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BmmW: A DNN-based joint BLE and mmWave radar system for accurate 3D localization with-oriented communication

PERVASIVE AND MOBILE COMPUTING 2024年 102卷

作者： Li, Peizheng Singh, Jagdeep Cui, Han Boano, Carlo Alberto Toshiba Europe Ltd Bristol Res & Innovat Lab Staines Upon Thames England Univ Bristol Bristol England Graz Univ Technol Inst Tech Informat Graz Austria

Bluetooth Low Energy (BLE) has emerged as one of the reference technologies for the development of indoor localization systems, due to its increasing ubiquity, low-cost hardware, and to the introduction of direction -finding enhancements improving its ranging performance. However, the intrinsic narrowband nature of BLE makes this technology susceptible to multipath and channel interference. As a result, it is still challenging to achieve decimetre -level localization accuracy, which is necessary when developing location -based services for smart factories and workspaces. To address this challenge, we present BmmW, an indoor localization system that augments the ranging estimates obtained with BLE 5.1's constant tone extension feature with mmWave radar measurements to provide 3D localization of a mobile tag with decimetre -level accuracy. Specifically, BmmW embeds a deep neural network (DNN) that is jointly trained with both BLE and mmWave measurements, practically leveraging the strengths of both technologies. In fact, mmWave radars can locate objects and people with decimetre -level accuracy, but their effectiveness in monitoring stationary targets and multiple objects is limited, and they also suffer from a fast signal attenuation limiting the usable range to a few meters. We evaluate BmmW's performance experimentally, and show that its joint DNN training scheme allows to track mobile tags with a mean 3D localization accuracy of 10 cm when combining angle -of -arrival BLE measurements with mmWave radar data. We further assess two variations of BmmW: BmmW- Lira and BmmW- Lira+ , both tailored for single -antenna BLE devices, which eliminates the necessity for bulky and expensive multi -antenna arrays and represents a cost-effective solution that is easy to integrate into compact IoT devices. In contrast to classic BmmW (which utilizes angle -of -arrival info), BmmW- Lira uses raw in-phase/quadrature (I/Q) measurements, and achieves a mean localization accuracy of 3

关键词： Indoor localization Data fusion Deep neural networks Bluetooth direction-finding mmWave-radar sensing Goal-oriented communications variational autoencoder

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Deep multi-sphere support vector data description based on disentangled representation learning

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PATTERN RECOGNITION 2024年 156卷

作者： Xing, Hong-Jie Wu, Hui-Nan Zhang, Ping-Ping Hebei Univ Sch Cyber Secur & Comp Baoding 071000 Peoples R China Hebei Univ Sch Math & Informat Sci Hebei Key Lab Machine Learning & Computat Intellig Baoding 071002 Peoples R China Hebei Meteorol Bur Hebei Meteorol Informat Ctr Shijiazhuang 050021 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

Deep support vector data description (Deep SVDD) combines deep mapping network and support vector data description (SVDD) to jointly optimize network connection weights and hypersphere volume. However, when the parameters of deep mapping network are set improperly, Deep SVDD may face the problem of hypersphere collapse, where all input data are mapped as the hypersphere center. To overcome the hypersphere collapse problem of Deep SVDD and improve the feature learning ability of deep mapping network, deep multi-sphere SVDD based on disentangled representation learning (DMSVDD-DRL) is proposed. DMSVDD-DRL consists of a variational autoencoder (VAE) and multiple hyperspheres. The feature representations obtained by VAE are disentangled into discriminative representations and generative representations that obey mixture t-distribution and Gaussian distribution, respectively. In the pre-training phase of DMSVDD-DRL, the network parameters and the hypersphere centers are initialized. In the training phase, the augmented data are added into the training set. The discriminative representations of both the input and augmented data are generated through the mapping network. Furthermore, multiple hyperspheres are constructed by the obtained discriminative representations in the feature space. Finally, the VAE loss of the input data, the reconstruction error of the augmented data, the augmentation loss between the input and augmented data, the average radius of the multiple hyperspheres, and the average distance from discriminative representations to their corresponding hypersphere centers are jointly minimized to obtain the optimal network connection weights and the multiple minimum volume hyperspheres. The effectiveness of the proposed DMSVDD-DRL is validated through the comparative and ablation experiments on the benchmark data sets. In addition, it is verified that DMSVDD-DRL is more robust against outliers in comparison with its related methods.

关键词： Deep support vector data description Disentangled representation learning variational autoencoder Hypersphere collapse Anomaly detection

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A survey on neural topic models: methods, applications, and challenges

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第2期57卷 18-18页

作者： Wu, Xiaobao Nguyen, Thong Luu, Anh Tuan Nanyang Technol Univ Sch Comp Sci & Engn 50 Nanyang Ave Singapore 639798 Singapore Natl Univ Singapore Sch Comp 21 Lower Kent Ridge Rd Singapore 119077 Singapore

Topic models have been prevalent for decades to discover latent topics and infer topic proportions of documents in an unsupervised fashion. They have been widely used in various applications like text analysis and context recommendation. Recently, the rise of neural networks has facilitated the emergence of a new research field-neural topic models (NTMs). Different from conventional topic models, NTMs directly optimize parameters without requiring model-specific derivations. This endows NTMs with better scalability and flexibility, resulting in significant research attention and plentiful new methods and applications. In this paper, we present a comprehensive survey on neural topic models concerning methods, applications, and challenges. Specifically, we systematically organize current NTM methods according to their network structures and introduce the NTMs for various scenarios like short texts and cross-lingual documents. We also discuss a wide range of popular applications built on NTMs. Finally, we highlight the challenges confronted by NTMs to inspire future research.

关键词： Neural topic model Cross-lingual topic modeling Dynamic topic modeling Short text topic modeling variational autoencoder

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Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7952页

作者： Pan, Jianguo Dong, Zhengyang Yan, Lijun Cai, Xia Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 200234 Peoples R China

Knowledge tracing is a significant research area in educational data mining, aiming to predict future performance based on students' historical learning data. In the field of programming, several challenges are faced in knowledge tracing, including inaccurate exercise representation and limited student information. These issues can lead to biased models and inaccurate predictions of students' knowledge states. To effectively address these issues, we propose a novel programming knowledge tracing model named GPPKT (Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing), which enhances performance by using knowledge graphs and personalized answer sequences. Specifically, we establish the associations between well-defined knowledge concepts and exercises, incorporating student learning abilities and latent representations generated from personalized answer sequences using variational autoencoders (VAE) in the model. This deep knowledge tracing model employs Long Short-Term Memory (LSTM) networks and attention mechanisms to integrate the embedding vectors, such as exercises and student information. Extensive experiments are conducted on two real-world programming datasets. The results indicate that GPPKT outperforms state-of-the-art methods, achieving an AUC of 0.8840 and an accuracy of 0.8472 on the Luogu dataset, and an AUC of 0.7770 and an accuracy of 0.8799 on the Codeforces dataset. This demonstrates the superiority of the proposed model, with an average improvement of 9.03% in AUC and 2.02% in accuracy across both datasets.

关键词： knowledge graph programming learning variational autoencoder knowledge tracing LSTM

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PCGen: A Fully Parallelizable Point Cloud Generative Model

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SENSORS 2024年第5期24卷 1414页

作者： Vercheval, Nicolas Royen, Remco Munteanu, Adrian Pizurica, Aleksandra Univ Ghent Fac Engn & Architecture Dept Telecommun & Informat Proc Res Grp Artificial Intelligence & Sparse Modelling B-9000 Ghent Belgium Univ Ghent Fac Engn & Architecture Dept Elect & Informat Syst Clifford Res Grp B-9000 Ghent Belgium Vrije Univ Brussel Dept Elect & Informat ETRO Fac Engn B-1050 Brussels Belgium

Generative models have the potential to revolutionize 3D extended reality. A primary obstacle is that augmented and virtual reality need real-time computing. Current state-of-the-art point cloud random generation methods are not fast enough for these applications. We introduce a vector-quantized variational autoencoder model (VQVAE) that can synthesize high-quality point clouds in milliseconds. Unlike previous work in VQVAEs, our model offers a compact sample representation suitable for conditional generation and data exploration with potential applications in rapid prototyping. We achieve this result by combining architectural improvements with an innovative approach for probabilistic random generation. First, we rethink current parallel point cloud autoencoder structures, and we propose several solutions to improve robustness, efficiency and reconstruction quality. Notable contributions in the decoder architecture include an innovative computation layer to process the shape semantic information, an attention mechanism that helps the model focus on different areas and a filter to cover possible sampling errors. Secondly, we introduce a parallel sampling strategy for VQVAE models consisting of a double encoding system, where a variational autoencoder learns how to generate the complex discrete distribution of the VQVAE, not only allowing quick inference but also describing the shape with a few global variables. We compare the proposed decoder and our VQVAE model with established and concurrent work, and we prove, one by one, the validity of the single contributions.

关键词： point clouds autoencoder variational autoencoder vector-quantized variational autoencoder real-time computing

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An Analysis on Disentanglement in Machine Learning 30

An Analysis on Disentanglement in Machine Learning

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30th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Mogultay, Hazal Kalkan, Sinan Vural, Fatos T. Yarman Orta Dogu Tekn Univ Bilgisayar Muhendisligi Ankara Turkey

ISBN: (纸本)9781665450928

Learnt representations by Deep autoencoders is not capable of decomposing the complex information into simple notion. In other words, attributes of samples are entangled in the basis vectors spanning the learned space. This leads to significant errors in deep learning algorithms. In order to avoid these errors, it is necessary to separate the feature space according to the common features shared between classes and to define a simple subspace for each feature. This approach has led to the birth of a new paradigm in Machine Learning, called disentanglement. Roughly, disentangled models can be defined as models that can independently learn the different components of the probability density function that produces the dataset in the feature space. Unfortunately, it is not always possible to learn these models. For this reason, there is still no easily applicable mathematical definition of disentanglement in the literature. In this study, a mathematical definition of the concept of disentanglement will be made and methods and metrics related to this approach will be discussed.

关键词： Disentanglement variational autoencoder representation learning

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ROBUST DISENTANGLED variational SPEECH REPRESENTATION LEARNING FOR ZERO-SHOT VOICE CONVERSION 47

ROBUST DISENTANGLED VARIATIONAL SPEECH REPRESENTATION LEARNI...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Lian, Jiachen Zhang, Chunlei Yu, Dong Univ Calif Berkeley EECS Berkeley CA 94720 USA Tencent AI Lab Bellevue WA 98004 USA

ISBN: (纸本)9781665405409

Traditional studies on voice conversion (VC) have made progress with parallel training data and known speakers. Good voice conversion quality is obtained by exploring better alignment modules or expressive mapping functions. In this study, we investigate zeroshot VC from a novel perspective of self-supervised disentangled speech representation learning. Specifically, we achieve the disentanglement by balancing the information flow between global speaker representation and time-varying content representation in a sequential variational autoencoder (VAE). A zero-shot voice conversion is performed by feeding an arbitrary speaker embedding and content embeddings to the VAE decoder. Besides that, an on-the-fly data augmentation training strategy is applied to make the learned representation noise invariant. On TIMIT and VCTK datasets, we achieve state-of-the-art performance on both objective evaluation, i.e., speaker verification (SV) on speaker embedding and content embedding, and subjective evaluation, i.e., voice naturalness and similarity, and remains to be robust even with noisy source/target utterances.

关键词： Self-supervised Disentangled representation learning zero-shot style transfer voice conversion variational autoencoder

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Learning Based Joint Coding-Modulation for Digital Semantic Communication Systems 14

Learning Based Joint Coding-Modulation for Digital Semantic ...

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14th IEEE International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Bo, Yufei Duan, Yiheng Shao, Shuo Tao, Meixia Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Cyber Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781665450850

In learning-based semantic communications, neural networks have replaced different building blocks in traditional communication systems. However, the digital modulation still remains a challenge for neural networks. The intrinsic mechanism of neural network based digital modulation is mapping continuous output of the neural network encoder into discrete constellation symbols, which is a non-differentiable function that cannot be trained with existing gradient descend algorithms. To overcome this challenge, in this paper we develop a joint coding-modulation scheme for digital semantic communications with BPSK modulation. In our method, the neural network outputs the likelihood of each constellation point, instead of having a concrete mapping. A random code rather than a deterministic code is hence used, which preserves more information for the symbols with a close likelihood on each constellation point. The joint coding-modulation design can match the modulation process with channel states, and hence improve the performance of digital semantic communications. Experiment results show that our method outperforms existing digital modulation methods in semantic communications over a wide range of SNR, and outperforms neural network based analog modulation method in low SNR regime.

关键词： Semantic communications variational autoencoder digital modulation

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3D U-Net with Trans-coder for Brain Tumor Segmentation 13

3D U-Net with Trans-coder for Brain Tumor Segmentation

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13th International Conference on Graphics and Image Processing (ICGIP)

作者： Zhang, Tingting Xu, Dan He, Kangjian Zhang, Hao Fu, Yuting Yunnan Univ Sch Informat Sci & Engn Kunming Yunnan Peoples R China

ISBN: (数字)9781510650435

ISBN: (纸本)9781510650435;9781510650428

3D Medical image segmentation is a basic and key task in computer-aided diagnosis, and glioma has a high degree of non-uniformity and irregular shape in multimodal MRI images. Therefore, accurate and reliable segmentation of brain tumors is still a challenging work in medical image analysis. U-Net has become the de facto standard in various medical image segmentation tasks and has achieved great success. However, due to the inherent locality of convolution operations, U-Net is generally limited in terms of explicitly modeling long-term dependencies. Although this problem is solved in Transformer, it has extreme complexity in terms of calculation and space when processing high-resolution 3D feature maps. In this paper, we propose the Trans-coder, which is embedded in the end of the U-Net encoder to improve the segmentation performance while reducing the amount of calculation. The Trans-coder takes the feature map from U-Net as the input sequence, and extracts the relative position information of the feature map, so as to get more detailed information of the image, and input it into the decoder to obtain good segmentation performance. At the same time, a variational autoencoder is used for regularization to prevent over-fitting problems. our method achieves superior performances to certain competing methods on the Multimodal Brain Tumor Segmentation Challenge (Brats) dataset.

关键词： 3D Medical image segmentation Trans-coder variational autoencoder 3D U-Net

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Proposing Novel High-Performance Compounds by Nested VAEs Trained Independently on Different Datasets 35th

Proposing Novel High-Performance Compounds by Nested VAEs Tr...

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35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Osakabe, Yoshihiro Asahara, Akinori Hitachi Ltd Res & Dev Grp Tokyo Japan

ISBN: (纸本)9783031085307;9783031085291

Materials informatics (MI), which uses artificial intelligence and data analysis techniques to improve the efficiency of materials development, is attracting interests from industry. One of its main applications is the rapid development of novel high-performance compounds. Recently, several deep generative models have been utilized to suggest candidate compounds that are expected to satisfy the desired performance. However, it is practically difficult to obtain sufficient amount of experimental data to train such models. Thus, the authors proposed a deep generative model with nested two variational autoencoders (VAEs). The outer VAE learns the structural features of compounds using large-scale public data, while the inner VAE learns the relationship between the latent variables of the outer VAE and the properties from small-scale experimental data. To generate high performance compounds beyond the range of the training data, the authors also proposed a loss function that amplifies the correlation between a component of latent variables of the inner VAE and material properties. The results indicated that this loss function contributes to improve the probability of generating high-performance candidates. Furthermore, as a result of verification test with an actual customer in chemical industry, it was confirmed that the proposed method is effective in reducing the number of experiments to 1/4 compared to a conventional method.

关键词： variational autoencoder Deep generative model Automatic compounds design Materials informatics

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