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Autoencoder-based OFDM for Agricultural image Transmission 10

Autoencoder-based OFDM for Agricultural Image Transmission

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10th international conference on advanced Cloud and Big Data (CBD)

作者： Li, Dongbo Liu, Xiangyu Shao, Yuxuan Sun, Yuchen Cheng, Siyao Liu, Jie Harbin Inst Technol Fac Comp IoT & Ambient Intelligence Res Ctr Harbin Peoples R China Harbin Inst Technol Fac Comp Harbin Peoples R China Harbin Inst Technol Shenzhen Int Res Inst AI Shenzhen Peoples R China

ISBN: (纸本)9798350309713

With the rapid development of the Internet of Things (IoT), smart agricultural puts forward higher demands on the transmission of agricultural big data. This paper proposes an end-to-end learning communication with autoencoderbased orthogonal frequency division multiplexing (OFDM-AE) for agricultural image transmission, which solves the problems of delay, congestion and high complexity caused by the processing method to information of independent modularization for the conventional OFDM. It is proposed to construct AE based on convolutional neural network (CNN) to realize global joint optimization of end-to-end communication system. In this paper, the network architecture of OFDM-AE is designed and trained on massive agricultural image data. We analyze the performance of the proposed OFDM-AE in different signal-to-noise ratio (SNR) cases. The experimental results show that the OFDM-AE can retain the image feature information and has a very advantageous complexity performance compared to the conventional OFDM with various modulation methods.

关键词： Internet of Things big data autoencoder OFDM agricultural image transmission

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Deep Learning for Visual Data Compression 21

Deep Learning for Visual Data Compression

引用

29th ACM international conference on Multimedia (MM)

作者： Lu, Guo Yang, Ren Wang, Shenlong Liu, Shan Timofte, Radu Beijing Inst Technol Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland UIUC Urbana IL USA Tencent Palo Alto CA USA Univ Wurzburg Wurzburg Germany

ISBN: (纸本)9781450386517

In this paper, we will introduce the recent progress in deep learning based visual data compression, including image compression, video compression and point cloud compression. In the past few years, deep learning techniques have been successfully applied to various computer vision and image processing applications. However, for the data compression task, the traditional approaches (i.e., block based motion estimation and motion compensation, etc.) are still widely employed in the mainstream codecs. Considering the powerful representation capability of neural networks, it is feasible to improve the data compression performance by employing the advanced deep learning technologies. To this end, the deep leaning based compression approaches have recently received increasing attention from both academia and industry in the field of computer vision and signal processing.

关键词： image compression video compression point cloud compression

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NOISE SUPPRESSION FOR IMPROVED FEW-SHOT LEARNING 47

NOISE SUPPRESSION FOR IMPROVED FEW-SHOT LEARNING

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47th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Chen, Zhikui Ji, Tiandong Zhang, Suhua Zhong, Fangming Dalian Univ Technol Sch Software Technol Dalian 116620 Liaoning Peoples R China

ISBN: (纸本)9781665405409

Few-shot learning (FSL) aims to generalize from few labeled samples. Recently, metric-based methods have achieved surprising classification performance on many FSL benchmarks. However, those methods ignore the impact of noise, making the few-shot learning still tricky. In this work, we identify that noise suppression is important to improve the performance of FSL algorithms. Hence, we proposed a novel attention-based contrastive learning model with discrete cosine transform input (ACL-DCT), which can suppress the noise in input images, image labels, and learned features, respectively. ACL-IX:T takes the transformed frequency domain representations by IX:T as input and removes the high-frequency part to suppress the input noise. Besides, an attention-based alignment of the feature maps and a supervised contrastive loss are used to mitigate the feature and label noise. We evaluate our ACL-DCT by comparing previous methods on two widely used datasets for few-shot classification (i.e., miniimageNet and CUB). The results indicate that our proposed method outperforms the state-of-the-art methods.

关键词： Few-shot learning image classification noise suppression contrastive learning

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A Fast image Denoising Method Based on CP Tensor Analysis 6

A Fast Image Denoising Method Based on CP Tensor Analysis

引用

6th IEEE international conference on Computer and Communication Systems, ICCCS 2021

作者： Pan, Xinhong Zhang, Guoyi Wang, Li Wang, Guangbo Dong, Jingyi Cao, Jiali Guangxi Power Grid Dispatching Control Center Nanning China China Southern Power Grid Dispatching Control Center Guangzhou China Communication University of China Beijing China

ISBN: (纸本)9780738126043

The data represented by tensor can maintain its original form, which is conductive to preserving high dimensional structure and adjacent relation information of data. Tensor factorization plays an instrumental role in signal processing, especially image processing. However, the existing tensor-based fitting algorithms require a lot of time cost and have low fitting accuracy, which is more obvious when the image data is large. In this paper, an accelerated fitting algorithm based on the Canonical Polyadic (CP) tensor analysis is designed to process images. Compared with the existing tensor-based fitting algorithm, the developed algorithm greatly improves the fitting speed with high fitting accuracy, and can quickly reduce the noise of the image. Simulation results for color image show that our method have greatly improved the efficiency of image denoising. © 2021 IEEE.

关键词： Tensors

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GLNet: Low-light image enhancement via grayscale priors 13

GLNet: Low-light image enhancement via grayscale priors

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13th international conference on signal processing Systems, ICSPS 2021

作者： Guo, Li Xie, Junwei Xue, Yuyang Li, Ru Zheng, Weixin Tong, Tong Gao, Qinquan College of Physics and Information Engineering Fuzhou University Tsukuba Japan Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology Fuzhou University Tsukuba Japan Imperial Vision Technology Fujian China Department of Computer Science University of Tsukuba Tsukuba Japan

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

Low-light images are generally produced by shooting in a low light environment or a tricky shooting angle, which not only affect people's perception, but also leads to the bad performance of some artificial intelligence algorithms, such as object detection, super-resolution, and so on. There are two difficulties in the low-light enhancement algorithm: in the first place, applying image processing algorithms independently to each low-light image often leads to the color distortion;the second is the need to restore the texture of the extremely low-light area. To address these issues, we present two novel and general approaches: firstly, we propose a new loss function to constrain the ratio between the corresponding RGB pixel values on the low-light image and the high-light image;secondly, we propose a new framework named GLNet, which uses the dense residual connection block to obtain the deep features of the low-light images, and design a gray scale channel network branch to guide the texture restoration on the RGB channels by enhancing the grayscale image. The ablation experiments have demonstrated the effectiveness of the proposed module in this paper. Extensive quantitative and perceptual experiments show that our approach obtains state-of-the-art performance on the public dataset. © COPYRIGHT SPIE.

关键词： image enhancement

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Three-Dimensional Foot Contact Position on a Smart Fitness Trampoline with a Upward Looking Wide View Imaging System 12

Three-Dimensional Foot Contact Position on a Smart Fitness T...

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12th international conference on Ubiquitous and Future Networks (ICUFN)

作者： Park, Sekyung Park, Junkyu Ahn, Jongsik Cho, Boram Lee, Suwoong Kim, Minyoung Korea Inst Ind Technol Adv Mechatron Grp Cheonan South Korea Kyungpook Natl Univ Dept Elect & Elect Engn Daegu South Korea

ISBN: (纸本)9781728164762

This study estimates the position and depth of the user's foot contacts of playing on a trampoline by acquiring the image of the footprint shadow and processing it to enable quantitative analysis of jumping (as a fitness exercise) or to link it to computer games. First, a hardware prototype of a jumping fitness monitoring system is proposed, consisting of a trampoline, an upward-looking wide-angle fish-eye camera module, and a signal processing embedded board. Second, image-based three-dimensional foot contact position estimation (I3D-FCE) algorithms are proposed, comprising the foot contact position estimation algorithm in the transverse plane and the foot contact depth estimation algorithm in the vertical direction. The foot contact positions were estimated using the binary image of the foot shadow obtained by extracting the binary masks of the trampoline and foot. The foot contact depth was estimated using the similarity ratio of the footprint shadow sizes. To verify the usefulness of the proposed system and the accuracy of the proposed contact position estimation algorithms, a series of experiments were conducted using a robotic manipulator. The experimental results show that the X and Y coordinates of the foot contact position and the Z coordinate of the foot contact depth were successfully estimated within an average error of 3.40 (mm), 3.26 (mm), and 4.49 (mm), respectively.

关键词： image processing Embedded board Smart Fitness

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The influence of different preprocessing on radar signal recognition based on time-frequency analysis and deep learning 3

The influence of different preprocessing on radar signal rec...

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3rd international conference on advanced Information Science and System, AISS 2021

作者： Li, Qin Liu, Wei Niu, Chaoyang Wang, Yanyun Gao, Ouyang Bao, Yintu Zou, Weiqi Zhang, Haobo Hu, Qing Lin, Zhikang Pan, Chaofan PLA Strategic Support Force Information Engineering University China

ISBN: (纸本)9781450385862

Aiming at the problem of the lack of rigorous comparative analysis in the preprocessing step in the classification and recognition of radar signals based on time-frequency diagrams and deep learning, this paper analyzes the influence of signal denoising, different time-frequency analysis, and time-frequency graph denoising in preprocessing on the classification and recognition of radar signals through control variables. Lay the foundation for further research on radar signal classification and recognition. © 2021 ACM.

关键词： image processing

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image processing based Smart Weed Removal and Organic Fertilizer Sprinkling Bot-A Systematic Review

Image processing based Smart Weed Removal and Organic Fertil...

引用

2021 international conference on Artificial Intelligence and Smart Systems, ICAIS 2021

作者： Uppada, Rajyalakshmi Kandubothula, Vanaja Padma, Uppalapati Dept. of Ece Surampalem India

ISBN: (纸本)9781728195377

Most of our country's economy depends on agriculture production. The production of the crop decreases due to unwanted plants (weeds) and the pests' infection around the crop. Weeds are removed manually and also controlled by spraying herbicides. To eradicate the pests, fertilizers are used. Due to the fertilizers and herbicides, the fert ility of the soil decreases and also causes pollution. Manually, these processes are time-taking and need more money to complete both the tasks. In this paper, the weed and diseased plant control using machine learning algorithms based on image processing is discussed. Dataset consists of samples of some healthy and diseased crops. The images in the real field are captured through a vision sensor and pre-processed. The resultant image is compared with the threshold value of the trained dataset. Feature Extraction is done through color, size, and shape. Based on the error rate, the system classifies the plant either as a weed or a diseased plant. If the error rate is high, the plant is classified as a weed and the signal is sent to the robotic arm to pluck the plant through serial communication. If the error rate is less, then it is detected as a diseased plant, and the signal is sent to the sprinkler to spray the organic fertilizer to the infected area. If there is no error, then the plant is treated as a healthy plant. Thus, the machine controls the weeds and the diseased plants around the field autonomously. © 2021 IEEE.

关键词： Crops

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COMPUTATIONAL IMAGING IN 3D X-RAY MICROSCOPY: RECONSTRUCTION, image SEGMENTATION AND TIME-EVOLVED EXPERIMENTS

COMPUTATIONAL IMAGING IN 3D X-RAY MICROSCOPY: RECONSTRUCTION...

引用

IEEE international conference on image processing (ICIP)

作者： Niverty, Sridhar Torbatissaraf, Hamid Nikitin, V De Andrade, Vincent Niauzorau, S. Kublik, N. Azeredo, B. Tekawade, A. De Carlo, Francesco Chawla, Nikhilesh Purdue Univ Sch Mat Engn W Lafayette IN 47907 USA Argonne Natl Lab Adv Photon Source Argonne IL 60439 USA Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ USA

ISBN: (纸本)9781665441155

Time-dependent X-ray Microscopy (XRM) is an excellent technique to develop a fundamental mechanistic understanding of material behavior. Computational imaging plays a critical role in XRM, in a variety of ways. 2D projections are acquired and the resulting datasets are reconstructed using a filtered back projection algorithm. Several imaging artifacts are typically present, such as beam hardening, misalignment of the data, drift during time-evolved experiments (particularly at high temperatures and/or nanometer resolution scans), etc. Minimizing and removing these artifacts is, thus, very important. This is all the more important, because image segmentation is then done to quantify the statistics of the microstructure (often, as a function of time). The efficiency and accuracy of image segmentation is directly proportional to the quality of the initial reconstructed data. Thus, there is a need to develop efficient, robust algorithms that can handle large datasets obtained by 4D, time-dependent x-ray microscopy. In this paper, we will describe the challenges associated with computation imaging during x- ray microscopy. The use of Convolutional Neural Network (CNN) architectures based on a deep learning approach, as means of automating and handling x-ray microscopy data sets, in both lab-scale and synchrotron, will be discussed. The use of CNN techniques to robustly process ultra-large volumes of data in relatively small-time frames can exponentially accelerate tomographic data analysis, opening up novel avenues for performing 4D characterization experiments.

关键词： image segmentation Temperature distribution Three-dimensional displays Microscopy Synchrotrons Tomography Convolutional neural networks

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Deep Learning Model for Detecting and Diagnosing Plant Disease

Deep Learning Model for Detecting and Diagnosing Plant Disea...

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2021 international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2021

作者： Moon, Nazmun Nessa Sharmin, Ms. Shayla Hossain, Refath Ara Jahan, Israt Nur, Fernaz Narin Rahman, Md. Mosfikur Daffodil Internationsl University Department of Computer Science and Engineering Dhaka Bangladesh Notre Damn University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665425032

This work deals with some plant disease detection like Potato, Pepper, and Tomato, Rice, etc. based on CNN image processing Technology. An enormous improvement has been made in the field of picture preparation and AI and its application in different parts of designing. The authors have entered the time of digitization. The authors have caught pictures with the assistance of an advanced camera. All the clearer the pictures are better and produce the outcomes. In this report, the authors have done the arrangement of sickness-free, somewhat infected, and sick leaves. The information related to plant features is especially useful for its applications in plant growth modeling, agricultural research, and on-farm production. Traditional direct measurement methods are generally simple and reliable, but they are time-consuming, laborious, and cumbersome. In contrast, the proposed vision-based methods are efficient in detecting and observing the exterior disease features. In the present investigation, CNN image processing algorithms are developed to detect disease by identifying the color feature of the defected area. Subsequently, the rotted area was segmented from an image, and the area of the rotted leaf portion was deduced from the Pepper and tomato plant feature data. The results showed a promising performance of this automatic vision-based system it is found 94% accuracy average in practice with easy validation in a large amount of data. © 2021 IEEE.

关键词： image processing

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