Hosein Marzi

An Enhanced Bio-inspired Trust and Reputation Model for Wireless Sensor Network

Abstract Wireless Sensor Networks (WSNs) are designed to perform a variety of information processing functionalities. Security is one of the major concerns for WSNs. This study focuses on trust and reputation system management. The proposed approach entitled Enhanced Bio-inspired Trust and Reputation Model (EBTRM), is Bio-inspired and extending Trust and Reputation Model (BTRM-WSN) and Peer Trust System. The objective of the current proposed system is to provide an efficient security solution to WSN which can provide a high level of security taking into account energy conservation.

Use of Neural Networks in Forecasting Financial Market

In todays volatile financial market the demand for an accurate option price forecaster has been a focal point for researchers. The purpose of this study is to forecast option prices using neural networks. Initially simple neural network was implemented using twenty year period data from S&P 500 index call option prices. The prediction result was better than that of traditional Black-Scholes model. A hybrid neural network was developed that utilized aspects of Black- Scholes model into the neural network and tested against the traditional approach and simple neural network. The hybrid neural network outperformed performance of the tradition forecasting model and improved prediction results of simple neural networks.

Fuzzy Control of an Inverted Pendulum using AC Induction Motor Actuator

It has been proven that fuzzy controllers are capable of controlling non-linear systems where it is cumbersome to develop conventional controllers based on mathematical modeling. This article describes designing fuzzy controllers for an inverted pendulum. It compares performance of two controllers designed based on Mamdani and Takagi-Sugeno in a short track length, following a high disturbance. The work then focuses on the AC electrical motor that produces the torque required for the horizontal movements of the inverted pendulum. A simplified control model for the AC motor is used which includes the motors time constant as the crucial parameter in producing rapid responses to the disturbances. In the currently modeling of fuzzy controllers for the inverted pendulum, the input to the pendulum block is a torque. This torque is produced by an electrical motor which is not included in the model. A disadvantage in this modeling is that the electrical motor dynamics is not built-in in the control system independently. Here, a simplified model of an AC electrical motor is incorporated to the system. The electrical motor receives its inputs as current or voltage and produces a torque as output to control the balance of the inverted pendulum. The new approach in modeling a fuzzy control system assists in 1) examining use of AC motors in producing rapid response, 2) selecting sensitive parameters for an optimum high performance electrical motor capable to stabilize the inverted pendulum system and 3) designing a Takagi-Sugeno type fuzzy controller

Modular Neural Network Architecture for Precise Condition Monitoring

Unplanned production shutdown due to equipment failure is the source of the highest cost in the manufacturing and process industries. Traditional fault detection methods are able to monitor the process and detect deterioration of the equipment after their degradation and malfunction occurs. This paper presents an intelligent technique based on a neural network (NN) that monitors the health of the equipment and forecasts faults by detecting any onset of failures. In this approach, an adaptive modular NN architecture that is capable of monitoring the health of industrial machines is introduced. This technique is applied to a subsystem of a machining center. The high accuracy of the technique is verified by extensive tests, resulting in over 99% precision.

A security model for wireless sensor networks

Until recently use of sensors to collect sensitive parameters had only few risk factors such as sensor malfunction, uncertainty of data collection, or missing data coverage. Some issues in these categories were addressed by multi-sensor application or sensor fusion. However, advancement in technology and advent of wireless sensors in a networked environment, brought along a new risk factor related to the security in wireless sensor network. Therefore security in Wireless Sensor Network WSNs is challenging and critical to the functionality of the networked sensors. This is very important in cases of highly secure environment, especially in industrial, military, and medical domains. The standard WSN protocols focus on energy efficiency; transmission efficiency, and routing. WSN is known for limitations on hardware and software and for resources-constrained in general. An adaptive model of security that meets requirements and constraints in WSN is Intrusion Detections. This article investigates security in WSN and presents a design process for achieving optimum security based on requirements and constraints in WSNs. Further, comparative results between a proposed technique and other security current approaches are discussed.

A Secure Platform of Wireless Sensor Networks

Security was not considered when current wireless sensor nodes were designed. As a result providing high level of security on current WSNs platforms is unattainable, especially against attacks based on key resolving and node compromise. In this paper we scrutinize the security holes in current WSNs platforms, and compare the main approaches to implementing the cryptographic primitives used to provide security services for these platforms, in terms of security, energy, and time efficiency. To secure these holes and provide more efficiency we propose a custom hardware platform for WSNs. The choice of cryptographic primitives for our suggested platform is based on their compatibility with the constrained nature of WSNs and their security status. We also discuss the most efficient configurations and implementation methodologies of these primitives, and review their specialized implementations for WSNs in recent literature. Based on that, we provide a hardware implementation of a crypto-processor using Very high speed integrated circuit Hardware Description Language (VHDL). Experimental results using synthesis for Spartan-6 low-power FPGA shows that the proposed protocol outperforms related work in terms of computation time and energy consumption.

Multi-Input Fuzzy control of an inverted pendulum using an armature controlled DC motor

This paper presents a design methodology to stabilize a class of multi-variant nonlinear system after a high disturbance occurs. It investigates application of Takagi-Sugeno type fuzzy controller (T-S-FC) to an inverted pendulum mechanism, actuated by an armature-controlled DC electrical motor.Fuzzy controllers use heuristic information in developing design methodologies for control of non-linear dynamic systems. This approach eliminates the need for comprehensive knowledge and mathematical modeling of the system, and in cases of more complex systems, approximation and simplifications in order to achieve feasible mathematical model is not required.The paper presents the stages of development of the Fuzzy Controller for an inverted pendulum by developing a two-input, Mamdani type system. It evaluates the performance of the system. Then a four-input T-S-FC type is developed. The research compares performances of each controller and presents the result of tests. A model for a DC motor is developed in this study, in order to measure the effect of time delays and response time caused by inherent properties of the physical system. The final part will demonstrate the complete operational system with the DC electrical motor included in the test system.

SN-SEC: a secure wireless sensor platform with hardware cryptographic primitives

Security was not considered when current wireless sensor nodes were designed. As a result, providing high level of security on current WSNs platforms is unattainable, especially against attacks based on key resolving and node compromise. In this paper, we scrutinize the security holes in current WSNs platforms and compare the main approaches to implementing their cryptographic primitives in terms of security, time, and energy efficiency. To secure these holes and provide more efficiency, we propose SN-SEC, a 32-bit RISC secure wireless sensor platform with hardware cryptographic primitives. The choice of cryptographic primitives for SN-SEC is based on their compatibility with the constrained nature of WSNs and their security. SN-SEC is implemented using very high-speed integrated circuit hardware description language. Experimental results using synthesis for Spartan-6 low-power FPGA show that the proposed design has a very reasonable computational time and energy consumption compared to well-known WSN processers.

Optimizing interprocess communication for best performance in real-time systems

This paper describes a new approach in designing communications between processes in a multi-threaded environment. This technique supports real-time performance and can be adapted for embedded operating systems. At present, Message-Passing is a widely used technique in designing Interprocess Communication Systems. This design may degrade performance of Realtime Systems. The present research introduces a library-based architecture for Interprocess Communication Systems (IPC) adaptable in Real-time Embedded Operating Systems. It achieves improved real-time performance by running IPC as a set of library functions.

Use of neural networks in forecasting financial market

This paper studies solutions for forecasting option prices in a volatile financial market. It reviews a mathematical model based on traditional Black-Scholes parametric solution. Then, uses neural networks and compares the results with the conventional method. Twenty year data from S&P 500 index call option prices was used in this study. Initially simple neural network was implemented. The prediction results of simple neural networks were better than that of conventional Black-sholes. A hybrid neural network was designed that employed some aspects of Black-Scholes model. The hybrid neural network outperformed the tradition forecasting model and improved prediction results of simple neural networks.