Design, fabrication and modelling of four-wheeled mobile robot platform with two differential and two caster wheels

This paper presents a design and modeling of wheeled mobile robot (MWR) when navigating autonomously in environment such as road and factory. It needs a good and robust design and control for wheeled mobile robot to move from one to another points with smooth moving and small tracking errors. This paper is focused on mechanical design and modeling of wheeled mobile robot. Autodesk inventor software is used to draw the design of the WMR because this software is simple to make any design and a wheeled mobile robot structure is designed with a center of gravity to be located below the axle wheels level. The wheeled mobile robot is driven using two differential drive and two castor wheels to balance robot while it is moving in the environment. Two kinds of coordinate systems are used to describe the movement of the robot in the environment; namely are Local and global coordinate system; where local is related to the heading angle and the deferential wheel shaft, however the global describes the motion in x, y and z directions. The kinematic model is derived for the four wheeled mobile robot using angular velocities equations for the left and right wheels with estimation the heading angle of the robot

Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis

The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. This paper proposes two robust methods: i) Wavelet packet decomposition (WPD), and ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: i) Difference in the signal to noise ratio ({\Delta}SNR) and ii) Percentage reduction in motion artifacts ({\eta}). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average {\Delta}SNR (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average {\eta} (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique i.e. the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average {\Delta}SNR and {\eta} values of 30.76 dB and 59.51%, respectively for all the EEG recordings. On the other hand, the two-stage motion artifacts removal technique i.e. WPD-CCA has produced the best average {\Delta}SNR (16.55 dB, utilizing db1 wavelet packet) and largest average {\eta} (41.40%, using fk8 wavelet packet). The highest average {\Delta}SNR and {\eta} using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed.Comment: 25 pages, 10 figures and 2 table

Modeling and design of two link robotic manipulator for grading and sorting of rotationally symmetric products

This paper present a design and modeling for a two link robotic manipulator for grading and sorting system. The mechanical design calculation of the robotic manipulator is accomplished firstly to estimate the torques and positions of manipulator that are required to move a certain payloads from one to another position, which is resulted by choosing of the right electrical motors. The mechanical design drawings for this manipulator system are fully done using Autodesk Inventor Software which concerns the real joint of the robotic manipulator. The dynamic equation of the robotic manipulator system is derived using the Lagrange equation which is then represented in the state space method to make simple for utilization in Simulation and real-time systems

Design a Low Voltage Energy Harvesting SoC System for Ultra-Low-Power Bio- medical Application

Abstract: This Paper presents a designing of low voltage energy harvesting SoC system based on Ultra-Low-Power Bio-medical applications. A novel technique (i.e., Genetic Algorithm) has chosen for designing the ultra-low-power circuit with a low voltage energy. This platform harvests ambient vibration energy as its power source, and is capable of self starting, and self-powered operation without the need of a battery. The proposed method consumes very little power, and is especially suitable for the environments, where ambient harvested power is very low. System modeling and analysis of the method will be developed using HSPICE software. The ultimate goal of this research work is to design a low voltage smart electronics circuit of SoC system. To implement our SoC design, the Analog/Digital software from Mentor Graphics will be considered. Experimental results will be presented at our next possible journal publication in future accordingly

A review on control of robotic manipulator for performing grading and sorting of rotational symmetric products

This paper presents a literature review on the common control systems that have been used for robotic manipulators with a very higher concern on PID and active force control (AFC).The control of manipulator is divided into two main systems, namely are linear and non-linear control systems. A nonlinear system is used to overcome un-modeled dynamics, variable payload, fiction and disturbance torque, variation, and noise. PID controller has enhanced the performance of the manipulator in certain cases such as reducing system vibration and maintaining the tracking errors of the manipulator. On the other hand, AFC is a robust and much viable controller in comparison with others ordinary strategies in controlling dynamical systems such as robotic manipulato

Review on real-time control schemes for wheeled mobile robot

The purpose of this paper is to review real-time control motion algorithms for wheeled mobile robot (WMR) when navigating in environment such as road. Its need a good controller to avoid collision with any disturbance and maintain a track error with zero level. The controllers is used with and other aiding sensors to measure the WMR’s velocities, posture, and interference to estimate the needed torque of mobile robot due to wheel rotating. Four main categories for wheeled mobile robot control that have been found in literature which are namely: Kinematic based controller, Dynamic based controllers, artificial intelligence based control system, and Active Force control. A MATLAB/Simulink software is the main software to simulate and implement control system. The real-time toolbox in MATLAB/SIMULINK are used to receive/send data from sensors/to actuator with existing of real path disturbances

A Machine Learning-Based Severity Prediction Tool for the Michigan Neuropathy Screening Instrument

Diabetic sensorimotor polyneuropathy (DSPN) is a serious long-term complication of diabetes, which may lead to foot ulceration and amputation. Among the screening tools for DSPN, the Michigan neuropathy screening instrument (MNSI) is frequently deployed, but it lacks a straightforward rating of severity. A DSPN severity grading system has been built and simulated for the MNSI, utilizing longitudinal data captured over 19 years from the Epidemiology of Diabetes Interventions and Complications (EDIC) trial. Machine learning algorithms were used to establish the MNSI factors and patient outcomes to characterise the features with the best ability to detect DSPN severity. A nomogram based on multivariable logistic regression was designed, developed and validated. The extra tree model was applied to identify the top seven ranked MNSI features that identified DSPN, namely vibration perception (R), 10-gm filament, previous diabetic neuropathy, vibration perception (L), presence of callus, deformities and fissure. The nomogram’s area under the curve (AUC) was 0.9421 and 0.946 for the internal and external datasets, respectively. The probability of DSPN was predicted from the nomogram and a DSPN severity grading system for MNSI was created using the probability score. An independent dataset was used to validate the model’s performance. The patients were divided into four different severity levels, i.e., absent, mild, moderate, and severe, with cut-off values of 10.50, 12.70 and 15.00 for a DSPN probability of less than 50, 75 and 100%, respectively. We provide an easy-to-use, straightforward and reproducible approach to determine prognosis in patients with DSPN

Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques

Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corrobo-rated by domain experts, based on a temperature distribution parameter-the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset. 2022 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This research was funded by Qatar National Research Fund (QNRF), International Research Collaboration Co-Fund (IRCC)-Qatar University and University Kebangsaan Malaysia with grant number NPRP12S-0227-190164, IRCC-2021-001 and DPK-2021-001 respectively.Scopu

Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques

Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corroborated by domain experts, based on a temperature distribution parameter—the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset