Mohsin I. Tiwana

Design, simulation and fabrication of a low cost capacitive tactile shear sensor for a robotic hand

In this paper a novel shear sensor design is described. The proposed design is able to measure static shear forces. The sensor is fabricated on a printed circuit board and uses a differential capacitor arrangement to detect shear force applied to the sensor surface. Both mathematical and COMSOL models have been developed for the described sensor. The sensor is intended for use in robotic hands for the detection of shear force at the tactile interface. The device can be fabricated at a low cost in both low and high volumes. Prototype sensors with a ±0.525 mm displacement range were fabricated. Fixed displacements (over the range ±0.5 mm) are applied to the capacitor common plate, without a silicone covering, and a range of shear forces (over the range ±4 N) applied to the sensor, once covered with a silicone skin, and the differential capacitance of the transducer is recorded. The maximum standard deviation of the differential capacitance across all force values is 1.35e-15 F. The maximum standard deviation, at each force value, across a range of ±2 N is 4.28e-16 F.

A Novel Framework of Automated RRM for LTE SON Using Data Mining: Application to LTE Mobility

With the evolution of broadband mobile networks towards LTE and beyond, the support for the internet and internet based services is growing. However, the size and operational costs of mobile networks are also growing. Self Organizing Networks (SON) are introduced as a part of the specifications of the LTE standard with the purpose of reducing the Operation and Maintenance costs of the mobile networks. This paper introduces a novel framework for automated Radio Resource Management (RRM) in LTE SON. This framework deals with the self-optimization and self-healing features of SON. The data mining technique of linear regression has been used to derive the functional relationship, known as model, between Key Performance Indicators and RRM parameters. The proposed framework uses this model in two ways: first, for network monitoring, which is the first step of the self-healing procedure and secondly, to devise a handover auto-tuning algorithm as part of the self-optimization procedure. The detailed results obtained for the finished case studies, demonstrate the effectiveness and usefulness of this approach.

Development of FPGA-based system for control of an Unmanned Ground Vehicle with 5-DOF robotic arm

This paper discusses the development of a customizable FPGA based system for implementing control algorithms on an Unmanned Ground Vehicle (UGV) and its 5 Degree of Freedom (DOF) manipulator. The compact RIO-9012 is used as a controller which is a reconfigurable embedded control and acquisition system using LabVIEW as the programming platform. The developed system enables the control of UGV and its manipulator using a remote joystick controller via Wi-Fi communication. Apart from Joystick, the system can also be controlled optionally using a keyboard. Accuracy of Joystick control has been enhanced by using point to point mapping technique. A user friendly GUI has been developed to view live video feedback obtained from the onboard cameras to control the UGV accordingly. Different features of UGV like path tracker (tracks its path on Google Maps), variable speed modes, battery indicator, camera switch and selector etc. are also managed in the GUI. The system has been developed so that, in future, it can easily be extended to a fully autonomous system.

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%.

Inverse kinematics control of redundant planar manipulator with joint constraints using numerical method

In this paper, an efficient numerical scheme is associated with the scheming of the inverse kinematics for a four degrees of freedom redundant planar robotic manipulator. The manipulator under study is an RRPR redundant manipulator with joint angle constraints at each joint. Firstly, using the forward kinematics transformation matrix, a plot of the reachable workspace was obtained. This plot was used to determine equations for the possible end-effector positions. Then the analytic approach was used to determine equations for the inverse kinematic scheme. The inverse kinematics scheme initially sets the first joint angle as a constant at the current position in order to determine the inverse solution. However, if the solution calculated is not within the joint constraints then it uses numerical techniques to determine the least displacement new first joint parameter to determine the next possible solution. This results in a faster and more accurate convergence to the desired solution as compared to the traditional approaches.

Comparative Analysis of EMG Signal Features in Time-domain and Frequency-domain using MYO Gesture Control

Feature extraction is a pronounced method to infer the information utility which is concealed in electromyography (EMG) signal to study the characteristic properties and behavior of signal. This study gives a comparative analysis of thirteen complete and most up-to-date EMG feature signals in Time-domain and Frequency-domain. Particularly, the EMG signals are obtained from a device MYO gesture control on an embedded system. For this purpose, four healthy male volunteers are considered to perform four different hand movements based on stationary, double tap, single finger movement and finger spread. To be a successful classification of these EMG features in both domains, we prefer attribute selected classifier as it gives the better performance and higher rate of accuracy i.e. 93.8%. The experimental results prove that features in time-domain are superfluity and redundant while features in frequency-domain (measured by statistical parameters of EMG power spectral density) show the ultimate dominance and signal characterization. The findings of this study are highly beneficial for further use in order to predict the behavior of EMG in pattern recognition and in classification of EMG signals for assistive devices or in powered human arm prosthetics.

Efficient FIR Filter Implementations for Multichannel BCIs Using Xilinx System Generator

Background. Brain computer interface (BCI) is a combination of software and hardware communication protocols that allow brain to control external devices. Main purpose of BCI controlled external devices is to provide communication medium for disabled persons. Now these devices are considered as a new way to rehabilitate patients with impunities. There are certain potentials present in electroencephalogram (EEG) that correspond to specific event. Main issue is to detect such event related potentials online in such a low signal to noise ratio (SNR). In this paper we propose a method that will facilitate the concept of online processing by providing an efficient filtering implementation in a hardware friendly environment by switching to finite impulse response (FIR). Main focus of this research is to minimize latency and computational delay of preprocessing related to any BCI application. Four different finite impulse response (FIR) implementations along with large Laplacian filter are implemented in Xilinx System Generator. Efficiency of 25% is achieved in terms of reduced number of coefficients and multiplications which in turn reduce computational delays accordingly.

Self-organizing inter-cell interference coordination in 4G and beyond networks using genetic algorithms

ABSTRACT The design objective of the 4G and beyond networks is not only to provide high data rate services but also ensure a good subscriber experience in terms of quality of service. However, the main challenge to this objective is the growing size and heterogeneity of these networks. This paper proposes a genetic-algorithm-based approach for the self-optimization of interference mitigation parameters for downlink inter-cell interference coordination parameter in Long Term Evolution (LTE) networks. The proposed algorithm is generic in nature and operates in an environment with the variations in traffic, user positions and propagation conditions. A comprehensive analysis of the obtained simulation results is presented, which shows that the proposed approach can significantly improve the network coverage in terms of call accept rate as well as capacity in terms of throughput.

EEG signals classification and determination of optimal feature-classifier combination for predicting the movement intent of lower limb

Recent advancements in brain computer-interfacing (BCI) and neuro-robotics have played an indispensable role for people suffering from neural injuries to expect better quality of life by restoring sensory functions and replacement of neuro-muscular pathways as BCI systems work on imagination of movements to control prosthetic limbs. In this research, multiple combinations of features and classifiers have been used to classify electroencephalographic (EEG) signals in order to acquire maximum classification accuracy for a four class EEG based BCI system. Knee and ankle joint movements are executed in a predefined manner during which EEG signals are acquired from sensorimotor cortex of four healthy test subjects, using Emotiv headset. After removing artifacts, five features; average band power, peak, kurtosis, mean and skewness are calculated. Multiple combinations of calculated features are used to classify ankle dorsi-planter flexion and knee extension-flexion movements using five different classifiers; Naïve Bayes, A-nearest neighbor (ANN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). It was found that features set comprising; average band power, peak and mean value yielded significantly higher classification accuracy of 81.31% among all other combinations whereas in classifiers, maximum accuracy of 81.48% is obtained through LDA as compared to other classifiers. These novel findings clearly exhibit the feasibility of achieving good classification accuracies using average band power, mean and peak values of EEG signals as feature vectors and LDA as classifier.

Towards development of vacuum forming system for lower limb prosthetics

In lower limb prosthetics, pistoning and increased volume change of residual limb inside the socket is the main problem. To overcome this problem, a vacuum suspension system has been developed to hold the limb firmly into the socket. This paper aims at the design of the suction geometry made of neoprene rubber to create a vacuum forming system for lower limb prosthetics. The designed geometry was integrated with bionic foot for below knee amputation. The proposed design was modeled using SOLIDWORKS 14 and analyzed by using finite element method (FEM) in ANSYS 14.5. The deformed geometry was further used to calculate the change in internal volume which gives the volume of air evacuated during each step. The value of required pressure to create a vacuum in the socket is -69 KPa (508 mmHg). Further calculations were made to find out the steps to achieve such pressure.