Base64 Color Code

QR code widely used as an url to link to a website or data that contains the essential text to fetch. Its nearly a decade has been used in day today life on products. An idea about re-inventing the qr code in such a way that making files in transformation of base64 (code text)which then formatted to a pattern of qr code which enables qr code has the content of the file. Now when a person scans the QR(Quick Response) code without the interaction with the internet he gets the app or the file in his device. While in this if the scanner of the app has the predefined AR(Augmented reality) object which helps the today's world to make an advertisable mode for attracting customers. Like adding a predefined cube with rotating animation. Now the user just has to add text on what side has to be placed. so image code just has to contain the text on which side and what speed the cube to rotate. By this a new way already defined library holding in decoder or scanner app helps the user interaction and content delivery easily. In this adding color QR code which gives the possibility to explore more data into it .

Research on QR image code recognition system based on artificial intelligence algorithm

The QR code recognition often faces the challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult, and therefore, to deal with this problem, artificial intelligence-based systems came into existence. To improve the recognition rate of QR image codes, this article adopts an improved adaptive median filter algorithm and a QR code distortion correction method based on backpropagation (BP) neural networks. This combination of artificial intelligence algorithms is capable of fitting the distorted QR image into the geometric deformation pattern, and QR code recognition is accomplished. The two-dimensional code distortion is addressed in this study, which was a serious research issue in the existing software systems. The research outcomes obtained after emphasizing on the preprocessing stage of the image revealed that a significant improvement of 14% is observed for the reading rate of QR image code, after processing by the system algorithm in this article. The artificial intelligence algorithm adopted has a certain effect in improving the recognition rate of the two-dimensional code image.

The “Temple Cycle” in the Book of Poems by the Emigrant Poet Georgiy Golokhvastov as a Text in a Text

The article examines the artistic system of the book of poems Polusonety [HalfSonnets] by Georgiy Golohvastov, the ideological leader of a group of poets of the first wave of emigrants (E.V. Khristiani, D.A. Magula, V.S. Ilyashenko) that originated in the 1920s-1930s in New York. The article deals with the specific phenomenon of a “temple cycle” as a text in the text of Polusonety. The cycle can be specified by the author as a structural unit of the book, or it can “stand out” as a united text space based on thematic or figurative unity. The latter is our case. The object of the research is the realization in the half-sonnets of the image-code of the Temple, which is a symbol of Russian culture within the model of the poet’s world, the basis of the traditional world order reverently stored in the memory of the emigrant poet. The “temple cycle” is considered as a single lyrical text. The cross-cutting theme of the cycle is the Holy Rus, represented in the image of the world lucent Temple; this image is in the memory of every emigrant. The signs of the Temple in Golokhvastov’s half-sonnets are “dark lamps at the kiot”, “monk”, “poor temple with a wrecked bell tower”, “skete”. The poet voluntarily emigrated to the United States after the revolution of 1917, but his memory preserved the idyllic image of Cathedral Russia that shaped in his poetic system as the conceptual artistic and aesthetic model of the “Russian world” closely connected with the Orthodox faith. The poet perceived faith as the embodiment of spiritual coordinates of Russian people forced to live in a foreign land, associated it with images of monastery, cross, icons, monk in his poetry. These semiotic codes illustrate Golokhvastov’s “Russian thought”. The code of the Temple in Golokhvastov’s art system has various transformations: it is a lonely monastic skete, an ancient monastery, and a small city parish. The space of the Temple is individualized under the influence of the historical and cultural situation, psycho-mental characteristics, and personal experience of the author. The temple for Golokhvastov’s lyrical hero is a locus of gaining strength and peace of mind, in which he can get rid of loneliness among the hostile alien crowd. The altar, icons, and deacon’s singing remind the lyrical hero of his ancestral estate, where he “trembled in his chlid’s prayers.” The renewing and purifying space of Christian symbols leads him by prayer and uncomplicated contemplation. Staying in the space of the House of God, the lyrical hero regains the forever lost Holy Rus. We come to the following conclusions: the figurative world of Golokhvastov’s “temple cycle” is deeply consistent with the spirit of the Christian doctrine of God, which was represented by the poet in various lyrical forms and images throughout his creative life. Being a text in the text, this cycle extends the boundaries of the understanding of the emigrant poet’s artistic model and the integrity of his poetic system.

Research on Low-Resolution Pattern Coding Recognition Method Based on Hu-DBN

The feature image code represented by the two-dimensional code is the key reference for global positioning in the visual navigation of mobile robots. Although reducing the acquired low-resolution image helps to reduce the real-time performance of the algorithm, the acquired feature image is more susceptible to motion blur-based interference and affects the accuracy of recognition, which causes the positioning failure of the whole multi-intelligence, in which the body control system is invalid. In this paper, an optimized low-resolution feature image code recognition method is proposed. In the preprocessing part, the characteristic image is converted into the characteristic signal matrix of Hu invariant moments, and then the characteristic image is added to the characteristic signal matrix as a characteristic component, and then the Hu-DBN neural network signal classifier is used to construct the signal matrix so as to achieve accurate recognition of low-resolution custom image signature images under high motion tolerance conditions. It not only avoids the problem of classical pattern recognition relying on model experience and poor adaptability of the scene, but also avoids the problem of high computational complexity and recognition efficiency of directly deep learning methods such as YOLO. The deployment of the mobile robot instance deployment test shows that the average recognition rate is of 96.3% at a resolution of 640×480@Pixs and motion speed of 0.5 m/s, which proves the effectiveness of the present method.

Rapid Estimation of the Entropy of Long Codes with Dependent Bits on Low-Power, Low-Bit Microcontrollers (Review of Literature on Reducing the Dimension of a Problem)

Introduction. The aim of the work is to reduce the requirements for bit depth and processor performance of a trusted computing environment when estimating the entropy of long codes with dependent bits. Materials and Methods. Testing procedures recommended by the Russia national standards are used. The transition from the analysis of ordinary long codes to Hamming distances between random Alien codes and the Own image code is used. Results. It is shown that the transition to the presentation of data by the normal distribution law in the space of Hamming distances makes the relationship between mathematical expectation and entropy almost linear. Low-bit tables are constructed that relate the first statistical moments of the distribution of Hamming distances to the entropy of long codes. In calculations, the correlation index of the digits of the studied codes can vary widely. Discussion and Conclusion. The calculation of the mathematical expectation and standard deviation is easily feasible on low-discharge low-power microcontrollers. The use of the synthesized tables makes it possible to pass easily from the lower statistical moments of the Hamming distances to the entropy of long codes. The task of calculating entropy is accelerated many times in comparison with Shannon’s procedures and becomes feasible on cheap low-bit processors.