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7th International Symposium on Test Automation and Instrumentation, ISTAI 2018

作者： Zhao, Huihuang Wang, Yaonan Zheng, Jinhua Qiao, Zhijun Zhang, Yun Department of Computer Science and Technology College of Computer Science and Technology Hengyang Normal University Hengyang Hunan China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang China Department of Electrical and Engineering College of Electrical and Information Engineering Hunan University Changsha China School of Mathematical and Statistical Sciences University of Texas Rio Grande ValleyTX United States Institute of Radio and TV Technology Communication University of Zhejiang Hangzhou310018 China

This study aims to improve the performance in solder joint image compression and reconstruction. A novel adaptive block compressive sensing with convex optimisation and Gini index (Ad_BCSGB_Gini) methodology for solder joint image compression and reconstruction is proposed. At first, the image is split into square blocks and each block is resized into a row which consists of a new image. Then, the new image is transformed into a sparse signal by an orthogonal basis matrix, and the image reconstruction is handled as a convex optimisation problem. Moreover, a gradient-based method which has fast computational speed is used to reconstruct image. There is a control factor which controls a norm l1 in the optimisation problem. To achieve the best performance, at last, the proposed method adaptively selects the best result by comparing Gini index of the reconstruction results based on different control factor values. Experimental results with different methods indicate that the Ad_BCSGB_Gini method is able to achieve the best performance in quantisation comparison than several classical algorithms, and Ad_BCSGB_Gini has a good robustness. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Convex optimization

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Dendrite Net with Acceleration Module for Faster Nonlinear Mapping and System Identification

arXiv

引用

arXiv 2020年

作者： Liu, Gang Pang, Yajing Yin, Shuai Niu, Xiaoke Wang, Jing Wan, Hong School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Institute of Robotics and Intelligent Systems Xi'an Jiaotong University Xi'An710049 China Henan Provincial Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001 China

Nonlinear mapping is an essential and common demand in online systems, such as sensor systems and mobile phones. Accelerating nonlinear mapping will directly speed up online systems. Previously the authors of this paper proposed a Dendrite Net (DD) with enormously lower time complexity than the existing nonlinear mapping algorithms;however, there still are redundant calculations in DD. This paper presents a DD with an acceleration module (AC) to accelerate nonlinear mapping further. We conduct three experiments to verify whether DD with AC has lower time complexity while retaining DD's nonlinear mapping properties and system identification properties: The first experiment is the precision and identification of unary nonlinear mapping, reflecting the calculation performance using DD with AC for basic functions in online systems. The second experiment is the mapping precision and identification of the multi-input nonlinear system, reflecting the performance for designing online systems via DD with AC. Finally, this paper compares the time complexity of DD and DD with AC and analyzes the theoretical reasons through repeated experiments. Results: DD with AC retains DD's excellent mapping and identification properties and has lower time complexity. Significance: DD with AC can be used for most engineering systems, such as sensor systems, and will speed up computation in these online systems. The code of DD with AC is available on GitHub:Gang neuron. © 2020, CC BY-NC-SA.

关键词： Online systems

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Multi-view commercial hotness prediction using context-aware neural network ensemble

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018年第4期2卷 1–19页

作者： He, Zhiyuan Yang, Su School of Computer Science Xi'an University of Technology China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

Prediction over heterogeneous data attracts much attention in urban computing. Recently, satellite imagery provides a new chance for urban perception but raises the problem of how to fuse visual and non-visual features. So far, the practice is to concatenate the multimodal features into a vector, which may suppress important features. Therefore, we propose a new ensemble learning framework: (1) An estimator is developed for each predictor to score its confidence, which is input adaptive. (2) By applying the output of each predictor to the input of the corresponding estimator as feedback, the estimator learns the performance of the predictor in the input-output space. When a new input is applied to produce a prediction, the similar situations will be recalled by the estimator to score the confidence of the prediction. (3) Using end-to-end training, the estimator learns the weights automatically to minimize the total loss of the neural networks. With the proposed method, data mining based urban computing and computer vision rendered urban perception can be bridged at the task of commercial activeness prediction, where the prediction based on satellite images and social context data are fused to yield better prediction than those based on single view data in the experiments. © 2018 Association for Computing Machinery.

关键词： Forecasting

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Learning Hierarchical Representations for Expressive Speaking Style in End-to-End Speech Synthesis

Learning Hierarchical Representations for Expressive Speakin...

引用

IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Xiaochun An Yuxuan Wang Shan Yang Zejun Ma Lei Xie Shaanxi Provincial Key Laboratory of Speech and Image Information Processing School of Computer Science Northwestern Polytechnical University Xi'an China ByteDance AI Lab

ISBN: (数字)9781728103068

ISBN: (纸本)9781728103075

Although Global Style Tokens (GSTs) are a recently-proposed method to uncover expressive factors of variation in speaking style, they are a mixture of style attributes without explicitly considering the factorization of multiple-level speaking styles. In this work, we introduce a hierarchical GST architecture with residuals to Tacotron, which learns multiple-level disentangled representations to model and control different style granularities in synthesized speech. We make hierarchical evaluations conditioned on individual tokens from different GST layers. As the number of layers increases, we tend to observe a coarse to fine style decomposition. For example, the first GST layer learns a good representation of speaker IDs while finer speaking style or emotion variations can be found in higher-level layers. Meanwhile, the proposed model shows good performance of style transfer.

关键词： Speech Spectrogram Speech synthesis Data models Training data Predictive models Training

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A spatial correlation based partial coverage scheduling scheme in wireless sensor networks

Journal of Network Intelligence

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Journal of Network Intelligence 2020年第2期5卷 34-43页

作者： Zhang, Jing Nian, Hongrui Ye, Xiucai Ji, Xiaorong He, Yao College of Information Science and Engineering Institute of Artificial Intelligence Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology 33th Xueyuan Road University Town Minhou FuzhouFujian350118 China College of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Department of Computer Science University of Tsukuba Tsukuba3058573 Japan

Wireless Sensor Networks are formed by a mass of various sensors, they are adopted in lots of applications. Because of the existence of spatial correlation in WSNs, the information from some geographic independence sensors are more significant than that from high correlation ones. Thus, it is unnecessary to transmit information from every sensor to the sink node. This paper focus on the coverage area for data monitoring in WSNs. To construct the virtual backbone network, we propose an efficient connected dominating set construction method based on clique, by taking advantage of the high spatial correlation characteristics of clique construction. Furthermore, by minimizing the number of sensors to activate, we propose a Spatial Correlation based partial Coverage Scheduling Scheme (SCCS) to prolong the network lifetime. The corresponding experiment indicate the efficiency and the feasibility of the proposed SCCS scheme. Moreover, this algorithm is able to guarantee better performance by comparing to the existing schemes. © 2020, Taiwan Ubiquitous information. All rights reserved.

关键词： Scheduling

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Improving Mandarin End-to-End Speech Synthesis by Self-Attention and Learnable Gaussian Bias

Improving Mandarin End-to-End Speech Synthesis by Self-Atten...

引用

IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Fengyu Yang Shan Yang Pengcheng Zhu Pengju Yan Lei Xie Shaanxi Provincial Key Laboratory of Speech and Image Information Processing School of Computer Science Northwestern Polytechnical University Xian China Tongdun AI Lab

ISBN: (数字)9781728103068

ISBN: (纸本)9781728103075

Compared to conventional speech synthesis, end-to-end speech synthesis has achieved much better naturalness with more simplified system building pipeline. End-to-end framework can generate natural speech directly from characters for English. But for other languages like Chinese, recent studies have indicated that extra engineering features are still needed for model robustness and naturalness, e.g, word boundaries and prosody boundaries, which makes the front-end pipeline as complicated as the traditional approach. To maintain the naturalness of generated speech and discard language-specific expertise as much as possible, in Mandarin TTS, we introduce a novel self-attention based encoder with learnable Gaussian bias in Tacotron. We evaluate different systems with and without complex prosody information and results show that the proposed approach has the ability to generate stable and natural speech with minimum language-dependent front-end modules.

关键词： Decoding Speech synthesis Pipelines Robustness Spectrogram Natural languages

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Jointly learning structured analysis discriminative dictionary and analysis multiclass classifier

arXiv

引用

arXiv 2019年

作者： Zhang, Zhao Jiang, Weiming Qin, Jie Zhang, Li Li, Fanzhang Zhang, Min Yan, Shuicheng School of Computer Science and Technology Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China Computer Vision Laboratory ETH Zürich Zürich8092 Switzerland Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

In this paper, we propose an analysis mechanism based structured Analysis Discriminative Dictionary Learning (ADDL) framework. ADDL seamlessly integrates the analysis discriminative dictionary learning, analysis representation and analysis classifier training into a unified model. The applied analysis mechanism can make sure that the learnt dictionaries, representations and linear classifiers over different classes are independent and discriminating as much as possible. The dictionary is obtained by minimizing a reconstruction error and an analytical incoherence promoting term that encourages the sub-dictionaries associated with different classes to be independent. To obtain the representation coefficients, ADDL imposes a sparse l2,1-norm constraint on the coding coefficients instead of using l0 or l1-norm, since the l0 or l1-norm constraint applied in most existing DL criteria makes the training phase time consuming. The codes-extraction projection that bridges data with the sparse codes by extracting special features from the given samples is calculated via minimizing a sparse codes approximation term. Then we compute a linear classifier based on the approximated sparse codes by an analysis mechanism to simultaneously consider the classification and representation powers. Thus, the classification approach of our model is very efficient, because it can avoid the extra time-consuming sparse reconstruction process with trained dictionary for each new test data as most existing DL algorithms. Simulations on real image databases demonstrate that our ADDL model can obtain superior performance over other state-of-the-arts. Copyright © 2019, The Authors. All rights reserved.

关键词： Codes (symbols)

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A Fragile Watermarking Algorithm for Speech Authentication by Modifying Least Significant Digits

A Fragile Watermarking Algorithm for Speech Authentication b...

引用

International Conference on computers, Communications, and Systems (ICCCS)

作者： Qing Qian Yunhe Cui School of Information International Joint Research Center for Data Science and High-Performance Computing Guizhou University of Finance and Economics Guiyang China School of Computer Science and Technology Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

Speech content is often faced with malicious forgery and tampering in transit. Therefore, we introduce a novel fragile watermarking algorithm by modifying the least significant digits for speech content authentication and tampering recovery. In this letter, the proposed authentication scheme consists of two processes: the first one is watermark generation and embedding, the second one is content authentication and recovery. In the former process, the compressed speech signal, frame number and check information are generated based on speech content to form a watermark. Subsequently, the generated watermark is embedded into the least significant digits of speech sampling points using the proposed watermarking algorithm. In the latter process, the embedded watermark is utilized to locate the tampered area and recover that tampered content synchronously. Experimental results show that the proposed watermarking algorithm is fragile to common signal processing. Additionally, it has favorable inaudibility, accurate location ability and high recovery quality.

关键词： Watermarking Authentication Speech coding Signal to noise ratio Speech processing

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Sea Experiment of Positioning Underwater Mobile Vehicles Using Multiple Surface Beacons

Sea Experiment of Positioning Underwater Mobile Vehicles Usi...

引用

OCEANS

作者： Huifang Chen Luyixin Zhang Feng Liu Chuanlei Lv Lei Xie Shengjun Xiong Haijun Wang Shaoqiong Yang Wen Xu Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Science and Technology on Sonar Laboratory Hangzhou Applied Acoustic Research Institute Hangzhou China No.710 Research Institute of China Shipbuilding Industry Corporation Yichang China School of Mechanical Engineering Tianjin University Tianjin China Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province Zhoushan China

ISBN: (数字)9781728154466

ISBN: (纸本)9781728184098

In the paper, we present the sea experiment of positioning underwater vehicles using multiple surface beacons. A system is designed to realize the underwater vehicles' positioning using the time difference of arrivals (TDOA) technique, where no strict clock synchronization between beacons and underwater vehicles is required. Moreover, an Extended Kalman Filter (EKF) method is introduced to improve the robustness. A sea experiment is conducted to verify the proposed positioning method. In the experiment, three surface beacons broadcast positioning messages synchronously at a given interval. The underwater node uses the received positioning messages and its corresponding arrival time to perform the position calculation. The experimental results show that the proposed positioning method works well and the average positioning error is less than 20 meters.

关键词： Location awareness Sea surface Time difference of arrival Robustness Mobile nodes Kalman filters Underwater vehicles

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Sparse Low-Rank Component-Based Representation for Face Recognition With Low-Quality Images

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IEEE Transactions on information Forensics and Security 2019年第1期14卷 251-261页

作者： Shicheng Yang Le Zhang Lianghua He Ying Wen Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Department of Computer Science and Technology Tongji University Shanghai China

Sparse-representation-based classification (SRC) has been showing a good performance for face recognition in recent years. But SRC is not good at face recognition with low quality images (e.g., disguised, corrupted, occluded, and so on) which often appear in practical applications. To solve the problem, in this paper, we propose a novel SRC-based method for face recognition with low quality images named sparse low-rank component-based representation (SLCR). In SLCR, we utilize low-rank matrix recovery on the training data set to obtain low-rank components and non-low-rank components, which are used to construct the dictionary. The new dictionary is capable of describing facial features better, especially for low quality face samples. Furthermore, the minimum class-wise reconstruction residual is used as the recognition rule, leading to a substantial improvement on the proposed SLCR's performance. Extensive experiments on benchmark face databases demonstrate that the proposed method is consistently superior to other sparse-representation-based approaches for face recognition with low quality images.

关键词： Training Dictionaries Face recognition Principal component analysis Face Image reconstruction Image recognition

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