Active sensing methods of ionic polymer metal composite (IPMC) : Comparative study in frequency domain

Ionic polymer-metal composites (IPMCs) are soft transducers that bend in response to low-voltage input, and generate voltage in response to deformations. Their potential applications include compliant locomotion systems, small-scale robotics, energy harvesting and biomedical instrumentation. The materials are inherently compliant, simple to shape, simple to miniaturize and simple to integrate into a system. Compared to actuation, IPMC sensing has not been intensively studied. The existing reports focus on the sensing phenomenon, but provide insufficient characterization for implementation purposes. This work aims to address this gap by studying and comparing the frequency responses and noise dynamics of different IPMC active sensing signals, i.e. voltage, charge and current. These characteristics are experimentally identified by mechanically exciting IPMC samples, and simultaneously measuring the respective signals and material deformations. The results provide a systematic comparison between different implementations of active sensing with IPMCs, and give insights into their strengths and limitations

The diagnosis of diabetic retinopathy by means of transfer learning with conventional machine learning pipeline

Diabetic Retinopathy is one of the common eye diseases due to the complication of diabetes mellitus. Cotton wool spots, rough exudates, haemorrhages and microaneurysms are the symptoms of the diabetic retinopathy due to the fluid leakage that is caused by the high blood glucose level disorder. Early treatment to prevent a permanent blindness is important as it could save the diabetic retinopathy vision. Hence, in this study, we proposed to employ an automated detection method to diagnose the diabetic retinopathy. The dataset was obtained from the Kaggle Database and been divided for training, testing and validation purposes. Furthermore, Transfer Learning models, namely VGG19 were employed to extract the features before being processed by Machine Learning classifiers which are SVM, kNN and RF to classify the diabetic retinopathy. VGG19-SVM pipeline produced the best accuracy in training, testing and validation processes, achieving 99, 99 and 96 percents respectively

Articulated robot arm

In medical rehabilitation programs, trajectory tracking is used to increase the repeatability of joint movement and the patient's recovery in the early phases of rehabilitation. In order to achieve that, the robotic arm has been implemented since it can provide a precise and move in almost perfect motion. This manuscript aim to develop and simulate a 2DOF robotic arn that will able to tracking the trajectory successfully. Hence, in order to achieved that a modeling, simulation, and control of a Two Degree of Freedom (2-DOF) Robot Arm is being discussed in this manuscript. First, the robot specifications, as well as Robot Kinematics forward and inverse kinematics of a 2-DOF robot arm, are provided. The dynamics of the 2-DOF robot arm were then formulated in order to obtain motion equations by using the Eular-Lagrange Equation. For the controller of the robot, a control design was created utilising a PID controller. All the data is recorded from the margin of error as well as the overshoot and peak settling time is being record via matlab. The data is differentiate by with with controller, with PI and PID, in which the error is less than 12.5 and 1.63 consecutively. The data that being gathered show that a controller best suited in this rehabilitation robo

Sign language recognition using deep learning through LSTM and CNN

This study presents the application of using deep learning to detect, recognize and translate sign language. Understanding sign language is crucial for communication between the deaf and mute people and the general society. This helps sign language users to easily communicate with others, thus eliminating the differences between both parties. The objectives of this thesis are to extract features from the dataset for sign language recognition model and the formulation of deep learning models and the classification performance to carry out the sign language recognition. First, we develop methodology for an efficient recognition of sign language. Next is to develop multiple system using three different model which is LSTM, CNN and YOLOv5 and compare the real time test result to choose the best model with the highest accuracy. We used same datasets for all algorithms to determine the best algorithm. The YOLOv5 has achieved the highest accuracy of 97% followed by LSTM and CNN with 94% and 66.67%

Effects of varied planar dimensions of IPMC on simulated actuation using COMSOL

This study focuses on mechatronic systems and their use of bending smart materials, specifically the ionic polymer metal composite (IPMC), for compliant actuation. The advantages of IPMC actuators, such as low power consumption and high flexibility, are highlighted. The actuation mechanism of IPMCs involving ion migration, water transport, and mechanical stress imbalance is discussed. The influence of geometric parameters, specifically length and width, on IPMC performance is investigated through simulations. Results show a positive correlation between IPMC lengths exceeding 30 mm and displacement, with longer lengths leading to higher displacements. The relationship between width and maximum displacement is attributed to factors like increased active area, larger polymer volume, and potential effects on mechanical properties. Further electromechanical analysis is needed for a comprehensive understanding of these mechanisms

The classification of oral squamous cell carcinoma (OSCC) by means of transfer learning

Patients that are diagnosed with oral cancer has more than an 83% survival chance if it is detected in its early stages. However, through conventional labour-intensive means, only 29% of cases are detected. It is worth mentioning that 90% of oral cancer is Oral Squamous Cell Carcinoma (OSCC) and is often caused by smoking and alcohol consumption. Computer-aided diagnostics could further increase the rate of detection of this form of oral cancer. The present study sought to employ a class of deep learning techniques known as transfer learning. The Inception V3 pre-trained convolutional neural network model is used to extract the features from texture-based images. Consequently, the malignant and benign nature of the cancer is identified from three different machine learning models, i.e., Support Vector Machine (SVM), k-Nearest Neighbors (kNN) and Random Forest (RF). It was shown from the study that an average of 91% classification accuracy was obtained from the test and validation dataset from the Inception V3-RF pipeline. The outcome of the present study could serve useful in an objective-based automatic diagnostic of OSCC and hence could possibly increase its detection

The diagnosis of diabetic retinopathy : An evaluation of different classifiers with the inception V3 model as a feature extractor

Diabetic Retinopathy (DR) is a type of eye disease that is caused by diabetes mellitus. The elevated blood glucose level causes the expansion of the blood vessels that results in the leaking of the blood and other fluids. DR is a silent disease in which those inflicted with it are unaware until irregularities in the retina have advanced to the point where treatment is difficult or impossible to administer, resulting in them losing their sight completely. However, it is worth noting that early treatment can solve this problem. Hence, the purpose of this study is to develop a transfer learning pipeline for diagnosing DR. The data in the present study was obtained from the Kaggle database, and the pre-trained InceptionV3 model was employed to extract the features from the images acquired. The features are fed into the three different classifiers, namely, Support Vector Machine (SVM), k-Nearest Neighbour (kNN) and the Random Forest (RF). It was shown from the present investigation that the InceptionV3-SVM pipeline demonstrated the best performance by achieving 100%, 98% and 96% classification accuracy for the training, testing and validation dataset. The results further suggest the possible deployment of the pipeline for the diagnosis of DR

Sensing and self-sensing actuation methods for ionic polymer–metal composite (IPMC) : a review

Ionic polymer–metal composites (IPMC) are smart material transducers that bend in response to low-voltage stimuli and generate voltage in response to bending. IPMCs are mechanically compliant, simple in construction, and easy to cut into desired shape. This allows the designing of novel sensing and actuation systems, e.g., for soft and bio-inspired robotics. IPMC sensing can be implemented in multiple ways, resulting in significantly different sensing characteristics. This paper will review the methods and research efforts to use IPMCs as deformation sensors. We will address efforts to model the IPMC sensing phenomenon, and implementation and characteristics of different IPMC sensing methods. Proposed sensing methods are divided into active sensing, passive sensing, and self-sensing actuation (SSA), whereas the active sensing methods measure one of IPMC-generated voltage, charge, or current; passive methods measure variations in IPMC impedances, or use it in capacitive sensor element circuit, and SSA methods implement simultaneous sensing and actuation on the same IPMC sample. Frequency ranges for reliable sensing vary among the methods, and no single method has been demonstrated to be effective for sensing in the full spectrum of IPMC actuation capabilities, i.e., from DC to ∼100 Hz. However, this limitation can be overcome by combining several sensing methods

Geometrical Analysis on Cap-Shaped Coils for Power Optimization of the Vibration-Based Electromagnetic Harvesting System

This paper presents the new form of coils for electromagnetic energy harvesting system based on topology optimization method which look-liked a cap to maximize the power output. It could increase the number of magnetic flux linkage interception of a cylindrical permanent magnet which in this case is of 10mm diameter. Several coils with different geometrical properties have been build and tested on a vibration generator with frequency of 100Hz. The results showed that the coil with lowest number of winding transduced highest power output of 680μW while the highest number of windings generated highest voltage output of 0.16V

Design a precision motion control of an upper limb robotic arm

Trajectory tracking is utilized in medical rehabilitation programs at the early stage of rehabilitation in order to track the performance of the patient in performing the prescribed task. The robotic arm has been utilized to accomplish this due to it precision and provide repetitive motion. The goal of this study is to design and simulate a two-degree-of-freedom robotic arm that can effectively track a trajectory. As a result, this study discusses the modelling, simulation, and control of a Two Degree of Freedom (2-DOF) Robot Arm to attain that goal. First, the robot specifications are provided, as well as the forward and inverse kinematics of a 2-DOF robot arm. The dynamics of the 2-DOF robot arm were then defined using the Euler- Lagrange Equation to obtain motion equations. A PID controller was used to construct a control design for the robot's controller. MATLAB is used to record all the data, including the margin of error, overshoot, and peak settling time. The data is identified using the PI and PID controllers, in which the error is smaller than 7 and 1.5, respectively. The controller was then used to create a prototype model by using MATLAB Sim Mechanics. The data obtained indicates that a PID controller is the best fit for this rehabilitation robot