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Hexagonal image Compressionusing Singular Value Decomposition in Python 2

Hexagonal Image Compressionusing Singular Value Decompositio...

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2nd international conference on Advances in Computing, Communication, Embedded and Secure systems, ACCESS 2021

作者： Varghese, Prathibha Selva Saroja, G. Arockia Electronics and Communication Department Noorul Islam Centre for Higher Education Tamil Nadu India Dept. Electronics and Communication Engineering Noorul Islam Centre for Higher Education Tamil Nadu India

ISBN: (纸本)9781728171364

With the advent of multimedia technologies in last two decades, there is a widespread need for efficient storage and transmission of data. Dealing with the vast information interchangein this digital era, image compression for reduction in byte size of graphics image file without loss of image quality to an acceptable level becomes the large interest area. Inspired from the biological models of human fovea, hexagonal image processing has gained a lot of attention in artificial intelligence era that deals with the application of image processing system that combines the benefits of biologically motivated structures. In this paper a singular value decomposition (SVD) over hexagonal image compression which is a missing stone in computer vision which provides higher packing density, higher angular symmetry and uniform connectivity. Due to lack of developments in hexagonal imaging devices, different resampling methods like alternate pixel shift method, half pixel shift method, pseudo hexagonal pixel method for sourcing hexagonal images. SVD is one of the powerful cutting-edge technology for image compression algorithms. SVD based image compression is performed on hexagonal grid and is compared with square grid using different parameters like compression ratio, compression size, PSNR and MSE using PYTHON’s SVD function. SVD based hexagonal image achieves the goal of compression by preserving good image quality at higher compression ratios, high computational efficiency, provides low mean square error (MSE), acceptable compression size depending on application and high peak to signal ratio (PSNR). © 2021 ieee

关键词： Singular value decomposition

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Lightweight Real-Time Detection Model for Multi-Sheep Abnormal Behaviour based on Yolov7-Tiny

Lightweight Real-Time Detection Model for Multi-Sheep Abnorm...

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ieee/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Haotian Zhang Yuan Ma Xiaobo Wang Rui Mao Meili Wang College of Information Engineering Northwest A&F University Yangling China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture Yangling China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Yangling China

Animal behaviour can reflect the health and physiological stage of the animal. Animal behaviour recognition is a vital part of automated farming systems. Although image-based deep learning algorithms can accurately identify animal behaviour, the lack of data on animal abnormal behaviour makes the practical deployment of models of limited significance. At the same time, the ageing of farm monitoring equipment is also a key factor hindering automated farming. This paper constructs a sheep abnormal behaviour dataset ABSB to address these issues and proposes a lightweight real-time multi-sheep abnormal behaviour detection model YOLOv7-Lrab based on the YOLOv7-tiny network. The abnormal behaviour dataset includes four normal behaviours: standing, lying, eating and drinking, and three abnormal behaviours: lameness, attack and death. In the proposed YOLOv7-Lrab model, the small target detection layer, Coordinate attention module, SPD-Conv and Mobileone module are added compared to YOLOv7-tiny. The experimental results show that with a 7:3 ratio of training data to test data, 96.5% recognition accuracy and 95.5% recall can be achieved, and the model size is only 4.5MB with fps of 156. The model is compressed to a minimum without loss of accuracy, providing a new idea for deploying deep learning model in practical application scenarios.

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AAD-DCE: An Aggregated Multimodal Attention Mechanism for Early and Late Dynamic Contrast Enhanced Prostate MRI Synthesis

AAD-DCE: An Aggregated Multimodal Attention Mechanism for Ea...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Divya Bharti Sriprabha Ramanarayanan Sadhana S Kishore Kumar M Keerthi Ram Harsh Agarwal Ramesh Venkatesan Mohanasankar Sivaprakasam Indian Institute of Technology Madras (IITM) India Healthcare Technology Innovation Centre (HTIC) India GE HealthCare (GE) India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a medical imaging technique that plays a crucial role in the detailed visualization and identification of tissue perfusion in abnormal lesions and radiological suggestions for biopsy. However, DCE-MRI involves the administration of a Gadolinium-based (Gad) contrast agent, which is associated with a risk of toxicity in the body. Previous deep learning approaches that synthesize DCE-MR images employ unimodal non-contrast or low-dose contrast MRI images lacking focus on the local perfusion information within the anatomy of interest. We propose AAD-DCE, a generative adversarial network (GAN) with an aggregated attention discriminator module consisting of global and local discriminators. The discriminators provide a spatial embedded attention map to drive the generator to synthesize early and late response DCE-MRI images. Our method employs multimodal inputs - T2 weighted (T2W), Apparent Diffusion Coefficient (ADC), and T1 pre-contrast for image synthesis. Extensive comparative and ablation studies on the ProstateX dataset show that our model (i) is agnostic to various generator benchmarks and (ii) outperforms other DCE-MRI synthesis approaches with improvement margins of +0.64 dB PSNR, +0.0518 SSIM, -0.015 MAE for early response and +0.1 dB PSNR, +0.0424 SSIM, -0.021 MAE for late response, and (ii) emphasize the importance of attention ensembling. Our code is available at https://***/bhartidivya/AAD-DCE

关键词： Visualization Toxicology image synthesis Magnetic resonance imaging signal processing Drives Generative adversarial networks Generators Lesions Speech processing

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Coprime Array DoA Estimation based On Improved Multi-label cGAN

Coprime Array DoA Estimation Based On Improved Multi-label c...

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international conference on signal Processing Proceedings (ICSP)

作者： Song Liu Yang Guo Guojian Ou Chongqing University of Posts and Telecommunications Chongqing China School of Information Technology Xichang University China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

An improved multi-label conditional Generative Adversarial Network (mcGAN) is proposed to obtain Direction-of-arrival (DoA) estimates for coprime phased arrays. First, data preprocessing is performed. The real and imaginary part data of a normalized coprime array covariance matrix are used as inputs to the mcGAN generator with its outputs as the covariance matrix of a uniform array. The DoAs are treated as label information, and the label dimension equals the number of signals. The multi-dimensional labels are only used as the inputs to the discriminator and not fed into the generator by avoiding information leakage. Finally, estimation of signal parameters via rotational invariant techniques (ESPRIT) is applied to the recovered uniform array covariance matrix to yield the DoA estimates. Simulation results have verified the efficiency and accuracy of mcGAN. It is revealed that mcGAN can effectively control the generation of expected patterns without pattern mismatch or pattern collapse; moreover, the generator shows good generalization abilities. based on the reconstructed data, the DoAs can be estimated quickly and accurately, and closely spaced spatial signals can be differentiated properly as well.

关键词： Training Direction-of-arrival estimation Accuracy Simulation Estimation Generative adversarial networks Generators Arrays Covariance matrices image reconstruction

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Fast NMPC Design for image-based Visual Servoing of Autonomous Underwater Vehicles

Fast NMPC Design for Image-Based Visual Servoing of Autonomo...

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Industrial Cyber-Physical systems (ICPS)

作者： Hang Gu Chao Shen Department of Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

This paper presents a new approach to image-based visual servoing (IBVS) for Autonomous Underwater Vehicles (AUVs) with the goal of improved performance and computational efficiency. Traditional IBVS methods, when combined with Model Predictive Control (MPC), face high computational demands due to the nonlinear dynamics and the large degrees of freedom (DOFs) in the variables of the associated optimization problem. Our method addresses this by reducing the DOFs of the optimization variable in the cost function while maintaining a good control performance. To further consider the smoothness of the MPC control signal, a soft constraint handling method is developed. The fast nonlinear MPC, combined with smoother control trajectories and effective constraint handling, makes our method particularly suitable for AUV IBVS applications in dynamic environments. Comparisons with standard strategies confirm the improved performance of our approach in terms of both speed and trajectory quality. Simulation results show that our approach can achieve an improved computation up to 100 times faster than conventional MPC-based IBVS methods, which highlights the great potential for real-time IBVS applications.

关键词： Constraint handling Autonomous underwater vehicles Service robots Simulation Real-time systems Visual servoing Computational efficiency

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Rectifying Irregular Boundaries Stereoscopic images Using Deep Learning and Evolutionary Algorithms

Rectifying Irregular Boundaries Stereoscopic Images Using De...

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ieee SSD international Multi-conference on systems, signals and Devices

作者： Muntasser A. Wahsh Zainab M. Hussain Department of Computer Science Informatics Institute for Postgraduate Studies (IIPS) Baghdad Iraq Department of Political Science Al-Iraqia University Baghdad Iraq

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Application of stereoscopic technology has increased significantly in several industries, including the media and entertainment sectors, medical imaging, and virtual reality. However, the overall viewing experience and the feeling of depth can be negatively impacted by uneven boundaries in stereoscopic images. To have a realistic and immersive stereoscopic experience, fixing these wavy edges is an essential process. This work present a distinctive approach for optimizing the rectangular bounds of stereoscopic images using a hybrid of deep learning and evolutionary algorithms. The suggested method uses a genetic algorithm to optimize the parameters of a convolutional neural network (CNN) to extract useful features for residual regression. Due to the lack of compatible datasets, a set of stereoscopic images was constructed from monocular images by calculating the depth of the objects using MiDaS pretrained depth estimation algorithm. Several metrics, including as mean squared error, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Fréchet inception distance (FID), were used to evaluate the performance of the suggested technique. The experimental findings show that the proposed method effectively optimizes rectangular borders with excellent accuracy, even for images with complicated shape and orientations. Various parameter settings were also tested, revealing that increasing the number of generations and population size contributes to improved performance. The combination of the deep learning algorithm, specifically the CNN, and the optimization algorithm, specifically the genetic algorithm (GA), leads to an increase in the accuracy of rectified images. Notably, the average PSNR reached 24.42, SSIM measured at 0.7793, and FID scored 18.26. The proposed method holds promise for a wide range of applications, including image cropping, object detection, and image segmentation, among others. By effectively optimizing rectangular boundaries in ster

关键词： Deep learning Visualization Accuracy PSNR Stereo image processing Depth measurement Evolutionary computation Feature extraction Convolutional neural networks Genetic algorithms

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Supernova Detection based on Multi-scale Fusion Faster RCNN 6

Supernova Detection Based on Multi-scale Fusion Faster RCNN

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6th ieee international conference on Intelligent Computing and signal Processing, ICSP 2021

作者： Guo, Dongjiao Qiu, Bo Liu, Yanping Xiang, Guanjie Hebei University of Technology Joint Laboratory for Astronomical Information School of Electronic and Information Engineering Tianjin China

ISBN: (纸本)9780738143705

Supernovae are of great significance in the study of life evolution and expansion history of the universe. In this paper, multi-scale fusion Faster RCNN model is used to realize automatic detection of supernovae. Firstly, the dataset was synthesized and rotated to enhance supernova features. Secondly, based on the Faster RCNN architecture, the network combined with Resnet50 and PAFPN is used to extract image features, so as to obtain multi-scale feature maps and improve the ability of the network to extract small target features. Finally, OHEM network is introduced into the network to balance the difference between positive and negative sample in number. The prepared dataset was used for the training of the multi-scale fusion Faster RCNN model, and the MACRO-F1 of the test set was up to 96.6%. The method proposed in this paper can realize the detection of supernovae from astronomical imagery with relatively high accuracy. © 2021 ieee.

关键词： image enhancement

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Combining CNN and Transformer for Noise-Robust image Compressed Sensing

Combining CNN and Transformer for Noise-Robust Image Compres...

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Pattern Recognition and Artificial Intelligence (PRAI), international conference on

作者： Lingjun Liu Xiaohui Lin Haoyi Tang Zhonghua Xie School of Computer Science and Engineering Huizhou University Huizhou China

Compressed sensing (CS), as a signal acquisition and reconstruction method, benefits from deep learning technology and can quickly and accurately reconstruct signals from a small number of observations. Emerging Transformer networks bring significant gains to CS while also leaving unresolved issues. One obvious issue is that the reconstruction performance of CS methods drops sharply in the presence of noise. To achieve accurate CS reconstruction in noisy scenes, this paper proposes to improve the latest Transformer-based CS method with a CNN correction module, resulting in a joint scheme for noise-robust image CS recovery. The scheme adopts a combination of a deep learning reconstruction module and a denoising module. Specifically, it consists of a reconstruction module with a sampling subnet for a trainable perceptual matrix strategy, an initial reconstruction subnet, a hybrid reconstruction subnet, and a trainable denoising module. Extensive experiments show that the scheme can effectively improve the image quality and noise robustness of compressed sensing reconstruction.

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Local 5G mmWave signal Measurement and Analysis for Spectrum Database 12

Local 5G mmWave Signal Measurement and Analysis for Spectrum...

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

作者： Nakajo, Hirofumi Fujii, Takeo Univ Electrocommun Adv Wireless & Commun Res Ctr AWCC 1-5-1 Chofugaoka Chofu Tokyo 1828585 Japan

ISBN: (纸本)9781728164762

The concept of the smart spectrum has been proposed to deal with the problem of shortage of spectrum, which is a limited resource. In smart spectrum, a database is constructed based on the measured data of the radio environment to manage the spectrum, thus realizing highly efficient spectrum utilization, and its usefulness has been confirmed in several systems. On the other hand, for 5G NR signals, the database based on the smart spectrum has not been studied and its usefulness has not been confirmed. In particular, since 5G millimeter wave (mmWave) signals adopt beamforming technology, it is necessary to estimate the directivity and propagation characteristics of multiple beams. Therefore, in the database construction, we need to measure and manage not only the conventional management for each base station and frequency but also each beam. In this paper, we conduct a measurement campaign of mmWave signals from a local 5G base station in Imabari City, Ehime Prefecture, Japan, and construct a spectrum database. We also discuss the analysis results of mmWave signals based on the constructed database, including the radio propagation of each beam and the comparison with anchor band signals, and confirm the usefulness.

关键词： Smart spectrum spectrum database measurement campaign 5G mmWave radio map

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MCNeT: Measurement-Consistent Networks Via A Deep Implicit Layer For Solving Inverse Problems

MCNeT: Measurement-Consistent Networks Via A Deep Implicit L...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Rahul Mourya João F. C. Mota Institute of Sensors Signals and Systems Heriot-Watt University Edinburgh UK

End-to-end deep neural networks (DNNs) have become the state-of-the-art (SOTA) for solving inverse problems. Despite their outstanding performance, during deployment, such networks are sensitive to minor variations in the testing pipeline and often fail to reconstruct small but important details, a feature critical in medical imaging, astronomy, or defence. Such instabilities in DNNs can be explained by the fact that they ignore the forward measurement model during deployment, and thus fail to enforce consistency between their output and the input measurements. To overcome this, we propose a framework that transforms any DNN for inverse problems into a measurement-consistent one. This is done by appending to it an implicit layer (or deep equilibrium network) designed to solve a model-based optimization problem. The implicit layer consists of a shallow learnable network that can be integrated into the end-to-end training while keeping the SOTA DNN fixed. Experiments on single-image super-resolution show that the proposed framework leads to significant improvements in reconstruction quality and robustness over the SOTA DNNs.

关键词： Training Inverse problems Superresolution signal processing algorithms Transforms Extraterrestrial measurements Inference algorithms

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