Depth-Assisted Semantic Segmentation, Image Enhancement and Parametric Modeling

This dissertation addresses the problem of employing 3D depth information on solving a number of traditional challenging computer vision/graphics problems. Humans have the abilities of perceiving the depth information in 3D world, which enable humans to reconstruct layouts, recognize objects and understand the geometric space and semantic meanings of the visual world. Therefore it is significant to explore how the 3D depth information can be utilized by computer vision systems to mimic such abilities of humans. This dissertation aims at employing 3D depth information to solve vision/graphics problems in the following aspects: scene understanding, image enhancements and 3D reconstruction and modeling. In addressing scene understanding problem, we present a framework for semantic segmentation and object recognition on urban video sequence only using dense depth maps recovered from the video. Five view-independent 3D features that vary with object class are extracted from dense depth maps and used for segmenting and recognizing different object classes in street scene images. We demonstrate a scene parsing algorithm that uses only dense 3D depth information to outperform using sparse 3D or 2D appearance features. In addressing image enhancement problem, we present a framework to overcome the imperfections of personal photographs of tourist sites using the rich information provided by large-scale internet photo collections (IPCs). By augmenting personal 2D images with 3D information reconstructed from IPCs, we address a number of traditionally challenging image enhancement techniques and achieve high-quality results using simple and robust algorithms. In addressing 3D reconstruction and modeling problem, we focus on parametric modeling of flower petals, the most distinctive part of a plant. The complex structure, severe occlusions and wide variations make the reconstruction of their 3D models a challenging task. We overcome these challenges by combining data driven modeling techniques with domain knowledge from botany. Taking a 3D point cloud of an input flower scanned from a single view, each segmented petal is fitted with a scale-invariant morphable petal shape model, which is constructed from individually scanned 3D exemplar petals. Novel constraints based on botany studies are incorporated into the fitting process for realistically reconstructing occluded regions and maintaining correct 3D spatial relations. The main contribution of the dissertation is in the intelligent usage of 3D depth information on solving traditional challenging vision/graphics problems. By developing some advanced algorithms either automatically or with minimum user interaction, the goal of this dissertation is to demonstrate that computed 3D depth behind the multiple images contains rich information of the visual world and therefore can be intelligently utilized to recognize/ understand semantic meanings of scenes, efficiently enhance and augment single 2D images, and reconstruct high-quality 3D models

Fast Adaptive S-ALOHA Scheme for Event-driven Machine-to-Machine Communications

Machine-to-Machine (M2M) communication is now playing a market-changing role in a wide range of business world. However, in event-driven M2M communications, a large number of devices activate within a short period of time, which in turn causes high radio congestions and severe access delay. To address this issue, we propose a Fast Adaptive S-ALOHA (FASA) scheme for M2M communication systems with bursty traffic. The statistics of consecutive idle and collision slots, rather than the observation in a single slot, are used in FASA to accelerate the tracking process of network status. Furthermore, the fast convergence property of FASA is guaranteed by using drift analysis. Simulation results demonstrate that the proposed FASA scheme achieves near-optimal performance in reducing access delay, which outperforms that of traditional additive schemes such as PB-ALOHA. Moreover, compared to multiplicative schemes, FASA shows its robustness even under heavy traffic load in addition to better delay performance.Comment: 5 pages, 3 figures, accepted to IEEE VTC2012-Fal

A cohesive law for interfaces in graphene/hexagonal boron nitride heterostructure

Graphene/hexagonal boron nitride (h-BN) heterostructure has showed great potential to improve the performance of graphene device. We have established the cohesive law for interfaces between graphene and monolayer or multi-layer h-BN based on the van der Waals force. The cohesive energy and cohesive strength are given in terms of area density of atoms on corresponding layers, number of layers, and parameters in the van der Waals force. It is found that the cohesive law in the graphene/multi-layer h-BN is dominated by the three h-BN layers which are closest to the graphene. The approximate solution is also obtained to simplify the expression of cohesive law. These results are very useful to study the deformation of graphene/h-BN heterostructure, which may have significant impacts on the performance and reliability of the graphene devices especially in the areas of emerging applications such as stretchable electronics

Reliability Evaluation of Direct Current Distribution System for Intelligent Buildings Based on Big Data Analysis

In intelligent buildings, the power is distributed in the direct current (DC) mode, which is more energy-efficient than the traditional alternating current (AC) mode. However, the DC distribution system for intelligent buildings faces many problems, such as the stochasticity and intermittency of distributed generation, as well as the uncertain reliability of key supply and distribution devices. To solve these problems, this paper evaluates and predicts the reliability of the DC distribution system for intelligent buildings through big data analysis. Firstly, the authors identified the sources of the big data on DC distribution system for reliability analysis, and constructed a scientific evaluation index system. Then, association rules were mined from the original data on the evaluation indices with MapReduce, and a reliability evaluation model was established based on Bayesian network. Finally, the proposed model was proved valid through experiments. The research provides reference for reliability evaluation of the DC distribution system in various fields

On Achieving Secure Message Authentication for Vehicular Communications

Vehicular Ad-hoc Networks (VANETs) have emerged as a new application scenario that is envisioned to revolutionize the human driving experiences, optimize traffic flow control systems, etc. Addressing security and privacy issues as the prerequisite of VANETs' development must be emphasized. To avoid any possible malicious attack and resource abuse, employing a digital signature scheme is widely recognized as the most effective approach for VANETs to achieve authentication, integrity, and validity. However, when the number of signatures received by a vehicle becomes large, a scalability problem emerges immediately, where a vehicle could be difficult to sequentially verify each received signature within 100-300 ms interval in accordance with the current Dedicated Short Range Communications (DSRC) protocol. In addition, there are still some unsolved attacks in VANETs such as Denial of Service (Dos) attacks, which are not well addressed and waiting for us to solve. In this thesis, we propose the following solutions to address the above mentioned security related issues. First of all, to address the scalability issues, we introduce a novel roadside unit (RSU) aided message authentication scheme, named RAISE, which makes RSUs responsible for verifying the authenticity of messages sent from vehicles and for notifying the results back to vehicles. In addition, RAISE adopts the k-anonymity property for preserving user privacy, where a message cannot be associated with a common vehicle. Secondly, we further consider the situation that RSUs may not cover all the busy streets of a city or a highway in some situations, for example, at the beginning of a VANETs' deployment period, or due to the physical damage of some RSUs, or simply for economic considerations. Under these circumstances, we further propose an efficient identity-based batch signature verification scheme for vehicular communications. The proposed scheme can make vehicles verify a batch of signatures once instead of one after another, and thus it efficiently increases vehicles' message verification speed. In addition, our scheme achieves conditional privacy: a distinct pseudo identity is generated along with each message, and a trust authority can trace a vehicle's real identity from its pseudo identity. In order to find invalid signatures in a batch of signatures, we adopt group testing technique which can find invalid signatures efficiently. Lastly, we identify a DoS attack, called signature jamming attack (SJA), which could easily happen and possibly cause a profound vicious impact on the normal operations of a VANET, yet has not been well addressed in the literature. The SJA can be simply launched at an attacker by flooding a significant number of messages with invalid signatures that jam the surrounding vehicles and prevent them from timely verifying regular and legitimate messages. To countermeasure the SJA, we introduces a hash-based puzzle scheme, which serves as a light-weight filter for excluding likely false signatures before they go through relatively lengthy signature verification process. To further minimize the vicious effect of SJA, we introduce a hash recommendation mechanism, which enables vehicles to share their information so as to more efficiently thwart the SJA. For each research solution, detailed analysis in terms of computational time, and transmission overhead, privacy preservation are performed to validate the efficiency and effectiveness of the proposed schemes

Influence of Section Orientation of Ultrasound Shear Wave Elastography on the Measurement of TI-RADS Category 4 Thyroid Nodules Stiffness

Thyroid shear wave elastography (SWE) is widely used as a noninvasive screening tool for thyroid nodules (TN) diagnosis. Herein, we assessed the effect of SWE section orientation on the stiffness measurement of TI-RADS category 4 TN. In this retrospective study, we followed up patients who had 2D ultrasound and elastography of the thyroid with pathological findings at our institution. The reliability and agreement between the aforementioned evaluations were further examined via calculation of the mean and maximum modulus values of the TN in both section orientations. As a result, there was good agreement in the measurement of the shear wave modulus of TN between the two different views, which provides relative flexibility for patients with anatomical or physiological defects