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Connected autonomous vehicles (CAVs) currently promise cooperation between vehicles, providing abundant and real-time information through wireless communication technologies. In this paper, a two-level fusion of classifiers (TLFC) approach is proposed by using deep learning classifiers to perform accurate road detection (RD). The proposed TLFC-RD approach improves the classification by considering four key strategies such as cross fold operation at input and pre-processing using superpixel generation, adequate features, multi-classifier feature fusion and a deep learning classifier. Specifically, the road is classified as drivable and non-drivable areas by designing the TLFC using the deep learning classifiers, and the detected information using the TLFC-RD is exchanged between the autonomous vehicles for the ease of driving on the road. The TLFC-RD is analyzed in terms of its accuracy, sensitivity or recall, specificity, precision, F1-measure and max F measure. The TLFC- RD method is also evaluated compared to three existing methods: U-Net with the Domain Adaptation Model (DAM), Two-Scale Fully Convolutional Network (TFCN) and a cooperative machine learning approach (i.e., TAAUWN). Experimental results show that the accuracy of the TLFC-RD method for the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset is 99.12% higher than its competitors.
Prabu Subramani; Khalid Nazim Abdul Sattar; Rocío Pérez de Prado; Balasubramanian Girirajan; Marcin Wozniak. Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles. Applied Sciences 2021, 11, 7984 .
AMA StylePrabu Subramani, Khalid Nazim Abdul Sattar, Rocío Pérez de Prado, Balasubramanian Girirajan, Marcin Wozniak. Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles. Applied Sciences. 2021; 11 (17):7984.
Chicago/Turabian StylePrabu Subramani; Khalid Nazim Abdul Sattar; Rocío Pérez de Prado; Balasubramanian Girirajan; Marcin Wozniak. 2021. "Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles." Applied Sciences 11, no. 17: 7984.
In recent years, the enhancement in technology has been envisioning for people to complete tasks in an easier way. Every manufacturing industry requires heavy machinery to accomplish tasks in a symmetric and systematic way, which is much easier with the help of advancement in the technology. The technological advancement directly affects human life as a result. It is found that humans are now fully dependent on it. The online game industry is one example of technology breakthrough. It is now a prominent industry to develop online games at world level. In this paper, our main objective is to analyze major factors which encourage mobile games industry to expand. Analyzing the system and symmetric relations inside can be done into two phases. The first phase is through a TAM Model, which is a very efficient way to solve statistical problems, and the second phase is with machine learning (ML) techniques, such as SVM, logistic regression, etc. Both strategies are popular and efficient in analyzing a system while maintaining the symmetry in a better way. Therefore, according to results from both the TAM model and ML approach, it is clear that perceived usefulness, attitude, and symmetric flow are important factors for game industry. The analytics provide a clear insight that perceived usefulness is an important parameter over behavior intention for the online mobile game industry.
Shaifali Chauhan; Mohit Mittal; Marcin Woźniak; Swadha Gupta; Rocío Pérez de Prado. A Technology Acceptance Model-Based Analytics for Online Mobile Games Using Machine Learning Techniques. Symmetry 2021, 13, 1545 .
AMA StyleShaifali Chauhan, Mohit Mittal, Marcin Woźniak, Swadha Gupta, Rocío Pérez de Prado. A Technology Acceptance Model-Based Analytics for Online Mobile Games Using Machine Learning Techniques. Symmetry. 2021; 13 (8):1545.
Chicago/Turabian StyleShaifali Chauhan; Mohit Mittal; Marcin Woźniak; Swadha Gupta; Rocío Pérez de Prado. 2021. "A Technology Acceptance Model-Based Analytics for Online Mobile Games Using Machine Learning Techniques." Symmetry 13, no. 8: 1545.
Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the ‘Automatic and Intelligent Data Collector and Classifier’ framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The ‘Custom-Net’ model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the ‘Custom-Net’. Furthermore, the impact of transfer learning on the ‘Custom-Net’ and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the ‘Custom-Net’ extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the ‘Custom-Net’ model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of ‘Custom-Net’ is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.
Nidhi Kundu; Geeta Rani; Vijaypal Dhaka; Kalpit Gupta; Siddaiah Nayak; Sahil Verma; Muhammad Ijaz; Marcin Woźniak. IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet. Sensors 2021, 21, 5386 .
AMA StyleNidhi Kundu, Geeta Rani, Vijaypal Dhaka, Kalpit Gupta, Siddaiah Nayak, Sahil Verma, Muhammad Ijaz, Marcin Woźniak. IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet. Sensors. 2021; 21 (16):5386.
Chicago/Turabian StyleNidhi Kundu; Geeta Rani; Vijaypal Dhaka; Kalpit Gupta; Siddaiah Nayak; Sahil Verma; Muhammad Ijaz; Marcin Woźniak. 2021. "IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet." Sensors 21, no. 16: 5386.
Due to Internet of Things (IoT), it has become easy to surveil the critical regions. Images are important parts of Surveillance Systems, and it is required to protect the images during transmission and storage. These secure surveillance frameworks are required in IoT systems, because any kind of information leakage can thwart the legal system as well as personal privacy. In this paper, a secure surveillance framework for IoT systems is proposed using image encryption. A hyperchaotic map is used to generate the pseudorandom sequences. The initial parameters of the hyperchaotic map are obtained using partial-regeneration-based non-dominated optimization (PRNDO). The permutation and diffusion processes are applied to generate the encrypted images, and the convolution neural network (CNN) can play an essential role in this part. The performance of the proposed framework is assessed by drawing comparisons with competitive techniques based on security parameters. It shows that the proposed framework provides promising results as compared to the existing techniques.
Gopal Ghosh; Kavita; Divya Anand; Sahil Verma; Danda B. Rawat; Jana Shafi; Zbigniew Marszałek; Marcin Woźniak. Secure Surveillance Systems Using Partial-Regeneration-Based Non-Dominated Optimization and 5D-Chaotic Map. Symmetry 2021, 13, 1447 .
AMA StyleGopal Ghosh, Kavita, Divya Anand, Sahil Verma, Danda B. Rawat, Jana Shafi, Zbigniew Marszałek, Marcin Woźniak. Secure Surveillance Systems Using Partial-Regeneration-Based Non-Dominated Optimization and 5D-Chaotic Map. Symmetry. 2021; 13 (8):1447.
Chicago/Turabian StyleGopal Ghosh; Kavita; Divya Anand; Sahil Verma; Danda B. Rawat; Jana Shafi; Zbigniew Marszałek; Marcin Woźniak. 2021. "Secure Surveillance Systems Using Partial-Regeneration-Based Non-Dominated Optimization and 5D-Chaotic Map." Symmetry 13, no. 8: 1447.
In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.
Vijaypal Dhaka; Sangeeta Meena; Geeta Rani; Deepak Sinwar; Kavita Kavita; Muhammad Ijaz; Marcin Woźniak. A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases. Sensors 2021, 21, 4749 .
AMA StyleVijaypal Dhaka, Sangeeta Meena, Geeta Rani, Deepak Sinwar, Kavita Kavita, Muhammad Ijaz, Marcin Woźniak. A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases. Sensors. 2021; 21 (14):4749.
Chicago/Turabian StyleVijaypal Dhaka; Sangeeta Meena; Geeta Rani; Deepak Sinwar; Kavita Kavita; Muhammad Ijaz; Marcin Woźniak. 2021. "A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases." Sensors 21, no. 14: 4749.
In the contemporary world, with ever-evolving internet models in the process of automating and digitalizing various industrial and domestic implementations, the Internet of Things (IoT) has made remarkable advancements in sharing the healthcare data and triggering the associated necessary actions. Healthcare-related data sharing among the intermediate nodes, privacy, and data integrity are the two critical challenges in the present-day scenario. Data needs to be encrypted to ensure the confidentiality of the sensitive information shared among the nodes, especially in the case of healthcare-related data records. Implementing the conventional encryption algorithms over the intermediate node may not be technically feasible, and too much burden on the intermediate nodes is not advisable. This article has focused on various security challenges in the existing mechanism, existing strategies in security solutions for IoT driven healthcare monitoring frameworks and proposes a context-aware state of art model based on Blockchain technology that has been deployed for encrypting the data among the nodes in the architecture of a 5G network. The proposed strategy was examined through various performance evaluation metrics, and the proposed approach had outperformed compared to its counterparts.
Parvathaneni Srinivasu; Akash Bhoi; Soumya Nayak; Muhammad Bhutta; Marcin Woźniak. Blockchain Technology for Secured Healthcare Data Communication among the Non-Terminal Nodes in IoT Architecture in 5G Network. Electronics 2021, 10, 1437 .
AMA StyleParvathaneni Srinivasu, Akash Bhoi, Soumya Nayak, Muhammad Bhutta, Marcin Woźniak. Blockchain Technology for Secured Healthcare Data Communication among the Non-Terminal Nodes in IoT Architecture in 5G Network. Electronics. 2021; 10 (12):1437.
Chicago/Turabian StyleParvathaneni Srinivasu; Akash Bhoi; Soumya Nayak; Muhammad Bhutta; Marcin Woźniak. 2021. "Blockchain Technology for Secured Healthcare Data Communication among the Non-Terminal Nodes in IoT Architecture in 5G Network." Electronics 10, no. 12: 1437.
In view of the problem that the traditional learning service recommendation does not fully consider the distinct differences between individuals, it is easy to lead to the contradiction between unchanging learning resources and learners’ personalized learning needs that are constantly improving, so an adaptive learning service recommendation improvement algorithm based on big data is proposed. Idea is based on adaptive learning platform and function modules. We consider the individual differences between students, to students as the center, collect students’ personalized learning demand data, and according to the data information to build student demand model. On the basis of using data mining methods for clustering recommendation service resources in learning, the adaptive recommend according to students’ individual need is proposed. The experimental results show that the adaptive learning service recommendation algorithm based on big data has high recommendation accuracy, coverage rate and recall rate, which is of great significance in the actual learning service recommendation.
Ya-Zhi Yang; Yong Zhong; Marcin Woźniak. Improvement of Adaptive Learning Service Recommendation Algorithm Based on Big Data. Mobile Networks and Applications 2021, 1 -12.
AMA StyleYa-Zhi Yang, Yong Zhong, Marcin Woźniak. Improvement of Adaptive Learning Service Recommendation Algorithm Based on Big Data. Mobile Networks and Applications. 2021; ():1-12.
Chicago/Turabian StyleYa-Zhi Yang; Yong Zhong; Marcin Woźniak. 2021. "Improvement of Adaptive Learning Service Recommendation Algorithm Based on Big Data." Mobile Networks and Applications , no. : 1-12.
To fully mine the relationship between temporal features in load data, improve the accuracy and efficiency of short-term load forecasting and overcome the difficulties caused by load nonlinearity and volatility in accurate load forecasting. In this paper, a hybrid neural network short-term load forecasting model based on temporal convolutional network (TCN) and gated recurrent unit (GRU) is proposed. Firstly, the correlation between meteorological features and load is measured with the distance correlation coefficient, and the fixed-length sliding time window method is used to reconstruct the features. Next, temporal convolutional network is adopted to extract the hidden historical information and time relationship including meteorological features, electricity price, etc., and a better-performing gated recurrent unit is utilized for perdition. Furthermore, the state-of-the-art AdaBelief optimizer and Attention mechanism are utilized to enhance the prediction accuracy and efficiency. The effectiveness and superiority of the proposed model are verified by load and weather data from Spain and PJM power system data. Short-term load forecasting results in different periods and comprehensive comparisons with the performance of different models show that the proposed model can provide accurate load forecasting results rather quickly. The highlights of this paper are that temporal convolutional network and gated recurrent unit are combined for load forecasting for the first time, and the forecasting performance is improved by the novel optimizer AdaBelief and feature selection based on distance correlation coefficient.
Hanhong Shi; Lei Wang; Rafal Scherer; Marcin Wozniak; Pengchao Zhang; Wei Wei. Short-Term Load Forecasting Based on Adabelief Optimized Temporal Convolutional Network and Gated Recurrent Unit Hybrid Neural Network. IEEE Access 2021, 9, 66965 -66981.
AMA StyleHanhong Shi, Lei Wang, Rafal Scherer, Marcin Wozniak, Pengchao Zhang, Wei Wei. Short-Term Load Forecasting Based on Adabelief Optimized Temporal Convolutional Network and Gated Recurrent Unit Hybrid Neural Network. IEEE Access. 2021; 9 ():66965-66981.
Chicago/Turabian StyleHanhong Shi; Lei Wang; Rafal Scherer; Marcin Wozniak; Pengchao Zhang; Wei Wei. 2021. "Short-Term Load Forecasting Based on Adabelief Optimized Temporal Convolutional Network and Gated Recurrent Unit Hybrid Neural Network." IEEE Access 9, no. : 66965-66981.
Modern medical clinics support medical examinations with computer systems which use Computational Intelligence on the way to detect potential health problems in more efficient way. One of the most important applications is evaluation of CT brain scans, where the most precise results come from deep learning approaches. In this article, we propose a novel correlation learning mechanism (CLM) for deep neural network architectures that combines convolutional neural network (CNN) with classic architecture. The support neural network helps CNN to find the most adequate filers for pooling and convolution layers. As a result, the main neural classifier learns faster and reaches higher efficiency. Results show that our CLM model is able to reach about 96% accuracy, and about 95% precision and recall. We have described our proposed mechanism and discussed numerical results to draw conclusions and show future works.
Marcin Woźniak; Jakub Siłka; Michał Wieczorek. Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Computing and Applications 2021, 1 -16.
AMA StyleMarcin Woźniak, Jakub Siłka, Michał Wieczorek. Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Computing and Applications. 2021; ():1-16.
Chicago/Turabian StyleMarcin Woźniak; Jakub Siłka; Michał Wieczorek. 2021. "Deep neural network correlation learning mechanism for CT brain tumor detection." Neural Computing and Applications , no. : 1-16.
Road segmentation for synthetic aperture radar (SAR) images is of great practical significance. With the rapid development and wide application of SAR imaging technology, this problem has attracted much attention. At present, there are numerous road segmentation methods. This paper analyzes and summarizes the road segmentation methods for SAR images over the years. Firstly, the traditional road segmentation algorithms are classified according to the degree of automation and the principle. Advantages and disadvantages are introduced successively for each traditional method. Then, the popular segmentation methods based on deep learning in recent years are systematically introduced. Finally, novel deep segmentation neural networks based on the capsule paradigm and the self-attention mechanism are forecasted as future research for SAR images.
Zengguo Sun; Hui Geng; Zheng Lu; Rafał Scherer; Marcin Woźniak. Review of Road Segmentation for SAR Images. Remote Sensing 2021, 13, 1011 .
AMA StyleZengguo Sun, Hui Geng, Zheng Lu, Rafał Scherer, Marcin Woźniak. Review of Road Segmentation for SAR Images. Remote Sensing. 2021; 13 (5):1011.
Chicago/Turabian StyleZengguo Sun; Hui Geng; Zheng Lu; Rafał Scherer; Marcin Woźniak. 2021. "Review of Road Segmentation for SAR Images." Remote Sensing 13, no. 5: 1011.
The advances in the Internet of Things (IoT) provide several chances to develop a variety of innovations supporting smart home users in several industries including healthcare, energy management, etc. Ubiquitous support by intelligent appliances at modern homes, which constantly work to gather information can help us to solve everyday issues. In this paper, we present a comparative study of recent advances in smart home development. The study aims to present the main trends in this field. During the analysis of the research reports and patents, we identify the propositions that constitute the main research streams. Through extensive analysis, we provide an outlook on the wide spectrum of the proposed solutions. We also analyze the main market to present which publishers are leading with the innovative science in this field. We also show the leaders of science and technology in the World. Finally, we define the ratio of the developments and outline the next stage of the development in the smart home industry.
Adam Zielonka; Marcin Wozniak; Sahil Garg; Georges Kaddoum; Jalil Piran; Ghulam Muhammad. Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances. IEEE Access 2021, 9, 26388 -26419.
AMA StyleAdam Zielonka, Marcin Wozniak, Sahil Garg, Georges Kaddoum, Jalil Piran, Ghulam Muhammad. Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances. IEEE Access. 2021; 9 (99):26388-26419.
Chicago/Turabian StyleAdam Zielonka; Marcin Wozniak; Sahil Garg; Georges Kaddoum; Jalil Piran; Ghulam Muhammad. 2021. "Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances." IEEE Access 9, no. 99: 26388-26419.
BrushLess Direct-Current (BLDC) motors are characterized by high efficiency and reliability due to the fact that the BLDC motor does not require power to the rotor. The rotor of the BLDC motor consists of permanent magnets. When examining the waveform of the current supplied to the motor windings, significant current ripple was observed within one power cycle, where the optimum value would be the constant value of this current during one power cycle. The variability of this current in one motor supply cycle results from the variability of the electromotive force induced in the motor winding. The paper presents a diagram of the power supply system consisting of an electronic commutator and a DC/DC converter made by the authors, and a proposed modification of the power supply system reducing the current pulsation of the motor windings and thus the possibility of reducing energy losses in the motor windings. The paper presents numerous results of measurements which showed a significant reduction in energy losses in the case of low-load operation.
Andrzej Sikora; Marcin Woźniak. Impact of Current Pulsation on BLDC Motor Parameters. Sensors 2021, 21, 587 .
AMA StyleAndrzej Sikora, Marcin Woźniak. Impact of Current Pulsation on BLDC Motor Parameters. Sensors. 2021; 21 (2):587.
Chicago/Turabian StyleAndrzej Sikora; Marcin Woźniak. 2021. "Impact of Current Pulsation on BLDC Motor Parameters." Sensors 21, no. 2: 587.
The recent years have seen a vast development in various methodologies for object detection and feature extraction and recognition, both in theory and in practice
Marcin Woźniak. Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments. Sensors 2020, 21, 45 .
AMA StyleMarcin Woźniak. Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments. Sensors. 2020; 21 (1):45.
Chicago/Turabian StyleMarcin Woźniak. 2020. "Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments." Sensors 21, no. 1: 45.
The idea of the paper concentrates on an iterative learning process in Graph Convolution Networks (GCNs) involved in two vital steps: one is a message propagation (message passing) step to aggregate neighboring node features via aggregators performed, and another is an encoding output step to encode node feature representations by using updaters. In our model, we propose a novel affinity-aware encoding as an updater in GCNs, which aggregates the neighboring nodes of a node while updating this node’s features. By utilizing affinity values of our encoding, we order the neighboring nodes to determine the correspondence between encoding functions and the neighboring nodes. Furthermore, to explicitly reduce the model size, we propose a lightweight variant of our updater that integrates Depth-wise Separable Convolution (DSC) into it, namely Depth-wise Separable Graph Convolution (DSGC). Comprehensive experiments conducted on graph data demonstrate that our models’ accuracy improved significantly for graphs of low-dimensional node features. Also, performed in the low-dimensional node feature space we provide state-of-the-art results on two metrics (Macro-f1 and Matthews correlation coefficient (MCC)). Besides, our models are robust when taking different low-dimensional feature selection strategies.
Wei Dong; Junsheng Wu; Zongwen Bai; Yaoqi Hu; Weigang Li; Wei Qiao; Marcin Woźniak. MobileGCN applied to low-dimensional node feature learning. Pattern Recognition 2020, 112, 107788 .
AMA StyleWei Dong, Junsheng Wu, Zongwen Bai, Yaoqi Hu, Weigang Li, Wei Qiao, Marcin Woźniak. MobileGCN applied to low-dimensional node feature learning. Pattern Recognition. 2020; 112 ():107788.
Chicago/Turabian StyleWei Dong; Junsheng Wu; Zongwen Bai; Yaoqi Hu; Weigang Li; Wei Qiao; Marcin Woźniak. 2020. "MobileGCN applied to low-dimensional node feature learning." Pattern Recognition 112, no. : 107788.
Technological development increases capacity of information systems, which with development of faster data transfer will be able to host variety of new devices. In this article we present electronic modules, infrastructure and fuzzy rules control model with implemented software for new generation home environment. The system is developed for the next IoT level based on 6G network communication standards. Proposed control model is efficient in water flow management, wind shield control, security aspects and carbon dioxide limitation via adaptive ventilation. Developed infrastructure is ready for new 6G communication standard, which will additionally improve efficiency and data flow at end-user devices and local area level.
Marcin Wozniak; Adam Zielonka; Andrzej Sikora; Jalil Piran; Atif Alamri. 6G-Enabled IoT Home Environment Control Using Fuzzy Rules. IEEE Internet of Things Journal 2020, 8, 5442 -5452.
AMA StyleMarcin Wozniak, Adam Zielonka, Andrzej Sikora, Jalil Piran, Atif Alamri. 6G-Enabled IoT Home Environment Control Using Fuzzy Rules. IEEE Internet of Things Journal. 2020; 8 (7):5442-5452.
Chicago/Turabian StyleMarcin Wozniak; Adam Zielonka; Andrzej Sikora; Jalil Piran; Atif Alamri. 2020. "6G-Enabled IoT Home Environment Control Using Fuzzy Rules." IEEE Internet of Things Journal 8, no. 7: 5442-5452.
A new speckle suppression algorithm is proposed for high-resolution synthetic aperture radar (SAR) images. It is based on the nonlocal means (NLM) filter and the modified Aubert and Aujol (AA) model. This method takes the nonlocal Dirichlet function as a linear regularization item, which constructs the weight by measuring the similarity of images. Then, a new despeckling model is introduced by combining the regularization item and the data item of the AA model, and an iterative algorithm is proposed to solve the new model. The experiments show that, compared with the AA model, the proposed model has more effective performance in suppressing speckle; namely, ENL and DCV measures are 21.75% and 4.5% higher, respectively, than for NLM. Moreover, it also has better performance in keeping the edge information.
Qiao Ke; Sun Zeng-Guo; Yang Liu; Wei Wei; Marcin Woźniak; Rafał Scherer. High-Resolution SAR Image Despeckling Based on Nonlocal Means Filter and Modified AA Model. Security and Communication Networks 2020, 2020, 1 -8.
AMA StyleQiao Ke, Sun Zeng-Guo, Yang Liu, Wei Wei, Marcin Woźniak, Rafał Scherer. High-Resolution SAR Image Despeckling Based on Nonlocal Means Filter and Modified AA Model. Security and Communication Networks. 2020; 2020 ():1-8.
Chicago/Turabian StyleQiao Ke; Sun Zeng-Guo; Yang Liu; Wei Wei; Marcin Woźniak; Rafał Scherer. 2020. "High-Resolution SAR Image Despeckling Based on Nonlocal Means Filter and Modified AA Model." Security and Communication Networks 2020, no. : 1-8.
The significant development of classifiers has made object detection and classification by using neural networks more effective and more straightforward. Unfortunately, there are images where these operations are still difficult due to the overlap of objects or very blurred contours. An example is images obtained from various microscopes, where bacteria or other biological structures can merge, or even have different shapes. To this end, we propose a novel solution based on convolutional auto-encoders and additional two-dimensional image processing techniques to achieve better efficiency in the detection and classification of small objects in such images. In our research, we have included elements such as very weak contours of shapes that may result from the merging of biological objects. The presented method was compared with others, such as a faster recurrent convolutional neural network to indicate the advantages of the proposed solution.
Dawid Połap; Marcin Wozniak; Marcin Korytkowski; Rafał Scherer. Encoder-Decoder Based CNN Structure for Microscopic Image Identification. Algorithms and Data Structures 2020, 301 -312.
AMA StyleDawid Połap, Marcin Wozniak, Marcin Korytkowski, Rafał Scherer. Encoder-Decoder Based CNN Structure for Microscopic Image Identification. Algorithms and Data Structures. 2020; ():301-312.
Chicago/Turabian StyleDawid Połap; Marcin Wozniak; Marcin Korytkowski; Rafał Scherer. 2020. "Encoder-Decoder Based CNN Structure for Microscopic Image Identification." Algorithms and Data Structures , no. : 301-312.
The application of the traditional single frame character image super-resolution reconstruction method has some problems, such as noise can not be removed completely and anti-interference performance is poor. A new method for the super-resolution reconstruction of single frame character image based on wavelet neural network is proposed. The structure and interface of image acquisition unit of solid state image sensor are designed. Combined with pinhole imaging model and camera self-calibration, image acquisition of Internet of Things is completed. An image degradation model was established to simulate the degradation process of ideal high-resolution image to low-resolution image. Wavelet threshold denoising method is used to remove the noise in a single frame character image and improve the anti-interference performance of the method. The wavelet neural network reflection model is used to reconstruct the single frame feature image and improve the resolution of the image. The experimental results show that the blur degree of the reconstructed image is always less than 5%. In the whole experiment, the accuracy of this method can be maintained at 80% ~ 90%. The image detail retention rate of the research method is relatively stable. With the increase of the number of experimental images, the retention rate of image details remains between 80% and 95%, indicating that the method is effective in practical application.
Ling-Li Guo; Marcin Woźniak. An Image Super-Resolution Reconstruction Method with Single Frame Character Based on Wavelet Neural Network in Internet of Things. Mobile Networks and Applications 2020, 26, 390 -403.
AMA StyleLing-Li Guo, Marcin Woźniak. An Image Super-Resolution Reconstruction Method with Single Frame Character Based on Wavelet Neural Network in Internet of Things. Mobile Networks and Applications. 2020; 26 (1):390-403.
Chicago/Turabian StyleLing-Li Guo; Marcin Woźniak. 2020. "An Image Super-Resolution Reconstruction Method with Single Frame Character Based on Wavelet Neural Network in Internet of Things." Mobile Networks and Applications 26, no. 1: 390-403.
The streamflow of the upper reaches of the Yangtze River exhibits different timing and periodicity characteristics in different quarters and months of the year, which makes it difficult to predict. Existing sliding window-based methods usually use a fixed-size window, for which the window size selection is random, resulting in large errors. This paper proposes a dynamic sliding window method that reflects the different timing and periodicity characteristics of the streamflow in different months of the year. Multiple datasets of different months are generated using a dynamic window at first, then the long-short term memory (LSTM) is used to select the optimal window, and finally, the dataset of the optimal window size is used for verification. The proposed method was tested using the hydrological data of Zhutuo Hydrological Station (China). A comparison between the flow prediction data and the measured data shows that the prediction method based on a dynamic sliding window LSTM is more accurate by 8.63% and 3.85% than the prediction method based on fixed window LSTM and the dynamic sliding window back-propagation neural network, respectively. This method can be generally used for the time series data prediction with different periodic characteristics.
Limei Dong; Desheng Fang; Xi Wang; Wei Wei; Robertas Damaševičius; Rafał Scherer; Marcin Woźniak. Prediction of Streamflow Based on Dynamic Sliding Window LSTM. Water 2020, 12, 3032 .
AMA StyleLimei Dong, Desheng Fang, Xi Wang, Wei Wei, Robertas Damaševičius, Rafał Scherer, Marcin Woźniak. Prediction of Streamflow Based on Dynamic Sliding Window LSTM. Water. 2020; 12 (11):3032.
Chicago/Turabian StyleLimei Dong; Desheng Fang; Xi Wang; Wei Wei; Robertas Damaševičius; Rafał Scherer; Marcin Woźniak. 2020. "Prediction of Streamflow Based on Dynamic Sliding Window LSTM." Water 12, no. 11: 3032.
Many types of biomaterial analysis require numerous repetition of the same operations. We suggest applying the principles of Total Laboratory Automation (TLA) for analysis of dental tissues in in-vitro conditions. We propose an innovative robotic platform with ABB high precision industrial robotic arm. We programmed the robot to achieve 3000 cycles of submerging for analysis of the stability and thermal wear of dental adhesive materials. We address the problem of robot trajectory planning to achieve smooth and precise trajectory while minimizing jerk. We generate different variants of trajectory using natural cubic splines and adopt the NSGA II multiobjective evolutionary algorithm to find a Pareto-optimal set of robot arm trajectories. The results demonstrate the applicability of the developed robotic platform for in-vitro experiments with dental materials. The platform is suitable for small or medium size dental laboratories.
Robertas Damaševičius; Rytis Maskeliūnas; Gintautas Narvydas; Rūta Narbutaitė; Dawid Połap; Marcin Woźniak. Intelligent automation of dental material analysis using robotic arm with Jerk optimized trajectory. Journal of Ambient Intelligence and Humanized Computing 2020, 11, 6223 -6234.
AMA StyleRobertas Damaševičius, Rytis Maskeliūnas, Gintautas Narvydas, Rūta Narbutaitė, Dawid Połap, Marcin Woźniak. Intelligent automation of dental material analysis using robotic arm with Jerk optimized trajectory. Journal of Ambient Intelligence and Humanized Computing. 2020; 11 (12):6223-6234.
Chicago/Turabian StyleRobertas Damaševičius; Rytis Maskeliūnas; Gintautas Narvydas; Rūta Narbutaitė; Dawid Połap; Marcin Woźniak. 2020. "Intelligent automation of dental material analysis using robotic arm with Jerk optimized trajectory." Journal of Ambient Intelligence and Humanized Computing 11, no. 12: 6223-6234.