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9th International Symposium on Computing and Networking Workshops, CANDARW 2021

作者： Saito, Daiki Hirofuchi, Takahiro Arai, Hiroko Sato, Yukinori Toyohashi University of Technology Department of Computer Science And Engineering Aichi Japan Digital Architecture Research Center Tokyo Japan Research Center For Emerging Computing Technologies Tsukuba Japan

ISBN: (纸本)9781665428354

In order to reduce power consumption of the main memory of computers, the use of MRAM (magnetoresistive random access memory) instead of DRAM is attractive, since MRAM does not require periodic refresh energy while achieving sufficiently small read/write latencies. Among various types of MRAM devices, VC-MRAM (voltage-controlled MRAM) has a great potential to realize massively energy-efficient memory subsystems. Its write energy is 2 times orders of magnitudes smaller than that of STT-MRAM. The problem with VC-MRAM, however, is that its write error rate (i.e., 10-4 with today's typical technologies) is much higher than the acceptable error rate that has been supposed to be required for the main memory (i.e., 10-9). In this paper, we investigate whether such errors derived from an erroneous memory device cause a problem in actual program execution in the memory subsystem with hierarchical cache memory. We develop a memory subsystem simulator that mimics the structure of the multi-level caches and the main memory based on a VC-MRAM model, and observe cell-level operations in the main memory throughout the execution of benchmark programs. From results, we find the number of bit corruptions depends greatly on the cache size and the error model of the device. This is because the number of memory accesses itself can be significantly reduced by passing through the cache. Furthermore, we observe that the occurrences of bit corruptions more than 3 bits that cannot be handled properly by the hamming ECC codes seldom happen. This observation leads us to the insight that VC-MRAM with Hamming ECC can be used as main memory in some applications if these are executed on CPUs with a large cache memory. © 2021 IEEE.

关键词： MRAM devices

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Sentiment Analysis on Students’ Evaluation of Higher Educational Institutions

Sentiment Analysis on Students’ Evaluation of Higher Educat...

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International Conference on Intelligent and Fuzzy Systems, INFUS 2020

作者： Toçoğlu, Mansur Alp Onan, Aytuğ Department of Software Engineering Faculty of Technology Manisa Celal Bayar University Manisa45400 Turkey Department of Computer Engineering Faculty of Engineering and Architecture İzmir Katip Çelebi University İzmir35620 Turkey

ISBN: (纸本)9783030511555

Sentiment analysis is the method of identifying and classifying the views of users from text documents into different sentiments, such as positive, negative or neutral. Sentiment analysis can be employed to extract structured and informative knowledge from unstructured text pieces. This knowledge can serve as an important source of information for decision support systems and individual decision makers. Sentiment analysis plays an important role in many fields like education, where student input is crucial for assessing the effectiveness of educational institutions. In this paper, we present a machine learning based approach to sentiment analysis on students’ evaluation of higher educational institutions. We analyze a corpus containing approximately 700 student reviews written in Turkish, with the use of conventional text representation schemes and machine learning classifiers. In the experimental analysis, three conventional text representation schemes (i.e., term-presence, term-frequency, TF-IDF scheme) and three N-gram models (1-gram, 2-gram and 3-gram) have been considered in conjunction with four classifiers (i.e., support vector machines, Naïve Bayes, logistic regression, and random forest algorithm). The predictive performance of four ensemble learners (i.e., AdaBoost, Bagging and Random Subspace and voting algorithm) have been also evaluated. The empirical results indicate that machine learning based approach yields promising results on students’ evaluation of higher educational institutions. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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From Detection to Traversal: a Probabilistic Framework for UAS-Assisted Landmine Mapping and Circumvention

From Detection to Traversal: a Probabilistic Framework for U...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： J. Josiah Steckenrider Dongbin Kim Pratheek Manjunath Jack Simpson Charles Rosbrook Micah Weiss Department of Civil and Mechanical Engineering United States Military Academy West Point NY USA College of Engineering Technology and Architecture University of Hartford West Hartford CT USA Department of Electrical Engineering and Computer Science United States Military Academy West Point NY USA

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This research presents a robust probabilistic framework for minefield localization, mapping, and avoidance, addressing a technological gap in the field of aerial countermine intelligence, while bypassing the well-established techniques of landmine detection. Our approach propagates the pose uncertainty matrix delivered by a drone's flight controller's Kalman filter to probabilistically estimate the location of detected mines. This probability map then seeds an artificial potential field path generator which creates a safe path for ground traversal by producing waypoints through the minefield. The system's performance is evaluated in simulations and validated through flight trials, demonstrating its potential to improve the efficiency and safety of UAV-assisted minefield navigation and threat avoidance.

关键词： Location awareness Landmine detection Uncertainty System performance Atmospheric modeling Probabilistic logic Aircraft navigation Generators Safety Kalman filters

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Innovative Approaches in Network architecture: Enhancing Connectivity and Performance in the Era of 5G and Beyond

Innovative Approaches in Network Architecture: Enhancing Con...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Sunita Pachar Satti Sudha Mohan Reddy Lakshmi Narasimhan Srinivasagopalan Balla Mounica B Srinivasa Rao Sivakoteswararao Katta IBM Department GLA University Department of ECE SRKR Engineering College India Software Development Enterprise Architecture Plano TX USA Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science Hyderabad Ace Engineering College Hyderabad Department of Mechanical KIETW Kakinada

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

This research study examines the evolving ecosystem of network applications for enhancing connectivity and performance in 5G and beyond (B5G) networks. The objective is to streamline large-scale deployment of vertical applications through a middleware layer, facilitating interactions among network operators and third parties based on varying trust levels. The proposed model addresses the complexity and computational demands of enabling adaptable, secure, and scalable applications for diverse 5G platforms. In particular, this study highlights the 5G-EPICENTRE project, which enables traffic management and dynamic control, especially for missioncritical Public Protection and Disaster Relief (PPDR) applications. By optimizing resource allocation, the model reduces computational costs while meeting the unique demands of PPDR services, such as high-quality video and data for emergency operations. The model's effectiveness will be evaluated through experiments leveraging 5G Core (5GC) control-plane capabilities, with a focus on quality of service (QoS) and latency. Practical limitations, including the integration challenges of multi-network APIs, are discussed in the conclusion.

关键词： Adaptation models 5G mobile communication Computational modeling Scalability Ecosystems Quality of service Telecommunications Complexity theory Resource management Protection

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Towards Spiking Control for Dielectric Elastomer Actuators

Towards Spiking Control for Dielectric Elastomer Actuators

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Lukas Sohlbach René Harmann Fernando M. Quintana Heng Wei Zhang Fernando Perez-Peña Karsten Schmidt Department of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt/Main Germany Department of Informatics and Technology Engineering School of Engineering University of Cá diz Cádiz Spain Department of Automation Electronics and Computer Architecture and Networks School of Engineering University of Cádiz Cádiz Spain

Dielectric Elastomer Actuators (DEAs) can be seen as flexible capacitors, where an incompressible elastomer is sandwiched between two compliant electrodes. From the elastomer non-linear and time-dependent phenomena arise during the activation of the actuator. This results in the need for appropriate control methods, especially when they are used in the field of soft robots. Therefore, this paper presents a feedforward controller which is based on Spiking Neural Networks (SNNs). To the best of our knowledge this is the first time DEAs and SNNs are combined to make a step towards true soft robots. It was demonstrated that it is necessary to include feedback into the control scheme. Also, it was found that in the context of a SpiNN-3 development board as a neuromorphic hardware platform, neuron encoding is the most promising encoding method. This also implies that a classification approach is preferable to a regression approach.

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Event Graphs: Syntax, Semantics, and Implementation 23

Event Graphs: Syntax, Semantics, and Implementation

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Proceedings of the Winter Simulation Conference

作者： Murat M. Gunal Yahya Ismail Osais Gerd Wagner Faculty of Engineering and Architecture Fenerbahce University Ataşehir Istanbul TURKEY Computer Engineering Department Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran SAUDI ARABIA Brandenburg University of Technology Cottbus GERMANY

ISBN: (纸本)9798350369663

This tutorial aims to introduce Event Graphs (EGs), invented 40 years ago by Lee Schruben to allow event-based modeling of discrete dynamic systems. Their simplicity and naturalness in causality modelling and simulation modelling made EGs popular in research and practice. In a simulation, an event causes state changes in a system as well as other events to happen in the future. EGs provide a parsimonious diagram representation for the Event Scheduling paradigm of Discrete Event Simulation. We first introduce their visual syntax and informal semantics, and then present a recent extension by adding objects to EGs. Our tutorial also includes an introduction to the formal semantics of EGs and a Python implementation for executing EGs.

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Brain Surface Can Predict Fiber Connections

Brain Surface Can Predict Fiber Connections

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IEEE International Symposium on Biomedical Imaging

作者： Zili Kang Yifan Lv Mengshen He Yiheng Liu Tianming Liu Bao Ge School of Physics & Information Technology Shaanxi Normal University Xi’an China Department of Computer Science and Bioimaging Research Center Cortical Architecture Imaging and Discovery Lab The University of Georgia GA USA

It has been of great interest to study relationship between white matter and gray matter in brain science. Previous studies have found that that the formation of gray matter is closely related to white matter. This paper attempts explore the relationship between cerebral cortex and white matter from the perspective of brain image, which tries to predict fiber connection information by using brain cortical information alone. The extensive experiments on HCP dataset demonstrate that brain cortical surface can be used to predict the presence or absence of fibers between any two cortical regions with about 80% accuracy, furtherly, it can be used to predict the fractional anisotropy (FA) value for each fiber, the experiments show that the FA value can be also predicted with a percentage error of about 20%. These results demonstrate that the brain surface can be used to predict fiber connection information, and there is a close relationship between the gray matter and white matter fiber. This work provides a new perspective for further study of the relationship between cerebral cortex and axonal fiber.

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Deep Learning for Brassica Oleracea Instance Segmentation in UAV Imagery

Deep Learning for Brassica Oleracea Instance Segmentation in...

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Jonel R. Macalisang Alvin Sarraga Alon Julie Ann B. Susa Yolanda D. Austria Mark Melegrito Sammy V. Militante College of Industrial Technology Technological University of the Philippines Manila City Philippines Department of Trade and Industry Center for AI Research Makati City Philippines Department of Computer Engineering Southern Luzon State University Quezon Philippines Department of Computer Engineering Adamson University Manila Philippines Department of Electronics Engineering Technological University of the Philippines Manila Philippines College of Engineering and Architecture University of Antique Antique Philippines

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

This work introduces a new method for quantifying and sizing Brassica Oleracea instances via the use of unmanned aerial vehicle (UAV) imagery for instance segmentation. The goal of the project is to create a deep learning-based model that can recognize and classify Brassica Oleracea occurrences in images taken by unmanned aerial vehicles. The study uses the Mask R-CNN architecture and transfer learning strategies to train the model using a dataset of COCO-formatted annotated data and images of cabbage that was obtained from Mendeley Data. To improve resilience and variability, augmentation methods are used in dataset preparation. Evaluation criteria including mean Average Precision (mAP), precision, recall, and confidence levels are used to assess the model's performance. Findings show that the model performed very well, obtaining a remarkable mAP value of 98.6%. Moreover, inferencing test data shows how reliable the model is in identifying Brassica Oleracea occurrences in a variety of settings. The model continuously obtains high identification rates despite obstacles such as item incompleteness and illumination fluctuations, demonstrating its dependability and applicability for practical use in agricultural settings. The suggested strategy has the potential to advance crop management techniques and precision agriculture methods, enhancing agricultural sustainability and production.

关键词： Instance segmentation Accuracy Image recognition Crops Lighting Production Autonomous aerial vehicles Data models Sustainable development Context modeling

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Relip: Reliable in-Band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xinyu Zhou, Wangqiu Zhou, Hao Jiang, Shenyao Liu, Zhi Wang, Xiaoyan Ji, Yusheng Song, Qi City University of Hong Kong Department of Computer Science Hong Kong University of Science and Technology of China School of Computer Science and Technology Hefei230027 China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Ibaraki University Graduate School of Science and Engineering Ibaraki310-8512 Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-0003 Japan

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Inductive power transmission

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Energy-Efficient Clustered Data Dissemination Based Routing in D2D Collaborative Vehicular Networks

Energy-Efficient Clustered Data Dissemination Based Routing ...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Shital Y. Gaikwad G. Anjaneyulu Dalia Al-Tarazi M. Rama Vivek M. Korde Kaniga Perumal Department of Computer Science and Engineering Mahatma Gandhi Missions College of Engineering and Technology Nanded M.S India Department of CE QIS College of Engineering and Technology (QISCET) Ongole Department of Architecture Engg Faculty of Engineering Zarqa university Zarqa Jordan College of Engineering University of Business and Technology Jeddah SA Department of ECE QIS College of Engineering and Technology Ongole AP Dept of Mechanical Engg Yeshwantrao Chavan College of Engineering Nagpur India Faculty of Science and Technology Spectrum International University College Selangor Malaysia

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

VANETs are crucial for intelligent transportation systems, allowing V2V and V2I communication. A study introduces the ECDDR model using D2D for efficient data distribution and transmission in VANETs. The model optimizes shared uplink resources between mobile and vehicle user devices in a single-cell setup, focusing on large-scale fading for multi-hop ad-hoc routing. To enhance information dissemination, energy-efficient vehicle clustering is applied, with buses as Cluster Heads (CH) for improved resource utilization in urban settings. Experiments show the ECDDR model outperforms existing methods in data delivery ratio, network throughput, and energy efficiency while reducing routing cost and energy consumption. The model integrates D2D communication, multi-hop routing, and strategic clustering to boost reliability and efficiency in VANET operations, supporting smart city initiatives and autonomous vehicle technologies.

关键词： Costs Vehicle-to-infrastructure Vehicular ad hoc networks Spread spectrum communication Routing Throughput Energy efficiency Data models Device-to-device communication Reliability

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