IAES Journals

Network-on-chip (NoC) is a new paradigm for system-on-chip (SoC) design, which facilitates the interconexion and integration of complex components. Since this technology is still new, significant research efforts are needed to accelerate and simplify the design phases. Mapping is a critical phase in the NoC design process, as a mismatch of application software components can have a significant impact on the performance of the final system. Therefore, it is essential to develop automated tools and methods to ensure this step. The main objective of this project is to develop a new approach that can be used to map applications on the NoC architecture to reduce communication costs. We have opted for an optimization algorithm, specifically the differential evolution algorithm, to achieve this goal.

This work addresses the solution of localizing and enhancing hands-free speech inside the car environment. Cars have different types of sounds from outside, co-passengers dialogue and noise. To provide better-quality speech, a microphone array-based beamforming technique is used. This research work proposes the method for selected source localization, source separation, and enhancement. An estimation of the direction of arrival (DOA) to localize the signal direction and preferred direction is selected for speech enhancement. The spiral and sine-cosine algorithm (SSCA) algorithm is combined with an adaptive least mean square to adapt the system for different environments. The algorithm is implemented in hardware and tested in a real-time car environment. The results showed significant improvement in signal-to-noise ratio (SNR) of 5.2 dB and perceptual evaluation of speech quality (PESQ) of 2.3. Finally, the model is fine-tuned for the car to get better quality. The proposed technique is efficient, and results are compared with existing methods.

This article examines the widespread introduction of artificial intelligence technologies, means of their implementation and support as a determining factor in the development of scientific and technological progress. On this basis, various advanced objects of various functional purposes are created, which are characterized as "smart". Their distinguishing feature is the ability to implement a "reasonable" way of functioning, taking into account the prevailing circumstances. This ability is expressed in the fact that in the object automatically, i.e., without human participation, or with minimal human participation, the most rational or optimal modes of functioning are supported, the definition of which involves the performance of operations containing signs of rational activity. This "smart" behavior of technical objects is mainly determined by the "intelligent" functioning of the control systems built into them. In particular, intelligent automated systems for optimal control. In accordance with this, the development of new approaches and methods that expand the possibilities of building such control systems should be considered as an urgent and priority task.

People with impairments who utilize robotic arms have a great deal more independence in their daily lives. The robotic arm can help persons with specific needs by using voice control, which is practical. The working mechanism is fashioned to have four degrees of freedom (4-DOF) with the inclusion of a jaw or gripper. The Arduino microcontroller, the speech module, and the arm are the main components of the robotic arm, which is a lightweight model driven by four motors. The servo motor is used to supply the rotational motion in the arm's three rotary joints and end effector. Rotation in two directions is made possible by the geared DC motors employed, which alter their direction of rotation if the polarity is flipped. The automatic voice recognition technology aids in comprehending spoken words picked up by a microphone. The HM2007 processor, the brains of the speech recognizer, is necessary for the speech recognizer to function. Digital commands are created from analog speech input. These commands are used as input to the Arduino thereby resulting in a system whereby the human voice gives a continuous control signal to operate a real and functional robotic arm.

Following the development of artificial intelligence technology, a new trend has emerged in which this technology is increasingly used in case investigations. In this study, we developed a lie detection system that can instantly determine whether an interrogee is lying depending on their emotional responses to specific questions. Investigators then use these data, in addition to their personal experiences and case information, to adjust their interrogation strategies and techniques, thereby leading the interrogee to confess and accelerating the investigation process. Our system collects data using OpenFace and performs deep learning using gcForest. Deep learning training was performed using a real-life trial dataset, the Miami University Deception Detection Database, and a bag-of-lies dataset, and their corresponding trained systems achieved a detection accuracy of 95.11%, 90.83%, and 88.19%, respectively.

In the ultra-high temperature (UHT) process, fluid temperature is raised above 135 °C for a short period of time (typically 4 seconds) and then quickly cooled ensuring no microbes remain in the final product. To have better quality processed milk, a stringent temperature control system is necessary. To solve this problem a detailed control-oriented mathematical model of the heating system for UHT application is developed and a detailed block diagram is established by identifying various systems and signals. To draw the merits of a feedforward controller (transfer function or fuzzy logic based) and proportional-integral-derivative (PID) feedback compensator, a fuzzy PID hybrid controller is designed and simulated in a MATLAB environment. Findings of the simulation results indicate that the fuzzy-PID hybrid compensator concatenates the benefits of both controllers. PID controller processes the error signal and tracks the setpoint whereas the feedforward controller (transfer function or fuzzy) effectively rejects the disturbance signal's effect on the controlled variable. The fuzzy-PID hybrid controller performs better than the individual PID or fuzzy controller.

In recent years, there has been a great interest in the transition to digital and automation services for dangerous and menial working processes. Due to its MH50-35 industrial robot, Motoman's properties allow us to improve the control system of an electric drive for industrial robots. The structure of the electric drive for six-axis robot manipulator performance can be superior to conventional Drive Control servos for motor excitation, and a novel automation system can be implemented for its servo performance. To solve these issues, we propose an optimization strategy that allows us to achieve an increase in productivity and labor safety in the industry, reduce the percentage of defects, guarantee product uniformity, and reduce the prime cost of production of items. Ideal conditions were anticipated using a mathematical model. In this study, by using a statistical model, the ideal conditions were synthesized. The optimization of the control system of an electric drive for industrial robot analysis was carried out, and our findings suggest using this model in industrial production to elucidate problems such as high accuracy and speed indicators.

Population aging becomes one of the most significant 21 st century social challenges. These challenges strongly reflect on the industry, labor, and financial markets. Population aging increases the demand for medicines, diagnostic equipment, and medical services. Both developed and developing countries have problems resulting from the current shortage of health workers and a limited supply of medical equipment. An alternative for medical staff growth is the robotization of medical services. However, robotics is economically justified when the costs of medical robots are lower than the construction of additional medical clinics and the increase in medical staff. Medical robotics appeared on the market later than the industrial and military ones but has recently found increasing use in highincome countries. Low and middle-income countries could not acquire expensive medical robots in sufficient quantities. The increased competition in the medical robotics market will lead to price reductions and make robotized services available for wide use. The article analyses the competition in segments of the medical robotics markets, connected with population aging.

Nowadays, many industries use robots and cameras in tandem to detect specific objects and perform specific tasks. However, misdetection can occur due to inconsistencies in lighting, background, and environment. In order to address the aforementioned issues, this study proposes using a dual arm sixdegree-of-freedom (6-DoF) collaborative robot, ABB YuMi, and red, green, blue-depth (RGB-D) camera with YOLOv5 in a pick-and-place application. In order to prepare the dataset, the images are collected and labeled. The dataset has been trained with the YOLOv5 machine learning algorithm. It has taken on the role of weight for real-time detection. When RGB images from a camera are sent to YOLOv5, data pertaining to the bottle's position x-y and color are extracted from the depth and color images. The position of the robot is used to control its movement. There are three parts to the experiment. To begin, YOLOv5 is tested with and without trained images. Second, YOLOv5 is tested with real-time camera images. Finally, we assume that YOLOv5 has perfect detection and grasping ability. The results were 95, 90, and 90%.

The paradigm of the smart factory is thought of as an innovative outline for the fourth industrial revolt. The GLOVA G7-DR20U is set as a programmable logic controller (PLC) for monitoring the performance of the smart factory while using the NodeMCU-V3 esp8266 as the internet of things (IoT) board for interaction between managers and the factory using the personal digital assistant (PDA) programming that has been written in the RabitMQ platform. The program logged inner PLC by applying ladder language for monitoring the performance of PLC. With the completion of intelligent PLC, it is likely to extend the existing making capability in the factory with simplicity. This work joins a PLC used as a parent control unit, apps, user programs, and human-machine interface, with the Internet. The proposed model of the smart factory holds two motors one for the parallel drive and the other for the upright drive. While running the system, we observe that the proposal is working correctly, and the reply to the interaction method via IoT is excellent.

This study proposes a control method for servo motor position using a proportional-integral-derivative (PID) controller with particle swarm optimization (PSO). We use an AX-12 servo motor that is commonly used for robotic manipulator applications. The angular position of the servo motor will be controlled using the PID control method with PSO as a controller gain optimizer. Firstly, the transfer function model of the servo motor is generated using open-loop model identification. Then, the integral error of the closed-loop system is used as PSO input in producing PID controller gain. As an objective function of the PSO algorithm, the integral time absolute error (ITAE) index performance is used. The proposed controller was tested and compared with PID with the Ziegler-Nichols (ZN) method. We also conduct the hardware experiment using Arduino Uno as a microcontroller using one AX-12 servo motor on the base joint of the manipulator robot. Based on the simulation result, the PID-PSO controller can achieve the best control response performance if compared to PID-ZN with a rise time is less than 0.5 s, a settling time of fewer than 8 s, and an overshoot under 1.2%. The effectiveness of the proposed PID-PSO controller is also validated by hardware experimental results.

There are some scenarios where the images taken are of low resolution and it is hard to judge the features from them, resulting in the need for enhancement. Super-resolution is a technique to produce a high-resolution image from a lower-resolution image. The intention here is to develop a system that enhances images of faces and satellite images by integrating these models and providing an interface to access this model. There have been various ways of achieving super-resolution using different techniques. Throughout the years, techniques involving deep learning methods, interpolation techniques, and recursive networks have been explored. We find it promising to use generative adversarial networks (GANs). The system has been deployed through Google Collaborate, Python libraries, and the TensorFlow framework. To assess the developed system, which consists of images, three metrics have been calculated. namely, peak signal-to-noise ratio, mean squared error, and structural similarity index. The model successfully demonstrated the capability of GANs by efficiently generating a high-resolution image from a low-resolution image for the given cases. The model would then be run on a standalone server for free Internet access for users to use super-resolution facial images and satellite images.

Recently, structural vibration control has proved its capacity to save lives and keep structures safe during earthquakes. Furthermore, there is a wealth of research in both numerical and experimental studies. As a result, due to its simplicity and performance in mitigating structural vibrations generated by ground motions, semi-active control played a significant role in the majority of these studies. Nonetheless, the magnetorheological damper is the most often used semi-active device. In particular, the rheological fluid properties have gained adequate attention in earthquake energy dissipation and structural vibrations management, particularly in the civil engineering field. The semi-active control of three scaled excited structures is addressed in this study. A magnetorheological damper operated by a hybrid fuzzy sliding mode controller ensures the proposed control. However, to provide the appropriate current for the damper to operate, this proposed intelligent controller is combined with a clipped optimum algorithm. Otherwise, the numerical simulation results of the seismic excited scaled structure demonstrate the resilience of the suggested controller. As a result, four time-scaled seismic data are applied to the tested structure. Finally, the usefulness of the suggested semi-active control technique in mitigating earthquake structural vibration is demonstrated clearly in the compared controlled and uncontrolled responses.

This study constructed a real-time microreaction recognition system that can give real-time assistance to investigators. Test results indicated that the number of frames per second (30 or 190); angle of the camera, namely the front view of the interviewee or left (+45°) or right (−45°) view; and image resolution (480 or 680 p) did not have major effects on the system's recognition ability. However, when the camera was placed at a distance of 300 cm, recognition did not always succeed. Value changes were larger when the camera was placed at an elevation 45° than when it was placed directly in front of the person being interrogated. Within a specific distance, the recognition results of the proposed real-time microreaction recognition system concurred with the six reaction case videos. In practice, only the distance and height of the camera must be adjusted in the real-time microreaction recognition system.

The aim of this study was to determine the motivation of science teachers and students towards science after participating in the activity of assembling, simulating, and recording line follower robots as an effort to motivate middle school students and teachers towards science in Bengkulu Province. The research was done by direct practicing, where 60 students and 15 teachers of three junior high school (SMP): SMP Negeri 06 Seluma, SMP Negeri 02 Kota Bengkulu, and SMP Negeri 8 Rejang Lebong, were involved as the research subjects. The research activity concluded that the schools are ready to prepare simple electronics/robot laboratories for the three research subjects and the science teachers and students were motivated to learn science. It was seen from the score of 3.95 (scale of 1 to 5) for students, and for the science teacher, the score was 3.83 (scale of 1 to 5). The science teachers will follow up on robotics activities so that students will be interested in learning science at home and school.

The purpose of this review is to highlight the current research on aerial robot swarms and their applications. It focuses on the system architecture and follows the current trend in aerial robotics promoting research in this field along with its impact on society. Further, it explores the dynamics as well as the flying mechanisms of a drone and sheds light on the different algorithms being used to control aerial swarms. Due to a lot of research going on in this field, we also discuss the different trends that are active and of keen interest to the researchers, including the swarm pattern formation behavior.

Human hands are essential in everyday tasks, mainly manipulating and grasping objects. Thus, accurate and precise three-dimensional (3D) models of digitally reconstructed hands are valuable to the world of ergonomics. A 3D scan-to-render system called the “3D hands model rendering using a 6-degrees of freedom (DoF) collaborative robot” is proposed to ensure that a person receives the best possible outcome for their unique anatomy. The description implies this is using a 6-DoF robot with a two-dimensional (2D) camera sensor that will encompass all forms of the production line in a timely, low-cost, precise, and accurate manner so that an individual can go to and scan their hand and have an actual 3D reconstruction print within the same facility, the same day. It is expected to generate an accurate hand model using structure from motion (SFM) system techniques to create a dense point cloud using photogrammetry. The point cloud is used to develop the tetrahedral mesh of the surface of the hand. This mesh is then refined to filter out the noise of the point cloud. The mesh can produce a precise 3D model that can tailor products to the consumer's needs. The results show the effectiveness of the 3D model of the hand.

ORCA is a low cost remotely operated vehicle which was indigenously developed for underwater inspection and survey. As the underwater environment is quite unpredictable, dynamic modeling and simulation of the remotely operated vehicle are essential to understand the behavior of the vehicle and accomplish stabilized navigation. This paper discusses a detailed approach to the mathematical modeling of ORCA based on Newtonian dynamics and simulating the position and velocity responses in Simulink. The open loop nonlinear model of the remotely operated vehicle was used to study the navigation challenges due to the various perturbations present underwater namely Coriolis and centripetal force, added mass, hydrodynamic damping force, and restoring forces. The six-thruster open loop ORCA model was subjected to various thrust inputs (25%, 50%, and 75%) to achieve six degrees of freedom (DoF) respectively and it was observed that there was significant instability in the other DOFs along with the principal direction of motion. Further, the authors will incorporate the various control systems in ORCA and analyze the stability in navigation induced due to each of them.

Direct current (DC) motor speed control is useful. Speed can be modified based on needs and operations. DC motors cannot control their speed. To control the DC motor's speed, a dependable controller is needed. The DC motor speed will be controlled by a fuzzy logic proportional integral derivative controller (FLC-PID). The DC motor circuit's electrical and mechanical components have been modeled mathematically. Ziegler-Nichols is used to tune the PID controller's gain parameters. The FLC controller employs 3×3 membership function rules in conjunction with the MATLAB/Fuzzy Simulink toolbox. Real hardware was attached to the simulation to evaluate the DC motor speed control using the fuzzy logic PID controller. DC motors with FLC PID controllers, FLC controllers, and DC motors alone will be compared for the transient response. The DC motor with an FLC PID controller performed better in this study.

Researchers have been attempting to make the car drive autonomously. Environment perception, together with safe guidance and control, is an important task and is one of the big challenges when developing this kind of system. Geometrical or physical-based models, machine learning-based models, and those based on a mixture of both models are the three types of navigation methods used to resolve this problem. The last method takes advantage of the learning capability of machine learning models and uses the safeness of geometric models in order to better perform the navigation task. This paper presents a hybrid autonomous navigation methodology, which takes advantage of the learning capability of machine learning and uses the safeness of the dynamic window approach geometric method. Using a single camera and a 2D lidar sensor, this method actuates as a high-level controller, where optimal vehicle velocities are found, then applied by a low-level controller. The final algorithm is validated in the CARLA Simulator environment, where the system proved to be capable to guide the vehicle in order to achieve the following tasks: lane keeping and obstacle avoidance. This is an open access article under the CC BY-SA license.

This paper proposes a model-free continuous integral sliding mode controller for robust control of robotic manipulators. The highly nonlinear dynamics of robots and load disturbances cause control challenges. To achieve tracking control under load disturbances and nonlinear parameter variations, the controller is constructed with three continuous terms including an integral term that acts as an adaptive controller. The proposed controller is able to accomplish a nonovershoot transient response, a short settling time, and strong disturbance rejection performance for robotic manipulators. The developed model-free control method is implemented on the PUMA 560 robotic manipulator, and its performance is compared with the proportional-derivative (PD) plus gravity controller. Numerical results under measurement noise and load disturbances are provided in order to show the efficacy, validity, and feasibility of the method. This is an open access article under the CC BY-SA license.

Solid waste management is one of the critical challenges seen everywhere, and the coronavirus disease (COVID-19) pandemic has only worsened the problems in the safe disposal of infectious waste. This paper outlines a design for a mobile robot that will intelligently identify, grasp, and collect a group of medical waste items using a six-degree of freedom (DoF) arm, You Only Look Once (YOLO) neural network, and a grasping algorithm. Various designs are generated before running simulations on the selected virtual model using Robot Operating System (ROS) and Gazebo simulator. A lidar sensor is also used to map the robot's surroundings and navigate autonomously. The robot has good scope for waste collection in medical facilities, where it can help create a safer environment.

In this paper, a nonlinear controller is designed for a magnetic levitation system (MLS) based on serial invariant manifolds. Synthesized controller based on the method of synergetic control theory (SCT) through invariant manifolds, asymptotically stable. In this method, the control law is synthesized to ensure the motion of the closed-loop control object from an arbitrary initial state into the vicinity of the desired invariant manifold. Thereby, the control system not only ensures the necessary control quality but also ensures the asymptotic stability of the entire system. The quality and efficiency of the control law are proven through simulation results and comparison with the sliding mode controller (SMC).

Process industry needed a fast executed automatic control system capable of handling uncertain, vague problems and nonlinear control variables. Liquid level control is one of the emerging control problems getting the interest of technical experts in the area of control. This paper is based on a fuzzy logic control strategy to maintain and stabilize the liquid level in a tank system that deals with pumping of liquid in tanks as well as regulating liquid level and pushing off the liquid into another tank. Fuzzy controller attains optimum performance by eliminating perturbation in steady state and vanishing the overshoot as compared to proportional, integral, and derivative (PID) controller. The proposed fuzzy logic controller shows minimal steady error as compared to PID controller. The defuzzification of the proposed scheme is based on the centroid method to obtain optimum results. The settling time is nearly 50 second while using fuzzy logic control as compared to 80 seconds in PID control strategy. The overshoot observed is minimal, nearly less than 1% using a fuzzy logic control scheme.

This study presents the development of water injection system for turbocharged spark ignition engine. The water injection control system is built for turbocharged spark ignition (SI) engine where water was injected at the intake port just before the throttle body. The data was collected from the simulation through the GT-Power software to determine the optimized injection output for the engine. Single-stage statistical engine responses and boundary models were established by using Model-Based Calibration (MBC) Toolbox. Control system was built using Simulink and simulation tests were conducted based on the speed and throttle position as the variables. The highest value of brake torque achieved in the GT-Power simulation was taken as the base value to determine the injection amount. The mean value of the predicted injection was recorded at 12.29 g/s while the variance of the predicted injection to the optimized injection was below 1%. The control system was simulated with the set predicted injection and the standard deviation of the predicted injection was 1.18. The control system simulation recorded a low percentage of 0.04% variance to the optimized injection with the pulse width modulation signal. The control system is ideal to be constructed and tested on actual engine test bed.

The search for disaster victims carried out by search-and-rescue (SAR) team mainly uses traditional methods, which are considered to take much time and effort, and pose a high risk for the search team and the victim. Based on this problem, we conducted research to assist disaster victim search. In this research, we designed a system using passive infrared (PIR) sensors to detect and measure the direction angle of the victim. Given the direction angle from different observation points' perspectives, we determine the victim's position using the triangulation method. we also designed a quadcopter unmanned aerial vehicle (UAV) to carry this sensor system across the disaster area. For monitoring purposes, a local website was designed to display data generated by the system. Based on test results, the system can determine a victim's position with a distance difference of less than 5 m. The system can search victim in an empty land ±35 m×15 m wide in 14 minutes 20 seconds. The data monitoring system also displayed the victim's position, the position of the observation points, and the UAV's flight path.

An industrial robot is mainly used for manufacturing. Industrial robots are 6 or more axes, which can be automatically controlled by programming. Typical applications of robots include welding, painting, picking, and placing printed circuit boards, packaging and labelling, palletizing, product inspection, and testing with high accuracy, precision, and fast speed. Robotic welding is a complex, nonlinear and time varying process which can be affected by various natural or any random disturbances. Due to the effect of various factors, the actual welding path may differ. So, welding robots should be able to detect the actual welding path, then adjust the difference in welding path and complete the welding process accurately. Laser welding is one of the most important technologies in the manufacturing field. It is the most frequently used technology which has made new demands. So, the manufacturer ensures to meet the quality of laser welding and improve the production efficiency. Due to the increase in demand of quality, accuracy, precise, productivity, flexibility and adaptive control of welding robot, an automatic laser seam tracking system is developed with welding robot to precisely follow the welding path and make the necessary corrections during welding operations.

This paper presents the implementation of a complex fractional order proportional integral derivative (CPID) and a real fractional order PID (RPID) controllers. The analysis and design of both controllers were carried out in a previous work done by the author, where the design specifications were classified into easy (case 1) and hard (case 2) design specifications. The main contribution of this paper is combining CRONE approximation and linear phase CRONE approximation to implement the CPID controller. The designed controllers-RPID and CPID-are implemented to control flowing water with low pressure circuit, which is a first order plus dead time system. Simulation results demonstrate that while the implemented RPID controller fails to stabilize the system in case 2, the implemented CPID controller stabilizes the system in both cases and achieves better transient response specifications.

Over the past decades, brain-computer interface (BCI) has gained a lot of attention in various fields ranging from medicine to entertainment, and electroencephalogram (EEG) signals are widely used in BCI. Braincomputer interface made human-computer interaction possible by using information acquired from EEG signals of the person. The raw EEG signals need to be processed to obtain valuable information which could be used for communication purposes. The objective of this paper is to identify the best combination of features that could discriminate cognitive stimuli-based tasks. EEG signals are recorded while the subjects are performing some arithmetical based mental tasks. Statistical, power, entropy, and fractional dimension (FD) features are extracted from the EEG signals. Various combinations of these features are analyzed and validated using random forest classifier, K-nearest neighbors (KNN), multilayer perceptron, linear discriminant analysis, and support vector machine. The combination of entropy-FD features gives the highest accuracy of 90.47% with the KNN algorithm when compared to individual entropy and FD features which achieves 79.36% with random forest classifier, multilayer perceptron, and 82.53% with linear discriminant analysis, respectively. Our results show that the hybrid of entropy-FD features with KNN classifier can efficiently classify the cognition-based stimuli.