Identification of wire network topology using impedance spectroscopy

Peer Reviewe

Design study of a magnetoelectric-electromagnetic vibration energy converter for energy harvesting

The aim of this paper is to design a combination of a magnetoelectric-electromagnetic (ME-EM) vibration converter in order to reach an improved energy outcome. In this paper, the influence of magnets polarization and magnetoelectric transducer and coil direction are investigated. For this purpose, a finite element model is developed using one coil, one ME transducer in a magnetic circuit. Simulation results show that a better magnetic field distribution and variation is reached, if the magnetic circuit magnets are placed in attraction. Radial polarization shows decisive advantages in comparison with axial polarization. The placement of coil parallel to the magnetic circuit direction and the magnetization of the ME transducer along its width is the optimal direction relative to the magnetic circuit

Concept for an event-triggered wireless sensor network for vibration-based diagnosis in trams

Trams are the most durable and resource efficient forms of public transportation. However, because of the varying wear in dependence on their operation mode and load levels, there is a need for condition monitoring and early maintenance. Vibration sensors provide interesting possibilities to monitor the relevant tram drive components. In this contribution we investigate their use under real conditions. On the basis of cable bound vibration measurements, the influence of the crossed track section, the tram speed and the tram condition is shown. Based on the investigation results, a concept is proposed in which a meshed and wireless sensor network, event-triggered, can acquire vibration measurement data, which are suitable for the diagnosis of tram drive components. The proposed concept has the potential to operate the sensor nodes in an energy efficient way through decentralized data evaluation taking place on the sensor node

Evaluation of 3D current injection patterns for human lung monitoring in electrical impedance tomography

Electrical impedance tomography (EIT) is a non-invasive imaging technique for monitoring the lungs continuously. During EIT Measurements, currents propagate intrinsically in 3D, since electrical current propagates diffusely in the human tissues, so a 2D EIT remains not sufficient to study the out-of-electrodes plane effects on the images. Until now, not enough effort has been made to evaluate the performance of 3D measurement patterns for lung monitoring. In this paper, to investigate 3D current injection patterns for 3D EIT, a 3D model mimicking the geometrical and electrical characteristics of the human thorax has been developed based on Finite Element Method (FEM) along with the Complete Electrode Model (CEM). Simulations have been performed with aligned (“planar,” “zigzag”, “square”, “zigzag opposite”, and “planar opposite”), and offset (“planar offset”, and “zigzag offset”) current injection patterns. Analysis shows the greatest current density diffusion results using the “zigzag opposite” current injection pattern

Machine learning approach to EIT image reconstruction of the human forearm section for different hand signs

Electrical impedance tomography (EIT) is an imaging technique used to reconstruct the conductivity of a target object from boundary voltages. In this study, we investigate suitable image reconstruction algorithms for EIT to enable the reconstruction of the conductivity distribution in the forearm section inferring muscle contractions at different hand signs. As EIT image reconstruction is an ill-posed inverse problem, the Gauss-Newton algorithm needs many iterations for the determination of suitable values of the regularization parameter and corresponding calculations of the Jacobian matrix. To reduce computational effort, we propose to use machine learning algorithms to directly reconstruct the EIT image. We explore the Radial Basis Neural Network (RBNN) and a one-dimensional Convolutional Neural Network (1D-CNN), which has been trained based on the measured EIT data for eight subjects, ten hand signs with ten trials. Both methods reach a low deviation at 0.0017 for RBNN and 0.0109 for CNN

Synergy of nanocomposite force myography and optical fiber-based wrist angle sensing for ambiguous sign classification

This paper aims at understanding the capabilities and limitation of combining Nanocomposite Force myography sensors (FMG) and optical fiber sensors in standalone systems and their synergy influence on the classification of ambiguous hand gestures. A set of 10 highly similar hand signs from the fingerspelling of the American sign language is adopted in this study. Force myography (FMG) signals are collected from one healthy subject performing the selected set of gestures with 40 repetitions for each gesture. The K-Tournament Grasshopper Extreme Learner (KTGEL) classifier has been implemented to perform an automated feature selection and hand sign classification with an efficient network size and a high accuracy

Edge Devices for Internet of Medical Things: Technologies, Techniques, and Implementation

The health sector is currently experiencing a significant paradigm shift. The growing number of elderly people in several countries along with the need to reduce the healthcare cost result in a big need for intelligent devices that can monitor and diagnose the well-being of individuals in their daily life and provide necessary alarms. In this context, wearable computing technologies are gaining importance as edge devices for the Internet of Medical Things. Their enabling technologies are mainly related to biological sensors, computation in low-power processors, and communication technologies. Recently, energy harvesting techniques and circuits have been proposed to extend the operating time of wearable devices and to improve usability aspects. This survey paper aims at providing an overview of technologies, techniques, and algorithms for wearable devices in the context of the Internet of Medical Things. It also surveys the various transformation techniques used to implement those algorithms using fog computing and IoT devices

Requirements for Energy-Harvesting-Driven Edge Devices Using Task-Offloading Approaches

Energy limitations remain a key concern in the development of Internet of Medical Things (IoMT) devices since most of them have limited energy sources, mainly from batteries. Therefore, providing a sustainable and autonomous power supply is essential as it allows continuous energy sensing, flexible positioning, less human intervention, and easy maintenance. In the last few years, extensive investigations have been conducted to develop energy-autonomous systems for the IoMT by implementing energy-harvesting (EH) technologies as a feasible and economically practical alternative to batteries. To this end, various EH-solutions have been developed for wearables to enhance power extraction efficiency, such as integrating resonant energy extraction circuits such as SSHI, S-SSHI, and P-SSHI connected to common energy-storage units to maintain a stable output for charge loads. These circuits enable an increase in the harvested power by 174% compared to the SEH circuit. Although IoMT devices are becoming increasingly powerful and more affordable, some tasks, such as machine-learning algorithms, still require intensive computational resources, leading to higher energy consumption. Offloading computing-intensive tasks from resource-limited user devices to resource-rich fog or cloud layers can effectively address these issues and manage energy consumption. Reinforcement learning, in particular, employs the Q-algorithm, which is an efficient technique for hardware implementation, as well as offloading tasks from wearables to edge devices. For example, the lowest reported power consumption using FPGA technology is 37 mW. Furthermore, the communication cost from wearables to fog devices should not offset the energy savings gained from task migration. This paper provides a comprehensive review of joint energy-harvesting technologies and computation-offloading strategies for the IoMT. Moreover, power supply strategies for wearables, energy-storage techniques, and hardware implementation of the task migration were provided

A review of nanocomposite-modified electrochemical sensors for water quality monitoring

Electrochemical sensors play a significant role in detecting chemical ions, molecules, and pathogens in water and other applications. These sensors are sensitive, portable, fast, inexpensive, and suitable for online and in-situ measurements compared to other methods. They can provide the detection for any compound that can undergo certain transformations within a potential window. It enables applications in multiple ion detection, mainly since these sensors are primarily non-specific. In this paper, we provide a survey of electrochemical sensors for the detection of water contaminants, i.e., pesticides, nitrate, nitrite, phosphorus, water hardeners, disinfectant, and other emergent contaminants (phenol, estrogen, gallic acid etc.). We focus on the influence of surface modification of the working electrodes by carbon nanomaterials, metallic nanostructures, imprinted polymers and evaluate the corresponding sensing performance. Especially for pesticides, which are challenging and need special care, we highlight biosensors, such as enzymatic sensors, immunobiosensor, aptasensors, and biomimetic sensors. We discuss the sensors’ overall performance, especially concerning real-sample performance and the capability for actual field application