Review of Contemporary Energy Harvesting Techniques and Their Feasibility in Wireless Geophones

Energy harvesting converts ambient energy to electrical energy providing numerous opportunities to realize wireless sensors. Seismic exploration is a prime avenue to benefit from it as energy harvesting equipped geophones would relieve the burden of cables which account for the biggest chunk of exploration cost and equipment weight. Since numerous energies are abundantly available in seismic fields, these can be harvested to power up geophones. However, due to the random and intermittent nature of the harvested energy, it is important that geophones must be equipped to tap from several energy sources for a stable operation. It may involve some initial installation cost but in the long run, it is cost-effective and beneficial as the sources for energy harvesting are available naturally. Extensive research has been carried out in recent years to harvest energies from various sources. However, there has not been a thorough investigation of utilizing these developments in the seismic context. In this survey, a comprehensive literature review is provided on the research progress in energy harvesting methods suitable for direct adaptation in geophones. Specifically, the focus is on small form factor energy harvesting circuits and systems capable of harvesting energy from wind, sun, vibrations, temperature difference, and radio frequencies. Furthermore, case studies are presented to assess the suitability of the studied energy harvesting methods. Finally, a design of energy harvesting equipped geophone is also proposed

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AI Prediction of Brain Signals for Human Gait Using BCI Device and FBG Based Sensorial Platform for Plantar Pressure Measurements

Artificial intelligence (AI) in developing modern solutions for biomedical problems such as the prediction of human gait for human rehabilitation is gaining ground. An attempt was made to use plantar pressure information through fiber Bragg grating (FBG) sensors mounted on an in-sole, in tandem with a brain-computer interface (BCI) device to predict brain signals corresponding to sitting, standing and walking postures of a person. Posture classification was attained with an accuracy range between 87–93% from FBG and BCI signals using machine learning models such as K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and naïve Bayes (NB). These models were used to identify electrodes responding to sitting, standing and walking activities of four users from a 16 channel BCI device. Six electrode positions based on the 10–20 system for electroencephalography (EEG) were identified as the most sensitive to plantar activities and found to be consistent with clinical investigations of the sensorimotor cortex during foot movement. A prediction of brain EEG corresponding to given FBG data with lowest mean square error (MSE) values (0.065–0.109) was made with the selection of a long-short term memory (LSTM) machine learning model when compared to the recurrent neural network (RNN) and gated recurrent unit (GRU) models

A Secure Optical Body Area Network Based on Free Space Optics and Time-Delayed 2D-Spectral/Spatial Optical CDMA

Free space optics (FSO)-based optical body area networks (OBANs) are receiving massive attention as an opportunity to address the limitations of their radio frequency (RF)-based counterparts. This boom in research interests is primarily due to multitude of benefits, including high capacity, immunity to electromagnetic interference (EMI), rapid installation, cost efficiency, and license-free use of spectrum. Securing the transmission of patient health data against interception in OBANs using insecure FSO channels is a challenging task. Therefore, we propose a low-cost, flexible, and secure OBAN based on FSO technology and a time-delayed two dimensional (2D) spectral/spatial optical code-division multiple access (OCDMA) system. The proposed architecture consists of eight sensors attached to the bodies of patients. The sensors operate at a rate of 50 kbps. Electrical data generated from each sensor are used to modulate an optical carrier and then encoded using 2D-spectral/spatial double weight–zero cross correlation (DW-ZCC) code. The 2D encoded optical signals are then time delayed to eliminate the multiple parallel FSO channels between the transmitter and medical center. The combined optical signal consists of eight 2D-encoded time-delayed optical signals transmitted towards a remote medical center over an FSO channel with a range of 1 km. The received signal is decoded and the data from each sensor are recovered after photodetection at the medical center for further analysis. The overall performance of the sensors is analyzed using bit-error rate (BER) and quality factor (Q-factor) plots for different weather conditions and lengths of the FSO channel, considering the log-normal channel model. The capital expenditure (CAPEX) of the proposed architecture is analyzed and compared with the conventional 2D-spectral/spatial FSO system to determine the overall impact of introducing time delay units on the cost of implementation

Design of a Highly Sensitive Photonic Crystal Fiber Sensor for Sulfuric Acid Detection

In this research, a photonic crystal fiber (PCF)-based sulfuric acid detector is proposed and investigated to identify the exact concentration of sulfuric acid in a mixture with water. In order to calculate the sensing and propagation characteristics, a finite element method (FEM) based on COMSOL Multiphysics software is employed. The extensive simulation results verified that the proposed optical detector could achieve an ultra-high sensitivity of around 97.8% at optimum structural and operating conditions. Furthermore, the proposed sensor exhibited negligible loss with suitable numerical aperture and single-mode propagation at fixed operating conditions. In addition, the circular air holes in the core and cladding reduce fabrication complexity and can be easily produced using the current technology. Therefore, we strongly believe that the proposed detector will soon find its use in numerous industrial applications

Calcium Carbonate Scale Inhibition with Ultrasonication and a Commercial Antiscalant

In this study, ultrasonication-assisted calcium carbonate scale inhibition was investigated compared with a commercial antiscalant ATMP (amino tris(methyl phosphonic acid)). The effects of varying ultrasound amplitude, pH, and inhibition duration were evaluated. The inhibition of calcium carbonate scale formation was measured based on the concentration of calcium in the solution after subjecting to different conditions. Scale deposits were also characterized using scanning electron microscopy and X-ray diffraction spectroscopy. Inhibition of scale formation was supported at a pH of 7 for an ultrasound amplitude of 150 W. A 94% calcium carbonate inhibition was recorded when the experiment was carried out with ultrasonication. The use of 5 mg/L ATMP achieved a 90% calcium carbonate inhibition of ATMP. The result of the characterization revealed that the morphology of the crystals was unaffected by ultrasonic irradiation. Sample treatment was performed with two different membranes to evaluate the calcium carbonate deposition, and data reveals that, at identical conditions, ultrasonication provides less deposition when compared to the control experiments