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Dark-Field Imaging on a Clinical CT System: Sample Data processing and Reconstruction 7

Dark-Field Imaging on a Clinical CT System: Sample Data Proc...

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7th International conference on Image Formation in X-Ray Computed Tomography

作者： Haeusele, Jakob Schmid, Clemens viermetz, Manuel Gustschin, Nikolai Lasser, Tobias Bergner, Frank Koehler, Thomas Pfeiffer, Franz Biomedical Physics Department of Physics Technical University of Munich Garching85748 Germany Munich Institute of Biomedical Engineering Technical University of Munich Garching85748 Germany Computational Imaging and Inverse Problems Department of Informatics Technical University of Munich Garching85748 Germany Philips Research Hamburg22335 Germany Institute for Advanced Study Technical University of Munich Garching85748 Germany Department of Diagnostic and Interventional Radiology School of Medicine and Klinikum rechts der Isar Technical University of Munich München81675 Germany

ISBN: (纸本)9781510656697

Grating-based phase-contrast and dark-field X-ray imaging is a promising technology for improving the diagnosis and imaging capabilities of breast cancer and lung diseases. While traditional X-ray techniques only consider the attenuation coefficient, phase-contrast and dark-field imaging are also capable of measuring the refractive index decrement and the so-called linear diffusion coefficient, a measure of a sample's small-angle scattering strength. Consequently, the technique provides additional information about the micro-structure of a sample. While it is already possible to perform human chest dark-field radiography, it is assumed that its diagnostic value increases when performed in a tomographic setup. The thereby acquired three-dimensional mappings of the three modalities yield detailed information about morphological changes without being obscured by overlaying structures. This work presents the sample data processing and reconstruction pipeline of the first human-sized clinical dark-field CT system. In this novel setting we require a processing concept which is (1) compatible with continuous rotation, (2) can compensate for perturbances induced by system vibrations, and (3) still enables short processing and reconstruction times. An advanced sliding window approach was chosen for the sample data extraction to meet requirements (1) and (3). Furthermore, we present the corrective measures that have to be applied in the employed processing and reconstruction algorithms to mitigate the effects of vibrations and deformations of the interferometer gratings. The developed techniques are shown to successfully reduce the emergence of artefacts in the reconstructed images. © 2022 SPIE.

关键词： Image reconstruction

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Artificial Intelligence-Powered Internet of Things for Farming and Plant Research

Artificial Intelligence-Powered Internet of Things for Farmi...

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Electronics and Renewable systems (ICEARS), International conference on

作者： A. Padma P. Suresh G. Narsimlu M Amarnath B. R Sreedhar Department of Mathematics Chaitanya Bharathi Institute of Technology

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The wearable fiber optic sensors for plants to monitor microenvironment and growth wearable fiber optic plant sensors for microclimate and growth monitoring. The plant agricultural sectors are facing challenges because to population expansion and climate ***-powered computer vision-based plant growth monitoring system the goal of this research project is to use computer vision to create an AI-based plant growth monitoring system. The technology will allow for automatic and real-time evaluation of plant growth and health by utilizing computer vision algorithms and artificial intelligence approaches. Conventional techniques for tracking plant growth require a lot of work and time. By employing image processing techniques to analyze plant photos and extract pertinent features leaf area, plant height, and width, the suggested solution will overcome these drawbacks. Using the collected information, machine learning methods will be used to train models that can identify patterns and forecast plant development. The system will be made to adapt to various plant species and environmental circumstances. This AI-based plant growth monitoring system will help farmers, researchers, and plant enthusiasts by offering precise and timely insights into plant health and growth. This will allow them to make well-informed decisions about plant care and optimize growth conditions. By providing an economical, scalable, and effective system with numerous applications in horticulture, agriculture, and environmental research, this research project seeks to transform plant growth monitoring.

关键词： Temperature sensors optical fiber sensors Computer vision Plants (biology) Feature extraction Internet of Things Biomedical monitoring Monitoring Intelligent sensors Wearable sensors

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Advanced AI Techniques for Grid-Connected Renewable Energy systems

Advanced AI Techniques for Grid-Connected Renewable Energy S...

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International conference on information Science and Communications Technologies (ICISCT)

作者： Laith H. Fezaa Ahmed Alkhayyat J.S. Chohan Raj Kumar Jatinder Kumar Nilesh Bhosle Department of Optical Techniques Al-Zahrawi University College Karbala Iraq College of Technical Engineering The Islamic University Najaf Iraq School of Mechanical Engineering Rayat Bahra University Mohali India Dept. of CSE Chandigarh University Mohali Punjab India Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India NIMS School of Computing Science and Artificial Intelligence NIMS University Jaipur Rajasthan India

ISBN: (数字)9798331527921

ISBN: (纸本)9798331527938

This research paper explores the ability to integrate innovative AI methods to optimise grid-integrated renewable energy systems. Consequently, this study assesses the benefits of energy demand forecasting, fault detection, and real-time energy management using Long Short Term Memory LSTM networks, SVM and RL models focusing on three broad areas. The LSTM model performed well in the energy demand forecast with low MAE and RMSE than other models while the SVM model performed well in fault detection with a 92% accuracy. LBC revealed that the RL-based system contributed to the reduction of energy cost while regulating the grid stability through optimizing energy in different demand situations. This paper also presents several key issues associated with data quality, model generalization, computational overhead, and interfaces to extant systems. This emphasizes the gaps on the effectiveness of data acquisition techniques, flexibility of AI mod- els and robustness of the algorithms to perform within resource constraints. There are some issues arise while integrating new AI technologies with existing traditional systems which must be considered and standardized. Possible future works are provided to overcome these issues, which include the refinement of pre processing techniques, flexi- bility of models, and solution extensibility. The studies highlight multiple possibilities of using AI in the enhancement of energy systems and their managing factor, decrease of operation costs, as well as increase of the robustness of grids, opening the way to further development of the more effective and stable energy managing solutions.

关键词： Support vector machines Renewable energy sources Energy consumption Accuracy Costs Computational modeling Predictive models Data models Artificial intelligence Long short term memory

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Vehicle Registration Plate Recognition Using YoloV8 and IOT

Vehicle Registration Plate Recognition Using YoloV8 and IOT

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AIML-Applications for Engineering & Technology (ICAET), International conference on

作者： Juber Nadaf Amol Kadam Pramod Jadhav Prasad Kadam vinod Patil Milind Gaikwad Department of Computer Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of CSBS Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of E & TC Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of Information Technology Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Number Plate recognition or the vehicle License Plate recognition is an interesting topic of research in smart cities which utilizes Image processing techniques. Since the number of cars is rising at an exponential rate, automated systems are supposed to store vehicle data for a variety of applications. In this intended algorithm, an efficient technique has been identified for recognition of different vehicle number registration plates. Through this technique, we can easily deal with problems like low light, irregular font number plates, wrong angles. We use the method of optical Character Recognition to identify characters that are extracted from the registration plate. Even though this project works efficiently for common use cases, it has its own drawbacks and limitations even though we are not using deep learning or any other machine learning algorithms. One of the important steps in vehicle plate recognition is the character recognition which is a technique to obtain individual text characters. YOLOv8 (You Only Look Once), and Darknet- 53 are the methods that is used to extract the features of the data. In this model, the input data is the image of the vehicle that is captured from the camera sensors or cell phones and later cropped to display only the registration plate. We undergo data-pre-processing to improve the quality or the pixels of image on the number plate. Testing is done using two different models one is the model which is obtained with more pre-processing data and the other obtained without any pre-processing data. We evaluate two datasets of number plate images: one with no interference and one where the colour intensity of the image has been decreased to test, the accuracy of the model

关键词： Deep learning YOLO Training Image recognition Accuracy Feature extraction Data models Internet of Things Character recognition License plate recognition

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Fourier ptychographic microscopy based on Gaussian apodization coherent transfer function constraints 3

Fourier ptychographic microscopy based on Gaussian apodizati...

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3rd International Computing Imaging conference, CITA 2023

作者： Cheng, Hong Li, Ju Liu, Hong Zhang, Hongyi Li, Yang School of Electronic and Information Engineering Anhui University Anhui Hefei230601 China Key Laboratory of Intelligent Computing & Signal Processing Anhui University Anhui Hefei230601 China

ISBN: (纸本)9781510671409

Fourier ptychographic microscopy technique combines the concept of phase retrieval algorithm and synthetic aperture, which can solve the problem that large field of view and high resolution cannot coexist. However, describing the linear transfer process of the imaging system by coherent transfer function in conventional calculations leads to ringing artifacts. Gaussian apodization coherent transfer function constraints is proposed and embedded into the software platform, and a high-resolution Fourier ptychographic microscopy imaging system is constructed together with the designed hardware. The proposed algorithm can reduce the ringing artifacts and ensure the accuracy and resolution of the reconstructed results by simulating experiments and acquiring real images. © 2023 SPIE.

关键词： Imaging systems

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Symbol Synchronizers in High-Speed Adaptive Receivers of optical and Radio Frequency Range

Symbol Synchronizers in High-Speed Adaptive Receivers of Opt...

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2021 systems of Signals Generating and processing in the Field of on Board Communications, SOSG 2021

作者： Kazakov, G.N. Shevtsov, V.A. Moscow Aviation Institute National Research University Moscow Russia

ISBN: (纸本)9780738130897

When developing modern high-speed receivers, the problem of designing high-speed symbol synchronization systems is quite urgent. Usually these are open-loop systems, built according to the Feedforward algorithm without using additional synchronizing pulses (blind receiver). In this paper, a structural diagram of such an adaptive device for symbol synchronization is drawn up, algorithms for the functioning of its main blocks are selected and described: the block for determining the symbol frequency, the block for estimating and correcting the temporal position of the sample and the decimator. Simulation of signal processing in the digital part of a high-speed optical signal receiver with an information transmission rate of 100 Gbit/s, using DP-QPSK modulation and coding of each transmitted frame in accordance with the OTH hierarchy using standard FECs with different redundancy. Simulation confirmed the correct operation of the synchronization device and made it possible to obtain numerical indicators of the normalized synchronization error depending on the bit signal-to-noise ratio and the number of samples used. Based on the simulation results, it has been proven that the investigated symbol synchronization device retains its operability up to the signal-to-noise ratio of 10 dB. This is sufficient to provide a bit error rate of less than 10-3 even when using low position digital modulation without applying error correction coding. © 2021 IEEE.

关键词： Signal to noise ratio

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A Hybrid-Layered Framework for Detection and Diagnosis of Alzheimer’s Disease (AD) from Fundus Images

A Hybrid-Layered Framework for Detection and Diagnosis of Al...

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Artificial Intelligence and Smart systems (ICAIS), International conference on

作者： V. Srilakshmi Anupama Anumolu M. Aruna Safali Vallabhaneni Siva Parvathi Department of CSE DVR & Dr.HS MIC College of Technology Vijayawada AP India Department of Mathamatics DVR & Dr.HS MIC College of Technology Vijayawada AP India Department of CSE Dhanekula Institute of Engineering and Technology Vijayawada AP India Department of Information Technology Prasad V. Potluri Siddhartha Institute of Technology Vijayawada AP India

Alzheimer’s disease (AD) is the most common disease that can cause a brain disorder in a human aged above 65. Detecting and diagnosing AD becomes a more complicated and complex task by using various manual processes. DL and ML algorithms are most widely used to analyze the complex features from the medical data used to detect AD from various samples. Several types of sample formats are used to detect AD. This paper mainly focused on detecting the AD from the retinal fundus images. Analyzing the early symptoms of AD can prevent the patient’s life from permanent eye loss. ML algorithms are having various drawbacks that use complex computations and more computation time for the processing of data. The AD prediction is done by using the fundus color images collected from the Kaggle dataset. ML follows various steps to complete the task such as training, pre-processing and algorithm implementation. In the existing approaches, a limited number of parameters are used. Another disadvantage of the traditional algorithms shows the low accuracy and unmatched results. This paper introduced the hybrid-layered framework is developed to detect the AD from the fundus images dataset. Several performance metrics such as precision, recall, F1-score, and accuracy are used to show the results.

关键词： Training Measurement Manuals Learning (artificial intelligence) Prediction algorithms Retina optical imaging

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visual SLAM for Dynamic Environments Based on Static Key-Points Detection

Visual SLAM for Dynamic Environments Based on Static Key-Poi...

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virtual Reality (ICVR), IEEE International conference on

作者： Liangtao Zhong Xiaoqiang Zhang Lingyan Ran Yamin Han Hongyu Chu School of Information Engineering Southwest University of Science and Technology Mianyang China School of Computer Science Northwestern Polytechnical University Xi’an China College of Information Engineering Northwest A&F University Xianyang China

Recently, the technique of virtual reality has drawn great attention from both academics and industry fields. In many VR applications, the time-varying orientations and the positions of the VR headset are needed, where the technique of simultaneous localization and mapping (SLAM) can be applied. While great progress has been made in vision-based SLAM algorithms and systems in the recent two decades, the problem of visual SLAM in dynamic environments with moving objects is still unaddressed. In this paper, a static key-point detection approach is proposed based on optical flow feature clustering and potential moving region detection and is utilized for RGB-D SLAM in a dynamic environment. Firstly, considering the fact that there could be multiple moving objects in the scene, an optical flow feature is designed to cluster the multiple motion patterns in the scene. Secondly, an ultra-lightweight object detection network is utilized to detect potential moving regions in the scene, without using GPU resources. Finally, by utilizing the key-point clustering and potential moving regions detection, the static key-points are detected and inputted into the keypoint-based visual SLAM methods. The proposed approach is easy to insert as a pre-processing module in various key-point based visual SLAM systems. Extensive experimental results with qualitative and quantitative evaluations have demonstrated the performance with satisfactory accuracy of the proposed approach in various challenging dynamic scene scenarios.

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Circular Synthetic Aperture Radar for Near-field Terahertz Imaging

Circular Synthetic Aperture Radar for Near-field Terahertz I...

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International conference on Multimedia, Signal processing and Communication Technologies, (IMPACT)

作者： Mavis Gezimati Ghanshyam Singh Department of Electrical and Electronic Engineering Science Centre for Smart Information and Communication Systems University of Johannesburg Johannesburg South Africa

ISBN: (纸本)9781665476485

The potential of synthetic aperture radar (SAR) systems to yield high resolution images has led to its exploration in new applications. The conventional microwave radar imaging suffer limited range resolution in the sub-millimeter range and optical imaging methods are constrained by the diffraction limit. In the Terahertz (THz) spectrum, SAR systems are capable of operating in all weather, all time and penetrating through clouds, smoke, dust etc. to achieve better resolution images beyond the diffraction limit. In this paper, the different aspects of Multimedia computing are investigated in THz circular SAR as a new mode of radar imaging for near-field applications for example indoor environment. We have explored a new toolbox that enables the rapid development of near-field THz imaging SAR systems and generation of large near-field THz imaging scenarios that could be used for data driven applications. A circular SAR imaging system is presented for 2D (two-dimensional) imaging of targets in THz-SAR and the radar images are reconstructed using the Back Propagation and Polar Formatting algorithms. The SAR performance evaluation metrics are reported and scope for future work is discussed.

关键词： Terahertz wave imaging Image resolution Imaging Signal processing algorithms Multimedia computing Radar imaging Reconstruction algorithms

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Autostereoscopic Measurement System for Rapid 3D Inspection of Wire Bonding

Autostereoscopic Measurement System for Rapid 3D Inspection ...

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2021 International conference of optical Imaging and Measurement, ICOIM 2021

作者： Gao, Sanshan Cheung, Chi Fai Li, Da The Hong Kong Polytechnic University State Key Laboratory of Ultra-Precision Machining Technology Department of Industrial and Systems Engineering Hong Kong

ISBN: (纸本)9780738112121

The quality of wire bonding is an important factor in the manufacturing of various precision electronic components in semiconductor industry. The wire bonding defects, including high loop and low loop, determined by the bonding height information, inevitably affect the performance of the product. It is vital to measure height information quickly and accurately to reduce the failure rate in the manufacturing process. However, traditional measurement system often needs to move the target samples for a number of times. To address the key measurement problems, a three-dimensional (3D) optical measurement system is developed based on autostereoscopy technologies. A micro-lens array is used to divide the light emitted by the sample into different directions. The elemental images from different view angles contain original 3D information and is recorded by a high-resolution image sensor. On the basis of the multi-view stereo information contained in the image, the elemental images can be refocused into multi-focus images. The 3D information can be directly extracted from the multi-focus images through self-developed algorithms. Experiment results show that the system can complete the measurement in 2 seconds and reach a high measurement accuracy at a sub-micrometer level. This verifies the feasibility of the proposed measurement system which is efficient and accurate for 3D inspection of wire bonding. © 2021 IEEE.

关键词： Stereo image processing

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