In automated guided vehicle (AGV) magnetic navigation systems, magnetic markers typically serve solely as spatial references, offering limited informational content. This paper presents a novel Magnetic Moment Encoding (MME) system, which enables magnetic markers to simultaneously function as both physical landmarks and information carriers. The system leverages three-dimensional magnetic moment parameters of permanent magnets to achieve high-capacity information encoding. Compared to conventional binary magnetic pole encoding methods, MME increases information density by approximately threefold. To address the challenge of decoding MME signals, we propose a neural network-based Magnetic Moment Decoder (MMD), which enables robust decoding of encoded information even in complex magnetic field environments. Integrated into AGV navigation, the MME system achieves a positioning accuracy of 11.88±9.12 mm and a path yaw error of 4.21 ± 2.23°, while also supporting real-time adaptive path planning in dynamic environments. This study introduces a new paradigm for high-density, low-power magnetic information interaction, with broad application potential in industrial automation and the Internet of Things.

In recent years, significant progress has been made in detecting, localizing, and classifying multiple magnetic dipole sources (DLCMMS) based on magnetic gradient tensors. Traditional DLCMMS methods face challenges due to limited resolution and high-dimensional parameter optimization. In response, we propose a novel DLCMMS method within the Cooperative Coordination (CC) framework. Specifically, we introduce a decomposition strategy within the CC framework to partition the original DLCMMS problem into lower-dimensional sub-problems. We perform joint parameter optimization for source number, positions, and magnetic moment parameters in each sub-problem covering a subset of magnetic sources and measurement points. Moreover, this letter enhances the hybrid optimization algorithm, combined with differential evolution and Levenberg-Marquardt, for joint parameter optimization. In simulations, the number recognition accuracy of sources is 99.3% without noise, about three times higher than traditional methods. During a field demonstration, the position estimation error was below 0.053m.

The Tolley-Lawson(TL) model plays an important role in the aeromagnetic survey. The TLG model based on the TL model introduced the correction of the geomagnetic gradient in the real world and improved the precision of aeromagnetic compensation. The key to geomagnetic gradient compensation is to introduce the system’s position information. However, in general, there is an offset between the origin of the coordinates of the magnetometer and the fuselage, which will lead to sensor position errors, especially during maneuvers. Correcting the positional error will eliminate the offset and increase the precision of the aeromagnetic survey. In this paper, the principle of the positional error is analyzed and an improved model(TLG-C) aims to correct the positional error is proposed. The positional error is modeled as a function of the attitude angle of the aircraft and the distance between the fuselage and magnetometer. An improved aeromagnetic compensation algorithm is derived based on the TLG-C model. The results of 24 cases in actual calibration flights show that the proposed method can effectively enhance the improvement ratio.

To detect frame duplication in degraded videos, we proposed a coarse-to-fine approach based on locality-sensitive hashing and image registration. The proposed method consists of a coarse matching stage and a duplication verification step. In the coarse matching stage, visually similar frame sequences are preclustered by locality-sensitive hashing and considered as potential duplication candidates. These candidates are further checked by a duplication verification step. Being different from the existing methods, our duplication verification does not rely on a fixed distance (or correlation) threshold to judge whether two frames are identical. We resorted to image registration, which is intrinsically a global optimal matching process, to determine whether two frames coincide with each other. We integrated the stability information into the registration objective function to make the registration process more robust for degraded videos. To test the performance of the proposed method, we created a dataset, which consists of 3 subsets of different kinds of degradation and 117 forged videos in total. The experimental results show that our method outperforms state-of-the-art methods for most cases in our dataset and exhibits outstanding robustness under different conditions. Thanks to the coarse-to-fine strategy, the running time of the proposed method is also quite competitive.

Nowadays with rapid advances in computer vision and deep learning, it is possible to create highly realistic synthetic faces in digital videos. This situation increases anxiety and suspicion in the video content. It can be a big challenge for humans and machines to differentiate between real and fake faces in a video, especially when the video is compressed or has low resolution. In this paper, an efficient method is proposed for detecting Face2Face manipulations. With the analysis of manipulated videos, we found that there are some visual artifacts that can be exploited to detect fake faces. The proposed method utilizes the histogram of oriented gradients for feature extraction that can be effective in exposing Face2Face manipulations. Experimental results clarify that our method effectively detects the manipulated faces with a high-performance accuracy under various compression quality levels.

Most data used to develop the magnetic interference compensation algorithms are derived from the actual measurement of aerial magnetometer. However, it takes a long time and high cost to get the actual data, which will increase the time and difficulty for algorithms development. In practice, simulation magnetic data can be generated for the development of magnetic compensation algorithms. In this paper, the components of the actual output data of aeromagnetometer are analyzed and simulated separately to make the simulation data as close as possible to the actual measured data. In addition, the attitude coupling and height drop during actual maneuvering can be reflected effectively in the simulation data. Results indicate that the proposed method is able to generate magnetic field data for compensation algorithms development.

Aeromagnetic compensation is an effective technique to reduce magnetic interference of the aircraft during an aero-magnetic survey. As an important indicator to evaluate the compensation effect, the figure of merit (FOM) is widely applied. However, calculating FOM needs to label the maneuvers manually. In order to solve this problem, we propose an automatic method to calculate the FOM. This method labels the maneuvers through classifying the flight direction and recognizing the attitude by combining two clustering algorithms, k-means and Gaussian mixture model (GMM). The required magnetic fields are collected by a vector-magnetometer, which is commonly used during the magnetic surveys. Results of the tests demonstrate the availability of the proposed method.

Inter‐frame forgery marks a central type of forgery in surveillance videos, and involves three aspects – frame duplication, insertion, and deletion – under temporal domain. However, this forgery type has received little attention from scholars. More efforts have been on detecting only a single aspect of inter‐frame forgery. Furthermore, studies have confirmed that previous methods did not achieve high accuracy for all forgeries types with low computational loads at the same time. In this study, the proposed method establishes a framework that can simultaneously detect all aspects of inter‐frame forgeries. During the decoding process, the authors extract residue data of each frame from a video stream. Then spatial and temporal energies are exploited to illustrate data flow, and abnormal points are determined to detect forged frames. Noise ratios of forged and original frames are estimated for differentiating insertion from duplication attacks. Experimental results indicate that the proposed method achieves higher accuracy and lower computational time for detecting inter‐frame forgery.

Image width is an important factor for making the partially recovered data perceptually meaningful in image file carving. The authors conduct a comprehensive comparison of the performance of the representative methods for estimating the JPEG image width. Experimental results show that the best methods based on pixels are always better than the best methods based on quantised discrete cosine transform (DCT) coefficients. To keep the good performance of the pixel‐based methods when the correct quantisation tables are unavailable, the authors replace the correct quantisation tables with the standard ones. Experimental results certify that such a replacement has only a little effect on the performance of the pixel‐based methods, the best of which still outperform the best methods based on quantised DCT coefficients. The two results indicate that it may be enough to just focus on the pixel‐based methods for future work. Finally, they propose a pixel‐based method, which derives the candidate image widths from the most likely adjacent minimum coded unit (MCU) pairs in the vertical direction. The candidate width which appears most frequently is chosen as the estimated image width. Experimental results show that the proposed method usually has the best performance when most MCUs of an image are recovered.

The estimating of the Tolles-Lawson model's coefficients plays an important role in aeromagnetic compensation. The directional cosines, which are indispensable for estimating the coefficients, are usually measured by a three-axis strapdown magnetometer in a general aeromagnetic survey system. However, in some cases, the scalar magnetometer may have a much higher accuracy than the three-axis strapdown magnetometer. This imbalance of the measurement accuracy is then introduced into the Tolles-Lawson model and affects the estimation of the coefficients. In this letter, a modified Tolles-Lawson model is introduced to reduce the imbalance through substituting the error model of the three-axis strapdown magnetometer into the calculation of the directional cosines. The characteristics of the modified model are analyzed and the corresponding coefficient-estimating system is developed. Simulation results illustrate that the modified model is more robust to the larger errors of the three-axis strapdown magnetometer than the classical model.

Most existing methods used in aeromagnetic compensation assume a constant geomagnetic field. These methods do not take into consideration the geomagnetic gradient, which can reduce the accuracy of the estimated coefficients of the Tolles-Lawson model. In practice, aside from the magnetic interference from the aircraft, a residual component of the geomagnetic field is contained in the filtered magnetic field signal. In this letter, the component of the geomagnetic field in the filtered magnetic field signal is modeled as a function of the longitude, latitude, and altitude of the aircraft. The linear equation for estimating the coefficients is then extended by combining the Tolles-Lawson model with the proposed model. Simulation results indicate that the proposed method is able to include the variance caused by the geomagnetic gradient into the Tolles-Lawson model calculation and results in a better aeromagnetic compensation.

In aeromagnetic compensation, the multicolinearity of the Tolles-Lawson model is one of the main factors limiting the performance of the coefficient-estimating methods. Aside from the intrinsic characteristic of the Tolles-Lawson model, data filtering as a preprocessing step in estimating the coefficients has a significant impact on the multicolinearity. To solve this problem, an adaptive filter based on wavelet multiresolution analysis is proposed. By modifying the wavelet decomposition results of the Mallat algorithms and reconstructing the signal, the adaptive filter can be used to establish a system of linear equations not only by capturing the frequency characteristics of the aircraft maneuvers as is usual but also by having a weakened multicolinearity. Simulation results illustrate the effectiveness of the proposed adaptive filter.

Reassembling fragmented image files plays a crucial role in seizing digital evidence from scattered digital image files. However, little attention has been paid to the quality assessment for reassembled image files. We propose a method to assign a reassembled image file with a score indicating its quality, defined as the amount of information that the reassembled image affords for its original image. The score ranges from 0 to 1. A higher score indicates better quality. The basic idea of the proposed method is to disintegrate the quality assessment of a reassembled image file into the calculations of the contributions of the fragments contained in the reassembled image file. For each fragment, if it is not from the original image file, it affords the least contribution. Otherwise its contribution depends on the extent to which the fragment and its neighbor fragments are concatenated correctly. We validate the performance of the proposed method with an extensive subjective study involving 588 reassembled image files. We also show that the existing quality assessments for images cannot be directly used to assess the quality of reassembled image files.

Due to its simplicity and effectiveness, copy-move forgery has become a commonly used approach to forging images and detection of this behavior has attracted much attention in recent years. Usually integrated in the copy-move forgery process is the rotation operation, which makes rotation invariance a critical property of the copy-move forgery detection methods. In this paper, we propose to use screened Harris Corner Detector and the MROGH descriptor as feature detector and descriptor respectively, to gain better feature coverage and robustness against rotation. Experimental results illustrates the efficacy of our method.

Figure-ground segmentation benefits lots of tasks in the field of computer vision. Exemplar-based approaches are capable of performing segmenting automatically without user interaction. However, most of them adopt fixed parameters for all the target images, which blocks their segmentation performances. We present a novel sparse representation based transfer scheme to gain adaptive parameters automatically. The proposed scheme transfers the segmentation masks of some windows from training images to obtain the soft mask of the target window from any given test image, when the target window can be represented by the linear combination of those windows. On the challenging PASCAL VOC 2010 segmentation dataset, experimental results and comparisons with the state-of-the-art methods show the effectiveness of the proposed scheme.

Biometric technologies are becoming much more important in various applications. Among them, iris recognition is considered as one of the most reliable and accurate technologies. In the preparation of iris recognition, the iris location will influence the performance of the entire system. This paper proposes a novel algorithm to locate iris and eyelids. Morphological operation is applied to remove eyelashes during iris boundary location. An optimal step length is calculated to reduce the searching time. Experimental results demonstrate that the proposed iris location algorithm is able to achieve a good performance with accuracy higher than 97.6%.