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Neuromorphic circuits on segmented crossbar architectures with enhanced properties 24

Neuromorphic circuits on segmented crossbar architectures wi...

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24th ieee European conference on Circuit Theory and Design (ECCTD)

作者： Ntinas, Vasileios Karakolis, Panagiotis Sirakoulis, Georgios Ch Dimitrakis, Panagiotis Democritus Univ Thrace Dept Elect & Comp Engn Xanthi Greece NCSR Demokritos Inst Nanosci & Nanotechnol Athens Greece Univ Polytecn Catalunya Dept Elect Engn Barcelona Spain

ISBN: (纸本)9781728171838

General purpose processors have been used in a wide variety of computational and modeling applications. However, their performance is not always sufficient when simulating neural networks, which are widely applied to signal processing and pattern recognition. In this work, after a systematic study of the computational requirements of such neural networks and an exploration of the available hardware solutions through which the aforementioned applications can be accelerated, a modern neuromorphic circuit structure is proposed with its operation attributed to memristor devices and segmented crossbar architecture. By coupling these two technologies, neuromorphic circuits have been designed with high computational performance versus integration scale and power consumption. An Ex-Situ training paradigm based on the advantageous memristor segmented crossbar is proposed, using the MNIST dataset and resulting at 97% accuracy. At the same time, a novel memristor tuning method on 1D1M configuration has been developed, able to increase the memristor programming speed.

关键词： Memristors

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Using a Novel Unbiased Dataset and Deep Learning architectures to Predict Protein-Protein Interactions

Using a Novel Unbiased Dataset and Deep Learning Architectur...

引用

ieee International conference on Bioinformatics and Biomedicine (ieee BIBM)

作者： Silva, Luis Pereira, Carlos Arrais, Joel P. Univ Coimbra Dept Informat Engn Coimbra Portugal Polytech Inst Coimbra Inst Engn IPC ISEC Coimbra Portugal

ISBN: (纸本)9781728162157

Proteins are indispensable to the living organisms and are the backbone of almost all cellular processes. However, these macromolecules rarely act alone, forming the protein-protein interactions. Given their biological significance it should come as no surprise that their deregulation is one of the main causes to several disease states. The sudden surge of interest in this field of study motivated the development of innovative in silico methods. Despite the obvious advances in recent years, the effectiveness of these computational methods remains questionable. There is still not enough evidence to support the exclusive use of in silico techniques to predict protein-protein interactions not yet experimentally determined. It is proved that one of the primary reasons leading to this situation is the non-existence of a "gold-standard" negative interactions dataset. Contrary to the high abundance of publicly available positive interactions, the negative examples are often artificially generated, culminating in biased samples. In this paper a new unbiased dataset that does not overly constraint the negative interactions distribution is presented. Beyond the novel dataset, also distinct deep learning models are proposed as a tool to predict whether two individual proteins are capable of interacting with each other, using exclusively the complete raw amino acid sequences. The obtained results firmly indicate that the proposed models are actually a valuable tool to predict protein-protein interactions, mainly when compared with the existing approaches, while also highlighting that there is still some room for improvement when implemented in unbiased datasets.

关键词： Protein-Protein Interaction Datasets Deep Learning

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Deep Learning for Linear Inverse Problems Using the Plug-and-Play Priors Framework

Deep Learning for Linear Inverse Problems Using the Plug-and...

引用

ieee International conference on Acoustics, Speech and Signal Processing

作者： Wei Chen David Wipf Miguel Rodrigues State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University China Amazon AI Research Lab Shanghai China University College London UK

Linear inverse problems appear in many applications, where different algorithms are typically employed to solve each inverse problem. Nowadays, the rapid development of deep learning (DL) provides a fresh perspective for solving the linear inverse problem: a number of well-designed network architectures results in state-of-the-art performance in many applications. In this overview paper, we present the combination of the DL and the Plug-and-Play priors (PPP) framework, showcasing how it allows solving various inverse problems by leveraging the impressive capabilities of existing DL based denoising algorithms. open challenges and potential future directions along this line of research are also discussed.

关键词： Deep learning Inverse problems Noise reduction Neural networks Signal processing algorithms Signal processing network architecture

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Raspberry Pi as Reconfigurable Fiber-based Router and Its Performance Analysis 8

Raspberry Pi as Reconfigurable Fiber-based Router and Its Pe...

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8th ieee International conference on Photonics (ICP)

作者： Nasir, Muhammad Haqeem Mohd Ahmad, Wan Siti Halimatul Munirah Wan Radzi, Nurul Asyikin Mohd Abdullah, Fairuz Abdullah, Azlina Ismail, Aiman Jamaludin, Md Zaini Univ Tenaga Nas Inst Power Engn Kajang Selangor Malaysia Univ Tenaga Nas Dept Elect & Elect Engn Coll Engn Kajang Selangor Malaysia

ISBN: (纸本)9781728105185

Passive Optical network (PON) has evolved drastically in order to provide greater speed and better reliability such as Next Generation PON1 (NG-PON1) and NG-PON2 which able to provide more than 40 Gbps and 128 Gbps of bandwidth respectively. Due to rapid growth of PON technologies, researchers have conducted many thorough experiments in order to enhance current PON-base technologies in a form of testbeds. However, most of the studies show that the testbeds have limitations either on its configurability, compactness or cost-efficiency. Hence, this motivates us to propose a lab-scale router testbed in PON architecture by using Raspberry Pi for its promising features and size. The experiments were done in a lab-scale area because the proposed router testbed is to provide a proof-of-concept solution of a reconfigurability router in PON. The results of the experiments are in terms of throughput, end-to-end delay and jitter for both downstream and upstream transmissions. After conducting the experiments, we have come to a conclusion that Raspberry Pis are suitable to be used as lab-scale routers in fiber networking architectures due to their reconfigurability, open source kernel, space-friendly and cost-efficient.

关键词： Raspberry Pi Passive Optical network router

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Bidirectional multi-tier cognitive swarm drone 5G network

Bidirectional multi-tier cognitive swarm drone 5G network

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2020 ieee INFOCOM conference on Computer Communications Workshops, INFOCOM WKSHPS 2020

作者： Jacob, Sunil Menon, Varun G. Shynu, P.G. Fathima Shemim, K.S. Mahapatra, Bandana Joseph, Saira SCMS Center for Robotics SCMS School of Engineeeirng and Technology Ernakulam India SCMS School of Engineeeirng and Technology Department of Computer Science and Engineering Ernakulam India School of Information Technology and Engineering Vellore Institute of Technology Vellore India University of Bolton Department of Computing and IT Ras Al Khaimah United Arab Emirates Symbiosis Skills and Open University Department of Data Science Kiwale Pune India SCMS School of Engineeeirng and Technology Department of Electronics and Communication Engineering Ernakulam India

ISBN: (纸本)9781728186955

The use of unmanned aerial vehicle (UAV) as a dynamic node in point to point communication of 5G and beyond 5G cellular networks is an emerging technology. It provides a reliable and efficient mode of wireless communication network in the sky. Existing architectures include single tier and multi-tier drone architecture. Major challenges in multi-tier architecture is in establishing an air traffic control system and in efficient resource sharing between the drones. To overcome the challenges associated with the multitier drone and cellular drone networks, a bidirectional multitier cognitive swarm drone network (BMCSDN) is proposed and investigated in terms of spectrum efficiency and allocation of optimized altitude and power for down link transmission. The proposed method uses the cognitive swarm drone (CSD) concept and provides enhancement of capacity and coverage with optimized transmission efficiency. The work also analyses the effect of interference when all CSDs transmit and receive with static and dynamic frequency allocations. © 2020 ieee.

关键词： 5G mobile communication systems

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CNN-based Ego-Motion Estimation for Fast MAV Maneuve

CNN-based Ego-Motion Estimation for Fast MAV Maneuve

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ieee International conference on Robotics and Automation (ICRA)

作者： Yingfu Xu Guido C. H. E. de Croon Micro Air Vehicle Laboratory Faculty of Aerospace Engineering Delft University of Technology The Netherlands

In the field of visual ego-motion estimation for Micro Air Vehicles (MAVs), fast maneuvers stay challenging mainly because of the big visual disparity and motion blur. In the pursuit of higher robustness, we study convolutional neural networks (CNNs) that predict the relative pose between subsequent images from a fast-moving monocular camera facing a planar scene. Aided by the Inertial Measurement Unit (IMU), we mainly focus on translational motion. The networks we study have similar small model sizes (around 1.35MB) and high inference speeds (around 10 milliseconds on a mobile GPU). Images for training and testing have realistic motion blur. Departing from a network framework that iteratively warps the first image to match the second with cascaded network blocks, we study different network architectures and training strategies. Simulated datasets and a self-collected MAV flight dataset are used for evaluation. The proposed setup shows better accuracy over existing networks and traditional feature-point-based methods during fast maneuvers. Moreover, self-supervised learning outperforms supervised learning. Videos and open-sourced code are available at https://github. com/tudelft/PoseNet_Planar

关键词： Training Visualization Supervised learning Estimation network architecture Cameras Unmanned aerial vehicles

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Mixed Integer Linear programming Models for Scheduling Elective Surgical Procedures 1

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20th International conference on Computational Science and Its Applications (ICCSA)

作者： Hortencio, Hanna Pamplona Ronconi, Debora Pretti Univ Sao Paulo Dept Prod Engn EP USP Av Prof Almeida Prado 128Cidade Univ BR-05508900 Sao Paulo Brazil

ISBN: (数字)9783030588083

ISBN: (纸本)9783030588083;9783030588076

The problem of scheduling surgeries consists of allocating patients and resources to each surgical stage, considering the patient's needs, as well as sequencing and timing constraints. This problem is classified as NP-hard and has been widely discussed in the literature for the past 60 years. Nevertheless, many authors do not take into account the multiple stages and resources required to address the complex aspects of operating room management. The general goal of this paper is to propose a mathematical model to represent and solve this problem. Computational tests were also performed to compare the proposed model with a similar model from the literature, with a 64% average reduction in computational time.

关键词： Scheduling Surgery Mixed integer linear programming Multiple resources Multiple stages

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Optimization of the Use of Critical Resources in the Development of Offshore Oil Fields 20th

Optimization of the Use of Critical Resources in the Develop...

引用

20th International conference on Computational Science and Its Applications (ICCSA)

作者： Bassi, Sergio Ronconi, Debora Pretti Univ Sao Paulo Prod Engn Dept BR-05508010 Sao Paulo Brazil

ISBN: (纸本)9783030587994;9783030587987

This paper focuses on a real case of connection of subsea oil wells to offshore platforms using pipe laying support vessels. The objective of this study is to maximize the oil production curve through an optimized use of the out-sourced fleet. Specific features of this scenario are considered, such as technical constraints of each vessel, the availability of the vessels, materials for connection, and the end of the previous phase, called completion. A mixed integer linear programming model is developed considering several constraints that structure this complex situation, among which a relevant characteristic of the problem: the increase of the production curve using injection wells to fight the natural decline of producing wells over time. This mathematical model was tested in small computational instances, showing adequate behavior, which demonstrates that it faithfully represents the situation portrayed and can be used, combined with more advanced computational resources, to achieve better results.

关键词： Oil wells Routing problem Mixed integer linear programming

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An Approximation Algorithm for Maximizing Product Modularity 6

An Approximation Algorithm for Maximizing Product Modularity

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6th International conference on Control, Automation and Robotics (ICCAR)

作者： Liu, Fang Du, Shaofeng Hong, Zhenjun Wu, Yanzhao Wang, Feng Yin, Yong Li, Dongni Beijing Inst Technol Sch Comp Sci Beijing Peoples R China Inner Mongolia First Machinery Grp Corp State Key Lab Smart Mfg Special Vehicles & Transm Baotou Peoples R China Doshisha Univ Grad Sch Business Kyoto Japan

ISBN: (纸本)9781728161396

A complex product can be described in terms of its product architecture. There are two product architectures: integral and modular. Advantages of modular products have been noted in the literature. Maximizing modularity is a critical issue in modular product design. In this study, a polynomial approximation algorithm with a 0.422 approximation ratio is proposed to find hidden modules. It is observed that better modularity can be achieved when the product is partitioned into 3 to 8 modules. Numerical experiments with applications in the products of bicycle, starter, and fruit chute system are conducted to illustrate the developed algorithm. Performance of the algorithm is demonstrated by comparisons with other well-known algorithms.

关键词： roduct modularity approximation algorithm semidefinite programming product design

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A Resource Constraint Approach for One Global Constraint MINLP 1

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20th International conference on Computational Science and Its Applications (ICCSA)

作者： Muts, Pavlo Nowak, Ivo Hendrix, Eligius M. T. Hamburg Univ Appl Sci Hamburg Germany Univ Malaga Comp Architecture Malaga 29080 Spain

ISBN: (数字)9783030588083

ISBN: (纸本)9783030588083;9783030588076

Many industrial optimization problems are sparse and can be formulated as block-separable mixed-integer nonlinear programming (MINLP) problems, where low-dimensional sub-problems are linked by a (linear) knapsack-like coupling constraint. This paper investigates exploiting this structure using decomposition and a resource constraint formulation of the problem. The idea is that one outer approximation master problem handles sub-problems that can be solved in parallel. The steps of the algorithm are illustrated with numerical examples which shows that convergence to the optimal solution requires a few steps of solving sub-problems in lower dimension.

关键词： Decomposition Parallel computing Column generation Global optimization Mixed-integer nonlinear programming

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