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DEMONSTRATION OF applications IN COMPUTER VISION AND NLP ON ULTRA POWER-EFFICIENT CNN DOMAIN SPECIFIC ACCELERATOR WITH 9.3TOPS/WATT

DEMONSTRATION OF APPLICATIONS IN COMPUTER VISION AND NLP ON ...

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IEEE International conference on Multimedia and Expo (IEEE ICME)

作者： Sun, Baohua Yang, Lin Zhang, Wenhan Dong, Patrick Young, Charles Dong, Jason Lin, Michael Gyrfalcon Technol Inc 1900 McCarthy Blvd Milpitas CA 95035 USA

ISBN: (纸本)9781538692141

Computer Vision and Natual Language processing (NLP) applications are becoming available at edge devices and mobile platforms with the mass production of low-power and high-performance AI chips. SPR2801s is the CNN Domain Specific Accelerator (CNN-DSA) with inference speed of more than 140fps for input image size of 224x224x3 and only 300mW power consumption. The convolution computations are all completed on SPR2801s chip which works as a co-processor. In this demo, we will show demos running on SPR2801s for computer vision and NLP applications, including image classification, text classification, sentiment analysis, and Compact Descriptor for Video Analysis (CDVA). The applications are demonstrated on a single chip and also demonstrated on a multi-chip board. The single chip is shown as a dongle with USB connecting to a host processor. And the eight-chip board shows the power of parallel processing with PCIe or M.2 interface.

关键词： CNN accelerator AI chip CDVA MPEG7 NLP

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A High speed Area Efficient Compression technique of Dadda multiplier for image Blending Application 3

A High speed Area Efficient Compression technique of Dadda m...

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3rd International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2019

作者： Savithaa, N. Poornima, A. Sri Ramakrishna Institute of Technology Department of ECE Coimbatore India

ISBN: (纸本)9781728143651

Multimedia and image processing are the two widely used applications in Digital signal processing domain through which the information/data will be delivered in a compact size. The interesting concept lies with approximate computing where the arithmetic designs are performed with reduced power, area and delay. In this paper, the analyses of four different compression techniques are done in a 8x8 dadda multiplier and it is further applied on image blending where pixel by pixel multiplication is been carried out. While comparing the results of both existing and proposed designs, the proposed design operates with greater speed, consumes less power and occupies less area. In addition to that, the proposed multipliers is well suited for image blending with respect to Peak signal to Noise Ratio (PSNR). © 2019 IEEE.

关键词： signal to noise ratio

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Multiscale Analysis for Higher-order Tensors 18

Multiscale Analysis for Higher-order Tensors

引用

conference on wavelets and Sparsity xVIII

作者： Ozdemir, Alp Zare, Ali Iwen, Mark A. Aviyente, Selin Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Michigan State Univ Dept Computat Math Sci & Engn E Lansing MI 48824 USA Michigan State Univ Dept Math E Lansing MI 48824 USA

ISBN: (数字)9781510629707

ISBN: (纸本)9781510629707

The widespread use of multisensor technology and the emergence of big datasets have created the need to develop tools to reduce, approximate, and classify large and multimodal data such as higher-order tensors. While early approaches focused on matrix- and vector-based methods to represent these higher-order data, more recently it has been shown that tensor decomposition methods are better equipped to capture couplings across their different modes. For these reasons, tensor decomposition methods have found applications in many different signal processing problems including dimensionality reduction, signal separation, linear regression, feature extraction, and classification. However, most of the existing tensor decomposition methods are based on the principle of finding a low-rank approximation in a linear subspace structure, where the definition of rank may change depending on the particular decomposition. Since many datasets are not necessarily low-rank in a linear subspace, this often results in high approximation errors or low compression rates. In this paper, we introduce a new adaptive, multi-scale tensor decomposition method for higher-order data inspired by hybrid linear modeling and subspace clustering techniques. In particular, we develop a multi-scale higher-order singular value decomposition (MS-HoSVD) approach where a given tensor is first permuted and then partitioned into several sub-tensors each of which can be represented as a low-rank tensor with increased representational efficiency. The proposed approach is evaluated for dimensionality reduction and classification for several different real-life tensor signals with promising results.

关键词： Higher-order singular value decomposition tensor decomposition multi-scale decomposition data reduction big data applications

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Deep Learning Based Gun Classification in x-Ray images

Deep Learning Based Gun Classification in X-Ray Images

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IEEE signal processing and Communications applications (SIU)

作者： İsmail Karakaya Orkun Öztürk Murat Bal Yunus Emre Esin HAVELSAN A.Ş. Ankara Türkiye

ISBN: (数字)9781728172064

ISBN: (纸本)9781728172071

In this study, deep learning based methods, which classifies the x-Ray images according to the presence of weapon, is proposed. In the first method the classification network has been trained with previously separated training images. In the second method, regions with weapons in the training images were cropped from the image. The training network was run with cropped images. In the third method, training was made with the regions determined in the images by Selective Search. In the last method, the training images were first given as an input image to the Faster R-CNN object detection method. Then, the regions determined as weapons were clipped from the image and included in the training set. Updated training set is given to the classification network. When the proposed four methods were compared, it was seen that the success was higher in the model in which the Faster R-CNN result was included in the training and test steps.

关键词： Reactive power Object recognition Visualization Training Testing Support vector machines Security

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Development of a new methodology for thermal image reconstruction 41

Development of a new methodology for thermal image reconstru...

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conference on Thermosense - Thermal Infrared applications xLI

作者： Kordatou, T. Z. Exarchos, D. A. Kordatos, E. Z. Matikas, T. E. Univ Ioannina Dept Mat Sci & Engn GR-45110 Ioannina Greece

ISBN: (数字)9781510626744

ISBN: (纸本)9781510626744

Infrared thermography (IRT) is a well-established and well-documented nondestructive evaluation (NDE) technique which has been proved as one of the critical assessment tools providing not only qualitative but also quantitative results useful for various applications. Even though many post processing methodologies have been used for thermal imaging analysis, there is still a need for a methodology that could possibly reduce the noise, improve the signal to Noise Ratio (SNR) and focus on a specific area of interest reconstructing automatically the thermal image. This work deals with fine-tuning the IRT method in order to assess the detectability of damage in composite materials.

关键词： InfraRed Thermography (IRT) Lock-in Thermography (LT) Damage assessment signal-to-Noise Ratio (SNR)

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Dilated Convolutional Neural Networks for Panoramic image Saliency Prediction

Dilated Convolutional Neural Networks for Panoramic Image Sa...

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IEEE International conference on Acoustics, Speech and signal processing

作者： Feng Dai Youqiang Zhang Yike Ma Hongliang Li Qiang Zhao Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Saliency prediction is an important way to understand human's behavior and has a wide range of applications. Although lots of algorithms have been designed to predict saliency for planar images, there are few works for 360° images. In this paper, we propose an encoder-decoder network for panoramic image saliency prediction. Dilated convolutional layers are deployed in the network, which can extract more representative features and improve the accuracy of saliency prediction. To deal with the image distortions in 360° images, our network takes cube map format as input and processes six faces of cube map simultaneously. Respecting the saliency distribution of ground truth, we also propose a new data augmentation method to train the network, which is validated to be helpful for performance improvement. Extensive experiments show that our method gives new state-of-the-art results on 360° image saliency prediction.

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Deep Multi-Scale Gabor Wavelet Network for image Restoration

Deep Multi-Scale Gabor Wavelet Network for Image Restoration

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IEEE International conference on Acoustics, Speech and signal processing

作者： Hang Dong xinyi Zhang Yu Guo Fei Wang College of Artificial Intelligence Xi’an Jiaotong University School of Software Engineering Xi’an Jiaotong University

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Due to the limitations of the imaging processors and complex weather conditions, image degradation is often inevitable. Existing deep learning-based image restoration methods often rely on the powerful feature representation capacity of deep networks and pay less attention to the inherent properties of the degradation signal, e.g. variations in spatial scale and orientations across the image, which makes them ineffective for the image restoration tasks. In this paper, we propose a Multiscale Gabor Wavelet Network (MsGWN) for image restoration. We apply the multi-scale architecture to extract the contaminated feature from input at different spatial scales, and thus the contaminated feature can be effectively restored in a corse- to-fine manner. However, using multi-scale architecture alone cannot remove the degradations with different orientations. To overcome this problem, we introduce a Gabor Wavelet Module (GWM) to further extract the contaminated features from four orientations. By decomposing the features into four multi-orientation components, the restoration process can be facilitated by avoiding learning the mixed degradations all-in- one. We evaluate the proposed method on image demoirding, image deraining, and image dehazing. Experiments on these applications demonstrate that the proposed method can achieve favorable results against the state-of-the-art approaches.

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Medical image Quality Assessment Method based on Residual Learning

Medical Image Quality Assessment Method based on Residual Le...

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IEEE International conference on signal and image processing (ICSIP)

作者： xiangyue Cui Weili Shi Ke Zhang Yu Miao Zhengang Jiang School of computer science and technology Changchun University of Science and Technology Changchun China

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

In clinical applications, the task of medical image quality assessment is done by radiologists and the existence of subjective factors leads to unstable the accuracy of the assessment. Deep learning methods bring the opportunity for objective assessment image quality. In order to avoid the overfitting problem of deep learning methods in small-sample data assessment, this paper proposes a medical image quality assessment method based on residual learning. This method adds a residual module to the traditional quality assessment network, so that the problem of overfitting can be effectively alleviated in the process of deepening the network structure. At the same time, this paper initially establishes a chest x-ray image quality assessment dataset. In the experiment, the correlation coefficients SRCC and PLCC of this method reaches 0.9692 and 0.9589, respectively, and the mean square error is 0.6697. Compared with the six assessment methods, three indicators are optimal. The method proposed in this paper can meet the clinical assessment needs of medical image quality, and the effectiveness of the method has been fully verified.

关键词： image quality Deep learning Neural networks Quality assessment Task analysis Medical diagnostic imaging x-ray imaging

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Wavelet-based Techniques Applied to Digital processing of ECG signals

Wavelet-based Techniques Applied to Digital Processing of EC...

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International conference and Exposition on Electrical and Power Engineering (EPE)

作者： Ionut Adrian Iftode Cristian Fosalau Faculty of Electrical Engineering “Gheorghe Asachi” Technical University of Iasi Iași Romania

ISBN: (数字)9781728181264

ISBN: (纸本)9781728181271

Electrocardiogram (ECG) is an important biomedical signal for diagnosing heart diseases, but now it can have different other applications, such as using it as a stress recognition biomarker. Taking into account that the ECG signal is always overlapped with noise generated by muscles, body movement, electrodes skin contact, breathing and electronics, a de-noising stage must be added. This research paper analyzes and compares the noise removal by using different decomposition levels of undecimated wavelet transform (UWT) and discrete wavelet transform (DWT) based on different types of mother wavelets like orthogonal (Haar, Daubechies, Coiflets, Symmlet) and biorthogonal, whereas the other artifacts that are low frequency carriers have been already removed from the signal. During the analysis phase for each of the de-noising methods, several parameters are modified to check the accuracy and performance in a more elaborated way. The raw ECG data used in the study has been obtained by using a proposed data acquisition system.

关键词： Electrocardiography Discrete wavelet transforms Wavelet transforms Graphical user interfaces Noise reduction Wavelet analysis Data acquisition

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A Viterbi Algorithm-Based IF estimator for overlapped multicomponent signal 11

A Viterbi Algorithm-Based IF estimator for overlapped multic...

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11th International conference on Digital image processing (ICDIP)

作者： Li, Po Nanjing Inst Ind Technol Coll Energy & Elect Engn 1 YangShan North Rd Nanjing Jiangsu Peoples R China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630765

Instantaneous frequency (IF) estimation of multicomponent signals with time frequency (TF) overlapped components is a challenging task in radar, sonar and other applications. Viterbi algorithm (VA) on TF distribution can be potentially applied to estimate IFs of multicomponent signals, but it may track wrong IFs when signal components intersect each other in the TF domain. In order to suppress this switch problem in the original VA, this paper assumes the linearity of IFs in the overlapped regions remains unchanged, then, a novel penalty function in the modified VA based on linear least square fitting technique using adjacent several TF points is developed. Performance of the proposed algorithm on several artificial multicomponent FM signals indicates the proposed algorithm can obtain more accurate IFs by suppressing the switch problem.

关键词： Viterbi algorithm instantaneous frequency estimator multicomponent signals time-frequency analysis

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