Brain age estimation:premise,promise,and problems

Premise:The com bined effects of modern healthcare practices which prolong lifespan and declining birthrates have created unprecedented changes in age demographics worldwide that are especially pronounced in Japan,South Korea,Europe,and North America.Since old age is the most significant predictor of dementia,global healthcare systems must rise to the challenge of providing care for those with neurodegenerative disorders.

Hourglass-GCN for 3D Human Pose Estimation Using Skeleton Structure and View Correlation

Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused.Moreover,existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs,making the correlation weights between nodes in the graph and their neighborhood nodes shared.Existing Graph Convolutional Networks(GCNs)cannot extract global and deeplevel skeleton structure information and view correlations efficiently.To solve these problems,pre-estimated multiview 2D poses are designed as a multi-view skeleton graph to fuse skeleton priors and view correlations explicitly to process occlusion problem,with the skeleton-edge and symmetry-edge representing the structure correlations between adjacent joints in each viewof skeleton graph and the view-edge representing the view correlations between the same joints in different views.To make graph convolution operation mine elaborate and sufficient skeleton structure information and view correlations,different correlation weights are assigned to different categories of neighborhood nodes and further assigned to each node in the graph.Based on the graph convolution operation proposed above,a Residual Graph Convolution(RGC)module is designed as the basic module to be combined with the simplified Hourglass architecture to construct the Hourglass-GCN as our 3D pose estimation network.Hourglass-GCNwith a symmetrical and concise architecture processes three scales ofmulti-viewskeleton graphs to extract local-to-global scale and shallow-to-deep level skeleton features efficiently.Experimental results on common large 3D pose dataset Human3.6M and MPI-INF-3DHP show that Hourglass-GCN outperforms some excellent methods in 3D pose estimation accuracy.

Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile networks.These networks are sufficiently scaled to interconnect billions of users and devices.Researchers in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous vehicles.NextG continuously improves its network functionality to support these applications.Multiple input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in conjunctionwithNextG.This article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial attacks.The technique aims to create long-lasting and secure NextG networks using this extended approach.The viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this research.Moreover,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

Model-driven full system dynamics estimation of PMSM-driven chain shell magazine

Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.

Permanent Magnet Temperature Estimation for PMSMs Using Virtual Position-offset Injection

This paper proposes a virtual position-offset injection based permanent magnet temperature estimation approach for permanent magnet synchronous machines(PMSMs). The concept of virtual position-offset injection is mathematically transforming the machine model to a virtual frame with a position-offset. The virtual frame temperature estimation model is derived to calculate the permanent magnet temperature(PMT) directly from the measurements with computation efficiency. The estimation model involves a combined inductance term, which can simplify the establishment of saturation compensation model with less measurements. Moreover, resistance and inverter distorted terms are cancelled in the estimation model, which can improve the robustness to the winding temperature rise and inverter distortion. The proposed approach can achieve simplified computation in temperature estimation and reduced memory usage in saturation compensation. While existing model-based approaches could be affected by either the need of resistance and inverter information or complex saturation compensation. Experiments are conducted on the test machine to verify the proposed approach under various operating conditions.

Deep learning for joint channel estimation and feedback in massive MIMO systems

The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation metrics.In this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised information.For this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the network.Specifically,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd images.In addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density estimation.The experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

Efficient Unsupervised Image Stitching Using Attention Mechanism with Deep Homography Estimation

Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lackingunique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenesseverely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deeplearning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheralcomputing devices. To address these challenges, this study proposes a novel unsupervised image stitching methodbased on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networksand attentionmechanisms. Themethodology is partitioned into three distinct stages. The initial stage combines theattention mechanism with a pooling pyramid model to enhance the detection and recognition of compact objectsin images, the task of the deep homography networks module is to estimate the global homography of the inputimages consideringmultiple viewpoints. The second stage involves preliminary stitching of the masks generated inthe initial stage and further enhancement through weighted computation to eliminate common stitching artifacts.The final stage is characterized by adaptive reconstruction and careful refinement of the initial stitching results.Comprehensive experiments acrossmultiple datasets are executed tometiculously assess the proposed model. Ourmethod’s Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) improved by 10.6%and 6%. These experimental results confirm the efficacy and utility of the presented model in this paper.

Cascaded ELM-Based Joint Frame Synchronization and Channel Estimation over Rician Fading Channel with Hardware Imperfections

Due to the interdependency of frame synchronization(FS)and channel estimation(CE),joint FS and CE(JFSCE)schemes are proposed to enhance their functionalities and therefore boost the overall performance of wireless communication systems.Although traditional JFSCE schemes alleviate the influence between FS and CE,they show deficiencies in dealing with hardware imperfection(HI)and deterministic line-of-sight(LOS)path.To tackle this challenge,we proposed a cascaded ELM-based JFSCE to alleviate the influence of HI in the scenario of the Rician fading channel.Specifically,the conventional JFSCE method is first employed to extract the initial features,and thus forms the non-Neural Network(NN)solutions for FS and CE,respectively.Then,the ELMbased networks,named FS-NET and CE-NET,are cascaded to capture the NN solutions of FS and CE.Simulation and analysis results show that,compared with the conventional JFSCE methods,the proposed cascaded ELM-based JFSCE significantly reduces the error probability of FS and the normalized mean square error(NMSE)of CE,even against the impacts of parameter variations.

High-Precision Doppler Frequency Estimation Based Positioning Using OTFS Modulations by Red and Blue Frequency Shift Discriminator

Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.

Enhancing microseismic/acoustic emission source localization accuracy with an outlier-robust kernel density estimation approach

Monitoring sensors in complex engineering environments often record abnormal data,leading to significant positioning errors.To reduce the influence of abnormal arrival times,we introduce an innovative,outlier-robust localization method that integrates kernel density estimation(KDE)with damping linear correction to enhance the precision of microseismic/acoustic emission(MS/AE)source positioning.Our approach systematically addresses abnormal arrival times through a three-step process:initial location by 4-arrival combinations,elimination of outliers based on three-dimensional KDE,and refinement using a linear correction with an adaptive damping factor.We validate our method through lead-breaking experiments,demonstrating over a 23%improvement in positioning accuracy with a maximum error of 9.12 mm(relative error of 15.80%)—outperforming 4 existing methods.Simulations under various system errors,outlier scales,and ratios substantiate our method’s superior performance.Field blasting experiments also confirm the practical applicability,with an average positioning error of 11.71 m(relative error of 7.59%),compared to 23.56,66.09,16.95,and 28.52 m for other methods.This research is significant as it enhances the robustness of MS/AE source localization when confronted with data anomalies.It also provides a practical solution for real-world engineering and safety monitoring applications.

Side Information-Based Stealthy False Data Injection Attacks Against Multi-Sensor Remote Estimation

Dear Editor,This letter investigates a novel stealthy false data injection(FDI)attack scheme based on side information to deteriorate the multi-sensor estimation performance of cyber-physical systems(CPSs).Compared with most existing works depending on the full system knowledge,this attack scheme is only related to attackers'sensor and physical process model.The design principle of the attack signal is derived to diverge the system estimation performance.Next,it is proven that the proposed attack scheme can successfully bypass the residual-based detector.Finally,all theoretical results are verified by numerical simulation.

Protocol-Based Non-Fragile State Estimation for Delayed Recurrent Neural Networks Subject to Replay Attacks

Dear Editor, This letter focuses on the protocol-based non-fragile state estimation problem for a class of recurrent neural networks(RNNs). With the development of communication technology, the networked systems have received particular attentions. The networked system brings advantages such as easy to implement.

State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.

Double-check in Lizard Age Estimation:Use of Phalanx Bone and Keratin Claw Sheath Lamellas

Dear Editor,The problem of age estimation in amphibians and reptiles with annual fluctuations of growth pattern has been considered to be mostly solved since the skeletochronological method was introduced(Kleinenberg and Smirina,1969).This method is based on counting the number of lines of arrested growth(LAGs)—cyclical growth marks that are usually formed annually and characterized by different optical aspects within the tubular bones.

Non-cooperative Space Target Estimation Algorithm Without Prior Information Dependence Based on Temporal Line of Sight Constraint

Under single-satellite observation,the parameter estimation of the boost phase of high-precision space noncooperative targets requires prior information.To improve the accuracy without prior information,we propose a parameter estimation model of the boost phase based on trajectory plane parametric cutting.The use of the plane passing through the geo-center and the cutting sequence line of sight(LOS)generates the trajectory-cutting plane.With the coefficient of the trajectory cutting plane directly used as the parameter to be estimated,a motion parameter estimation model in space non-cooperative targets is established,and the Gauss-Newton iteration method is used to solve the flight parameters.The experimental results show that the estimation algorithm proposed in this paper weakly relies on prior information and has higher estimation accuracy,providing a practical new idea and method for the parameter estimation of space non-cooperative targets under single-satellite warning.

Report for Type 2 Bayes-Fuzzy Estimation in No-Data Problem

It is well known that the system (1 + 1) can be unequal to 2, because this system has both observation error and system error. Furthermore, we must provide our mustered service within our cool head and warm heart, where two states of nature are existing upon us. Any system is regarded as the two-dimensional variable error model. On the other hand, we consider that the fuzziness is existing in this system. Though we can usually obtain the fuzzy number from the possibility theory, it is not fuzzy but possibility, because the possibility function is as same as the likelihood function, and we can obtain the possibility measure by the maximal likelihood method (i.e. max product method proposed by Dr. Hideo Tanaka). Therefore, Fuzzy is regarded as the only one case according to Vague, which has both some state of nature in this world and another state of nature in the other world. Here, we can consider that Type 1 Vague Event in other world can be obtained by mapping and translating from Type 1 fuzzy Event in this world. We named this estimation as Type 1 Bayes-Fuzzy Estimation. When the Vague Events were abnormal (ex. under War), we need to consider that another world could exist around other world. In this case, we call it Type 2 Bayes-Fuzzy Estimation. Where Hori et al. constructed the stochastic different equation upon Type 1 Vague Events, along with the general following probabilistic introduction method from the single regression model, multi-regression model, AR model, Markov (decision) process, to the stochastic different equation. Furthermore, we showed that the system theory approach is Possibility Markov Process, and that the making decision approach is Sequential Bayes Estimation, too. After all, Type 1 Bays-Fuzzy estimation is the special case in Bayes estimation, because the pareto solutions can exist in two stochastic different equations upon Type 2 Vague Events, after we ignore one equation each other (note that this is Type 1 case), we can obtain both its system solution and its decision solution. Here, it is noted that Type 2 Vague estimation can be applied to the shallow abnormal decision problem with possibility reserved judgement. However, it is very important problem that we can have no idea for possibility reserved judgement under the deepest abnormal envelopment (ex. under War). Expect for this deepest abnormal decision problem, Bayes estimation can completely cover fuzzy estimation. In this paper, we explain our flowing study and further research object forward to this deepest abnormal decision problem.

Possible effects of selecting different station distributions in the optimal sequence estimation method

Since the inception of the optimal sequence estimation (OSE) method,various research teams have substantiated its efficacy as the optimal stacking technique for handling array data,leading to its successful application in numerous geoscience studies.Nevertheless,concerns persist regarding the potential impact of aliasing resulting from the choice of distinct station distributions on the outcomes derived from OSE.In this investigation,I employ theoretical deduction and experimental analysis to elucidate the reasons behind the immunity of the Y_(l'm')-related common signal obtained through OSE to variations in station distribution selection.The primary objective of OSE is also underscored,i.e.,to restore/strip a Y_(l'm')-related common periodic signal from various stations.Furthermore,I provide additional clarification that the‘Y_(l'm')-related common signal’and the‘Y_(l'm')-related equivalent excitation sequence’are distinct concepts.These analyses will facilitate the utilization of the OSE technique by other researchers in investigating intriguing geophysical phenomena and attaining sound explanations.

RE-SMOTE:A Novel Imbalanced Sampling Method Based on SMOTE with Radius Estimation

Imbalance is a distinctive feature of many datasets,and how to make the dataset balanced become a hot topic in the machine learning field.The Synthetic Minority Oversampling Technique(SMOTE)is the classical method to solve this problem.Although much research has been conducted on SMOTE,there is still the problem of synthetic sample singularity.To solve the issues of class imbalance and diversity of generated samples,this paper proposes a hybrid resampling method for binary imbalanced data sets,RE-SMOTE,which is designed based on the improvements of two oversampling methods parameter-free SMOTE(PF-SMOTE)and SMOTE-Weighted Ensemble Nearest Neighbor(SMOTE-WENN).Initially,minority class samples are divided into safe and boundary minority categories.Boundary minority samples are regenerated through linear interpolation with the nearest majority class samples.In contrast,safe minority samples are randomly generated within a circular range centered on the initial safe minority samples with a radius determined by the distance to the nearest majority class samples.Furthermore,we use Weighted Edited Nearest Neighbor(WENN)and relative density methods to clean the generated samples and remove the low-quality samples.Relative density is calculated based on the ratio of majority to minority samples among the reverse k-nearest neighbor samples.To verify the effectiveness and robustness of the proposed model,we conducted a comprehensive experimental study on 40 datasets selected from real applications.The experimental results show the superiority of radius estimation-SMOTE(RE-SMOTE)over other state-of-the-art methods.Code is available at:https://github.com/blue9792/RE-SMOTE(accessed on 30 September 2024).