This paper aims to establish an Arduino and IoT-based Hierarchical Multi-Agent System (HMAS) for management of loads’ side with incentive approach in a micro-grid. In this study, the performance of the proposed algorithm in a micro-grid has been verified. The micro-grid contains a battery energy storage system (BESS) and different types of loads known as residential consumer (RC), commercial consumer (CC), and industrial consumer (IC). The user interface on a smartphone directly communicates with the load management system via an integrated Ethernet Shield server which uses Wi-Fi communication protocol. Also, the communication between the Ethernet Shield and the Arduino microcontroller is based on Wi-Fi communication. A simulation model is developed in Java Agent Development Environment (JADE) for dynamic and effective energy administration, which takes an informed decision and chooses the most feasible action to stabilize, sustain, and enhance the micro-grid. Further, the environment variable is sensed through the Arduino microcontroller and sensors, and then given to the MAS agents in the IoT environment. The test results indicated that the system was able to effectively control and regulate the energy in the micro-grid.

Background: Bioimpedance spectroscopy (BIS) is a device used to measure electrical impedance at frequencies from 0 Hz to 1 MHz. Many clinical diagnosis and fundamental researches, especially in the field of physiology and pathology, rely on this device. The device can be used to estimate human body composition, through the information of total body water, extracellular fluid and intracellular fluid, fat-free mass, and fat mass from its impedance. BIS analysis can provide physiological statuses such as ischemia, pulmonary edema, skin cancer, and intramuscular tumors. BIS is expected to be used even more widely, both for hospital or home-based use, particularly because BIS handy, compact, inexpensive, and less power-consuming with adequately accurate real-time. In previous research, the BIS design was based on the magnitude-ratio and phase-difference detection using the AD8302 gain-phase detector method which resulted in an operating range between 20 kHz and 1 MHz. However, the impedance was obtained from the logarithmic ratio magnitude which caused the device to have limited accuracy at frequencies <20 kHz. Methods: In this research, we conduct design and development of a low-cost Arduino-based electrical bioimpedance spectrometer. Results: The low-cost bioimpedance spectrometry was successfully developed using AD9850 as the programmable function generator, OPA2134 as the OpAm of voltage-controlled current source, AD620A as the instrument amplifier and AD536A as the alternating current to direct current converter which could work accurately from 0 Hz to 100 kHz. Conclusion: The multi-frequency bioimpedance device developed in this research has the capability to safely measure the impedance of the human body due to its relatively stable electric current, which is equal to (0. 370 ± 0. 003) mA with frequencies ranging from 5 to 200 kHz and has an accuracy of over 90% in the frequency range of 10 Hz to 100 kHz.

BLDC motors are used in various industries due to their efficiency, reliability and controllability. Among these applications, electric and hybrid vehicles, robotics and automation systems, residential electronics, aerospace industries, medical equipment, wind turbines and solar systems can be mentioned. In many of these applications, dynamic motor speed control is needed. In this paper, due to the importance of the subject, at first, by applying the PLECS software and using the PWM method along with the PID controller, the simulation of the dynamic speed control of the BLDC motor with dynamic speed references is performed, and then, the experimental implementation of the BLDC motor drive using Arduino Mega microcontroller board, in the conditions of dynamic changes of speed references, is accomplished and the corresponding results are presented. Labview software is used to display and record speed data on the computer. The simulation and experimental results show that the BLDC motor has good efficiency in tracking the dynamic speed references up to the dynamic frequency of 0.3 Hz.

Nowadays recording the performances and body activities of the athletes during the exercises and the matches have noticeable importance which is the reason for growth of the sports wearable’ s technology, for the past few years. Accordingly, we designed new wearable equipment on the basis of internet of things, using Arduino Boards and different kind of sensors for recording the performance and physical activities of the athletes. The process data of the sensors are available in real time and could be displayed in the PC through the internet connection which makes the coach capable of analyzing the performance of the athlete during or after the activity. The weight of this equipment is less than 100 gr and works with a frequency of 5 Hz (the data will be updated every 200 ms). The advantages of the designed equipment comparing to similar imported ones are the smaller size and the less ultimate cost.

Inertial Navigation System (INS) is one of the navigation systems widely used in various land-based, aerial, and marine applications. Among all types of INS, Microelectromechanical System (MEMS)-based INS can be widely utilized, owing to their low cost, lightweight, and small size. However, due to the manufacturing technology, MEMS-based INS suffers from deterministic and stochastic errors, which increase positioning errors over time. In this paper, a new effective noise reduction method is proposed that can provide more accurate outputs of MEMS-based inertial sensors. The intelligent method in this paper is a combined denoising method that combines Wavelet Transform (WT), Permutation Entropy (PE), Support Vector Regression (SVR), and Genetic Algorithm (GA). Firstly, WT is employed to obtain a time-frequency representation of raw data. Secondly, a four-element feature vector is formed. These four features are (1) amplitude of frequency, (2) its ratio to mean of amplitudes of all frequencies, (3) location of frequency in time-frequency representation, and (4) judgment on behaviors of frequency that is obtained by utilizing PE. Thirdly, based on the feature vector, the GA-SVR algorithm predicts amplitudes of all frequencies in the time-frequency representation of the denoised signal. Finally, by employing inverse WT the denoised signal is obtained. In this work, the outputs of the Inertial Measurement Unit (IMU) in ADIS16407 sensor, as a low-cost and MEMS-based INS, have been utilized for data collection. The proposed method has been compared with other noise reduction methods and the achieved results verify superior improvement than other methods.

A High-gain, fully balanced preamplifier is presented. The proposed structure advantages flipped voltage follower scheme to achieve a compact current conveyor with very low input impedance. The presented current conveyor then is used as a core element to realize a high-gain, gm-enhanced trans-conductance amplifier. The presented amplifier is suitable for application as a preamplifier. The high gain of amplifier makes it very suitable to be configured in a feedback form to deliver a high-precision predefined or programmable amplification gain. The proposed structure draws a very low power of 150nW from a 0.6V supply voltage. The Spectre Post-layout simulations with TSMC 180nm CMOS technology have been performed. The proposed amplifier exhibits an open-loop DC gain of 141.5dB and 3-dB frequency bandwidth of 2.4kHz at 60dB closed-loop configuration. The load capacitance is set to be 5pF. The proposed structure also delivers high CMRR and PSRR values of 148.3dB and 153.7dB, respectively.