Motion Estimation and Compensation in the Redundant Wavelet Domain

Despite being the prefered approach for still-image compression for nearly a decade, wavelet-based coding for video has been slow to emerge, due primarily to the fact that the shift variance of the discrete wavelet transform hinders motion estimation and compensation crucial to modern video coders. Recently it has been recognized that a redundant, or overcomplete, wavelet transform is shift invariant and thus permits motion prediction in the wavelet domain. In this dissertation, other uses for the redundancy of overcomplete wavelet transforms in video coding are explored. First, it is demonstrated that the redundant-wavelet domain facilitates the placement of an irregular triangular mesh to video images, thereby exploiting transform redundancy to implement geometries for motion estimation and compensation more general than the traditional block structure widely employed. As the second contribution of this dissertation, a new form of multihypothesis prediction, redundant wavelet multihypothesis, is presented. This new approach to motion estimation and compensation produces motion predictions that are diverse in transform phase to increase prediction accuracy. Finally, it is demonstrated that the proposed redundant-wavelet strategies complement existing advanced video-coding techniques and produce significant performance improvements in a battery of experimental results

Perspective Chapter: Deep Reinforcement Learning for Co-Resident Attack Mitigation in The Cloud

Cloud computing brings convenience and cost efficiency to users, but multiplexing virtual machines (VMs) on a single physical machine (PM) results in various cybersecurity risks. For example, a co-resident attack could occur when malicious VMs use shared resources on the hosting PM to control or gain unauthorized access to other benign VMs. Most task schedulers do not contribute to both resource management and risk control. This article studies how to minimize the co-resident risks while optimizing the VM completion time through designing efficient VM allocation policies. A zero-trust threat model is defined with a set of co-resident risk mitigation parameters to support this argument and assume that all VMs are malicious. In order to reduce the chances of co-residency, deep reinforcement learning (DRL) is adopted to decide the VM allocation strategy. An effective cost function is developed to guide the reinforcement learning (RL) policy training. Compared with other traditional scheduling paradigms, the proposed system achieves plausible mitigation of co-resident attacks with a relatively small VM slowdown ratio

Stability analysis of differential scheme for dynamic equations of mooring cable system

The mooring cable system in plane motion can be modeled as two coupled partial differential equations, which can be numerical solved by finite difference method directly. The difference scheme is analyzed, and parameters selection for time-marching of displacement and velocity are deduced. The stability condition of the scheme is analyzed through Fourier series method, and parameters range which match stable scheme is given. Then, the parameters range is verified by a numerical example

Galloping behavior analysis of transmission line with thin ice accretions

A dynamic model of a galloping transmission line able to describe for the coupling of its longitudinal, in-plane, out-of-plane and torsional vibrations is established. It also considers the effects of geometrical nonlinearity and aerodynamic nonlinearity. By the static configuration, the reduced model is obtained. Then, the equations of motion are obtained through the Galerkin method. It contains two in-plane, two out-of-plane and two torsional components. By numerical calculation, the maximum amplitudes at wind speeds are drawn and the galloping behavior of transmission line with thin ice accretions is analyzed. The obtained results show that the second galloping mode is more triggered. The double-mode galloping occurs in all motions, in which the maximum amplitude is bigger than in single-mode galloping. And the double-mode galloping presents the track of inclined ‘8’ in longitudinal direction

Compressive Sensing Based Image Compression and Transmission for Noisy Channels

This paper presents the design of an optimized Compressive Sensing image compression technique for data transmission over noisy mobile wireless channel. The proposed technique is more robust to channel noise. It uses individual measurement driven coding scheme, which facilitates simpler encoder design. The shift of computational burden from encoder to decoder is more suitable for mobile devices applications where computational power and battery life are limited. This paper also presents a novel quantizer which allows the encoder to dynamically adapt to the channel conditions and provides optimum performance

Connecting Incoming Freshmen With Engineering Through Hands-On Projects

Engineering programs suffer a high attrition rate, which causes the nation to graduate much less engineers. A survey of the literature reveals that the high attrition rate is due mainly to the fact that the first year of an engineering program is all fundamental theory and students don't see the connection to their future engineering careers. To address this problem, educators in the Roy G. Perry College of Engineering at Prairie View A&M University launched a five-week summer camp entitled “College of Engineering Enhancement Institute (CE2I)” aimed at improving the performance of incoming freshmen in mathematics by one level and a smoother transition between high school and college. Each department in the college participated by introducing their individual curriculum through hands-on projects designed by faculty members. Computer Engineering, Computer Science and Computer Engineering Technology programs implemented multimedia projects to tie the incoming freshman to their selected majors. Results show that the camp met the expectations and successfully points the directions for our future engineering education practices.

Expansions of Functions Based on Rational Orthogonal Basis with Nonnegative Instantaneous Frequencies

We consider in this paper expansions of functions based on the rational orthogonal basis for the space of square integrable functions. The basis functions have nonnegative instantaneous frequencies so that the expansions make physical sense. We discuss the almost everywhere convergence of the expansions and develop a fast algorithm for computing the coefficients arising in the expansions by combining the characterization of the coefficients with the fast Fourier transform