—Transfer learning have been frequently used to improve deep neural network training through incorporating weights of pre-trained networks as the starting-point of optimization for regularization. While deep transfer...
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The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (ME...
The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a deep Reinforcement learning (DRL) multi-objective algorithm, called a Double deep Q-learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 % improvement in energy efficiency, and a 20 % decrease in the deadline-miss ratio compared to benchmark algorithms.
A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition. Different to the 2DPCA, 2DPCA-L1 and G2DPCA, the R2DPCA utilizes the label information (if known) of training ...
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Hyperspectral Document Image (HSDI) analysis allows for efficient and accurate differentiation of inks with visually similar color but unique spectral response, which is a crucial step in authentication of documents. ...
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Hyperspectral Document Image (HSDI) analysis allows for efficient and accurate differentiation of inks with visually similar color but unique spectral response, which is a crucial step in authentication of documents. Various HSDI based ink discrimination methods are available in the current literature, however, more accurate and robust methods are required to empower document authentication. Contrary to the former ink mismatch detection methods based on spectral features only, we present a novel method based on deeplearning that exploits the spectral correlation as well as the spatial context to enhance ink mismatch detection. Spectral responses of the target pixel and its neighboring pixels are organized in an image format and fed to a Convolutional Neural Network (CNN) for classification. The proposed method achieves the highest accuracy among the other ink mismatch detection methods on the UWA Writing Ink Hyperspectral Images database (WIHSI), which demonstrates the effectiveness of deeplearning models employing spatio-spectral hybrid features for document authentication. Detailed experimental analysis for selection of appropriate CNN architecture, spatio-spectral data format and training ratio is presented along with a comparison with the previous methods on this subject.
Part information has been shown to be resistant to occlusions and viewpoint changes, which is beneficial for various vision-related tasks. However, we found very limited work in car pose estimation and reconstruction ...
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For applications such as augmented reality, autonomous driving, self-localization/camera pose estimation and scene parsing are crucial technologies. In this paper, we propose a unified framework to tackle these two pr...
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Handwriting is a behavioral characteristic of human beings that is one of the common idiosyncrasies utilized for litigation purposes. Writer identification is commonly used for forensic examination of questioned and s...
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Handwriting is a behavioral characteristic of human beings that is one of the common idiosyncrasies utilized for litigation purposes. Writer identification is commonly used for forensic examination of questioned and specimen documents. Recent advancements in imaging and machine learning technologies have empowered the development of automated, intelligent and robust writer identification methods. Most of the existing methods based on human defined features and color imaging have limited performance in terms of accuracy and robustness. However, rich spectral information content obtained from hyperspectral imaging (HSI) and suitable spatio-spectral features extracted using deeplearning can significantly enhance the performance of writer identification in terms of accuracy and robustness. In this paper, we propose a novel writer identification method in which spectral responses of text pixels in a hyperspectral document image are extracted and are fed to a Convolutional Neural Network (CNN) for writer classification. Different CNN architectures, hyperparameters, spatio-spectral formats, train-test ratios and inks are used to evaluate the performance of the proposed system on the UWA Writing Inks Hyperspectral Images (WIHSI) database and to select the most suitable set of parameters for writer identification. The findings of this work have opened a new arena in forensic document analysis for writer identification using HSI and deeplearning.
Depression has long been recognized as one of the leading causes of disability and burden worldwide. In psychology, it is well known that the self is not only the cognitive subject, but also the core of personality. A...
ISBN:
(数字)9781728132488
ISBN:
(纸本)9781728132495
Depression has long been recognized as one of the leading causes of disability and burden worldwide. In psychology, it is well known that the self is not only the cognitive subject, but also the core of personality. And the high incidence of suicide and pervasive hopelessness in depressed individuals suggested that the self might be abnormal among them. In order to expand the application of the depression detection, we employ classical scientific psychology paradigms on abnormalities of self-related processing in depressed individuals to develop a Chinese depressed speech corpus. Eleven depressed individuals and ten healthy subjects, who are gender-balanced and age-balanced, were recruited to participate in this study. Currently we have preliminarily collected 6 and 2.5 hours of speech data respectively, with the results of preliminary analysis indicating that there exist abnormalities in the depressed speech. The study results will provide a new perspective and strategy for further study on the building and application of Chinese speech corpus in depression.
Building intelligent agents that can communicate with and learn from humans in natural language is of great value. Supervised language learning is limited by the ability of capturing mainly the statistics of training ...
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This paper is mainly about a speech synthesis system based on deep Neural Network (DNN) model of Yi languages, a kind of minority language in China. The system is composed of relatively complete text analysis of Yi, m...
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ISBN:
(数字)9781728155869
ISBN:
(纸本)9781728155876
This paper is mainly about a speech synthesis system based on deep Neural Network (DNN) model of Yi languages, a kind of minority language in China. The system is composed of relatively complete text analysis of Yi, model training and speech synthesis module. Especially in front-end, the word segmentation, pause handling, word-to-phoneme conversion and label processing are used to analysis text of Yi language. We designed the question set for decision tree of DNN model training and used vocoder: WORLD for synthesis. The system achieves a relatively good Mean Opinion Score (MOS) of 3.93 by Yi undergraduates as evaluators compared with a MOS of 4.58 of original speech. To investigate the factors affecting the quality of synthesized Yi speech, this paper also objectively evaluates the performance of different training set and DNN model. The system successfully synthesized Yi speech for the first time and synthesized speech is relatively good as the result of an only complete minority language speech synthesis system.
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