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Intrusion detection systems are a necessary tool to protect computer networks from cyber-attacks. Analyzing the payload of a packet can help in identifying strings that can help to detect attacks. Machine learning can be used to train models based on feature extraction of packet payloads. One important issue is that payload based intrusion detection systems may be too slow for standard processing...
In the industry of integrated circuits, defect patterns shown on a wafer map contain crucial information for quality engineers to find the cause of defect to increase yield. This paper proposes a method for wafer defect pattern recognition which could recognize more than one defect patterns based on Ordering Point to Identify the Cluster Structure(OPTICS) and Support Vector Machine(SVM). The effectiveness...
Emotions play a significant and powerful role in everyday life of human beings. Developing algorithms for computers to recognize emotional expression is a widely studied area. In this study, emotion recognition from Galvanic signals was performed using time domain and wavelet based features. Feature extraction has been done with various feature set attributes. Various length windows have been used...
EMG pattern recognition has been studied for control of prostheses and rehabilitation systems for decades. Existing research platforms for developing EMG pattern recognition algorithms are typically based on MATLAB and the collection of EMG signals is often done by expensive, non-portable data acquisition systems. The requirement of these resources usually limits the use of these platforms in the...
Early and accurate detection of rotor faults is crucial for optimal performance of rotating machinery. Unbalance and misalignment are the most common faults occurring in the machinery. Using vibration-based conventional frequency analysis methods, it is often difficult to identify these faults because they exhibit similar frequency patterns. The balancing procedure of an unbalanced rotor is based...
For solving the problem of performance degradation assessment of train rolling bearings, the degradation pattern recognition and assessment method based on segmentation vote and SVM was proposed. Firstly, in order to obtain effective features indexes of bearing performance degradation, with the collected vibration acceleration data, the data was decomposed by LMD and state features were extracted...
In this paper we propose a variant oisf the TASPG algorithm for texture recognition. TASPG (Texture Analysis based on Shortest Paths in Graphs) is a recently proposed texture recognition method that extracts features from paths along texture images. Although TASPG achieved promising results, its application may be limited by its high computational cost which stems from the extensive use of Dijkstra's...
We propose a Convolutional Neural Network model to learn spatial footstep features end-to-end from a floor sensor system for biometric applications. Our model's generalization performance is assessed by independent validation and evaluation datasets from the largest footstep database to date, containing nearly 20,000 footstep signals from 127 users. We report footstep recognition performance as Equal...
Tongue diagnosis is one of the main components of traditional Chinese medicine (TCM). Developing an objective and quantitative recognition model is very importantly and useful in the modernization of TCM. Currently, major problems in digital diagnoses of tongue images are extracting suitable features and building a high-performance classifier. To address these two issues, we present a robust approach...
In order to recognize faults of the high voltage circuit breaker (HVCB) in the whole fault state space precisely and minimize the impact of the lack of fault data on the accuracy of fault recognition, a method of fault recognition was proposed based on the incremental learning algorithm for SVM. Firstly, the incremental learning algorithm for SVM was analyzed theoretically, and the state monitoring...
Demographic change in the next few years will lead to a pronounced disparity in generation distribution. Hence there is a need to develop intelligent systems to support and maintain the autonomy of the elderly at home. A high priority in this case assumes the preparation-free acquisition of vital signs and patient parameters in long-term monitoring systems to detect early changes or deterioration...
Computational Auditory Scene Analysis (CASA) is typically achieved by statistical models trained offline on available data. Their performance relies heavily on the assumption that the process generating the data along with the recording conditions are stationary over time. Nowadays, there is a high demand for methodologies and tools dealing with a series of problems tightly coupled with non-stationary...
Handwriting Recognition HR is the old dream of all those who need to enter data into a computer. In this article, we present a state of the art in the field of handwriting recognition by focusing primarily on Arabic handwritten script which we present an overview of the Offline Automatic Arabic Handwriting Recognition Systems AHRS and the techniques and strategies used. Then, to try to resolve the...
This paper presents a novel humming feature extraction algorithm based on locality statistical analysis to tackle the problem of the instability of humming features in the query by humming (QBH) system. By carrying out statistics to humming notes sequences in both longitudinal vocal range distribution and horizontal temporal variation distribution, we can obtain the locality statistical humming features...
Discrete Krawtchouk moments are powerful tools in the field of image processing application and pattern recognition. In this paper, we propose a fast and accurate algorithm based on matrix multiplication to extract local features of 3D Krawtchouk moments. The center of interest region in an object can be shifted by varying three parameters. We also computed local Tchebichef moments from 3D object...
This article on implementing a machine vision process, such as to enable support processes of agriculture. Work is born of the problems presented by different crops, causing great losses in both time and costs. Using machine vision every inconvenience that occurs from the stage of germination and growth to the maturation phase of each culture was analyzed. To this end, different methods of image processing...
Analysis procedures for higher-dimensional data are generally computationally costly; thereby justifying the high research interest in the area. Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of...
The success of object categorization is heavily dependent on the extracted image descriptors. In general, image or region segmentation is usually performed to segment an image into several regions or objects, and then some level-level features, such as color and texture, are extracted from each region. As a result, the region descriptor or the combination of multiple region descriptors can be used...
The hedonic attributes of cutaneous elicitation play a crucial role in everyday life, influencing our behavior and psychophysical state. However, the correlation between such a hedonic aspect of touch and the Autonomic Nervous System (ANS)-related physiological response, which is intimately connected to emotions, still needs to be deeply investigated. This study reports on caress-like stimuli conveyed...
In myoelectric prosthetic control, the motion classification performance would be decayed if an electromyography (EMG) pattern to be recognized differs significantly from the one used for classifier training. Generally, the training signals are acquired when a subject performs motions with a proper force. In practical use of a myoelectric prosthesis, however, the variation of force levels to do a...
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