Search

Recently, multi-frequency multi-constellation receivers have been actively studied, which are single receivers that process multiple global navigation satellite system (GNSS) signals for high accuracy and reliability. However, in order for a single receiver to process multiple GNSS signals, it requires as many code generators as the number of supported GNSS signals, and this is one of the problems that must be solved in implementing an efficient multi-frequency multi-constellation receiver. This paper proposes an area-efficient universal code generator that can support both GPS L1C signals and BDS B1C signals. The proposed architecture alleviates the area problem by sharing common hardware in a time-multiplex mode without degrading the overall system performance. According to the result of the synthesis using the CMOS 65 nm process, the proposed universal code generator has an area reduced by 98%, 93%, and 60% compared to the previous memory-based universal code generator (MB UCG), the Legendre-generation universal code generator (LG UCG), and the Weil-generation universal code generator (WG UCG), respectively. Furthermore, the proposed generator is applicable to all Legendre sequence-based codes.

The main purpose of this work is to study the possibility of using the few-group approximation for calculation of some neutron-physical characteristics of VVER-1000 core by means of special version of Monte Carlo Universal code. The Monte Carlo method for VVER-1000 core neutron-physical characteristics calculation using the few-group approximation with an estimate of neutron cross sections “by location” was provided and tested in this research. The reduction of calculation time due to the transition from a pointwise model of representation of cross sections to the few-group approximation and methodical error of this approach were evaluated. Optimal number of energy groups was determined. It was found that consideration of the scattering anisotropy leads to a significant decrease in methodical error. Ways of further reduction of methodical error were worked out.

Although conventional global navigation satellite systems (GNSS) receivers were originally designed for single signals, studies on multi-signal receiver design have recently been actively conducted to achieve high accuracy, precision, and reliability. However, in order for a multi-signal receiver to support various codes, the receiver should support the generation of individual codes. Therefore, the resulting problem of increased complexity must be solved. This paper proposes a hardware structure for an area-efficient linear feedback shift register (LFSR)-based multi-frequency universal code generator. Whereas the existing universal code generators were configured so that feedback polynomials, output registers, and initial values can be selected by placing read-only memories (ROMs), multiplexers (MUXs), and exclusive ORs (XORs) by register bit, in the case of the proposed universal code generator, the circuit was implemented by applying the hardwiring technique to those register bits that have fixed values. According to the results of field programmable gate array (FPGA) implementation, the proposed LFSR-based universal code generator can improve look up table (LUT) by up to 37% and register by up to 78% when compared to conventional code generators, and LUT by up to 36% when compared to the previous universal code generator. Therefore, the proposed universal code generator is a good candidate for implementing multi-frequency receivers to achieve high precision and high reliability.

У статті запропоновано визначення символу в міждисциплінарному аспекті. Указано те, що символ – це універсальний інтелектуальний засіб кодування абстрактної дійсності, а тому пошук конкретних способів і методів його дешифрування є досить актуальним.Метою статті є аналіз різноаспектних теоретичних поглядів стосовно ролі символу як такого, завданням – розкрити концептуальні положення відомих учених щодо трактування функцій символу та варіативності його рецепції. Акцентовано на тому, що в сучасних розвідках символ наділений особливими поліфункціональними властивостями, він корелює з такими категоріями, як картина світу, феномен людського буття, екзистенціалізм, національні / загальнонаціональні особливості, духовність., The article describes the definition of a symbol in an interdisciplinary aspect. It is indicated that the symbol is a universal intellectual means of encoding abstract reality, and therefore the search for specific methods and methods for its decryption is very relevant. Therefore, the purpose of this article is to analyze the various aspects of theoretical research regarding the role of the symbol as such, and thus the task is to reveal the conceptual positions of the famous scientists regarding the interpretation of the functions of the symbol and the variability of its perception. It is emphasized is that in modern investigations the symbol is endowed with special polyfunctional qualities and correlates with such categories as the picture of the world, the phenomenon of human existence, existentialism, national / national features, spirituality.Considering the symbol as a fundamental multi-dimensional category and the most important concepts in relation to its definition, the following main directions are distinguished: 1) philosophical, the founder of which is E. Kassierer. According to his theory of “symbolic forms,” the symbol is regarded as a symbolic form, as a means of modeling reality and as a means of human mental and cultural activity, “the key to human nature”; 2) semiotic, which, according to G. G. Pocheptsov, is based on the close connection between the signifiant and the signifiй, on the sign nature of the symbol and on the study of the symbol through a sign that reflects not reality, but indicates a certain model of reality; 3) a mifopoetichesky that characterizes a symbol from theological positions, according to which the myth transmits a holistic, bilateral, that is, material and ideal, a picture of the world, the reflection of which we find in mystical-symbolic images; 4) lingophilosophical, according to which, according to A.F. Losev, a symbol is considered as a social and cultural sign, which reflects a certain idea, which is perceived intuitively and not expressed verbally; 6) a psychological one that S. Freud taught, considering a symbol as a secret code, which has the role of disguising the essence of the hidden desire, and the interpretation of the unconscious – its decipherment; 7) literary and artistic, according to which, according to V. V. Kolesov, the symbol is interpreted as a cultural communicative system that reveals the functioning of a symbol in the artwork.Summarizing the above, we can conclude that the symbol is a semiotic concept that reflects the author’s idea, depends on the psychological features of the author and his background knowledge, actualizes the contextual associative feelings. The main criteria are explicit expression of implicit contextual meaning and its cultural aspect.

In this paper, we are interested in the problem of strengthening the secrecy of distributed encryption in a particular case where the encryption keys are correlated to each other. We show that the post-encryption-compression (PEC) paradigm provides a suitable solution for the problem without requiring any additional secret randomness. More precisely, we show that in the case of one-time-pad encryption, we can use affine encoders with specific properties to strengthen the secrecy by using them to compress the ciphertexts before sending them to public communication channels. We show how to derive those affine encoders using universal code construction paradigm. The universal code construction gives us advantages as follows. 1) We can construct good encoders based on the transmission rates only, without knowing the value of the distribution of sources or keys. 2) Reliability and secrecy are achievable by construction even in particular cases such as follows: 1) only the range of correlation between keys is known but the exact amount is unknown or 2) the correlation between keys are changing time to time within a certain range. We also derive explicit lower bounds on the achievable reliability and secrecy exponents, i.e., the exponential rates of decay of the probability of decoding error and of the information leakage as the block length becomes large.

Many theories assume that a sensory neuron’s higher firing rate indicates a greater probability of its preferred stimulus. However, this contradicts (1) the adaptation phenomena where prolonged exposure to, and thus increased probability of, a stimulus reduces the firing rates of cells tuned to the stimulus; and (2) the observation that unexpected (low probability) stimuli capture attention and increase neuronal firing. Other theories posit that the brain builds predictive/efficient codes for reconstructing sensory inputs. However, they cannot explain that the brain preserves some information while discarding other. We propose that in sensory areas, projection neurons’ firing rates are proportional to optimal code length (i.e., negative log estimated probability), and their spike patterns are the code, for useful features in inputs. This hypothesis explains adaptation-induced changes of V1 orientation tuning curves and bottom-up attention. We discuss how the modern minimum-description-length (MDL) principle may help understand neural codes. Because regularity extraction is relative to a model class (defined by cells) via its optimal universal code (OUC), MDL matches the brain’s purposeful, hierarchical processing without input reconstruction. Such processing enables input compression/understanding even when model classes do not contain true models. Top-down attention modifies lower-level OUCs via feedback connections to enhance transmission of behaviorally relevant information. Although OUCs concern lossless data compression, we suggest possible extensions to lossy, prefix-free neural codes for prompt, online processing of most important aspects of stimuli while minimizing behaviorally relevant distortion. Finally, we discuss how neural networks might learn MDL’s normalized maximum likelihood (NML) distributions from input data.

A universal code for the (positive) integers is a variable length code that can be used to store or compress a sequence of integers. It also implies a probability distribution on integers which can be a natural choice when the true distribution of a source of integers is unknown; such a code and distribution may be useful in statistical inference. This paper provides two improvements to the theory and practice of universal codes. First, it defines and examines a new universal code omega* (omega-star) that asymptotically beats the Elias omega code. Second, it analyses the properties of a code proposed by Wallace based on trees, and shows it to be a universal code, to have desirable properties for use in inference, and to beat the Elias omega code on almost all integers up to the 1697-bit code-word mark. Encoding and decoding routines for the codes described here are implemented and available for interactive use.1 1The codes may be tried at www.allisons.org/ll/MML/Discrete/Universal/ $\leftarrow$ click.

Learning from source code usually requires a large amount of labeled data. Despite the possible scarcity of labeled data, the trained model is highly task-specific and lacks transferability to different tasks. In this work, we present effective pre-training strategies on top of a novel graph-based code representation, to produce universal representations for code. Specifically, our graph-based representation captures important semantics between code elements (e.g., control flow and data flow). We pre-train graph neural networks on the representation to extract universal code properties. The pre-trained model then enables the possibility of fine-tuning to support various downstream applications. We evaluate our model on two real-world datasets – spanning over 30M Java methods and 770K Python methods. Through visualization, we reveal discriminative properties in our universal code representation. By comparing multiple benchmarks, we demonstrate that the proposed framework achieves state-of-the-art results on method name prediction and code graph link prediction.

Digitalization of management objects allows using digital technologies to improve the efficiency of enterprises as links of supply chains. It is particularly difficult to establish communications between various objects of supply chain management—flows, systems, processes, and relationships—as well as to maintain their right combination for solving tasks in the most rational way. The research aims to develop a code of combinations of management objects that can solve the above problem. The research hypothesis assumes the possibility of using a universal code formed by a set of classification features of these objects, each of which is characterized by a binary code. System analysis, grouping, and classification research methods are used. The result is a code that provides digital information processing and increases the efficiency of supply chain management. The scientific contribution is to create a methodological basis for digitalization of management objects. The research results can be used in the development and application of computer software for supply chain management.

This paper discusses the vulnerabilities of automatic access monitoring and control systems widely used in everyday life. Two basic principles of information exchange in such devices are described. Study of amplitude-frequency characteristics of key fobs from various manufacturers of automatic access monitoring and control systems was undertaken. According to the results of the study and the vulnerabilities found, a universal code grabber model is shown. Knowing the principles of operation of this model, it will be possible to successfully counter hacking. A test model of the system based on the Arduino Uno microcontroller was also developed.

This paper reviews PRN code generation methods. Generated codes are used as ranging codes in the existing GNSSs. These codes are used to improve the accuracy of the ranging measurements. We present the structure of the universal code generator which can be used in the satellite navigation systems: NAVSTAR GPS, GLONASS, Galileo, Beidou. This generator provides the ability to generate ranging codes for almost 70% of all open navigation signals. Universal generator was implemented as a SystemVerilog module as part of the navigation receiver software. Using the receiver including this module, we have successfully received navigation signals that use different PRN codes. We also compared hardware resource utilization by universal generator and by GPS C/A ranging code generator. This analysis showed that the universal generator module uses less than 4 times more FPGA resources than the special GPS C/A generator module. However, the resource utilization of the correlator channel increases by only 5 percent.

Mammalian grid cells represent spatial locations in the brain via triangular firing patterns that tessellate the environment. They are regarded as the biological substrate for path integration and to provide an efficient code for space. However, grid cell patterns are strongly influenced by environmental manipulations, in particular, exhibiting local geometrical deformations and defects tied to the shape of the recording enclosure, challenging the view that grid cells constitute a universal code for space. We show that the observed responses to environmental manipulations arise as a natural result under the general framework of feedforward models with spatially unstructured feedback inhibition, which puts the development of triangular patterns in the context of a Turing pattern formation process over physical space. The model produces coherent neuronal populations with equal grid spacing, field size, and orientation.

We discuss work extraction from classical information engines (e.g., Szil\'ard) with $N$-particles, $q$ partitions, and initial arbitrary non-equilibrium states. In particular, we focus on their {\em optimal} behaviour, which includes the measurement of a set of quantities $\Phi$ with a feedback protocol that extracts the maximal average amount of work. We show that the optimal non-equilibrium state to which the engine should be driven before the measurement is given by the normalised maximum-likelihood probability distribution of a statistical model that admits $\Phi$ as sufficient statistics. Furthermore, we show that the minimax universal code redundancy $\mathcal{R}^*$ associated to this model, provides an upper bound to the work that the demon can extract on average from the cycle, in units of $k_{\rm B}T$. We also find that, in the limit of $N$ large, the maximum average extracted work cannot exceed $H[\Phi]/2$, i.e. one half times the Shannon entropy of the measurement. Our results establish a connection between optimal work extraction in stochastic thermodynamics and optimal universal data compression, providing design principles for optimal information engines. In particular, they suggest that: (i) optimal coding is thermodynamically efficient, and (ii) it is essential to drive the system into a critical state in order to achieve optimal performance., Comment: 26 pages, 5 figures. To appear in JSTAT

In the entropy coding, AIVF (almost instantaneous variable-to-fixed length) codes using multiple parsing trees can attain a better compression rate than the Tunstall code, which attains the best compression rate in the class of VF codes with a single parsing tree. Furthermore, the multiple parsing trees of an AIVF code can be multiplexed into a single parsing tree. In this paper, we propose a new universal data compression code based on the techniques of the AIVF code. The proposed universal code can also be considered as an improvement of the LZW code (Welch code). We explain how the AIVF coding techniques can be applied to universal coding by growing dynamically a single parsing tree, and we evaluate the compression rate of the proposed universal code theoretically and using several corpora.

The standard genetic code (SGC) is virtually universal among extant life forms. Although many deviations from the universal code exist, particularly in organelles and prokaryotes with small genomes, they are limited in scope and obviously secondary. The universality of the code likely results from the combination of a frozen accident, i.e., the deleterious effect of codon reassignment in the SGC, and the inhibitory effect of changes in the code on horizontal gene transfer. The structure of the SGC is nonrandom and ensures high robustness of the code to mutational and translational errors. However, this error minimization is most likely a by-product of the primordial code expansion driven by the diversification of the repertoire of protein amino acids, rather than a direct result of selection. Phylogenetic analysis of translation system components, in particular aminoacyl–tRNA synthetases, shows that, at a stage of evolution when the translation system had already attained high fidelity, the correspondence between amino acids and cognate codons was determined by recognition of amino acids by RNA molecules, i.e., proto-tRNAs. We propose an experimentally testable scenario for the evolution of the code that combines recognition of amino acids by unique sites on proto-tRNAs (distinct from the anticodons), expansion of the code via proto-tRNA duplication, and frozen accident.

Universal fixed-to-variable lossless source coding for memoryless sources is studied in the finite blocklength and higher-order asymptotics regimes. Optimal third-order coding rates are derived for general fixed-to-variable codes and for prefix codes. It is shown that the non-prefix Type Size code, in which codeword lengths are chosen in ascending order of type class size, achieves the optimal third-order rate and outperforms classical Two-Stage codes. Converse results are proved making use of a result on the distribution of the empirical entropy and Laplace's approximation. Finally, the fixed-to-variable coding problem without a prefix constraint is shown to be essentially the same as the universal guessing problem., 32 pages, 1 figure. Submitted to IEEE Transactions on Information Theory, Dec. 2014

In this article, a construction of an optimal tight conflict-avoiding code of length 3 d p e and weight 3 is shown for d ≡ 1 ( mod 3 ) , e ∈ N and a prime p ≡ 3 ( mod 8 ) with p ≠ 3 , assuming that p is a non-Wieferich prime if e ≥ 2 . This is a new series of optimal conflict-avoiding code for which the number of codewords can be exactly determined.

Proposed were new structures for concurrent error detection systems of the combinatorial logic circuits based on the codes with summation of the weighted transitions and their modifications. They were compared with the traditional systems of duplication and check by the Berger code. The structure based on the code with summation of the weighted transitions allowed one to improve the index of realization complexity as compared with the duplication system by 4 % on the average. The structure obtained by modifying the code with summation of the weighted transitions into the optimal code enables one to improve this index almost twice as much as compared with the system of checking by the Berger code. At that, this system has a better index of error detection. In certain cases, the structure of the concurrent error detection system on the basis of the optimal code is superior in complexity to the system of parity check.

The Minimum Description Length (MDL) principle is an information theoretic approach to inductive inference that originated in algorithmic coding theory. In this approach, data are viewed as codes to be compressed by the model. From this perspective, models are compared on their ability to compress a data set by extracting useful information in the data apart from random noise. The goal of model selection is to identify the model, from a set of candidate models, that permits the shortest description length (code) of the data. Since Rissanen originally formalized the problem using the crude ‘two-part code’ MDL method in the 1970s, many significant strides have been made, especially in the 1990s, with the culmination of the development of the refined ‘universal code’ MDL method, dubbed Normalized Maximum Likelihood (NML). It represents an elegant solution to the model selection problem. The present paper provides a tutorial review on these latest developments with a special focus on NML. An application example of NML in cognitive modeling is also provided.

A universal compression code can act as an estimator for the distribution of a finite alphabet finite-order Markov source. This property of universal codes was exploited by Jacob and Bansal in [5] to propose a modification of the CUSUM test in order to solve the change detection problem when the post-change distribution is not known. The performance of this test was proven to be asymptotically optimal for a memoryless sources and class of sources with memory under Lorden’s minimax formulation. This study was further extended in [9] where performance of the modified CUSUM for an i.i.d. setting under Lai’s criterion involving a constraint on the probability of false alarm within a window (PFAW) was analyzed. In this paper, we introduce a modified version of the window limited CUSUM (WL-CUSUM) test by incorporating strongly universal code. We closely follow the work of Lai [8] in order to prove the asymptotic optimality for the test under the PFAW criterion. We further prove the asymptotic optimality of the modified WL-CUSUM test in the Bayesian setting.

If we consider the neural network optimization process as a model selection problem, the implicit space can be constrained by the normalizing factor, the minimum description length of the optimal universal code. Inspired by the adaptation phenomenon of biological neuronal firing, we propose a class of reparameterization of the activation in the neural network that take into account the statistical regularity in the implicit space under the Minimum Description Length (MDL) principle. We introduce an incremental version of computing this universal code as normalized maximum likelihood and demonstrated its flexibility to include data prior such as top-down attention and other oracle information and its compatibility to be incorporated into batch normalization and layer normalization. The empirical results showed that the proposed method outperforms existing normalization methods in tackling the limited and imbalanced data from a non-stationary distribution benchmarked on computer vision and reinforcement learning tasks. As an unsupervised attention mechanism given input data, this biologically plausible normalization has the potential to deal with other complicated real-world scenarios as well as reinforcement learning setting where the rewards are sparse and non-uniform. Further research is proposed to discover these scenarios and explore the behaviors among different variants.

A new chain code named Unsigned Manhattan Chain Code (UMCC) is presented.UMCC is insensitive to rotation and mirroring, and enables shape magnification.UMCC is capable of detecting monotone parts of the shape's boundary.The proposed chain code achieves 50% better compression ratio than F8 code. This paper introduces a new chain code named Unsigned Manhattan Chain Code - UMCC. Although it exploits a pixel's neighbourhood of 8-connectivity, only two coding symbols are used for navigating through the geometric shape's boundary pixels. For this, the movements in the x - and y -coordinate direction are separated, whilst the sign of the moving direction is controlled by two flags - one for each coordinate direction. UMCC is insensitive to rotation and mirroring and enables shape magnification. However, the more unique property is the UMCC's ability to explicitly separate the monotonic parts of the geometric shape. UMCC's properties have been compared against the properties of other chain codes including the Freeman chain code in eight and four directions, the Vertex Chain Code, and the Three OrThogonal chain code. It has been shown that the UMCC has superior properties in regards to the up-to-date chain codes.

Information processing in the presence of noise has been a key challenge in multiple disciplines including computer science, communications, and neuroscience. Among such noise-reduction mechanisms, the shift-map code represents an analog variable by its residues with respect to distinct moduli (that are chosen as geometric scalings of an integer). Motivated by the multi-periodic neural code in the entorhinal cortex, i.e., the coding mechanism of grid cells, this work extends the shift-map code by generalizing the choices of moduli. In particular, it is shown that using similarly sized moduli (for instance, evenly and closely spaced integers, which tend to have large co-prime factors) results in a code whose codewords are separated in an interleaving way such that when the decoder has side information regarding the source, then error control is significantly improved (compared to the original shift map code). This novel structure allows the system to dynamically adapt to the side information at the decoder, even if the encoder is not privy to the side information. A geometrical interpretation of the proposed coding scheme and a method to find such codes are detailed. As an extension, it is shown that this novel code also adapts to scenarios when only a fraction of codeword symbols is available at the decoder.