Orthogonal Frequency Division Mtfltiplexing (OFDM)is the most preferred and widely used multiplexing tech- nique in current wireless environment for sidelining the spectrum scarcity problem by splitting a signal into ...Orthogonal Frequency Division Mtfltiplexing (OFDM)is the most preferred and widely used multiplexing tech- nique in current wireless environment for sidelining the spectrum scarcity problem by splitting a signal into N signals and subsequently modulating them through several orthogonal subcarriers (which bears high frequency). With several great features,OFDM is severely affected by undesirable affects of frequency offset errors and Local Oscillator (LO)frequency synchronization errors.This paper proposes an approach that devises a new hybrid technique,which is a combination of Maximum Likelihood Estimation (MLE)and Self Cancellation (SC)techniques through wavelet implication,to enhance BER performance of the OFDM system.A comparative analysis of SC,MILE,and wavelet-based hybrid InterCarrier Interference (ICI)cancellation techniques was conducted using 64-QAM and differential offset (0.02 and 0.01)for differential data bits (4 Mb and 16 Mb)to justify the outstanding results.The simulation results showed a significant byte-error-rate improvement over different legacy techniques based on fast Fourier transform.展开更多
There are various technologies like CVD. Radio Frequency sputtering, spin coating etc. present for thin film deposition for various applications and for gas sensors. In this review, special attention is focused on the...There are various technologies like CVD. Radio Frequency sputtering, spin coating etc. present for thin film deposition for various applications and for gas sensors. In this review, special attention is focused on the thin film deposition for gas sensing applications by using Langmuir Blodgett method. Langmuir Blodgett method also discussed briefly. Modified technique of Langmuir-Blodgett like Langmuir Schaefer method is discussed and various examples of Langmuir Blodgett techniques for gas sensing for space applications are included. Future prospects of gas sensing thin film deposition by Langmuir Blodgett technique are explained.展开更多
Rooftop units(RTUs)were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive.Fault detection and diagnosis(FDD)tools can be ...Rooftop units(RTUs)were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive.Fault detection and diagnosis(FDD)tools can be employed for RTU methods to ensure essential faults are addressed promptly.In this aspect,this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units(ODBNFDC-PRTU)model.The ODBNFDC-PRTU technique considers fault diagnosis as amulti-class classification problem and is handled usingDL models.For fault diagnosis in RTUs,the ODBNFDC-PRTU model exploits the deep belief network(DBN)classification model,which identifies seven distinct types of faults.At the same time,the chicken swarm optimization(CSO)algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process,showing the novelty of the work.To illustrate the enhanced performance of the ODBNFDC-PRTU algorithm,a comprehensive set of simulations are applied.The comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30%and TPR of 93.09%.展开更多
Skin cancer is one of the most dangerous cancer.Because of the high melanoma death rate,skin cancer is divided into non-melanoma and melanoma.The dermatologist finds it difficult to identify skin cancer from dermoscop...Skin cancer is one of the most dangerous cancer.Because of the high melanoma death rate,skin cancer is divided into non-melanoma and melanoma.The dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin lesions.Sometimes,pathology and biopsy examinations are required for cancer diagnosis.Earlier studies have formulated computer-based systems for detecting skin cancer from skin lesion images.With recent advancements in hardware and software technologies,deep learning(DL)has developed as a potential technique for feature learning.Therefore,this study develops a new sand cat swarm optimization with a deep transfer learning method for skin cancer detection and classification(SCSODTL-SCC)technique.The major intention of the SCSODTL-SCC model lies in the recognition and classification of different types of skin cancer on dermoscopic images.Primarily,Dull razor approach-related hair removal and median filtering-based noise elimination are performed.Moreover,the U2Net segmentation approach is employed for detecting infected lesion regions in dermoscopic images.Furthermore,the NASNetLarge-based feature extractor with a hybrid deep belief network(DBN)model is used for classification.Finally,the classification performance can be improved by the SCSO algorithm for the hyperparameter tuning process,showing the novelty of the work.The simulation values of the SCSODTL-SCC model are scrutinized on the benchmark skin lesion dataset.The comparative results assured that the SCSODTL-SCC model had shown maximum skin cancer classification performance in different measures.展开更多
Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so on.Statistical data mining(SDM)is an interdisciplinary dom...Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so on.Statistical data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data.It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves.Thus,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)model.In the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation complexity.For rainfall prediction.Convolution neural network with long short-term memory(CNN-LSTM)technique is exploited.At last,this study involves the pelican optimization algorithm(POA)as a hyperparameter optimizer.The experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive class.The comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.展开更多
Jaundice,common condition in newborns,is characterized by yellowing of the skin and eyes due to elevated levels of bilirubin in the blood.Timely detection and management of jaundice are crucial to prevent potential co...Jaundice,common condition in newborns,is characterized by yellowing of the skin and eyes due to elevated levels of bilirubin in the blood.Timely detection and management of jaundice are crucial to prevent potential complications.Traditional jaundice assessment methods rely on visual inspection or invasive blood tests that are subjective and painful for infants,respectively.Although several automated methods for jaundice detection have been developed during the past few years,a limited number of reviews consolidating these developments have been presented till date,making it essential to systematically evaluate and present the existing advancements.This paper fills this gap by providing a thorough survey of automated methods for jaundice detection in neonates.The primary focus of the survey is to review the existing methodologies,techniques,and technologies used for neonatal jaundice detection.The key findings from the review indicate that image-based bilirubinometers and transcutaneous bilirubinometers are promising non-invasive alternatives,and provide a good trade-off between accuracy and ease of use.However,their effectiveness varies with factors like skin pigmentation,gestational age,and measurement site.Spectroscopic and biosensor-based techniques show high sensitivity but need further clinical validation.Despite advancements,several challenges including device calibration,large-scale validation,and regulatory barriers still haunt the researchers.Standardization,regulatory compliances,and seamless integration into healthcare workflows are the key hurdles to be addressed.By consolidating the current knowledge and discussing the challenges and opportunities in this field,this survey aims to contribute to the advancement of automatic jaundice detection and ultimately improve neonatal care.展开更多
This paper describes an analytical model for bulk electron mobility in strained-Si layers as a function of strain. Phonon scattering, columbic scattering and surface roughness scattering are included to analyze the fu...This paper describes an analytical model for bulk electron mobility in strained-Si layers as a function of strain. Phonon scattering, columbic scattering and surface roughness scattering are included to analyze the full mobility model. Analytical explicit calculations of all of the parameters to accurately estimate the electron mobility have been made. The results predict an increase in the electron mobility with the application of biaxial strain as also predicted from the basic theory of strain physics of metal oxide semiconductor （MOS） devices. The results have also been compared with numerically reported results and show good agreement.展开更多
Frequency regulation in a generation mix having large wind power penetration is a critical issue, as wind units isolate from the grid during disturbances with advanced power electronics controllers and reduce equivale...Frequency regulation in a generation mix having large wind power penetration is a critical issue, as wind units isolate from the grid during disturbances with advanced power electronics controllers and reduce equivalent system inertia. Thus, it is important that wind turbines also contribute to system frequency control. This paper examines the dynamic contribution of doubly fed induction generator （DFIG）-based wind turbine in system frequency regulation. The modified inertial support scheme is proposed which helps the DFIG to provide the short term transient active power support to the grid during transients and arrests the fall in frequency. The frequency deviation is considered by the controller to provide the inertial control. An additional reference power output is used which helps the DFIG to release kinetic energy stored in rotating masses of the turbine. The optimal speed control parameters have been used for the DFIG to increases its participation in frequency control. The simulations carried out in a two-area interconnected power system demonstrate the contribution of the DFIG in load frequency control.展开更多
In a competitive and deregulated power scenario, the utilities try to maintain their real electric power generation in balance with the load demand, which creates a need for the precise real time generation scheduling...In a competitive and deregulated power scenario, the utilities try to maintain their real electric power generation in balance with the load demand, which creates a need for the precise real time generation scheduling （GS）. In this paper, the GS problem is solved to perform the unit commitment （UC） based on frequency prediction by using artificial neural network （ANN） with the objective to minimize the overall system cost of the state utility. The introduction of availability-based tariff （ABT） signifies the importance of frequency in GS. Under- prediction or over-prediction will result in an unnecessary commitment of generating units or buying power from central generating units at a higher cost. Therefore, an accurate frequency prediction is the first step toward optimal GS. The dependency of frequency on various parameters such as actual generation, load demand, wind power and power deficit has been considered in this paper. The proposed technique provides a reliable solution for the input parameter different from the one presented in the training data. The performance of the frequency predictor model has been evaluated based on the absolute percentage error （APE） and the mean absolute percentage error （MAPE）. The proposed predicted frequency sensitive GS model is applied to the system of Indian state of Tamilnadu, which reduces the overall system cost of the state utility by keeping off the dearer units selected based on the predicted frequency.展开更多
This paper presents the analysis of load frequency control （LFC） of a deregulated two-area hydro-thermal power system using fuzzy logic controller, with doubly fed induction generators （DFIGs） integrated into both...This paper presents the analysis of load frequency control （LFC） of a deregulated two-area hydro-thermal power system using fuzzy logic controller, with doubly fed induction generators （DFIGs） integrated into both the control areas. The deregulation of power sector has led to the formation of new companies for generation, transmission and distribution of power. The conventional two-area power system is modified to study the effects of the bilateral contracts of companies on the system dynamics. Deregulation creates highly competitive and distributed control environment, and the LFC becomes even more challenging when wind generators are also integrated into the system. The overall inertia of the system reduces, as the wind unit does not provide inertia and isolates from the grid during disturbances. The DFIGs integrated provide inertial support to the system through modified inertial control scheme, and arrests the initial fall in frequency after disturbance. The inertial control responds to frequency deviations, which takes out the kinetic energy of the wind turbine for improving the frequency response of the system. To enhance the participation of the doubly fed induction generator （DFIG） in the frequency control, optimal values of the speed control parameters of the DFIG-based wind turbine have been obtained using integral square error （ISE） technique. The dynamics of the system have been obtained for a small load perturbation, and for contract violation using fuzzy controller.展开更多
In this paper,a review of Cu/low-k,carbon nanotube（CNT）,graphene nanoribbon（GNR）and optical based interconnect technologies has been done,Interconnect models,challenges and solutions have also been discussed.Of al...In this paper,a review of Cu/low-k,carbon nanotube（CNT）,graphene nanoribbon（GNR）and optical based interconnect technologies has been done,Interconnect models,challenges and solutions have also been discussed.Of all the four technologies,CNT interconnects satisfy most of the challenges and they are most suited for nanometer scale technologies,despite some minor drawbacks.It is concluded that beyond 32nm technology,a paradigm shift in the interconnect material is required as Cu/low-k interconnects are approaching fundamental limits.展开更多
Sunoj et al.[(2009).Characterization of life distributions using conditional expectations of doubly(Intervel)truncated random variables.Communications in Statistics–Theory and Methods,38(9),1441–1452]introduced the ...Sunoj et al.[(2009).Characterization of life distributions using conditional expectations of doubly(Intervel)truncated random variables.Communications in Statistics–Theory and Methods,38(9),1441–1452]introduced the concept of Shannon doubly truncated entropy in the literature.Quantile functions are equivalent alternatives to distribution functions in modelling and analysis of statistical data.In this paper,we introduce quantile version of Shannon interval entropyfor doubly truncated random variable and investigate it for various types of univariate distribution functions.We have characterised certain specific lifetime distributions using the measureproposed.Also we discuss one fascinating practical example based on the quantile data analysis.展开更多
文摘Orthogonal Frequency Division Mtfltiplexing (OFDM)is the most preferred and widely used multiplexing tech- nique in current wireless environment for sidelining the spectrum scarcity problem by splitting a signal into N signals and subsequently modulating them through several orthogonal subcarriers (which bears high frequency). With several great features,OFDM is severely affected by undesirable affects of frequency offset errors and Local Oscillator (LO)frequency synchronization errors.This paper proposes an approach that devises a new hybrid technique,which is a combination of Maximum Likelihood Estimation (MLE)and Self Cancellation (SC)techniques through wavelet implication,to enhance BER performance of the OFDM system.A comparative analysis of SC,MILE,and wavelet-based hybrid InterCarrier Interference (ICI)cancellation techniques was conducted using 64-QAM and differential offset (0.02 and 0.01)for differential data bits (4 Mb and 16 Mb)to justify the outstanding results.The simulation results showed a significant byte-error-rate improvement over different legacy techniques based on fast Fourier transform.
文摘There are various technologies like CVD. Radio Frequency sputtering, spin coating etc. present for thin film deposition for various applications and for gas sensors. In this review, special attention is focused on the thin film deposition for gas sensing applications by using Langmuir Blodgett method. Langmuir Blodgett method also discussed briefly. Modified technique of Langmuir-Blodgett like Langmuir Schaefer method is discussed and various examples of Langmuir Blodgett techniques for gas sensing for space applications are included. Future prospects of gas sensing thin film deposition by Langmuir Blodgett technique are explained.
基金This work was supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2020-0-00107,Development of the technology to automate the recommendations for big data analytic models that define data characteristics and problems).
文摘Rooftop units(RTUs)were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive.Fault detection and diagnosis(FDD)tools can be employed for RTU methods to ensure essential faults are addressed promptly.In this aspect,this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units(ODBNFDC-PRTU)model.The ODBNFDC-PRTU technique considers fault diagnosis as amulti-class classification problem and is handled usingDL models.For fault diagnosis in RTUs,the ODBNFDC-PRTU model exploits the deep belief network(DBN)classification model,which identifies seven distinct types of faults.At the same time,the chicken swarm optimization(CSO)algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process,showing the novelty of the work.To illustrate the enhanced performance of the ODBNFDC-PRTU algorithm,a comprehensive set of simulations are applied.The comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30%and TPR of 93.09%.
基金supported by the Technology Development Program of MSS [No.S3033853]by the National University Development Project by the Ministry of Education in 2022.
文摘Skin cancer is one of the most dangerous cancer.Because of the high melanoma death rate,skin cancer is divided into non-melanoma and melanoma.The dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin lesions.Sometimes,pathology and biopsy examinations are required for cancer diagnosis.Earlier studies have formulated computer-based systems for detecting skin cancer from skin lesion images.With recent advancements in hardware and software technologies,deep learning(DL)has developed as a potential technique for feature learning.Therefore,this study develops a new sand cat swarm optimization with a deep transfer learning method for skin cancer detection and classification(SCSODTL-SCC)technique.The major intention of the SCSODTL-SCC model lies in the recognition and classification of different types of skin cancer on dermoscopic images.Primarily,Dull razor approach-related hair removal and median filtering-based noise elimination are performed.Moreover,the U2Net segmentation approach is employed for detecting infected lesion regions in dermoscopic images.Furthermore,the NASNetLarge-based feature extractor with a hybrid deep belief network(DBN)model is used for classification.Finally,the classification performance can be improved by the SCSO algorithm for the hyperparameter tuning process,showing the novelty of the work.The simulation values of the SCSODTL-SCC model are scrutinized on the benchmark skin lesion dataset.The comparative results assured that the SCSODTL-SCC model had shown maximum skin cancer classification performance in different measures.
基金This research was partly supported by the Technology Development Program of MSS[No.S3033853]by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A4A1031509).
文摘Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so on.Statistical data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data.It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves.Thus,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)model.In the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation complexity.For rainfall prediction.Convolution neural network with long short-term memory(CNN-LSTM)technique is exploited.At last,this study involves the pelican optimization algorithm(POA)as a hyperparameter optimizer.The experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive class.The comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.
基金funded by the Indian Council of Medical Research(ICMR),New Delhi,Government of India under Grant No.EM/SG/Dev.Res/124/0812-2023.
文摘Jaundice,common condition in newborns,is characterized by yellowing of the skin and eyes due to elevated levels of bilirubin in the blood.Timely detection and management of jaundice are crucial to prevent potential complications.Traditional jaundice assessment methods rely on visual inspection or invasive blood tests that are subjective and painful for infants,respectively.Although several automated methods for jaundice detection have been developed during the past few years,a limited number of reviews consolidating these developments have been presented till date,making it essential to systematically evaluate and present the existing advancements.This paper fills this gap by providing a thorough survey of automated methods for jaundice detection in neonates.The primary focus of the survey is to review the existing methodologies,techniques,and technologies used for neonatal jaundice detection.The key findings from the review indicate that image-based bilirubinometers and transcutaneous bilirubinometers are promising non-invasive alternatives,and provide a good trade-off between accuracy and ease of use.However,their effectiveness varies with factors like skin pigmentation,gestational age,and measurement site.Spectroscopic and biosensor-based techniques show high sensitivity but need further clinical validation.Despite advancements,several challenges including device calibration,large-scale validation,and regulatory barriers still haunt the researchers.Standardization,regulatory compliances,and seamless integration into healthcare workflows are the key hurdles to be addressed.By consolidating the current knowledge and discussing the challenges and opportunities in this field,this survey aims to contribute to the advancement of automatic jaundice detection and ultimately improve neonatal care.
文摘This paper describes an analytical model for bulk electron mobility in strained-Si layers as a function of strain. Phonon scattering, columbic scattering and surface roughness scattering are included to analyze the full mobility model. Analytical explicit calculations of all of the parameters to accurately estimate the electron mobility have been made. The results predict an increase in the electron mobility with the application of biaxial strain as also predicted from the basic theory of strain physics of metal oxide semiconductor （MOS） devices. The results have also been compared with numerically reported results and show good agreement.
文摘Frequency regulation in a generation mix having large wind power penetration is a critical issue, as wind units isolate from the grid during disturbances with advanced power electronics controllers and reduce equivalent system inertia. Thus, it is important that wind turbines also contribute to system frequency control. This paper examines the dynamic contribution of doubly fed induction generator （DFIG）-based wind turbine in system frequency regulation. The modified inertial support scheme is proposed which helps the DFIG to provide the short term transient active power support to the grid during transients and arrests the fall in frequency. The frequency deviation is considered by the controller to provide the inertial control. An additional reference power output is used which helps the DFIG to release kinetic energy stored in rotating masses of the turbine. The optimal speed control parameters have been used for the DFIG to increases its participation in frequency control. The simulations carried out in a two-area interconnected power system demonstrate the contribution of the DFIG in load frequency control.
文摘In a competitive and deregulated power scenario, the utilities try to maintain their real electric power generation in balance with the load demand, which creates a need for the precise real time generation scheduling （GS）. In this paper, the GS problem is solved to perform the unit commitment （UC） based on frequency prediction by using artificial neural network （ANN） with the objective to minimize the overall system cost of the state utility. The introduction of availability-based tariff （ABT） signifies the importance of frequency in GS. Under- prediction or over-prediction will result in an unnecessary commitment of generating units or buying power from central generating units at a higher cost. Therefore, an accurate frequency prediction is the first step toward optimal GS. The dependency of frequency on various parameters such as actual generation, load demand, wind power and power deficit has been considered in this paper. The proposed technique provides a reliable solution for the input parameter different from the one presented in the training data. The performance of the frequency predictor model has been evaluated based on the absolute percentage error （APE） and the mean absolute percentage error （MAPE）. The proposed predicted frequency sensitive GS model is applied to the system of Indian state of Tamilnadu, which reduces the overall system cost of the state utility by keeping off the dearer units selected based on the predicted frequency.
文摘This paper presents the analysis of load frequency control （LFC） of a deregulated two-area hydro-thermal power system using fuzzy logic controller, with doubly fed induction generators （DFIGs） integrated into both the control areas. The deregulation of power sector has led to the formation of new companies for generation, transmission and distribution of power. The conventional two-area power system is modified to study the effects of the bilateral contracts of companies on the system dynamics. Deregulation creates highly competitive and distributed control environment, and the LFC becomes even more challenging when wind generators are also integrated into the system. The overall inertia of the system reduces, as the wind unit does not provide inertia and isolates from the grid during disturbances. The DFIGs integrated provide inertial support to the system through modified inertial control scheme, and arrests the initial fall in frequency after disturbance. The inertial control responds to frequency deviations, which takes out the kinetic energy of the wind turbine for improving the frequency response of the system. To enhance the participation of the doubly fed induction generator （DFIG） in the frequency control, optimal values of the speed control parameters of the DFIG-based wind turbine have been obtained using integral square error （ISE） technique. The dynamics of the system have been obtained for a small load perturbation, and for contract violation using fuzzy controller.
文摘In this paper,a review of Cu/low-k,carbon nanotube（CNT）,graphene nanoribbon（GNR）and optical based interconnect technologies has been done,Interconnect models,challenges and solutions have also been discussed.Of all the four technologies,CNT interconnects satisfy most of the challenges and they are most suited for nanometer scale technologies,despite some minor drawbacks.It is concluded that beyond 32nm technology,a paradigm shift in the interconnect material is required as Cu/low-k interconnects are approaching fundamental limits.
基金The first author wishes to acknowledge the Science and Engineering Research Board(SERB)Government of India,for the financial assistance(Ref.No.ECR/2017/001987)for carrying out this research work.
文摘Sunoj et al.[(2009).Characterization of life distributions using conditional expectations of doubly(Intervel)truncated random variables.Communications in Statistics–Theory and Methods,38(9),1441–1452]introduced the concept of Shannon doubly truncated entropy in the literature.Quantile functions are equivalent alternatives to distribution functions in modelling and analysis of statistical data.In this paper,we introduce quantile version of Shannon interval entropyfor doubly truncated random variable and investigate it for various types of univariate distribution functions.We have characterised certain specific lifetime distributions using the measureproposed.Also we discuss one fascinating practical example based on the quantile data analysis.
