A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here.  The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditio...

In this paper, we analyze the performance of electrocardiogram (ECG) signal compression by comparing original and reconstructed signal on two problems. First, automatic sleep stage classification based on ECG signal; second, arrhythmia classification. An effective ECG signal compression method based on two-dimensional wavelet transform which employs set partitioning in hierarchical trees (SPIHT...

A Multiuser Automatic Modulation Classifier (MAMC) is an important signal processing component of a multi-antenna cognitive radio (CR) receiver that can identify the modulation format employed by multiple users simultaneously. In a typical wireless communication system, transmitted signals are subjected to multipath fading and interference from other users. Multipath fading not only affects sym...

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography...

In many practical detection and classi cation problems, the signals of interest exhibit some uncertain nuisance parameters, such as the unknown delay and Doppler in radar. For optimal performance, the form of such parameters must be known and exploited as is done in the generalized likelihood ratio test (GLRT). In practice, the statistics required for designing the GLRT processors are not avail...

Audio signal classification (ASC) consists of extracting relevant features from a sound, and of using these features to identify into which of a set of classes the sound is most likely to fit. The feature extraction and grouping algorithms used can be quite diverse depending on the classification domain of the application. This paper presents background necessary to understand the general resea...

In this paper, we present two feature extraction techniques for the classification of ultrasonic NDE signals acquired from weld inspection regions of boiling water reactor piping of nuclear power plants. The classification system consists of a pre-processing block that extracts features from the incoming patterns, and of an artificial neural network that assigns the computed features to a parti...

The paper presents new machine learning methods: signal composition, which classifies time-series regardless of length, type, and quantity; and self-labeling, a supervised-learning enhancement. The paper describes further the implementation of the methods on a financial search engine system using a collection of 7,881 financial instruments traded during 2011 to identify inverse behavior among t...

A modified neocognitron neural network suitable for medical signal classification is presented. The network's functionality is demonstrated on an application involving the classification of breathing signals measured on patients recovering from surgery. The performance of the system was found to be equivalent, and in some cases, better than a standard technique used for comparison. The main adv...