The improved cross-correlation algorithm for the strain demodulation of Vernier-effect-based optical fiber sensor(VE-OFS)is proposed in this article.The algorithm identifies the most similar spectrum to the measured o...The improved cross-correlation algorithm for the strain demodulation of Vernier-effect-based optical fiber sensor(VE-OFS)is proposed in this article.The algorithm identifies the most similar spectrum to the measured one from the database of the collected spectra by employing the cross-correlation operation,subsequently deriving the predicted value via weighted calculation.As the algorithm uses the complete information in the measured raw spectrum,more accurate results and larger measurement range can be obtained.Additionally,the improved cross-correlation algorithm also has the potential to improve the measurement speed compared to current standards due to the possibility for the collection using low sampling rate.This work presents an important algorithm towards a simpler,faster way to improve the demodulation performance of VE-OFS.展开更多
The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorit...The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision.展开更多
In this paper after analyzing the adaptation process of the proportionate normalized least mean square(PNLMS) algorithm, a statistical model is obtained to describe the convergence process of each adaptive filter coef...In this paper after analyzing the adaptation process of the proportionate normalized least mean square(PNLMS) algorithm, a statistical model is obtained to describe the convergence process of each adaptive filter coefcient. Inspired by this result, a modified PNLMS algorithm based on precise magnitude estimate is proposed. The simulation results indicate that in contrast to the traditional PNLMS algorithm, the proposed algorithm achieves faster convergence speed in the initial convergence state and lower misalignment in the stead stage with much less computational complexity.展开更多
Autism Spectrum Disorder(ASD)is a complex neurodevelopmental condition that causes multiple challenges in behavioral and communication activities.In the medical field,the data related to ASD,the security measures are ...Autism Spectrum Disorder(ASD)is a complex neurodevelopmental condition that causes multiple challenges in behavioral and communication activities.In the medical field,the data related to ASD,the security measures are integrated in this research responsibly and effectively to develop the Mobile Neuron Attention Stage-by-Stage Network(MNASNet)model,which is the integration of both Mobile Network(MobileNet)and Neuron Attention Stage-by-Stage.The steps followed to detect ASD with privacy-preserved data are data normalization,data augmentation,and K-Anonymization.The clinical data of individuals are taken initially and preprocessed using the Z-score Normalization.Then,data augmentation is performed using the oversampling technique.Subsequently,K-Anonymization is effectuated by utilizing the Black-winged Kite Algorithm to ensure the privacy of medical data,where the best fitness solution is based on data utility and privacy.Finally,after improving the data privacy,the developed approach MNASNet is implemented for ASD detection,which achieves highly accurate results compared to traditional methods to detect autism behavior.Hence,the final results illustrate that the proposed MNASNet achieves an accuracy of 92.9%,TPR of 95.9%,and TNR of 90.9%at the k-samples of 8.展开更多
In the era of digital signal processing,like graphics and computation systems,multiplication-accumulation is one of the prime operations.A MAC unit is a vital component of a digital system,like different Fast Fourier ...In the era of digital signal processing,like graphics and computation systems,multiplication-accumulation is one of the prime operations.A MAC unit is a vital component of a digital system,like different Fast Fourier Transform(FFT)algorithms,convolution,image processing algorithms,etcetera.In the domain of digital signal processing,the use of normalization architecture is very vast.The main objective of using normalization is to performcomparison and shift operations.In this research paper,an evolutionary approach for designing an optimized normalization algorithm is proposed using basic logical blocks such as Multiplexer,Adder etc.The proposed normalization algorithm is further used in designing an 8×8 bit Signed Floating-Point Multiply-Accumulate(SFMAC)architecture.Since the SFMAC can accept an 8-bit significand and a 3-bit exponent,the input to the said architecture can be somewhere between−(7.96872)_(10) to+(7.96872)_(10).The proposed architecture is designed and implemented using the Cadence Virtuoso using 90 and 130 nm technologies(in Generic Process Design Kit(GPDK)and Taiwan Semiconductor Manufacturing Company(TSMC),respectively).To reduce the power consumption of the proposed normalization architecture,techniques such as“block enabling”and“clock gating”are used rigorously.According to the analysis done on Cadence,the proposed architecture uses the least amount of power compared to its current predecessors.展开更多
This research introduces a challenge in integrating and cleaning the data,which is a crucial task in object matching.While the object is detected and then measured,the vibration at different light intensities may influ...This research introduces a challenge in integrating and cleaning the data,which is a crucial task in object matching.While the object is detected and then measured,the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage,abnormal stopping,and disaster.Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement.To solve all these problems,this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to a predetermined portion of the image.A novel white color sticker of the known dimensions marked with a color dot is pasted on the surface of an object for the best result in the template matching using the Improved Up-Sampled Cross-Correlation(UCC)algorithm.The vibration measurement is calculated using the Finite-Difference Algorithm(FDA),a machine vision systemfitted with a macro lens sensor that is capable of capturing the image at a closer range,which does not affect the quality of displacement measurement from the video frames.Thefield test was conducted on the TAFE(Tractors and Farm Equipment Limited)tractor parts,and the percentage of error was recorded between 30%and 50%at very low vibration values close to zero,whereas it was recorded between 5%and 10%error in most high-accelerations,the essential range for vibration analysis.Finally,the suggested system is more suitable for measuring the vibration of stationary machinery having low frequency ranges.The use of a macro lens enables to capture of image frames at very close-ups.A 30%to 50%error percentage has been reported when the vibration amplitude is very small.Therefore,this study is not suitable for Nano vibration analysis.展开更多
The feedback vertex set (FVS) problem is to find the set of vertices of minimum cardinality whose removal renders the graph acyclic. The FVS problem has applications in several areas such as combinatorial circuit desi...The feedback vertex set (FVS) problem is to find the set of vertices of minimum cardinality whose removal renders the graph acyclic. The FVS problem has applications in several areas such as combinatorial circuit design, synchronous systems, computer systems, and very-large-scale integration (VLSI) circuits. The FVS problem is known to be NP-hard for simple graphs, but polynomi-al-time algorithms have been found for special classes of graphs. The intersection graph of a collection of arcs on a circle is called a circular-arc graph. A normal Helly circular-arc graph is a proper subclass of the set of circular-arc graphs. In this paper, we present an algorithm that takes time to solve the FVS problem in a normal Helly circular-arc graph with n vertices and m edges.展开更多
A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all...A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.展开更多
文摘The improved cross-correlation algorithm for the strain demodulation of Vernier-effect-based optical fiber sensor(VE-OFS)is proposed in this article.The algorithm identifies the most similar spectrum to the measured one from the database of the collected spectra by employing the cross-correlation operation,subsequently deriving the predicted value via weighted calculation.As the algorithm uses the complete information in the measured raw spectrum,more accurate results and larger measurement range can be obtained.Additionally,the improved cross-correlation algorithm also has the potential to improve the measurement speed compared to current standards due to the possibility for the collection using low sampling rate.This work presents an important algorithm towards a simpler,faster way to improve the demodulation performance of VE-OFS.
基金Project(50905037) supported by the National Natural Science Foundation of ChinaProject(20092304120014) supported by Specialized Research Fund for the Doctoral Program of Higher Education of China+2 种基金 Project(20100471021) supported by the China Postdoctoral Science Foundation Project(LBH-Q09134) supported by Heilongjiang Postdoctoral Science-Research Foundation,China Project (HEUFT09013) supported by the Foundation of Harbin Engineering University,China
文摘The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response,by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm.The command input was corrected by weights to generate the desired input for the algorithm,and the feedback was brought into the feedback correction,whose output was the weighted feedback.The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback.Thus,the updated weights were copied to the input correction.The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input,making the feedback track the command.The above concept was used as a basis for the development of amplitude phase control.The method has good real-time performance without estimating the system model.The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision.
文摘In this paper after analyzing the adaptation process of the proportionate normalized least mean square(PNLMS) algorithm, a statistical model is obtained to describe the convergence process of each adaptive filter coefcient. Inspired by this result, a modified PNLMS algorithm based on precise magnitude estimate is proposed. The simulation results indicate that in contrast to the traditional PNLMS algorithm, the proposed algorithm achieves faster convergence speed in the initial convergence state and lower misalignment in the stead stage with much less computational complexity.
文摘Autism Spectrum Disorder(ASD)is a complex neurodevelopmental condition that causes multiple challenges in behavioral and communication activities.In the medical field,the data related to ASD,the security measures are integrated in this research responsibly and effectively to develop the Mobile Neuron Attention Stage-by-Stage Network(MNASNet)model,which is the integration of both Mobile Network(MobileNet)and Neuron Attention Stage-by-Stage.The steps followed to detect ASD with privacy-preserved data are data normalization,data augmentation,and K-Anonymization.The clinical data of individuals are taken initially and preprocessed using the Z-score Normalization.Then,data augmentation is performed using the oversampling technique.Subsequently,K-Anonymization is effectuated by utilizing the Black-winged Kite Algorithm to ensure the privacy of medical data,where the best fitness solution is based on data utility and privacy.Finally,after improving the data privacy,the developed approach MNASNet is implemented for ASD detection,which achieves highly accurate results compared to traditional methods to detect autism behavior.Hence,the final results illustrate that the proposed MNASNet achieves an accuracy of 92.9%,TPR of 95.9%,and TNR of 90.9%at the k-samples of 8.
基金This work was supported by Research Support Fund(RSF)of Symbiosis International(Deemed University),Pune,India。
文摘In the era of digital signal processing,like graphics and computation systems,multiplication-accumulation is one of the prime operations.A MAC unit is a vital component of a digital system,like different Fast Fourier Transform(FFT)algorithms,convolution,image processing algorithms,etcetera.In the domain of digital signal processing,the use of normalization architecture is very vast.The main objective of using normalization is to performcomparison and shift operations.In this research paper,an evolutionary approach for designing an optimized normalization algorithm is proposed using basic logical blocks such as Multiplexer,Adder etc.The proposed normalization algorithm is further used in designing an 8×8 bit Signed Floating-Point Multiply-Accumulate(SFMAC)architecture.Since the SFMAC can accept an 8-bit significand and a 3-bit exponent,the input to the said architecture can be somewhere between−(7.96872)_(10) to+(7.96872)_(10).The proposed architecture is designed and implemented using the Cadence Virtuoso using 90 and 130 nm technologies(in Generic Process Design Kit(GPDK)and Taiwan Semiconductor Manufacturing Company(TSMC),respectively).To reduce the power consumption of the proposed normalization architecture,techniques such as“block enabling”and“clock gating”are used rigorously.According to the analysis done on Cadence,the proposed architecture uses the least amount of power compared to its current predecessors.
文摘This research introduces a challenge in integrating and cleaning the data,which is a crucial task in object matching.While the object is detected and then measured,the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage,abnormal stopping,and disaster.Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement.To solve all these problems,this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to a predetermined portion of the image.A novel white color sticker of the known dimensions marked with a color dot is pasted on the surface of an object for the best result in the template matching using the Improved Up-Sampled Cross-Correlation(UCC)algorithm.The vibration measurement is calculated using the Finite-Difference Algorithm(FDA),a machine vision systemfitted with a macro lens sensor that is capable of capturing the image at a closer range,which does not affect the quality of displacement measurement from the video frames.Thefield test was conducted on the TAFE(Tractors and Farm Equipment Limited)tractor parts,and the percentage of error was recorded between 30%and 50%at very low vibration values close to zero,whereas it was recorded between 5%and 10%error in most high-accelerations,the essential range for vibration analysis.Finally,the suggested system is more suitable for measuring the vibration of stationary machinery having low frequency ranges.The use of a macro lens enables to capture of image frames at very close-ups.A 30%to 50%error percentage has been reported when the vibration amplitude is very small.Therefore,this study is not suitable for Nano vibration analysis.
文摘The feedback vertex set (FVS) problem is to find the set of vertices of minimum cardinality whose removal renders the graph acyclic. The FVS problem has applications in several areas such as combinatorial circuit design, synchronous systems, computer systems, and very-large-scale integration (VLSI) circuits. The FVS problem is known to be NP-hard for simple graphs, but polynomi-al-time algorithms have been found for special classes of graphs. The intersection graph of a collection of arcs on a circle is called a circular-arc graph. A normal Helly circular-arc graph is a proper subclass of the set of circular-arc graphs. In this paper, we present an algorithm that takes time to solve the FVS problem in a normal Helly circular-arc graph with n vertices and m edges.
基金supported by the Institutional Fund Projects(IFPIP-1481-611-1443)the Key Projects of Natural Science Research in Anhui Higher Education Institutions(2022AH051909)+1 种基金the Provincial Quality Project of Colleges and Universities in Anhui Province(2022sdxx020,2022xqhz044)Bengbu University 2021 High-Level Scientific Research and Cultivation Project(2021pyxm04)。
文摘A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.
