一种改进的心电图QRS波群检测算法

在利用小波变换检测QRS波群时,最关键的部分就是模极值配对,提出一种区域极值配对算法来检测R波。首先利用二次样条小波基函数和多孔(ATrous)算法对心电(ECG)信号进行小波变换求取模极值,用正极大值来确定搜索区域,以这个正极大值为起点,以这个确定区域为搜索范围,向左搜索负极大值点,将这两个极值配对,他们之间的过零点就是R波的对应点,然后在检测到R波的基础上检测出Q波与S波,再结合距离最大值法检测出QRS波群的起止点。并采用医学相关理论对检测结果进行优化,进一步去除错检点,补偿漏检点。最后利用MIT-BIH心率失常数据库中记录的数据对该算法进行验证,实验结果表明所提算法能准确检测QRS波群,平均检出率达到了99.97%。     

一种视频监控图像条纹噪声的快速检测方法

针对视频监控图像中存在各类条纹噪声的问题,根据条纹特性和受干扰图像帧的频域特征,提出了一种快速检测监控录像周期性条纹的算法。根据相对距离将频谱图分成两个子块,再运用行列累积函数或阈值检测各子带是否存在异常亮点,进而确定图像帧是否存在条纹噪声。利用频率谱中异常点的对称特性可减少遍历次数,有效提高了算法的运行效率。实验结果表明,该算法对监控视频序列中的多种周期性条纹具有良好的检测效果,并提高了计算速度。     

An efficient method for defect detection during the manufacturing of web materials

Defect detection is becoming an increasingly important task during the manufacturing process. The early detection of faults or defects and the removal of the elements that may produce them are essential to improve product quality and reduce the economic impact caused by discarding defective products. This point is especially important in the case of products that are very expensive to produce. In this paper, the authors propose a method to detect a specific type of defect that may occur during the production of web materials: periodical defects. This type of defect is very harmful, as it can generate many surface defects, greatly reducing the quality of the end product and, on occasions, making it unsuitable for sale. To run the proposed method, two different functions must be executed a large number of times. Since the time available to perform the detection of these defects may be limited, it is very important to consume the least amount of time possible. In order to reduce the overall time required for detection, an analysis of how the method accesses the input data is performed. Thus, the most efficient data structure to store the information is determined. At the end of the paper, several experiments are performed to verify that both the proposed method and the data structure used to store the information are the most suitable to solve the aforementioned problem.     

Detection of electrocardiogram signals using an efficient method

Automatic detection of electrocardiogram (ECG) signals is very important for clinical diagnosis of heart disease. This paper investigates the design of a three-step system for recognition of the five types of ECG beat. In the first step, stationary wavelet transform (SWT) is used for noise reduction of the electrocardiogram (ECG) signals. Feature extraction module extracts higher order statistics of ECG signals in combination with three timing interval features. Then hybrid Bees algorithm-radial basis function (RBF_BA) technique is used to classify the five types of electrocardiogram (ECG) beat. The suggested method can accurately classify and discriminate normal (Normal) and abnormal heartbeats. Abnormal heartbeats include left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contractions (APC) and premature ventricular contractions (PVC). Finally, the classification capability of five different classes of ECG signals is attained over eight files from the MIT/BIH arrhythmia database. Simulation results show that classification accuracy of 95.79% for the first dataset (4000 beats) and an overall accuracy of detection of 95.18% are achieved over eight files from the MIT/BIH arrhythmia database.     

An efficient fire detection method based on orientation feature

This paper proposes a novel method for reliable fire detection. The burning fire usually causes rich moving features in terms of directions, which can offer the best chance to distinguish between the fire region and the non-fire one. Motivated by this observation, we design a novel orientation feature to represent this characteristic. Based on this feature, a method is proposed to detect the fire efficiently. First, fire color is utilized to extract the fire candidate areas from the surveillance video. Then, the direction is obtained by computing the optical flow for each pixel in the candidate area. The directions are discretized to four parts. By counting the percentage of pixels whose moving directions fall into these four parts in a period of time, and combining with the two parameters, i.e., both of the number of frames without the moving directions and the number of consecutive frames in the candidate area, we use these six parameters as the fire orientation feature. In the end, by training a support vector machine (SVM) classifier with the input of our fire orientation feature, the candidate area is judged whether it is a fire. Our main contribution is that we design the novel fire orientation feature. The feature can not only characterize the fire intrinsic dynamic properties accurately but also is very efficient. Compared with the art-of-state methods, the experimental results confirm that our approach significantly improves the accuracy of fire detection and impressively decreases the false alarm rate. The detection speed of our approach is also very competitive with the art-of-state fire detection methods.     

Identification of QRS complexes in 12-lead electrocardiogram

A method for the detection of QRS complexes in 12-lead electrocardiogram (ECG) using support vector machine (SVM) is presented in this paper. Digital filtering techniques are used to remove base line wander and power line interference. SVM is used for the identification of QRS complexes in the processed signal. The performance of the algorithm is evaluated against the standard CSE ECG database. The results indicated that the algorithm achieved 99.75% of the identification rate. The percentage of false positive and false negative is 1.61% and 0.26%, respectively. The performance of the proposed algorithm is found to be better than published results of the other QRS detectors tested on the same database. The proposed method functions reliably even under the conditions of poor signal quality in the ECG data.     

An efficient blind detection watermarking algorithm using range conversion method

Microsystem Technologies - In recent years digital technology plays a vital role in data transmission. Most of the digital content or data exchange happens through the internet. Provision of...     

一种基于像素差值特征的车辆检测方法

为了快速定位监控场景中的车辆位置,提出了一种基于像素差值特征的车辆检测方法。首先提取图像的归一化像素差值特征(NPD),之后使用深度二次树(DQT)学习最优的特征子集,最后使用Ada Boost算法筛选最具区分力的特征构建强分类器。以含有正面、侧面及背面三个角度超过3 500个样本为测试集进行了快速车辆检测测试,并与梯度方向直方图(HOG)和Haar的组合特征进行了对比。对比实验表明,基于NPD的车辆检测方法最优,其检测率和检测时间分别为85.47%和200 ms。     

An efficient cascaded method for network intrusion detection based on extreme learning machines

Machine learning techniques are widely used for network intrusion detection (NID). However, it has to face the unbalance of training samples between classes as it is hard to collect samples of some intrusion classes. This would produce false positives for these intrusion classes. Meanwhile, since there are various types of intrusions, classification boundaries between different classes are seriously nonlinear. Due to the huge amount of training data, computational efficiency is also required. This paper therefore proposes an efficient cascaded classifier for NID. This classifier consists of a collection of binary base classifiers which are serially connected. Each base classifier corresponds to a type of intrusion. The order of these base classifiers is automatically determined based on the number of false positives to cope with the unbalance of training samples. Extreme learning machine algorithm, which has low computational cost, is used to train these base classifiers to delineate the nonlinear boundaries between classes. This proposed NID method is evaluated on the KDD99 data set. Experimental results have shown that this proposed method outperforms other state-of-the-art methods including decision tree, back-propagation neural network and support vector machines.     

A new and efficient transform for curve detection

A new transform for curve detection, called the Curve-Fitting Hough Transform (CFHT), is proposed. In the conventional Hough Transform (HT) and its variants, both storage and computation grow exponentially with the number of parameters. The CFHT is advantageous over the conventional HT and variants in its high speed, small storage, arbitrary parameter range, and high parameter resolution. This is achieved by fitting a segment of the curve to be detected to a small neighborhood of edge points. If the fitting error is less than a given threshold, the parameters obtained from curve fitting are used to map an edge element to a single point in the parameter space. A multidimensional ordered parameter list is used to accumulate the occurrences of the curve to be detected.     

An efficient and low-error mesh simplification method based on torsion detection

To preserve the major characteristics of the simplified model, this study proposes the use of torsion detection to improve the quadric error metric of vertex-pair contraction, and retain the physical features of the models. Besides keeping the physical features of the models, the proposed method also decreases the preprocessing time cost associated with analysis. To verify the conclusion, this research not only presents the effects of simplification and compares them with the vertex-pair contraction, but also employs Metro detection and image comparison to verify the error measurements. The experimental results demonstrate that the proposed method improves the error rate and keeps the precision of the object features efficiently.     

An efficient method for image forgery detection based on trigonometric transforms and deep learning

Multimedia Tools and Applications - Image forgery detection is the basic key to solve many problems, especially social problems such as those in Facebook, and court cases. The common form of image...     

An efficient method for the computation of Legendre moments

Legendre moments are continuous moments, hence, when applied to discrete-space images, numerical approximation is involved and error occurs. This paper proposes a method to compute the exact values of the moments by mathematically integrating the Legendre polynomials over the corresponding intervals of the image pixels. Experimental results show that the values obtained match those calculated theoretically, and the image reconstructed from these moments have lower error than that of the conventional methods for the same order. Although the same set of exact Legendre moments can be obtained indirectly from the set of geometric moments, the computation time taken is much longer than the proposed method.     

An efficient method for tracking failure detection using parallel correlation filtering and Siamese network

Applied Intelligence - In recent years, object tracking based on Siamese network has attracted the attention by taking advantage of both speed and accuracy. However, when similar object...     

An efficient volumetric method for non-rigid registration

We propose a novel and efficient volumetric method for registering 3D shapes with non-rigid deformations. Our method uses a signed distance field to represent the 3D input shapes and registers them by minimizing the difference between their distance fields. With the assumptions that the sampling points in each cell of the object volume follow the same rigid transformation, and the transformations of the sampling cells vary smoothly inside the object volume, a two-step method is used for the non-rigid registration. The first step is the locally rigid registration, which minimizes the difference between the source and target distance fields of the sampling cells. The second step is the globally non-rigid registration, which minimizes the difference between the transformations of adjacent cells. In just a few iterations, our method rapidly converges for the registration. We tested our method on several datasets, and the experimental results demonstrate the robustness and efficiency of our method.     

An efficient naturalness-preserving image-recoloring method for dichromats

We present an efficient and automatic image-recoloring technique for dichromats that highlights important visual details that would otherwise be unnoticed by these individuals. While previous techniques approach this problem by potentially changing all colors of the original image, causing their results to look unnatural to color vision deficients, our approach preserves, as much as possible, the image's original colors. Our approach is about three orders of magnitude faster than previous ones. The results of a paired-comparison evaluation carried out with fourteen color-vision deficients (CVDs) indicated the preference of our technique over the state-of-the-art automatic recoloring technique for dichromats. When considering information visualization examples, the subjects tend to prefer our results over the original images. An extension of our technique that exaggerates color contrast tends to be preferred when CVDs compared pairs of scientific visualization images. These results provide valuable information for guiding the design of visualizations for color-vision deficients.     

An efficient swap algorithm for the lattice Boltzmann method

During the last decade, the lattice-Boltzmann method (LBM) as a valuable tool in computational fluid dynamics has been increasingly acknowledged. The widespread application of LBM is partly due to the simplicity of its coding. The most well-known algorithms for the implementation of the standard lattice-Boltzmann equation (LBE) are the two-lattice and two-step algorithms. However, implementations of the two-lattice or the two-step algorithm suffer from high memory consumption or poor computational performance, respectively. Ultimately, the computing resources available decide which of the two disadvantages is more critical. Here we introduce a new algorithm, called the swap algorithm, for the implementation of LBE. Simulation results demonstrate that implementations based on the swap algorithm can achieve high computational performance and have very low memory consumption. Furthermore, we show how the performance of its implementations can be further improved by code optimization.