no code implementations • 12 May 2024 • Alzayat Saleh, Alex Olsen, Jake Wood, Bronson Philippa, Mostafa Rahimi Azghadi
Weeds present a significant challenge in agriculture, causing yield loss and requiring expensive control measures.
no code implementations • 13 Mar 2024 • Alzayat Saleh, Alex Olsen, Jake Wood, Bronson Philippa, Mostafa Rahimi Azghadi
We propose ShadowRemovalNet, a novel method designed for real-time image processing on resource-constrained hardware.
no code implementations • 13 Feb 2024 • Ethan Kane Waters, Carla Chia-Ming Chen, Mostafa Rahimi Azghadi
This paper offers a comprehensive analysis of previous research to aid in unlocking this potential and advancing the development of an effective sugarcane health monitoring system using satellite technology.
no code implementations • 19 Oct 2023 • Alzayat Saleh, Alex Olsen, Jake Wood, Bronson Philippa, Mostafa Rahimi Azghadi
WeedCLR is evaluated on two public weed datasets: CottonWeedID15, containing 15 weed species, and DeepWeeds, containing 8 weed species.
no code implementations • 5 Oct 2023 • Tao Huang, Jianan Liu, Xi Zhou, Dinh C. Nguyen, Mostafa Rahimi Azghadi, Yuxuan Xia, Qing-Long Han, Sumei Sun
To address this gap, this paper provides a comprehensive overview of the evolution of CP technologies, spanning from early explorations to recent developments, including advancements in V2X communication technologies.
no code implementations • 15 Jul 2023 • Alzayat Saleh, Md Mehedi Hasan, Herman W Raadsma, Mehar S Khatkar, Dean R Jerry, Mostafa Rahimi Azghadi
In this study, we applied a novel DL approach to automate weight estimation and morphometric analysis using the black tiger prawn (Penaeus monodon) as a model crustacean.
no code implementations • 19 Apr 2023 • Jingjin Li, Chao Chen, Lei Pan, Mostafa Rahimi Azghadi, Hossein Ghodosi, Jun Zhang
The privacy issues include technical-wise information stealing and policy-wise privacy breaches.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
1 code implementation • 18 Dec 2022 • Alzayat Saleh, Marcus Sheaves, Dean Jerry, Mostafa Rahimi Azghadi
This makes it difficult to train supervised deep learning models on large and diverse datasets, which can limit the model's performance.
1 code implementation • 30 Sep 2022 • Gideon Vos, Kelly Trinh, Zoltan Sarnyai, Mostafa Rahimi Azghadi
Finally, we propose and evaluate the use of ensemble techniques by combining gradient boosting with an artificial neural network to measure predictive power on new, unseen data.
no code implementations • 29 Sep 2022 • Gideon Vos, Kelly Trinh, Zoltan Sarnyai, Mostafa Rahimi Azghadi
This study reviewed published works contributing and/or using datasets designed for detecting stress and their associated machine learning methods, with a systematic review and meta-analysis of those that utilized wearable sensor data as stress biomarkers.
no code implementations • 13 Sep 2022 • Alzayat Saleh, David Jones, Dean Jerry, Mostafa Rahimi Azghadi
Transformer-based models, such as the Vision Transformer (ViT), can outperform onvolutional Neural Networks (CNNs) in some vision tasks when there is sufficient training data.
no code implementations • 23 Aug 2022 • Alzayat Saleh, Marcus Sheaves, Dean Jerry, Mostafa Rahimi Azghadi
In the third stage, the refined labels are used to train a segmentation network.
1 code implementation • 20 Jun 2022 • Chenqi Li, Corey Lammie, Xuening Dong, Amirali Amirsoleimani, Mostafa Rahimi Azghadi, Roman Genov
During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly.
no code implementations • 11 Jun 2022 • Alzayat Saleh, Marcus Sheaves, Dean Jerry, Mostafa Rahimi Azghadi
This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts.
no code implementations • 11 Jun 2022 • Alzayat Saleh, Marcus Sheaves, Dean Jerry, Mostafa Rahimi Azghadi
Our proposed model is trained on videos -- without any annotations -- to perform fish segmentation in underwater videos taken in situ in the wild.
1 code implementation • 13 May 2022 • Tim Zhang, Corey Lammie, Mostafa Rahimi Azghadi, Amirali Amirsoleimani, Majid Ahmadi, Roman Genov
Spike sorting algorithms are used to separate extracellular recordings of neuronal populations into single-unit spike activities.
no code implementations • 14 Mar 2022 • Alzayat Saleh, Marcus Sheaves, Mostafa Rahimi Azghadi
This information is essential for developing sustainable fisheries for human consumption, and for preserving the environment.
1 code implementation • 15 Feb 2022 • Jason K. Eshraghian, Corey Lammie, Mostafa Rahimi Azghadi, Wei D. Lu
Spiking and Quantized Neural Networks (NNs) are becoming exceedingly important for hyper-efficient implementations of Deep Learning (DL) algorithms.
no code implementations • 18 Jan 2022 • Corey Lammie, Jason K. Eshraghian, Chenqi Li, Amirali Amirsoleimani, Roman Genov, Wei D. Lu, Mostafa Rahimi Azghadi
The impact of device and circuit-level effects in mixed-signal Resistive Random Access Memory (RRAM) accelerators typically manifest as performance degradation of Deep Learning (DL) algorithms, but the degree of impact varies based on algorithmic features.
no code implementations • 30 Sep 2021 • Alzayat Saleh, Issam H. Laradji, Corey Lammie, David Vazquez, Carol A Flavell, Mostafa Rahimi Azghadi
US images can be used to measure abdominal muscles dimensions for the diagnosis and creation of customized treatment plans for patients with Low Back Pain (LBP), however, they are difficult to interpret.
no code implementations • 11 Mar 2021 • Corey Lammie, Jason K. Eshraghian, Wei D. Lu, Mostafa Rahimi Azghadi
Stochastic Computing (SC) is a computing paradigm that allows for the low-cost and low-power computation of various arithmetic operations using stochastic bit streams and digital logic.
1 code implementation • 6 Nov 2020 • Issam Laradji, Alzayat Saleh, Pau Rodriguez, Derek Nowrouzezahrai, Mostafa Rahimi Azghadi, David Vazquez
Leading automatic approaches rely on fully-supervised segmentation models to acquire these measurements but these require collecting per-pixel labels -- also time consuming and laborious: i. e., it can take up to two minutes per fish to generate accurate segmentation labels, almost always requiring at least some manual intervention.
1 code implementation • 11 Jul 2020 • Mostafa Rahimi Azghadi, Corey Lammie, Jason K. Eshraghian, Melika Payvand, Elisa Donati, Bernabe Linares-Barranco, Giacomo Indiveri
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge.
1 code implementation • 23 Apr 2020 • Corey Lammie, Wei Xiang, Bernabé Linares-Barranco, Mostafa Rahimi Azghadi
Memristive devices have shown great promise to facilitate the acceleration and improve the power efficiency of Deep Learning (DL) systems.
Emerging Technologies
no code implementations • 8 Jan 2020 • Corey Lammie, Wei Xiang, Mostafa Rahimi Azghadi
While hardware implementations of inference routines for Binarized Neural Networks (BNNs) are plentiful, current realizations of efficient BNN hardware training accelerators, suitable for Internet of Things (IoT) edge devices, leave much to be desired.
no code implementations • 14 Oct 2019 • Corey Lammie, Olga Krestinskaya, Alex James, Mostafa Rahimi Azghadi
Moreover, we introduce means to mitigate the adverse effect of memristive variations in our proposed networks.
1 code implementation • 15 May 2019 • Corey Lammie, Wei Xiang, Mostafa Rahimi Azghadi
Consequently, the performance and complexity of Artificial Neural Networks (ANNs) is burgeoning.
1 code implementation • 9 Oct 2018 • Alex Olsen, Dmitry A. Konovalov, Bronson Philippa, Peter Ridd, Jake C. Wood, Jamie Johns, Wesley Banks, Benjamin Girgenti, Owen Kenny, James Whinney, Brendan Calvert, Mostafa Rahimi Azghadi, Ronald D. White
This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust classification methods to make robotic weed control viable.
no code implementations • 30 Mar 2013 • Mostafa Rahimi Azghadi, Said Al-Sarawi, Derek Abbott, Nicolangelo Iannella
Additionally, it has been shown that the behaviour inherent to the spike rate-based Bienenstock-Cooper-Munro (BCM) synaptic plasticity rule can also emerge from the TSTDP rule.