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Cascadable Stochastic Logic for DNA Storage

Cascadable Stochastic Logic for DNA Storage

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IEEE visual communications and image processing (VCIP)

作者： Arnav Solanki Tonglin Chen Marc Riedel University of Minnesota Minneapolis Minnesota USA

ISBN: (纸本)9781728173221

Ever since Watson and Crick first described the molecular structure of DNA, its information-bearing potential has been apparent to computer scientists. This has led to a concerted effort in academia and industry to deliver practical DNA data storage systems. This paper presents a novel approach for both storage and computation with DNA. Data is stored in the form of analog values of the relative concentration of different DNA molecules. Computation is in the form of cas-cadable NAND operations, effected via toehold-mediated strand displacement reactions operating on these concentration values. Results were verified with the “Peppercorn Enumerator,” a recent software tool for analyzing domain-level strand displacement. In all cases, the relative error in output concentration was less than 0.03%. The approach is robust to encoding errors and cross-hybridization. It does not rely on long DNA strands, which are expensive to synthesize. It opens new avenues for storage and computing, including the implementation of a wide range of useful mathematical functions in vitro.

关键词： Industries image coding visual communication Buffer storage DNA Software tools In vitro

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9th International conference on image processing and communications, IP and C 2017

9th International Conference on Image Processing and Communi...

引用

9th International conference on image processing and communications, IP and C 2017

ISBN: (纸本)9783319687193

The proceedings contain 22 papers. The special focus in this conference is on image processing and communications. The topics include: Binary line oriented histogram;image search enhanced by using external data sources and reasoning;linguistic description of images based on fuzzy histograms;using toboggan segmentation in detection of centers and radius of cell nuclei;evaluation of the pre-processing methods in image-based palmprint biometrics;on the way to perfect steganography;PET waste classification method and plastic waste database – wadaba;estimation of geometrical deformations of 3D prints using local cross-correlation and monte carlo sampling;quality assessment of 3D printed surfaces in fourier domain;the adaboost algorithm with linear modification of the weights;evaluation of hashing methods performance on binary feature descriptors;efficient parallelization methods of labeling algorithm;modeling telecommunication networks with the use of reference graphs;QoS concept for protecting network resources - a case study;acceleration of genome sequencing with intelligent cloud brokers;learning management systems on blended learning courses;technology and software for calculating correct normalization of correlation functions;running sports decision aid tool based on reinforcement learning approach;framework for evaluating QoE for remote control of autonomous cars in mobile wireless networks;practical advices for university teachers and comparison of visual descriptors for 3D reconstruction of non-rigid planar surfaces.

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CAESR: Conditional Autoencoder and Super-Resolution for Learned Spatial Scalability

CAESR: Conditional Autoencoder and Super-Resolution for Lear...

引用

IEEE visual communications and image processing (VCIP)

作者： Charles Bonnincau Wassim Hamidouche Jean-François Travers Naty Sidaty Jean-Yves Aubié Olivier Deforges IRT b<>com Cesson-Sevigne France TDF Cesson-Sevigne France Univ Rennes INSA Rennes CNRS IETR - UMR 6164 Rennes France

ISBN: (纸本)9781728173221

In this paper, we present CAESR, an hybrid learning-based coding approach for spatial scalability based on the versatile video coding (VVC) standard. Our framework considers a low-resolution signal encoded with VVC intra-mode as a base-layer (BL), and a deep conditional autoencoder with hyperprior (AE-HP) as an enhancement-layer (EL) model. The EL encoder takes as inputs both the upscaled BL reconstruction and the original image. Our approach relies on conditional coding that learns the optimal mixture of the source and the upscaled BL image, enabling better performance than residual coding. On the decoder side, a super-resolution (SR) module is used to recover high-resolution details and invert the conditional coding process. Experimental results have shown that our solution is competitive with the VVC full-resolution intra coding while being scalable.

关键词： Video coding Training image coding visual communication Scalability Superresolution Encoding

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Two-stage Parallax Correction and Multi-stage Cross-view Fusion Network Based Stereo image Super-Resolution

Two-stage Parallax Correction and Multi-stage Cross-view Fus...

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IEEE visual communications and image processing (VCIP)

作者： Yijian Zheng Sumei Li School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781728173221

Stereo image super-resolution (SR) has achieved great progress in recent years. However, the two major problems of the existing methods are that the parallax correction is insufficient and the cross-view information fusion only occurs in the beginning of the network. To address these problems, we propose a two-stage parallax correction and a multi-stage cross-view fusion network for better stereo image SR results. Specially, the two-stage parallax correction module consists of horizontal parallax correction and refined parallax correction. The first stage corrects horizontal parallax by parallax attention. The second stage is based on deformable convolution to refine horizontal parallax and correct vertical parallax simultaneously. Then, multiple cascaded enhanced residual spatial feature transform blocks are developed to fuse cross-view information at multiple stages. Extensive experiments show that our method achieves state-of-the-art performance on the KITTI2012, KITTI2015, Middlebury and Flickr1024 datasets.

关键词： Fuses visual communication Convolution Superresolution Transforms Benchmark testing Spatial resolution

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UHP-SOT: An Unsupervised High-Performance Single Object Tracker

UHP-SOT: An Unsupervised High-Performance Single Object Trac...

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IEEE visual communications and image processing (VCIP)

作者： Zhiruo Zhou Hongyu Fu Suya You Christoph C. Borel-Donohue C.-C. Jay Kuo Media Communications Lab University of Southern California Los Angeles CA USA Army Research Laboratory Adelphi Maryland USA

ISBN: (纸本)9781728173221

An unsupervised online object tracking method that exploits both foreground and background correlations is proposed and named UHP-SOT (Unsupervised High-Performance Single Object Tracker) in this work. UHP-SOT consists of three modules: 1) appearance model update, 2) background motion modeling, and 3) trajectory-based box prediction. A state-of-the-art discriminative correlation filters (DCF) based tracker is adopted by UHP-SOT as the first module. We point out shortcomings of using the first module alone such as failure in recovering from tracking loss and inflexibility in object box adaptation and then propose the second and third modules to overcome them. Both are novel in single object tracking (SOT). We test UHP-SOT on two popular object tracking benchmarks, TB-50 and TB-100, and show that it outperforms all previous unsupervised SOT methods, achieves a performance comparable with the best supervised deep-learning-based SOT methods, and operates at a fast speed (i.e. 22.7-32.0 FPS on a CPU).

关键词： Adaptation models Correlation visual communication image processing Predictive models Benchmark testing Object tracking

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CRC-Based Multi-Error Correction of H.265 Encoded Videos in Wireless communications

CRC-Based Multi-Error Correction of H.265 Encoded Videos in ...

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IEEE visual communications and image processing (VCIP)

作者： Vivien Boussard Stéphane Coulombe François-Xavier Coudoux Patrick Corlay Anthony Trioux École de technologie supérieure Montreal Canada UPHF CNRS Univ. Lille ISEN Centrale Lille UMR 8520 - IEMN DOAE Valenciennes France

ISBN: (纸本)9781728173221

This paper analyzes the benefits of extending CRC-based error correction (CRC-EC) to handle more errors in the context of error-prone wireless networks. In the literature, CRC-EC has been used to correct up to 3 binary errors per packet. We first present a theoretical analysis of the CRC-EC candidate list while increasing the number of errors considered. We then analyze the candidate list reduction resulting from subsequent checksum validation and video decoding steps. Simulations conducted on two wireless networks show that the network considered has a huge impact on CRC-EC performance. Over a Bluetooth low energy (BLE) channel with Eb/No=8 dB, an average PSNR improvement of 4.4 dB on videos is achieved when CRC-EC corrects up to 5, rather than 3 errors per packet.

关键词： Bluetooth visual communication Wireless networks image processing Redundancy Error correction Decoding

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A Novel Self-Knowledge Distillation Approach with Siamese Representation Learning for Action Recognition

A Novel Self-Knowledge Distillation Approach with Siamese Re...

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IEEE visual communications and image processing (VCIP)

作者： Duc-Quang Vu Thi-Thu-Trang Phung Jia-Ching Wang National Central University Taoyuan Taiwan Thai Nguyen University Thai Nguyen Vietnam

ISBN: (纸本)9781728173221

Knowledge distillation is an effective transfer of knowledge from a heavy network (teacher) to a small network (student) to boost students' performance. Self-knowledge dis-tillation, the special case of knowledge distillation, has been proposed to remove the large teacher network training process while preserving the student's performance. This paper intro-duces a novel Self-knowledge distillation approach via Siamese representation learning, which minimizes the difference between two representation vectors of the two different views from a given sample. Our proposed method, SKD-SRL, utilizes both soft label distillation and the similarity of representation vectors. Therefore, SKD-SRL can generate more consistent predictions and representations in various views of the same data point. Our benchmark has been evaluated on various standard datasets. The experimental results have shown that SKD-SRL significantly improves the accuracy compared to existing supervised learning and knowledge distillation methods regardless of the networks.

关键词： Representation learning Knowledge engineering Training Computer vision visual communication image processing Supervised learning

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A Full-Reference Quality Assessment Metric for Fine-Grained Compressed images

A Full-Reference Quality Assessment Metric for Fine-Grained ...

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IEEE visual communications and image processing (VCIP)

作者： Zicheng Zhang Wei Sun Xiongkuo Min Tao Wang Wei Lu Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9781728173221

Compressed image quality assessment (IQA) has been a crucial part of a wide range of image services such as storage and transmission. Due to the effect of different bit rates and compression methods, the compressed images usually have different levels of quality. Nowadays, the mainstream full-reference (FR) metrics are effective to predict the quality of compressed images at coarse-grained levels, however, they may perform poorly when quality differences of the compressed images are quite subtle. To better improve the Quality of Experience (QoE) and provide useful guidance for compression algorithms, we propose an FR-IQA metric for fine-grained compressed images, which estimates the image quality by analyzing the difference of structure and texture. Our metric is mainly validated on the fine-grained compression IQA (FGIQA) database and is tested on other commonly used compression IQA databases as well. The experimental results show that our metric outperforms mainstream FR-IQA metrics on the fine-grained compression IQA database and also obtains competitive performance on the coarse-grained compression IQA databases.

关键词： Measurement image quality image coding visual communication Feature extraction Stability analysis Quality assessment

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5th International conference on Cognitive Systems and Signal processing, ICCSIP 2020

5th International Conference on Cognitive Systems and Signal...

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5th International conference on Cognitive Systems and Signal processing, ICCSIP 2020

ISBN: (纸本)9789811623356

The proceedings contain 59 papers. The special focus in this conference is on Cognitive Systems and Signal processing. The topics include: Application of Broad Learning System for image Classification Based on Deep Features;emotion Recognition Based on Graph Neural Networks;a Light-Weight Stereo Matching Network with Color Guidance Refinement;overview of Monocular Depth Estimation Based on Deep Learning;Non-contact Physiological Parameters Detection Based on MTCNN and EVM;texture Classification of a Miniature Whisker Sensor with Varied Contact Pose;the Realtime Indoor Localization Unmanned Aerial Vehicle;semantic-Based Road Segmentation for High-Definition Map Construction;transformer Region Proposal for Object Detection;a Feature Fusion Based Object Tracking Algorithm;Detection and Reconstruction of Transparent Objects with Infrared Projection-Based RGB-D Cameras;a Simulation-to-Real Autonomous Driving System Based on End-to-End Learning;Underwater SLAM Based on Forward-Looking Sonar;robust visual Odometry Using Semantic Information in Complex Dynamic Scenes;A Multi-sensor Data Fusion Method Based on Improved XGBoost Model for AGV Localization;Multi UAV Target Tracking Based on the Vision and Communication Information;an Accurate Positioning Method for Robotic Manipulation Based on Vision and Tactile Sensors;l1-Norm and Trace Lasso Based Locality Correlation Projection;episodic Training for Domain Generalization Using Latent Domains;A Novel Attitude Estimation Algorithm Based on EKF-LSTM Fusion Model;METAHACI: Meta-learning for Human Activity Classification from IMU Data;preface;Quantized Separable Residual Network for Facial Expression Recognition on FPGA;fusing Knowledge and Experience with Graph Convolutional Network for Cross-task Learning in visual Cognitive Development;factored Trace Lasso Based Linear Regression Methods: Optimizations and Applications.

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An Industrial Instrument Panel Identification System Design Based on an Improved YOLOv7 and UAV

An Industrial Instrument Panel Identification System Design ...

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International conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Miao Ye Yuejin Huang Qiuxiang Jiang Peng Wen Hongwen Hu Jin Cheng School of Information and Communications Guilin University of Electronic Technology China

Industrial production often requires collecting and reading visual information on instrument panels. Traditional manual inspection and identification methods require considerable work, resulting in inefficient and inaccurate inspections. It is important to design an industrial instrument panel inspection and recognition system that can be applied in real time. In this paper, an industrial instrument panel recognition system is designed by combining an unmanned aerial vehicle (UAV) platform and deep learning techniques. For mobile UAVs, an ultrawideband (UWB) positioning module is used to implement the real-time UAV positioning and its fixed-point cruising function at each instrument panel location point. As the UAV moves around the instrument panel, a modified YOLOv7 model, compressed through a combination of pruning and quantization, is loaded to perform initial detection and recognition of the instrument panel image to ensure real-time transmission of the captured raw image to the ground side. On the ground-side server, the original YOLOv7 model is deployed to perform instrument panel detection on the initially captured images, filter out redundant background information, and then extract the tick marks and pointer information on the instrument panel through the U-Net semantic segmentation model, after which the tick marks and pointer information are processed using traditional image-processing methods. Finally, the instrument panel is identified through the angle method. This method can alleviate the accuracy loss caused by a lightweight model on the mobile side, thus ensuring the quality of accuracy. Finally, a web client is designed using Flask technology to display the uploaded instrument panel images and recognition results via a web terminal. A series of test experiments and their results show that the designed industrial instrument panel detection and recognition system is reliable and efficient in reading industrial instrument panel information in real tim

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