Search Results for author:
Found 27 papers, 10 papers with code
ShadowRemovalNet: Efficient Real-Time Shadow Removal
We propose ShadowRemovalNet, a novel method designed for real-time image processing on resource-constrained hardware.
WeedCLR: Weed Contrastive Learning through Visual Representations with Class-Optimized Loss in Long-Tailed Datasets
WeedCLR is evaluated on two public weed datasets: CottonWeedID15, containing 15 weed species, and DeepWeeds, containing 8 weed species.
V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges
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.
Prawn Morphometrics and Weight Estimation from Images using Deep Learning for Landmark Localization
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.
Security and Privacy Problems in Voice Assistant Applications: A Survey
The privacy issues include technical-wise information stealing and policy-wise privacy breaches.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Adaptive Uncertainty Distribution in Deep Learning for Unsupervised Underwater Image Enhancement
This makes it difficult to train supervised deep learning models on large and diverse datasets, which can limit the model's performance.
Ensemble Machine Learning Model Trained on a New Synthesized Dataset Generalizes Well for Stress Prediction Using Wearable Devices
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.
Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review
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.
A lightweight Transformer-based model for fish landmark detection
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.
Unsupervised Fish Trajectory Tracking and Segmentation
In the third stage, the refined labels are used to train a segmentation network.
Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks
During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly.
Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey
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.
Transformer-based Self-Supervised Fish Segmentation in Underwater Videos
Our proposed model is trained on videos -- without any annotations -- to perform fish segmentation in underwater videos taken in situ in the wild.
Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation
Spike sorting algorithms are used to separate extracellular recordings of neuronal populations into single-unit spike activities.
Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey
This information is essential for developing sustainable fisheries for human consumption, and for preserving the environment.
Navigating Local Minima in Quantized Spiking Neural Networks
Spiking and Quantized Neural Networks (NNs) are becoming exceedingly important for hyper-efficient implementations of Deep Learning (DL) algorithms.
Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures
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.
A Deep Learning Localization Method for Measuring Abdominal Muscle Dimensions in Ultrasound Images
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.
Memristive Stochastic Computing for Deep Learning Parameter Optimization
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.
Affinity LCFCN: Learning to Segment Fish with Weak Supervision
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.
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
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.
MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems
Memristive devices have shown great promise to facilitate the acceleration and improve the power efficiency of Deep Learning (DL) systems.
Emerging Technologies
Training Progressively Binarizing Deep Networks Using FPGAs
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.
Variation-aware Binarized Memristive Networks
Moreover, we introduce means to mitigate the adverse effect of memristive variations in our proposed networks.
Accelerating Deterministic and Stochastic Binarized Neural Networks on FPGAs Using OpenCL
Consequently, the performance and complexity of Artificial Neural Networks (ANNs) is burgeoning.
DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning
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.
A Neuromorphic VLSI Design for Spike Timing and Rate Based Synaptic Plasticity
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.
