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IEEE International Conference on Systems, Man and Cybernetics

作者： Chengpei Wu Siyi Xu Junli Li School of computer science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu Sichuan China

The connectivity and controllability of complex networks play an important role in ensuring the proper functioning of network systems. Robustness of connectivity and controllability is the ability of a network to maintain its basic functions against various malicious attacks. Convolutional neural network (CNN)-based approaches provide an efficient framework to approximate the network robustness, which significantly reduces computation time compared to attack simulations. In this paper, the performance of CNN-based prediction for connectivity and controllability robustness is investigated, when there are noises in the network input representation. Two CNN-based predictors are compared and investigated, 1) convolutional neural network-based robustness predictor (CNN-RP), and 2) spatial pyramid pooling-based convolutional neural network (CNN-SPP). Two aspects of network information noises are considered and investigated, 1) the random node information noises (RNIN), and 2) the random edge information noises (REIN). The following main conclusions are obtained from extensive experimental studies on synthetic networks: 1) CNN-RP is more tolerant than CNN-SPP to network noises, 2) The characteristics of small-world and scale-free networks make them have a favorable anti-noise ability, and 3) RNIN has less impact on CNN-based prediction performance than REIN, RNIN and REIN show opposite effects on prediction performance when the size of the predicted network is out of the training network size range.

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Cross-Angle Facial Individual Verification of Giant Panda

Cross-Angle Facial Individual Verification of Giant Panda

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wu, Jiawei Su, Han Min, Peng He, Mengnan Wu, Pengcheng Luo, Gai Hou, Rong Chen, Peng College Computer Science Of Sichuan Normal University Chengdu China Visual Computing and Visual Reality Key Laboratory of Sichuan Province Chengdu China College of Sichuan Normal University Chengdu China Chengdu Research Base of Giant Panda China Chengdu Research Base of Giant Panda Breeding China Sichuan Normal University Chengdu Research Base of Giant Panda Breeding Chengdu China

ISBN: (纸本)9781665410205

Enhancing the protection of giant pandas necessitates a precise means of verifying their individual information. With the development of deep learning technology, some individual recognition methods for giant pandas have emerged. However, these methods often neglect cross-angle facial recognition, a common occurrence in natural settings, and predominantly focus on identification rather than verification, rendering them unsuitable for wild populations of unknown individuals. Cross-angle facial verification poses novel challenges, notably feature misalignment and geometric deformation induced by varying angles, particularly in the verification process reliant on comparing feature discrepancies. To address this issue, we developed the CrossPandaFace model. This model employs a Pixel Drift Unit (PDU) to adjust feature pixels and utilizes template features generated by the Template Generation Module (TGM) as a reference to align giant pandas from different angles onto a unified template. Furthermore, Multi-scale Feature Supplement (MSFS) compensates for the potential risk of losing local features of aligned features. Experimental results show state-of-the-art performance, thus affirming the efficacy of our model for cross-angle giant panda verification. © 2024 IEEE.

关键词： Deep learning

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Fast Collection of Target Tags Information for Large-Scale RFID Systems 20

Fast Collection of Target Tags Information for Large-Scale R...

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20th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Chu, Chu Fu, Ning Xu, Rui College of Computer Science Sichuan Normal University Chengdu610101 China The Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610101 China School of Network and Communication Engineering Chengdu Technological University Chengdu611730 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

Radio Frequency Identification (RFID) technology is greatly supporting a variety of life and industry. Enhanced by sensors, RFID tags can monitor real-time information about attached objects and the environment, making efficient collection of tag information an important task. However, most works focus on collecting all tags but lack effective mechanisms to avoid interfering from irrelevant tags when the application only cares about a part of the tags in some cases. Besides, in more common scenarios with multiple RFID readers, existing works fail to effectively determine the surrounding tags for each reader, increasing the execution time. To conquer these defects, we propose a new solution called the efficient Vector-based target tag Information Collection (VIC) protocol to accelerate the collection rate. First, we introduce a composite vector to silence irrelevant tags and determine the local tags. During this process, the expected empty slots are skipped and the useless information transmitted by tags is reduced. Second, we use an allocation vector to collect tag information by resolving tag collision, thus improving the time efficiency. Finally, we provide theoretical analyses to optimize the parameter settings. Extensive experiments prove that VIC outperforms existing solutions by up to 32% increase in time efficiency. © 2023 IEEE.

关键词： Efficiency

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ReconGait: Giant Panda Gait Recognition Based on Spatio-Temporal Feature Reconstruction

ReconGait: Giant Panda Gait Recognition Based on Spatio-Temp...

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International Joint Conference on Neural Networks (IJCNN)

作者： Peng Min Han Su Mengnan He Rong Hou Pinjia Que Peng Chen School of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu China Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu Research Base of Giant Panda Breeding Chengdu China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

As an endangered species, protecting and identifying individual giant pandas has always been a focus. However, traditional methods based on feces and molecular biology have inherent limitations. With the progress of deep learning, image analysis methods can deliver good results, but they rely on high-quality frontal facial images, which are challenging to acquire in the wild. Furthermore, these approaches have neglected the fact that a substantial portion of data from the wild is predominantly in the form of video, and have inadequately exploited the temporal information. Therefore, we apply gait recognition to identify individual giant pandas. By examining the unique walking patterns of individuals in the video, gait recognition allows accurate identification from various angles, long ranges, and without subject cooperation. This makes it ideal to identify wild individual giant pandas, but it also introduces new challenges, such as quadrupeds having more complex body gestures compared to humans, and the generally lower image quality taken by cameras in the wild. To tackle issues in natural environments, we design ReconGait, a giant panda gait recognition model capable of aligning multi-view images and restoring low-quality regions effectively. Experiments shows state-of-the-art results, proving the effectiveness of using gait analysis for individual giant panda identification. Through our work, we strive to contribute significantly to the preservation of this rare species and set a precedent for the integration of gait recognition in wildlife conservation efforts.

关键词： Endangered species Wildlife Neural networks Image restoration Quadrupedal robots Object recognition Environmental management

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Utilizing Multipath Effects for Mobile Charging

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IEEE Transactions on Mobile computing 2025年

作者： Gao, Jing Wu, Die Zhang, Linglin Li, Jingwen Yang, Jin Peng, Jian Liu, Tang Sichuan Normal University College of Computer Science Sichuan Chengdu610101 China Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University College of Computer Science Chengdu610065 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Chengdu610068 China

Recently, Wireless Rechargeable Sensor Networks (WRSNs) have emerged as a promising solution to address the energy limitations of wireless sensor networks. In practical applications of WRSNs, environmental objects are ubiquitous, reflecting radio waves and causing them to reach sensors via multiple paths. These multipath effects significantly impact the power intensity received by sensors. In this paper, we study a fundamental issue of charGing schEduling with mulTipath effectS (GETS), that is, how to schedule a mobile charger by comprehensively considering the multipath effects to maximize the overall charging utility. To this end, we first establish a charging model with environmental objects to investigate the impact of multipath effects on power distribution. Then, we propose a charging scheduling scheme that not only selects a series of sojourn locations for the MC (Mobile Charger) to maximize the total power received by nearby sensors but also construct a charging path that avoids environmental objects. We conduct extensive simulations as well as indoor and outdoor field experiments to evaluate the performance of our scheme. The results demonstrate that, on average, our scheme outperforms baseline algorithms by 48.87%. © 2002-2012 IEEE.

关键词： Charging stations

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Age Estimation of Giant Pandas Based on Narrowed Window Ordered Regression

Age Estimation of Giant Pandas Based on Narrowed Window Orde...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zihao Zhang Han Su Rong Hou Pengcheng Wu Ping Xu Peng Chen School of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu China Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu Research Base of Giant Panda Breeding Chengdu China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The age estimation of giant pandas is a crucial aspect of panda conservation efforts. Traditional methods for giant panda age estimation are time-consuming and expensive. Previous research has explored computer vision approaches for this task, primarily focusing on standardized frontal images of giant pandas. However, capturing clear frontal photos in the wild is challenging. In this study, we proposed a method based on Deep Convolutional Neural Networks (DCNN) for age estimation using multi-angle facial images of giant pandas. We expanded the original giant panda frontal age dataset into a multi-angle dataset, consisting of 13,524 facial images from 285 pandas. To enhance the model’s ability to fit multi-angle data, we incorporated deformable convolutions during feature extraction. We introduced a narrowed window ordered regression (NWR), and comparisons across multiple images to reduce spatial distances between feature vectors from different angles. The final model achieved a Mean Absolute Error (MAE) of 3.38. demonstrating superior performance even on frontal data. The results indicate the effectiveness of our approach in utilizing multi-angle facial data for wild giant panda age estimation. Computer vision technologies showcase their potential for meaningful contributions to wildlife conservation efforts.

关键词： Deformable models Computer vision Wildlife Neural networks Estimation Focusing Feature extraction

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An Effective Deployment Scheme for Elimination of Phase Cancellation in Backscatter-based WPCN

An Effective Deployment Scheme for Elimination of Phase Canc...

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IEEE Conference on Wireless Communications and Networking

作者： Qiang Wang Yuzhuo Ma Chenglong Zhang Tang Liu Jilin Yang Dié Wu College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Lab Sichuan Normal University Chengdu China

Without the need for batteries, backscatter-based Wireless Powered Communication Network (WPCN) has been envisioned as a promising alternative to conventional wireless networks. Unfortunately, the unique phase cancellation problem in backscatter-based WPCN is essentially a phenomenon that severely affects connectivity and reliability of the network. Many arts have tried to tackle this issue either by using multiple antennas to employ the signal diversity, which increases the size and is not cost-efficient, or by making a repetition of the same information with different load impedances, which significantly decreases the throughput of network. In our paper, we propose an effective deployment scheme, aiming to fundamentally eliminate the phase cancellation problem. Specifically, we first build a practical communication model seeking the blind areas caused by phase cancellation. Then, a greedy algorithm and a minimum-weight graph based algorithm are proposed to elaborate topology of the network to ensure the connectivity. Finally, extensive experiments are carried out to evaluate the performance.

关键词： Greedy algorithms Network topology Wireless networks Throughput Topology Reliability Communication networks

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Dynamic Semi-structured Data Clustering Based on Frequently Changing Structure

Dynamic Semi-structured Data Clustering Based on Frequently ...

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Cloud computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Junren Yang Wei Li College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Visual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China

In order to cluster dynamic semi-structured data documents, a dynamic semi-structured data clustering algorithm based on frequently changing structure is proposed. The algorithm uses dynamic model to store the historical change information of dynamic semi-structured data, builds frequent structure path tree based on the spatial frequent changing structure, uses frequent structure path tree to calculate document similarity, and finally uses DBSCAN algorithm to cluster. Experimental results show that the Jaccard coefficient and Rand index of the proposed method are above 0.99, and the performance consumption and clustering effect are better than the traditional static clustering algorithm.

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IC-CViT: Inverse-Consistent Convolutional Vision Transformer for Diffeomorphic Image Registration

IC-CViT: Inverse-Consistent Convolutional Vision Transformer...

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International Joint Conference on Neural Networks (IJCNN)

作者： Tao Xu Ting Jiang Xiaoning Li College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu China Sichuan 2011 Collaborative Innovation Center for Educational Big Data Chengdu China

Diffeomorphic registration plays a crucial role in medical image analysis due to the invertible and one-to-one mapping transformation. In recent years, with the development of deep learning technology, convolutional neural networks (CNNs) have been a broad focus of research in medical image registration, and CNN-based methods have made great progress. However, the results of most existing methods generally are not necessarily diffeomorphic, generating implausibly bijective mappings between images due to the interpolation and discrete representation. Furthermore, the performances of CNNs may be limited by a lack of precise comprehension of global and long-range cross-image spatial relevance. Vision Transformer (ViT) is capable of enhancing the long-distance information interaction ability to identify the semantically anatomically correspondences of medical images. Compared with CNN, ViT has weak local feature extraction ability due to less inductive bias, especially in small-scale training datasets, meaning that the samples between adjacent pixels cannot be exploited adequately. To address the above challenges, we propose a novel Inverse-Consistent Convolutional Vision Transformer (IC-CViT) network for diffeomorphic image registration. Specifically, image pairs can explicitly conduct bi-directional registration through the predicted deformation filed, generated within the diffeomorphic mappings space and restricted by the proposed inverse consistent loss term. We verify our method on two 3D brain MRI scan datasets including OASIS and LPBA40. Comprehensive results demonstrate that IC-CViT achieves state-of-the-art registration accuracy while maintaining desired diffeomorphic properties.

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An Optimization Method of Primer Design Based on Attention-BiLSTM

An Optimization Method of Primer Design Based on Attention-B...

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Robotics, Artificial Intelligence and Intelligent Control (RAIIC), International Conference on

作者： Binhao Bai Jinyu Long Zhibo Yang Junli Li Ping Wei College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China Sichuan Key Laboratory of Translational Medicine of Traditional Chinese Medicine Sichuan Academy of Traditional Chinese Medicine Sichuan Center of Translational Medicine Chengdu China

In this paper, we propose a method to predict the success of primer amplification based on the relationship existing between the sequence of primer and template, which can optimize the primer design and select the primer with better amplification from the candidate primer set. The double-stranded structure between primer and template nucleotide sequences is represented here by a number of words, each consisting of five characters that form sentences, as the dataset for the experiment, which is learned using an attention-based mechanism of bidirectional long short-term memory neural network model (Attention-BiLSTM), and then predicts primer amplification. The model predicted the results of polymerase chain reaction (PCR) involving specific primers and specific DNA templates with 82% accuracy, an improvement of about 2% over the performance of the LSTM with more stable value. These results show that the model can be used to effectively predict the results of PCR. This is the first paper to optimize primer design by screening the candidate primer set with a neural network model.

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