Maximum Correntropy Criterion-Based UKF for Loosely Coupling INS and UWB in Indoor Localization

Indoor positioning is a key technology in today’s intelligent environments,and it plays a crucial role in many application areas.This paper proposed an unscented Kalman filter(UKF)based on the maximum correntropy criterion(MCC)instead of the minimummean square error criterion(MMSE).This innovative approach is applied to the loose coupling of the Inertial Navigation System(INS)and Ultra-Wideband(UWB).By introducing the maximum correntropy criterion,the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise,thus enhancing its adaptability to diverse environmental localization requirements.Particularly in the presence of non-Gaussian noise,especially heavy-tailed noise,the MCCUKF exhibits superior accuracy and robustness compared to the traditional UKF.The method initially generates an estimate of the predicted state and covariance matrix through the unscented transform(UT)and then recharacterizes the measurement information using a nonlinear regression method at the cost of theMCC.Subsequently,the state and covariance matrices of the filter are updated by employing the unscented transformation on the measurement equations.Moreover,to mitigate the influence of non-line-of-sight(NLOS)errors positioning accuracy,this paper proposes a k-medoid clustering algorithm based on bisection k-means(Bikmeans).This algorithm preprocesses the UWB distance measurements to yield a more precise position estimation.Simulation results demonstrate that MCCUKF is robust to the uncertainty of UWB and realizes stable integration of INS and UWB systems.

Semi-supervised surface defect detection of wind turbine blades with YOLOv4

Timely inspection of defects on the surfaces of wind turbine blades can effectively prevent unpredictable accidents.To this end,this study proposes a semi-supervised object-detection network based on You Only Looking Once version 4(YOLOv4).A semi-supervised structure comprising a generative adversarial network(GAN)was designed to overcome the difficulty in obtaining sufficient samples and sample labeling.In a GAN,the generator is realized by an encoder-decoder network,where the backbone of the encoder is YOLOv4 and the decoder comprises inverse convolutional layers.Partial features from the generator are passed to the defect detection network.Deploying several unlabeled images can significantly improve the generalization and recognition capabilities of defect-detection models.The small-scale object detection capacity of the network can be improved by enhancing essential features in the feature map by adding the concurrent spatial and channel squeeze and excitation(scSE)attention module to the three parts of the YOLOv4 network.A balancing improvement was made to the loss function of YOLOv4 to overcome the imbalance problem of the defective species.The results for both the single-and multi-category defect datasets show that the improved model can make good use of the features of the unlabeled images.The accuracy of wind turbine blade defect detection also has a significant advantage over classical object detection algorithms,including faster R-CNN and DETR.

Time and Space Efficient Multi-Model Convolution Vision Transformer for Tomato Disease Detection from Leaf Images with Varied Backgrounds

A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20%of the total consumption.An increase of 3.3%in consumption is predicted from 2024 to 2032.Tomatoes are also rich in iron,potassium,antioxidant lycopene,vitamins A,C and K which are important for preventing cancer,and maintaining blood pressure and glucose levels.Thus,tomatoes are globally important due to their widespread usage and nutritional value.To face the high demand for tomatoes,it is mandatory to investigate the causes of crop loss and minimize them.Diseases are one of the major causes that adversely affect crop yield and degrade the quality of the tomato fruit.This leads to financial losses and affects the livelihood of farmers.Therefore,automatic disease detection at any stage of the tomato plant is a critical issue.Deep learning models introduced in the literature show promising results,but the models are difficult to implement on handheld devices such as mobile phones due to high computational costs and a large number of parameters.Also,most of the models proposed so far work efficiently for images with plain backgrounds where a clear demarcation exists between the background and leaf region.Moreover,the existing techniques lack in recognizing multiple diseases on the same leaf.To address these concerns,we introduce a customized deep learning-based convolution vision transformer model.Themodel achieves an accuracy of 93.51%for classifying tomato leaf images with plain as well as complex backgrounds into 13 categories.It requires a space storage of merely 5.8 MB which is 98.93%,98.33%,and 92.64%less than stateof-the-art visual geometry group,vision transformers,and convolution vision transformermodels,respectively.Its training time of 44 min is 51.12%,74.12%,and 57.7%lower than the above-mentioned models.Thus,it can be deployed on(Internet of Things)IoT-enabled devices,drones,or mobile devices to assist farmers in the real-time monitoring of tomato crops.The periodicmonitoring promotes timely action to prevent the spread of diseases and reduce crop loss.

A Robust Indoor Localization Algorithm Based on Polynomial Fitting and Gaussian Mixed Model

Wireless sensor network(WSN)positioning has a good effect on indoor positioning,so it has received extensive attention in the field of positioning.Non-line-of sight(NLOS)is a primary challenge in indoor complex environment.In this paper,a robust localization algorithm based on Gaussian mixture model and fitting polynomial is proposed to solve the problem of NLOS error.Firstly,fitting polynomials are used to predict the measured values.The residuals of predicted and measured values are clustered by Gaussian mixture model(GMM).The LOS probability and NLOS probability are calculated according to the clustering centers.The measured values are filtered by Kalman filter(KF),variable parameter unscented Kalman filter(VPUKF)and variable parameter particle filter(VPPF)in turn.The distance value processed by KF and VPUKF and the distance value processed by KF,VPUKF and VPPF are combined according to probability.Finally,the maximum likelihood method is used to calculate the position coordinate estimation.Through simulation comparison,the proposed algorithm has better positioning accuracy than several comparison algorithms in this paper.And it shows strong robustness in strong NLOS environment.

Computational Investigation of Multiband EMNZ Metamaterial Absorber for Terahertz Applications

This study presents an Epsilon Mu near-zero(EMNZ)nanostructured metamaterial absorber(NMMA)for visible regime applications.The resonator and dielectric layers are made of tungsten(W)and quartz(fused),where the working band is expanded by changing the resonator layer’s design.Due to perfect impedance matching with plasmonic resonance characteristics,the proposed NMMA structure is achieved an excellent absorption of 99.99%at 571 THz,99.50%at 488.26 THz,and 99.32%at 598 THz frequencies.The absorption mechanism is demonstrated by the theory of impedance,electric field,and power loss density distributions,respectively.The geometric parameters are explored and analyzed to show the structure’s performance,and a near-field pattern is used to explain the absorption mechanism at the resonance frequency point.The numerical analysis method describes that the proposed structure exhibited more than 80%absorbability between 550 and 900 THz.The Computer Simulation Technology(CST Microwave Studio 2019)software is used to design the proposed structure.Furthermore,CSTHFSS interference is validated by the simulation data with the help of the finite element method(FEM).The proposed NMMA structure is also exhibits glucose concentration sensing capability as applications.So the proposed broadband absorber may have a potential application in THz sensing,imaging(MRI,thermal,color),solar energy harvesting,light modulators,and optoelectronic devices.

Bilateral Coupled Epsilon Negative Metamaterial for Dual Band Wireless Communications

This work presents a dual band epsilon negative(ENG)metamaterial with a bilateral coupled split ring resonator(SRR)for use in C and X band wireless communication systems.The traditional split-ring resonator(SRR)has been amended with this engineered structure.The proposed metamaterial unit cell is realized on the 1.6 mm thick FR-4 printed media with a dimension of 10×10 mm2.The resonating patch built with a square split outer ring.Two interlinked inner rings are coupled vertically to the outer ring to extend its electrical length as well as to tune the resonance frequency.Numerical simulation is performed using CST studio suite 2019 to design and performance analysis.The transmission coefficient(S21)of the proposed unit cell and array configuration exhibits two resonances at 6.7 and 10.5 GHz with wide bandwidth extended from 4.86 to 8.06 GHz and 10.1 to 11.2 GHz,respectively.Negative permittivity is noted at frequencies between 6.76–9.5 GHz and 10.5–12 GHz,consecutively,with near-zero refractive index and permeability.The optimal EMR value depicts the compactness of the proposed structure.The 1×2,2×2 and 4×4 arrays are analyzed that shows similar response compared to the unit cell.Measured results of the 2×2 array shows the close similarity of S21 response as compared to simulation.The observed properties of the proposed unit cell ascertain its suitability for wireless communications by enhancing the gain and directivity of the antenna system.

Improving the intensity and efficiency of compressed echo in rare-earth-ion-doped crystal

We investigate the intensity and efficiency of a compressed echo, which is important in arbitrary waveform generation（AWG）. A new model of compressed echo is proposed based on the optical Bloch equations, which exposes much more detailed parameters than the conventional model, such as the time delay of the chirp lasers, the nature of the rare-earth-iondoped crystal, etc. According to the novel model of compressed echo, we find that reducing the time delay of the chirp lasers and scanning the lasers around the center frequency of the inhomogeneously broadened spectrum, while utilizing a crystal with larger coherence time and excitation lifetime can improve the compressed echo＇s intensity and efficiency. The theoretical analysis is validated by numerical simulations.

The Study and Application of the IoT in Pet Systems

The interaction between human and physical devices and devices in the real world is gaining more attention, and requires a natural and intuitive methodology to employ. According to this idea and living well, life has been a growing demand. Thus, how to raise pets in an easy way has been the main issue recently. This study examines the ability of computation, communication, and control technologies to improve human interaction with pets by the technology of the Internet of Things. This work addresses the improvement through the pet application of the ability of location-awareness, and to help the pet owners raise their pet on the activity and eating control easily. Extensive experiment results demonstrate that our proposed system performs significantly help on the kidney disease and reduce the symptoms. Our study not only presents the key improvement of the pet monitor system involved in the ideas of the Internet of Things, but also meets the demands of pet owners, who are out for works without any trouble.

Distributed Resource Allocation in Dispersed Computing Environment Based on UAV Track Inspection in Urban Rail Transit

With the rapid development of urban rail transit,the existing track detection has some problems such as low efficiency and insufficient detection coverage,so an intelligent and automatic track detectionmethod based onUAV is urgently needed to avoid major safety accidents.At the same time,the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices.As a result,the Dispersed Computing(DCOMP)architecture enables collaborative computing between devices in the Internet of Everything(IoE),promotes low-latency and efficient cross-wide applications,and meets users’growing needs for computing performance and service quality.This paper focuses on examining the resource allocation challenge within a dispersed computing environment that utilizes UAV inspection tracks.Furthermore,the system takes into account both resource constraints and computational constraints and transforms the optimization problem into an energy minimization problem with computational constraints.The Markov Decision Process(MDP)model is employed to capture the connection between the dispersed computing resource allocation strategy and the system environment.Subsequently,a method based on Double Deep Q-Network(DDQN)is introduced to derive the optimal policy.Simultaneously,an experience replay mechanism is implemented to tackle the issue of increasing dimensionality.The experimental simulations validate the efficacy of the method across various scenarios.

Image Steganography by Pixel-Value Differencing Using General Quantization Ranges

A new steganographic method by pixel-value differencing(PVD)using general quantization ranges of pixel pairs’difference values is proposed.The objective of this method is to provide a data embedding technique with a range table with range widths not limited to powers of 2,extending PVD-based methods to enhance their flexibility and data-embedding rates without changing their capabilities to resist security attacks.Specifically,the conventional PVD technique partitions a grayscale image into 1×2 non-overlapping blocks.The entire range[0,255]of all possible absolute values of the pixel pairs’grayscale differences in the blocks is divided into multiple quantization ranges.The width of each quantization range is a power of two to facilitate the direct embedding of the bit information with high embedding rates.Without using power-of-two range widths,the embedding rates can drop using conventional embedding techniques.In contrast,the proposed method uses general quantization range widths,and a multiple-based number conversion mechanism is employed skillfully to implement the use of nonpower-of-two range widths,with each pixel pair being employed to embed a digit in the multiple-based number.All the message bits are converted into a big multiple-based number whose digits can be embedded into the pixel pairs with a higher embedding rate.Good experimental results showed the feasibility of the proposed method and its resistance to security attacks.In addition,implementation examples are provided,where the proposed method adopts non-power-of-two range widths and employsmultiple-based number conversion to expand the data-hiding and steganalysis-resisting capabilities of other PVD methods.

Single-Layer Wideband Circularly Polarized Antenna Using Non-Uniform Metasurface for C-band Applications

A single-layer design of non-uniform metasurface(MS)based circularly polarized(CP)antenna with wideband operation characteristic is proposed and investigated in this paper.The antenna is excited by a truncated corner squared patch as a primary radiating CP source.Then,a non-uniform MS is placed in the same layer with the driven patch.Besides increasing the impedance bandwidth,the non-uniform MS also generates two additional CP bands in the high frequency band,leading to significantly increase the antenna’s overall performances.The use of non-uniform MS distinguishes our design from the other CP MS based antennas in literature,in which the MS is formed by multiple uniform unit cells and placed in different layer with the radiating element.For validation,an antenna prototype,whose overall dimensions of 0.94λ_(o)×0.94λ_(o)×0.06λ_(o) at the center operating frequency,is fabricated and experimentally tested.The measured operating bandwidth with|S11|≤−10 dB and AR≤3 dB is 27.1%(5.1–6.7 GHz)and the broadside gain within this band is from 5.7 to 7.2 dBic.Compared to the other related works,the proposed antenna has advantage of very wideband operation with single-layer design.

Investigation of three-pulse photon echo in thick crystal using finite-difference time-domain method

This paper investigates the phenomenon of three-pulse photon echo in thick rare-earth ions doped crystal whose thickness is far larger than 0.002 cm which is adopted in previous works.The influence of thickness on the three-pulse photon echo＇s amplitude and efficiency is analyzed with the Maxwell-Bloch equations solved by finite-difference timedomain method.We demonstrate that the amplitude of three-pulse echo will increase with the increasing of thickness and the optimum thickness to generate three-pulse photon echo is 0.3 cm for Tm^（3＋）：YAG when the attenuation of the input pulse is taken into account.Meanwhile,we find the expression 0.09 exp（α＇L）,which is previously employed to describe the relationship between echo＇s efficiency and thickness,should be modified as 1.3 · 0.09 exp（2.4 ·α＇L） with the propagation of echo considered.

Polarization Insensitive Broadband Zero Indexed Nano-Meta Absorber for Optical Region Applications

Broadband response metamaterial absorber(MMA)remains a challenge among researchers.A nanostructured new zero-indexed metamaterial(ZIM)absorber is presented in this study,constructed with a hexagonal shape resonator for optical region applications.The design consists of a resonator and dielectric layers made with tungsten and quartz(Fused).The proposed absorbent exhibits average absorption of more than 0.8972(89.72%)within the visible wavelength of 450–600 nm and nearly perfect absorption of 0.99(99%)at 461.61 nm.Based on computational analysis,the proposed absorber can be characterized as ZIM.The developments of ZIM absorbers have demonstrated plasmonic resonance characteristics and a perfect impedance match.The incidence obliquity in typically the range of 0◦–90◦both in TE and TM mode with maximum absorbance is more than 0.8972(∼89.72%),and up to 45◦angular stability is suitable for solar cell applications,like exploiting solar energy.The proposed structure prototype is designed and simulated by studying microwave technology numerical computer simulation(CST)tools.The finite integration technique(FIT)based simulator CST and finite element method(FEM)based simulator HFSS also helps validate the numerical data of the proposed ZIM absorber.The proposed MMA design is appropriate for substantial absorption,wide-angle stability,absolute invisible layers,magnetic resonance imaging(MRI),color images,and thermal imaging applications.

CONSTRUCTION OF THE ENCRYPTION MATRIX BASED ON CHEBYSHEV CHAOTIC NEURAL NETWORKS

The paper proposes a novel algorithm to get the encryption matrix. Firstly, a chaotic sequence generated by Chebyshev chaotic neural networks is converted into a series of low-order integer matrices from which available encryption matrices are selected. Then, a higher order encryption matrix relating real world application is constructed by means of tensor production method based on selected encryption matrices. The results show that the proposed algorithm can produce a "one-time pad cipher" encryption matrix with high security; and the encryption results have good chaos and auto-correlation with the natural frequency of the plaintext being hidden and homogenized.

Remarks on the Complexity of Signed k-Domination on Graphs

This paper is motivated by the concept of the signed k-domination problem and dedicated to the complexity of the problem on graphs. For any fixed nonnegative integer k, we show that the signed k-domination problem is NP-complete for doubly chordal graphs. For strongly chordal graphs and distance-hereditary graphs, we show that the signed k-domination problem can be solved in polynomial time. We also show that the problem is linear-time solvable for trees, interval graphs, and chordal comparability graphs.

An FPGA-Based HOG Accelerator with HW/SW Co-Design for Human Detection and Its Application to Crowd Density Estimation

Human detection is important in many applications and has attracted significant attention over the last decade. The Histograms of Oriented Gradients (HOG) as effective local descriptors are used with binary sliding window mechanism to achieve good detection performance. However, the computation of HOG under such framework is about billion times and the pure software implementation for HOG computation is hard to meet the real-time requirement. This study proposes a hardware architecture called One-HOG accelerator operated on FPGA of Xilinx Spartan-6 LX-150T that provides an efficient way to compute HOG such that an embedded real-time platform of HW/SW co-design for application to crowd estimation and analysis is achieved. The One-HOG accelerator mainly consists of gradient module and histogram module. The gradient module is for computing gradient magnitude and orientation;histogram module is for generating a 36-D HOG feature vector. In addition to hardware realization, a new method called Histograms-of-Oriented-Gradients AdaBoost Long-Feature-Vector (HOG-AdaBoost-LFV) human classifier is proposed to significantly decrease the number of times to compute the HOG without sacrificing detection performance. The experiment results from three static image and four video datasets demonstrate that the proposed SW/HW (software/hardware) co-design system is 13.14 times faster than the pure software computation of Dalal algorithm.

Tapered optical fiber DNA biosensor for detecting Leptospira DNA

Objective: To establish a DNA detection platform based on a tapered optical fiber to detect Leptospira DNA by targeting the leptospiral secY gene.Methods: The biosensor works on the principle of light propagating in the special geometry of the optical fiber tapered from a waist diameter of 125 to 12 μm. The fiber surface was functionalized through a cascade of chemical treatments and the immobilization of a DNA capture probe targeting the secY gene. The presence of the target DNA was determined from the wavelength shift in the optical transmission spectrum.Results: The biosensor demonstrated good sensitivity, detecting Leptospira DNA at 0.001 ng/μL, and was selective for Leptospira DNA without cross-reactivity with non-leptospiral microorganisms. The biosensor specifically detected DNA that was specifically amplified through the loop-mediated isothermal amplification approach.Conclusions: These findings warrant the potential of this platform to be developed as a novel alternative approach to diagnose leptospirosis.

Optimal Resource Allocation for NOMA Wireless Networks

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user accesses.Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user signals.Although its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power domain.In contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum utilization.However,it will increase the complexity of its receiver.Different power allocation techniques will have a direct impact on the system’s throughput.As a result,in order to boost the system capacity,an efficient power allocation mechanism must be investigated.This research developed an efficient technique based on conjugate gradient to solve the problem of downlink power distribution.The major goal is to maximize the users’maximum weighted sum rate.The suggested algorithm’s most notable feature is that it converges to the global optimal solution.When compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

Seeker optimization algorithm:a novel stochastic search algorithm for global numerical optimization

A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.