A Real-Time Control System for a Frequency Response-based Permitivity Sensor (Electronics Project)

Permittivity is an important property of dielectric materials. By measuring the permittivity of a material, it is possible to obtain information about the material’s physical and chemical properties, which are of great importance to many applications.

In this study, a realtime control system for a frequency-response (FR) permittivity sensor was developed. The core of the hardware was a kitCON167 microcontroller (PHYTEC America, LLC), which controlled and communicated with peripheral devices. The system consisted of circuits for waveform generation, signal conditioning, signal processing, data acquisition, data display, data storage, and temperature measurement. A C program was developed in the TASKING Embedded Development Environment (EDE) to control the system.

The control system designed in this study embodied improvements over a previously designed version in the following aspects: 1) it used a printed circuit board (PCB); 2) the measurement frequency range was extended from 120 MHz to 400 MHz; 3) the resolution of measured FR data was improved by using programmable gain amplifiers; 4) a data storage module and a real-time temperature measurement module were added to the system; 5) an LCD display and a keypad were added to the system to display the FR data with corresponding frequencies and to allow users to enter commands.

Impedance transformation models for the sensor probe, the coaxial cable that connects the control system with the sensor probe, and the signal processing circuit were studied in order to acquire information on the permittivity of measured materials from measured FR data. Coaxial cables of the same length terminated with different loads, including an open circuit, a short circuit, a 50 resistor, and a 50 resistor paralleled by a capacitor, were tested. The results indicated that the models were capable of predicting the impedances of these specific loads using the FR data. Sensor probes with different sizes and coaxial cables with two different lengths terminated with the same sensor probe were also tested. The results were discussed.

Additional tests for the gain and phase detector were conducted to compare FR data measured by the gain and phase detector with those observed on an oscilloscope. 

to download full project click on the below link:

http://krex.k-state.edu/dspace/bitstream/2097/12199/1/NingTang2009.pdf

Piezo-electric Power Scavenging for Mining Applications (ECE/EEE Project)

The growing need of creating a network of sensors in critical environment to monitor, sense and alert an operator about the environment gives rise to the research work carried out in the area of power supply to these sensors.

Wireless sensors are usually designed to run on batteries. However, as the number of sensors increases and the devices decrease in size, there is clearly a need to explore alternatives to battery power for wireless sensors. Reliable, efficient and environmentally friendly energy harvesting methods could be adopted to design and build a new electronic device that could be used to replace or supplement batteries in wireless sensors.

This thesis focuses on potential ambient sources of power that can be harvested to run low power wireless sensors in mining environments. It discusses several techniques for converting energy from such sources into useful electrical power. In particular, piezoelectric power conversion technique is described in detail.

Wireless sensor or sensor networks hold significant potential in the mining environment. The need for deployment of such sensor networks is increasing daily as mining companies are looking to adopt the system developed in the “Intelligent Mine – Technology Program (IMTP)”. The objectives of the IMTP are to increase the mine’s productivity, decrease the total costs and to improve the working conditions. To complement these objectives, there have to be improved methods for powering sensor devices to deploy them in large numbers.

Drilling is a crucial component in both underground and surface mining. Water jet assisted drilling is an example of a new drilling technology employing wireless sensors. There are various forms of energy that could potentially be used to power wireless electronic sensors provided the waste energy can be tapped in an intrinsically safe way. In this particular project, the required power to run sensors could be generated by converting mechanical vibration produced from water jet assisted drilling into electrical energy with an intrinsically safe circuit. Various power scavenging methods were researched, but vibration-to-electricity conversion using piezo-ceramic material was selected as the most promising method for this project.

Piezo-based energy conversion is not normally good for mining applications because of intrinsic safety issues. In the case of water jet assisted drilling, however, the environment is much more suitable for piezo-electric conversion. A detailed computer model for this type of power conversion has been developed. The mechanical model of the vibration spectrum is based on test data from the Contents 2 CRC-Mining group. A power conversion circuit has been built, detailed circuit simulations studied and the experimental results are demonstrated.

An example vibration scenario consisting of (20×10^-6)rms strain is considered. Based on this, and a detailed model of a 70mmx25mm PZT piezoelectric patch with 0:2mm thickness, our computer simulation studies and experiments demonstrate the ability to harvest up to 210mW of power.
Source: The University of Newcastle
Author: Upendra K. Singh

to down load full project click on below link:

http://www.mediafire.com/file/joj5b9c4ag8eu2v/ATTACHMENT02.pdf

Q-Learning for Robot Control (Robotics Project)

Q-Learning is a method for solving reinforcement learning problems. Reinforcement learning problems require improvement of behaviour based on received rewards. Q-Learning has the potential to reduce robot programming effort and increase the range of robot abilities.

However, most current Q-learning systems are not suitable for robotics problems: they treat continuous variables, for example speeds or positions, as discretised values. Discretisation does not allow smooth control and does not fully exploit sensed information. A practical algorithm must also cope with real-time constraints, sensing and actuation delays, and incorrect sensor data.

This research describes an algorithm that deals with continuous state and action variables without discretising. The algorithm is evaluated with vision-based mobile robot and active head gaze control tasks. As well as learning the basic control tasks, the algorithm learns to compensate for delays in sensing and actuation by predicting the behaviour of its environment. Although the learned dynamic model is implicit in the controller, it is possible to extract some aspects of the model. The extracted models are compared to theoretically derived models of environment behaviour.

The difficulty of working with robots motivates development of methods that reduce experimentation time. This research exploits Q-learning’s ability to learn by passively observing the robot’s actions—rather than necessarily controlling the robot. This is a valuable tool for shortening the duration of learning experiments.

Author: Gaskett, Chris

Source: The Australian National University

to download full project click on the below link:
http://www.mediafire.com/file/ceuhj2fs3silhb9/02whole.pdf

Touch Screen GLCD based Digital Devices Control System (ece project)

The aim of this project is to build a Graphical LCD Touch Screen interface for switching electrical devices. The controlled devices can be of high voltage or low voltage. A virtual on screen keypad and control board can be developed by the program running inside microcontroller. The status of devices can be viewed on Graphical LCD. No needs to have mechanical push buttons or LED indicators.

Users can control the devices with gentle finger touch. Controlling of Electrical appliances such as Television can be Passwordprotected. By this we can limit the access to certain electrical devices to children or any other un-authorized persons.

This project consists of a microcontroller that takes input from touch screen and processes the request. Then it processes the data and takes necessary action and updates the status on Graphical LCD.

The major building blocks of this project are:
1.Microcontroller Mother Board with regulated power supply.
2.Electromagnetic Relay (controls 230V, 10 Amps loads).
3.Graphical LCD with Touch Screen and Controller interface.
4.Electrical devices to be controlled.

MOTOR CYCLE BATTERY CHARGER

Motorcycle Battery Charger

In the market widely available variety of battery chargers with different technical features ranging from a low amperage to charge the system automatically. This feature is very necessary to improve battery charger system is good and reliable for various purposes, such as a motorcycle battery charger.



Here is a motorcycle battery charger circuit which also has a good feature. This feature is a feedback control circuit where the battery is fully charged condition at the maximum rate. If the battery is fully charged condition encountered, it will be marked with an LED that is turned on.



This motorcycle battery charger is specifically designed for a 12V battery. Apart from some small-sized electronic components like IC LM350, LM1458, and the passive components, used a 18V step-down transformer 80 watt. You simply select a single output voltage 0-18V with a current of about 4-5A. The cable used should have the ability matching a little larger than the current passing. In order to prevent any change electrical energy into heat in the cables that can lower the voltage level.



When construction of the motorcycle battery charger is finished turn the TR1 in place zero value, then the steps for setting the control.



check without connecting the battery, that both LED’s light up.

Connect a car battery charger. Check that the LD2 is off and that a current (typically 2 until 4 A), flows to the battery.

Turn the TR1 and check that the LD2 can turn and charge current to cut.

Turn the TR1 to null value and charge the battery using the standard technique hydrometer (if not available, use a battery in good condition and fully charged).

Turn carefully so that the TR1 LD2 begins to turn and charge current drops to a few hundred mA. If TR1 installed correctly then the next load will see the first LD2 will start to flicker, and charging the battery. When fully charged the battery then the LD2 will turn on fully.





Motorcycle Battery Charger Circuit

To TR1 no longer needs another adjustment. The Q1 is connected in series with the circuit of the battery and can be fired from the circuit R3-4 and LD2. The battery terminal voltage is obtained from the circuit R2, C1, TR1, D2 and activates the Q2 when the voltage terminals exceeds the value we are striving to TR1.



When an uncharged battery put on charge the terminal voltage is low. under this situation the Q2 turn off and Q1, fired in each half cycle of the circuit R3-4, LD2. The Q1 functions as a simple rectifier. While charging the battery, the terminal voltage increases. If the terminal voltage rises above the level that we have set to TR1, then shifts the Q2 gate drive of Q1, it turns off, stop giving power to the battery and lights LD2, showing us that the loading is complete.



The Q1 and the bridge rectifier GR1, should be placed on a good heatsink for proper cooling. The M1 is an Amperemeter DC 5A, so we can monitor the charging current. Optionally can be placed a Voltmeter in parallel with the poles of the battery should have high input impedance, however, not affect the circuit measuring device.

mini project for electronic students

skin response meter
in this the abstract of the project and circuit design of the skin response is given .To download please click the below link
https://docs.google.com/fileview?id=0B8ZeTiGCaM3wNmFlN2MwOWEtYTNmMC00NGQxLTgxYTUtYjVhODczNmJhNzk4&hl=en