Nasir Rashid - Profile on Academia.edu (original) (raw)

Papers by Nasir Rashid

Unorthodox Approach to Classify EEG Signals for Upper Limb Prosthesis Application

Brain computer interface (BCI) is targeted for decoding the EEG (Electroencephalogram) signals th... more Brain computer interface (BCI) is targeted for decoding the EEG (Electroencephalogram) signals that the human brain generates which are beneficial for the paraplegic patients. These EEG signals are slow cortical potentials that are directly recorded from scalp thus cortical neuronal activity is explored via non-invasive electrodes. These EEG signals are then further utilized for performing various operations which the paraplegic patients are unable to perform. This research article presents a novel architecture of classification of four finger movements (thumb movement, index finger movement, middle and index finger combined movement and fist movement) of the right hand on the basis of EEG (Electroencephalogram) data of the movements. The presented architecture utilizes Guided filter for reduction of noise (artefacts) from EEG signals alpha and beta band (8-30 Hz). As this band contains the maximum information of movement in terms of motor imagery. Rank Transform is employed as feature extraction approach for further enhancement of processed EEG signals. Two stage Logistic Regression classifier is finally employed for classification of movements using processed EEG signals. The experimental results demonstrate the accuracy, robustness and computational complexity of the proposed approach and have significant improvement as compared with recent architectures for EEG classification.

Biomedical Research-tokyo, 2017

Brain computer interface decodes signals that the human brain generates and uses them to control ... more Brain computer interface decodes signals that the human brain generates and uses them to control external devices. The signals that are acquired are classified into movements on the basis of feature vector after being extracted from raw signals. This paper presents a novel method of classification of four finger movements (thumb movement, index finger movement, middle and index finger combined movement and fist movement) of the right hand on the basis of EEG (Electroencephalogram) data of the movements. The data-set was obtained from a right-handed neurologically intact volunteer using a noninvasive BCI (Brain-Computer Interface) system. The signals were obtained using a 14 channel electrode headset. The EEG signals that are obtained are first filtered to retain alpha and beta band (8-30 Hz) as they contain the maximum information of movement. Power Spectral Density (PSD) is used for analysis of the filtered EEG data. Classification of the features is done using various classifiers....

PLOS ONE

Robotics and artificial intelligence have played a significant role in developing assistive techn... more Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. Brain-Computer Interface (BCI) is a communication system that allows humans to communicate with their environment by detecting and quantifying control signals produced from different modalities and translating them into voluntary commands for actuating an external device. For that purpose, classification the brain signals with a very high accuracy and minimization of the errors is of profound importance to the researchers. So in this study, a novel framework has been proposed to classify the binary-class electroencephalogram (EEG) data. The proposed framework is tested on BCI Competition IV dataset 1 and BCI Competition III dataset 4a. Artifact removal from EEG data is done through preprocessing, followed by feature extraction for recognizing discriminative information in the recorded brain signals. Signal preprocessing involves the application...

Patch-wise weed coarse segmentation mask from aerial imagery of sesame crop

Computers and Electronics in Agriculture

Development of NI my-RIO Based Control Module for Versatile Terrain Autonomous Mobility Platform (VTAMP)

2019 International Conference on Robotics and Automation in Industry (ICRAI)

In recent years, much progress has been made in the field of Unmanned Ground Vehicles (UGV). None... more In recent years, much progress has been made in the field of Unmanned Ground Vehicles (UGV). Nonetheless, safe mobility and traversal of a UGV on rough terrains such as snow and mountainous regions is still a daunting task. This paper discusses the increased maneuverability and robustness of a UGV by developing its control on NI myRIO. A Versatile Terrain Autonomous Mobility Platform (VTAMP) has been designed for an increase traversing capability through rough and challenging terrains. Extended maneuverability is achieved with the assistance of arm-like structures in the vehicle known as "Flippers". An open loop control system based on NI myRIO is developed with the addition of few add-ons like night vision camera. To achieve robustness, a fail-safe has been developed in both hardware and software and its reliability is experienced in various testing conditions.

Object tracking with a robotic manipulator mounted on ground vehicle using Image Based Visual Servoing

2017 17th International Conference on Control, Automation and Systems (ICCAS), 2017

This paper presents an application of a novel approach for detecting and tracking an object with ... more This paper presents an application of a novel approach for detecting and tracking an object with a 2 DOF robotic manipulator which can be equipped with an array of electrically controlled actuators. The said approach utilizes the Image Based Visual Servoing (IBVS) technique. The developed system is able to determine the object pose in real time from features in the image. Object is detected using shaped based approach algorithms of image processing. The position and orientation of the world coordinates of the object being tracked are calculated from the coordinates of the object in image plane using camera's intrinsic and extrinsic parameters. Experimental results demonstrate the effectiveness of this proposed approach.

Characterization of Motor Imagery based EEG signals with Hilbert Transform

2021 International Conference on Robotics and Automation in Industry (ICRAI), 2021

Brain-computer interface (BCI) is a tool for non-muscular contact between computer and the brain,... more Brain-computer interface (BCI) is a tool for non-muscular contact between computer and the brain, used to acquire Electroencephalograms (EEG). Motor Imagery (MI) is the psychic implementation of any movement without any muscle awakening. Imagination of movement of the limbs can result in spatially noticeable brain signals that can be used to classify patterns. In this research, the application of Hilbert Transform (HT) for the classification of MI based EEG data is shown. A publicly available BCI Competition IV dataset, by Berlin BCI group, containing EEG recordings of 7 subjects performing MI task has been used for this study. Hilbert Transform has been implemented on the EEG data to draw phase plots for the detection of activity in each trial. An average accuracy of 93.6% has been achieved using the proposed methodology. Conclusions of this research manifest that better classification accuracy can be obtained using phase plots of EEG signals which would result in a more viable threshold.

Design and development of a semi-autonomous stair climbing robotic platform for rough terrains

2017 17th International Conference on Control, Automation and Systems (ICCAS), 2017

This paper presents the design of a stair climbing fixed flipper unmanned ground vehicle (UGV) fo... more This paper presents the design of a stair climbing fixed flipper unmanned ground vehicle (UGV) for urban search and rescue purposes. Mobile flippers are being used in certain UGVs for enhanced mobility in rough terrains, however, the control algorithm of these platforms is complex. To add this enhanced mobility in the UGV and to reduce the intricacy of the control algorithm, anterior end of the tracks are lifted up which enables the UGV to pass over obstacles with relative ease. To prevent the rollover of UGV while moving on an inclined surface, an image processing algorithm was developed which halts the motion of UGV if the calculated slope exceeds the threshold value with a maximum error of about 8%. Furthermore, left and right track velocities along with the turn radius were also calculated.

Motor Imagery based Multivariate EEG Signal Classification for Brain Controlled Interface Applications

2019 7th International Conference on Mechatronics Engineering (ICOM), 2019

Brain computer interface (BCI) can be defined as a pathway that enables human brain to communicat... more Brain computer interface (BCI) can be defined as a pathway that enables human brain to communicate and voluntarily command an external device and generate output instead of depending upon peripheral nerves and muscular movements. Achieving maximum classification accuracy is the greatest challenge in developing a BCI system to correctly interpret the brain signals. This paper aims at investigating various classification algorithms in combination with different pre-processing techniques and comparing their results for maximum classification accuracy. Independent component analysis (ICA), principal component analysis (PCA) and notch filters are used for artifact removal, dimension reduction and noise cancellation, respectively. Left and right hand movements were recorded from the scalp using non-invasive electrodes. Fine KNN, with independent components as feature, gives highest classification accuracy in comparison with various classification techniques used in this research.

Design and Experimental Testing of an In-Parallel Actuated 3 DOF Serial Robotic Manipulator for Unmanned Ground Vehicle

2018 3rd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), 2018

This paper presents the design of a 3 degree of freedom manipulator which can be easily mounted o... more This paper presents the design of a 3 degree of freedom manipulator which can be easily mounted on an Unmanned Ground Vehicle (UGV). UGVs operate in closed spaces and rough terrains where human manipulation is difficult for applications involving urban search and rescue. The manipulator therefore needs to have high performance, be light-weight and compact. The proposed design of the manipulator can be easily used with different end-effectors like a camera, gripper or cutter, relevant to the application. In order to maximize the payload capacity and minimize power consumption, in-parallel actuation of the elbow joint is proposed which nullifies the load of actuator on shoulder joint. A chain mechanism is used to transfer torque from motor at base to the relevant joint. Self-locking has been achieved by using worm and gear. This approach results in an increase of 1.42 kilograms in the payload capacity of the manipulator. The results are verified through experimentation.

Kinematic and energy analysis of single axis solar panel tracker system (a comparative study)

Journal of Emerging Trends in Engineering and Applied Sciences, 2013

Energy needs are becoming more and more complex, especially in underdeveloped countries. The sola... more Energy needs are becoming more and more complex, especially in underdeveloped countries. The solar energy is one of best solution for increasing demand of energy by mankind. Sun energy can fulfil our domestic and irrigation requirement because fossil fuels are running short day by day. Therefore, it is one of the most important source of energy to explore for its maturity. In this paper, kinematic and energy analysis of two solar trackers panel systems are studied comparatively for maximising efficiency. Structure of model 1 (single axis tracker) previously designed was bulky, causing the linear actuator to consume more electrical energy. Kinematic and force analysis of model 1 showed the drawbacks of displaced centre of gravity and excessive energy consumption due to weight of frame which supports the panel. Based on kinematic and force analysis of model 1, new model 2 has been designed in which all the above problems are addressed by shifting the centre of gravity on the axis of r...

IEEE Access, 2021

Amputees with lower limb loss need special care during daily life activities to make the movement... more Amputees with lower limb loss need special care during daily life activities to make the movement natural as before amputation. No such work exists covering the main aspects from causes of amputation to the psycho-social impact of the amputees after using the prosthetic device. This review presents for lower limb prosthesis; the study of lower limb amputation, design & development, control strategies & machine learning algorithms, the psycho-social impact of prosthetic users, and design trends in patents. Research articles, review papers, magazines, letters, study reports, surveys, and patents, etc. have been used as sources for this review. Traumatic injuries and different diseases have been found as common causes of amputation. Design & development section illustrates design mechanisms, the categories of passive, active, & semi-active prostheses, an overview of a subset of commercially available prosthetic devices, and 3D printing of the accessories. The control section provides information about control techniques, sensors used, machine learning algorithms, and their key outcomes. Quality of life, phantom limb pain, and psycho-social impact of prosthetic users have been summarized for different countries that are believed to attract the interest of the readers. We have also developed an open-source database ''FAKH-50'' for patents to emphasize the design trends and advancements in lower limb prostheses from 1970 to 2020. Overall trend analysis determined is in the descending order as the knee (48%) > ankle (28%) > foot (22%) > hip (2%) patents in the current version of our database. The forthcoming section highlights the challenges and prospects of the domain. A mutual observation demands the design of a bio-compatible, lightweight, and economic prosthesis to track the normal human gait by eliminating phantom limb pain. This will empower the amputees to live a quality life in society. This work may be beneficial for researchers, technicians, clinicians, and amputees. INDEX TERMS Causes of amputation, lower limb amputation, lower limb prosthesis, design mechanisms, semi-active prosthesis, human gait cycle.

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

Proceedings of the 2018 4th International Conference on Mechatronics and Robotics Engineering, 2018

Feature extraction is a pronounced method to infer the information utility which is concealed in ... more 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.

IEEE Access

The exponential growth in road accidents has led to a need for continuous driver monitoring to en... more The exponential growth in road accidents has led to a need for continuous driver monitoring to enhance road safety. Existing techniques rely on vehicle sensor-based and behavior analysis-based approaches, where the behavior analysis-based approaches are generally considered more desirable as they enable reliable detection of a more elaborate set of driver behaviors. They are categorized as intrusive and non-intrusive approaches. Unlike intrusive approaches that generally rely on constant direct human contact with sensors (physiological signals) and are sensitive to artifacts, non-intrusive approaches offer a more effective behavior monitoring using computer vision-based techniques. This paper proposes an end-to-end non-intrusive IoT-based automated framework to monitor driver behaviors, designed specifically for logistic and public transport applications. It consists of an embedded system, edge computing and cloud computing modules, and a mobile phone application, in an attempt to provide a holistic unified solution for drowsiness detection, monitoring, as well as evaluation of drivers. Drowsiness detection is based on detecting sleeping, yawning, and distraction behaviors using an image processing-based technique. To minimize the effects of latency, throughput, and packet losses, edge computing is performed using commercial off-the-shelf embedded boards. Moreover, a cloud-hosted real-time database for remote monitoring on interactive Android mobile application has been set up, where admin can add multiple drivers to get drowsiness notifications along with other useful related information for driver evaluation. An extensive experimental testing has been performed, obtaining encouraging results. An overall accuracy of 96% is achieved along with an enhanced robustness, portability, and usability of the proposed framework.

Improved Classification Accuracy of Four Class FNIRS-BCI

2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2020

A new method for pixel classification for rice variety identification using spectral and time series data from Sentinel-2 satellite imagery

Computers and Electronics in Agriculture, 2022

Analysis of visual features and classifiers for Fruit classification problem

Computers and Electronics in Agriculture, 2021

2013 IEEE 9th International Conference on Emerging Technologies (ICET), 2013

Brain Computer Interface (BCI) is a communication system, which avoiding the brain's normal outpu... more Brain Computer Interface (BCI) is a communication system, which avoiding the brain's normal output pathways of muscles and peripheral nerves and allows a patient to control its external world only by means of brain signals. For successful implementation of BCI, dimensionality reduction and classification are fundamental task. In this paper, we used a publically available EEG signals data of the Upper Limb Motion. First the dimensionality of the data is being reduced by using Principal Component Analysis (PCA) followed by classification of the reduced dimensioned dataset by well-known classifiers e.g. Artificial Neural Networks (ANN), Linear Discriminant Analysis (LDA) and Decision trees (DT). To identify a classifier which does the classification task more efficiently, we compare their performances on the basis of Confusion Matrices and Percentage Accuracies. The experimental results show that ANN is the best classifier for the classification of brain signals and has the percentage accuracy of 81.6%.

IEEE Access

Weeds affects crops health as it shares water and nutrients from the soil, as a result it decreas... more Weeds affects crops health as it shares water and nutrients from the soil, as a result it decreases crop yield. Manual weedicide spray through bag-pack is hazardous to human health. Localized autonomous weedicide spray through aerial spraying units can help save water, weedicide chemical and effect less on human health. Such systems require multi-spectral cues to classify crop, weed, and soil surface. Our focus in this paper is on the detection of weeds in the sugar beet crop, using airborne multispectral camera sensors, which is considered as an alternative crop to sugarcane to obtain sugar in Pakistan. We developed a new framework for weed identification; a patch-based classification approach as appose to semantic segmentation that is more realistic for real-time intelligent aerial spraying systems. Our approach converts 3-class pixel classification problem into a 2-class crop-weed patch classification problem which in turns improves crop and weed classification accuracy. For classification, we developed a new VGG-Beet convolutional neural network (CNN), which is based on generic CNN (VGG16) model with 11 convolutional layers. For experiments, we captured a sugar beet dataset with 3-channel multispectral sensor with a ground sampling distance (GSD) of 0.2 cm/pixel and a height of 4 meters. For better comparison, we used two publicly available sugar beet crop aerial imagery datasets, captured using a 5-channel multispectral sensor and a 4-Channel multispectral sensor with a ground sampling distance of 1cm and a height of 10 meters. We observed that patch-based method is more robust to different lighting conditions. To produce low cost weed detection system usage of Agrocam sensor is recommended, for higher accuracy Red Edge and Sequoia multispectral sensors with more channels should be deployed. We observed higher crop-weed accuracy and lower testing time for our patch-based approach as compared to state-of-the-art UNet and Deeplab semantic segmentation networks. INDEX TERMS autonomous weed detection, drone weed detection, deep learning in agriculture, multispectral image processing.

Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC)

Infrared Physics & Technology