Relative Location Information Research Articles

A spatial map: a propitious choice for constraining the binding problem.

Many studies have shown that the human visual system has two major functionally distinct cortical visual pathways: a ventral pathway, thought to be important for object recognition, and a dorsal pathway, thought to be important for spatial cognition. According to our and others previous studies, artificial neural networks with two segregated pathways can determine objects' identities and locations more accurately and efficiently than one-pathway artificial neural networks. In addition, we showed that these two segregated artificial cortical visual pathways can each process identity and spatial information of visual objects independently and differently. However, when using such networks to process multiple objects' identities and locations, a binding problem arises because the networks may not associate each object's identity with its location correctly. In a previous study, we constrained the binding problem by training the artificial identity pathway to retain relative location information of objects. This design uses a location map to constrain the binding problem. One limitation of that study was that we only considered two attributes of our objects (identity and location) and only one possible map (location) for binding. However, typically the brain needs to process and bind many attributes of an object, and any of these attributes could be used to constrain the binding problem. In our current study, using visual objects with multiple attributes (identity, luminance, orientation, and location) that need to be recognized, we tried to find the best map (among an identity map, a luminance map, an orientation map, or a location map) to constrain the binding problem. We found that in our experimental simulations, when visual attributes are independent of each other, a location map is always a better choice than the other kinds of maps examined for constraining the binding problem. Our findings agree with previous neurophysiological findings that show that the organization or map in many visual cortical areas is primarily retinotopic or spatial.

Reversed trend comprehensive assessment-based inverter fault diagnosis under various complex interferences in microgrid

The healthy operation of inverter is the basic issues to ensure the quality of engineering power supply. However, the various complex interferences from microgrid engineering applications are relatively a difficult problem for fault diagnosis of inverter, e.g. harmonic components and unbalanced state. It increases the difficulty of the failure analysis, model design, algorithm realization and thresholds selection, even may lead to false alarm and erroneous triggering of protection units. Hence, a new fault diagnosis algorithm is proposed. Firstly, the reversed coordinate two-dimensional processing (RCT-dP) method is proposed to obtain reversed two-dimensional data (RTD) by inserting a moving reversed coordinate, which is very convenient for data attenuation segmentation and reversed trend extraction. Secondly, the obtained RTD are used for a proposed reversed trend comprehensive assessment (RTCA) method to obtain assessment variables based on multiple reversed trends and corresponding reliability, which can effectively extract the fault component, effectively reduce the influence of harmonic components and unbalanced state. Thirdly, the relative location information of detected signals are extracted. Then, the obtained assessment variables and relative location information are processed by variable regularization processing. Finally, the diagnosis results are obtained through a fault decision. Compared with existing fault diagnosis methods, the proposed fault diagnosis method can accurately detect and locate fault for any switch of inverter under harmonic components and unbalanced state. Meanwhile, because the fault diagnosis algorithm is based on saltatory driving, it can realize very low number of start-up without affecting the accuracy of the diagnosis. The experimental results are shown to validate the proposed algorithm.

Relative Localization Estimation for Multiple Robots via the Rotating Ultra-Wideband Tag

Most distributed algorithms for robot coordination require relative location information, but how to obtain relative locations in a distributed manner is still a primary problem to address in multi-robot applications. In order to obtain the relative locations between robots, no matter whether they are in relative motion or stationary situation, we design a rotating ultra-wideband tag to provide the persistency of excitation condition and two estimation algorithms to estimate the relative locations in a distributed manner. Moreover, our approach relies only on on-board sensors and requires only one ultra-wideband tag per robot, eliminating the need for any ground anchors, thus allowing deployment in GNSS-denied environments without range restrictions. The proposed approach in this letter is also tested in simulations and experiments to verify the theoretical findings and effectiveness in practice.

EEG emotion recognition based on the attention mechanism and pre-trained convolution capsule network

Given the rapid development of brain–computer interfaces, emotion identification based on EEG signals has emerged as a new study area with tremendous importance in recent years. EEG-based emotion recognition remains a challenging task in pattern recognition due to the complexity and diversity of the emotion signal, even though the deep learning models have significantly outperformed the conventional techniques in this area. In this paper, we propose an EEG emotion recognition model on the basis of the attention mechanism and a pre-trained convolution capsule network to recognize various emotions more effectively. This model employs coordinate attention to endow the input signal with relative spatial information and then maps the EEG signal to higher dimensional space, which enriches the emotion-related information in EEG. The pre-trained model, which excels at extracting features, is adopted as the feature extractor. Last but not least, a double-layer capsule network is constructed for emotion recognition to completely utilize the relative location information of EEG data. The subject-dependent and subject-independent experiments using DEAP datasets are individually conducted in this study, with the findings demonstrating that the suggested strategy presented excellent recognition accuracy and generalizability.

BDR-Net: Bhattacharyya Distance-Based Distribution Metric Modeling for Rotating Object Detection in Remote Sensing

Rotating object detection in remote sensing image has made substantial progress. However, the widely used feature extraction and fusion modules cannot accurately represent the spatial location and angle information of remote sensing objects in arbitrary orientation. A detection scheme that accurately expresses spatial angle characteristics is necessary when remote sensing objects are densely distributed. This article proposes an effective network for rotating object detection in optical remote sensing image (BDR-Net). First, a feature aggregation module (FAM) is designed to refine the feature extraction of rotating objects, which can generate refined feature maps. Second, 2-D Gaussian modeling simulates the rotation angle information of the object. Third, given that the Bhattacharyya distance is mathematically symmetric and rigorous, we design a Bhattacharyya-distance-based learning module (BDLM). We improve the loss function to more accurately guide the regression of the relative location information of a rotating box. The effectiveness of the proposed method is validated by the results of experiments on three public datasets: DOTA, DIOR, and HRSC2016. The results of our research prove that a Bhattacharyya distance with a Gaussian distribution could be used as an approximate regression loss of the object detection model. This article offers an original approach to combining object detection with metric learning to apply rotating object detection to instrumentation in a deployable and rapid manner.

Import vertical characteristic of rain streak for single image deraining

Recently, deep convolutional neural networks show good effect for single image deraining. These networks always adopt the conventional convolution method to extract features, which may neglect the characteristic of rain streak. A novelty vertical module is proposed to focus on the vertical characteristic of rain streak. Such module uses 1 \(\times X\) convolution kernel to extract the vertical information of rain streaks and a \(X \times X\) convolution kernel to keep relative location information. Use this module in the front of deraining network can better detach rain streaks from background. In addition, the contrastive learning is employed to improve the performance of the model. Extensive experimental results demonstrated the superiority of the deraining methods with the proposed methods in comparison with the base ones.

Embedding Refinement Framework for Targeted Aspect-Based Sentiment Analysis

The state-of-the-art approaches to targeted aspect-based sentiment analysis (TABSA) are mostly built on deep neural networks with attention mechanisms. One problem is that embeddings of targets and aspects are either pre-trained from large external corpora or randomly initialized. We argue that affective commonsense knowledge and words indicative of sentiment could be used to learn better target and aspect embeddings. We therefore propose an embedding refinement framework called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAEC</b> ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> efining <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ffective <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> mbedding from <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ontext), in which sentiment concepts extracted from affective commonsense knowledge and word relative location information are incorporated to derive context-affective embeddings. Furthermore, a sparse coefficient vector is exploited in refining the embeddings of targets and aspects separately. In this way, embeddings of targets and aspects can capture the highly relevant affective words. Experimental results on two benchmark datasets show that our framework can be easily integrated with existing embedding-based TABSA models and achieves state-of-the-art results compared to models relying on pre-trained word embeddings or built on other embedding refinement methods.

Structure-Coherent Deep Feature Learning for Robust Face Alignment.

In this paper, we propose a structure-coherent deep feature learning method for face alignment. Unlike most existing face alignment methods which overlook the facial structure cues, we explicitly exploit the relation among facial landmarks to make the detector robust to hard cases such as occlusion and large pose. Specifically, we leverage a landmark-graph relational network to enforce the structural relationships among landmarks. We consider the facial landmarks as structural graph nodes and carefully design the neighborhood to passing features among the most related nodes. Our method dynamically adapts the weights of node neighborhood to eliminate distracted information from noisy nodes, such as occluded landmark point. Moreover, different from most previous works which only tend to penalize the landmarks absolute position during the training, we propose a relative location loss to enhance the information of relative location of landmarks. This relative location supervision further regularizes the facial structure. Our approach considers the interactions among facial landmarks and can be easily implemented on top of any convolutional backbone to boost the performance. Extensive experiments on three popular benchmarks, including WFLW, COFW and 300W, demonstrate the effectiveness of the proposed method. In particular, due to explicit structure modeling, our approach is especially robust to challenging cases resulting in impressive low failure rate on COFW and WFLW datasets. The model and code are publicly available at https://github.com/BeierZhu/Sturcture-Coherency-Face-Alignment.

Indirect PIN Entry Method for Mobile Banking Using Relative Location Information of Secret Code

Indirect PIN Entry Method for Mobile Banking Using Relative Location Information of Secret Code

Point clouds learning with attention-based graph convolution networks

Point clouds, as a kind of 3D objects representation, are the most primitive outputs obtained by 3D sensors. Unlike 2D images, point clouds are disordered and unstructured. Hence the classification techniques such as the convolution neural network are not applicable to point cloud analysis directly. To solve this problem, we propose a novel network to extract point clouds feature, named attention-based graph convolutional network (AGCN). Taking the learning process as a message propagation between adjacent points, we specifically introduce attention mechanism to construct a point attention layer for analyzing the relationship between local points feature. The object classification is implemented by stacking multiple layers of point attention layer. In addition, the proposed network is extended to an attention-based encoder-decoder structure for segmentation tasks. We also introduce an additional global graph structure network to compensate for the relative location information of the individual points in the graph structure network. Experimental results show that our network has lower computational complexity and faster convergence speed. Compared with existing methods, the proposed network can achieve comparable performance in classification and segmentation tasks.

A new car logo extraction method based on license plate detection

Nowadays, intelligent transportation system is becoming the new trend because of the development of modern traffic, which serves to help gather traffic data, control traffic and improve road safety. One big challenge within intelligent transportation system is car model detection, which provides important information for traffic monitoring and planning. Currently, most car model extraction focus more on one-pass solution, which directly finds out the car logo region. In this paper, a new hybrid car logo extraction method based on license plate detection is proposed. First, a text detection is applied on car images to locate the license plate. Then, within a region of interest above license plate, a sub region is extracted to locate the car logo based on edge information. The fundamental concept is that using the relative location information between the license plate and car logo can take the most advantage of existing state-of-the-art license plates detection methods which is practically accurate, robust and efficient. Experimental results on OpenALPR dataset demonstrate its good performance in extracting car logos.

Cooperative localization for multiple AUVs based on the rough estimation of the measurements

The accuracy of cooperative localization for multiple autonomous underwater vehicles (AUVs) equipped with low precise proprioceptive localization sensors can be improved by using relative location information between individuals and Bayesian filtering. However, when the relative location measurement errors are high, its accuracy will be reduced. Two measurement for rough estimation algorithms under the constraint environment of the cooperative structure are developed in this paper: the first algorithm is based on the underwater acoustic isotropic transmission. And the second algorithm is based on the common observation environment. In the first algorithm, it builds under the assumption that the distance errors calculated from the simultaneous omnidirectional response signals from the same transmitting source have correlated. Similarly, in the second algorithm, the assumption that “common observation environment” is correlated is made. First, the correlation between the errors is used roughly to estimate the measurement of information. Then, a suitable filter is applied to fuse the rough estimation measurement with dead-reckoning estimation that improves the location estimation accuracy. The final simulation, by changing the AUV formation navigation paths and the sensor observation noises, shows the proposed processing methods have effectiveness and consistency compared to the traditional algorithm.

Distributed mobile sensor load balancing deployment algorithm based on probability coverage model

Load imbalance of distributed mobile sensors in distributed mobile sensor networks can easily lead to premature death of low-energy nodes, resulting in network partition and even network collapse, thus reducing the practicability of the network. Therefore, a distributed mobile sensor load balancing deployment algorithm based on Probabilistic Coverage Model (PCM) is proposed to deal with the relationship between residual energy of nodes and transmission power. With PCM as load balancing deployment condition, Lyapunov second method is used to deploy load balancing. The proposed distributed control law minimises the load difference among nodes. The algorithm only needs the relative location information of single hop neighbour nodes, and only carries out single hop communication. Therefore, the delay and communication load are small, and the scalability is strong. It confirms that the power system control module has a stable solution. The simulation results show that the algorithm can balance the load and energy consumption of nodes and improve the practicability of the network.

Distributed mobile sensor load balancing deployment algorithm based on probability coverage model

Load imbalance of distributed mobile sensors in distributed mobile sensor networks can easily lead to premature death of low-energy nodes, resulting in network partition and even network collapse, thus reducing the practicability of the network. Therefore, a distributed mobile sensor load balancing deployment algorithm based on Probabilistic Coverage Model (PCM) is proposed to deal with the relationship between residual energy of nodes and transmission power. With PCM as load balancing deployment condition, Lyapunov second method is used to deploy load balancing. The proposed distributed control law minimises the load difference among nodes. The algorithm only needs the relative location information of single hop neighbour nodes, and only carries out single hop communication. Therefore, the delay and communication load are small, and the scalability is strong. It confirms that the power system control module has a stable solution. The simulation results show that the algorithm can balance the load and energy consumption of nodes and improve the practicability of the network.

TagSort: Accurate Relative Localization Exploring RFID Phase Spectrum Matching for Internet of Things

The radio frequency identification (RFID) technologies, which have been widely adopted in different Internet of Things (IoT) applications, are the fundamental building block for achieving smart factories, smart logistics, smart stores, etc. Besides knowing the ID of tags, the relative location information of different tags is of great importance since it contains the spatial relationship among different tags, which is essential for many object localization applications beyond the capacity of absolute localization approaches. In this article, we propose TagSort , an RFID-based sorting system that exploits the physical layer information, i.e., the phase of RFID wireless signals to achieve the relative localization of different tags. Several novel filtering and peak detection algorithms are proposed to achieve accurate and robust detection of the order of tags. Extensive evaluation shows promising results (over 95% accuracy) and make TagSort a promising system for future RFID sorting systems, thus enabling a variety of IoT applications and services.

Action Recognition with Multiple Relative Descriptors of Trajectories

Dense trajectory has become one of the most successful hand-crafted features for action recognition. However, most of the existing dense trajectories based methods ignore the relationship between trajectories. In this paper, we propose multiple relative descriptors of trajectories to model the relative information of pairs of trajectories. Specifically, we present relative motion descriptors and relative location descriptors, which are utilized to capture the relative motion information and relative location information respectively. Moreover, we present relative deep feature descriptors which combine the deep features with hand-crafted features. By aggregating the above descriptors, we obtain the fixed-length representation regardless of the various duration of input video. The experimental results on three standard datasets demonstrate the superiority of our method.

Dense networks with relative location awareness for thorax disease identification.

Chest X-ray is one of the most common examinations for diagnosing heart and lung diseases. Due to the existing of a large number of clinical cases, many automated diagnosis algorithms based on chest X-ray images have been proposed. To our knowledge, almost none of the previous auto-diagnosis algorithms consider the effect of relative location information on disease incidence. In this study, we propose to use relative location information to assist the identification of thorax diseases. In this work, U-Net is used to segment lung and heart from chest image. The relative location maps are computed through Euclidean distance transformation from segmented masks. By introducing the relative location information into the network, the usual location of disease is combined with the incidence. The proposed network is the fusion of two branches: mask branch and image branch. A mask branch is designed to be a bottom-up and top-down structure to extract relative location information. The structure has a large receptive field, which can extract more information for large lesion and contextual information for small lesion. The features learned from mask branch are fused with image branch, which is a 121-layers DenseNet. We compare our proposed method with four state-of-the-art methods on the largest public chest X-ray dataset: ChestX-ray14. The proposed method achieves the area under a curve of 0.820, which outperforms all the existing models and algorithms. This paper proposed a dense network with relative location information to identify thorax disease. The method combines the usual location of disease with the incidence for the first time and performs good.

多谱段共孔径跟踪/引导系统光学设计

One of the important direction of the optical system is the co-aperture optical system, which takes advantage of optical system characters with long-focus and high resolution. This paper designed a tracking guiding system whose aperture of the optical system for visible light imaging, laser imaging and laser ranging was shared. The co-aperture design combined the high resolution visible light system with high resolution laser system, which made the system not only obtain high-definition images, but also get the relative location information. The co-aperture optical system could compress system size, reduce the rotational inertia when the optical system was in the process of tracking for the overall implementation of the system. The focal length of visible light subsystem is 1 200 mm; F number is 6; field of 1.2. The focal length of laser imaging subsystem is 1 500 mm; F number is 7.5. Each system imaging quality is close to the diffraction limit, and tolerance allocation results is verified by tolerance analysis.

多谱段共孔径跟踪/引导系统光学设计

One of the important direction of the optical system is the co-aperture optical system, which takes advantage of optical system characters with long-focus and high resolution. This paper designed a tracking guiding system whose aperture of the optical system for visible light imaging, laser imaging and laser ranging was shared. The co-aperture design combined the high resolution visible light system with high resolution laser system, which made the system not only obtain high-definition images, but also get the relative location information. The co-aperture optical system could compress system size, reduce the rotational inertia when the optical system was in the process of tracking for the overall implementation of the system. The focal length of visible light subsystem is 1 200 mm; F number is 6; field of 1.2. The focal length of laser imaging subsystem is 1 500 mm; F number is 7.5. Each system imaging quality is close to the diffraction limit, and tolerance allocation results is verified by tolerance analysis.

Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System.

Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream.