Performance Improvement of Device-Free Localization Using Bayesian Optimization

For interior tomography based on truncated Hilbert transform (THT), the recently proposed truncated singular value decomposition (TSVD) reconstruction method uses a regularizati on parameter given directly. In this paper, a method of choosing the regularization parameter is presented based on L-curve to get an optimal regularization parameter in theoretical sense. Furthermore, we develop a Tikhonov regula rization method in comparison to TSVD. Simulation results indicate that both of the two regularization methods with the optimal regularization parameters have good performances on the image quality for both cases of noise-free and noisy projections. Keywords: Interior Tomography, Truncated Hilbert Transform, L-curve, Truncated Singular Value Decomposition, Tikhonov Regularization 1. INTRODUCTION It had been believed that the solution to the interior tomography is not unique fo r a long time. Recently, inspired by the concept of the differential backprojection (DBP) proposed by Noo et al [1] and Pan et al [2], a novel solution for the interior problem was proposed with numerical results demonstrat ed that the interior problem can be uniquely and stably solved if a sub-region inside a region of interest (ROI) is known [3]-[5]. Similar results were also independently proposed by other researchers [6]-[8]. In these papers, image reconstruction was reduced to the inversion of THT via the projection onto convex set (POCS) method, which is iterativ e and computationally expensive. To over above limitations, TSVD as a regularization method has been proposed for inversing the THT, which runs two orders of magnitude faster than the iterative method [9]. However the regularization parame ter in this method is given directly without theoretical analysis. The selection of the regularization parameter that controls the trade-off between fidelity to the data and constraint on the reconstruction result is a primary problem in the regularization method. In this paper, we present a method of selecting the optimal regularization based on the L-curve for TSVD method for solving the interior tomography based on truncated Hilbert transform. In this method, the optimal regularization parameter is computed theoretically using the maximum curvature of the L-curve. Moreover, we develop a Tikhonov regularization method in comparison to TSVD. Root Mean Sq uare Error (RMSE) of reconstr ucted image with respect to different regularization parameters validates our selecting regularization parameter method. The paper is organized as follows: section 2 gives Mathematical formulation, TSVD, Tikhonov regularization, the L-curve. Section 3 exhibits the simulation results. Section 4 is conclusion.

Device-free localization (DFL), which can detect and locate a person by measuring the changes in received signals, is one of the primary techniques in wireless sensor networks. Recently, research on fingerprint-based localization in changing environments has been receiving increasing attention. However, when the environment changes due to furniture or other objects are moved, there is still much room for localization accuracy improvement in fingerprint-based DFL. In this paper, we propose a novel DFL algorithm for changing environments: this algorithm features an enhanced channel-selection method and adopts the logistic regression classifier to improve the localization accuracy. The proposed frequency channel-selection method selects two correlated channels with higher Pearson correlation coefficient both in the training and testing procedures, which would be more robust to the environmental change. Meanwhile, the logistic regression classifier could counteract the negative influence on the localization accuracy, without the need for rebuilding the database in fingerprint-based DFL. Experimental results demonstrate that the logistic regression classifier has the lowest error rate among three related methods (k-nearest neighbours classifier, linear discriminant analysis classifier, and random forests classifier). In addition, the localization accuracy has been further improved by the proposed DFL algorithm than by the other state-of-the-art fingerprint-based methods.

An enhanced RSS-distance-angle weighted geometric filter for device-free localization

Device-free localization (DFL) based on wireless sensor networks (WSNs) is a technology that can detect and locate a person by measuring the changes in received signals without the need for any wireless devices. As an emerging important technology in WSNs, radio tomographic imaging (RTI) has received increasing attention. However, there is much room to improve localization accuracy in RTI. To address this issue, an enhanced channel-selection method and a new distance-based elliptical model are proposed to improve the localization accuracy. The enhanced frequency channel-selection method selects two channels with the lowest received signal strength (RSS) variances to collect data. This approach is more robust to environmental change. The new distance-based elliptical model is based on the distance between the voxels and sensors. Meanwhile, the communication links are divided into line-of-sight (LOS) paths and nonline-of-sight (NLOS) paths. Experimental results demonstrate that the proposed algorithm improves the accuracy of positioning by up to 44.8% over some state-of-the-art RTI methods with low cost.

Accurate endoscopic camera pose estimation is crucial for real-time AR navigation systems. While current methods primarily use depth and optical flow, they often ignore structural inconsistencies between images. Leveraging the RAFT framework, we process sequential stereo RGB pairs to extract optical flow and depth features for pose estimation. To address structural inconsistencies, we refine the weights for both 2D and 3D residuals by computing SSIM indices for the left and right views, as well as pre- and post-optical flow transformations. The SSIM metric is also used in the loss function. Experiments on the StereoMIS dataset demonstrate our method's improved pose estimation accuracy compared to rigid SLAM methods, showing a lower accumulated trajectory error (ATE-RMSE: 18.5mm). Additionally, ablation experiments achieved an 11.49% reduction in average error. The pose estimation accuracy has been improved by incorporating SSIM. The code is available at: https://github.com/lianrq/pose-estimation-by-SSIM-Integration.

A novel surface heat flux calibration method applicable to nonlinear inverse heat conduction problems and a new strategy for obtaining an optimal regularization parameter independent of the regularization technique are presented herein. For the proposed nonlinear calibration integral equation, quasi-linearization of the nonlinear heat equation is achieved through both time domain and heat flux magnitude rescaling based on the local temperature measurement. The inverse heat conduction problem is then resolved in terms of rescaled variables through a calibration framework. The proposed calibration equation has a form of the Volterra equation of the first kind that relates the rescaled net unknown heat flux to the rescaled net calibration heat flux and their corresponding rescaled in-depth temperature measurements during both calibration and reconstruction tests. Because the functional form of a Volterra integral equation of the first kind is ill-posed, three different regularization methods are considered and compared. The three methods consist of 1) local future time method, 2) singular-value-decomposition-based regularization, and 3) Tikhonov regularization. A new strategy is proposed to determine the corresponding optimal regularization parameters. This strategy uses a Gaussian filter for estimating the variance in a group of predictions. The best regularization parameters are obtained by balancing the weighted bias and variance. Encouraging numerical results are observed independent of applied regularization approaches in presence of a significant noise, verifying the nonlinear calibration method and the merit of the new regularization parameter strategy.

Many visual quality metrics are used to measure the quality of lossy compressed images and videos, and are integrated in the rate-distortion optimization of hybrid video codecs. However, most of the metrics focus on the average objective quality in a picture. In certain applications, like medical image processing, the maximum absolute error should be more weighted. In this paper, the rate-distortion optimization of HEVC is extended by integrating this error metric. Thus, rate, average error, and maximum absolute error are jointly optimized. Furthermore, a weighting factor α is included into the calculation of the optimization for balancing the ratio between average and maximum absolute error. For HEVC intra with $\alpha =0.25$ an average maximum absolute error reduction of −25.63 can be achieved, while the bitrate increases slightly by 0.59%. Furthermore, the visual quality of the medical volumes improves and the data fidelity increases, i.e. less block artifacts appear and less structure disappear.

Cooperative autonomous driving in 5G and smart cities environment is expected to further improve safety, security and efficiency of transportation systems. To this end, involved vehicles is imperative to have accurate knowledge of both their own and neighboring vehicles’ location, a task known as cooperative awareness. In this paper, we have formulated two novel distributed localization and tracking schemes, based on Gradient Descent and Extended Kalman Filter algorithms, to cope with erroneous GPS location. Sensor-rich vehicles exploit Vehicle-to-Vehicle communications and a multitude of integrated sensors, like LIDAR and Cameras, to generate and fuse heterogeneous data. Each vehicle interacts only with its own connected neighboring vehicles, formulating individual star topologies. Extensive simulation studies using CARLA autonomous driving simulator, verify the significant reduction of GPS error achieved by the two methods in various experimental conditions. Distributed tracking proves to be much superior than Gradient descent algorithm, both in the case of self (58% reduction of GPS error) and neighboring vehicles location estimation (38% reduction of average GPS error).

NUMBER of methods have been developed for damage detection in the last two decades. Many of the methods are based on the observation of dynamic behavior of a structure (Doebling et al. [1], Brincker et al. [2], Maeck and DeRoeck [3]), among which the sensitivity approach via a model-updating technique is commonly accepted and applied extensively in the engineering industry because of its clear mathematical background andquantitativeindications.However,thistypeofmethodisweakat accommodating the influence of measurement errors, leading to illconditioned problems, as demonstrated by Friswell et al. [4] and Humar et al. [5]. This shortcoming means that the existence and uniqueness of the solution is not ensured and numerical instability is likely to take place in the course of the solution process [6,7]. Investigations have since been conducted to deal with the illconditioned problems in model updating. Hansen [8,9] and Vogel [10] proposed regularization methods for obtaining a solution of the inverse problem. It is recognized in regularization theory that the conventional output error, which is usually the vector of differences between thecomputedandmeasuredresponses,canbemadeunrealistically small if the process of damage identification is allowed to behave badly, such that the variable has arbitrarily large deviations from the true set of parameter change, or there may be infinite sets of ill-posed solutions. A stable solution scheme can be achieved by imposing certain constraints in the form of added penalty terms with adjustable weighting parameters based on posterior knowledge. Recently,TiturusandFriswell[11]presentedthesensitivity-based model-updating method with an additional regularization criterion and computed the solutions based on the generalized singular value decomposition.Specificfeaturesoftheparameterandresponsepaths were explored when the regularization parameter varies. The four different types of spaces that arise in the solution were discussed, together with the characteristics of the families of the regularized solution. Weber et al. [12] applied the Tikhonov regularization and truncated singular value decomposition consistently to a nonlinear updating problem. Line-search and stopping criteria known from numericaloptimizationwereadaptedtotheregularizedproblem.The optimal regularization parameter was determined by generalized cross-validation. From experiences gained in model updating with laboratory test structures, the authors found that Tikhonov regularization can give the optimal solution when there is no noise or very small noise in the measurement. But in the updating procedure for a nonlinear inverse problem with the inclusion of noise and model error, the signal-tonoise ratio is getting smaller [13,14] with each iteration, and the solution obtained from a poor regularization parameter is usually unacceptable. The algorithm does not accurately converge, and the results depend strongly on the convergence criteria and tolerances. In this paper, two techniques are proposed in a new regularization method for the identification of local damages in a structure. One technique proposed a new side condition that classified the elements aspossiblydamagedandundamagedelements,whichwillbetreated differently later. The other technique restricts the range of variation of the regularization parameter, such that the regularization solution will be in a realistic range, and the correct optimal point from the curvature of the L-curve is consistently chosen to ensure a continuousconvergingprocess.Bothtechniquesmakefulluseofthe information from results obtained in previous iteration steps. A plane frame structure is studied with two damaged elements and different levels of noise and model errors to illustrate the application of the proposed method. Numerical results show that the proposed consistent regularization method is very effective at improving the results in the inverse problem with ill-conditioning, compared with the conventional Tikhonov regularization.

ABSTRACTAlthough traditional BP neural networks have shown some improvement in ultrasonic indoor localization accuracy, it has a tendency to fall into the problem of local optimal solutions, which limits the localization accuracy. To address this issue, we propose the use of the artificial rabbit optimization (ARO) algorithm as an optimization strategy. The ARO algorithm dynamically adjusts and searches for weights and thresholds during the initialization and training of BP neural networks to find the global optimal solution. This approach efficiently explores the weight space and enhances the BP neural network's performance in ultrasonic localization tasks. Experiments have confirmed that the hybrid ARO‐BP localization algorithm performs well in matching predicted trajectories with actual positions, especially in a 3D localization scenario constructed by six base stations. The algorithm produces excellent results in both line‐of‐sight (LOS) and non–line‐of‐sight (NLOS) environments, which are typical indoor settings. The ARO‐BP neural network effectively reduces the average localization error and ensures high‐precision localization under various transmission conditions and obstacle effects. In NLOS conditions, the positioning accuracy is improved by 16.05% with four tags and 10.92% with six tags, resulting in an average error reduction of 8.02 cm. The ARO‐BP algorithm enhances positioning accuracy by 13.99% with four tags and 21.76% with six tags, resulting in an average error reduction of 12.01 cm. In conclusion, ARO‐BP significantly improves the accuracy of ultrasonic localization in both LOS and NLOS indoor environments with reflections and diffractions. This advancement provides a new direction for the development of indoor positioning technology and is expected to lead to significant progress in practical applications within related fields.

In the domain of intelligent driving vehicles, radar and camera sensors play a crucial role, and their fusion has been widely acknowledged to enhance sensing range and accuracy. This article delves into the challenge of asynchronous information fusion for object detection between radar and camera, taking into consideration different sampling frequencies of radar and camera. Initially, different state observation and prediction models for radar and camera are formulated, leading to the proposal of a comprehensive target tracking model. Subsequently, the matching and fusion process of detection results is undertaken, yielding an optimal estimation as the output of the sensor information fusion system. Furthermore, the article introduces the intelligent vehicle sensor fusion task and describes the stepwise approach to sensor fusion in traffic scenarios, including coordinate transform, sensor data preprocessing, and target track. To validate the proposed sensor asynchronous fusion algorithm, a simulation experiment and a road test are conducted focusing on interaction between ego and environment vehicles. The simulation results underscore the effectiveness of the algorithm, with an average lateral error reduction of 32% and an average longitudinal error reduction of 63%. Road test shows that effective fusion can expand the detection range of sensor system and improve the detection performance.

In this paper the tiny nodes are deployed in target areas according to the deployment nature of target but nodes are easily targeted by attacker with physical attack of node capture. So, secure communications in some wireless sensor networks are critical because these networks are highly vulnerable to internal and external attacks. In our paper, we present an enhanced High-Resolution Robust Localization for security of wireless sensor networks. This combination of scheme provides the good performances and efficiency in terms of network connectivity. We propose a range-independent localization algorithm called HiRLoc that allows sensors to passively determine their location with high resolution, without increasing the number of reference points, or the complexity of the hardware of each reference point. In HiRLoc, sensors determine their location based on the intersection of the areas covered by the beacons transmitted by multiple reference points. we show that HiRLoc is robust against known attacks on WSN.. Finally, our performance evaluation shows that HiRLoc leads to a significant improvement in localization accuracy compared to state-of-the-art range independent localization schemes, while requiring fewer reference points. Index Terms: Localization, Algorithm, Design, Performance, Security.

This paper examines efficient methods for the calculation of open circuit voltage distortion in salient pole synchronous generators with damper windings. It then proceeds to examine the role of damper bars in the level of distortion. The simplified finite element method employed is inclusive of eddy current effects, dynamic field current and full interaction of all MMF sources. It allows accurate total harmonic distortion values to be calculated approximately 25 to 30 times quicker than a fully rotating finite element simulation. Tests have been conducted on eight built machines and results compared to both analytical and alternative finite element methods. The proposed method has an average reduction in error of 55%, compared to an existing analytical method. The presence of damper bars and their position within the pole face is examined, and shown to have a large influence over the level of total harmonic distortion produced. (6 pages)

BackgroundInteractions of microbes and diseases are of great importance for biomedical research. However, large-scale of microbe–disease interactions are hidden in the biomedical literature. The structured databases for microbe–disease interactions are in limited amounts. In this paper, we aim to construct a large-scale database for microbe–disease interactions automatically. We attained this goal via applying text mining methods based on a deep learning model with a moderate curation cost. We also built a user-friendly web interface that allows researchers to navigate and query required information.ResultsFirstly, we manually constructed a golden-standard corpus and a sliver-standard corpus (SSC) for microbe–disease interactions for curation. Moreover, we proposed a text mining framework for microbe–disease interaction extraction based on a pretrained model BERE. We applied named entity recognition tools to detect microbe and disease mentions from the free biomedical texts. After that, we fine-tuned the pretrained model BERE to recognize relations between targeted entities, which was originally built for drug–target interactions or drug–drug interactions. The introduction of SSC for model fine-tuning greatly improved detection performance for microbe–disease interactions, with an average reduction in error of approximately 10%. The MDIDB website offers data browsing, custom searching for specific diseases or microbes, and batch downloading.ConclusionsEvaluation results demonstrate that our method outperform the baseline model (rule-based PKDE4J) with an average F_1-score of 73.81%. For further validation, we randomly sampled nearly 1000 predicted interactions by our model, and manually checked the correctness of each interaction, which gives a 73% accuracy. The MDIDB webiste is freely avaliable throuth http://dbmdi.com/index/

Fine-grained power estimation is fundamental to modern MPSoC design and operation, enabling early power and thermal budgeting before fabrication, predictive thermal control at runtime, post-silicon microarchitectural energy debugging, and hardware security analysis. Existing System-on-Chip (SoC) designs often rely solely on total power consumption measurements because of the cost, size, and complexity of power sensors. However, the lack of precision and granularity in power measurements leads to inefficient power and thermal management and limits the ability to detect failed or compromised sensors. To tackle these challenges, we propose the Improved Clustering Blind Power Identification (ICBPI) approach, an innovative approach for fine-grained power estimation in SoCs. ICBPI leverages total power measurements and per-unit temperature data, even with potentially unreliable sensors, to deliver accurate per-unit power estimates. This dual focus not only enhances power estimation, leading to better power management decisions, but also improves the detection of malicious sensors—such as those compromised by hardware Trojan attacks—that manipulate sensor data to induce potential system failures. Extensive evaluations across four SoC architectures demonstrate ICBPI’s superior performance in reducing per-core power estimation errors by \({77.56\%} \) compared to the baseline Blind Power Identification (BPI) and by \({68.44\%} \) compared to the state-of-the-art approaches. Furthermore, ICBPI achieves \({100\%} \) detection of malicious thermal sensor attacks and enhances the localization process compared to the Blind Identification Countermeasure (BIC) approach. In the comparative analysis between ICBPI and the most recent model, Alternating Blind Power Identification (ABPI), ICBPI shows error rates as low as \({0.5\%} \) compared to \({2.8\%} \) in a 2 × 2 mesh benchmark and outperforms ABPI on heterogeneous architectures. The NVIDIA Jetson Xavier AGX development board serves as a platform for validating ICBPI. Consequently, ICBPI demonstrates an approximate reduction in average error of \(79.71\% \) on the CPU and \(79.59\% \) on the GPU relative to three existing state-of-the-art approaches.