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With the prevalence of artificial intelligence technologies, big data has been utilized to higher extent in many cross-domain fields. This paper concentrates on the digital assessment of vocational education level in some specific areas, and proposes a graph neural network-based assessment model for this purpose. Assume that all vocational colleges inside a specific region are with a social graph, in which each college is a node and the relations among them are the edges. The graph neural network (GNN) model is formulated to capture global structured features of all the nodes together. The GNN is then employed for the sequential modeling pattern, and the evolving characteristics of all the colleges can be captured. Some experiments are also conducted to evaluate the performance of the proposed GNN-VEL. It is compared with two typical forecasting methods under evaluation of two metrics. The results show that it performs better than other two methods. [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]

This paper proposes a novel hybrid technique for enhancing power quality (PQ) in distributed generation (DG) systems by deploying a unified power quality conditioner (UPQC). Here, the proposed hybrid method is the joint execution of white shark optimizer (WSO) and recalling-enhanced recurrent neural network (RERNN), called the WSO-RERNN technique. The primary objective of this novel approach is to effectively mitigate voltage sag and reduce voltage harmonics under varying load conditions. It is important to investigate the voltage sag, swell and harmonic distortion of the system to obtain an enhanced PQ of the energy supply. Therefore, this paper shows the brief impact of PQ in DG utilizing the proposed unified PQ conditioner controller. The WSO-RERNN control technique enhances the performance of the UPQC controller by providing the optimal control signal. By then, the efficiency of the proposed approach is done in MATLAB, and the performance is compared with those of existing optimization techniques, including Ant Lion Optimizer (ALO), Grey wolf optimization (GWO) and Salp swarm algorithm (SSA) methods. [ABSTRACT FROM AUTHOR]

As a widely used equation in electrostatics, the Poisson equation has significant research value in numerical solution. The basic principle of existing methods is to divide the solution domain into various grids and solve the numerical solutions at each grid node. Therefore, the accuracy of the solution is strongly correlated with the grid density divided. Based on this, this paper proposes a grid-free numerical calculation method that requires far fewer model parameters than traditional methods, and can ignore the order of the equation to solve high-dimensional Poisson equations. Given a Poisson equation, which has a certain type of boundary condition. A certain number of coordinate points are selected on the solution space and its boundary to construct a dataset. Using automatic differentiation technique to fit the differential operator in the equation, a loss function is constructed by incorporating the given boundary conditions or initial conditions, and the final numerical solution is obtained through iterative optimization algorithms. In the numerical experiment section, the algorithm proposed in this paper was used to solve the two-dimensional and three-dimensional Poisson equations with given exact solutions. The relative errors between the numerical solution and the true solution were 8. 5 3 e − 4 and 6. 4 0 e − 2 , which are within the acceptable range. This proves that the proposed algorithm is feasible for solving the two-dimensional and three-dimensional Poisson equations with precise solutions. Secondly, the proposed algorithm is used to solve the four-dimensional Poisson equation with first-type boundary conditions, and the relative error range of the solution was within [0,0.56], which successfully extends the algorithm to solve high-dimensional Poisson equations and verifies its feasibility and efficiency in solving high-dimensional Poisson equations regardless of the dimension restriction. [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, 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]

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]

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]

When applying visual SLAM systems to underground coal mines, several challenges arise. First, there are non-ideal texture areas in the scene, which make feature extraction and matching difficult and reduce the accuracy of positioning and mapping. Second, the limited computing resources of mobile robots prevent the real-time execution of complex algorithms. To address these challenges, this paper proposes an edge computing-based SLAM system that fuses point and line features. The visual odometer of point and line feature fusion solves the problem of insufficient feature extraction in texture sparse areas and incorrect feature matching in texture repetitive areas, thereby improving the accuracy of visual positioning and mapping. The distributed deployment strategy of edge computing enables the algorithm to be executed in real-time on the underground coal mine mobile robot. The experiment demonstrated that using the visual odometer method with ORB-SLAM 2 reduced the absolute trajectory error by 8.87% in dense repetitive texture areas and 9.96% in low texture areas. [ABSTRACT FROM AUTHOR]

With 3D products being widely applied, more attention has focused on studying stereo image super-resolution (SR). Current stereo image SR studies mainly aim to improve the performance by the additional information from a pair of low-resolution stereo images. However, it is challenging for stereo image SR to fully exploit self-similarity information from its own image and parallax information between stereo image pairs. In line with these challenges, this paper presents a Two-Branch Network (TBNet) to integrate self-similarity information and parallax information for SR. In the TBnet, a stereo parallax transfer module with an encoder–decoder structure was first proposed to sufficiently transfer multi-scale parallax information and preserve the stereo consistency between stereo images. This paper further presented a residual pyramid self-attention module to employ self-similarity information to take advantage of self-predictive power. Finally, extensive experiments demonstrate the superiority of our model over the state-of-the-art performance in terms of objective and perceptual quality and the accuracy of disparity estimation. [ABSTRACT FROM AUTHOR]

At present, there are many semantic segmentation algorithms with excellent performance for intelligent driving vehicles, but most of them only work well on scenes with good illumination. In order to solve the problem of scene segmentation under low illumination, this paper proposes a novel semantic segmentation algorithm that combines visible and infrared images. In this algorithm, two parallel encoders are designed as the input of the images, and the decoder divides the fused images output from the encoder. The model is based on ResNet algorithm, and the residual attention module is used in each branch to mine and enhance the spatial features of multilevel channels to extract images information. Experiments are carried out on publicly available thermal infrared and visible datasets. The results show that the algorithm proposed in this paper is superior to the algorithm using only visible images in semantic segmentation of traffic environment. [ABSTRACT FROM AUTHOR]

In this paper, we study the path-planning problem of emergency fire control robots in the nuclear environment. Given the high risk of the atomic environment, the irregularity of spatial shape, and the complex distribution of obstacles, a robot path planning method is proposed based on the combination of Q -learning and BCD raster map decomposition method. It realizes the automatic elimination control of the nuclear-contaminated environment and reduces the exposure risk of manual intervention operation. First, Q -learning, a reinforcement learning model, is used to establish the optimal path between the start and end points of the operation area. Second, the BCD raster map decomposition method is used to realize the global division of the operation area. Then, an improved partition merging method based on the Q -learning optimal path is proposed to complete the job sub-region merging and cover path planning. Finally, the simulation experiment proves that the technique can quickly and stably achieve the global path coverage of the unique operating environment of the nuclear domain. [ABSTRACT FROM AUTHOR]

This paper aims to propose an improved image classification model to reduce the cost of model construction. Aiming at the problem that network training usually requires the support of a large number of labeled samples, an image classification model based on semi-supervised deep learning is proposed, which uses labeled samples to guide the network to learn unlabeled samples. A convolutional neural network model for simultaneous processing of labeled and unlabeled data is constructed. The tagged data is used to train the Softmax classifier and provide the initial K-means clustering center for the untagged data. The nonsubsampling contourlet layer is used to replace the first convolutional layer of the full convolutional neural network to extract multi-scale depth features, and the nonsubsampling contourlet full convolutional neural network is constructed. The network can extract multi-scale information of the images to be classified, and extract more discriminative deep image features. In addition, the parameters of the nonsubsampled contourlet layers are pre-set and do not require network training. The proposed method has higher classification accuracy than the contrast method on polarimetric SAR images using the nonsubsampled contourlet full convolutional neural network. [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 paper, we focus on two tasks: semantic segmentation and target detection in the visual semantic understanding of tennis sports images and we optimize the network structure to achieve a more complete location contour information mining of the target. In detail, we focus on a weakly supervised image semantic segmentation method based on null convolution pixel relations. To address the problem of incomplete pixel-level pseudo-labeling, we introduce a cavity convolution unit with multiple cavity rates and a self-attentive mechanism in the classification model to adaptively enhance the target regions and suppress other irrelevant regions while expanding the perceptual field to generate high-quality pixel-level pseudo-labeling and then train the semantic segmentation model. The final experimental results show that the hierarchical fusion algorithm proposed in this paper significantly outperforms other algorithms, and the overall classification accuracy of the tandem cavity neural network algorithm reaches 81% with good overall classification results. The recognition accuracy of static movements is higher than that of dynamic movements. [ABSTRACT FROM AUTHOR]

To address the issues of blurred details and distortion of color in the images recovered by the original AOD-Net dehazing method, this paper proposes a CPAD-Net dehazing network model based on attention mechanism and dense residual blocks. The network is improved on the basis of AOD-Net, which can reduce the errors arising from the separately determined transmittance and atmospheric light values. A new dense residual block structure is designed to replace the traditional convolution method, which effectively improves the detail processing capability and the representation ability of the network model for image feature information. On this basis, the attention module determines how to learn the weights according to the feature importance of distinct channels and distinct pixels, and then obtain the recovery of images in terms of color and texture. The experiments showed that the dehazing efficiency of our method are richer in texture detail information and more natural in color recovery. Compared with other algorithms, the PSNR and SSIM indexes of our method are considerably superior to those listed algorithms, which definitively demonstrates that the dehazing effect of our method is more effective, and the recovered images are more realistic and natural. [ABSTRACT FROM AUTHOR]

Concrete crack detection is essential for infrastructure safety, and its detection efficiency and accuracy are the key issues. An improved YOLOV5 and three measurement algorithms are proposed in this paper, where the original prediction heads are replaced by Transformer Heads (TH) to expose the prediction potential with one self-attention model. Experiments show that the improved YOLOV5 effectively enhances the detection and classification of concrete cracks, and the Mean Average Precision (MAP) value of all classes increases to 99.5%. The first method is more accurate for small cracks, whilst the average width obtained based on the axial traverse correction method is more exact for large cracks. The crack width obtained from the concrete picture sample is the same as that obtained from the manual detection, with a deviation rate of 0–5.5%. This research demonstrates the recognition and classification of concrete cracks by integrating deep learning and machine vision with high precision and high efficiency. It is helpful for the real-time measurement and analysis of concrete cracks with potential safety hazards in bridges, high-rise buildings, etc. [ABSTRACT FROM AUTHOR]

Currently, the prevalence of various Internet intrusion technologies has brought much challenge to the enterprise management. For many core documents, the information leakage may lead to the loss of secrets of enterprises. Therefore, some core official documents in enterprises are in the format of papers, rather than electronic format. As a consequence, it is of significance to develop automatic information processing techniques for official documents in the format of papers, so as to improve the working efficiency of enterprises. In this paper, a novel business scheduling approach for enterprises via vision sensing-based automatic documental information extraction is proposed. For the first stage, the vision sensing-based optical character recognition (OCR) technique is utilized to extract textual information from official documents in the format of papers. For the second stage, the deep neural network is utilized to output business scheduling results on the basis of digital recognition contents from the first stage. Finally, the experimental simulation is also carried out to verify efficiency of the proposal. [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]

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]

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]

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]

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]

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 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]

Low-resource utilization and high-energy consumption have become progressively protuberant issues in cloud data centers. Virtual Machine (VM) migration is the key objective to resolve this issue. Moreover, extreme VM migration might empower Service-Level Agreement (SLA) violations. Few works are considered for optimizing throughput and energy consumption. An efficient VM migration must consider different parameters like network communication overhead, migration overhead, resource utilization, energy consumption and quality of service which is a multi-objective issue. Hence, in this paper, a Modified Pelican Optimization-based Variable Load Mean Function (MPO-VLMF)-based host overload detection is presented and security enhancement is developed. The main motive of this study is to achieve host overload detection and security enhancement. To obtain host overload detection, the variable load mean function is developed. In this mean function, the weight parameter is selected by considering Modified Pelican Optimization (MPO). The Levy flight (LF) is considered for enhancing the updating process of Pelican Optimization (PO). To enhance the security of the system, the Digital Signature-based Encryption (DSE) is developed. Based on this proposed approach, security and host overload detection are obtained. The proposed technique is implemented and evaluated by performance measures. It is compared with the conventional approaches to justify the performance of the system. [ABSTRACT FROM AUTHOR]

This paper presents the structure and operational principles of high-frequency electronically controllable sinusoidal oscillators employing current-feedback operational amplifiers (CFOAs) with externally accessible compensation node z as active components. The resonant circuits in the oscillators are connected to the terminal z , and for them in the selective network varactor diodes or varicaps as the variable capacitances are utilized, which are controlled by external DC voltage. Based on an analysis of the proposed circuit configurations, analytical expressions for the characteristic equation in a steady-state mode of operation and formulas for the basic electrical parameters have been obtained. In order to verify the functionality of the proposed electronic circuits and the effectiveness of the derived analytical expressions, an experimental and simulation study of sample electronic circuits with commercially available integrated circuit CFOAs AD844 in the frequency range up to about 10 MHz is performed. The obtained results for the studied electronic circuits confirm the theoretical studies and hypotheses, and the comparative analysis shows that the maximum value for the relative error does not exceed 10%, which is acceptable considering the tolerances of the technological parameters of the used passive and active elements. [ABSTRACT FROM AUTHOR]

This research paper presents a 12-bit 8–4-segmented current-steering DAC (digital-to-analog converter) that offers notable advantages, particularly in terms of its reduced power consumption compared to other similar designs. The exceptional performance of our DAC can be attributed to two key factors: the use of a thermometer encoder and an efficient digital input bit segmentation. These features contribute to enhanced precision and reduced power requirements, setting our DAC apart from the existing solutions in the field. These enhancements significantly reduce glitches and improve the accuracy of the DAC's output. DAC dissipates a total power of about 44.95 mW when running at a sample rate of 1.2 GHz with the CMOS technology of 45 nm. While the supply input voltage is held constant at 1.2 V, the DAC's digital input is pulsating in nature in which the ON voltage is 1 V and OFF voltage is 0 V. Also, the DAC has a spurious-free dynamic range (SFDR) of 41.77 dB. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) schemes eliminate the need for intellectual human knowledge of crypto algorithms to facilitate side-channel attacks on security implementations. Side channel attack analysis for circuit-level hardware (VLSI) implementations of symmetric-key block ciphers through artificial machine models are yet to be addressed. The proposed design of block cipher architecture, which is the implementation under attack features, secure adiabatic logic style the Charge Balancing Symmetric Pre-resolve Adiabatic Logic (CBSPAL). This style exhibits uniform power consumption through its inherent circuit arrangement, which makes it robust toward side-channel power attacks. This paper considers several Multilayer Perceptron (MLP) architectures to mount non-profiled side-channel attacks on round 1- PRESENT circuit-level implementation. Supervised learning of the MLP models through training with power waveforms employs binary LSB labeling. An attack on the custom dataset with 50,000 supply power traces collected from the CBSPAL implementation fails to retrieve secret information in any of the 8 bytes. The traditional Correlation Power Attack (CPA) was also not thriving with the proposed implementation. Comparison is made through a similar attack approach on benchmark AES (ASCAD dataset), which distinguishes the secret information throughout all the parameters of interest: normalized accuracy, normalized NMM accuracy and rank plots on byte 3. [ABSTRACT FROM AUTHOR]

In the era of low-power very large-scale integration design, approximate computing is a soaring design paradigm that assures energy-efficient circuit design at the cost of some accuracy. Multiplication is a very important operation of an arithmetic unit and compressor is the inherent part of the multiplier. In this paper, an energy-efficient design of a 4:2 approximate compressor is proposed. The inclusion of the extra leakage control transistors in the logic has led to leakage power reduction, which ultimately reduces the power dissipation in the circuits. The proposed 4:2 compressor is simulated at 10 nm fin-field effect transistor (FinFET) technology. The proposed compressor has been verified for its functionality and robustness. The proposed design shows a power dissipation of 154.48 nW and a delay of 9.16 ps. In comparison to other 4:2 approximate compressors, the proposed compressor shows the least power dissipation, the lowest power delay product (PDP) and the highest robustness. The power dissipation of the proposed compressor is 92.84% less than that of the base compressor. The PDP of the designed compressor is 80.58% less than the minimum PDP among other compressors. The aging analysis for the negative bias temperature instability (NBTI) effect is also checked for the proposed compressor and compared with the existing designs. [ABSTRACT FROM AUTHOR]

This paper presents an innovative general structure for a single-loop Sigma-Delta (Σ Δ) modulator that combines low-distortion and noise-coupled techniques to achieve high-resolution for low-power applications. The low-distortion technique adjusts the signal transfer function of the proposed structure to unity, while the noise-coupled technique enhances the order of quantization noise-shaping at the modulator output. These techniques were incorporated into the design to increase the modulator order without requiring additional operational amplifiers during its circuit implementation, resulting in a low-power modulator compared to similar structures. To reduce the number of required amplifiers, a second-order infinite impulse response (IIR) filter was used in the modulator loop filter, in place of two integrators. To assess the proposed structure's performance, a third-order modulator sample was implemented and simulated for speech recognition applications, specifically, digital hearing aids, using 180 nm complementary metal oxide semiconductor (CMOS) technology. The results indicate that for a third structure with a sampling rate of 64, an input sine signal of −6 dBFS and a sampling frequency of 2.56 MHz, the signal to noise plus distortion (SNDR) was 81.9 dB, and the dynamic range (DR) was 88 dB for a 20 KHz bandwidth. The modulator's power consumption was 126.9 μ W. Circuit and system-level simulations demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper introduces a low-power fully differential fixed window level-crossing analog-to-digital converter (LC-ADC) for wireless medical implantable devices. The LC-ADC could be an excellent candidate for low-power systems due to the reduction of sampling points for bio-potential signals. Different from existing fixed window LC-ADCs, which use a 1-bit DAC or scaler and two reference levels to move the input signal to the comparison window, a simplified scheme is proposed in which the DAC or scaler is removed and a single-reference level is used to create the comparison window. The proposed LC-ADC utilizes a single-reference comparator, which leads to a simplified implementation and significant reduction in power consumption and circuit area. In addition, using a single reference level and removing DAC, leads to a decrease in the complexity of the controlling logic. The proposed LC-ADC is simulated in 0.18 μ m CMOS technology. The simulation results achieve an effective number of bits (ENOB) of up to 6.5 bits with about 59–141 nW power consumption under 0.8 V supply and input signal bandwidth from 5 Hz to 4 kHz. [ABSTRACT FROM AUTHOR]

This paper presents a complementary split-ring resonator (CSRR) loaded coplanar waveguide (CPW) fed with a circular shape, miniaturized diamond slot planar monopole antenna. The proposed antenna for healthcare monitoring biomedical applications uses the industrial medical and scientific band. The antenna design and development to implant the human phantom are proposed. The primary goal of this work is to continuously monitor the patient's ability to detect abnormal conditions as soon as possible as a result of improvements in quality of life. In this case, an antenna design methodology must prioritize features such as miniaturization, increased gain and bandwidth, and biocompatibility. Simulated and measured antenna characteristics for biomedical applications are performed at ISM Band frequency. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is rapidly transforming the healthcare, finance and transportation industries. This paper presents a field-programmable gate array (FPGA)-based neural network accelerator (NNA) design for power allocation in downlink nonorthogonal multiple access (NOMA) networks. The proposed hardware accelerator effectively cuts computational costs while delivering performance on par with the highest sum capacity. Numerical results show that this NNA offers a remarkable computational speed increase of up to 99% compared to the conventional exhaustive search method. Furthermore, the deep learning (DL) model achieved high accuracy (0.92 training, 0.93 testing), and the hardware accelerator design for this DL inference model was implemented on the PYNQ-Z2 board-constrained edge device to predict power allocation coefficients in NOMA systems. [ABSTRACT FROM AUTHOR]

In the domain of process virtual machine (PVM) binary translation, the difference in address space layout between the guest program and the translated program requires the recalculation of jump instruction targets, resulting in suboptimal execution efficiency. This paper presents a novel method called SPC-Indexed Indirect Branch Hardware Cache Redirecting (SPCIC) technique. SPCIC utilizes specialized branch instruction to represent indirect branches from guest programs while frequently-used target addresses are cached in a customized hardware mapping table. When translating an indirect branch, SPCIC queries the jump target cache first to achieve a fast redirection unless the destination address is not cached. Besides, SPCIC merely falls back to the software-based remapping approach when the query fails, improving the translation efficiency to the greatest extent. SPCIC is implemented on the QEMU platform to accelerate the translation of ARM payloads into RISC-V. Experiments are carried on SPEC2006 to demonstrate the effectiveness of SPCIC for reducing the runtime overhead of indirect branch translation. The experimental results indicate up to 11% average improvement and 35% maximum improvement are obtained on the selected benchmark. [ABSTRACT FROM AUTHOR]

Recently, wireless body area network (WBAN) becomes a hot research topic in the advanced healthcare system. The WBAN plays a vital role in monitoring the physiological parameters of the human body with sensors. The sensors are small in size, and it has a small-sized battery with limited life. Hence, the energy is limited in the multi-hop routing process. The patient data is collected by the sensor, and the data are transmitted with high energy consumption. It causes failure in the data transmission path. To avoid this, the data transmission process should be optimized. This paper presents an advanced authentication and energy-efficient routing protocol (AAERP) for optimal routing paths in WBAN. Patients' data are aggregated from the WBAN through the IoMT devices in the initial stage. To secure the patient's private data, a hybrid mechanism of the elliptic curve cryptosystem (ECC) and Paillier cryptosystem is proposed for the data encryption process. Data security is improved by authenticating the data before transmission using an encryption algorithm. Before the routing process, the data encryption approach converts the original plain text data into ciphertext data. This encryption approach assists in avoiding intrusions in the network system. The encrypted data are optimally routed with the help of the teamwork optimization algorithm (TOA) approach. The optimal path selection using this optimization technique improves the effectiveness and robustness of the system. The experimental setup is performed by using Python software. The efficacy of the proposed model is evaluated by solving parameters like network lifetime, network throughput, residual energy, success rate, number of packets received, number of packets sent, and number of packets dropped. The performance of the proposed model is measured by comparing the obtained results with several existing models. [ABSTRACT FROM AUTHOR]

The imperative for lightweighting technologies, paramount in mission-critical cyber-physical systems (CPSs) including aerospace, automotive and allied sectors, hinges upon optimizing energy efficiency and curbing product weight. Honeycomb structures, celebrated for their exceptional strength-to-weight ratio, have indisputably guided the pursuit of lightweight design. This paper expounds upon the versatility of honeycomb structures by scrutinizing their in-plane mechanical attributes. Leveraging finite element simulations and polynomial fitting, we enhance the prevailing equivalent elastic modulus model for uniform honeycomb structures, expanding its domain to encompass a broader spectrum of relative density values. Deliberations ensue concerning the model's constraints and its inapplicability to nonuniform honeycomb structures. The investigation introduces nodes as pivotal influencers in the mechanical comportment of nonuniform honeycomb structures, delineating the nexus between the equivalent elastic modulus and node dimensions through a fusion of finite element simulations and mechanical experimentation. Furthermore, this research delves into the tenets and constructs of density-based variable density methodologies within the ambit of topology optimization, with an overarching goal of maximizing stiffness. We furnish a holistic design protocol for optimizing honeycomb structures, underscored by a pragmatic instantiation of the density-based variable density approach. Scrutinizing the geometric interplay between honeycomb structures and design spaces, we posit an innovative paradigm employing concentric circles to approximate cellular envelopes, streamlining numerical cartography and the conversion of optimization outputs into variable density honeycomb configurations. Evaluation of the in-plane mechanical attributes of variable density honeycomb structures reveals that TPU material augments the resilience of both uniform and variable density honeycomb structures, whereas topology optimization amplifies specific stiffness and resilience modulus in variable density honeycomb structures relative to their uniform counterparts. This study sheds light on the complexities of honeycomb structures, providing valuable insights for their optimization in lightweight applications. [ABSTRACT FROM AUTHOR]

The present prevailing loop closure detection algorithm is mainly applicable for simultaneous localization and mapping (SLAM). Its effectiveness is contingent upon environmental conditions, which can fluctuate due to variations in lighting or the surrounding scenario. Vision-based algorithms, while adept during daylight hours, may falter in nocturnal settings. Conversely, lidar methods hinge on the sparsity of the given scenario. This paper proposes an algorithm that comprehensively utilizes lidar and image features to assign weighted factors for loop closure detection based on multi-modal sensor fusion. First, we use k -means clustering to produce a point cloud spatial global bag of words. Second, an improved deep learning method is used to train feature descriptors of images while scan context is also used to detect candidate point cloud features. After that, different feature-weighted factors are assigned for homologous feature descriptors. Finally, the detection result related to the maximum weight factor is designated to the optimal loop closure. The adaptive weighted loop closure (AWLC) algorithm we proposed inherits the advantages of different loop closure detection algorithms and hence it is accurate and robust. The AWLC method is compared with popular loop detection algorithms in different datasets. Experiments show that the AWLC can maintain the effectiveness and robustness of detection even at night or in highly dynamic complex environment. [ABSTRACT FROM AUTHOR]

In this paper, a complementary split ring resonator (CSRR) loaded compact CPW-fed circular shaped monopole UWB antenna with dual band notch characteristics is designed on an FR-4 substrate. The propounded band-notched UWB antenna is integrated with two frequency selective surface layers referred as bandpass frequency selective surface (FSS-1) and reflector (FSS-2) to offer enhanced radiation performance. Bandpass FSS-1 enhances the gain of the antenna by improving directivity and reflector FSS-2 reflects back the backside radiation of the proposed antenna towards the forward direction thereby increasing gain and directivity of the proposed antenna. Furthermore, a two-element orthogonal MIMO antenna is proposed with improved gain using two FSS layers. The complete configuration of the proposed two-element MIMO antenna consists of CSRR-based radiating patches, monopole ground planes and two integrated FSS layers. The proposed MIMO antenna operates in UWB range from 3.46 GHz to 16.02 GHz with dual notches at 3.9 GHz and 7.2 GHz. Isolation between MIMO elements is greater than 23 dB. Both antennas are fabricated and investigated at 5.9 GHz for vehicular communications. Maximum gain improvements of 8 dB and 6.4 dB are observed in case of the presented single element and MIMO antenna within the entire operating band due to the integration of dual FSS layers. The diversity performance of the suggested MIMO antenna is investigated by analyzing mean effective gain, envelope correlation coefficient, channel capacity loss and diversity gain parameters. The proposed MIMO antenna is a suitable candidate for vehicular and UWB wireless communication applications. [ABSTRACT FROM AUTHOR]

To identify whether the circuit under test is fault-free or faulty, fault diagnosis is conducted in an analog circuit. However, in the case of fault detection (FD) techniques, the size of FD is the main challenge in testing, especially for complex analog circuits. Hence to reduce the size of FD, specific test nodes are chosen to perform fault diagnosis from the available accessible test nodes. Specific test nodes are selected based on the faults classification efficiency of fault diagnosis. Therefore, this paper proposes an approach for single and multiple soft fault diagnosis using minimum test nodes in analog electronic circuits. The proposed double deep Q-learning-based hybrid Simulated annealing–Tabu search (DDQN-Hybrid SATS) technique determines minimum test nodes with the utilization of distance metric for fault classification. The DDQN-Hybrid SATS technique is used to identify minimum nodes for testing. In this, the double deep Q learning network (DDQN) ensures better reliability and faster convergence in learning but suffers from catastrophic forgetting issues. To prevent such issues, the DDQN approach is optimized using a hybrid SATS algorithm. The hybrid optimization of simulated annealing (SA) and Tabu search (TS) coalesce the advantages of individual optimization procedures to provide the optimum solution in a fast and effective way. The results for a fourth-order low pass filter are presented (i.e., first CUT) and an eight-bit digital to analog converter (i.e., second CUT). Moreover, the simulation experiment reveals that the proposed DDQN-Hybrid SATS technique achieves greater overall fault classification accuracy of about 96.25% than other compared techniques with minimum computation time. [ABSTRACT FROM AUTHOR]

This paper presents a background calibration method assisted with noise-shaping, for a 10-b bridged SAR ADC. It proposes calibrating the mismatches from the MSB capacitors using the LSBs. First-order noise-shaping has been employed to facilitate the calibration as well as the analog-to-digital conversion. Since noise-shaping is able to shape both the comparator input and quantization noise, it is expected to improve the calibration effects as well as the SNR, for the SAR ADC. Measurement results show, upon the proposed calibration, the SAR's INL and SNR are improved by 3 LSB and 9 dB, respectively, compared to when the noise shaping is off. Fabricated using a 55 nm CMOS MOMCAP technology and tested with a −2.5 dB sinewave input, the bridged SAR ADC achieves an SNR of 62.2 dB and an INL better than 1 LSB, when consuming a power of 7 2 μ W , at a supply voltage of 1.2 V and sampling frequency of 50 kHz. The ADC's active area is 3 0 0 × 3 8 0 μ m 2 . [ABSTRACT FROM AUTHOR]

Network Intrusion Detection Systems (NIDSs) have been proposed in the literature as security tools for detecting anomalous and intrusive network data traffic. However, the existing NIDS frameworks are computation-intensive, thereby making them unsuitable for deployment in resource-constrained networks with limited computational capabilities. This paper aims to address this issue by proposing computationally efficient NIDS framework for detecting anomalous data traffic in resource-constrained networks. The proposed NIDS framework uses an ensemble-based classifier model comprising multiple classifiers, which enables it to achieve high accuracy and detection rate across a wide range of low-footprint and stealth network attacks. The proposed framework also uses feature scaling and dimensionality reduction techniques to minimize the overall computational overhead. The proposed framework consists of two stages. In the first stage, four distinct base-level classifiers are utilized. The classification probabilities of the first stage are used in the modified meta-level classifier. The modified meta-level classifier is trained on the class probabilities of the base-level classifiers combined using a novel proposed probability function. The performance of the proposed NIDS framework is evaluated on a proprietary testbed dataset and two benchmark datasets namely CICIDS-2017 and UNSW-NB15. The results reveal that the proposed NIDS framework provides better performance than the existing NIDS frameworks in terms of false positive rate, despite using a significantly lower number of input features for its analysis. [ABSTRACT FROM AUTHOR]

A novel non-isolated high-gain DC–DC converter for green energy employment is presented and analyzed. The proposed converter comprises a switched capacitor cell, passive clamp circuit, coupled inductors, and voltage multiplier unit. An interleaved boost converter (IBC) is placed on the input side of the proposed design. The voltage multiplier unit (VMU) with the secondary windings of the coupled inductors is located on the load side. It is used to accomplish interleaved power storage. The leakage energy of coupled inductor is recirculated to the load side, and the reverse recovery problem of diodes is effectively suppressed. As a result, a low-on-state resistance power switch with a low voltage rating is employed and minimizing conduction losses. The presented topology is suitable for sustainable energy applications due to its low operating duty cycle, high voltage conversion ratio, and higher efficiency. The output voltage causes substantially low voltage stress on semiconductor switches and passive elements. This proposed paper deals with simulation, parameter selection, experimental design, results and discussion. The simulation is verified using MATLAB/Simulink with a DC voltage of 30–517 V. To validate the design analysis, a 1.5-kW experimental prototype is designed with a switching frequency of 10 kHz and attained an efficiency of 96.07%. [ABSTRACT FROM AUTHOR]

In recent years, video surveillance has become an integral part of computer vision research, addressing a variety of challenges in security, memory management and content extraction from video sequences. This paper introduces the Robust Object Detection using Fire Hawks Optimizer with Deep Learning (ROD-FHODL) technique, a novel approach designed specifically for video surveillance applications. Combining object detection and classification the proposed technique employs a two-step procedure. Utilizing the power of the Mask Region-based Convolutional Neural Network (Mask-RCNN) for object detection, we optimize its hyperparameters using the Fire Hawks Optimizer (FHO) algorithm to improve its efficacy. Our experimental results on the UCSD dataset demonstrate the significant impact of the proposed work. It achieves an extraordinary RUNNT of 1.34 s on the pedestrian-1 dataset, significantly outperforming existing models. In addition, the proposed system surpasses in accuracy, with a pedestrian-1 accuracy rate of 97.45% and Area Under the Curve (AUC) values of 98.92%. Comparative analysis demonstrates the superiority of the proposed system in True Positive Rate (TPR) versus False Positive Rate (FPR) across thresholds. In conclusion, the proposed system represents a significant advancement in video surveillance, offering advances in speed, precision and robustness that hold promise for enhancing security, traffic management and public space monitoring in smart city infrastructure and other applications. [ABSTRACT FROM AUTHOR]