Binary Sign Research Articles

ORGANIZATION OF DISPENSARY OBSERVATION OF PATIENTS WITH PATHOLOGY OF THE MACULAR RETINA USING ARTIFICIAL INTELLIGENCE SYSTEMS

Background: Despite the mention in the provision of medical care for diseases of the organ of vision, adnexa and orbit about equipping the medical consultative and diagnostic department of the clinic with an optical coherence tomograph, dynamic monitoring of patients with retinal pathology after the start of treatment is most often carried out in a medical ophthalmological center, which reduces accessibility for patients with primary (first identified) morbidity requiring initiation of treatment. The required technology needs to be changed, intensified, incl. using artificial intelligence technologies.
 Aim: development of methodological foundations for organizational technology for dispensary observation of patients with pathology of the posterior segment of the eye using clinical decision support system (CDSS) based on artificial intelligence.
 Materials and methods: The assessment of the existing regulatory framework was carried out on the basis of an analysis of the Constitution of the Russian Federation, Federal laws, by-laws, and judicial practice. The creation of a methodology for a structured medical document describing an optical coherence tomography image was carried out using an expert method: a survey of 100 ophthalmologists with the appropriate level of education, incl. additional professional, engaged in the provision of medical services - specialized medical care (treatment) to patients with pathology of the posterior segment of the eye.
 Results: Using an expert method, 123 binary signs were selected and described to describe the structure of the macular region of the retina in normal and pathological conditions, of which 26 signs were identified that can be interpreted as predictors of worsening of the clinical course of the disease.
 Conclusion: The developed classifier made it possible to create and train a medical decision support system based on 60,000 medical images, which, as an information service, without making a diagnosis, can change the organization of the dynamic observation process: the formation of patient routing - a primary service using the developed CDSS; if there are signs of deterioration in the clinical picture - routing to a medical ophthalmological center to assess the dynamics, provide specialized services, incl. high-tech medical care.

Признаки прогрессирования заболевания сетчатки глаза, используемые для повышения валидности системы поддержки принятия врачебных решений на основе искусственного интеллекта

Purpose is to determine the most significant diagnostic signs of retinal disease progression, which are used to increase the validity of the medical decision support system (mdSS) for describing oct images using the method of an expert survey. Material and methods. An expert analysis of the binary signs used in mdSS when describing the oct image of the macular area was carried out: each sign was assessed in terms of the development of the pathological process, the need for telemedicine consultation and (or) referral of patients to a medical organization. The expert method included a survey of 15 ophthalmologists with the necessary qualifications. the experts were asked to rank on a ten-point scale 123 binary signs used by mdSS to form a conclusion about the need for referral to an ophthalmological department for specialized medical care. Results and discussion. The conducted expert survey showed a similar understanding of the signs obtained in the description of retina oct image, which can be used as predictors of the deterioration of the clinical course of the disease in patients with a previously diagnosed diagnosis. If there are signs identified as predictors of the pathological process, mdSS offers to go to the portal «telemedical system of remote consultations of the federal and regional levels» (https://fcmk.minzdrav.gov.ru/), upload the received documents to receive advice on patient routing. Conclusion. The established list of diagnostic features allowed to increase the validity of mdSS in terms of substantiating the conclusion about the need to refer the patient for a consultation with an ophthalmologist to resolve the issue of providing timely specialized medical care. this mdSS can be used as an information service in the work of specialists with higher medical education, a certificate of a medical specialist in the field of «ophthalmology», helps to structure data when uploading to an existing portal of telemedicine consultations. Keywords: retinal diseases, OCT, medical decision support system, signs of disease progression

Binary endocardial appearance for Fabry disease diagnosis: a systematic review and meta-analysis

Abstract Objective This study aimed to determine the performance of the endocardial binary appearance (binary sign [BS]) on echocardiography for Fabry-Anderson disease (FD) diagnosis. Methods A diagnostic systematic review was performed, which included studies with >20 patients selected, with enough reported data to compute the 2 × 2 tables, and with a BS evaluation against a genetic test. Evidence was searched up to October 4, 2020, in the following databases: PubMed, Scopus, EMBASE, and Web of Science. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies 2 tool and certain evidence using the Grading of Recommendations Assessment, Development, and Evaluation approach. Results Four studies were included in the systematic review. All studies were cross-sectional that included 524 patients that compared the BS vs. the alpha-galactosidase-A and/or genetic test, alpha-galactosidase-A and globotriaosylceramide isoform, and genetic test alone. The BS was found to have a sensitivity of 41% (confidence interval [CI] 95% = 17–71) and specificity of 91% (CI 95% = 60–99) and area under the curve of 0.71 (CI 95% = 0.64–0.78) for diagnostic of FD, with a certain of evidence low. Conclusion The BS has a good performance in confirming the diagnosis of FD, but not for screening. However, the evidence had low certainty, thus future studies could modify these results. Funding Acknowledgement Type of funding sources: None.

The role of echocadiography in the differential diagnosis between PRKAG2 syndrome and Fabry disease

Abstract Funding Acknowledgements Type of funding sources: None. Background Storage of glycogen and glycosphingolipids occurs in the heart with PRKAG2 Syndrome and Fabry Disease (FD), respectively. Identification of specific echocardiographic parameters could contribute to early diagnosis, which is fundamental to initiate appropriate treatment and improve prognosis. Material and methods: Prospective, transversal, observational study involving 45 patients, 30 genetically identified with the PRKAG2 gene mutation and 15 with FD. Conventional transthoracic, bi-, and tridimensional echocardiography as well as strain/strain rate speckle tracking were measured during one examination. Specific echocardiographic measurements were obtained and the data exported to a workstation for subsequent evaluation. Analyses were performed with 5% significance using the R program, version 3.1.3. Results: Compared to FD patients, PRKAG2 patients presented increased left atrium volume index, mean LV septum, posterior wall thickness, right ventricle (RV) dimensional values, and RV thickness in diastole. LV hypertrophy was found in varying degrees in 25 (86%) of PRKAG2 and 8 (53%) of FD Pts. We detected a binary appearance of LV endocardial border ("binary sign") and hypertrophy of the anterolateral papilary muscle in 60% of FD Pts. PRKAG2 Pts showed reduced global longitudinal strain (2DGLS), 4D GLS, 4D global radial strain (GRS), and aortic diameter compared to FD patients. In PRKAG2 Pts the bull’s-eye plot showed a stripe-like pattern. In FD Pts, however, it showed a peak systolic strain reduction in basal and mid- anterolateral segments. Conclusion: Echocardiography is an important technique in differentiating both diseases to determine adequate treatment and prognosis. We have demonstrated that PRKAG2 Pts present moderately greater cardiac involvement than FD Pts.

Design and implementation of fast RBSD multiplier

In the developing world, we need to meet the high-speed requirements of users in chips. It has become a necessity to have faster and more efficient gadgets in all sectors which can work beyond our expectations and imaginations and give a better experience. So, for that we have designed a multiplier which is faster and can provide results precisely. In this paper we are using RBSD encodings which make multiplier faster by not doing carry propagations and thereby reducing delay for getting output. Redundant Binary Signed Decimals (RBSD) multipliers are designed in such a way that it reduces partial products in multiplication and thereby the multiplier is carry-free. The RBSD multiplier proposed in this paper can perform both signed and unsigned number multiplication. The proposed system is being implemented in VIVADO 2020.2 version in Verilog language. This paper is a comparative analysis with the existing model of RBSD multiplier.

Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis.

In patients presenting with blunt hepatic injury (BHI), the utility of CT for triage to hepatic angiography remains uncertain since simple binary assessment of contrast extravasation (CE) as being present or absent has only modest accuracy for major arterial injury on digital subtraction angiography (DSA). American Association for the Surgery of Trauma (AAST) liver injury grading is coarse and subjective, with limited diagnostic utility in this setting. Volumetric measurements of hepatic injury burden could improve prediction. We hypothesized that in a cohort of patients that underwent catheter-directed hepatic angiography following admission trauma CT, a deep learning quantitative visualization method that calculates % liver parenchymal disruption (the LPD index, or LPDI) would add value to CE assessment for prediction of major hepatic arterial injury (MHAI). This retrospective study included adult patients with BHI between 1/1/2008 and 5/1/2017 from two institutions that underwent admission trauma CT prior to hepatic angiography (n = 73). Presence (n = 41) or absence (n = 32) of MHAI (pseudoaneurysm, AVF, or active contrast extravasation on DSA) served as the outcome. Voxelwise measurements of liver laceration were derived using an existing multiscale deep learning algorithm trained on manually labeled data using cross-validation with a 75-25% split in four unseen folds. Liver volume was derived using a pre-trained whole liver segmentation algorithm. LPDI was automatically calculated for each patient by determining the percentage of liver involved by laceration. Classification and regression tree (CART) analyses were performed using a combination of automated LPDI measurements and either manually segmented CE volumes, or CE as a binary sign. Performance metrics for the decision rules were compared for significant differences with binary CE alone (the current standard of care for predicting MHAI), and the AAST grade. 36% of patients (n = 26) had contrast extravasation on CT. Median [Q1-Q3] automated LPDI was 4.0% [1.0-12.1%]. 41/73 (56%) of patients had MHAI. A decision tree based on auto-LPDI and volumetric CE measurements (CEvol) had the highest accuracy (0.84, 95% CI 0.73-0.91) with significant improvement over binary CE assessment (0.68, 95% CI 0.57-0.79; p = 0.01). AAST grades at different cut-offs performed poorly for predicting MHAI, with accuracies ranging from 0.44-0.63. Decision tree analysis suggests an auto-LPDI cut-off of ≥ 12% for minimizing false negative CT exams when CE is absent or diminutive. Current CT imaging paradigms are coarse, subjective, and limited for predicting which BHIs are most likely to benefit from AE. LPDI, automated using deep learning methods, may improve objective personalized triage of BHI patients to angiography at the point of care.

Internet addiction in adolescents in Central Siberia: analysis of prevalence and structure of consumed content

The aim of this study was to investigate the prevalence of Internet addiction (IА) and the structure of consumed content among adolescents in Central Siberia. Materials and methods . From January to May 2019, 3,012 adolescents (45.8% of boys and 54.2% of girls) aged 12–18 years were examined (average age 14.5 ± 1.3 years). Younger adolescents (12–14 years old) accounted for 52.5%, and seniors (15–18 years old) accounted for 47.5%. Peculiarities of online behavior were evaluated according to Chen’s Internet Addiction Scale (CIAS) adapted by V.L. Malygin and K.A. Feklisov; a total CIAS score of ≥ 65 indicated Internet addiction. Gambling addiction was rated according to the Game Addiction Scale for Adolescents, and addiction to social networks was rated according to the Social Media Disorder Scale. The structure of the consumed content was estimated in the overall sample and in two age groups. The data obtained were processed by non-parametric statistical methods using the Statistica 12.0 software. Quantitative characteristics are presented as the median and the interquartile range Me (Q 25 – Q 75 ), binary signs are represented as a share (%) and the confidence interval. The significance of the differences ( p ) for quantitative indicators was evaluated by Mann – Whitney U -test and for binary characters by Pearson’s χ2 criterion. The differences between the groups were considered statistically significant at p ≤ 0.05. Results. The overall prevalence of IА was 6.9%, adaptive use of the Internet was observed in 49.4% of adolescents, non-adaptive use was registered in 43.6% of cases. The frequency of Internet addiction increases with age: from 6.0% in 12–14 year-old adolescents to 8.0% in 15–18 year-old adolescents ( p = 0.0324). Content consumed by adolescents included gambling addiction (11%), addiction to social networks (8.0%), mixed IA (2.6%), and undifferentiated Internet addiction (2.8%). Younger adolescents are more often dependent on online games (12.2%), social networks (9.3%), and mixed IА (3.3%), while in older adolescents, undifferentiated IА is more often observed (39.5%). Conclusion . High level of IA prevalence in adolescents in Central Siberia confirms the relevance of this problem and indicates the need to develop preventive measures aimed at maintaining child and adolescent health.

Відновлення таблиць з частково відсутніми даними

The problem of data recovery found in a wide variety of statistical analysis applications. For example, it related to the survey in various sectors of the economy. When filling out a questionnaire specialist specific subject area sometimes there is a situation where he cannot answer all the questions for technical reasons (failure of the communication channel) or for personal reasons (he may not prefer one criterion to another). Survey data in many cases are formed in the form of rectangular binary tables, where columns (rows) are the studied variables that correspond to binary numbers (1, 0) or signs ( + , -). This article is devoted to the analysis and recovery of binary data in cases when the table of values of variables for the above reasons is absent. The solution of this problem proposed to carried out by the method of homogeneous groups. The essence of this method is to find in the General population of binary data of homogeneous groups of subjects of the survey and determine the affiliation of the subject of the survey, in the response of which for one of the above reasons there was a transition to one of the selected homogeneous groups. This method works well for large amounts of binary data with relatively few spaces. Otherwise, stricter methods, such as simulation methods using maximum likelihood functions or Bayesian strategies, are used. Ref. 8, tabl. 2

Statistical evaluation of the information content of attributes for the task of searching for semantically close sentences

The paper presents the results of evaluating the informative value of quantitative and binary signs to solve the problem of finding semantically close sentences (paraphrases). Three types of signs are considered in the article: those built on vector representations of words (according to the Word2Vec model), based on the extraction of numbers and structured information and reflecting the quantitative characteristics of the text. As indicators of information content, the percentage of paraphrases among examples with a characteristic, and the percentage of paraphrases with a attribute (for binary characteristics), as well as estimates using the accumulated frequency method (for quantitative indicators) are used. The assessment was conducted on the Russian paraphrase corps. The set of features considered in the work was tested as input for two machine learning models for defining semantically close sentences: reference vector machines (SVMs) and a recurrent neural network model. The first model accepts only the considered set of signs as input parameters, the second - the text in the form of sequences and the set of signs as an additional input. The quality of the models was 67.06% (F-measure) and 69.49% (accuracy) and 79.85% (F-measure) and 74.16% (accuracy), respectively. The result obtained in the work is comparable with the best results of the systems presented in 2017 at the competition for the definition of paraphrase for the Russian language (the second result for the F-measure, the third result for accuracy). The results proposed in the work can be used both in the implementation of search models for semantically close fragments of texts in natural language, and for the analysis of Russian-language paraphrases from the point of view of computer linguistics.

Unary Coding Controlled Simultaneous Wireless Information and Power Transfer

Radio frequency (RF) signals have been relied upon for both wireless information delivery and wireless charging to the massively deployed low-power Internet of Things (IoT) devices. Extensive efforts have been invested in physical layer and medium-access-control layer design for coordinating simultaneous wireless information and power transfer (SWIPT) in RF bands. Different from the existing works, we study the coding controlled SWIPT from the information theoretical perspective with practical transceiver. Due to its practical decoding implementation and its flexibility on the codeword structure, unary code is chosen for joint information and energy encoding. Wireless power transfer (WPT) performance in terms of energy harvested per binary sign and of battery overflow/underflow probability is maximised by optimising the codeword distribution of coded information source, while satisfying required wireless information transfer (WIT) performance in terms of mutual information. Furthermore, a Genetic Algorithm (GA) aided coding design is proposed to reduce the computational complexity. Numerical results characterise the SWIPT performance and validate the optimality of our proposed GA aided unary coding design.

Polarity-Header Optical OFDM for Visible Light Communication Systems

Orthogonal frequency division multiplexing (OFDM) is a candidate modulation technique to achieve high-speed data transmission in visible light communication systems that provide both illumination and downlink data transmission. This paper proposes a method called polarity-header optical OFDM (PHO-OFDM) specially designed to account for the nonlinear response of the LED transmitters. To make the transmitted signal positive, as required for intensity modulation and direct detection systems, a signal header containing the sign information of the bipolar OFDM signal is transmitted followed by the amplitude of the signal. The binary sign information can be encoded and transmitted via a few samples using $M$ -ary pulse-amplitude modulation. The amplitude of the OFDM signal that is larger than the maximum LED power is sent in a subsequent frame to reduce the nonlinear distortion due to peak-power clipping. Furthermore, illumination requirements are taken into account, and their effects on the system performance are discussed. This paper also considers the overhead cost of adding a cyclic prefix to minimize intersymbol interference in dispersive channels. Compared with state-of-the-art optical OFDM techniques, the proposed PHO-OFDM achieves a better BER performance and higher data throughput, and provides a larger illumination flexibility.

АКТУАЛИЗАЦИЯ РОДОВЫХ ЗНАЧЕНИЙ В ДЕТСКОЙ ПОЭЗИИ КОНЦА ХХ НАЧАЛА ХХI В.В.

The article deals with peculiarities of the functioning of generic meanings in children's poetry of the late XX beginning of the XXI century. Expressiveness, semantic actualization and neutralization of the gender category in children’s poetry are researched. Juxtaposition gender grammes and the actualization of the meaning of sex in animate nouns in the works of children's authors are described. The cases of the use of masculine and feminine grammes for the formation of binary sign complexes, as well as the use of universal gender complexes in the plural form for the generalization and transmission of ‘children’ are considered. Examples of the use of generic opposites for the actualization of a certain part of the grammatical field and the formation of the structure and plot of the poem are described. The use of paired by the same-rooted animated nouns in the meaning of “family”, “couple” is analyzed. Examples of the use of productive word-building tools for creating occasional author formations are described. We studied the mechanisms of violation of predictability for creating a comic effect. Examples of juxtaposition of the three genders for transferring of the value of completeness and inclusiveness are considered The use of gender pairs to create a model of figurative parallelism is described. The mechanisms of the functioning of the gender paradigm in the structure of children's poetic text are studied. Key words: gender meaning, expressiveness, actualization, neutralization, gender paradigm, gender complex, binary sign complex

Development of the disable software reporting system on the basis of the neural network

The PE structure of malicious and secure software is analyzed, features are highlighted, binary sign vectors are obtained and used as inputs for training the neural network. A software model for detecting malware based on the ART-1 neural network was developed, optimal similarity coefficients were found, and testing was performed. The obtained research results showed the possibility of using the developed system of identifying malicious software in computer systems protection systems

Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchange data.

We extend the concept of statistical symmetry as the invariance of a probability distribution under transformation to analyze binary sign time series data of price difference from the foreign exchange market. We model segments of the sign time series as Markov sequences and apply a local hypothesis test to evaluate the symmetries of independence and time reversion in different periods of the market. For the test, we derive the probability of a binary Markov process to generate a given set of number of symbol pairs. Using such analysis, we could not only segment the time series according the different behaviors but also characterize the segments in terms of statistical symmetries. As a particular result, we find that the foreign exchange market is essentially time reversible but this symmetry is broken when there is a strong external influence.

Design and Development of Tamil Sign Alphabets using Image Processing with Right Hand Palm to Aid Deaf-Dumb People

Hand recognition is a recent active area of research in the computer vision for the purpose of Human - Computer Interaction. This paper mainly concentrates on Tamil sign alphabets (TSL) into speech which could be helpful for deaf-dumb people. In this paper, a set of 32 (25) combinations of binary number sign images are introduced to propose a system to recognize Tamil sign alphabets. These Tamil alphabets have 12 vowels, 18 consonants, and one Aayutha Ezhuthu. The proposed system is based on four main stages: Pre-processing method, Training phase, Sign detection, and Conversion of Binary to voice. The binary sign images are loaded at a run time or static as 310 images which are taken ten times in different distances at the same position. The Ave fingertip positions represent (‘1’ or ‘0’) and are identified by using image processing techniques with proposed right hand palm angular-based analysis. Then, the binary values are assigned to the corresponding Tamil letters and voice. The experiments were performed with ten different signer palms and the results demonstrated that the system could successfully recognize Tamil sign alphabets with better accuracy with 99.35% of static images and 98.36% of dynamic images (runtime).

DETERMINING THE ORIENTATION OF THE ELEMENTARY COMPONENTS OF CHARACTERS MODELS, SUBJECT TO AUTOMATIC NAMING ON THE SET OF ATOMIC ELEMENTS

Arbitrary nature digital binary images signs automatic modeling, naming and recognition are actual scientific and practical problems, which are existing in many areas of information technologies application scopes, particularly in electronic documents processing and analyzing. This article is devoted to a constructive definition of images structure components, that are modeled after skeletonization during preprocessing, and their properties, which will give an opportunity to perform automatic detection of described objects in order to form description of future signs, which will be named and recognized on atomic elements discrete set

Fast calculation of number of binary signed digit representations of an integer

Binary Signed Digit(BSD) representation of an integer is widely used in computer arithmetic,cryptography and digital signal processing.An integer of length n bits can have several BSD representations.In this paper,the authors studied the properties of the number of BSD representation of an integer,and presented two improved non-recursion algorithms.They can rapidly calculate the exact number of BSD representations of an integer of a certain length,and the storage requirements get reduced.

Classifying two-dimensional orbits using pattern recognition

We present a fast algorithm to identify both regular and irregular orbits that map out a sustained shape in configuration space. The method, which we dub ‘pattern autocorrelation’ (PACO), detects a repeating pattern in time-series constructed from binary sign changes in phase-space coordinates reduced to two dimensions. This is achieved by computing the autocorrelation function of the time-series, and by retrieving a pattern and a pattern-to-signal ratio. We apply the method to two-dimensional orbits in the logarithmic potential in an application to spiral galaxies with an asymptotically flat rotation curve; the general case of three-dimensional orbits is sketched. We find that irregular orbits can yet sustain the smooth morphological features of a galaxy for a substantial fraction of a Hubble time: this fraction is quantified through the pattern-to-signal ratio. In the case where a central supermassive black hole is added to the potential, we find that up to ≈16% of initial conditions space yields irregular motion that may sustain long-lived regular features. The method further detects and distinguishes orbits that are not based on Lissajous theory of resonant motion.

Intelligent Sign Language Recognition Using Image Processing

Computer recognition of sign language is an important research problem for enabling communication with hearing impaired people. This project introduces an efficient and fast algorithm for identification of the number of fingers opened in a gesture representing an alphabet of the Binary Sign Language. The system does not require the hand to be perfectly aligned to the camera. The project uses image processing system to identify, especially English alphabetic sign language used by the deaf people to communicate. The basic objective of this project is to develop a computer based intelligent system that will enable dumb people significantly to communicate with all other people using their natural hand gestures. The idea consisted of designing and building up an intelligent system using image processing, machine learning and artificial intelligence concepts to take visual inputs of sign language's hand gestures and generate easily recognizable form of outputs. Hence the objective of this project is to develop an intelligent system which can act as a translator between the sign language and the spoken language dynamically and can make the communication between people with hearing impairment and normal people both effective and efficient. The system is we are implementing for Binary sign language but it can detect any sign language with prior image processing

Coexisting Order and Disorder Hidden in a Quasi-Two-Dimensional Frustrated Magnet

Frustrated magnetic interactions in a quasi-two-dimensional [111] slab of pyrochlore lattice were studied. For uniform nearest neighbor (NN) interactions, we show that the complex magnetic problem can be mapped onto a model with two independent degrees of freedom, tricolor and binary sign. This provides a systematic way to construct the complex classical spin ground states with collinear and coplanar bipyramid spins. We also identify "partial but extended" zero-energy excitations amongst the ground states. For nonuniform NN interactions, the coplanar ground state can be obtained from the collinear bipyramid spin state by collectively rotating two spins of each tetrahedron with an angle α in an opposite direction. The latter model with α~30° fits the experimental neutron data from SrCr(9p)Ga(12-9p)O(19) well.