WSEAS transactions on signal processing最新文献

Using Hybrid Models of AI for Identification of Trees by UAV Images of Forests: I. Machine-learning Component of the Models 使用人工智能混合模型通过无人机拍摄的森林图像识别树木：I. 模型的机器学习部分

WSEAS transactions on signal processing Pub Date : 2024-07-04 DOI: 10.37394/232014.2024.20.5

Zurab Bosikashvili, Giorgi Kvartskhava

{"title":"Using Hybrid Models of AI for Identification of Trees by UAV Images of Forests: I. Machine-learning Component of the Models","authors":"Zurab Bosikashvili, Giorgi Kvartskhava","doi":"10.37394/232014.2024.20.5","DOIUrl":"https://doi.org/10.37394/232014.2024.20.5","url":null,"abstract":"Artificial intellect models (machine learning, logical reasoning, etc.) are currently the focus of many remote sensing approaches for forest inventory management. Although they return satisfactory results in many tasks, some challenges remain, especially in the case of the highly dense distribution of trees in forests. In this paper, we propose a novel hybrid approach using together deep learning models and symbolic logic methods for identifying single-tree species in highly dense areas. The use of deep learning methods in solving high dimensional problems in face recognition has some issues due to low accuracy and interpretability of results. The paper proposes a hybrid approach for solving complex image classification problems. This approach involves the use of both machine learning methods and symbolic knowledge. The paper presents the structure and formal model of the hybrid system, which includes a new component, an operations manager. The first part of the paper proposes a new architecture of deep neural networks with attentional mechanisms built on blocking meta-functions. The corresponding module has been developed in Python language. The results of the module's work are provided to the knowledge base. As a result of symbolic conclusions, the teaching module is reorganized. The experiments conducted showed the effectiveness of the presented approach.","PeriodicalId":486203,"journal":{"name":"WSEAS transactions on signal processing","volume":" 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141679973","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

Improved UFIR Filter for Fusing Recent INS-assisted Visual Measurement under Colored Measurement Noise in UAV Landing 改进的 UFIR 滤波器，用于在无人机着陆时融合彩色测量噪声下的最近 INS 辅助视觉测量结果

WSEAS transactions on signal processing Pub Date : 2024-06-06 DOI: 10.37394/232014.2024.20.4

Yide Zhang, Teng Li, Xin Zang, Jingwen Yu, Yuan Xu, Yuriy S. Shmaliy

引用次数: 0

Denoising ECG Signals using Weighted Iterative UFIR Filtering 利用加权迭代 UFIR 滤波对心电图信号去噪

WSEAS transactions on signal processing Pub Date : 2023-12-07 DOI: 10.37394/232014.2023.19.16

Carlos Lastre-Dominguez, Victor Jímenez-Ramos, Hector Azcaray-Rivera, Eduardo Pérez-Campos, J. Muñoz-Minjares, Yuriy Shmaliy

引用次数: 0

Time Series Analysis with Application in Public Health and Biomedical Data 时间序列分析及其在公共卫生和生物医学数据中的应用

WSEAS transactions on signal processing Pub Date : 2023-11-14 DOI: 10.37394/232014.2023.19.15

Theodor D. Popescu

引用次数: 0

Comparison of Adaptive Kalman Filters in Aircraft State Estimation 自适应卡尔曼滤波器在飞机状态估计中的比较

WSEAS transactions on signal processing Pub Date : 2023-10-30 DOI: 10.37394/232014.2023.19.14

Mert Sever, Tuncay Yunus Erkeç, Chingiz Hajiyev

引用次数: 0

The Improvement in the AFM Image After Applying a Lucy- Richardon Deconvolution Algorithm Using a New Technique for Estimating the AFM Tip Shape From the Square Sample 应用Lucy- richardson反卷积算法对AFM图像的改进——利用方形样本估计AFM针尖形状的新技术

WSEAS transactions on signal processing Pub Date : 2023-10-04 DOI: 10.37394/232014.2023.19.11

Ahmed Ahtaiba

引用次数: 0

Robust Recursive Least-Squares Wiener Filter for Linear Continuous-Time Uncertain Stochastic Systems 线性连续时间不确定随机系统的鲁棒递推最小二乘维纳滤波器

WSEAS transactions on signal processing Pub Date : 2023-10-04 DOI: 10.37394/232014.2023.19.12

Seiichi Nakamori

{"title":"Robust Recursive Least-Squares Wiener Filter for Linear Continuous-Time Uncertain Stochastic Systems","authors":"Seiichi Nakamori","doi":"10.37394/232014.2023.19.12","DOIUrl":"https://doi.org/10.37394/232014.2023.19.12","url":null,"abstract":"For linear continuous-time systems with uncertainties in the system and observation matrices, an original robust RLS Wiener filter is designed in this study. The robust RLS Wiener filter does not assume norm-bounded uncertainty for the system and observation matrices, in contrast to the robust Kalman filter. In the design of the robust RLS Wiener filter, the degraded signal, affected by the uncertainties in the system and observation matrices, is modeled by an autoregressive (AR) model. The system and observation matrices for the degraded signal are formulated from the relationship between the AR model of the degraded signal and the state-space model. Estimation formulas for the system and observation matrices are proposed in Section 2. The robust filtering problem is introduced based on the minimization of the mean-square value of the filtering errors for the system states. The robust filtering estimate is given as an integral transformation of the degraded observations using the impulse response function. The integral equation that an optimal impulse response function satisfies is given in Section 3. Theorem 1 presents a robust RLS Wiener filtering algorithm starting from this integral equation. The proposed robust RLS Wiener filter outperforms the existing robust Kalman filter regarding estimate accuracy, as shown by a numerical simulation example.","PeriodicalId":486203,"journal":{"name":"WSEAS transactions on signal processing","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135592235","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

Automated Alzheimer’s Disease Diagnosis using Convolutional Neural Networks and Magnetic Resonance Imaging 使用卷积神经网络和磁共振成像自动诊断阿尔茨海默病

WSEAS transactions on signal processing Pub Date : 2023-10-04 DOI: 10.37394/232014.2023.19.13

Asmaa Nasr Mohammed, Abdulgani Albagul, Moamer Musbah Ahmad

{"title":"Automated Alzheimer’s Disease Diagnosis using Convolutional Neural Networks and Magnetic Resonance Imaging","authors":"Asmaa Nasr Mohammed, Abdulgani Albagul, Moamer Musbah Ahmad","doi":"10.37394/232014.2023.19.13","DOIUrl":"https://doi.org/10.37394/232014.2023.19.13","url":null,"abstract":"Alzheimer’s disease is a debilitating neuro-logical condition affecting millions globally; therefore, correct diagnosis plays a significant role in treating or managing it effectively. Convolutional neural networks (CNNs), which are popular deep learning algorithms are applied to image processing tasks, offer a good technique to study and investigate images processing. In this study, a CNN model for classifying Alzheimer’s patients is proposed. The research yielded impressive results: recall and precision scores as high as 0.9958 which indicate trustworthy identification of true positives while maintaining few false positives; test accuracy exceeding 99% confirming desirable generalization capabilities from the training dataset to live scenarios; ROC AUC score at an astronomical height of 0.9999 signifying great potential in distinguishing between afflicted individuals from their non-affected counterparts accurately. The proposed network achieved a classification accuracy of 99.94% on LMCI vs EMCI, 99.87% on LMCI vs MCI, 99.95% on LMCI vs AD, 99.94% on LMCI vs CN, 99.99% on CN vs AD, 99.99% on CN vs EMCI, 99.99% on CN vs MCI, 99.99% on AD vs EMCI, 99.98% on AD vs MCI, and 99.96% on MCI vs EMCI. The proposed CNNs model is compared with two ultramodern models such as VGG19 and ResNet50. The results show that the proposed model achieved a superior performance in diagnostic precision and effectiveness of Alzheimer’s disease, leading to early detection, enhanced treatment plans, and enriching the quality of life for those affected.","PeriodicalId":486203,"journal":{"name":"WSEAS transactions on signal processing","volume":"232 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135592085","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

Performance of Some Modified Test Statistics for Testing the Population Signal to Noise Ratio: Simulation and Applications 一些改进的测试统计量在总体信噪比测试中的性能:仿真与应用

WSEAS transactions on signal processing Pub Date : 2023-10-03 DOI: 10.37394/232014.2023.19.10

Samantha Menendez, Florence George, B. M. Golam Kibria

引用次数: 0

Recognising Image Shapes from Image Parts, not Neural Parts 识别图像形状从图像部分，而不是神经部分

WSEAS transactions on signal processing Pub Date : 2023-10-02 DOI: 10.37394/232014.2023.19.9

Kieran Greer

引用次数: 0