the smart manufacturing model to measure and monitor the arising temperatures throughout the machining process in order to build a new tool path rapidly and efficiently. The placement of a non-contact infrared “MLX90614” temperature sensor allowed measuring of the average cutting temperature as well as the maximum cutting temperature. The data acquisition process was carried out using the “Xampp” Control Panel software, which communicated with the ESP32 “microcontroller” board using the Arduino IDE program. Experiments were carried out, so that the temperature of the cutting, which was measured, was recorded, and the results of the experiments were analyzed.

In this paper, a novel flow control strategy which is the inlet throttled pump was used to design an angular velocity control system for rotary actuator. Inlet throttled systems have good performance in addition to their high efficiency compared to traditional valve controlled systems. The flow in the proposed system is adjusted by a valve that is positioned at the pump inlet with the purpose of reducing the energy loses across the valve. This regulated flow is used then to control the actuator angular velocity. The system was modeled and the open loop stability and performance were studied. In order to improve the system performance, Robust-Proportional- Integral-Derivative (RPID) and structured singular value (????????) controllers have been designed. The multiplicative uncertainty was analyzed to assess the robustness of the feedback control system where six parameters were considered uncertain within a range of +10%. The robust stability and performance requirements of the closed-loop angular velocity control system were assessed in the frequency domain. The time response of the system showed that the system is stable with both (RPID) and (Mu) controllers. The Mu controller can handle parametric uncertainty without requiring pure integral term which is a significant advantage over the (RPID) controller. On the other hand, the (RPID) controller could achieve robust performance, making it much suitable for systems that require high levels of performance and robustness. In summary, the the (RPID) and Mu controller is a more comprehensive solution for ensuring the best performance of a system. The results for each (RPID) and Mu-controllers showed no oscillations, zero percent overshoot. Each of the (RPID) and Mu-controllers meets the robustness needs.

The steady increase in reliance on robotic systems in industrial applications arises the need to ensure their reliability despite the presence of external disturbances in the environment they work in and their parameters variations due to several factors like: change of temperature, wear and tear effect, and payload variation. Traditional controllers usually fail to maintain the system's stability and performance in the presence of uncertainty, while robust controllers aim to guarantee stability and desired performance across a range of possible uncertainties in the system through incorporating these uncertainties into their design process. This paper investigates the robustness of three controllers namely, H2 controller, H-infinity controller, and mixed H2/H- infinity controller, applied to the triple inverted pendulum system which translates to robustly control legged robots and robotic arms. The results show that among the three controllers, only the mixed H2/H-infinity control system is robustly stable and maintains its performance with stability margin 1.1864 and performance margin 1.1662.