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Moment Transform-based Compressive Sensing in image Processing 28th

Moment Transform-Based Compressive Sensing in Image Processi...

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28th international conference on systems, signals and image Processing (IWSSIP)

作者： Kalampokas, Theofanis Papakostas, George A. Int Hellen Univ Dept Comp Sci MLV Res Grp Kavala Greece

ISBN: (纸本)9783030968786;9783030968779

Over the last decades, images have become an important source of information in many domains, thus their high quality has become necessary to acquire better information. One of the important issues that arise is image denoising, which means recovering a signal from inaccurately and/or partially measured samples. This interpretation is highly correlated to the compressive sensing theory, which is a revolutionary technology and implies that if a signal is sparse then the original signal can be obtained from a few measured values, which are much less, than the ones suggested by other used theories like Shannon's sampling theories. A strong factor in Compressive Sensing (CS) theory to achieve the sparsest solution and the noise removal from the corrupted image is the selection of the basis dictionary. In this paper, Discrete Cosine Transform (DCT) and moment transform (Tchebichef, Krawtchouk) are compared in order to achieve image denoising of Gaussian additive white noise based on compressive sensing and sparse approximation theory. The experimental results revealed that the basis dictionaries constructed by the moment transform perform competitively to the traditional DCT. The latter transform shows a higher PSNR of 30.82 dB and the same 0.91 SSIM value as the Tchebichef transform. Moreover, from the sparsity point of view, Krawtchouk moments provide approximately 20-30% more sparse results than DCT.

关键词： Compressive sensing image moments Denoising Tchebichef moments Krawtchouk moments DCT

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Assessing signal Noise Effects on Machine Learning Models for ECG-based Cardiac Diagnosis

Assessing Signal Noise Effects on Machine Learning Models fo...

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18th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Guglielmi, Emanuela Russo, Davide Donato Trinchese, Pasquale Laudato, Gennaro Scalabrino, Simone Testa, Gianluca Oliveto, Rocco Department of Bioscience and Territory University of Molise Italy Department of Medicine and Health Sciences University of Molise Italy

The internet of Medical Things (IoMT) plays a vital role in healthcare by enhancing preventive careand chronic disease management through continuous monitoring using smart sensors and wearable devices. However, the reliability of IoMT systems can be compromised by noise in the acquired vital signals, which can negatively impact the accuracy of Machine Learning (ML) models used for anomaly detection. This study evaluates the impact of various disturbances on the performance of ML models in predicting cardiac conditions, with a focus on assessing the reliability and effectiveness of these systems in real-world applications. We investigated the effects of three types of noise—baseline wander, muscle artifact noise, and electrode motion artifact—on the performance of two advanced ML models designed to predict cardiac conditions, specifically atrial fibrillation (AF) and ventricular tachycardia (VT). Our analysis centered on how different noise intensities (i.e., the "loudness" of the noise) and durations (i.e., the length of time the noise persists) impacted the classification performance of these models. The VT detection model showed robust performance, with minimal impact even under intense and prolonged noise conditions. In contrast, AF detection was affected by all types of noise, with classification accuracy decreasing by up to ∼59% in the most challenging scenarios. © 2025 by SCITEPRESS – Science and technology Publications, Lda.

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Design of Wellsite Environment Monitoring System based on internet of Things technology

Design of Wellsite Environment Monitoring System Based on In...

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6th international conference on Intelligent Computing and signal Processing (ICSP)

作者： Xin Fang Lan Shen Haowen Zhao Fangxing Lyu Directional Drilling Laboratory of CNOOC Key Laboratory of Well Logging and Directional Drilling School of Computing Xi'an Shiyou University Xi'an China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

In order to guarantee China's energy security and promote the digitalisation of oil and gas construction, a wellsite environment monitoring system based on internet of Things technology has been constructed. By building a model to simulate the operating environment of the wellsite, collecting data in real time using sensor technology and uploading it to the cloud platform for storage and analysis, together with the intelligent back-end management system, which can monitor in real time, remotely supervise and assist in decision-making, provide the management personnel with comprehensive and accurate data support, reduce the risk of accidents occurring, promote technological innovation, and enhance the ability of sustainable development.

关键词： Cloud computing Oils Face recognition Decision making Production Real-time systems internet of Things Security Personnel Natural gas

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Evaluating Lora's Transmission Range and Reliability for Connected Vehicles 10

Evaluating Lora's Transmission Range and Reliability for Con...

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10th international conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Elshafei, Mohamed Ossama Shammaa, Maggie Maher, Engy Aly El-Aasser, Minar German University Faculty of Information Engineering and Technology Cairo Egypt

ISBN: (纸本)9798350329674

LoRa (Long-Range) technology has imposed itself recently as a strong name in the wireless module market used in IoT (internet of Things) applications. Smart cities and long-range monitoring systems are examples of accomplishments made by the IoT that connect the physical and digital worlds to facilitate human lifestyles, but Vehicle-to-Vehicle (V2V) communication is our concern. Dedicated Short Range Communication (DSRC) and Cellular Vehicle to Everything (C-V2X) are the common wireless technologies used in the V2V industry, as they were originally designed for this purpose, while LoRa is yet to be explored. Testing our experiment using a hardware setup instead of software-based simulations will produce practical and realistic results that can really help improve the performance of V2V networks by discovering new factors that can degrade the signal. LoRa does not consume huge power, as it was originally designed to be a good option for Low Power Wide Area Network (LPWAN) devices, because LoRa can only handle small data packets like data coming from sensors. Consequently, the On-Board Units (OBU) supporting LoRa will be affordable to most individuals in society, which will help in increasing road safety and improving traffic management. This paper focuses on designing and establishing communication between two-enabled devices using Arduino MKR WAN 1310, as well as analyzing their performance after deploying a prototype in a real-world context. This prototype is developed with unidirectional connectivity between two nodes. Since the speed of the vehicle and its location from the sender node can affect the flow of data transmission due to the Doppler effect that occurs, the performance of V2V is evaluated based on data transfer consistency and the quality of the transmitted LoRa signals in the real-world environment. The experimental results demonstrated that LoRa is reliable and suitable for V2V communication. © 2023 IEEE.

关键词： internet of things

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A Cooperative Gene Regulatory Network Target Entrapping in GNSS-denied Environments 12

A Cooperative Gene Regulatory Network Target Entrapping in G...

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12th international conference on CYBER technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Shi, Ze Fan, Zhun Ma, Peili Hong, Juncao Li, Wenji Zhu, Guijie Huang, Huaxing Cai, Yuwei Dong, Zhaohui Zhu, Xiaomin Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shantou515063 China College of Systems Engineering National University of Defense Technology China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In recent years, the use of gene regulatory network (GRN) to guide swarm robots for target-entrapping tasks has received increasing attention. Even though many GRN-based algorithms for swarm aggregation morphogenesis generation are designed and verified in two-dimensional space, they are not suit-able for target trapping in three-dimensional space with GNSS-denied environments. Therefore, we propose a cooperative GRN (Co-GRN), which can guide swarm robots to complete target-entrapping tasks in GNSS-denied three-dimensional environments. The average distance and enveloping strength are analysed in several simulated scenes, and the effectiveness and flexibility of the proposed method are verified. © 2022 IEEE.

关键词： Genes

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Channel-Stationary Entropy Model for Multispectral image Compression 11th

Channel-Stationary Entropy Model for Multispectral Image Com...

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11th international conference on Communications, signal Processing, and systems, CSPS 2022

作者： Ren, Guanglong Cao, Tongbo Kong, Fanqiang Tang, Jiahui College of Astronautics Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9789819912599

Multispectral image have a large number of complex features. The existing network models have good compression performance for multispectral image, but the encoding and decoding time is long. In order to extract latent statistical features to guide arithmetic coding and save compression time as much as possible, a multispectral image compression framework based on channel-stationary is proposed. The multi-spectral image first go through the feature extraction network to get the feature maps, they can be down-sampling and compressed and then input to the arithmetic encoder and entropy priori module respectively. The weight features obtained by the entropy prior module feed the aid into the image features of the autoregressive module for entropy coding. The auto-regressive module can realize decoding of images, thus saving the time required for encoding and decoding. In addition, the balance between image reconstruction quality and bit rate is achieved by using distortion optimization technology. Then the image is reconstructed by inverse quantization, up-sampling and deconvolution module. The experimental results show that the method is better than JPEG2000 under the condition of similar bit rate, the compression time is shorter, and the network time complexity of the method is higher. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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Multi-Channel Adaptive Partitioning Network for Block-based image Compressive Sensing

Multi-Channel Adaptive Partitioning Network for Block-Based ...

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2022 IEEE international conference on Multimedia and Expo, ICME 2022

作者： Hui, Chen Liu, Shaohui Jiang, Feng School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

image compressive sensing (CS) technology has attracted increasing attentions in the past few years, and a great deal deep learning-based methods have been proposed. However, the existing methods use fixed-scale blocks for sampling and re-construction. Such practice will inevitably result in the in-ability to distinguish between significant regions and background regions, and even waste excessive sampling resources on the background ones to a large extent. In this paper, we propose a novel multi-channel adaptive partitioning network for block-based image CS, in which image blocks of different scales are utilized to distinguish regions of different saliency. Specifically, an adaptive block partitioning method based on image saliency is put forward, using which significant regions are divided into large blocks and background regions are divided into small blocks. Subsequently, blocks of different scales are fed to different-channel networks for sampling to yield the compressed measurements. To improve the re-construction quality of the image, a scalable multi-scale re-construction network is proposed to recover the compressed measurements into the reconstructed image. Experimental results compared with the state-of-the-art show that the proposed scheme achieves significant improvements in terms of objective metrics and subjective visual image quality. © 2022 IEEE.

关键词： image compression

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Exploration on Quality Enhancement of Art Works based on image Processing technology

Exploration on Quality Enhancement of Art Works Based on Ima...

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Integrated Intelligence and Communication systems (ICIICS), international conference on

作者： Lin Jin Shaanxi Fashion Engineering University Xi’an City Shaanxi Province China

In response to the problems existing in current methods for enhancing the quality of art works, this paper introduced a method for enhancing the quality of art works based on image processing technology. First, the edge of the image in the art works was enhanced by fuzzy enhancement technology. Then the image was balanced by the improved histogram equalization technology and the image was denoised based on the gray transformation algorithm. Finally, the processed image was processed based on the local mean algorithm. The experimental results showed that after using the proposed method to enhance the quality of art works, the peak signal-to-noise ratio and average gradient of the image have been significantly improved. The expert’s peak signal-to-noise ratio score could reach up to 89.8 points and the average gradient score could reach up to 88.3 points.

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Lightweight helmet detection algorithm based on improved YOLOv5 2

Lightweight helmet detection algorithm based on improved YOL...

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2nd international conference on Advanced Algorithms and signal image Processing, AASIP 2022

作者： Li, Jianning Zhang, Junzhao Zhang, Xinyu Wang, Shuai College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Software Institute Nanjing University Jiangsu Nanjing210093 China Shanxi Electric Drive and Internet of Things Engineering Research Center Shanxi Taiyuan030024 China Shanxi Province 1331 Project-Smart City Based on Big Data Lighting Data Sharing and Public Service Engineering Technology Research Center Shanxi Taiyuan030024 China Shanxi Intelligent Digital Lighting Union Laboratory Shanxi Taiyuan030024 China Shanxi Intelligent Digital Cultural Tourism Industry Research Institute Shanxi Taiyuan030024 China

ISBN: (纸本)9781510660571

In the construction process, wearing safety helmet can effectively reduce the injury caused by falling objects and hard objects. In order to detect the wearing of safety helmet during the construction process, a lightweight safety helmet wearing detection algorithm YOLOv5-CS is proposed and applied to the embedded equipment at the construction site. The algorithm is based on the improvement of YOLOv5 target detection algorithm. The improved CAShuffle module is used to form the backbone feature extraction network, which reduces the size of the original algorithm and ensures the detection accuracy of the algorithm;DIoU as the loss function, which makes the algorithm more conducive to detecting small targets such as hard hats;modify dataset, add the dataset on the basis of the original dataset and mark the safety helmet and the anchor frame of the person separately, and judge whether the worker wears the safety helmet by analyzing positional relationship of the identified anchor frame, use the algorithm combining K-means and genetic algorithm to predict the anchor frame of the dataset, and use Mosaic algorithm to enhance the data. The results show that the parameter quantity of YOLOv5-CS algorithm is 2.4M, less than one third of YOLOv5;The average precision decreased by only 4.2%;640x640 pictures can be recognized at 31 frames per second on Jetson Nano, a embedded platform. © 2022 SPIE.

关键词： signal detection

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Performance evaluation of a many/multi-core implementation for signal filtering problem

Performance evaluation of a many/multi-core implementation f...

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international IEEE conference on signal-image Technologies and internet-based System

作者： Vincenzo D’Alò Pasquale De Luca Science and Technologies Department Parthenope University of Naples Naples Italy

signal filtering plays an important role in preprocessing procedures used in several application fields. In last years, several methods have been proposed by achieving good results. When large data are given in input, performance are likely to get worse. based on the Savitzky-Golay filter, we have developed, in this paper, a parallel implementation with engaging a multi-core architecture. Experiments and tests highlight the gain of performance in time terms.

关键词： Performance evaluation Filtering Shape Computer architecture Filtering algorithms Software Digital filters

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