Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

Dynamic UEP of embedded image bit streams over noisy channels

We present a dynamic unequal error protection (UEP) framework for the embedded image bit streams over noisy channels. A general source model for M-rate UEP schemes is derived and an optimization algorithm for 2-rate dynamic UEP schemes is developed. To facilitate the design and implementation of UEP schemes, we also derived a necessary condition and an upper bound for UEP gains. Simulation results demonstrate about 0.3 dB improvements over equal error protection (EEP) schemes at the price of negligible overheads while the progressiveness of the original bit streams is still kept

Compressed domain MPEG-2 video editing with VBV requirement

A novel method is proposed to achieve efficient MPEG-2 video editing in compressed domain while preserving Video Buffer Verifier (VBV) requirements. Different cases are determined, according to the VBV modes of the bitstreams to be concatenated. For each case, the minimum number of zero-stuffing bits or shortest waiting time between two streams is determined analytically, so that the resulting bit-stream is still VBV-compliant. The simulation:results show that the proposed method is applicable to any MPEG-2 bit-stream independent of its encoder

Comparative analysis of hidden Markov models for multi-modal dialogue scene indexing

A class of audio-visual content is segmented into dialogue scenes using the state transitions of a novel hidden Markov model (HMM). Each shot is classi ed using both audio track and visual content to determine the state/scene transitions of the model. After simulations with circular and left-to-right HMM topologies, it is observed that both are performing very good with multi-modal inputs. More- over, for circular topology, the comparisons between different training and observation sets show that audio and face information together gives the most consistent results among different observation sets.Article Pre-prin