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This paper is concerned with the measurement outlier-resistant mobile robot localization problem by using multiple Doppler-azimuth radars under round-robin protocol (R-RP). In the considered robot localization system, multiple Doppler-azimuth radars are equipped on the robot platform to produce the measurement including the Doppler frequency shift and the azimuth. In order to assuage communication link congestion, the R-RP is used. For mitigating the influence of outliers, a time-varying state estimator is constructed which contains a saturation function with variable saturation levels. This paper aims at seeking out a practicable yet effective solution to the addressed robot localization problem by devising the constructed estimator which can assure that, over a finite horizon, the localization error satisfies the given H ∞ performance index. By constructing an appropriate Lyapunov function, the sufficient condition, which can guarantee the localization error to fulfill the given H ∞ performance, is established. Then, by resorting to the solution to a set of linear matrix inequalities, the constructed estimator can be devised. In the light of the estimator design strategy proposed in this paper, the corresponding robot localization algorithm is developed. At last, some simulations are conducted to testify the usefulness of the developed robot localization algorithm. • The measurement outlier and the round-robin protocol are considered simultaneously for the robot localization problem for the first time. • A discrete state estimator containing a saturation function with the variable saturation level is constructed. • By using Lyapunov theory and the RLMI method, the estimator is designed. • The corresponding measurement outlier-resistant robot localization algorithm is given. [ABSTRACT FROM AUTHOR]

Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the fields of autonomous driving, robot navigation, and urban map updating. However, the domain gap between heterogeneous point clouds and the difficulty of feature characterization in large-scale complex street scenes pose significant challenges for existing PCPR methods. Most PCPR methods only take into account point clouds collected by the same platforms and sensors, thus they are with poor domain transferability. In this paper, we propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (HPCPR) tasks. Firstly, multi-scale local features are extracted by the pyramid feature extraction module. Secondly, local features are enhanced by the patch feature augmentation module to overcome the domain gap problem and achieve better feature representation as well as network generalizability. Finally, a global feature is generated using an adaptive pyramid feature aggregation module, which automatically adjusts and balances the proportion of intra-scale and inter-scale features according to the scene content. To evaluate the performance of PatchAugNet, a large-scale heterogeneous point cloud dataset consisting of high-precision Mobile Laser Scanning (MLS) point clouds and helmet-mounted Portable Laser Scanning (PLS) point clouds is collected. The dataset covers various street scenes with a length of over 20 km. The comprehensive experimental results indicate that PatchAugNet achieves State-Of-The-Art (SOTA) performance with 83.43 % recall@top1% and 60.34 % recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by + 9.57 recall@top1% and + 15.50 recall@top1, while exhibiting better generalizability. For source code and detailed experimental results, please refer to: https://github.com/WHU-USI3DV/PatchAugNet. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) mobile communication technology is now widely available in several countries, with millions of 5G customers. Therefore, it's time for academia and business to focus on the next generation. This paper will overview the sixth-generation (6G) mobile network, including motivations, use case scenarios, requirements, supported research projects, and technologies. We discuss the Beyond 5G (B5G) evolution and advanced 5G features to predict the critical 6G requirements and highlight the 6G capabilities. We also introduce the 6G scenarios, requirements, and technological components compared to 5G. Moreover, the current status of 6G research is discussed, and a rough roadmap for specification and regulation is explored. Then we describe a few prospective applications, their benefits, concepts, and research directions. We explore the business direction for 6G by introducing the most recently 6G projects in the vertical markets. We also propose a network architectural vision and the evolution of hardware-software designs to satisfy the higher requirements of 6G applications. This paper also presents a comprehensive survey of existing 6G trends, technologies, applications, industrial markets, and network structures for the most promising 6G applications. [ABSTRACT FROM AUTHOR]

Modern and new integrated technologies have changed the traditional systems by using more advanced machine learning, artificial intelligence methods, new generation standards, and smart and intelligent devices. The new integrated networks like the Internet of Things (IoT) and 5G standards offer various benefits and services. However, these networks have suffered from multiple object detection, localization, and classification issues. Conventional Neural Networks (CNN) and their variants have been adopted for object detection, classification, and localization in IoT networks to create autonomous devices to make decisions and perform tasks without human intervention and helpful to learn in-depth features. Motivated by these facts, this paper investigates existing object detection and recognition techniques by using CNN models used in IoT networks. This paper presents a Conventional Neural Networks for 5G-Enabled Internet of Things Network (CNN-5GIoT) model for moving and static objects in IoT networks after a detailed comparison. The proposed model is evaluated with existing models to check the accuracy of real-time tracking. The proposed model is more efficient for real-time object detection and recognition than conventional methods. [ABSTRACT FROM AUTHOR]

[Display omitted] The time evolution of a physical system is generally described by a differential equation, which can be solved numerically by adopting a difference scheme with space–time discretization. This discretization, as a numerical artifact, results in accumulated errors during evolution and thus usually plays a negative role in simulations. In a quantum circuit, however, the "evolution time" is represented by the depth of the circuit layer, and thus is intrinsically discrete. Hence, the discretization-induced error therein is not a numerical artifact, but a physical observable effect responsible for remarkable nonequilibrium phenomena absent in conventional quantum dynamics. In this paper, we show that the combination of measurement feedback and temporal discretization can give rise to a new type of quantum dynamics. As physical consequences of this interactive quantum dynamics, a nonequilibrium steady state with spontaneous symmetry breaking is revealed in a zero-dimensional (single-qubit) system. A localization mechanism distinct from that in the well-established Anderson localization has also been proposed in a one-dimensional interactive quantum system. [ABSTRACT FROM AUTHOR]

Sensor node location information is critical in many Wireless Sensor Network (WSN) applications with random sensor node deployment. In such applications, node localization using a faster area exploration mechanism is needed to initiate precise sensing and communication. In this paper, faster area exploration approach for mobility-assisted localization scheme is proposed for mission-critical WSN applications. Each Mobile Anchor (MA) node embedded with a localization module moves in the sensor field in a coordinated manner while localizing the deployed static nodes. The proposed area exploration scheme is implemented using a hybrid of max-gain approach and cost-utility based frontier (HMF) approach. The paper addresses the issue of sparse anchor node condition during the localization process. The simulation results obtained using Cooja simulator shows that the proposed mobility-assisted localization scheme results in accurate localization with minimum delay. The proof-of-concept of the proposed scheme is demonstrated using Berkeley static nodes and custom designed MA nodes. [ABSTRACT FROM AUTHOR]

• A new method for locating liver tumors without radiation is proposed. • An improved ICP algorithm is proposed to complete accurate registration of livers. • The deformation model of liver is proposed on the basis of position dynamics. • A 3D non-rigid localization framework for liver tumors is proposed. • The localization accuracy of intraoperative liver tumor is verified by experiment. In this paper, a new localization method of liver tumors based on structured light is proposed to achieve the accurate resection of liver tumors. Firstly, the preoperative liver model and intraoperative liver surface are completed respectively based on CT sections and structured light. Secondly, the 3D reconstruction of liver surface based on structured light is used as the reference surface for registration. A common ICP algorithm and an improved ICP algorithm based on statistics are proposed to complete the rough and fine registration of liver surface. In this paper, the theory of PBD is introduced, and the registration results are used to guide the preoperative deformation of the liver model. Finally, the method of 3D non-rigid localization of liver tumors proposed in this paper is verified by experiments. In addition, the effectiveness of the proposed method is compared with that of the tumor localization obtained by the finite element analysis and Coherent point drift methods. The experimental results show that the average localization error of liver tumor is 1.24 mm. The time spent during the entire process of tumor localization is kept under fifteen seconds. The method presented in this paper meets the needs of surgeons in the intraoperative process. [ABSTRACT FROM AUTHOR]

Radio Frequency Identification (RFID) systems have remarkable potential to be used in the supply chain management systems of the construction industry. RFID's ability to identify, track and locate objects provides tremendous flexibility in planning and operation of the industry. Consequently, RFID systems can contribute to on-time project completion, cost savings, enhanced reputation, and avoidance of regulatory fines. Moreover, RFID enhances visibility into inventory, equipment, and workforce, ensuring real-time safety measures. This paper conducts an in-depth analysis of state-of-the-art RFID detection and localization techniques, identifying gaps specific to the construction sector. The paper proposes the integration of emerging technologies, with a particular focus on the Internet of Things (IoT) and security mechanisms. This integration aims to solve challenges in using RFID systems in the construction industry's supply chain. It promises to enhance efficiency, security, and effectiveness of RFID technology, leading to a more efficient and resilient supply chain management system. [Display omitted] • Provides an in-depth analysis of state-of-the-art RFID detection and localization techniques, uncovering gaps and limitations specific to construction, and proposing innovative solutions. • Discovers how RFID, combined with emerging technologies like IoT, enhances supply chain efficiency and resilience, addressing specific challenges unique to the construction sector. • Addresses security concerns related to RFID localization and proposes a practical solution tailored to the construction industry's needs. [ABSTRACT FROM AUTHOR]

Public food procurement, involving public sector entities purchasing and distributing food for government-run programs or facilities, is gaining traction as a strategy to counter the dominance of global corporations in food systems. This paper examines the potential and challenges of a place-based procurement model that links urban and rural areas through direct supply chains to promote local, environmentally sustainable food production and consumption, drawing on an in-depth analysis of South Korea's Urban-rural Coexistence Public Meal Program. The findings of this study highlight that program agendas focused on locality and sustainability may misalign, limiting transformative potential, especially when 'place' is narrowly defined within territorial or administrative boundaries. The paper advocates for a comprehensive understanding of 'place' that transcends spatial limits, as a key to rebuild regional supply chains, acknowledging its multi-scalar nature and socio-ecological connections between urban and rural spheres. Evidence from South Korea informs the design of place-based food systems, emphasizing the importance of aligning local realities, coordinating different food systems agendas, and exchanging context-specific knowledge. This study contributes to the theoretical elaboration of place-based approaches and the envisioning of urban policies that support food system sustainability and localization. [ABSTRACT FROM AUTHOR]

The ability of self-driving vehicles to carry out navigation tasks successfully relies heavily on the implementation of a strong localization system. Global navigation satellite systems have been used to provide such information to the vehicle. However, this faces challenges related to the position computation in certain environments, such as in tunnels, under bridges, or in urban canyons. This paper proposes an autoencoder based method to predict a vehicle's position based on a LiDAR point cloud (converted into a reference system) without using additional sensors. The proposed model modifies the objective function of the autoencoder to include an additional objective responsible for adapting the output of the encoder layer (the latent space) to predict the position. Moreover, a penalty parameter was used in the objective function to emphasize the learning in the latent space. The convergence of the proposed method was proven by showing that it is a stochastic-local-quasi-convex function, which ensures the effectiveness of the Stochastic Normalized Gradient Descent algorithm in achieving convergence. The NCLT dataset was used to train our model in PyTorch. In comparison to various advanced registration techniques, our method achieves excellent results for the majority of the Kitti dataset sequences as well. The method achieved a mean absolute error of about 0.5, which is desirable for autonomous-driving vehicles. • First paper tailoring outcomes for vehicle location detection in latent space. • Modified auto-encoder cost function to learn vehicle positions. • Offers mathematical proofs for algorithm convergence. • Achieves excellent accuracy compared to state-of-the-art methods. • Attains 0.5 mean absolute error for long sequences, crucial for safe navigation. [ABSTRACT FROM AUTHOR]

The increase in population and vehicle ownership causes high levels of traffic density. The very high mobility of the population ultimately has an impact on routine congestion which is felt to be getting worse over time. There are several factors that cause traffic jams to get worse. One of them is due to the increasing number of motorized vehicles. The number of vehicles that continues to increase is not in accordance with the road capacity that can accommodate these cars. For this reason, a system is needed that can categorize cars that can run on certain days according to the number plate (eg odd or even) to reduce congestion. This built system can extract the license plates of vehicles passing on the highway using image processing and character recognition methods. This research paper proposed an Automatic Number Plate Recognition ANPR system is an image processing and character recognition system that is used to recognize a car's license plate using Optical Character Recognition (OCR). The inputted license plate is automatically localized, segmented, and recognized using the OCR algorithm provided in the Tesseract library. The experiment shows 83.3% accuracy due to the difference in license plate format, background, and fonts. [ABSTRACT FROM AUTHOR]

This study demonstrates the result of a systematic literature review using the preferred reporting items for systematic review and meta-analysis (PRISMA) method. The main objective is to find issues that still need research on heterogeneous communication, especially 5G cellular and localization networks for small and medium-sized enterprises (SMEs) in the context of Industry 4.0. The research query is formulated based on the research questions and implemented in three selected databases. Then, the query results are categorized into six sections. Consequently, open issues addressed by the assessed papers in five general categories are analyzed and discussed. [ABSTRACT FROM AUTHOR]

Nowadays, determining the exact position of sensor nodes in WSNs is an important factor in many applications. As the need of the localization accuracy varies between the applications, many localization techniques are used in different applications. Hence, node localization becomes one of the fundamental challenges in WSNs. Localization is categorized in two groups: range free and range based. In the range free techniques, localization is related between nodes and topological information of sensor nodes. On the other hand, in range based techniques, it is required to calculate distance between nodes. The scope of this paper is on range free localization. We survey different range free localization techniques and discuss some localization-based applications where the location of these sensor nodes is vital and sensitive. On the second part of the paper, we describe five algorithms namely: Centroid, Amorphous, APIT, DV-Hop and DV-HopMax algorithms. We simulate these algorithms using MATLAB based on different setups. Moreover, we make a comparative study between these localization algorithms based on different performance metrics showing their pros and cons. [ABSTRACT FROM AUTHOR]

• Methods of integrating ground and aerial meta-data for localization and reconstruction are reviewed. • Localization methods are reviewed in terms of image based methods and structure based methods. • Reconstruction methods are reviewed in terms of image based methods and laser based methods. Localization and reconstruction are two highly related research areas. Both of them have developed rapidly in recent years. Apparently, with the help of ground and aerial meta-data integration, the performance of both localization and reconstruction can go a step further. For localization, aerial meta-data provides a global reference, by which the ground query can achieve a cumulative error free absolute localization. As for reconstruction, a complete and detailed model can be reconstructed by integrating ground and aerial meta-data. Though with many advantages, the integration itself is non-trivial. It is difficult to obtain ground-to-aerial correspondences neither in 2D manner nor in 3D manner. That is because: (1) The differences between the ground and aerial images in viewpoint, scale, illumination, etc. are notable; (2) The discrepancies between the ground and aerial point clouds in terms of point density, accuracy, noise level, etc. are very large. To deal with these problems, lots of methods have been proposed recently. In this paper, the methods of integrating ground and aerial meta-data for localization and reconstruction are reviewed respectively. Though many intermediate results with high quality have been achieved, we hope that inspired by the reviewed methods in this paper, more thorough methods and impressive results would emerge. [ABSTRACT FROM AUTHOR]

Localization is a self-position estimation problem and is one of the most critical research areas in autonomous mobile robots. This study presents novel solutions to two significant practical challenges in localization. The initial challenge pertains to the lack of distinct features within the environment, while the second involves the imperfectness of the map. Many studies are based on Extended Kalman Filter (EKF) and they need specific features from the environment for localization. The edges and corners are the popular natural feature types to be used in grid maps. The proposed study promises accurate and fast position tracking in the grid maps which have very few corners and edges. To achieve high performance in such a challenging map, we propose the virtual dynamic point landmark (VDPL) approach in this paper. VDPL does not need specific features such as edges and corners, and any part of the map can be used as a landmark. Since the real-world applications are based on imperfect maps, mostly generated by SLAM (Simultaneous Localization and Mapping) algorithm, the probabilistic distribution of the measurement model and measurement predictions are incorrect. In this study, the position errors that occur as a result of this incorrectness are alleviated by modifying the EKF algorithm. The modified equations of EKF for taking into account the map errors are clearly shown in the paper. The efficiency of the proposed solution is demonstrated in multiple simulations which are performed in randomly generated maps. Moreover, the benefits and real-time performance of the proposed approach are provided by real-world tests using an autonomous wheelchair platform. We believe that the methods developed in this study will improve the localization performance of autonomous robots that operate in challenging environments in terms of feature structure. [ABSTRACT FROM AUTHOR]

Despite the superelastic deformation of NiTi has been documented and analyzed elaborately, its shape-memory behavior during stress-biased thermal cycling has not been thoroughly unveiled. This paper examines the evolution of transformation pattern in NiTi upon thermal cycling over a wide range of biasing stress. For the present shape-memory NiTi, both forward and reverse transformations proceed via the growth of localized deformation band (LDB) under low biasing stress (σ bias ≤ 150 MPa), while LDB only appears during forward transformation and reverse transformation is uniform under high biasing stress (σ bias ≥ 200 MPa). This is the first time that delocalization (conversion of deformation mode from localization to homogeneity) is observed during stress-biased thermal cycling. We clarify that the intrinsic undercooling/overheating of martensitic transformation results in unstable thermomechanical response, and it is the origin of localization in shape-memory NiTi. It is found that transformation-induced plasticity (TRIP), which is dominated by dislocation slip and deformation twining, not only causes irreversibility in the present shape-memory NiTi but also leads to delocalization through stabilization of intrinsic thermomechanical response of reverse transformation. • The first time that delocalization is observed under stress-biased thermal cycling of shape-memory NiTi. • TRIP dominated by dislocations and deformation twins causes irreversibility in the present shape-memory NiTi. • The intrinsic undercooling/overheating of martensitic transformation is the origin of localization in shape-memory NiTi. • Stabilization of intrinsic thermomechanical response by TRIP leads to delocalization during stress-biased thermal cycling. [ABSTRACT FROM AUTHOR]

• Single surface fully coupled thermomechanical constitutive model for SMAs. • Transformation strain and entropy depends on temperature. • Inhomogeneous deformation modeled as softening. • Structural multiphysics simulations for set of isobaric tests. • Interaction of helical localization patterns and latent heat reproduces their rate. In an isobaric test, typically used for establishing the transformation temperatures of SMAs, a specimen is taken through a cool/heat cycle at a prescribed stress level. The companion paper, Part I, demonstrated that such tests on NiTi tubes result in rather complex interactions between the helical bands of localized deformation induced by phase transformations, the associated latent heat, and the thermal exchange with the environment. These interactions were quantified using accurately controlled testing conditions and full-field diagnostics, and provide a challenging platform for evaluating analyses. In Part II this challenge is met by first extending the thermomechanical constitutive framework developed by our group to include variations of transformation stress, strain, and latent heat with temperature. Unique features of the model include: modeling the reversible phase transformations of NiTi through a single surface in the deviatoric stress–temperature space with the transformation strain and entropy as the internal variables; and use of softening to model the inhomogeneous deformation exhibited in tension. The model is calibrated to the isothermal results of Part I. The constitutive model is then incorporated into a fully coupled static displacement-thermally transient finite element analysis that is used to simulate the isobaric experiments on NiTi tubes of Part I over a range of stresses. The clamped ends of the experiment are idealized as radial constraints. Heat exchange between the structure and the environment is strictly by convection. Isobaric testing is simulated by taking the model tube through the cool/heat cycle of the experiment at the prescribed stress level. A small thickness depression at one of the ends is used to initiate localized transformation. The temperature-strain response is reproduced with the two transformations initiating at essentially the same temperatures as in the experiments, producing the correct transformation strains for all stress levels. Transformation of M propagates via a helical band at similar speeds as in the experiments, while transformation of A is via multipronged fronts for all cases. Successful reproduction of the velocities of the banded transformations is governed by the value of the convection coefficient. Overall, the reproduction of the isobaric experiments over a range of stress levels, validates the constitutive and structural models. It also points to the limitations of modeling the heat exchange between the specimen and the environment only by convection. [ABSTRACT FROM AUTHOR]

The online self-calibration is a required capability from an autonomous vehicle that should operate lifelong in a safe manner. This paper introduces relative weighting by a neural network into the batch Gauss–Newton calibration method of the wheel odometry model. A wheel odometry model with accurately estimated parameters could improve the motion estimation task of an autonomous vehicle, but the online parameter identification from only onboard measurements is a challenge due to the noises and the nonlinear behavior of the dynamic system. A possible solution to deal with the effect of noises is to calibrate the model with more segments at once forming a batch, but this can only reduce the distortion effect, not eliminate it. Our proposed algorithm improves this batch formulation by integrating relative weights for the segments to mitigate the distorting effect of noisy measurements. The method applies an AI-based tool to extract a proper weighting strategy from the previously recorded data that utilizes only online signals during operation. With the usage of the proposed architecture, the calibration accuracy significantly increased with the reduction of the distortion effect of faulty measurements, while the same amount of data is used as the raw batch estimation. The performance of the method is demonstrated with real measurements in a city driving with a passenger vehicle, where the calibration signals come from the equipped automotive-grade type of Global Navigation Satellite System and Inertial Measurement Unit. [ABSTRACT FROM AUTHOR]

Point cloud-based place recognition is crucial for mobile robots and autonomous vehicles, especially when the global positioning sensor is not accessible. LiDAR points are scattered on the surface of objects and buildings, which have strong shape priors along different axes. To enhance message passing along particular axes, Stacked Asymmetric Convolution Block (SACB) is designed, which is one of the main contributions in this paper. Comprehensive experiments demonstrate that asymmetric convolution and its corresponding strategies employed by SACB can contribute to the more effective representation of point cloud feature. On this basis, Selective Feature Fusion Block (SFFB), which is formed by stacking point- and channel-wise gating layers in a predefined sequence, is proposed to selectively boost salient local features in certain key regions, as well as to align the features before fusion phase. SACBs and SFFBs are combined to construct a robust and accurate architecture for point cloud-based place recognition, which is termed SelFLoc. Comparative experimental results show that SelFLoc achieves the state-of-the-art (SOTA) performance on the Oxford and other three in-house benchmarks with an improvement of 1.6 absolute percentages on mean average recall@1. [ABSTRACT FROM AUTHOR]

Digital image forgery has become hugely widespread, as numerous easy-to-use, low-cost image manipulation tools have become widely available to the common masses. Such forged images can be used with various malicious intentions, such as to harm the social reputation of renowned personalities, to perform identity fraud resulting in financial disasters, and many more illegitimate activities. Image splicing is a form of image forgery where an adversary intelligently combines portions from multiple source images to generate a natural-looking artificial image. Detection of image splicing attacks poses an open challenge in the forensic domain, and in recent literature, several significant research findings on image splicing detection have been described. However, the number of features documented in such works is significantly huge. Our aim in this work is to address the issue of feature set optimization while modeling image splicing detection as a classification problem and preserving the forgery detection efficiency reported in the state-of-the-art. This paper proposes an image-splicing detection scheme based on textural features and Haralick features computed from the input image's Gray Level Co-occurrence Matrix (GLCM) and also localizes the spliced regions in a detected spliced image. We have explored the well-known Columbia Image Splicing Detection Evaluation Dataset and the DSO-1 dataset, which is more challenging because of its constituent post-processed color images. Experimental results prove that our proposed model obtains 95% accuracy in image splicing detection with an AUC score of 0.99, with an optimized feature set of dimensionality of 15 only. • Image splicing detection using Texture and Haralick features from GLCM. • Achieved accuracy of 95% and AUC score of 0.99. • Feature set is optimized with dimension 15 only. • Non-overlapping block segmentation for splicing localization. • Low computational overhead as it is feature-based with low-dimension. [ABSTRACT FROM AUTHOR]

Satellites extend the 5G Internet of Things (IoT) applications to remote areas by providing global coverage. In applications like environmental and wildlife monitoring, the location information of IoT sensors is equally important as the data itself. This location information can also be utilized for spectrum management between the satellite and IoT networks. The localization is performed using nodes with known positions called anchor nodes (ANs). Optimization analysis of ANs significantly enhance the localization performance. In this paper, we propose a single AN-based localization algorithm for wireless sensors. The proposed localization approach uses directional information and mobility, focusing on finding the shortest path with only two beacon points. Simulation results demonstrate the superiority of the proposed localization algorithm over the state of the art approaches. [ABSTRACT FROM AUTHOR]

Advanced cervical screening via liquid-based cytology (LBC)/Pap smear is a highly efficient precancerous cell detection tool based on cell image analysis, in which cells are classified as normal/abnormal. This paper outlines the drawbacks by introducing a new framework for the accurate classification of cervical cells. The proposed methodology comprises three phases: segmentation, localization of nucleus, and classification. In the segmentation phase, we develop a hybrid system that incorporates two binary image patches obtained by a 19-layered convolutional neural network (ConvNet) model with an enhanced deep high dimensional dissimilarity translation (HDDT) based conspicuous segmentation. To get the relevant information from binary patched images, a technique called optimum semantic similarity selective search (OSS-SS) is proposed that returns the localized RGB patched image. A pre-trained ResNet-50 model is retrained using transfer learning on localized patched images in the classification phase. Following that, the selected features from the average pool and fully connected layers are down-sampled using the t-distribution stochastic neighbor embedding (t-SNE) approach. Finally, these combined features are fed into a multi-class weighted kernel extreme learning machine (WKELM) classifier via a sparse multicanonical correlation (SMCCA) method. Three datasets (SIPaKMed, CRIC, and Harlev) are used to evaluate the segmentation and classification task. The proposed approach obtained an accuracy of 99.12 %, specificity of 99.45 %, sensitivity of 99.25 % with an execution time 99.6248 on SIPaKMed. The experimental analysis indicate that our model is more effective than existing techniques. [ABSTRACT FROM AUTHOR]

This paper connects the localization of government leaders with the theory of "sense of place" and constructs a theoretical framework reflecting the relationship between government leaders' localization and farmland conversion based on that theory. Then, the paper explores in depth the theoretical influence of government leaders' localization on farmland conversion and discusses a corresponding empirical test we conducted by constructing an econometric model and applying the statistical data of 284 cities in mainland China from 2004 to 2014. The results showed that, relative to outlanders, natives acting as government leaders could have the effect of inhibiting large-scale farmland conversion to some extent, and such an inhibition effect would be expanded by a drop in the promotion probability of the involved government leaders. The inhibition effect of government leaders' promotion at local levels on farmland conversion was not obvious, generally. The tenure extension of government leaders sometimes had a positive influence on the change of the farmland conversion scale, which was closely related to leaders' ongoing hopes for promotion at different stages in their careers. The research findings of this paper also provided policy implications for the Chinese government to effectively protect land resources and develop the local economy in an orderly manner based on perspectives for improving the administration system. • We connect the localization of government leaders with the "sense of place" theory. • Leaders' localization could inhibit overconversion of farmland to some extent. • Leaders' place attachment will expand with a drop in promotion probability. • Leaders' place attachment has clearer effects than place identity or dependence. [ABSTRACT FROM AUTHOR]

Abstract In sensors localization, it is not economically argumentative to equip many static nodes with GPS. The most widespread solution is to utilize from mobile location-aware nodes called mobile anchors. A substantial amount of research to propose mobile anchor trajectory to improve localization accuracy, localization latency, network coverage and traversed path has been reported. However, none of the existing static mobile anchor path planning mechanisms has emphasized on increasing mobile anchor lifetime and reliability that is necessary due to limited energy. In this paper we propose a novel mobile anchor trajectory planning scheme called Optimal Priority based Trajectory with Energy Constraint (OPTEC) in order to address these issues. The proposed scheme utilizes the Mixed Integer Linear Programming optimization (MILP) approach for optimal route planning in the presence of location uncertainty for deployed sensors. Several important evaluation metrics including localization coverage, localization success, ineffective beacon points and energy-location uncertainty product are also defined for a comprehensive comparison of static mobile anchor trajectory plans. In this paper, static sensors utilize a range-free localization algorithm. Simulation results reveal that the proposed mobile anchor trajectory planning approach can surpass other existing trajectories in terms of localization error, mobile anchor energy consumption and even sensors lifetime. [ABSTRACT FROM AUTHOR]

Abstract When localization methods used in reservoir history matching were introduced from outside the oil and gas industry, the differences between data assimilation for other industries and reservoir history matching were not considered. However, these differences can be extremely important when considering the validity of these methods. This problem is discussed within the context of the roles of covariance localization in detail in this paper. This paper starts with discussion of relations between two significant problems, ill-posedness and spurious correlations, and their solution methods. This analysis leads to a conclusion about two appropriate roles of the covariance localization method. This conclusion is used to validate the cross-covariance localization methods used in reservoir simulation. In addition, for the cross-covariance localization in reservoir history matching, there are many different processes, especially different correlation functions, proposed by different authors. This makes it difficult to choose a proper method when solving history matching or data assimilation problems. The performance of different cross-covariance localization correlation functions is also analyzed theoretically and experimentally in this paper. Highlights • This paper builds a relation between two significant problems, ill-posedness and spurious correlations. • Localization is described as a method which performs two important roles. • This paper discusses the differences between data assimilation for other industries and reservoir history matching. • The performance of different correlation functions is analyzed theoretically and experimentally in this paper. [ABSTRACT FROM AUTHOR]

Nowadays, determining the exact position of sensor nodes in WSNs is an important factor in many applications. As the need of the localization accuracy varies between the applications, many localization techniques are used in different applications. Hence, node localization becomes one of the fundamental challenges in WSNs. Localization is categorized in two groups: range free and range based. In the range free techniques, localization is related between nodes and topological information of sensor nodes. On the other hand, in range based techniques, it is required to calculate distance between nodes. The scope of this paper is on range free localization. We survey different range free localization techniques and discuss some localization-based applications where the location of these sensor nodes is vital and sensitive. On the second part of the paper, we describe five algorithms namely: Centroid, Amorphous, APIT, DV-Hop and DV-HopMax algorithms. We simulate these algorithms using MATLAB based on different setups. Moreover, we make a comparative study between these localization algorithms based on different performance metrics showing their pros and cons. [ABSTRACT FROM AUTHOR]

Highlights • The shaft-bending is firstly considered to the shaft-disk-blades coupling vibration problem. • The evolution mechanism relies on the changes of the mistuned features parameters. • The vibration localization phenomenon appear due to the mistuned features. • The mistuned features have not obviouse influencs on the Campbell results. Abstract The purpose of this paper is to investigate the coupling vibration characteristics of a flexible shaft-disk-blades system with mistuned features. There are some new phenomena due to the coupling effects of shaft-bending, shaft-torsion, disk-transverse and blade-bending. In this investigation, this paper mainly focuses on the influence of mistuned features of the blade's length and the stagger angle. It is found that there are four types of coupling modes: the coupling mode of shaft bending, disk transverse and blade bending (SDB), the coupling mode of shaft torsion disk transverse-blade bending (TDB), the coupling mode of disk transverse and blade bending (DB), the repeated mode of blade bending-blade bending (BB). With the effect of mistuned features, the natural frequencies and the coupling mode type will change correspondingly. With the mistuning value of blade length employed in this study, the TDB mode in the tuned system will disappear and shift into TSDB mode instead, and one of the repeated SDB modes will be replaced by STDB modes. Due to this mistuned features, the blades and disk experience a certain degree of vibration localization phenomenon. Different from the length feature, the influence of mistuning values of blade's stagger angle mainly take effect on the coupling modes. At last, by inspection on the Campbell diagrams, the influence of rotational speed on the transformation of natural frequencies is illustrated on the tuned/mistuned flexible shaft-disk-blades coupling structure. [ABSTRACT FROM AUTHOR]

Highlights • An adaptive windows based algorithm to localize and classify PCG heart sounds. • Introduction of a peak based clustering technique to handle varying beat duration. • Effective extraction of cycle/period from clean patches of noisy signals. • Extension of cycle/period to complete signal. • A novel memory-based classifier to guard against varying heart rate. Abstract Automated analysis of heart sound recordings (phonocardiograms or PCG) for classification of heart beats in noisy environments is a challenging problem. The major challenges are introduced due to inter- and intra-beat variation, spectral overlap of heart and lung sounds and noise picked from environment. This paper presents a robust algorithm for heart beat localization and classification using variable length window and peak amplitude threshold variation. The technique presented in this paper first attempts to find peaks which correspond to the fundamental heart sound cycle (FHSC) i.e. S 1, S 2, S 1, ... or fundamental heart sound period (FHSP) i.e. S 1, S 1, .... If FHSC/FHSP is found, an attempt is made to extend it to the whole signal. Once extended, the signal is directly labeled using systole and diastole durations. During classification, history of labels is used to counter the heart rate variations. The proposed technique is evaluated on publicly available Pascal Heart Sound Challenge dataset and reports smaller average error for localization and has high sensitivity and accuracy for heart beat classification. [ABSTRACT FROM AUTHOR]

Abstract Ensemble data assimilation methods are among the most successful techniques for assisted history matching. However, these methods suffer from sampling errors caused by the limited number of ensemble members employed in practical applications. A typical consequence of sampling errors is an excessive loss of ensemble variance. In practice, the posterior ensemble tends to underestimate the uncertainty range in the parameter values and production predictions. Distance-based localization is the standard method for mitigating sampling errors in ensemble data assimilation. A properly designed localization matrix works remarkably well to improve the estimate of gridblock properties such as porosity and permeability. However, field history-matching problems also include non-local model parameters, i.e., parameters with no spatial location. Examples of non-local parameters include relative permeability curves, fluid contacts, global property multipliers, among others. In these cases, we cannot use distance-based localization. This paper presents an investigation on several methods proposed in the literature that can be applied to non-local model parameters to mitigate erroneous loss of ensemble variance. We use a small synthetic history-matching problem to evaluate the performance of the methods. For this problem, we were able to compute a reference solution with a very large ensemble. We compare the methods mainly in terms of the preservation of the ensemble variance without compromising the ability of matching the observed data. We also analyse the robustness and difficulty to select proper configurations for each method. Highlights • Sampling errors due to limited ensemble size typically lead to excessive loss of posterior ensemble variance and, consequently, underestimation of uncertainty. • This paper presents an investigation on methods to mitigate loss of ensemble variance that can be applied to non-local model parameters. • The methods are compered in a small synthetic history matching problem. • All methods investigated were able to reduce the loss of variances, but in some cases, at a cost of a poor data match. [ABSTRACT FROM AUTHOR]

Fluorescently labeled cellulose nanofibrils (CNFs) were used to evaluate CNF leaching from paper according to standard safety assays for food contact materials. Enzymatically pretreated pulp was first labeled with 5-([4,6-Dichlorotriazin-2-yl]amino)fluorescein hydrochloride (DTAF), followed by homogenization to produce fluorescent CNFs of varying degrees of fibrillation. Labeling at the μmolar DTAF/g cellulose level imparted quantitative ppb fluorescence detection of CNFs (LOD of approximately 20 ppb), without significantly altering other material properties, suggesting that DTAF-labeled CNFs are an appropriate mimic for native CNFs and that this approach can be used to detect low CNF concentrations. Cold and hot-water extractions of laboratory papers (100 % CNFs and CNF-fiber blended papers) showed loss values below 3 wt% CNFs, with the finest CNF quality showing the least loss overall and with greater loss experienced under hot water conditions compared with cold water. DTAF-labeled CNFs can be used to address questions related to CNF distribution, localization, and loss. [ABSTRACT FROM AUTHOR]

The 3D point cloud can directly provide accurate distance information, which facilitates many applications such as autonomous driving and environmental modeling. In order to construct more complete environmental information for these applications, it is necessary to register point clouds that are obtained from different poses. Registration after coarse localization is an effective method for understanding the pose of the device among the context of its environment. Here, a method for extracting plane features based on voxels is proposed and used for coarse registration and localization. The point cloud is divided into voxels by an octree, and voxels with the same plane characteristics are merged to obtain plane features. The candidate transformation matrix is calculated by using the corresponding plane set, and the RANSAC process with two-level transformation matrix verification, including quick verification and fine verification, is used to find the optimum transformation matrix from the candidates after clustering. Then, the coarse registration can be achieved. With the extracted plane features, a combined plane feature description of the point cloud frame is constructed to fulfill fast frame-level global localization. With the integration of the registration method presented in the current paper, pose localization can also be achieved. Experimental results show that the proposed method can achieve more than 85% successful registration rate with short time consumptions. This implies that the proposed method is more efficient than the benchmark method. Even when the map is large, frame-level localization is still fast, and has a successful localization rate of over 90%. The corresponding code are available at https://github.com/zhanjiawang/VPFBR-L. [ABSTRACT FROM AUTHOR]

Accurate pose estimation of monocular ground-level images with respect to a satellite/aerial photogrammetric dataset is an extremely challenging task. Existing solutions often perform an offline post-registration on 3D results from both sources, which however, suffers from non-rigid geometric distortions of the 3D monocular reconstruction and the lack of overlaps between the air to ground content. This paper provides an online solution that performs accurate pose estimation of the ground images with respect to a 3D model derived from satellite images, followed by a dense 3D reconstruction. Our solution takes advantage of the simultaneous localization and mapping (SLAM) paradigm to dynamically incorporate reference observations from the satellite 3D model during the incremental pose estimation, called a cross-view SLAM solver, which leverages both ground-to-satellite error and image-level reprojection errors at the frame level to yield image poses that are well-registered to the satellite 3D model for facade point cloud reconstruction. This process also has the advantage of correcting non-rigid distortions and trajectory drifts that are often presented in monocular SLAM systems. In addition, our solution leverages both the geometric and semantic information from the satellite model and ground images to perform a per-frame correction for frame-level pose initialization, in which a novel scheme called pose buffer is introduced to initialize the pose of each keyframe through robust visual hull alignment of ground objects. The proposed approach has been experimented using four trajectories of monocular videos collections (around 7,000 frames per trajectory on average) and a 3D semantic model from multi-view satellite images to estimate the poses of the video frames and yield point clouds consistent with the satellite 3D models, evaluated by using LiDAR ground-truth. Both qualitative and quantitative experiments demonstrate that our solution yields accurate, drift-free poses and point clouds consistent with the satellite data and visually much more pleasing 3D models with facade information. Compared to the LiDAR ground-truth, the derived 3D models with ground-level images have achieved a mean absolute error of 1.78 m (improved from 3.15 m achieved using SLAM without utilizing satellite 3D models) (A testing program will be made available through https://github.com/GDAOSU/Cross-View-SLAM). [ABSTRACT FROM AUTHOR]

Local Positioning Systems (LPS) are supposing an attractive research topic over the last few years. LPS are ad-hoc deployments of wireless sensor networks for particularly adapt to the environment characteristics in harsh environments. Among LPS, those based on temporal measurements stand out for their trade-off among accuracy, robustness and costs. But, regardless the LPS architecture considered, an optimization of the sensor distribution is required for achieving competitive results. Recent studies have shown that under optimized node distributions, time-based LPS cumulate the bigger error bounds due to synchronization errors. Consequently, asynchronous architectures such as Asynchronous Time Difference of Arrival (A-TDOA) have been recently proposed. However, the A-TDOA architecture supposes the concentration of the time measurement in a single clock of a coordinator sensor making this architecture less versatile. In this paper, we present an optimization methodology for overcoming the drawbacks of the A-TDOA architecture in nominal and failure conditions with regards to the synchronous TDOA. Results show that this optimization strategy allows the reduction of the uncertainties in the target location by 79% and 89.5% and the enhancement of the convergence properties by 86% and 33% of the A-TDOA architecture with regards to the TDOA synchronous architecture in two different application scenarios. In addition, maximum convergence points are more easily found in the A-TDOA in both configurations concluding the benefits of this architecture in LPS high-demanded applications. [ABSTRACT FROM AUTHOR]

As an effective means of thermal management, thermal rectification (TR) has been studied in a variety of complex asymmetric or heterogeneous structures. Due to experimental conditions, many complex structures cannot be realized easily, and the low-temperature thermal bath temperature far below room temperature does not conform to the actual application environment of the device. In this paper, we studied the thermal rectification inside graphene supported by a heterogeneous substrate of SiO 2 and GaN alternately. Heat flux is easier to transfer from the graphene covered on the SiO 2 side to the GaN side. When the temperature of the cold bath is maintained at 300 K, only the temperature of the high-temperature hot bath is changed, and the maximum TR rate is about 52%. TR rate can be tuned by heat source temperature and size, with heat source size of 2 nm and temperature above 450 K, the highest TR rate can be obtained. The phonon density of states and participation ratio reveal the influence of the substrate and position on the phonon transport in graphene. Besides, the spectral energy density analysis confirmed that the substrate effect caused the shift of the graphene flexural phonon mode and the reconstruction of the phonon spectrum, which was in good agreement with PDOS, and further explain the basic mechanism of TR from the scattering rate and the degree of localization. [Display omitted] The thermal rectification effect of graphene covered on a heterojunction substrate composed of SiO 2 and GaN was studied by using non-equilibrium molecular dynamics (NEMD). The interface thermal resistance between the thermal bath and central area also needs to be considered, at high temperature, the whole system cannot reach the ideal thermal rectification rate. Moreover, the phonon participation ratio and spectral energy density are calculated to understand the mechanism of thermal rectification and the physical nature of the substrate effect, respectively. • Propose a non-destructive thermal rectification method for the pristine graphene. • Thermal rectification effect of the graphene system at high temperature is studied. • The non-linear interface thermal resistance between thermal bath and central area is considered. • Phonon participation ratio proves the localization characteristics of different positions. • SED analysis proves the drift and reconstruction of graphene phonon spectrum under substrate effect. [ABSTRACT FROM AUTHOR]

Great interest is directed toward real-time applications to determine the exact location of sensor nodes deployed in an area of interest. In this paper, we present a novel approach using a combination of the Kalman filter and regularized bounding box method for localizing unknown nodes in an area using an FPGA-enhanced edge computing UAV whose trajectory is known and is represented as the position of many anchors. The UAV is equipped with a GPS system that allows it to gather location data of sensor nodes as it moves around its environment. We employ a regularized bounding box to predict the positions of the unknown nodes using regularization factors and we use the Kalman filter algorithm to smooth and improve the accuracy of the sensor nodes to be localized. In order to localize the unknown nodes, the UAV receives the number of hops from each node and uses this information as input to the localization algorithm. Furthermore, the use of an FPGA board allows for real-time processing of sensory data, enabling the UAV to make fast and accurate decisions in dynamic environments. The localization algorithm was implemented on the FPGA board "Zynq MiniZed 7007s evaluation board" using Xilinx blocks in Simulink, and the generated code was converted into VHDL using Xilinx System Generator. The algorithm was simulated and synthesized using "Vivado" software. In fact, the proposed system was evaluated by comparing the performances achieved through two different implementations: Hardware and Software implementation. In effect, the performance of FPGA hardware implementation presents a new achievement in localization due to its easy testing and fast implementation. Our results show that this approach can efficiently locate unknown nodes with good latency and high accuracy. In fact, the execution time of the FPGA-integrated algorithm is reduced by about 60 times compared to the software implementation and the power consumption is about 100 mW, which proves the suitability of FPGA for localization in WSNs, offering a promising solution for various mobile WSN applications. [ABSTRACT FROM AUTHOR]

This paper proposes a novel node localization method in sparse underwater wireless sensor networks (UWSNs) where the locations of only a small number of anchor nodes are available. The proposed method estimates the Euclidean distances from anchor nodes to multi-hop sensor nodes with the help of angle of arrival (AoA) measurements in the local coordinate systems of the routing nodes. A new distance estimation method is proposed for sensor nodes with greater-than-two-hops to an anchor node by accurately estimating the rotation matrix between the routing nodes. By forwarding distances hop-by-hop through the network, the sensor nodes are able to obtain distance estimates to more than four or five anchor nodes. Then the location of the sensor node is solved by a weighted Least Squares method. This paper develops a practical table of weights for different number of hops and AoA estimation errors. Simulation results show that the proposed localization method outperforms the existing multi-hop localization algorithms, such as DV-hop, DV-distance, Euclidean, and Cosine-law methods, in terms of distance estimation and location estimation, even if the AoA measurement error is large. Meanwhile, the proposed method maintains almost the same high localization coverage as the existing methods. [ABSTRACT FROM AUTHOR]

Delamination is one of the most common types of damage in carbon fiber reinforced polymer composites. This paper analyzes the interaction of fundamental symmetric (S0) and anti-symmetric (A0) mode Lamb waves with the start and the end of a delamination using finite element simulations. Propagations of Lamb waves in a 4-layer [0/90]s laminate were compared with propagations obtained in a 1-layer [0] sub-laminate and in a 3-layer [90/90/0] sub-laminate, which are the two sub-laminates separated by the delamination. Results show that the Lamb waves travel at different velocities in the delaminated region and in the healthy region. Comparing the signals obtained in the delaminated region with the excitation, it was found that mode conversions happen at the edges of a delamination for both S0 and A0 waves. The mode conversion of S0 wave at the end of a delamination generates large A0 waves and causes most significant changes to the detection signal. Based on this feature, a reference-free localization method is proposed in this paper. By measuring the difference between the arrival times of two modes, the distance between pick-up sensor and the end of delamination can be estimated. This method was verified by signals obtained in finite element simulation. [ABSTRACT FROM AUTHOR]

This paper presents a novel robotic system that applies spray foam insulation in underfloor voids in order to improve the energy efficiency of buildings. The work focuses on solving the mapping and localization problems in such environments, since they are a key factor in the autonomy of the robot. Solving these tasks in underfloor voids is especially challenging because the terrain is extremely uneven due to the presence of stones, bricks and sand. Within these environments, the robot should be able to localize itself and apply the insulation foam to the underside of the floor. The robot is equipped with a 2D laser sensor which permits building point clouds from several positions of the underfloor environment. The localization process is solved by estimating the position of the robot with respect to previously known positions. For this purpose, the alignment between point clouds is calculated. This paper describes two algorithms to robustly obtain the alignment between two positions. The proposed algorithms are tested with a set of point clouds captured with a laser scan in several environments under real working conditions. The results show that the localization problem can be solved in such challenging underfloor voids by using depth information. [ABSTRACT FROM AUTHOR]

The paper presents an innovative approach to identifying voltage fluctuation sources in power networks, also considering the localization understood as the indication of supply points of disturbing loads. The presented approach considers disturbance sources that change their operating state with a frequency higher than the power frequency. The implementation of the proposed solution is also proposed in such a way that its implementation in the smart meter infrastructure allows automatic localization of disturbance sources without additional expert knowledge. In the proposed approach, the modulation signal is estimated using a carrier signal estimator, which allows the estimation of a modulation signal with a frequency higher than the power frequency. The estimated modulating signal is decomposed into component signals associated with individual disturbing loads by decomposition by approximation using pulse waves. The decomposition process allows for the estimation of selected parameters associated with disturbing loads, on the basis of which the assessment of propagation of voltage fluctuations associated with the impact of individual disturbance sources is performed, which allows for the indication of their supply point. The proposed approach was verified in numerical simulation studies using MATLAB/SIMULINK and in experimental studies carried out in a real low-voltage power grid. • A new method using effective demodulation and decomposition techniques allowing identification of disturbing loads in power networks. • The proposed approach allows the identification of disturbing loads that change their operating state with a frequency higher than the power frequency. • The proposed approach allows the identification of disturbing loads that have comparable changes in their operating state. • A verification of the approach and comparison with other methods using data that considers actual conditions occurring in modern power networks. • A possibility of automatic identification of sources of voltage fluctuations without additional expert knowledge. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to attain accurate and automatic detection of brain tumors in gray magnetic resonance images using reducing the local dimensions. We propose a novel model called Improved Mixtures of Factor Analyzers based on Dynamic Co-Clustering (IMFADCC) for the detection and localization of brain tumors. After image preprocessing and enhancement, the optimal numbers of row clusters and column clusters corresponding to each row cluster, alongside other model parameters, are determined concurrently based on the size of the tumor by our model. Then, using the optimal values obtained, the image is co-clustered by simultaneously clustering rows and columns until the block containing the tumor is identified. The output image is divided into a certain number of blocks, one of which contains the tumor. For post-processing, the identified block is binarized using minimum error thresholding. The proposed model accurately detects and locates the block binarized within the original image while considering the remainder of the image as background. The effectiveness of the proposed method was assessed by evaluating its accuracy, Dice, intersection on union, geometric mean, receiver operating characteristic curve, specificity, and sensitivity criteria on the BraTS2018, BraTS2019, and BraTS2020 datasets. The results show that our method has significant performance and is highly accurate in detecting tumors of different sizes in complex and high-size images. Moreover, our method outperforms the existing diagnostic methods. [ABSTRACT FROM AUTHOR]

Possible lamellar coarsening in the as-cast microstructure of TiAl alloys significantly affects the subsequent processing of ingot metallurgy, and a systematic study of it is necessary. In this paper, two TiAl alloys with different solidification modes (TNM-45Al alloy and TNM-0.3Si-0.7 C alloy) were obtained by compositional minor adjustment basing on TNM alloy. The effect of different adjustment routes on the localized lamellar structure coarseing behaviour and the resulting differences in the uniformity of the nanohardness distribution were investigated by comparing with TNM alloy. It is found that increasing the Al content of the TNM alloy while keeping the solidification mode as β-solidification unchanged leads to severe localised lamellar coarsening in the S-segregation zone of TNM-45Al alloy, including continuous coarsening (CC) and discontinuous coarsening (DC). In contrast, the solidification mode of the TNM-0.3Si-0.7 C alloy with the addition of the strong α stabilizer elements Si and C is transformed into hypoperitectic solidification with severe peritectic segregation, but the lamellar structure exhibits surprising full-domain stability. The results show that the thermodynamic tendency caused by high Al content and the absence of net-like β-segregation leads to severe localised lamellar coarsening in TNM-45Al alloy. CC can be accomplished through the migration of interface defects. DC is formed by the nucleation and expansion of γ-phase Shockley incomplete dislocations, and the interfaces consist of the α 2 /γ interface and the first discovered γ /γ twin interface. The lamellar coarsening of TNM-0.3Si-0.7 C alloy can be effectively suppressed by the solid solution of Si/C atoms and the near net-like β-segregation in the primary β-phase region. As a result, the distribution uniformity of the nanohardness of the lamellar structure in TNM-0.3Si-0.7 C alloy is excellent, with the variance decreasing from ±0.24 of TNM alloy to ±0.20, while it increases to ±0.37 for the TNM-45Al alloy. • This is the first systematic study of the correlation between microsegregation and lamellar coarsening in TiAl alloys with different solidification modes. • Aggravated S-segregation of β-solidified TiAl alloys after increasing Al content leads to localised lamellar coarsening. • Solid solution of α-stabilising elements and β-segregation stabilises the full-domain lamellar structure of hypoperitectic TiAl alloys. • This is the first discovery of γ/γ twin interfaces in the continuous coarsening structure of TiAl alloys. [ABSTRACT FROM AUTHOR]

• A GNSS-LiDAR-inertial odometry and mapping system is developed with high efficiency and stability at the same time. • A coarse-to-fine state estimation framework based on iterated error-state Kalman filter is provided for multi-sensor navigation systems. • GNSS initialization, multi-layer outlier rejection, and multi-sensor motion compensation are designed for the filter-based state estimation architecture. • A series of comprehensive experiments on multiple levels are conducted to verify the performance and feasibility of the method. Simultaneous localization and mapping (SLAM) aims to solve the problems of robot localization and mapping in unknown environments. Recent related research usually uses closed-loop correction or integrate GNSS (Global Navigation Satellite System) into the optimization framework to ensure the long-term system accuracy and stability at the cost of huge computational resources. To balance efficiency and accuracy, this paper presents a fast and stable GNSS-LiDAR-inertial state estimator: GNSS, LiDAR and IMU are fused to achieve state estimation from coarse to fine, thereby improving the system accuracy and stability; the overall framework based on iterated error-state Kalman filter makes our system faster than most multi-sensor fusion SLAM. We also design a fast GNSS online initialization method and a multi-layer outlier rejection mechanism for our system. In addition, we apply backward propagation for multi-sensor motion compensation to overcome the limitations of fast motion. Finally, comprehensive experiments demonstrate that our system achieves higher accuracy and computational efficiency than the state-of-the-art navigation systems on the latest challenging public datasets, and perform equally well in the real environment. [ABSTRACT FROM AUTHOR]

Propagation and localization of light in one-dimensional nonlinear photonic crystals (1D-NPhCs) are analyzed in this paper, which consist of alternating layers with varying dielectric constants. The nonlinear dielectric layers are composed of polyvinylidene fluoride (PVDF)/graphene oxide (GO), which act as a defect and are embedded between two types of linear dielectric layers, enabling manipulation of their optical susceptibility. The definitions, classifications, and the optimal structure are provided, as well as formulas for transmittance and reflectance with the transmission matrix method (TMM). The defect layer of PVDF/GO enhances the localization capability of the electric field of light, while the stacking orders and symmetry determine transmission or reflectance at the specified wavelength or frequency. The translational symmetric structure, particularly those with single and double layers of PVDF/GO, exhibit high transmittance (0.99 and 0.62, respectively). In contrast, mirror symmetric and other asymmetric structures have broad bandgaps, with the maximum bandwidth reaching 87.5 THz. The 1D-NPhCs with PVDF/GO could provide a potential solution for improving optical filtering and limiting devices in the future. • Utilizing the TMM and FEM to provide definitions, classifications, and the optimal structure of 1D-NPhCs. • The defect layer with PVDF/RGO enhances the localization capability of the electric field of light. • The translational symmetric and mirror symmetric structures can create an F-P resonator and a Bragg mirror, respectively. [ABSTRACT FROM AUTHOR]

Localization models, particularly for indoor applications like autonomous vehicles and smart manufacturing in Industry 4.0, have emerged as a crucial research area because of the growing need for precise positioning. In this context, distributed cell-free MIMO have demonstrated capabilities for a higher fine-grained resolution and accuracy. This paper explores different approaches to address challenges related to positioning and corresponding data transmission and model compression problems in cell-free massive MIMO systems. We present a novel deep-learning powered CSI compression model for reducing the signalization data associated with the localization task, minimizing the fronthaul capacity requirement by at least a factor of 2, without a significant loss of accuracy. In our research, we also focus on the challenges of deploying deep end-to-end neural networks on resource-constrained embedded platforms. Two approaches are proposed: model modification and magnitude-based pruning. Our results show that the pruned end-to-end localization model has a smaller size by 36% at the cost of an increase on the localization error by a factor of 1.5 (precision is reduced from approximately 5 mm to 7.8 mm). [ABSTRACT FROM AUTHOR]

A new approach is proposed to account for the impact of sprain variables for computational simulation of fracture in quasi-brittle materials. For an isotropic solid, these variables are equal to the Laplacians of the components of the displacement field, and they were introduced in a recent study by Zhang and Bazant to resolve spurious mesh effects for cases involving softening material response. This is possible by establishing localization-resisting forces which are conjugate to the displacements with respect to a sprain energy functional, defined as a function of the sprain variables. To enable the evaluation of the sprain variables in a conventional, C 0 continuum finite element approximation, this paper introduces a set of scalar fields, each being weakly equal to the Laplacian of one of the components of the displacement vector field. This leads to a straightforward implementation with standard continuum (solid) elements, eliminating the need for approximations satisfying higher continuity requirements or for ad-hoc, finite-difference algorithms which were adopted in the original pertinent work for the calculation of the sprain variables. • Use of localization-resisting forces in continuum simulations of strain softening, to eliminate spurious mesh-size effects. • These forces are governed by a set of scalar fields, weakly equivalent to the Laplacians of the displacement components. • Weak equivalence allows to obtain the Laplacians of the displacement field with conventional, C0 finite element approximations. • The method is found to eliminate spurious effects of mesh size and of mesh orientation with respect to crack propagation path. [ABSTRACT FROM AUTHOR]

Traditional localization approaches, e.g., angle of arrival clustering (AOA-clustering), subspace data fusion, and minimum variance distortionless response, entail eigenvalue decomposition or global grid search within the target area, which could pose significant challenges when applied to massive antenna arrays as a result of their high computational complexity. In this paper, we present a low-complexity localization approach for multiple sources with two-dimensional discrete Fourier transform (2D-DFT). Firstly, we compute the cross-covariance, which could effectively suppress noise and obtain aligned AOAs via 2D-DFT. Then, we utilize phase offset method and total least square solution to obtain accurate position estimates. The proposed approach could achieve a good compromise between performance and computational complexity. The Cramér-Rao Bound (CRB) and simulation results are provided to corroborate the effectiveness and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a new method to increase odometric sensor accuracy by systematic and non-systematic errors processing. Mobile robot localization is improved combining this technique with a filter that fuses the information from several sensors characterized by their covariance. The process focuses on calculating the odometric speed difference with respect to the filter to implement an error type detection module in real time. The correction of systematic errors consists in an online parameter adjustment using the previous information and conditioned by the filter accuracy. This data is also applied to design a variable odometric covariance which describes the sensor reliability and determines the influence of both errors on the robot localization. The method is implemented in a low-cost autonomous wheelchair with a LIDAR, IMU and encoders fused by an UKF algorithm. The experimental results prove that the estimated poses are closer to the real ones than using other well-known previous methods. [ABSTRACT FROM AUTHOR]