IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

Simultaneously and location mapping (SLAM) is an important technique for achieving a full autonomous navigation system by constructing a 2D map of the surrounding environment. A performance of two popular distance sensor (Asus Xtion Pro Camera and 2D LiDAR) in building a map of the indoor environment using Hector SLAM is presented in this paper. Navigation system using 2D LiDAR can only detect object on a certain level of plane. This leads to miss the obstacles that are below and/or above the level of laser scan. So the generated map will be inaccurate that causes collision during autonomous navigation. Asus Xtion Pro sensor can be a low cost alternative for a laser distance sensor in addition to its ability to provide 3D data. Using data of depth image, the entire obstacle will be detected to prevent collision. Many experiments in real time scenarios in indoor environment have been conducted to evaluate the performance of the RGB-D sensor vs. 2D LiDAR in constructing a 2D map. Furthermore, the results also indicate that some modifications on the parameters of Hector SLAM method are able to enhance the accuracy of map which is constructed by Asus Xtion Pro camera. Therefore, Asus Xtion Pro offers a good alternative to build a 2D map using Hector SLAM. This work is implemented in ROS on Raspberry Pi 3 B+.

This paper aims to present the literature related to the regulation of Type 1 Diabetes Mellitus (T1DM) via positive control and constrained control. This idea of positive control was derived because the control input (insulin) can only be infused/injected (one direction control). The main operation of insulin is to reduce glycemia back to euglycemia. If glycemia goes into hypoglycemia; the only possible way is to stop insulin injection temporarily, and the patient must take some carbohydrates to raise glycemia. Also, hyperglycemia can be treated by estimating the amount of meals taken by the patient using an estimator. Since meals are a positive factor, the controller gives an adequate positive action to eliminate the effect of meals. This paper reviews the research work related to regulating glycemia that considered the positivity of insulin as a control input. The impact of considering the positive control in the design is the fact that any negative decision will be cut off to zero. In such case, the system is left open-loop and will be out of control.

Petroleum is the economic infrastructure in Iraq because it generates a significant portion of the country's revenue and can be considered as the primary source of financial costs each year. As a consequence, it is critical to protect the sector and continue to develop it. Therefore, it is important to track and maintain pipelines regularly to detect defects on time. In pipeline monitor and control, the advent of the Internet of Things (IoT) technology and the deployment of embedded sensing systems enable successful pipeline maintenance with the simple requirement for real-time precise measurements. In this paper, a wireless network based on an IoT system and integrated with cloud service is proposed for structure monitoring of oil pipelines, to detect the risks on the structure such as tampering and/or wear and tear effects. The method is based on collecting data from a sensor node equipped with an RF module attached to the pipeline structure. These nodes collectively form a network of IoT devices connected to the cloud server. The raw data is collected, stored, and statistically analyzed to be accessible by the user anytime and anywhere through the Internet. The performance of the system is evaluated in different cases, including the distance about the node to detect events on the pipe and to discriminate the distance of event to determine the location the event it was tested by using four different states of the transmitted data.

With the speedy expansion of e-commerce, credit cards have also become rising vogue, and that makes online transactions sleek and suitable. In conjunction with rising in online transactions, credit card fraud also increasing, which contributes to losses incurred yearly. As a result, many deep and machine learning methods are produced to fix such as problems like Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), and other algorithms, but the current models are still not accurate. Moreover, sometimes the used datasets still need further preprocessing, since that has been approved the important role of feature engineering in performance optimization. In this paper, effective feature engineering and feature selection methods have been produced for preprocessing the raw dataset, which was transformed with Exploratory Data Analysis (EDA). Then LightGBM, XGboost, and Random forest classifiers are used for fraud detection. Experiments show that the LightGBM and XGboost models achieved the best accuracy with 100% after applying further preprocessing on the dataset.

Automatic Revocation means performing the revocation task automatically by the proxy Re-Encryption (PRE), without any command from the data owner. For the lack of survey studies that tackle the automatic Revocation Process, this paper demonstrates a rich survey on the recent auto-revocation schemes proposed by the research community. To accomplish the survey, a literature review methodology, which includes seven steps, is followed. The study concluded with the following results: clarifying the concept of automatic revocation identifying the current proposed automatic revocation schemes, classifying the proposed automatic user revocation schemes, and presenting suggestions of future research directions for revocation schemes.

In this work, the design of a quantitative PID controller is proposed for Ball and Beam system. This controller is designed to robustly compensate for the nonlinear and uncertain behavior of the ball and beam system. The PID controller parameters are obtained using the Black Hole Optimization (BHO) method subject to Quantitative Feedback Theory (QFT) consteaints. The QFT is used to design a simple and robust controller with a more desirable performance. The simulation results show that the proposed quantitative PID controller can effectively compensate the ball and beam system with robust behavior and desirable time response specifications.

Today's insurance industry plays a significant role in a variety of fields, particularly in health insurance. As IoT technology advances, health insurers in IoT networks can obtain real-time medical data for individuals and issue individual insurance policies based on a person's lifestyle. However, sharing personal data requires a guarantee of privacy and security. This paper suggests a Blockchain technology to solve this problem. The work presents a novel framework that integrates health insurers, IoT-based networks, and Blockchain technology to implement access control protocol using a smart contract for sharing the financial premium of insureds with the stakeholders as non-participants/authorized parties. The evaluation of the proposal results in authorized access within less time compared to traditional data-sharing systems, and the security analysis shows that proposal can protect data from potential threats.

A recently-derived alternative method for computing the relative gain array (RGA) for singular and/or non-square systems has been proposed, which provably guarantees unit invariance. This property is not offered by the conventional method that uses the Moore-Penrose (MP) pseudoinverse. In this paper, we note that the absence of the scale-invariance property by the conventional MP-RGA does not *necessarily* imply a practical disadvantage in real-world applications. In other words, while it is true that performance of a controller should not depend on the choice of units via its input and output variables, this does not necessarily imply that the resulting MP-RGA measures of component interaction lead to different controller-design input-output pairings. In this paper we consider the application of the MP-RGA to a realistic transfer function relating to a Sakai fractional distillation system. Specifically, for this transfer function we assess whether or not the choice of unit, which in this case relates to temperature, affects the choice of loop pairings implied by the resulting RGA matrix. Our results show that it does, thus confirming that unit-sensitivity of the MP-RGA undermines its rigorous use for MIMO controller design.

Finger veins are different from other biometric signs; it is a special characteristic of the human body. The challenge for an imposter to explore and comprehend it, since the veins are below the skin, it is impossible to tell which one is, and which one stands out because the person has more than one finger to examine. Impostor recognition based on applying three machine-learning methods will be presented in this article, and then there is a discussion at preprocessing, Linear Discriminant Analysis (LDA) for feature extraction, and k fold cross-validation as an evaluation method. These measures were carried out on two different datasets, which are the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT) Dataset and the University of Twente Finger Veins (UTFV) dataset. The classifier with the best results was Support Vector Machine (SVM) and Linear Regression (LR) had the lowest classifier accuracy.

As compared to other conventional biometrics systems, voice is a unique and important metric, where it is used in many vital fields as the security and communication domains that do not need to be expensive to achieve. The purpose of this article is to see how machine learning (ML) algorithms perform for speaker Authentication to recognize impostors. To boost the audios usable in real environments, it was suggested the preprocessing of audio, like noise decreasing and voiced improving. Mel Frequency Cepstral Coefficients (MFCC) and the four features (Amplitude, Zero Crossing, Mean, and Standard Division) are extracted for all audio metrics, straight beside their differentials and accelerations. Then, Vector Quantization (VQ) is applied to these files. The algorithms were prepared and examined on two datasets, by applying k-fold cross-validation. The preparation for testing and comparing the three (ML) approaches are as follows: Support Vector Machine (SVM), One Rule (One-R), Linear Regression (LR). The result of the (SVM) algorithm average accuracy of 96.33 percent was superior.