The present study screened out 16 papers from SSCI-indexed and SCI-indexed journals for a literature review on the topic of adaptive algorithms applied in various adaptive learning systems. It is found that most of th...
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ISBN:
(纸本)9781728136608
The present study screened out 16 papers from SSCI-indexed and SCI-indexed journals for a literature review on the topic of adaptive algorithms applied in various adaptive learning systems. It is found that most of the reviewed articles directly adopted some long-standing algorithms, although a few of them employed game engines or developed their own algorithms for the construction of the adaptive learning systems. The literature review also shows that the majority of the studies have testified the effectiveness of the adopted algorithms, although many of them cannot achieve the ideal dynamic adaptivity.
Deep neural network (DNN) is a popular model implemented in many systems to handle complex tasks such as image classification, object recognition, natural language processing etc. Consequently DNN structural vulnerabi...
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With recent advances in the study of biometrics, gait analysis has drawn much attention for its potential use in forensics, surveillance, and legal systems. In this paper, we present WIAGE, a contactless and non-intru...
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ISBN:
(纸本)9781728181059
With recent advances in the study of biometrics, gait analysis has drawn much attention for its potential use in forensics, surveillance, and legal systems. In this paper, we present WIAGE, a contactless and non-intrusive gait-based age estimation system, which leverages wireless sensing to perform gait analysis to infer the age of individuals. Traditional age estimation systems either require users to carry wearable devices that are inconvenient or rely on imageprocessing that is computationally intensive and sensitive to lighting conditions and occlusion. In contrast, WIAGE utilizes the incumbent WiFi infrastructure to infer the age of users with minimal interference to their activities. We adopt a series of signal processing techniques to recover clear gait patterns from the noisy WiFi signals and extract the most relevant features from steps that can be used for robust age estimation. The experimental results show that WIAGE can achieve an age estimation accuracy of 95.2% for 23 users, which demonstrates the feasibility and effectiveness of our proposed system.
Coronary artery blockage is a vital issue of occurring heart attack. There are several techniques to diagnose coronary artery blockage as well as other type heart diseases. In this paper, we discuss about computerized...
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ISBN:
(纸本)9781510626300
Coronary artery blockage is a vital issue of occurring heart attack. There are several techniques to diagnose coronary artery blockage as well as other type heart diseases. In this paper, we discuss about computerized full automated model for the detection of coronary artery blockage using imageprocessing techniques so that the system does not have to rely on human's inspection. Using efficient imageprocessing technique and AI algorithms, the system allows a faster and reliable detection of the narrowing area of the wall of coronary arteries due to the condensation of different artery blocking agents. The system requires a 64-slice/128-slice CTA image as input. After the acquisition of the desired input image, it goes through several steps to determine the region of interest. This research proposes a two stage approach that includes the pre-processing stage and decision stage. The pre-processing stage involves common imageprocessing strategies while the decision stage involves the extraction and calculation of features to finally determine the intended result using AI algorithms. This type of model effectively enables early detection of coronary artery blockage through segmentation, quantification, identification of degree of blockage and risk factors of heart attack.
Pedestrian detection algorithms based on deep learning often rely on high-performance imageprocessing platforms, and it does not adapt to high portability requirements of practical engineering. In order to solve the ...
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The proceedings contain 41 papers. The topics discussed include: target re-identification in low-quality camera networks;robust face recognition algorithm for identification of disaster victims;improved image copyrigh...
ISBN:
(纸本)9780819494283
The proceedings contain 41 papers. The topics discussed include: target re-identification in low-quality camera networks;robust face recognition algorithm for identification of disaster victims;improved image copyright protection scheme exploiting visual cryptography in wavelet domain;HDR image multi-bit watermarking using bilateral-filtering-based masking;body-part estimation from Lucas-Kanade tracked Harris points;hue processing in tetrachromatic spaces;locally tuned inverse sine nonlinear technique for color image enhancement;embedding high dynamic range tone mapping in JPEG compression;visual quality analysis for images degraded by different types of noise;graph cut and image intensity-based splitting improves nuclei segmentation in high-content screening;and near real-time skin deformation mapping.
As a biomimetic model of visual information processing, predictive coding (PC) has become increasingly popular for explaining a range of neural responses and many aspects of brain organization. While the development o...
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As a biomimetic model of visual information processing, predictive coding (PC) has become increasingly popular for explaining a range of neural responses and many aspects of brain organization. While the development of PC model is encouraging in the neurobiology community, its practical applications in machine learning (e.g., image classification) have not been fully explored yet. In this paper, a novel imageprocessing model called fast inference PC (FIPC) is presented for image representation and classification. Compared with the basic PC model, a regression procedure and a classification layer have been added to the proposed FIPC model. The regression procedure is used to learn regression mappings that achieve fast inference at test time, while the classification layer can instruct the model to extract more discriminative features. In addition, effective learning and fine-tuning algorithms are developed for the proposed model. Experimental results obtained on four image benchmark data sets show that our model is able to directly and fast infer representations and, simultaneously, produce lower error rates on image classification tasks.
To achieve higher requirements for industrial robots in practical applications, the image pre-processingalgorithms, key feature extraction and recognition algorithms, and trajectory planning and generation algorithms...
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