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Multimodal Slot vision Transformer for SAR Image Classification 2

Multimodal Slot Vision Transformer for SAR Image Classificat...

引用

2nd IEEE international conference on signal, Information and Data processing, ICSIDP 2024

作者： Yu, Tianyi Han, Fangzhou Zhang, Lamei Zou, Bin Harbin Institute of Technology Department of Information Engineering Harbin150001 China

ISBN: (纸本)9798331515669

In recent years, deep learning network models, particularly vision Transformer (ViT), have achieved remarkable progress in the domain of computer vision. Radar remote sensing has garnered attention due to its all-weather operational capabilities;However, due to limitations in imaging mechanisms, radar data is susceptible to noise interference, affecting data quality and interpretation accuracy. Hyperspectral data, on the other hand, is known for its extensive spectral coverage. To effectively utilize the modal features of both and promote information exchange between different modalities, addressing the shortcomings of single-modal data and solving the issue of feature indistinguishability in multimodal networks, a ViT network based on slot attention encoder, referred to as SLViT, is proposed. To better achieve feature decoupling, parallel branches are employed to handle remote sensing image blocks from various modalities, while position-based weight sharing is applied, providing a new means to simultaneously leverage spatial information and specific modal information. Meanwhile, unlike traditional multimodal fusion methods, attention mechanisms are employed for feature fusion, and a depthwise separable convolution module is introduced for processing remote sensing image blocks. © 2024 IEEE.

关键词： Radar imaging

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Research and Design of a Lower Limb Rehabilitation Exoskeleton robot Driven by Cables 2

Research and Design of a Lower Limb Rehabilitation Exoskelet...

引用

2nd IEEE international conference on signal, Information and Data processing, ICSIDP 2024

作者： Su, Ziying Gao, Weize Gao, Yongfeng Beijing University of Aeronautics and Astronautic School of Mechanical Engineering and Automation Beijing China Baotou Steel Group Corporation Bayan Obo Iron Mine Baotou China

ISBN: (纸本)9798331515669

With the development of science and technology and economic society, research on postoperative rehabilitation of lower limb fractures continues to deepen, and postoperative rehabilitation products are emerging one after another. However, traditional postoperative rehabilitation products for lower limb fractures have problems such as single function, low control precision, and bulky structure. At the same time, research on exoskeleton robots in the field of postoperative rehabilitation for fractures is not in-depth. Based on existing exoskeleton robots and driving methods, this paper designs a lower limb fracture postoperative rehabilitation exoskeleton robot that can provide axial mechanical stimulation and assistive torque. The design adopts the driving method of cable driving, and through the unique design of the coupled movable pulley group, it realizes the combination of two working modes, making the exoskeleton capable of providing axial mechanical stimulation and knee joint assistive torque. Finally, the axial load characteristics and torque characteristics of the exoskeleton device were verified by 3D printing prototype, meeting the requirements of perfect working mode and precise control system. © 2024 IEEE.

关键词： Exoskeleton (robotics)

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TENSOR-GUIDED INTERPOLATION FOR OFF-GRID POWER SPECTRUM MAP CONSTRUCTION 49

TENSOR-GUIDED INTERPOLATION FOR OFF-GRID POWER SPECTRUM MAP ...

引用

49th IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Sun, Hao Chen, Junting Luo, Yuan Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China Chinese Univ Hong Kong Future Network Intelligence Inst Shenzhen Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China

ISBN: (纸本)9798350344868;9798350344851

This paper addresses the off-grid tensor-guided interpolation problem, aiming to reconstruct a 3D power spectrum map from sparse observations. A segmented polynomial model is employed to handle off-grid measurements, while a nuclear norm regularization is incorporated to account for the inherent low-rank characteristics of signals. An alternating regression and singular value thresholding algorithm is developed to solve the proposed method. The numerical results demonstrate the superiority of the proposed method, showcasing a remarkable improvement of over 10% in power spectrum map reconstruction accuracy when the sampling rate exceeds 6%, as compared to state-of-the-art approaches.

关键词： Tensor-guided interpolation off-grid alternating minimization power spectrum map.

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A LIGHT-WEIGHT STATE DETECTION MODEL FOR KALMAN-FILTER-BASED ACOUSTIC FEEDBACK CANCELLATION WITH RAPID RECOVERY FROM ABRUPT PATH CHANGES 49

A LIGHT-WEIGHT STATE DETECTION MODEL FOR KALMAN-FILTER-BASED...

引用

49th IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Guo, Haocheng Le, Xiaohuai Chen, Kai Lu, Jing Nanjing Univ Key Lab Modern Acoust Nanjing 210093 Peoples R China Horizon Robot NJU Horizon Intelligent Audio Lab Beijing 100094 Peoples R China ByteDance RTC Lab Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

The partitioned block frequency domain Kalman Filter (PBFDKF) has been applied in acoustic feedback cancellation (AFC) due to its fast convergence and low steady-state misalignment. However, in cases where the feedback path experiences abrupt changes, the Kalman filter, once it reaches a steady state, might encounter the issue of deadlock and exhibit suboptimal tracking capabilities. In this paper, the Kalman filter with a light-weight state detection model (KF-SD) is proposed to effectively improve the robustness of AFC against abrupt path changes. The feedback return loss enhancement (FRLE) is proposed as the input to a state detection model with only 789 parameters to track the abrupt feedback path changes, and the state detection results are merged into the Kalman filter for a better re-convergence performance. A refined training label is proposed to ensure the robustness of model. Experimental results illustrate the superior performance of the proposed KF-SD algorithm, showcasing a high true positive rate, a low false alarm rate, and a short state detection latency. These advantages lead to faster re-convergence and enhanced sound quality when compared to the commonly used shadow filter strategy.

关键词： Acoustic Feedback Cancellation (AFC) Kalman filter Abrupt path change Neural network (DNN)

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Contextual Deep Learning Approaches for Time Series Reconstruction

Contextual Deep Learning Approaches for Time Series Reconstr...

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IEEE international conference on Omni-Layer Intelligent Systems (IEEE COINS)

作者： Ibarra-Fiallo, Julio Lara, Juan A. Univ San Francisco Quito Colegio Ciencias & Ingn Cumbaya Ecuador Univ Cordoba Cordoba Spain

ISBN: (纸本)9798350349603;9798350349597

The reconstruction of signals from partial data is a fundamental task in various applications, such as signal processing, computer vision, and time series analysis. Traditional interpolation techniques like polynomial fitting and frequency decomposition often struggle with complex or non-linear signals, particularly when the available data is limited or noisy. In this work, we explore the use of deep learning methods to address this signal reconstruction problem. Specifically, we present two neural network-based approaches: a simple autoencoder network and a convolutional neural network (CNN) autoencoder. The autoencoder is used to reconstruct 250-point time series from only 50 scattered data points, while the CNN autoencoder is applied to reconstruct 5000-point signals from 1000 downsampled points. Our results demonstrate that both neural network architectures significantly outperform classical polynomial interpolation, high-lighting the potential of deep learning as a powerful alternative for signal reconstruction tasks. The CNN-based method, in particular, exhibits a strong ability to capture relevant local patterns, allowing effective generalization even from heavily subsampled inputs. This work contributes to the growing body of research on applying deep learning to solve challenging problems in signal processing and time series analysis.

关键词： signal reconstruction time series deep learning interpolation

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ESTATE: EXPERT-GUIDED STATE TEXT ENHANCEMENT FOR ZERO-SHOT INDUSTRIAL ANOMALY DETECTION 31

ESTATE: EXPERT-GUIDED STATE TEXT ENHANCEMENT FOR ZERO-SHOT I...

引用

2024 international conference on Image processing

作者： Zhu, Bingke Li, Hao Chen, Changlin Hua, Liujie Wang, Jinqiao Chinese Acad Sci Inst Automat Fdn Model Res Ctr Beijing Peoples R China Cent South Univ Sch Comp Sci & Engn Hunan Peoples R China Hefei Univ Technol Sch Instrument Sci & Opto Elect Engn Hefei Peoples R China

ISBN: (纸本)9798350349405;9798350349399

The Expert-Guided State Text Enhancement Anomaly Detection (ESTATE) framework addresses the challenges in industrial anomaly detection arising from diverse product categories and limited defective samples. This framework, integrating expert insights through comparative state prompts, leverages two innovative text-guided networks, CLS-Refiner and SEG-Refiner, enhancing model training. These networks, connected to residual textual features of standard vision-language pre-trained models, focus on amplifying adjectives' significance in text for improved image block and pixel-level alignment. ESTATE's effectiveness is demonstrated through evaluations on MVTecAD and VisA datasets, achieving AUROC scores of 89.6%/89.6% for classification and 95.1%/85.0% for segmentation tasks, alongside setting new benchmarks in F1Max and PRO *** AUC-cls on MVTecAD and VisA demonstrated an enhancement of 5.06% and 8.97%, respectively, compared to the APRIL-GAN approach.

关键词： anomaly detection zero-shot vision-language model text-guided

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FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION WITH UNKNOWN ANOMALIES ESTIMATED BY METADATA-ASSISTED AUDIO GENERATION 49

FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION WITH UNKNO...

引用

49th IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhang, Hejing Zhu, Qiaoxi Guan, Jian Liu, Haohe Xiao, Feiyang Tian, Jiantong Mei, Xinhao Liu, Xubo Wang, Wenwu Harbin Engn Univ Grp Intelligent Signal Proc Coll Comp Sci & Technol Harbin Peoples R China Harbin Engn Univ Natl Engn Lab Modeling & Emulat EGovernment Harbin Peoples R China Univ Technol Sydney Ctr Audio Acoust & Vibrat Ultimo Australia Univ Surrey Ctr Vision Speech & Signal Proc Guildford Surrey England

ISBN: (纸本)9798350344868;9798350344851

First-shot (FS) unsupervised anomalous sound detection (ASD) is a brand-new task introduced in DCASE 2023 Challenge Task 2, where the anomalous sounds for the target machine types are unseen in training. Existing methods often rely on the availability of normal and abnormal sound data from the target machines. However, due to the lack of anomalous sound data for the target machine types, it becomes challenging when adapting the existing ASD methods to the first-shot task. In this paper, we propose a new framework for the first-shot unsupervised ASD, where metadata-assisted audio generation is used to estimate unknown anomalies, by utilising the available machine information (i.e., metadata and sound data) to fine-tune a text-to-audio generation model for generating the anomalous sounds that contain unique acoustic characteristics accounting for each different machine type. We then use the method of Time-Weighted Frequency domain audio Representation with Gaussian Mixture Model (TWFR-GMM) as the backbone to achieve the first-shot unsupervised ASD. Our proposed FS-TWFR-GMM method achieves competitive performance amongst top systems in DCASE 2023 Challenge Task 2, while requiring only 1% model parameters for detection, as validated in our experiments.

关键词： Unsupervised learning anomalous sound detection audio generation metadata latent diffusion model

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PID Fuzzy and Neural Fuzzy Depth Controllers for an underwater robot: the UROV 7

PID Fuzzy and Neural Fuzzy Depth Controllers for an underwat...

引用

IEEE 7th international conference on Advanced Technologies, signal and Image processing (ATSIP)

作者： Ben Jabeur, Chiraz Seddik, Hassene Univ Tunis El Manar ISI 2 Rue Abou Raihane Bayrouni Ariana 2080 Tunisia Univ Tunis RIFTSI ENSIT 5 Ave Taha Hussein Tunis 1008 Tunisia Univ Tunis Dept Elect Engn RIFTSI ENSIT 5 Ave TahaHussein Tunis 1008 Tunisia

ISBN: (纸本)9798350351491;9798350351484

In this work, a complete strategy of modeling and auto-depth control of an Underwater Remotely Operated Vehicle (UROV) is presented. The effectiveness of the depth control is a position keeping of the UROV such as maintaining the stationary behavior at a depth set point. It is a task to control the attitude of an UROV with a PID, a fuzzy and a neural fuzzy (ANFIS) controllers. The objective of this research is to create an intelligent controller to control the UROV to follow a chosen depth. It is to note that the UROV is progressing in an aggressive milieu filled with a number of disadvantages that are the underwater currents. The UROV must have quick responses with a higher stability and precision facing these turbulences. This technique principle allows some recompenses if compared with the conventional control procedure such as the control with PID. The simulations where done with the Matlab/Simulink program with which a comparative training is completed concerning PID, fuzzy and neural fuzzy controllers beside underwater troubles. A degree of strength is selected for these troubles to examine the strength of the UROV. The results of these simulations are considered to be satisfactory and have definite the effectiveness of the proposed fuzzy and neural fuzzy technics. Effectively, these controllers present better performances in terms of overshoot, settling time and steady state error over PID controller and are efficient for disturbances rejection. Essentially, for the depth control, the system overshoot response should be considered since overshoot is mainly dangerous for the system.

关键词： PID controller fuzzy controller neural fuzzy controller hostile environment UROV control disturbance rejection

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Al-Powered Autonomous Building Cleaning robots 7

Al-Powered Autonomous Building Cleaning Robots

引用

7th international conference on signal processing and Information Security, ICSPIS 2024

作者： Jaiswal, Akash Abdulaziz, Nidhal Heriot-Watt University School of Engineering and Physical Sciences Dubai United Arab Emirates

ISBN: (纸本)9798350368673

Cleaning the exterior of high-rise buildings is hazardous and demanding, often resulting in accidents due to human error or unfavourable weather. To mitigate these risks, we propose an autonomous building cleaning robot that utilizes advanced AI capabilities. The robot employs a sophisticated adhesive system and cutting-edge Electric Ducted Fans (EDF) to stabilize its vertical position, alongside an integrated AI control system to enhance efficiency and adaptability. It is equipped with multiple real-time sensors, including ultrasonic and infrared, for accurate navigation and effective obstacle avoidance. Additionally, the system incorporates remote control functionality with robust security features to ensure worker safety and optimize efficiency. Early results indicate a notable decrease in cleaning time and improvements in safety, making this innovation transformative for the building maintenance industry. A detailed comparison with existing technologies demonstrates the advantages of our approach in terms of safety, efficiency, and sustainability. © 2024 IEEE.

关键词： Adhesive mechanism AI Autonomous robot Building cleaning Electric Ducted Fan (EDF)

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Automated robot with UV-C Sterilizer for Disinfection of Public Places

Automated Robot with UV-C Sterilizer for Disinfection of Pub...

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2024 IEEE international conference on signal processing and Advance Research in Computing, SPARC 2024

作者： Choudhary, Amar Vardhan, Doddamreddy Vishnu Madhu Sudhan, B. Vineetha, Devireddy Karnataka Bangalore India

ISBN: (纸本)9798350385199

The novel coronavirus pandemic (COVID-19), has fundamentally altered our way of life and by means of how we connect with the outside world. The pandemic has made it imperative to develop practical disinfection techniques that may be used on a regular basis. It is highly necessary, especially at public places, to prevent the airborne and surface transmission of illnesses. Chemical disinfectants are used in most of the current approaches, which can be time-consuming and labor-intensive. Irradiation with ultraviolet (UV) light is a tried-and-true, effective disinfectant. Hospitals assisted living facilities, airports, and retail centers are among the public organizations that are becoming more interested in using UV disinfection robots. In this present paper the glimpse of a functional UV-C Sterilizer robot shall be provided. © 2024 IEEE.

关键词： Sterilizers

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