Supervisory Control and Data Acquisition

Since the advent of control systems, SCADA has played an important role in the field of automation. SCADA systems offer a means of controlling remotely located devices in an industrial process. Supervisory control can be combined with data acquisition wherein the data is obtained from the devices and it is processed further according to the user’s needs. This paper offers an insight into the functioning of a typical SCADA system and its applications in the real world. The types of architecture of such control systems have been studied, along with an overview of the security concerns pertaining to them. General Terms Control Systems

Design of an Adaptive Controller of a Satellite using Thruster Actuator

this paper, an attitude control algorithm for a satellite is developed based on adaptive control using thruster actuators. For this purpose a twelve-thruster arrangement has been considered and the control torque for each thruster has been calculated. Then a Pulse Width-Pulse Frequency (PWPF) modulator is used for converting continuous controller signals into equivalent discrete one. Then, uncertainties in the moment of inertia matrix and disturbances torque has been considered and adaptive attitude control using feedback linearization controller with self-tuning regulator (Least Square Estimator With Bounded Gain Factor) is used. Finally, the performance of the designed attitude controller is investigated by simulations. KeywordsControl; Adaptive control; Satellite; Reaction

Modelling and Simulation of Monopolar HVDC Transmission System Feeding a Strong AC Network with Firefly Algorithm based Optimal PI Controller

A Highly Adaptive Method for Moving Target Detection in Dynamic Background with a Simplified Manner

Target detection is an approach to extract object from image, however it is difficult task when object is moving. Moving target detection is a key area in image processing such as traffic control system, activity monitoring security system, CCTV footage etc. For detecting a moving object in dynamic background, a background subtraction based method has already been suggested. These methods does not give better results when object is moving very fast, object is very tiny and presence of lighting effect. To overcome these problems, we propose a new method for Moving Target Detection in Dynamic Background. It achieves dynamic scene using certain probability of time and subsequent frame difference method and addresses the difficult scenario, where object is moving very fast and background changes frequently. In order to increase the accuracy of a proposed method, rate of change in background is calculated in fixed time of interval which will maintain dynamic behavior of object as well as background. The experimental results show that the proposed method can detect moving object more efficiently and completely in both cases online as well as offline video

Artificial Neural Network Learning Enhancement using Bacterial Foraging Optimization Algorithm

The artificial neural network (ANN) is a mathematical model capable of representing any non-linear relationship between input and output data. ANN is an abstract representation of the biological nervous system which has the ability to solve many complex problems. It has been successfully applied to a wide variety of classification and function approximation problems. The information processing capability of artificial neural networks (ANNs) is related to its architecture and weights. To have a high efficiency in ANN, selection of an appropriate architecture and learning algorithm is very important. In this study, the adaption of neural network connection weights using Bacterial Foraging Optimization Algorithm (BFO) is proposed as a mechanism to improve the performance of Artificial Neural Network in classification of Software Defect Dataset. The problem concerns the classification of software as defective or non defective on the basis of software metrics data. The results show that BFOANNs have better accuracy than traditional ANNs. The experimental results showed that BFOA-ANN has an improvement of 2.55 % in software defect prediction accuracy than the original feed forward artificial neural network and 2.80 % in case of cascade forward neural network. General Terms Learning enhancement, Optimization

A Novel Approach for Image Steganography using LCG

modification, interception and sniffing normally exist. Steganography is a common security technique that is utilized to solve or reduce those problems. A large number of methods is used for implementing steganography; as least significant bits (LSB), discrete cosine transform (DCT), discrete Fourier transform (DFT), Spread Spectrum coding and Perceptual Masking. This paper proposes a random and sequential LSB to embed the secret message inside the color image. The linear congruent generator (LCG) is a random generator that is used with LSB to hide a stream of bits in a bitmap image (cover image) to give a new image (stego-image) comparable to the cover image. Secret key for random LSB is a combination of four parameters (Seed, Multiplier, Noncommon factor, and Cycle length). The proposed method employees red, green or blue channel to hide the secret message. Selection of channel based on the modification rate for each channel. The minimum modified channel in cover image is utilized to embed the secret message. Results show that random LSB is better than Sequential LSB in term of visual effect while the worst in term of execution time. Random LSB satisfies sufficient security to secret message due to requirements for random function parameters in the extraction process.

Building an Object Cloud Storage Service System using OpenStack Swift

Cloud Storage Systems are increasingly noticed now-a-days as they are promising elastic capability and high reliability at low cost. In these services, the files are stored in an authenticated cloud storage service center. The most important feature is storage is adjusted dynamically, and there won’t be any worry about space being inadequate or wasted. This paper presents a solution for deploying an Object Cloud Storage Service System based on the open-source cloud Operating System OpenStack and Swift.

Disease Diagnosis using Soft Computing Model: A Digest

In today's era, medical diagnosis has become one of the most progressive disciplines. Since people are taking hybrid food in the daily life, different disease came into existence. It made people very careful for their health. In some cases, lack of knowledge of disease in doctor causes the patient death. Therefore, we require such diagnosis system for non-expertise so that right prescription can come for the patient. Over the years, soft computing plays the major role for computer aided disease diagnosis in physician decision process. Considering these issues, a lot work has been devoted to this discipline. Several disease diagnosis systems based on fuzzy, rule based reasoning, case based reasoning etc. have been proposed for different disease using their symptoms. Hepatitis diagnosis system is good example which is based on CBR. In this paper, we reviewed such types of diagnosis system and their techniques used. We also emphasizes on the liver disease diagnosis system. Moreover, we also found shortcoming in the present systems that cause the vague diagnosis system. Hence, patient gets more illness or sometimes costs as death. We also directed future work that will help to make the present system more robust, reliable, and helpful for nonexpertise.

Subject based Clustering for Digital Forensic Investigation with Subject Suggestion

A Framework for Reducing Impediments in Agile Projects