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Efficient Noise Cancellation in Lung Signals Using Pipelined Adaptive LMS Algorithm

Efficient Noise Cancellation in Lung Signals Using Pipelined...

Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC), International Conference on

作者： K. Sathesh T. Manivannan Kowsalya P E. Sathiyanarayanan P. Ramanathan E. Aravindraj Dept. of Electronics and Communication Engineering Madanapalle Institute of Technology & Science Madanapalle India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

The proposed work focuses on the use of the Pipelined Adaptive LMS algorithm as an effective noise cancellation technique in lung signals. The LMS algorithm is an adaptive filtering technique that has the capability to remove noise from signals by adjusting filter coefficients based on the input data. The study employs MATLAB and ModelSim-Altera as software tools to implement the algorithm. The outcomes showcase the efficacy of the Pipelined Adaptive LMS algorithm for canceling noise in lung signals. This research provides valuable insights into the application of adaptive filtering methods in medical applications, highlighting the potential of these techniques to improve the quality of medical signal processing. Additionally, the study emphasizes the importance of optimizing algorithm performance to ensure successful Application in real-world scenarios is crucial. This study contributes to the advancement of sophisticated noise cancellation techniques for medical applications and underscores the significance of adapting these techniques to improve patient care.

关键词： software algorithms Signal processing algorithms Lung Medical services Filtering algorithms Noise cancellation Mathematical models

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Preparing an on-Demand Cloud Processing Workflow for NISAR Ecosystems Science Products

Preparing an on-Demand Cloud Processing Workflow for NISAR E...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Alexandra Christensen Paul Siqueira Bruce Chapman Josef Kellndorfer Kyle McDonald Sassan Saatchi K C Cushman Brandi Downs Adriana Parra Naveen Ramachandran Jet Propulsion Laboratory California Institute of Technology University of Massachusetts Amherst Massachusetts Earth Big Data LLC City College of New York Oak Ridge National Laboratory

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

In preparation for the NISAR launch and data collection in 2024, the NISAR Project Science Team is building workflows for each Science Team discipline (Ecosystems, Cryosphere, and Solid Earth). This abstract focuses on the Ecosystem disciplines and the development of on-demand cloud-processing workflows for wetlands inundation, forest biomass, agricultural active crop area, and forest disturbance. The workflow simulates NISAR data using UAVSAR or ALOS-2 Single Look Complex data, which are processed to Level 2 geocoded polarimetric covariance matrix products using InSAR Scientific Computing Environment 3.0 software and to Level 3 science products using the Algorithm Theoretical Basis Documents. In this presentation, we describe these workflows and efforts to improve efficiency and data accessibility by using a cloud processing system. We present preliminary sample products from each Ecosystem discipline: inundation, forest biomass, crop area, and forest disturbance..

关键词： Scientific computing software algorithms Crops Geoscience and remote sensing Forestry Solids software

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Real-time Performance Study of Information Transmission in EPA Industrial Ethernet

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Chinese Journal of Electronics 2023年第1期21卷 125-130页

作者： Ning Liu Chongquan Zhong Yuqin Bai School of Mechanical and Power Engineering Henan Polytechnic University Jiaozuo China School of Control Science and Engineering Dalian University of Technology Dalian China

In order to propose the method of evaluating and improving the real-time performance of EPA (Ethernet for plant automation) industrial Ethernet, the real-time performance of EPA information transmission was studied from theoretical and experimental perspectives. By analyzing information transmission regularity and EPA deterministic scheduling mechanism, several indicators including delivery time, time synchronization accuracy, utilization percentage of configured timeslice, datasending time offset accuracy and throughput RTE that can be used to specify the real-time performance of EPA information transmission were presented and investigated. On this basis, the test principles and test methods of the indicators were studied. Furthermore, an experiment platform was developed to test the real-time indicators of EPA information transmission. According to the analysis and the experiment, some methods to improve the real-time performance of EPA information transmission were proposed.

关键词： Performance evaluation Job shop scheduling software algorithms Information processing Ethernet Throughput Real-time systems

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Improving Robustness and Natural Accuracy in Adversarial Distillation with Important Sample Features

Improving Robustness and Natural Accuracy in Adversarial Dis...

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2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Peichao Qiu Lifeng Huang Ning Liu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China College of Mathematics and Informatics South China Agricultural University Guangzhou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

Although adversarial training proves to be an effective method for training robust neural networks, its application is constrained by model capacity, making it challenging to implement in scenarios with limited computing and storage resources. To tackle this issue, adversarial distillation emerges as a viable solution to transfer knowledge from a robust teacher model to a smaller-capacity student model. However, most existing research overlooks the importance of inconsistencies in adversarial example features during distillation, which can potentially lead to a degradation in the student model's performance. In this paper, we propose a novel method named importance-aware intermediate distillation method (IID), which enhances the efficiency of distillation by leveraging the teacher model's prior knowledge to perceive the varying importance of features in different samples. Through extensive experiments on different datasets, we demonstrate the effectiveness of IID in improving model robustness.

关键词： Training Degradation Frequency modulation Computational modeling software algorithms Neural networks Network security Feature extraction Robustness software engineering

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Data Frequency Coverage Impact on AI Performance

Data Frequency Coverage Impact on AI Performance

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IEEE International Conference on software Testing Verification and Validation Workshop, ICSTW

作者： Erin Lanus Brian Lee Jaganmohan Chandrasekaran Laura J. Freeman M S Raunak Raghu N. Kacker D. Richard Kuhn National Security Institute Virginia Tech Arlington VA USA Sanghani Center for Artificial Intelligence & Data Analytics Virginia Tech Arlington VA USA Information Technology Lab National Institute of Standards and Technology MD USA

ISBN: (数字)9798331534677

ISBN: (纸本)9798331534684

Artificial Intelligence (AI) models use statistical learning over data to solve complex problems for which straightforward rules or algorithms may be difficult or impossible to design; however, a side effect is that models that are complex enough to sufficiently represent the function may be uninterpretable. Combinatorial testing, a black-box approach arising from software testing, has been applied to test AI models. A key differentiator between traditional software and AI is that many traditional software faults are deterministic, requiring a failureinducing combination of inputs to appear only once in the test set for it to be discovered. On the other hand, AI models learn statistically by reinforcing weights through repeated appearances in the training dataset, and the frequency of input combinations plays a significant role in influencing the model’s behavior. Thus, a single occurrence of a combination of feature values may not be sufficient to influence the model’s behavior. Consequently, measures like simple combinatorial coverage that are applicable to software testing do not capture the frequency with which interactions are covered in the AI model’s input space. This work develops methods to characterize the data frequency coverage of feature interactions in training datasets and analyze the impact of imbalance, or skew, in the combinatorial frequency coverage of the training data on model performance. We demonstrate our methods with experiments on an open-source dataset using several classical machine learning algorithms. This pilot study makes three observations: performance may increase or decrease with data skew, feature importance methods do not predict skew impact, and adding more data may not mitigate skew effects.

关键词： Training Hands Machine learning algorithms Statistical learning software algorithms Training data Data models Frequency measurement software measurement Artificial intelligence

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Development of LoRa Communication System Generator Simulation Model

Development of LoRa Communication System Generator Simulatio...

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Systems of Signals Generating and Processing in the Field of on Board Communications

作者： Konstantin Yu. Ryumshin Angela I. Sattarova Alexey P. Zhuravlev Vladimir E. Rusanov Lev M. Kazadaev Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

Today LoRa data transmission technology is gaining more and more popularity among Internet devices for collecting information and communication. The main purpose of LoRa technology is to collect simple information in small packets transmitted at a low speed. Information is collected from various sensors and devices located within the range of the main receiver. This technology complements Wi-Fi. It is suitable for applications with a long range, low transmission speed and power consumption. WiFi provides a short range, low speed and power consumption. LoRa technology is constantly being improved, in connection with which monitoring radio networks built on its basis is of current importance for implementation and study (during the educational process). Modeling of radio communication algorithms in LoRa data transmission technology is one of the important research areas for the development of radio networks of this type. In this regard, this paper develops software that generates files of digital readings of the LoRa radio signal quadratures.

关键词： Underwater communication Power demand software algorithms LoRa software Radio networks Wireless fidelity Standards Spectrogram Radio spectrum management

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A Framework for PCB Design File Reconstruction from X-Ray CT Annotations

A Framework for PCB Design File Reconstruction from X-Ray CT...

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IEEE International Symposium on Quality Electronic Design

作者： Carson Sobolewski David S. Koblah Domenic Forte Florida Institute for National Security University of Florida Gainesville FL USA

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

Reverse engineering (RE) is often used in security-critical applications to determine the structure and functionality of various systems, including printed circuit boards (PCBs). Although it has both beneficial and malicious uses, it is particularly vital within the realm of hardware trust and assurance. PCB RE enhances legacy electronic system replacement, intellectual property (IP) protection, and supply chain integrity. To contribute to the requirements of effective PCB RE, extensive research has been conducted on the analysis of PCBs using X-ray computed tomography (CT) scans, including image segmentation focusing on via and trace annotation. Applying extracted annotations, this work outlines a Python-based framework, coupled with the open-source KiCaD software, for the automated reconstruction of PCB design files. Given the via, pad and trace annotations, in addition to board dimensions, the algorithm automatically recognizes board shape, trace size, and connections to reconstruct the bare PCB accurately. This technique was tested on three distinct layers of a sample multilayer PCB with great success. Its feasibility holds great promise for future extensions to complete the entire PCB RE framework. The project source code is available for reference on GitHub. 1 1 https://***/koblahdavid/xray_ct_reconstruction

关键词： Annotations Computed tomography Source coding Printed circuits Reverse engineering Supply chains software algorithms software Image reconstruction X-ray imaging

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Research on modifiable digital watermarking scheme based on QRcode and blockchain

Research on modifiable digital watermarking scheme based on ...

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2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Yukun Hui School of Cyberspace Security Xi'an University of Posts& Telecommunications Xi'an China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

Existing digital watermarking techniques for vector geographic data based on spatio-temporal domains face problems such as high computational complexity, low embedding capacity of effective watermark information, and low security of watermark storage. To cope with these challenges, a digital watermarking scheme based on quick response code (QR code) and blockchain is proposed. In this scheme, the QR code is used as a carrier for the real watermark information, which increases the payload of the watermark. The authentic watermark is stored using blockchain and proprietary file system (IPFS) technology, and the hash index value returned by the blockchain is embedded into the vector map after folding, which alleviates the security problem of centralized storage to a certain extent. When a watermark verification or modification event occurs, the blockchain records user identity and timestamp information. Experimental results show that the scheme is robust to attacks such as data cropping, compression and geometric transformation, and can effectively extract correct watermark data.

关键词： software algorithms Watermarking QR codes Vectors Robustness Blockchains software reliability Security Secure storage software engineering

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Research on Sound Source Localization Based on Random Forest

Research on Sound Source Localization Based on Random Forest

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2025 2nd International Conference on algorithms, software Engineering and Network Security (ASENS)

作者： Tianqi Wang Guanqun Liu Rubo Zhang College of Mechanical and Electronic Engineering Dalian Minzu University Dalian China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

To address the issue in location fingerprinting methods where the Euclidean distance between data points tends to equalize as feature dimensionality increases, leading to a loss of metric differentiation, a random forest-based localization method is proposed. The method involves two stages: an offline sampling stage and an online localization stage. During the offline stage, sound source signals are captured at each localization reference point, and time-delay features are extracted to construct a localization database using the extracted features and reference point location information. In the online stage, a time delay estimation algorithm combining the initial segment of the envelope with cross-correlation (ISE-CC) is used to extract the time-delay features of the signal at the target point, and localization is achieved using random forest (RF) predictions. Simulation experiments comparing the proposed method with nearest neighbor (NN) and weighted k-nearest neighbor (WKNN) demonstrate that the proposed method achieves minimal deviation in sound source position estimation and satisfies the requirements for practical applications.

关键词： Location awareness Radio frequency Delay effects software algorithms Estimation Fingerprint recognition Feature extraction Prediction algorithms Data mining software engineering

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Hybird Clustering Algorithm Based on BP Neural Network and K-means

Hybird Clustering Algorithm Based on BP Neural Network and K...

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software Engineering and Computer Science (CSECS), International Conference on

作者： Yongzhi Zhu Qian Zhang College of Information Engineering Shandong Vocational University of Foreign Affair Weihai China Department of Electronic Information Engineering Jining Vocational and Technical College Jining China

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

In the era of big data, efficient and accurate clustering algorithms are indispensable for organizing and mining information. This paper focuses on the optimization of clustering algorithms, particularly by integrating the advantages of the Backpropagation (BP) neural network and the K-means algorithm to enhance clustering accuracy and stability. This paper proposed improvement strategy introduces a novel fusion method that combines the self-learning characteristic of the BP neural network with the crowd wisdom of the K-means algorithm, resulting in a hybrid model that dynamically adjusts cluster centroids. In this model, the BP network is responsible for generating and updating initial cluster centroids, while the K-means algorithm allocates samples based on these centroids. This complementary approach forms an iterative optimization process. The aim is to address the limitations of traditional K-means when dealing with complex data structures and the initialization challenge faced by the BP network in clustering tasks. The implementation section involves programming the improved algorithm using the sklearn library and testing it on the classic iris dataset. Through comparisons with the original K-means algorithm and clustering solely with the BP neural network,this demonstrates the superiority of the improved algorithm in clustering performance.

关键词： Iris Accuracy Heuristic algorithms Neural networks software algorithms Clustering algorithms Big Data Stability analysis Classification algorithms Optimization

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