Discrete Signals Research Articles

Optimization for the quick-code methods for the synthesis of discrete signals – physical carriers of data in information-communication systems

The paper presents the theoretical foundations of synthesis and analysis of complex nonlinear discrete cryptographic signals, the basis for the synthesis of which are random (pseudo-random) processes, including algorithms for cryptographic transformation of information, as well as methods for optimizing the synthesis of these signals using decimation and discrete programming. namely, the method of branches and boundaries. In order to improve the performance of signal generation and processing, estimates of the effectiveness of the decimation procedure are proposed and presented. It is shown that the use of the studied signal systems will improve the efficiency of modern ICS (speed of data generation and processing devices, noise immunity, information security, secrecy, protection against input (imposition) of false messages, message falsification, data integrity, etc.).

High-order harmonic generation in 2D transition metal disulphides

In this paper, we explore the capabilities of MoS2 and WS2 2D monolayers to produce radiation in the terahertz range by the generation of high-order harmonics. This phenomenon, which is a result of the non-linear response of the electronic carrier population to the applied electric field, is studied by using a particle ensemble stochastic simulation approach based on the Monte Carlo method. The power of the produced harmonic signals is studied against the electric field amplitude, the external temperature, and the frequency of the excitation. Additionally, the stochastic nature of the simulation tool enables us to discern the purely discrete harmonic signal from the background spectral noise that comes from intrinsic carrier velocity fluctuations in the diffusive regime, permitting to set bandwidth thresholds for harmonic extraction. It was found that both TMDs showed similar bandwidth thresholds when compared to the III–V semiconductor at low temperatures, while WS2 would be a far better choice over MoS2 for exploitation of the seventh and ninth harmonic generation.

Noise-like discrete signals for asynchronous code division radio systems

This article discusses noise-like discrete signals (pseudo-random sequences) for asynchronous code division systems for radio channels. Asynchrony implies the use of sequences that are statistically uncorrelated for an arbitrary cyclically shifted copy of the signals, i.e. their cross-correlation coefficient for arbitrarily chosen starting points is close to zero. The fundamental theoretical limit for this characteristic is the well-known Welch boundary. In this paper, we compare the correlation properties of various sets (Gold codes, Kasami sequences, etc.) with this fundamental limit. The parameters of different codes are estimated, the corresponding bound is shown and compared with the real correlation characteristics of the codes. For the approximation, the Laurent series expansion and the Puiseau series were used. The asymptotic properties were also estimated. The paper also considers new ensembles of noise-like discrete signals for asynchronous systems. These codes are statistically uncorrelated, asymptotically the square of their cross-correlation for arbitrary starting points tends to the theoretical Welch bound. Moreover, the cardinality (power of the set) of new signal ensembles is much higher than that of Gold codes and Kasami sets. Consequently, the practical use of such noise-like discrete signals will increase the capacity of asynchronous code division systems for radio channels and reduce the cost of communication services. In addition, new sets of spreading signals will be useful for the implementation of the so-called. soft capacity, i.e. when, if necessary, the base station can increase the subscriber capacity with a slight decrease in the quality of service.

The (Un)identification of Continuous-Action Rational Inattention Decision Models

In this paper, we discuss some properties of optimal consideration set in binary state continuous-action rational inattention decision problems. First, we show that it is the form of the utility function that determines the form of posterior distribution, i.e. whether it is a binary distribution or a triple or quadruple distribution, etc. Besides, we will also show that the posterior distribution may not be resulted after information acquisition in some circumstances. Second, we show that the optimal consideration set is essentially the optimal action strategies. Therefore, without further conditions, the rational inattention decision model is unidentifiable. Third, in order to identify the rational inattention decision model, we introduce another condition to the model. The discrete actions after information acquisition that satisfy the endogenously-determined probability (optimal action strategies) should satisfy the properties of probabilities, and therefore the discrete actions should also follow an exogenous probability distribution. This idea is tested by an example with quadratic utility function. Through this example, we find that under this condition, the optimal consideration set can be identified. The elements in the optimal consideration set, or equivalently the discrete signals after information acquisition, exhibit a curve or curves.

Devising methods to synthesize discrete complex signals with required properties for application in modern information and communication systems

Information and communication systems (ICSs) must comply with increasingly stringent requirements to ensure the reliability and speed of information transmission, noise immunity, information security. This paper reports the methods to synthesize discrete complex cryptographic signals, underlying the construction of which are random (pseudo-random) processes; the methods for synthesizing characteristic discrete complex signals whose construction is based on using the nature of the multiplicative group of a finite field; the results of studying the properties of the specified signal systems. It is shown that the methods built provide a higher synthesis performance than known methods and make it possible to algorithmize the synthesis processes for the construction of software and hardware devices to form such signals. The win in the time when synthesizing nonlinear signals in finite fields using the devised method is, compared to the known method, for the period of 9,972 elements is 1,039.6 times. The proposed method for synthesizing the entire system of such signals, based on decimation operation, outperforms the known method of difference sets in performance. Thus, for a signal period of 2,380 elements, the win in time exceeds 28 times. It has also been shown that the application of such systems of complex signals could improve the efficiency indicators of modern ICSs. Thus, the imitation resistance of the system, when using complex discrete cryptographic signals with a signal period of 1,023 elements, is four orders of magnitude higher than when applying the linear signal classes (for example, M-sequences). For a signal period of 1,023 elements, the win (in terms of structural secrecy) when using the signal systems reported in this work exceeds 300 times at a period of 8,192, compared to the signals of the linear form (M-sequences)

Model and method of contextual help to develop skills of power unit operator using training simulator

Development of operator skills at the facility may lead to significant risks of accidents, and emergency drills in case of accidents “on-site” is impossible. Therefore, the use of computer simulators for operator training is an established international practice. Skill honing means repetition of exercises and is prone to errors. To increase the efficiency of training, computer simulators provide the ability to stop simulation to refer to tutorials. However, tutorials do not provide an immediate solution of the problem, as for what the mistake is and how one should act in a particular situation. The aim of the study is to reduce the time for contextual information search by, first, developing a dynamic model of training scripts, and second, developing a method to interpret the above model and to find error description. Petri net (PN) is an adequate model of action scenarios for a user of a computer application. Petri net links actions and the conditions of their execution. However, in contrast to the actions of the application user, the ability of the operator to perform actions is determined not only by the state of the control interface, but also by the state of the object itself. Thus, it is necessary to improve previously proposed models and methods of their interpretation. A scenario model in the form of an open Petri net (OPN) is proposed. The model novelty is the dynamic marking of positions with the values of the variables of the control object and the presence of transitions that are triggered when discrete signals of the control system are received. The authors propose the method of interpreting OPN queries which returns reference information relevant to the context. A method of integrating contextual help into simulator which projects the state of the object and the operator's actions on the structure of the OPN is proposed. The results of the study have been implemented in 215 MW power unit simulator of Surgut State District Power Station №1, which is being developed by Tekon-Avtomatika LLC. The results can be used during development of computer simulators of similar objects. Linear computation efficiency of the access to context information is provided.

METHOD OF OBTAINING THE SPECTRAL CHARACTERISTICS OF THE SCANNING PROBE MICROSCOPE

The article discusses methods and algorithms for digital processing and filtering when carrying out nano-measurements using a scanning probe microscope. The paper discusses frequency methods for improving images, in particular, the use of the Fourier transforms with various filtering methods to improve the quality of the resulting image. Stable computational algorithms have been developed for transforming discrete signals based on the Fourier transform. Methods for the interpretation of the numerical results of the discrete Fourier transform in such packages as Matlab, MathCad, Matematica are presented. It is proposed to use a window transform, developed based on the Fourier transform, which makes it possible to single out the informative features of the signal and to reduce the influence of the destabilizing factors that arise when processing signals from a scanning gold microscope in real conditions.

Processing Methods for Digital Image Data Based on the Geographic Information System

Digital image data processing is mainly to input digital image data into a computer to complete the conversion of a continuous spatially distributed image model into a discrete digital model so that the computer can identify, process, and store the processing process of digital image information. Geographic information system (GIS) is a computer system that integrates multiple forms of information expression, and it integrates functions such as collection, processing, transmission, storage, management, analysis, expression, and query retrieval, which can quickly discover the spatial distribution of things and their attributes and can express the results accurately and vividly in various intuitive forms. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of processing methods for digital image data, elaborated the development background, current status, and future challenges of the GIS technology, introduced the methods and principles of permutation matrix algorithm and subimage averaging method, constructed the processing model for digital image data based on GIS, analyzed the data structure and its database establishment for digital image, proposed the processing methods for digital image data based on GIS, performed the enhancement processing and calculation classification of digital image data, and finally conducted a case analysis and its result discussion. The study results show that the proposed processing methods for digital image data based on GIS can perform analogue-to-digital conversion of continuous images, complete the steps of sampling, layering, and quantization, and then encode the obtained discrete digital signal into the computer to form an in-plane collection of pixels; this processing method can also organically combine spatial information and image data and identify, process, and store digital image data from both spatial and attribute aspects. The study results of this paper provide a reference for further research on the processing methods for digital image data based on GIS.

Vehicle-to-grid management for multi-time scale grid power balancing

The mitigation of peak-valley difference and transient power fluctuation are both of great significance to the economy and stability of the power grid. This paper proposes a novel vehicle-to-grid behavior management method that can provide peak-shaving and fast power balancing service to the grid simultaneously. Firstly, a multi-time scale vehicle-to-grid behavior management framework is designed to enable large-scale optimization and real-time control at the same time in vehicle-to-grid scheduling. Then, the grid peak-shaving requirement is modeled as an optimization problem in a centralized V2G state coordinator, where the charging behavior of all grid-connected electric vehicles can be synergistically scheduled. The optimization variable is designed as a group of vehicle-to-grid state control signals that can respond to grid peak-shaving requirements. Further, a V2G power controller is designed to manage the vehicle charging power in real time based on the sampled grid frequency state and discrete state control signals. In the developed scheduling method, the charging power of grid-connected electric vehicles is scheduled by the cooperation between the V2G state coordinator and the power controller. The effectiveness of the proposed methodologies is verified on a microgrid system, and results indicate that the V2G scheduling can achieve multi-time scale grid power balancing. This work can bring dual benefits, enabling system operators to use cheap solutions to manage energy networks and allowing vehicle owners to gain profits from providing V2G services to the grid.

Circle-to-circle amplification coupled with microfluidic affinity chromatography enrichment for in vitro molecular diagnostics of Zika fever and analysis of anti-flaviviral drug efficacy

Sensitive viral diagnostic methods are increasingly in demand to tackle emerging epidemics. The Zika virus (ZIKV) is particularly relevant in tropical resource limited settings (RLS) and is associated with intermittent epidemics such as the recent 2016 ZIKV outbreak in South America, wherein Zika fever was classified by WHO as a public health emergency of international concern. Thus, there is an urgent need for widespread Zika fever diagnostics and efficient drug therapies. ZIKV diagnostics are typically performed using RT-qPCR in centralized laboratories. While extremely sensitive, RT-qPCR requires rapid heating-cooling cycles, combined with continuous fluorescence measurements to allow quantification, implying high costs and limiting availability of molecular diagnostics in RLS. Here, we report isothermal amplification of ZIKV cDNA using padlock probes followed by two rounds of Rolling Circle Amplification (RCA), termed as circle-to-circle amplification (C2CA), combined with a microfluidic affinity chromatography enrichment (μACE) platform. This platform allowed the detection of <17 vRNA copies per reaction mixture, equivalent to ∼3 aM, showed a positive correlation with RT-qPCR in both average (r = 0.80) and discrete (r = 0.95) signal modes, and was validated for drug efficiency tests using in vitro infected peripheral blood mononuclear cells from 3 healthy donors. This performance shows significant promise towards highly sensitive, albeit simple and cost-effective point-of-care viral diagnostics.

Signal processing on higher-order networks: Livin’ on the edge... and beyond

In this tutorial, we provide a didactic treatment of the emerging topic of signal processing on higher-order networks. Drawing analogies from discrete and graph signal processing, we introduce the building blocks for processing data on simplicial complexes and hypergraphs, two common higher-order network abstractions that can incorporate polyadic relationships. We provide brief introductions to simplicial complexes and hypergraphs, with a special emphasis on the concepts needed for the processing of signals supported on these structures. Specifically, we discuss Fourier analysis, signal denoising, signal interpolation, node embeddings, and nonlinear processing through neural networks, using these two higher-order network models. In the context of simplicial complexes, we specifically focus on signal processing using the Hodge Laplacian matrix, a multi-relational operator that leverages the special structure of simplicial complexes and generalizes desirable properties of the Laplacian matrix in graph signal processing. For hypergraphs, we present both matrix and tensor representations, and discuss the trade-offs in adopting one or the other. We also highlight limitations and potential research avenues, both to inform practitioners and to motivate the contribution of new researchers to the area.

Discrete uncertainty principle in quaternion setting and application in signal reconstruction

In this paper, a novel discrete uncertainty principle associated with discrete Quaternion Fourier transform is established to give the relationship between the nonzero numbers of the discrete quaternion-valued signals and their Quaternion Fourier transforms. We obtain that the product of the numbers of nonzero elements of a sequence [Formula: see text], [Formula: see text] and its Quaternion Fourier transform is no less than [Formula: see text] and the result is sharp. Then we extend the uncertainty principle of discrete signals proved by Donoho and Starkin to two dimensional case. It suggests how sparsity helps in the recovery of missing frequency. The experimental results on the recovery of Lena also demonstrate the necessity of the two-dimensional discrete uncertainty principle associated with Quaternion Fourier transforms.

Estimation of the Randomness of Continuous and Discrete Signals Using the Disentropy of the Autocorrelation

The amount of randomness in a signal generated by a physical or non-physical process can reveal important information about that process. Thus, to quantify the amount of randomness in a signal is an important task in many different areas. In this direction, the present work proposes to use the disentropy of the autocorrelation function as a measure of randomness. Since the proposed measure uses only the autocorrelation of the signal analyzed, the knowledge of the probability density function of the signal analyzed is not necessary. Several examples using noisy and chaotic signals are considered. In particular, it is shown that the logistic map working in the chaotic regime does not necessarily produce a maximally random sequence of values.

Analysis of misaligned optical rotational Doppler effect by modal decomposition.

The optical rotational Doppler effect (RDE) is closely related to the unique orbital angular momentum (OAM) carried by optical vortex, whose topological charge means the mode of OAM. Compared with the coaxial incidence, the rotational Doppler frequency shift spectrum of a misaligned optical vortex (misaligned RDE) widens according to a certain law. In this paper, an OAM modal decomposition method of the misaligned optical RDE is proposed and the relative intensity of different OAM modes, namely the OAM spectrum, is derived based on an inner product computation. Analyses show that lateral displacements and angular deflections change the distribution of OAM modes relative to the rotation axis of the object. A misaligned Laguerre-Gaussian (LG) vortex can be represented as a specific combination of coaxial LG modes, and the difference between the topological charge of two adjacent modes is 1 or 2 with lateral displacements or angular deflections respectively. An experiment of misaligned optical RDE using a superimposed LG vortex is executed, and the obtained frequency shift spectrum with misaligned incidence expands into a set of discrete signals, which agrees well with the theoretical results. Moreover, we can get the rotation frequency of the object from an expanded frequency spectrum more quickly and accurately based on the difference between two adjacent signal peaks. The proposed method contributes to analyze the misaligned optical RDE comprehensively, which is significant in remote sensing and optical metrology.

Quantifying persistence in the T-cell signaling network using an optically controllable antigen receptor.

T cells discriminate between healthy and infected cells with remarkable sensitivity when mounting an immune response, which is hypothesized to depend on T cells combining stimuli from multiple antigen‐presenting cell interactions into a more potent response. To quantify the capacity for T cells to accomplish this, we have developed an antigen receptor that is optically tunable within cell conjugates, providing control over the duration, and intensity of intracellular T‐cell signaling. We observe limited persistence within the T‐cell intracellular network on disruption of receptor input, with signals dissipating entirely in ~15 min, and directly show sustained proximal receptor signaling is required to maintain gene transcription. T cells thus primarily accumulate the outputs of gene expression rather than integrate discrete intracellular signals. Engineering optical control in a clinically relevant chimeric antigen receptor (CAR), we show that this limited signal persistence can be exploited to increase CAR‐T cell activation threefold using pulsatile stimulation. Our results are likely to apply more generally to the signaling dynamics of other cellular networks.

Hybrid Disturbance Observer-Based Anti-Disturbance Composite Control With Applications to Mars Landing Mission

In this paper, a hybrid anti-disturbance composite control scheme is presented to address the problem subject to systems remarkably affected by external disturbances and discrete dynamics of actuators. By explicitly considering the hybrid feature resulting from discrete actuation signals acted on a continuous system, the hybrid disturbance observer is designed to estimate the effect of external disturbance. Meanwhile, ${H_{\infty }}$ technique is used to mitigate the effect of continuous command executing error caused by discontinuous operation, which cannot be modeled in priori . Within the proposed composite control scheme, the combination of the disturbance estimation information and the ${H_{\infty }}$ technique enables the entire hybrid system precisely performing. The stability of the whole continuous–discrete control system is analyzed. Finally, based on the Mars lander dynamics, comparative simulation studies illustrate that the developed control scheme can preserve a satisfactory level of performance, even in the presence of external disturbances and actuator quantization errors.

A machine learning approach for classifying and quantifying acoustic diversity.

1. Assessing diversity of discretely varying behavior is a classical ethological problem. In particular, the challenge of calculating an individuals' or species' vocal repertoire size is often an important step in ecological and behavioral studies, but a reproducible and broadly applicable method for accomplishing this task is not currently available. 2. We offer a generalizable method to automate the calculation and quantification of acoustic diversity using an unsupervised random forest framework. We tested our method using natural and synthetic datasets of known repertoire sizes that exhibit standardized variation in common acoustic features as well as in recording quality. We tested two approaches to estimate acoustic diversity using the output from unsupervised random forest analyses: (i) cluster analysis to estimate the number of discrete acoustic signals (e.g., repertoire size) and (ii) an estimation of acoustic area in acoustic feature space, as a proxy for repertoire size. 3. We find that our unsupervised analyses classify acoustic structure with high accuracy. Specifically, both approaches accurately estimate element diversity when repertoire size is small to intermediate (5-20 unique elements). However, for larger datasets (20-100 unique elements), we find that calculating the size of the area occupied in acoustic space is a more reliable proxy for estimating repertoire size. 4. We conclude that our implementation of unsupervised random forest analysis offers a generalizable tool that researchers can apply to classify acoustic structure of diverse datasets. Additionally, output from these analyses can be used to compare the distribution and diversity of signals in acoustic space, creating opportunities to quantify and compare the amount of acoustic variation among individuals, populations, or species in a standardized way. We provide R code and examples to aid researchers interested in using these techniques.

Neural network method of the decision Of the nonlinear problem of optimum distribution of the non-uniform resource

Given article is devoted features of the decision of a problem of integer nonlinear programming, by means of developed neural network method and algorithm of nonlinear optimization of means «decision Search» tabular processor Microsoft Excel. In offered neural network method the task in view decision is made by means of a recurrent neural network (RNN) matrix architecture with m neurons in each line and n neurons in each column. All neurons such network are connected with each other by communications, and the signal from an exit neuron can move on its input. Neural network method is characterized by that on inputs mentioned RNN the entrance vector of values of parameters of optimized nonlinear criterion function of a problem of distribution of a non-uniform resource moves, calculation of values of weight factors connected among themselves neurons is carried out and signal RNN is formed. This signal by means of nonlinear function will be transformed to the discrete target signal characterizing values quasi-optimal of the decision of the mentioned problem which size changes from 0 to 1. The estimation of efficiency of the decision of a considered problem was carried out at its various values of an indicator of efficiency on the basis of developed imitating model RNN. As indicators of efficiency of application offered neural network method were used – an average relative error and time of the decision of a problem. The value received by means of algorithm of nonlinear optimization of means was accepted to the exact decision «decision Search» tabular processor Microsoft Excel. The analysis of the received results of the experimental researches, offered neural network method, has allowed to make the conclusion that in comparison with an existing method of nonlinear optimization of tabular processor Microsoft Excel use offered neural network method allows essentially (in 9,4 times) to lower time of the decision of a problem dimension 10 × 8 (m ×n) and thus to provide accuracy of its decision not less than 99,8 %.

Synchronization scheme of photon-counting underwater optical wireless communication based on PPM

In order to realize underwater optical wireless communication (UOWC) under long-distance, single photon avalanche diode (SPAD) is used as the receiver. Since the SPAD outputs discrete single photon pulse signals, the key issue is how to recover data from the discrete single photon pulses. Traditional photon-counting UOWC uses SPAD array with high gain to recover data directly. The disadvantage of this method is that the number of photons required to transmit 1 bit data is large, and the transmission distance is also limited. In order to reduce the number of photons required to transmit 1 bit data, we proposed a method of extracting the time slot synchronization clock (TSSC) from the received discrete photon-counting pulses and recovering the data. Based on the random Poisson process of the receiver and the attenuation process of the underwater channel, a photon-counting UOWC model was established. Meanwhile, to verify the method proposed, an underwater light emitting diode (LED) communication system based on SPAD was built. The experimental results show that without considering the background light noise, the photon-counting UOWC system can achieve a satisfactory performance of 0.875 photons/bit with a symbol error rate (SER) of 1.8 × 10−3 by using 256-pulse-position modulation (PPM) and the TSSC extraction method we proposed.

Цифровые инновации в финансовой системе исламских стран Ближнего Востока

В ближневосточном регионе функционируют международные финансовые центры. Кроме Тель-Авива, Стамбула и Баку все эти финансовые центры функционируют на основе исламской финансовой системы. Военно-политическая обстановка в регионе сплотила некоторые исламские государства при посредничестве США и Тель-Авива. После Египта и Иордании с сентября 2020 года ОАЭ, Оман и Бахрейн, а с октября 2020 года Судан возобновили дипломатические и экономические отношения с Израилем. Саудовская Аравия активно участвует в этих процессах. Цифровые инновации в финансовой системе исламских стран Ближнего Востока в основном принимаются в &#x0D; институтах и учреждениях этих центров. Цифровые технологии, которые работают с дискретными сигналами, активно внедряются в отчетно-учётных системах, в системах хранения данных, в торговле, в сферах страхования,кредитования и банковских услуг. При этом строго соблюдается требовании норм шариата.