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Defects on the surface of rolling bearing elements are some of the most frequent causes of malfunctions and breakages of rotating machines. Defect detection in rolling bearings via techniques that examine changes in measured signal is a very important topic of research due to increasing demands for quality and reliability. In this paper, incipient defect identification method based on adaptive lifting scheme packet is proposed. Adaptive lifting scheme packet operators which adapt to the signal characteristic are constructed. The shock pulse value in defect sensitive frequency band is used as the defect indicator to identify the defect location and severity of rolling bearing. The proposed defect identification method is applied to analyze the experimental signal from rolling bearing with incipient inner raceway defect. The result confirms that the proposed method is accurate and robust in rolling bearing incipient defect identification. [ABSTRACT FROM AUTHOR]

Vibration signals of rolling bearings usually are corrupted by heavy noise and it is very important to extract fault features from such signals. In this paper, an adaptive lifting scheme is proposed for fault diagnosis of rolling bearings. The kurtosis indexes of scale decomposition signals are used as the optimization indicator to select the prediction operator and update operator, which can adapt to the dominant signal characteristics, and reveal the fault feature. Fourier transform is adopted to remove the overlapping signal frequency components at every scale decomposition signal. Experimental results confirm the advantage of the adaptive lifting scheme over lifting scheme for feature extraction, and the typical features of rolling bearing in time domain are successfully extracted by adaptive lifting scheme. [ABSTRACT FROM AUTHOR]

A novel adaptive lifting scheme is proposed to effectively extract features of fault-generated high-frequency transient waveforms in power transmission lines. The accurate and fast fault location is achieved. [ABSTRACT FROM AUTHOR]

In this work, we extract prosodic features from subjects while they are talking while walking using human-gait-speech data. These human-gait-speech data are separated into 1D data (human-speech) and 2D data (human-gait) using the adaptive-lifting scheme of wavelet transform. We carry out extraction of prosodic features from human-speech data, such as speech duration, pitch, speaking rate, and speech momentum, using five different natural languages (Hindi, Bengali, Oriya, Chhattisgarhi, and English) for the detection of behavioral patterns. These behavioral patterns form real-valued measured parameters, stored in a knowledge-based model called the human-gait-speech model. Extraction of geometrical features from human-gait data, such as step length, energy or effort, walking speed, and gait momentum, is carried out for the authentication of behavioral patterns. In this paper, the data of 25 subjects of different ages, talking in five different natural languages while walking, are analyzed for the detection of behavioral patterns. [ABSTRACT FROM AUTHOR]

This paper presents a novel algorithm for matching image interest points. Potential interest points are identified by searching for local peaks in Difference-of-Gaussian (DoG) images. We refine and assign rotation, scale and location for each keypoint by using the SIFT algorithm refid="ref1"/. Pseudo log-polar sampling grid is then applied to properly scaled image patches around each keypoint, and a weighted adaptive lifting scheme transform is designed for each ring of the log-polar grid. The designed adaptive transform for a ring in the reference keypoint and the general non-adaptive transform are applied to the corresponding ring in a test keypoint. Similarity measure is calculated by comparing the corresponding transform domain coefficients of the adaptive and non-adaptive transforms. We refer to the proposed versatile system of Rotation And Scale Invariant Matching as RASIM. Our experiments show that the accuracy of RASIM is more than SIFT, which is the most widely used interest point matching algorithm in the literature. RASIM is also more robust to image deformations while its computation time is comparable to SIFT. [ABSTRACT FROM AUTHOR]

Wavelet transform has been widely used for vibration-based machine fault diagnosis. However, it is a difficult task to choose or design appropriate wavelet or wavelets for a given application. In this paper, a new signal-adapted lifting scheme for rotating machinery fault diagnosis is proposed, which allows us to construct a wavelet directly from the statistics of a given signal. The prediction operator based on genetic algorithms is designed to maximize the kurtosis of detail signal produced by the lifting scheme, and the update operator is designed to minimize a reconstruction error. The signal-adapted lifting scheme is applied to analyze bearing and gearbox vibration signals. The conventional diagnosis techniques and non-adaptive lifting scheme are also used to analyze the same signals for comparison. The results demonstrate that the signal-adapted lifting scheme is more effective in extracting inherent fault features from complex vibration signals. [Copyright &y& Elsevier]

Underperformance in higher frequency signal regions denoising is a common problem for many denoising methods. Wavelet transforms are, generally, less prone to the problem than the pure spatial or frequency domain transforms, but there is still much room for improvements. In this paper, we propose a point-wise adaptive wavelet transform for signal denoising applications. It is very efficient in denoising higher frequency regions, without compromising the performance on smooth, lower frequency, regions. The transform uses statistical method of intersection of confidence intervals rule to adapt to local signal properties. Its performance was extensively tested on various signal classes. The results proved validity of theoretical assumptions and showed significant performance improvements when compared to other denoising methods. [ABSTRACT FROM AUTHOR]

Wavelet transform is one of the most acceptable tools to analyze vibration signals for gear fault detection. However, there are still some limitations of the traditional wavelet transforms due to the utilization of fixed linear filters. This investigation presents an adaptive morphological gradient lifting wavelet (AMGLW) to remedy the shortcomings of traditional wavelet transform schemes. A novel nonlinear filter, named morphological gradient filter, is designed for enhancing the impulsive features of the original signal. Then the adaptability of AMGLW is implemented by selecting between two filters, namely the average filter and the morphological gradient filter, to update the approximation signal dependent upon the local gradient of the analyzed signal. This new scheme is evaluated on a simulated signal and a practical vibration signal measured from a gearbox. Experimental results demonstrate that the presented AMGLW outperforms the traditional linear wavelet (LW) transform obviously for detecting gear defects. Furthermore, the computational cost of AMGLW is much less than the traditional LW. Thus the AMGLW scheme is quite suitable for the online condition monitoring of gears. [ABSTRACT FROM PUBLISHER]

In this paper, we propose novel adaptive wavelet filter bank structures based on the lifting scheme. The filter banks are nonseparable, based on quincunx sampling, with their properties being pixel-wise adapted according to the local image features. Despite being adaptive, the filter banks retain a desirable number of primal and dual vanishing moments. The adaptation is introduced in the predict stage of the filter bank with an adaptation region chosen independently for each pixel, based on the intersection of confidence intervals (ICI) rule. The image denoising results are presented for both synthetic and real-world images. It is shown that the obtained wavelet decompositions perform well, especially for synthetic images that contain periodic patterns, for which the proposed method outperforms the state of the art in image denoising. [ABSTRACT FROM AUTHOR]

As the use of digital media becomes ever more widespread, the data distribution process is becoming faster and easier, while requiring less effort to make exact copies. One of the major challenges for intellectual property protection of digital media is to discourage unauthorized copying and distribution. Digital watermarking has been proposed as a way to claim the ownership of the source and owner. To achieve maximum protection, the watermark should be perceptually invisible, statically undetectable, resistant to lossy data compression, and resistant to common image processing operations. Besides perceptual invisibility, private control of the watermark is also very important. In this paper, a private key-dependent, wavelet-based lifting scheme is proposed to improve the security of the image authentication algorithm. Experimental results show that the embedded watermark is robust against various signal processing and compression attacks. [ABSTRACT FROM AUTHOR]

Researchers face the trade-off between publishing mobility data along with their papers while protecting the privacy of the individuals. In addition to the anonymization process, other techniques, such as spatial discretization and location concealing or removal, are applied to achieve these dual objectives. The primary research question is whether concealing the observation area is an adequate form of protection or whether human mobility patterns in urban areas are inherently revealing of location. The characteristics of the mobility data, such as the number of activity records in a given spatial unit, can reveal the silhouette of the urban landscape, which can be used to infer the identity of the city in question. The presented locating method was tested on multiple cities using different open datasets and coarser spatial discretization units. While publishing mobility data is essential for research, concealing the observation area is insufficient to prevent the identification of the urban area. Instead of obscuring the observation area, noise should be added to the trajectories to mitigate privacy risks regarding the individuals. [ABSTRACT FROM AUTHOR]

Image compression is a vital component for domains in which the computational resources are usually scarce such as automotive or telemedicine fields. Also, when discussing real-time systems, the large amount of data that must flow through the system can represent a bottleneck. Therefore, the storage of images, alongside the compression, transmission, and decompression procedures, becomes vital. In recent years, many compression techniques that only preserve the quality of the region of interest of an image have been developed, the other parts being either discarded or compressed with major quality loss. This paper proposes a study of relevant papers from the last decade which are focused on the selection of a region of interest of an image and on the compression techniques that can be applied to that area. To better highlight the novelty of the hybrid methods, classical state-of-the-art approaches are also analyzed. The current work will provide an overview of classical and hybrid compression methods alongside a categorization based on compression ratio and other quality factors such as mean-square error and peak signal-to-noise ratio, structural similarity index measure, and so on. This overview can help researchers to develop a better idea of what compression algorithms are used in certain domains and to find out if the presented performance parameters are of interest for the intended purpose. [ABSTRACT FROM AUTHOR]

In the process of microassembly, aligning the end effectors with the micro-parts using image information is the basis of automated assembly. In order to realize the flexible and accurate clamping center locating of the micro-gripper with various shapes of jaws, this paper proposes an iterative-based processing algorithm. First, the locating problem is transformed into a multi-parameter optimization problem through the geometric analysis of the clamping process. Second, an iterative optimal algorithm based on the block coordinate descent is developed, in which a scaling golden section (SGS) scheme is proposed to calculate the iteration scaling parameters. Third, the lookup table and variable threshold iteration techniques are utilized to further improve the performance of the SGS scheme. Simulation results show that the proposed algorithm can efficiently locate the clamping center for various types of jaws with sub-pixel accuracy. Finally, a microassembly experiment is carried out to demonstrate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Deep learning-based optical flow estimation achieved impressive success with faster inference time and outperformed performance. Optical flow estimation networks are usually treated as a black box relying on large amounts of synthetic data for training, therefore the generalization and robustness of the network applying in realities remains a challenge. To overcome these problems, a dual-frequency paradigm is proposed for optical flow estimation. The proposed dual-frequency encoder captures discriminative features with both high-frequency and low-frequency biases. It is experimentally demonstrated that our method achieves better generalization while only pre-trained on FlyingChiars. Furthermore, our method improves the prediction of optical flow in occluded regions by enhancing the perception of high-frequency features that further improve the robustness of the network. Compared to the start-of-the-art RAFT, our approach obtains an improvement of the average end-point error by 10.6% on the Sintel Clean datasets and 11.7% on the challenging Sintel Final dataset. [ABSTRACT FROM AUTHOR]

Solving the problem of the transmission of mechanical equipment is complicated, and the interconnection between equipment components in a complex industrial environment can easily lead to faults. A multi-scale-sensor information fusion method is proposed, overcoming the shortcomings of fault diagnosis methods based on the analysis of one signal, in terms of diagnosis accuracy and efficiency. First, different sizes of convolution kernels are applied to extract multi-scale features from original signals using a multi-scale one-dimensional convolutional neural network (1DCNN); this not only improves the learning ability of the features but also enables the fine characterization of the features. Then, using Dempster–Shafer (DS) evidence theory, improved by multi-sensor information fusion strategy, the feature signals extracted by the multi-scale 1DCNN are fused to realize the fault detection and location. Finally, the experimental results of fault detection on a flash furnace show that the accuracy of the proposed method is more than 99.65% and has better fault diagnosis, which proves the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Electric induction motors are one of the most important and widely used classes of machines in modern industry. Large motors, which are commonly process-critical, will usually have built-in condition-monitoring systems to facilitate preventive maintenance and fault detection. Such capabilities are usually not cost-effective for small (under ten horsepower) motors, as they are inexpensive to replace. However, large industrial sites may use hundreds of these small motors, often to drive cooling fans or lubrication pumps for larger machines. Multiple small motors may further be assigned to a single electrical circuit, meaning a failure in one could damage other motors on that circuit. There is thus a need for condition monitoring of aggregations of small motors. We report on an ongoing project to develop a machine-learning-based solution for fault detection in multiple small electric motors. Shallow and deep learning approaches to this problem are investigated and compared, with a hybrid deep/shallow system ultimately being the most effective. [ABSTRACT FROM AUTHOR]