The electromagnetic levitation system(EMLS)serves as the most important part of any magnetic levitation system.However,its characteristics are defined by its highly nonlinear dynamics and instability.Furthermore,the u...The electromagnetic levitation system(EMLS)serves as the most important part of any magnetic levitation system.However,its characteristics are defined by its highly nonlinear dynamics and instability.Furthermore,the uncertainties in the dynamics of an electromagnetic levitation system make the controller design more difficult.Therefore,it is necessary to design a robust control law that will ensure the system’s stability in the presence of these uncertainties.In this framework,the dynamics of an electromagnetic levitation system are addressed in terms of matched and unmatched uncertainties.The robust control problem is translated into the optimal control problem,where the uncertainties of the electromagnetic levitation system are directly reflected in the cost function.The optimal control method is used to solve the robust control problem.The solution to the optimal control problem for the electromagnetic levitation system is indeed a solution to the robust control problem of the electromagnetic levitation system under matched and unmatched uncertainties.The simulation and experimental results demonstrate the performance of the designed control scheme.The performance indices such as integral absolute error(IAE),integral square error(ISE),integral time absolute error(ITAE),and integral time square error(ITSE)are compared for both uncertainties to showcase the robustness of the designed control scheme.展开更多
In recent years,Speech Emotion Recognition(SER)has developed into an essential instrument for interpreting human emotions from auditory data.The proposed research focuses on the development of a SER system employing d...In recent years,Speech Emotion Recognition(SER)has developed into an essential instrument for interpreting human emotions from auditory data.The proposed research focuses on the development of a SER system employing deep learning and multiple datasets containing samples of emotive speech.The primary objective of this research endeavor is to investigate the utilization of Convolutional Neural Networks(CNNs)in the process of sound feature extraction.Stretching,pitch manipulation,and noise injection are a few of the techniques utilized in this study to improve the data quality.Feature extraction methods including Zero Crossing Rate,Chroma_stft,Mel⁃scale Frequency Cepstral Coefficients(MFCC),Root Mean Square(RMS),and Mel⁃Spectogram are used to train a model.By using these techniques,audio signals can be transformed into recognized features that can be utilized to train the model.Ultimately,the study produces a thorough evaluation of the models performance.When this method was applied,the model achieved an impressive accuracy of 94.57%on the test dataset.The proposed work was also validated on the EMO⁃BD and IEMOCAP datasets.These consist of further data augmentation,feature engineering,and hyperparameter optimization.By following these development paths,SER systems will be able to be implemented in real⁃world scenarios with greater accuracy and resilience.展开更多
In this research paper,we have presented variable area type capacitive sensor signal conditioning system for angular displacement measurement and for this purpose we have used timer LM555 based astable multivibrator a...In this research paper,we have presented variable area type capacitive sensor signal conditioning system for angular displacement measurement and for this purpose we have used timer LM555 based astable multivibrator and universal frequency to digital converter (UFDC). Due to variation in angular displacement in the variable area type capacitor which is connected in the timer based astable circuit,capacitance changes which in turn changes the time period of the timer circuit output. The time period of the timer output waveform is linear with the capacitance and hence linear with angular displacement. The timer output is further processed with UFDC for the measurement. The experimental results show that the time period is linear with the angular displacement in the range of 0- 180° and the uncertainty we should associate it with this average time period value is the standard deviation of the mean,often called the standard error (SE),which is ± 0.023 μs. Because of the simplicity,this measurement system can be used in both electronic and industrial instrumentation.展开更多
Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of ...Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea(SA)activity.In the proposed method,the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted.These features are applied to the Classification and Regression Tree(CART)-Particle Swarm Optimization(PSO)classifier which classifies the signal into normal breathing signal and sleep apnea signal.The proposed method is validated to measure the performance metrics like sensitivity,specificity,accuracy and F1 score by applying time domain and frequency domain features separately.Additionally,the performance of the CART-PSO(CPSO)classification algorithm is evaluated through comparing its measures with existing classification algorithms.Concurrently,the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.展开更多
Registers and counters are the most important devices in any system of computations.In this paper we have communicated the trinary registers and counters in modified trinary number(MTN) system.It is suitable for the o...Registers and counters are the most important devices in any system of computations.In this paper we have communicated the trinary registers and counters in modified trinary number(MTN) system.It is suitable for the optical computing and other applications in multivalued logic system.Here the savart plate and spatial light modulator(SLM) based optoelectronic circuits have been used to exploit the optical tree architecture(OTA) in optical interconnection network.展开更多
The inverted pendulum is a classic problem in dynamics and control theory and is widely used as a benchmark for testing control algorithms. It is unstable without control. The process is non linear and unstable with o...The inverted pendulum is a classic problem in dynamics and control theory and is widely used as a benchmark for testing control algorithms. It is unstable without control. The process is non linear and unstable with one input signal and several output signals. It is hence obvious that feedback of the state of the pendulum is needed to stabilize the pendulum. The aim of the study is to stabilize the pendulum such that the position of the carriage on the track is controlled quickly and accurately. The problem involves an arm, able to move horizontally in angular motion, and a pendulum, hinged to the arm at the bottom of its length such that the pendulum can move in the same plane as the arm. The conventional PID controller can be used for virtually any process condition. This makes elimination the offset of the proportional mode possible and still provides fast response. In this paper, we have modelled the system and studied conventional controller and LQR controller. It is observed that the LQR method works better compared to conventional controller.展开更多
There is a widespread agreement that lung cancer is one of the deadliest types of cancer,affecting both women and men.As a result,detecting lung cancer at an early stage is crucial to create an accurate treatment plan...There is a widespread agreement that lung cancer is one of the deadliest types of cancer,affecting both women and men.As a result,detecting lung cancer at an early stage is crucial to create an accurate treatment plan and forecasting the reaction of the patient to the adopted treatment.For this reason,the development of convolutional neural networks(CNNs)for the task of lung cancer classification has recently seen a trend in attention.CNNs have great potential,but they need a lot of training data and struggle with input alterations.To address these limitations of CNNs,a novel machine-learning architecture of capsule networks has been presented,and it has the potential to completely transform the areas of deep learning.Capsule networks,which are the focus of this work,are interesting because they can withstand rotation and affine translation with relatively little training data.This research optimizes the performance of CapsNets by designing a new architecture that allows them to perform better on the challenge of lung cancer classification.The findings demonstrate that the proposed capsule network method outperforms CNNs on the lung cancer classification challenge.CapsNet with a single convolution layer and 32 features(CN-1-32),CapsNet with a single convolution layer and 64 features(CN-1-64),and CapsNet with a double convolution layer and 64 features(CN-2-64)are the three capsulel networks developed in this research for lung cancer classification.Lung nodules,both benign and malignant,are classified using these networks using CT images.The LIDC-IDRI database was utilized to assess the performance of those networks.Based on the testing results,CN-2-64 network performed better out of the three networks tested,with a specificity of 98.37%,sensitivity of 97.47%and an accuracy of 97.92%.展开更多
A generic analytical model and the ATLAS simulation of a homojunction light emitting diode(LED) based on p+-InAs0.91Sb0.09/n0-InAs0.91Sb0.09/n+-InAs0.91Sb0.09 materials grown on lattice matched p+-GaSb substrate are p...A generic analytical model and the ATLAS simulation of a homojunction light emitting diode(LED) based on p+-InAs0.91Sb0.09/n0-InAs0.91Sb0.09/n+-InAs0.91Sb0.09 materials grown on lattice matched p+-GaSb substrate are presented.This LED is suitable for use as source in the optical absorption gas spectroscopy in the mid-infrared spectral region at 300 K.The various electro-optical properties of the homojunction LED are evaluated using analytical techniques and ATLAS device simulation software.The current-voltage characteristics of the structure are computed analytically and simulated,and the results are found to be in good agreement.The output power of the homojunction LED is estimated as a function of bias current under high carrier injection and compared with the reported experimental results.展开更多
This research implements a novel segmentation of mammographic mass.Three methods are proposed,namely,segmentation of mass based on iterative active contour,automatic region growing,and fully automatic mask selectionba...This research implements a novel segmentation of mammographic mass.Three methods are proposed,namely,segmentation of mass based on iterative active contour,automatic region growing,and fully automatic mask selectionbased active contour techniques.In the first method,iterative threshold is performed for manual cropped preprocessed image,and active contour is applied thereafter.To overcome manual cropping in the second method,an automatic seed selection followed by region growing is performed.Given that the result is only a few images owing to over segmentation,the third method uses a fully automatic active contour.Results of the segmentation techniques are compared with the manual markup by experts,specifically by taking the difference in their mean values.Accordingly,the difference in the mean value of the third method is 1.0853,which indicates the closeness of the segmentation.Moreover,the proposed method is compared with the existing fuzzy C means and level set methods.The automatic mass segmentation based on active contour technique results in segmentation with high accuracy.By using adaptive neuro fuzzy inference system,classification is done and results in a sensitivity of 94.73%,accuracy of 93.93%,and Mathew’s correlation coefficient(MCC)of 0.876.展开更多
The Solar Flare Index is regarded as one of the most important solar indices in the field of solarterrestrial research. It has the maximum effect on Earth of all other solar activity indices and is being considered fo...The Solar Flare Index is regarded as one of the most important solar indices in the field of solarterrestrial research. It has the maximum effect on Earth of all other solar activity indices and is being considered for describing the short-lived dynamo action inside the Sun. This paper attempts to study the short as well as long-term temporal fluctuations in the chromosphere region of the Sun using the Solar Flare Index. The daily Solar Flare Index for Northern, Southern Hemisphere and Total Disk are considered for a period from January 1976 to December 2014(total 14 245 days) for chaotic as well as periodic analysis.The 0–1 test has been employed to investigate the chaotic behavior associated with the Solar Flare Index.This test revealed that the time series data is non-linear and multi-periodic in nature with deterministic chaotic features. For periodic analysis, the Raleigh Power Spectrum algorithm has been used for identifying the predominant periods within the data along with their confidence score. The well-known fundamental period of 27 days and 11 years along with their harmonics are well affirmed in our investigation with a period of 28 days and 10.77 years. The presence of 14 days and 7 days periods in this investigation states the short-lived action inside the Sun. Our investigation also demonstrates the presence of other mid-range periods including the famous Rieger type period which are very much confirming the results obtained by other authors using various solar activity indicators.展开更多
Sensor networks are regularly sent to monitor certain physical properties that run in length from divisions of a second to many months or indeed several years.Nodes must advance their energy use for expanding network ...Sensor networks are regularly sent to monitor certain physical properties that run in length from divisions of a second to many months or indeed several years.Nodes must advance their energy use for expanding network lifetime.The fault detection of the network node is very significant for guaranteeing the correctness of monitoring results.Due to different network resource constraints and malicious attacks,security assurance in wireless sensor networks has been a difficult task.The implementation of these features requires larger space due to distributed module.This research work proposes new sensor node architecture integrated with a self-testing core and cryptoprocessor to provide fault-free operation and secured data transmission.The proposed node architecture was designed using Verilog programming and implemented using the Xilinx ISE tool in the Spartan 3E environment.The proposed system supports the real-time application in the range of 33 nanoseconds.The obtained results have been compared with the existing Microcontroller-based system.The power consumption of the proposed system consumes only 3.9 mW,and it is only 24%percentage of AT mega-based node architecture.展开更多
This paper deals with Furuta Pendulum(FP)or Rotary Inverted Pendulum(RIP),which is an under-actuated non-minimum unstable non-linear process.The process considered along with uncertainties which are unmodelled and ana...This paper deals with Furuta Pendulum(FP)or Rotary Inverted Pendulum(RIP),which is an under-actuated non-minimum unstable non-linear process.The process considered along with uncertainties which are unmodelled and analyses the performance of Linear Quadratic Regulator(LQR)with Kalman filter and H∞filter as two filter configurations.The LQR is a technique for developing practical feedback,in addition the desired x shows the vector of desirable states and is used as the external input to the closed-loop system.The effectiveness of the two filters in FP or RIP are measured and contrasted with rise time,peak time,settling time and maximum peak overshoot for time domain performance.The filters are also tested with gain margin,phase margin,disk stability margins for frequency domain performance and worst case stability margins for performance due to uncertainties.The H-infinity filter reduces the estimate error to a minimum,making it resilient in the worst case than the standard Kalman filter.Further,when theβrestriction value lowers,the H∞filter becomes more robust.The worst case gain performance is also focused for the two filter configurations and tested where H∞filter is found to outperform towards robust stability and performance.Also the switchover between the two filters is dependent upon a user-specified co-efficient that gives the flexibility in the design of non-linear systems.The non-linear process is tested for set point tracking,disturbance rejection,un-modelled noise dynamics and uncertainties,which records robust performance towards stability.展开更多
Due to the development of transportation, population growth and industrial activities, air quality has become a major issue in urban areas. Poor air qualityleads to rising health issues in the human’s life in many wa...Due to the development of transportation, population growth and industrial activities, air quality has become a major issue in urban areas. Poor air qualityleads to rising health issues in the human’s life in many ways especially respiratory infections, heart disease, asthma, stroke and lung cancer. The contaminatedair comprises harmful ingredients such as sulfur dioxide (SO2), nitrogen dioxide(NO2), and particulate matter of PM10, PM2.5, and an Air Quality Index (AQI).These pollutant ingredients are very harmful to human’s health and also leads todeath. So, it is necessary to develop a prediction model for air quality as regularon the basis of monthly or seasonaly. In this work, a new hybrid model for airquality prediction (AQP) is developed by using reed deer metaheuristic optimizedLong Short Term Memory (LSTM) Deep Learning network. To overcome thedrawback of the existing autoregressive integrated moving average model(ARIMA) model, the residual errors are processed by using an optimized LSTMnetwork. The red deer optimization (RDO) is a new type of metaheuristic methodwhich is motivated by the mating behaviour of Red Deer. The proposed model isbetter in terms of all prediction performance parameters when compared withother models.展开更多
Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique techniq...Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements;perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm.This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease(PD).To play out this handling method,electroencepha-logram(EEG)signals are gained while the subject is performing different wrist and elbow movements.Then,the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass filter.Then,feature extraction is carried out using Fast Fourier Transform(FFT).Subse-quently,the extraction process is done by Daubechies(db4)and Haar wavelet(db1)in MATLAB and classified using the Levenberg-Marquardt Algorithm.The results of the frequency changes that occurred during various wrist move-ments in the parietal region are compared with the frequency changes that occurred in frontal EEG signals.This proposed algorithm also uses the deep learn-ing pattern analysis network to evaluate the matching sequence for each action that takes place.Maximum accuracy of 97.2%and maximum error range of 0.6684%are achieved during the analysis.Results of this research confirm that the Levenberg-Marquardt algorithm,along with the newly developed deep learn-ing hybrid PatternNet,provides a more accurate range of frequency changes than any other classifier used in previous works of literature.Based on the analysis,the peak-to-peak value is used to define the threshold for the prototype arm,which performs all the intended degrees of freedom(DOF),verifying the results.These results would aid the specialists in their decision-making by facilitating the ana-lysis and interpretation of brain signals in the field of neuroscience,specifically in tremor analysis in PD.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
The increasing trends in SoCs and SiPs technologies demand integration of large numbers of buses and metal tracks for interconnections. On-Chip SerDes Transceiver is a promising solution which can reduce the number of...The increasing trends in SoCs and SiPs technologies demand integration of large numbers of buses and metal tracks for interconnections. On-Chip SerDes Transceiver is a promising solution which can reduce the number of interconnects and offers remarkable benefits in context with power consumption, area congestion and crosstalk. This paper reports a design of a new Serializer and Deserializer architecture for basic functional operations of serialization and deserialization used in On-Chip SerDes Transceiver. This architecture employs a design technique which samples input on both edges of clock. The main advantage of this technique which is input is sampled with lower clock (half the original rate) and is distributed for the same functional throughput, which results in power savings in the clock distribution network. This proposed Serializer and Deserializer architecture is designed using UMC 180 nm CMOS technology and simulation is done using Cadence Spectre simulator with a supply voltage of 1.8 V. The present design is compared with the earlier published similar works and improvements are obtained in terms of power consumption and area as shown in Tables 1-3 respectively. This design also helps the designer for solving crosstalk issues.展开更多
The air continues to be an extremely substantial part of survival on earth.Air pollution poses a critical risk to humans and the environment.Using sensor-based structures,we can get air pollutant data in real-time.How...The air continues to be an extremely substantial part of survival on earth.Air pollution poses a critical risk to humans and the environment.Using sensor-based structures,we can get air pollutant data in real-time.However,the sensors rely upon limited-battery sources that are immaterial to be alternated repeatedly amid extensive broadcast costs associated with real-time applications like air quality monitoring.Consequently,air quality sensor-based monitoring structures are lifetime-constrained and prone to the untimely loss of connectivity.Effective energy administration measures must therefore be implemented to handle the outlay of power dissipation.In this study,the authors propose outdoor air quality monitoring using a sensor network with an enhanced lifetime-enhancing cooperative data gathering and relaying algorithm(E-LCDGRA).LCDGRA is a cluster-based cooperative event-driven routing scheme with dedicated relay allocation mechanisms that tackle the problems of event-driven clustered WSNs with immobile gateways.The adapted variant,named E-LCDGRA,enhances the LCDGRA algorithm by incorporating a non-beacon-aided CSMA layer-2 un-slotted protocol with a back-off mechanism.The performance of the proposed E-LCDGRA is examined with other classical gathering schemes,including IEESEP and CERP,in terms of average lifetime,energy consumption,and delay.展开更多
Electrical Capacitance Tomography (ECT) determines the dielectric permittivity of the interior object depending on the measurements of exterior capacitance. Generally, the electrodes are placed outside the PVC cylinde...Electrical Capacitance Tomography (ECT) determines the dielectric permittivity of the interior object depending on the measurements of exterior capacitance. Generally, the electrodes are placed outside the PVC cylinder where the medium to be imaged is present;but in ECT using inter-electrode capacitance measurements can be achieved by placing inside of the dielectric medium. In the proposed ECT system, the ECT sensor is modeled using ANSYS software and the model is implemented in real ECT system. For each step of measurement, a stable AC signal is applied to a pair of electrodes that form a capacitor. The novel system is to measure the capacitance range variation in picofarad and the corresponding voltage ranges from 1 volt to 4 volts. The switching speed of all combinational electrodes is implemented using embedded system to achieve higher speed performance of AC ECT system which eliminates the drift and stray capacitance error. This is yielding the original image of unknown multiphase medium inside the electrodes using Lab VIEW. This paper investigates several advantages such as improved overall system performance;simple structure, avoids stray capacitance effect, reduces the drift problem and achieves high signal to noise ratio.展开更多
In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But...In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But they still face challenges in terms of computational requirements,overfitting and generalization issues,etc.To resolve such issues,optimization algorithms provide greater control and transparency in designing digital filters for image enhancement and denoising.Therefore,this paper presented a novel denoising approach for medical applications using an Optimized Learning⁃based Multi⁃level discrete Wavelet Cascaded Convolutional Neural Network(OLMWCNN).In this approach,the optimal filter parameters are identified to preserve the image quality after denoising.The performance and efficiency of the OLMWCNN filter are evaluated,demonstrating significant progress in denoising medical images while overcoming the limitations of conventional methods.展开更多
文摘The electromagnetic levitation system(EMLS)serves as the most important part of any magnetic levitation system.However,its characteristics are defined by its highly nonlinear dynamics and instability.Furthermore,the uncertainties in the dynamics of an electromagnetic levitation system make the controller design more difficult.Therefore,it is necessary to design a robust control law that will ensure the system’s stability in the presence of these uncertainties.In this framework,the dynamics of an electromagnetic levitation system are addressed in terms of matched and unmatched uncertainties.The robust control problem is translated into the optimal control problem,where the uncertainties of the electromagnetic levitation system are directly reflected in the cost function.The optimal control method is used to solve the robust control problem.The solution to the optimal control problem for the electromagnetic levitation system is indeed a solution to the robust control problem of the electromagnetic levitation system under matched and unmatched uncertainties.The simulation and experimental results demonstrate the performance of the designed control scheme.The performance indices such as integral absolute error(IAE),integral square error(ISE),integral time absolute error(ITAE),and integral time square error(ITSE)are compared for both uncertainties to showcase the robustness of the designed control scheme.
文摘In recent years,Speech Emotion Recognition(SER)has developed into an essential instrument for interpreting human emotions from auditory data.The proposed research focuses on the development of a SER system employing deep learning and multiple datasets containing samples of emotive speech.The primary objective of this research endeavor is to investigate the utilization of Convolutional Neural Networks(CNNs)in the process of sound feature extraction.Stretching,pitch manipulation,and noise injection are a few of the techniques utilized in this study to improve the data quality.Feature extraction methods including Zero Crossing Rate,Chroma_stft,Mel⁃scale Frequency Cepstral Coefficients(MFCC),Root Mean Square(RMS),and Mel⁃Spectogram are used to train a model.By using these techniques,audio signals can be transformed into recognized features that can be utilized to train the model.Ultimately,the study produces a thorough evaluation of the models performance.When this method was applied,the model achieved an impressive accuracy of 94.57%on the test dataset.The proposed work was also validated on the EMO⁃BD and IEMOCAP datasets.These consist of further data augmentation,feature engineering,and hyperparameter optimization.By following these development paths,SER systems will be able to be implemented in real⁃world scenarios with greater accuracy and resilience.
文摘In this research paper,we have presented variable area type capacitive sensor signal conditioning system for angular displacement measurement and for this purpose we have used timer LM555 based astable multivibrator and universal frequency to digital converter (UFDC). Due to variation in angular displacement in the variable area type capacitor which is connected in the timer based astable circuit,capacitance changes which in turn changes the time period of the timer circuit output. The time period of the timer output waveform is linear with the capacitance and hence linear with angular displacement. The timer output is further processed with UFDC for the measurement. The experimental results show that the time period is linear with the angular displacement in the range of 0- 180° and the uncertainty we should associate it with this average time period value is the standard deviation of the mean,often called the standard error (SE),which is ± 0.023 μs. Because of the simplicity,this measurement system can be used in both electronic and industrial instrumentation.
文摘Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea(SA)activity.In the proposed method,the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted.These features are applied to the Classification and Regression Tree(CART)-Particle Swarm Optimization(PSO)classifier which classifies the signal into normal breathing signal and sleep apnea signal.The proposed method is validated to measure the performance metrics like sensitivity,specificity,accuracy and F1 score by applying time domain and frequency domain features separately.Additionally,the performance of the CART-PSO(CPSO)classification algorithm is evaluated through comparing its measures with existing classification algorithms.Concurrently,the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.
文摘Registers and counters are the most important devices in any system of computations.In this paper we have communicated the trinary registers and counters in modified trinary number(MTN) system.It is suitable for the optical computing and other applications in multivalued logic system.Here the savart plate and spatial light modulator(SLM) based optoelectronic circuits have been used to exploit the optical tree architecture(OTA) in optical interconnection network.
文摘The inverted pendulum is a classic problem in dynamics and control theory and is widely used as a benchmark for testing control algorithms. It is unstable without control. The process is non linear and unstable with one input signal and several output signals. It is hence obvious that feedback of the state of the pendulum is needed to stabilize the pendulum. The aim of the study is to stabilize the pendulum such that the position of the carriage on the track is controlled quickly and accurately. The problem involves an arm, able to move horizontally in angular motion, and a pendulum, hinged to the arm at the bottom of its length such that the pendulum can move in the same plane as the arm. The conventional PID controller can be used for virtually any process condition. This makes elimination the offset of the proportional mode possible and still provides fast response. In this paper, we have modelled the system and studied conventional controller and LQR controller. It is observed that the LQR method works better compared to conventional controller.
文摘There is a widespread agreement that lung cancer is one of the deadliest types of cancer,affecting both women and men.As a result,detecting lung cancer at an early stage is crucial to create an accurate treatment plan and forecasting the reaction of the patient to the adopted treatment.For this reason,the development of convolutional neural networks(CNNs)for the task of lung cancer classification has recently seen a trend in attention.CNNs have great potential,but they need a lot of training data and struggle with input alterations.To address these limitations of CNNs,a novel machine-learning architecture of capsule networks has been presented,and it has the potential to completely transform the areas of deep learning.Capsule networks,which are the focus of this work,are interesting because they can withstand rotation and affine translation with relatively little training data.This research optimizes the performance of CapsNets by designing a new architecture that allows them to perform better on the challenge of lung cancer classification.The findings demonstrate that the proposed capsule network method outperforms CNNs on the lung cancer classification challenge.CapsNet with a single convolution layer and 32 features(CN-1-32),CapsNet with a single convolution layer and 64 features(CN-1-64),and CapsNet with a double convolution layer and 64 features(CN-2-64)are the three capsulel networks developed in this research for lung cancer classification.Lung nodules,both benign and malignant,are classified using these networks using CT images.The LIDC-IDRI database was utilized to assess the performance of those networks.Based on the testing results,CN-2-64 network performed better out of the three networks tested,with a specificity of 98.37%,sensitivity of 97.47%and an accuracy of 97.92%.
文摘A generic analytical model and the ATLAS simulation of a homojunction light emitting diode(LED) based on p+-InAs0.91Sb0.09/n0-InAs0.91Sb0.09/n+-InAs0.91Sb0.09 materials grown on lattice matched p+-GaSb substrate are presented.This LED is suitable for use as source in the optical absorption gas spectroscopy in the mid-infrared spectral region at 300 K.The various electro-optical properties of the homojunction LED are evaluated using analytical techniques and ATLAS device simulation software.The current-voltage characteristics of the structure are computed analytically and simulated,and the results are found to be in good agreement.The output power of the homojunction LED is estimated as a function of bias current under high carrier injection and compared with the reported experimental results.
文摘This research implements a novel segmentation of mammographic mass.Three methods are proposed,namely,segmentation of mass based on iterative active contour,automatic region growing,and fully automatic mask selectionbased active contour techniques.In the first method,iterative threshold is performed for manual cropped preprocessed image,and active contour is applied thereafter.To overcome manual cropping in the second method,an automatic seed selection followed by region growing is performed.Given that the result is only a few images owing to over segmentation,the third method uses a fully automatic active contour.Results of the segmentation techniques are compared with the manual markup by experts,specifically by taking the difference in their mean values.Accordingly,the difference in the mean value of the third method is 1.0853,which indicates the closeness of the segmentation.Moreover,the proposed method is compared with the existing fuzzy C means and level set methods.The automatic mass segmentation based on active contour technique results in segmentation with high accuracy.By using adaptive neuro fuzzy inference system,classification is done and results in a sensitivity of 94.73%,accuracy of 93.93%,and Mathew’s correlation coefficient(MCC)of 0.876.
基金the support extended by Jadavpur UniversityWest Bengal India. This work is a part of RUSA 2.0 Faculty Major Research Project under Jadavpur University。
文摘The Solar Flare Index is regarded as one of the most important solar indices in the field of solarterrestrial research. It has the maximum effect on Earth of all other solar activity indices and is being considered for describing the short-lived dynamo action inside the Sun. This paper attempts to study the short as well as long-term temporal fluctuations in the chromosphere region of the Sun using the Solar Flare Index. The daily Solar Flare Index for Northern, Southern Hemisphere and Total Disk are considered for a period from January 1976 to December 2014(total 14 245 days) for chaotic as well as periodic analysis.The 0–1 test has been employed to investigate the chaotic behavior associated with the Solar Flare Index.This test revealed that the time series data is non-linear and multi-periodic in nature with deterministic chaotic features. For periodic analysis, the Raleigh Power Spectrum algorithm has been used for identifying the predominant periods within the data along with their confidence score. The well-known fundamental period of 27 days and 11 years along with their harmonics are well affirmed in our investigation with a period of 28 days and 10.77 years. The presence of 14 days and 7 days periods in this investigation states the short-lived action inside the Sun. Our investigation also demonstrates the presence of other mid-range periods including the famous Rieger type period which are very much confirming the results obtained by other authors using various solar activity indicators.
文摘Sensor networks are regularly sent to monitor certain physical properties that run in length from divisions of a second to many months or indeed several years.Nodes must advance their energy use for expanding network lifetime.The fault detection of the network node is very significant for guaranteeing the correctness of monitoring results.Due to different network resource constraints and malicious attacks,security assurance in wireless sensor networks has been a difficult task.The implementation of these features requires larger space due to distributed module.This research work proposes new sensor node architecture integrated with a self-testing core and cryptoprocessor to provide fault-free operation and secured data transmission.The proposed node architecture was designed using Verilog programming and implemented using the Xilinx ISE tool in the Spartan 3E environment.The proposed system supports the real-time application in the range of 33 nanoseconds.The obtained results have been compared with the existing Microcontroller-based system.The power consumption of the proposed system consumes only 3.9 mW,and it is only 24%percentage of AT mega-based node architecture.
文摘This paper deals with Furuta Pendulum(FP)or Rotary Inverted Pendulum(RIP),which is an under-actuated non-minimum unstable non-linear process.The process considered along with uncertainties which are unmodelled and analyses the performance of Linear Quadratic Regulator(LQR)with Kalman filter and H∞filter as two filter configurations.The LQR is a technique for developing practical feedback,in addition the desired x shows the vector of desirable states and is used as the external input to the closed-loop system.The effectiveness of the two filters in FP or RIP are measured and contrasted with rise time,peak time,settling time and maximum peak overshoot for time domain performance.The filters are also tested with gain margin,phase margin,disk stability margins for frequency domain performance and worst case stability margins for performance due to uncertainties.The H-infinity filter reduces the estimate error to a minimum,making it resilient in the worst case than the standard Kalman filter.Further,when theβrestriction value lowers,the H∞filter becomes more robust.The worst case gain performance is also focused for the two filter configurations and tested where H∞filter is found to outperform towards robust stability and performance.Also the switchover between the two filters is dependent upon a user-specified co-efficient that gives the flexibility in the design of non-linear systems.The non-linear process is tested for set point tracking,disturbance rejection,un-modelled noise dynamics and uncertainties,which records robust performance towards stability.
文摘Due to the development of transportation, population growth and industrial activities, air quality has become a major issue in urban areas. Poor air qualityleads to rising health issues in the human’s life in many ways especially respiratory infections, heart disease, asthma, stroke and lung cancer. The contaminatedair comprises harmful ingredients such as sulfur dioxide (SO2), nitrogen dioxide(NO2), and particulate matter of PM10, PM2.5, and an Air Quality Index (AQI).These pollutant ingredients are very harmful to human’s health and also leads todeath. So, it is necessary to develop a prediction model for air quality as regularon the basis of monthly or seasonaly. In this work, a new hybrid model for airquality prediction (AQP) is developed by using reed deer metaheuristic optimizedLong Short Term Memory (LSTM) Deep Learning network. To overcome thedrawback of the existing autoregressive integrated moving average model(ARIMA) model, the residual errors are processed by using an optimized LSTMnetwork. The red deer optimization (RDO) is a new type of metaheuristic methodwhich is motivated by the mating behaviour of Red Deer. The proposed model isbetter in terms of all prediction performance parameters when compared withother models.
文摘Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements;perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm.This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease(PD).To play out this handling method,electroencepha-logram(EEG)signals are gained while the subject is performing different wrist and elbow movements.Then,the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass filter.Then,feature extraction is carried out using Fast Fourier Transform(FFT).Subse-quently,the extraction process is done by Daubechies(db4)and Haar wavelet(db1)in MATLAB and classified using the Levenberg-Marquardt Algorithm.The results of the frequency changes that occurred during various wrist move-ments in the parietal region are compared with the frequency changes that occurred in frontal EEG signals.This proposed algorithm also uses the deep learn-ing pattern analysis network to evaluate the matching sequence for each action that takes place.Maximum accuracy of 97.2%and maximum error range of 0.6684%are achieved during the analysis.Results of this research confirm that the Levenberg-Marquardt algorithm,along with the newly developed deep learn-ing hybrid PatternNet,provides a more accurate range of frequency changes than any other classifier used in previous works of literature.Based on the analysis,the peak-to-peak value is used to define the threshold for the prototype arm,which performs all the intended degrees of freedom(DOF),verifying the results.These results would aid the specialists in their decision-making by facilitating the ana-lysis and interpretation of brain signals in the field of neuroscience,specifically in tremor analysis in PD.
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.
文摘The increasing trends in SoCs and SiPs technologies demand integration of large numbers of buses and metal tracks for interconnections. On-Chip SerDes Transceiver is a promising solution which can reduce the number of interconnects and offers remarkable benefits in context with power consumption, area congestion and crosstalk. This paper reports a design of a new Serializer and Deserializer architecture for basic functional operations of serialization and deserialization used in On-Chip SerDes Transceiver. This architecture employs a design technique which samples input on both edges of clock. The main advantage of this technique which is input is sampled with lower clock (half the original rate) and is distributed for the same functional throughput, which results in power savings in the clock distribution network. This proposed Serializer and Deserializer architecture is designed using UMC 180 nm CMOS technology and simulation is done using Cadence Spectre simulator with a supply voltage of 1.8 V. The present design is compared with the earlier published similar works and improvements are obtained in terms of power consumption and area as shown in Tables 1-3 respectively. This design also helps the designer for solving crosstalk issues.
文摘The air continues to be an extremely substantial part of survival on earth.Air pollution poses a critical risk to humans and the environment.Using sensor-based structures,we can get air pollutant data in real-time.However,the sensors rely upon limited-battery sources that are immaterial to be alternated repeatedly amid extensive broadcast costs associated with real-time applications like air quality monitoring.Consequently,air quality sensor-based monitoring structures are lifetime-constrained and prone to the untimely loss of connectivity.Effective energy administration measures must therefore be implemented to handle the outlay of power dissipation.In this study,the authors propose outdoor air quality monitoring using a sensor network with an enhanced lifetime-enhancing cooperative data gathering and relaying algorithm(E-LCDGRA).LCDGRA is a cluster-based cooperative event-driven routing scheme with dedicated relay allocation mechanisms that tackle the problems of event-driven clustered WSNs with immobile gateways.The adapted variant,named E-LCDGRA,enhances the LCDGRA algorithm by incorporating a non-beacon-aided CSMA layer-2 un-slotted protocol with a back-off mechanism.The performance of the proposed E-LCDGRA is examined with other classical gathering schemes,including IEESEP and CERP,in terms of average lifetime,energy consumption,and delay.
文摘Electrical Capacitance Tomography (ECT) determines the dielectric permittivity of the interior object depending on the measurements of exterior capacitance. Generally, the electrodes are placed outside the PVC cylinder where the medium to be imaged is present;but in ECT using inter-electrode capacitance measurements can be achieved by placing inside of the dielectric medium. In the proposed ECT system, the ECT sensor is modeled using ANSYS software and the model is implemented in real ECT system. For each step of measurement, a stable AC signal is applied to a pair of electrodes that form a capacitor. The novel system is to measure the capacitance range variation in picofarad and the corresponding voltage ranges from 1 volt to 4 volts. The switching speed of all combinational electrodes is implemented using embedded system to achieve higher speed performance of AC ECT system which eliminates the drift and stray capacitance error. This is yielding the original image of unknown multiphase medium inside the electrodes using Lab VIEW. This paper investigates several advantages such as improved overall system performance;simple structure, avoids stray capacitance effect, reduces the drift problem and achieves high signal to noise ratio.
文摘In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But they still face challenges in terms of computational requirements,overfitting and generalization issues,etc.To resolve such issues,optimization algorithms provide greater control and transparency in designing digital filters for image enhancement and denoising.Therefore,this paper presented a novel denoising approach for medical applications using an Optimized Learning⁃based Multi⁃level discrete Wavelet Cascaded Convolutional Neural Network(OLMWCNN).In this approach,the optimal filter parameters are identified to preserve the image quality after denoising.The performance and efficiency of the OLMWCNN filter are evaluated,demonstrating significant progress in denoising medical images while overcoming the limitations of conventional methods.
