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IEEE International Symposium on Circuits and systems (ISCAS)

作者： R. de la Rosa-vidal J. A. Leñero-Bardallo F. J. Garrido-Flores Á. Rodríguez-vázquez J. Bernabéu-Wittel Instituto de Microelectrónica de Sevilla IMSE-CNM (CSIC Universidad de Sevilla) and Virgen del Rocío Hospital Seville Spain

Thermography imaging has great potential for the diagnosis and follow-up of clinical entities that alter body temperature locally [1]. Infrared (IR) cameras have dropped in price during the last ten years. Nowadays, they are compatible with clinical use because they provide immediate results, do not require physical contact with the patient, and their images are easy to interpret. In particular, vascular anomalies constitute a vast family of clinical entities that alter body temperature and can exploit this emerging imaging modality [2]. Many IR systems devoted to the medical field are available in the market. However, to the best of our knowledge, there is a lack of customizable systems easily adaptable to particular medical application scenarios by incorporating different peripherals or sensors and embedding custom image processing algorithms. Commercial IR systems are limited to image acquisition.

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Efficient Framework for Diagnosis of Acute Lymphoblastic Leukemia Using Random Forest Classifier

Efficient Framework for Diagnosis of Acute Lymphoblastic Leu...

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Conference on Modeling of systems Circuits and Devices (MOS-AK India)

作者： Ramya v Karthikeyan B Kalluri Srinath Reddy Jayashree R Harikrishna M Department of ECE MVJ College of Engineering Bengaluru India

ISBN: (数字)9798350395556

ISBN: (纸本)9798350395563

The aberrant growth of white blood cells in the blood and myeloid tissue is the hallmark of leukaemia, which pathologists find it by observing at a blood smear viewed through a microscope. To identify and define leukaemia, pathologists look at the quality of the cells and their physical properties. Microscopic image analysis is crucial for the early leukaemia screening and accurate diagnosis, because current approaches depend on manual inspection, which is tedious and strongly reliant on province specialist's knowledge. Computerized leukaemia diagnosis reveals new possibilities for the eliminating human intrusion while also giving better accurate objective data. This paper provides a framework for automatic identification of acute lymphoblastic leukaemia from outermost blood smear pictures predicted on traditional digital image processing techniques and Machine Learning algorithms. This method of automated leukaemia identification was found to be more effective, fast and precise.

关键词： White blood cells Technological innovation image analysis Accuracy Machine learning algorithms Microscopy Process control

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Crop Selection and Yield Prediction using Machine Learning algorithms

Crop Selection and Yield Prediction using Machine Learning A...

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Augmented Intelligence and Sustainable systems (ICAISS), International Conference on

作者： J. Ashok Kumar N. Parimala R. Pitchai B V Raju Institute of Technology Narsapur India

Agriculture monitoring, particularly in developing nations, can assist prevent famine and aid human efforts. Estimating crop yields before harvest, often known as yield estimation, is difficult. Our technique predicts agricultural yields using publicly available remote sensing data in a scalable manner. Accurate, and economical manner. This strategy might assist all farmers use the least amount of fertilizer feasible, thereby maintaining soil health, while also giving them with the option to make the greatest money from the same piece of land through government-established soil health centers. As a result, it would be a win-win situation for all parties involved. This can be accomplished using machine learning algorithm KNN and image processing. Based on the results of the prediction analysis, machine learning algorithms are utilized to anticipate the best crop as well as the related bio-fertilizer. image processing could be utilized to further improve automated drones or tractors because it generates the shortest number of turns through the field. A predictive study is performed to recommend the top three most suitable crops based on soil nutrient levels, temperature, and possible revenue that this crop could generate. The outcomes can be applied in two ways. The first option is the automatic method, in which the farmer just selects their area and a suitable crop is selected based on prior experiments completed nearby. Second, you can manually enter soil information and get a crop recommendation based on the value you enter.

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Fast guided filter for power-efficient real-time 1080p streaming video processing

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JOURNAL OF REAL-TIME image processing 2020年第3期17卷 511-525页

作者： El Mezeni, Dragomir Saranovac, Lazar Univ Belgrade Sch Elect Engn Blvd Kralja Aleksandra 73 Belgrade Serbia

With the advent of embedded vision systems, smart sensors with integrated image signal processing (ISP) become a hot topic. This poses a need for efficient hardware implementation, regarding resource utilization and power consumption, of core image processing algorithms. Power consumption is especially important, since many of the target devices are usually battery operated. Edge-aware filtering, although it is used in many core image processing algorithms, is still challenging operation, especially in cases where large kernels are needed. In this paper, efficient hardware realization of fast guided filter (FGF) is proposed. It is based on idea that large filter of size R=K.S can be calculated by downsampling input image by factor S and using filter of size K. Besides reduced memory and logic requirements, this optimization enables that, for the scaling factor S, core processing is done at 1/S2 pixel clock, providing significantly lower power consumption. Experimental results on Cyclone v FPGA chip demonstrate that, for FGF of size 35x35 with downsampling factor S=7 the proposed design achieves 60 fps for 1080p video. Memory utilization is 147.3 kB without need for any off-chip memory. Core dynamic power consumption is 79.89 mW. Proposed design consumes less total power than state-of-the-art guided filter realizations including ASIC-based solutions. This module can be seamlessly integrated into smart sensors ISP units, because it is designed for power-efficient streaming processing.

关键词： Guided image filtering Real-time video processing Power-efficient video processing

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Development of a Phasogrammetric Measurement System for Error Estimation in Close-Range Photogrammetry

Development of a Phasogrammetric Measurement System for Erro...

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Wave Electronics and its Application in Information and Telecommunication systems (WECONF)

作者： A. Yu. Poroykov N. Yu. Sivov E. v. Shmatko Department of Physics Named After V.A. Fabrikant National Research University "MPEI" Moscow Russia

In aeronautics, engineering, medicine, robotics and other industries, optical methods are widely used to measure the geometry and surface deformation of various objects from their images. These methods are based on digital image processing algorithms and can be summarized in a group of methods called close-range photogrammetry. When developing photogrammetric measurement systems, one of the problems to be solved is the estimation of their error. In this work, it is proposed to use physical simulation to estimate the error, which gives more reliable results than theoretical calculations or computer simulation. The phasogrammetric method has been chosen as the reference method for surface shape measurement. This paper presents the results of the development of an optical phasogrammetric measurement system based on the structured illumination method and an evaluation of its accuracy characteristics.

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Prediction of Herbs with its Benefits using Deep Learning Techniques

Prediction of Herbs with its Benefits using Deep Learning Te...

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2022 International Conference on Sustainable Computing and Data Communication systems, ICSCDS 2022

作者： Begum, M. Sharmila Haris, R. vetrimaran, v. Raj, P. Ashwin B.Tech Computer Science and Engineering Pmist Tamil Nadu Vallam Thanjavur India

ISBN: (纸本)9781665478847

Automated plant identification is a very promising solution for bridging the taxonomic gap, which is receiving much attention from botany and computer science. As machine learning technology advances, more complex models have been proposed to automate crop identification. Herbal remedies are considered in the pharmaceutical industry due to fewer harmful side effects and less expensive than modern medicine. Based on these data, many researchers have shown great interest in studying the recognition of natural herbal medicines. There are various possibilities for moving towards solid phase production capable of accurately discriminating medicinal plants in real time. In this project, efficient and reliable machine learning algorithms for plant catalogues using leaf images used in recent years are being studied. The review covers image processing techniques used to locate leaves and extract important leaf features from other machine learning steps. These deep learning stages are classified according to their function when it comes to discriminating leaf images based on common plant characteristics, i.e. shapes, ridges, textures and combinations of many elements. Then you get results using herbs with improved accuracy. The test results indicate that the proposed system provides an improved level of accuracy. © 2022 IEEE.

关键词： Deep neural networks

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BISHENG: A 49.38PPM Flexible Multi-Function Printer SoC in 28nm CMOS

BISHENG: A 49.38PPM Flexible Multi-Function Printer SoC in 2...

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IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)

作者： Dongliang Xiong Xiaoxu Zhang Siwen Xiu Zhili Liu Dandan Zheng Li Ding Aiguo Yin Peiyong Zhang Kai Huang Zhejiang University Zhejiang Gongshang University China Jiliang University Geehy Semiconductor Pantum Electronics

ISBN: (数字)9798331530723

ISBN: (纸本)9798331530730

Innovative AI functions and imaging algorithms are important for multi-function printers (MFP) to provide good user experience. However, prior printer system on chips (SoC) using hardwired hardware or customized processors have limited flexibility and programmability. This paper proposes a flexible MFP SoC called BISHENG, which uses customized RISC-v processors and a hybrid interconnect with sequential pipeline and partial crossbar. The chip was fabricated in 28 nm CMOS. Experiment result shows that it can achieves 49.38 pages per minute (PPM) in prototype printer with a typical power 2.55 W. Keywords-System on Chip, Multi-Function Printer, Document image processing, On-Chip Network

关键词： Program processors image color analysis Pipelines Integrated circuit interconnections Imaging Prototypes User experience Hybrid power systems System-on-chip Printers

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A MobileUnet++-based Abnormality Segmentation and Multi-scale Network Approach for Brain Tumor Classification

A MobileUnet++-based Abnormality Segmentation and Multi-scal...

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Intelligent algorithms for Computational Intelligence systems (IACIS), International Conference on

作者： veesam Pavan Kumar Satya Ranjan Pattanaik v Sunil Kumar Dept of Computer Science and Engineering Biju Patnaik University of Technology(BPUT) Rourkela India Dept of Computer Science and Engineering Gandhi Institute for Technology(GIFT) Bhubaneswar India Dept of Computer Science and Engineering PBR Visvodaya Institute of Technology & Science Kavali India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Tumors are a pervasive concern in modern life, driven by cellular irregularities that disrupt the orderly division necessary for healthy cell growth. However, brain tumors present unique challenges compared to tumors in other regions due to the intricacies of brain structure and function. A brain tumor occurs when cells in the cerebral cortex undergo uncontrolled proliferation. Diagnosing brain tumors, especially in their early stages is a formidable task that often relies on manual examination of medical images by radiologists or healthcare professionals. Despite various approaches to brain tumor classification, precision remains paramount while ensuring practical utility in segmentation and classification tasks. Traditional methods are often manual, time-consuming and lacking in automated classification capabilities, hampering efficient decision-making. The advent of deep learning technology has revolutionized medical image processing, particularly in the realm of brain tumor analysis. Deep learning algorithms, powered by vast datasets and computational prowess, offer remarkable capabilities in automating segmentation and classification tasks, thereby enhancing accuracy and efficiency in diagnosing brain tumors. Consequently, this study introduces a method for smart brain tumor segmentation and classification leveraging deep learning techniques. The process begins with acquiring essential images through established benchmarking techniques. These images are then processed in the abnormality segmentation phase, utilizing Mobile-Unet++ (MUnet++) for segmentation. Following segmentation, the images undergo a classification phase, where brain tumors are classified using Multi-scale Mobilenet (MMNet). To validate effectiveness of proposed technique, an efficacy study is conducted, comparing it against traditional methodologies for segmentation and classification of brain.

关键词： Deep learning image segmentation Accuracy Decision making Manuals Medical services Brain modeling Oncology Medical diagnostic imaging Diseases

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A Light Weight Approach for Real-time Background Subtraction in Camera Surveillance systems

A Light Weight Approach for Real-time Background Subtraction...

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International image processing, Applications and systems Conference (IPAS)

作者： Ege Ince Sevdenur Kutuk Rayan Abri Sara Abri Salih Cetin Mavinci Informatics Inc. Ankara Turkey

ISBN: (纸本)9781665462204

Real time processing in the context of image processing for topics like motion detection and suspicious object detection requires processing the background more times. In this field, background subtraction solutions can overcome the limitations caused by real time issues. Different methods of background subtraction have been investigated for this goal. Although more background subtraction methods provide the required efficiency, they do not make produce a real-time solution in a camera surveillance environment. In this paper, we propose a model for background subtraction using four different traditional algorithms; viBe, Mixture of Gaussian v2 (MOG2), Two Points, and Pixel Based Adaptive Segmenter (PBAS). The presented model is a lightweight real time architecture for surveillance cameras. In this model, the dynamic programming logic is used during preprocessing of the frames. The CDnet 2014 data set is used to assess the model's accuracy, and the findings show that it is more accurate than the traditional methods whose combinations are suggested in the paper in terms of Frames per second (fps), F1 score, and Intersection over union (IoU) values by 61.31, 0.552, and 0.430 correspondingly.

关键词： Adaptation models Surveillance image processing Heuristic algorithms Object detection Programming Cameras

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Development of an Intelligent Robotic System for Recognizing and Transporting Postal Objects

Development of an Intelligent Robotic System for Recognizing...

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IEEE Conference on Quality Management, Transport and Information Security, Information Technologies (IT&MQ&IS)

作者： Mariya v. Khokhlova Igor v. Barsuk Liliya I. voronova Nikita v. Belov Department of Intelligent Control Systems and Automation Moscow Technical University of Communications and Informatics Moscow Russia

Now computer science methods are actively used in different areas of Industry 4.0., including in the postal service to automate the transportation of postal items. The problem of transportation is particularly acute for postal objects of large volumes and weights, as it is performed manually and is one of the most time-consuming operations. The article describes the development of a prototype intelligent robotic system with computer vision to recognize and transport such postal objects (parcels). An analysis of modern scientific and practical developments, methods and means of image processing, including methods of contour analysis, and video stream processing algorithms is carried out. The stages of design and integration of the computer vision subsystem into the robotic complex are described.

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