Promoting smart cities into the 5G era with multi-field Internet of Things (IoT) applications powered with advanced mechanical energy harvesters

The energy harvesting (EH) from unused natural waste energy sources is common nowadays because of rising power demand. The sources have the potential of producing micro to milliwatts power depending on the ambient conditions. Many researchers have been concentrating on micro-level energy harvesting to provide power to the micro-devices in a remote area. The concept leads to a drastic reduction in cost. Once the structure is established, it can generate electricity with minimal cost or effort like renewable sources. This paper reviews the two key areas of the piezoelectric energy harvesting system (PEHS), namely, mechanical and electronic approaches, developed by several researchers. From the thorough review, it is realized that the existing technologies more or less can capable to EH by using the piezoelectric elements; however, the consistency and stability of the systems are not up to the mark yet. In this study, vibration-based PEHS has been considered for the application of the optimization technique to enhance its performance. This review has been focused on numerous challenges and recommendations for next-generation EH by utilizing vibration-based piezoelectric elements.

Review of piezoelectric energy harvesting system and application of optimization techniques to enhance the performance of the harvesting system

Modeling and Characterization of Piezoelectric d(33)-Mode MEMS Energy Harvester

Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system.

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

As worldwide awareness about global climate change spreads, green electronics are becoming increasingly popular as an alternative to diminish pollution. Thus, nowadays energy efficiency is a paramount characteristic in electronics systems to obtain such a goal. Harvesting wasted energy from human activities and world physical phenomena is an alternative to deal with the aforementioned problem. Energy harvesters constitute a feasible solution to harvesting part of the energy being spared. The present research work provides the tools for characterizing, designing and implementing such devices in electronic systems through their equivalent structural models.

https://www.mdpi.com/2079-9292/8/5/486/pdf

Energy Harvesting Technologies and Equivalent Electronic Structural Models—Review

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Electrostatic Energy Harvesting Systems: A Better Understanding of Their SustainabilityElectrostatic Energy Harvesting Systems: A Better Understanding of Their Sustainability

This article presents two new approaches of fetal electrocardiogram (ECG) signal (FECG) separation using the input-mode adaptive filter (IMAF) and the output-mode adaptive filter (OMAF). Both approaches use the recursive least-squares (RLS) and the least-mean-squares (LMS) algorithms and a single-reference-generation block. In the IMAF, the filter’s primary input is connected directly to the abdominal signal. The reference signal is generated by windowing the abdominal signal according to the locations of the QRS MECG pulses. In the OMAF, the filter’s primary input is connected to the output stage of a blind source separation block. The reference signal is generated by windowing the raw FECG signal, from the BSS output, according to the locations of the QRS pulses of the extracted MECG signal. We selected the null space idempotent transformation matrix (NSITM) as the BSS algorithm used in this work. Results from real Daisy and Physionet databases show the successful extraction of the FECG signal. Results from synthesized data from Physionet databases, using OMAF, show considerable improvement in extraction performances over NSITM and IMAF when the fetal-to-maternal signal-to-noise ratio (fmSNR) increases from −30 to 0 dB. This study demonstrated that the OMAF is a feasible algorithm for FECG extraction.

Fetal ECG Extraction Using Input-Mode and Output-Mode Adaptive Filters With Blind Source Separation

Heartbeats are important aspects for the study of heart diseases in medical sciences as they provide vital information on heart disorders and abnormalities in heart rhythms. Each heartbeat provides a QRS complex in the electrocardiogram (ECG) which is centered at the R-peak. The analysis of ECG is hindered by low-frequency noise, high-frequency noise, interference from P and T waves, and changes in QRS morphology. This paper presents a new peak detection algorithm that can suppress the noise and adapt to changes in ECG signal morphology for better detection performance. The proposed algorithm is based on median and moving average (MA) filtering, segmentation, time and amplitude thresholds, and statistical false peak elimination (SFPE). The filters are first used in preprocessing to reduce unwanted noise and interference. The data is then divided into smaller segments and each segment is then analyzed using two distinct thresholds, a time axis (x-axis) threshold and an amplitude (y-axis) threshold. Next, the false peaks are eliminated resulting from any residue of noise using an average value of peak-to-peak interval. A post-processing stage is added to eliminate any peak that is detected twice and to search for missed low-amplitude peaks. The proposed method is tested on MIT-BIH arrhythmia and Fantasia databases and provides better results in comparison to several state-of-the-art methods in the field. The mean sensitivity, positive predictivity, and detection error rates for the proposed method are 99.82%, 99.88%, and 0.31%, respectively, for the MIT-BIH arrhythmia database and 99.92%, 99.90%, and 0.18%, respectively, for the Fantasia database.

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A Novel Method of QRS Detection Using Time and Amplitude Thresholds With Statistical False Peak Elimination

/pdf/modeling-of-low-power-electrostatic-wind-energy-harvester-18pe7pu23y.pdf

Modeling of Low Power Electrostatic Wind Energy Harvester for Macro-Scale Applications

This study presents a novel blind source extraction of a noisy mixture using a class of parallel linear predictor filters. Analysis of a noisy mixture equation is carried out to address new autoregressive source signal model based on the covariance matrix of the whitened data. A method of interchanging the rules of filter inputs is proposed such that this matrix becomes the filter input while the estimated source signals are considered as the parallel filter coefficients. As the matrix has unity norm and unity eigenvalues, the filter becomes independent on the mixture signal norm and eigenvalues variations, thus solving drastically the ambiguity due to the dependency of the filter on the mixture power levels if the mixture is considered as the filter input. Furthermore, the unity eigenvalues of the matrix result in a very fast convergence in two iterations. Simulation results show that the model is capable of extracting the unknown source signals and removing noise when the input signal-to-noise ratio is varied from −20 to 80 dB.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-spr.2017.0512

Luay Yassin Taha

Papers

Electrostatic Energy Harvesting Systems: A Better Understanding of Their SustainabilityElectrostatic Energy Harvesting Systems: A Better Understanding of Their Sustainability

Fetal ECG Extraction Using Input-Mode and Output-Mode Adaptive Filters With Blind Source Separation

A Novel Method of QRS Detection Using Time and Amplitude Thresholds With Statistical False Peak Elimination

Modeling of Low Power Electrostatic Wind Energy Harvester for Macro-Scale Applications

Efficient blind source extraction of noisy mixture utilising a class of parallel linear predictor filters