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Wyświetlanie 1-3 z 3
Tytuł:
Realization of multiplexer logic-based 2-D block firfilter using distributed arithmetic
Autorzy:
Chowdari, Ch. Pratyusha
Seventline, J. Beatrice
Powiązania:
https://bibliotekanauki.pl/articles/38699398.pdf
Data publikacji:
2023
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
2-D FIR filter
switching-based LUT
distributed arithmetic
block processing
2-D FIR filtr
arytmetyka rozproszona
przetwarzanie blokowe
Opis:
This paper presents a novel systolic two-dimensional (2D) block finite impulse response(FIR) filter architecture using a distributed arithmetic (DA)-based multiplexer look-uptable (DA-MUX-LUT). The proposed DA-MUX-LUT architecture computes the instan-taneous partial-product using the bit vector. The switching-based LUT replaces memory-based structures and reduces hardware complexity. Block processing allows memory reuse,which reduces the number of registers to store the previous input samples. Parallel addersare substituted by a modified carry look-ahead adder (MCLA), which minimizes the delay.Moreover, a resource-sharing concept is introduced to the DA-MUX-LUT block that drastically reduces the adder requirement. The application specific integrated circuit (ASIC)synthesis results show that the proposed DA-MUX-LUT-based 2-D block FIR filter forfilter size 8x8 and block size 4 has 31.22% less delay, 28.66% less area-delay product(ADP), 37.70% less power-delay product (PDP), and occupies almost the same area thanthe existing architecture.
Źródło:
Computer Assisted Methods in Engineering and Science; 2023, 30, 1; 89-103
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A supervised approach to musculoskeletal imaging fracture detection and classification using deep learning algorithms
Autorzy:
Karanam, Santoshachandra Rao
Srinivas, Y.
Chakravarty, S.
Powiązania:
https://bibliotekanauki.pl/articles/38702595.pdf
Data publikacji:
2023
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
musculoskeletal image
image processing
image enhancement
fracture diagnosis
fracture classification
deep neural network
obraz układu mięśniowo-szkieletowego
przetwarzanie obrazu
wzmocnienie obrazu
diagnoza złamania
klasyfikacja złamań
głęboka sieć neuronowa
Opis:
Bone fractures break bone continuity. Impact or stress causes numerous bone fractures. Fracture misdiagnosis is the most frequent mistake in emergency rooms, resulting in treatment delays and permanent impairment. According to the Indian population studies, fractures are becoming more common. In the last three decades, there has been a growth of 480 000, and by 2022, it will surpass 600 000. Classifying X-rays may be challenging, particularly in an emergency room when one must act quickly. Deep learning techniques have recently become more popular for image categorization. Deep neural networks (DNNs) can classify images and solve challenging problems. This research aims to build and evaluate a deep learning system for fracture identification and bone fracture classification (BFC). This work proposes an image-processing system that can identify bone fractures using X-rays. Images from the dataset are pre-processed, enhanced, and extracted. Then, DNN classifiers ResNeXt101, InceptionResNetV2, Xception, and NASNetLarge separate the images into the ones with unfractured and fractured bones (normal, oblique, spiral, comminuted, impacted, transverse, and greenstick). The most accurate model is InceptionResNetV2, with an accuracy of 94.58%.
Źródło:
Computer Assisted Methods in Engineering and Science; 2023, 30, 3; 369-385
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Classification and detection of skin disease based on machine learning and image processing evolutionary models
Autorzy:
Bordoloi, Dibyahash
Singh, Vijay
Kaliyaperumal, Karthikeyan
Ritonga, Mahyudin
Jawarneh, Malik
Kassanuk, Thanwamas
Quiñonez-Choquecota, Jose
Powiązania:
https://bibliotekanauki.pl/articles/38700501.pdf
Data publikacji:
2023
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Tematy:
skin disorder
machine learning
classification
image enhancement
image segmentation
disease detection
schorzenie skóry
nauczanie maszynowe
klasyfikacja
ulepszenie obrazu
segmentacja obrazów
wykrywanie choroby
Opis:
Skin disorders, a prevalent cause of illnesses, may be identified by studying their physical structure and history of the condition. Currently, skin diseases are diagnosed using invasive procedures such as clinical examination and histology. The examinations are quite effective and beneficial. This paper describes an evolutionary model for skin disease classification and detection based on machine learning and image processing. This model integrates image preprocessing, image augmentation, segmentation, and machine learning algorithms. The experimental investigation makes use of a dermatology data set. The model employs the machine learning methods: the support vector machine (SVM), the k-nearest neighbors (KNN), and random forest algorithms for image categorization and detection. This suggested methodology is beneficial for the accurate identification of skin disease using image analysis. The SVM algorithm achieved an accuracy of 98.8%. The KNN algorithm achieved a sensitivity of 91%. The specificity of KNN was 99%.
Źródło:
Computer Assisted Methods in Engineering and Science; 2023, 30, 2; 247-256
2299-3649
Pojawia się w:
Computer Assisted Methods in Engineering and Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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