Showing papers in "Journal of Nanoelectronics and Optoelectronics in 2022"

Theoretical Study on Defective g-GaN for CO2 Adsorption

Abstract: Carbon neutrality is one of ultimate goals of global population. The detection of CO2, now, is a research hotspot, and two-dimensional materials are undoubtedly play an important role. In this paper, the first-principles approach based on density functional theory was used to study the adsorption behavior of CO2 on intrinsic and defective g-GaN. The results are as follows. The adsorption energy is relatively bigger, the band gap and the structures of g-GaN and CO2 have no obvious changes when CO2 is adsorbed on the intrinsic g-GaN. It indicates that intrinsic g-GaN is inert to CO2. Defective g-GaN still maintains a planar structure, but g-GaN are transformed from semiconductors to half-metal and metals after the introduction of Ga and N single vacancies, respectively. The CO2 adsorption energy and adsorption distance are reduced, the structure of defective g-GaN is obviously deformed when CO2 is adsorbed on defective g-GaN. It indicates that the adsorption between g-GaN and CO2 is stronger. CO2 is physically adsorbed on these three structures from the perspective of charge exchange which is good for desorption. Defective g-GaN still remain half-metallic and metallic properties after CO2 is adsorbed on it. From the adsorption energy, the introduction of Ga vacancy enhances the detection ability of g-GaN for CO2, and it is better than N vacancy. This provides theoretical support for g-GaN materials as a nanoscale gas sensor materials.

Convolutional Neural Network Enable Optoelectronic System for Predicting Cardiac Response by Analyzing Auction-Based Optimization Algorithms

Abstract: One of the body’s most important organs is the heart. An electrocardiogram (ECG) is a common diagnostic tool because it provides continuous tracings of the heart’s electrophysiological activity. The study’s overarching objective is the development and implementation of an artificial intelligence (AI)-based abnormal heart beat detection system with potential applications in the early diagnosis and timely treatment of cardiovascular diseases. Through the transmission of signals to the healthcare monitoring system, these wearable devices enable doctors to keep constant, reliable tabs on their patients’ health statuses. In addition to alerting the doctors and nurses, this serves as a warning to the patient so that they, too, can take preventative measures. Several scientific teams utilizing AI contributed to the victory. Predicting cardiovascular disease using information gathered from smart devices is challenging due to low accuracy and time complexity. We propose a new optimization strategy based on deep learning to tackle these problems. In particular, it relies on the Condition-Convolutional Neural Network (Condition-CNN) based Auction-based Optimization algorithm, which analyzes optimization algorithms (ABO) while also considering Opto electronics property (sensor and detector characteristics, MOSFET) mechanism details, and the active element triumvirate.

Photonic Filter-Free Scheme for D-Band mm-Wave Signal Generation with Frequency 12-Tupling Only via One Integrated In-Phase and Quadrature Modulator

Abstract: In this work, we have proposed a new scheme of generating continuously tunable and filter-less D-band millimeter-wave (mm-wave) signal with frequency 12-tupling only via one integrated in-phase and quadrature (I/Q) modulator. The two sub-MZMs in the I/Q modulator, biased at peak point, are applied to eliminate odd order optical tones. By controlling the phase and amplitude of RF driving signals, I/Q modulator bias point, and the optical phase shifter embed in the lower arm of I/Q modulator, a frequency 12-tupling signal can be obtained through the beat frequency in the photodetector. Employing 10 GHz RF drive signal, 120 GHz millimeter wave signal can be realized by detailed mathematical formula derivation and simulation, and optical sideband suppression ratio (OSSR) can reach 37.65 dB and radio frequency sideband suppression ratio (RFSSR) can reach 32.08 dB.

The Influence of 10 MeV Proton Irradiation on Silicon Carbide Power Metal-Oxide-Semiconductor Field-Effect Transistor

Abstract: The effects of 10 MeV proton irradiation on the threshold voltage and gate oxide reliability of SiC MOSFET are investigated. The negative shift of the threshold voltage was observed after irradiation, and the magnitude of the shift is exclusively related to the fluence and not the drain voltage. Moreover, proton irradiation leads up to the degeneration of oxide reliability. Experiment and simulation results indicate that the shift of the threshold voltage is caused by the total ionizing dose effect. Due to the superior blocking capabilities of the SiC MOSFET, the electric field of gate oxide is almost unaffected by the voltage applied to the drain, so the drift of threshold voltage is only related to particle fluence. The single event effect is responsible for the degradation of gate oxide reliability. The single event effect induces a transient high electric field in the gate oxide, which generates defects and affects the reliability of the gate oxide.

Behavior Prediction Scheme Using Hierarchical Clustering and Deep Neural Networks

Abstract: Nowadays, most companies are utilizing customer behavior mining frameworks to improve their business strategies. These frameworks are used to predict different business patterns, such as sales, forecasting, or marketing. Different data mining and machine learning concepts have been applied to predict customer behaviors. However, traditional approaches consume more time and fail to predict exact user behaviors. In this paper, intelligent techniques, such as fuzzy clustering and deep learning approaches, are utilized to investigate customer portfolios to detect customers’ purchasing patterns. To accomplish this objective, hierarchical fuzzy clustering was applied to compute the relationship between products and purchasing criteria. According to the analysis, similar data are grouped together, which reduces the maximum error classification problem. Then, an optimized deep recurrent neural network is incorporated into this process to improve the prediction rate. The discussed system efficiency is evaluated using a number of datasets with respective performance metrics. The proposed approach was compared to other single model-based and hybrid model-based approaches and was found to attain maximum accuracy and minimum error rate in comparison.

On-Line Monitoring Method of Carbon Fiber Composite Drilling Tool Wear Driven by Digital Twin

Abstract: Aiming at the problems of not being able to directly monitor the wear state of drill bit during vibration drilling and not being able to collect relevant dynamic data online during machining, a digital twin-driven online monitoring method for vibration drilling bit wear was proposed. Firstly, feature extraction of multi-source signals in drilling process is carried out by wavelet analysis, and a double neural network model for bit wear recognition is established. Based on this, an online monitoring method for bit wear is proposed. The digital twinning system for bit wear is implemented, and the dynamic data in drilling process is collected online, and the simulation of bit wear process is realized synchronously. Finally, the proposed prediction method is compared with Support vector machine (SVM) recognition method. The results show that the proposed method can effectively predict the tool wear condition and realize the real-time identification of tool wear degree in machining process.

Metalloporphyrin-Based Fluorescent Sensor for the Discrimination of Volatile Organic Compounds Using Density Functional Theory

Abstract: This spectral property of the fluorescent sensors were investigated using density functional theory (DFT) and time-dependent density functional theory (TD-DFT). The considered silver porphyrin (AgP) was selected as a representative dye for the theoretical study of the fluorescent sensors. The molecular structures of AgP and its complexes were optimized at B3LYP/LANL2DZ basis set. The calculated geometry structures, front-line molecular orbitals, absorption spectra, and electronic structures were analyzed to reveal the molecular reaction between AgP-based fluorescent sensors and volatile organic compounds (VOCs). The energy gaps indicated that the efficient orders of AgP-based fluorescent sensor reacted with volatile organic compounds were shown as O2 < N2 < propane (L3) < propaldehyde (L5) < H2S < propanol (L2) < trimethylamine (L1) < ethyl acetate (L6) < butanone (L4). The calculated results all reveal that the AgP-based fluorescent sensor possesses significant changes (i.e., molecular structure, frontline molecular orbital, and absorption spectra) before and after reacting with volatile organic compounds, which are closely related to the selectivity and sensitivity property of AgP-based fluorescent sensor. Therefore, this study may be useful for the AgP-based fluorescent sensor in a special application region.

Optimization of ZnO Nano Particles Preparation and Its Performance as Electron Transfer Layer in Quantum Light-Emitting Diode

Abstract: In this paper, different diameters ZnO Nano Particles (ZnO NPs) are prepared through wet chemistry method. The size effects on Quantum light-emitting diodes (QLED) performance are investigated. In addition, the influences of reaction temperature, reaction time and reactant ratio on ZnO size and performance of QLED are also studied. Transmission electron microscope (TEM), Ultraviolet-Visible spectroscopy (UV-Vis) absorption spectra and photoluminescence (PL) spectra are employed to analyze the influence of preparation conditions on optical properties of the ZnO NPs packaged QLED. The results show that 2.5 nm ZnO NPs can be obtained at 25 °C for 6 hours when the Zn2+:OH- ratio is 1:1. In comparison to the 5 nm ZnO QLED, the EQE of the 2.5 nm ZnO QLED has increased from 1% to 7.8%, and the brightness has increased from 8000 to 13000 cd/m2. When ZnO NPs solution concentration is 30 mg/ml and spin speed is 4000 rpm, the optimal turn-on voltages and luminous intensity of QLED can also be attained.

Influence of Co on the Microstructure, Electrochemical and Pseudocapacitive Properties of Amorphous Manganese Dioxide

Abstract: Manganese dioxide is a cathode material for zinc-ion batteries which is low in cost and high in performance, whereas, traditional manganese dioxide materials have poor cycle stability and poor conductivity. In response to this problem, many researchers have carried out related research, such as the preparation of various crystal forms of manganese dioxide, element doping (Co, Sn, V, etc.) and so on. However, the research on doping amorphous materials to solve the above problems is in scarcity. In this paper, amorphous manganese oxide and Co doping were combined for the first time, and Co-doped amorphous manganese dioxide was prepared by in-situ liquid phase dispersion coprecipitation method. The influence of Co doping on the microstructure and electrochemical properties of amorphous manganese dioxide were also investigated for the first time. As can be observed from the results, despite that Co doping preserves the abundant structural defects in amorphous manganese dioxide, it transforms the amorphous manganese dioxide particles from spherical to nano-sheet shape, significantly increases the particular area of surface and pore size of amorphous manganese dioxide, and stabilizes its crystal structure. Furthermore, Co doping can not only reduce the impedance of the amorphous manganese dioxide cathode, but also extremely boost the pseudocapacitive performance of amorphous manganese dioxide, thereby greatly improving its discharge specific capacity, the rate performance and the cycle stability. The prepared Co-doped amorphous manganese dioxide cathode has a maximum discharge specific capacity of 325 mAh g−1, and a capacity retention rate of 64% at 600 cycles under a current density of 1 A g−1.

Numerical Study of Surface Plasmon Resonance Biosensor Using Aluminium Oxide and Bismuth Telluride Nanomaterials for Skin Cancer Cell Detection

Abstract: In the present article, a highly angular sensitive surface plasmon resonance sensor has been studied for early detection of skin cancer cell. The device’s basic design is based on the Kretschmann configuration, which uses an angular interrogation technique. The surface plasmon resonance biosensor has a high potential for detecting skin cancer cells. The variation of refractive index has been taken 1.35–1.38 for basal cell cancer (skin cancer). The proposed device has been stacked with multilayers having silver metal, CaF2 prism, Al2O3, and Bi2Te3 layers. In this article detection accuracy, angular sensitivity, the distribution of electric field intensity and figure of merit as performance parameters have been reported. The optimized value of angular sensitivity is 257.33°RIU−1. Similarly, some other performance parameters like detection accuracy, penetration depth, quality factor and distribution of electric field intensity have also been evaluated and the values are 0.3143 deg−1, 80.8883 RIU−1, 4.82×105 V/m and 112 nm respectively. The numerical simulation has been evaluated by COMSOL multiphysics and MATLAB software. The proposed biosensor may have been used in biological and chemical sensor applications.

A Secure Convergence of Data Transmission and Intelligent Authentication Using a Sensing Approach for the Internet of Thing

Abstract: The IoT’s millions of sensor-equipped gadgets upload data to computers for management and use in smart grids, intelligent transportation networks, healthcare networks, and smart cities. Sensor data needs a safe server-sensor connection. Studying altered data could have catastrophic consequences. Internet-of-Things sensors must authenticate with the reader and base station before sending data. IoT sensors can securely and effectively communicate data. Correction evidence in the suggested technique reveals that the required data is created at the receiver using the normal Euclidean parameters of IoT sensors. The proposed approach is compatible with most assaults, making it a good security option. A framework for edge IoT devices can optimize latency, security, and other performance characteristics. Constraints Application Protocol (COAP) and The Stream Control Transmission Protocol (AESCQTTP) combine cooperative resource management, machine learning, context-aware computing, and flexible infrastructure to handle communication and computing difficulties. To reduce end-to-end latency by (7.13–7.35)%, raise security by 98.99%, and increase efficient pocket distribution to 98%. This study examined existing research issues and edge-computing technologies and proposed a novel strategy for optimizing edge-IoT system performance metrics. This arrangement could let future networks communicate securely.

Investigation of Addition of Manganese Ferrite Nanoparticles (MnFe₂O₄ NPs) on Polyvinyl Alcohol/Polyethylene Glycol Blend for Magnetoelectronics and Optoelectronics Applications

Abstract: In this study, manganese ferrite nanoparticles (MnFe 2 O 4 NPs) were successfully prepared using the solvothermal method. XRD, HRTEM, FT-IR, and VSM techniques were used to characterize pure MnFe 2 O 4 NPs. After that, the prepared MnFe 2 O 4 NPs were added with different compositions to PVA/PEG polymer blend using a simple casting method. The XRD and FT-IR confirmed the insertion of MnFe 2 O 4 NPs inside the PVA/PEG blend. Optical parameters affirmed that the addition of MnFe 2 O 4 decreases the optical band gap for PVA/PEG blend samples. FESEM and cross-section examination evident that the incorporation of MnFe 2 O 4 NPs creates small pores inside PVA/PEG blend and also increased its surface roughness. Dielectric properties were measured at room temperature in the range (0.1 Hz–20 MHz). Both dielectric constant and dielectric loss were shown to decrease with increasing frequency while increasing with increasing MnFe 2 O 4 NPs. Loss tangent (tan δ ) showed a peak with frequency, indicating that these nanocomposites were dielectrically relaxed, with relaxation time decreasing with increasing MnFe 2 O 4 NPs. The results showed that increasing the concentration of MnFe 2 O 4 NPs leads to increase electrical conductivity. VSM results showed a great enhancement of magnetic properties of PVA/PEG with increasing MnFe 2 O 4 NPs which makes these nanocomposites were perfectly used in magnetoelectronics and optoelectronics devices.

Design and Fabrication of Near Ultraviolet Enhanced Composite Single Photon Avalanche Diode for Fluorescence Lifetime Imaging

Abstract: The near ultraviolet photon detection probability (PDP) of single photon avalanche diodes (SPADs) is very important for the fluorescence lifetime imaging. However, the PDP of traditional SPAD (T-SPAD) devices in the near-ultraviolet is not ideal, which is difficult to meet the requirements of fluorescence lifetime imaging. In response to the above problems, this paper realizes a near ultraviolet enhanced composite SPAD (NUEC-SPAD) based on photogate. The device is based on a photogate and a PN junction formed by P+/N-Well to detect photons. Therefore, the PDP of the device in the near ultraviolet is greatly improved. In addition, the shallow trench isolation (STI) and multiplication regions are isolated by photogate, and the dark count rate (DCR) of the device is greatly reduced. The principle of NUEC-SPAD device is simulated and verified based on the Technology-Computer-Aided-Design (TCAD). The NUEC-SPAD device and the T-SPAD device are fabricated based on the 0.18 μm BCD process. The experimental data show that the avalanche breakdown voltage of NUEC-SPAD device is 12 V. The device has good PDP in the range of 360 nm to 700 nm. Under the excess bias voltage of 0.5 V, the PDP of NUEC-SPAD device is 43.81% (@460 nm), which is 45.50% higher than that of T-SPAD device. Under the excess bias voltage of 1 V, the DCR of NUEC-SPAD device is only 0.24 Hz/μm2.

Intelligent Healthcare Provided by Nano-Enhanced Biosensing Systems: Progress in COVID-19 Management via the Artificial Neural Network Approach

Abstract: Biosensors using opto electronics mechanisms are evolving as efficient (sensitive and selective) and low-cost analytical diagnostic devices for early-stage disease diagnosis, which is crucial for person-centered health and wellness management. Due to advancements in nanotechnology in the areas of sensing unit fabrication, device integration, interfacing, packaging, and sensing performance at the point-of-care (POC), personalized diagnostics are now possible, allowing doctors to tailor tests to each patient’s unique disease profile and management requirements. Innovative biosensing technology is being pushed as the diagnostic tool of the future because of its potential to provide accurate results without requiring intrusive procedures. Because of this, this visionary piece of writing explores analytical methods for managing personalised health care that can enhance the health of the general population. The end goal is to take control of a healthier tomorrow as soon as possible. Right now, the most crucial part of controlling the COVID-19 pandemic, a potentially fatal respiratory viral disease, is the rapid, specific, and sensitive detection of human beta severe acute respiratory system coronavirus (SARS-CoV-2) protein.

Instantaneous Frequency Estimation of Nonlinear FM Radar Signal Based on Multi-Scale Chirplet Path

Abstract: Instantaneous frequency is an important parameter to non-linear frequency modulated (NLFM) signal in low probability intercept (LPI) radar. For electronic intelligence, it is very important to accurately estimate instantaneous frequency of NLFM signal. A multi-scale chirplet path pursuit (MCPP) method and its improved method are proposed for electronic intelligence systems to estimate instantaneous frequency of NLFM radar signal in this paper. Firstly, signal duration is divided into a set of dynamic time interval, multi-scale chirplet basis function is established on each time interval simultaneously. And then, projection coefficient in each dynamic interval is calculated basing on chirplet basis functions. And then, chirplet basis functions which have the largest projection coefficient with the analysis signal in each time interval are connected by path pursuit algorithm. Rough estimation of instantaneous frequency will be achieved by connecting the linear frequency of those chirplet basis functions. At last, to solve the problem that instantaneous frequency curve is not smooth for the impact of noise and chirplet errors, least square fitting method is used to further improve estimation accuracy. Experimental results show that, proposed improved MCPP algorithm is suitable for the instantaneous frequency of the NLFM radar signal at low SNR. Compared with time-frequency analysis method, it has higher estimation accuracy. Proposed method can also be applied to the instantaneous frequencies estimation of other NLFM signal without prior knowledge, such as seismic signals and fault diagnosis signals.

First Order Plus Dead Time (FOPDT) Model Approximation and Proportional-Integral-Derivative Controllers Tuning for Multi-Volume Process

Abstract: Analysis and controller design for the FOPDT model is widely used because of its simpleness and convenience. In this paper, the problem of the multi-volume process (MVP) being approximated to first order plus dead time (FOPDT) model, as well as its proportional-integral-derivative (PID) controller arguments tuning is studied. These have been heavily studied in recent years and the methods developed for its optimal design rely on the idea of including several robust performance specifications in the objective function the method presents fast convergence and consists of mentioning a desired closed-loop transfer function. Particle swarm optimization (PSO) algorithm and performance index of the integral of the time-weighted absolute error (ITAE) minimum is presented to determine the approximate FOPDT model coefficients of MVP processes, where the order is from two to fifteen. In addition, the approximate FOPDT model is used to design the PID controller, which is used to control MVP. A large number of tuning methods are provided to analyze and compare the closed-loop control performances. At the end of the paper, two simulation examples illustrate the superiority and effectiveness of the PID controller design based on the proposed model reduction method. The simulation results show that the reduced-order controller can control a high-order system well, but the process of order reduction is complicated and it needs a long computation time. The FOPID is a generalization of the conventional PID controller. This is based on an extension calculus. A new method for approximating MVP to the FOPDT model is presented in this paper with more effectiveness.

The Research of Spark Memory Optimization Based on Non-Volatile Memory

Abstract: With the advent of the significant data era, more and more data information needs to be processed, bringing tremendous challenges to storage and computing. The spark amount of data is getting larger and larger, and the I/O bottleneck of computing and scheduling from the disk has increasingly become an essential factor restricting performance. The spark came into being and proposed in-memory computing, which significantly improved the computing speed. In addition, the high rate of the memory is easy to lose without power, and the small but expensive feature is also an urgent need to improve. The emergence of new non-volatile memory (NVM) not only brings the characteristics of non-volatile, large capacity, low latency but also brings new opportunities and challenges to the storage system. Therefore, based on the emergence of NVM and the problems to be improved in Spark memory, this paper proposes an NVM-based Spark memory optimization method. Add NVM to the Spark memory system, build a hybrid storage structure of NVM and memory, and make the partition management for NVM storage. What’s more, add some new persistence levels and optimize RDDs and other vital data. In the end, make the related optimization for cache and recovery.

Connection-Adjustable Network Slicing Process for Heterogeneous Service Handling in Real-Time Applications

Abstract: The use of fiber optics in computer networks improves the data handling rate and aids in high-level real-time application support for different user categories. Design and modeling of optical communications for computer networks requires difficult slicing and connectivity process for preventing signal losses. In this article, a Connection-Adjustable Network Slicing (CANS) process is introduced to prevent signal losses due to heterogeneous application support. The proposed process identifies service demands and the actual network transmit capacity for acknowledging services. The optical features are improved using the recommended learning preferences in order to achieve high service delivery. In the amplification process, the infrastructure support and slicing delays are accounted for preventing signal losses. To improve network stability with low-level computer networks, the service-to-loss forecast is predicted using recommendation learning. Therefore, the proposed process’s performance is validated using the metrics service latency, slicing rate, service sharing ratio, and outage.

A Novel Hybrid Reconfigurable Architecture for Prediction of Side Channel Attacks with Its Countermeasure Mechanism

Abstract: The Internet of Things (IoT) has pushed everyone‘s normal life zone to their comfort zone by making them use embedded IoT devices for controlling and monitoring their daily gadgets. IoT devices find their applications in health care, agriculture, industrial automation, and even vehicles. Since IoT involves numerous devices’ data sharing, which causes network traffic and makes them vulnerable to security breaches, especially Side-Channel Attacks (SCA), it creates demand for an intelligent framework. As of now, many secured cryptoengines are integrated into embedded chips, but still, SCAs play a very vital role in IoT networks. As a result, the paper proposes a novel reconfigurable architecture as Field Programmable Gate Arrays (FPGA) that integrates deep learning models for effective SCA prediction as well as a countermeasure mechanism based on chaotic maps. Finally, the experimentation results prove the excellence of the proposed framework in terms of prediction accuracy (99%), sensitivity (98.9%), and specificity (98.9%) in comparison with LSTM, ELM SVM, RF, NB, and ANN. The results also demonstrated that the proposed framework requires only 50% of the total energy for network operation.

Dynamic Channel Allocation Algorithm for Heterogeneous Cellular Networks in Power Communication

Abstract: In order to solve the problems of low efficiency and high noise in the application of modern channel resource allocation methods, an anti-interference dynamic channel allocation algorithm for heterogeneous cellular networks in power communication is proposed. First, the heterogeneous cellular network model of power communication and the architecture of power multi-channel transmission platform are constructed. Then, according to the receiving and noise characteristics of communication resources, the anti-interference algorithm of power communication resources under multi-channel transmission is designed to obtain the communication index of power communication heterogeneous cellular network, and the anti-interference dynamic allocation algorithm of power communication heterogeneous cellular network resources is realized, Finally, the comparison method is used to prove the practicability of the method in this paper. The experimental results show that the average value of communication resources after interference suppression is 950 Mb, the average signal to noise ratio is 10.5 dB, and the resource allocation time is less than 3 min after 5000 iterations, which is superior to the comparison method. The method has good channel anti-interference ability and channel resource allocation efficiency, and has certain application value.

Total Ionizing Dose Effects of the Color Complementary Metal Oxide Semiconductor (CMOS) Image Sensor at Different Bias

Abstract: The experiments of total ionizing dose radiation effects on color CMOS image sensors at different biases were presented. Two bias conditions operated during 60Co γ irradiation, biased and unbiased. In the data processing, the data of four channels were extracted according to the sequence of Bayer array respectively to study the difference between each channel. The full well capacity and dark signal non-uniformity versus the cumulative doses were investigated. After γ-ray irradiation, the full well capacity of the samples decreased along with dark signal non-uniformity increased. Meanwhile, the descending amplitude of full well capacity was different in each channel, but the variation of dark signal non-uniformity was almost consistent. Moreover, the degradations of full well capacity and dark signal non-uniformity were more seriously under-biased.

Switchable Terahertz Metasurfaces Based on Patterned Vanadium Dioxide and Graphene

Abstract: This paper proposes a bi-functional switchable broadband terahertz metasurface ground on U-shaped vanadium dioxide (VO2) and graphene. The proposed design can effectively switch the current working state through a two-parameter regulation mechanism. Specifically, as we fix graphene’s Fermi level at 1 eV, and VO2 is in the form of insulating, the proposed design can be seen as a broadband terahertz absorber. Shifting the Fermi level of graphene can dynamically modulate the amplitude of the broadband absorption spectrum. In other words, by arbitrarily tailoring the Fermi level of graphene, the proposed design can freely switch states, i.e., mode switching from broadband absorption to broadband reflection, in the frequency range of interest. As graphene’s Fermi level is equal to 0.01 eV, and the vanadium dioxide in the structure is in the metallic state; the designed metasurface can be seen as a broadband terahertz linear polarization converter. It can convert the incident linearly polarized terahertz wave into its orthogonal polarization. By varying the conductivity of vanadium dioxide in the simulation, the proposed design can freely tune the current operating state over the operating frequency range, similar to “ON” and “OFF”. Metasurfaces can work efficiently in different frequency ranges by changing the geometric parameters. Therefore, the designed structure has switchable and tunable functions simultaneously, providing additional options for integrated, intelligent, and miniaturized devices.

Research on Inversion Technology of Surface Defect Depth of Ultra-Smooth Optical Surfaces

Abstract: For identifying the surface features of ultra-smooth optical or non-optical surfaces, light scattering analysis and measurement is a very useful method. Aiming at the requirement of detecting depth information of surface defects on ultra-smooth surfaces, the author propose a method of measuring the depth information about the surface defects of optical elements by using a relationship model between surface roughness and surface defects. The relationship between surface roughness and surface scratches is analyzed, and the relationship model is established. Then, by simulating the surface roughness with scratches and without scratches, according to the relationship model, the depth information of the surface of the optical component is calculated and the correctness of the model is verified. Finally, the length and width information of the surface scratches are measured according to the microscopic scattering dark field imaging method, the surface roughness is measured by white light interferometry, and the depth information of the surface scratches is calculated according to the above relationship model. The results are compared with the conclusion of the white light interferometer. The depth calculated by the roughness is basically consistent with the measured scratch depth, and the error is between 0.205 nm and 4.246 nm. Therefore, the experimental results demonstrate the effectiveness and feasibility of this proposed method.

Investigation on the Nonlinear Chiroptical Activity of Cysteine Capped Ag20 Nanoclusters

Abstract: Exploring chiral phenomena on nanoscale level by capping organic ligand on the surface of metal nanoclusters has drawn increased attention in both theoretical and experimental aspects. Here, we perform a systematical theoretical investigation on the linear and nonlinear chiroptical properties of chiral ligand capped Ag20 nanoclusters. The influences of chiral ligand (D-cysteine) and structural configuration (V- and S-type connections) of Ag20 nanoclusters on the chiroptical behaviors of their complexes are investigated. The calculated results demonstrate that the electronic circular dichroism (ECD) and two-photon absorption (TPA) spectra of complexes are sensitive to their structural configuration. It is found that S-type connection will result in stronger ECD and two-photon circular dichroism (TPCD). And, the V-type connection will result in stronger TPA spectra. Additionally, based on analysis of the frontier molecular orbitals, it is confirmed that the hybridization between HOMOs plays a pivotal role in the induced chirality.

Research on Parameter Design and Simulation of Infrared Optical Fiber Imaging System Based on Error Theory

Abstract: As the microlens array is attached to the infrared optical fiber imaging bundle, forming a rigid assemble is a new method to optimize the imaging quality of the infrared optical fiber imaging system. The alignment accuracy of the assembly is essential to the overall imaging quality of the system. To ensure that the assembly can still produce quality images under a certain alignment error, the imaging system model of assembly was established. Then introduced the concept of error margin and error margin coefficient to analyze the alignment error types and corresponding error effects of the assembly. Based on these concepts, the parameter design of the optical fiber imaging system was started. The limiting alignment errors were calculated at the error margin factor ξ = 1.25: transverse δx = 9 μm, longitudinal dz = 50 μm, and deflection angle γ = 1.5°. Lastly, a ZEMAX simulation study was carried out. The simulation results showed that: under a certain alignment error, the light did not leak but be completely coupled into the core of the fiber, which confirmed the accuracy of error analysis and parameter design. And the assembly was realized in certain high-quality imaging under an alignment error. So the error analysis and parameters were designed correctly, and high imaging quality transfer of the system with a certain alignment error was achieved for the assembly.

Managing Optical Sensor Data Security and Mitigating Data Variation in Real-Time Applications Using Fractional Analytical Method

Abstract: Optical sensors are employed in different real-time applications for an object, temperature, etc., sensing, improving the analysis accuracy. The optical sensors utilized in medical applications collect more sensitive data. Therefore, sensor data must be maintained in terms of providing security and privacy. The optical sensor recorded information influenced by the intermediate attacker that affects the data processing accuracy. The problem with the sensor input analysis is the intensity variation and failures in observed sequences. This article addresses the above problem using reinforcement learning, and the method is named Fractional Analytical Method with Feature Variation (FAM-FV). This method relies on data intensity features observed in sensing, transmission, and analysis time instances which are used to manage data security. Reinforcement learning identifies the analysis required based on ceasing intensity in different intervals. The required analysis is provided for restoring the intensity validations for reducing mean errors. This is performed based on the reinforced data attributes and matching features observed in the previous interval. The matching is performed from sensing to analysis interval to improve accuracy. The learning paradigm identifies and recommends the analysis level in different intervals. The proposed method is verified using accuracy, precision, mean error, analysis time, and complexity metrics.

Graphene and Nickel Nanomaterials Based Surface Plasmon Resonance (SPR) Biosensor: A Theoretical Study

Abstract: An extremely sensitive surface plasmon resonance (SPR) based biosensor has been simulated in the present study using an angular interrogation technique. The large surface area of the graphene layer facilitates biomolecule absorption. The SPR biosensor is proposed in a five-layer Kretschmann configuration with a ferromagnetic material and a silver layer. The proposed SPR biosensor’s sensitivity has been significantly raised in comparison to traditional film-based SPR biosensors. By refining the proposed structure to include a ferromagnetic materials nickel and monolayer of graphene with thicknesses of 15 nm and 0.34 nm and a silver layer of 45 nm, respectively, it is possible to increase sensitivity to 266°/RIU. Furthermore, the proposed SPR sensor design has a very small FWHM, a high detection accuracy (DA), and a high-quality factor (QF). Monolayer of graphene with a fixed mono-layer Nickle configuration were found to have the highest sensitivity of 266°/RIU. Additionally, it should be noted that the proposed SPR biosensor exhibits superior performance compared to SPR sensor parameters previously recorded.

Printed Circuit Board Electromagnetic Compatibility Design of LLC Resonant Converter Based on Field-Line Coupling Effect

Abstract: With the development of switching power supply in the direction of high efficiency, high integration, miniaturization and intelligence, LLC resonant converter has been widely used for its advantages of low loss and high efficiency. As an indispensable part in the design, the circuit board is responsible for the dual connection of the electrical part and the mechanical part of the LLC resonant converter. With the increasing of integration and distribution density of components, the EMC problem becomes more and more serious. In this paper, LLC resonant converter PCB is taken as an example to guide the selection and layout of active components based on field-circuit combination method, and the signal integrity of the system is analyzed. Finally, an experimental platform is built to verify the feasibility and effectiveness of the proposed method.

A Novel Multi-Objective Learning Algorithm for Disease Identification and Classification in Electronic Healthcare System

Abstract: Data is a commodity in today’s electronic world, and massive amount of data is being generated in many fields. Medical files and disease-related data are two types of data in the healthcare industry. This electronics health data and machine learning methods would enable us all to evaluate vast amount of data in order to uncover hidden patterns in disease, offer individualized treatment to the patients, and anticipate disease progression. In this paper, a general architecture for illness prediction in the health industry is proposed. The Internet of Things (IoT), as a helpful model wherein reduced electronics body sensors and smart multimedia medical equipment, are used to enable remote monitoring of body function, plays a critical role, particularly in areas when medical care centers are few. To tackle these challenges, we have proposed Deep Reinforcement Learning with Gradient-based Optimization (DRL with BRO) model for various disease detection and classification such as skin disease, lung disease, heart, and liver disease. Initially, the IoT-enabled data are collected and stored in the cloud storage. After that, the medical decision support system based DRL with the GBO model classifies various diseases. The maximum classification accuracy with the minimum delay is the multi-objective function and finally, the proposed study satisfies the objective functions. Based on the experimental study, the proposed method offers good results than other existing methods.

A Smart Diseases Diagnosis and Classification Strategy of Electronic Healthcare Application Using Novel Hybrid Artificial Intelligence Approaches

Abstract: In today’s world, the healthcare industry faces difficulties like a scarcity of healthcare professionals, ageing, and rising healthcare costs. Also the classification and decision making process using the data generated via electronic health sensors is of major concern. In the fields of research and medical services, artificial intelligence (AI) is widely employed. However, correct estimate for various illnesses is a significant issue. The implementation of a new hybrid artificial intelligence (AI)-based classifier for helping prediction diagnosis in patients with chronic cancer conditions is examined in this work. Unknown qualities are predicted and given using the Hierarchical Red deer optimization (HRDO) based feature extraction, which is based on realworld cases. The Self-Systemized Generative Fuzzy Algorithm (SSGFA), which finds irregularities in patient data and predicts sickness, is used to create the hybrid classification design. This study’s simulation analysis included datasets for colon, lung, and brain cancer illnesses. The new combination of classifiers’ better performance resulted in total classification with increased accuracy, precision, recall, and F-measure, respectively. In terms of performance indicators, the suggested strategy is also compared to traditional methods. This demonstrates the suggested classification model’s ability to appropriately categorize various illnesses information for categorization.