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
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
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
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
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
Microuidic channels play a vital role in separation of analytes of interest such as bacteria and platelet cells,etc.,in various biochemical diagnosis procedures including urinary tract infections(UTI)and bloodstream i...Microuidic channels play a vital role in separation of analytes of interest such as bacteria and platelet cells,etc.,in various biochemical diagnosis procedures including urinary tract infections(UTI)and bloodstream infections.This paper presents the multi physics computational model specifically designed to study the effects of design parameters of a microuidics channel for the separation of Escherichia coli(E.coli)from various blood constituents including red blood cells(RBC)and platelets.A standard two inlet and a two outlet microchannel of length 805µm with a channel width of 40µm is simulated.The effect of electrode potentials and the effect of electrode placement along the channel length and also the levitation of electrodes from the channel wall are studied to optimize the selective particle separation throughput.Simulated results show the efcient separation of E-coli with a mean diameter 0.68µm is achieved at low voltages(less than 20 V)when electrodes placed near to the micro channel and also noticed that the applied electric potential is inversely proportional to the number of electrodes placed along the microuidic channel.The computer aided multi physics simulations with multiple governing parameters could be advantage in design optimization of microuidics channels and support precise bioparticle separation for better diagnosis.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
Energy saving is one of the most important research hotspots, by which operational expenditure and CO2 emission can be reduced. Optimal cooling capacity scheduling in addition to temperature control can improve energy...Energy saving is one of the most important research hotspots, by which operational expenditure and CO2 emission can be reduced. Optimal cooling capacity scheduling in addition to temperature control can improve energy efficiency. The main contribution of this work is modeling the telecommunication building for the fabric cooling load to schedule the operation of air conditioners. The time series data of the fabric cooling load of the building envelope is taken by simulation by using Energy Plus, Building Control Virtual Test Bed （BCVTB）, and Matlab. This pre-computed data and other internal thermal loads are used for scheduling in air conditioners. Energy savings obtained for the whole year are about 4% to 6% by simulation and the field study, respectively.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
This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma(CMB)using a well-defined deep learning architecture.A deep learning architecture could be designed using ideas fro...This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma(CMB)using a well-defined deep learning architecture.A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images.First,a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes.A 10-layer deep learning architecture is designed to extract deep features.The introduction of pooling layers in the architecture reduces the feature dimension.The extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling layers are used to classify histopathological images using a neural network classifier.The performance of the CMB classification system is evaluated using 1414(10×magnification)and 1071(100×magnification)augmented histopathological images with five classes of CMB such as desmoplastic,nodular,large cell,classic,and normal.Experimental results show that the average classification accuracy of 99.38%(10×)and 99.07%(100×)is attained by the proposed CNB classification system.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
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
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
Greenhouse system (GHS) is the worldwide fastest growing phenomenon in agricultural sector. Greenhouse models are essential for improving control efficiencies. The Relative Gain Analysis (RGA) reveals that the GHS con...Greenhouse system (GHS) is the worldwide fastest growing phenomenon in agricultural sector. Greenhouse models are essential for improving control efficiencies. The Relative Gain Analysis (RGA) reveals that the GHS control is complex due to 1) high nonlinear interactions between the biological subsystem and the physical subsystem and 2) strong coupling between the process variables such as temperature and humidity. In this paper, a decoupled linear cooling model has been developed using a feedback-feed forward linearization technique. Further, based on the model developed Internal Model Control (IMC) based Proportional Integrator (PI) controller parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to achieve minimum Integral Square Error (ISE). The closed loop control is carried out using the above control schemes for set-point change and disturbance rejection. Finally, closed loop servo and servo-regulatory responses of GHS are compared quantitatively as well as qualitatively. The results implicate that IMC based PI controller using PSO provides better performance than the IMC based PI controller using GA. Also, it is observed that the disturbance introduced in one loop will not affect the other loop due to feedback-feed forward linearization and decoupling. Such a control scheme used for GHS would result in better yield in production of crops such as tomato, lettuce and broccoli.展开更多
In this paper, a novel 10 Transistor Static Random Access Memory (SRAM) cell is proposed. Read and Write bit lines are decoupled in the proposed cell. Feedback loop-cutting with single bit line write scheme is employe...In this paper, a novel 10 Transistor Static Random Access Memory (SRAM) cell is proposed. Read and Write bit lines are decoupled in the proposed cell. Feedback loop-cutting with single bit line write scheme is employed in the 10 Transistor SRAM cell to reduce active power consumption during the write operation. Read access time and write access time are measured for proposed cell architecture based on Eldo SPICE simulation using TSMC based 90 nm Complementary Metal Oxide Semiconductor (CMOS) technology at various process corners. Leakage current measurements made on hold mode of operation show that proposed cell architecture is having 12.31 nano amperes as compared to 40.63 nano amperes of the standard 6 Transistor cell. 10 Transistor cell also has better performance in terms of leakage power as compared to 6 Transistor cell.展开更多
Identifying faces in non-frontal poses presents a significant challenge for face recognition(FR)systems.In this study,we delved into the impact of yaw pose variations on these systems and devised a robust method for d...Identifying faces in non-frontal poses presents a significant challenge for face recognition(FR)systems.In this study,we delved into the impact of yaw pose variations on these systems and devised a robust method for detecting faces across a wide range of angles from 0°to±90°.We initially selected the most suitable feature vector size by integrating the Dlib,FaceNet(Inception-v2),and“Support Vector Machines(SVM)”+“K-nearest neighbors(KNN)”algorithms.To train and evaluate this feature vector,we used two datasets:the“Labeled Faces in the Wild(LFW)”benchmark data and the“Robust Shape-Based FR System(RSBFRS)”real-time data,which contained face images with varying yaw poses.After selecting the best feature vector,we developed a real-time FR system to handle yaw poses.The proposed FaceNet architecture achieved recognition accuracies of 99.7%and 99.8%for the LFW and RSBFRS datasets,respectively,with 128 feature vector dimensions and minimum Euclidean distance thresholds of 0.06 and 0.12.The FaceNet+SVM and FaceNet+KNN classifiers achieved classification accuracies of 99.26%and 99.44%,respectively.The 128-dimensional embedding vector showed the highest recognition rate among all dimensions.These results demonstrate the effectiveness of our proposed approach in enhancing FR accuracy,particularly in real-world scenarios with varying yaw poses.展开更多
Low contrast of Magnetic Resonance(MR)images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis.State-of-the-art contrast boosting techniques intole...Low contrast of Magnetic Resonance(MR)images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis.State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images.Drastic changes in brightness features,induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings.To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well.This method termed as Power-law and Logarithmic Modification-based Histogram Equalization(PLMHE)partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression.After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization,cumulative histograms are computed.Enhanced grey level values are computed from the resultant cumulative histograms.The performance of the PLMHE algorithm is comparedwith traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression,a significant change in mean brightness,and contrast-overshoot.展开更多
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Microuidic channels play a vital role in separation of analytes of interest such as bacteria and platelet cells,etc.,in various biochemical diagnosis procedures including urinary tract infections(UTI)and bloodstream infections.This paper presents the multi physics computational model specifically designed to study the effects of design parameters of a microuidics channel for the separation of Escherichia coli(E.coli)from various blood constituents including red blood cells(RBC)and platelets.A standard two inlet and a two outlet microchannel of length 805µm with a channel width of 40µm is simulated.The effect of electrode potentials and the effect of electrode placement along the channel length and also the levitation of electrodes from the channel wall are studied to optimize the selective particle separation throughput.Simulated results show the efcient separation of E-coli with a mean diameter 0.68µm is achieved at low voltages(less than 20 V)when electrodes placed near to the micro channel and also noticed that the applied electric potential is inversely proportional to the number of electrodes placed along the microuidic channel.The computer aided multi physics simulations with multiple governing parameters could be advantage in design optimization of microuidics channels and support precise bioparticle separation for better diagnosis.
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support and facilities provieded by Bharat Sanchar Nigam Limited Chennai Telephones and Department of Telecommunications,India for this study
文摘Energy saving is one of the most important research hotspots, by which operational expenditure and CO2 emission can be reduced. Optimal cooling capacity scheduling in addition to temperature control can improve energy efficiency. The main contribution of this work is modeling the telecommunication building for the fabric cooling load to schedule the operation of air conditioners. The time series data of the fabric cooling load of the building envelope is taken by simulation by using Energy Plus, Building Control Virtual Test Bed （BCVTB）, and Matlab. This pre-computed data and other internal thermal loads are used for scheduling in air conditioners. Energy savings obtained for the whole year are about 4% to 6% by simulation and the field study, respectively.
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This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma(CMB)using a well-defined deep learning architecture.A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images.First,a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes.A 10-layer deep learning architecture is designed to extract deep features.The introduction of pooling layers in the architecture reduces the feature dimension.The extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling layers are used to classify histopathological images using a neural network classifier.The performance of the CMB classification system is evaluated using 1414(10×magnification)and 1071(100×magnification)augmented histopathological images with five classes of CMB such as desmoplastic,nodular,large cell,classic,and normal.Experimental results show that the average classification accuracy of 99.38%(10×)and 99.07%(100×)is attained by the proposed CNB classification system.
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.
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Greenhouse system (GHS) is the worldwide fastest growing phenomenon in agricultural sector. Greenhouse models are essential for improving control efficiencies. The Relative Gain Analysis (RGA) reveals that the GHS control is complex due to 1) high nonlinear interactions between the biological subsystem and the physical subsystem and 2) strong coupling between the process variables such as temperature and humidity. In this paper, a decoupled linear cooling model has been developed using a feedback-feed forward linearization technique. Further, based on the model developed Internal Model Control (IMC) based Proportional Integrator (PI) controller parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to achieve minimum Integral Square Error (ISE). The closed loop control is carried out using the above control schemes for set-point change and disturbance rejection. Finally, closed loop servo and servo-regulatory responses of GHS are compared quantitatively as well as qualitatively. The results implicate that IMC based PI controller using PSO provides better performance than the IMC based PI controller using GA. Also, it is observed that the disturbance introduced in one loop will not affect the other loop due to feedback-feed forward linearization and decoupling. Such a control scheme used for GHS would result in better yield in production of crops such as tomato, lettuce and broccoli.
文摘In this paper, a novel 10 Transistor Static Random Access Memory (SRAM) cell is proposed. Read and Write bit lines are decoupled in the proposed cell. Feedback loop-cutting with single bit line write scheme is employed in the 10 Transistor SRAM cell to reduce active power consumption during the write operation. Read access time and write access time are measured for proposed cell architecture based on Eldo SPICE simulation using TSMC based 90 nm Complementary Metal Oxide Semiconductor (CMOS) technology at various process corners. Leakage current measurements made on hold mode of operation show that proposed cell architecture is having 12.31 nano amperes as compared to 40.63 nano amperes of the standard 6 Transistor cell. 10 Transistor cell also has better performance in terms of leakage power as compared to 6 Transistor cell.
基金funding for the project,excluding research publication,from the Board of Research in Nuclear Sciences(BRNS)under Grant Number 59/14/05/2019/BRNS.
文摘Identifying faces in non-frontal poses presents a significant challenge for face recognition(FR)systems.In this study,we delved into the impact of yaw pose variations on these systems and devised a robust method for detecting faces across a wide range of angles from 0°to±90°.We initially selected the most suitable feature vector size by integrating the Dlib,FaceNet(Inception-v2),and“Support Vector Machines(SVM)”+“K-nearest neighbors(KNN)”algorithms.To train and evaluate this feature vector,we used two datasets:the“Labeled Faces in the Wild(LFW)”benchmark data and the“Robust Shape-Based FR System(RSBFRS)”real-time data,which contained face images with varying yaw poses.After selecting the best feature vector,we developed a real-time FR system to handle yaw poses.The proposed FaceNet architecture achieved recognition accuracies of 99.7%and 99.8%for the LFW and RSBFRS datasets,respectively,with 128 feature vector dimensions and minimum Euclidean distance thresholds of 0.06 and 0.12.The FaceNet+SVM and FaceNet+KNN classifiers achieved classification accuracies of 99.26%and 99.44%,respectively.The 128-dimensional embedding vector showed the highest recognition rate among all dimensions.These results demonstrate the effectiveness of our proposed approach in enhancing FR accuracy,particularly in real-world scenarios with varying yaw poses.
基金This work was supported by Taif university Researchers Supporting Project Number(TURSP-2020/114),Taif University,Taif,Saudi Arabia.
文摘Low contrast of Magnetic Resonance(MR)images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis.State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images.Drastic changes in brightness features,induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings.To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well.This method termed as Power-law and Logarithmic Modification-based Histogram Equalization(PLMHE)partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression.After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization,cumulative histograms are computed.Enhanced grey level values are computed from the resultant cumulative histograms.The performance of the PLMHE algorithm is comparedwith traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression,a significant change in mean brightness,and contrast-overshoot.
