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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

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SwiftCNN: A Deep Learning Model for B-ALL Diagnosis and Subtype Classification from Blood Smear Images

SwiftCNN: A Deep Learning Model for B-ALL Diagnosis and Subt...

引用

IEEE International conference on signal processing, Information, Communication & Systems (SPICSCON)

作者： Farhana Islam Chowdhury Shatabdi Dey Taslima Akter Mila K. M. Safin Kamal Ahmed Wasif Reza Mohammad Shamsul Arefin Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

The most advanced methods available today are convolutional neural networks (CNNs), which are frequently used for image categorization tasks. This article uses sophisticated neural network models to explore the categorization of peripheral blood smear pictures for B-ALL diagnosis and its subtypes. We introduce a method for classifying images using a modified VGG19 model. Images are pre-processed at first before being input into the multi-class classification algorithms. We discovered throughout this study that the suggested methods improve model performance. Our study focused on two types of images: benign and malignant, as well as three subtypes of malignant lymphoblasts: Early Pre-B, Pre-B, and Pro-B ALL. The model has a validation loss of 0.1499 and an accuracy of 94.63%, whereas its training loss is 0.1127 and 96.97%, respectively. These findings demonstrate how well the VGG19-based model performs in terms of categorization.

关键词： Training Deep learning Measurement Accuracy Neural networks signal processing Robustness Convolutional neural networks Blood Cancer

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Efficient FPGA Implementation of Syndrome-Assisted Layered Belief Propagation Decoder for CV-QKD Systems

Efficient FPGA Implementation of Syndrome-Assisted Layered B...

引用

Communication, Image and signal processing (CCISP), International conference on

作者： Yong Chen Xue-Qin Jiang Genlong Chen Peng Huang Enjian Bai Han Hai School of Information Science and Technology Donghua University Shanghai China State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

In this paper, we propose a QC-LDPC decoder architecture with low complexity as well as excellent decoding performance in CV-QKD systems, which employs the syndrome-assisted layered belief propagation decoding algorithm. To minimize the complexity of hardware implementation and resource consumption in iterative decoding, we use fixed-point numbers instead of floating-point numbers to update node messages. Additionally, two-segment nonuniform lookup tables are employed to implement $\psi(x)$ efficiently. Besides, the layered syndrome-assisted early termination (LS-ET) scheme is introduced, resulting in the reduction of the number of decoding iterations while maintaining superior frame error rate (FER) performance. The experimental results indicate that the QC-LDPC decoder implemented on an FPGA is capable of achieving throughput of 50.8 Mbps when the code length is 26112 and code rate is 22/68.

关键词： Performance evaluation Codes signal processing algorithms Throughput Decoding Complexity theory Table lookup Iterative decoding Field programmable gate arrays Belief propagation

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Digital Implementation of Radar Receiver and signal processing algorithms

Digital Implementation of Radar Receiver and Signal Processi...

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2021 International conference on System, Computation, Automation and Networking, ICSCAN 2021

作者： Rathor, Suraj Bhan Dayalan, Shashikiran Bazil Raj, A.A. Defence Inst. of Advanced Technology Department of Electronics Engg. Pune India Defence Research Development Orgn. Electronics Radar Development Estt. Bangalore India

ISBN: (纸本)9781665439862

This paper details the implementation of Radar Receiver and signal processing algorithms on a Kintex-7 FPGA based board and the results obtained. Radar receiver receives and enhances the target echoes with noise and clutter. The major blocks of a Digital Radar Receiver are Digital Down Converter (DDC) which is used to convert an RF or IF signal down to baseband by sampling the signal at suitably high sample rates and then using purely digital techniques to obtain baseband data. Radar signal processing algorithms extracts targets from the background interference (noise + jammer) and passes the information to Data Processor for tracking and further action. The major blocks of Radar signal processing are Pulse Compression, MTI, Doppler Filtering, CFAR and target angle estimation. The conventional DDC (Single Stage FIR filter) was implemented on a Kintex-7 FPGA - XC7K325TFBG900-1 device using Vivado 2014.4 tool and the Radar signal processing chain was implemented using Code Blocks IDE Software using C/C++ language. The implementation was targeted to reduce hardware complexity, enhance speed and reduce power dissipation. The results are analysed using Vivado Simulator and MATLAB tools. © 2021 IEEE.

关键词： Radar receivers

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IoT Based Air Quality and Noise Pollution Monitoring System

IoT Based Air Quality and Noise Pollution Monitoring System

引用

International conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Amit Datta Md. Monjurul Islam Md. Sabbir Hassan Kuasha Bosu Aka Istiaque Ahamed Abir Ahmed Department of Electrical and Electronics Engineering (EEE) American International University - Bangladesh (AIUB) Dhaka Bangladesh

Air pollution is the presence of contaminants or poisonous substances that interfere with human health or welfare or create destructive natural impacts. With the fast improvement of communication innovations, remote sensing technology, and air pollution monitoring systems, it is possible to check the air concentration and take appropriate action. In this paper, a system is developed that can monitor different parameters, like O 3 , NO 2 , CO 2 , and temperature in real time. The control system converts all the data to human-readable values. With the development of a communication system, all data is stored in a cloud database. A decision-making calculation algorithm is developed using advanced technology like cloud computing. Further, a visual platform was created to allow the user to access the data remotely.

关键词： Temperature sensors Temperature measurement Industries Cloud computing Visualization Technological innovation signal processing algorithms

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Dimensionality Reduction for Hyperspectral Image Classification

Dimensionality Reduction for Hyperspectral Image Classificat...

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International conference on Image and signal processing and their Applications (ISPA)

作者： Mohamed Cherifi Ammar Mesloub Mohammed Nabil El Korso Tayeb Touhami Abdennour Hacine Gharbi Laboratoire Traitement du Signal Ecole Militaire Polytechnique Alger Algérie Laboratoire Antennes et Dispositifs Micro-Ondes Ecole Militaire Polytechnique Alger Algérie L2S CNRS Universite Paris-Saclay Gif Sur Yvette France LMSE Laboratory University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj Bordj Bou Arréridj Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

The information explosion in most scientific and research domains over recent decades, driven by advances in data collection and storage, has introduced new challenges in handling, analyzing, and classifying large datasets. advanced datasets come with a multitude of observations, posing challenges in analysis due to the increase in the number of variables associated with each observation, commonly referred to as high-dimensional data. Much of this data is highly redundant, and relevant feature extraction is required to reveal essential *** this context, our study focuses on dimensionality reduction, a crucial technique to address the challenge of classifying complex hyperspectral data and to avoid the curse of dimensionality phenomenon caused by the increasing number of features in high-dimensional data, and also to reduce the complexity in terms of memory space and computing time. We examine two commonly used methods, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and evaluate their performance using various supervised classification algorithms. Our aim is to compare these techniques in the context of hyperspectral image classification.

关键词： Dimensionality reduction High-dimensional data Support vector machines Streaming media signal processing Feature extraction Complexity theory

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jaCappella Corpus: A Japanese a Cappella Vocal Ensemble Corpus

jaCappella Corpus: A Japanese a Cappella Vocal Ensemble Corp...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Tomohiko Nakamura Shinnosuke Takamichi Naoko Tanji Satoru Fukayama Hiroshi Saruwatari Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan National Institute of Advanced Industrial Science and Technology (AIST) Koto Tokyo Japan

We construct a corpus of Japanese a cappella vocal ensembles (ja-Cappella corpus) for vocal ensemble separation and synthesis. It consists of 35 copyright-cleared vocal ensemble songs and their audio recordings of individual voice parts. These songs were arranged from out-of-copyright Japanese children’s songs and have six voice parts (lead vocal, soprano, alto, tenor, bass, and vocal percussion). They are divided into seven subsets, each of which features typical characteristics of a music genre such as jazz and enka. The variety in genre and voice part match vocal ensembles recently widespread in social media services such as YouTube, although the main targets of conventional vocal ensemble datasets are choral singing made up of soprano, alto, tenor, and bass. Experimental evaluation demonstrates that our corpus is a challenging resource for vocal ensemble separation. Our corpus is available on our project page.

关键词： Video on demand Social networking (online) signal processing algorithms signal processing Lead Audio recording Acoustics

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signal Quality Assurance of ECG signals for Automated signal processing Techniques 1

引用

1st International conference on Intelligent Computing and Big Data Analytics, ICICBDA 2024

作者： Arora, Neha Mishra, Biswajit Ramrao Adik Institute of Technology D Y Patil Deemed to be University Maharashtra Navi Mumbai India Dhirubhai Ambani Institute of Information and Communication Technology Gujarat Gandhinagar India

ISBN: (数字)9783031747014

ISBN: (纸本)9783031747007

Assessing the ECG signal quality is necessary for the automated detection methods. If a noisy signal is sent to cardiologists or automated systems, it may lead to false positive or false negative interpretations which can lead to misdiagnosis. In this work, we have utilized heuristic rules of ECG signals and R-R interval based parameters to develop a novel algorithm to assess the signal quality. The signal quality assurance algorithm can be placed before the automated detection or classification algorithms to identify if the acquired signal is suitable for further processing or if it needs to be reacquired. The signal quality assurance algorithm has been validated using the MIT-BIH Normal Sinus Rhythm and MIT-BIH Noise Stress Test Databases. The algorithm has efficiently identified the signal below and equal to SNR of 12 dB as noisy signals and unacceptable for further signal processing. This work also identifies the impact of noise addition on feature point detection such as QRS, P and T waves which is imperative for any signal processing classification technique beginning from the traditional empirical methods to the most advanced neural networks approaches. Along with the database signals we have also validated it with ECG signals obtained in controlled laboratory settings by deliberately inducing noise by various means. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electrocardiography

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Cold Diffusion for Speech Enhancement

Cold Diffusion for Speech Enhancement

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Hao Yen François G. Germain Gordon Wichern Jonathan Le Roux Mitsubishi Electric Research Laboratories (MERL) Cambridge MA USA School of Electrical and Computer Engineering Georgia Institute of Technology GA USA

Diffusion models have recently shown promising results for difficult enhancement tasks such as the conditional and unconditional restoration of natural images and audio signals. In this work, we explore the possibility of leveraging a recently proposed advanced iterative diffusion model, namely cold diffusion, to recover clean speech signals from noisy signals. The unique mathematical properties of the sampling process from cold diffusion could be utilized to restore high-quality samples from arbitrary degradations. Based on these properties, we propose an improved training algorithm and objective to help the model generalize better during the sampling process. We verify our proposed framework by investigating two model architectures. Experimental results on benchmark speech enhancement dataset VoiceBank-DEMAND demonstrate the strong performance of the proposed approach compared to representative discriminative models and diffusion-based enhancement models.

关键词： Training Degradation signal processing algorithms Speech enhancement signal processing Mathematical models Robustness

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Next-generation Digital Receiver Platforms for Cosmology/Radio Astronomy experiments

Next-generation Digital Receiver Platforms for Cosmology/Rad...

引用

IEEE International conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Srivani K.S. Girish B.S. Mayuri S. Rao Saurabh Singh Kamini P.A. Madhavi S. Keerthipriya S. Adarsh Kumar Dash Vishakha S. P. Yash Agrawal Prabu Thiagaraj Somashekar R. Shiv K. Sethi Udaya Shankar N. Seetha S. Electronics Engineering Group Raman Research Institute Bengaluru India Astronomy and Astrophysics Raman Research Institute Bengaluru India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Over the last decade, Field-Programmable Gate Array (FPGA) technology has made significant strides, notably with advanced Micro Devices Inc. (AMD) pioneering Multi-processor Systems-on-Chip (MPSoC). The addition of RF-class analog technology has further advanced MPSoCs, resulting in Radio Frequency System-on-a-Chip (RFSoC). RFSoCs combine data converters with programmable logic, dedicated processors, and high-speed transceivers, making them ideal for large-N applications in radio astronomy/cosmology experiments. The Raman Research Institute has embarked upon building next-generation digital correlation spectrometers using RFSoC evaluation boards like AMD's ZCU111 and ZCU216, advancing towards a compact, custom-designed third generation RFSoC-based platform named "Cosmology and radio astronomy experiments with RFSoC Integrated signal processing" (CRISP). These technological advancements are pivotal for high precision cosmology experiments, detecting subtle signals from recombination and reionization epochs. The paper describes the digital correlation receivers based on evaluation platforms, signal processing firmware, and the architecture of CRISP leveraging Size, Weight, and Power (SWaP) attributes.

关键词： Correlation Prototypes signal processing algorithms Receivers Transceivers System-on-chip Logic

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Research on Intelligent Estimation Model of Carbon Stock of Rocky Desertification Plantations Based on LiDAR Point Cloud Data

Research on Intelligent Estimation Model of Carbon Stock of ...

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

作者： Yin Beilei Liu Feng Wei Shengling Jiang Guijuan Zhang Gentao Guangxi Eco-Engineering Vocational and Technical College Liuzhou China Guangxi Laibin Forestry Survey and Design Institute Co. Ltd Laibin China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

This paper proposes an intelligent estimation model of carbon stock of rocky desertification plantations based on LiDAR point cloud data. The model uses an advanced point cloud segmentation algorithm to finely segment the forest data collected by LiDAR, so as to accurately distinguish the vegetation type and density. Through the processing and feature extraction of point cloud data, combined with the geographical characteristics of rocky desertification areas, this study designed a carbon stock estimation algorithm model suitable for rocky desertification plantations. The system model includes modules such as data acquisition, point cloud segmentation, and carbon stock estimation, which are optimized by combining machine learning methods. The simulation part verifies the accuracy of the model through a large amount of field measurement data, and accurately estimates the carbon stock of different rocky desertification levels through quantitative analysis. The results show that the error rate of the model in carbon stock estimation is less than 5%, which has high application potential and practicality. The research in this paper provides an effective technical means for the accurate assessment of carbon stocks in rocky desertification areas, which will help further promote the implementation of carbon sink mechanisms and the restoration of ecosystems.

关键词： Point cloud compression Laser radar Accuracy Estimation Plantations signal processing algorithms Vegetation mapping Desertification Data models Carbon

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