With the rapid expansion of electronic data transmission, unauthorized data access is growing. Information security against unwanted access is becoming increasingly essential in data storage and data transfer. Images are a common type of information that is used in almost every aspect of life, so the security of image data against unwanted access is critical. In this article, we propose an encryption technique that uses a symmetric encryption method based on XOR operation between the plain image and another image that will be used as a key agreed upon by both the sender and recipient. To ensure the security of the plain images, the scheme performs pixel permutation procedures dynamically using random numbers on the key image and plain image in each encryption process, which renders the encryption algorithm strong and brute-force resistant. Simulation results on several standard images showed a random distribution of pixel values and a higher pass rate correlated with entropy and ideal values for analysis parameters. Thus, through the use of randomization, the proposed algorithm is resistant to various attacks and offers promising security measurements while maintaining efficient and fast execution.

Using wireless sensor network technology in structure health monitoring applications results in generating large amount of data. To sift through this data and extract useful information an extensive data analysis should be applied. In this paper, a Wireless Sensor Network (WSNs) is proposed for the oil pipeline monitoring system with proposed method for event detection and classification. The method depends on the Principal Component Analysis (PCA). It applied to features extracted from vibration signals of the monitored pipeline. These vibration signals are collected while applying damage events (knocking and drilling) to the oil pipeline. PCA is applied to features extracted from both time domain and frequency domain. The results manifest that this method is able to detect the existence of damage and also to distinguish between the different levels of harmful events applied to the pipeline.

The world's economy is dominate by the oil export business, which is heavily reliant on oil pipelines. Due to the length of the pipes and the harsh environment through which they pass, continuous structural health monitoring of pipelines using normal methods is difficult and expensive. In this paper, an IoT system integrated with cloud services is propose for oil pipeline structure monitoring. The system is based on collecting data from sensor nodes attached to the pipeline structure, which collectively form a network of IoT devices connected to the AWS cloud. Measurements from sensor nodes are collect, stored, and filtered in AWS cloud. Measurements are also make accessible to users through the internet in real-time using Python web framework, Flask, and sending alarms via email in real-time. The performance of the system is evaluate by applying damaging events (hard knocking) on the oil pipeline at several distances. Analysis of IoT data by machine learning classification algorithms, apply and comparison between SVM, Random Forest Classifier, and Decision Tree to determine the best one, and then built in EC2 Linux in AWS to analyses the measurements and classify new events according to their distances from the sensor nodes. The proposed system is test on field measurements that were collect in Al-Mussaib Gas Turbine Power Station in Baghdad. Among the three classifiers, Random Forest achieved 90% classification rate.

Interval Type2 Fuzzy Logic Control (IT2FLC) has been applied to a number of industrial, medical, home and military applications. Hardware implementation of IT2FLC can be achieved in a number of ways.One of these ways is the use of a Field Programmable Gate Array (FPGA).In this paper, the design and implementation of an IT2FLC using FPGA has been presented. The proposed controller is of Mamdani type. It works in different structures (P/PI/PD/PID like IT2FLC) depending on two control lines, different number of triangular shape memberships (2-7) depending on three control lines, six tunable gains and within a range of sampling time of (0.01-1024) seconds. Three type reduction algorithms are used and it is found that the Enhanced Iterative Algorithm with Stop Condition (EIASC) produced the minimum reduction in FPGA size. Thus less execution time. The reduction size is about 75% than Karnick Mendel (KM) and is about 3% than Enhanced KM (EKM). Linear and nonlinear models are used to test the designed Controller. Gains are tuned manually to reach minimum overshoot, settling time and steady state error.Simulation and Implementation results showed that the proposed controller works in an efficient way under no-load, load and uncertainty in the nonlinear model parameters.

Pipeline Monitoring Systems (PMS) benefits the most of recent developments in wireless remote monitoring since each pipeline would span for long distances which make conventional methods unsuitable. Precise monitoring and detection of damaging events requires moving large amounts of data between sensor nodes and base station for processing which require high bandwidth communication protocol. To overcome this problem, In-Situ processing can be practiced by processing the collected data locally at each node instead of the base station. However, this introduce a challenge to the limited resources available on the nodes. In this paper, a Wireless Sensor Network (WSN) was implemented for In-Situ Pipeline Monitoring System with proposed algorithms for event location estimation. The proposed algorithms include feature extraction (using ANOVA), dimensionality reduction using statistical procedure that is (Principle Component Analysis PCA) and data classification using supervised learning K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). The proposed system was tested on pipelines in Al-Mussaib Gas Turbine Power Plant. During test, knocking events are applied at several distances relative to the nodes locations. Data collected at each node is filtered and processed locally in real time in each two adjacent nodes. The results of the estimation is then sent to the supervisor at base-station for display. The results show the proposed system ability to estimate the location of knocking event.