Sequence Of Symbols Research Articles

A construction of a 𝜆-Poisson generic sequence

Years ago Zeev Rudnick defined the λ \lambda -Poisson generic sequences as the infinite sequences of symbols in a finite alphabet where the number of occurrences of long words in the initial segments follow the Poisson distribution with parameter λ \lambda . Although almost all sequences, with respect to the uniform measure, are Poisson generic, no explicit instance has yet been given. In this note we give a construction of an explicit λ \lambda -Poisson generic sequence over any alphabet and any positive λ \lambda , except for the case of the two-symbol alphabet, in which it is required that λ \lambda be less than or equal to the natural logarithm of 2 2 . Since λ \lambda -Poisson genericity implies Borel normality, the constructed sequences are Borel normal. The same construction provides explicit instances of Borel normal sequences that are not λ \lambda -Poisson generic.

Chaotic Pulse-Shaping Filter Based on Root-Raised-Cosine Division.

Chaotic baseband wireless communication system (CBWCS) suffers bit error rate (BER) degradation due to their intrinsic intersymbol interference (ISI). To this end, an ISI-free chaotic filter based on root-raised-cosine (RRC) division is constructed to generate a chaotic signal. A wireless communication system using this chaotic signal as a baseband waveform is proposed. The chaotic property is proved by the corresponding new hybrid dynamical system with topological conjugation to symbolic sequences and a positive Lyapunov exponent. Simulation results show that under a single-path channel and multi-path channel, the proposed method outperforms CBWCS in both BER performance and computational complexity.

On the Use of Variability Measures to Analyze Source Coding Data Based on the Shannon Entropy

Source coding maps elements from an information source to a sequence of alphabetic symbols. Then, the source symbols can be recovered exactly from the binary units. In this paper, we derive an approach that includes information variation in the source coding. The approach is more realistic than its standard version. We employ the Shannon entropy for coding the sequences of a source. Our approach is also helpful for short sequences when the central limit theorem does not apply. We rely on a quantifier of the information variation as a source. This quantifier corresponds to the second central moment of a random variable that measures the information content of a source symbol; that is, considering the standard deviation. An interpretation of typical sequences is also provided through this approach. We show how to use a binary memoryless source as an example. In addition, Monte Carlo simulation studies are conducted to evaluate the performance of our approach. We apply this approach to two real datasets related to purity and wheat prices in Brazil.

ФОРМИРОВАНИЕ ОДНОПОЛОСНОЙ КВАДРАТУРНОЙ ФАЗОВОЙ МАНИПУЛЯЦИИ РАДИОСИГНАЛОВ НА ПОДНЕСУЩИХ ЧАСТОТАХ В КОГЕРЕНТНОЙ ОПТИЧЕСКОЙ СИСТЕМЕ КОММУНИКАЦИИ

The coherent optical communication technology allows to create a transmission system overlong distances with high bandwidth. Higher order modulation formats such as Quadrature PhaseShift Keying (QPSK) provide high data rates in a simple, reliable and cost-effective manner. Thesemulti-bit modulation methods are transmitted as a symbol implementing simple in-phase andquadrature (IQ) modulation formats. To increase throughput in coherent optical systems, opticalsubcarrier multiplexing is used, when several radio signals are multiplexed on one optical carrier.This multiplexing is limited by double sideband, resulting in reduced received subcarrier powerand degraded carrier-to-noise ratio. To accumulate these effects, many ways of implementingoptical single sideband (OSSB) are proposed. The paper proposes an analysis of a coherent opticaltransmission system for generating single-sideband QPSK modulation using an optical I/Qmodulator and M-ary RF pulse shaping. The pseudo-random bit sequence generator generates a10 Gb/s modulation baseband signal. Each bit sequence is converted into an M-ary sequence ofsymbols from binary signals using phase shift keying (PSK). The M-ary pulse generator generatesa multi-level pulse shaping according to the sequence of M-ary input symbols. Electrical pulsesare applied to the electrodes of the electro-optical modulator according to the Mach–Zehnderinterferometer (MZM) scheme. The I/Q modulator operated at the quadrature operating point inboth the in-phase and quadrature branches, with an extinction ratio 60 dB and 3 V switching voltage.The light wave is emitted by a CW laser with a wavelength of 1550 nm.

Detecting renewal states in chains of variable length via intrinsic Bayes factors

Markov chains with variable length are useful parsimonious stochastic models able to generate most stationary sequence of discrete symbols. The idea is to identify the suffixes of the past, called contexts, that are relevant to predict the future symbol. Sometimes a single state is a context, and looking at the past and finding this specific state makes the further past irrelevant. States with such property are called renewal states and they can be used to split the chain into independent and identically distributed blocks. In order to identify renewal states for chains with variable length, we propose the use of Intrinsic Bayes Factor to evaluate the hypothesis that some particular state is a renewal state. In this case, the difficulty lies in integrating the marginal posterior distribution for the random context trees for general prior distribution on the space of context trees, with Dirichlet prior for the transition probabilities, and Monte Carlo methods are applied. To show the strength of our method, we analyzed artificial datasets generated from different models and one example coming from the field of Linguistics.

Information is non-physical: The rules connecting representation and meaning do not obey the laws of physics

The proposition that information is physical is widely accepted in the scientific community. Information is composed of physical representation, abstract meaning and rules, which interpret representation to meaning. In this article, I demonstrated that the rules connecting representation with meaning cannot be the laws of physics, because all quantities appearing in the laws of physics are physical, observable and measurable, and the meaning, however, is abstract, unobservable and unmeasurable. For linguistic information, the rules that determine how the ordered symbol sequence of language corresponds to its meaning are language vocabulary and grammar, not the laws of physics. This characteristic of information – its rules that link representation with meaning do not obey the laws of physics – ontologically distinguishes information from physical substances that obey the laws of physics, and inspires me to define entities that do not obey the laws of physics (such as information) as non-physical.

Chain code strategy for lossless storage and transfer of segmented binary medical data

Obtaining a 3D medical visualization is a tedious process requiring several processing steps (such as segmentation) and assigning various rendering parameters (such as color and opacity). Current systems use video/image exporting or snapshots to save results. Such vendor-dependent tools not only prevent the possibility of further interactions but also creates additional large-size data that is problematic to store in PACS over time and hard to transfer for teleradiology applications. To overcome, alternative strategies propose a representation of the visualizations, which only store segmentation masks that contains the binary form of segmented data. Unfortunately, existing compression methods are limited to effectively compress the volumetric data. In this study, lossless storage of binary segmented data is effectively performed by two newly-proposed chain code approaches. Particularly-two novel contributions are presented: 1. The dictionary of normalized angle difference is improved as a new chain symbol coding procedure, namely normalized angle difference, by adding new symbols to the dictionary aiming to generate a low-entropy symbol sequence for medical volumes. 2. A new volumetric approach that utilizes 26 symbols to encode volumetric data is developed. Each slice is visited, and the contour of the segmented object is codified such that eight different vectors for each slice (pointing to one of the four faces of each voxel, plus four towards one of its edges) are obtained. The developed methods are tested on diverse volumetric segmented data and compared to existing standards. It is shown that the proposed methods outperform well-established techniques.

Memory-aware end-to-end learning of channel distortions in optical coherent communications.

We implement a new variant of the end-to-end learning approach for the performance improvement of an optical coherent-detection communication system. The proposed solution enables learning the joint probabilistic and geometric shaping of symbol sequences by using auxiliary channel model based on the perturbation theory and the refined symbol probabilities training procedure. Due to its structure, the auxiliary channel model based on the first order perturbation theory expansions allows us performing an efficient parallelizable model application, while, simultaneously, producing a remarkably accurate channel approximation. The learnt multi-symbol joint probabilistic and geometric shaping demonstrates a considerable bit-wise mutual information gain of 0.47 bits/2D-symbol over the conventional Maxwell-Boltzmann shaping for a single-channel 64 GBd transmission through the 170 km single-mode fiber link.

EXK-SC: A Semantic Communication Model Based on Information Framework Expansion and Knowledge Collision.

Semantic communication is not focused on improving the accuracy of transmitted symbols, but is concerned with expressing the expected meaning that the symbol sequence exactly carries. However, the measurement of semantic messages and their corresponding codebook generation are still open issues. Expansion, which integrates simple things into a complex system and even generates intelligence, is truly consistent with the evolution of the human language system. We apply this idea to the semantic communication system, quantifying semantic transmission by symbol sequences and investigating the semantic information system in a similar way as Shannon's method for digital communication systems. This work is the first to discuss semantic expansion and knowledge collision in the semantic information framework. Some important theoretical results are presented, including the relationship between semantic expansion and the transmission information rate. We believe such a semantic information framework may provide a new paradigm for semantic communications, and semantic expansion and knowledge collision will be the cornerstone of semantic information theory.

Global Repeat Map (GRM) Application: Finding All DNA Tandem Repeat Units

Tandem repeats (TRs) are important components of eukaryotic genomes; they have both structural and functional roles: (i) they form essential chromosome structures such as centromeres and telomeres; (ii) they modify chromatin structure and affect transcription, resulting in altered gene expression and protein abundance. There are established links between variations in TRs and incompatibilities between species, evolutionary development, chromosome mis-segregation, aging, cancer outcomes and different diseases. Given the importance of TRs, it seemed essential to develop an efficient, sensitive and automated application for the identification of all kinds of TRs in various genomic sequences. Here, we present our new GRM application for identifying TRs, which is designed to overcome all the limitations of the currently existing algorithms. Our GRM algorithm provides a straightforward identification of TRs using the frequency domain but avoiding the mapping of the symbolic DNA sequence into numerical sequence, and using key string matching, but avoiding the statistical methods of locally optimizing individual key strings. Using the GRM application, we analyzed human, chimpanzee and mouse chromosome 19 genome sequences (RefSeqs), and showed that our application was very fast, efficient and simple, with a powerful graphical user interface. It can identify all types of TRs, from the smallest (2 bp) to the very large, as large as tens of kilobasepairs. It does not require any prior knowledge of sequence structure and does not require any user-defined parameters or thresholds. In this way, it ensures that a full spectrum of TRs can be detected in just one step. Furthermore, it is robust to all types of mutations in repeat copies and can identify TRs with various complexities in the sequence pattern. From this perspective, we can conclude that the GRM application is an efficient, sensitive and automated method for the identification of all kinds of TRs.

OFDM LOW COMPLEXITY CHANNEL ESTIMATION USING TIME-FREQUENCY ADJUSTABLE WINDOW FUNCTIONS

In this paper, we introduce a low complexity algorithm for estimation of the channel transfer function in the OFDM communication system that is using a scattered pilot symbol grid. Although, the use of the scattered pilot grid enables implementation of the flexible, and adaptive radio interface, it suffers from a high estimation error at the edges of the symbol sequence. Due to the sampling in time, and frequency, the signal is circularly expanded in both domains, and this has to be taken into account when the signal is processed. The proposed algorithm is shaping the pilot symbol estimates in time, and frequency domain, such that the aliasing in both domains are reduced or eliminated. We achieve a significant reduction of the estimation error, with a linear increase in computational complexity.

Permutation entropy variations in seismic noise before and after eruptive activity at Shinmoedake volcano, Kirishima complex, Japan

Permutation entropy (PE) is a complexity metric that encodes a time series into sequences of symbols and can be used to decipher between deterministic and stochastic behavior. This study investigates PE variations in seismic noise during three eruption cycles in 2011, 2017, and 2018 at Shinmoedake volcano, Japan. The volcano is monitored by a dedicated seismic network and by infrasound microphones that recorded continuously during the aforementioned eruptions. The frequency range 1–7 Hz was used in order to infer temporal changes of PE in seismic noise and minimize any human contributions. The results showed that PE values decreased before the occurrence of each eruption. By combining these results with other observations we can attribute this decrease in PE to two reasons: first, to the occurrence of volcanic tremor that is a deterministic signal, and second, to magma migration at shallower depth beneath Shinmoedake which can attenuate high-frequency seismic waves and thus result in a less stochastic signal. PE also exhibited a spike-like increase just before the onset of the three eruptions. In 2011 and 2017, this feature was probably associated with bubble growth and collapse due to the interaction between the aquifer and high temperature magma. In 2018 the aquifer had mostly evaporated; hence, the spike in PE values was likely generated by fracturing of solidified magma within the conduit as fresh magma was pushing its way upwards. These results show that PE is a potentially useful tool for monitoring seismic noise at volcanoes and can contribute toward forecasting volcanic eruptions in conjunction with other widely used methodologies.Graphical

The Effects of Symbol Format and Psycholinguistic Features on Receptive Syntax Outcomes of Children Without Disability.

The graphic symbol is the foundation of augmentative and alternative communication (AAC) for many preliterate individuals; however, research has focused primarily on static graphic symbol sequences despite mainstream commercial technologies such as animation. The goal of this study was to compare static and animated symbol sequences across receptive communication outcome measures and psycholinguistic features (e.g., word frequency). A counterbalanced, 2 × 2 × 2 mixed design was used to investigate the effects of symbol format (animated and static), first condition (animated or static), and first experimental task (identification or labeling) on identification accuracy and labeling accuracy of graphic symbol sequences (five symbols) in 24 children with typical development ages 7 and 8 years old. Additionally, three 2 × 2 repeated-measures analyses of variance were conducted using symbol format (animated and static) and (a) word frequency (low, high), (b) imageability (low, high), and (c) concreteness (low, high). In addition to superior identification and labeling accuracy of animated sequences, a significant interaction between symbol format and the first condition was observed for both experimental tasks. When the animation format was the first condition, then the children's performance improved in the subsequent static condition. Finally, word frequency, imageability, and concreteness ratings for all verbs and prepositions had significant effects on labeling accuracy of verbs and prepositions. Significant interactions between symbol format and psycholinguistic features were also found. For example, highly imageable, animated verbs were labeled with greater accuracy when compared with all other variables. Animation technology appears to alleviate some of the burden associated with word- and sentence-level outcomes in children with typical development. Moreover, animation appears to reduce the effects of psycholinguistic features such as word frequency and imageability by increasing the transparency of the symbol. Given the increase in research in this area, speech-language pathologists may consider adopting animated graphic symbols on a case-by-case basis as a tool to augment the learning of word classes in which movement is integral to comprehension.

A New Symbolic Time Series Analysis Method Based on Time-to-Space Mapping, through a Symmetric Magnetic Field, Quantized by Prime Numbers

This work presents a new analysis method for two-symbol symbolic time series based on the time-to-space mapping achieved through a device of current carrying circular rings. An algorithm based on the theory of prime numbers is proposed for the approximate estimation of the stratified magnetic field produced by the aforementioned device. The main property of the specific algorithm is that it quantizes the stratified magnetic field. If a two-symbol symbolic time series is used to determine the flow directions of the rings’ currents, a time-to-space mapping of the dynamics of the system producing the time series is observed. A unique “fingerprint” of the symbolic dynamics is shaped by the spatial allocation of the values of the six-valued symmetric quantized magnetic field produced by the device. This allows for the quantitative evaluation of the original system’s dynamics by analyzing the resultant quantized magnetic field values space allocation, in a spectrum ranging from the lack of dynamics (randomness) to the presence of dynamics at all scales (criticality). Two examples of application–corresponding to the extremes of the dynamics spectrum, specifically, for symbolic time series resulting from (a) a random numbers generator and (b) the spin alternation of 2D-Ising in its critical state, verify the reliable time-to-space mapping of the involved symbolic dynamics. Moreover, an application to the symbolic sequence produced by the DNA of the GAPDH (Glyceraldehyde-3-Phosphate Dehydrogenase) human gene is presented as a real-world, intermediate dynamics case. The proposed symbolic time series analysis method presents the advantage that can take into account information related to both symbols, which is particularly useful in analyzing two-symbol time series of relatively short length where the probabilities of occurrence of the two symbols are not equal. By inferring the universality class of an artificial-neural-network-based hybrid spin model through the value of the critical exponent δ, it is shown that for such time series, the proposed method provides a unique way to expose the real dynamics of the underlying complex system, in contrast to the analysis of waiting times in the time domain that leads to an ambiguous quantitative result.

A Pattern Mining Approach for Improving Speech Emotion Recognition

Speech-driven user interfaces are becoming more common in our lives. To interact with such systems naturally and effectively, machines need to recognize the emotional states of users and respond to them accordingly. At the heart of the emotion recognition research done to this end lies the emotion representation that enables machines to learn and predict emotions. Speech emotion recognition studies use a wide range of low-to-high-level acoustic features for representation purposes such as LLDs, their functionals, and BoAW. In this paper, we present a new method for extracting a novel set of high-level features for classifying emotions. For this purpose, we (1) reduce the dimension of discrete-time speech signals, (2) perform a quantization operation on the new signals and assign a distinct symbol to each quantization level, (3) use the symbol sequences representing the signals to extract discriminative patterns that are capable of distinguishing different emotions from each other, and (4) generate a separate set of features for each emotion from the extracted patterns. Experimental results show that pattern features outperform Energy, Voicing, MFCC, Spectral, and RASTA feature sets. We also demonstrate that combining the pattern-based features and the acoustic features further improves the classification performance.

Variable-to-Variable Huffman Coding: Optimal and Greedy Approaches

In this paper, we address the problem of m-gram entropy variable-to-variable coding, extending the classical Huffman algorithm to the case of coding m-element (i.e., m-grams) sequences of symbols taken from the stream of input data for . We propose a procedure to enable the determination of the frequencies of the occurrence of m-grams in the input data; we formulate the optimal coding algorithm and estimate its computational complexity as , where n is the size of the input data. Since such complexity is high in terms of practical applications, we also propose an approximate approach with linear complexity, which is based on a greedy heuristic used in solving backpack problems. In order to verify the practical effectiveness of the proposed approximate approach, experiments involving different sets of input data were conducted. The experimental study shows that the results obtained with the approximate approach were, first, close to the optimal results and, second, better than the results obtained using the popular DEFLATE and PPM algorithms in the case of data that can be characterized by highly invariable and easy to estimate statistics.

A Mixed-Integer Solution for TDOA Based Direct Positioning of Digital Modulation Signal

This paper proposes a direct position determination (DPD) method for a digital modulation signal based on time difference of arrival (TDOA) measurements. Unlike the two-step positioning process, the measurements are used to directly estimate the source position. Fully utilizing information in a transmitted waveform can improve the accuracy of source localization in a single-step method. Based on maximum likelihood (ML) estimates, when the digital modulation scheme is known to the location system, an alternating iterative DPD method is developed to locate the emitter. The objective function of the proposed algorithm takes into account the source position and transmitted symbol sequence, which makes it a mixed-integer optimization problem. In particular, ML sequence estimation is adopted and the complex envelope is restored using a genetic algorithm. Then, based on Newton’s method, an alternating iterative algorithm is proposed to update the position and symbol sequence results. Compared with the existing DPD method, the proposed algorithm gives more accurate location results for unknown waveforms. In addition, the proposed algorithm can reach the Cramér-Rao bound (CRB) for known signal waveforms, as verified using comprehensive simulations.

An approach for applying natural language processing to image classification problems

A growing interest in applying Natural Language Processing (NLP) models to computer vision problems has recently emerged. This interest is motivated by the success of NLP models in tasks such as translation and text summarization. In this paper, we propose a new method for applying NLP to image classification problems. We aim to represent the visual patterns of objects by using a sequence of alphabet symbols and then train a Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), or Transformer using these sequences to classify objects. An extensive experimental evaluation using a limited number of images for training has been conducted to compare our method with the ResNet-50 deep learning architecture. The results obtained by the proposed method outperform ResNet-50 in all test scenarios. In one test, the method achieved an average accuracy of 95.3% compared to 89.9% of ResNet-50. The source code ( http://git.inovisao.ucdb.br/inovisao/applying-npl-to-image-classification) and dataset ( https://doi.org/10.6084/m9.figshare.20055602.v1) are publicly available.

Order pattern recurrence for the analysis of complex systems

In this paper, order pattern recurrence plot (OPRP) and order pattern recurrence quantification analysis (OPRQA) are proposed to quantify recurrence characteristics of complex systems. The method uses the recurrence of order patterns in symbolic sequence to explore the order structure of the original data, in which the color of the symbol is employed to distinguish different order patterns. The main advantage of the approach is its robustness with respect to non-stationary data and low requirements for the length of the required time series. The method is demonstrated to be effective in synthetic data and real data. As demonstrated in simulation models, the conclusion is that the method can not only make a distinction between chaotic system and random noise but also quantify various bifurcation transition scenarios like period doubling or other phenomena associated with chaos-to-chaos transitions in logistic map. In empirical analysis, this approach helps observe the different performance of the time series before, during and after the financial crisis. In addition, differences of stocks between developing countries and developed countries are reflected in OPRP and quantified by the corresponding OPRQA. The proposed method brings together recurrence plots and symbolic dynamics to empower researchers with effective means to visualize and quantify complex behavior in dynamic system.

Language Inference Using Elman Networks with Evolutionary Training

In this paper, a novel Elman-type recurrent neural network (RNN) is presented for the binary classification of arbitrary symbol sequences, and a novel training method, including both evolutionary and local search methods, is evaluated using sequence databases from a wide range of scientific areas. An efficient, publicly available, software tool is implemented in C++, accelerating significantly (more than 40 times) the RNN weights estimation process using both simd and multi-thread technology. The experimental results, in all databases, with the hybrid training method show improvements in a range of 2% to 25% compared with the standard genetic algorithm.