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Resource Allocation Optimization in 5G Networks Using Variational Quantum Regressor

Resource Allocation Optimization in 5G Networks Using Variat...

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Quantum communications, Networking, and Computing (QCNC), International Conference on

作者： Param Pathak Vidhi Oad Aditya Prajapati Nouhaila Innan Dept. of Computer Engineering Sardar Vallabhbhai Patel Institute of Technology India Dept. of Electronics and Communication Vishwakarma Government Engineering College India Dept. of Information and Communication Technology Dhirubhai Ambani Institute of Information and Communication Technology India Quantum Physics and Magnetism Team LPMC Faculty of Sciences Ben M'sick Hassan II University of Casablanca Morocco

ISBN: (数字)9798350366778

ISBN: (纸本)9798350366785

Quantum Machine Learning (QML) is a powerful tool for addressing complex telecommunications challenges. As 5G technology continues to evolve, our research provides a roadmap for integrating QML into the resource allocation framework, potentially improving the efficiency and performance of 5G networks. Through the application of the Variational Quantum Regressor (VQR), our study demonstrates the potential for quantum models to enhance predictive accuracy in resource allocation scenarios. Our VQR accurately aligns predicted quantum states with their intended targets, thereby reducing discrepancies. Such precision in regression analysis, particularly in the context of 5G networks, represents a significant breakthrough. A key highlight of our algorithm is its ability to substantially reduce the mean squared error to an impressively low value of 0.0081, indicating near-perfect prediction accuracy. This innovative approach is set against the backdrop of the evolving landscape of communication networks and computational systems, where the challenge of resource allocation is increasingly pronounced due to the rising demand for bandwidth-intensive and computation-heavy services.

关键词： Accuracy Quantum computing systematics 5G mobile communication Heuristic algorithms Telecommunications Resource management

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Performance Study of ZnO/SnO Thin Film Heterojunction Solar Cell

Performance Study of ZnO/SnO Thin Film Heterojunction Solar ...

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International Conference on computers and Devices for communication (CODEC)

作者： Lipika Mandal Syed Sadique Anwer Askari Md. Asraful Sekh Md. Aref Billaha Dept. of Computer Science and Business Administration Asansol Engineering College Asansol India Dept. of Electronics and Communication Engineering IIIT Ranchi Ranchi India Dept of Electronics & Communication Engineering Aliah University Kolkata India Dept. of Electronics & Telecommunication Engineering Jalpaiguri Polytechnic Institute Jalpaiguri India

The interesting properties of both Tin monoxide (SnO) and Zinc Oxide (ZnO) semiconductors allow to predict promising photovoltaic performance of n-ZnO/p-SnO heterojunction. In this work, one-dimensional numerical simulation is used to study and optimize the n-ZnO/p-SnO thin film based heterojunction solar cell. The effects of ZnO and SnO thickness on the performance of ZnO/SnO heterojunction solar cell is investigated. A highest efficiency of 15.85% is obtained for ~1.75-2.0 μm of SnO absorber layer thickness.

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Deep-Learning-based VCSEL transmitter emulator

Deep-Learning-based VCSEL transmitter emulator

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Deligiannidis, Stavros Argyris, Nikos Dris, Stefanos Kalavrouziotis, Dimitrios Bakopoulos, Paraskevas Mesaritakis, Charis Bogris, Adonis Department of Informatics and Computer Engineering University of West Attica Aghiou Spiridonos Egaleo Athens12243 Greece NVIDIA Ermou 56 Athens10563 Greece University of the Aegean Dept. Information and Communication Systems Engineering Palama 2 str. Samos Karlovasi83200 Greece

ISBN: (纸本)9798350345995

The design of photonic/opto-electronic structures is a crucial field of research due to the wide range of applications in which these devices are utilized, such as communications, sensing and imaging. The traditional approach to design, which involves numerical simulation and iterative adjustments, is both time-consuming and computationally intensive. The ability to design-emulate photonic structures quickly and efficiently could bring new technologies to market at a faster pace and can also facilitate the process of extracting critical performance metrics from complex fabricated devices. The Vertical Cavity Surface Emitting Laser (VCSEL) and its use as a transmitter broadly used in data center interconnects is an example of a rather complex nonlinear system also exhibiting bandwidth limitation mechanisms attributed to its frequency response and parasitic effects, and thus its numerical simulation based on conventional rate equations can only partially approximate its behavior. When it comes to modelling fabricated devices, such physical models are notoriously difficult to parameterize in order to fit to observed laboratory data;in contrast, the emulation of the experimentally recorded behavior with the use of nonlinear models based on neural networks provides a much more efficient path to modelling, and with potentially higher prediction accuracy [1]. In this work, two types of bidirectional recurrent neural networks (RNN), namely Long Short-Term Memory (bi-LSTM) and Vanilla (bi-VRNN) were used to simulate the dynamic behavior of an experimentally characterized VCSEL-based, PAM-4 transmitter, exhibiting a high prediction accuracy approaching 100%. © 2023 IEEE.

关键词： Frequency response

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A Virtual Reality 3D Game: A Comparison Between an Immersive Virtual Reality Application and A Desktop Experience

A Virtual Reality 3D Game: A Comparison Between an Immersive...

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Image Processing Challenges (ICIPC), IEEE International Conference on

作者： Michalis Vrigkas Christophoros Nikou Dept. Communication and Digital Media University of Western Macedonia Kastoria Greece Dept. Computer Science and Engineering University of Ioannina Ioannina Greece

The work aims to design and implement a 3D interactive and addictive object avoidance game using the Unity platform. The implementation of the immersive virtual reality application uses any smart mobile device as an input and output device, utilizing its accelerometer and compass to record the orientation and rotation data of the device in 3D space and capture the digital environment stereoscopically on the device screen. A comparative study between a virtual reality and a desktop real-time 3D game is performed to analyze the various attributes of the game and determine which medium is most effective.

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ULung:A Novel Approach for Lung Image Segmentation

ULung:A Novel Approach for Lung Image Segmentation

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Computing and Informatics (ICCI), International Conference on

作者： Osim Kumar Pal Shuvasish Roy Anup Kumar Modok Tajmihir Islam Teethi Siddhartha Kumer Sarker Dept. of Biomedical Engineering University of Kragujevac Kragujevac Serbia Dept. of Electrical & Electronic Engineering American International University-Bangladesh Dhaka Bangladesh Dept. of Computer Science & Engineering Daffodil International University Ashulia Savar Bangladesh Dept. of Space Communication & Navigation Technology BSMRAAU Old Airport Dhaka Bangladesh

ULung is an innovative medical technology used for segmenting medical images in pulmonary diagnostics. It introduces a modified approach to analyzing and understanding pulmonary conditions. ULung employs cutting-edge deep learning and image processing techniques to isolate intricate characteristics in medical lung images properly. The model uses advanced convolutional neural network architecture as UNet layer stricture to detect abnormalities and delineate anatomical regions reliably. ULung exhibits versatility in handling various lung ailments after extensive training using diverse datasets. The innovative methodology ensures enhanced segmentation performance, enhancing diagnostic precision and expediting medical evaluations. The advent of ULung has greatly enhanced medical imaging, providing clinicians with a potent tool for precise and expeditious lung evaluation. ULung is an impressive advancement in medical image segmentation that can revolutionize standards in respiratory healthcare due to its robustness and adaptability. The model's accuracy achieved using EfficientNetV2 is 99%, while its precision and recall rates are 98% and 96%, respectively. The model achieves a Mean Intersection over Union (MeanIoU) of 91.48% after the 14th epoch.

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Thermal Satellite Imagery Analysis and Emissivity Characteristics for the Prediction of Oil Reservoirs Existence 7

Thermal Satellite Imagery Analysis and Emissivity Characteri...

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7th International Conference on Space Science and communication, IconSpace 2021

作者： Tarish, Ali Badri Sali, Aduwati Mohamed Shariff, Abdul Rashid Hanafi, Marsyita Ismail, Alyani Mohammed, Intehaa A. Upm Dept. Of Computer & Communication Systems Engineering Serdang Selangor Malaysia Upm WiPNET Research Centre Dept. Of Computer & Communication Systems Engineering Serdang Selangor Malaysia Upm Dept. Of Biological & Agricultural Engineering Serdang Selangor Malaysia Uob College Of Education For Pure Sciences Dept. Of Physics Babylon Iraq

ISBN: (纸本)9781665425230

Simultaneous transformation of natural land cover contributes significantly to changing the surface phenomena making accurate forecasting difficult. Ground surveys would permit Land Use Land Cover (LULC) classification, but they are burdensome, expensive, and time-consuming and lacks accuracy in the exploration for all target areas at the same time, which highlights satellite imagery an evident and preferred alternative. A new mathematical method has been used for calculating the highest and lowest Land Surface Temperature (LST) to improve model accuracy for a particular location and detect oil seep-induced alteration from Landsat Data Continuity Mission (LDCM). Two sites within different patterns were chosen to represent a diversity of thermal anomalies positions in the study area. The results show that there are no traces of oil in either zone 1 lies which in the Kanoya province and zone 2 located east of the coast of Kagoshima which is overlooking the Pacific Ocean. In the end, the night-time scenes of the Thermal Infrared Sensor (TIRS) achieve a high gain of the thermal image with radiance 3.2-12.65 (Watts/m2 sr μm). Moreover, the analysis of the (TIRS) image is an optimal explanation for geomorphology and subsurface geology in southern Japan via the potentiality of the simulators set. © 2021 IEEE.

关键词： Satellite imagery

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Convolutional Recurrent Neural Network with Attention Gates for Real-time Single-channel Speech Enhancement

Convolutional Recurrent Neural Network with Attention Gates ...

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2021 International Symposium on Intelligent Signal Processing and communication systems, ISPACS 2021

作者： Wu, Wen-Yu Li, Pin-Hsuan Liang, Kai-Wen Chang, Pao-Chi National Central University Dept. Communication Engineering Taoyuan Taiwan National Central University Dept. Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9781665419512

In this paper, we incorporate the attention gates (AG) into the convolutional recurrent neural network (CRNN) to perform speech enhancement. The attention gates, which enhance important features and suppress irrelevant parts, can help the system effectively generate more accurate complex ratio mask (CRM). Because the model takes into account the phase information, better speech quality can be obtained. Since the parameters of the proposed model can be reduced to only 2.3M, the computational complexity is low, and the objective of real-time speech enhancement can be achieved. © 2021 IEEE.

关键词： Recurrent neural networks

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Stripe Removal from Hyperspectral Food Images acquired by Handheld Camera using ℓ2,1 Norm Minimization and SSTV Regularization

Stripe Removal from Hyperspectral Food Images acquired by Ha...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and communications Technology (IAICT)

作者： K S Shanthini Sudhish N George Sony George Binu Melit Devassy Dept. of Electronics and Communication Engineering National Institute of Technology Calicut India Dept. of Computer Science Norwegian University of Science and Technology Norway

Hyperspectral imaging offers the capacity to quickly and noninvasively monitor a food product’s physical, chemical and morphological properties. Specim IQ is a handheld push broom camera with basic data handling and data analysis capabilities within the camera software. However, the recordings of the Specim IQ camera showed a line pattern (stripes) that was evident in all images. Stripes significantly reduce the visual quality of the images and lower the results of further processing. Hence an efficient destriping model is developed, which specifically addresses this issue. The proposed model uses a spatial gradient term to analyze the directional characteristics and group sparsity to describe the structural characteristics of the stripe component. In addition to this, a spatial spectral total variation regularization is used to ensure piecewise smoothness in the spatial and spectral domains and to remove Gaussian noise. The ensuing optimisation problem is solved using the alternating direction method of multipliers (ADMM). The proposed method is tested in real stripe noise environments, and the findings demonstrate that it outperforms some of the best approaches in terms of visual quality and quantitative evaluations. When compared with the other approaches, the proposed method attained the highest noise reduction (NR) and lowest mean relative deviation (MRD) values (NR=1.67, MRD=1.02%).

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Impacts of Image Compression on the Detection Quality of a Novel Real-Time Image Processing Platform

Impacts of Image Compression on the Detection Quality of a N...

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International Conference on Intelligent Transportation

作者： Jannik Mehrke Georg Volk Yannik Stumpp Oliver Bringmann Anestis Terzis Institute of Communication Technology Ulm University of Applied Sciences Dept. of Computer Engineering University of Tübingen

Cloud-based data processing latency mainly depends on the transmission delay of data to the cloud and the used data processing algorithm. To minimize the transmission delay, it is important to compress the transferred data without reducing the quality of the data. When using data compression algorithms, it is important to validate the impact of these algorithms on the detection quality. This work evaluates the effects of image compression and transmission over wireless interfaces on state of the art neural networks. Therefore, a modern image processing platform for next generation automotive processing architectures, as used in software defined vehicles, is introduced. The impacts of different image encoders as well as data transmission parameters are investigated and discussed.

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RoseVision: An Android Application To Detect Rose Leaf Diseases Using Modified Convolutional Neural Network

RoseVision: An Android Application To Detect Rose Leaf Disea...

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International Conference on computer and Information Technology (ICCIT)

作者： Saidul Islam Nayan Syed Mahfuzur Rahman Nosin Ibna Mahbub Dept. of Computer Science and Engineering University of Global Village Barishal Bangladesh Dept. of Information and Communication Technology Islamic University Kushtia Bangladesh

ISBN: (数字)9798350359015

ISBN: (纸本)9798350359022

Every year 28% of the global cut rose production is hampered by various rose plant diseases. Early detection and classification could diminish the cost of cultivation lost. However, without proper knowledge and expert assistance, farmers are facing challenges to identify the diseases in time to take action. Previously, a few works have been conducted on this. So far, no study has been done to develop an efficient automatic mobile application detection system with faster, lighter, and more accurate predictions. In this study, we presented an artificially intelligent mobile application using image processing to identify rose leaf diseases. The primary goal of this work is to create a simple but exact identification with minimal computing power and time. We proposed a Modified Convolutional Neural Network (MCNN) to classify different rose leaf diseases. The proposed MCNN model outperforms the famous pre-trained models including VGG16, VGG19, DenseNet121, MobileNetV2, Resnet50, InceptionV3, and Extreme Inception (Xception) by achieving 97.94% test accuracy. Additionally, based on the trained MCNN model, we developed an android-based mobile application that will allow the farmers to quickly identify the disease using their android phones.

关键词： Plant diseases Accuracy Computational modeling Production Mobile applications Convolutional neural networks Information technology Smart phones Diseases Residual neural networks

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