Training-based symbol detection with temporal convolutional neural network in single-polarized optical communication system

In order to reduce the physical impairment caused by signal distortion,in this paper,we investigate symbol detection with Deep Learning(DL)methods to improve bit-error performance in the optical communication system.Many DL-based methods have been applied to such systems to improve bit-error performance.Referring to the speech-to-text method of automatic speech recognition,this paper proposes a signal-to-symbol method based on DL and designs a receiver for symbol detection on single-polarized optical communications modes.To realize this detection method,we propose a non-causal temporal convolutional network-assisted receiver to detect symbols directly from the baseband signal,which specifically integrates most modules of the receiver.Meanwhile,we adopt three training approaches for different signal-to-noise ratios.We also apply a parametric rectified linear unit to enhance the noise robustness of the proposed network.According to the simulation experiments,the biterror-rate performance of the proposed method is close to or even superior to that of the conventional receiver and better than the recurrent neural network-based receiver.

Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability programming.Inspired by evolutionary algorithms,which can iteratively find the nearoptimal solution,different Evolutionary Algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic Algorithm(GA),Evolution Strategy Algorithm(ES),Differential Evolution Algorithm(DE),and Evolutionary Programming Algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language.With the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms.It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT.

State-Based Opacity Verification of Networked Discrete Event Systems Using Labeled Petri Nets

The opaque property plays an important role in the operation of a security-critical system,implying that pre-defined secret information of the system is not able to be inferred through partially observing its behavior.This paper addresses the verification of current-state,initial-state,infinite-step,and K-step opacity of networked discrete event systems modeled by labeled Petri nets,where communication losses and delays are considered.Based on the symbolic technique for the representation of states in Petri nets,an observer and an estimator are designed for the verification of current-state and initial-state opacity,respectively.Then,we propose a structure called an I-observer that is combined with secret states to verify whether a networked discrete event system is infinite-step opaque or K-step opaque.Due to the utilization of symbolic approaches for the state-based opacity verification,the computation of the reachability graphs of labeled Petri nets is avoided,which dramatically reduces the computational overheads stemming from networked discrete event systems.

Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Binary Program Vulnerability Mining Based on Neural Network

Software security analysts typically only have access to the executable program and cannot directly access the source code of the program.This poses significant challenges to security analysis.While it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining methods.However,these tools suffer from some shortcomings.In terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search space.Additionally,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information loss.In this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation techniques.By leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion method.This combination allows for the unified handling of binary programs across various architectures,compilers,and compilation options.Subsequently,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)network.Finally,the graph embedding network is utilized to evaluate the similarity of program functionalities.Based on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target function.The solved content serves as the initial seed for targeted fuzzing.The binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity scores.This approach facilitates cross-architecture analysis of executable programs without their source codes and concurrently reduces the risk of symbolic execution path explosion.

A Study on the Symbol-Based Narrative in the International Communication of Cities

Cultural symbols,a manifestation of cities’cultural resources,are not only signs that frame concepts but also forms that express meanings.Exploring the international communication of cities from the perspective of symbols,this paper analyzes in depth how cities create their cultural symbols in the dynamic process of international communication in an era of symbol-based digital media,and how they develop their narratives and explain meanings through the dissemination of symbols when telling their stories to international audiences,thus enhancing the efficiency and effectiveness of their international communication efforts.

Symbols in The Magic Barrel

The article takes the famous modern American Jewish writer Bernard Malamud’s novel The Magic Barrel as the object of study,and uses symbolism to interpret it,analyzing it one by one in terms of the magic barrel,the colors,the seasons and the windows,respectively,so as to reveal the process of getting rid of the childishness within the novel’s male protagonist,Leo Finkel,who is maturing,as well as the novel’s Jewish theme of searching for the soul of the self.

Camilla Guerrieri’s Portrait of Guidobaldo II della Rovere:Symbols of Honor and Power

The Altomani&Sons Collection owns a remarkable newly discovered portrait of Guidobaldo II della Rovere,Duke of Urbino(1514-1574),a historical military figure who was a condottiere,ruler of Urbino,Commander-in-chief of the Papal Estate,and Perfect of Rome,as well as a collector and patron of the Fine Arts.Camilla Guerrieri Nati(1628-1694),a seventeenth-century Italian painter from Fossombrone(in the province of Pesaro and Urbino),portrayed this heroic personage surrounded by emblems associated with his military courage and leadership,including his plumed burgonet helmet,metal gilded armor,a necklace with the golden fleece,and batons of secular and religious dominions.This oil painting on copper-considered a precious metal at the time-emphasizes the importance of the commission.The material and technique also reveals a unique artistic achievement in that it provides the painting with a smooth,reflective surface and vibrant coloration,symbolizing precious imagery.

Evolutionary Dynamics Modeling of Symbolic Social Network Structure Equilibrium

The use of symbol attributes on the side of symbolic social networks to analyze,understand,and predict the topology,function,and dynamic behaviour of complex networks,and has important theoretical significance for personalized recommendations,attitude prediction,user feature analysis,and clustering and application value.However,due to the huge scale of online social networks,this poses a challenge to traditional symbolic social network analysis methods.Based on the theory of structural equilibrium,this paper studies the evolutionary dynamics of symbolic social networks,proposes the energy function of weak structural equilibrium theory,and uses the evolution of evolutionary algorithms to obtain the weak imbalance of the network.The simulation experiment results show that the calculation method in this paper can get the optimal solution faster.It provides an idea for the study of real and complex social networks.

Non-Coherent Multi-Symbol Detection of PR-CPM for Deep Space Relay Satellites with Physical-Layer Network Coding

The demand for wireless spectrum and data transmission has increased dramatically with the rapid growth of deepspace exploration and communication.The satellite relay communication is an essential technique to solve the issues above.The method of combining Physical-layer Network Coding(PNC)with Continuous Phase Modulation(CPM)on relay satellites can improve communication efficiency and perform the collection and transmission of space data effectively.Partial-Response CPM(PR-CPM)signals possess excellent spectrum and power characteristics and are suitable for deep-space communications with limited bandwidth and massive data transmission.In this paper,a partial response non-coherent multi-symbol detection algorithm is proposed based on the Maximum-Likelihood principle.The proposed algorithm fully utilizes the memory properties of PR-CPM signals and makes decisions on specified symbols by observing a number of symbols each time.Simulation results indicate that the performance gain under the Bit Error Rate of 10-4 is about 2 dB when 5 symbols are inspected,compared with the case where the observation length is 3.

Symbol Detection Based on Temporal Convolutional Network in Optical Communications

Deep learning(DL)is one of the fastest developing areas in artificial intelligence,it has been recently gained studies and application in computer vision,automatic driving,automatic speech recognition,and communication.This paper uses the DL method to design a symbol detection algorithm in receiver for optical communication systems.The proposed DL based method is implemented by a non-causal temporal convolutional network(ncTCN),which is a convolutional neural network and appropriate for sequence processing.Meanwhile,we adopt three methods to realize the training process for multiple signal-to-noise ratios of the AWGN channel.Furthermore,we apply two nonlinear activation functions for the noise robustness to the proposed ncTCN.Without losing generality,we apply the ncTCN-based receiver to the 16-ary quadrature amplitude modulation optical communication system in the simulation experiment.According to the experiment results,the proposed method can obtain some bit error rate performance gain compared to some conventional receivers.

MODIFIED DOUBLE-GRAPH DECOMPOSITION ANALYSIS FOR FINDING SYMBOLIC NETWORK FUNCTIONS

The concepts of complementary cofactor pairs, normal double-graphs and feasible torn vertex seta are introduced. By using them a decomposition theorem for first-order cofactor C(Y) is derived. Combining it with the modified double-graph method, a new decomposition analysis-modified double-graph decomposition analysis is presented for finding symbolic network functions. Its advantages are that the resultant symbolic expressions are compact and contain no cancellation terms, and its sign evaluation is very simple.

COATES GRAPH APPROACH FOR GENERATING SYMBOLIC NETWORK FUNCTION

In this paper, symbolic code matrix ,constant matrix and count matrix are defined .The first twomatrices are used to describe the elemental expression of augmented matrix and the nede admittance equa-tion is thus obtained. The third matrix is used to obtain the incoming degree matrix, and according to thematrix all the 1- factors of the Coates graph are given. By using the data code, the determinant is expandedand the same items in the expansion are merged. Thus the symbolic network function in which no term can-cellation occurs is generated.

Symbolic Computation and Graphic Presentation of Magneto-Static Field and Its Associated Vector Potential for a Steady Looping Current

With the advent of Computer Algebra System (CAS) such as Mathematica [1], challenging symbolic longhand calcula-tions can effectively be performed free of error and at ease. Mathematica’s integrated features allow the investigator to combine the needed symbolic, numeric and graphic modules all in one interactive environment. This assists the author to focus on interpreting the output rather than exerting the efforts of relating the scattered separate modules. In this note the author, utilizing these three features, explores the magneto-static field and its associated vector potential of a steady looping current. In particular by deploying the numeric features of Mathematica the exact value of the vector potential of the looping current conducive to its 3D graph is presented.

Accurate Symbolic Solution of Ginzburg-Landau Equations in the Circular Cell Approximation by Variational Method: Magnetization of Ideal Type II Superconductor

In this paper I access the degree of approximation of known symbolic approach to solving of Ginzburg-Landau (GL) equations using variational method and a concept of vortex lattice with circular unit cells, refine it in a clear and concise way, identify and eliminate the errors. Also, I will improve its accuracy by providing for the first time precise dependencies of the variational parameters;correct and calculate magnetisation, compare it with the one calculated numerically and conclude they agree within 98.5% or better for any value of the GL parameter k and at magnetic field , which is good basis for many engineering applications. As a result, a theoretical tool is developed using known symbolic solutions of GL equations with accuracy surpassing that of any other known symbolic solution and approaching that of numerical one.

Improved Genetic Programming Algorithm Applied to Symbolic Regression and Software Reliability Modeling

The present study aims at improving the ability of the canonical genetic programming algorithm to solve problems, and describes an improved genetic programming (IGP). The proposed method can be described as follows: the first inves-tigates initializing population, the second investigates reproduction operator, the third investigates crossover operator, and the fourth investigates mutation operation. The IGP is examined in two domains and the results suggest that the IGP is more effective and more efficient than the canonical one applied in different domains.

A robust symbol timing strategy for cellular systems

In cellular systems,establishing the initial symbol timing of potential preambles is the first step of a cell search.The envelope fluctuation of the downlink signal hinders the successful timing of conventional symbol timing methods.To solve this problem,a hybrid timing strategy is proposed with two novel detectors,namely the normalized replica-based detector and normalized differential detector.The strategy first detects all potential preambles via the normalized replica-based detector and then employs the normalized differential detector to verify the target preamble,which comes from the target cell and has the highest power.The strategy is unaffected by envelope fluctuation and has computational complexity comparable to that of conventional methods.Simu-lations and real-data tests show that the hybrid timing strategy is robust and practical for initial symbol timing.

Crack Growth Rate Model Derived from Domain Knowledge-Guided Symbolic Regression

Machine learning(ML)has powerful nonlinear processing and multivariate learning capabilities,so it has been widely utilised in the fatigue field.However,most ML methods are inexplicable black-box models that are difficult to apply in engineering practice.Symbolic regression(SR)is an interpretable machine learning method for determining the optimal fitting equation for datasets.In this study,domain knowledge-guided SR was used to determine a new fatigue crack growth(FCG)rate model.Three terms of the variable subtree ofΔK,R-ratio,andΔK_(th)were obtained by analysing eight traditional semi-empirical FCG rate models.Based on the FCG rate test data from other literature,the SR model was constructed using Al-7055-T7511.It was subsequently extended to other alloys(Ti-10V-2Fe-3Al,Ti-6Al-4V,Cr-Mo-V,LC9cs,Al-6013-T651,and Al-2324-T3)using multiple linear regression.Compared with the three semi-empirical FCG rate models,the SR model yielded higher prediction accuracy.This result demonstrates the potential of domain knowledge-guided SR for building the FCG rate model.

Application of Blank Symbols in Japanese Architecture and Indoor Designs

A symbol is an expression of meaning,while blank symbols express special meanings.By focusing on the application of blank symbols in Japanese architecture and indoor designs,we analyzed the aesthetic principles in Japanese architecture and indoor designs from the perspective of semiotics,such as“Kongji,”“Emptiness,”and“Dying out,”and their minimalist and pure design concepts.Traditional Chinese culture was also further explored,especially the profound influence of the“Chan sect”and the philosophy of“unity of heaven and mankind”on Japanese architecture and designs.This study aims to facilitate the coexistence and mutual appreciation of Chinese and Japanese architectural designs.

Analysis of the Influence of Clothing on Characterization from Symbolism: Taking The Scarlet Letter, Daisy Miller, and Dry September as Examples

In the novels, the clothing worn by the characters are symbols made and used by the authors to express the characters’ personality and deepen the theme, and grasping their symbolism helps to enter into the interior of the novels and understand their essence. This paper will analyze and interpret the clothing of typical female characters in The Scarlet Letter, Daisy Miller and Dry September from the perspective of symbolism, so as to illustrate how the symbolism of clothing, as a kind of symbols, shapes the image of the main characters.