Coherent optical communication technology is a key technology for building high-speed optical fibre communication networks, while the amplitude and phase imbalance between the in-phase and quadrature (IQ) signals at t...
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ISBN:
(数字)9798350356328
ISBN:
(纸本)9798350356335
Coherent optical communication technology is a key technology for building high-speed optical fibre communication networks, while the amplitude and phase imbalance between the in-phase and quadrature (IQ) signals at the receiving end will lead to the degradation of the received signal quality and affect the performance of the communication system. In this thesis, a narrowband zero-intermediate frequency (ZIF) receiver IQ imbalance estimation and compensation algorithm is proposed with the research objective of designing IQ imbalance estimation and compensation algorithms for the 16 quadrature amplitude modulation (16QAM) signal in coherent optical communication systems that can be implemented in hardware circuits. The algorithm is based on the Gram-Schmidt orthogonalization procedure (GSOP) for approximate estimation of the IQ imbalance parameters and improves the estimation accuracy of the IQ imbalance parameters by means of iterative optimization. The IQ imbalance signal is effectively compensated by the inverse matrix of the IQ mismatch parameter matrix. Based on the estimation error of the IQ imbalance parameters, the bit error rate (BER) of the signal transmission at different signal to noise ratio (SNR), and the simulation results of the 16QAM constellation diagram before and after compensation, it is concluded that the iterative optimized algorithm based on GSOP reduces the complexity of hardware implementation and improves the efficiency and accuracy of the IQ imbalance parameters estimation effectively. The findings of this study hold practical engineering significance for estimating and calibrating IQ imbalance in coherent optical communication systems using high-order modulated signals through digital signal processing techniques.
The authors present the development of eyewear that incorporates stereoscopic and thermal imaging cameras for the purpose of highlighting objects/views of interest. imageprocessingalgorithms that simplify complex el...
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ISBN:
(数字)9781728127828
ISBN:
(纸本)9781728127828
The authors present the development of eyewear that incorporates stereoscopic and thermal imaging cameras for the purpose of highlighting objects/views of interest. imageprocessingalgorithms that simplify complex elements in a scene have the ability to improve the utility of blind and low vision aids. Thermal imaging can be used to highlight important objects such as people or animals, while stereoscopic imaging can be used to filter background imagery beyond a certain distance. The methods used have been successful in providing utility to retinal prosthesis users. The stereoscopic camera systems involved strict requirements on the relative orientation of the cameras for calibrated distance filtering. A mechanical design is presented that fixes the relative camera locations on a 3D printed titanium structure that can float in the frame to maintain orientations even when the eyewear is flexed during wearing.
Thermal imaging has long been utilized across industries to maintain electrical equipment and detect faults in machines, ensuring their reliable operation. Infrared thermography, or thermal imaging, has emerged as a p...
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In recent years, Electroencephalogram (EEG) has gradually become one of the important means of early Parkinson's disease (PD) detection because of its non-invasive and real-time monitoring capability. This paper d...
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ISBN:
(数字)9798350355413
ISBN:
(纸本)9798350355420
In recent years, Electroencephalogram (EEG) has gradually become one of the important means of early Parkinson's disease (PD) detection because of its non-invasive and real-time monitoring capability. This paper discusses the EEG signal analysis method from the effective feature extraction techniques and the application of traditional machine learning algorithms. Although traditional methods are able to provide some degree of feature interpretability, there are limitations in handling high-dimensional feature space and redundant features, resulting in decreased classification performance. Therefore, this paper proposes a Support Vector Machine (SVM) classification model based on artificial channel optimization, through channel selection of EEG signals recorded by 64-lead devices, screening out the optimal channel combination highly related to PD, and then achieving efficient classification. Specifically, the study uses publicly available Parkinson's disease EEG datasets, employs power spectral density (PSD) features, and combines manual channel selection with the strong classification capabilities of SVM to achieve effective classification of PD EEG signals. In addition, the article highlights that model performance may vary on different datasets, and more clinical validation is needed in the future, and suggests to combine other machine learning technologies such as deep learning to further improve model performance.
As the use of Healthcare Information Technology has continued to rise, the issue of safe transfer of medical information with especial emphasis on images has become a major concern. The findings of this study propose ...
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Gait as a biometric has become a popular research topic in recent years as a result of its numerous applications in sectors such as surveillance, authentication, and so on. It is capable of achieving detection at a di...
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ISBN:
(数字)9781665482509
ISBN:
(纸本)9781665482509
Gait as a biometric has become a popular research topic in recent years as a result of its numerous applications in sectors such as surveillance, authentication, and so on. It is capable of achieving detection at a distance that few other technologies can equal. It is still a difficult problem to solve since real human gait is influenced by several variable elements such as alterations in clothing, speed, and carrying situation. Also, unknown covariate circumstances may impact the training and testing settings for a specific individual in gait recognition. image sequences are typically used by computer-aided gait recognition systems without taking into account variables such as clothes and the contents of carrier bags while on the move. In this work, we provide a technique for selecting gait energy image-based (GEI) features, that is both effective and robust. The covariate factors have less impact on the given gait representation. A simple ten-layered convolutional neural network (CNN) is designed which intakes GEI as input. Several typical variations and occlusions that impact and worsen gait recognition ability are less susceptible to the suggested method. For both clothing and mobility variations, we used the CASIA datasets to assess our observations. The experimental findings reveal that in numerous circumstances, the deep neural network model created in this study achieved better results when compared with other existing algorithms.
Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for...
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Snapshot compressive imaging (SCI) uses a 2D sensor to obtain higher dimensional data and then reconstructs the underlying high-dimension data by elaborate algorithms. Applying SCI to capture hyperspectral images is k...
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Waste management is a pressing global issue, and the need for efficient waste separation processes is becoming increasingly important. Incorporating Machine Learning techniques with waste separation has yielded promis...
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With the development of digital imageprocessing technology, the demand and application of wide-view video continue to grow. However, the existing stitching algorithms may result in unnatural appearance, misalignment,...
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ISBN:
(数字)9798350359985
ISBN:
(纸本)9798350359992
With the development of digital imageprocessing technology, the demand and application of wide-view video continue to grow. However, the existing stitching algorithms may result in unnatural appearance, misalignment, and ghosting issues when stitching scenes with inconsistent brightness or dynamic objects. To address the above problems, we propose a real-time multi-camera video stitching algorithm with adaptive sensing of illumination and dynamic objects. Within the proposed illumination compensation module, brightness equalization across varying illumination conditions is attained through the application of gain compensation to image blocks. We propose a histogram-based dynamic object detection approach to dynamically detect the presence of moving objects along the stitching seam. This method facilitates the adaptive detection of dynamic entities by analyzing the histogram disparities between the local vicinity of the stitching seam and the broader image context. The experimental results show that it can effectively solve the problem of unnatural stitching results arising from the uneven brightness of the image, and can effectively detect the presence of dynamic objects near the stitching seam so that the effect of the video stitching has been improved.
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