Foveated Vision Models for Search and Recognition

Computer vision has made a significant progress in recent years thanks to advancement in neural network architectures and computing power. At the sensory level, the current machine vision systems sample the visual data uniformly to make predictions about the scene. This is in contrast with the human vision system that has high visual acuity only in a small central region, the fovea, and much coarser sampling away from the center. There has been a renewed interest, particularly in the context of active vision for robotics navigation and scene exploration, to develop biologically motivated methods that can leverage such foveated computations. While foveated vision offers computational savings at or near the region of interest, it requires eye movements to scan the scene for effective image understanding. The hypothesis is that methods that can leverage non-uniform sampling of the field of view together with eye-movements will lead to a new class of active vision systems that are optimized computationally for specific tasks of interest.Inspired by the above observations, this research provides, for the first time, a comprehensive study of the human visual search in the constrained setting of person identification in the wild. A novel video database is created that systematically tests how different parts of a person contribute towards eye-movements and person identification. Our study shows that the search errors can dominate the overall recognition accuracy in human subject experiments. This calls for new strategies for integrating eye tracking with foveated image representations. Towards this two specific approaches are investigated further.In the first approach, a deep neural network based method is developed to model eye movements. Using the long-short-term-memory to model the successive fixations. The proposed method outperforms state of the state of the art performance while simplifying the feature extraction procedure. The second approach focuses on the foveated image model that leverages multiple fixations. A convolutional neural network method is proposed that works directly with the foveated input images that achieves competitive recognition rates compared to standard neural networks operating on the same number of input pixels. Overall the thesis investigates the requirements and implementations that could support active foveated vision, and lays down the ground work for future studies in this area

Artifact elimination in ECG signal using wavelet transform

Electrocardiogram signal is the electrical actvity of the heart and doctors can diagnose heart disease based on this electrocardiogram signal. However, the electrocardiogram signals often have noise and artifact components. Therefore, one electrocardiogram signal without the noise and artifact plays an important role in heart disease diagnosis with more accurate results. This paper proposes a wavelet transform with three stages of decomposition, filter, and reconstruction for eliminating the noise and artifact in the electrocardiogram signal. The signal after decomposing produces approximation and detail coefficients, which contains the frequency ranges of the noise and artifact components. Hence, the approximation and detail coefficients with the frequency ranges corresponding to the noise and artifact in the electrocardiogram signal are eliminated by filters before they are reconstructed. For the evaluation of the proposed algorithm, filter evaluation metrics are applied, in which signal-to-noise ratio and mean squared error along with power spectral density are employed. The simulation results show that the proposed wavelet algorithm at level 8 is effective, in which the with the “dmey” wavelet function was selected be the best based power spectrum density

Development of the Monitoring Program for an Integrated Small-Scale Wind and Solar Systems based on IoT Technology

For monitoring the energy supply from the hybrid small-scale wind turbine generator (WTG) and rooftop solar Photovoltage (PV) systems, this paper presents the design of a management program of the studied system based on the Internet of Things (IoT) technology. The proposed studied system consists of digital power meters that communicate wirelessly to the Programmable Logic Controller (PLC) through the ZigBee communication standard. By using a free cloud platform will greatly facilitate the Supervisory Control and Data Acquisition (SCADA) interface design work for a Human Machine Interface (HMI) or mobile phone. This system configuration may be easy to be fitted for collecting electrical information such as voltage, current, power, frequency of the system to be monitored. This is one of the cheap solutions deployed in small-scale hybrid power systems (HPS) or factories because wireless communication is very convenient in construction and installation

ROBUST MPPT OBSERVER-BASED CONTROL SYSTEM FOR WIND ENERGY CONVERSION SYSTEM WITH UNCERTAINTIES AND DISTURBANCE

The problem of tracking the maximum power point for the wind energy conversion system (WECS) is taken into consideration in this paper. The WECS in this article is simultaneously affected by the uncertainties and the arbitrary disturbance that cause the WECSs to be much more challenging to control. A new method to synthesize a polynomial disturbance observer for estimating the aerodynamic torque, wind speed, and electromagnetic torque without using sensors is proposed in this paper. Unlike the previous methods, in this work, both the uncertainties and the disturbance are estimated, then estimations of the uncertainties and disturbance are transmitted to the Linear Quadratic Regulator (LQR) controller for eliminating the influences of the uncertainties and disturbance; and tracking the optimal power point of WECS. It should be noted that the uncertainties in this work are time-varying and both uncertainties and disturbance do not need to satisfy the bounded constraints. The wind speed and aerodynamic torque are arbitrary and unnecessary to fulfill the low-varying constraint or r th time derivative bound. On the basis of Lyapunov methodology and the sum-of-square technique (SOS), the main theorems are derived to design the polynomial disturbance observer. Finally, the simulation results are provided to demonstrate the effectiveness and merit of the proposed method

Common Mode Voltage Elimination for Quasi-Switch Boost T-Type Inverter Based on SVM Technique

In this paper, the effect of common-mode voltage generated in the three-level quasi-switched boost T-type inverter is minimized by applying the proposed space-vector modulation technique, which uses only medium vectors and zero vector to synthesize the reference vector. The switching sequence is selected smoothly for inserting the shoot-through state for the inverter branch. The shoot-through vector is added within the zero vector in order to not affect the active vectors as well as the output voltage. In addition, the shoot-through control signal of active switches of the impedance network is generated to ensure that its phase is shifted 90 degrees compared to shoot through the signal of the inverter leg, which provides an improvement in reducing the inductor current ripple and enhancing the voltage gain. The effectiveness of the proposed method is verified through simulation and experimental results. In addition, the superiority of the proposed scheme is demonstrated by comparing it to the conventional pulse-width modulation technique

POLYNOMIAL OBSERVER-BASED CONTROLLER SYNTHESIS AND FAULT-TOLERANT CONTROL FOR TRACKING OPTIMAL POWER OF WIND ENERGY CONVERSION SYSTEMS

This article proposes a new approach to design a fault-tolerant control (FTC) scheme for tracking the optimal power of wind energy conversion systems (WECSs). In this article, the considered fault will not only impact on actuator but also sensors. As the fault severely affects the performance of WECSs, the FTC are required to be worked accurately and effectively. The polynomial observer, as a part of the proposed FTC system, is synthesized to estimate the aerodynamic torque, electromagnetic torque, and fault simultaneously without using sensors to measure. The information of these parameters is sent back to the LQR (Linear Quadratic Regular) controller of WECSs. Both fault and aerodynamic torque in this study are unnecessary to fulfil any constraint. It should be noted that WECSs is reconstructed to a new form based on the descriptor technique, then the observer will design for this new form instead of the original system. Based on Lyapunov methodology and with the aid of SOS (Sum-Of-Square) technique, the conditions for polynomial observer design are derived in the main theorems. Finally, the simulation results have proved the effectiveness and merit of the proposed FTC method

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset