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of 26
pro vyhledávání: '"Loni, Mohammad"'
Detecting human actions is a crucial task for autonomous robots and vehicles, often requiring the integration of various data modalities for improved accuracy. In this study, we introduce a novel approach to Human Action Recognition (HAR) based on sk
Externí odkaz:
http://arxiv.org/abs/2410.01962
The increasing demand for autonomous machines in construction environments necessitates the development of robust object detection algorithms that can perform effectively across various weather and environmental conditions. This paper introduces a ne
Externí odkaz:
http://arxiv.org/abs/2312.16516
Sparse Neural Networks (SNNs) can potentially demonstrate similar performance to their dense counterparts while saving significant energy and memory at inference. However, the accuracy drop incurred by SNNs, especially at high pruning ratios, can be
Externí odkaz:
http://arxiv.org/abs/2305.10964
Sparse matrix-vector multiplication (SpMV) is an essential linear algebra operation that dominates the computing cost in many scientific applications. Due to providing massive parallelism and high memory bandwidth, GPUs are commonly used to accelerat
Externí odkaz:
http://arxiv.org/abs/2302.05662
Autor:
Asadi, Mehdi, Poursalim, Fatemeh, Loni, Mohammad, Daneshtalab, Masoud, Sjödin, Mikael, Gharehbaghi, Arash
This paper presents a novel machine learning framework for detecting Paroxysmal Atrial Fibrillation (PxAF), a pathological characteristic of Electrocardiogram (ECG) that can lead to fatal conditions such as heart attack. To enhance the learning proce
Externí odkaz:
http://arxiv.org/abs/2301.10173
Publikováno v:
ACM Transactions on Embedded Computing Systems, Vol. 22, No. 5s, Article 105. Publication date: September 2023
The deployment of Deep Neural Networks (DNNs) on edge devices is hindered by the substantial gap between performance requirements and available processing power. While recent research has made significant strides in developing pruning methods to buil
Externí odkaz:
http://arxiv.org/abs/2207.06968
Autor:
Loni, Mohammad
Convolutional Neural Networks (CNNs) suffer from energy-hungry implementation due to requiring huge amounts of computations and significant memory consumption. This problem will be more highlighted by the proliferation of CNNs on resource-constraine
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-52113
Publikováno v:
In Microprocessors and Microsystems March 2020 73
Akademický článek
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Autor:
Loni, Mohammad
Deep Neural Networks (DNNs) are increasingly being processed on resource-constrained edge nodes (computer nodes used in, e.g., cyber-physical systems or at the edge of computational clouds) due to efficiency, connectivity, and privacy concerns. This
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______258::49534af75f458ce77162a574348bd726
http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-59946
http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-59946