Wykaz publikacji wybranego autora

Michał Karwatowski, mgr inż.

asystent

Wydział Informatyki, Elektroniki i Telekomunikacji
WIEiT-ke, Instytut Elektroniki

  • 2023

    [dyscyplina 1] dziedzina nauk inżynieryjno-technicznych / informatyka techniczna i telekomunikacja

  • 2018

    [dyscyplina 1] dziedzina nauk inżynieryjno-technicznych / informatyka techniczna i telekomunikacja

    [dyscyplina 2] dziedzina nauk inżynieryjno-technicznych / automatyka, elektronika i elektrotechnika (25%)

Identyfikatory Autora Informacje o Autorze w systemach zewnętrznych

ORCID: 0000-0001-6285-136X orcid iD

ResearcherID: ISA-6516-2023

Scopus: 57189294331

PBN: 5e70938d878c28a0473ac8d5

System Informacyjny AGH (SkOs)

1
  • A blockchain service for science data safety
2
  • A custom co-processor for the discovery of low autocorrelation binary sequences
3
  • A study of parallel techniques for dimensionality reduction and its impact on the quality of text processing algorithms
4
  • A system for the fast and accurate cytology images annotation
5
  • Advances in the system for the automatic dogs’ skin cancer detection
6
  • Canine age classification using convolutional neural network
7
  • Canine age classification using Deep Learning as a step toward preventive medicine in animals
8
  • Classification of images of cytological samples for the purposes of initial analysis
9
  • Comparison of GPU and CPU implementations of new variants of SDLS algorithms for LABS problem
10
  • Comparison of one and two-step deep learning models for cytology image classification
11
  • Comparison of semantic vectors with reduced precision using the cosine similarity measure
12
  • Compressing sentiment analysis CNN models for efficient hardware processing
13
  • Deep learning classification of cytology images with uncertain training data
14
  • Detection of electronic components for mobile applications
15
  • Dog gait assessment using temporal graph neural networks
16
  • Energy efficient calculations of text similarity measure on FPGA-accelerated computing platforms
17
  • Fast compression and optimization of deep learning models for natural language processing
18
  • Fast pre-diagnosis of neoplastic changes in cytology images using machine learning
19
  • FPGA implementation of procedures for video quality assessment
20
  • High level framework for mapping deep learning neural models to FPGAs
21
  • Improving hip dysplasia diagnosis in dogs through augmented 3D video simulation
22
  • Improving text classification with vectors of reduced precision
23
  • Mapping neural networks to FPGA-based IoT devices for ultra-low latency processing
24
  • Neoplastic cells segmentation using deep neural networks
25
  • NLP semi-supervised PU learning with reduced number of labeled examples