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A 1.8mW Perception Chip with Near-Sensor processing Scheme for Low-Power AIoT Applications 18

A 1.8mW Perception Chip with Near-Sensor Processing Scheme f...

引用

18th ieee-Computer-Society Annual symposium on VLSI (ISVLSI)

作者： Liu, Zheyu Ren, Erxiang Luo, Li Wei, Qi Wu, Xing Li, Xueqing Qiao, Fei Liu, Xin-Jun Yang, Huazhong Tsinghua Univ Dept EE BNRist Beijing Peoples R China Tsinghua Univ Dept PI ERCNT Beijing Peoples R China Tsinghua Univ Dept ME Beijing Peoples R China Beijing Jiaotong Univ Dept EE Beijing Peoples R China East China Normal Univ Dept EE Shanghai Peoples R China

ISBN: (纸本)9781728133911

1) In the past few years, the demand for intelligence of IoT front-end devices has dramatically increased. However, such devices face challenges of limited on-chip resources and strict power or energy constraints. Recent progress in binarized neural networks has provided promising solutions for front-end processing system to conduct simple detection and classification tasks by making trade-offs between the processing quality and the computation complexity. In this paper, we propose a mixed-signal perception chip, in which an ADC-free 32x32 image sensor and a BNN processing array arc directly integrated with a 180mm standard CMOS process. Taking advantage of the ADC-free processing architecture, the whole processing system only consumes 1.8mW power, while providing up to 545.4 GOPS/W energy efficiency. The implementation performance and energy efficiency are comparable with the state-of-the-art designs in much more advanced CMOS technologies. This work provides a promising alternative for low-power IoT intelligent applications.

关键词： Near-sensor processing Artificial intelligence Smart sensor Low power

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Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning

Multiclass Yeast Segmentation in Microstructured Environment...

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ieee symposium on computational intelligence and Bioinformatics and computational Biology (CIBCB)

作者： Tim Prangemeier Christian Wildner André O. Françani Christoph Reich Heinz Koeppl Centre for Synthetic Biology Technische Universität Darmstadt

ISBN: (数字)9781728194684

ISBN: (纸本)9781728194691

Cell segmentation is a major bottleneck in extracting quantitative single-cell information from microscopy data. The challenge is exasperated in the setting of microstructured environments. While deep learning approaches have proven useful for general cell segmentation tasks, existing segmentation tools for the yeast-microstructure setting rely on traditional machine learning approaches. Here we present convolutional neural networks trained for multiclass segmenting of individual yeast cells and discerning these from cell-similar microstructures. We give an overview of the datasets recorded for training, validating and testing the networks, as well as a typical use-case. We showcase the method's contribution to segmenting yeast in microstructured environments with a typical synthetic biology application in mind. The models achieve robust segmentation results, outperforming the previous state-of-the-art in both accuracy and speed. The combination of fast and accurate segmentation is not only beneficial for a posteriori data processing, it also makes online monitoring of thousands of trapped cells or closed-loop optimal experimental design feasible from an image processing perspective.

关键词： Computer architecture Microprocessors image segmentation Microscopy Training Microstructure Deep learning

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AI in space: applications examples and challenges

AI in space: applications examples and challenges

引用

ieee International symposium on Defect and Fault Tolerance in VLSI Systems

作者： Gianluca Furano Antonis Tavoularis Marco Rovatti SA ESTEC Noordwijk The Netherlands

ISBN: (数字)9781728194578

ISBN: (纸本)9781728194585

While AI is being successfully applied in space (e.g. in the areas of enhanced monitoring and diagnostics, in prediction, image analysis etc.), it is still not applied *** potential applications could benefit from AI on-board capabilities at different levels. Probably the most straightforward approach is the use of AI for remote sensing missions at payload processing level to perform image processing tasks. Nevertheless, other applications at instrument, satellite or system levels could also represent important breakthroughs in the way we use and operate satellites for any kind of mission.A possible way forward would be to train machine learning models on-ground, up-link the trained models and use them on-board. This would enable an increased level of autonomy (e.g. opportunistic science) and added-value on-board, for a little extra computational cost. Even the most computational intensive AI models (e.g. deep learning) have now versions that allow trained models to be run on smartphones (“on the edge”).

关键词： Artificial intelligence Satellites image coding Training Erbium Metadata Bandwidth

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Accelerate Randomized Low Dimensional Manifold Method for image Reconstruction

Accelerate Randomized Low Dimensional Manifold Method for Im...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Ruifeng Hu Jun Zhang School of Science Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

In this paper, an image processing method based on low-dimensional manifold model is improved by a structured stochastic method. In the original low-dimensional manifold image processing method, the key step is to solve a large linear system involving the matrix of image patch similarity, but the computation of the similarity matrix and large linear equations solving will bring great computational complexity. In this paper, by using the structural information of the image data, the similarity of image patches is obtained by structured local random computation, so as to quickly calculate the similarity matrix and solve the related linear equations. The experimental results show that the computational efficiency of the proposed method is greatly improved, and results similar to the original method can be obtained.

关键词： Manifolds Linear systems Stochastic processes Gray-scale computational efficiency Mathematical model image reconstruction

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Cryo-CARE: Content-Aware image Restoration for Cryo-Transmission Electron Microscopy Data 16

Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmis...

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16th ieee International symposium on Biomedical Imaging (ISBI)

作者： Buchholz, Tim-Oliver Jordan, Mareike Pigino, Gaia Jug, Florian CSBD Dresden Germany Max Planck Inst Mol Cell Biol & Genet Dresden Germany

ISBN: (纸本)9781538636411

Multiple approaches to use deep learning for image restoration have recently been proposed. Training such approaches requires well registered pairs of high and low quality images. While this is easily achievable for many imaging modalities, e.g. fluorescence light microscopy, for others it is not. Cryo-transmission electron microscopy (cryo-TEM) could profoundly benefit from improved denoising methods, unfortunately it is one of the latter. Here we show how recent advances in network training for image restoration tasks, i.e. denoising, can be applied to cryo-TEM data. We describe our proposed method and show how it can be applied to single cryo-TEM projections and whole cryo-tomographic image volumes. Our proposed restoration method dramatically increases contrast in cryo-TEM images, which improves the interpretability of the acquired data. Furthermore we show that automated downstream processing on restored image data, demonstrated on a dense segmentation task, leads to improved results.

关键词： image restoration cryo-electron microscopy deep learning demising

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MSIT: A Modified Lightweight Algorithm for Secure Internet of Things

MSIT: A Modified Lightweight Algorithm for Secure Internet o...

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Sustainable Energy, signal processing and Cyber Security (iSSSC), ieee International symposium on

作者： Manoja Kumar Nayak Prasanta Kumar Swain North Orissa University India

ISBN: (数字)9781728188805

ISBN: (纸本)9781728188812

Internet of Things (IoT) has become an interesting topic of the research community, a way of doing the last years. Traditional encryption algorithms are computationally expensive on account of their complexity and require number of rounds for encryption. Device communication in IoT required algorithms with less complexity for secure communication. In this paper, we address the problem of security in IoT using a modified lightweight encryption and decryption algorithm called Modified Secure IoT (MSIT) for Secure data communication. A block cipher of 64 bits that accepts key of 64-bits and uses +2 function for encryption and decryption. Here, +2 appear as the binary addition which avoid the carry which is reproduced by the most significant bit addition. The proposed work has been evaluated in terms of entropy, correlation, image histogram and differential analysis. We also demonstrates the merits of the proposed formulation using computational cost and total computational time.

关键词： Wireless sensor networks signal processing algorithms Ubiquitous computing Encryption Complexity theory Internet of Things Security

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Multi Pitch Estimation of Piano Music using Cartesian Genetic Programming with Spectral Harmonic Mask

Multi Pitch Estimation of Piano Music using Cartesian Geneti...

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ieee symposium Series on computational intelligence (SSCI)

作者： Rolando Miragaia Gustavo Reis Francisco Fernandéz de Vega Francisco Chávez School of Technology and Management Polytechnic Institute of Leiria Leiria Portugal niversity of Extremadura Merida Spain

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

Piano notes recognition, or pitch estimation of piano notes has been a popular research topic for many years, and is still investigated nowadays. It is a fundamental task during the process of automatic music transcription (extracting the musical score from an acoustic signal). We take advantage of Cartesian Genetic Programming (CGP) to evolve mathematical functions that act as independent classifiers for piano notes. These classifiers are then used to identify the presence of piano notes in polyphonic audio signals. This paper describes our technique and the latest improvements made in our research. The main feature is the introduction of spectral harmonic masks in the binarization process for measuring the fitness values that has allowed to improve the classification rate: 10% in the F-measure mean result. Our system architecture is also described to show the feasibility of its parallelization, which will reduce the computing time.

关键词： Genetic programming Statistics Sociology Estimation Music Task analysis signal processing

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Long-Bone Fracture Detection using Artificial Neural Networks based on Line Features of X-ray images

Long-Bone Fracture Detection using Artificial Neural Network...

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ieee symposium Series on computational intelligence (SSCI)

作者： Yang, Alice Yi Cheng, Ling Shimaponda-Nawa, Mulundumina Zhu, Hai-Yun Univ Witwatersrand Sch Elect & Informat Engn ZA-2000 Johannesburg South Africa Hosp PLA Dept Radiol Shanghai 200052 Peoples R China

ISBN: (纸本)9781728124858

Two line-based fracture detection schemes are developed and discussed, namely Standard line-based fracture detection and Adaptive Differential Parameter Optimized (ADPO) line-based fracture detection. The purpose of the two line-based fracture detection schemes is to detect fractured lines from X-ray images using extracted features based on recognised patterns to differentiate fractured lines from non-fractured lines. The schemes reduce the number of images required for training, as the training is performed line-wise. The difference between the two schemes is the detection of lines. The ADPO scheme optimizes the parameters of the Probabilistic Hough Transform, such that granule lines within the fractured regions are detected. The lines are given in the form of points, (x, y), which includes the starting and ending points. Based on the given line points, 13 features are extracted from each line as a summary of line information. These features are used for fracture and non-fracture classification of the detected lines. The classification is carried out by the Artificial Neural Network (ANN). The Standard Scheme is capable of achieving an average accuracy of 71.57%, whereas the ADPO scheme achieved an average accuracy of 72.89%. The ADPO scheme is opted for over the Standard scheme, however it can be further improved with contour fracture detection.

关键词： artificial neural network x-ray images feature ex-traction image processing

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Plug-And-Pipeline: Efficient Regularization for Single-Step Adversarial Training

Plug-And-Pipeline: Efficient Regularization for Single-Step ...

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ieee Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： B.S. Vivek Ambareesh Revanur Naveen Venkat R. Venkatesh Babu Video Analytics Lab Indian Institute of Science Bangalore India

The following topics are dealt with: learning (artificial intelligence); neural nets; feature extraction; image classification; object detection; convolutional neural nets; image segmentation; computer vision; video s... 详细信息

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

关键词： Training Robustness computational modeling Perturbation methods Iterative methods Backpropagation Data models

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Research on image Recognition of Supermarket Commodity Based on Convolutional Neural Network 12

Research on Image Recognition of Supermarket Commodity Based...

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12th International symposium on computational intelligence and Design, ISCID 2019

作者： Liu, Yanmei Liu, Ben Chen, Yuda Wuhan Institute of Design and Sciences Wuhan China China Geological Survey Wuhan Center of Geological Survey Wuhan China

ISBN: (纸本)9781728146522

For the actual shopping scenes in supermarkets, there will be more complex background environment, more diverse detection objects, and more changeable actual scenes, so the traditional image recognition network cannot meet the existing needs. In this paper, Convolutional Neural Network technology and YOLOv3 target detection algorithm are adopted to realize the recognition and detection of target objects in images, automated analysis and processing of these product image data with computer vision algorithms. The convolution neural network and COCO data set are used to train the neural network. Finally, the well-trained network is tested. Tested, the effect is good. © 2019 ieee.

关键词： image recognition

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