Information Processing Tools Research Articles

A Neural Network Classification Model Based on Covering and Immune Clustering Algorithm

Inspired by the information processing mechanism of the human brain, the artificial neural network (ANN) is a classic data mining method and a powerful soft computing technique. The ANN provides a valuable tool for information processing and pattern recognition, thanks to its advantages in distributed storage, parallel processing, fast problem-solving and adaptive learning. The constructive neural network (CNN) is a popular emerging neural network model suitable for processing largescale data. In this paper, a novel neural network classification model was established based on the covering algorithm (CA) and the immune clustering algorithm (ICA). The CA is highly comprehensible, and enjoys fast computing speed, and high recognition rate. However, the learning effect of this algorithm is rather poor, because the training set is randomly selected from the original data, and the number of nodes (covering number) and area being covered are greatly affected by the learning sequence. To solve the problem, the ICA was introduced to preprocess the data samples, and calculate the cluster centers based on the antibody-antigen affinity. The CA and the ICA work together to determine the covering center and radius automatically, and convert them into the weights and thresholds of the hidden layer of neural network. The number of hidden layer neurons equals the number of covering. In addition, the McCulloch-Pitts (M-P) neurons were adopted for the output layer. Based on the input feature of the hidden layer, the output feature completes the mapping from input to output, creating the final classes of the original data. The introduction of the ICA fully solves the defect of the CA. Finally, our neural network classification model was verified through experiments on real-world datasets.

METHODOLOGICAL ASPECTS OF DIGITAL TRANSFORMATION APPLIED TO PUBLIC SERVICES

The article covers outputs of the research on development of methodological aspects applied under digital transformation of public services and based on the procedures suggested for formalizing the reengineering process used for related service provision administrative processes in accordance with the capacity delivered by advanced information technologies. The Introduction outlines the issue in general, which is mostly related to the fact that the most of the general population enjoy no public services at all despite the implementation of the e-governance concept. It also analyzes the reasons resulting in a problematic situation described above which, among others, include complicated procedures used to both provide and access public (administrative services); lack of related information; lengthy terms of service provision caused by unavailability of information systems capable of ensuring proper data exchange between state electronic information resources and administrative bodies or agencies; lack of state standardization efforts in public service provision, etc. The analysis used to study recent research and publications confirmed that only general aspects of administrative processes used prior to public service provision in the electronic format has been more or less fully processed so far. As for the formalization of public service digitalization implemented through methodological approaches deemed as compatible for use, the matter at hand remains open and requires further research. With the above in mind, the paper states the purpose of related research formulated as the development of methodological support to digital transformation of public services while objectives of the above are focused on the formalization of the reengineering process for public services and development of related procedures fit for practical use. The outputs of the research describe the general methodological approach to digital transformation of public services based on “as is” and “to be” models. The paper proposes to consider the Administrative Service Register as the information basis used to develop an “as is” model. A register as such contains unified service names, documents, standardized administrative process descriptions as well as results of an audit covering state electronic information resources. The developed “as is” model should be used as a frame to define service information links, to implement audit and streamlining (reengineering) of public service provision procedures (administrative processes) as well as to integrate real life services. As a result, there will be designed the vision of public service provision (a “to be” model) to be implemented further on. The vision is defined as a technical option used to provide public services as a synthesis of service provision administrative processes and information processing tools. At the same time, the objective implying the vision development for a set of public services involves the selection of a specific option used to implement a set of related administrative processes while ensuring ultimate performance in terms of provision of a public service set concerned. The idea of the formalized objective is to select a set of administrative processes used for public service provision at service providers and data exchange between them with the use of related technologies ensuring minimum costs for the whole service set. According to the objective defined and aggregative–decomposition method applied, a respective solution is brought down to two stages: building a graph-based structure for administrative process options used under a certain set of public services (the “as is” model) in accordance with their links; afterwards, the above graph-based structure allows tracking the shortest route to define the appropriate option for administrative processes to be applied under the whole set of public services with the use of respective information technologies (the “to be” model, vision). The Conclusion describes the major research outputs and further explorative prospects.

Fuzzy cognitive maps in the dependability analysis of systems

Aim. Dependability simulation of a complex system starts with its structuring, i.e. partitioning into components (blocks, units, elements), for which probabilities of failure are known. The classical dependability theory uses the concept of structural function that allows ranking elements by their importance, which is required for optimal distribution of the resources allocated to ensuring system dependability. Man-machine systems are structured using an algorithmic description of discrete processes of operation, where the presence of clear boundaries between individual operations allows collecting statistical data on the probabilities of error that is required for modeling. Algorithmization is complicated in case of man-machine systems with continuous human activity, where the absence of clear boundaries between operations prevents the correct assessment of the probability of their correct performance. For that reason, the process of operation has to be considered as a single operation, whose correct performance depends on heterogeneous and interconnected human-machine system-related, technical, software-specific, managerial and other factors. The simulated system becomes a “black box” with unknown structure (output is dependability, inputs are contributing factors), while the problem of element ranking typical to the dependability theory comes down to the problem of factor ranking. Regression analysis is one of the most popular means of multifactor dependability simulation of man-machine systems. It requires a large quantity of experimental data and is not compatible with qualitative factors that are measured by expert methods. The “if – then” fuzzy rule is a convenient tool for expert information processing. However, regression analysis and fuzzy rules have a common limitation: they require independent input variables, i.e. contributing factors. Fuzzy cognitive maps do not have this restriction. They are a new simulation tool that is not yet widely used in the dependability theory. The Aim of the paper is to raise awareness of dependability simulation with fuzzy cognitive maps.Method. It is proposed – based on the theory of fuzzy cognitive maps – to rank factors that affect system dependability. The method is based on the formalization of causal relationships between the contributing factors and the dependability in the form of a fuzzy cognitive map, i.e. directed graph, whose node correspond to the system’s dependability and contributing factors, while the weighted edges indicate the magnitude of the factors’ effect on each other and the system’s dependability. The rank of a factor is defined as an equivalent of the element’s importance index per Birnbaum, which, in the probabilistic dependability theory is calculated based on the structure function.Results. Models and algorithms are proposed for calculation of the importance indexes of single factors and respective effects that affect system dependability represented with a fuzzy cognitive map. The method is exemplified by the dependability and safety of an automobile in the “driver-automobile-road” system subject to the driver’s qualification, traffic situation, unit costs of operation, operating conditions, maintenance scheduling, quality of maintenance and repair, quality of automobile design, quality of operational materials and spare parts, as well as storage conditions.Conclusions. The advantages of the method include: a) use of available expert information with no collection and processing statistical data; b) capability to take into account any quantitative and qualitative factors associated with people, technology, software, quality of service, operating conditions, etc.; c) ease of expansion of the number of considered factors through the introduction of additional nodes and edges of the cognitive map graph. The method can be applied to complex systems with fuzzy structures, whose dependability strongly depends on interrelated factors that are measured by means of expert methods.

Deterministic twirling with low resources

Twirling operations, which average a quantum state with respect to a unitary subgroup, have become a frequently-employed tool in quantum information processing. We investigate the efficient implementation of twirling operations with minimal resources, without necessitating the ability to perform all possible unitary operations on the quantum system of interest. We present a general algebraic method allowing us to choose a set of - typically very few - unitary operators which, when applied randomly and repeatedly, produce the given twirling operation exponentially quickly. The method is applied to twirling operations for bipartite quantum systems with respect to the unitary group U(d)⊗U(d), an essential ingredient in entanglement distillation protocols. In particular, we provide a complete classification of sets of unitary operators capable of performing twirling on two qubits. Moreover, we construct a generic set containing at most three unitary operators achieving the twirling operation for a general two-qudit system.

Maximal nonlocality from maximal entanglement and mutually unbiased bases, and self-testing of two-qutrit quantum systems

Bell inequalities are an important tool in device-independent quantum information processing because their violation can serve as a certificate of relevant quantum properties. Probably the best known example of a Bell inequality is due to Clauser, Horne, Shimony and Holt (CHSH), which is defined in the simplest scenario involving two dichotomic measurements and whose all key properties are well understood. There have been many attempts to generalise the CHSH Bell inequality to higher-dimensional quantum systems, however, for most of them the maximal quantum violation---the key quantity for most device-independent applications---remains unknown. On the other hand, the constructions for which the maximal quantum violation can be computed, do not preserve the natural property of the CHSH inequality, namely, that the maximal quantum violation is achieved by the maximally entangled state and measurements corresponding to mutually unbiased bases. In this work we propose a novel family of Bell inequalities which exhibit precisely these properties, and whose maximal quantum violation can be computed analytically. In the simplest scenario it recovers the CHSH Bell inequality. These inequalities involvedmeasurements settings, each havingdoutcomes for an arbitrary prime numberd≥3. We then show that in the three-outcome case our Bell inequality can be used to self-test the maximally entangled state of two-qutrits and three mutually unbiased bases at each site. Yet, we demonstrate that in the case of more outcomes, their maximal violation does not allow for self-testing in the standard sense, which motivates the definition of a new weak form of self-testing. The ability to certify high-dimensional MUBs makes these inequalities attractive from the device-independent cryptography point of view.

Software Tools for Handling Information for the Internet of Things

The analysis of methods of creation of software for information processing for the Internet of things is described. The methods of creating separate stages of the development of applications for reading information and managing Internet speeches are explored. Comparative tables of efficiency of different methods are given, depending on development criteria. A table for analyzing the effectiveness of a combination of different methods for creating front-end and back-end parts of information processing tools for the Internet of Things has also been developed. In this stage of the development of modern technologies, the important place does young, but a very prospective direction is the internet. Specifically, special warnings a technical realization is provided. Differential developments, including the following, before connecting to the network, connect to the user through any wireless network, and enhanced the use of this device. They are constantly improving and appearing new. Urgently important to provide comfort use with a maximum remote control and number of functions, an important role will play the speed and comfort the data management additional. Software internet risk is strongly risks, and that and users 'requirements to contribute and functionality of applications rise the quantity of fast. That the developer appears complex, currently intractable, problem: you need to develop application with a convenient and pleasant user interface designs, maximum performance and response to user actions, and with broad functionality. Theme is actual, that this sphere is very strongly developing, in all your funds investigated in that foreign year. Internet range development is used in different industries, as industry, and sustainable life. Specially in medicine; in the military sphere; in the sales area; in the field of personnel selection, and others. This technology makes life of people and makes it faster. The client, going into the application, sees the display of the front-end parts of web application, that is, the client side of the user interface to the software and hardware part of the service. It contains information about the object, which is taken from the database through the Internet through back-end, that is, software-hardware part of the service. To change certain parameters of the object, the client makes adjustments to the corresponding field of the application, this data fall into the client side of the user interface, from which are redirected to the software and hardware part of the service. After processing – data through the Internet goes to the server. Checking whether the user's requirements for object changes are to display the status of the object in the application. This method is not the only, but the simplest and most visual. In the research component of this article, only the most effective and most common methods of developing the front-end and back-end parts of the application are considered. Ref. 12, fig. 3, tabl. 3.

APPROXIMATION OF NON-PERIODIC SIGNALS IN SYSTEMS OF MULTI-CLASS DIAGNOSTICS

Background. Improvement of systems of multi-class diagnostics is a very urgent task of the present. In most cases, it occurs due to increased accuracy of primary converters. However, the widespread use of microprocessor technology, a powerful mathematical base, the development of methods and tools for diagnostic information processing, the rapid development of new promising methods of research of objects and processes, new opportunities for modern computer and information technology contribute to the improvement process. In this paper one of the directions of improvement of functional diagnostics systems of engineering and construction structures is considered, namely: realization of the possibility of approximation of nonperiodic signals by the least squares method.Objective. The purpose of the work is to improve the least squares method for the application to non-periodic vibrational signals and to develop software for the measured data approximation with the help of preliminary use of interpolation algorithms for efficient use in systems of multi-class diagnostics.Conclusions. In order to achieve the goal, in this paper an investigation of the efficiency of an interpolation algorithm by Lagrange polynomials was performed using simulated nonperiodic signals without the presence of noise. It is shown that the methodological errors of the chosen method appear when using too many interpolation nodes. This leads to a significant increase in the interpolation error at the edges of the segment. The choice of the optimal number of interpolation nodes was conducted. An algorithm and software for nonperiodic signals approximation by the least squares method in MatLab mathematical package was developed. The standard algorithm of approximation by least squares method is improved by using piecewise approximation by polynomials up to the seventh order. The algorithm of piecewise approximation is improved by using anti-aliasing in adjacent segments. The algorithm and software of data approximation have been developed due to preliminary use of interpolation methods for changing the length of the initial signal to the value of 2i (i - the natural number). This is necessary for the use of fast data processing algorithms in systems of multi-class diagnostics, as well as possible increase of the accuracy of prediction of the technical state of objects by such systems.

Superadiabatic population transfer in a three-level superconducting circuit

Adiabatic manipulation of the quantum state is an essential tool in modern quantum information processing. Here, we demonstrate the speedup of the adiabatic population transfer in a three-level superconducting transmon circuit by suppressing the spurious nonadiabatic excitations with an additional two-photon microwave pulse. We apply this superadiabatic method to the stimulated Raman adiabatic passage, realizing fast and robust population transfer from the ground state to the second excited state of the quantum circuit.

Застосування програмного забезпечення в процесі навчання елементів теорії ймовірностей та математичної статистики студентів спеціальностей «Математика» та «Інформатика»

У статті показано необхідність доповнення класичного курсу теорії ймовірностей та математичної статистики для спеціальностей «Математика» та «Інформатика» комп’ютерним практикумом, який спрямований на оволодіння студентами інструментарієм опрацювання статистичних даних з використанням засобів комп’ютерних технологій, що значно полегшує й прискорює обчислення статистичних показників, складання статистичних таблиць і побудову графіків, а також розширює можливості аналізу та наочного подання статистичних даних. На прикладі однієї лабораторної роботи в статті описані можливі шляхи набуття практичних навичок, умінь та досвіду роботи студентів в даному напрямі.

SEARCH OF OPTIMAL COMPROMISE BETWEEN REQUIREMENTS IN SPHERE OF INFORMATION PROCESSING AND RESOURCES REQUIRED FOR THESE TASKS

The possibility of using the fundamentals of system analysis when choosing the optimal information processing strategy in the design of complex information systems with the aim of obtaining a complete and objective assessment of information resources, implemented in the interest of analyzing the technical characteristics of integrated information processing tools of complex automated systems, is considered. Information risk analysis was carried out, i.e. the process of integrated assessment of information processing in an automated system with the transition to quantitative or qualitative indicators. In addition, each indicator represents the probable damage, which depends on the information processing in the system. The process of identifying and reducing risks that can affect the information system is understood as the process of risk management. The results of the analysis are used in the selection of information processing facilities and in evaluating the effectiveness of existing and projected subsystems.

JARINGAN AD-HOC VEHICULAR (VANET) : TINJAUAN TENTANG ARSITEKTUR, KARAKTERISTIK, APLIKASI, DAN PROTOKOL MEDIUM ACCESS CONTROL (MAC)

VANET (Vehicular Ad-hoc Network) is an ad-hoc wireless network which nodes are composed by mostly moving vehicles and infrastructure communications or access points along the road. It integrates adhoc networking, cellular technology, wireless LAN and other technologies, which include GPS(Global Positioning System), digital mapping, sensing technologies and advanced information processing tools to support Intelligent Traffic Systems (ITS). VANET is developed mainly to improve road safety and to increase the traffic efficiency. It is designed to support the exchange of warning messages among the vehicles to increase the passenger safety and other information related to VANET applications and services . To achieve these goals, there are several issues that should be addressed. One of these challenges is designing reliable and efficient Medium Access Control (MAC) protocol for VANET. Due to VANET’s distinguishing characteristics, such as high mobility of the nodes, dynamic network topology, enough battery power, high processing power, etc, the MAC protocols designed for MANET (Mobile Ad-hoc Network) are not suitable for VANET. The research community has been working to develop MAC protocols appropriate for this ad-hoc network by proposing new design or modifying the existing one.In this paper, the VANET’s architecture, characteristics, applications and standard are discussed and then a comprehensive study of several MAC protocols available in the literature is presented.

Factors Impacting the Use of Terminology to Convey Diagnostic Certainty in Radiology Reports

Background: Variable use of phrases expressing diagnostic uncertainty can lead to ambiguous radiology reports, a concern for information processing. Objective: This study aimed to quantify the usage of phrases conveying diagnostic certainty for abdominal imaging findings and assess factors that impact use of phrases with “good agreement” between radiologists and referring providers. Methods: This retrospective, Institutional Review Board-Approved study included all diagnostic reports generated by the Abdominal Radiology Division at an academic medical center July-September 2016. We assessed the use of 16 diagnostic certainty phrases using information retrieval from the Impression section of radiology reports. Phrases with good provider agreement for conveying the level of certainty are defined as “good agreement” phrases - including “diagnostic of”, “represents” and “unlikely.” We assessed the impact of imaging modality, trainee contribution to report generation, and individual radiologists. Results: In 5,598 radiology reports, 2,071 (37%) contained diagnostic certainty phrases, 119 (6%) of which were “good agreement” phrases. There was a significant difference between how frequently “good agreement” phrases were used in Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and X-ray reports (p=0.0003). There was a significant variation among attending radiologists on the use of “good agreement” phrases (p<0.0019). There was no difference in the use of “good agreement” phrases in reports generated by attending radiologists alone compared to reports with trainees. Conclusion: Although phrases to convey diagnostic certainty were commonly used in radiology reports, the use of phrases with a good agreement was uncommon. Standardizing terminology to convey diagnostic certainty may reduce ambiguity in radiology reports and generate more accurate information processing tools.

Evolution of quantum correlation undergoing decoherence using the information entropy inequality

Using the information-theoretic inequality proposed by Braunstein and Caves, we study the evolution of quantum correlation in the presence of different noisy channels. First, we experimentally prepare the high-fidelity two-bit entangled state and simulated the bit-flip and phase-shift noise using an all-optical experimental setup. Then, we study the quantum correlation evolution of a two-bit entangled system using the information difference of entropy under different noisy channels and noisy environments. It is found that the quantum correlation degradation depends on the type of noise and noisy channels. Our work provides a new essential tool for quantum information processing and measurement, and offers new insight into the evolution of quantum correlation in open systems.

Perfect Quantum State Transfer in a Superconducting Qubit Chain with Parametrically Tunable Couplings

Faithfully transferring the quantum state is essential for quantum information processing. Here we demonstrate a fast (in 84 ns) and high-fidelity (99.2%) transfer of arbitrary quantum states in a chain of four superconducting qubits with nearest-neighbor coupling. This transfer relies on full control of the effective couplings between neighboring qubits, which is realized only by our parametrically modulating the qubits without increasing circuit complexity. Once the couplings between qubits fulfill a specific ratio, perfect quantum state transfer can be achieved in a single step, and is therefore robust to noise and accumulation of experimental errors. This quantum state transfer can be extended to a larger qubit chain and thus adds a desirable tool for future quantum information processing. The demonstrated flexibility of the coupling tunability is suitable for quantum simulation of many-body physics, which requires different configurations of qubit couplings.

Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors.

Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco industry. In this paper, a novel method based on the support vector machine (SVM) is proposed to discriminate the tobacco cultivation region using the near-infrared (NIR) sensors, where the genetic algorithm (GA) is employed for input subset selection to identify the effective principal components (PCs) for the SVM model. With the same number of PCs as the inputs to the SVM model, a number of comparative experiments were conducted between the effective PCs selected by GA and the PCs orderly starting from the first one. The model performance was evaluated in terms of prediction accuracy and four parameters of assessment criteria (true positive rate, true negative rate, positive predictive value and F1 score). From the results, it is interesting to find that some PCs with less information may contribute more to the cultivation regions and are considered as more effective PCs, and the SVM model with the effective PCs selected by GA has a superior discrimination capacity. The proposed GA-SVM model can effectively learn the relationship between tobacco cultivation regions and tobacco NIR sensor data.

Transforming Bell\u2019s inequalities into state classifiers with machine learning

In quantum information science, a major challenge is to look for an efficient means for classifying quantum states. An attractive proposal is to utilize Bell’s inequality as an entanglement witness, for classifying entangled state. The problem is that entanglement is necessary but not sufficient for violating Bell’s inequalities, making these inequalities unreliable in state classification. Furthermore, in general, classifying the separability of states, even for only few qubits, is resource-consuming. Here we look for alternative solutions with the methods of machine learning, by constructing neural networks that are capable of simultaneously encoding convex sets of multiple entanglement witness inequalities. The simulation results indicated that these transformed Bell-type classifiers can perform significantly better than the original Bell’s inequalities in classifying entangled states. We further extended our analysis to classify quantum states into multiple species through machine learning. These results not only provide an interpretation of neural network as quantum state classifier, but also confirm that neural networks can be a valuable tool for quantum information processing.

LARS: A Logic-based framework for Analytic Reasoning over Streams

The increasing availability of streaming data has accelerated advances in information processing tools that no longer store data for static querying but push information to consumers as soon as it becomes available. Stream processing aims at providing languages and tools for data that changes at a high rate. To cope with the volume of data, query languages often extend existing approaches for static data by means of window operators that return snapshots of recent data. However, the semantics of these languages are often given only informally or operationally, which makes their analysis and comparison difficult. A formal means to express the declarative semantics of such systems seems to be missing. This lack of theory is of particular relevance for the emerging research in stream reasoning which shifts the focus from throughput to higher expressiveness. To fill this gap, we present LARS, a Logic-based framework for Analytic Reasoning over Streams. At its core, LARS formulas extend propositional logic with generic window operators and additional controls to handle temporal information. On top of this, LARS programs extend Answer Set Programming (ASP) with rich stream reasoning capabilities; the latter can be exploited to target AI applications in a streaming context, such as diagnosis, configuration or planning. Specifically, we study in this article the computational complexity of LARS formulas and programs, their relationship to Linear Temporal Logic (LTL) and the well-known Continuous Query Language (CQL). Furthermore, we discuss the modeling capabilities of LARS in notes on the SPARQL extensions C-SPARQL and CQELS, and on the interval-based approach of the complex event processing language ETALIS. We finally briefly touch available implementations, in particular, the recent prototype engines Laser and Ticker that aim for high throughput and high expressiveness, respectively. Notably, both engines specify their semantics in LARS, indicating the desired flexibility of the framework and its potential as stream reasoning language itself, which is further explored in other works.

Magneto-optical rotation in cavity QED with Zeeman coherence

We investigate theoretically the magneto-optical rotation in cavity QED system with atomic Zeeman coherence, which is established via coherent population trapping. Owing to Zeeman coherence, the ultranarrow transmission spectrum less than 1 MHz with gain can be achieved with a flat-top Faraday rotation angle. By controlling the parameters appropriately, the input probe components within the flat-top regime rotate with almost the same angle, and transmit through the cavity perpendicularly to the other components outside the flat-top regime. The concepts discussed here provide an important tool for perfect ultranarrow Faraday optical filter and quantum information processing.

Gene-gene interaction network analysis of hepatocellular carcinoma using bioinformatic software.

Information processing tools and bioinformatics software have markedly advanced the ability of researchers to process and analyze biological data. Data from the genomes of humans and model organisms aid researchers to identify topics to study, which in turn improves predictive accuracy, facilitates the identification of relevant genes and simplifies the validation of laboratory data. The objective of the present study was to investigate the regulatory network constituted by long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and mRNA in hepatocellular carcinoma (HCC). Microarray data from HCC datasets were downloaded from The Cancer Genome Atlas database, and the Limma package in R was used to identify the differentially expressed genes (DEGs) between HCC and normal samples. Gene ontology enrichment analysis of DEGs was conducted using the Database for Annotation, Visualization, and Integrated Discovery. TargetScan, microcosm, miRanda, miRDB and PicTar were used to predict target genes. lncRNAs associated with HCC were probed using the lncRNASNP database, and a lncRNA-miRNA-mRNA regulatory network was visualized using Cytoscape. The present study identified 114 differentially expressed miRNAs and 2,239 differentially expressed mRNAs; of these, 725 were downregulated genes that were primarily involved in complement and coagulation cascades, fatty acid metabolism and butanoate metabolism, among others. The remaining 1,514 were upregulated genes principally involved in DNA replication, oocyte meiosis and homologous recombination, among others. Through the integrated analysis of associations between different types of RNAs and target gene prediction, the present study identified 203 miRNA-mRNA pairs, including 28 miRNAs and 170 mRNAs, and identified 348 lncRNA-miRNA pairs, containing 28 miRNAs. Therefore, owing to the association between lncRNAs-miRNAs-mRNAs, the present study screened out 2,721 regulatory associations. The data in the present study provide a comprehensive bioinformatic analysis of genes, functions and pathways that may be involved in the pathogenesis of HCC.

Gyrator wavelet transform

The gyrator transform is a linear canonical transform, which generates the rotation of an optical signal in position-spatial frequency planes. Gyrator wavelet transform is a relatively newer optical information processing tool obtained by combining the gyrator transform with the wavelet transform. This combination provides multi-resolution analysis of an image which is twisted in spatial frequency planes. The proposed tool satisfies basic algebraic properties, such as the linearity property and Parseval's theorem. Considering the usefulness of this tool, here a study of features, applications, and implementation of the gyrator wavelet transform is presented. This work studies the features of the gyrator wavelet transform, which can find a role in different applications such as edge enhancement, image encryption, image hiding, and watermarking.