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Challenges and Prospective Solutions for Non-Uniform Radar Waveforms in a Shared Spectrum

Challenges and Prospective Solutions for Non-Uniform Radar W...

引用

IEEE National conference on Radar

作者： Jason C. Harrington Shannon D. Blunt Nathan Goodman Justin G. Metcalf Jonathan W. Owen Christian C. Jones Callin Schone Rylee G. Mattingly Sensors Division STR Arlington VA USA Radar Systems Laboratory University of Kansas Lawrence KS KS Advanced Radar Research Center The University of Oklahoma Norman OK USA

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

The purpose of this paper is to provide an overview of challenges and methods to design and process a non-uniform radar waveform in a shared spectrum with computationally efficient algorithms to mitigate co-channel interference and en-able standard constant false alarm rate detection and tracking approaches. Computationally efficient processing approaches for non-uniform radar waveforms paired with emerging computing architectures are paving the way for real-time implementation. However, these non-uniform processing schemes need to be paired with interference informed radar waveform design to support interference mitigation processing of OFDM signals to achieve acceptable performance in real world environments. Current challenges associated with optimizing a non-linear radar processing chain for a non-uniform waveform discussed in this paper include processing and signal environment informed wave-form design, OFDM interference rejection, convergence speed of reiterative minimum mean square error (RMMSE) based range Doppler map (RDM) formation, and preservation of Gaussian noise statistics for down-stream processing. Potential solutions discussed to these problems include a waveform optimization based on a restricted isometry property (RIP) constraint, re-mod/demod inference rejection, standard RMMSE, and reduced dimension RMMSE RDM processing.

关键词： OFDM Radar detection Radar tracking Radar signal processing Real-time systems Doppler radar Computational efficiency

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Auris Guardian - A Smartphone Application for Hearing Loss Detection

Auris Guardian - A Smartphone Application for Hearing Loss D...

引用

Machine Learning and Autonomous Systems (ICMLAS), International conference on

作者： Legin M L Lohit Akshaa M Dafni Rose J Department of CSE St. Joseph's Institute of Technology Chennai

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Hearing loss is a common problem world over, which often resulted in reduced communication skills and quality of life. Conventionally, tests were carried out by the audiologists on the patient after equipping him with precise audiometers, and it was expensive, inaccessible, and time consuming as well, especially in less fortunate health resource regions. Therefore, for this, applications have been developed on the smartphones to diagnose hearing losses using air-conduction pure-tone audiometry techniques. This project evaluates the validity and reliability of such applications, particularly their performance in the event of using the “10 dB down and 5 dB up” method to hear threshold detection. The paper extends into the design and realization of an audiometric testing application based on advanced audio signal processing algorithms to diagnose hearing loss. Some smartphone apps have demonstrated the ability to achieve up to 90% precision in identifying mild hearing loss compared to the standard audiological tests. However, inconsistencies are possible due to varying device quality, earphone calibrations, and background noise. The new system combines data acquisition along with handling audio inputs, interaction with the user, and data analysis to ascertain hearing loss more accurately. Such applications may serve as low-cost, user-friendly alternatives to traditional audiology and facilitate hearing tests even in remote locations or among those not accessible to the audiologists.

关键词： Data analysis Working environment noise signal processing algorithms Auditory system Transforms User interfaces Reliability Smart phones Standards Testing

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Labeled Random Finite Sets-Based Group Target Tracking with Multi-Detection Model

Labeled Random Finite Sets-Based Group Target Tracking with ...

引用

signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Longxiang Jiao Qi Jiang Rui Wang Radar Technology Research Institute School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China The Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In radar tracking applications for low-altitude group targets (such as bird flocks or drone swarms), the traditional point target measurement model is often unsuitable due to the complex micro-Doppler signatures and multipath effects. algorithms based on labeled random finite sets (RFS) challenges with multi-detection problems. This paper presents a new method for tracking low-altitude group targets using labeled RFS in radar systems. To mitigate the disturbance from multi-detection, we propose modified assignment cost matrices in the update process, which reduces the number of false associations, and thus achieves quality enhancement of trajectory estimations. The effectiveness of the proposed method is validated through simulation results.

关键词： Radio frequency Target tracking Costs Simulation Estimation Radar Radar tracking Trajectory Reliability Standards

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AI-enhanced Healthcare IoT System: advanced ML Detection and Classification algorithms for Real-Time Cardiovascular Monitoring

AI-enhanced Healthcare IoT System: Advanced ML Detection and...

引用

International conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Lakis Christodoulou Andreas Chari Michael Georgiades R&D Technology and Innovation Biomed Medical Systems Paphos Cyprus ISLab CS Neapolis University & Infostrada Communications Paphos Cyprus

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Cardiovascular diseases (CVDs), including coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other conditions affecting the heart and blood vessels, are identified by both the Pan American Health Organization and the World Health Organization as the leading cause of global mortality, underscoring their significant impact on health worldwide. In this paper we utilize emerging technologies in the fields of Internet of Medical Things (IoMT) and AI/Machine Learning (ML) and propose an end-to-end prototype platform for real-time detection, classification, prediction, and monitoring of cardiovascular anomalies. In addition we introduce Cardio-ECG-Heart Arrhythmia algorithms using advanced AI/ML, combining mathematical-statistical and computational techniques to intelligently detect critical conditions related to CVDs and arrhythmias. The proposed innovative AI/ML Peak Detection algorithm based on adaptive thresholds, coupled with the ML Decision Tree Classification algorithm, has been tested with numerous ECG signals and exhibits remarkable performance, achieving exceptional precision, accuracy, sensitivity, specificity, and F1 Score rates between 99% and 100%.

关键词： Heart Machine learning algorithms Standards organizations signal processing algorithms Prototypes Organizations Machine learning

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QG-DETR: Query-Guided Detection Transformer for Audio Moment Retrieval

QG-DETR: Query-Guided Detection Transformer for Audio Moment...

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advanced algorithms and Control Engineering (ICAACE), International conference on

作者： Peng Wang Chengxu Han Zhaohui Zhang Beijing Normal-Hong Kong Baptist University Zhuhai China Qinghai Qaidam Vocational & Technical College Qinghai China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Audio Moment Retrieval (AMR) has garnered increasing attention due to the rising demand for efficient audio content understanding. AMR aims to accurately locate temporal intervals in untrimmed audio based on natural language queries. However, existing approaches often struggle with cross-modal alignment, primarily because they fuse textual queries with audio representations containing irrelevant content, thereby impairing retrieval accuracy. To address this, we introduce Query-Guided DETR (QG-DETR), a novel AMR framework incorporating two critical components: a Query-Guided Audio Enhancement Module, which leverages textual queries to refine audio representations by suppressing irrelevant information, and a Cross-modal Alignment Module, explicitly designed to fuse refined audio features and textual representations to ensure accurate localization of relevant audio moments. Extensive experiments conducted on benchmark datasets including Clotho-Moment, UnAV-100, and TUT Sound Events 2017 demonstrate that our QG-DETR achieves state-of-the-art performance.

关键词： Training Measurement Location awareness Accuracy Noise reduction Natural languages Benchmark testing Transformers Robustness Acoustic signal processing

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Classifying EEG-based Upper Limb Motor Imagery Tasks for Brain-Robot Interface-based System Development

Classifying EEG-based Upper Limb Motor Imagery Tasks for Bra...

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International conference on advanced Robotics and Mechatronics (ICARM)

作者： Kuldeep Gurjar Sanskar Agrawal Kazi Newaj Faisal Rishi Raj Sharma Department of Electronics Engineering Defence Institute of Advanced Technology Pune India

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Cognitive robotics and engineering strive to develop brain-robot interfaces (BRI) that enable effective collaboration between robots and humans. The continued advancements in neuroscience, robotics, and machine learning are projected to broaden the scope of BRI applications, ultimately improving human-robot cooperation. A major area of focus for BRI-based system development involves mapping brain activities to control actions through motor imagery (MI) based technologies such as electroencephalogram (EEG). In this paper, a novel technique for classifying different tasks involving MI of upper limb movements from EEG signals is presented. To emphasize the inter-related information within various bands of EEG signals, ratio of band power (RBP)-based features are proposed for categorizing different upper limb-based MI tasks. The combination of these proposed features and an optimized KNN classifier produced remarkable accuracy in classifying diverse MI tasks from EEG signals. This approach was compared with recent methods applied to the same dataset, illustrating its superiority in performance. Furthermore, a comparison between the proposed RBP-based features and conventional band power-based features underscored the effectiveness of RBP-based attributes for classifying MI-related tasks. The improved effectiveness of the proposed approach could lead to progress in BRI-based systems development across a range of applications.

关键词： Accuracy Sensitivity signal processing algorithms Machine learning Nearest neighbor methods signal processing Motors Electroencephalography Reliability signal to noise ratio

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Using active asymmetry control and blind source separation to improve the accuracy of after develop overlay measurements 37

Using active asymmetry control and blind source separation t...

引用

conference on Metrology, Inspection, and Process Control XXXVII

作者： Lee, Jungmin Lee, Doogyu Lee, Eunji Yim, Inbeom Lee, Jeongjin Lee, Seung Yoon Hwang, Chan Noot, Marc van Leest, Arno Mathijssen, Simon Gao, Yao Yang, Seung-Bin Baek, Mi-Yeon Lee, Do-Haeng Park, Han-Gyeol Yim, Jong-Hyuk Kim, Thomas Lee, Ho-Hyuk Dahha, Kemal Smith-Meerman, Stefan Van Witteveen, Koen McNamara, Elliott McLaren, Matthew Samsung Seoul South Korea ASML Netherlands BV Veldhoven Netherlands

ISBN: (纸本)9781510660991;9781510661004

During metrology overlay recipe setup typically a wide range of different target designs are present to select from. The main goal of recipe setup is to select the most accurate target type-recipe combination at ADI (after develop inspection) without additional external information that can be used for production on a large set of wafers. We will introduce a method based on blind source separation to disentangle the contributions of target asymmetry and the overlay of the targets. Based on this separation, the most accurate target-recipe combination can be selected. On top of selecting the most accurate target-recipe combination, it is important to stabilize the difference between the overlay on device and the overlay as measured on the target. In order to increase that stability we will introduce advanced algorithms in the ADI measurements that use measured asymmetry parameters to correct for inline target asymmetry variation. We will show a metrology to device matching improvement of up to 40% on product wafers.

关键词： Overlay metrology Semiconducting wafers Metrology Matrices Polarization signal processing Wafer testing Diffraction Education and training Independent component analysis

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Research on Intelligent System of Key Technology of Long Distance Guided Wave Radar in Level Measurement of Nuclear Power Unit

Research on Intelligent System of Key Technology of Long Dis...

引用

IEEE International conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Hongwei Yu Jian Wang CGN Digital Technology Co. Ltd. Beijing China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

In this paper, a key technology intelligent system based on long distance guided wave radar is proposed to meet the special requirements of liquid level measurement in nuclear power units. By integrating advanced signal processing algorithm model, the system effectively overcomes the problem of insufficient accuracy and poor stability of traditional liquid level measurement method in complex nuclear power environment. An efficient signal processing algorithm is developed, which can accurately identify and analyze the guided wave radar echo signal, so as to achieve accurate measurement of liquid level. In addition, in order to verify the effectiveness of the algorithm and the reliability of the system, simulation modeling is also carried out in this paper. The liquid level variation under various practical conditions is simulated, and the response speed and measurement error of the system are evaluated strictly. The experimental results show that the proposed intelligent system can run stably in the complex nuclear power environment, its measurement accuracy is significantly higher than the state of the art, and it has good anti-interference ability and fast response characteristics.

关键词： Accuracy Power measurement Liquids Radar measurements signal processing algorithms Radar Stability analysis Level measurement Intelligent systems Nuclear measurements

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An Improved RT-DETR Model for Detection of Tobacco Impurities

An Improved RT-DETR Model for Detection of Tobacco Impuritie...

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International conference on Vision, Image and signal processing (ICVISP)

作者： Chengjin Tao Zhongxin Ma Lijun Yun Zaiqing Chen Bin Li Cheng Shen Yunnan Provincial Tobacco Company Kunming China Yunnan Normal University Engineering Research Center of Computer Vision and Intelligent Control Technology Kunming China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Tobacco, as a crop of significant economic importance, plays a pivotal role in global agriculture and industry. However, during the processing of tobacco products, the presence of tobacco impurities (TIs) poses a major challenge, as it substantially diminishes product quality. Traditional methods for detecting TIs are often inefficient and limited in scope, underscoring the need for precise and reliable detection techniques. This study addresses these challenges by introducing an improved Real-Time Detection Transformer (RT-DETR) model. By integrating the ShuffleNet V2 architecture and a novel Multi-Branch-Edge-Information-Select (MBEIS) block, the model enhances feature extraction and fusion capabilities, enabling efficient detection even in complex scenarios. Furthermore, a standardized imaging environment, consisting of controlled lighting and a black box setup, was employed to ensure consistent data collection, and the dataset was augmented using advanced data enhancement techniques. Experimental evaluations demonstrate that the proposed model achieves a precision of 92.7%, a recall of 93.5 %, and a mean average precision (mAP) of 87.9%, outperforming mainstream detection algorithms. In addition, the reduction in model parameters and computational complexity ensures superior real-time performance, making it both lightweight and effective for industrial applications. The results highlight the model's strong potential as a robust solution for detection of TIs, providing a significant technological advancement for the tobacco industry. This research not only addresses critical quality assurance challenges but also offers a valuable reference for applying advanced detection technologies in other industrial domains.

关键词： Accuracy Impurities Computational modeling Computer architecture signal processing Transformers Real-time systems Product design Quality assessment Sorting

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Alzheimer's disease MRI image recognition method based on deep learning

Alzheimer's disease MRI image recognition method based on de...

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signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Huang Jiahao College of Mechanical and Electronic Engineering Nanjing Forestry University Nanjing China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Globally, the incidence of Alzheimer's disease is increasing. Although there are many diagnostic methods, traditional methods often rely on professional knowledge and consume much time. Therefore, there is an urgent need to introduce automated technology to improve diagnostic efficiency and accuracy. This study used three deep learning models, namely convolutional neural network (CNN), ResNet, and VGGNet. It optimized them according to data characteristics for Alzheimer's MRI image recognition, providing a reference for diagnosing Alzheimer's disease. This study used the dataset from Dataset Alzheimer, based on the data preprocessing of Alzheimer's Disease Neuroimaging Initiative (ADNI). The model was trained after the dataset was processed by data augmentation, pixel value scaling and MOTE oversampling. The experimental results indicated that the VGGNet model attained the highest overall accuracy at 76%, and the CNN and ResNet models reached accuracies of 75% and 74%, respectively. Nonetheless, the model's proficiency in reliably identifying categories with limited samples necessitates enhancement. Furthermore, further analysis is necessary to assess the model's generalisation potential and its relevance in clinical environments. Future research may explore more advanced model architectures, incorporating attention processes and generative adversarial networks (GANs), to enhance the model's recognition capabilities and its suitability for therapeutic applications.

关键词： Deep learning Accuracy Image recognition Magnetic resonance imaging Transfer learning signal processing algorithms signal processing Convolutional neural networks Reliability Alzheimer's disease

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