Robust Ellipse Fitting Based on Maximum Correntropy Criterion With Variable Center.

The presence of radically irregular data points (RIDPs), which are referred to as the subset of measurements that represents no or little information, can significantly degrade the performance of ellipse fitting methods. We develop an ellipse fitting method that is robust to RIDPs based on the maximum correntropy criterion with variable center (MCC-VC), where an adaptable Laplacian kernel is used. For single ellipse fitting, we formulate a non-convex optimization problem and divide it into two subproblems, one to estimate the kernel bandwidth and the other the kernel center. We design sufficiently accurate convex approximation to each subproblem that will lead to computationally efficient closed-form solutions. The two subproblems are solved in an alternate manner until convergence is reached. We also investigate coupled ellipses fitting. While there exist multiple ellipses fitting methods in the literature, we develop a coupled ellipses fitting method by exploiting the underlying special structure, where the associations between the data points and ellipses are absent in the problem. The proposed method first introduces an association vector for each data point and then formulates a non-convex mixed-integer optimization problem to establish the data associations, which is approximately solved by relaxing it into a second-order cone program. Using the estimated data associations, we then extend the proposed single ellipse fitting method to accomplish the final coupled ellipses fitting. The proposed method is shown to perform significantly better than the existing methods using both simulated data and real images.

Approaches to characterizing oscillatory burst detection algorithms for electrophysiological recordings.

BACKGROUND: Cognitive processes are associated with fast oscillations of the local field potential and electroencephalogram. There is a growing interest in targeting them because these are disrupted by aging and disease. This has proven challenging because they often occur as short-lasting bursts. Moreover, they are obscured by broad-band aperiodic activity reflecting other neural processes. These attributes have made it exceedingly difficult to develop analytical tools for estimating the reliability of detection methods. NEW METHOD: To address this challenge, we developed an open-source toolkit with four processing steps, that can be tailored to specific brain states and individuals. First, the power spectrum is decomposed into periodic and aperiodic components, each of whose properties are estimated. Second, the properties of the transient oscillatory bursts that contribute to the periodic component are derived and optimized to account for contamination from the aperiodic component. Third, using the burst properties and aperiodic power spectrum, surrogate neural signals are synthesized that match the observed signal's spectrotemporal properties. Lastly, oscillatory burst detection algorithms run on the surrogate signals are subjected to a receiver operating characteristic analysis, providing insight into their performance. RESULTS: The characterization algorithm extracted features of oscillatory bursts across multiple frequency bands and brain regions, allowing for recording-specific evaluation of detection performance. For our dataset, the optimal detection threshold for gamma bursts was found to be lower than the one commonly used. COMPARISON WITH EXISTING METHODS: Existing methods characterize the power spectrum, while ours evaluates the detection of oscillatory bursts. CONCLUSIONS: This pipeline facilitates the evaluation of thresholds for detection algorithms from individual recordings.

Non-Invasive Heart Rate Estimation From Ballistocardiograms Using Bidirectional LSTM Regression.

Non-invasive heart rate estimation is of great importance in daily monitoring of cardiovascular diseases. In this paper, a bidirectional long short term memory (bi-LSTM) regression network is developed for non-invasive heart rate estimation from the ballistocardiograms (BCG) signals. The proposed deep regression model provides an effective solution to the existing challenges in BCG heart rate estimation, such as the mismatch between the BCG signals and ground-truth reference, multi-sensor fusion and effective time series feature learning. Allowing label uncertainty in the estimation can reduce the manual cost of data annotation while further improving the heart rate estimation performance. Compared with the state-of-the-art BCG heart rate estimation methods, the strong fitting and generalization ability of the proposed deep regression model maintains better robustness to noise (e.g., sensor noise) and perturbations (e.g., body movements) in the BCG signals and provides a more reliable solution for long term heart rate monitoring.

Robust Ellipse Fitting With Laplacian Kernel Based Maximum Correntropy Criterion.

The performance of ellipse fitting may significantly degrade in the presence of outliers, which can be caused by occlusion of the object, mirror reflection or other objects in the process of edge detection. In this paper, we propose an ellipse fitting method that is robust against the outliers, and thus maintaining stable performance when outliers can be present. We formulate an optimization problem for ellipse fitting based on the maximum entropy criterion (MCC), having the Laplacian as the kernel function from the well-known fact that the l1 -norm error measure is robust to outliers. The optimization problem is highly nonlinear and non-convex, and thus is very difficult to solve. To handle this difficulty, we divide it into two subproblems and solve the two subproblems in an alternate manner through iterations. The first subproblem has a closed-form solution and the second one is cast as a convex second-order cone program (SOCP) that can reach the global solution. By so doing, the alternate iterations always converge to an optimal solution, although it can be local instead of global. Furthermore, we propose a procedure to identify failed fitting of the algorithm caused by local convergence to a wrong solution, and thus, it reduces the probability of fitting failure by restarting the algorithm at a different initialization. The proposed robust ellipse fitting method is next extended to the coupled ellipses fitting problem. Both simulated and real data verify the superior performance of the proposed ellipse fitting method over the existing methods.

Life cycle assessment (LCA) of electrically-enhanced POME filtration: Environmental impacts of conductive-membrane formulation and process operating parameters.

This study assessed the environmental impacts of the formulation of graphene oxide (GO)/multi-walled carbon nanotubes (MWCNTs) conductive membranes and of the process operating parameters of electrically-enhanced palm oil mill effluent (POME) filtration. Two different analyses approaches were employed, cradle-to-gate approach for conductive membrane production and gate-to-gate approach for the POME filtration process. The parameters in conductive-membrane formulation (e.g. the weight ratio of carbon nanomaterials, and concentration of GO/MWCNT nanohybrids) and process operating parameters (e.g. electric field strength and electricity operating mode) were investigated. The findings herein are twofold. Firstly, for the fabrication of GO/MWCNT conductive membranes, the best weight ratio of GO:MWCNTs was found to be 1:9, given its superior membrane electrical conductivity with lower environmental impacts by 8.51% compared to pristine MWCNTs. The most suitable concentration of carbon nanomaterials was found to be 5 wt%, given its lowest impacts on resource depletion, human health, and ecosystems. Secondly, for the electrically-enhanced POME filtration, the optimum process operating parameters were found to be the application of an electric field of 300 V/cm in the continuous mode, given its lower environmental impacts (22.99%-89.30%) secondary to its requirement of the least electricity to produce permeate. The present study has established not only the optimized conditions in membrane formulation but also the operating parameters of electrically-enhanced filtration; such findings enable the use of cleaner production and sustainable approach to minimize fouling for industrial applications, whilst maintaining excellent efficiency.

Classification of Brainwaves Using Convolutional Neural Network.

Classification of brainwaves in recordings is of considerable interest to neuroscience and medical communities. Classification techniques used presently depend on the extraction of low-level features from the recordings, which in turn affects the classification performance. To alleviate this problem, this paper proposes an end-to-end approach using Convolutional Neural Network (CNN) which has been shown to detect complex patterns in a signal by exploiting its spatiotemporal nature. The present study uses time and frequency axes for the classification using synthesized Local Field Potential (LFP) data. The results are analyzed and compared with the FFT technique. In all the results, the CNN outperforms the FFT by a significant margin especially when the noise level is high. This study also sheds light on certain signal characteristics affecting network performance.

Monitoring the Relative Blood Pressure Using a Hydraulic Bed Sensor System.

We propose a nonwearable hydraulic bed sensor system that is placed underneath the mattress to estimate the relative systolic blood pressure of a subject, which only differs from the actual blood pressure by a scaling and an offset factor. Two types of features are proposed to obtain the relative blood pressure, one based on the strength and the other on the morphology of the bed sensor ballistocardiogram pulses. The relative blood pressure is related to the actual by a scale and an offset factor that can be obtained through calibration. The proposed system is able to extract the relative blood pressure more accurately with a less sophisticated sensor system compared to those from the literature. We tested the system using a dataset collected from 48 subjects right after active exercises. Comparison with the ground truth obtained from the blood pressure cuff validates the promising performance of the proposed system, where the mean correlation between the estimate and the ground truth is near to 90% for the strength feature and 83% for the morphology feature.

Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring From Ballistocardiograms.

A multiple instance dictionary learning approach, dictionary learning using functions of multiple instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a "heartbeat concept" that represents an individual's personal ballistocardiogram heartbeat pattern. DL-FUMI formulates heartbeat detection and heartbeat characterization as a multiple instance learning problem to address the uncertainty inherent in aligning BCG signals with ground truth during training. Experimental results show that the estimated heartbeat concept obtained by DL-FUMI is an effective heartbeat prototype and achieves superior performance over comparison algorithms.

Enhancement of Chlorella vulgaris harvesting via the electro-coagulation-flotation (ECF) method.

This article explores the potential of using an electro-coagulation-flotation (ECF) harvester to allow flotation of microalgae cells for surface harvesting. A response surface methodology (RSM) model was used to optimize ECF harvesting by adjusting electrode plate material, electrode plate number, charge of the electrodes, electrolyte concentration, and pH value of the culture solution. The result revealed that three aluminum electrode plates (one anode and two cathodes), brine solution (8 g/L), and acidity (pH = 4) of culture solution (optimized ECF harvester) The highest flocculant concentration was measured at 2966 mg/L after 60 min and showed a 79.8 % increase of flocculation concentration. Such results can provide a basis for designing a large-scale microalgae harvester for commercial use in the future.

Maximization of cell growth and lipid production of freshwater microalga Chlorella vulgaris by enrichment technique for biodiesel production.

Chlorella vulgaris was cultivated under limitation and starvation and under controlled conditions using different concentrations of nitrate (NaNO3) and phosphate (K2HPO4 and KH2PO4) chemicals in modified Bold basal medium (BBM). The biomass and lipid production responses to different media were examined in terms of optical density, cell density, dry biomass, and lipid productivity. In the 12-day batch culture period, the highest biomass productivity obtained was 72.083 mg L-1 day-1 under BBM - NcontrolPlimited condition. The highest lipid content, lipid concentration, and lipid productivity obtained were 53.202 %, 287.291 mg/L, and 23.449 mg L-1 day-1 under BBM - NControlPDeprivation condition, respectively. Nitrogen had a major effect in the biomass concentration of C. vulgaris, while no significant effect was found for phosphorus. Nitrogen and phosphorus starvation was found to be the strategy affecting the lipid accumulation and affected the lipid composition of C. vulgaris cultures.

Cultivation of Chlorella vulgaris in Column Photobioreactor for Biomass Production and Lipid Accumulation.

Microalgae have been used as energy resources in recent decades to mitigate the global energy crisis. As the demand for pure microalgae strains for commercial use increases, designing an effective photobioreactor (PBR) for mass cultivation is important. Chlorella vulgaris, a local freshwater microalga, was used to study the algal biomass cultivation and lipid production using various PBR configurations (bubbling, air-lift, porous air-lift). The results show that a bubbling column design is a better choice for the cultivation of Chlorella vulgaris than an air-lift one. The highest biomass concentration in the bubbling PBR was 0.78 g/L while the air-lift PBR had a value of 0.09 g/L. Key operating parameters, including draft-tube length and bubbling flowrate, were then optimized based on biomass production and lipid yield. The highest lipid content was in the porous air-lift PBR and the air-lift PBR with shorter draft tube (35 cm) was also better than a longer one (50 cm) for algal cultivation, but the microalgae attachment on the inner tube of PBR always occurred. The highest biomass concentration could be produced under the highest gas flowrate of 2.7 L/min, whereas the lowest dry cell mass was under the lowest gas flowrate of 0.2 L/min.

Testing non-wearable fall detection methods in the homes of older adults.

In this paper, we describe two longitudinal studies in which fall detection sensor technology was tested in the homes of older adults. The first study tested Doppler radar, a two-webcam system, and a depth camera system in ten apartments for two years. This continuous data collection allowed us to investigate the real-world setting of target users and compare the advantages and limitations of each sensor modality. Based on this study, the depth camera was chosen for a current ongoing study in which depth camera systems have been installed in 94 additional older adult apartments. We include a discussion of the different sensor systems, the pros and cons of each, and results of the fall detection and false alarms in the older adult homes.

Scenedesmus quadricauda for Nutrient Removal and Lipid Production in Wastewater.

Scenedesmus quadricauda, a local freshwater microalga, was used to treat primary settled and filtrate wastewater and to produce algal lipid. For the primary settled wastewater, the maximum biomass concentrations of acclimated and unacclimated microalgae were 0.995 g/L and 0.940 g/L, respectively. Over 90% orthophosphate and 95% ammonia nitrogen in the acclimated and unacclimated cultures, respectively, were removed after five days. The lipid contents of the microalgae were higher than 13% in all cultures. The highest growth rate occurred in the 25% filtrate culture. Over 80% phosphate was removed under the 25% and 50% filtrate cultures within six days, while over 90% ammonia nitrogen was removed within five days under both conditions. The lipid content was the highest (18.1%) under the 50% filtrate condition. C16:0, C18:2n6, and C18:3n3 were dominant fatty acids. In conclusion, S. quadricauda is a viable candidate for wastewater treatment and lipid production.

Robust heartbeat detection from in-home ballistocardiogram signals of older adults using a bed sensor.

We propose a simple and robust method to detect heartbeats using the ballistocardiogram (BCG) signal that is produced by a hydraulic bed sensor placed under the mattress. The proposed method is found beneficial especially when the BCG signal does not display consistent J-peaks, which can often be the case for overnight, in-home monitoring, especially with frail seniors. Heartbeat detection is based on the short-time energy of the BCG signal. Compared with previous methods that rely on the J-peaks observed from the BCG amplitude, we are able to achieve considerable improvement even when significant distortions are present. Test results are included for different BCG waveform patterns from older adults.

Doppler radar fall activity detection using the wavelet transform.

We propose in this paper the use of Wavelet transform (WT) to detect human falls using a ceiling mounted Doppler range control radar. The radar senses any motions from falls as well as nonfalls due to the Doppler effect. The WT is very effective in distinguishing the falls from other activities, making it a promising technique for radar fall detection in nonobtrusive inhome elder care applications. The proposed radar fall detector consists of two stages. The prescreen stage uses the coefficients of wavelet decomposition at a given scale to identify the time locations in which fall activities may have occurred. The classification stage extracts the time-frequency content from the wavelet coefficients at many scales to form a feature vector for fall versus nonfall classification. The selection of different wavelet functions is examined to achieve better performance. Experimental results using the data from the laboratory and real inhome environments validate the promising and robust performance of the proposed detector.

CBAP promotes thymocyte negative selection by facilitating T-cell receptor proximal signaling.

T-cell receptor (TCR)-transduced signaling is critical to thymocyte development at the CD4/CD8 double-positive stage, but the molecules involved in this process are not yet fully characterized. We previously demonstrated that GM-CSF/IL-3/IL-5 receptor common ß-chain-associated protein (CBAP) modulates ZAP70-mediated T-cell migration and adhesion. On the basis of the high expression of CBAP during thymocyte development, we investigated the function of CBAP in thymocyte development using a CBAP knockout mouse. CBAP-deficient mice showed normal early thymocyte development and positive selection. In contrast, several negative selection models (including TCR transgene, superantigen staphylococcal enterotoxin B, and anti-CD3 antibody treatment) revealed an attenuation of TCR-induced thymocyte deletion in CBAP knockout mice. This phenotype correlated with a reduced accumulation of BIM upon TCR crosslinking in CBAP-deficient thymocytes. Loss of CBAP led to reduced TCR-induced phosphorylation of proteins involved in both proximal and distal signaling events, including ZAP70, LAT, PLCγ1, and JNK1/2. Moreover, TCR-induced association of LAT signalosome components was reduced in CBAP-deficient thymocytes. Our data demonstrate that CBAP is a novel component in the TCR signaling pathway and modulates thymocyte apoptosis during negative selection.

Efficient source separation algorithms for acoustic fall detection using a microsoft kinect.

Falls have become a common health problem among older adults. In previous study, we proposed an acoustic fall detection system (acoustic FADE) that employed a microphone array and beamforming to provide automatic fall detection. However, the previous acoustic FADE had difficulties in detecting the fall signal in environments where interference comes from the fall direction, the number of interferences exceeds FADE's ability to handle or a fall is occluded. To address these issues, in this paper, we propose two blind source separation (BSS) methods for extracting the fall signal out of the interferences to improve the fall classification task. We first propose the single-channel BSS by using nonnegative matrix factorization (NMF) to automatically decompose the mixture into a linear combination of several basis components. Based on the distinct patterns of the bases of falls, we identify them efficiently and then construct the interference free fall signal. Next, we extend the single-channel BSS to the multichannel case through a joint NMF over all channels followed by a delay-and-sum beamformer for additional ambient noise reduction. In our experiments, we used the Microsoft Kinect to collect the acoustic data in real-home environments. The results show that in environments with high interference and background noise levels, the fall detection performance is significantly improved using the proposed BSS approaches.

Assessment of heavy metals/metalloid (As, Pb, Cd, Ni, Zn, Cr, Cu, Mn) concentrations in edible fish species tissue in the Pearl River Delta (PRD), China.

The major aim of this study was to investigate heavy metal content of edible fish in the PRD. Eleven species of fish (consisting of 711 individuals) [catfish (Clarias fuscus), tilapia (Oreochromis mossambicus), grass carp (Ctenopharyngodon idellus), bighead carp (Aristichthys nobilis), mandarin fish (Siniperca kneri), snakehead (Channa asiatiea), black bass (Micropterus salmoides), mangrove snapper (Lutjanus griseus), star snapper (Lutjanu stellatus), snubnose pompano (Trachinotus blochii) and orange-spotted grouper (Epinephelus coioides)] were collected for the analyses of heavy metals. Overall concentrations (mg/kg, ww) in the fish muscles were: As (0.03-1.53), Pb (0.03-8.62), Cd (0.02-0.06), Ni (0.44-9.75), Zn (15.7-29.5), Cr (0.22-0.65), Cu (0.79-2.26), Mn (0.82-6.91). Significant level of Pb were found in tilapia at all locations. It is recommended that heavy metal concentrations in different fish species must be determined on a regular basis in the future so as to reduce human health risks from acute and chronic food intoxication.

A theoretical study on the placement of microphone arrays for improving the localization accuracy of a fall.

Falling is a common health problem for elderly. To address the problem, we are currently developing an acoustic fall detection system, FADE, which automatically detects a fall and reports to the caregiver. Of great importance of the fall detection system is a low false alarm rate that can be achieved by knowing where the acoustic signal comes from. The previous work showed the sound source localization can be determined by using an 8-microphone circular array, but the accuracy varies when placing the array at different positions. To further improve the localization accuracy, a second array can be added. In this paper we investigate the variations of localization accuracy of a fall signal when one or two arrays are placed at different positions in a room. The accuracy is evaluated by the Cramér-Rao Lower Bound (CRLB). The CRLB aids the determination of the best theoretical placement of one or two arrays in a room for locating the sound source.

Computed tomography analysis of knee pose and geometry before and after total knee arthroplasty.

Using a three-dimensional (3D) modality to image patients' knees before and after total knee arthroplasty (TKA) allows researchers and clinicians to evaluate causes of pain after TKA, differences in implant design, and changes in the articular geometry as a result of surgery. Computed tomography (CT) has not been fully utilized to date for evaluating the knee after TKA due to metal artifacts obscuring part of the image. We describe an accurate, validated protocol, which has been implemented in vivo, that improves visibility of the patellofemoral joint, matches implant models automatically in 3D, segments preoperative bone semi-automatically, detects and sets coordinate systems automatically, determines the six degrees of freedom of knee pose and geometry, and allows for multiple other measurements that are clinically relevant. Subjects are imaged at 0° and 30° knee flexion, while pushing on a custom-made knee rig to provide partial loadbearing. With some modifications, the protocol can be adopted by any group with access to a CT scanner and image analysis software, allowing for the investigation of numerous clinical and biomechanical questions.