Frontiers in signal processing最新文献_第10页

PancreaSys: An Automated Cloud-Based Pancreatic Cancer Grading System 胰:基于云的胰腺癌自动分级系统

Frontiers in signal processing Pub Date : 2022-02-11 DOI: 10.3389/frsip.2022.833640

Muhammad Nurmahir Mohamad Sehmi, M. F. A. Fauzi, Wan Siti Halimatul Munirah Wan Ahmad, E. Chan

{"title":"PancreaSys: An Automated Cloud-Based Pancreatic Cancer Grading System","authors":"Muhammad Nurmahir Mohamad Sehmi, M. F. A. Fauzi, Wan Siti Halimatul Munirah Wan Ahmad, E. Chan","doi":"10.3389/frsip.2022.833640","DOIUrl":"https://doi.org/10.3389/frsip.2022.833640","url":null,"abstract":"Pancreatic cancer is one of the deadliest diseases which has taken millions of lives over the past 20 years. Due to challenges in grading pancreatic cancer, this study presents an automated cloud-based system, utilizing a convolutional neural network deep learning (DL) approach to classifying four classes of pancreatic cancer grade from pathology image into Normal, Grade I, Grade II, and Grade III. This cloud-based system, named PancreaSys, takes an input of high power field images from the web user interface, slices them into smaller patches, makes predictions, and stitches back the patches before returning the final result to the pathologist. Anvil and Google Colab are used as the backbone of the system to build a web user interface for deploying the DL model in the classification of the cancer grade. This work employs the transfer learning approach on a pre-trained DenseNet201 model with data augmentation to alleviate the small dataset’s challenges. A 5-fold cross-validation (CV) was employed to ensure all samples in a dataset were used to evaluate and mitigate selection bias during splitting the dataset into 80% training and 20% validation sets. The experiments were done on three different datasets (May Grunwald-Giemsa (MGG), hematoxylin and eosin (H&E), and a mixture of both, called the Mixed dataset) to observe the model performance on two different pathology stains (MGG and H&E). Promising performances are reported in predicting the pancreatic cancer grade from pathology images, with a mean f1-score of 0.88, 0.96, and 0.89 for the MGG, H&E, and Mixed datasets, respectively. The outcome from this research is expected to serve as a prognosis system for the pathologist in providing accurate grading for pancreatic cancer in pathological images.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"137 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76548458","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

Low Dose CT Denoising by ResNet With Fused Attention Modules and Integrated Loss Functions 融合注意模块和综合损失函数的ResNet低剂量CT去噪

Frontiers in signal processing Pub Date : 2022-02-07 DOI: 10.3389/frsip.2021.812193

Luella Marcos, J. Alirezaie, P. Babyn

{"title":"Low Dose CT Denoising by ResNet With Fused Attention Modules and Integrated Loss Functions","authors":"Luella Marcos, J. Alirezaie, P. Babyn","doi":"10.3389/frsip.2021.812193","DOIUrl":"https://doi.org/10.3389/frsip.2021.812193","url":null,"abstract":"X-ray computed tomography (CT) is a non-invasive medical diagnostic tool that has raised public concerns due to the associated health risks of radiation dose to patients. Reducing the radiation dose leads to noise artifacts, making the low-dose CT images unreliable for diagnosis. Hence, low-dose CT (LDCT) image reconstruction techniques have offered a new research area. In this study, a deep neural network is proposed, specifically a residual network (ResNet) using dilated convolution, batch normalization, and rectified linear unit (ReLU) layers with fused spatial- and channel-attention modules to enhance the quality of LDCT images. The network is optimized using the integration of per-pixel loss, perceptual loss via VGG16-net, and dissimilarity index loss. Through an ablation experiment, these functions show that they could effectively prevent edge oversmoothing, improve image texture, and preserve the structural details. Finally, comparative experiments showed that the qualitative and quantitative results of the proposed network outperform state-of-the-art denoising models such as block-matching 3D filtering (BM3D), Markovian-based patch generative adversarial network (patch-GAN), and dilated residual network with edge detection (DRL-E-MP).","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86238383","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

Analysis of a 2D Representation for CPS Anomaly Detection in a Context-Based Security Framework 基于上下文的安全框架中CPS异常检测的二维表示分析

Frontiers in signal processing Pub Date : 2022-01-21 DOI: 10.3389/frsip.2021.814129

Sara Baldoni, M. Carli, F. Battisti

引用次数: 2

Static: Low Frequency Energy Harvesting and Power Transfer for the Internet of Things 静态:用于物联网的低频能量收集和功率传输

Frontiers in signal processing Pub Date : 2022-01-19 DOI: 10.3389/frsip.2021.763299

A. Thangarajan, T. D. Nguyen, Meng-Liang Liu, Sam Michiels, F. Yang, K. Man, Jieming Ma, W. Joosen, D. Hughes

{"title":"Static: Low Frequency Energy Harvesting and Power Transfer for the Internet of Things","authors":"A. Thangarajan, T. D. Nguyen, Meng-Liang Liu, Sam Michiels, F. Yang, K. Man, Jieming Ma, W. Joosen, D. Hughes","doi":"10.3389/frsip.2021.763299","DOIUrl":"https://doi.org/10.3389/frsip.2021.763299","url":null,"abstract":"The Internet of Things (IoT) is composed of wireless embedded devices which sense, analyze and communicate the state of the physical world. To achieve truly wireless operation, today’s IoT devices largely depend on batteries for power. However, this leads to high maintenance costs due to battery replacement, or the environmentally damaging concept of disposable devices. Energy harvesting has emerged as a promising approach to delivering long-life, environmentally friendly IoT device operation. However, with the exception of solar harvesting, it remains difficult to ensure sustainable system operation using environmental power alone. This paper tackles this problem by contributing Static, a Radio Frequency (RF) energy harvesting and wireless power transfer platform. Our approach comprises autonomous energy management techniques, adaptive power transfer algorithms and an open-source hardware reference platform to enable further research. We evaluate Static in laboratory conditions and show that 1) ambient RF energy harvesting can deliver sustainable operation using common industrial sources, while 2) wireless power transfer provides a simple means to power motes at a range of up to 3 m through a variety of media.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"34 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77231112","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

Optimization of Network Throughput of Joint Radar Communication System Using Stochastic Geometry 基于随机几何的联合雷达通信系统网络吞吐量优化

Frontiers in signal processing Pub Date : 2022-01-10 DOI: 10.3389/frsip.2022.835743

S. S. Ram, Shubhi Singhal, Gourab Ghatak

{"title":"Optimization of Network Throughput of Joint Radar Communication System Using Stochastic Geometry","authors":"S. S. Ram, Shubhi Singhal, Gourab Ghatak","doi":"10.3389/frsip.2022.835743","DOIUrl":"https://doi.org/10.3389/frsip.2022.835743","url":null,"abstract":"Recently joint radar communication (JRC) systems have gained considerable interest for several applications such as vehicular communications, indoor localization and activity recognition, covert military communications, and satellite based remote sensing. In these frameworks, bistatic/passive radar deployments with directional beams explore the angular search space and identify mobile users/radar targets. Subsequently, directional communication links are established with these mobile users. Consequently, JRC parameters such as the time trade-off between the radar exploration and communication service tasks have direct implications on the network throughput. Using tools from stochastic geometry (SG), we derive several system design and planning insights for deploying such networks and demonstrate how efficient radar detection can augment the communication throughput in a JRC system. Specifically, we provide a generalized analytical framework to maximize the network throughput by optimizing JRC parameters such as the exploration/exploitation duty cycle, the radar bandwidth, the transmit power and the pulse repetition interval. The analysis is further extended to monostatic radar conditions, which is a special case in our framework. The theoretical results are experimentally validated through Monte Carlo simulations. Our analysis highlights that for a larger bistatic range, a lower operating bandwidth and a higher duty cycle must be employed to maximize the network throughput. Furthermore, we demonstrate how a reduced success in radar detection due to higher clutter density deteriorates the overall network throughput. Finally, we show a peak reliability of 70% of the JRC link metrics for a single bistatic transceiver configuration.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77382171","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: 8

Rethinking Pooling Operation for Liver and Liver-Tumor Segmentations 肝脏及肝肿瘤分割池化手术的再思考

Frontiers in signal processing Pub Date : 2022-01-10 DOI: 10.3389/frsip.2021.808050

Ju-hong Lei, Tao Lei, Weiqiang Zhao, Min-Qi Xue, Xiaogang Du, A. Nandi

引用次数: 0

AIDA: An Active Inference-Based Design Agent for Audio Processing Algorithms AIDA:一种基于推理的音频处理算法设计代理

Frontiers in signal processing Pub Date : 2021-12-26 DOI: 10.3389/frsip.2022.842477

Albert Podusenko, B. V. Erp, Magnus T. Koudahl, B. Vries

{"title":"AIDA: An Active Inference-Based Design Agent for Audio Processing Algorithms","authors":"Albert Podusenko, B. V. Erp, Magnus T. Koudahl, B. Vries","doi":"10.3389/frsip.2022.842477","DOIUrl":"https://doi.org/10.3389/frsip.2022.842477","url":null,"abstract":"In this paper we present Active Inference-Based Design Agent (AIDA), which is an active inference-based agent that iteratively designs a personalized audio processing algorithm through situated interactions with a human client. The target application of AIDA is to propose on-the-spot the most interesting alternative values for the tuning parameters of a hearing aid (HA) algorithm, whenever a HA client is not satisfied with their HA performance. AIDA interprets searching for the “most interesting alternative” as an issue of optimal (acoustic) context-aware Bayesian trial design. In computational terms, AIDA is realized as an active inference-based agent with an Expected Free Energy criterion for trial design. This type of architecture is inspired by neuro-economic models on efficient (Bayesian) trial design in brains and implies that AIDA comprises generative probabilistic models for acoustic signals and user responses. We propose a novel generative model for acoustic signals as a sum of time-varying auto-regressive filters and a user response model based on a Gaussian Process Classifier. The full AIDA agent has been implemented in a factor graph for the generative model and all tasks (parameter learning, acoustic context classification, trial design, etc.) are realized by variational message passing on the factor graph. All verification and validation experiments and demonstrations are freely accessible at our GitHub repository.","PeriodicalId":93557,"journal":{"name":"Frontiers in signal processing","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83273457","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: 3

Multi-Frequency Radar Micro-Doppler Based Classification of Micro-Drone Payload Weight 基于多频雷达微多普勒的微型无人机载荷重量分类

Frontiers in signal processing Pub Date : 2021-12-22 DOI: 10.3389/frsip.2021.781777

D. Dhulashia, N. Peters, C. Horne, P. Beasley, M. Ritchie

引用次数: 7

Persymmetric Adaptive Union Subspace Detection 超对称自适应联合子空间检测

Frontiers in signal processing Pub Date : 2021-12-15 DOI: 10.3389/frsip.2021.782182

Liyan Pan, Yongchan Gao, Z. Ye, Yuzhou Lv, Ming Fang

引用次数: 0

Interference Utilization Precoding in Multi-Cluster IoT Networks 多集群物联网网络中的干扰利用预编码

Frontiers in signal processing Pub Date : 2021-11-22 DOI: 10.3389/frsip.2021.761559

Yuanchen Wang, Eng Gee Lim, Xiaoping Xue, Guangyu Zhu, Rui Pei, Zhongxiang Wei

引用次数: 1