Development of an intelligent scorpion detection technique using vibration analysis

A possible solution to address the problem of Scorpion stings is the capability of detecting its presence earlier before it stings. This paper presents efforts in Scorpion detection using substrate vibration modelling approach. An eight stage approach has been presented in this work. Using sinusoidal signal, signal representing Scorpion behaviour was firstly sampled and then amplified before transmitting to a nearby receiving module. The received signal undergoes filtering for noise removal before being modelled for coefficients determination. The computed coefficients were then clustered for analysis of behavioural determination. Results obtained in this work show that the proposed technique can be used for Scorpion detection

Hardware implementation of intelligent braking system

Intelligent braking system has a lot of potential applications especially in developed countries where research on smart vehicle and intelligent highway are receiving ample attention. The system when integrated with other subsystems like automatic traction control system, intelligent throttle system, and auto cruise system, etc will result in smart vehicle maneuver. The driver at the end of the day will become the passenger, safety accorded the highest priority and the journey will be optimized in term of time duration, cost, efficiency and comfortability. The impact of such design and development will cater for the need of contemporary society that aspires quality drive as well as to accommodate the advancement of technology especially in the area of smart sensor and actuator. The emergence of digital signal processor enhances the capacity and features of universal microcontroller. This paper introduces the use of TI DSP, TMS320LF2407 as an engine of the system. The overall system is designed so that the value of inter-vehicle distance from infrared laser sensor and speed of follower car from speedometer are fed into the DSP for processing, resulting in the DSP issuing commands to actuator to function appropriately

Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding

One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. The performance of Multilayer Feed Forward Artificial Neural Network performance in image compression using different learning algorithms is examined in this paper. Based on Gradient Descent, Conjugate Gradient, Quasi-Newton techniques three different error back propagation algorithms have been developed for use in training two types of neural networks, a single hidden layer network and three hidden layers network. The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms

Design and implementation of an intelligent fuzzy logic controller (FLC) for Air Handling Unit (AHU) for smart house

Intelligent Building Automation System (IBAS) is one of the heaviest researched areas motivated by the continuous high demand on economically-effective systems that are designed to provide a desirable controlled space for various organizations. IBAS has been developed along with the rapid sophistication of the information and control technologies in this study. The main objective of the continuous effort is to provide an intelligent monitor and control of various facilities within the building so as to offer its users or occupants with effective security, improved productivity, human comfort, and efficient energy management. Heat, Ventilation and Air Conditioning (HVAC), Lighting Systems, Life and Safety System, and Access Control are some of the typical systems that formed IBAS in most modern building. HVAC and Lighting systems constitute the major energy consumer in an entire building that focuses particularly on the improvement of monitor and control of these systems

Two level Differential Evolution algorithms for ARMA parameters estimatio

The problem of determining simultaneously the model order and coefficient of an Autoregressive Moving Average (ARMA) model is examined in this paper. An Evolutionary Algorithm (EA) comprising two-level Differential Evolution (DE) optimization scheme is proposed. The first level searches for the appropriate model order while the second level computes the optimal/sub-optimal corresponding parameters. The performance of the algorithm is evaluated using both simulated ARMA models and practical rotary motion system. The results of both examples show the effectiveness of the proposed algorithm over a well known conventional technique

Autonomous biomimitic robot based multi-agent system for disaster management and rescue

This paper discusses the scope and feasibility of autonomous biomimitic robot based multi-agent systems for disaster management and rescue. Search and rescue operations in disastrous situations like earthquake, landslide, fire hazards, mineshaft breakdown etc. are still handled manually. Manual operations in these cases often fail due to complicated nature of the catastrophe. Especially in the case of human entrapment in areas inaccessible to either human or traditional rescue equipment. As such rescue operation suffers from improper strategy and even leads to unintentional further destruction due to lack of proper information along the rescue site. It is clear, proper information in and around the disaster can help successful handling of the catastrophe. Thus information like location of the survivor, state of the obstructions around him/her, state of injury, level of oxygen and hazardous gases are of crucial importance. To gather such widespread information from such difficult terrain, autonomous robots equipped with multiple sensors and capable to move inside difficult to access areas is a good choice. Autonomous biomimitic robot like Snake robot is meant to mimic motion of a natural snake, which does not possess any limb. Natural snakes can undergo wide range of motion and are able to move over rough terrains without the danger of entanglement. Slender structure of the snake body helps a snake to go inside narrow holes. Thus a snake robot able to mimic these features of a natural snake will be of extreme use in handling search and rescue operations. Snake robots equipped with multiple sensors and controlled under multiagent collaborative protocol are expected to bring about acceptable solution to disaster management and rescue. The other such biomimitic robots that can be considered in the autonomous robot team are flapping wing flyers and robot Monkeys. A team consisting of such robots will help in collecting information, distributing food and medicine in disastrous location

Application of intelligent technique for development of Colpitts oscillator

In this paper, new method of Colpitts oscillator designing through combination of Genetic Algorithm and Artificial Neural Network (ANN) has been suggested. The Thevenin's resistors for the common base Colpitts oscillator are optimized through application of GA and ANN. The developed common base Colpitts oscillator has shortest transient time response and stable Direct Current (DC) stability in the long term operation. Involvement of GA and ANN successfully optimize between transient time response and steady state response of common base oscillator. Application of these two artificial intelligent techniques assist faster selection of optimizes components values such as resistance values during circuit development rather than conventional method which used intuition techniques to develop the circuit

A new method of vascular point detection using artificial neural network

Vascular intersection is an important feature in retina fundus image (RFI). It can be used to monitor the progress of diabetes hence accurately determining vascular point is of utmost important. In this work a new method of vascular point detection using artificial neural network model has been proposed. The method uses a 5x5 window in order to detect the combination of bifurcation and crossover points in a retina fundus image. Simulated images have been used to train the artificial neural network and on convergence the network is used to test (RFI) from DRIVE database. Performance analysis of the system shows that ANN based technique achieves 100% accuracy on simulated images and minimum of 92% accuracy on RFI obtained from DRIVE database