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
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
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
Large-scale wireless sensor networks(WSNs)play a critical role in monitoring dangerous scenarios and responding to medical emergencies.However,the inherent instability and error-prone nature of wireless links present ...Large-scale wireless sensor networks(WSNs)play a critical role in monitoring dangerous scenarios and responding to medical emergencies.However,the inherent instability and error-prone nature of wireless links present significant challenges,necessitating efficient data collection and reliable transmission services.This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs.The primary goal is to enhance the reliability of data collection and transmission services,ensuring a comprehensive and practical approach.Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability.Additionally,it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops.These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios.Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes.The evaluation spans varying sensor densities,wireless channel conditions,and packet transmission rates,showcasing the protocol’s superiority in ensuring reliable and efficient data transfer.Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs.By prioritizing fairness,reliability,and efficiency,the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services,thereby offering a valuable contribution to the field of medical event-drivenWSNs.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
A wireless sensor network(WSN)consists of several tiny sensor nodes to monitor,collect,and transmit the physical information from an environment through the wireless channel.The node failure is considered as one of th...A wireless sensor network(WSN)consists of several tiny sensor nodes to monitor,collect,and transmit the physical information from an environment through the wireless channel.The node failure is considered as one of the main issues in the WSN which creates higher packet drop,delay,and energy consumption during the communication.Although the node failure occurred mostly due to persistent energy exhaustion during transmission of data packets.In this paper,Artificial Neural Network(ANN)based Node Failure Detection(NFD)is developed with cognitive radio for detecting the location of the node failure.The ad hoc on-demand distance vector(AODV)routing protocol is used for transmitting the data from the source node to the base station.Moreover,the Mahalanobis distance is used for detecting an adjacent node to the node failure which is used to create the routing path without any node failure.The performance of the proposed ANN-NFD method is analysed in terms of throughput,delivery rate,number of nodes alive,drop rate,end to end delay,energy consumption,and overhead ratio.Furthermore,the performance of the ANN-NFD method is evaluated with the header to base station and base station to header(H2B2H)protocol.The packet delivery rate of the ANN-NFD method is 0.92 for 150 nodes that are high when compared to the H2B2H protocol.Hence,the ANN-NFD method provides data consistency during data transmission under node and battery failure.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
This study presents a simple methodology for implementation of all optical JK flip flop for future optical high speed networks.The scheme utilizes electronic model of JK flip flop for implementation of all optical JK ...This study presents a simple methodology for implementation of all optical JK flip flop for future optical high speed networks.The scheme utilizes electronic model of JK flip flop for implementation of all optical JK flip flop at the bit rate of 7 Gbit/s.Firstly,all-optical AND and NOR gates are implemented.Furthermore,with the combination of these basic gate structures,the optical model of JK flip flop is verified.This structure makes use of two optical AND gates and two optical NOR gates.This technique uses a semiconductor optical amplifier(SOA)as the nonlinear medium to produce considerable amount of cross gain and cross phase modulation to attain truth table conditions of optical JK flip flop.In this method,the number of gates is reduced as compared to earlier schemes.Rise time and fall time of 5.6 ps with contrast ratio more than 60 dB are achieved in this design.展开更多
Video inpainting is a technique that fills in the missing regions or gaps in a video by using its known pixels.The existing video inpainting algorithms are computationally expensive and introduce seam in the target re...Video inpainting is a technique that fills in the missing regions or gaps in a video by using its known pixels.The existing video inpainting algorithms are computationally expensive and introduce seam in the target region that arises due to variation in brightness or contrast of the patches.To overcome these drawbacks,the authors propose a novel two-stage framework.In the first step,sub-bands of wavelets of a low-resolution image are obtained using the dualtree complex wavelet transform.Criminisi algorithm and auto-regression technique are then applied to these subbands to inpaint the missing regions.The fuzzy logic-based histogram equalisation is used to further enhance the image by preserving the image brightness and improve the local contrast.In the second step,the image is enhanced using super-resolution technique.The process of down-sampling,inpainting and subsequently enhancing the video using the super-resolution technique reduces the video inpainting time.The framework is tested on video sequences by comparing and analysing the structural similarity index matrix,peak-signal-to-noise ratio,visual information fidelity in pixel domain and execution time with the state-of-the-art algorithms.The experimental analysis gives visually pleasing results for object removal and error concealment.展开更多
This paper presents a halfway signaling exchange shared path protection(HSE-SPP)on the backup route for a fast connection recovery strategy.In the proposed HSE-SPP,a pre-assigned intermediate node on the backup route ...This paper presents a halfway signaling exchange shared path protection(HSE-SPP)on the backup route for a fast connection recovery strategy.In the proposed HSE-SPP,a pre-assigned intermediate node on the backup route is chosen for signaling exchange.When connection fails,source and destination nodes simultaneously generate backup connection setup messages to the pre-assigned intermediate node on the reserved backup route.At the intermediate node,signaling process occurs,and acknowledgment is generated for data transmission to the respective end nodes.Consequently,connection recovery time by applying HSE-SPP becomes very low.Simulations are performed for network parameters and results are verified with existing strategies.The average recovery time(RT),bandwidth blocking probability(BBP),bandwidth provisioning ratio(BPR),and resource overbuild(RO)ratio of HSE-SPP for ARPANET is 13.54 ms,0.18,3.02,0.55,and for dedicated path protection(DPP)are 13.20 ms,0.56,6.30,3.75 and for shared path protection(SPP)22.19 ms,0.22,3.23,0.70 respectively.Similarly,average RT,BBP,BPR and RO of HSE-SPP for COST239 are8.33 ms,0.04,1.64,0.26,and for DPP 4.23,0.47,3.50,2.04,and for SPP 11.81,0.08,1.66,0.27 respectively.Hence,results of the proposed strategy are better in terms of RT,BBP,BPR,and RO ratio.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
Automatic recognition of human emotions in a continuous dialog model remains challenging where a speaker’s utterance includes several sentences that may not always carry a single emotion.Limited work with standalone ...Automatic recognition of human emotions in a continuous dialog model remains challenging where a speaker’s utterance includes several sentences that may not always carry a single emotion.Limited work with standalone speech emotion recognition(SER)systems proposed for continuous speech only has been reported.In the recent decade,various effective SER systems have been proposed for discrete speech,i.e.,short speech phrases.It would be more helpful if these systems could also recognize emotions from continuous speech.However,if these systems are applied directly to test emotions from continuous speech,emotion recognition performance would not be similar to that achieved for discrete speech due to the mismatch between training data(from training speech)and testing data(from continuous speech).The problem may possibly be resolved if an existing SER system for discrete speech is enhanced.Thus,in this work the author’s existing effective SER system for multilingual and mixed-lingual discrete speech is enhanced by enriching the cepstral speech feature set with bi-spectral speech features and a unique functional set of Mel frequency cepstral coefficient features derived from a sine filter bank.Data augmentation is applied to combat skewness of the SER system toward certain emotions.Classification using random forest is performed.This enhanced SER system is used to predict emotions from continuous speech with a uniform segmentation method.Due to data scarcity,several audio samples of discrete speech from the SAVEE database that has recordings in a universal language,i.e.,English,are concatenated resulting in multi-emotional speech samples.Anger,fear,sad,and neutral emotions,which are vital during the initial investigation of mentally disordered individuals,are selected to build six categories of multi-emotional samples.Experimental results demonstrate the suitability of the proposed method for recognizing emotions from continuous speech as well as from discrete speech.展开更多
Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the au...Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.展开更多
Wireless sensor networks(WSN)encompass a set of inexpensive and battery powered sensor nodes,commonly employed for data gathering and tracking applications.Optimal energy utilization of the nodes in WSN is essential t...Wireless sensor networks(WSN)encompass a set of inexpensive and battery powered sensor nodes,commonly employed for data gathering and tracking applications.Optimal energy utilization of the nodes in WSN is essential to capture data effectively and transmit them to destination.The latest developments of energy efficient clustering techniques can be widely applied to accomplish energy efficiency in the network.In this aspect,this paper presents an enhanced Archimedes optimization based cluster head selection(EAOA-CHS)approach for WSN.The goal of the EAOA-CHS method is to optimally choose the CHs from the available nodes in WSN and then organize the nodes into a set of clusters.Besides,the EAOA is derived by the incorporation of the chaotic map and pseudo-random performance.Moreover,the EAOA-CHS technique determines a fitness function involving total energy consumption and lifetime of WSN.The design of EAOA for CH election in the WSN depicts the novelty of work.In order to exhibit the enhanced efficiency of EAOA-CHS technique,a set of simulations are applied on 3 distinct conditions dependent upon the place of base station(BS).The simulation results pointed out the better outcomes of the EAOA-CHS technique over the recent methods under all scenarios.展开更多
Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop.Plant diseases are one of the underlying causes in the decrease in the number of quantity and qu...Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop.Plant diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming crops.Recognition of diseases from the plant images is an active research topic which makes use of machine learning(ML)approaches.A novel deep neural network(DNN)classification model is proposed for the identification of paddy leaf disease using plant image data.Classification errors were minimized by optimizing weights and biases in the DNN model using a crow search algorithm(CSA)during both the standard pre-training and fine-tuning processes.This DNN-CSA architecture enables the use of simplistic statistical learning techniques with a decreased computational workload,ensuring high classification accuracy.Paddy leaf images were first preprocessed,and the areas indicative of disease were initially extracted using a k-means clustering method.Thresholding was then applied to eliminate regions not indicative of disease.Next,a set of features were extracted from the previously isolated diseased regions.Finally,the classification accuracy and efficiency of the proposed DNN-CSA model were verified experimentally and shown to be superior to a support vector machine with multiple cross-fold validations.展开更多
The Coronavirus disease 2019(COVID-19)outbreak was rst discovered in Wuhan,China,and it has since spread to more than 200 countries.The World Health Organization proclaimed COVID-19 a public health emergency of intern...The Coronavirus disease 2019(COVID-19)outbreak was rst discovered in Wuhan,China,and it has since spread to more than 200 countries.The World Health Organization proclaimed COVID-19 a public health emergency of international concern on January 30,2020.Normally,a quickly spreading infection that could jeopardize the well-being of countless individuals requires prompt action to forestall the malady in a timely manner.COVID19 is a major threat worldwide due to its ability to rapidly spread.No vaccines are yet available for COVID-19.The objective of this paper is to examine the worldwide COVID-19 pandemic,specically studying Hubei Province,China;Taiwan;South Korea;Japan;and Italy,in terms of exposed,infected,recovered/deceased,original conrmed cases,and predict conrmed cases in specic countries by using the susceptible-exposed-infectious-recovered model to predict the future outbreak of COVID-19.We applied four differential equations to calculate the number of conrmed cases in each country,plotted them on a graph,and then applied polynomial regression with the logic of multiple linear regression to predict the further spread of the pandemic.We also compared the calculated and predicted cases of conrmed population and plotted them in the graph,where we could see that the lines of calculated and predicted cases do intersect with each other to give the perfect true results for the future spread of the virus.This study considered the cases from 22 January 2020 to 25 April 2020.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
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.
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.
文摘Large-scale wireless sensor networks(WSNs)play a critical role in monitoring dangerous scenarios and responding to medical emergencies.However,the inherent instability and error-prone nature of wireless links present significant challenges,necessitating efficient data collection and reliable transmission services.This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs.The primary goal is to enhance the reliability of data collection and transmission services,ensuring a comprehensive and practical approach.Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability.Additionally,it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops.These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios.Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes.The evaluation spans varying sensor densities,wireless channel conditions,and packet transmission rates,showcasing the protocol’s superiority in ensuring reliable and efficient data transfer.Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs.By prioritizing fairness,reliability,and efficiency,the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services,thereby offering a valuable contribution to the field of medical event-drivenWSNs.
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A wireless sensor network(WSN)consists of several tiny sensor nodes to monitor,collect,and transmit the physical information from an environment through the wireless channel.The node failure is considered as one of the main issues in the WSN which creates higher packet drop,delay,and energy consumption during the communication.Although the node failure occurred mostly due to persistent energy exhaustion during transmission of data packets.In this paper,Artificial Neural Network(ANN)based Node Failure Detection(NFD)is developed with cognitive radio for detecting the location of the node failure.The ad hoc on-demand distance vector(AODV)routing protocol is used for transmitting the data from the source node to the base station.Moreover,the Mahalanobis distance is used for detecting an adjacent node to the node failure which is used to create the routing path without any node failure.The performance of the proposed ANN-NFD method is analysed in terms of throughput,delivery rate,number of nodes alive,drop rate,end to end delay,energy consumption,and overhead ratio.Furthermore,the performance of the ANN-NFD method is evaluated with the header to base station and base station to header(H2B2H)protocol.The packet delivery rate of the ANN-NFD method is 0.92 for 150 nodes that are high when compared to the H2B2H protocol.Hence,the ANN-NFD method provides data consistency during data transmission under node and battery failure.
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.
基金supported by the Science&Engineering Research Board,New Delhi,India vide Sanction(No.EMR/2017/004162)。
文摘This study presents a simple methodology for implementation of all optical JK flip flop for future optical high speed networks.The scheme utilizes electronic model of JK flip flop for implementation of all optical JK flip flop at the bit rate of 7 Gbit/s.Firstly,all-optical AND and NOR gates are implemented.Furthermore,with the combination of these basic gate structures,the optical model of JK flip flop is verified.This structure makes use of two optical AND gates and two optical NOR gates.This technique uses a semiconductor optical amplifier(SOA)as the nonlinear medium to produce considerable amount of cross gain and cross phase modulation to attain truth table conditions of optical JK flip flop.In this method,the number of gates is reduced as compared to earlier schemes.Rise time and fall time of 5.6 ps with contrast ratio more than 60 dB are achieved in this design.
文摘Video inpainting is a technique that fills in the missing regions or gaps in a video by using its known pixels.The existing video inpainting algorithms are computationally expensive and introduce seam in the target region that arises due to variation in brightness or contrast of the patches.To overcome these drawbacks,the authors propose a novel two-stage framework.In the first step,sub-bands of wavelets of a low-resolution image are obtained using the dualtree complex wavelet transform.Criminisi algorithm and auto-regression technique are then applied to these subbands to inpaint the missing regions.The fuzzy logic-based histogram equalisation is used to further enhance the image by preserving the image brightness and improve the local contrast.In the second step,the image is enhanced using super-resolution technique.The process of down-sampling,inpainting and subsequently enhancing the video using the super-resolution technique reduces the video inpainting time.The framework is tested on video sequences by comparing and analysing the structural similarity index matrix,peak-signal-to-noise ratio,visual information fidelity in pixel domain and execution time with the state-of-the-art algorithms.The experimental analysis gives visually pleasing results for object removal and error concealment.
文摘This paper presents a halfway signaling exchange shared path protection(HSE-SPP)on the backup route for a fast connection recovery strategy.In the proposed HSE-SPP,a pre-assigned intermediate node on the backup route is chosen for signaling exchange.When connection fails,source and destination nodes simultaneously generate backup connection setup messages to the pre-assigned intermediate node on the reserved backup route.At the intermediate node,signaling process occurs,and acknowledgment is generated for data transmission to the respective end nodes.Consequently,connection recovery time by applying HSE-SPP becomes very low.Simulations are performed for network parameters and results are verified with existing strategies.The average recovery time(RT),bandwidth blocking probability(BBP),bandwidth provisioning ratio(BPR),and resource overbuild(RO)ratio of HSE-SPP for ARPANET is 13.54 ms,0.18,3.02,0.55,and for dedicated path protection(DPP)are 13.20 ms,0.56,6.30,3.75 and for shared path protection(SPP)22.19 ms,0.22,3.23,0.70 respectively.Similarly,average RT,BBP,BPR and RO of HSE-SPP for COST239 are8.33 ms,0.04,1.64,0.26,and for DPP 4.23,0.47,3.50,2.04,and for SPP 11.81,0.08,1.66,0.27 respectively.Hence,results of the proposed strategy are better in terms of RT,BBP,BPR,and RO ratio.
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This work was partially supported by the Research Groups Program(Research Group Number RG-1439-033),under the Deanship of Scientific Research,King Saud University,Riyadh,Saudi Arabia.
文摘Automatic recognition of human emotions in a continuous dialog model remains challenging where a speaker’s utterance includes several sentences that may not always carry a single emotion.Limited work with standalone speech emotion recognition(SER)systems proposed for continuous speech only has been reported.In the recent decade,various effective SER systems have been proposed for discrete speech,i.e.,short speech phrases.It would be more helpful if these systems could also recognize emotions from continuous speech.However,if these systems are applied directly to test emotions from continuous speech,emotion recognition performance would not be similar to that achieved for discrete speech due to the mismatch between training data(from training speech)and testing data(from continuous speech).The problem may possibly be resolved if an existing SER system for discrete speech is enhanced.Thus,in this work the author’s existing effective SER system for multilingual and mixed-lingual discrete speech is enhanced by enriching the cepstral speech feature set with bi-spectral speech features and a unique functional set of Mel frequency cepstral coefficient features derived from a sine filter bank.Data augmentation is applied to combat skewness of the SER system toward certain emotions.Classification using random forest is performed.This enhanced SER system is used to predict emotions from continuous speech with a uniform segmentation method.Due to data scarcity,several audio samples of discrete speech from the SAVEE database that has recordings in a universal language,i.e.,English,are concatenated resulting in multi-emotional speech samples.Anger,fear,sad,and neutral emotions,which are vital during the initial investigation of mentally disordered individuals,are selected to build six categories of multi-emotional samples.Experimental results demonstrate the suitability of the proposed method for recognizing emotions from continuous speech as well as from discrete speech.
基金supported by Researchers Supporting Program(TUMAProject-2021-14)AlMaarefa University,Riyadh,Saudi Arabia.Mohd Anul Haq would like to thank Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2022-173.
文摘Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A6A1A03039493).
文摘Wireless sensor networks(WSN)encompass a set of inexpensive and battery powered sensor nodes,commonly employed for data gathering and tracking applications.Optimal energy utilization of the nodes in WSN is essential to capture data effectively and transmit them to destination.The latest developments of energy efficient clustering techniques can be widely applied to accomplish energy efficiency in the network.In this aspect,this paper presents an enhanced Archimedes optimization based cluster head selection(EAOA-CHS)approach for WSN.The goal of the EAOA-CHS method is to optimally choose the CHs from the available nodes in WSN and then organize the nodes into a set of clusters.Besides,the EAOA is derived by the incorporation of the chaotic map and pseudo-random performance.Moreover,the EAOA-CHS technique determines a fitness function involving total energy consumption and lifetime of WSN.The design of EAOA for CH election in the WSN depicts the novelty of work.In order to exhibit the enhanced efficiency of EAOA-CHS technique,a set of simulations are applied on 3 distinct conditions dependent upon the place of base station(BS).The simulation results pointed out the better outcomes of the EAOA-CHS technique over the recent methods under all scenarios.
文摘Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop.Plant diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming crops.Recognition of diseases from the plant images is an active research topic which makes use of machine learning(ML)approaches.A novel deep neural network(DNN)classification model is proposed for the identification of paddy leaf disease using plant image data.Classification errors were minimized by optimizing weights and biases in the DNN model using a crow search algorithm(CSA)during both the standard pre-training and fine-tuning processes.This DNN-CSA architecture enables the use of simplistic statistical learning techniques with a decreased computational workload,ensuring high classification accuracy.Paddy leaf images were first preprocessed,and the areas indicative of disease were initially extracted using a k-means clustering method.Thresholding was then applied to eliminate regions not indicative of disease.Next,a set of features were extracted from the previously isolated diseased regions.Finally,the classification accuracy and efficiency of the proposed DNN-CSA model were verified experimentally and shown to be superior to a support vector machine with multiple cross-fold validations.
基金funded by the Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydney。
文摘The Coronavirus disease 2019(COVID-19)outbreak was rst discovered in Wuhan,China,and it has since spread to more than 200 countries.The World Health Organization proclaimed COVID-19 a public health emergency of international concern on January 30,2020.Normally,a quickly spreading infection that could jeopardize the well-being of countless individuals requires prompt action to forestall the malady in a timely manner.COVID19 is a major threat worldwide due to its ability to rapidly spread.No vaccines are yet available for COVID-19.The objective of this paper is to examine the worldwide COVID-19 pandemic,specically studying Hubei Province,China;Taiwan;South Korea;Japan;and Italy,in terms of exposed,infected,recovered/deceased,original conrmed cases,and predict conrmed cases in specic countries by using the susceptible-exposed-infectious-recovered model to predict the future outbreak of COVID-19.We applied four differential equations to calculate the number of conrmed cases in each country,plotted them on a graph,and then applied polynomial regression with the logic of multiple linear regression to predict the further spread of the pandemic.We also compared the calculated and predicted cases of conrmed population and plotted them in the graph,where we could see that the lines of calculated and predicted cases do intersect with each other to give the perfect true results for the future spread of the virus.This study considered the cases from 22 January 2020 to 25 April 2020.
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.
