Miracle-dz/UniFAR-FTUs huggingface dataset:
The Detection of P300 Potential Based on Deep Belief Network

It is a key issue in P300 Brain-computer interface (BCI)system that how to extract effective features of P300 potential from raw electroencephalography (EEG)data and get better classification performance. In this paper, we proposed a P300 potential detection method based on deep belief network (DBN)which can extract useful feature information from raw data without data preprocessing. First we converted the original EEG data as input data according to the time and spatial features, then exacted the features by the constructed DBN model, and finally realized the classification of P300 potential by the Softmax classifier. The results showed that the DBN has a good feature learning ability for the P300 potential and has achieved the better detection results.
Assessment of Intracardiac Blood Flow in Patients with Functional Single Ventricle after Fontan Surgery and Children Volunteers

Repaired Fontan patients always suffer from the risk of long-term complications in a long time. It is so essential that followup and periodic assessment of cardiac function using MRI. 4D flow MRI sequence evaluate the temporal evolution of various quantitative information about blood flow patterns within a 3D volume and to assess intracardiac flow functional components. In our study, we intend to analyze on 3D intracardiac hemodynamic analysis of six repaired Fontan patients and six children volunteers, from mitral valve inflow to aortic outflow. The four different component of ventricular blood flow were found in intracardiac model. We found the percentage of direct flow has a significantly decrease and residual volume has a significantly increase in post-Fontan patients. It is feasible to analyze of 4D flow data allows analysis of difference functional flow components between normal LV volunteers and post-Fontan patients. The assessment of flow component may be sensitive index for early assessment of the hemodynamics difference when ejection fraction did not decrease.
A Crop-Based Multi-Branch Network for Matching Cost Computation

Stereo matching is a challenging problem in computer vision. An excellent matching cost computation method is useful for enhancing stereo matching performance. Traditional matching cost computation is lack of robustness. In this paper, we propose a crop-based multi-branch convolution neural network (CBMBNet) for robust matching cost computation. We employ ResNeXt block for feature extraction and introduce a new crop-based multi-branch network structure to enhance the accuracy of matching. Several post-processing techniques are used further to enhance disparity map equality. The experimental results show that the proposed CBMBNet can reduce error rates than MC-CNN-fst and MC-CNN-acrt approaches based on Middlebury stereo data set.
Detection of the Nonlinearity in Human ECG Physiological Signals

In this paper, we defined and investigated multifractal singularity spectrum area lnsf(α) of the 12-lead ECG signals taken from healthy and diseased human subjects of different age stages. One-way ANOV A analysis reveals all the crowds are statistically distinguishable using this indicator. The results showed that the arithmetic mean values of lnsf(a) from the 12 leads ECG signals is significantly larger for healthy young people but smaller for heart diseased sufferer. While the dispersion of lnsf(a) from the 12 leads ECG signals is obviously smaller for diseased sufferer but larger for healthy people. We also think, the 12-leads mean value of singularity spectrum area of human ECG signal can reflect the self-discipline control function of human autonomic nervous system to some degree. When people are getting unhealthy and older, the self-discipline function weakens. It tends to turn from multifractality to monofractality.
Characterizing Adversarial Samples of Convolutional Neural Networks

Adversarial samples aim to make deep convolutional neural networks predict incorrectly under small perturbations. This paper investigates non-targeted adversarial samples of convolutional neural networks and makes a primitive attempt to characterize adversarial samples. Two observations are made: first, adversarial perturbations are mainly in the high-frequency domain; second, adversarial categories usually have strong semantic relevance to the original categories. Our two observations provide a solid basis to understand the behavior of convolutional neural networks and thus to improve their robustness against adversarial samples.
Cone Transform for Size-Tolerant Object Recognition

In this paper, we propose a cone transform method, which is robust against size variations in object recognition. A main problem in computer vision is that objects may show large size variations. This makes it difficult to develop robust object recognition algorithms since most recognition algorithms assume certain target size ranges. The proposed method represents an input image by two angles. The first angle is measured from a center point counter-clockwise and the other angle is measured from the radius. The proposed method shows better tolerance against size variations. Experiment results show that the proposed method provided better classification performance when objects were reduced in size.
Blind source separation based on NNAE-GCC

The performance of blind source separation algorithm based on non-negative matrix factorization is good, but the complexity of algorithm limits to on-line separation. We propose an acoustic separation method based on Non-Negative Automatic Encoder (NNAE) and Generalized Cross Correlation (GCC), which reduces the complexity of the algorithm and provides the possibility of online separation. The time-frequency feature of speech is mapped to high-dimensional space via NNAE. The time delay of sources is calculated by GCC. According to the time delay information, the delay masking vector of each frame of speech is calculated in high-dimensional space. The NNAE-GCC combines high-dimensional feature and spatial position information to implement blind source separation. The complementary spectral and spatial information can be simultaneously exploited to improve the performance of speech separation. Experiments on simulation mixed speech and the real-world data, which is collected from a circular microphone array with a diameter of 4 cm, show the potential of the proposed algorithm for speech separation in reverberant environments. By replacing the NMF with NNAE, we found that the neural network is given a clear physical meaning.
Human Pose Estimation Based on Step Deep Convolution Neural Network

To improve the accuracy and robustness of human pose estimation, a step deep convolution neural network is proposed, which consists of a feed forward module and several step modules. Image features output from the last four layers of feed forward module are fused with the context features, the fused features are used as the input of the first step module; Image features of each step module fused with context features are used as the input of the next step module; The confidence map output from the last step module is used to predict joint position. This stepwise approach increases the receptive fields, which is good to learn the long-distance relationship between joints and predicting the position of occluded joints. At the same time, the confidence calculated by the previous module provides more and more accurate estimation of the position of each joint in the subsequent modules. In addition, the network also provides a way to strengthen the intermediate supervision by learning the objective function, so as to supplement the gradient of back-propagation and adjust the learning process, which effectively solves problem that the gradient disappears during training. Our approach is tested on two standard datasets of Leeds Sports Poses (LSP) and Frames Labeled In Cinema (FLIC), whose results indicate that our network has better performance in pose estimation of human body.
Low-Complexity Channel Estimation in 3D Lens Millimeter-Wave Massive MIMO Systems

Lens-based millimeter-wave massive MIMO technologies could reduce the number of required radio frequency chains at the base station without a loss of performance, but require the beamspace channel information, which is often costly to obtain. In this paper, a low-complexity scheme to estimate the channel for lens-based 3D millimeter wave massive MIMO systems is proposed. Beamspace analysis shows that the main entries of the channel matrix form a dual crossing (DC) shape. Existing DC-based channel estimation algorithms have a very high computational complexity. The proposed algorithm eliminates the process of repeatedly selecting the main row and column that is required in existing algorithms, and provides a solution even when certain entries exceed the matrix boundary. It thus has a much lower computational complexity than existing algorithms. Additionally it achieves a smaller normalized mean-squared error of the channel estimates than existing DC-based algorithms.
GVF and CV Model-Based Pulmonary Artery Edge Detection of CTPA Image Sequence

Since the gradient vector flow (GVF)model is not suitable for multi-objects edges detection and cannot adapt to the change of the object's geometric topology, and the Chan-Vese (CV)model is easy to result in false detection, this paper proposes a new method for detecting the edges of the pulmonary artery for the computed tomographic pulmonary angiography (CTPA)image sequences which combines the advantages of GVF model and CV model. Firstly, the initial contour is driven by the GVF field. After getting the converged contour curve, the image inside the curve is extracted; Secondly, CV model-based edge detection is performed on the segmented image to solve the problem of multi-objects detection. Experiments show that the proposed method can effectively solve the problem of pulmonary artery edges detection in CTPA images. Applying the algorithm to targets tracking for image sequence, the method can detect the edges of each target separately, and obtain the independent contours, when the pulmonary artery is split into two.
Downlink Channel Reconstruction for FDD 3D Multi-Antenna Systems

This paper faces to the frequency-division-duplex (FDD) three-dimensional (3D) multi-antenna system and proposes a novel downlink channel reconstruction scheme which costs only a small amount of overhead. Uniform planar array (UPA) is employed at the base station (BS) to exploit the 3D space. In the uplink, we first estimate the frequency-independent parameters (delay, vertical and horizontal angles of arrival) by introducing and extending the Newtonized orthogonal matching pursuit (NOMP) algorithm. Both the orthogonal matching pursuit dictionary and the Newton refinement step (NRS) are redesigned according to the spatial propagation model of the signal for the NOMP algorithm. Then, we calculate the downlink gains and feed them back to the BS. The costs for downlink pilots and uplink feedback can be very small. Finally, downlink channel is reconstructed by using the frequency-independent parameters and the downlink gains. Simulation results show that using more antennas helps improve the mean-squared error (MSE) performance of the proposed scheme and can even achieve a better MSE performance than using the linear minimum mean-square error method.
Shape Adaptive Texture Coding Based on Wavelet-Based Contourlet Transform

Transform coding of arbitrary shape object is one of the core technologies of MPEG-4 video coding. In this chapter, we propose a shape adaptive Wavelet-Based Contourlet Transform framework for MPEG4 texture coding. In the shape adaptive texture coding framework, Wavelet-Based Contourlet Transform is divided into two parts: the Wavelet-Based Contourlet Transform of the shape mask, and the Wavelet-Based Contourlet Transform of the object texture. In the Wavelet-Based Contourlet Transform phase, the shape coding is carried out using lazy Wavelet-Based Contourlet Transform, and the texture coding is carried out by 9/7 wavelets in the form of lifting. In the zerotree quantization stage of wavelet coefficients, three quantization methods are adopted according to different needs. The experimental results show that the Wavelet-Based Contourlet Transform texture coding based on object has similar low complexity with the texture coding, such as SADCT, and the performance is better. Moreover, the coding effect is better than that of SADCT at low bit rate.
Classification of Motor Imagery Based on Multi-Decision Fusion for Brain Computer Interface

Brain-computer interface (BCI) based on motor imagery (MI) is considered to be a promising cognitive tool for rehabilitation therapy of movement disorders. Feature extraction and classification are important in MI-BCI, and affect the BCI's performance. Label is the classifying result of motor imagery in MI-BCI. For example, 1 represents imaging left hand movement and 2 represents imaging right hand movement in a MI-BCI system. In this paper, we combined labels from different feature extraction and classification, and fused labels to get a new label which has higher accuracy. We named the method multi-decision fusion. We used the multi-decision fusion on BCI competition's MI datasets for classification. By comparing the results with those using conventional methods of feature extraction and classification, we have verified multi-decision fusion is an effective method. Multi-decision fusion can effectively improve classification accuracy of motor imagery.
A Route Selection Scheme for Supporting Virtual Tours in Sites with Cultural Interest Using Drones

Virtual tourism is a novel trend that enhances the experience the users perceive from touristic places, such as archaeological sites. Drones are equipped with 360° video cameras and used for video capturing of the heritage sites. The video material is streamed to the users in real time, enriched with additional 3D, Augmented Reality (AR) or Mixed Reality (MR) material. Furthermore, the selection of the appropriate flying route for each drone should be performed, in order to provide a satisfactory tour experience to the user, considering his preferences about specific monuments. To address this issue, this paper describes a heritage route selection scheme for supporting real-time virtual tours in sites with cultural interest using drones. The proposed scheme applies a Fuzzy Multiple Attribute Decision Making (FMADM) algorithm, the Trapezoidal Fuzzy Topsis for Heritage Route Selection (TFT-HRS), to accomplish the ranking of the candidate heritage routes. The algorithm uses Interval-Valued Trapezoidal Fuzzy Numbers (IVTFN) for the representation of heritage routes evaluation values. Performance evaluation shows that the suggested method produces better results compared to the Fuzzy Topsis (FTOPSIS) by selecting the most appropriate flying route for the drone.
Comparative Study of Inhomogeneous Simulated Annealing Algorithms for Quadratic Assignment Problem

Simulated annealing (SA)algorithm is a popular metaheuristic which has been widely used to solve many continuous and combinatorial optimization problems. Temperature cooling scheme is one of the key factors for its promising performance. Several studies have shown that reannealing can improve SA's performance on quadratic assignment problem (QAP). Using the benchmark instances from QAPLIB, this paper carried systematic experiments to compare the effect of rean-nealing on performance of inhomogeneous SA (ISA). Simulation results show that: (1)although reannealing can improve ISA's performance in general, it may has no effect on some type of QAP instances; (2)parameters for cooling schedule are not only sensitive to the type of QAP instance, but sensitive to the specific instance also; (3)the timing to activate the reannealing is sensitive to the type of QAP instances also. The source code is made publicly available as a benchmark for future comparisons.
The Evolution of People'S Interest in Video Games

Understanding human behavior is becoming a key issue in many areas of research. Based on the data set of product reviews from Amazon. , this paper gives an intuitive understanding of human interest evolution through the study of people's rating behavior on video games. Results disclose the increasing inequality in active users' interests distribution. , and reveal the time-invariant power law distribution of active users'interest span. All these findings have significant application value in both science and commerce.
A Non-Linguistic Approach for Human Emotion Recognition from Speech

One of the most important issues in several aspects of human-computer interaction is the understanding of the users' emotional state. In several applications such as monitoring of humans in assistive living environments, or assessing students' affective state during a course, it is imperative to use an unobtrusive method, so as to avoid discomforting or distracting the user. Thus, one should opt for approaches that use either visual or audio sensors which may observe users without any kind of direct contact. In this work, our goal is to recognize the emotional state of humans using only the non-linguistic aspect of speech information, i.e., the acoustic properties of speech. Therefore, we propose an emotion classification that is based on the bag-of-visual words model that has been previously applied in many computer vision tasks. A given audio segment is transformed to a spectrogram, i.e., a visual representation of its spectrum. From this representation we first extract SURF features and using a previously constructed visual vocabulary, we quantize them into a set of visual words. Then a histogram is constructed per image; These feature vectors are used to train SVM classifiers. Our approach is evaluated using a) 3 publicly available datasets that contain speech from different languages and b) a custom dataset that has been constructed during a real-life classroom experiments, involving middle-school students.
Classification of Fabric Patterns Image Based on Improved Log-AlexNet

Fabrics are filled with aspects of our lives. Facing the massive fabric image data, how to help people quickly and efficiently classify these images becomes urgent. Aiming at the problem of low classification efficiency and accuracy, this paper proposes a binary hash classification framework based on AlexNet. By improving the activation function Log-ReLU and adding hidden layer to learn binary hash coding and other optimized network parameters, the proposed framework (Log-AlexNet)extracts rich abstract features and improves the classification efficiency and precision of fabric patterns. Compared with the traditional image feature extraction and classification methods, the experimental results show that the improved Log-AlexN et for classification of cloth fabric patterns is feasible and much better than the traditional state-of-the-art methods.
Predicting human trajectory with virtual HRI environment

Although the topic of HRI (human-robot interaction) has prevailed for a long time, we still require a great endeavor to achieve the vision which robot collaborates with human friendly. One of the reasons for this is the lacking of security. Since robot cannot have the same inteligence and physical experience as human, at least for now, they won’t communicate with us fluently, and may do harm to human physically. In order to stay clear from these accidents, we need to experiment with a safer environment and make robot more smart which can be done with predicting human trajectory. In this paper a virtual environment which built up for HRI is introduced firstly. In this model, interactive object models and human body model which can be controlled by skeleton tracking algorithm. The code of human body model is published on the Github, hoping to help researchers who do HRI debugging in virtual environment Then we propose human trajectory prediction model using neural network and implement it in real world. The results show our prediction model outperforms other typical models and the video of our experiment provides more intuitive perspective.
A Novel Video-Surveillance-Based Algorithm of Fall Detection

With the development of the society and medical technologies, the average age of population keeps increasing, health care. I has been a worldwide problem need to be solved. Though hospitals can heal many patients send to them, falls are in fact the first rated incentive that causes disabilities among the elderly people due to long-time unnoticed can lead to serious sequences especially for the elderly and patients who live alone. Fall detection would play a key role in preventing elderly people and patients harm from longtime lying without noticed. The most important part of a fall detection algorithm is to distinguish fall from the other daily fall-like activities such as lie down and squat. This paper studied the algorithm of fall detection based on vision-approach, compared different approaches to detect falls and presents a new algorithm based on vision using multi-parameters to discriminate falls and other activities which turns out have a higher accuracy on fall detecting than some other vision-based fall down detection algorithms and the experimental results reveal this method made the fall detection more reliable.
Smart Mosquitoes Trap Using Favoriot Monitoring System

This paper explains the project development of a smart mosquito trap prototype that functioned to control and protect the environment from mosquito bites. Mosquitoes are harmful insects that carry diseases, which sometimes dangerous and can kill human lives. It is very important to be able to trap and also analyze the quantity of mosquitoes in order to determine the step that should be taken to prevent the transmission of diseases. Besides that, a mosquito trap that is implemented with remote transmittance of data could be extremely useful, as it would not only reduce the amount of diseases but also provide real-time data of the trapped mosquitoes. The current mosquitoes trap in the market does not integrate the Internet of Things (IoT) capabilities. The developed prototype trap was designed with the novel capability of sensing the nearby mosquitoes and transmitted the collected real-time data of the average number of trapped mosquitoes to the Favoriot IoT cloud platform for remote monitoring. Performance evaluation of the prototype was carried out on the university campus for 24 hours. The result shows that the prototype has successfully trapped a lot of mosquitoes and provided the real-time analytics data of the number of trapped mosquitoes.
Chinese Character Recognition Based on Residual Separable Convolutional Neural Network

In this paper, we present an end-to-end deep learning system for Chinese character recognition. We applied the system to recognize ID card photographed by smart phone in natural environment. The quality of ID card images was affected by illumination and shadow. We developed a preprocessing procedure to enhance the images and then to detect and extract Chinese characters and numbers from the images. The end-to-end deep convolutional neural network (CNN)consists of deep separable convolutional (DSC)layers, batch normalization (BN)layers, ReLu activation layers and residual connections to mitigate over-fitting and gradient vanish. To train the network, we constructed a dataset containing the 3765 most frequent Chinese characters. We tested the proposed system on a self-constructed Chinese character dataset and achieved an accuracy of 92.73%.