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Aiming at solving the problems of low recognition accuracy and poor practicability caused by the small amount of data and insufficient training of the network weights when the deep neural network algorithm is directly used to classify the 4-class motor-imagery electroencephalogram signals(MI-EEG), We combine the one-versus-the-rest common spatial pattern (OVR-CSP) algorithm and a novel convolution neural networks (CNN) algorithm to extract features and classify 4-class MI-EEG signals. Firstly, the original EEG signal data is truncated and expanded by using a fixed-size overlapping window, the features of intercepted sub-signals are extracted by CSP algorithm and the obtained feature vectors are merged as the input sample matrix of CNN. Secondly, CNN algorithm performs second feature extraction and final classification on the input sample matrix. Finally, the validity of the proposed algorithm was verified by the datasets IIIa of the BCI2005 competition. The average recognition accuracy of the three subjects has reached 91.9%, this is an improvement over other algorithms in recent years.

Electrical Capacitance Tomography (ECT) is a branch of Process Tomography based on capacitance-sensitive mechanism, which has the advantages of simple structure, low cost, high speed and relatively safe. To ensure the accuracy of image reconstruct, a large amount of research on sensitivity field is necessary. In this paper, we compared the differences of sensitivity fields between Electrical Capacitance Volume Tomography (ECVT) and Adaptive Electrical Capacitance Tomography (AECVT) system at first, studied the imaging characteristics of a regular symmetrical square sensor by a fast calculation method of three-dimensional AECVT sensitivity based on electric intensity, to simulate and analyze square tubes applied in some industries. Excited by different voltage envelopes synthetic plates adopted in AECVT system provides more variations and possibilities for imaging. The experiment explores the effects of different voltage packaging parameters on a series of characteristics such as the size and uniformity of the sensitivity field, in the hope that it will stimulate further research.

Adaptive electrical capacitance volume tomography (AECVT) is a powerful and flexible imaging technique that has received widespread attention in recent years. However, the accuracy of reconstructed image in AECVT system is still not ideal enough to meet the needs of industrial problems. Although differential evolution (DE) algorithm has played a great role in many other areas, very few studies have noted the role it plays in electrical capacitance volume tomography (ECVT) system, let alone AECVT system. Therefore, this paper contrasts the quality of the reconstruction image among the Particle Swarm Optimization algorithm and the DE algorithm with different strategies respectively applied in AECVT system when the adaptive plates are only excited by single voltage. Then we present a modified DE algorithm with a novel mutation strategy, which has a satisfied capability of avoiding convergence to the local optimum. By preliminary discussion, the modified DE algorithm is demonstrated to be slightly superior and stable to DE algorithm for single voltage excitation. In addition, in order to make full use of the flexibility of AECVT system, we conduct a series of experiments to find the best voltage envelope for the performance of proposed algorithm.

A key issue in brain-computer interface systems (BCI) based on motor-imagery electroencephalogram signals (MI-EEG) is the classification accuracy of EEG signals. Although deep learning (DL) methods have achieved great success in many research fields, only a limited number of works investigate its potential in BCI application research. In order to optimize the classification performance of MI-EEG signals, we propose a deep learning end-to-end classification model which is combined with convolutional neural network (CNN) and stacked autoencoders (SAE). A new type of CNN is introduced into the model for learning generalized features from time and spatial domains and for dimension reduction. Finally, the features extracted in the CNN are classified by a deep network SAE. The effectiveness of the proposed approach has been evaluated by using datasets of BCI competition data III and BCI competition data Ⅳ. Our results show that DL should be considered as an alternative to other state of art approaches, if the amount of data is large enough.

In order to realize the fast and accurate automated detection and classification of EEG signals during the normal, inter-ictal and ictal periods of patients, we propose an automated classification method for feature extraction of epileptic EEG signals based on the sample entropy and fast-slow-wave energy ratio (FSR)of each frequency sub-band in this paper. EEG signals are decomposed into frequency sub-bands using wavelet packet decomposition (WPD)in this method. The SampEn and FSR of different sub-bands are calculated, which are used to form feature vectors and these vectors are used as inputs to three different classifiers: support vector machine (SVM), k-nearest neighbor (KNN), and probabilistic neural network (PNN), to evaluate four famous classification problems. Our results show that the SVM classifier using radial basis function (RBF)is able to distinguish the above four problems with high accuracy more than 98.67%.

As an important research direction in the field of artificial intelligence, emotion recognition has become a hot topic in current research. Because of the use of multi-channel EEG acquisition equipment nowadays, which brings many problems in practical use and post-calculation, we have also studied channel selection. In this paper, the DEAP database is used as EEG data. The multi-feature fusion in a time domain and the composite features based on wavelet feature and information entropy are used as EEG features for emotion recognition. The average recognition accuracy reached 72.03% and 71.7% respectively. We also use the ReliefF algorithm to select EEG channels. Under the premise of a slight loss of emotional recognition preparation rate, we selected the optimal combination of 6 and 13 channels, and the brain data was reduced from 32 channels to 13 channels. It lays a foundation for the development of portable, wearable devices.

Most smart home products on the market today only stay at the level of multi-channel control of home appliances and have a single product function. For those parts that are not involved, this article describes a smart home cloud system based on Kinect. The system uses the STM32 as the control core and utilizes Kinect depth sensors to establish an identity and posture recognition system based on human skeletons, combined with support vector machine (SVM) and Internet of Things technology to realize smart home control systems. What is more, intelligent alarm system and child anti-drop window control system truly realize home Intelligence. After verification, the system can greatly improve the current level of intelligent home security capabilities, make up for the vacancy of the existing identity system, real intelligence for the home rather than intelligent control of home appliances is finally achieved.

Deep learning approaches have been used successfully in computer vision, natural language processing and speech processing. However, the number of studies that employ deep learning on brain-computer interface (BCI) based on electroencephalography (EEG) is very limited. In this paper, we present a deep learning approach for motor imagery (MI) EEG signal classification. We perform spatial projection using common spatial pattern (CSP) for the EEG signal and then temporal projection is applied to the spatially filtered signal. The signal is next fed to a single-layer neural network for classification. We apply backpropagation (BP) algorithm to fine-tune the parameters of the approach. The effectiveness of the proposed approach has been evaluated using datasets of BCI competition III and BCI competition IV.

Electrical capacitance volume tomography(ECVT) is a simple-structure and low-cost imaging technique. ECVT is used to reconstruct an image of the permittivity distribution in the three-dimensional(3-D) domain with the limited capacitance measurements and sensitivity map by a suitable reconstruction algorithm. We can get the predicted image of higher resolution by improving the sensitivity map and the capacitance value and image reconstruction methods. In this paper, we contrast the quality of the reconstruction image between ECVT system and adaptive electrical capacitance volume tomography(AECVT) system. The experiment sensors used for comparison are the same size but different plates: conventional and synthetic which are both excited by single voltage. Then, we investigate the effect of different voltage envelopes on the quality of reconstruction image in AECVT. The examples are presented to demonstrate that AECVT system is slightly superior to ECVT system for single voltage excitation. Further more, for AECVT system, it can improve the 3-D reconstruction performance by using voltage excitations of multiple amplitudes and changing the voltage position.

Using multi-channel EEG acquisition equipment for mental fatigue detection has several limitations to its application. Therefore, this paper will explore single-channel EEG signals based on portable EEG acquisition equipment to detect mental fatigue. In addition, improved single sub-band reconstruction of wavelet packet algorithm is proposed to eliminate frequency aliasing. This aliasing phenomenon occurs in the EEG signals analysis process when using the fast wavelet packet algorithm. Two feature parameters, 8 energy ratios of the EEG four rhythms and the variance of various sub-bands wavelet packet coefficients, are extracted. The experimental results show that 5 of the energy ratios and the variance of 3 sub-bands wavelet packet coefficients have significant differences between the awake and mental fatigue states and, therefore, taking these features as mental fatigue evaluation indices is feasible. In conclusion, using portable EEG acquisition equipment to conduct mental fatigue detection is a portable and effective method which is both practical and convenient.

Combining computer technology with traditional Chinese medical treatment mechanisms, the paper deals with a new approach to the design of medical sensor, designs the main circuit of an EMG sensor by evolution. A framework of evolvable sensor is proposed based on the combining of the characteristics of conventional sensor and evolvable hardware. Integrated with the hierarchical coding characteristic of GA and analog/digital circuit, hierarchical structures evolve method which suits the evolvement of analog sensor circuit is proposed. In the present case study low pass filter circuits are evolved successfully in the usage of an improved elitist preserving and adaptive GA through off-line evolution, whatpsilas more, simulated by OrCAD/Pspice. The preliminary experiments and results attempt the meaningful explore to the entire design of the evolvable sensor and the transform from EHW theory based on analog circuit to practical application.

Using edge detection to do news video retrieval may cause the caption location inaccurately. A method of news caption location is proposed in the paper, using C-mean clustering to extract the caption, then using vertical differential coefficient and median filter to weaken the image background and noise. Comparing with the existing algorithm, it is simple, it adapts to the complexity background. The experimental results show that the proposed algorithm of news caption location performs well.

In order to overcome the restrictions of conventional sensors, and get the mechanisms of smart sensing and self-adapting to ambient conditions, a framework of evolvable sensor is proposed based on the combining of the characteristics of conventional sensor and evolvable hardware. The paper introduces the preliminary research of evolvable sensor which has the capacity of self-adaptation and fault-tolerance based on the evolutionary algorithm and evolvable hardware. It is a kind of real intelligent sensor.