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A Study of Multilayer Omega Networks Operating with Cut-Through Mechanism under Uniform Traffic 5

A Study of Multilayer Omega Networks Operating with Cut-Thro...

5th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2020

作者： Stergiou, Eleftherios Garofalakis, John Liarokapis, Dimitrios Margariti, Spiridoula V. University of Ioannina Dept. Informatics and Telecommunications Arta Greece Dept. of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9781728164458

There is an enduring interest in developing more efficient multistage interconnection networks (MINs), as they are a key aspect of various switching sectors and the demands for higher data transfer rates. Here, semi-layer Omega type networks operating under the cut-through forwarding policy are selected as a test-bed subject for performance evaluation. This special shuffle-exchange network was modelled, analysed, and simulated. Moreover, this type of fabric is compared with corresponding single-layer Omega networks operating via cut-through or a 'store and forward' forwarding technique. The results show, in a quantitative manner, how superior is the under study fabric compared to the corresponding single-layer constructions, in terms of network size, showing the advantages that are provided by both usages: fan-out and cut-through mechanisms. Moreover, the roles of the switch element's size and the reliability metric were investigated. The findings that are derived can be valuable assets for MIN designers, in order to achieve more efficient delivering networks with low overall deployment costs. © 2020 IEEE.

关键词： Data transfer

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Fall Detection System for Elderly (FDS-E) using Low-cost Camera Based on LSTM and OpenPose

Fall Detection System for Elderly (FDS-E) using Low-cost Cam...

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IEEE Instrumentation and Measurement Technology Conference

作者： Herti Miawarni Tri Arief Sardjono Eko Setijadi Wijayanti Mauridhi Hery Purnomo Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering Dept. of Biomedical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Architecture Faculty of Engineering Universitas Diponegoro Semarang Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember University Center of Excellence on Artificial Intelligence for Healthcare and Society (UCE AIHeS) Surabaya Indonesia

Recently, many countries have been dealing with an increasing amount of elderly who are living alone. Unfortunately, fall incidents in the elderly tend to happen, and without proper dependable care would lead to fatal injuries. The elderly must be closely monitored under constant observation. Accidental falls are a leading cause of injury, mortality, and mobility limitations in the elderly. The expenses of national healthcare systems are also significantly impacted by accidental falls. These findings highlight the importance of investing heavily in the study and advancement of fall detection and intervention technologies. It is essential to recognize them early and provide fast assistance by developing technologies that can simultaneously improve the standard and the safety of the elderly's living environment. In this paper, we present a computer vision algorithm for falling incident detection using a low-cost camera and deep learning model. Ultimately, this algorithm can be used for the cases of elderly, in which falling risk is high and fatal. Furthermore, one self-developed dataset was used to validate the suggested approach experimentally. We used an OpenPose-based feature using a low-cost camera input and then classified each event or activity as either fall or non-fall. Classification tasks are carried out by Long Short-Term Memory (LSTM) with multiple validation metrics. The LSTM in this work is also optimized using Bayesian method. Finally, our approach to recognize falls has accuracy of 99.5%, which is distinct from other past works.

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Connectivity Framework for Rendezvous and Mobile Producer Nodes Using NDN Interest Flooding

Connectivity Framework for Rendezvous and Mobile Producer No...

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2021 International Congress of Advanced Technology and engineering, ICOTEN 2021

作者： Ahmed, Muhammed Zaharadeen Hashim, Aisha Hassan Abdallah Khalifa, Othman Omran Saeed, Rashid Abdelhaleem Alsaqour, Raed Ali Alkali, Abdulkadir Hamidu International Islamic University Malaysia Dept. of Electric and Computer Engineering Kuala Lumpur Malaysia Taif26571 Saudi Arabia Saudi Electronic University College of Computing and Informatics Saudi Arabia University of Maiduguri Deparmtent of Computer Engineering Maiduguri Nigeria

ISBN: (纸本)9781665412247

Named Data Networking is a promising application of Information Concentric Networking. More research is required to improve NDN mobility especially when Interest and Data contents are flooded during transmission. In named data networking framework, data integrity and security are highly guaranteed due to its nature of operation. Although, Information Concentric Networks are to some extend vulnerable to some form of Denial of Service (DoS) attacks. these attacks are mostly Interest Flooding Attacks. In this paper, a scalable solution that is capable of moderating complex interest flooding framework for rendezvous and producer nodes mobile routes is implemented by simulation. The simulation involves mobile producer (MP) transmission between the two interfaces (in-out bound) on a network. We further adopt the use an efficient protocol known as the Listen First Broadcast Later for Interest content as they are transmitted by consumer in search for data between nodes of producer and rendezvous in motion. Our implementation ensures preventing attackers at both ends of the links without affecting non attacking consumers and producers. The protocol depends on the mobile producer's Data content reply and use a fix rate adaptation to initiate secured handoff. Simulation results in ndnSIM 2.1 and Wireshark present successful implementation of the producer to rendezvous scalable content delivery between the links. © 2021 IEEE.

关键词： Denial-of-service attack

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Comparison of Neural Network and Random Forest Classifier Performance on Dragon Fruit Disease 23

Comparison of Neural Network and Random Forest Classifier Pe...

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23rd International Electronics Symposium, IES 2021

作者： Lado, Anita Jaquiline Sooai, Adri Gabriel Mamulak, Natalia Magdalena Rafu Nani, Paskalis Andrianus Bria, Yulianti Paula Batarius, Patrisius Aliandu, Paulina Ngaga, Emerensiana Sinlae, Alfry Aristo Jansen Mau, Sisilia Daeng Bakka Tedy, Frengky Meolbatak, Emiliana Metan Siki, Yovinia Carmeneja Hoar Gumelar, Agustinus Bimo Fanani, Nurul Zainal Yuhana, Umi Laili Universitas Katolik Widya Mandira Dept. of Computer Science Kupang85225 Indonesia Universitas Narotama Faculty of Computer Science Surabaya60117 Indonesia Dept. of Engineering Politeknik Negeri Jember Indonesia Institut Teknologi Sepuluh Nopember Dept. of Informatics Engineering Surabaya60117 Indonesia

ISBN: (纸本)9781665443463

Measuring the performance of several classifiers in the modeling process, based on datasets with certain criteria, becomes important. It used to determine which classifier is more reliable for a particular task. Neural network will be compared its performance against Random Forest using dragon fruit datasets. Consisting of 41 images of healthy and sick fruit and leaf, it is divided into four classes, both classifiers were used in two comparing experiments. The validation used is 10-fold cross-validation. The results obtained are not much different from the prediction accuracy in the range of 70% to 82.9% for both classifiers. © 2021 IEEE.

关键词： Fruits

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Integrated Photonic Accelerator Based on Optical Spectrum Slicing for Convolutional Neural Networks

arXiv

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arXiv 2023年

作者： Tsirigotis, Aris Sarantoglou, George Deligiannidis, Stavros Sozos, Kostas Bogris, Adonis Mesaritakis, Charis Dept. Information and Communication Systems Engineering University of the Aegean Palama 2 str. Karlovasi Samos83200 Greece Dept. Informatics and Computer Engineering University of West Attica Ag. Spiridonos Egaleo12243 Greece

In this work we numerically analyze a passive photonic integrated neuromorphic accelerator based on hardware-friendly optical spectrum slicing nodes. The proposed scheme can act as a fully analogue convolutional layer, preprocessing information directly in the optical domain. The proposed scheme allows the extraction of meaningful spatio-temporal features from the incoming data, thus when used prior to a simple fully connected digital single layer network it can boost performance with negligible power consumption. Numerical simulations using the MNIST dataset confirmed the acceleration properties of the proposed scheme, where 10 neuromorphic nodes can replace the convolutional layers of a sophisticated LeNet-5 network, thus reducing the number of total floating point operations per second (FLOPS) by 98% while offering a 97.2% classification accuracy. ©2022 The Author(s) Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Signal-Interference Bandpass Filters Using Resonant Transversal Filtering Sections With Asymmetrical Transfer Function Characteristics

Signal-Interference Bandpass Filters Using Resonant Transver...

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European Conference on Microwave

作者： David Chatzichristodoulou Symeon Nikolaou Photos Vryonides Dimitra Psychogiou Frederick Research Center Cyprus RF and Microwave Solutions Cyprus Dept. of Electrical and Computer Engineering and Informatics Frederick University Cyprus School of Engineering University College Cork Ireland Tyndall National Institute Ireland

This paper introduces a new class of planar bandpass filters (BPFs) with quasi-elliptic transfer functions obtained by cascading resonant transversal filtering sections (TFS) with asymmetric transfer function characteristics. Each TFS includes a parallel-type resonator in one of its two RF signal paths which in turn leads to two transmission zeros (TZs) without increasing the circuit size. Furthermore, it enables high-order filtering transfer functions to be realized by directly cascading multiple TFSs without the need for inter-stage impedance inverters. Theoretical examples of spectrally asymmetrical BPF responses using linear-circuit analysis illustrate the resonator-based TFS BPF concept’s operating principles. Additionally, a microstrip prototype was manufactured and measured at 1 GHz to demonstrate the proof of concept. The prototype exhibited the following RF performance characteristics: a center frequency of 1 GHz, a 3-dB bandwidth of 150 MHz, a minimum in-band insertion loss of 0.8 dB, and in-band group-delay variation: 4.2-7.5 ns.

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Borneo Smart Forest Information System for Management of Dipterocarp Plants in Kalimantan Rainforest

Borneo Smart Forest Information System for Management of Dip...

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Information Technology International Seminar (ITIS)

作者： Masna Wati Novianti Puspitasari Ummul Hairah Joan Angelina Widians Anindita Septiarini Ade Fiqri Tjikoa Dept. of Informatics Faculty of Engineering Universitas Mulawarman Samarinda Indonesia Dept. of Informatics Universitas Mulawarman Doctoral Program Dept. of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9798350398205

Kalimantan’s tropical rainforest is home to indigenous plants of the Dipterocarpaceae family (tribe), which is renowned for having the most endemic species. Dipterocarp is a family of pantropical plants, many of which are used in the wood industry. This study aims to provide information on the potential of Dipterocarp in Kalimantan forests. Education and conservation efforts for Dipterocarp trees, a prominent component of the forest ecosystem, are crucial in light of the significant harm done to Kalimantan’s rainforests. The authors develop the Borneo Smart Forest information system. This system digitally organizes data on the diversity of Dipterocarp in the Kalimantan rainforest based on Web and QR-Code technology. Borneo Smart Forest (BSF) system for monitoring plant inventory and plant markers can be read by smartphones using QR codes. It gives visitors a comprehensive overview of the plants. A QR code is a printed, two-dimensional barcode with limited space for data storage. This technology allows the plant to show earlier administrator-made additions to the inventory information system. The QR code will direct the user to the BSF website, which has data about the plants. The development of this system is one of the efforts to support the achievement of Dipterocarp conservation.

关键词： Seminars Memory Forestry QR codes Vegetation Wood industry Information technology

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Proactive Network Traffic Prediction using Generative Adversarial Network

Proactive Network Traffic Prediction using Generative Advers...

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International Conference on Information Networking

作者： Gyurin Byun Van-Vi Vo Syed M. Raza Duc-Tai Le Huigyu Yang Hyunseung Choo Dept. of AI Systems Engineering Sungkyunkwan University Suwon South Korea Convergence Research Institute Sungkyunkwan University Suwon South Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea College of Computing and Informatics Sungkyunkwan University Suwon South Korea Dept. of Superintelligence Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Proactive traffic management in 5G networks is crucial for optimizing network efficiency, ensuring quality of service, adapting to dynamic conditions, and enhancing the user experience. In this paper, we propose a Generative Adversarial Network (GAN) architecture that leverages spatiotemporal features in network traffic data to predict future traffic. Our approach incorporates a Convolutional Long Short-Term Memory (ConvLSTM) model within the generator of the GAN, which collaborates with the discriminator, utilizing a Convolutional Neural Network (CNN) model, to provide essential feedback for training the generator. This integration ensures that our model not only predicts future traffic with improved accuracy but also adapts to dynamic network conditions. Based on experimental results using network traces, our model significantly outperforms the baseline, reducing prediction error by 12% while forecasting network traffic for the next 1 minute. These findings represent a significant advancement in proactive network management, particularly in addressing the challenges posed by real-time streaming and other latency-sensitive applications in 5G networks.

关键词： Training Adaptation models 5G mobile communication Telecommunication traffic Predictive models Generative adversarial networks Generators

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Exploring the Semantic Complexity of Adjective-Noun Collocations between Uzbek and English for Improved Machine Translation

Exploring the Semantic Complexity of Adjective-Noun Collocat...

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International Conference on computer Science and engineering (UBMK)

作者： Nilufar Abdurakhmonova Nargiza Shamieva Eşref Adali Dept. of Computational and applied linguistics National University of Uzbekistan Tashkent Uzbekistan Dept. of Foreign language and Literature National Univ. of Uzbekistan Tashkent Uzbekistan Computer Engineering and Informatics Faculty Istanbul Technical University Istanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

The bilingual thesaurus of Uzbek-English adjectives is a field that has not been studied well explored yet for machine translation. Several websites and electronic dictionaries created nearby show that not enough semantically close adjectives are included in them. Despite extensive research on the meaning the adjectives in both languages, there remains a lack of understanding about the relationship between the adjective itself and the noun, which has an absolutely new connotation in collaboration. Our result shows that a recent bilingual thesaurus demonstrates how one adjective could acquire numerous new synonyms while combining with different nouns. The meanings of words, in both languages have been extensively studied, Little is understood about the dynamic connection between these words and the nouns they describe. The complexity of how adjectives pair with nouns is evident in how this relationship leads to meanings and connotations. Our study aims to bridge this gap by demonstrating how a bilingual thesaurus can capture the meanings and variations of adjectives when combined with nouns. The findings indicate that depending on the noun it accompanies a contemporary bilingual thesaurus can generate synonyms, for a single adjective.

关键词： Vocabulary Dictionaries Sensitivity Semantics Linguistics Thesauri Complexity theory Machine translation Cultural differences Probes

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Action Localization and Recognition through Unsupervised I3D and TSN

Action Localization and Recognition through Unsupervised I3D...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Munzir Umran Kahlil Muchtar Taufik Fuadi Abidin Fitri Arnia Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh Indonesia Department of Informatics Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Banda Aceh Indonesia

Since CCTV technology was widely introduced as a video surveillance system, it has become very popular due to increased security concerns. However, detecting suspicious behavior is still done manually by humans, which is inefficient in terms of accuracy and manpower (prone to distraction and fatigue). Many studies use the entire frame of a video to recognize human action, whereas in this study first localize the area of object movement using the unsupervised inflated 3D (I3D) network which produces several frames that have been determined as the region of interest (ROI) from the total frames in the video. Then the collection of ROI frames is generated into a video which will then be classified to recognize human action through the Temporal Segment Networks (TSN). For efficiency purposes, the proposed pipeline can be run separately in the edge (for localization), and cloud environment (for recognition), respectively. The results show that there are 54 classes of ROI video data sets that have an average human action recognition prediction similarity rate above 84.6% which is the average of the prediction similarity rate of the entire ROI video data. In terms of prediction accuracy, 37 classes of data (36.63%) are better than the original class, including 25 classes of data (24.75%) above the threshold, and the processing speed of classification with ROI video data is on average 5 times faster than full-frame video data.

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