The article is devoted to the problems of constructing electromagnetic field with given parameters and both to the study of electromagnetic environment. For solving the problems, the corresponding theoretical material is presented. The functional relationships are considered that make it possible to construct the device for generating electromagnetic field with specified parameters in circular orthogonal polarization basis. The block diagram, which can ensure the specified field forming with acceptable errors are synthesized. Measurement of radiation characteristics, including polarization characteristics, requires the appropriate orientation of the receiving antenna to the direction of wave propagation. Corresponding algorithm and antenna system for this purpose is proposed. The study of the field polarization characteristics formed using the ring antenna elements is carried out. It is shown that in the broad frequency band, the ring elements can be replaced with spiral radiators, as well as that the antenna system for electromagnetic waves reception and their subsequent decomposition in circular polarization orthogonal basis, must contain at least eight antenna elements. Applied spiral flat antenna elements ensure the low level of cross-polarization due to the matched load on the spiral end, which is one of the conditions for successful polarization analysis. Besides, a device for polarization analysis of incident electromagnetic waves and the algorithm for measurement of the effective reflection area are considered.

Medical data protection against illegal manipulations has become an essential and urgent issue. Unfortunately, images exchanged via networks are not absolutely protected against the preservation of integrity, authenticity and the right of use. Watermarking can play a very important role in dealing with this problem. For this reason, it becomes necessary to perform such watermarking in image regions where the disorder regarding the pixels distribution should be less when embedding secret data called watermark. In this paper, we propose a new approach of medical image watermarking in spatial domain with a non-blind way. This approach is based on one of the essential properties of image called Local Entropy (LE). The watermarking in the zones with less local entropy values guarantees a high imperceptibility of the watermark. The choice of the low local entropy is based on measuring or estimating changes in a zones or regions of the image. The watermark embedding consists only on pixels with less local entropy values since these pixels represent less disorder within the image. The results obtained are very encouraging and have been evaluated in terms of imperceptibility through the Peak Signal Noise Rate (PSNR) metric and evaluated also in terms of robustness by measuring the Correlation Coefficients (CC), Bit Error Ratio (BER) and Structural Similarity Index Measure (SSIM) between the original watermark and the extracted ones.

The fundamental component of the work contains a summary of the theoretical foundations of the algorithms of the scale-self-similar approach for the analysis of digital Mueller-matrix images of birefringent architectonics of biological tissues. The theoretical consideration of multifractal analysis and determination of singularity spectra of fractal dimensions of coordinate distributions of matrix elements (Mueller-matrix images - MMI) of biological tissue preparations is based on the method of maxima of amplitude modules of the wavelet transform (WTMM). The applied part of the work is devoted to the comparison of diagnostic capabilities for determining the prescription of mechanical brain injury using algorithms of statistical (central statistical moments of the 1st - 4th orders), fractal (approximating curves to logarithmic dependences of power spectra) and multifractal (WTMM) analysis of MMI linear birefringence of fibrillar networks of neurons of nervous tissue. Excellent (~95%) accuracy of differential diagnosis of the prescription of mechanical injury has been achieved.

Low poly image abstraction is an art form that has gained popularity in recent years, particularly in the digital art community. The process involves simplifying an image by reducing the number of polygons used to represent it while preserving its overall appearance and details. This paper proposes a new approach to low poly image abstraction that utilizes edge-preserved seed points to preserve important details while reducing triangle count. The proposed approach involves six steps. First, the input image is smoothed using an anisotropic diffusion filter. Second, the entropy of each pixel in the smoothed image is computed and stored in an entropy map. Third, seed points for Delaunay triangulation are selected by identifying pixels with maximum entropy values in the entropy map. Fourth, the Delaunay triangulation is generated using the seed points as input. Fifth, colors are assigned to the triangles in the Delaunay triangulation using a color selection module. Finally, the final low poly image is generated by rendering the colored Delaunay triangulation. The effectiveness of the proposed method was evaluated through qualitative and quantitative experiments, comparing its results with other comprehensive methods using a diverse range of images from a benchmark dataset. The results showed that the proposed method outperformed other methods in preserving image details while maintaining low polygon count. Additionally, the proposed method was found to be efficient and capable of producing visually appealing results.

In the case of Sensorineural Hearing Loss (SNHL) persons speech perception diminishes in a noisy environment because of masking. The present work aims mainly at improving speech perception in sensorineural hearing-impaired subjects, as there is no known medical treatment for this condition. Speech perception can be improved by reducing the impact of masking. This is accomplished by splitting the speech signal into two parts for binaural dichotic presentation using time-varying comb filters having complementary magnitude responses. Using the frequency sampling method time-varying comb (FIR) filters with magnitude responses complementary to each other with 512 order are designed to split the speech signal for dichotic presentation. For the purpose of designing filters, 22 kHz sampling frequency and twenty-two one-third octave bands spanning from 0 to 11 kHz are taken into consideration. Magnitude responses of filters are continuously swept with a time shift less than just noticeable difference (JND) so that capacity to detect gaps in speech signal enhances without negating the benefits of the spectral splitting technique. Filter functioning is evaluated by using objective and subjective measures. Using Perceptual Evaluation of Speech Quality (PESQ) and spectrographic analysis an objective evaluation is made. The subjective measure is done using Mean Opinion Score (MOS) for quality of speech. MOS test is examined on normal hearing subjects by adding white noise to study materials at different SNR levels. For the evaluation of intelligibility of speech Modified Rhyme Test (MRT) is considered and evaluated on normal hearing subjects as well as bilateral moderate SNHL persons by adding white noise to study materials at different SNR levels. Study materials used for the evaluation of quality are VC syllable /aa-b/ & vowel /aa/. 300 monosyllabic words of consonant-vowel-consonant (CVC) are used as study materials for the evaluation of speech intelligibility.
The outcomes showed an improvement in PESQ values and MOS test scores for lower SNR values comparing unprocessed speech with processed speech and also an improvement in the intelligibility of processed speech in a noisy atmosphere for both types of subjects. Thus there is an enhancement in speech perception of processed speech in a noisy environment.

A colorization method based on a fully convolutional neural network for grayscale images is presented in this paper. The proposed colorization method includes color space conversion, grayscale image preprocessing and implementation of improved U-Net network. The training and operating of the U-Net network take place for images represented in the space of the Lab color model. The trained U-Net network integrates realistic colors (generate data of a and b components) into grayscale images based on L-component data of the Lab color model. Median cut method of quantization is applied to L-component data before the training and operating of the U-Net network. Logistic activation function is applied to normalized results of convolution layers of the U-Net network. The proposed colorization method has been tested on ImageNet database. The evaluation results of the proposed method according to various parameters are presented. Colorization accuracy by the proposed method reachers more than 84.81%. The colorization method proposed in this paper is characterized by optimized architecture of convolution neural network that is able to train on a limited image set with a satisfactory training duration. The proposed colorization method can be used to improve the image quality and restoring data in the development of computer vision systems. The further research can be focused on the study of a technique of defining optimal number of the gray levels and the implementation of the combined quantization methods. Also, further research can be focused on the use of HSV, HLS and other color models for the training and operating of the neural network.

For ensuring the safety issues, a country should establish a secure monitoring system around the most important places. Due to the huge development in unmanned aerial vehicles (UAV), drone detection is a vital part of the safety monitoring system for reducing threats from neighboring countries or terrorist groups. This paper presents a deep learning-based drone detection method. A You Only Look Once (YOLO) v7 architecture is used to train on the dataset. The training dataset consists of drone images in various environments. The trained model was tested on multiple videos of drones from YouTube. Experimental results demonstrate that the model exhibited a recall of 0.9656 and a precision of 0.9509. In addition, the performance of the model compares with the state-of-art models with YOLOv8, YOLO-NAS, Faster-RCNN architectures and it outperforms the other models by maintaining a more stable precision and recall curve.

To prevent the loss of the yield of food crops and to attain sustainable agricultural growth, accurate detection of plant disease at an early stage is crucial. However, the extraction of crucial features from infected plant leaves to differentiate the properties associated with different diseases is a complex task, as the diseases exhibit huge variations, which insists on the need for developing precise disease detection. Hence in this research, the early detection of plant disease is performed by utilizing a Modified political optimization adapted deep Neural Network (MPO-adapted deep NN) model, in which the continuous learning capability of the deep NN classifier helps in the deeper analysis of the information in the image and identifies the plant disease more accurately. Identification of the plant disease posse’s challenges due to complexities present in the image and the neural networks effectively dwells with the complex relationships and the non-linear characteristics of the network help in achieving adaptability and makes the system more suitable for real-time applications. The main contribution relies on the modified political optimization algorithm that efficiently tunes the parameters of the deep NN classifier to analyze the disease patterns effectively and provides disease detection with high accuracy. Further, the Adaptive K-means algorithm is utilized for the effective segmentation of diseased parts, and the Grey level co-occurrence matrix (GLCM) features are extracted in the method that enhances the accuracy of the detection. When compared to the existing techniques, the MPO-adapted deep NN model attains high accuracy, sensitivity, and specificity values of 98.95%, 97.45%, and 98.95% for cotton leaf, 94.47%, 94.58%, 94.54% for cotton root, 99.10%, 99.10%, 99.10% for cotton stem, respectively concerning the k-fold. Analysis demonstrating the superiority of the research's metrics values measurement. When compared to existing methods, detecting the disease in cotton stems is very effective.