Soft Compression for Lossless Image Coding Based on Shape Recognition

Soft compression is a lossless image compression method that is committed to eliminating coding redundancy and spatial redundancy simultaneously. To do so, it adopts shapes to encode an image. In this paper, we propose a compressible indicator function with regard to images, which gives a threshold of the average number of bits required to represent a location and can be used for illustrating the working principle. We investigate and analyze soft compression for binary image, gray image and multi-component image with specific algorithms and compressible indicator value. In terms of compression ratio, the soft compression algorithm outperforms the popular classical standards PNG and JPEG2000 in lossless image compression. It is expected that the bandwidth and storage space needed when transmitting and storing the same kind of images (such as medical images) can be greatly reduced with applying soft compression.

Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening

With the development of information technology, band expansion technology is gradually applied to college English listening teaching. This technology aims to recover broadband speech signals from narrowband speech signals with a limited frequency band. However, due to the limitations of current voice equipment and channel conditions, the existing voice band expansion technology often ignores the high-frequency and low-frequency correlation of the audio, resulting in excessive smoothing of the recovered high-frequency spectrum, too dull subjective hearing, and insufficient expression ability. In order to solve this problem, a neural network model PCA-NN (principal components analysis-neural network) based on principal component image analysis is proposed. Based on the nonlinear characteristics of the audio image signal, the model reduces the dimension of high-dimensional data and realizes the effective recovery of the high-frequency detailed spectrum of audio signal in phase space. The results show that the PCA-NN, i.e., neural network based on principal component analysis, is superior to other audio expansion algorithms in subjective and objective evaluation; in log spectrum distortion evaluation, PCA-NN algorithm obtains smaller LSD. Compared with EHBE, Le, and La, the average LSD decreased by 2.286 dB, 0.51 dB, and 0.15 dB, respectively. The above results show that in the image frequency band expansion of college English listening, the neural network algorithm based on principal component analysis (PCA-NN) can obtain better high-frequency reconstruction accuracy and effectively improve the audio quality.

Technique for Removing Unnecessary Superimposed Patterns from Image using Generative Network

A technique for removing unnecessary patterns from captured images by using a generative network is studied. The patterns, composed of lines and spaces, are superimposed onto a blue component image of RGB color image when the image is captured for the purpose of acquiring a depth map. The superimposed patterns become unnecessary after the depth map is acquired. We tried to remove these unnecessary patterns by using a generative adversarial network (GAN) and an auto encoder (AE). The experimental results show that the patterns can be removed by using a GAN and AE to the point of being invisible. They also show that the performance of GAN is much higher than that of AE and that its PSNR and SSIM were over 45 and about 0.99, respectively. From the results, we demonstrate the effectiveness of the technique with a GAN.

A Novel Pix2Pix Enabled Traveling Wave-Based Fault Location Method

This paper proposes a new Image-to-Image Translation (Pix2Pix) enabled deep learning method for traveling wave-based fault location. Unlike the previous methods that require a high sampling frequency of the PMU, the proposed method can translate the scale 1 detail component image provided by the low frequency PMU data to higher frequency ones via the Pix2Pix. This allows us to significantly improve the fault location accuracy. Test results via the YOLO v3 object recognition algorithm show that the images generated by pix2pix can be accurately identified. This enables to improve the estimation accuracy of the arrival time of the traveling wave head, leading to better fault location outcomes.

Psychological components of professional image of inspectors of the road patrol service of the State road safety Inspectorate and directions for its improvement

The article deals with psychological components of the phenomenon of the professional image of the inspectors of the Road Patrol Service of the State Road Safety Inspectorate are the subject of the study. The purposes of the scientific article are to present psychological characteristics of leading components of the professional image of the inspectors of the Road Patrol Service of the State Road Safety Inspectorate and to determine the trends of its improvement. It’s established that the most significant elements of the discussed professional image are appearance, knowledge of normative legal acts, the culture of dialogue, competent speech and the ability to handle stress and require urgent attention and development – a normative legal acts and speech skills. A synthesis of the literature and an empirical study carried out using methods of a written survey, observation, ranking, and analysis of situations has allowed not only to reveal the psychological meaning of each component image, but also to identify the likely directions of its improvement. A special place among these directions is given to improving the quality of legal and psychological training, and training in practical linguistics in the field of business communication with traffic participants. The scientific novelty of research consists in the reflection of the modern view on the problem of professional image of the employee through the eyes of the subjects of its formation and in the formulation of specific proposals based on the experience of the respondents and sought after in the course of further modernization of the image of the inspectors of the Road Patrol Service of the State Road Safety Inspectorate.

Formation of Tourist Image of the City as a Strategic Direction of Development of Territory Marketing

The article is aimed at forming the tourist image of the city as a strategic direction of development of territory marketing. The article proves that a strong and positive image of the city is considered to be the effective and efficient mechanism allowing to fight for limited resources, competitive advantages and high competitiveness, representing a holistic set of characteristics, combining unique and original features of the city, as well as figurative ideas or perceptions that makes it possible for consumers, tourists, investors to distinguish and identify the city from among many other. The structure of the city’s potential, i. e., its economic, political, social and environmental components, on which the image of the territory is based, is provided. In the research, the existing instruments for creating and promoting a territorial image that can be used in both external and internal communications are proposed to be classified according to the four-component image structure, which includes conceptual, active, personal and attributive components. Studying the currently established image of the city and getting an idea of what position it occupies in the minds of target groups is an exceptionally important stage in the planning of urban marketing. Studying the image of the city is anything but easy task. That is why the article suggests to use an aggregate of different approaches, methods and specific tools in order to study the entire complex and multi-aspect nature of the image of the city of Kharkiv. An evaluation of such kind is needed in order to develop an algorithm for strategic management of the image of the territory. Whereas a positive and properly constructed relevant image can act as one of the instruments for attracting investments aimed at developing the territory and achieving the goals of the city development.

Sugarcane stem nodes based on the maximum value points of the vertical projection function

ABSTRACT: In order to solve the problem that the stem nodes are difficult to identify in the process of sugarcane seed automatic cutting, a method of identifying the stem nodes of sugarcane based on the extreme points of vertical projection function is proposed in this paper. Firstly, in order to reduce the influence of light on image processing, the RGB color image is converted to HSI color image, and the S component image of the HSI color space is extracted as a research object. Then, the S component image is binarized by the Otsu method, the hole of the binary image is filled by morphology closing algorithm, and the sugarcane and the background are initially separated by the horizontal projection map of the binary image. Finally, the position of sugarcane stem is preliminarily determined by continuously taking the derivative of the vertical projection function of the binary image, and the sum of the local pixel value of the suspicious pixel column is compared to further determine the sugarcane stem node. The experimental results showed that the recognition rate of single stem node is 100%, and the standard deviation is less than 1.1 mm. The accuracy of simultaneous identification of double stem nodes is 98%, and the standard deviation is less than 1.7 mm. The accuracy of simultaneous identification of the three stem nodes is 95%, and the standard deviation is less than 2.2 mm. Compared with the other methods introduced in this paper, the proposed method has higher recognition and accuracy.