Real time sobel square edge detector for night vision analysis

Vision analysis with low or no illumination is gaining more and more attention recently, especially in the fields of security surveillance and medical diagnosis. In this paper, a real time sobel square edge detector is developed as a vision enhancer in order to render clear shapes of object in targeting scenes, allowing further analysis such as object or human detection, object or human tracking, human behavior recognition, and identification on abnormal scenes or activities. The method is optimized for real time applications and compared with existing edge detectors. Program codes are illustrated in the content and the results show that the proposed algorithm is promising to generate clear vision data with low noise

A low variance error boosting algorithm

This paper introduces a robust variant of AdaBoost, cw-AdaBoost, that uses weight perturbation to reduce variance error, and is particularly effective when dealing with data sets, such as microarray data, which have large numbers of features and small number of instances. The algorithm is compared with AdaBoost, Arcing and MultiBoost, using twelve gene expression datasets, using 10-fold cross validation. The new algorithm consistently achieves higher classification accuracy over all these datasets. In contrast to other AdaBoost variants, the algorithm is not susceptible to problems when a zero-error base classifier is encountered

Artificial intelligent vision analysis in obstructive sleep apnoea (OSA)

Although polysomnography is a generally adopted approach for diagnosing obstructive sleep apnoea (OSA), there are several critical drawbacks with it, including massive equipment cost, large expense on replacing damaged components and more importantly invasive devices required to be worn while patients are struggling to sleep. Furthermore, there is no proof that polymonography obtains higher accuracy in detecting patients with OSA than more simple investigations [1]. Video monitoring has been adopted to assist diagnosis on obstructive sleep apnoea. From practical researches [3], the best predictors of morbidity in individual patients, as assessed by improvements with CPAP therapy, are nocturnal oxygen saturation [4, 5] and movement during sleep [4]. Hence, we purpose a robotic, objective and reliable video monitoring system with AI intelligence for analysis on human behavior during sleep, automatically generating a statistics report on body activity, including arm movement, limb movement, head movement and body rotation movement and arousal movement

Vision analysis in detecting abnormal breathing activity in application to diagnosis of obstructive sleep apnoea

Recognizing abnormal breathing activity from body movement is a challenging task in machine vision. In this paper, we present a non-intrusive automatic video monitoring technique for detecting abnormal breathing activities and assisting in diagnosis of obstructive sleep apnoea. The proposed technique utilizes infrared video information and avoids imposing geometric or positional constraints on the patient. The technique also deals with fully or partially obscured patients’ body. A continuously updated breathing activity template is built for distinguishing general body movement from breathing behavior

Detect-and-Avoid: Flight Test 6 Scripted Encounters Data Analysis

The Unmanned Aircraft System (UAS) in the National Airspace System (NAS) project conducted Flight Test 6 (FT6) in 2019. The ultimate goal of this flight test was to produce data to inform RTCA SC-228's Phase II Minimum Operational Performance Standards (MOPS) for Detect and Avoid (DAA) and Low Size, Weight, and Power Sensors. This report documents the analysis of scripted encounters' data. Scripted encounters own were analyzed and categorized based on the outcome of alert, maneuver guidance, and effectiveness of pilots' maneuver in resolving conflicts. Results indicate that UAS pilots' decisions as well as intruder maneuvers are leading factors that contribute to ineffective DAA maneuvers. Results also show that adding buffers to the DAA's suggested minimum turn angle improves effectiveness of the DAA maneuvers

Acoustic Lens Design Using Machine Learning

This thesis aims to contribute to the development of a novel approach and efficient method for the inverse design of acoustic metamaterial lenses using machine learning, specifically, deep learning, generative modeling, and reinforcement learning. Acoustic lenses can focus incident plane waves at the focal point, enabling them to detect structures non-intrusively. These lenses can be utilized in biomedical engineering, medical devices, structural engineering, ultrasound imaging, health monitoring, etc. Finding the global optimum through a traditional iterative optimization process for designing the acoustic lens is challenging. It may become infeasible due to high dimensional parameter space and the compute resources needed. Machine learning techniques have been shown promising for finding the global optimum. Generative modeling is a powerful technique enabling recent advancements in drug discoveries, organic molecule development, and photonics. We combined generative modeling with global optimization and an analytical form of gradients computed by means of multiple scattering theory. In addition, reinforcement learning can potentially outperform traditional optimization algorithms. Thus, in this thesis, the acoustic lens is modeled using two machine learning techniques, such as generative modeling, using 2D-Global Topology Optimization Networks (2D-GLOnets), and reinforcement learning using the Deep Deterministic Policy Gradient (DDPG) algorithm. Results from the aforementioned methods are compared with traditional optimization algorithms

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. Surrogate models are widely used in the field of simulation sciences to efficiently analyze computationally expensive simulation models. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic

Lung Metastases Presenting as Multiple Bleeding Ulcers in the Small Bowel: A Case Report

Introduction: Lung cancer is a leading cause of cancer mortality worldwide and approximately half of the patients are diagnosed at an advanced stage.  Gastrointestinal metastases from lung cancer are very rare.Case Report: Here, we present a case of a 73-year-old gentleman with gastrointestinal metastases from lung cancer, presenting as acute gastrointestinal bleeding from multiple bleeding ulcers in the small bowel.Conclusion: Early detection of gastrointestinal metastases will help with determining clinical management. Whilst likely palliative in nature, treatment may incorporate surgical resection which if to be undertaken, should be performed early for prompt palliation of symptoms and improvement of quality of remaining life