<abstract>
<title style='display:none'>Abstract</title>
The ball and beam system is a laboratory equipment with high nonlinearity in its dynamics. The aims of this research are to model the ball and beam system considering nonlinear factors and coupling effect and to design controllers to control the ball position. The LQR is designed considering two Degrees-of-Freedom and coupling dynamics. The parameters of the LQR are tuned using Genetic Algorithm (GA). Jacobian linearization method is used to linearize the system around operating-point. Due to the noise of the sensor in the experimental setup, a state observer is designed to observe the velocity of the ball. In order to compare the performance of the LQR and study the effect of simplifying assumptions, two control strategies are designed and implemented: Proportional Derivative Integral (PID) as non-model based control strategy, hybrid PID and Linear Quadratic Regulator (LQR) as combination of model based and non-model based control strategies. The experimental results of this research prove the model based control strategies outperforms the non-model based or hybrid controllers in a nonlinear and noisy ball and beam system. In addition, it is shown that the coupling dynamics cannot be eliminated as a simplifying assumption in designing the controller.
</abstract>

Modeling and Control of Ball and Beam System using Model Based and Non-Model Based Control Approaches

<abstract>
<title style='display:none'>Abstract</title>
When a human gives an order to a robot, the robot must often use its vision to ascertain the human’s indication. In our previous paper, to develop a system where robots precisely receive and obey human orders in daily work spaces, we proposed an experimental system for finger direction recognition (FDR) in 3D space using stereo matching by mounting two cameras on the robot. In this paper, we evaluate this system for FDR in 3D space by performing a series of evaluation experiments using a turntable capable of fixing a hand in a specific finger direction. We estimated various finger directions and distances for two major pointing hand forms (Forms 1 and 2) and evaluated the precision of θ and Φ. We conclude that the θ and Φ estimations are valid because the estimation error is almost within 10 ° when the distance between the camera and object is less than 110 and 80 cm for θ and Φ for Forms 1 and 2, respectively. Finally, we applied our FDR system to the communication between a robot and a person by visual and tactile sensations. In the application test, the robot recognized the object at which the person pointed and put it in the person’s palm.</abstract>

Evaluation of Finger Direction Recognition Method for Behavior Control of Robot

<abstract>
<title style='display:none'>Abstract</title>
This project is concerned to analyze and increase the efficiency of patrol inspection and thus reduces time consumption for inspection. Inspection is the process of checking whether the specification of the product meet the standard specification provided by the customer. Inspector takes more time for checking the parameter that meet the specification at all time than Checking the parameters that does not satisfy the customer specification which leads to rejection .Thus the inspector should inspect the parameter that increase the rejection or rework with more attention .on the whole this concept helps the inspector to improve the quality of the product by inspecting more components according to the frequency of the component.
</abstract>

Patrol Inspection and Time Study to Improve Quality

<abstract>
<title style='display:none'>Abstract</title>
There has been a constant demand for the development of non-invasive, sensitive glucose sensor system that offers fast and real-time electronic readout of blood glucose levels. In this article, we propose a new system for detecting blood glucose levels by estimating the concentration of acetone in the exhaled breath. A TGS822 tin oxide (SnO2) sensor has been used to detect the concentration of acetone in the exhaled air. Acetone in exhaled breath showed a correlation with the blood glucose levels. Effects of pressure, temperature and humidity have been considered. Artificial Neural Network (ANN) has been used to extract features from the output waveform of the sensors. The system has been trained and tested with patient data in the blood glucose ranges from 80 mg/dl to 180 mg/dl. Using the proposed system, the blood glucose concentration has been estimated within an error limit of ±7.5 mg/dl.</abstract>

Breath Acetone-Based Non-Invasive Detection of Blood Glucose Levels

<abstract>
<title style='display:none'>Abstract</title>
This study focuses on finding the shortest paths among cities in Java Island by repeatedly combining the start node’s nearest neighbor to implement Dijkstra algorithm. Node combination is used to find the shortest path among cities in Java by deleting the node nearest to the start node. The use of memory by node combination is more efficient than the use of memory by the original Disjkstra algorithm. The 46 cities in Java Island will be used to evaluate the performance of finding shortest path. The experimental results show that the accuracy of node combination is 92.88% with the Google Map as the reference. The successful implementation of algorithm in finding the shortest path on the real problem is a good point; therefore, the algorithm can be developed to solve the transportation network problem.
</abstract>

Finding the Shortest Paths Among Cities in Java Island Using Node Combination Based on Dijkstra Algorithm

<abstract><title style='display:none'>Abstract</title>The intention of this paper is to provide an overview on the subject of Human-Computer Interaction. The overview includes the basic definitions and terminology, a survey of existing technologies and recent advances in the field, common architectures used in the design of HCI systems which includes unimodal and multimodal configurations, and finally the applications of HCI. This paper also offers a comprehensive number references for each concept, method, and application in the HCI.</abstract>

Human-Computer Interaction: Overview on State of the Art

<abstract>
<title style='display:none'>Abstract</title>
The evolving cyber-attack landscape demands flexible and precise protection for information and networks. Network anomaly detection (NAD) systems play a crucial role in preventing and detecting abnormal activities on the network that may lead to catastrophic outcomes when undetected. This paper aims to provide a comprehensive analysis of NAD using unsupervised learning (UL) methods to evaluate the effectiveness of such systems. The paper presents a detailed overview of several UL techniques, lists the current developments and innovations in UL techniques for network anomaly and intrusion detection, and evaluates 13 unsupervised anomaly detection algorithms empirically on benchmark datasets such as NSL-KDD, UNSW-NB15, and CIC-IDS 2017 to analyze the performance of different classes of UL approaches for NAD systems. This study demonstrates the effectiveness of NAD algorithms, discusses UL approaches' research challenges, and unearths the potential drawbacks in the current network security environment.
</abstract>

A Holistic review and performance evaluation of unsupervised learning methods for network anomaly detection

<abstract>
<title style='display:none'>Abstract</title>
This paper proposes and evaluates a novel control approach for trajectory tracking, stability enhancement, and vibration reduction of a flexible joint robot (FJR). The FJR is a 2-degree-of-freedom underactuated nonlinear system that is challenging to control due to vibration, underactuation, uncertainties, and external disturbances. The control objectives are high trajectory tracking performance together with suppressing vibration. The proposed control approach uses integral sliding mode control (ISMC) combined with a barrier function based on back-stepping. This ensures robust and smooth performance by eliminating the reaching phase where sliding mode control (SMC) is typically not robust. Robustness is thus guaranteed from the start. The FJR is modeled as a 4th-order system using Lagrangian mechanics and decomposed into two 2nd-order subsystems for control design. Sliding variables are defined for each subsystem. Using these variables, the proposed control achieves robust trajectory tracking, stability, and vibration reduction. Numerical simulations in MATLAB validate the superior performance of the proposed ISMC-barrier function control compared to conventional ISMC for the FJR. This novel control approach addresses the challenges of controlling this FJR.
</abstract>

Barrier Function-Based Integral Sliding Mode Controller Design for a Single-Link Rotary Flexible Joint Robot

<abstract>
<title style='display:none'>Abstract</title>
Parkinson's disease (PsD) is a prevalent neurodegenerative malady, which keeps intensifying with age. It is acquired by the progressive demise of the dopaminergic neurons existing in the substantia nigra pars compacta region of the human brain. In the absence of a single accurate test, and due to the dependency on the doctors, intensive research is being carried out to automate the early disease detection and predict disease severity also. In this study, a detailed review of various artificial intelligence (AI) models applied to different datasets across different modalities has been presented. The emotional intelligence (EI) modality, which can be used for the early detection and can help in maintaining a comfortable lifestyle, has been identified. EI is a predominant, emerging technology that can be used to detect PsD at the initial stages and to enhance the socialization of the PsD patients and their attendants. Challenges and possibilities that can assist in bridging the differences between the fast-growing technologies meant to detect PsD and the actual implementation of the automated PsD detection model are presented in this research. This review highlights the prominence of using the support vector machine (SVM) classifier in achieving an accuracy of about 99% in many modalities such as magnetic resonance imaging (MRI), speech, and electroencephalogram (EEG). A 100% accuracy is achieved in the EEG and handwriting modality using convolutional neural network (CNN) and optimized crow search algorithm (OCSA), respectively. Also, an accuracy of 95% is achieved in PsD progression detection using Bagged Tree, artificial neural network (ANN), and SVM. The maximum accuracy of 99% is attained using K-nearest Neighbors (KNN) and Naïve Bayes classifiers on EEG signals using EI. The most widely used dataset is identified as the Parkinson's Progression Markers Initiative (PPMI) database.
</abstract>

Automated Parkinson's Disease Detection: A Review of Techniques, Datasets, Modalities, and Open Challenges

<abstract>
<title style='display:none'>Abstract</title>
The paper reviews the milestones and various modern-day approaches in developing phonocardiogram (PCG) signal analysis. It also explains the different phases and methods of the Heart Sound signal analysis. Many physicians depend heavily on ECG experts, inviting healthcare costs and ignorance of stethoscope skills. Hence, auscultation is not a simple solution for the detection of valvular heart disease; therefore, doctors prefer clinical evaluation using Doppler Echo-cardiogram and another pathological test. However, the benefits of auscultation and other clinical evaluation can be associated with computer-aided diagnosis methods that can help considerably in measuring and analyzing various Heart Sounds. This review covers the most recent research for segmenting valvular Heart Sound during preprocessing stages, like adaptive fuzzy system, Shannon energy, time-frequency representation, and discrete wavelet distribution for analyzing and diagnosing various heart-related diseases. Different Convolutional Neural Network (CNN) based deep-learning models are discussed for valvular Heart Sound analysis, like LeNet-5, AlexNet, VGG16, VGG19, DenseNet121, Inception Net, Residual Net, Google Net, Mobile Net, Squeeze Net, and Xception Net. Among all deep-learning methods, the Xception Net claimed the highest accuracy of 99.43 + 0.03% and sensitivity of 98.58 + 0.06%. The review also provides the recent advances in the feature extraction and classification techniques of Cardiac Sound, which helps researchers and readers to a great extent.
</abstract>

Recent Advances in PCG Signal Analysis using AI: A Review

<abstract>
<title style='display:none'>Abstract</title>
In leukemia diagnosis, automating the process of decision-making can reduce the impact of individual pathologists' expertise. While deep learning models have demonstrated promise in disease diagnosis, combining them can yield superior results. This research introduces an ensemble model that merges two pre-trained deep learning models, namely, VGG-16 and Inception, using transfer learning. It aims to accurately classify leukemia subtypes using real and standard dataset images, focusing on interpretability. Therefore, the use of Local Interpretable Model-Agnostic Explanations (LIME) is employed to achieve interpretability. The ensemble model achieves an accuracy of 83.33% in binary classification, outperforming individual models. In multi-class classification, VGG-16 and Inception reach accuracies of 83.335% and 93.33%, respectively, while the ensemble model reaches an accuracy of 100%.
</abstract>

Symbiosis Centre of Applied AI (SCAAI), Symbiosis International (Deemed) University

Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering & IT, University of Technology Sydney

Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model

<abstract>
<title style='display:none'>Abstract</title>
Temperature measurement and transmission of a signal safely to the control room for further processing is important for the process industry. In this paper, a modified head-mounted temperature measurement system using a thermocouple with opto-isolation has been developed. Here, the thermocouple is connected to the terminals, mounted on the ceramic base in the head of the thermo-well. It consists of two signal conditioners for thermocouple and AD590, both signal conditioning outputs applied to a summer circuit. The output of the summer circuit which is in the range of 1.73–3.43V, adjusted by a signal conditioning circuit, is applied to the middle electrode of Mach-Zehnder interferometer (MZI). MZI produces normalized optical signals according to the variations in temperature. These optical signals are then transmitted to the control room safely in the inflammable process industry. The transmitted signals are demodulated in the control room and then sent to the PC through an Opto-isolator circuit and DAS card. The necessary theory as well as mathematical equation has been derived. The experimental and simulation results are reported here.
</abstract>

A temperature measurement technique using optical channel as a signal transmitting media

<abstract>
<title style='display:none'>Abstract</title>
The development of dynamic rehabilitation devices can be evaluated as a research fastgrowing field. Indeed, robot-assisted therapy is an advanced new technology mainly in stroke rehabilitation. Although patients benefit from this enormous development of technology, including the presence of rehabilitation robots, the therapeutic field still suffering a lack in hand robotic rehabilitation devices. In this context, this work proposes a new design of a 3D printed hand exoskeleton for the stroke rehabilitation. Based on the EMG signals measured from the muscles responsible for the hand motion, the designed mechatronic system detects the intention of hand opening or hand closing from the stroked subject. Based on an embedded controller and five servomotors, the low cost robotic system is able to drive in real time three degrees of freedom (DOFs) for each finger. The real tests with stroked subjects showed that the designed hand exoskeleton architecture has a positive effect on the motion finger range and mainly in the hand ability to perform some simple tasks. The case studies showed a good recovery of the motor functions and consequently the developed system efficiency.
</abstract>

DESIGN AND DEVELOPMENT OF 3D PRINTED MYOELECTRIC ROBOTIC EXOSKELETON FOR HAND REHABILITATION

<abstract>
<title style='display:none'>Abstract</title>
In this paper, Self Tuning Fuzzy PID controller is developed to improve the performance of the electro-hydraulic actuator. The controller is designed based on the mathematical model of the system which is estimated by using System Identification technique. The model is performed in linear discrete model to obtain a discrete transfer function for the system. Model estimation procedures are done by using System Identification Toolbox in Matlab. Data for model estimation is taken from an experimental works. Fuzzy logic is used to tune each parameter of PID controller. Through simulation in Matlab by selecting appropriate fuzzy rules are designed to tune the parameters Kp, Ki and Kd of the PID controller, the performance of the hydraulic system has improved significantly compare to conventional PID controller.
</abstract>

Application Of Self-Tuning Fuzzy Pid Controller On Industrial Hydraulic Actuator Using System Identification Approach

<abstract>
<title style='display:none'>Abstract</title>
Strategies to facilitate detection enhancement in various methods of nitrate detection are presented in this paper. The main nitrate detection methods for the past 10 years will be reviewed according to their advantages and disadvantages, followed by a focus on the techniques on increasing detection capabilities. For each detection method, an investigation of the technique of improvement is carried out. The utilisation of advance material such as membranes, reduction agents etc. is also identified as the key aspect for system improvement. Many researchers in the field of potentiometry, electrochemical, and biosensors have focused on miniaturising their detection systems to enhance the capability of nitrate in-situ measurement. The performance of miniaturised sensor systems is comparable to that of conventional systems.
</abstract>

TECHNIQUES IN ADVANCING THE CAPABILITIES OF VARIOUS NITRATE DETECTION METHODS: A REVIEW

<abstract>
<title style='display:none'>Abstract</title>
Technological advances in manufacturing smart high-performances electronic devices, increasingly available at lower costs, nowadays allow one to improve users’ quality of life in many application fields. In this work, the human–machine interaction obtained by using a next generation device (Myo armband) is analyzed and discussed, with a particular focus to healthcare applications such as upper-limb prostheses. An overview on application fields of the Myo armband and on the latest research works related to its use in prosthetic applications is presented; subsequently, the technical features and functionalities of this device are examined. Myo armband is a wearable device provided with eight electromyographic electrodes, a 9-axes inertial measurement unit and a transmission module. It sends the data related to the detected signals, via Bluetooth Low Energy technology, to other electronic devices which process them and act accordingly, depending on how they are programmed (in order to drive actuators or perform other specific functions). Applied to the prosthetic field, Myo armband allows one to overcome many issues related to the existing prostheses, representing a complete electronic platform that detects in real-time the main signals related to forearm activity (muscles activation and forearm movements in the three-dimensional space) and sends these data to the connected devices. Nowadays, several typologies of prostheses are available on the market; they can be mainly distinguished into low-cost prostheses, which are light and compact but allow for a limited number of movements, and high-end prostheses, which are much more complex and featured by high dexterity, but also heavy, bulky, difficult to control and very expensive. Finally, the Myo armband is an optimum candidate for prosthetic application (and many others) and offers an excellent low-cost solution for obtaining a reliable, easy to use system.
</abstract>

Technical Features and Functionalities of Myo Armband: An Overview on Related Literature and Advanced Applications of Myoelectric Armbands Mainly Focused on Arm Prostheses

<abstract>
<title style='display:none'>Abstract</title>
It is of little surprise that falling is often accepted as a natural part of the aging process. In fact, it is the impact rather than the occurrence of falls in the elderly, which is of most concern. Aging people are typically frailer, more unsteady, and have slower reactions, thus are more likely to fall and be injured than younger individuals. Typically, research and industry presented various practical solutions for assisting the elderly and their caregivers against falls via detecting falls and triggering notification alarms calling for help as soon as falls occur in order to diminish fall consequences. Furthermore, fall likelihood prediction systems have been emerged lately based on the manipulation of the medical and behavioral history of elderly patients in order to predict the possibility of falls occurrence. Accordingly, response from caregivers may be triggered prior to most fall occurrences and accordingly prevent falls from taking place. This paper presents an extensive review for the state-of-the- art trends and technologies of fall detection and prevention systems assisting the elderly people and their caregivers. Furthermore, this paper discusses the main challenges, facing elderly fall prevention, along with suggestions for future research directions.
</abstract>

Fall Detection and Prevention for the Elderly: A Review of Trends and Challenges

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International Journal on Smart Sensing and Intelligent Systems

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The International Journal on Smart Sensing and Intelligent Systems (S2IS) is an on-line journal to be available to everybody at no...