AFFECT-PRESERVING VISUAL PRIVACY PROTECTION

The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this dissertation, we propose to balance the privacy protection and the utility of the data by preserving the privacy-insensitive information, such as pose and expression, which is useful in many applications involving visual understanding. The Intellectual Merits of the dissertation include a novel framework for visual privacy protection by manipulating facial image and body shape of individuals, which: (1) is able to conceal the identity of individuals; (2) provide a way to preserve the utility of the data, such as expression and pose information; (3) balance the utility of the data and capacity of the privacy protection. The Broader Impacts of the dissertation focus on the significance of privacy protection on visual data, and the inadequacy of current privacy enhancing technologies in preserving affect and behavioral attributes of the visual content, which are highly useful for behavior observation in educational and medical settings. This work in this dissertation represents one of the first attempts in achieving both goals simultaneously

A Fast and Robust Extrinsic Calibration for RGB-D Camera Networks

From object tracking to 3D reconstruction, RGB-Depth (RGB-D) camera networks play an increasingly important role in many vision and graphics applications. Practical applications often use sparsely-placed cameras to maximize visibility, while using as few cameras as possible to minimize cost. In general, it is challenging to calibrate sparse camera networks due to the lack of shared scene features across different camera views. In this paper, we propose a novel algorithm that can accurately and rapidly calibrate the geometric relationships across an arbitrary number of RGB-D cameras on a network. Our work has a number of novel features. First, to cope with the wide separation between different cameras, we establish view correspondences by using a spherical calibration object. We show that this approach outperforms other techniques based on planar calibration objects. Second, instead of modeling camera extrinsic calibration using rigid transformation, which is optimal only for pinhole cameras, we systematically test different view transformation functions including rigid transformation, polynomial transformation and manifold regression to determine the most robust mapping that generalizes well to unseen data. Third, we reformulate the celebrated bundle adjustment procedure to minimize the global 3D reprojection error so as to fine-tune the initial estimates. Finally, our scalable client-server architecture is computationally efficient: the calibration of a five-camera system, including data capture, can be done in minutes using only commodity PCs. Our proposed framework is compared with other state-of-the-arts systems using both quantitative measurements and visual alignment results of the merged point clouds

Video surveillance system for human detection

The problem I need to solve is the pedestrian detection in campus monitor. Computer vision studies based on deep learning algorithm provide relatively precise result for frame and video detection. Among all kinks of deep learning frameworks and algorithms, Caffe and Faster R-CNN perform outstandingly in both detection speed and accuracy rate. In this dissertation, Caffe and Faster R-CNN are applied on both CPU and GPU to detect the people in campus station video based on VGG network and ZF network. To visualized display the detection process and result, I created an interface using python. In the interface, functions of video selection, algorithm detection, result displaying are integrated. After the test of algorithm, the mean average precision for people detection is around 0.76. In the detection of campus monitor, most of complete pedestrians with proper size can be detected. The original request of the dissertation is satisfied. To further improve the performance of the algorithm, the network is trained using VOC 2007, VOC2012, VOC 2007+VOC 2012 and the superposition of other labeled pedestrian datasets. After the enhance training, the number of detected objects in campus monitor video increased. These results proved that increasing training samples of a specific class contributes to the performance of Faster R-CNN algorithm.Master of Science (Communications Engineering

On the Stability of a General Mixed Additive-Cubic Functional Equation in Random Normed Spaces

<p/> <p>We prove the generalized Hyers-Ulam stability of the following additive-cubic equation <inline-formula> <graphic file="1029-242X-2010-328473-i1.gif"/></inline-formula> in the setting of random normed spaces.</p

Probabilistic Safety Analysis for Loss of Offsite Power Accident in Dual-units Nuclear Power Plant

In order to explore the risk assessment method of the multi-unit nuclear power plant site, this paper selects the dual-unit plant nuclear site to analyze lose off-site power accident. By combining and improving the single-unit ET/FT model, to establish the dual-unit ET/FT model. From the analysis of the accident sequence, it can be concluded that the common cause failure of equipment is the main challenge faced by the dual-units. Especially the RPC sub-channel in the reactor protection system and the failure of emergency diesel engine circuit breaker. As can be seen from the high proportion of core CD occurring simultaneously in both uints, it has a great significance to study the risk of mult-units sites

The Method of Calculating the Frequency of the Initiating Event in a Dual-Unit Site with the Example of LOOP Events

In a nuclear power plant, the consequences of a multi-unit event occurring concurrently are more serious than those of a single-unit event. The first step in the probabilistic safety analysis of multi-units is to analyze the initiating events and calculate the frequency of initiating events for simultaneous events of multiple units. The difficulty in using the fault tree model is that the known data are all frequency data from a single unit and cannot be logically multiplied. In this paper, taking a dual unit as an example, we used the formula to convert the probability of failure of the second unit within 72 h and then build a fault tree model. After analyzing the results of the dual unit, the most frequent cut set was the common cause of failure of the main transformer and of the switching failure of the main and auxiliary external power. The final calculation of the frequency of simultaneous loss of off-site power events for the dual units within 72 h was 3.22 × 10−4/year. After comparing with the single-unit results, it was found that the common cause failure of each unit’s independent equipment was the main reason for the occurrence of a loss of off-site power. Shared equipment in a single unit was ranked low in all the cut sets (such as the stability of the external grid for the main and auxiliary power systems) but was ranked high in multiple units. The calculation results of the frequency of initiating events of double units were two orders of magnitude lower than those of a single unit. However, the consequences of simultaneous events of multiple units were higher than those of single reactors. Therefore, attention should be paid to the risk of a simultaneous loss of off-site power event of multiple units

Ultrasonic Non-Destructive Testing System of Semi-Enclosed Workpiece with Dual-Robot Testing System

With the rapid development of material science, more and more workpieces, especially workpieces with complex curved surfaces, are being made of composite materials. Robotic non-destructive testing (NDT) systems for complex curved surface composite material parts are being used more and more. Despite the emergence of such flexible NDT systems, the detection of semi-enclosed parts is also a challenge for robotic NDT systems. In order to overcome the problem, this paper establishes an NDT solution for semi-enclosed workpieces based on a dual-robot system of synchronous motion, in which an extension arm is installed on one of the robots and presents a trajectory planning method that always ensures the extension arm is parallel to the rotary axis of a semi-enclosed workpiece and that the ultrasonic probes are perpendicular to the workpiece surface. Trajectory analysis experiments and ultrasonic NDT experiments utilizing the optimal water path distance determined by simulation result of multi-Gaussian beam model for two types of semi-enclosed workpieces are performed with the dual-robot NDT system. Experimental results prove that the dual-robot NDT scheme functions well and the planned trajectories are correct. All the hole-shaped artificial defects with diameters &ge;3 mm are detected by using 2.25 MHz ultrasonic probes through the transmission testing method. Vivid 3D C-scan image of a small diameter cylindrical workpiece based on the testing result is provided for convenience of observation

Effective and Efficient Porous CeO₂ Adsorbent for Acid Orange 7 Adsorption

A porous CeO2 was synthesized following the addition of guanidine carbonate to a Ce3+ aqueous solution, the subsequent addition of hydrogen peroxide and a final hydrothermal treatment. The optimal experimental parameters for the synthesis of porous CeO2, including the amounts of guanidine carbonate and hydrogen peroxide and the hydrothermal conditions, were determined by taking the adsorption efficiency of acid orange 7 (AO7) dye as the evaluation. A template−free hydrothermal strategy could avoid the use of soft or hard templates and the subsequent tedious procedures of eliminating templates, which aligned with the goals of energy conservation and emission reduction. Moreover, both the guanidine carbonate and hydrogen peroxide used in this work were accessible and eco−friendly raw materials. The porous CeO2 possessed rapid adsorption capacities for AO7 dye. When the initial concentration of AO7 was less than 130 mg/L, removal efficiencies greater than 90.0% were obtained, achieving a maximum value of 97.5% at [AO7] = 100 mg/L and [CeO2] = 2.0 g/L in the first 10 min of contact. Moreover, the adsorption–desorption equilibrium between the porous CeO2 adsorbent and the AO7 molecule was basically established within the first 30 min. The saturated adsorption amount of AO7 dye was 90.3 mg/g based on a Langmuir linear fitting of the experimental data. Moreover, the porous CeO2 could be recycled using a NaOH aqueous solution, and the adsorption efficiency of AO7 dye still remained above 92.5% after five cycles. This study provided an alternative porous adsorbent for the purification of dye wastewater, and a template−free hydrothermal strategy was developed to enable the design of CeO2−based catalysts or catalyst carriers

A Fast and Robust Extrinsic Calibration for RGB-D Camera Networks.

From object tracking to 3D reconstruction, RGB-Depth (RGB-D) camera networks play an increasingly important role in many vision and graphics applications. Practical applications often use sparsely-placed cameras to maximize visibility, while using as few cameras as possible to minimize cost. In general, it is challenging to calibrate sparse camera networks due to the lack of shared scene features across different camera views. In this paper, we propose a novel algorithm that can accurately and rapidly calibrate the geometric relationships across an arbitrary number of RGB-D cameras on a network. Our work has a number of novel features. First, to cope with the wide separation between different cameras, we establish view correspondences by using a spherical calibration object. We show that this approach outperforms other techniques based on planar calibration objects. Second, instead of modeling camera extrinsic calibration using rigid transformation, which is optimal only for pinhole cameras, we systematically test different view transformation functions including rigid transformation, polynomial transformation and manifold regression to determine the most robust mapping that generalizes well to unseen data. Third, we reformulate the celebrated bundle adjustment procedure to minimize the global 3D reprojection error so as to fine-tune the initial estimates. Finally, our scalable client-server architecture is computationally efficient: the calibration of a five-camera system, including data capture, can be done in minutes using only commodity PCs. Our proposed framework is compared with other state-of-the-arts systems using both quantitative measurements and visual alignment results of the merged point clouds