Conventional refractive detection is not appropriate for daily use due to its complicated operation, bulky device, and need for patient and doctor communication. In this study, a refractive detection system based on E...
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ISBN:
(数字)9798350361445
ISBN:
(纸本)9798350361452
Conventional refractive detection is not appropriate for daily use due to its complicated operation, bulky device, and need for patient and doctor communication. In this study, a refractive detection system based on EEG and singular spectrum analysis is proposed. The system consists of single channel EEG device, 3D glasses and signalprocessing module. People only need to wear a lightweight device to obtain EEG signals, and then the signalprocessing module uses singular spectrum analysis and machine learning to process and analyze the refractive EEG to complete the refractive detection. The process is independent of location and expertise. The results of the experiment suggest that the refractive detection system accuracy can be increased to 80.32% by applying a time-frequency algorithm, serial feature fusion, and machine learning model construction. Furthermore, the system hardware implementation costs are inexpensive, and it is simple to wear, which will help in the development of a portable and daily refractive detection system.
advanced diagnostic technologies are necessary for early diagnosis and treatment of cardiovascular disease (CVD), a rising global health concern. By combining raw ECG signals with patient demographic information, the ...
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ISBN:
(数字)9798331512248
ISBN:
(纸本)9798331512255
advanced diagnostic technologies are necessary for early diagnosis and treatment of cardiovascular disease (CVD), a rising global health concern. By combining raw ECG signals with patient demographic information, the suggested approach enhances the prediction of heart illness. To improve the system's anticipated accuracy, this method incorporates signalprocessing, feature extraction, and selection processes. Thus, we create an application that uses 12 lead-ECG signal images from PTB-XL dataset along with additional parameters like age, gender, height, and weight to forecast the vulnerability of a heart condition. This system method derives valuable properties from ECG data using a new Fast Fourier Transform (FFT) feature extraction algorithm. Furthermore, the random forest classification technique is used to select highly discriminative characteristics for prediction. Acquired features are input into a Multi-Layer Perceptron (MLP) model, which is trained by employing an extensive dataset of patient demographics and ECG signals.
Improvements in deep learning have benefited signalprocessing on spherical surfaces in particular. This work addresses deep learning for close-range spherical surface signalprocessing. Traditional signalprocessing ...
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ISBN:
(数字)9798350305463
ISBN:
(纸本)9798350305470
Improvements in deep learning have benefited signalprocessing on spherical surfaces in particular. This work addresses deep learning for close-range spherical surface signalprocessing. Traditional signalprocessingalgorithms may struggle to handle complex data on spherical surfaces. Deep learning has the potential to close this gap. Our deep learning in signalprocessing examination begins with its principles, difficulties, and accomplishments. The article goes on to illustrate how spherical geometry influences data encoding and signalprocessing, underscoring the need for understanding signalprocessing on spherical manifolds. The research emphasises the need for cutting-edge solutions to tackle non-Euclidean data representation challenges quickly. We are experimenting with several deep learning architectures for spherical manifold data. Among these architectures are attention mechanisms, GNNs, CNNs, and so on. Integrating these models with spherical data layers and methodologies should allow for a thorough evaluation of their ability to detect complex close-range *** learning algorithms increase near-field signalprocessing on spherical manifolds in terms of accuracy, efficiency, and resilience, according to the results. Deep learning architectures for curved spherical data processing are advanced in this work. This objective necessitates research on its theoretical underpinnings, existing approaches, and practical applications. These unique and adaptable data management solutions provide remarkable data management on spherical manifolds for near-field applications. Their data management systems are more precise and trustworthy than earlier ones.
Finding the features with the highest correlation degree in the source APT data from network security devices is essential for preprocessing the data in order to train models that will increase the accuracy of APT att...
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ISBN:
(数字)9798350376548
ISBN:
(纸本)9798350376555
Finding the features with the highest correlation degree in the source APT data from network security devices is essential for preprocessing the data in order to train models that will increase the accuracy of APT attack classification. The preprocessing, cleaning, and feature transformation of the dataset are the first steps in the application of data mining techniques in this work. VarianceThreshold is ultimately chosen for feature selection to create the optimal feature subset after a range of feature selection techniques are used for experimental comparison. The best feature subset is then used to compare and evaluate several machine learning algorithms, and by integrating ensemble learning methods, the VT-stacking model is recommended. The experimental results demonstrate that the VT-stacking model developed in this paper achieves 97.3% accuracy in categorizing APT assaults after preprocessing the APT data.
We propose a convex signal recovery method for block sparsity under arbitrary linear transform with unknown block structure. The proposed method is a generalization of an adaptive block sparse regularization named LOP...
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ISBN:
(数字)9789464593617
ISBN:
(纸本)9798331519773
We propose a convex signal recovery method for block sparsity under arbitrary linear transform with unknown block structure. The proposed method is a generalization of an adaptive block sparse regularization named LOP-l2/l1 and enables us to apply LOP-l2/l1 into different domain under non-invertible linear transform. Our work broadens the scope of block sparse regularization, enabling more versatile and powerful applications across various signalprocessing domains. We derive an iterative algorithm for solving the proposed method and provide conditions for its convergence to the optimal solution. Numerical experiments demonstrate the effectiveness of the proposed method for signal and image recovery.
A network of physical things that are outfitted with sensors, smart networking, and RFID technology is referred to as the "Internet of Things" (IoT), which is an abbreviation for the phrase. The items in que...
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ISBN:
(数字)9798350368949
ISBN:
(纸本)9798350368956
A network of physical things that are outfitted with sensors, smart networking, and RFID technology is referred to as the "Internet of Things" (IoT), which is an abbreviation for the phrase. The items in question are able to communicate with one another and share data with other systems and devices respectively. Information security is of the utmost importance since by the year 2020, there will be more than fifty billion devices connected to the internet. Traditional cryptography, such as the advanced Encryption Standard (DES) and the advanced Encryption Standard (AES), does not take into account the necessary hardware and software, the amount of power consumed, the size of the code, or the amount of memory overhead. NLBSIT, an Energy Efficient and Low-Pooled Memory Cryptographic Algorithm, and a Robust Lightweight Algorithm are the three lightweight encryption methods that are described in this thesis. NLBSIT is an acronym for natural language based secure information transfer. These algorithms are intended for use with devices connected to the Internet of Things. In order to solve the problems that Internet of Things devices encounter, such as excessive memory usage, power consumption, and energy consumption, these algorithms have been developed.
Security in signalprocessing involves implementing robust encryption techniques and authentication measures to safeguard sensitive information from unauthorized access or manipulation, ensuring the integrity and conf...
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ISBN:
(数字)9798350372120
ISBN:
(纸本)9798350372137
Security in signalprocessing involves implementing robust encryption techniques and authentication measures to safeguard sensitive information from unauthorized access or manipulation, ensuring the integrity and confidentiality of processed data in various applications. This paper presents a pioneering VLSI architecture merging Discrete Wavelet Transform (DWT) with robust encryption algorithms for fortified security in data processing. Aimed at embedded systems, it ensures data integrity and confidentiality. By marrying DWT’s computational efficiency for analysis and encryption prowess of AES, it enables real-time processing while safeguarding sensitive information. The design harnesses parallel processing, augmenting throughput and reducing latency for extensive data volumes. Comparative results highlight superior performance in speed, security, and resource utilization over traditional systems. This adaptable framework finds application in secure communications, biomedical signalprocessing, multimedia encryption, and IoT devices, addressing the critical intersection of signalprocessing and data security. This VLSI integration of DWT and advanced encryption presents a potent solution for secure, efficient data processing in resource-limited environments. The proposed system exhibits an on-chip power consumption of 535mW, a memory capacity of 819.922 MB, and a gain of 519.445 dB, all while maintaining a stable junction temperature of 33°C.
The integration of electroencephalography (EEG) and photoplethysmography (PPG) in consumer-grade wearable devices is revolutionizing healthcare by enabling continuous health monitoring. However, these devices face sig...
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ISBN:
(数字)9798350353983
ISBN:
(纸本)9798350353990
The integration of electroencephalography (EEG) and photoplethysmography (PPG) in consumer-grade wearable devices is revolutionizing healthcare by enabling continuous health monitoring. However, these devices face significant challenges in terms of power consumption, computational complexity, and data storage, necessitating efficient data compression techniques. This paper proposes a modified Golomb-Rice coding method tailored for lossy compression of multi-channel EEG and PPG signals. By leveraging the statistical properties of filtered data and employing an optimized parameter estimation strategy, the proposed method achieves high compression ratios while minimizing computational overhead and preserving critical signal information. The proposed algorithm shifts data to handle negative values, scales and converts it into an appropriate format for encoding, and uses a novel parameter estimation technique to optimize the Golomb-Rice parameter. Extensive simulations on EEG and PPG datasets demonstrate that this parameter estimation method yields significantly better performance, improving both efficiency and processing speed. Results underscore the importance of preprocessing in data compression and provide a robust framework for future advancements in biomedical signalprocessing.
Digital radio frequency memory (DRFM) devices can quickly intercept radar waveforms and has become the main technique to achieve advanced radar jamming. Multiple-input multiple-output (MIMO) radars can counter advance...
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ISBN:
(数字)9798350355895
ISBN:
(纸本)9798350355901
Digital radio frequency memory (DRFM) devices can quickly intercept radar waveforms and has become the main technique to achieve advanced radar jamming. Multiple-input multiple-output (MIMO) radars can counter advanced DRFM jamming based on waveform agility technology by emitting waveform sets which are orthogonal between groups. To design multiple groups of orthogonal waveform sets simultaneously, an optimization design model for group agile orthogonal waveform sets is developed in this paper. The correlation function peak of intergroup waveforms is multiplied by a deviation weight, serving as the objective function. In order to solve the proposed optimization model, we propose an optimization design algorithm for group agile orthogonal waveform sets. The quantum genetic algorithm (QGA), employing a partial search strategy and having no special requirements on the properties of the objective function, is capable of solving such complex non-convex optimization problems. Numerical simulation results demonstrate that the proposed algorithm yields waveform sets with good intra- and intergroup orthogonality performance, adjustable via changing weight coefficients.
In present era, aviation communication technologies provides safe, effective, and efficient transfer of data between aircraft and facilities on the ground. Cryptography is essential to maintain the confidentiality, in...
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ISBN:
(数字)9798350385205
ISBN:
(纸本)9798350385212
In present era, aviation communication technologies provides safe, effective, and efficient transfer of data between aircraft and facilities on the ground. Cryptography is essential to maintain the confidentiality, integrity, and legitimacy of data transfer in aviation systems. The safe key exchange and authentication that are essential components of aviation communication security are expertly ensured algorithms. advanced Encryption Standard (AES), Rivest- Shamir- Adleman (RSA), Blowfish, Fernet, and Elliptic curve cryptog- raphy (ECC) are a few of the symmetric and asymmetric encryption method explored for aviation system. In this paper, the development and application of AES and Fernet encryption algorithms, specifically designed for safe client- server communication for the aviation industry sector, are the mainly implemented and performance analyzed. The public key is used for encryption, while the private key is used for decryption. The effectiveness of these algorithms in safeguarding data transfer between a client and server within an airline network is put to the test with great rigor.
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