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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Carrillo-Perez, Francisco Morales, Juan Carlos Castillo-Secilla, Daniel Guillen, Alberto Rojas, Ignacio Herrera, Luis Javier Department of Computer Architecture and Technology University of Granada. C.I.T.I.C. Periodista Rafael Gómez Montero 2 Granada18014 Spain

ISBN: (纸本)9783030881627

Lung cancer is one of the most frequent cancer types, and one among those causing more deceases worldwide. Nowadays, in order to improve the diagnosis of cancer more screenings are performed to the same patient and various biological sources are being gathered. Fusing the information provided by these sources can lead to a more robust diagnosis, which can improve the prognosis of the patient. In this work, a comparison of fusion methodologies (early and intermediate) using RNA-Seq and Copy Number Variation data for Non-Small-Cell Lung Cancer classification is performed. We found that great results can be attained using both fusion methodologies, with an AUC of 0.984 for the early fusion and 0.989 for the intermediate fusion, improving those obtained by each source of information independently (0.978 RNA-Seq and 0.910 Copy Number Variation). This work shows that fusion methodologies can enhance the classification of non-small-cell lung cancer, and that these methodologies can be promising for the diagnosis of other cancer types. © 2021, Springer Nature Switzerland AG.

关键词： RNA

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Analysis and Design of Mobile Model for DTN

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Chinese Journal of Electronics 2023年第4期18卷 635-638页

作者： Zhiting Lin Yugui Qu Yusheng Ji Baohua Zhao Department of Computer Science University of Science and Technology of China Hefei China Province Key Laboratory of Software in Computing and Communication Hefei China Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

Delay tolerant networks may become unexpectedly partitioned due to node mobility or variation in signal strength. However, most widely used models in some relative works are generally very simplistic. In order to exploit intelligent forwarding algorithms, a novel nodal mobile model of delay tolerant networks is presented to map reality with more accuracy. And several approaches are introduced to analyze the network structure, such as n-cliques, n-clans, degree, closeness and betweenness. Our research results showed that the centralizations became smaller when the wireless connections were concerned. It meant that quite a number of nodes became potential relays because of the new structure of networks.

关键词： Wireless communication Analytical models Wireless sensor networks Protocols Social networking (online) Sensitivity analysis Social network theory

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COVID-19 Detection Method from Chest CT Scans via the Fusion of Slice Information and Lung Segmentation 1st

COVID-19 Detection Method from Chest CT Scans via the Fusion...

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1st International Conference on Bioengineering and Biomedical Signal and Image Processing, BIOMESIP 2021

作者： Toledano Pavón, Jesús Morales Vega, Juan Carlos Carrillo-Perez, Francisco Herrera, Luis Javier Rojas, Ignacio Department of Computer Architecture and Technology University of Granada. C.I.T.I.C. Periodista Rafael Gómez Montero 2 Granada18014 Spain

ISBN: (纸本)9783030881627

Due to the high spread of the COVID-19 virus, several diagnosis support systems are being developed in order to detect the disease in a faster and accurate way. In this paper, a stacking method for Computed Tomography (CT) scans has been implemented for the pre-processing step. The method combines both slice normalization and lung segmentation in a single output image using RGB color channels, providing more information from the input slices to the CNN models. The binary classification step starts with a slice-level prediction, which applies fine-tuning to the whole model and dense layers are changed by a custom scheme to improve the performance. Then, a patient-level prediction is performed by fixing a threshold percentage of COVID positive slices that allows to make the final prediction, classifying patients as COVID or NORMAL. The accuracy and metrics obtained show the robustness of the presented method in comparison to using the normalised slices or the masks independently. Given the results obtained, the proposed method can accurately detect the COVID-19 disease and the fusion of information improves the results obtained. © 2021, Springer Nature Switzerland AG.

关键词： COVID-19

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A simulation of a memory subsystem using a highly energy-efficient but erroneous MRAM 9

A simulation of a memory subsystem using a highly energy-eff...

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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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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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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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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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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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