Cognitive load is crucial in assessing mental effort in tasks. This paper discusses using EEG signals and a 2D-CNN model to classify cognitive load during mental arithmetic tasks, aiming to optimize performance. EEG signals help evaluate mental workload, although they can be sensitive to noise. The

0 views • 19 slides

A computer system is a programmable digital electronics device that processes data as per program instructions to provide meaningful output. It comprises hardware and software components, with hardware being the physical parts and software essential for driving the hardware. Computer organization fo

14 views • 71 slides

Develop flows showing interaction between SMO modules in the context of open-source architecture using OSC, ONAP, and other code. The objective is to align open-source work with O-RAN trends, improve synergy, reduce duplication, and provide feedback to O-RAN discussions. Related work includes Decoup

4 views • 27 slides

Explore the fundamentals of recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in the context of downstream applications. Delve into LSTM, GRU, and RNN variants, alongside CNN architectures like ConvNext, ResNet, and more. Understand the mathematical formulations of RNNs and c

1 views • 76 slides

In the electronic meeting of 3GPP TSG-RAN-WG4, discussions were held on the RF architecture and waveform assumptions for NR V2X intra-band operation in band n79. Various options and recommendations were presented regarding RF architecture, antenna architecture, and waveform definitions for efficient

1 views • 7 slides

Computer architecture and organization are fundamental aspects of computing systems. Computer architecture focuses on the functional design and implementation of various computer parts, while computer organization deals with how operational attributes come together to realize the architectural speci

3 views • 40 slides

Anti-patterns in software architecture are commonly occurring solutions to problems that lead to negative consequences. These arise due to insufficient knowledge or experience, misuse of design patterns, and lack of attention to evolving project architecture. Examples include Jumble, Stovepipe, Spag

1 views • 7 slides

PowerPC is a RISC instruction set architecture developed by IBM in collaboration with Apple and Motorola in the early 1990s. It is based on IBM's POWER architecture, offering both 32-bit and 64-bit processors popular in embedded systems. The architecture emphasizes a reduced set of pipelined instruc

2 views • 13 slides

Client-server architecture is a computing model where a central server hosts and manages resources and services for client computers over a network. There are different types of clients and servers, each with unique characteristics and roles. This architecture offers various advantages and disadvant

3 views • 15 slides

In an ideal scenario, the digital architecture for children would encompass systems such as Enterprise Architecture, Functional Architecture, and Solution Architecture to support UNICEF's high-impact interventions. It would involve integrated platforms for Health Information Exchange, Supply Chain M

1 views • 19 slides

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

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

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

This project focuses on real-time cough and sneeze detection for assessing disease likelihood and individual well-being. Deep learning, particularly CNN and CRNN models, is utilized for efficient detection and classification. The team conducted a literature survey on keyword spotting techniques and

1 views • 15 slides

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

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

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

Detection of coughs and sneezes plays a crucial role in assessing an individual's health condition. This project by Group 71 focuses on real-time detection using deep learning techniques to analyze audio data from various datasets. The use of deep learning models like CNN and CRNN showcases improved

0 views • 15 slides

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

Proposed establishment of a Working Group by the CESG to develop a Service-Oriented Architecture (SOA) framework for space mission operations within the CCSDS. The focus includes identifying services, use cases, architecture definitions, and business cases to enhance CCSDS-wide interoperability and

0 views • 7 slides

The evolution of sentiment analysis on tweets from SemEval competitions in 2013 to 2017 is discussed, showcasing advancements in technology and the shift from SVM and sentiment lexicons to CNN with word embeddings. Aspect-Based Sentiment Analysis, as explored in SemEval2014, involves determining asp

0 views • 23 slides

Y86 Instruction Set Architecture is a simplified pseudo-language based on x86 (IA-32) architecture. It involves implementing the Fetch-Decode-Execute cycle, where instructions are fetched from memory, decoded, and executed. The Y86 ISA offers a simpler set of instructions and formats compared to x86

0 views • 25 slides

This paper explores how Service-Oriented Architectures (SOA) can be integrated with the HL7 Clinical Document Architecture to facilitate the sharing of Summary Care Records between healthcare information systems. It highlights the benefits of a federated architecture based on SOA and coding standard

0 views • 51 slides

Delve into the world of modern architecture trends, focusing on Expressionist architecture in Europe during the early 20th century and the influential Bauhaus movement in Germany. Expressionist architecture emphasized emotional effects through distorted forms inspired by nature, while the Bauhaus sc

0 views • 10 slides

Experiments show that a simple CNN with minimal hyperparameter tuning and static vectors achieves excellent results for sentence-level classification tasks. Fine-tuning task-specific vectors further improves performance. A dataset from Rotten Tomatoes is used for the experiments, showcasing results

0 views • 10 slides

This research initiative by Suraj Vathsa focuses on using transfer learning and hybridization techniques to detect adventitious lung sounds such as wheezes and crackles from patient lung sound recordings. By developing an AI system that combines deep learning models and generative modeling for data

0 views • 6 slides

This content showcases various aspects of 5G system architecture, including system handover, non-roaming architecture, service-based architecture, and user plane functionality. It delves into the control plane functions, user plane functions, and core network endpoints of the 5G network. The images

0 views • 49 slides

Learn how to install Apache Singa, prepare data using SINGA recognizable records, and convert programs for DataShard for efficient handling of CIFAR-10 and MNIST datasets. Explore examples on creating shards, generating records, and implementing CNN layers for effective deep learning.

0 views • 23 slides

Investigating the impact of different channel configurations in detecting artifacts in scalp EEG records for seizure detection. A deep learning algorithm, CNN/LSTM, was employed on various channel setups to minimize loss of spatial information. Results show sensitivities between 33%-37% with false a

0 views • 12 slides

Client/Server Computing architecture separates clients and servers over a network, allowing for file sharing, resource allocation, and service requests. Clients initiate services from servers, with transparent server locations and message-passing transactions. Systems with C/S architecture include f

0 views • 18 slides

Our project aims to create an assistive system for individuals with disabilities by combining IMU action recognition, speech recognition, and image recognition to understand intentions and perform corresponding actions. We use deep learning for intent recognition, gesture identification, and object

0 views • 14 slides

Delve into the concepts of memory hierarchy, cache optimizations, RISC architecture, and other architecture styles in embedded computer architecture. Learn about Accumulator and Stack architectures, their characteristics, advantages, and example code implementations. Explore the differences between

0 views • 52 slides

Covering topics like Instruction-Set Architecture (ISA), 5-stage pipeline, and Pipelined instructions, this course delves into the intricacies of advanced computer architecture, with a focus on achieving high performance by optimizing data flow to execution units. The course provides insights into t

0 views • 12 slides

This presentation delves into the software architecture design and implementation of a Document Filter System (DFS) aimed at efficiently finding relevant information. It discusses the architecture's effectiveness in supporting diverse applications, multilingual document searching, complex query func

0 views • 33 slides

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

This content describes the MOIMS Protocol Viewpoint inputs to the SEA Reference Architecture updates by Roger Thompson from ESA SAWG. It includes details about the graphical conventions, data store elements, organizational domains, network layers, communications protocols, and space communications c

0 views • 21 slides

The research presents a method utilizing visual understanding to detect UI display issues in mobile devices. By recruiting testers and employing visual techniques, the severity of issues like component occlusion, text overlap, and missing images was confirmed. CNN-based models aid in issue detection

0 views • 17 slides

Using a multimodal recurrent attention neural network, MRACNN, this study proposes a unified approach for image aesthetic prediction by jointly learning visual and textual features. Inspired by human attention mechanisms, the network utilizes datasets like AVA and photo.net comments to enhance multi

0 views • 14 slides

This presentation delves into the intricacies of Neural Image Captioning, focusing on a model known as Neural Image Caption (NIC). The NIC's primary goal is to automatically generate descriptive English sentences for images. Leveraging the Encoder-Decoder structure, the NIC uses a deep CNN as the en

0 views • 13 slides

Neural network accelerators are achieving high energy efficiency and performance for recognition and mining applications. To overcome memory bandwidth constraints, the proposal suggests mapping the entire CNN into SRAM and moving closer to sensors to minimize memory access for I/O. Placing the CNN a

0 views • 24 slides