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The widespread adoption of e-commerce has rendered online reviews an indispensable source for acquiring knowledge about product features. The Kano model serves as a classic framework for categorizing customer requirements. It was proposed by the Japanese scholar Noriaki Kano in 1984 as a tool primarily used to help businesses better understand and categorize customer needs, guiding product development and service quality improvement. However, existing research on mining online review information often relies predominantly on static analysis, lacking comprehensive categorization of user demands. To address this gap, this paper introduces an enhanced Kano model for the identification and dynamic analysis of user demands. This model classifies review data based on emotional polarity and user attention, leveraging the characteristics inherent in online reviews. Furthermore, it constructs a user demand evolution model using time series analysis and utilizes SnowNLP for emotional calculations. By conducting word frequency statistics and employing LDA models to analyze product features, this paper identifies the evolving trends in user demands. Additionally, the exponential smoothing method is used to forecast the trend in users’ emotional values towards the product. The findings demonstrate that this model effectively extracts valuable insights regarding product feature information from user reviews, thereby offering novel perspectives and methodologies for related fields. Consequently, this contributes significantly to advancing the identification and dynamic analysis of user demands. [ABSTRACT FROM AUTHOR]

This paper takes the wind turbine of the new energy pumping unit as the study subject, which mainly analyzes realistic wind speed estimation on the windward side of the wind turbine and then proposes a wind turbine speed control method based on wind speed estimation. The support vector regression (SVR) model is built first in this study, and the parameters of the SVR model are optimized by the particle swarm algorithm. The SVR II wind speed prediction model is constructed and the working characteristic curve of the wind turbine is generated by gathering wind speed and output power data from the wind turbine at the assigned test location. The prediction model can compensate for the disturbance of system inertia and make the output wind speed more accurate. The effective wind speed acting on the turbine surface can be obtained by substituting the predicted wind speed into the optimal blade tip velocity ratio equation. Suppose the wind turbine is in maximum power tracking control. In that case, the optimal speed can be given directly by the model SCR III, and the speed can be tracked in real-time by the controller to accomplish the largest wind power capture. The optimal speed tracking control uses the calculated effective wind speed if the wind turbine is under constant power output control. In this paper, 14,000 sets of characteristic samples were extracted within the range of wind speed variation of 4–20 m/s, and model verification was carried out for models SVR-I, SVR-II and SVR-III. Finally, the predicted effective wind speed value and the actual value are within ±0.3 m/s of each other. In practical engineering applications, the forecasted effective wind speed value can entirely substitute the actual value. This work put forward a maximum power tracking control approach that can satisfy new energy pumping units' energy conservation needs. [ABSTRACT FROM AUTHOR]

This paper proposes a new dielectrically modulated (DM) twin cavity nanotube biosensor and its sensitivity evaluation suitability by the transit time (τ) and device efficiency (g m / I ds ). In addition, the vertical structure of the device also helps the uniform spreading of biomolecules within the nanogap cavity region. The inner cavity of the proposed biosensor offers more space for the stabilization of biomolecules and uses the benefits of material solubility. In this paper, the sensing ability of the biosensor is examined in terms of τ and g m / I ds for five neutral biomolecules. Furthermore, the charged biomolecule and deoxyribonucleic acid (DNA) are separately investigated for positive as well as negative charge densities. Also, various optimizations on the device are analyzed for different biomolecules in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the linear active disturbance rejection control (LADRC) is applied to the position control of a dicing saw chuck table, which is used to improve the steady-state accuracy and anti-interference ability of the control system. Aiming at the problem that LADRC parameters are difficult to adjust, a parameter adjustment method based on an improved sand cat swarm algorithm is proposed. The nonlinear convergence factor strategy and multiple chaotic map optimization strategy are used to improve the algorithm. In order to verify the performance of the proposed method, the improved sand cat swarm optimization algorithm is compared with the standard sand cat swarm optimization algorithm, grey wolf optimization algorithm and salp swarm optimization algorithm. The final experimental results show that using the improved sand cat swarm optimization algorithm to optimize the LADRC controller has higher steady-state accuracy, stronger anti-interference ability, and better optimization results. [ABSTRACT FROM AUTHOR]

In this paper, an example of an intelligent security system based on deep learning approach has been analyzed and described. Security is an important element in many aspects of human life. Depending on customer requirements, different levels of security may be required and implemented. Nowadays, artificial intelligence is becoming an indispensable part in many areas of human life and accordingly security systems are also based on the new achievements in this field. In this paper, an intelligent security system based on two methods for identifying persons, gait recognition and face recognition, was described. The two methods mentioned above can be used to give persons access to different buildings, facilities, institutions, etc. or to different indoor or outdoor areas. In light of the above, the aforementioned system uses face and gait recognition to identify persons who should be granted access to a facility or a specific area within the facility. Accordingly, two deep learning models for gait and face recognition have been developed and described. The analysis, the settings defined and the results obtained in relation to the models developed were also described. [ABSTRACT FROM AUTHOR]

The increasing demand for low voltage, power efficient, high-speed analog-to-digital converters (ADCs) results in the improvement of speed and power of regenerative dynamic comparator. In this paper, a dual-tail dynamic comparator is used with two extra transistors in the latch stage. These extra transistors help in the increase of transconductance of the latch stage, which helps decrease the delay of the proposed comparator. Mathematical analysis is done for the proposed architecture; this gives the idea of reducing the delay of the comparator with an increase in the transconductance of the comparator. The simulation and layout of the proposed comparator are done on the Cadence software with 90 nm CMOS technology. This proposed design is simulated with a 2 GHz clock frequency at supply voltage of 1 V. The proposed architecture consumes a power of 39.19 μ W and a delay of 143.12 ps at 1 V supply voltage, 5 mV input difference voltage and 0.9 V common mode voltage. The Monte Carlo simulation of the proposed architecture for power, delay, power delay product (PDP) and offset is also demonstrated in this paper. Process corner analysis is done for power, delay and PDP. [ABSTRACT FROM AUTHOR]

To address the issue of frequency stability in microgrids with limited communication resources and bandwidth, this paper proposed an event-triggered control method based on a cloud-edge collaborative architecture. Firstly, to address the limited communication resources, event-triggered detector is set up at the edge to upload state variables and update control instructions only when the frequency deviation exceeds a pre-set threshold. Secondly, a fractional-order PI–PD controller is designed at the cloud control center, which achieves higher control degrees of freedom and stronger robustness than traditional integer-order PID control schemes. Additionally, to obtain better control parameters, an optimized design method based on an improved Big Bang-Big Crunch method (BB-BC3) is proposed for the fractional-order PI–PD controller. At last, a simulation example is given to prove the validity of the proposed method. Simulation results show that compared to the integer-order PI–PD controller, the fractional-order PI–PD controller based on the BB-BC3 algorithm proposed in this paper reduces the peak value of the response curve by 5%, and shortens the setting time by 0.14 s. [ABSTRACT FROM AUTHOR]

A charge and discharge management scheme is proposed. The stored electric energy in distributed storage devices will converge to the consistent. The consistency of the stored electric energy of the devices helps to maintain the maximum load capacity and maximum consumption capacity of distributed storage devices. The charging and discharging process is constructed as a time-varying optimization problem, and the proposed algorithm can respond to the time-varying parameters of the distributed storage devices in real time. The time-varying neurodynamic algorithms can obtain time-varying optimal solution trajectories to give the optimal charging and discharging strategy in real time. In addition, the proposed approach in this paper focuses on the privacy protection of device data. Each device can calculate the power of discharging or charging by communicating with the partially connected nodes. Numerical simulations of the proposed scheme in the paper are given to verify the effectiveness of the scheme. Numerical simulations show that our scheme can make the electric energy stored in each storage device converge and maintain the maximum load capacity or maximum consumption capacity of the whole distributed storage device. [ABSTRACT FROM AUTHOR]

Confronted with the fast social development, the speed of garbage generation has increased. Therefore, from every aspect, especially staying green and healthy, it is urgent and vital to ensure accurate and stable garbage classification. In the field of garbage classification, household garbage has a large volume and the classification process still requires manual intervention. Besides, most of the current studies focus on industrial waste and medical waste, while the existing studies on household garbage are not effective enough. Furthermore, these studies are mostly trained and validated in self-built datasets, making the experimental results insufficiently representative and severely limiting the development of research. Aiming to facilitate the study of household garbage classification, this paper proposes a simple yet effective network model (FConvNet) to improve classification performance. In this paper, an effective convolution block with fused convolution is designed to enhance the potential correlation of the features in the spatial dimension, and then a lightweight symmetric structure is constructed to infer more information about the feature representation. The experiments on public datasets have justified the effectiveness of FConvNet with accuracy rates of 95%, 95% and 97%, achieving better performance on public datasets compared to baselines. Additionally, an online system based on the WeChat mini program framework is designed to provide convenient household garbage classification services and enable the collection of uploaded images to perform model optimizations. [ABSTRACT FROM AUTHOR]

The prediction of price trends in the stock market has always been a hot research topic in the financial field. However, due to the high instability and volatility of stock prices, it is very difficult to accurately predict stock trends. How to remove the noise of stock data, extract effective features, and pursue maximum value returns has always been a challenge. This paper proposes a hybrid model (DWT-DQN) that combines discrete wavelet transform with deep reinforcement learning to improve the accuracy and return rate of stock predictions. First, the model captures price fluctuation information on different scales by performing discrete wavelet transformation on the difference between long- and short-term moving averages of stock prices, and well extracts the changing characteristics of stock price data in the time domain and frequency domain. Then the feature data are input into the built DQN network for model training. The network can select the optimal trading action based on market status and historical experience and returns. At the same time, during the data sampling process, an attention mechanism is introduced to allow the model to further learn in the direction of maximizing returns. Through testing and verification on SSEC, HSI, NDX and SPX data sets, experiments show that the hybrid model proposed in this paper has excellent performance in terms of accuracy and return rate. [ABSTRACT FROM AUTHOR]

The 21st century is an era of rapid development of high-tech industry. Through continuous collection and sharing of data, IoT technology connected by things has penetrated into every aspect of human life. Modern organizations find that they increasingly rely on knowledge and information sharing and interconnection to enhance their innovation and development capabilities. Therefore, this paper studies the strategic decision-making of knowledge sharing among employees on the IoT platform drawing on evolutionary game theory, this paper constructs a three-party game model composed of "organization-knowledge sharers-knowledge seekers" from the perspective of stakeholders, and discusses the strategic choice of organization and employee behavior under the dynamic decision mechanism and when the game reaches equilibrium and stability. Moreover, this paper uses MATLAB 2016a to simulate the model. The results show that under the premise of higher organizational rewards, synergistic benefits, high-shared extra income, knowledge-sharing preferences, incentive preferences of the organization, the system is easier to reach the ideal state. With the smaller cost of sharing and incentive, organization and employees are more willing to be motivators and sharers, while the incentive degree of organization should be controlled within a reasonable range. This paper can provide specific theoretical and practical guidance for the practice of organizational knowledge management. [ABSTRACT FROM AUTHOR]

Battery Management System (BMS) functions to monitor individual cell in a battery pack and its crucial task is to maintain stability throughout the battery pack. The BMS is responsible for maintaining the safety of the battery as well as not to harm the user or environment. The parameters that are to be monitored in a battery are Voltage, Current and Temperature. With the collected data, BMS carefully monitors the charging–discharging behavior of the battery particularly in the Lithium-ion (Li-ion) batteries in which charging and discharging behavior are completely different. This paper proposes a real-time IOT connected deep learning algorithm for estimation of State-of-Charge (SoC) of Li-ion batteries. This paper provides unique objectives and congruence between model-based conventional methods and state-of-the-art deep learning algorithm, specifically Feed Forward Neural Network (FNN) which is nonRecurrent. This paper also highlights the advantages of Internet-of-Things (IoT) connected deep learning algorithm for estimation of State-of-Charge of Li-ion batteries in Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs). The major advantage of the proposed method is that the Artificial Intelligence (AI)-based techniques aim to bring the estimation error less than 2% at a low cost and time without the model of the battery, at par with conventional method of Extended Kalman Filter (EKF) which is the best ever practical estimation theory. Another advantage of the proposed method is that in an abnormal condition (i.e., Unsafe Temperature) the IF This Then That (IFTTT) IoT mobile application interfaced with BMS through ThingSpeak cloud, sends a notification alert to the battery expert or to the user prior to an emergency. Finally, the real-time data of the battery parameters are collected through ThingSpeak cloud platform for future research and analysis. [ABSTRACT FROM AUTHOR]

Accurately predicting the maximum temperature is essential for studying human comfort, ecological environment development and social progress. However, traditional prediction methods are inefficient and inaccurate when dealing with large volumes of meteorological data. To tackle these challenges, this paper introduces an integrated approach, the ARIMA–LSTM–XGBoost model, which combines the strengths of autoregressive integrated moving average (ARIMA), long short-term memory network (LSTM) and eXtreme Gradient Boosting (XGBoost) to predict the maximum temperature. The proposed model enhances the prediction accuracy and convergence rate through techniques like MAPE reciprocal weight (MAPE-RW) and Schedule Sampling. Additionally, the model selects the best performing model using the early stopping method. This paper compares and analyzes the prediction results of the ARIMA, LSTM, XGBoost and ARIMA–LSTM–XGBoost models. The experimental results indicate that the ARIMA–LSTM–XGBoost model proposed in this paper achieves superior prediction accuracy, performance, and confidence. The ARIMA–LSTM–XGBoost model shows a Root-Mean-Squared Error (RMSE) of 1.381, significantly outperforming the ARIMA model (3.828), LSTM model (3.360) and XGBoost model (1.422). The coefficient of determination ( R 2) is 0.977, surpassing the values of 0.905 for the ARIMA model, 0.922 for the LSTM model and 0.910 for the XGBoost model. The ARIMA–LSTM–XGBoost model also exhibits a higher confidence level compared to the individual models. [ABSTRACT FROM AUTHOR]

The low-light image enhancement task aims to improve the visibility of information in the dark to obtain more data and utilize it, while also improving the visual quality of the image. In this paper, we propose a dual cross-attention multi-stage embedding network (DCMENet) for fast and accurate enhancement of low-light images into high-quality images with high visibility. The problem that enhanced images tend to have more noise in them, which affects the image quality, is improved by introducing an attention mechanism in the encoder–decoder structure. In addition, the encoder–decoder can focus most of its attention on the dark areas of the image and better attend to the detailed features in the image that are obscured by the dark areas. In particular, the poor performance of the Transformer when the dataset size is small is solved by fusing the CNN-Attention and Transformer in the encoder. Considering the purpose of the low-light image enhancement task, we raise the importance of recovering image detail information to the same level as reconstructing the lighting. For features such as texture details in images, cascade extraction using spatial attention and pixel attention can reduce the model complexity while the performance is also improved. Finally, the global features obtained by the encoder–decoder are fused into the shallow feature extraction structure to reconstruct the illumination while guiding the network for the focused extraction of information in the dark. The proposed DCMENet achieves the best results in both objective quality assessment and subjective evaluation, while for the computer vision tasks working in low-light environments as well, the enhanced images using the DCMENet proposed in this paper show the best performance. [ABSTRACT FROM AUTHOR]

The fast warning for financial risk of enterprises has always been a realistic demand for their managers. Currently, this mainly relies on expert experience to make comprehensive analysis from massive business data. Benefitting from the strong computational performance of deep learning, this paper proposes a fuzzy neural network (FNN)-based intelligent warning method for financial risk of enterprises. An improved FNN structure with time-varying coefficients and time-varying time lags is established to extract features of enterprises from complex financial context. The algorithm of fuzzy C-means and fuzzy clustering based on sample data are studied. In this paper, the fuzzy C-means algorithm is used to cluster the samples, the input sample set is preprocessed, a new set of learning samples is formed, and then the neural network is trained. The enterprise financial risk sample and its modular FNN model are established, and the evaluation of the enterprise financial risk sample is simulated. Then, a decision part is added following the FNN part to output the warning results. After that, we have also conducted a case study as simulation experiments to evaluate the proposed technical framework. The obtained results show that it can perform well in the fast warning of financial risk for enterprises. [ABSTRACT FROM AUTHOR]

Image semantic segmentation technology is one of the core research contents in the field of computer vision. With the improvement of computer performance and the continuous development of deep learning technology, researchers have more and more enthusiasm to study the actual effect and performance of image semantic segmentation. The results of deep semantic segmentation allow computers to have a more detailed and accurate understanding of images, and have a wide range of application needs in the fields of autonomous driving, intelligent security, medical imaging, remote sensing images, etc. However, the existing image semantic segmentation algorithms have the disadvantages of easy discontinuous results and insufficient prediction accuracy. In this paper, we take deep learning-based image semantic segmentation technology as the research object to explore the improvement of the image semantic segmentation algorithm and its application in road scenarios. First, this paper proposes MCU-Net method based on residual fusion and multi-scale contextual information. MCU-Net uses residual fusion module to deepen the network structure and improve the ability of U-Net to acquire deeper features. Then a top-down and bottom-up path is constructed for feature information between different levels, and the spatial and semantic information contained in shallow and deep features in the network is fully utilized by fusing features from different levels. In addition, an enhanced void space pyramid pooling module is added for feature information between the same levels, which enables the output features to have a larger range of semantic information. Second, this paper proposes the DAMCU-Net method based on attention mechanism and edge detection based on MCU-Net. DAMCU-Net extracts global contextual information by the attention mechanism optimization module, while fusing features using dense jump connections to facilitate the network to recover more spatial detail information during upsampling, and uses the FReLU activation function to improve the segmentation capability of the network for complex targets. For the edge information lost in the feature extraction process, the edge detection branch is added to supplement the feature information of the main path by feature fusion to achieve the optimization of the edge information. [ABSTRACT FROM AUTHOR]

This paper focuses on deep vision sensing-assisted gesture recognition for athletes in dynamic scenes. Although many research attention had been devoted to this field in recent years, most of existing works failed to fully take characteristics of dynamic scenes into consideration. To deal with this challenge, this paper proposes a diffusion convolution neural network-based multiview gesture recognition approach in dynamic scenes. For one thing, the dynamic spatiotemporal slice position selection based on the body mask heatmap is adopted to calculate positions of horizontal and vertical slices. Thus, the dynamic selection of slice positions in two directions can be realized, and then the extraction of bi-directional spatiotemporal slice images can be completed. For another, action sequences through the 3D residual neural network are learned, and the spatiotemporal information among frames are mined through recurrent networks. Through their combination, a multi-view gesture recognition approach for athletes is constructed. In the experiments, two standard datasets UCF101 and HMDB51 are utilized to establish simulation environment. The proposed method can reach the accuracy beyond 95% on the two datasets. Compared with several typical recognition methods, the proposed method shows higher accuracy. [ABSTRACT FROM AUTHOR]

This paper deals with the design, simulation, and practical modeling of metamaterial-based multiband conformal bandpass filter (BPF) for various wireless communication applications with improved quality factors. The novel metamaterial in the form of a split ring resonator is loaded on the ground plane face of the proposed BPF. The overall dimension of the designed BPF is only 2 8 × 2 8 mm 2 . The proposed BPF is tuned initially for quality factor enhancement based on the thickness of the substrate, physical parameters of the f transmission line, ground plane, externally loaded elements, and the gap in the metamaterial loading. The suggested filter operates at triple band covering the frequency bands from 1.4 to 2.2, 3.6 to 3.9, and 4.8 to 5.9 GHz, which are suitable for sub-6 GHz 5G and other wireless applications. The insertion loss is observed as 1 dB, which is suitable for the proposed BPF. The conformal behavior of the filter is judged through bending deformation analysis at various bending positions like (15 ∘ , 30 ∘ , 45 ∘ , 60 ∘ , and 90 ∘ ). The proposed BPF retains triple pass band characteristics at various bending deformations, which makes it suitable to be used in curved structures or flexible circuitry. The theory of equivalent circuits and quality factor (Q) of the designed BPF is discussed in this paper. The results are analyzed experimentally through ANRITSU-MS2037C combinational analyzer. The proposed BPF is suitable for sub-6 GHz 5G, WLAN, and Wi-Max applications. [ABSTRACT FROM AUTHOR]

The development of modern process CMOS integrated circuits has reduced the feature sizes and thus the reliability of the chip continuously. First, this paper proposed two kinds of single-node upset self-recovery feedback loops with low overhead. One is called P-RFL which is composed of P-type complementary element (CP) and Clocked CP (C2P), and the other is called N-RFL which is composed of N-type complementary element (CN) and Clocked CN (C2N). Second, in order to fully tolerate triple-node upsets (TNUs), this paper presents three TNU-hardened latches: C2P-C2N, DMR-C2P and DMR-C2N. Using the blocking ability of the C-element, the outputs of two RFLs are connected to the C-element array. Therefore, when any three nodes upset at the same time, the transient pulse propagates inside the latch step by step, and disappears after being blocked by the C-element, ensuring that the TNU-hardened latches can restore to the correct logic state. HSPICE simulations show that all the three proposed latches achieve lower power, delay and APDP, compared with other six TNU-hardened latches. DMR-C2N achieves the lowest power, delay and APDP. In addition, the PVT variations analysis show that three proposed TNU-hardened latches are less sensitive to the variations of process, voltage and temperature. [ABSTRACT FROM AUTHOR]

For the actual circuit with fractional order characteristics, the fractional order circuit model is more concise and precise than the classical circuit model. So studying the synthesis of fractional order immittance is of great significance. This paper investigates the passive synthesis method of the three-element-kind fractional order immittance function. It means designing a network with three kinds of passive fractional-order elements that exhibit a specific impedance function. This paper proposes a variable substitution method to solve this problem. The method consists of three steps: The first step of the process is to establish the correspondence between the fractional order and lossy reactance. This way, the fractional order immittance function is transformed into an integer order immittance function. Then the Brune procedure realizes the immittance function as a network with lossy reactance. Finally, a fractional order network is obtained by replacing the lossy reactance with the corresponding fractional reactance. A simulation example and an experiment are also given for verification. This method applies to all three-element-kind network impedance functions that meet the passive condition. [ABSTRACT FROM AUTHOR]

The concealment of rootkits makes them a significant security threat. Kernel-level rootkits can be extremely dangerous as they have high system privileges. A typical type of kernel-level rootkits is to hook system calls which are essential for overall system functionality. This paper presents drootkit, a tool to detect kernel-level rootkits that hook system calls. Additionally, drootkit can recover damaged systems. This tool utilizes eBPF technology, ensuring both flexibility and security. When installing new kernel modules, the virtual address range of the initial kernel code will not be affected. In light of this, drootkit conducts bounds checking on all system calls within the system. In the case of system calls being hooked, drootkit can detect and recover them while issuing warning messages. For testing purposes, this paper also implements a malicious kernel module that can hook system calls and run on the arm64 platform. We have conducted an experiment that confirms drootkit's capability to detect rootkits while also effectively restoring the system. Moreover, drootkit has very low system overhead and does not significantly affect system performance, making it a reliable choice for a backend program that can run for an extended period of time. [ABSTRACT FROM AUTHOR]

The limitations of scaling in CMOS technology pose challenges in meeting the requirements of future applications. To address these challenges, researchers are exploring various design techniques, including Approximate Computing (AC), which leverages the inherent error resilience of applications to achieve high performance and energy gains with desired quality. AC has gained popularity as a computer paradigm for error-resilient applications, and many researchers have studied AC across computing layers and developed tools for implementing these techniques. This paper provides a comprehensive survey of AC techniques at the abstraction levels of software and hardware and discusses the tools to implement AC in hardware and software, quality evaluation tools and comparison points. The paper also covers existing frameworks for AC, potential applications, future research directions, challenges and limitations. This information can guide researchers in identifying promising avenues for further advancements and innovations in this domain. Additionally, this paper compares state-of-the-art surveys of AC and highlights the unique features and contributions of this work that distinguish our work from previous surveys. [ABSTRACT FROM AUTHOR]

In this paper, a portable low-power low dropout regulator (LDO) with no output capacitance has been designed in 0.18- μ m CMOS technology. In this paper, a PMOS power transistor is used and combined with an overshoot and undershoot suppression circuit to improve the transient response of the circuit. A modified Class-AB operational transconductance amplifier (OTA) is used to reduce power consumption and act as an error amplifier to eliminate low frequency poles. At the same time, a super source follower and a Miller capacitor are used for frequency compensation of the system. Finally, the system can provide a stable voltage for a load step from 0.2 to 25 mA in 500 ns edge-time for 140 pF-load capacitance. The test results show that the circuit achieves a good voltage regulation rate and high transient response. [ABSTRACT FROM AUTHOR]

This paper presents in-depth research and analysis of the optimization method of tourist traffic routes using the adaptive cuckoo algorithm. The traditional cuckoo algorithm has the disadvantages of slow convergence speed and easy falling into local extremes. This paper proposes the iterative adaptive CHSACS algorithm based on iteration. Based on the original CS algorithm, a dynamic adaptive step control amount and a segmented weighted position update formula are introduced to give a class of improved CS algorithms to coordinate the problem of local search and global search of the CS algorithm and speed up the convergence speed at the later stage. To address the problem that the out-of-bounds nests interfere with the convergence of the algorithm, a memory strategy is introduced to relocate the out-of-bounds nests in the search space to improve the stability of the algorithm. Experiments are conducted on the iterative adaptive-based conductive CHSACS algorithm with test function sets. Compared with the original CS algorithm, and ACO algorithm, the CHSACS algorithm has faster convergence, higher search accuracy and a better ability to avoid local optima when dealing with continuous function optimization problems. For the dynamic travel path problem of travel time optimization, the road travel time and sightseeing time of different periods are predicted based on historical data, and the most traveled distance of travel time between two sights is dynamically searched by using the search mechanism based on time windows. The iterative adaptive CHSACS algorithm is used to continuously find the tourist traffic route with the shortest travel time travel path from all combinations of tour sequences. This paper combines the characteristics of bus vehicle scheduling itself, takes into account the interests of both bus companies and tourists, establishes a bus vehicle scheduling model with the departure interval as the independent variable, introduces this hybrid cuckoo algorithm into bus scheduling, verifies the scientific and feasibility of the algorithm through examples, and provides a new idea for solving bus scheduling optimization problems. [ABSTRACT FROM AUTHOR]

This paper proposes a method that considers the bus net Fuzzy control strategy quickly and accurately for power. Due to the intermittent and fluctuating characteristics of distributed generation units in microgrid, the power quality will be reduced and the normal operation of microgrid will be affected. In this paper, empirical modal analysis fused with convolutional neural network algorithm is applied to the power control strategy of microgrid energy storage system to improve the economy and reliability of microgrid. The design process of the controller is analyzed, and fuzzy rules and membership functions are designed according to the effect requirements of the controller. Then, the center of gravity method is used to obtain the output power of the energy storage system. The simulation results show that the configuration scheme based on the optimization of the hybrid energy storage system in the whole life cycle in this paper not only ensures the stability of the energy storage system but also effectively avoids power fluctuations and stabilizes the peak shaving and valley filling rate. While improving the economy of the energy storage system, it also increases the service life of the energy storage system. [ABSTRACT FROM AUTHOR]

In this growing world of the internet, most of our daily routine tasks are somehow connected to the internet, from smartphones to internet of things (IoT) devices to cloud networks. Internet users are growing rapidly, and the internet is accessible to everyone from anywhere. Data phishing is a cyber security attack that uses deception to trick internet users to get their content and information. In this attack, malicious users try to steal personal data such as login credentials, credit card details, health care information, etc., of the users on the internet. They exploit users' sensitive information using vulnerabilities. Information stealers are known as phishers. Phishers use different techniques for phishing. One of the most common methods is to direct the users to a false website to enter their login credentials and their details on these phishing sites. Phishing websites look like the original websites. Phishers use these details to get access to the user's accounts and hijack them for monetary purposes. Many internet users fall for this trap of phishing sites and share their personal and sensitive details. In this paper, we will analyze and implement machine learning (ML) techniques to detect phishing attacks. There are different methods to identify phishing attacks, one of them is by checking the uniform resource locator (URL) address using ML. ML is used to teach a machine to differentiate between phishing and original site URLs. There are many different techniques to overcome this attack. This research paper aims to provide accurate and true phishing detection with less time complexity. [ABSTRACT FROM AUTHOR]

In recent years, cloud computing technologies have been developed rapidly in this computing world to provide suitable on-demand network access all over the world. A cloud service provider offers numerous types of cloud services to the user. But the most significant issue is how to attain optimal virtual machine (VM) allocation for the user and design an efficient big data storage platform thereby satisfying the requirement of both the cloud service provider and the user. Therefore, this paper presents two novel strategies for optimizing VM resource allocation and cloud storage. An optimized cloud cluster storage service is introduced in this paper using a binarization based on modified fuzzy c-means clustering (BMFCM) algorithm to overcome the negative issues caused by the repetitive nature of the big data traffic. The BMFCM algorithm utilized can be implemented transparently and can also address problems associated with massive data storage. The VM selection is optimized in the proposed work using a hybrid COOT-reverse cognitive fruit fly (RCFF) optimization algorithm. The main aim of this algorithm is to improve the massive big data traffic and storage locality. The CPU utilization, VM power, memory dimension and network bandwidth are taken as the fitness function of the hybrid COOT-RCFF algorithm. When implemented in CloudSim and Hadoop, the proposed methodology offers improvements in terms of completion time, overall energy consumption, makespan, user provider satisfaction and load ratio. The results show that the proposed methodology improves the execution time and data retrieval efficiency by up to 32% and 6.3% more than the existing techniques. [ABSTRACT FROM AUTHOR]

This paper introduces a Multi-Level Cascade Inverter (MLI) based on Enhanced Quasi-Z Source Inverter (MQZSI) to connect photovoltaic (PV) systems based on the proposed method. Usually the interface among PV power supply and load is completed with MQZS-CMLI. In this paper, the proposed control scheme is comprehensive implementation of Recalling Enhanced Recurrent Neural Network (RERNN) and Quasi-Opposite Chemical Reaction Optimization (QOCRO) named as RENCO. The main objective of proposed method is to decide the efficiency of the PV system by the maximal power extraction. Here, MQZS-CMLI's modeling design contains suitable number of components, other than their capacitors and semiconductors comply with low-voltage stress. It is improved for providing that maximum power of the PV power generation system. At first, the goal function is described according to the parameters and limitations of controller (like voltage, current, power, modulation index, so on). These parameters apply to recommend RENCO technology input. The proposed RENCO technology improves voltage distribution, power transmission, and minimizes power fluctuations while sharing power with load. The proposed MPPT-based technology ensures that the maximum power is provided to load. The proposed method adjust the duty cycle of MQZS-CMLI and reduces the modulation load. Finally, the proposed technology is executed on the MATLAB/Simulink platform, and its output efficiency is compared to existing systems under dissimilar load circumstances. [ABSTRACT FROM AUTHOR]

In this paper, the six degrees of freedom adaptive tracking control for chaser spacecraft with uncertainty and disturbance to approach a noncooperative target are studied. The system equations with coupled relative translation and relative rotation are modeled. Considering the unknown inertia uncertainties and state-dependant disturbances, a backstepping-based adaptive tracking control law is developed to achieve high-precision proximity operations between chaser and noncooperative target. The unknown inertia and disturbance are estimated by adaptive update laws. The properties of the developed control law are discussed through the comparison of simulation results. [ABSTRACT FROM AUTHOR]

This paper presents a Schmitt trigger (ST) circuit with anti-clockwise hysteresis and its utility as an adjustable square/triangular wave generator. The ST circuit employs a single dual-X second-generation current conveyor and two resistors. The threshold voltages of the circuit are adjustable such that it can also function as a zero crossing detector. The circuit has a wide operational frequency range and also consumes low power. Without using an additional active block, a square/triangular wave generator circuit is also realized within the same circuit topology. The generator circuit uses a single active element, one grounded resistor and one grounded capacitor only. The use of grounded passive components makes the proposed generator circuit easily integrable. Additionally, the generator circuit is adjustable as its duty cycle is tunable by means of an external current source. The generator circuit also has a wide operational frequency range from 2.1 Hz to 19.2 MHz. The theoretical aspects of the proposed circuits are validated via Cadence simulations. Additionally, a prototype of DXCCII, which is implemented by using current feedback operational amplifier ICs AD844, is used to verify the ST experimentally. [ABSTRACT FROM AUTHOR]

Nowadays, multilevel inverter (MLI)-based induction heating (IH) technology is the best-suited option in many domestic, commercial and medicinal applications because of its higher efficiency, low cost, fast heating and lower electromagnetic interference (EMI). This paper presents reduced components count high-frequency (HF) single phase five-level (5-L) MLI for IH applications. The working methodology of the proposed system has been analyzed in the multicarrier phase disposition pulse width modulation (MC-PD-PWM) method in high switching frequency i.e., 10 kHz. In addition, the proposed topology can produce output voltage twice of input voltage without any external source. Moreover, the detailed power loss and reliability analysis have been evaluated for the proposed system. In terms of switch count and efficiency, the single-phase 5-L inverter architecture is compared to some conventional and current topologies. Finally, to validate and justify the operation of the proposed system, the simulation and experimental results of five-level inverter are presented. [ABSTRACT FROM AUTHOR]

Urban user water demand prediction (WDP) is of significant importance for smart water supply system, which can provide a strong decision-making basis for the dispatching and management of smart water supply system. However, owing to the fluctuation, intermittence and nonstationarity of the user's water consumption in urban buildings, it is extremely difficult to predict accurately. Therefore, a novel short-term WDP model (Singular Spectrum Analysis Convolutional Neural Network Bidirectional Gate Recurrent Unit, SSA-CNN-BiGRU) is proposed to promote the stability and accuracy of WDP, which successfully introduces organic combinations including deep learning, decomposition technique, and data partitioning policies into the domain of WDP. First, raw data are decomposed into components that carry distinct frequency signals for weakening its nonstationarity and complexity. Then, all the components are automatically divided into several groups using clustering algorithm based on their entropy, after which deep learning method is adopted to predict by groups. Finally, the predicted result of each group is summed up to be fused as the final value. To validate the predictive performance of SSA-CNN-BiGRU, real data have been selected for this study. In experiments, SSA-CNN-BiGRU achieved a fitting of 94.73%. Comparison by relevant evaluation metrics demonstrates that the proposed model exhibits superior performance, thus providing a more accurate basis for WDP. [ABSTRACT FROM AUTHOR]

In this paper, the combination of a high step-up boost converter with extended voltage multiplier cell and isolated SEPIC converter with a series output module is presented. Since these converters use the same current source in their inputs, they can share the common parts, so the structure of the proposed converter is simple. In this converter, the high frequency transformer used in the SEPIC converter and the inductor of the extended voltage multiplier cell are coupled, so the volume of the magnetic parts is reduced. In addition, an active-clamp circuit is used to solve the problem of leakage inductor and achieve to Zero Voltage Switching (ZVS) turn on/off for switches and ZCS for diodes. Compared to its high step-up counterpart, the proposed converter has the following: (1) distributed voltage stress on the diodes and voltage of the output capacitors are fair. (2) Higher voltage gain. (3) Voltage stresses on the diodes are lower and the reverse-recovery problem of the diodes is eliminated. (4) Minimum root mean square (RMS) leakage inductor current. The operational principle and characteristics of the proposed converter are presented, and in order to verify the proposed converter, a 200 W, 20–400 V prototype converter is implemented and experimental results are provided. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things (IoT) technology and the digital transformation of the financial industry, the financial IoT is becoming an important trend in the future financial field. In the era of financial IoT, a large amount of data information is recorded by sensors and devices, which poses new challenges and opportunities for user credit evaluation. In this context, conventional deep learning-based user credit evaluation methods often cannot meet privacy needs. The paper combines the privacy security ability of federated learning with deep learning, and proposes a deep federated learning-based user credit evaluation model under financial IoT scenarios. First, a particle swarm optimization-based backpropagation neural network algorithm is formulated to extract user credit evaluation features, in order to obtain user behavior patterns and credit features. Then, the XGBoost algorithm is employed to output credit evaluation results. As for the training process, the thought of federated learning is integrated to distribute the model to individual participants. In such mode, the model can be updated and trained without exposing user data to the central cloud. The experiments are conducted on real-world data to assess the proposal’s performance. The results show that the proposed credit evaluation framework can achieve better accuracy, under the guarantee of user privacy. [ABSTRACT FROM AUTHOR]

In the fields of structural health monitoring, civil engineering safety monitoring, aerospace and other fields, the key challenge is to increase the range of strain measurements to solve with the information monitoring of large strains. Reducing the strain sensitivity of the sensor is an important way to solve the problem of small sensor ranges. In this paper, the inclined transfer structure is proposed to achieve large strain distributed optical fiber sensing. When the fiber is axially stretched directly to fracture, the inclined transfer structure is used to increase the strain measurement range of the system. The theoretical relationship between the strain monitored by single mode fiber sensors and the matrix strain is established. It is experimentally verified that the inclined transfer structure using polypropylene material as the matrix achieves the strain measurement from 36667μ휀 at sensing spatial resolution of 8mm, 10nm wavelength tuning range of the laser source and 45∘ inclined angle, obtaining a good linear fit of more than 0.99. One hour stability of less than 11pm is obtained, corresponding to the strain of 9μ휀. [ABSTRACT FROM AUTHOR]

This paper presents a reconfigurable image confusion scheme, which uses Linear Congruential Generators (LCGs)-based Pseudorandom Bits Generator (PRBG). The PRBG is based on the variable input-coupled LCG with a reconfigurable clock divider. The proposed algorithm encrypts the input image up to four times successively using different random sequences in every attempt. This new scheme aims to efficiently extract statistically strong pseudorandom sequences from a proposed PRBG with a large keyspace and simultaneously increase the security level of the encrypted image. This PRBG was initially designed on Virtex-5 (XC5VLX110T), Virtex-7 (XC7VX330T) and Artix-7 (XC7A100T) Field Programmable Gate Arrays (FPGAs). The statistical properties of the proposed PRBG for four different configurations are verified by the National Institute of Standards and Technology (NIST) tests. Thereafter, a reconfigurable encryption/decryption algorithm that uses the proposed PRBG is developed for secure image encryption. The encryption process was accomplished using the MATLAB tool after obtaining the PRBG keys from the FPGA. To show the quality and strength of the encryption process, security analysis [correlations and Number of Pixels Change Rate (NPCR)] is performed. Security analysis results are compared with the conventional encryption algorithm to show that the developed reconfigurable encryption scheme provides better results in security tests. [ABSTRACT FROM AUTHOR]

In this paper, a second-order sinusoidal oscillator (SO) has been presented using current-feedback operational amplifiers (CFOAs) as the active element. The proposed oscillator uses five resistors and two grounded capacitors in its circuit design, making it simple and cost-effective to implement. Its design allows for greater control and flexibility in designing the oscillator circuit, making it an attractive option for a variety of applications. It also provides independent resistive tuning, allowing for greater control and flexibility in designing the oscillator circuit. Additionally, it offers low parameter sensitivity, makes it more stable and reliable. These features make it a valuable tool for engineers and designers in a variety of applications, including signal processing, communication systems, and control systems. The frequency tuning expression achieved has the ability to tune frequencies in a broad range of frequencies. PSPICE tool is used to simulate and analyze the behavior of the proposed circuit. The total harmonic distortion recorded was 5 %. [ABSTRACT FROM AUTHOR]

This paper puts forward a new vertical-tunneling-attributed dual-material double-gate heterojunction-TFET (VTDMDG-HTFET). The device structure of VTDMDG-HTFET includes Si 0. 7 Ge 0. 3 material for the designing of source region. The mechanism of vertical tunneling in VTDMDG-HTFET provides superior electric field at tunneling interface and leads to improved transfer characteristics. VTDMDG-HTFET also includes metal gate work-function engineering approach to facilitate high ON-current and small OFF-current. Application of high- k dielectric material HfO˙2 in the form of gate oxide enhances the capacitive-coupling at channel-source interface. Moreover, the work includes the comparison of proposed VTDMDG-HTFET with the conventional vertical-tunneling based dual-material double-gate-TFET (VTDMDG-TFET). It has been observed from the simulation results that SiGe source-based VTDMDG-HTFET offers better DC characteristics in terms of superior ON-current, smaller OFF-current, steeper subthreshold-slope, lower threshold voltage, higher transconductance along with smaller drain-induced barrier-lowering (DIBL) effects as compared to its counterpart silicon-source-based VTDMDG-TFET. In this work, reliability of VTDMDG-HTFET has also been examined and compared with VTDMDG-TFET in terms of temperature sensitivity and effect of different interface trap charges. The device simulation and investigations have been carried out using technology computer aided design (TCAD) tool. Moreover, device circuit mixed-mode simulation analysis is carried out for conventional and proposed device based resistive load inverters. A comparison is performed between both the inverters in terms of prorogation delay and switching threshold voltages. Further, the mixed mode simulation analysis is also performed for the proposed device based inverter under the influence of different interface trap charges (ITCs). Study shows that reliability performance of the proposed device is better as compared to its counterpart conventional device. Hence, vertical tunneling and SiGe source-based VTDMDG-HTFET can be a better choice for various applications such as analog/RF, analog and digital electronic circuits, energy harvesting, gas-sensing and memory devices. [ABSTRACT FROM AUTHOR]

The smooth operation of the power system is of significant importance for ensuring societal stability. Therefore, regular inspections of the power system are necessary. The most efficient method for power inspection is to utilize Unmanned Aerial Vehicles (UAVs) to assist inspection. However, the UAV inspection still needs human resources to operate and manage the UAVs effectively. This paper proposes a UAV-airport power inspection system, where the airport can operate and manage UAVs instead of humans. The key contribution of the system is the computation offloading technology and the Wireless Power Transfer (WPT) capability. In other words, the airport can help UAVs execute electrical image fault detection and the UAVs can be charged by the airport. We propose a joint optimization strategy for UAV flight speed, local execution frequency for fault detection, and WPT time to maximize the number of images in the electrical dataset captured by the UAV that have undergone power fault detection within the inspection time. Comprehensive experiments are conducted to verify the performance of the UAV-airport power inspection system and our optimization strategy. [ABSTRACT FROM AUTHOR]

This research paper deals with a directional high gain PCB 1 × 8 antenna array for 2.4 ISM band utilizations. To achieve this antenna array, a well-matched equal-split 9 dB-power splitter is designed and integrated with the suggested antenna array. It exhibits a wide operating range of 506 MHz (2.022–2.528 GHz) and splits the feed power to 8 equal-in-phase output quantities. Then eight identical patch elements with good reflection coefficient, high gain and excellent radiation efficiency are connected at the eight-output ports of the 1 × 8-power divider in order to obtain an array antenna consisting of eight radiating elements. A quarter-wave impedance adapter is utilized to obtain a perfect matching of impedance between patches and the power divider. The suggested directional antenna resonates at 2.4 GHz with good impedance matching via offering reflection coefficient S 1 1 = − 2 4. 3 4 dB and voltage standing wave ratio (VSWR) of 1.16. Moreover, it offers good radiation traits like the enhanced gain of 14.76 dB, an excellent radiating efficiency equals about 98.86% and a directional radiation pattern. Initially, the proposed planar directional antenna array has been designed and simulated utilizing high-frequency structure simulator (HFSS) EM simulation tool, the results are validated with another simulation tool via computer simulation technology (CST) software. This printed antenna array is a potential candidate to operate at around 2.4 GHz for RFID reader utilizations due to its outstanding impedance and radiation characteristics. [ABSTRACT FROM AUTHOR]

Distributions of electroencephalogram (EEG) data vary greatly across different subjects. It is a very important issue how to generalize models across subjects. In this paper, an algorithm is proposed to build high-performance cross-subject motor-imagery brain–computer interfaces (BCIs) for a new subject. First, a novel distance metric is proposed to quantify the joint distribution discrepancy (JDD) between data from different subjects. It gives better evaluations for discrepancies between different distributions than conventional probabilistic metrics. Moreover, it can be extended to design many novel algorithms. Second, a support vector machine combined with JDD (JDMSVM) is proposed for cross-subject classification. For dataset dataIVa, the JDMSVM runs best under 9 out of 15 situations and averagely outperforms counterparts by 10.1%, 9.5%, 3.2% and 1.7%, respectively. For GigaDataset, JDMSVM runs best under 8 of 12 conditions. It averagely outperforms its counterparts by 10.4%, 5.3%, 2.7% and 2.4%, respectively. The experiments demonstrate that the proposed algorithm is effective and competitive for cross-subject BCI. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to present novel applications of two recently proposed CMOS transconductors in realizing voltage mode/current mode Tow–Thomas (TT)-equivalent biquads and a linear voltage-controlled oscillator (VCO), all exhibiting a number of advantages over the previously known circuits to realize the same functions. The workability of all the propositions has been demonstrated by SPICE simulation results using TSMC 0. 1 8 μ m CMOS technology parameters. [ABSTRACT FROM AUTHOR]

The performance of electrical connectors can be significantly impacted by periodic variations in contact resistance caused by vibrational stress. Intermittent faults resulting from such stress are characterized by their random and fleeting nature, making it difficult to study and replicate them. This paper proposes a novel method for reproducing intermittent faults in electrical connectors. To implement this method, intermittent fault data are first collected from electrical connectors subjected to different vibration loads. Next, a statistical distribution model is constructed using kernel density estimation (KDE). Based on this model, a fault injector is designed to simulate intermittent faults under varying vibration loads. The simulated faults are then compared to real-world intermittent fault signals in a controlled environment to validate the accuracy of the method. The results demonstrate that the proposed method effectively reproduces intermittent faults in electrical connectors under varying vibration conditions. This approach can be used to better understand the behavior of connectors under vibrational stress and to develop more effective testing and fault diagnosis methods. [ABSTRACT FROM AUTHOR]

This paper presents two new transconductance-boosted architectures of the voltage differencing transconductance amplifier (VDTA). The first architecture (VDTA-I) relies on the technique of transconductance enhancement using partial positive feedback which is realized through negative impedance implemented using a cross-coupled amplifier. The second architecture (VDTA-II) incorporates the concept of gate-to-source voltage enhancement using an inverting amplifier for transconductance boosting. Detailed mathematical formulations for computing transconductance gain and bandwidth are presented using high-frequency small-signal equivalent circuits of both propositions. The functionality of the proposals is validated through SPICE simulations using TSMC 0.18 μ m technology parameters. Both DC and AC analyses are carried out through simulations. An enhancement in transconductance gain of 140% is observed for VDTA-I whereas for VDTA-II it is found to be 53% in comparison to the conventional VDTA. The 3 dB frequencies are recorded to be 160 and 630 MHz, respectively. The PVT analyses are also carried out to test the efficacy of both propositions. [ABSTRACT FROM AUTHOR]

Object tracking for motion scenes is a common research concern in field of computer vision. Its goal is to accurately track targets in different time periods and predict their future states by utilizing the motion information in video sequences. However, traditional target-tracking methods in motion scenes often face challenges such as target blur, occlusion, and changes in lighting. To deal with this issue, this paper proposes a diffusion neural network-enhanced object-tracking approach under sports scenarios. In order to further improve tracking performance, the diffusion convolution operation is introduced, which propagates features at different time steps to enhance the modeling ability of target motion. Then, suitable influencing factors are selected based on motion scene object feature parameters. Finally, a target tracking method is established by integrating these two methods. In the experiment, we used a large number of real motion scene datasets to evaluate the proposed method. The experimental results show that by comparing with traditional moving object tracking methods, the proposal achieves significant improvement in tracking accuracy and methodology robustness. In addition, we also conducted stability experiments, proving that this method has good stability for models with varying kernel numbers. [ABSTRACT FROM AUTHOR]

This paper proposes a novel analog front end to filter edges in captured images. The proposed architecture employs the Sobel algorithm and uses a 3 × 3 kernel to process the input images. We used 180 nm Semi-Conductor Laboratory (SCL) CMOS fabrication technology for implementation with a power supply of 1.8 V. The proposed design employs six analog differential pairs in each horizontal and vertical gradient unit. The inputs to the differential pairs are the active signals fed from the 3 × 3 kernel using the Sobel weight masks. We connected the differential datum pairs in cascade to estimate the gradient index in horizontal/vertical direction using the net difference current between the output terminals. The horizontal and vertical gradient units, viz., G H and G V , respectively, are configured based on the polarity of weights in respective Sobel masks. The gradient (G) of the processing pixel (PP) is estimated using the net difference current in the combined circuitry employing G H and G V cells. The key advantages of the proposed design are very low area realization, high accuracy, and low power dissipation. The efficacy of the proposed edge filter analog front end is estimated through simulations and analysis with two sets of test signals in the processing window. [ABSTRACT FROM AUTHOR]

The location and detection of switch faults is a crucial step in improving the dependability of inverters, which is a requirement for critical applications. This paper focuses on a machine learning-based approach for the open-switch fault detection system for cascaded H-bridge (CHB) multilevel inverters (MLI) used in high- and medium-power applications. Each switch failure is taken into account in this analysis, and the problem is used to model logically different machine learning algorithms, namely Gaussian Naive Bayes (GNB), support vector machines (SVM) and multinomial logistic regression (MLR) models for fault identification and localization based on multi-class classification (MCC) methods. This work includes the simulation and hardware-based data collection from five-level CHB-MLI and correlation-based selection of prominent features in the final dataset. The results of three MCC models for all possible combinations of predictors (features) prove that the mean voltage of bridges is the dominant feature for fault detection in the CHB inverter. Other major findings are machine learning-assisted fault identification and diagnosis is undoubtedly accurate and significantly more promising in condition monitoring and fault diagnosis in CHB-MLIs. The results reveal 100% accuracy in fault diagnosis. Therefore, the presented work confirms switch fault detection in CHB-MLI with mean value as dominant fault feature along with machine learning techniques simple, robust and accurate fault detection. [ABSTRACT FROM AUTHOR]

In the colloquy concerning human rights, equality, and human health, mental illness and therapy regarding mental health have been condoned. Mental disorder is a behavioral motif that catalyzes the significant anguish or affliction of personal functioning. The symptoms of a mental disorder may be tenacious, degenerative, or transpire as a single episode. Brain sickness is often interpreted as a combination of how a person thinks, perceives, contemplates and reacts. This may be analogous to a specific region or workings of the brain frequently in a social context. Anxiety disorders, psychotic disorders, personality disorders, mood disorders, eating disorders, and many more are examples of mental disorders, while complications include social problems, suicides, and cognitive impairment. These days, mental disorders are quotidian worldwide, and clinically consequential levels of derangement rise adversely. The purpose of this paper is to aid in prognosis of the type of mental disorder by analyzing the brainwaves such as Alpha (α), Beta (β), Gamma (γ), Theta (), Delta (δ) with the help of big data analysis and the Internet of Medical Things (IoMT). IoMT helps in gathering the required data and data transmission, while big data analysis helps in predicting the type of disorder. [ABSTRACT FROM AUTHOR]

In this paper, a new voltage-mode (VM) multifunction filter including only two grounded capacitors, three resistors, and two current feedback operational amplifiers is introduced. This filter with one high input impedance and two low output impedances can simultaneously provide band-pass filter (BPF) and low-pass filter (LPF) responses with an adjustable gain. A universal VM filter topology with three inputs and one output generating high-pass filter (HPF), BPF, LPF, notch filter, and all-pass filter (APF) responses is obtained from the multifunction one. It has the same number of active and passive elements as the multifunction and a low output impedance. Sensitivities of both introduced filters are low enough. However, the APF of the introduced universal filter has a passive element-matching problem. As an example, parasitic impedance effects are considered for the LPF of the universal filter, while non-ideal gain analyses are given for the HPF. To see how the introduced universal filter works, countless simulations are performed through the SPICE (Simulation Program with Integrated Circuit Emphasis) program in which 0.13 μ m IBM CMOS technology parameters are utilized. DC supply voltages are selected as ± 0.9 V. Also, several experiments using commercially available AD844s with ± 10 V supply voltages are carried out to see the performance. [ABSTRACT FROM AUTHOR]

Social media blockchain is emerging as a promising solution to deal with privacy issues, by putting user privacy in edge nodes rather than centralized nodes. Under the protection of information encryption, only those who have cryptographic keys can get access to key information. This work aims at multimedia information in social media blockchain and utilizes the RSA encryption mechanism to construct the information encryption system. Due to the resilience of biometric features, the biometric cryptographic keys are not easy to be fabricated. Thus, this paper proposes a biometric keys-enhanced multimedia encryption algorithm for social media blockchain. First of all, the wiener filter is adopted to make some preprocessing operations to images, such as noise reduction. On this basis, the discrete wavelet transform is adopted to extract feature representation from images, and nonlinear approximation of contourlet transform is adopted to make feature fusion. Next, cryptographic keys can be generated from the fused biometric feature vectors to encrypt biometric data. Finally, some simulation experiments are conducted to evaluate performance of the proposal from three aspects: key generation time, security level and encryption-decryption time complexity. For key generation time, processing speed of the proposal is approximately 1–2 ms per sample. For security level, the proposal can reach an index value beyond 95% which is higher than comparison methods. For encryption-decryption time complexity, the proposal is about 30% lower than comparison methods. [ABSTRACT FROM AUTHOR]