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IEEE Joint 22nd International Symposium on Computational Intelligence and Informatics / 8th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo)

作者： Takacs, Kristof Elek, Renata Nagyne Haidegger, Tamas Obuda Univ Antal Bejczy Ctr Intelligent Robot Budapest Hungary Obuda Univ Doctoral Sch Appl Informat & Appl Math Budapest Hungary Obuda Univ John von Neumann Fac Informat Budapest Hungary Obuda Univ Univ Res & Innovat Ctr EKIK Budapest Hungary Austrian Ctr Med Innovat & Technol Wiener Neustadt Austria

ISBN: (纸本)9798350398823

image processing techniques are having a huge impact on most fields of robotics and industrial automation. Real-time methods are usually employed in complex automation tasks, assisting with decision making or directly guiding robots and machinery, while post-processing is usually used for retrospective assessment of systems and processes. While artificial intelligence-based image processing algorithms (relying usually on neural networks) are more common nowadays, "classical" image processing methods can also be used effectively for most modern applications. This paper focuses on optical flow-based image processing, proving its efficiency by presenting optical flow-based solutions for modern challenges in different fields of robotics, such as robot-assisted surgery and food processing. The application domain introduced in this paper is based on a smart robotic gripper designed to support automated robot cells in the meat industry. The gripper is capable of slip detection and secure gripping of soft, slippery tissues with the help of the implemented real-time algorithm.

关键词： image processing Optical flow Slip detection Robotic gripping

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Detection and Analysis of Oil Spill using image processing

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND applications 2022年第4期13卷 381-387页

作者： Al-Battbootti, Myssar Jabbar Hammood Goga, Nicolae Marin, Iuliana Univ Politehn Bucharest UPB Fac Engn Foreign Languages Bucharest Romania

Thousands of oil spills occur every year on offshore oil production platforms. Moreover, ships that crosses rivers to reach the harbor cause spills each year. The current study focuses on IRAQI marine and rivers, especially Al-Bakr, Khor al-Amaya, ABOT oil terminal and SHAT AL-Arab river inside Al Ba??rah oil terminal. In order to mitigate and manage oil spill impacts, an unmanned aerial vehicle has proven to be a valuable tool in mitigating and managing incidents. To achieve high accuracy, the objective of the current research is to analyze captured images for rivers, identify oil pollution and determine its location. The images were taken from the Iraqi Regional Organization for the Protection, General Company for Ports of Iraq, Iraqi Ministry of Environment and online websites. In the current paper is presented a software framework for detecting oil spills, pollution in rivers and other kinds of garbage. The framework based on artificial intelligence is divided into two parts: a training model and an operational model. In the training model part, a machine learning model is applied, which is one of the fastest and most accurate methods, integrated inside PipelineMLML. Thus, the object detection technique used can identify one or more categories of objects in a picture or video. Furthermore, the locations of objects can be identified with the help of neural networks. In the operational mode, models can identify oil spills in images.

关键词： PipelineMLML oil spill artificial intelligence machine learning

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Spiking Generative Models Based on Variational Autoencoder and Adversarial Training

Spiking Generative Models Based on Variational Autoencoder a...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Wenchuan Zhang Jie Zhang Ricky Yuen-Tan Hou Weifeng Su Wentao Fan Department of Computer Science Guangdong Provincial/Zhuhai Key Laboratory IRADS Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China Department of Computer Science and Technology Huaqiao University Xiamen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep neural networks (DNNs) have demonstrated exceptional performance across a variety of applications, yet they require substantial computing and power resources. In contrast, Spiking neural networks (SNNs) offer significant potential for energy-efficient computing due to their binary, event-driven properties. However, existing deep generative SNNs often struggle to produce high-quality low-dimensional representations in latent space, adversely affecting the quality of their generated samples. To address this limitation, we introduce a novel generative model that integrates the principles of variational autoencoders (VAEs) with adversarial training techniques. Our model consists of a generative module based entirely on an SNN-structured VAE and a discriminator employing artificial neural networks. This discriminator enhances the training process of our SNN-based generative model by applying adversarial principles similar to those used in Generative Adversarial networks (GANs). Additionally, we have developed a conditional generation capability within our model, enabling the controlled production of specific images based on label inputs. Experimental evaluations on multiple datasets demonstrate that our model achieves superior image quality compared to existing top-performing SNN-based generative models. The source code of our model is accessible at https://***/zxj8806/SGM-VaGAN.

关键词： Training Adaptation models image synthesis Source coding Autoencoders Spiking neural networks Production Signal processing Real-time systems Speech processing

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DNCS: artificial Intelligence Enabled Food Recognition and Associated Calorie Level Estimation System Using Deep neural Classification Scheme

DNCS: Artificial Intelligence Enabled Food Recognition and A...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： A. P. Ramesh Ovss Prasad Venkat. S Jagadeeshwaran A P. Adi Lakshmi R. Singaravelu Department of Electronics Sri Ramakrishna College of Arts and Science Coimbatore Department of ECE QIS College of Engineering and Technology Ongole AndhraPradesh Department of ECE S.A. Engineering College Chennai Department of EEE Sona College of Technology Salem Department of CSE QIS College of Engineering and Technology Ongole AndhraPradesh Department of ECE Vel Tech Multi Tech Dr. Rangarajan Dr Sakunthala Engineering College Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The DNCS (Cascaded CNN-ANN) enabled Food Recognition (FR) and Associated Calorie Level Estimation System presents a novel approach to food classification and calorie estimation using a cascaded neural network architecture. The system combines Convolutional neural networks (CNN) for food image recognition with artificial neural networks (ANN) for calorie estimation, leveraging fine-tuned pre-trained models such as InceptionV3 and EfficientNet. The system demonstrated high FR accuracy across various datasets, achieving a peak accuracy of 96.2% for common food items like burgers and pizzas, with an overall accuracy range between 89.2% and 96.2%. For calorie estimation, the system achieved a Mean Absolute Error (MAE) of less than 6.1%, demonstrating reliability in predicting calorie content based on food portion size. The use of image segmentation techniques like graph-cut and GrabCut further enhanced the accuracy of FR. The system was tested for real-time applications, achieving an average processing time of 350 to 450 milliseconds per image, making it suitable for integration into mobile applications. User feedback highlighted ease of use, speed, and accuracy as key strengths, with an overall satisfaction rating of 4.7 out of 5. These results suggest that the DNCS system offers an efficient solution for dietary management through real-time calorie tracking.

关键词： Deep learning image segmentation Accuracy image recognition Estimation Computer architecture Real-time systems Mobile applications Convolutional neural networks Reliability

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Learning Efficient Hash Codes for Fast Graph-Based Data Similarity Retrieval

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IEEE TRANSACTIONS ON image processing 2021年 30卷 6321-6334页

作者： Wang, Jinbao Xu, Shuo Zheng, Feng Lu, Ke Song, Jingkuan Shao, Ling Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Anhui Univ Sch Elect & Informat Engn Hefei 230039 Peoples R China Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

Traditional operations, e.g. graph edit distance (GED), are no longer suitable for processing the massive quantities of graph-structured data now available, due to their irregular structures and high computational complexities. With the advent of graph neural networks (GNNs), the problems of graph representation and graph similarity search have drawn particular attention in the field of computer vision. However, GNNs have been less studied for efficient and fast retrieval after graph representation. To represent graph-based data, and maintain fast retrieval while doing so, we introduce an efficient hash model with graph neural networks (HGNN) for a newly designed task (i.e. fast graph-based data retrieval). Due to its flexibility, HGNN can be implemented in both an unsupervised and supervised manner. Specifically, by adopting a graph neural network and hash learning algorithms, HGNN can effectively learn a similarity-preserving graph representation and compute pair-wise similarity or provide classification via low-dimensional compact hash codes. To the best of our knowledge, our model is the first to address graph hashing representation in the Hamming space. Our experimental results reach comparable prediction accuracy to full-precision methods and can even outperform traditional models in some cases. In real-world applications, using hash codes can greatly benefit systems with smaller memory capacities and accelerate the retrieval speed of graph-structured data. Hence, we believe the proposed HGNN has great potential in further research.

关键词： Task analysis Measurement Computational modeling Binary codes Graph neural networks Data models Training Graph representation graph neural networks hash codes similarity retrieval

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On the Robustness of Digital Modulation Recognition for Cooperative Relaying networks under Imperfect CSI 7

On the Robustness of Digital Modulation Recognition for Coop...

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7th International Conference on image and Signal processing and their applications, ISPA 2022

作者： Tayakout, Hakim Boutellaa, Elhocine Bouchibane, Fatma Zohra Centre de Développement des Technologies Avancées Division Télécommunication Algiers Algeria

ISBN: (纸本)9781665480420

Cognitive radio (CR) technology offers the ability to adapt the transmission or reception parameters, thus efficiently improving the wireless spectrum resources utilization. As one of the important components of CR, automatic modulation recognition (AMR) aims to improve the overall communication performance besides promoting efficient and secure transmissions. In this paper, we tackle the semi blind modulation recognition problem in a distributed space-time block coding (D-STBC) scheme for a cooperative relaying network of three nodes, employing the Amplify-and-Forward (AaF) protocol. As classifier, we design a multi-layer artificial neural network (ANN) and train it using higher-order statistics (HOS) as features of the received signals. We evaluate two D- STBC configurations identifying the modulation with and without channel state information (CSI) by exploiting both the temporal and spatial signal dimensions. Experimental results demonstrate that the proposed identifier ensures a good probability for achieving correct modulation identification in acceptable signal-to-noise ratio (SNR) ranges. © 2022 IEEE.

关键词： Cognitive radio

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Deep Learning-Based Approaches for Accurate Brain Tumor Detection in MRI images

Deep Learning-Based Approaches for Accurate Brain Tumor Dete...

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2024 International Conference on Decision Aid Sciences and applications, DASA 2024

作者： Noori, Mohammed Ahmed, Mohsin Ibrahim, Amer Saeed, Ali Khishe, Mohammad Mahfuri, Mahmoud Al-ma'Moon University College Baghdad Iraq College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates Northern Technical University Computer Center Ninevah Iraq Yuan Ze University Innovation Center for Artificial Intelligence Applications Taiwan Jadara Research Center Jadara University Jordan International University for Science and Technology Computer and Information Technology Department Syria

ISBN: (纸本)9798350369106

Brain tumor classification is considered one of the major tasks in medical image analysis, where correct and timely diagnosis could be achieved to serve as the key to effective treatment. This research paper proposes a deep learning-based approach for automatic classification of brain tumors from MRI images by fine tuning the ResNet50V2 CNN model. The dataset is made up of four classes, namely: Glioma Tumor, Meningioma Tumor, No Tumor, and Pituitary Tumor, totaling 2,844 images. We have done some strategy data augmentation and class weighting in order to prevent class imbalance, which ensures good performance in all tumor classes. Pre-processing and augmentation of the input images are done via rotation, shifting, zooming, and flipping. It yielded 95.29% on the validation set, with the major highlights of performance metrics for the minority class being class precision equal to 0.97 and class recall equal to 0.92, hence proving the efficiency of performed class balancing. The range of F1-scores, from 0.93 to 0.97, means fantastic predictive capability across all tumor types. Other techniques were also used to make this model optimal, such as early stopping, learning rate reduction, and checkpointing. The high performance of this model shows great promise in helping clinicians toward the right diagnosis of brain tumors in an effective way. Future efforts will involve incorporating larger datasets, exploring advanced augmentation techniques to enhance further model generalizability. Model explainability tools such as Grad-CAM will be used to extract insight into the model's decision-making process. This enhances the clinical interpretability of the results. © 2024 IEEE.

关键词： Convolutional neural networks

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Blind detection of glow-based facial forgery

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MULTIMEDIA TOOLS AND applications 2021年第5期80卷 7687-7710页

作者： Guo, Zhiqing Hu, Lipin Xia, Ming Yang, Gaobo Hunan Univ Sch Informat Sci & Engn Changsha 410082 Peoples R China Southwest Minzu Univ Coll Elect & Informat Engn Chengdu 610041 Peoples R China

With the rapid development of artificial intelligence technologies, various generative models can synthesize fake face images with photo-realistic effects. Glow, a generative flow using invertible 1x1 convolution, is a state-of-the-art technique for efficient synthesis of face images with high resolution and fidelity. However, facial forgeries bring serious challenges to morality, ethics and public confidence. Especially, facial forgeries might change the semantic content conveyed by a face image. A Convolutional neural Network (CNN) based model, namely SCnet, is proposed to expose the Glow-based facial forgery. Specifically, an image sharpening operator is embedded in the convolutional layer as the pre-processing layer of the network to highlight the traces left by Glow. Then, SCnet is specifically designed to automatically learn high-level forensics features from pre-processing results. Moreover, a fake face dataset is built by exploiting the CelebA face image dataset and the Glow-based forgery technique. A series of experiments are conducted to prove the effectiveness of the proposed approach. Experimental results show that the proposed approach achieves a classification accuracy up to 95.92% under various post-processing operations.

关键词： Convolutional neural networks Fake face datasets Facial forgery Passive image forensics

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FPGA-based implementation of deep neural network using stochastic computing

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APPLIED SOFT COMPUTING 2023年 137卷

作者： Nobari, Maedeh Jahanirad, Hadi Univ Kurdistan Dept Elect & Commun Engn Kurdistan Iran

A serious challenge in artificial real-time applications is the hardware implementation of deep neural networks (DNN). Among various methods, stochastic computing (SC)-based implementations received tremendous attention due to the low hardware overhead. However, the slow convergence rate is a major problem in SC-based neural networks' implementation, and millions of clock cycles are required to generate a relatively accurate output. The reconfigurability and parallel nature of field programmable gate array (FPGA) chips make them a preferable platform for SC-based DNN implementation. A fully or semi-parallel implementation of DNNs requires extensive hardware resources. In this paper, an efficient method for DNN implementation on an FPGA chip is presented to address these problems. The FPGA chip reconfiguration feature allows a DNN with several different neurons and topologies to be implemented on a single chip. Convergence time is significantly reduced by limiting the length of the stochastic bitstreams and establishing synchronization between the processing units based on precise timing. Furthermore, due to the limited number of input-output pins in the FPGA chip, a sequential architecture is proposed wherein the DNN inputs enter through only three 8-bit ports. This makes it possible to implement DNNs for image-processing applications. The proposed method is implemented using the Verilog hardware description language on the Xilinx FPGA Virtex-7 xc7v2000t chip. The results show a more than 82% reduction in hardware resources and the minimum rate of power consumption compared to state-of-the-art methods. In addition, the average error rate of the implemented DNN is reduced by 2%.(c) 2023 Elsevier B.V. All rights reserved.

关键词： artificial neural network (ANN) Multi -layer perceptron (MLP) Stochastic computing Probability estimator (PE) Field programmable gate array (FPGA)

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Quality Evaluation of Dried Carrot Obtained in Different Drying Conditions Using Deep Convolutional neural networks 14

Quality Evaluation of Dried Carrot Obtained in Different Dry...

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14th International Conference on Digital image processing, ICDIP 2022

作者： Koszela, Krzysztof Adamski, Franciszek Szychta, Marek Przybyl, Krzysztof Gierz, Lukasz Department of Biosystems Engineering Poznan University of Life Sciences Wojska Polskiego 50 Poznan60-625 Poland Department of Dairy and Process Engineering Faculty of Food Sciences and Nutrition Poznań University of Life Sciences Wojska Polskiego 31 Poznan60-624 Poland Lukasiewicz Research Network Poznań Institute of Technology Starolecka 31 Poznan60-963 Poland Institute of Machine Design Faculty of Mechanical Engineering Poznan University of Technology Piotrowo 3 Poznan60-965 Poland

ISBN: (纸本)9781510657564

In recent years one can observe a continuous growth of demand for dried vegetables. This tendency has an impact on the development of dehydrated food market segment in Poland, which enables to manage a surplus of vegetable production. More and more often dried vegetables are used in various sectors of food industry, both because of their high nutritional values and because of the changing nutritional habits among customers. Among dried vegetables, dried carrots seem to play a strategic role on account of the fact that this produce has a wide spectrum of applications and is famous for its high nutritional value. The research was conducted in order to evaluate the quality of dried carrot cubes using three different techniques of drying. It should be noted that during the research both correct and incorrect dried carrots were used. What is more, the process of deep learning of convolutional artificial neural networks was carried out with MobileNet architecture for classification, for a selected research sample. The classification included both the type of drying process and the quality of drying for binary division on account of the applied parameters. The obtained models were characterized by high capability to classify samples at the level of 85 - 100% with the exception of lyophilized dried carrots where the trained network reached the effectiveness of classification at the level of 71% for the validation set. The research proved that fast and noninvasive evaluation of the quality of dried carrot cubes in different conditions is possible and highly effective using artificial neural networks. © 2022 SPIE.

关键词： Vegetables

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