Convolutional network - PowerPoint PPT Presentation


Apache MINA: High-performance Network Applications Framework

Apache MINA is a robust framework for building high-performance network applications. With features like non-blocking I/O, event-driven architecture, and enhanced scalability, MINA provides a reliable platform for developing multipurpose infrastructure and networked applications. Its strengths lie i

3 views • 13 slides


Modeling and Generation of Realistic Network Activity Using Non-Negative Matrix Factorization

The GHOST project focuses on the challenges of modeling, analyzing, and generating patterns of network activity. By utilizing Non-Negative Matrix Factorization (NMF), realistic network activity patterns can be created and injected into live wireless networks. Understanding and predicting user behavi

4 views • 28 slides



Automated Anomaly Detection Tool for Network Performance Optimization

Anomaly Detection Tool (ADT) aims to automate the detection of network degradation in a mobile communications network, reducing the time and effort required significantly. By utilizing statistical and machine learning models, ADT can generate anomaly reports efficiently across a large circle network

8 views • 7 slides


Localised Adaptive Spatial-Temporal Graph Neural Network

This paper introduces the Localised Adaptive Spatial-Temporal Graph Neural Network model, focusing on the importance of spatial-temporal data modeling in graph structures. The challenges of balancing spatial and temporal dependencies for accurate inference are addressed, along with the use of distri

3 views • 19 slides


Exploring a Cutting-Edge Convolutional Neural Network for Speech Emotion Recognition

Human speech is a rich source of emotional indicators, making Speech Emotion Recognition (SER) vital for intelligent systems to understand emotions. SER involves extracting emotional states from speech and categorizing them. This process includes feature extraction and classification, utilizing tech

1 views • 15 slides


Revolutionizing Network Management with Intent-Based Networking

Explore the concept and benefits of Intent-Based Networking (IBN) in simplifying network configuration and enhancing efficiency. Learn how IBN automates network operations, aligns with business objectives, improves security, and ensures scalability and reliability. Discover the potential of IBN tool

0 views • 14 slides


Network Compression Techniques: Overview and Practical Issues

Various network compression techniques such as network pruning, knowledge distillation, and parameter quantization are discussed in this content. The importance of pruning redundant weights and neurons in over-parameterized networks is highlighted. Practical issues like weight pruning and neuron pru

0 views • 37 slides


Network Slicing with OAI 5G CN Workshop Overview

Overview of Network Slicing with OAI 5G CN workshop focusing on the crucial role of network slicing in realizing the service-oriented 5G vision. This workshop covers topics like multiple logical networks creation on shared infrastructure, different types of network slices, preparation and instantiat

1 views • 6 slides


Network Design Challenges and Solutions in Business Data Communications

Issues in designing a Local Area Network (LAN) include needs analysis, technological design, and cost assessment. The traditional approach involves structured systems analysis, but faces challenges due to rapidly changing technology and increasing network traffic. The Building Blocks Approach recomm

1 views • 20 slides


Understanding Convolutional Codes in Digital Communication

Convolutional codes provide an efficient alternative to linear block coding by grouping data into smaller blocks and encoding them into output bits. These codes are defined by parameters (n, k, L) and realized using a convolutional structure. Generators play a key role in determining the connections

0 views • 19 slides


Automated Melanoma Detection Using Convolutional Neural Network

Melanoma, a type of skin cancer, can be life-threatening if not diagnosed early. This study presented at the IEEE EMBC conference focuses on using a convolutional neural network for automated detection of melanoma lesions in clinical images. The importance of early detection is highlighted, as exper

0 views • 34 slides


Understanding 5G RAN Network Slicing and Architecture

Explore the intricate world of 5G Radio Access Network (RAN) and Network Slicing, delving into concepts such as SO Service Orchestrator, SDN-C Service Design, and Core Network Elements. Discover the significance of managing and designing mobile slice services, including eMBB, Massive IoT, and Missio

0 views • 26 slides


Understanding Snort: An Open-Source Network Intrusion Detection System

Snort is an open-source Network Intrusion Detection System (NIDS) developed by Cisco, capable of analyzing network packets to identify suspicious activities. It can function as a packet sniffer, packet logger, or a full-fledged intrusion prevention system. By monitoring and matching network activity

0 views • 23 slides


Data Flows and Network Challenges in Particle Physics Infrastructure

This overview delves into the data flows and network challenges faced in particle physics infrastructure, focusing on the JUNO project. It discusses the process of data reception, storage, and replication across various data centers, highlighting the bidirectional nature of data flows. Additionally,

0 views • 24 slides


Recent Developments on Super-Resolution: A Comprehensive Overview

Super-resolution technology aims to reconstruct high-resolution images from low-resolution inputs, with applications in video surveillance, medical diagnosis, and remote sensing. Various convolutional neural network (CNN) models have been developed, such as SRCNN, VDSR, ESPCN, and FSRCNN, each with

0 views • 12 slides


Understanding U-Net: A Convolutional Network for Image Segmentation

U-Net is a convolutional neural network designed for image segmentation. It consists of a contracting path to capture context and an expanding path for precise localization. By concatenating high-resolution feature maps, U-Net efficiently handles information loss and maintains spatial details. The a

0 views • 8 slides


EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization

This study introduces the EEG Conformer, a Convolutional Transformer model designed for EEG decoding and visualization. The research presents a cutting-edge approach in neural systems and rehabilitation engineering, offering advancements in EEG analysis techniques. By combining convolutional neural

1 views • 6 slides


Understanding Convolutional Neural Networks: Architectural Characterizations for Accuracy Inference

This presentation by Duc Hoang from Rhodes College explores inferring the accuracy of Convolutional Neural Networks (CNNs) based on their architectural characterizations. The talk covers the MINERvA experiment, deep learning concepts including CNNs, and the significance of predicting CNN accuracy be

0 views • 21 slides


Progress of Network Architecture Work in FG IMT-2020

In the Network Architecture Group led by Namseok Ko, significant progress has been made in defining the IMT-2020 architecture. The work has involved gap analysis, draft recommendations, and setting framework and requirements. Phase 1 focused on identifying 19 architectural gaps, such as demands for

1 views • 11 slides


CNN-based Multi-task Learning for Crowd Counting: A Novel Approach

This paper presents a novel end-to-end cascaded network of Convolutional Neural Networks (CNNs) for crowd counting, incorporating high-level prior and density estimation. The proposed model addresses the challenge of non-uniform large variations in scale and appearance of objects in crowd analysis.

1 views • 17 slides


Exploring DRONET: Learning to Fly by Driving

DRONET presents a novel approach to safe and reliable outdoor navigation for Autonomous Underwater Vehicles (AUVs), addressing challenges such as obstacle avoidance and adherence to traffic laws. By utilizing a Residual Convolutional Neural Network (CNN) and a custom outdoor dataset, DRONET achieves

0 views • 20 slides


Understanding Interconnection Networks Topology

Exploring the topology of interconnection networks helps determine the arrangement of channels and nodes, impacting network cost, performance, latency, energy consumption, and complexity of implementation. Abstract metrics such as degree, hop count, and network diameter play crucial roles in evaluat

1 views • 56 slides


Transportation Network Modeling and Analysis with C.Coupled SE Platform

This content outlines the features and functionalities of the C.Coupled SE Platform (CSET Platform) developed by the Connetics Transportation Group. It covers aspects such as interface design, inputs merging, purposes, platform development using Cube, TAZs merging, and network attributes. The platfo

0 views • 11 slides


Wavelet-based Scaleograms and CNN for Anomaly Detection in Nuclear Reactors

This study utilizes wavelet-based scaleograms and a convolutional neural network (CNN) for anomaly detection in nuclear reactors. By analyzing neutron flux signals from in-core and ex-core sensors, the proposed methodology aims to identify perturbations such as fuel assembly vibrations, synchronized

3 views • 11 slides


Convolutional Neural Networks for Sentence Classification: A Deep Learning Approach

Deep learning models, originally designed for computer vision, have shown remarkable success in various Natural Language Processing (NLP) tasks. This paper presents a simple Convolutional Neural Network (CNN) architecture for sentence classification, utilizing word vectors from an unsupervised neura

0 views • 15 slides


Human Disease Symptom Network: Understanding Disease Relationships Through Symptoms and Genes

The Human Disease Symptom Network (HSDN) is constructed using a large-scale medical bibliographic records database to form a network of human diseases based on symptom similarities. By integrating disease-gene associations and protein-protein interaction data, correlations between symptom similarity

0 views • 37 slides


Introduction to Network Analysis Using .NET

This presentation introduces the concept of network analysis using .NET in the humanities classroom. It provides a template for teaching and adapting network analysis tools for educational purposes. The guide explains the relevance of networks in processing and visualizing data, emphasizing the coll

0 views • 20 slides


Meridian: An SDN Platform for Cloud Network Services

Meridian is an SDN platform developed by Mohammad Banikazemi, David Olshefski, Anees Shaikh, John Tracey, and GuohuiWang at IBM T. J. Watson Research Center. The platform focuses on providing cloud network services efficiently. It encompasses an architecture that enables faster and more convenient n

0 views • 21 slides


Enhancing Network Security with Software-Defined Snort and OpenFlow

Explore the implementation of Snort, Barnyard, and PulledPork within a Software-Defined Network framework using OpenFlow technology. Learn how these tools enhance network security through intrusion detection engines, rule management, and network traffic control mechanisms. Dive into the architecture

0 views • 15 slides


Unified Features Learning for Buggy Source Code Localization

Bug localization is a crucial task in software maintenance. This paper introduces a novel approach using a convolutional neural network to learn unified features from bug reports in natural language and source code in programming language, capturing both lexicon and program structure semantics.

0 views • 18 slides


Understanding Network Metrics Through Centrality Analysis

This presentation introduces network metrics as tools to describe network characteristics and answer important questions. Using centrality metrics as an example, participants learn how to identify the most important nodes in a network based on different criteria such as degree centrality and closene

0 views • 15 slides


Deep Learning for Plant Disease Resistance Analysis

Utilizing deep learning facilitated microscopy, a research team led by Hening Cui from Columbia University aims to dissect durable resistance to plant diseases. The project focuses on segmenting hyphal networks of fungal and host plant cells using a deep convolutional neural network architecture cal

0 views • 13 slides


Understanding Network Analysis: Whole Networks vs. Ego Networks

Explore the differences between Whole Networks and Ego Networks in social network analysis. Whole Networks provide comprehensive information about all nodes and links, enabling the computation of network-level statistics. On the other hand, Ego Networks focus on a sample of nodes, limiting the abili

0 views • 31 slides


Exploring Efficient Hardware Architectures for Deep Neural Network Processing

Discover new hardware architectures designed for efficient deep neural network processing, including SCNN accelerators for compressed-sparse Convolutional Neural Networks. Learn about convolution operations, memory size versus access energy, dataflow decisions for reuse, and Planar Tiled-Input Stati

0 views • 23 slides


Unveiling Convolutional Neural Network Architectures

Delve into the evolution of Convolutional Neural Network (ConvNet) architectures, exploring the concept of "Deeper is better" through challenges, winner accuracies, and the progression from simpler to more complex designs like VGG patterns and residual connections. Discover the significance of layer

0 views • 22 slides


Understanding Convolutional Neural Networks (CNN) in Depth

CNN, a type of neural network, comprises convolutional, subsampling, and fully connected layers achieving state-of-the-art results in tasks like handwritten digit recognition. CNN is specialized for image input data but can be tricky to train with large-scale datasets due to the complexity of replic

0 views • 22 slides


Network Function Virtualization (NFV) Overview

Network Function Virtualization (NFV) focuses on virtualizing network functions to improve efficiency and reduce costs in network infrastructure. The lecture discusses key readings, devices that compose a network, specialization of devices, benefits of one-device-does-anything approach, and the goal

0 views • 21 slides


Enhancing Network Stability with Network Monitoring Systems

Network monitoring is crucial for efficient management and proactive issue detection in a network environment. Factors influencing an effective network system include choosing the best OEM, SLA agreements, and selecting a reliable System Integrator. Reactive monitoring can lead to financial losses a

0 views • 12 slides


Accelerating Systemic Change Network Inaugural Workshop Summary

The Accelerating Systemic Change Network held its inaugural workshop at Howard Hughes Medical Institute in July 2016 to address the lack of coordination in improving higher education. With a vision to become a professional hub for change researchers in STEM education, the network aims to enhance ind

0 views • 5 slides


University Network Section Overview July 2015 - March 2016

The presentation covers the network team structure, team members, objectives, goals, report outline, network statistics, accomplishments, and future plans of the university network section from July 2015 to March 2016. It highlights efforts to provide stable internet and intranet services, restructu

0 views • 16 slides