Future networks - PowerPoint PPT Presentation


Computational Physics (Lecture 18)

Neural networks explained with the example of feedforward vs. recurrent networks. Feedforward networks propagate data, while recurrent models allow loops for cascade effects. Recurrent networks are less influential but closer to the brain's function. Introduction to handwritten digit classification

0 views • 55 slides


Evolution and Potential of 5G Technology

Explore the evolving landscape of 5G technology, from enhanced mobile broadband to groundbreaking use cases and standalone networks. Learn how supportive regulations and spectrum allocation are vital for unlocking 5G's full potential. Discover the transformative impact of Standalone 5G networks on i

8 views • 10 slides



Future Connect 2023 Summary Document Overview

Future Connect 2023 is a core guidance resource for Auckland's strategic network, providing a whole-system view of the networks across different modes of transport. It outlines key challenges, informs investment planning decisions, and has been updated in preparation for the 2024-2034 Regional Land

7 views • 18 slides


Introduction to Deep Learning: Neural Networks and Multilayer Perceptrons

Explore the fundamentals of neural networks, including artificial neurons and activation functions, in the context of deep learning. Learn about multilayer perceptrons and their role in forming decision regions for classification tasks. Understand forward propagation and backpropagation as essential

2 views • 74 slides


Understanding Computer Networks: Types and Characteristics

In the realm of computer networks, nodes share resources through digital telecommunications networks. These networks enable lightning-fast data exchange and boast attributes like speed, accuracy, diligence, versatility, and vast storage capabilities. Additionally, various types of networks exist tod

9 views • 12 slides


Graph Neural Networks

Graph Neural Networks (GNNs) are a versatile form of neural networks that encompass various network architectures like NNs, CNNs, and RNNs, as well as unsupervised learning models such as RBM and DBNs. They find applications in diverse fields such as object detection, machine translation, and drug d

2 views • 48 slides


Understanding Artificial Neural Networks From Scratch

Learn how to build artificial neural networks from scratch, focusing on multi-level feedforward networks like multi-level perceptrons. Discover how neural networks function, including training large networks in parallel and distributed systems, and grasp concepts such as learning non-linear function

1 views • 33 slides


Understanding Back-Propagation Algorithm in Neural Networks

Artificial Neural Networks aim to mimic brain processing. Back-propagation is a key method to train these networks, optimizing weights to minimize loss. Multi-layer networks enable learning complex patterns by creating internal representations. Historical background traces the development from early

1 views • 24 slides


Exploring Samsung SmartThings Hub and Zigbee/Zwave Networks

The Samsung SmartThings hub is a versatile device connecting Zigbee and Zwave networks, offering secure access to SkySpark via HTTPS. Zigbee and Zwave networks operate on distinct frequencies, enabling efficient communication without interference with WiFi. These networks support various devices for

0 views • 19 slides


Understanding Wireless Wide Area Networks (WWAN) and Cellular Network Principles

Wireless Wide Area Networks (WWAN) utilize cellular network technology like GSM to facilitate seamless communication for mobile users by creating cells in a geographic service area. Cellular networks are structured with backbone networks, base stations, and mobile stations, allowing for growth and c

2 views • 17 slides


Understanding Interconnection Networks in Multiprocessor Systems

Interconnection networks are essential in multiprocessor systems, linking processing elements, memory modules, and I/O units. They enable data exchange between processors and memory units, determining system performance. Fully connected interconnection networks offer high reliability but require ext

1 views • 19 slides


Understanding Computer Networks in BCA VI Semester

Computer networks are vital for sharing resources, exchanging files, and enabling electronic communications. This content explores the basics of computer networks, the components involved, advantages like file sharing and resource sharing, and different network computing models such as centralized a

1 views • 96 slides


Understanding Computer Communication Networks at Anjuman College

This course focuses on computer communication networks at Anjuman College of Engineering and Technology in Tirupati, covering topics such as basic concepts, network layers, IP addressing, hardware aspects, LAN standards, security, and administration. Students will learn about theoretical and practic

0 views • 72 slides


Introduction to Neural Networks in IBM SPSS Modeler 14.2

This presentation provides an introduction to neural networks in IBM SPSS Modeler 14.2. It covers the concepts of directed data mining using neural networks, the structure of neural networks, terms associated with neural networks, and the process of inputs and outputs in neural network models. The d

0 views • 18 slides


Enhancing Agriculture Through Global Knowledge Networks and Information Management Systems

Global and regional knowledge networks play a vital role in agriculture by facilitating information sharing, collaboration, capacity building, and coordination among stakeholders. These networks improve access to information, foster collaboration, enhance capacity building, and strengthen coordinati

0 views • 5 slides


Understanding Router Routing Tables in Computer Networks

Router routing tables are crucial for directing packets to their destination networks. These tables contain information on directly connected and remote networks, as well as default routes. Routers use this information to determine the best path for packet forwarding based on network/next hop associ

0 views • 48 slides


P-Rank: A Comprehensive Structural Similarity Measure over Information Networks

Analyzing the concept of structural similarity within Information Networks (INs), the study introduces P-Rank as a more advanced alternative to SimRank. By addressing the limitations of SimRank and offering a more efficient computational approach, P-Rank aims to provide a comprehensive measure of si

0 views • 17 slides


Understanding Advanced Classifiers and Neural Networks

This content explores the concept of advanced classifiers like Neural Networks which compose complex relationships through combining perceptrons. It delves into the workings of the classic perceptron and how modern neural networks use more complex decision functions. The visuals provided offer a cle

0 views • 26 slides


Understanding Future Progressive Tense in English Grammar

Future Progressive Tense, also known as Future Continuous Tense, is used to indicate actions that will be ongoing at a specific point in the future. This tense emphasizes the continuous nature of an action happening in the future. Learn how to form and use the Future Progressive Tense through exampl

0 views • 22 slides


Mastering the Future Tense in Spanish

Learn how to use the future tense in Spanish to improve your language skills and achieve better grades. Explore the two future tenses in Spanish, the near future structure with "ir + infinitive," and the simple future with verb endings. Practice translating sentences to and from Spanish to strengthe

0 views • 9 slides


Understanding Future Simple Tense in English Grammar

The Future Simple Tense in English is used to express decisions made at the moment of speaking, announce planned future events, show willingness or unwillingness for future actions, express thoughts, and talk about events certain to happen. It is often accompanied by adverbs to indicate timing. Ques

0 views • 11 slides


Understanding Relational Bayesian Networks in Statistical Inference

Relational Bayesian networks play a crucial role in predicting ground facts and frequencies in complex relational data. Through first-order and ground probabilities, these networks provide insights into individual cases and categories. Learning Bayesian networks for such data involves exploring diff

0 views • 46 slides


Understanding Overlay Networks and Distributed Hash Tables

Overlay networks are logical networks built on top of lower-layer networks, allowing for efficient data lookup and reliable communication. They come in unstructured and structured forms, with examples like Gnutella and BitTorrent. Distributed Hash Tables (DHTs) are used in real-world applications li

0 views • 45 slides


Understanding Networks: An Introduction to the World of Connections

Networks define the structure of interactions between agents, portraying relationships as ties or links. Various examples such as the 9/11 terrorists network, international trade network, biological networks, and historical marriage alliances in Florence illustrate the power dynamics within differen

0 views • 46 slides


Understanding Graph Theory and Networks: Concepts and Applications

Explore the concepts of graph theory and management science, focusing on networks, spanning trees, and their practical applications. Learn about the difference between a snowplow tracing streets, a traveler visiting cities, and connecting towns with cables. Discover how networks like Facebook evolve

0 views • 15 slides


Parallel Prefix Networks in Divide-and-Conquer Algorithms

Explore the construction and comparisons of various parallel prefix networks in divide-and-conquer algorithms, such as Ladner-Fischer, Brent-Kung, and Kogge-Stone. These networks optimize computation efficiency through parallel processing, showcasing different levels of latency, cell complexity, and

1 views • 21 slides


Bio-inspired Networking and Complex Networks: A Survey

This survey explores the challenges in future wireless networks, bio-inspired networking principles, complex networks, and bio-inspired math modeling. It covers topics like self-organization, autonomous actions, foraging behavior of ant colonies, and more.

0 views • 22 slides


Diverse Social Entities Mining from Linked Data in Social Networks

This research focuses on mining diverse social entities from linked data in social networks using a DF-tree structure and DF-growth mining algorithm. The study explores the extraction of important linked data in social networks and the mining of various social entities such as friends. Prominence va

0 views • 13 slides


Understanding Bayesian Networks: A Comprehensive Overview

Bayesian networks, also known as Bayes nets, provide a powerful tool for modeling uncertainty in complex domains by representing conditional independence relationships among variables. This outline covers the semantics, construction, and application of Bayesian networks, illustrating how they offer

0 views • 17 slides


Machine Learning and Artificial Neural Networks for Face Verification: Overview and Applications

In the realm of computer vision, the integration of machine learning and artificial neural networks has enabled significant advancements in face verification tasks. Leveraging the brain's inherent pattern recognition capabilities, AI systems can analyze vast amounts of data to enhance face detection

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 Job Information Networks and Neighborhood Effects

The study delves into the impact of job information networks and neighborhood effects on inequality, emphasizing the use of personal connections in job searches. It discusses the role of network size, resources, and types of connections in job acquisition, highlighting key stylized facts observed in

0 views • 20 slides


Evolution of Networking: Embracing Software-Defined Networks

Embrace the future of networking by transitioning to Software-Defined Networks (SDN), overcoming drawbacks of current paradigms. Explore SDN's motivation, OpenFlow API, challenges, and use-cases. Compare the complexities of today's distributed, error-prone networks with the simplicity and efficiency

0 views • 36 slides


Intersectional STEM Network Formation for Underrepresented Students

Addressing the underrepresentation of women and people of color in STEM, this study explores the impact of peer networks on the persistence of underrepresented high school students of color in STEM at the postsecondary level. It delves into how race and gender intersect to influence the creation and

0 views • 16 slides


Exploring Network Slicing in Future 5G Networks

The research delves into the design of a 5G-ready architecture known as NFV-based Network Store, aiming to serve as a digital distribution platform for 5G applications. By focusing on network slicing and programmability, the architecture enhances resource efficiency and agility in deploying software

0 views • 17 slides


Innovation in Campus Networks Through OpenFlow Technology

OpenFlow technology pioneered by researchers from top universities facilitates innovation in campus networks by providing an open platform for testing new ideas on production networks. It addresses the limitations of existing solutions, enabling researchers to bring the future Internet to legacy sys

0 views • 24 slides


New Approaches in Learning Complex-Valued Neural Networks

This study explores innovative methods in training complex-valued neural networks, including a model of complex-valued neurons, network architecture, error analysis, Adam optimizer, gradient calculation, and activation function selection. Simulation results compare real-valued and complex-valued net

0 views • 12 slides


Understanding Bayesian Networks for Efficient Probabilistic Inference

Bayesian networks, also known as graphical models, provide a compact and efficient way to represent complex joint probability distributions involving hidden variables. By depicting conditional independence relationships between random variables in a graph, Bayesian networks facilitate Bayesian infer

0 views • 33 slides


Future Vision for IEEE 802.11 in 5G Networks

This submission discusses the potential role of IEEE 802.11 as a component in future 5G networks, addressing challenges and opportunities. It presents insights on managing 802.11 within non-proprietary interfaces, emphasizing compatibility and defined functions for seamless integration in advanced n

0 views • 37 slides


Cloud Computing and Future Networks: Driving Innovation in Africa

Explore how cloud computing, SDN, NFV, and big data are shaping the future of networks in Africa, with insights from industry leaders and experts. Learn about cloud services, network transformation, converged cloud solutions, and cloud system architecture for a better Africa.

0 views • 23 slides