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Accommodation and Vergence Responses to Electronic Holographic Displays Compared with Those to Stereoscopic Displays

Accommodation and Vergence Responses to Electronic Holograph...

引用

conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： Haruki Mizushina Ippei Negishi Junya Nakamura Yasuhiro Takaki Hiroshi Ando Shinobu Masaki Tokushima University Tokushima Japan Kanazawa Institute of Technology Ishikawa Japan Tokyo University of Agriculture and Technology Tokyo Japan National Institute of Information and Communications Technology Osaka Japan Advanced Telecommunications Research Institute International Kyoto Japan

ISBN: (纸本)9781728164021

Electronic holography is an ideal 3D display technique to ameliorate vergence-accommodation conflict, which is a possible cause of visual fatigue and discomfort from viewing conventional stereoscopic 3D displays. Previous studies have measured accommodative and vergence responses to holographic images and real objects, and reported that they are in good agreement. To demonstrate the effectiveness of electronic holography as a solution of vergence-accommodation conflict caused by viewing conventional stereoscopic 3D displays, we measured accommodative and vergence responses to reconstructed images of holograms and two-view stereoscopic images. We also measured responses to real objects located at various distances as a baseline condition. The results indicate that the accommodative response to the electro-holographic display changes with the vergence response in a similar manner, as is the case with real objects as compared to conventional two-view stereo display. This suggests that the holographic display technology is a promising candidate for resolving the vergence-accommodation conflict.

关键词： visualization Three-dimensional displays image resolution Stereo image processing Industry applications Virtual reality Human factors

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Optimize CDMA system capacity with location

Optimize CDMA system capacity with location

引用

IEEE conference on Vehicular Technology (VTC)

作者： W.C.Y. Lee D.J.Y. Lee Link Air Communications Santa Clara CA USA

The introduction of location technology shifts the paradigm of network engineering. It opens the door for providing a revolutionary approach to network engineering. An algorithm is discussed for a revolutionary way of combining parameters made available through location technology and currently available system parameters for future network engineering and operation. "Continuous" snapshots of the network can be parsed and intelligently stored into the network performance data warehouse as images. These images can be analyzed off-line to select certain "troubled" instances of the network (this can be site specific). These instances can then be analyzed through simulation and/or engineer intervention. An optimized solution can be developed. These "troubled" instances and associated solutions can be fed back into the "virtual network engineer" simulated by the AI engine to increase its knowledge base. Later, the intelligent pseudo "network engineer" can identify "troubled" network instances and compare them with the existing images and associated solutions in the data warehouse. The existing solution then can be applied to perform real time automated network engineering. This is done through image processing by selecting the most similar network instance and associated solutions. By saving more and more "troubled" instances and developed associated solutions, the "virtual network engineer" increases its knowledge base and becomes more and more "intelligent" and can handle future "troubled" instances based on the "experience". For example, the dynamic power allocation and individual specific dedicated dynamic SHO (soft handoff) thresholds for each mobile can be combined to engineer the network performance efficiently once the mobile location and associated network characteristics can be identified.

关键词： Multiaccess communication Intelligent networks Data warehouses Knowledge engineering Power engineering and energy image analysis Analytical models Artificial intelligence Engines Data engineering

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A Review on Feature Extraction Techniques in Content Based image Retrieval

A Review on Feature Extraction Techniques in Content Based I...

引用

International conference on Wireless communications, Signal processing and Networking

作者： Jigisha M. Patel Nikunj C. Gamit Department of Computer Engineering C.G.P.I.T Uka Tarsadia University Bardoli India

ISBN: (纸本)9781467393393

The Content Based image retrieval (CBIR) is a very important for retrieving the most visual relevant images from the large image database. The various low level features are extracted based on their visual content which are color, shape, texture etc. The paper gives the overview of color and texture feature extraction techniques like color histogram, color correlogram, color co-occurrence matrix and tamura texture feature, steerable pyramid, wavelet transform, Gabor wavelet transform respectively and also the comparative analysis of this techniques is shown in the paper.

关键词： CBIR Feature extraction Color histogram Color correlogram DCD CCM Tamura feature Wavelet transform GWT

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Machine Vision Aided Adaptive Beamforming Decision for IRS-Assisted Wireless Networks

Machine Vision Aided Adaptive Beamforming Decision for IRS-A...

引用

European conference on Networks and communications (EuCNC)

作者： M. Munawar M. Guenach I. Moerman IMEC Leuven IDLab Ghent University

ISBN: (数字)9798350344998

ISBN: (纸本)9798350345001

This study leverages machine vision to assist communication in wireless networks, with a specific focus on intelligent reflecting surface (IRS)-assisted wireless networks. Instead of depending on traditional schemes such as alternating optimization or semidefinite relaxation to maximize signal strength in an IRS-assisted network, which are computationally expensive and impractical, we use visual data to make low-complexity beamforming decisions for users. Our approach involves employing a ceiling camera with a fish-eye view, covering a wide communication area. The user within the network is initially detected using the YOLOv2 object detection method. Subsequently, we propose closed-form analytical expressions to determine the distances between the access point (AP), user, and IRS. Accounting for the non-uniform nature of the fish-eye image, we introduce a novel method to determine non-uniform pixel weightages using trigonometric techniques. Based on the calculated distances, we make beamforming decisions depending on the user’s proximity to the AP or IRS. The proposed method significantly reduces computational complexity, making it nearly independent of the number of reflecting elements at the IRS. Simulation results indicate that the proposed approach exhibits extremely lower computational costs compared not only to conventional schemes such as alternating optimization and semidefinite relaxation-based convex solvers but also to low-complexity heuristic schemes.

关键词： Degradation visualization Array signal processing Wireless networks Machine vision Simulation Object detection

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An intricate spectrogram processing in speech enhancement using machine learning with cloud computing

An intricate spectrogram processing in speech enhancement us...

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International conference on image Information processing

作者： Yenna Geetha Reddy G Mahendran Ameer Al-Khaykan Omar A. Alkawak S. A. Kalaiselvan A. Priya Department of Computer Science and Engineering MAlla Reddy Engineering College for Women (UGC Autonomous)Kompally Secunderabad Telangana India Department of Mechancal Engineering R.M.K Engineering Collegekavaraipettai Chennai Tamil Nadu India Department of Electronic and Communications Engineering University of Kufa Najaf Iraq Department of Air Conditioning and Refrigeration Technical Engineering Al-Mustaqbal University College Hilla Babil Iraq Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science (SIMATS) Chennai Tamil Nadu India School of Architecture Koneru Lakshmaiah Educational Foundation Guntur Andhra Pradesh India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

The spectrogram is defined as the visual representation of the signal strength with a particular interval of time regarding frequencies in the appropriate waveform. It is demonstrated using the Fourier transform. It is illustrated in a three-dimensional plot of signal composed with amplitude versus frequency and time. They are classified as long signal sections and short signal sections with filters. It demonstrates the consecutive spectacle of the sound signal at a particular interval of time. The time-frequency representation of audio signals is obtained through spectrograms. This helps in the understanding of the speech data. Machine learning plays an important role in the identification of the relevant features in the spectrogram. The intricate patterns in the time and frequency domains are represented through CNN and RNN. These models are trained and tested through a large dataset which includes both clean and noisy speech samples. This helps to learn complex interrelationships and patterns associated with varied environmental conditions and interference systems. The computational demands of these systems are handled through the cloud computing techniques. They provide a platform for effective solutions for performing real-time interference. These cloud services provide various collaborations to numerous models to achieve desired outcomes. The proposed system provides higher adaptability in varied environmental and noisy profiles. Thus the integration of cloud computing with machine learning provides real-time processing with a natural seamless user experience.

关键词： Cloud computing Time-frequency analysis visualization Computational modeling Machine learning Interference Speech enhancement

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Diabetic Retinopathy Detection Using CNN With Res-LSTMDN

Diabetic Retinopathy Detection Using CNN With Res-LSTMDN

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International conference on communications and Signal processing

作者： R. Gayathri S Karthikeyan T KausheekRaj S Keerthana Department of Electronics and Communication Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

For diabetic patients, early diagnosis and treatment of diabetic retinopathy (DR) are essential since they can avert irreversible visual loss. The ways we currently find DR aren't very accurate or fast, so we need better ways to do it automatically. CNNs, a type of machine learning algorithm, have been shown to work well for analyzing medical images and could be a good option for detecting DR. Among CNN architectures, ResNet-152 has gained reputation for its depth and effectiveness in image classification tasks. However, to further enhance its performance in DR detection, this study does a modification to the final layer of ResNet-152. Specifically, the final layer is replaced with Long-Short Term Memory (LSTM) and Deep Neural Network (DNN) components, resulting in an ensemble approach termed Res-LSTMDN. This novel architecture aims to improve the hierarchical feature learning of ResNet-152 while integrating the sequential memory capabilities of LSTM and the flexibility of DNN. Res-LSTMDN uses advanced technology to study eye images, specifically those from people with diabetes. By finding unique patterns in these images, this technique aims to improve the accuracy of detecting diabetes-related eye problems. This will allow for early intervention, potentially preventing vision loss. As part of the ongoing efforts to develop advanced computer tools for diabetes diagnosis, Res-LSTMDN has the potential to significantly impact global diabetes management strategies.

关键词： Representation learning Diabetic retinopathy visualization Machine learning algorithms Memory management Signal processing Convolutional neural networks

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Classifications of Citrus Fruit Using image processing -GLCM Parameters

Classifications of Citrus Fruit Using Image Processing -GLCM...

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International conference on communications and Signal processing

作者： Chandan Kumar Siddharth Chauhan R. Narmadha Alla Harika Mounica Gurram National Institute of Standards and Technology Department of Physics Colorado State University Electrical Engineering Department University of Colorado

ISBN: (纸本)9781479980826

In recent years, automatic visual inspection system has become useful tool in industrial process. The citrus fruit sorting and grading process has now been completely automated with the exception of the final stage of classification concerned with visual inspection. Due to the growing demand of quality citrus fruit, an automated and reliable identification and classification mechanism in order to handle the bulk of data is implemented. Human vision sometimes leads to mismatch between the fruits. Using machine vision system, it will be simple to reduce human effort and achieve a better output. The objective of this project is to assess the fruit by color grading. In this paper we describe an integrated system developed for the sorting on citrus fruit and for the color grading of the defected fruits. The result suggests that the performance is adequate to provide the basis for a viable commercial visual inspection system. The following project aims at implementing such an automated system using image processing with images of citrus fruit as a basis of classification depending on its color. The goal of the inspection is not to give a statistical analysis of the production but to classify every citrus fruit into quality constant batches.

关键词： Quality inspection Machine vision image processing GLCM parameters Citrus fruits Sorting and grading

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Smart Sorting: Enhancing Almond Quality with DenseNet-201-Based image Classification

Smart Sorting: Enhancing Almond Quality with DenseNet-201-Ba...

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International conference on Information Science and communications Technologies (ICISCT)

作者： Arpanpreet Kaur Amit Kumar Mishra Bura Vijay Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science and Artificial Intelligence SR University Warangal India

ISBN: (数字)9798331527921

ISBN: (纸本)9798331527938

Considered as two varieties: damaged and undamaged, almonds are a rather consumed agricultural product. Maintaining consumer satisfaction and industry standards depends on good detection of damaged almonds., which could be influenced by mechanical damage., mold, or pests. On the other hand, slow, labor-intensive hand inspection methods prone to mistake demand automated classification systems. This work meets that demand by automating the categorization process using DenseNet-121, a convolutional neural network (CNN), therefore lowering labor expenses and increasing accuracy. Particularly considering the tiny dataset of 1,000 photos split equally between damaged and undamaged almonds, DenseNet-121's architecture-with its dense connections and capacity to reuse features-is well-suited for the almond classification challenge. Pre-trained on imageNet and polished for this particular purpose, the model-trained across 60 epochs using a 32-batch size-achieved an accuracy of 95.27%. Random horizontal flips, resizing., and center cropping among other data augmentation techniques improved the model's generalizing capability. This work demonstrates the model's ability to substitute human methods in actual environments by showing how well it employs little visual information to classify almonds. Automating almond sorting speeds and provides higher consistency in quality control as well as efficiency of sorting. This work opens the road for further research of machine learning techniques in agricultural processing with possible applications in food quality assurance.

关键词： Accuracy Quality assurance Process control Quality control Machine learning Data augmentation Data models Convolutional neural networks Sorting image classification

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A Review towards Enhancing Authentication Scheme Using image Fusion and Multishared Cryptography

A Review towards Enhancing Authentication Scheme Using Image...

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International conference on communications and Signal processing

作者： V.B. Gadicha A.S. Alvi Department of Computer Sci. & Engg P.R.Patil College of Engg & Tech Amravati (MH) Department of Computer Sci. & Engg PRMIT&R Badnera Amravati (MH).

ISBN: (纸本)9781479980826

Passwords are used for user authentication, validation, and verification in various software applications (may be Commercial, or Research based). All the existing schemes which are used to generate the password will always provide enough scope for the Intruder (Cracker) to crack or guess the password. All the existing schemes of password generation works on a basic assumption that the resultant password should be easy to remember for a user. This ultimately brings the downfall of the scheme. Therefore, a Password Generator Scheme is proposed here which will restrict the Intruders to crack the password or a scheme should exist which will cause sufficient delay in cracking of a password so that the Generator of the password will change the previous password. Here a strong password generator scheme is proposed by using image Fusion & visual key Cryptography.

关键词： visual Crypto Algorithm image Fusion Two-Share Cryptography

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Object observation in detail by the AUV "Tri-Dog 1" with laser pointers

Object observation in detail by the AUV "Tri-Dog 1" with las...

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OCEANS

作者： H. Kondo Soncheol Yu T. Ura Underwater Technology Research Center Institute of Industrial Science IIS University of Tokyo Meguro Tokyo Japan

AUV (autonomous underwater vehicles) can be used to observe and get video images of underwater structures. This paper proposes a navigation method of AUV for observing large objects which disturb magnetic fields, based on images by a CCD camera, assuming that visibility is good enough. The AUV uses dead reckoning navigation until it comes near to the object. For detecting and observing the object, it uses two laser pointers, which are mounted in parallel on the head of the AUV, and a CCD camera. Through visual processing, the AUV calculates distances to two reflected points of laser beams, then determines angle between the object and the AUV. The AUV can then turn around the object keeping constant distance and angle. The proposed algorithm was implemented in the developed test bed AUV "Tri-Dog 1" and tested through tank tests. The vehicle successfully traced a straight wall and a curved wall, detected piles of 1 meter in diameter, and turned around them. It finds the precise distance to an object. An AUV will thus be able to carry out a survey of a target object in detail without any help from outside.

关键词： Testing Navigation Charge coupled devices Charge-coupled image sensors Underwater vehicles Underwater structures Magnetic fields Dead reckoning Object detection Laser beams

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