Real-time Monitoring Applications Research Articles

Transparent, Flexible, Conformal Capacitive Pressure Sensors with Nanoparticles.

The fundamental challenge in designing transparent pressure sensors is the ideal combination of high optical transparency and high pressure sensitivity. Satisfying these competing demands is commonly achieved by a compromise between the transparency and usage of a patterned dielectric surface, which increases pressure sensitivity, but decreases transparency. Herein, a design strategy for fabricating high-transparency and high-sensitivity capacitive pressure sensors is proposed, which relies on the multiple states of nanoparticle dispersity resulting in enhanced surface roughness and light transmittance. We utilize two nanoparticle dispersion states on a surface: (i) homogeneous dispersion, where each nanoparticle (≈500 nm) with a size comparable to the visible light wavelength has low light scattering; and (ii) heterogeneous dispersion, where aggregated nanoparticles form a micrometer-sized feature, increasing pressure sensitivity. This approach is experimentally verified using a nanoparticle-dispersed polymer composite, which has high pressure sensitivity (1.0 kPa-1 ), and demonstrates excellent transparency (>95%). We demonstrate that the integration of nanoparticle-dispersed capacitor elements into an array readily yields a real-time pressure monitoring application and a fully functional touch device capable of acting as a pressure sensor-based input device, thereby opening up new avenues to establish processing techniques that are effective on the nanoscale yet applicable to macroscopic processing.

Precipitation Clouds Delineation Scheme in Tropical Cyclones and Its Validation Using Precipitation and Cloud Parameter Datasets from TRMM

AbstractUtilizing the cloud parameters derived from the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner and the near-surface rainfall detected by the TRMM Precipitation Radar, the differences of cloud parameters for precipitating clouds (PCs) and nonprecipitating clouds (NPCs) are examined in tropical cyclones (TCs) during daytime from June to September 1998–2010. A precipitation delineation scheme that is based on cloud parameter thresholds is proposed and validated using the independent TC datasets in 2011 and observational datasets from Terra/MODIS. Statistical analysis of these results shows that the differences in the effective radius of cloud particles Re are small for PCs and NPCs, while thick clouds with large cloud optical thickness (COT) and liquid water path (LWP) can be considered as candidates for PCs. The probability of precipitation increases rapidly as the LWP and COT increase, reaching ~90%, whereas the probability of precipitation reaches a peak value of only 30% as Re increases. The combined threshold of a brightness temperature at 10.8 μm (BT4) of 270 K and an LWP of 750 g m−2 shows the best performance for precipitation discrimination at the pixel levels, with the probability of detection (POD) reaching 68.2% and false-alarm ratio (FAR) reaching 31.54%. From MODIS observations, the composite scheme utilizing BT4 and LWP also proves to be a good index, with POD reaching 77.39% and FAR reaching 24.2%. The results from this study demonstrate a potential application of real-time precipitation monitoring in TCs utilizing cloud parameters from visible and infrared measurements on board geostationary weather satellites.

Reversible Strategy of Water Monitoring Aimed at Amphiphilic Pollutants.

For monitoring diverse pollutants in a complicated water environment, the design and development of various detection strategies are necessary. Here, we introduce a general strategy of water monitoring aimed at amphiphilic pollutants using carbon nanotube based film. The pollutants with amphiphilic characteristics can tune the wetting behavior between carbon nanotubes and water molecules, leading to the change in the interface resistance of the carbon nanotube based film. The experimental results demonstrate that the change ratio of the film resistance is related to the concentration of pollutants in solution. This monitoring strategy is general for the detection of amphiphilic materials in mixed solution, such as surfactants and some organic solvent. The ability to achieve a sensitive and repeatable change in film resistance has potential applications in high-sensitivity, real-time, long-lasting, and multiple water monitoring.

A Comparative Study of FFT, DCT, and DWT for Efficient Arrhytmia Classification in RP-RF Framework

Computer-aided ECG classification is an important tool for timely diagnosis of abnormal heart conditions. This paper proposes a novel framework that combines the theory of compressive sensing with random forests to achieve reliable automatic cardiac arrhythmia detection. Furthermore, the paper evaluates the characterization power of FFT, DCT and DWT data transformations in order to extract significant features that will bring the additional boost to the classification performance. The experiments – carried out over MIT-BIH benchmark arrhythmia database, following the standards and recommended practices provided by AAMI – demonstrate that DWT based features exhibit better performances compared to other two feature extraction techniques for a relatively small number of random projected coefficients, i.e. after considerable (approx. 85%) dimensionality reduction of the input signal. The results are very promising, suggesting that the proposed model could be implemented for practical applications of real-time ECG monitoring, due to its low-complexity.

Biophysical Characteristics of Human Neuroblastoma Cell in Oligomeric $\beta $ -Amyloid (1-40) Cytotoxicity.

Beta amyloid ( ) peptide, which is a common neuropathological hallmark deposit in the brain of patients with Alzheimer's disease, typically comprises 39-43 amino acid residues. peptides exist as isoforms of and with various lengths. In this research, atomic force microscopy (AFM) was applied to investigate aggregations in Hank's Balanced Salt Solution. Toxic effect of oligomer was investigated in live SH-SY5Y neuroblastoma cells by characterizing cell morphology and cell mechanics using high-resolution AFM scanning. oligomer-induced cytoskeleton reorganization was also observed under confocal microscopy, and it can account for reduction in Young's modulus of cells. Meanwhile, phosphorylation of tau increased after oligomer treatment, possibly resulting in microtubule disassembly. This paper demonstrates the linkage between cellular mechanical changes and neurodegeneration mediated by . The method used implies promising applications of real-time monitoring of cellular mechanical properties given the toxic effects of on living neuronal cells.

Proteomic Characterization of Dermal Interstitial Fluid Extracted Using a Novel Microneedle-Assisted Technique.

As wearable fitness devices have gained commercial acceptance, interest in real-time monitoring of an individual's physiological status using noninvasive techniques has grown. Microneedles have been proposed as a minimally invasive technique for sampling the dermal interstitial fluid (ISF) for clinical monitoring and diagnosis, but little is known about its composition. In this study, a novel microneedle array was used to collect dermal ISF from three healthy human donors and compared with matching serum and plasma samples. Using a shotgun quantitative proteomic approach, 407 proteins were quantified with at least one unique peptide, and of those, 135 proteins were differently expressed at least 2-fold. Collectively, these proteins tended to originate from the cytoplasm, membrane bound vesicles, and extracellular vesicular exosomes. Proteomic analysis confirmed previously published work that indicates that ISF is highly similar to both plasma and serum. In this study, less than one percent of proteins were uniquely identified in ISF. Taken together, ISF could serve as a minimally invasive alternative for blood-derived fluids with potential for real-time monitoring applications.

Online denoising of eye-blinks in electroencephalography

Due to its high temporal resolution, electroencephalography (EEG) has become a broadly-used technology for real-time brain monitoring applications such as neurofeedback (NFB) and brain-computer interfaces (BCI). However, since EEG signals are prone to artifacts, denoising is a crucial step that enables adequate subsequent data processing and interpretation. The aim of this study is to compare manual denoising to unsupervised online denoising, which is essential to real-time applications. Denoising EEG for real-time applications requires the implementation of unsupervised and online methods. In order to permit genericity, these methods should not rely on electrooculography (EOG) traces nor on temporal/spatial templates of the artifacts. Two blind source separation (BSS) methods are analyzed in this paper with the aim of automatically correcting online eye-blink artifacts: the algorithm for multiple unknown signals extraction (AMUSE) and the approximate joint diagonalization of Fourier cospectra (AJDC). The chosen gold standard is a manual review of the EEG database carried out retrospectively by a human operator. Comparison is carried out using the spectral properties of the continuous EEG and event-related potentials (ERP). The AJDC algorithm addresses limitations observed in AMUSE and outperforms it. No statistical difference is found between the manual and automatic approaches on a database composed of 15 healthy individuals, paving the way for an automated, operator-independent, and real-time eye-blink correction technique.

Real-time monitoring of high-power disk laser welding based on support vector machine

In this paper, an efficient quality monitoring system for monitoring high-power disk laser welding in real time was developed. Fifteen features of laser-induced metal vapor plume and spatters were extracted and support vector machine was adopted to establish a classifier to evaluate the welding quality. Feature selection method was employed to choose suitable features. The experiment results demonstrated that this method had satisfactory performance and could be applied to real-time monitoring application.

Numerical calculation of wing-bending moment with real-time strain monitoring by FBG modulation

This paper presents an application of Structural Health Monitoring System based on Fiber Bragg Grating sensors (FBGs) dedicated to wing-bending moment. A numerical calculation of bending moment is proposed to the application of real-time wing-bending moment monitoring. With the advantage of anti-electromagnetic interference, small size and light weight, Fiber Bragg grating (FBG) sensors have been applied in structural health monitoring system (SHMS). An experiment was performed in full-scale fatigue test of an aircraft, and the wings of aircraft were subjected to specific loading conditions, and the strain data was collected by FBGs’ demodulation. The relationship matrix K between the strain and the wing-bending moment was established. It is a new approach for the wing-bending moment real-time monitoring with the simple FBG strain collection modulation.

Localization of Origins of Premature Ventricular Contraction by Means of Convolutional Neural Network From 12-Lead ECG.

This paper proposes a novel method to localize origins of premature ventricular contractions (PVCs) from 12-lead electrocardiography (ECG) using convolutional neural network (CNN) and a realistic computer heart model. The proposed method consists of two CNNs (Segment CNN and Epi-Endo CNN) to classify among ventricular sources from 25 segments and from epicardium (Epi) or endocardium (Endo). The inputs are the full time courses and the first half of QRS complexes of 12-lead ECG, respectively. After registering the ventricle computer model with an individual patient's heart, the training datasets were generated by multiplying ventricular current dipoles derived from single pacing at various locations with patient-specific lead field. The origins of PVC are localized by calculating the weighted center of gravity of classification returned by the CNNs. A number of computer simulations were conducted to evaluate the proposed method under a variety of noise levels and heart registration errors. Furthermore, the proposed method was evaluated on 90 PVC beats from nine human patients with PVCs and compared against ablation outcome in the same patients. The computer simulation evaluation returned relatively high accuracies for Segment CNN (∼78%) and Epi-Endo CNN (∼90%). Clinical testing in nine PVC patients resulted an averaged localization error of 11mm. Our simulation and clinical evaluation results demonstrate the capability and merits of the proposed CNN-based method for localization of PVC. This paper suggests a new approach for cardiac source localization of origin of arrhythmias using only the 12-lead ECG by means of CNN, and may have important applications for future real-time monitoring and localizing origins of cardiac arrhythmias guiding ablation treatment.

Performance Analysis of Tags Collection Time on A New Architecture of 2.4 GHz Embedded Active RFID System for Indoor and Outdoor Tracking and Monitoring Applications Based on IEEE 802.15.4 Standard

This paper focuses on the analysis of tags collection time of 2.4 GHz embedded active Radio Frequency Identification (RFID) system for indoor and outdoor real-time tracking and monitoring applications based on IEEE 802.15.4 standard. The main novelty of the system is the implementation of the communication method in order to provide Machine to Machine (M2M) communication and automated switching mechanism between indoor and outdoor location by utilizing active RFID, Wireless Sensor Network (WSN), Global Positioning System (GPS) and mobile communication on a single platform. In this work, GPS receiver covers outdoor location tracking, while active RFID provides identification and Receive Signal Strength Indicator (RSSI) reading for each tag holder to cover indoor location tracking especially near or inside building where location information is not detected by GPS. Several experiments were conducted on three different RFID tags which were active RFID tag embedded with GPS and GSM (ERFIDG2), active RFID tag embedded with GPS (ERFIDG) and standalone RFID tag communicating with the same active RFID reader. The experiment was done to evaluate the communication performance of the active RFID in terms of tags collection time using Transparent (AT) and Application Programming Interface (API) mode. The experiment was extended to measure tags collection time in single hop and multi hops communication for Tag Talk First (TTF) and Reader Talk First (RTF) protocols. The results show that the proposed active RFID system (ERFIDG2) is better than the standalone and ERFIDG systems. The in-depth research done in this work is to study the experience and identify the challenges that will be faced in the development and implementation of a wireless RFID-based system for tracking and monitoring applications.

A-optimal encoding weights for nonlinear inverse problems, with application to the Helmholtz inverse problem

The computational cost of solving an inverse problem governed by PDEs, using multiple experiments, increases linearly with the number of experiments. A recently proposed method to decrease this cost uses only a small number of random linear combinations of all experiments for solving the inverse problem. This approach applies to inverse problems where the PDE solution depends linearly on the right-hand side function that models the experiment. As this method is stochastic in essence, the quality of the obtained reconstructions can vary, in particular when only a small number of combinations are used. We develop a Bayesian formulation for the definition and computation of encoding weights that lead to a parameter reconstruction with the least uncertainty. We call these weights A-optimal encoding weights. Our framework applies to inverse problems where the governing PDE is nonlinear with respect to the inversion parameter field. We formulate the problem in infinite dimensions and follow the optimize-then-discretize approach, devoting special attention to the discretization and the choice of numerical methods in order to achieve a computational cost that is independent of the parameter discretization. We elaborate our method for a Helmholtz inverse problem, and derive the adjoint-based expressions for the gradient of the objective function of the optimization problem for finding the A-optimal encoding weights. The proposed method is potentially attractive for real-time monitoring applications, where one can invest the effort to compute optimal weights offline, to later solve an inverse problem repeatedly, over time, at a fraction of the initial cost.

Frequency Domain Analysis of Sensor Data for Event Classification in Real-Time Robot Assisted Deburring.

Process monitoring using indirect methods relies on the usage of sensors. Using sensors to acquire vital process related information also presents itself with the problem of big data management and analysis. Due to uncertainty in the frequency of events occurring, a higher sampling rate is often used in real-time monitoring applications to increase the chances of capturing and understanding all possible events related to the process. Advanced signal processing methods are used to further decipher meaningful information from the acquired data. In this research work, power spectrum density (PSD) of sensor data acquired at sampling rates between 40–51.2 kHz was calculated and the corelation between PSD and completed number of cycles/passes is presented. Here, the progress in number of cycles/passes is the event this research work intends to classify and the algorithm used to compute PSD is Welch’s estimate method. A comparison between Welch’s estimate method and statistical methods is also discussed. A clear co-relation was observed using Welch’s estimate to classify the number of cycles/passes. The paper also succeeds in classifying vibration signal generated by the spindle from the vibration signal acquired during finishing process.

Application of real-time continuous glucose monitoring in outpatient management system for type 1 diabetes mellitus

Objective To evaluate the effect of continuous glucose monitoring system(CGMS) in improving the current status of type 1 diabetes mellitus(T1DM) control and reducing the economic burden of the patients. Methods One hundred and fifteen patients with T1DM were randomly assigned to the CGMS group and the self-monitoring of blood glucose(SMBG) group respectively. The patients in CGMS group were on 72 h CGMS every 6 months, while SMBG group only with SMBG to guide the insulin dose adjustment. The levels of blood glucose and the statistics of the number of hypoglycemia and diabetic ketoacidosis were taken as the main observational indexes every 6 months. The chronic complication and the statistics of the number of hospitalizations and the total cost of treatment were made as the secondary observational index every 12 months. Results 2 h postprandial plasma glucose(2hPG) and mean blood glucose(MBG) in the CGMS group were lower than those in the SMBG group [(10.7±1.9 vs 11.5±2.7) mmol/L, (9.7±0.5 vs 10.6±0.7) mmol/L, P<0.05] in the clinical follow-up visit after 6 months. The per capita number of hypoglycaemia in the CGMS group was lower than that in the SMBG group[(7.9±2.6 vs 9.2±3.4) times, P<0.05]. In the outpatient follow-up re-visit to the patients after 6 months, fasting plasma glucose(FPG), 2hPG, MBG, and HbA1C of the patients in the CGMS group were lower than those in the SMBG group(t=4.71~9.75, P<0.05), the per capita numbers of hypoglycemia and DKA in the CGMS group were lower than those in the SMBG group(t=3.61~4.37, P<0.05). Conclusion The application of real-time continuous glucose monitoring in T1DM outpatient management may reduce the whole-day blood glucose of the patients, decrease the incidence risk of hypoglycemia, and improve the compliance of the treatment while without increasing the economic burden of the disease. (Chin J Endocrinol Metab, 2017, 33: 367-371) Key words: Real-time continuous glucose monitoring system; Diabetes mellitus, type 1; Outpatient management system; Control status; Economic burden of disease

Monitoring black tea fermentation using a colorimetric sensor array-based artificial olfaction system

The fermentation process plays an important role in black tea's quality control and determination. This article presented a portable artificial olfaction system for real-time monitoring of black tea. Herein, a colorimetric sensor array, which was fabricated by printing 12 chemical dyes, including 9 porphyrins/metalloporphyrins and 3 pH indicators on a C2 reverse silica-get flat plate, was utilized to develop the artificial olfaction system for data acquisition. With specific colorific fingerprint of volatile compounds from black tea, fermentation time can be achieved with efficient chemometrics. Additionally, a novel classification algorithm, which combined K-Nearest Neighbor and adaptive boosting, namely KNN-AdaBoost, was proposed with a comparison of two classical classification algorithms (e.g., KNN and BP-ANN). Results demonstrated that the optimum KNN-AdaBoost model was superior to others with discrimination rate of 100% in both the training and prediction set. The overall results sufficiently reveal that the colorimetric sensor has promising applications in real-time fermentation monitoring. Practical applications The fermentation process plays an important role in black tea's quality control and determination. However, traditional analytical chemical methods (i.e., mass spectrometry, liquid chromatography gas, and gas chromatography-mass spectrometry) can monitor the fermentation process and have some drawbacks, such as complicated operation, high costs of implementation, and lengthy analysis time. This research notably proposed a portable artificial olfaction system for real-time monitoring black tea; and examined the feasibility, accuracy and effectiveness of this sensor. The overall results obtained from this study sufficiently reveals that the colorimetric sensor has promising applications in real-time fermentation monitoring.

Evaluating the Air Pollution Impact Using Environmental Monitoring, Dispersion Modeling and Volunteered Geographic Information Systems

The paper describes the application of real-time environmental monitoring, local and long-range transport dispersion modeling and Volunteered Geographic Information (VGI) systems that can improve the fast knowledge regarding the air pollution status to determine the actual outdoor conditions for living in a specific urban area. A case study using such techniques is presented for a pollution event with fine particulate matter (PM2.5) in Targoviste, Romania. PM2.5 time series were recorded during the pollution event by two optical monitoring systems providing an average of 184.1, maximum of 323, and minimum of 107 �g m-3 (DustTrackTM 8533 EP system), and 177.4, 321 and 93 �g m-3 (Rokidair microstation), respectively. PM2.5 concentrations and forward trajectories were computed using two programs: BREEZE� AERMOD 7.9 and HYSPLIT dispersion model. The obtained results emphasize the usefulness of embedding dispersion modeling advanced tools to supplement monitoring results and to characterize the source apportionment.

Wearable Resistive Pressure Sensor Based on Highly Flexible Carbon Composite Conductors with Irregular Surface Morphology.

Wearable pressure sensors are crucial building blocks for potential applications in real-time health monitoring, artificial electronic skins, and human-to-machine interfaces. Here we present a highly sensitive, simple-architectured wearable resistive pressure sensor based on highly compliant yet robust carbon composite conductors made of a vertically aligned carbon nanotube (VACNT) forest embedded in a polydimethylsiloxane (PDMS) matrix with irregular surface morphology. A roughened surface of the VACNT/PDMS composite conductor is simply formed using a sandblasted silicon master in a low-cost and potentially scalable manner and plays an important role in improving the sensitivity of resistive pressure sensor. After assembling two of the roughened composite conductors, our sensor shows considerable pressure sensitivity of ∼0.3 kPa-1 up to 0.7 kPa as well as stable steady-state responses under various pressures, a wide detectable range of up to 5 kPa before saturation, a relatively fast response time of ∼162 ms, and good reproducibility over 5000 cycles of pressure loading/unloading. The fabricated pressure sensor can be used to detect a wide range of human motions ranging from subtle blood pulses to dynamic joint movements, and it can also be used to map spatial pressure distribution in a multipixel platform (in a 4 × 4 pixel array).

Portable High Surface Area TiO2 Nanotube Array Sensor for the Detection of Benzene at Room Temperature

Benzene is one of the few known human carcinogens as identified by the International Agency for Research on Cancer. It is a common indoor and outdoor pollutant, with sources ranging from cigarette smoke to traffic. Hence, there is an increasing need to detect low concentrations of benzene. Metal-oxide based nano-sensors, with their simple sensing mechanisms make for a good choice. Thin films based on titanium dioxide (TiO2) have previously been shown to respond to benzene (1), however these sensors are run at high temperatures (160-200°C), require a complex setup for operation, and have low surface area for sensing. This paper presents the development of TiO2 nanotube based sensor array (Figure 1) that has high surface area, is highly sensitive, and operates at room temperature using portable and simple instrumentation. TiO2 nanotubes were synthesized through anodization in electrolytic solution of ammonium fluoride (NH4F)-ethylene glycol (EG) and oxygen annealed for up to 500°C. The response of the synthesized hollow nanotubes was studied at room temperature for different concentrations of benzene vapor (100-500ppm). The response of the sensor coupons to benzene vapor was measured using an amperometric technique. The results showed an increase in current that was directly proportional to the concentration of benzene in the sample which indicates a quantitative nature of the sensor (Figure 2). The sensor was placed in a portable system which can be deployed to potential contamination sites for real-time monitoring applications. Results on sensing mechanism, stability of the sensor over time, sensor reusability, and detection limit will be presented. (1) Mabrook M., Hawkins P. (2001) Sensors and Actuators, B: Chemical 75, 197-202. Figure 1

Portable Agrichemical Detection System for Enhancing the Safety of Agricultural Products Using Aggregation of Gold Nanoparticles.

Organophosphorus (OP) and triazole chemicals have been commonly used as insecticides and fungicides to protect agricultural foods from harmful insects and fungi. However, these agrichemicals sometimes remain after distribution and can cause serious health and environmental issues. Therefore, it is essential to detect OPs and triazole chemicals in agricultural products. Nowadays, many detection techniques for OPs and triazole chemicals are expensive and time-consuming and require highly trained technicians. Thus, particularly rapid, simple, and sensitive detection methods are in demand for on-site screening of agrichemicals. Gold nanoparticles (AuNPs) have been utilized for applications in analytical assays and real-time monitoring in the biosensor field because of their biocompatibility and outstanding size-dependent optical properties. In this study, we used AuNPs as a detection probe, which have a size of 17 nm in diameter, a red color, and the absorbance peak at 520 nm. When imidazole was added to AuNPs mixed with the agrichemicals, the AuNPs aggregated and their colors changed to purple, causing the appearance of a new peak at 660–670 nm, which could be measured within approximately 20 s. Moreover, we developed a novel device for multiple agrichemical detections using an AuNP-aggregation-based spectrometric detection system. This portable device is light, simple, fast, and highly sensitive as well as selective. With this system, agrichemical residues can be easily detected on the spot at a low cost and in a short reaction time.

Engineering of Surface Chemistry for Enhanced Sensitivity in Nanoporous Interferometric Sensing Platforms.

We explore new approaches to engineering the surface chemistry of interferometric sensing platforms based on nanoporous anodic alumina (NAA) and reflectometric interference spectroscopy (RIfS). Two surface engineering strategies are presented, namely (i) selective chemical functionalization of the inner surface of NAA pores with amine-terminated thiol molecules and (ii) selective chemical functionalization of the top surface of NAA with dithiol molecules. The strong molecular interaction of Au3+ ions with thiol-containing functional molecules of alkane chain or peptide character provides a model sensing system with which to assess the sensitivity of these NAA platforms by both molecular feature and surface engineering. Changes in the effective optical thickness of the functionalized NAA photonic films (i.e., sensing principle), in response to gold ions, are monitored in real-time by RIfS. 6-Amino-1-hexanethiol (inner surface) and 1,6-hexanedithiol (top surface), the most sensitive functional molecules from approaches i and ii, respectively, were combined into a third sensing strategy whereby the NAA platforms are functionalized on both the top and inner surfaces concurrently. Engineering of the surface according to this approach resulted in an additive enhancement in sensitivity of up to 5-fold compared to previously reported systems. This study advances the rational engineering of surface chemistry for interferometric sensing on nanoporous platforms with potential applications for real-time monitoring of multiple analytes in dynamic environments.