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Design of HD Real-time Video Defogging System Based on FPGA

Design of HD Real-time Video Defogging System Based on FPGA

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International conference on Integrated Circuits and Microsystems (ICICM)

作者： Xinchun Wu Yanliang Li Xun Han Xiaojun Zhang Xiaobing Huang Biao Sun School of Information Science and Technology Southwest Jiaotong University Chengdu China Department of Transportation Management Sichuan Police College Luzhou China Qianghua times (Chengdu) Technology Co. Ltd Chengdu China

ISBN: (数字)9798331509453

ISBN: (纸本)9798331520243

The research in this paper focuses on three main aspects: Improving the dark channel prior defogging algorithm to make it suitable for implementation on FPGA; Designing a high-definition real-time video defogging system capable of supporting a resolution of 1920×1080 and a frame rate of 60 frames per second; Comparing the defogging results of different algorithms qualitatively and quantitatively. This paper optimizes the dark channel prior defogging algorithm by separately calculating the transmission rate for the sky region based on the high grayscale values in the dark channel image, aiming to reduce color deviation in the sky region after defogging. The proposed defogging algorithm demonstrates efficient performance on FPGA, producing vivid and clear defogged videos while maintaining real-time processing capability. This enhancement effectively improves the usability of video equipment in foggy weather conditions.

关键词： Micromechanical devices Image color analysis Streaming media Video equipment Gray-scale Real-time systems Usability Field programmable gate arrays Meteorology

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Malicious Address Identifier (MAI): A Browser Extension to Identify Malicious URLs

Malicious Address Identifier (MAI): A Browser Extension to I...

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International conference on Emerging Trends in Engineering and Technology, ICETET

作者： Om Jaiswal Parag Asare Jitesh Gadgilwar Kunal Rahangdale Pratik Adekar Leela Bitla Sampada Wazalwar Center of Excellence Information Security G H Raisoni College of Engineering Nagpur India Department of Information Technology G H Raisoni College of Engineering Nagpur India

Many websites have emerged since the internet was introduced. Every day, people visit a variety of websites, and they unknowingly click on malicious URLs because they simply neglect to check the URLs they visit. Clicking on a malicious URL has significant consequences. People might become victims of a phishing attack, which aims to steal people’s money or identities by tricking them into disclosing their personal data such as passwords, credit card numbers, and bank account numbers on websites that portray themselves as legitimate. They might also have malware automatically installed on their computer devices, which can harm and destroy their systems. Therefore, if people don’t know whether a URL is malicious or not, something more dangerous might happen. In this paper, we present a browser extension that employs a two-layer model to identify malicious URLs on the web using Bi-LSTM and CNN algorithms. This extension not only identifies malicious URLs but also prevents them from being visited. Furthermore, it blocks ads so that users do not click on malicious URLs through ads.

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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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Voice Command Recognition for Drone Control by Deep Neural Networks on Embedded System 8

Voice Command Recognition for Drone Control by Deep Neural N...

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8th International conference on Electrical and Electronics Engineering (ICEEE)

作者： Yapicioglu, Cengizhan Dokur, Zumray Olmez, Tamer Istanbul Tech Univ Dept Elect & Commun Engn Istanbul Turkey

ISBN: (纸本)9781665440714

Speech recognition and its applications for controlling a system has been an important and attractive issue over the last few decades. Controlling electronic devices by speech commands allows us to manage systems quickly and easily since users would not need any additional information or remote controller. Being able to communicate a system by using speech commands also brings with the requirements of fast and accurate response. So, at the present, speech recognition algorithms are extensively performing on high performance computers. However, the improvements of system on a chip (SoC) board and deep neural network based algorithms, make it possible to execute such kind of programs on them. The proposed study defines a model for controlling a drone system by using Turkish speech directional commands in real time which is based on deep learning approaches using spectrogram images. At first, speech commands are detected in real time with the help of signal energy and zero crossing rate and these are converted to log spectrogram images. A CNN (three convolutional layers and a fully connected layer) structure is created and trained by feeding those images. Then, the trained model is moved to embedded board to achieve real time, low-cost performance. Speech commands are provided by the user instantaneously and transferred to the model as an input for decision. Then, algorithm decides which directional command is given by the user and desired operation is performed on the drone system. It is observed that, by using the proposed model, accuracies of 95.72% for offline dataset and 92,88% for real time classification are obtained.

关键词： speech recognition spectrogram convolutional neural networks deep learning speech processing image processing embedded systems

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Decrease the time for classification of the incoming signals from BCI 2021

Decrease the time for classification of the incoming signals...

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3rd International Symposium on Signal processing systems, SSPS 2021

作者： Dimitrov, Georgi P. Panayotova, Galina Kovatcheva, Eugenia Petrov, Pavel Garvanova, Magdalena Petrova, Snejana Dimitrova, Inna Bychkov, Olexiy S. University of Library Studies and Information Technologies Sofia Bulgaria University of Economics Varna Bulgaria Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (纸本)9781450389587

In the recent years the attention to Brain-Computer Interface (BCI) devices and their potential for decoding human brain signals have risen considerably. However, a number of issues related to the classification of received signals still remain unresolved. This study focuses on increasing the speed of classification of data obtained from the Brain Computer Interface (BCI), without significantly affecting the accuracy of processing and classification. Our research team focuses on the possibilities to reduce the number of channels as one of the potential factors for increasing the speed of incoming data classification. Experimental data is obtained by using Emotiv Epoc 14+. For data processing we used Python. The data is classified with K-Neighbors algorithm. © 2021 ACM.

关键词： Brain computer interface

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Spiking Transformer Networks: A Rate Coded Approach for processing Sequential Data 7

Spiking Transformer Networks: A Rate Coded Approach for Proc...

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7th International conference on systems and Informatics, ICSAI 2021

作者： Mueller, Etienne Studenyak, Viktor Auge, Daniel Knoll, Alois Technical University of Munich Munich Germany

ISBN: (纸本)9781665426244

Machine learning applications are steadily increasing in performance, while also being deployed on a growing number of devices with limited energy resources. To minimize this trade-off, researchers are continually looking for more energy efficient solutions. A promising field involves the use of spiking neural networks in combination with neuromorphic hardware, significantly reducing energy consumption since energy is only consumed as information is being processed. However, as their learning algorithms lag behind conventional neural networks trained with backpropagation, not many applications can be found today. The highest levels of accuracy can be achieved by converting networks that are trained with backpropagation to spiking networks. Spiking neural networks can show nearly the same performance in fully connected and convolutional networks. The conversion of recurrent networks has been shown to be challenging. However, recent progress with transformer networks could change this. This type of network not only consists of modules that can easily be converted, but also shows the best accuracy levels for different machine learning tasks. In this work, we present a method to convert the transformer architecture to networks of spiking neurons. With only minimal conversion loss, our approach can be used for processing sequential data with very high accuracy while offering the possibility of reductions in energy consumption. © 2021 IEEE.

关键词： Learning algorithms

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Deep Unified Model for Face Recognition based on Convolution Neural Network and Edge Computing

Deep Unified Model for Face Recognition based on Convolution...

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International conference on Communication and Electronics systems (ICCES)

作者： V. Senthilkumar P. Saranya B Kezia Rani Srinivasan P Ramu Kuchipudi Md. Abul Ala Walid Department of CSE Kumaraguru College of Technology Coimbatore Tamilnadu India Department of ECE Dr. N.G.P. Institute of Technology Coimbatore Tamilnadu Department of Information Technology Vasavi College of Engineering Hyderabad Telangana India Department of ECE Sona College of Technology Salem Tamilnadu Inida Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Bangladesh

CCTV, communication, and alarm systems use face recognition technologies. Face detection in photos is a popular topic in science for practical reasons and because it challenges computer-generated vision systems. The variety of shooting situations (position, lighting, hairdo, emotion, backdrop, etc.) and face traits requires versatility. Deep learning-based image identification methods beat machine learning methods in efficiency and information processing. Modern computer systems have major authentication issues. Internet-connected smart devices are producing more data every day. A new model is needed to handle its vast data output. Deep learning and edge computing process vast volumes of data with high precision. Many trust facial recognition systems. SIFT and accelerated robust features are used in traditional facial recognition algorithms (SURF). This paper presents a convolutional neural network-based face identification and recognition solution that outperforms established methods. Tagged photographs of people taken in the outdoors teach the face-recognition algorithm (LFW). The suggested system had 99.1% accuracy on test data.

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Semiconductor Photonic devices for New Paradigms in Networking, Sensing, and Computing

Semiconductor Photonic Devices for New Paradigms in Networki...

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2022 27th OptoElectronics and Communications conference (OECC) and 2022 International conference on Photonics in Switching and Computing (PSC)

作者： Yoshiaki Nakano Department of Electrical Engineering and Information Systems The University of Tokyo

ISBN: (数字)9784885523366

ISBN: (纸本)9781665486064

Semiconductor photonic devices and their integrated circuits (photonic integrated circuits, PICs) have been a powerful tool in advancing optical telecom/datacom systems mainly over the past decades but now their application fields are expanding into those of sensing, imaging, and computing. Indium phosphide (InP), which is a traditional material for telecom PICs, is also good in other applications because it gives higher speed and functionality. Silicon PICs on the other hand give us higher level of integration and manufacturability. Both approaches are to be utilized, depending on user's requirements. In this talk, our recent semiconductor photonic devices and integrated circuits on InP and silicon are reviewed, which manipulate polarizations and phase-fronts (rather than traditional amplitudes and phases) for new-generation optical networking and sensing, together with some application examples. Then, our optical unitary converter integrated circuits (OUCs) toward all-optical multi-input multi-output (MIMO) processing and optical deep learning are introduced as an example of PICs for computing.

关键词： Integrated optics optical polarization optical imaging optical network units Silicon Telecommunications optical sensors

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Design and Implementation of a Low-cost Digital Stethoscope-Based Phonocardiogram (PCG) Acquisition System for Telemedicine Application

Design and Implementation of a Low-cost Digital Stethoscope-...

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International conference on Computer and information Technology (ICCIT)

作者： Dewan Md. Akib Md. Ashraf Uddin Ridoy Sheikh Arafat Nazmus Sakib Tasnuva Faruk Md. Kafiul Islam Electrical and Electronic Engineering Independent University Bangladesh Dhaka Bangladesh Dept. of Public Health Independent University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This paper presents the development of an affordable, digital stethoscope-based Phonocardiogram (PCG) acquisition system designed for telemedicine applications. The proposed system utilizes modern DSP and ML techniques with low-cost hardware components to capture and analyze heart sounds, providing real-time data transmission to healthcare professionals. The core design consists of a traditional stethoscope paired with a microphone, which records heart sounds via a smartphone or PC. By incorporating signal processing techniques, including denoising and machine learning algorithms, the device can classify heart conditions with promising accuracy. In trials involving 20 participants, PCG data was collected in both resting and post-exercise conditions, and heart rate measurements from the system were compared with ECG and oximeter readings. The results showed that the proposed PCG system maintained an overall error rate of less than 5% when compared to standard medical devices. Further classification of the PCG signals, using features like variance and skewness, demonstrated the system’s ability to distinguish between normal and abnormal heart sounds. With an average classification accuracy of 78.39%, sensitivity of 77.85%, and specificity of 78.94%, the device proved to be a practical, low-cost solution for telemedicine. By improving access to cardiac care, this PCG acquisition system could significantly enhance early detection and monitoring of heart diseases, particularly in remote areas where traditional medical services are scarce.

关键词： Accuracy Sensitivity Telemedicine Stethoscope Medical services Electrocardiography Signal processing Real-time systems Phonocardiography Standards

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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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