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2023 IEEE International Conference on Electro Information technology, eIT 2023

作者： Singh, Gurpreet Shekhar, Abhishek Raj Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

The new era of technology is being greatly influenced by the field of artificial intelligence. computer vision and deep learning have become increasingly important due to their ability to process vast amounts of data and provide insights and solutions in a variety of fields. computer vision, deep learning and signal analysis have been used in a growing number of applications and services including smart devices, image, and speech recognition, healthcare, etc., one such device is an infant monitoring system. It monitors the daily activities of the infant such as their sleeping patterns, sounds, and movements. In this paper, deep learning and computer vision libraries were used to develop algorithms to detect whether the infant was in any uncomfortable situation such as sleeping on its back, face being covered and whether the infant was awake. The smart infant monitoring system detects the infant's unsafe resting situation in real time and sent immediate alerts to the caretaker's device. This paper presents the design flow of a smart infant monitoring system consisting of a night vision camera, a Jetson Nano, and a Wi-Fi internet connection. The pose estimation and awake detection algorithms were developed and tested successfully for different infant resting/sleeping situations. The smart infant monitoring system provides significant benefits for safety and an improved understanding of infants' sleep patterns and behavior. © 2023 IEEE.

关键词： Face recognition

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BD-YOLO: Optimising Insect Imbalance Data Detection Models

BD-YOLO: Optimising Insect Imbalance Data Detection Models

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

作者： Wang, Jian-Yi Jia, Zhen-Hong Zhou, Gang Wang, Jia-Jia School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Uygur Autonomous Region Signal Detection and Processing Key Laboratory Xinjiang University Urumqi830046 China

ISBN: (纸本)9781665410205

Vision-based insect detection is an important task in smart agriculture. Field-collected insect image datasets usually show severe imbalances in categories. Previous models have insufficient accuracy as well as low real-time performance for detecting unbalanced data of insects. To address the issues described above, an improved framework Balance Detect YOLO(BD-YOLO) based on YOLOv8 is proposed in this paper. We innovatively designed Focaler-CPIoU loss to increase the weight of a few classes of samples to achieve the optimisation of pest imbalance data detection. We introduced lightweight convolutional MBConv to improve the recognition accuracy while reducing the number of model parameters. In order to verify the effectiveness of the method in this paper, we established an insect dataset containing six types of insects, in which the most numerous thrips is 431.5 times more than the least numerous chrysopaperla. The experimental results show that compared with YOLOv8, the mAP50 reaches 61.7% with a 73.1% reduction in the number of model parameters, an improvement of 5.4%, and an improvement of 769 in FPS. A feasible approach to optimise the detection of unbalanced number of insects is provided in this paper along with the ability to be deployed on lightweight hardware devices. © 2024 IEEE.

关键词： Invertebrates

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Improving Speaker Verification Back-End with Graph Neural Networks

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Chen, Jinfeng Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

Currently, research on speaker verification tasks is primarily concentrated on enhancing deep speaker models to extract high-quality speaker embeddings. Nevertheless, this speaker embeddings can be regarded as potential graph structures. Therefore, this paper introduces a graph neural network-based speaker verification back-end approach, treating the speaker embeddings derived from the front-end as graph structures and leveraging graph neural networks to uncover the relationships among embeddings, thereby achieving superior-quality speaker embeddings. In addition, we propose a group updating method to solve the problem of memory overflow when the number of nodes is excessive. Extensive experiments and ablation studies were carried out on the VoxCeleb dataset, and the experimental outcomes validate the effectiveness of our proposed graph neural network speaker back-end in significantly boosting the performance of speaker verification systems. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Graph embeddings

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Target detection algorithm with wide field of view in foggy weather based on improved CSLBP operator 4

Target detection algorithm with wide field of view in foggy ...

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4th International Conference on Image processing and Intelligent Control, IPIC 2024

作者： Lian, Xiya Jia, Zhenhong Zhou, Gang Wang, Jiajia Song, Sensen Xiao, Xiaohui Shi, Fei College of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510682313

In order to solve the problem of unclear foreground targets and false foregrounds after performing target detection tasks in foggy video images with wide fields of view, we propose an improved video target detection algorithm for the CSLBP operator to detect foreground targets in different scenes. The improved CSLBP operator is used to extract the texture features of the wide-field image, and the foreground target pixels are judged combined with background modeling. This method is not sensitive to changes in indoor lighting or outdoor sunlight, can effectively solve the shadow problem, and more completely extracts foreground moving targets. It also has obvious effects in bad weather such as foggy weather, and has good robustness. © 2024 SPIE.

关键词： Image enhancement

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Anomalous Sound Detection Using Time-Frequency Feature and Mixbatch

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Journal of Shanghai Jiaotong University (Science) 2025年 1-8页

作者： Huang, Shun Zhang, Yunxiang Fang, Zhihua Tang, Minrui Xu, Ruifeng He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The sound emitted by machines under abnormal working conditions exhibits various frequency patterns. Currently, the most advanced anomalous sound detection (ASD) approach is to apply a multi-head self-attention mechanism to the Log-Mel spectrogram for automatic frequency pattern analysis. However, the Log-Mel spectrogram may filter out high-frequency components of abnormal sounds;thus the use of self-attention mechanisms on Log-Mel spectrogram seems to have certain limitations. In this paper, we construct a simple convolutional neural network to extract comprehensive frequency features from raw audio to complement spectral-temporal information fusion. The parameters of this neural network are continuously updated during the training process to extract better frequency features for downstream classification neural networks in ASD. Additionally, a method for data augmentation in the batch dimension was developed to help the classification model learn both types of comprehensive features simultaneously. The proposed method achieved an AUC of 94.13% and a pAUC of 89.09% on the DCASE 2020 Challenge Task 2 dataset. Even when using only the features proposed in this paper, an AUC of 84.62% was achieved. (Figure presented.). © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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Dual-Path Spectrogram Refinement Network for Robust Speaker Verification

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Wang, Zonghui Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The accuracy and reliability of automatic speaker verification (ASV) face significant challenges in noisy environments. In recent years, joint training of speech enhancement front-end and ASV back-end has been widely applied to improve the robustness of ASV systems. Traditional joint training directly uses the enhanced speech features as the input of the back-end. However, the diversity of noise types and noise intensities will excessively suppress the enhanced features, resulting in speech distortion or residual noise. To alleviate this problem, we propose a dual-path spectrogram refinement network that enables the enhanced features to learn supplementary information from the separated noise features. In addition, we incorporate coordinate attention into the overall joint architecture to capture more comprehensive frequency and temporal information of the speaker from different spatial positions. We conduct extensive experiments on the VoxCeleb1 test set, the out-of-domain noise test set, and the VOiCES corpus. The experimental results demonstrate that our proposed method significantly improves the accuracy and robustness of speaker verification systems in both clean and noisy environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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Attention Mechanism Based Technical Phrase Extraction for Patent Text 8

Attention Mechanism Based Technical Phrase Extraction for Pa...

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8th International Conference on Intelligent Computing and signal processing, ICSP 2023

作者： Jiang, Xin Zhou, Peng Zhao, Shu Guo, Yufeng Anhui University School of Computer Science and Technology Hefei China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China

ISBN: (纸本)9798350302455

Technological innovation is becoming one of the critical factors in promoting social development all over the world. The vigorous development of patent applications in recent years provides an opportunity to reveal the inherent laws of innovation, but it also puts forward higher requirements for patent mining technology. An essential step in patent text mining is to build a technical portrait for each patent, that is, to identify the technical phrases involved, which can summarize and represent the patent from a technical perspective. Previous technical phrase extraction methods thoroughly used technical phrases' characteristics and the relationship between technical phrases. Regarding our observations, the relationship between patent texts and technical phrases is also essential. Specifically, critical technical phrases are more relevant to the patent text and can be discovered by the attention mechanism. Motivated by this, we propose an unsupervised technical phrase extraction method based on the attention mechanism named UTESC. Self-attention captures the importance of technical phrases in sentences, and cross-attention captures the relevance between technical phrases and patents. Extensive experiments and algorithm comparisons on patent datasets have proven the effectiveness of our algorithm. © 2023 IEEE.

关键词： Patents and inventions

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Meta-feature-based Concept Evolution Detection on Feature Streams 8

Meta-feature-based Concept Evolution Detection on Feature St...

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8th International Conference on Intelligent Computing and signal processing, ICSP 2023

作者： Guo, Yufeng Zhou, Peng Zhang, Yanping Jiang, Xin Anhui University School of Computer Science and Technology Hefei China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China

ISBN: (纸本)9798350302455

Data in the real world is often not static but generated and processed in streams, such as real-time adjustment of device setting parameters and real-time GPS positioning data. Feature streams means the number of samples is fixed, and their features are generated and arrive individually over time. A significant challenge of learning from online streaming data is a phenomenon known as concept evolution, that the concept of the data may change over time. In the streaming feature scenario, we define meta-features as univariate statistics describing data distribution and use meta-features to capture the data distribution and statistical properties of concepts. Therefore, an efficient Meta-Feature-based Concept Evolution Detection framework on Feature Streams (MF-CED-FS) is proposed, which consists of a sliding window, meta-feature vector similarity discrimination, and a concept detection method based on a weighted bipartite graph. Extensive experiments on real-world high-dimensional datasets verify the effectiveness of MF-CED-FS. © 2023 IEEE.

关键词： Feature extraction

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Tor encrypted traffic recognition and classification technology based on flow information graph and deep learning

Tor encrypted traffic recognition and classification technol...

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2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Zhang, Yu Jia, Zhenhong Huang, Xiaohui Wang, Jiajia Zhou, Gang Shi, Fei Lu, Changwu School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Xinjiang Uygur Autonomous Region Urumqi830046 China

ISBN: (纸本)9781510681514

Internet traffic analysis is the core approach to network management and security. In the rapidly changing environment of encrypted traffic, traditional plaintext-based analysis methods have become obsolete. Although there are currently some methods for analysing encrypted traffic, they overlook the inherent logic and hierarchy of different encrypted traffic analysis requirements and lack research into the essential characteristics of encrypted *** article proposes a framework FM-ENet based on Graph Neural Network and Deep learning for Tor encrypted traffic classification to meet the practical needs of network management and security monitoring.A Flow Information Graph has been designed on the basis of Graph Neural Network, which can effectively alleviate the problem of concept *** the area of deep neural networks, we have developed an end-to-end multi-level spatio-temporal feature fusion enhanced network module ST-FENet, which has the advantage of automatically learning the non-linear relationship between input and output data without the need for manual feature extraction,this structure can complement each other in classification performance and solve the problem of low classifier efficiency. We compared FM-ENet to current popular methods using the public ISCX-Tor dataset. FM-ENet can achieve higher performance while saving costs, with a 9% improvement in accuracy compared to FlowPrint and a 4.4%, 4.5% and 4.4% improvement in PR, RC and F1 indicators compared to TSCRNN. © 2024 SPIE.

关键词： Network security

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Coarse-to-fine change detection in low-illumination video sequences via classifier trained on ResNet18

Coarse-to-fine change detection in low-illumination video se...

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2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Lin, Jiajun Jia, Zhenhong School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510681514

In low-illumination environments, the contrast between targets and the background sharply decreases, and imaging sensors introduce complex random noise while capturing more light, leading to severe distortion in the target features of visible light images. Under these conditions, target feature-based detection algorithms face significant challenges. In this paper, we propose a coarse-to-fine change detection algorithm that detects targets in low-illumination environments by focusing on the change features generated by the targets. First, a binary classifier based on ResNet18 is trained with a specially curated dataset of targets and noise. Second, this classifier is applied to identify potential change objects in test data, preserve targets, and extract corresponding bi-temporal local regions of interest. Third, novel difference feature extraction operators are employed to generate local difference images. Next, a Laplacian-of-Gaussian-based graph cut algorithm is used to perform binary segmentation, distinguishing foreground from background in the images. We validated the feasibility of this algorithm in three challenging night scenes. Compared with the current state-of-the-art unsupervised change detection algorithms, the proposed algorithm shows overall better detection performance. © 2024 SPIE.

关键词： Change detection

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