TECHNOLOGY FOR THE DEVELOPMENT OF SOFTWARE ALGORITHMS FOR OPTOELECTRONIC MONITORING SYSTEMS FOR REMOTE OBJECTS

The issues of constructing algorithms for obtaining coordinate and non-coordinate information used to solve the problem of multiplexing images obtained from several optoelectronic systems are considered. A unique mathematical method for finding the corresponding points in images, based on algorithms for continuous wavelet transform of the brightness structure of an image, is proposed. The technology of development of algorithms intended for multi-position optoelectronic systems for monitoring remote objects based on software from National Instruments is considered. A technique for constructing software for obtaining information is proposed, which ensures high accuracy in determining the coordinates of the corresponding fragments in images. It is shown that the parallel use of several methods makes it possible to assess the reliability of the information obtained under conditions of changing observation parameters. The use of several methods makes it possible to assess the reliability of the information obtained under conditions of changing observation parameters. Computational experiments have confirmed that a more accurate search for image alignment regions is provided by the double wavelet transform method by increasing the number of extrema of the curves of the continuous wavelet transform coefficients, expanding the area from localization and additional filtering. An example of the practical implementation of the developed algorithms in a two-channel optoelectronic system is presented.

Comparative studies of multiscale edge detection using different edge detectors for MRI thigh

Edge detection plays an important role in computer vision to extract object boundary. Multiscale edge detection method provides a variety of image features by different resolution at multiscale of edges. The method extracts coarse and fine structure edges simultaneously in an image. Due to this, the multiscale method enables more reliable edges are detected. Most of the multiscale methods are not translation invariant due to the decimated process. They mostly depend on the corresponding transform coefficients. These methods need more computation and a larger storage space. This study proposes a multiscale method that uses an average filter to smooth image at three different scales. Three different classical edge detectors namely Prewitt, Sobel and Laplacian were used to extract the edges from the smooth images. The edges extracted from the different scales of smooth images were then combined to form the multiscale edge detection. The performances of the multiscale images extracted from the three classical edge detectors were then compared and discussed.

THE USE OF DISCRETE WAVELET ANALYSIS OF VIBRO-ACOUSTIC SIGNALS TO DETECT THE POWER SUPPLY ASYMMETRY OF ROTATING ACCOMPANYING ELECTRIC MACHINES

The work shows that among the existing sufficiently described and studied approaches that are suitable for analyzing the temporal realization of a vibosignal, which can be obtained during the operation of a real electric machine, one can single out Fourier transforms and discrete wavelet transformations. An analysis of the descriptions of vibro-acoustic signals given in the literature, caused by the asymmetry of the power supply, shows that this defect leads to the appearance of oscillations that contain a harmonic component localized at the frequency of the supply voltage of the electrical network. This fact justifies the expediency of analyzing the frequency range, which includes the frequency of the supply voltage, and the selection of the mother wavelet, based on the features inherent in a single harmonic oscillation. A method for detecting a defect in the asymmetry of power supply to rotating electric machines of alternating current using a discrete wavelet transformation of a vibro-acoustic signal is proposed. The frequency band has been established, which is advisable to analyze in order to identify the indicated defect. It was found that the detection of a power asymmetry defect with the use of the wavelet transform of the temporal realization of the vibroacoustic signal is advisable to carry out using the Haar maternal wavelet function, which provides a combination of a high degree of affinity of the maternal wavelet with the form of vibration change due to the introduced asymmetry and relative the simplicity of such a transformation. It is shown that when detecting power asymmetry, it is advisable to analyze the behavior of the wavelet coefficients of the frequency band, which includes the frequency of the supply voltage of the electric machine. Since the influence of the indicated defect on other frequency bands will be minimal, the analysis of the behavior of their wavelet transform coefficients in order to identify the indicated defect is ineffective. A numerical criterion for assessing the influence of power asymmetry on the wavelet transform coefficients is proposed in the form of the mean square value of the wavelet coefficients of the informative frequency band in the study of the time interval, which significantly exceeds the period of the supply voltage of the electric machine. It is shown that this criterion has a reduced sensitivity to the impact of non-informative single disturbances that may arise during the operation of an electric machine. Keywords: electric machine, rotor unbalance, defect, burst, wavelet transform.

Efficient Coding of the Embedded Signal in Steganographic Systems with Multiple Access

Today, steganographic systems with multiple access are of considerable importance. In such sys-tems, the orthogonal Walsh-Hadamard transform is most often used for multiplexing and divid-ing channels, which leads to the need for efficient coding of the Walsh-Hadamard transform coefficients for the convenience of their subsequent embedding. The purpose of the research is to develop a theoretical basis for efficient coding of the embedded signal in steganographic sys-tems with multiple access with an arbitrary number of users N, based on MC-CDMA technology. This purpose was fulfilled by forming the theoretical basis for constructing effective codes de-signed to encode the embedded signal in steganographic systems with multiple access. The most important results obtained are the proposed and proven relations that determine both the possible values of the Walsh-Hadamard transform coefficients, for a given value of the number of divid-ed channels, and the probability of occurrence of the given values of the Walsh-Hadamard transform coefficients, which allow the construction of effective codes to represent the embed-ded signal. In the case of the number of divided channels N=4, we propose to use a constant amplitude code that provides a smaller value of the average codeword length in comparison with the Huffman code, while the constructed code has correcting capabilities. The significance of the obtained results is determined by the possibility of using the developed theoretical basis when constructing effective codes for encoding the embedded signal in steganographic systems with multiple access at an arbitrary value of the number of divided channels N.

Sequential Transform Learning

This work proposes a new approach for dynamical modeling; we call it sequential transform learning. This is loosely based on the transform (analysis dictionary) learning formulation. This is the first work on this topic. Transform learning, was originally developed for static problems; we modify it to model dynamical systems by introducing a feedback loop. The learnt transform coefficients for the t th instant are fed back along with the t + 1st sample, thereby establishing a Markovian relationship. Furthermore, the formulation is made supervised by the label consistency cost. Our approach keeps the best of two worlds, marrying the interpretability and uncertainty measure of signal processing with the function approximation ability of neural networks. We have carried out experiments on one of the most challenging problems in dynamical modeling - stock forecasting. Benchmarking with the state-of-the-art has shown that our method excels over the rest.

Rotational Wavelet Filters for Analysis of Brain MRI in Detection of Alzheimer’s Disease

In this paper, a method is suggested for multi directional analysis of Magnetic Resonance Image (MRI) scans for detection of Alzheimer’s disease (AD). This is a novel technique which utilizes, two-dimensional (2-D) rotated complex wavelet filters (RCWF) for feature identification. DTCWT identifies the features in 6 directions (±150±450, ±750) while RCWT identifies the features in different 6 directions (-300,0, +300, +600, +900, +1200), which enhances the directional selectivity of the transform coefficients and results in well description of corresponding textures. Dual-tree rotated complex wavelet transform (DT- RCWF) and dual-tree complex wavelet transform (DT- CWT) are applied to the sample images at a time thus the transform coefficients in twelve different directions is obtained simultaneously. The obtained transform coefficients are used for calculation of various texture features such as energy, entropy and standard deviation. The obtained parameters form the feature vectors which are given as input to the classifiers to get the input classified as Normal control or AD sufferer. This proposed algorithm produces results which are superior in terms of accuracy, feature extraction rate, sensitivity, specificity, precision and recall necessary to realize the efficiency of diagnosis of Alzheimer’s Disease as compared to other existing methods.

Image Compression Using Permanent Neural Networks for Predicting Compact Discrete Cosine Transform Coefficients

This study proposes a new image compression technique that produces a high compression ratio yet consumes low execution times. Since many of the current image compression algorithms consume high execution times, this technique speeds up the execution time of image compression. The technique is based on permanent neural networks to predict the discrete cosine transform partial coefficients. This can eliminate the need to generate the discrete cosine transformation every time an image is compressed. A compression ratio of 94% is achieved while the average decompressed image peak signal to noise ratio and structure similarity image measure are 22.25 and 0.65 respectively. The compression time can be neglected when compared to other reported techniques because the only needed process in the compression stage is to use the generated neural network model to predict the few discrete cosine transform coefficients.