Umar Shahbaz Khan

Background removal from GPR data using Eigenvalues

A GPR radargram of an underground scan has reflections not only from the target but many unwanted objects known as clutters. Additionally, the signal is corrupted by the direct wave and coupling effect of the antennas and background noise. In order to successfully extract the target signature, these extra noise effects need to be eliminated. Though the clutters cannot be totally removed from the data, background removal techniques suppress their effect to quite an extent. Usually mean subtraction is used as a background removal technique but the results are just satisfactory and further improvements can be made. In this paper an Eigenvalue based background removal technique in collaboration with mean subtraction is presented. This proposed method decreases the effect of clutters and the output is much more refined. Even though this method takes slightly more time than the traditional background removal methods, the output eliminates major portion of the clutter therefore the segmentation and classification stages in an automated GPR data processing system would be much more efficient hence reducing the overall time consumption for near real time GPR data processing. The method has been implemented on a number of different data sets and the results indicate that the proposed method gives significant improvement in background removal over the existing background removal methods.

Detection of landmines and underground utilities from acoustic and GPR images with a cepstral approach

This paper introduces a cepstral approach for the automatic detection of landmines and underground utilities from acoustic and ground penetrating radar (GPR) images. This approach is based on treating the problem as a pattern recognition problem. Cepstral features are extracted from a group of images, which are transformed first to 1-D signals by lexicographic ordering. Mel-frequency cepstral coefficients (MFCCs) and polynomial shape coefficients are extracted from these 1-D signals to form a database of features, which can be used to train a neural network with these features. The target detection can be performed by extracting features from any new image with the same method used in the training phase. These features are tested with the neural network to decide whether a target exists or not. The different domains are tested and compared for efficient feature extraction from the lexicographically ordered 1-D signals. Experimental results show the success of the proposed cepstral approach for landmine detection from both acoustic and GPR images at low as well as high signal to noise ratios (SNRs). Results also show that the discrete cosine transform (DCT) is the most appropriate domain for feature extraction.

Automatic inspection system using machine vision

Man from the beginning of time, tried to automate things for comfort, accuracy, precision and speed. Technology advanced from manual to mechanical and then from mechanical to automatic. Vision based applications are the products of the future. Machine vision systems integrate electronic components with software systems to imitate a variety of human functions. This paper describes current research on a vision based inspection system. A computer using a camera as an eye has replaced the manual inspection system. The camera is mounted on a conveyor belt. The main objective is to inspect for defects, instead of using complicated filters like edge enhancement, and correlation etc. a very simple technique has been implemented. Since the objects are moving over the conveyor belt so time is a factor that should be counted for. Using filters or correlation procedures give better results but consume a lot of time. The technique discussed in this paper inspects on the basic pixel level. It checks on the basis of size, shape, color and dimensions. We have implemented it on five applications and the results achieved were good enough to prove that the algorithm works as desired

Optimized circuit for EMG signal processing

An optimized circuit for processing of EMG signals has been designed and presented in this paper. This circuit acquires EMG signals from surface of the skin using bipolar electrodes and enables the amputee to control the prosthetic hand in an efficient manner. EMG can be defined as the electrical potential produced due to the contraction of muscle. It can be picked from the residual portion of muscles of an amputee. EMG signal requires the processes of amplification, band limiting and rectification, before it can be fed to an analog to digital converter (ADC) and subsequently to motors driving the prosthetic device.

Multi-Response Optimization of Electrothermal Micromirror Using Desirability Function-Based Response Surface Methodology

The design of a micromirror for biomedical applications requires multiple output responses to be optimized, given a set of performance parameters and constraints. This paper presents the parametric design optimization of an electrothermally actuated micromirror for the deflection angle, input power, and micromirror temperature rise from the ambient for Optical Coherence Tomography (OCT) system. Initially, a screening design matrix based on the Design of Experiments (DOE) technique is developed and the corresponding output responses are obtained using coupled structural-thermal-electric Finite Element Modeling (FEM). The interaction between the significant design factors is analyzed by developing Response Surface Models (RSM) for the output responses. The output responses are optimized by combining the individual responses into a composite function using desirability function approach. A downhill simplex method, based on the heuristic search algorithm, is implemented on the RSM models to find the optimal levels of the design factors. The predicted values of output responses obtained using multi-response optimization are verified by the FEM simulations.

Design of non-conventional chain drive mechanism for a mini-robot

This paper describes the design of a non-conventional chain drive mechanism for a mini-robot. A mini-robot named RUDYCUDY™ was designed. Chain drives are normally used when power or motion or both of them are to be transferred over a short distance. Various systems were available in the markets that had certain standards. To drive small scale robots no standard chain was available. In this study a roller chain was designed because of its simplicity, strength, ability to work in harsh environment and little requirement for lubrication.

Design, Simulation and Optimization of Electrothermal Micro Actuator

This work presents the design and optimization of out of plane electrothermal MEMS actuators. The proposed concept is capable of generating large out of plane displacement at low driving power and a low actuation temperature. The performance of this actuator is evaluated and simulated in ANSYS. The out of plane displacement of 291µm at a temperature increase of 135°C from ambient has been achieved with the applied power of 2.7mW(0.7V). Moreover, a thermal time constant of 5.6ms and a frequency of 85Hz is accomplished for this actuator.

Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis

Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8–30 Hz) containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.

MEMS based Braille system

Visually impaired individuals are not able to peruse like ordinary people. Tactile technologies are helpful for those to read through sense of touch but these technologies are not yet widely available, due to their high cost, non ideal response and degree of safety. In this research an attempt is made on designing an affordable and useable MEMS (Microelectromechanical system) based Braille system, offering an efficient way in improving readability of visually impaired people. Our ultimate goal is to design a wearable system that converts digital text files into tactile signals using USB port. This system will be based on MEMS actuators that would mimic these Braille dots. In this paper such a Braille system is presented having overall size of 9mm* 16mm. In comparison with other MEMS based Braille systems, this design requires low input voltage i.e. 0.6V in order to produce required deflection of 0.25mm of Braille dot. With the help of this tactile display visual impaired person can read with the speed of sighted human beings. The device is easy to wear as the material and operating temperature are not harmful to the human skin.

Real Time Modeling of Interlocking Control System of Rawalpindi Cantt Train Yard

Interlocking system is a safety critical system which governs the safe movement of trains in a train yard. Recent advancement in technology has enabled railway organizations world over to optimize their operations by using software based automated solutions. Since interlocking systems are not only complex but also safety critical, these systems should be modeled and verified against safety requirements to weed out any design bugs which when discovered during testing or deployment phase in the system life-cycle, will result in high cost overruns and can cause catastrophes. Timed automata have effectively been used for the modeling and verification of real-time safety critical systems. In this paper, we model Rawalpindi Cantt (Pakistan) train yard using timed automata and verify its safety properties using UPPAAL model checker. This verified model can effectively be used to implement the design which will be more reliable as compared to the systems which are verified by classical methods of testing and simulation.