Position sensitive device(PSD)sensor is a vital optical element that is mainly used in tracking systems for visible light communication(VLC).Recently,a new reconfigurable PSD architecture emerged.The proposed architec...Position sensitive device(PSD)sensor is a vital optical element that is mainly used in tracking systems for visible light communication(VLC).Recently,a new reconfigurable PSD architecture emerged.The proposed architecture makes the PSD perform more functions by modifying its architecture.As the PSD is mainly formed of an array of photodiodes.The primary concept involves employing transistors to alternate between the operating modes of the photodiodes(photoconductive and photovoltaic).Additionally,alternating among output pins can be done based on the required function.This paper presents the mathematical modeling and simulation of a reconfigurable-multifunctional optical sensor which can perform energy harvesting and data acquisition,as well as positioning,which is not available in the traditional PSDs.Simulation using the MATLAB software tool was achieved to demonstrate the modeling.The simulation results confirmed the validity of the mathematical modeling and proved that the modified sensor architecture,as depicted by the equations,accurately describes its behavior.The proposed sensor is expected to extend the battery's lifecycle,reduce its physical size,and increase the integration and functionality of the system.The presented sensor might be used in free space optical(FSO)communication like cube satellites or even in underwater wireless optical communication(UWOC).展开更多
This paper presents the simulation and implementation of a reconfigurable pixel that serves both data acquisition and energy harvesting purposes.The main topic focuses on switching between the two operating modes of t...This paper presents the simulation and implementation of a reconfigurable pixel that serves both data acquisition and energy harvesting purposes.The main topic focuses on switching between the two operating modes of the photodiode:photoconductive and photovoltaic modes.This proposed model can be used to design novel optical sensors with energy harvesting capability,such as position sensitive device(PSD)and complementary metal oxide semiconductor(CMOS)image sensors,which can extend the battery lifetime of the whole optical system.Thus,we can overcome power supply problems like wiring and changing batteries frequently,especially in hard-to-reach places like space(cube satellites)or even underwater wireless optical communication(UWOC).The proposed pixel architecture offers the advantage of a minimalistic design with only four transistors.Nevertheless,it does come with a drawback in the form of higher noise levels.The simulation was achieved using MATLAB,and the implementation was performed using the programmable system-on-chip(PSoC)microcontroller.The results showed that the functionality of the dual-function pixel is correct,and the scheduling of both energy harvesting and signal sensing functions was successfully achieved.展开更多
This paper presents a review of the position-sensitive detector(PSD) sensor, covering different types of PSD and recent works related to this field. Furthermore, it explains the theoretical concepts and provides infor...This paper presents a review of the position-sensitive detector(PSD) sensor, covering different types of PSD and recent works related to this field. Furthermore, it explains the theoretical concepts and provides information about its structure and principles of operation. Moreover, it includes the main information about the available commercial PSDs from different companies, along with a comparison between the common modules. The PSD features include high position resolution, fast response, and a wide dynamic range. These features make it suitable for various fields and applications, such as imaging, spectrometry, spectroscopy and others.展开更多
In this investigation,all-optical Toggle flip-flop event-driven memory is explored with data rate of 16 Gbit/s.Single mode optical fiber model is used as a nonlinear medium to generate the output set and reset pulses ...In this investigation,all-optical Toggle flip-flop event-driven memory is explored with data rate of 16 Gbit/s.Single mode optical fiber model is used as a nonlinear medium to generate the output set and reset pulses of a Toggle flip-flop,and the model is based on the bidirectional optical transmission principle,considering the fundamental effects of cross phase modulation and self-phase modulation with change in polarization state.The performance of a flip-flop is evaluated using truth table conditions and performance parameters such as Q factor,which is obtained as 380.92 d B for Q and 272.9 d B for■,and rising and falling times of 7.304 ps and 5.79 ps,respectively are obtained,which makes flip-flop design fast as compared to earlier design techniques.展开更多
Applications for quanta and space sensing both depend on efficient low-light imaging.To precisely optimize and design image sensor pixels for these applications,it is crucial to analyze the mechanisms behind dark curr...Applications for quanta and space sensing both depend on efficient low-light imaging.To precisely optimize and design image sensor pixels for these applications,it is crucial to analyze the mechanisms behind dark current generation,considering factors such as temperature,trap cross-section and trap concentration.The thresholds for these generating effects are computed using optoelectrical technology computer aided design(TCAD)simulations,and the ensuing changes in pinned photo-diode(PPD)dynamic capacitance are observed.Various generation models along with an interfacial trap model are used to compare PPD capacitance fluctuations during light and dark environments.With the use of this comparison study,current compact models of complementary metal oxide semiconductor(CMOS)image sensors can be modified to accurately capture the impacts of dark current in low-light conditions.The model developed through this study demonstrates a deviation of only 6.85%from the behavior observed in physical devices.These results not only enhance our understanding of dark current generation mechanisms but also offer practical applications by improving the performance and accuracy of image sensors.展开更多
The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accide...The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.展开更多
The globe faces an urgent need to close the energy demand-supply gap.Addressing this difficulty requires constructing a Hybrid Renewable Energy System(HRES),which has proven to be the most appropriate solution.HRES al...The globe faces an urgent need to close the energy demand-supply gap.Addressing this difficulty requires constructing a Hybrid Renewable Energy System(HRES),which has proven to be the most appropriate solution.HRES allows for integrating two or more renewable energy resources,successfully addressing the issue of intermittent availability of non-conventional energy resources.Optimization is critical for improving the HRES’s performance parameters during implementation.This study focuses on HRES using solar and biomass as renewable energy supplies and appropriate energy storage technologies.However,energy fluctuations present a problem with the power quality of HRES.To address this issue,the research paper introduces the Generalized Dynamic Progressive Neural Fuzzy Controller(GDPNFC),which regulates power flow within the proposed HRES.Furthermore,a unique approach called Enhanced Multi-Objective Monarch Butterfly Optimization(EMMBO)is used to optimize technical parameters.The simulation tool used in the research work is HOMER(Hybrid Optimization of Multiple Energy Resources)-PRO,and the system’s power quality is assessed using MATLAB 2016.The research paper concludes with comparing the performance of existing systems to the proposed system in terms of power loss and Total Harmonic Distortion(THD).It was established that the proposed technique involving EMMBO outperformed existing methods in technical optimization.展开更多
In this paper,drain current transient characteristics ofβ-Ga2O3 high electron mobility transistor(HEMT)are studied to access current collapse and recovery time due to dynamic population and de-population of deep leve...In this paper,drain current transient characteristics ofβ-Ga2O3 high electron mobility transistor(HEMT)are studied to access current collapse and recovery time due to dynamic population and de-population of deep level traps and interface traps.An approximately 10 min,and 1 h of recovery time to steady-state drain current value is measured under 1 ms of stress on the gate and drain electrodes due to iron(Fe)–dopedβ-Ga2O3 substrate and germanium(Ge)–dopedβ-Ga2O3 epitaxial layer respectively.On-state current lag is more severe due to widely reported defect trap EC–0.82 e V over EC–0.78 e V,-0.75 e V present in Iron(Fe)-dopedβ-Ga2O3 bulk crystals.A negligible amount of current degradation is observed in the latter case due to the trap level at EC–0.98 e V.It is found that occupancy of ionized trap density varied mostly under the gate and gate–source area.This investigation of reversible current collapse phenomenon and assessment of recovery time inβ-Ga2O3 HEMT is carried out through 2 D device simulations using appropriate velocity and charge transport models.This work can further help in the proper characterization ofβ-Ga2O3 devices to understand temporary and permanent device degradation.展开更多
In this paper,an efficient 8-channel 32Gbps RoF(Radio over Fiber)system incorporating Bessel Filter(8/32 RoFBF)has been demonstrated to reduce the impact of non-linear transmission effects,specifically Four-Wave Mixin...In this paper,an efficient 8-channel 32Gbps RoF(Radio over Fiber)system incorporating Bessel Filter(8/32 RoFBF)has been demonstrated to reduce the impact of non-linear transmission effects,specifically Four-Wave Mixing(FWM).The simulation results indicate that the proposed 8/32 RoF-BF system provides an optimum result w.r.t.channel spacing(75 GHz),input source power(0 dBm)and number of input channels(8).In comparison with the existing RoF system,the proposed 8/32 RoF-BF system has been validated analytically and it is found that the performance of the proposed system is in close proximity particularly in FWM sideband power reduction of the order of 4 dBm for the 8-channel 32Gbps RoF system.展开更多
CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit ...CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the systemparameters.The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation.As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user throughput.Self-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of salps.In this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary users.The results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.展开更多
The radio-frequency (RF) performance of the p-type NiO-pocket based β-Ga_(2)O_(3)/black phosphorous heterostructureMOSFET has been evaluated. The key figure of merits (FOMs) for device performance evaluation include ...The radio-frequency (RF) performance of the p-type NiO-pocket based β-Ga_(2)O_(3)/black phosphorous heterostructureMOSFET has been evaluated. The key figure of merits (FOMs) for device performance evaluation include the transconductance(gm) gate dependent intrinsic-capacitances (Cgd and Cgs), cutoff frequency (fT), gain bandwidth (GBW) product and output-conductance(gd). Similarly, power-gain (Gp), power added efficiency (PAE), and output power (POUT) are also investigated for largesignalcontinuous-wave (CW) RF performance evaluation. The motive behind the study is to improve the β-Ga_(2)O_(3) MOS deviceperformance along with a reduction in power losses and device associated leakages. To show the applicability of the designeddevice in RF applications, its RF FOMs are analyzed. With the outline characteristics of the ultrathin black phosphorous layer belowthe β-Ga_(2)O_(3) channel region, the proposed device results in 1.09 times improvement in fT, with 0.7 times lower Cgs, and 3.27dB improved GP in comparison to the NiO-GO MOSFET. The results indicate that the designed NiO-GO/BP MOSFET has betterRF performance with improved power gain and low leakages.展开更多
Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or compu...Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques.The experienced evaluators take time to identify the disease which is highly laborious and too costly.If wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield quality.To solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease identification.In the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation techniques.The augmented images are preprocessed by histogram equalization.As a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch extraction.Different layers of region extraction models construct a feature vector that is later passed to the CE-SVM model.As a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.展开更多
COVID-19 has become one of the critical health issues globally,which surfaced first in latter part of the year 2019.It is the topmost concern for many nations’governments as the contagious virus started mushrooming o...COVID-19 has become one of the critical health issues globally,which surfaced first in latter part of the year 2019.It is the topmost concern for many nations’governments as the contagious virus started mushrooming over adjacent regions of infected areas.In 1980,a vaccine called Bacillus Calmette-Guérin(BCG)was introduced for preventing tuberculosis and lung cancer.Countries that have made the BCG vaccine mandatory have witnessed a lesser COVID-19 fatality rate than the countries that have not made it compulsory.This paper’s initial research shows that the countries with a longtermcompulsory BCGvaccination system are less affected by COVID-19 than those without a BCG vaccination system.This paper discusses analytical data patterns for medical applications regarding COVID-19 impact on countries with mandatory BCG status on fatality rates.The paper has tackled numerous analytical challenges to realize the full potential of heterogeneous data.An analogy is drawn to demonstrate how other factors can affect fatality and infection rates other than BCG vaccination only,such as age groups affected,other diseases,and stringency index.The data of Spain,Portugal,and Germany have been taken for a case study of BCG impact analysis.展开更多
In this work,we design a multisensory IoT-based online vitals monitor(hereinafter referred to as the VITALS)to sense four bedside physiological parameters including pulse(heart)rate,body temperature,blood pressure,and...In this work,we design a multisensory IoT-based online vitals monitor(hereinafter referred to as the VITALS)to sense four bedside physiological parameters including pulse(heart)rate,body temperature,blood pressure,and periph-eral oxygen saturation.Then,the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery.The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment,a powerful microcontroller,a reliable wireless communication module,and a big data analytics system.It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis.We use Apache Kafka(to gather live data streams from connected sen-sors),Apache Spark(to categorize the patient vitals and notify the medical pro-fessionals while identifying abnormalities in physiological parameters),Hadoop Distributed File System(HDFS)(to archive data streams for further analysis and long-term storage),Spark SQL,Hive and Matplotlib(to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals).In addition,we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely.Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing,data processing,and data transmission mechanisms.To validate the system accuracy,we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor,the Welch Allyn®Spot Check.Our pro-posed system provides improved care solutions,especially for those whose access to care services is limited.展开更多
Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise.Moreover,it is widely utilizing for preventing injuries of athletes during a practice session and in few cas...Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise.Moreover,it is widely utilizing for preventing injuries of athletes during a practice session and in few cases,it leads to muscle fatigue.At present,emerging technology like the internet of things(IoT)and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity.In this study,an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram(sEMG)sensor.Normally,the EMG signal is utilized to display muscle activity.Arduino controller,Wi-Fi module,and EMG sensor are utilized in developing the wearable device.The Time-frequency domain spectrum technique is employed for classifying the threemuscle fatigue conditions including meanRMS,mean frequency,etc.A real-time experiment is realized on six different individuals with developed wearable devices and the average RMS value assists to determine the average threshold of recorded data.The threshold level is analyzed by calculating the mean RMS value and concluded three fatigue conditions as>2V:Extensive);1–2V:Moderate,and<1V:relaxed.The warning alarm system was designed in LabVIEW with three color LEDs to indicate the different states of muscle fatigue.Moreover,the device is interfaced with the cloud through the internet provided with a Wi-Fi module embedded in wearable devices.The data available in the cloud server can be utilized for forecasting the frequency of an individual to muscle fatigue.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
Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehic...Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and classification.Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is difficult.The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem.This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking.The method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground areas.Compared to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.展开更多
Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unn...Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.展开更多
With the advent of Reinforcement Learning(RL)and its continuous progress,state-of-the-art RL systems have come up for many challenging and real-world tasks.Given the scope of this area,various techniques are found in ...With the advent of Reinforcement Learning(RL)and its continuous progress,state-of-the-art RL systems have come up for many challenging and real-world tasks.Given the scope of this area,various techniques are found in the literature.One such notable technique,Multiple Deep Q-Network(DQN)based RL systems use multiple DQN-based-entities,which learn together and communicate with each other.The learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully designed.As more complex DQNs come to the fore,the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training,need for high resources,more training time,and difficulty in fine-tuning leading to performance issues.Taking a cue from the parallel processing found in the nature and its efficacy,we propose a lightweight ensemble based approach for solving the core RL tasks.It uses multiple binary action DQNs having shared state and reward.The benefits of the proposed approach are overall simplicity,faster convergence and better performance compared to conventional DQN based approaches.The approach can potentially be extended to any type of DQN by forming its ensemble.Conducting extensive experimentation,promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks,and Atari 2600 games as compared to recent techniques.The proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task,259.2 on the LunarLander-v2 task,and state-of-the-art results on four out of five Atari 2600 games.展开更多
In recent years,the detection of fake job descriptions has become increasingly necessary because social networking has changed the way people access burgeoning information in the internet age.Identifying fraud in job ...In recent years,the detection of fake job descriptions has become increasingly necessary because social networking has changed the way people access burgeoning information in the internet age.Identifying fraud in job descriptions can help jobseekers to avoid many of the risks of job hunting.However,the problem of detecting fake job descriptions comes up against the problem of class imbalance when the number of genuine jobs exceeds the number of fake jobs.This causes a reduction in the predictability and performance of traditional machine learning models.We therefore present an efficient framework that uses an oversampling technique called FJD-OT(Fake Job Description Detection Using Oversampling Techniques)to improve the predictability of detecting fake job descriptions.In the proposed framework,we apply several techniques including the removal of stop words and the use of a tokenizer to preprocess the text data in the first module.We then use a bag of words in combination with the term frequency-inverse document frequency(TF-IDF)approach to extract the features from the text data to create the feature dataset in the second module.Next,our framework applies k-fold cross-validation,a commonly used technique to test the effectiveness of machine learning models,that splits the experimental dataset[the Employment Scam Aegean(ESA)dataset in our study]into training and test sets for evaluation.The training set is passed through the third module,an oversampling module in which the SVMSMOTE method is used to balance data before training the classifiers in the last module.The experimental results indicate that the proposed approach significantly improves the predictability of fake job description detection on the ESA dataset based on several popular performance metrics.展开更多
文摘Position sensitive device(PSD)sensor is a vital optical element that is mainly used in tracking systems for visible light communication(VLC).Recently,a new reconfigurable PSD architecture emerged.The proposed architecture makes the PSD perform more functions by modifying its architecture.As the PSD is mainly formed of an array of photodiodes.The primary concept involves employing transistors to alternate between the operating modes of the photodiodes(photoconductive and photovoltaic).Additionally,alternating among output pins can be done based on the required function.This paper presents the mathematical modeling and simulation of a reconfigurable-multifunctional optical sensor which can perform energy harvesting and data acquisition,as well as positioning,which is not available in the traditional PSDs.Simulation using the MATLAB software tool was achieved to demonstrate the modeling.The simulation results confirmed the validity of the mathematical modeling and proved that the modified sensor architecture,as depicted by the equations,accurately describes its behavior.The proposed sensor is expected to extend the battery's lifecycle,reduce its physical size,and increase the integration and functionality of the system.The presented sensor might be used in free space optical(FSO)communication like cube satellites or even in underwater wireless optical communication(UWOC).
文摘This paper presents the simulation and implementation of a reconfigurable pixel that serves both data acquisition and energy harvesting purposes.The main topic focuses on switching between the two operating modes of the photodiode:photoconductive and photovoltaic modes.This proposed model can be used to design novel optical sensors with energy harvesting capability,such as position sensitive device(PSD)and complementary metal oxide semiconductor(CMOS)image sensors,which can extend the battery lifetime of the whole optical system.Thus,we can overcome power supply problems like wiring and changing batteries frequently,especially in hard-to-reach places like space(cube satellites)or even underwater wireless optical communication(UWOC).The proposed pixel architecture offers the advantage of a minimalistic design with only four transistors.Nevertheless,it does come with a drawback in the form of higher noise levels.The simulation was achieved using MATLAB,and the implementation was performed using the programmable system-on-chip(PSoC)microcontroller.The results showed that the functionality of the dual-function pixel is correct,and the scheduling of both energy harvesting and signal sensing functions was successfully achieved.
文摘This paper presents a review of the position-sensitive detector(PSD) sensor, covering different types of PSD and recent works related to this field. Furthermore, it explains the theoretical concepts and provides information about its structure and principles of operation. Moreover, it includes the main information about the available commercial PSDs from different companies, along with a comparison between the common modules. The PSD features include high position resolution, fast response, and a wide dynamic range. These features make it suitable for various fields and applications, such as imaging, spectrometry, spectroscopy and others.
基金supported by the Science and Engineering Research Board,New Delhi for Research Grant Vide Sanction No:File No.EMR/2017/004162 dated 01-11-18。
文摘In this investigation,all-optical Toggle flip-flop event-driven memory is explored with data rate of 16 Gbit/s.Single mode optical fiber model is used as a nonlinear medium to generate the output set and reset pulses of a Toggle flip-flop,and the model is based on the bidirectional optical transmission principle,considering the fundamental effects of cross phase modulation and self-phase modulation with change in polarization state.The performance of a flip-flop is evaluated using truth table conditions and performance parameters such as Q factor,which is obtained as 380.92 d B for Q and 272.9 d B for■,and rising and falling times of 7.304 ps and 5.79 ps,respectively are obtained,which makes flip-flop design fast as compared to earlier design techniques.
文摘Applications for quanta and space sensing both depend on efficient low-light imaging.To precisely optimize and design image sensor pixels for these applications,it is crucial to analyze the mechanisms behind dark current generation,considering factors such as temperature,trap cross-section and trap concentration.The thresholds for these generating effects are computed using optoelectrical technology computer aided design(TCAD)simulations,and the ensuing changes in pinned photo-diode(PPD)dynamic capacitance are observed.Various generation models along with an interfacial trap model are used to compare PPD capacitance fluctuations during light and dark environments.With the use of this comparison study,current compact models of complementary metal oxide semiconductor(CMOS)image sensors can be modified to accurately capture the impacts of dark current in low-light conditions.The model developed through this study demonstrates a deviation of only 6.85%from the behavior observed in physical devices.These results not only enhance our understanding of dark current generation mechanisms but also offer practical applications by improving the performance and accuracy of image sensors.
基金This paper is financed by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.004-0001-C01.
文摘The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.
文摘The globe faces an urgent need to close the energy demand-supply gap.Addressing this difficulty requires constructing a Hybrid Renewable Energy System(HRES),which has proven to be the most appropriate solution.HRES allows for integrating two or more renewable energy resources,successfully addressing the issue of intermittent availability of non-conventional energy resources.Optimization is critical for improving the HRES’s performance parameters during implementation.This study focuses on HRES using solar and biomass as renewable energy supplies and appropriate energy storage technologies.However,energy fluctuations present a problem with the power quality of HRES.To address this issue,the research paper introduces the Generalized Dynamic Progressive Neural Fuzzy Controller(GDPNFC),which regulates power flow within the proposed HRES.Furthermore,a unique approach called Enhanced Multi-Objective Monarch Butterfly Optimization(EMMBO)is used to optimize technical parameters.The simulation tool used in the research work is HOMER(Hybrid Optimization of Multiple Energy Resources)-PRO,and the system’s power quality is assessed using MATLAB 2016.The research paper concludes with comparing the performance of existing systems to the proposed system in terms of power loss and Total Harmonic Distortion(THD).It was established that the proposed technique involving EMMBO outperformed existing methods in technical optimization.
基金an outcome of the collaborative R&D work undertaken in the project under the Visvesvaraya PhD Scheme of Ministry of Electronics&Information Technology,Govt.of India,being implemented by Digital India Corporation。
文摘In this paper,drain current transient characteristics ofβ-Ga2O3 high electron mobility transistor(HEMT)are studied to access current collapse and recovery time due to dynamic population and de-population of deep level traps and interface traps.An approximately 10 min,and 1 h of recovery time to steady-state drain current value is measured under 1 ms of stress on the gate and drain electrodes due to iron(Fe)–dopedβ-Ga2O3 substrate and germanium(Ge)–dopedβ-Ga2O3 epitaxial layer respectively.On-state current lag is more severe due to widely reported defect trap EC–0.82 e V over EC–0.78 e V,-0.75 e V present in Iron(Fe)-dopedβ-Ga2O3 bulk crystals.A negligible amount of current degradation is observed in the latter case due to the trap level at EC–0.98 e V.It is found that occupancy of ionized trap density varied mostly under the gate and gate–source area.This investigation of reversible current collapse phenomenon and assessment of recovery time inβ-Ga2O3 HEMT is carried out through 2 D device simulations using appropriate velocity and charge transport models.This work can further help in the proper characterization ofβ-Ga2O3 devices to understand temporary and permanent device degradation.
文摘In this paper,an efficient 8-channel 32Gbps RoF(Radio over Fiber)system incorporating Bessel Filter(8/32 RoFBF)has been demonstrated to reduce the impact of non-linear transmission effects,specifically Four-Wave Mixing(FWM).The simulation results indicate that the proposed 8/32 RoF-BF system provides an optimum result w.r.t.channel spacing(75 GHz),input source power(0 dBm)and number of input channels(8).In comparison with the existing RoF system,the proposed 8/32 RoF-BF system has been validated analytically and it is found that the performance of the proposed system is in close proximity particularly in FWM sideband power reduction of the order of 4 dBm for the 8-channel 32Gbps RoF system.
基金The authors would like to thank for the support from Taif University Researchers Supporting Project Number(TURSP-2020/239),Taif University,Taif,Saudi Arabia。
文摘CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the systemparameters.The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation.As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user throughput.Self-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of salps.In this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary users.The results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.
文摘The radio-frequency (RF) performance of the p-type NiO-pocket based β-Ga_(2)O_(3)/black phosphorous heterostructureMOSFET has been evaluated. The key figure of merits (FOMs) for device performance evaluation include the transconductance(gm) gate dependent intrinsic-capacitances (Cgd and Cgs), cutoff frequency (fT), gain bandwidth (GBW) product and output-conductance(gd). Similarly, power-gain (Gp), power added efficiency (PAE), and output power (POUT) are also investigated for largesignalcontinuous-wave (CW) RF performance evaluation. The motive behind the study is to improve the β-Ga_(2)O_(3) MOS deviceperformance along with a reduction in power losses and device associated leakages. To show the applicability of the designeddevice in RF applications, its RF FOMs are analyzed. With the outline characteristics of the ultrathin black phosphorous layer belowthe β-Ga_(2)O_(3) channel region, the proposed device results in 1.09 times improvement in fT, with 0.7 times lower Cgs, and 3.27dB improved GP in comparison to the NiO-GO MOSFET. The results indicate that the designed NiO-GO/BP MOSFET has betterRF performance with improved power gain and low leakages.
文摘Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques.The experienced evaluators take time to identify the disease which is highly laborious and too costly.If wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield quality.To solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease identification.In the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation techniques.The augmented images are preprocessed by histogram equalization.As a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch extraction.Different layers of region extraction models construct a feature vector that is later passed to the CE-SVM model.As a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.
文摘COVID-19 has become one of the critical health issues globally,which surfaced first in latter part of the year 2019.It is the topmost concern for many nations’governments as the contagious virus started mushrooming over adjacent regions of infected areas.In 1980,a vaccine called Bacillus Calmette-Guérin(BCG)was introduced for preventing tuberculosis and lung cancer.Countries that have made the BCG vaccine mandatory have witnessed a lesser COVID-19 fatality rate than the countries that have not made it compulsory.This paper’s initial research shows that the countries with a longtermcompulsory BCGvaccination system are less affected by COVID-19 than those without a BCG vaccination system.This paper discusses analytical data patterns for medical applications regarding COVID-19 impact on countries with mandatory BCG status on fatality rates.The paper has tackled numerous analytical challenges to realize the full potential of heterogeneous data.An analogy is drawn to demonstrate how other factors can affect fatality and infection rates other than BCG vaccination only,such as age groups affected,other diseases,and stringency index.The data of Spain,Portugal,and Germany have been taken for a case study of BCG impact analysis.
文摘In this work,we design a multisensory IoT-based online vitals monitor(hereinafter referred to as the VITALS)to sense four bedside physiological parameters including pulse(heart)rate,body temperature,blood pressure,and periph-eral oxygen saturation.Then,the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery.The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment,a powerful microcontroller,a reliable wireless communication module,and a big data analytics system.It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis.We use Apache Kafka(to gather live data streams from connected sen-sors),Apache Spark(to categorize the patient vitals and notify the medical pro-fessionals while identifying abnormalities in physiological parameters),Hadoop Distributed File System(HDFS)(to archive data streams for further analysis and long-term storage),Spark SQL,Hive and Matplotlib(to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals).In addition,we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely.Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing,data processing,and data transmission mechanisms.To validate the system accuracy,we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor,the Welch Allyn®Spot Check.Our pro-posed system provides improved care solutions,especially for those whose access to care services is limited.
基金This project was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under Grant No.(D-15-611-1443).
文摘Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise.Moreover,it is widely utilizing for preventing injuries of athletes during a practice session and in few cases,it leads to muscle fatigue.At present,emerging technology like the internet of things(IoT)and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity.In this study,an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram(sEMG)sensor.Normally,the EMG signal is utilized to display muscle activity.Arduino controller,Wi-Fi module,and EMG sensor are utilized in developing the wearable device.The Time-frequency domain spectrum technique is employed for classifying the threemuscle fatigue conditions including meanRMS,mean frequency,etc.A real-time experiment is realized on six different individuals with developed wearable devices and the average RMS value assists to determine the average threshold of recorded data.The threshold level is analyzed by calculating the mean RMS value and concluded three fatigue conditions as>2V:Extensive);1–2V:Moderate,and<1V:relaxed.The warning alarm system was designed in LabVIEW with three color LEDs to indicate the different states of muscle fatigue.Moreover,the device is interfaced with the cloud through the internet provided with a Wi-Fi module embedded in wearable devices.The data available in the cloud server can be utilized for forecasting the frequency of an individual to muscle fatigue.
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.
基金funded by Researchers Supporting Project Number(RSP2023R503),King Saud University,Riyadh,Saudi Arabia。
文摘Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and classification.Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is difficult.The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem.This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking.The method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground areas.Compared to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.
文摘Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.
文摘With the advent of Reinforcement Learning(RL)and its continuous progress,state-of-the-art RL systems have come up for many challenging and real-world tasks.Given the scope of this area,various techniques are found in the literature.One such notable technique,Multiple Deep Q-Network(DQN)based RL systems use multiple DQN-based-entities,which learn together and communicate with each other.The learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully designed.As more complex DQNs come to the fore,the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training,need for high resources,more training time,and difficulty in fine-tuning leading to performance issues.Taking a cue from the parallel processing found in the nature and its efficacy,we propose a lightweight ensemble based approach for solving the core RL tasks.It uses multiple binary action DQNs having shared state and reward.The benefits of the proposed approach are overall simplicity,faster convergence and better performance compared to conventional DQN based approaches.The approach can potentially be extended to any type of DQN by forming its ensemble.Conducting extensive experimentation,promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks,and Atari 2600 games as compared to recent techniques.The proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task,259.2 on the LunarLander-v2 task,and state-of-the-art results on four out of five Atari 2600 games.
文摘In recent years,the detection of fake job descriptions has become increasingly necessary because social networking has changed the way people access burgeoning information in the internet age.Identifying fraud in job descriptions can help jobseekers to avoid many of the risks of job hunting.However,the problem of detecting fake job descriptions comes up against the problem of class imbalance when the number of genuine jobs exceeds the number of fake jobs.This causes a reduction in the predictability and performance of traditional machine learning models.We therefore present an efficient framework that uses an oversampling technique called FJD-OT(Fake Job Description Detection Using Oversampling Techniques)to improve the predictability of detecting fake job descriptions.In the proposed framework,we apply several techniques including the removal of stop words and the use of a tokenizer to preprocess the text data in the first module.We then use a bag of words in combination with the term frequency-inverse document frequency(TF-IDF)approach to extract the features from the text data to create the feature dataset in the second module.Next,our framework applies k-fold cross-validation,a commonly used technique to test the effectiveness of machine learning models,that splits the experimental dataset[the Employment Scam Aegean(ESA)dataset in our study]into training and test sets for evaluation.The training set is passed through the third module,an oversampling module in which the SVMSMOTE method is used to balance data before training the classifiers in the last module.The experimental results indicate that the proposed approach significantly improves the predictability of fake job description detection on the ESA dataset based on several popular performance metrics.
