Browsing by Subject "Deep learning"
Now showing items 1-10 of 10
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ANALYZING AES POWER TRACES FOR SIDE-CHANNEL ATTACK: GENERATION, CLASSIFICATION, KEY DEDUCTION, AND MITIGATION STRATEGIES
(2024-08-06)Side-channel attacks (SCAs) exploit leakages from a system’s physical implementation, like acoustic signals, electromagnetic, and power emissions, to deduce sensitive information, thus bypassing traditional security ... -
BIOMASS ESTIMATION OF FISH USING DEEP NETWORKS AND STEREO VISION
(2020-05-15)Mass of an object is essential information for many an industrial application. The aquaculture industry estimates the fish's weight by scaling a sample of fish out of growing tanks. This process harms fish and reveals an ... -
Development of a machine vision system to estimate the physical attributes of potato tubers on-the-go at the post-harvest stage
(2023-08-08)This study presents a deep learning and image processing-based machine vision system for sampling and sizing full-size potato tubers on post-harvest conveyors. First, we present a method for sampling fully visible potato ... -
Generative Causal Modelling Techniques for Visual Model Explanation and Counterfactual Audio Generation
(2023-12-14)This thesis evaluates the effectiveness of two recent works in the area of nonlinear causal modelling, DeepSCM and ImageCFGen, in their ability to explain image classifiers and model audio data. First, techniques are ... -
Learning Optical Flow with Auxiliary Cost Aggregation
(2023-11-30)Optical flow represents motions for each pixel between two adjacent frames in a video sequence. Deep learning-based estimation approaches for optical flow have overshadowed the variational approaches over the past few ... -
Learning Stochastic Weight Masking to Resist Adversarial Attacks
(2019-12-02)Adding small perturbations to test images can drastically change the classification accuracy of machine learning models. These perturbed examples are called adversarial examples (Szegedy et al., 2013). Studying these ... -
Predictive modeling of damage and repair for disease and activity of daily living status in ELSA dataset using machine learning models
(2023-12-08)A good predictive model is useful in health sciences for predicting onset of disease, as well as damage or repair of health deficits. One can predict one or more of these quantities depending on the nature of the collected ... -
SEMANTIC SEGMENTATION OF MICROSCOPIC BLOOD IMAGE DATA USING SELF-TRAINING TO AUGMENT SMALL TRAINING SETS AND ITS APPLICATION FOR COUNTING CELLS
(2019-04-29)Semantic segmentation is a computer vision task of assigning a label describing the content to each pixel in an image. There has been a lot of progress in this area using deep neural networks with an encoder-decoder ... -
Subtractor-Based CNN Inference Accelerator
(2022-11-02)This paper presents a novel method to boost the performance of CNN inference accelerators utilizing subtractors. The proposed CNN preprocessing accelerator relies on sorting, grouping, and rounding the weights in order to ... -
Synchronization Techniques for Coherent Underwater Acoustic Receivers
(2019-03-12)In wireless communication systems, synchronization is one of the most important issues. The requirement for synchronization is especially intensified when there is strong channel distortion. For instance, the Doppler effect ...