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检索条件"主题词=Signal Processing Algorithms"

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Seismic Monitoring: A Seismic Alarm System Based on a Configurable Small Isolated Input/Output Digital Logic Array Model

Seismic Monitoring: A Seismic Alarm System Based on a Config...

引用

Chinese Automation Congress (CAC)

作者： Yuru Geng Shiyong Huang Jie Jiang Yong Han School of Network and Communication Engineering Chengdu Technological University Chengdu China Chengdu China Nuclear Haichuan Nuclear Technology Co. Ltd Nuclear Power Institute of China Chengdu China Guangyuan China Nuclear Vocational and Technical College Guangyuan China Chengdu Vocational & Technical College of Industry Chengdu China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This is a prototype product of a newly designed and manufactured earthquake alarm box, which is used for onsite logic operation, processing and indication of earthquake alarm signals. This paper introduces the concrete design, logical mathematical algorithm and functional components of the prototype product that supports the alarm signal processing of common seismic acceleration recorders. The function and performance demonstration of the prototype product has been successfully run in the laboratory and practical projects. Its design feature is a typical technical application of mathematical logic operation and processing in the current practical needs described in this paper. Therefore, many of its functions may be beneficial to the development and application of other similar products and verify the scientific nature of mathematical logic.

关键词： Automation Array signal processing Earthquakes Prototypes signal processing algorithms Alarm systems Mathematical models Logic Monitoring Logic arrays

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Assessing Algorithmic Bias in Machine Learning Classifiers: A Fairness Evaluation

Assessing Algorithmic Bias in Machine Learning Classifiers: ...

引用

IEEE signal processing in Medicine and Biology Symposium (SPMB)

作者： S. Hamdan S. Wagle S. Poudel Y. Zhou K. Poudel Computer Science Middle Tennessee State University Murfreesboro TN USA Computational and Data Science Middle Tennessee State University Murfreesboro TN USA

ISBN: (数字)9798350388572

ISBN: (纸本)9798350388589

In the realm of healthcare, AI stands as an invaluable tool that significantly contributes to streamlining healthcare records [1] . It excels in organizing medical documentation, enabling remote patient monitoring, and extracting critical information from EHR systems. Furthermore, AI profoundly influences drug discovery and development by predicting patient responses to medications and identifying those who will benefit most from specific treatments [2] . This technological advancement also enhances the efficacy of personalized medicine by tailoring treatments to patients based on their medical histories.

关键词： Patient monitoring Machine learning algorithms Precision medicine signal processing algorithms Medical services Machine learning signal processing Prediction algorithms Drug discovery History

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Transformer based Deep Learning Model for Underwater Image Enhancement

Transformer based Deep Learning Model for Underwater Image E...

引用

Innovations in High Speed Communication and signal processing (IHCSP), International Conference on

作者： Prachi Parashar Aparna Singh Kushwah Department of Electronics and Communication UIT-RGPV Bhopal Madhya Pradesh India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

A parallel-guided underwater image improvement technique utilising a Transformer and CNN is suggested to rectify low contrast and colour distortion in underwater images. The approach employs a 3D position embedding model to furnish the Transformer with relative positional data, colour distortion metrics, and global characteristics of the feature map. A CNN encoder extracts local image features, while global features obtained from the Transformer are combined with the CNN-extracted local features via a feature modulation matrix. The CNN decoder enhances the image resolution, and the resulting feature map is input into a feature enhancement network, which produces the final output. The algorithm is trained using the current EUVP paired dataset. To validate the algorithm's superiority, underwater images exhibiting varied levels of colour distortion were chosen for qualitative comparison and quantitative analysis. The findings indicate that the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of the enhanced underwater images surpass those of alternative comparison algorithms, with a notable enhancement in subjective quality, resulting in images characterised by more vibrant colours and greater clarity.

关键词： Training Deep learning Visualization PSNR Image color analysis signal processing algorithms Transformers Feature extraction Distortion Image enhancement

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Performance Evaluation of Coherent & Non-Coherent MIMO Radar for Target Detection

Performance Evaluation of Coherent & Non-Coherent MIMO Radar...

引用

Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： Devasis Pradhan Atul Khuntia Divya Dharshini R Kumar Aditya Mythri R Arun Agarwal Department of Electronics and Communication Dean Research and Development AIT Acharya Institute of Technology Bangalore India Department of Electronics and Communication Acharya Institute of Technology Bangalore India Department of Electronics & Communication Engineering Siksha OAnusandhan Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

Modern radar systems are essential for various applications, including defence, meteorology, and surveillance. Multiple-Input Multiple-Output (MIMO) radar systems have gained significant attention due to their ability to improve target detection and tracking capabilities. Coherent and non-coherent MIMO radar systems differ in their signal processing approaches, with coherent systems offering better performance at the cost of increased computational complexity. This study focuses on the performance evaluation of coherent and non-coherent MIMO radar systems for target detection, aiming to improve the capacity and quality of efficiency in radar systems. This paper presents a comprehensive performance evaluation of coherent and non-coherent Multiple Input Multiple Output (MIMO) radar systems for target detection. The study focuses on key parameters including signal-to-Noise Ratio (SNR), signal-to-Interference-plus-Noise Ratio (SINR), Probability of False Detection, and Probability of Arrival. Coherent and non-coherent MIMO radar systems offer distinct advantages and trade-offs, making it essential to assess their performance under various conditions. Through simulation and analysis, this research aims to provide valuable insights into the comparative effectiveness of these radar architectures in detecting targets amidst noise and interference. Such insights can inform the design and implementation of radar systems tailored to specific operational requirements and environmental conditions.

关键词： Performance evaluation Target tracking MIMO radar Surveillance signal processing algorithms Radar detection Object detection Interference MIMO communication signal to noise ratio

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Research on Methods for Interference Suppression in Satellite Internet

Research on Methods for Interference Suppression in Satellit...

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International Conference on Communication Technology (ICCT)

作者： Zixi Fang Shilei Shan Tian Li Chenhua Sun Academy for Network & Communications of CETC Shijiazhuang China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Aiming at the impact of Low Earth Orbit (LEO) satellite constellation interference to the downlink of Geosynchronous Orbit (GEO) satellites in Satellite Internet, this paper proposes interference suppression methods for terrestrial terminals based on signal processing. The overall operational process is divided into three parts, namely, high-resolution spatial filtering, interference margin estimation, and interference suppression algorithm. Firstly, an improved multiple signal classification (i-MUSIC) algorithm is used in spatial filtering to solve the problem of heteroscedasticity. Secondly, the interference margin of the receiver is defined combined with satellite link characteristics. It provides the prerequisite for an interference suppression algorithm. Next, the symbol weight extensive cancellation algorithm in the frequency domain (FD-SW-ECA) based on Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) is proposed to suppress the co-frequency interference. The simulation results show that using the proposed methods in this paper can significantly reduce errors, and the FD-SW-ECA algorithm is effective for interference suppression.

关键词： Interference suppression Satellites Filtering Simulation signal processing algorithms Pattern classification Receivers signal processing Classification algorithms Internet

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Integrating Airborne and Satellite Hyperspectral Data for Enhanced Spectral Unmixing

Integrating Airborne and Satellite Hyperspectral Data for En...

引用

Workshop on Hyperspectral Image and signal processing: Evolution in Remote Sensing, WHISPERS

作者： Rupsa Chakraborty Sam Thiele Parth Naik Moritz Kirsch Richard Gloaguen Center for Advanvced Systems Understanding Helmholtz-Zentrum Dresden-Rossendorf Görlitz Germany Helmholtz-Institute Freiberg for Resource Technology Helmholtz-Zentrum-Dresden Rossendorf Freiberg Germany

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

We combine 2 m resolution airborne (HySpex) and 30 m resolution satellite (EnMAP) hyperspectral data to address the challenge of mixed pixels in satellite imagery. Endmembers are manually selected from HySpex data, and Non-negative Least Squares (NNLS) spectral unmixing is applied to generate high-resolution spectral abundance maps. These maps are then resampled to match EnMAP's spatial resolution and used to predict an endmember library from the EnMAP scene. This predicted library is then used for unmixing the EnMAP data over a broader area. When compared to spectral abundance maps generated from direct endmember selection from EnMAP alone, the unmixing results using the predicted library closely align with the high-resolution output, despite some land cover changes over time. In contrast, the spectral abundance maps from low-resolution endmembers lack detail. We discuss the implications of our approach for improved spatial and temporal mapping.

关键词： Satellites Vegetation mapping Land surface signal processing algorithms Soil signal processing Libraries Spatial resolution Stress Hyperspectral imaging

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Joint Slot and Power Optimization for Grant Free Random Access with Unknown and Heterogeneous Device Activity

Joint Slot and Power Optimization for Grant Free Random Acce...

引用

European signal processing Conference (EUSIPCO)

作者： Alix Jeannerot Malcolm Egan Jean-Marie Gorce INSA Lyon Inria CITI UR3720 Villeurbanne France Inria INSA Lyon CITI UR3720 Villeurbanne France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Grant Free Random Access (GFRA) is a popular protocol in the Internet of Things (IoT) to reduce the control signaling. GFRA is a framed protocol where each frame is split into two parts: device identification; and data transmission part which can be viewed as a form of Frame Slotted ALOHA (FSA). A common assumption in FSA is device homogeneity; that is the probability that a device seeks to transmit data in a particular frame is common for all devices and independent of the other devices. Recent work has investigated the possibility of tuning the FSA protocol to the statistics of the network by changing the probability for a particular device to access a particular slot. However, power control with a successive interference cancellation (SIC) receiver has not yet been considered to further increase the performance of the tuned FSA protocols. In this paper, we propose algorithms to jointly optimize both the slot selection and the transmit power of the devices to minimize the outage of the devices in the network. We show via a simulation study that our algorithms can outperform baselines (including slotted ALOHA) in terms of expected number of devices transmitting without outage and in term of transmit power.

关键词： Performance evaluation Protocols Interference cancellation signal processing algorithms Probability signal processing Throughput Internet of Things Optimization Tuning

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A Deep Reinforcement Learning-Based Adaptive Transmission Design for Trustworthy URLLC Support in 5G and Beyond Wireless Systems

A Deep Reinforcement Learning-Based Adaptive Transmission De...

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IEEE Pacific Rim Conference on Communications, Computers and signal processing

作者： Hong-Chuan Yang Negin Sadat Saatchi University of Victoria Victoria BC Canada

ISBN: (数字)9798350362312

ISBN: (纸本)9798350362329

The natural tension between achieving high reliability and guaranteeing low latency is a key challenge in supporting ultra-reliable low-latency communications (URLLC) service in current and future wireless systems. To achieve the most effective solution, various reliability and latency mechanisms must be jointly designed. In this paper, we propose to optimally select the numerology, slot size, modulation and coding schemes for each transmission or retransmission attempt for a packet transmission over 5G and beyond wireless systems. We formulate this sequential decision-making problem into an Markov decision process (MDP) and find the optimal policy using a deep reinforcement learning algorithm. Through selected numerical examples, we demonstrate that the proposed joint design can achieve significant performance gains over the conventional schemes. We also show that such joint selection is especially important when the latency budget is stringent and/or channel quality is poor.

关键词： Wireless communication Adaptive systems 5G mobile communication Simulation Markov decision processes signal processing algorithms Modulation Ultra reliable low latency communication Performance gain Reliability engineering

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Optimization Method for Small Sample Communication Emitter Source Individual Recognition Classifier

Optimization Method for Small Sample Communication Emitter S...

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IEEE International Conference on signal and Image processing (ICSIP)

作者： Xiezhao Pan Lu Wang Xiaogang Tang Minghui Gao Binquan Zhang Space Engineering University College of Information Beijing China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Identification of specific communication emitters is a challenging and hot issue in the field of electromagnetic signal processing. In non-cooperative communication scenarios, the number of intercepted labeled signal samples is small, and the overlap of vibration and spectrum significantly increases the difficulty of classifying and identifying individual emitters. This paper addresses the issue of misclassification by the classification hyperplane of support vector machines (SVM) when dealing with nonlinear high-dimensional feature classification of communication signals. An adaptive optimization framework for the hyperplane is proposed. The square integral bispectrum of preprocessed signals is extracted and subjected to local linear embedding reduction as fingerprint features. The classification algorithm is adjusted based on the distance between the sample points to be classified and the optimal classification hyperplane. Validation with real signals shows that the recognition accuracy reaches 69.18% at OdB and 94.17% at 20dB, with an average recognition accuracy of 81.78% within the signal-to-noise ratio range of OdB to 20dB. This paper provides a new approach for the design of classifiers for the identification of individual emitters in non-cooperative communication scenarios with small samples.

关键词： Support vector machines Vibrations Accuracy Optimization methods signal processing algorithms signal processing Feature extraction

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Designing an Explainable AI Framework for Leukemia Diagnosis: Integrating Customized CNN-SVM Models with Optimized Deep Learning

Designing an Explainable AI Framework for Leukemia Diagnosis...

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Innovations in High Speed Communication and signal processing (IHCSP), International Conference on

作者： Lovish Dhingra Ranjusha J P Sanjiv Mathur Bhandari Mahesh Ashok K. Ranjith Singh Tannmay Gupta Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of S&H Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Mangement PG Management JAIN (Deemed to be University) Bangalore Karnataka IT Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

White blood cell appearance is one of the most important criteria for diagnosis for leukaemia. Since the conclusion of a diagnosis may be affected by the personal perspective of the person making the diagnosis, it is very advantageous to use a computerised methodology. To make this procedure easier, a doctor will look at blood smear slides under an electron microscope and make a diagnosis based on what they see. In terms of the deep learning frameworks, we are able to get measures like prospective accuracies. To get the most out of these frameworks, you need a big dataset with plenty of pictures. Due of its rarity, leukaemia databases often include photos of a smaller size. The suggested approach makes use of a tailored CNN integrated with a support vector machine. The classifier decides whether the cells are normal or infected. To verify the accuracy of this judgement, the widely used XAI framework LIME is used for interpretation. The present study makes use of the widely used and freely accessible ALL-IDB dataset. The suggested technique follows the beginning and follows by a presentation of relevant work. The findings, discussions, and conclusions are all included in the following section. It goes on to say that with a 96% accuracy rate, the suggested CNN-SVM method is much superior than GradCAM, Shaply, and LIME. The CNN-SVM model outperformed the others in terms of accuracy, whereas GradCAM outperformed them across the board. The explanations provided by Shaply and LIME were also rather accurate.

关键词： Deep learning Support vector machines White blood cells Technological innovation Accuracy Explainable AI System performance signal processing algorithms signal processing Feature extraction

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