Signal, Image Processing and Embedded Systems Trends最新文献

A Framework to Protect Iot Devices from Enslavement in a Home Environment 保护物联网设备免受家庭环境奴役的框架

Signal, Image Processing and Embedded Systems Trends Pub Date : 2022-11-19 DOI: 10.5121/csit.2022.122012

K. Al-Begain, Murad Khan, Basil Alothman, C. Joumaa, Ibrahim Rashed

引用次数: 0

Multi-Sink Convergecast Protocol for Large Scale Wireless Sensor Networks 大规模无线传感器网络的多sink汇聚协议

Signal, Image Processing and Embedded Systems Trends Pub Date : 2022-11-19 DOI: 10.5121/csit.2022.122016

Gokou Hervé Fabrice Diédié, Koigny Fabrice Kouassi, Tchimou N'Takpé

引用次数: 1

Deep Learning Technique to Denoise Electromyogram Artifacts from Single-Channel Electroencephalogram Signals 从单通道脑电图信号中去噪肌电信号伪影的深度学习技术

Signal, Image Processing and Embedded Systems Trends Pub Date : 2022-11-19 DOI: 10.5121/csit.2022.122006

Muhammad E. H. Chowdhury, Md. Shafayet Hossain, S. Mahmud, A. Khandakar

{"title":"Deep Learning Technique to Denoise Electromyogram Artifacts from Single-Channel Electroencephalogram Signals","authors":"Muhammad E. H. Chowdhury, Md. Shafayet Hossain, S. Mahmud, A. Khandakar","doi":"10.5121/csit.2022.122006","DOIUrl":"https://doi.org/10.5121/csit.2022.122006","url":null,"abstract":"The adoption of dependable and robust techniques to remove electromyogram (EMG) artifacts from electroencephalogram (EEG) is essential to enable the exact identification of several neurological diseases. Even though many classical signal processing-based techniques have been used in the past and only a few deep-learning-based models have been proposed very recently, it is still a challenge to design an effective technique to eliminate EMG artifacts from EEG. In this work, deep learning (DL) techniques have been used to remove EMG artifacts from single-channel EEG data by employing four popular 1D convolutional neural network (CNN) models for signal synthesis. To train, validate, and test four CNN models, a semi-synthetic publicly accessible EEG dataset known as EEGdenoiseNet has been used the performance of 1D CNN models has been assessed by calculating the relative root mean squared error (RRMSE) in both the time and frequency domain, the temporal and spectral percentage reduction in EMG artifacts and the average power ratios between five EEG bands to whole spectra. The U-Net model outperformed the other three 1D CNN models in most cases in removing EMG artifacts from EEG achieving the highest temporal and spectral percentage reduction in EMG artifacts (90.01% and 95.49%); the closest average power ratio for theta, alpha, beta, and gamma band (0.55701, 0.12904, 0.07516, and 0.01822, respectively) compared to ground truth EEG (0.5429; 0.13225; 0.08214; 0.002146; and 0.02146, respectively). It is expected from the reported results that the proposed framework can be used for real-time EMG artifact reduction from multi-channel EEG data as well.","PeriodicalId":105776,"journal":{"name":"Signal, Image Processing and Embedded Systems Trends","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124979261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards a Smart Multi-Modal Image Registration Process 面向智能多模态图像配准过程

Signal, Image Processing and Embedded Systems Trends Pub Date : 2022-11-19 DOI: 10.5121/csit.2022.122004

Marwa Chaabane, Bruno Koller, I. Rodriguez

引用次数: 1

Research on wireless Powered Communication Networks Sum Rate Maximization based on time Reversal OFDM 基于时间反转OFDM的无线供电通信网络和速率最大化研究

Signal, Image Processing and Embedded Systems Trends Pub Date : 2022-11-19 DOI: 10.5121/csit.2022.122002

Wei Liu, Fang Wei Li, Hai Bo Zhang, Bo-ping Li

{"title":"Research on wireless Powered Communication Networks Sum Rate Maximization based on time Reversal OFDM","authors":"Wei Liu, Fang Wei Li, Hai Bo Zhang, Bo-ping Li","doi":"10.5121/csit.2022.122002","DOIUrl":"https://doi.org/10.5121/csit.2022.122002","url":null,"abstract":"This paper studies a wireless power communication network(WPCN) based on orthogonal frequency division multiplexing (OFDM) with time reversal(TR). In this paper, the \" Harvest Then Transmit \" protocol is adopted, and the transmission time block is divided into three stages, the first stage is for power transmission, the second stage is for TR detection, and the third stage is for information transmission. The energy limited access point (AP) and the terminal node obtain energy from the radiofrequency signal sent by the power beacon (PB) to assist the terminal data transmission. The energy limited AP and the terminal node obtain energy from the radio frequency signal sent by the PB to assist the terminal data transmission. In the TR phase and the wireless information transmission (WIT) phase, the terminal transmits the TR detection signal to the AP using the collected energy, and the AP uses the collected energy to transmit independent signals to a plurality of terminals through OFDM. In order to maximize the sum rate of WPCN, the energy collection time and AP power allocation are jointly optimized. Under the energy causal constraint, the subcarrier allocation, power allocation and time allocation of the whole process are studied, and because of the binary variables involved in the subcarrier allocation, the problem belongs to the mixed integer non-convex programming problem. the problem is transformed into a quasiconvex problem, and then binary search is used to obtain the optimal solution. The simulation results verify the effectiveness of this scheme. The results show that the proposed scheme significantly improves the sum rate of the terminal compared to the reference scheme.","PeriodicalId":105776,"journal":{"name":"Signal, Image Processing and Embedded Systems Trends","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132819507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Economic Productivity of Water in Agriculture based on Ordered Weighted Average Operators 基于有序加权平均算子的农业用水经济生产率

Signal, Image Processing and Embedded Systems Trends Pub Date : 2022-11-19 DOI: 10.5121/csit.2022.122015

José Manuel Brotons Martínez

{"title":"The Economic Productivity of Water in Agriculture based on Ordered Weighted Average Operators","authors":"José Manuel Brotons Martínez","doi":"10.5121/csit.2022.122015","DOIUrl":"https://doi.org/10.5121/csit.2022.122015","url":null,"abstract":"Water productivity is one of the main indicators used in agriculture. Price of water change from some regions where the price is free to other with a very high price. When water productivity is measured in Euros, to make comparable the results of the regions where the price is free, we need to obtain a correct measurement, which will require setting a market price for water in areas where no price has yet been set. Therefore, the aim of this paper is to propose new productivity indicators based on fuzzy logic, whereby experts’ opinions about the possible price of the use of water as well as the annual variability of agricultural prices can be added. Therefore, the fuzzy willingness to pay (FWTP) and fuzzy willingness to accept (FWTA) methodology will be applied to create an artificial water market. The use of fuzzy logic will allow the uncertainty inherent in the experts’ answers to be collected. Ordered Weighted Averaging (OWA) operators and their different extensions will allow different aggregations based on the sentiment or interests reflected by the experts. These same aggregators, applied to the prices of the products at origin, will make it possible to create new indicators of the economic productivity of water. Finally, through an empirical application for a pepper crop in south-eastern Spain we can visualize the importance of the different indicators and their influence on the final results.","PeriodicalId":105776,"journal":{"name":"Signal, Image Processing and Embedded Systems Trends","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130973251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Utilizing Deep Machine Learning to Create a Contextally and Environmentally Aware Application to Prevent Spinal Tendonitis 利用深度机器学习创建上下文和环境意识应用程序，以预防脊髓肌腱炎

Signal, Image Processing and Embedded Systems Trends Pub Date : 2022-11-19 DOI: 10.5121/csit.2022.122009

Barry Li, Yu Sun

引用次数: 0

A Cryptographically Secured Real-Time Peer-to-Peer Multiplayer Framework For Browser WebRTC 浏览器webbrtc的加密安全实时点对点多人框架

Signal, Image Processing and Embedded Systems Trends Pub Date : 2022-11-19 DOI: 10.5121/csit.2022.122010

Haocheng Han, Yu Sun

{"title":"A Cryptographically Secured Real-Time Peer-to-Peer Multiplayer Framework For Browser WebRTC","authors":"Haocheng Han, Yu Sun","doi":"10.5121/csit.2022.122010","DOIUrl":"https://doi.org/10.5121/csit.2022.122010","url":null,"abstract":"P2P(peer-to-peer) multiplayer protocols, such as lockstep and rollback net-code, have historically been the cheaper, direct alternative to the Client-Server model. Recent advances in WebRTC technology raise interesting prospects for independent developers to build serverless, P2P multiplayer games on the browser. P2P has several advantages over the Client-Server model in multiplayer games, such as reduced latency, significantly cheaper servers that only handle handshakes, etc. However, as the browser environment does not allow for third-party anti-cheat software, having a secure protocol that catches potential cheaters is crucial. Furthermore, traditional P2P protocols, such as deterministic lockstep, are unusable in the browser environment because different players could be running the game on different browser engines. This paper introduces a framework called Peercraft for P2P WebRTC games with both security and synchronization. We propose two P2P cheat-proofing protocols, Random Authority Shuffle and Speculation-Based State Verification. Both are built on known secure cryptographic primitives. We also propose a time-based synchronization protocol that does not require determinism, Resynchronizing-at-Root, which tolerates desynchronizations due to browser instability while fixing the entire desynchronization chain with only one re-simulation call, greatly improving the browser game’s performance.","PeriodicalId":105776,"journal":{"name":"Signal, Image Processing and Embedded Systems Trends","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123714970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Robustness of Age and Gender Prediction based on Custom Speech Data 基于自定义语音数据的年龄和性别预测鲁棒性改进

Signal, Image Processing and Embedded Systems Trends Pub Date : 2022-11-19 DOI: 10.5121/csit.2022.122005

Veerapandiyan Kandasamy, Anup Bera

{"title":"Improving Robustness of Age and Gender Prediction based on Custom Speech Data","authors":"Veerapandiyan Kandasamy, Anup Bera","doi":"10.5121/csit.2022.122005","DOIUrl":"https://doi.org/10.5121/csit.2022.122005","url":null,"abstract":"With the increased use of human-machine interaction via voice enabled smart devices over the years, there are growing demands for better accuracy of the speech analytics systems. Several studies show that speech analytics system exhibits bias towards speaker demographics, such age, gender, race, accent etc. To avoid such a bias, speaker demographic information can be used to prepare training dataset for the speech analytics model. Also, speaker demographic information can be used for targeted advertisement, recommendation, and forensic science. In this research we will demonstrate some algorithms for age and gender prediction from speech data with our custom dataset that covers speakers from around the world with varying accents. In order to extract speaker age and gender from speech data, we’ve also included a method for determining the appropriate length of audio file to be ingested into the system, which will reduce computational time. This study also identifies the most effective padding and cropping mechanism for obtaining the best results from the input audio file. We investigated the impact of various parameters on the performance and end-to-end implementation of a real-time speaker age and gender information extraction system. Our best model has a RMSE value of 4.1 for age prediction and 99.5% for gender prediction on custom test dataset.","PeriodicalId":105776,"journal":{"name":"Signal, Image Processing and Embedded Systems Trends","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125483047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Classification of Viral, Bacterial, And Covid-19 Pneumonia using Deep Learning Framework from Chest X-Ray Images 利用胸部x射线图像的深度学习框架对病毒、细菌和Covid-19肺炎进行分类

Signal, Image Processing and Embedded Systems Trends Pub Date : 2022-11-19 DOI: 10.5121/csit.2022.122001

Muhammad E. H. Chowdhury, Tawsifur Rahman, A. Khandakar, S. Mahmud

{"title":"Classification of Viral, Bacterial, And Covid-19 Pneumonia using Deep Learning Framework from Chest X-Ray Images","authors":"Muhammad E. H. Chowdhury, Tawsifur Rahman, A. Khandakar, S. Mahmud","doi":"10.5121/csit.2022.122001","DOIUrl":"https://doi.org/10.5121/csit.2022.122001","url":null,"abstract":"The novel coronavirus disease (COVID-19) is a highly contagious infectious disease. Even though there is a large pool of articles that showed the potential of using chest X-ray images in COVID-19 detection, a detailed study using a wide range of pre-trained convolutional neural network (CNN) encoders-based deep learning framework in screening viral, bacterial, and COVID-19 pneumonia are still missing. Deep learning network training is challenging without a properly annotated huge database. Transfer learning is a crucial technique for transferring knowledge from real-world object classification tasks to domain-specific tasks, and it may offer a viable answer. Although COVID-19 infection on the lungs and bacterial and viral pneumonia shares many similarities, they are treated differently. Therefore, it is crucial to appropriately diagnose them. The authors have compiled a large X-ray dataset (QU-MLG-COV) consisting of 16,712 CXR images with 8851 normal, 3616 COVID-19, 1485 viral, and 2740 bacterial pneumonia CXR images. We employed image pre-processing methods and 21 deep pre-trained CNN encoders to extract features, which were then dimensionality reduced using principal component analysis (PCA) and classified into 4-classes. We trained and evaluated every cutting-edge pre-trained network to extract features to improve performance. CheXNet surpasses other networks for identifying COVID-19, Bacterial, Viral, and Normal, with an accuracy of 98.89 percent, 97.87 percent, 97.55 percent, and 99.09 percent, respectively. The deep layer network found significant overlaps between viral and bacterial images. The paper validates the network learning from the relevant area of the images by Score-CAM visualization. The performance of the various pre-trained networks is also thoroughly examined in the paper in terms of both inference time and well-known performance criteria.","PeriodicalId":105776,"journal":{"name":"Signal, Image Processing and Embedded Systems Trends","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127135063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1