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检索条件"主题词=Signal Processing Algorithms"

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High Speed Quantum Cloud Cryptography using Elliptic Paraboloid

High Speed Quantum Cloud Cryptography using Elliptic Parabol...

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

作者： Kunal Gagneja CSE Department Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Mathematical properties of an elliptic paraboloid were used for high-speed encryption on quantum cloud. Encryption time nanoseconds, diffusion and confusion were measured. These observations proved that elliptic paraboloid was more secure against side channel attacks. Circle was used for encryption. Decryption was done by elliptic paraboloid. It was proved that ciphertext size was less as compared to the plaintext size. Complexity of elliptic paraboloid cryptosystem was O(n 2 ). The design was independent of hardware. It could be implemented as both block cipher and stream cipher. Key management, distribution and handling were made easier. Block size and key size were flexible. Comparative analysis was made with both symmetric and public key cryptosystems. Experiments have proved that the novel design was better. Experiments were done with UTF-8 encoding. Modifications could be done to provide digital envelope and digital signature. Cryptosystem was compatible with quantum hardware. Dependance on insecure XOR and prime numbers was reduced. Elliptic paraboloid was applied both as symmetric and asymmetric key cryptosystem. Possibility of hardware implementation was also studied.

关键词： Ciphers Technological innovation signal processing algorithms Side-channel attacks signal processing Hardware Time measurement Encryption Sensors Field programmable gate arrays

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Automated emotion identification system utilizing EEG bands extracted via wavelet filter banks

Automated emotion identification system utilizing EEG bands ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Dhanhanjay Pachori Tapan Kumar Gandhi Department of Electronics and Communication Engineering Indian Institute of Information Technology Nagpur Nagpur Maharashtra India Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In this paper, a new framework in order to identify the human emotions for three classes using the electroencephalogram (EEG) signals has been proposed. It is difficult to reliably and effectively identify different kinds of emotion from nonstationary EEG data. With the EEG recordings, appropriate signal processing and machine learning techniques can be used to design an automated system in order to identify human emotions. The proposed framework is evaluated through extensive experiments utilizing the SJTU Emotion EEG dataset (SEED). The EEG signal is decomposed into several bands by the wavelet filter banks technique. Then from each band, two features are computed which are wavelet energy and Shannon entropy, and then both features are fused to obtain a combined feature. By using support vector machine (SVM), the features are classified into various different emotion states such as positive, neutral, and negative. An accuracy of $\mathbf{8 4. 8 \%}$ is achieved for human emotion detection using EEG signals in the proposed framework. The proposed human emotion recognition algorithm finds applications in brain-computer interface, psychology, and human-machine interface fields.

关键词： Support vector machines Emotion recognition Accuracy Filter banks signal processing algorithms Machine learning Electroencephalography Entropy Reliability signal resolution

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Image Encryption Based on Chen Chaotic System, OpenSSL S-Box and the Fibonacci Q-Matrix

Image Encryption Based on Chen Chaotic System, OpenSSL S-Box...

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signal processing algorithms, Architectures, Arrangements and Applications (SPA)

作者： Rana Lotfy Mohamed Gabr Eyad Mamdouh Amr Aboshousha Wassim Alexan Dina El-Damak Abdallah Fathy Marvy Badr Monir Mansour Faculty of MET German University in Cairo Cairo Egypt Physics Department German University in Cairo Cairo Egypt Faculty of IET German University in Cairo Cairo Egypt Department of Electronic Engineering Technology Faculty of Engineering Technology ElSewedy University of Technology - Polytechnic of Egypt Cairo Egypt Department of Electrical Engineering Faculty of Engineering The British University in Egypt Cairo Egypt

ISBN: (数字)9788362065486

ISBN: (纸本)9798350373806

This paper presents a novel image encryption algorithm that leverages the chaotic properties of the Chen system, the cryptographic strength of OpenSSL, and the mathematical robustness of the Fibonacci Q-Matrix. The proposed method begins by generating an encryption key using the Chen chaotic system, known for its sensitivity to initial conditions and complex dynamic behavior. This key is then utilized in conjunction with a substitution box (S box) generated through OpenSSL to introduce non-linearity and diffusion into the encryption process. To further enhance security, the resulting image data undergoes a series of multiplications by a large number of Fibonacci Q-Matrices, exploiting their recursive properties for added complexity and confusion. Numerical results demonstrate the proposed algorithm’s superior performance in terms of security and efficiency, making it a promising solution for safeguarding digital images against unauthorized access and cryptographic attacks.

关键词： Sensitivity Heuristic algorithms Digital images signal processing algorithms Robustness Encryption Complexity theory

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MSCMFusion: Multi-Stage Cross-Modal Fusion for Low-light Environments

MSCMFusion: Multi-Stage Cross-Modal Fusion for Low-light Env...

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International Conference on signal processing Proceedings (ICSP)

作者： Yuanchen Sun Shikui Wei Peng Sun Bo Li Qingwen Li Hong Li School of Computer Science & Technology Beijing Jiaotong University Beijing China NR Electric Co. Ltd. Nanjing China Information Technology Research Institute China Tower Beijing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Traditional visible light cameras often fall short in providing sufficient information under low-light environments, necessitating the use of thermal imaging to bolster model robustness. However, integrating images from diverse imaging mechanisms poses significant challenges. Existing visible-thermal detection algorithms typically rely on basic fusion techniques like concatenation, element-wise summation, and weighted summation. These methods fail to effectively capture the complementarity and disparity between different modal features, leading to reduced feature distinctiveness. To overcome this, we introduce the Multi-stage Fusion Network (MFNet), which strategically segments the fusion process into alignment and fusion phases for multispectral features. The network incorporates a Modality Consistency Loss (MCL) during the alignment phase to reduce disparities by calculating the Kullback-Leibler divergence between features across modalities. Additionally, we propose a Multi-granularity Cross-Attention (MCA) mechanism in the fusion phase, enhancing the integration of complementary information through spatial correlation across various granularities. Comparative evaluations with existing visible-thermal fusion methods using the FLIR-Aligned and LLVIP datasets demonstrate significant performance enhancements across multiple metrics. Visualization experiments further corroborate the superior detection capabilities of MFNet in diverse scenarios, underscoring its practical efficacy and innovation in image fusion technology.

关键词： Measurement Technological innovation Correlation signal processing algorithms Lighting signal processing Cameras Robustness Detection algorithms Image fusion

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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...

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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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Structured Loss for Deep Change-Point Detection

Structured Loss for Deep Change-Point Detection

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European signal processing Conference (EUSIPCO)

作者： Simon Blotas Charles Truong Université Paris Saclay Université Paris Cité ENS Paris-Saclay CNRS SSA INSERM Centre Borelli

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Change-point detection, which is finding abrupt changes in the behaviour of a signal, is a crucial step in numerous data processing pipelines. There are many available algorithms in the literature; choosing one and calibrating it is complex and time-consuming. In many applications, labels are available, but very few articles have proposed using that information to facilitate calibration. This work describes a methodology for learning a signal representation where change points are easily detected. Our approach relies on a loss function tailored for the change detection task, which we combine with a deep neural network to transform the signal. Experimental results on several real-world data sets show that our method improves segmentation accuracy over baseline methods.

关键词： Accuracy Pipelines signal processing algorithms Europe Artificial neural networks Transforms signal processing Data processing signal representation Data mining

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Empirical Studies on Wearable WSNs in Human Activity Recognition

Empirical Studies on Wearable WSNs in Human Activity Recogni...

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

作者： Dukhbhanjan Singh Varun Ojha Santhosh Kumar. C Manjunath H R Y. K. Sharma A. Amudha Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Civil Prince Shri Venkateshwara Padmavathy Engineering College Chennai India School of Engineering and Technology Physics JAIN (Deemed to be University) Bangalore Karnataka India Computer Engineering Vishwakarma Institute of Technology Pune India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Our research study, "Wearable WSNs in Human Activity Recognition," describes a complete method for detecting human activity using wireless sensor networks and wearable devices. Gathering and processing data, extracting characteristics, and applying them are three techniques to identify movement. We test with several WSN-equipped persons. Our portable WSNs include accelerometers, gyroscopes, and heart rate monitors to collect and analyze data. Continuously filtering and altering raw sensor data prepares it for usage. Feature extraction is essential to interpret mixed sensor data. No more MAD or Fast Fourier Transform. We identify human behavior using Random Forest, K-NN, and LSTM Network techniques. Our WSNHAR approach outperforms others in "Comparison of Recognition Metrics" and "Computational Efficiency and Energy Consumption" charts. Memory, F1-score, precision, accuracy, and processing time indicate system speed. The recommended approach computes swiftly and uses little power, making it suitable for energy-efficient programs.

关键词： Wireless sensor networks Filtering signal processing algorithms Feature extraction Computational efficiency Human activity recognition Biomedical monitoring Wearable devices Long short term memory Monitoring

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Automatic re-labeling of Google AudioSet for improved quality of learned features and pre-training

Automatic re-labeling of Google AudioSet for improved qualit...

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signal processing algorithms, Architectures, Arrangements and Applications (SPA)

作者： Tomasz Grzywalski Dick Botteldooren Department of Information Technology Ghent University Ghent Belgium

ISBN: (数字)9788362065486

ISBN: (纸本)9798350373806

AudioSet, comprising over 2 million human-labeled sound clips, remains one of the biggest and most versatile publicly available audio events datasets. Deep neural networks trained on this data are able to detect 527 types of sounds organized in a hierarchical (tree-like) structure named ontology. However, these models are also often used as feature extractors or serve as a basis for knowledge transfer to other sound detection and classification tasks. When describing the AudioSet recordings, raters were asked to choose one or more labels from the ontology. Analysis of the dataset reveals that raters were inconsistent and imprecise when dealing with the hierarchy of sounds. For example, some raters selected only the most precise labels while others selected all relevant labels (i.e. all parents of selected child labels). Additionally, a large fraction of sound clips are labeled with general labels without providing any fine-grained labels. These issues harm the quality of features learned by the models trained on AudioSet. As a remedy, we propose two ways in which the dataset can be automatically re-labeled to achieve specific, consistent and complete label definitions on all levels of the ontology tree. Experimental results show significant improvement in the performance of new models trained on features extracted from, or initialized with weights transferred from base models trained with re-labeled AudioSet data. In a more general view, this work highlights the importance of paying attention to the labeling of data as a way to improve model accuracy.

关键词： Training Machine learning algorithms signal processing algorithms Machine learning Ontologies Feature extraction Data models Recording Next generation networking Testing

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Context-based Marwari text clustering

Context-based Marwari text clustering

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IEEE International Conference on signal processing, Informatics, Communication and Energy Systems (SPICES)

作者： Pranav Malani Prafulla Bafna Symbiosis Institute of Computer Studies and Research Symbiosis International Deemed University Pune India

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

Today's news is not only produced quickly and in large quantities, but it also originates from a variety of internet sources, including news organizations and websites that help people find jobs. It is usual practice to extract information from English text, however, Marwari, a regional language, has not been fully investigated because of the scarcity of resources. Marwari Vector Space Model for (MVSM), Marwari Synset Vector Space Model for (MSVSM), & the Grouped Entity Marwari Document Matrix (GEMDM) were used to process over 1223 Marwari news items using entity extraction and clustering algorithms. To improve accuracy, we used synset-based dimension reduction techniques. MGEDM performed the best across several datasets when the Hierarchical Agglomerative Clustering (HAC) and K-means clustering was evaluated using three measures. MGEDM obtained a purity score of 1.8, an entropy score of 1.4, and positive silhouette’s coefficient.

关键词： Dimensionality reduction signal processing algorithms Lattices Organizations signal processing SPICE Vectors Entropy Data mining Informatics

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Towards Quantum-inspired Evolutionary algorithms for Denoising PCG signals

Towards Quantum-inspired Evolutionary Algorithms for Denoisi...

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International Conference on signal processing and Information Security (ICSPIS)

作者： Lubna Siddiqui Ashish Mani Jaspal Singh Amity School of Engineering and Technology Amity University Noida India Amity Innovation and Design Centre Amity University Noida India Centre for Development of Advanced Computing Chandigarh India

ISBN: (数字)9798350368673

ISBN: (纸本)9798350368680

This document aims to reaffirm the superiority of quantum-inspired evolutionary algorithms(QiEA) over conventional algorithms. The study proposes a novel QiEA with sorted mutation and greedy local restart (SMGLR) algorithm for finding the optimal coefficients of a filter using a multi-objective optimization function. PCG signals sourced from the Washington Hearts Sounds and Murmurs database and the Michigan Heart Sounds database were filtered using this enhanced filter. Comparative analysis was conducted against the proposed algorithm and established techniques such as Wavelet-based denoising and Variable Mode decomposition. Results: The results revealed that the filter designed using QiEA-SMGLR exhibited superior performance in terms of improved signal-to-Noise Ratio (SNR) in both comparisons. A comprehensive statistical analysis yielded a correlation coefficient of close to 0.99 indicating strong signal integrity. The QiEA-SMGLR algorithm outperforms Wavelet-based and VMD methods by improving the output SNR by 20% to 100%. This variance can be attributed to the intensity of noise introduced. The result reaffirms the hypothesis that the QiEA-SMGLR algorithm is superior in improving signal clarity and precision over traditionally popular techniques. The improvement in the analysis of Phonocardiogram (PCG) signals could help improve patient diagnostics, amidst the fight against global cardiac problems.

关键词： Heart Noise reduction signal processing algorithms Evolutionary computation Filtering algorithms Wavelet analysis Phonocardiography signal integrity Optimization signal to noise ratio

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