Muscle Fatigue Analysis With Optimized Complementary Ensemble Empirical Mode Decomposition and Multi-Scale Envelope Spectral Entropy

The preprocessing of surface electromyography (sEMG) signals with complementary ensemble empirical mode decomposition (CEEMD) improves frequency identification precision and temporal resolution, and lays a good foundation for feature extraction. However, a mode-mixing problem often occurs when the CEEMD decomposes an sEMG signal that exhibits intermittency and contains components with a near-by spectrum into intrinsic mode functions (IMFs). This paper presents a method called optimized CEEMD (OCEEMD) to solve this problem. The method integrates the least-squares mutual information (LSMI) and the chaotic quantum particle swarm optimization (CQPSO) algorithm in signal decomposition. It uses the LSMI to calculate the correlation between IMFs so as to reduce mode mixing and uses the CQPSO to optimize the standard deviation of Gaussian white noise so as to improve iteration efficiency. Then, useful IMFs are selected and added to reconstruct a de-noised signal. Finally, considering that the IMFs contain abundant frequency and envelope information, this paper extracts the multi-scale envelope spectral entropy (MSESEn) from the reconstructed sEMG signal. Some original sEMG signals, which were collected from experiments, were used to validate the methods. Compared with the CEEMD and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the OCEEMD effectively suppresses mode mixing between IMFs with rapid iteration. Compared with approximate entropy (ApEn) and sample entropy (SampEn), the MSESEn clearly shows a declining tendency with time and is sensitive to muscle fatigue. This suggests a potential use of this approach for sEMG signal preprocessing and the analysis of muscle fatigue

Abnormal Behavior Detection Using Trajectory Analysis in Camera Sensor Networks

Camera sensor networks have developed as a new technology for the wide-area video surveillance. In view of the limited power and computational capability of the camera nodes, the paper presents an abnormal behavior detection approach which is convenient and available for camera sensor networks. Trajectory analysis and anomaly modeling are carried out by single-node processing, whereas anomaly detection is performed by multinode voting. The main contributions of the proposed method are summarized as follows. First, target trajectories are reconstructed and represented as symbol sequences. Second, the sequences are taken into account using Markov model for building the transition probability matrix which can be used to automatically analyze abnormal behavior. Third, the final decision of anomaly detection is made through the majority voting of local results of individual camera nodes. Experimental results show that the proposed method can effectively estimate typical abnormal behaviors in real scenes

A Networking and Data Transmission Mode of Mobile Seismic Station

The networking and data transmission mode of the field mobile seismic station involves such matters as richness, real-time and effectiveness of seismic data. In this paper, we propose a networking and data transmission mode with CAN bus and embedded technology. In light of the effectiveness, distance, cost and construction relating to data transmission in the field environment, CAN bus was utilized to connect the scattered seismic collection nodes, and the embedded microprocessor ARM was used to manage collection nodes within a certain scope. In addition, both the multiple task operating system Linux and application framework Qt were employed to construct a seismic network local management system, which significantly simplified the management task of mobile station. Such networking and data transmission mode shows such features as lower cost, easy construction, and higher reliability and instantaneity, also enables the possibility of constructing a mobile collection station with larger scale. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.491

Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks

Data compression is very important in wireless sensor networks (WSNs) with the limited energy of sensor nodes. Data communication results in energy consumption most of the time; the lifetime of sensor nodes is usually prolonged by reducing data transmission and reception. In this paper, we propose a new Stacked RBM Auto-Encoder (Stacked RBM-AE) model to compress sensing data, which is composed of a encode layer and a decode layer. In the encode layer, the sensing data is compressed; and in the decode layer, the sensing data is reconstructed. The encode layer and the decode layer are composed of four standard Restricted Boltzmann Machines (RBMs). We also provide an energy optimization method that can further reduce the energy consumption of the model storage and calculation by pruning the parameters of the model. We test the performance of the model by using the environment data collected by Intel Lab. When the compression ratio of the model is 10, the average Percentage RMS Difference value is 10.04%, and the average temperature reconstruction error value is 0.2815 °C. The node communication energy consumption in WSNs can be reduced by 90%. Compared with the traditional method, the proposed model has better compression efficiency and reconstruction accuracy under the same compression ratio. Our experiment results show that the new neural network model can not only apply to data compression for WSNs, but also have high compression efficiency and good transfer learning ability

Compressive Background Modeling for Foreground Extraction

Robust and efficient foreground extraction is a crucial topic in many computer vision applications. In this paper, we propose an accurate and computationally efficient background subtraction method. The key idea is to reduce the data dimensionality of image frame based on compressive sensing and in the meanwhile apply sparse representation to build the current background by a set of preceding background images. According to greedy iterative optimization, the background image and background subtracted image can be recovered by using a few compressive measurements. The proposed method is validated through multiple challenging video sequences. Experimental results demonstrate the fact that the performance of our approach is comparable to those of existing classical background subtraction techniques

Attribute Value Weighted Average of One-Dependence Estimators

Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, semi-naive Bayesian classifiers which utilize one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth attribute dependencies; meanwhile, the probability estimation in ODEs is effective, thus leading to excellent performance. In previous studies, ODEs were exploited directly in a simple way. For example, averaged one-dependence estimators (AODE) weaken the attribute independence assumption by directly averaging all of a constrained class of classifiers. However, all one-dependence estimators in AODE have the same weights and are treated equally. In this study, we propose a new paradigm based on a simple, efficient, and effective attribute value weighting approach, called attribute value weighted average of one-dependence estimators (AVWAODE). AVWAODE assigns discriminative weights to different ODEs by computing the correlation between the different root attribute value and the class. Our approach uses two different attribute value weighting measures: the Kullback–Leibler (KL) measure and the information gain (IG) measure, and thus two different versions are created, which are simply denoted by AVWAODE-KL and AVWAODE-IG, respectively. We experimentally tested them using a collection of 36 University of California at Irvine (UCI) datasets and found that they both achieved better performance than some other state-of-the-art Bayesian classifiers used for comparison

CBN-VAE: A Data Compression Model with Efficient Convolutional Structure for Wireless Sensor Networks

Data compression is a useful method to reduce the communication energy consumption in wireless sensor networks (WSNs). Most existing neural network compression methods focus on improving the compression and reconstruction accuracy (i.e., increasing parameters and layers), ignoring the computation consumption of the network and its application ability in WSNs. In contrast, we pay attention to the computation consumption and application of neural networks, and propose an extremely simple and efficient neural network data compression model. The model combines the feature extraction advantages of Convolutional Neural Network (CNN) with the data generation ability of Variational Autoencoder (VAE) and Restricted Boltzmann Machine (RBM), we call it CBN-VAE. In particular, we propose a new efficient convolutional structure: Downsampling-Convolutional RBM (D-CRBM), and use it to replace the standard convolution to reduce parameters and computational consumption. Specifically, we use the VAE model composed of multiple D-CRBM layers to learn the hidden mathematical features of the sensing data, and use this feature to compress and reconstruct the sensing data. We test the performance of the model by using various real-world WSN datasets. Under the same network size, compared with the CNN, the parameters of CBN-VAE model are reduced by 73.88% and the floating-point operations (FLOPs) are reduced by 96.43% with negligible accuracy loss. Compared with the traditional neural networks, the proposed model is more suitable for application on nodes in WSNs. For the Intel Lab temperature data, the average Signal-to-Noise Ratio (SNR) value of the model can reach 32.51 dB, the average reconstruction error value is 0.0678 °C. The node communication energy consumption can be reduced by 95.83%. Compared with the traditional compression methods, the proposed model has better compression and reconstruction accuracy. At the same time, the experimental results show that the model has good fault detection performance and anti-noise ability. When reconstructing data, the model can effectively avoid fault and noise data

Structural assembly of the tailed bacteriophage ϕ29

The mature virion of the tailed bacteriophage ϕ29 is an ~33 MDa complex that contains more than 450 subunits of seven structural proteins assembling into a prolate head and a short non-contractile tail. Here, we report the near-atomic structures of the ϕ29 pre-genome packaging head (prohead), the mature virion and the genome-emptied virion. Structural comparisons suggest local rotation or oscillation of the head-tail connector upon DNA packaging and release. Termination of the DNA packaging occurs through pressure-dependent correlative positional and conformational changes in the connector. The funnel-shaped tail lower collar attaches the expanded narrow end of the connector and has a 180-Å long, 24-strand β barrel narrow stem tube that undergoes conformational changes upon genome release. The appendages form an interlocked assembly attaching the tail around the collar. The membrane active long loops at the distal end of the tail knob exit during the late stage of infection and form the cone-shaped tip of a largely hydrophobic helix barrel, prepared for membrane penetration.ISSN:2041-172