Muhammad Usman Asad

GPS-GSM Integration for Enhancing Public Transportation Management Services

This paper proposes and implements a solution for enhancing public transportation management services based on GPS and GSM in Punjab province of Pakistan. The system consists of four modules: BUS Station Module, In-BUS Module, BASE Station Module and BUS Stop Module. Equipped with PC and GSM modem, BUS Station Module sends the initialization information containing the bus number and license plate number to In-BUS Module and BASE Station Module using SMS. The microcontroller based In-BUS Module consisting mainly of a GPS receiver and GSM modem then starts transmitting its location and number of passengers to BASE Station Module. BASE Station Module equipped with a microcontroller unit and GSM modems interfaced to PCs is designed to keep track record of every bus, processes user request about a particular bus location out of BUS Station and updates buses location on bus stops. BUS Stop Module is installed at every bus stop and consists of a GSM modem, memory unit and dot matrix display all interfaced to a microcontroller. This module receives buses location information coming towards that stop from BASE Station module and displays the information on a dot matrix display. A per stop statistical analysis is carried out based on the number of passengers and a recommendation report along with this analysis is sent to Punjab Government Transportation Department to have a check on the performance and services offered by transporters to common people. The results have shown that the developed system is useful for facilitating people using public transportation services.

Microcontroller Based Neural Network Controlled Low Cost Autonomous Vehicle

In this paper, design of a low cost autonomous vehicle based on neural network for navigation in unknown environments is presented. The vehicle is equipped with four ultrasonic sensors for hurdle distance measurement, a wheel encoder for measuring distance traveled, a compass for heading information, a GPS receiver for goal position information, a GSM modem for changing destination place on run time and a nonvolatile RAM for storing waypoint data; all interfaced to a low cost AT89C52 microcontroller. The microcontroller processes the information acquired from the sensors and generates robot motion commands accordingly through neural network. The neural network running inside the microcontroller is a multilayer feed-forward network with back-propagation training algorithm. The network is trained offline with tangent-sigmoid as activation function for neurons and is implemented in real time with piecewise linear approximation of tangent-sigmoid function. Results have shown that upto twenty neurons can be implemented in hidden layer with this technique. The vehicle is tested with varying destination places in outdoor environments containing stationary as well as moving obstacles and is found to reach the set targets successfully.

RFID Based Security and Access Control System

 Abstract—This paper describes the design of RFID based security and access control system for use in hostels inside the Punjab University premises. The system combines RFID technology and biometrics to accomplish the required task. When the RFID reader installed at the entrance of hostel detects a number, the system captures the user image and scans the database for a match. If both the card and captured image belong to a registered user, access is granted; otherwise the system turns on the alarm and makes an emergency call to the security van through GSM modem. In this way, the suspicious persons can be caught.

Fuzzy Model Based Bilateral Control Design of Nonlinear Tele-Operation System Using Method of State Convergence

This paper presents the design of a state convergence (SC)-based bilateral controller for a nonlinear teleoperation system, which has been approximated by a Takagi-Sugeno (TS) fuzzy model. The selection of SC is made due to the advantages offered by this scheme both in the modeling and control design stages. The modeling stage considers master/slave systems, which can be represented by the n th order differential equations, while the control design stage offers an easy way to determine the control gains required for assigning desired closed loop dynamics to teleoperation system. After the master/slave systems are represented by TS fuzzy models, a stabilizing fuzzy law is adopted which allows deploying the SC scheme with all its benefits to design the fuzzy bilateral controller. In this way, not only the simplicity of the design scheme is ensured but also the existing SC scheme is able to control a nonlinear teleoperation system based on its TS fuzzy model description. As an additional advantage, the SC-based existing linear bilateral controller can be easily derived from the SC-based proposed fuzzy bilateral controller. Various cases of master/slave systems originally reported in terms of their linear model representation and communication in the absence/presence of time delay are all discussed in the corresponding fuzzy framework. The MATLAB simulations, considering a one-degree-of-freedom teleoperation system, are performed to validate the proposed methodology for controlling a nonlinear teleoperation system.

A low cost microcontroller implementation of neural network based hurdle avoidance controller for a car-like robot

This paper describes the implementation of a neural network based hurdle avoidance controller for a car like robot using a low cost single chip 89C52 microcontroller. The neural network is the multilayer feed-forward network with back propagation training algorithm. The network is trained offline with tangent-sigmoid as activation function for neurons and is implemented in real time with piecewise linear approximation of tangent-sigmoid function. Results have shown that up-to twenty neurons in hidden layer can be deployed with the proposed technique using a single 89C52 microcontroller. The vehicle is tested in various environments containing obstacles and is found to avoid obstacles in its path successfully.

Design and Development of an Image Processing Based Adaptive Traffic Control System with GSM Interface

With the increasing number of vehicles on the road, it has become difficult to properly control the flow of traffic especially in large cities like Lahore. The proposed system uses a digital camera mounted on a stepper motor to sense the traffic on the road. The decision to open the lane is done after monitoring the traffic load. The heavily loaded side is turned on for a longer time. Thus the system is intelligent because it is not using the fixed time frame. The system is also integrated with GSM, thus the signals can also be controlled with the help of mobile in case of an emergency. MATLAB® programming environment has been used in simulating and developing the system.

Neo-Fuzzy Integrated Adaptive Decayed Brain Emotional Learning Network for Online Time Series Prediction

Adaptive decayed brain emotional learning (ADBEL) network is recently proposed for the online time series forecasting problems. As opposed to other popular learning networks, such as multilayer perceptron, adaptive neuro-fuzzy inference system, and locally linear neuro-fuzzy model, ADBEL network offers lower computational complexity and fast learning, which make it an ideal candidate for the time series prediction in an online fashion. In fact, these prominent features are inherited from the mechanism employed by the limbic system of the mammalian brain in processing the external stimuli, which also forms the basis of the ADBEL network. This paper aims at further enhancing the forecasting performance of the ADBEL network through its integration with a neo-fuzzy network. The selection of the neo-fuzzy network is made as it offers features required for online prediction in real time environments including simplicity, transparency, accuracy, and lower computational complexity. Furthermore, this integration is only considered in the orbitofrontal cortex section of the ADBEL network and only three membership functions are employed to realize the neo-fuzzy neuron. Thus, the resultant neo-fuzzy integrated ADBEL (NF-ADBEL) network is still simple and can be deployed in online prediction problems. Few chaotic time series namely the Mackey glass, Lorenz, Rossler, and the Disturbance storm time index as well as the Narendra dynamic plant identification problem are used to evaluate the performance of the proposed NF-ADBEL network in terms of the root mean squared error and correlation coefficient criterions using MATLAB®programming environment.

Fuzzy logic based wall tracking controller for mobile robot navigation

This paper describes the design and implementation of a fuzzy logic based controller for wall tracking behavior of mobile robot. The controller accepts inputs from three ultrasonic sensors and generates speed commands for differentially steered robot to track wall on its left side at a set distance. The controller is implemented in real time using an inexpensive 8 bit microcontroller and is validated through experimental runs in following the corner trajectory.

State convergence based bilateral control of nonlinear teleoperation system represented by TS fuzzy models

This paper presents the bilateral control of nonlinear teleoperation system represented by Takago-Sugeno (TS) fuzzy models based upon the state convergence methodology. All features of state convergence method proposed for linear bilateral control including the provision of considering the master and slave systems modeled by nth order differential equations, achieving the desired convergence behavior between the master and slave systems along with posing the desired slave dynamics and simplicity of determining the control gains by solving a set of equations, are all kept while designing the fuzzy bilateral control for nonlinear teleoperation system. MATLAB simulations considering one degree of freedom (DoF) nonlinear teleoperation system are included to prove the validity of the proposed state convergence based fuzzy bilateral control.

A 2D simulator for vision based autonomous road following

A 2D simulator for vision based autonomous road following is designed using MATLAB. The simulator provides options for selecting road image, placing hurdles on road and the algorithm for following the road autonomously. With the road image containing hurdles and algorithm selected, the simulator draws the robot trajectory and also plots the error curves. A real time implementation of simulated algorithms is finally presented. The simulator can be employed to teach the fundamental concepts in autonomous road following tasks.