The front-end electronics (FEE) of the Compact Muon Solenoid (CMS) is needed very low power consumption and higher readout bandwidth to match the low power requirement of its Short Strip application-specific integrated circuits (ASIC) (SSA) and to handle a large number of pileup events in the High-Luminosity Large Hadron Collider (LHC). A low-noise, wide bandwidth, and ultra-low power FEE for the pixel-strip sensor of the CMS has been designed and simulated in a 0.35 µm Complementary Metal Oxide Semiconductor (CMOS) process. The design comprises a Charge Sensitive Amplifier (CSA) and a fast Capacitor-Resistor-Resistor-Capacitor (CR-RC) pulse shaper (PS). A compact structure of the CSA circuit has been analyzed and designed for high throughput purposes. Analytical calculations were performed to achieve at least 998 MHz gain bandwidth, and then overcome pileup issue in the High-Luminosity LHC. The spice simulations prove that the circuit can achieve 88 dB dc-gain while exhibiting up to 1 GHz gain-bandwidth product (GBP). The stability of the design was guaranteed with an 82-degree phase margin while 214 ns optimal shaping time was extracted for low-power purposes. The robustness of the design against radiations was performed and the amplitude resolution of the proposed front-end was controlled at 1.87% FWHM (full width half maximum). The circuit has been designed to handle up to 280 fC input charge pulses with 2 pF maximum sensor capacitance. In good agreement with the analytical calculations, simulations outcomes were validated by post-layout simulations results, which provided a baseline gain of 546.56 mV/MeV and 920.66 mV/MeV, respectively, for the CSA and the shaping module while the ENC (Equivalent Noise Charge) of the device was controlled at 37.6 e- at 0 pF with a noise slope of 16.32 e-/pF. Moreover, the proposed circuit dissipates very low power which is only 8.72 µW from a 3.3 V supply and the compact layout occupied just 0.0205 mm2 die area.
Epileptic seizures are temporary episodes of convulsions, where approximately 70 percent of the diagnosed population can successfully manage their condition with proper medication and lead a normal life. Over 50 million people worldwide are affected by some form of epileptic seizures, and their accurate detection can help millions in the proper management of this condition. Increasing research in machine learning has made a great impact on biomedical signal processing and especially in electroencephalogram (EEG) data analysis. The availability of various feature extraction techniques and classification methods makes it difficult to choose the most suitable combination for resource-efficient and correct detection. This paper intends to review the relevant studies of wavelet and empirical mode decomposition-based feature extraction techniques used for seizure detection in epileptic EEG data. The articles were chosen for review based on their Journal Citation Report, feature selection methods, and classifiers used. The high-dimensional EEG data falls under the category of '3N' biosignals-nonstationary, nonlinear, and noisy; hence, two popular classifiers, namely random forest and support vector machine, were taken for review, as they are capable of handling high-dimensional data and have a low risk of over-fitting. The main metrics used are sensitivity, specificity, and accuracy; hence, some papers reviewed were excluded due to insufficient metrics. To evaluate the overall performances of the reviewed papers, a simple mean value of all metrics was used. This review indicates that the system that used a Stockwell transform wavelet variant as a feature extractor and SVM classifiers led to a potentially better result.
This paper's novel focus is predicting the leaf nitrogen content of rice during growing and maturing. A multispectral image processing-based prediction model of the Radial Basis Function Neural Network (RBFNN) model was proposed. Moreover, this paper depicted three primary points as the following: First, collect images of rice leaves (RL) from a controlled condition experimental laboratory and new shoot leaves in different stages in the visible light spectrum, and apply digital image processing technology to extract the color characteristics of RL and the morphological characteristics of the new shoot leaves. Secondly, the RBFNN model, the General Regression Model (GRL), and the General Regression Method (GRM) model were constructed based on the extracted image feature parameters and the nitrogen content of rice leaves. Third, the RBFNN is optimized by and Partial Least-Squares Regression (RBFNN-PLSR) model. Finally, the validation results show that the nitrogen content prediction models at growing and mature stages that the mean absolute error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) of the RFBNN model during the rice-growing stage and the mature stage are 0.6418 (%), 0.5399 (%), 0.0652 (%), and 0.3540 (%), 0.1566 (%), 0.0214 (%) respectively, the predicted value of the model fits well with the actual value. Finally, the model may be used to give the best foundation for achieving exact fertilization control by continuously monitoring the nitrogen nutrition status of rice. In addition, at the growing stage, the RBFNN model shows better results compared to both GRL and GRM, in which MAE is reduced by 0.2233% and 0.2785%, respectively.
In this paper, a new approach for the periodical testing and the functionality evaluation of a fetal heart rate monitor device based on ultrasound principle is proposed. The design and realization of the device are presented, together with the description of its features and functioning tests. In the designed device, a relay element, driven by an electric signal that allows switching at two specific frequencies, is used to simulate the fetus and the mother's heartbeat. The simulator was designed to be compliant with the standard requirements for accurate assessment and measurement of medical devices. The accuracy of the simulated signals was evaluated, and it resulted to be stable and reliable. The generated frequencies show an error of about 0.5% with respect to the nominal one while the accuracy of the test equipment was within ±3% of the test signal set frequency. This value complies with the technical standard for the accuracy of fetal heart rate monitor devices. Moreover, the performed tests and measurements show the correct functionality of the developed simulator. The proposed equipment and testing respect the technical requirements for medical devices. The features of the proposed device make it simple and quick in testing a fetal heart rate monitor, thus providing an efficient way to evaluate and test the correlation capabilities of commercial apparatuses.
The application of compressed sensing (CS) to biomedical imaging is sensational since it permits a rationally accurate reconstruction of images by exploiting the image sparsity. The quality of CS reconstruction methods largely depends on the use of various sparsifying transforms, such as wavelets, curvelets or total variation (TV), to recover MR images. As per recently developed mathematical concepts of CS, the biomedical images with sparse representation can be recovered from randomly undersampled data, provided that an appropriate nonlinear recovery method is used. Due to high under-sampling, the reconstructed images have noise like artifacts because of aliasing. Reconstruction of images from CS involves two steps, one for dictionary learning and the other for sparse coding. In this novel framework, we choose Simultaneous code word optimization (SimCO) patch-based dictionary learning that updates the atoms simultaneously, whereas Focal underdetermined system solver (FOCUSS) is used for sparse representation because of a soft constraint on sparsity of an image. Combining SimCO and FOCUSS, we propose a new scheme called SiFo. Our proposed alternating reconstruction scheme learns the dictionary, uses it to eliminate aliasing and noise in one stage, and afterwards restores and fills in the k-space data in the second stage. Experiments were performed using different sampling schemes with noisy and noiseless cases of both phantom and real brain images. Based on various performance parameters, it has been shown that our designed technique outperforms the conventional techniques, like K-SVD with OMP, used in dictionary learning based MRI (DLMRI) reconstruction.
Magnetic field sensors are becoming an essential part of everyday life due to the improvements in their sensitivities and resolutions, while at the same time they have become compact, smaller in size and economical. In the work presented herein a Lorentz force based CMOS-MEMS magnetic field sensor is designed, fabricated and optically characterized. The sensor is fabricated by using CMOS thin layers and dry post micromachining is used to release the device structure and finally the sensor chip is packaged in DIP. The sensor consists of a shuttle which is designed to resonate in the lateral direction (first mode of resonance). In the presence of an external magnetic field, the Lorentz force actuates the shuttle in the lateral direction and the amplitude of resonance is measured using an optical method. The differential change in the amplitude of the resonating shuttle shows the strength of the external magnetic field. The resonance frequency of the shuttle is determined to be 8164 Hz experimentally and from the resonance curve, the quality factor and damping ratio are obtained. In an open environment, the quality factor and damping ratio are found to be 51.34 and 0.00973 respectively. The sensitivity of the sensor is determined in static mode to be 0.034 µm/mT when a current of 10 mA passes through the shuttle, while it is found to be higher at resonance with a value of 1.35 µm/mT at 8 mA current. Finally, the resolution of the sensor is found to be 370.37 µT.
Electroencephalography (EEG) signals have great impact on the development of assistive rehabilitation devices. These signals are used as a popular tool to investigate the functions and the behavior of the human motion in recent research. The study of EEG-based control of assistive devices is still in early stages. Although the EEG-based control of assistive devices has attracted a considerable level of attention over the last few years, few studies have been carried out to systematically review these studies, as a means of offering researchers and experts a comprehensive summary of the present, state-of-the-art EEG-based control techniques used for assistive technology. Therefore, this research has three main goals. The first aim is to systematically gather, summarize, evaluate and synthesize information regarding the accuracy and the value of previous research published in the literature between 2011 and 2018. The second goal is to extensively report on the holistic, experimental outcomes of this domain in relation to current research. It is systematically performed to provide a wealthy image and grounded evidence of the current state of research covering EEG-based control for assistive rehabilitation devices to all the experts and scientists. The third goal is to recognize the gap of knowledge that demands further investigation and to recommend directions for future research in this area.
In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.
Glucose oxidase (EC 1.1.3.4) sensors that have been developed and widely used for glucose monitoring have generally relied on electrochemical principle. In this study, the potential use of colorimetric method for glucose detection utilizing glucose oxidase-magnetic cellulose nanocrystals (CNCs) is explored. Magnetic cellulose nanocrystals (magnetic CNCs) were fabricated using iron oxide nanoparticles (IONPs) and cellulose nanocrystals (CNCs) via electrostatic self-assembly technique. Glucose oxidase was successfully immobilized on magnetic CNCs using carbodiimide-coupling reaction. About 33% of GOx was successfully attached on magnetic CNCs, and the affinity of GOx-magnetic CNCs to glucose molecules was slightly higher than free enzymes. Furthermore, immobilization does not affect the specificity of GOx-magnetic CNCs towards glucose and can detect glucose from 0.25 mM to 2.5 mM. Apart from that, GOx-magnetic CNCs stored at 4 °C for 4 weeks retained 70% of its initial activity and can be recycled for at least ten consecutive cycles.
We present the integration of a flow focusing microfluidic device in a dielectrophoretic application that based on a tapered aluminum microelectrode array (TAMA). The characterization and optimization method of microfluidic geometry performs the hydrodynamic flow focusing on the channel. The sample fluids are hydrodynamically focused into the region of interest (ROI) where the dielectrophoresis force (FDEP) is dominant. The device geometry is designed using 3D CAD software and fabricated using the micro-milling process combined with soft lithography using PDMS. The flow simulation is achieved using COMSOL Multiphysics 5.5 to study the effect of the flow rate ratio between the sample fluids (Q1) and the sheath fluids (Q2) toward the width of flow focusing. Five different flow rate ratios (Q1/Q2) are recorded in this experiment, which are 0.2, 0.4, 0.6, 0.8 and 1.0. The width of flow focusing is increased linearly with the flow rate ratio (Q1/Q2) for both the simulation and the experiment. At the highest flow rate ratio (Q1/Q2 = 1), the width of flow focusing is obtained at 638.66 µm and at the lowest flow rate ratio (Q1/Q2 = 0.2), the width of flow focusing is obtained at 226.03 µm. As a result, the flow focusing effect is able to reduce the dispersion of the particles in the microelectrode from 2000 µm to 226.03 µm toward the ROI. The significance of flow focusing on the separation of particles is studied using 10 and 1 µm polystyrene beads by applying a non-uniform electrical field to the TAMA at 10 VPP, 150 kHz. Ultimately, we are able to manipulate the trajectories of two different types of particles in the channel. For further validation, the focusing of 3.2 µm polystyrene beads within the dominant FDEP results in an enhanced manipulation efficiency from 20% to 80% in the ROI.
Diabetes patients are at risk of having chronic wounds, which would take months to years to resolve naturally. Chronic wounds can be countered using the electrical stimulation technique (EST) by dielectrophoresis (DEP), which is label-free, highly sensitive, and selective for particle trajectory. In this study, we focus on the validation of polystyrene particles of 3.2 and 4.8 μm to predict the behavior of keratinocytes to estimate their crossover frequency (fXO) using the DEP force (FDEP) for particle manipulation. MyDEP is a piece of java-based stand-alone software used to consider the dielectric particle response to AC electric fields and analyzes the electrical properties of biological cells. The prototypic 3.2 and 4.8 μm polystyrene particles have fXO values from MyDEP of 425.02 and 275.37 kHz, respectively. Fibroblast cells were also subjected to numerical analysis because the interaction of keratinocytes and fibroblast cells is essential for wound healing. Consequently, the predicted fXO from the MyDEP plot for keratinocyte and fibroblast cells are 510.53 and 28.10 MHz, respectively. The finite element method (FEM) is utilized to compute the electric field intensity and particle trajectory based on DEP and drag forces. Moreover, the particle trajectories are quantified in a high and low conductive medium. To justify the simulation, further DEP experiments are carried out by applying a non-uniform electric field to a mixture of different sizes of polystyrene particles and keratinocyte cells, and these results are well agreed. The alive keratinocyte cells exhibit NDEP force in a highly conductive medium from 100 kHz to 25 MHz. 2D/3D motion analysis software (DIPP-MotionV) can also perform image analysis of keratinocyte cells and evaluate the average speed, acceleration, and trajectory position. The resultant NDEP force can align the keratinocyte cells in the wound site upon suitable applied frequency. Thus, MyDEP estimates the Clausius-Mossotti factors (CMF), FEM computes the cell trajectory, and the experimental results of prototypic polystyrene particles are well correlated and provide an optimistic response towards keratinocyte cells for rapid wound healing applications.
The extraction of relevant wavelengths from a large dataset of Near Infrared Spectroscopy (NIRS) is a significant challenge in vibrational spectroscopy research. Nonetheless, this process allows the improvement in the chemical interpretability by emphasizing the chemical entities related to the chemical parameters of samples. With the complexity in the dataset, it may be possible that irrelevant wavelengths are still included in the multivariate calibration. This yields the computational process to become unnecessary complex and decreases the accuracy and robustness of the model. In multivariate analysis, Partial Least Square Regression (PLSR) is a method commonly used to build a predictive model from NIR spectral data. However, in the PLSR method and common commercial chemometrics software, there is no standard wavelength selection procedure applied to screen the irrelevant wavelengths. In this study, a new robust wavelength selection procedure called the modified VIP-MCUVE (mod-VIP-MCUVE) using Filter-Wrapper method and input scaling strategy is introduced. The proposed method combines the modified Variable Importance in Projection (VIP) and modified Monte Carlo Uninformative Variable Elimination (MCUVE) to calculate the scale matrix of the input variable. The modified VIP uses the orthogonal components of Partial Least Square (PLS) in investigating the informative variable in the model by applying the amount of variation both in X and y{SSX,SSY}, simultaneously. The modified MCUVE uses a robust reliability coefficient and a robust tolerance interval in the selection procedure. To evaluate the superiority of the proposed method, the classical VIP, MCUVE, and autoscaling procedure in classical PLSR were also included in the evaluation. Using artificial data with Monte Carlo simulation and NIR spectral data of oil palm (Elaeis guineensis Jacq.) fruit mesocarp, the study shows that the proposed method offers advantages to improve model interpretability, to be computationally extensive, and to produce better model accuracy.
Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.
Quorum sensing (QS), acts as one of the gene regulatory systems that allow bacteria to regulate their physiological activities by sensing the population density with synchronization of the signaling molecules that they produce. Here, we report a marine isolate, namely strain T47, and its unique AHL profile. Strain T47 was identified using 16S rRNA sequence analysis confirming that it is a member of Vibrio closely clustered to Vibrio sinaloensis. The isolated V. sinaloensis strain T47 was confirmed to produce N-butanoyl-L-homoserine lactone (C4-HSL) by using high resolution liquid chromatography tandem mass spectrometry. V. sinaloensis strain T47 also formed biofilms and its biofilm formation could be affected by anti-QS compound (cathechin) suggesting this is a QS-regulated trait in V. sinaloensis strain T47. To our knowledge, this is the first documentation of AHL and biofilm production in V. sinaloensis strain T47.
Quorum sensing (QS) is a bacterial cell-to-cell communication system controlling QS-mediated genes which is synchronized with the population density. The regulation of specific gene activity is dependent on the signaling molecules produced, namely N-acyl homoserine lactones (AHLs). We report here the identification and characterization of AHLs produced by bacterial strain ND07 isolated from a Malaysian fresh water sample. Molecular identification showed that strain ND07 is clustered closely to Pseudomonas cremoricolorata. Spent culture supernatant extract of P. cremoricolorata strain ND07 activated the AHL biosensor Chromobacterium violaceum CV026. Using high resolution triple quadrupole liquid chromatography-mass spectrometry, it was confirmed that P. cremoricolorata strain ND07 produced N-octanoyl-L-homoserine lactone (C8-HSL) and N-decanoyl-L-homoserine lactone (C10-HSL). To the best of our knowledge, this is the first documentation on the production of C10-HSL in P. cremoricolorata strain ND07.
We report the degradation of quorum sensing N-acylhomoserine lactone molecules by a bacterium isolated from a Malaysian marine water sample. MALDI-TOF and phylogenetic analysis indicated this isolate BM1 clustered closely to Labrenzia sp. The quorum quenching activity of this isolate was confirmed by using a series of bioassays and rapid resolution liquid chromatography analysis. Labrenzia sp. degraded a wide range of N-acylhomoserine lactones namely N-(3-hexanoyl)-L-homoserine lactone (C6-HSL), N-(3-oxohexanoyl)-L-homoserine lactone (3-oxo-C6-HSL) and N-(3-hydroxyhexanoyl)-L-homoserine lactone (3-hydroxy-C6-HSL). Re-lactonisation bioassays confirmed Labrenzia sp. BM1 degraded these signalling molecules efficiently via lactonase activity. To the best of our knowledge, this is the first documentation of a Labrenzia sp. capable of degrading N-acylhomoserine lactones and confirmation of its lactonase-based mechanism of action.
One obvious requirement for concerted action by a bacterial population is for an individual to be aware of and respond to the other individuals of the same species in order to form a response in unison. The term "quorum sensing" (QS) was coined to describe bacterial communication that is able to stimulate expression of a series of genes when the concentration of the signaling molecules has reached a threshold level. Here we report the isolation from aquatic environment of a bacterium that was later identified as Enterobacter sp.. Chromobacterium violaceum CV026 and Escherichia coli [pSB401] were used for preliminary screening of N-acyl homoserine lactone (AHL) production. The Enterobacter sp. isolated was shown to produce two types of AHLs as confirmed by analysis using high resolution tandem mass spectrometry. To the best of our knowledge, this is the first documentation of an Enterobacter sp. that produced both 3-oxo-C6-HSL and 3-oxo-C8-HSL as QS signaling molecules.
Enterobacter asburiae L1 is a quorum sensing bacterium isolated from lettuce leaves. In this study, for the first time, the complete genome of E. asburiae L1 was sequenced using the single molecule real time sequencer (PacBio RSII) and the whole genome sequence was verified by using optical genome mapping (OpGen) technology. In our previous study, E. asburiae L1 has been reported to produce AHLs, suggesting the possibility of virulence factor regulation which is quorum sensing dependent. This evoked our interest to study the genome of this bacterium and here we present the complete genome of E. asburiae L1, which carries the virulence factor gene virK, the N-acyl homoserine lactone-based QS transcriptional regulator gene luxR and the N-acyl homoserine lactone synthase gene which we firstly named easI. The availability of the whole genome sequence of E. asburiae L1 will pave the way for the study of the QS-mediated gene expression in this bacterium. Hence, the importance and functions of these signaling molecules can be further studied in the hope of elucidating the mechanisms of QS-regulation in E. asburiae. To the best of our knowledge, this is the first documentation of both a complete genome sequence and the establishment of the molecular basis of QS properties of E. asburiae.
N-acylhomoserine lactones (AHL) plays roles as signal molecules in quorum sensing (QS) in most Gram-negative bacteria. QS regulates various physiological activities in relation with population density and concentration of signal molecules. With the aim of isolating marine water-borne bacteria that possess QS properties, we report here the preliminary screening of marine bacteria for AHL production using Chromobacterium violaceum CV026 as the AHL biosensor. Strain T33 was isolated based on preliminary AHL screening and further identified by using 16S rDNA sequence analysis as a member of the genus Vibrio closely related to Vibrio brasiliensis. The isolated Vibrio sp. strain T33 was confirmed to produce N-hexanoyl-L-homoserine lactone (C6-HSL) and N-(3-oxodecanoyl)-L-homoserine lactone (3-oxo-C10 HSL) through high resolution tandem mass spectrometry analysis. We demonstrated that this isolate formed biofilms which could be inhibited by catechin. To the best of our knowledge, this is the first report that documents the production of these AHLs by Vibrio brasiliensis strain T33.
A multidrug-resistant clinical bacteria strain GB11 was isolated from a wound swab on the leg of a patient. Identity of stain GB11 as Pseudomonas aeruginosa was validated by using matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS). Detection of the production of signaling molecules, N-acylhomoserine lactones (AHLs), was conducted using three different bacterial biosensors. A total of four different AHLs were found to be produced by strain GB11, namely N-butyryl homoserine lactone (C4-HSL), N-hexanoylhomoserine lactone (C6-HSL), N-octanoyl homoserine lactone (C8-HSL) and N-3-oxo-dodecanoylhomoserine lactone (3-oxo-C12-HSL) using high resolution liquid chromatography tandem mass spectrometry (LC-MS/MS). Of these detected AHLs, 3-oxo-C12-HSL was found to be the most abundant AHL produced by P. aeruginosa GB11.