Sensors
1424-8220
Cơ quản chủ quản: Multidisciplinary Digital Publishing Institute (MDPI) , MDPI
Lĩnh vực:
Analytical ChemistryBiochemistryMedicine (miscellaneous)InstrumentationAtomic and Molecular Physics, and OpticsElectrical and Electronic EngineeringInformation Systems
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Single Channel EEG Artifact Identification Using Two-Dimensional Multi-Resolution Analysis As a diagnostic monitoring approach, electroencephalogram (EEG) signals can be decoded by signal processing methodologies for various health monitoring purposes. However, EEG recordings are contaminated by other interferences, particularly facial and ocular artifacts generated by the user. This is specifically an issue during continuous EEG recording sessions, and is therefore a key step in using EEG signals for either physiological monitoring and diagnosis or brain–computer interface to identify such artifacts from useful EEG components. In this study, we aim to design a new generic framework in order to process and characterize EEG recording as a multi-component and non-stationary signal with the aim of localizing and identifying its component (e.g., artifact). In the proposed method, we gather three complementary algorithms together to enhance the efficiency of the system. Algorithms include time–frequency (TF) analysis and representation, two-dimensional multi-resolution analysis (2D MRA), and feature extraction and classification. Then, a combination of spectro-temporal and geometric features are extracted by combining key instantaneous TF space descriptors, which enables the system to characterize the non-stationarities in the EEG dynamics. We fit a curvelet transform (as a MRA method) to 2D TF representation of EEG segments to decompose the given space to various levels of resolution. Such a decomposition efficiently improves the analysis of the TF spaces with different characteristics (e.g., resolution). Our experimental results demonstrate that the combination of expansion to TF space, analysis using MRA, and extracting a set of suitable features and applying a proper predictive model is effective in enhancing the EEG artifact identification performance. We also compare the performance of the designed system with another common EEG signal processing technique—namely, 1D wavelet transform. Our experimental results reveal that the proposed method outperforms 1D wavelet.
Tập 17 Số 12 - Trang 2895
Inertial Sensor-Based Gait Recognition: A Review With the recent development of microelectromechanical systems (MEMS), inertial sensors have become widely used in the research of wearable gait analysis due to several factors, such as being easy-to-use and low-cost. Considering the fact that each individual has a unique way of walking, inertial sensors can be applied to the problem of gait recognition where assessed gait can be interpreted as a biometric trait. Thus, inertial sensor-based gait recognition has a great potential to play an important role in many security-related applications. Since inertial sensors are included in smart devices that are nowadays present at every step, inertial sensor-based gait recognition has become very attractive and emerging field of research that has provided many interesting discoveries recently. This paper provides a thorough and systematic review of current state-of-the-art in this field of research. Review procedure has revealed that the latest advanced inertial sensor-based gait recognition approaches are able to sufficiently recognise the users when relying on inertial data obtained during gait by single commercially available smart device in controlled circumstances, including fixed placement and small variations in gait. Furthermore, these approaches have also revealed considerable breakthrough by realistic use in uncontrolled circumstances, showing great potential for their further development and wide applicability.
Tập 15 Số 9 - Trang 22089-22127
Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.
Tập 22 Số 3 - Trang 807
Biosensors for the Detection of Circulating Tumour Cells Metastasis is the cause of most cancer deaths. Circulating tumour cells (CTCs) are cells released from the primary tumour into the bloodstream that are considered the main promoters of metastasis. Therefore, these cells are targets for understanding tumour biology and improving clinical management of the disease. Several techniques have emerged in recent years to isolate, detect, and characterise CTCs. As CTCs are a rare event, their study requires multidisciplinary considerations of both biological and physical properties. In addition, as isolation of viable cells may give further insights into metastatic development, cell recovery must be done with minimal cell damage. The ideal system for CTCs analysis must include maximum efficiency of detection in real time. In this sense, new approaches used to enrich CTCs from clinical samples have provided an important improvement in cell recovery. However, this progress should be accompanied by more efficient strategies of cell quantification. A range of biosensor platforms are being introduced into the technology for CTCs quantification with promising results. This review provides an update on recent progress in CTCs identification using different approaches based on sensor signaling.
Tập 14 Số 3 - Trang 4856-4875
Biosensors and Bio-Bar Code Assays Based on Biofunctionalized Magnetic Microbeads This review paper reports the applications of magnetic microbeads in biosensors and bio-bar code assays. Affinity biosensors are presented through different types of transducing systems: electrochemical, piezo electric or magnetic ones, applied to immunodetection and genodetection. Enzymatic biosensors are based on biofunctionalization through magnetic microbeads of a transducer, more often amperometric, potentiometric or conductimetric. The bio-bar code assays relie on a sandwich structure based on specific biological interaction of a magnetic microbead and a nanoparticle with a defined biological molecule. The magnetic particle allows the separation of the reacted target molecules from unreacted ones. The nanoparticles aim at the amplification and the detection of the target molecule. The bio-bar code assays allow the detection at very low concentration of biological molecules, similar to PCR sensitivity.
Tập 7 Số 4 - Trang 589-614
Electrochemical Performance of a Carbon Nanotube/La-Doped TiO2 Nanocomposite and its Use for Preparation of an Electrochemical Nicotinic Acid Sensor A carbon nanotube/La-doped TiO2 (La-TiO2) nanocomposite (CLTN) was prepared by a procedure similar to a complex/adsorption process. Scanning electron microscopy (SEM) images show that the La-TiO2 distributes on the carbon nanotube walls. The CLTN was mixed with paraffin to form a CLTN paste for the CLTN paste electrode (CLTNPE). The electrochemical characteristics of CLTNPE were compared with that of conventional carbon electrodes such as the carbon paste electrode (CPE) and glass carbon electrode (GC). The CLTNPE exhibits electrochemical activity and was used to investigate the electrochemistry of nicotinic acid (NA). The modified electrode has a strong electrocatalytic effect on the redox of NA. The cyclic voltammetry (CV) redox potential of NA at the CLTNPE is 320 mV. The oxidation process of NA on the CLTNPE is pH dependent. A sensitive chronoamperometric response for NA was obtained covering a linear range from 1.0×10-6 mol·L-1 to 1.2×10-4 mol·L-1, with a detection limit of 2.7×10-7 mol·L-1. The NA sensor displays a remarkable sensitivity and stability. The mean recovery of NA in the human urine is 101.8%, with a mean variation coefficient (RSD) of 2.6%.
Tập 8 Số 11 - Trang 7085-7096
Muscle Co-Activation around the Knee during Different Walking Speeds in Healthy Females The purpose of this study was to examine the changes in co-activation around the knee joint during different walking speeds in healthy females using the co-activation index. Ten healthy females (age: 21.20 ± 7.21 years, height: 164.00 ± 4.00 cm, mass: 60.60 ± 4.99 kg) participated in this study and performed three walking speeds (slow, normal, and fast). A Qualisys 11-camera motion analysis system sampling at a frequency of 200 Hz was synchronized with a Trigno EMG Wireless system operating at a 2000 Hz sampling frequency. A significant decrease in the co-activation index of thigh muscles was observed between the slow and fast, and between the normal and fast, walking speeds during all walking phases. A non-significant difference was observed between the slow and normal walking speeds during most walking phases, except the second double support phase, during which the difference was significant. A negative relationship was found between walking speed and the co-activation index of thigh muscles in all speeds during walking phases: first double support (r = −0.3386, p < 0.001), single support (r = −0.2144, p < 0.01), second double support (r = −0.4949, p < 0.001), and Swing (r = −0.1639, p < 0.05). In conclusion, the results indicated high variability of thigh muscle co-activation in healthy females during the different walking speeds, and a decrease in the co-activation of the thigh muscles with the increase of speed.
Tập 21 Số 3 - Trang 677
Decision-Making of Underwater Cooperative Confrontation Based on MODPSO This paper proposes a multi-objective decision-making model for underwater countermeasures based on a multi-objective decision theory and solves it using the multi-objective discrete particle swarm optimization (MODPSO) algorithm. Existing decision-making models are based on fully allocated assignment without considering the weapon consumption and communication delay, which does not conform to the actual naval combat process. The minimum opponent residual threat probability and minimum own-weapon consumption are selected as two functions of the multi-objective decision-making model in this paper. Considering the impact of the communication delay, the multi-objective discrete particle swarm optimization (MODPSO) algorithm is proposed to obtain the optimal solution of the distribution scheme with different weapon consumptions. The algorithm adopts the natural number coding method, and the particle corresponds to the confrontation strategy. The simulation result shows that underwater communication delay impacts the decision-making selection. It verifies the effectiveness of the proposed model and the proposed multi-objective discrete particle swarm optimization algorithm.
Tập 19 Số 9 - Trang 2211
Raman Microspectroscopy of Individual Algal Cells: Sensing Unsaturation of Storage Lipids in vivo Algae are becoming a strategic source of fuels, food, feedstocks, and biologically active compounds. This potential has stimulated the development of innovative analytical methods focused on these microorganisms. Algal lipids are among the most promising potential products for fuels as well as for nutrition. The crucial parameter characterizing the algal lipids is the degree of unsaturation of the constituent fatty acids quantified by the iodine value. Here we demonstrate the capacity of the spatially resolved Raman microspectroscopy to determine the effective iodine value in lipid storage bodies of individual living algal cells. The Raman spectra were collected from three selected algal species immobilized in an agarose gel. Prior to immobilization, the algae were cultivated in the stationary phase inducing an overproduction of lipids. We employed the characteristic peaks in the Raman scattering spectra at 1,656 cm−1 (cis C=C stretching mode) and 1,445 cm−1 (CH2 scissoring mode) as the markers defining the ratio of unsaturated-to-saturated carbon-carbon bonds of the fatty acids in the algal lipids. These spectral features were first quantified for pure fatty acids of known iodine value. The resultant calibration curve was then used to calculate the effective iodine value of storage lipids in the living algal cells from their Raman spectra. We demonstrated that the iodine value differs significantly for the three studied algal species. Our spectroscopic estimations of the iodine value were validated using GC-MS measurements and an excellent agreement was found for the Trachydiscus minutus species. A good agreement was also found with the earlier published data on Botryococcus braunii. Thus, we propose that Raman microspectroscopy can become technique of choice in the rapidly expanding field of algal biotechnology.
Tập 10 Số 9 - Trang 8635-8651
Multi-Level Context Pyramid Network for Visual Sentiment Analysis Sharing our feelings through content with images and short videos is one main way of expression on social networks. Visual content can affect people’s emotions, which makes the task of analyzing the sentimental information of visual content more and more concerned. Most of the current methods focus on how to improve the local emotional representations to get better performance of sentiment analysis and ignore the problem of how to perceive objects of different scales and different emotional intensity in complex scenes. In this paper, based on the alterable scale and multi-level local regional emotional affinity analysis under the global perspective, we propose a multi-level context pyramid network (MCPNet) for visual sentiment analysis by combining local and global representations to improve the classification performance. Firstly, Resnet101 is employed as backbone to obtain multi-level emotional representation representing different degrees of semantic information and detailed information. Next, the multi-scale adaptive context modules (MACM) are proposed to learn the sentiment correlation degree of different regions for different scale in the image, and to extract the multi-scale context features for each level deep representation. Finally, different levels of context features are combined to obtain the multi-cue sentimental feature for image sentiment classification. Extensive experimental results on seven commonly used visual sentiment datasets illustrate that our method outperforms the state-of-the-art methods, especially the accuracy on the FI dataset exceeds 90%.
Tập 21 Số 6 - Trang 2136