Ghassan AlRegib

Distributed Estimation in Energy-Constrained Wireless Sensor Networks

In this paper, we consider distributed estimation of a noise-corrupted deterministic parameter in energy-constrained wireless sensor networks from energy-distortion perspective. Given a total energy budget allowable to be used by all sensors, there exists a tradeoff between the subset of active sensors and the energy used by each active sensor in order to minimize the estimation MSE. To determine the optimal quantization bit rate and transmission energy of each sensor, a concept of equivalent unit-energy MSE function is introduced. Based on this concept, an optimal energy-constrained distributed estimation algorithm for homogeneous sensor networks and a quasi-optimal energy-constrained distributed estimation algorithm for heterogeneous sensor networks are proposed. Moreover, the theoretical energy-distortion performance bound for distributed estimation is addressed and it is shown that the proposed algorithm is quasi-optimal within a factor 2 of the theoretical lower bound. Simulation results also show that the proposed method can achieve a significant reduction in the estimation MSE when compared with other uniform schemes. Finally, the proposed algorithm is easy to implement in a distributed manner and it adapts well to the dynamic sensor environments.

Cooperative MAC and routing protocols for wireless ad hoc networks

Cooperative diversity techniques exploit the spatial characteristics of the network to create transmit-diversity, in which the same information can be forwarded through multiple paths towards a single destination or a set of destination nodes. In this paper, we study the integration of cooperative diversity into wireless routing protocols by developing distributed cooperative MAC (C-MAC) and routing protocols. The proposed protocols employ efficient relay selection-coordination and power allocation techniques to maximize the cooperation benefits in the network. Simulation results show that the energy-saving performance of the minimum-energy routing protocols can be significantly improved when they are implemented together with the proposed C-MAC protocol (%50). We also show that the performance of the C-MAC protocol can be further enhanced when the initial path is selected using the cooperation characteristics of the network (%11 more energy-savings compared to the previous case, i.e., C-MAC with minimum-energy routing)

Hierarchical Hole-Filling For Depth-Based View Synthesis in FTV and 3D Video

Three-dimensional television (3DTV) is believed to be the future of television broadcasting that would replace current 2D HDTV technology. Future 3DTV would bring a more life-like and visually immersive home entertainment experience, in which users will have the freedom to navigate through the scene to choose a different viewpoint. A desired view can be synthesized at the receiver side using depth image-based rendering (DIBR). While this approach has many advantages, one of the key challenges in DIBR is how to fill the holes caused by disocclusion regions and wrong depth values. In this paper, we propose two new approaches for disocclusion removal in DIBR. Both approaches namely hierarchical hole-filling (HHF) and depth adaptive hierarchical hole-filling eliminate the need for any smoothing or filtering of the depth map. Both techniques use a pyramid-like approach to estimate the hole pixels from lower resolution estimates of the 3D wrapped image. The lower resolution estimates involve a pseudo zero canceling plus Gaussian filtering of the wrapped image. The depth adaptive HHF incorporates the depth information to produce a higher resolution rendering around previously occluded areas. Experimental results show that HHF and depth adaptive HHF yield virtual images and stereoscopic videos that are free of geometric distortions and a better rendering quality both subjectively and objectively than traditional hole-filling approaches.

Rate-Constrained Distributed Estimation in Wireless Sensor Networks

In this paper, we consider the distributed parameter estimation in wireless sensor networks where a total bit rate constraint is imposed. We study the optimal tradeoff between the number of active sensors and the quantization bit rate for each active sensor to minimize the estimation mean-square error (MSE). To facilitate the solution, we first introduce a concept of equivalent 1-bit MSE function. Next, we present an optimal distributed estimation algorithm for homogeneous sensor networks based on minimizing the equivalent 1-bit MSE function. Then, we present a quasi-optimal distributed estimation algorithm for heterogeneous sensor networks, which is also based on the equivalent 1-bit MSE function, and the upper bound of the estimation MSE of the proposed algorithm is addressed. Furthermore, a theoretical nonachievable lower bound of the estimation MSE under the total bit rate constraint is stated and it is shown that our proposed algorithm is quasi-optimal within a factor 2.2872 of the theoretical lower bound. Simulation results also show that significant reduction in estimation MSE is achieved by our proposed algorithm when compared with other uniform methods

Error-resilient transmission of 3D models

In this article, we propose an error-resilient transmission method for progressively compressed 3D models. The proposed method is scalable with respect to both channel bandwidth and channel packet-loss rate. We jointly design source and channel coders using a statistical measure that (i) calculates the number of both source and channel coding bits, and (ii) distributes the channel coding bits among the transmitted refinement levels in order to maximize the expected decoded model quality. In order to keep the total number of bits before and after applying error protection the same, we transmit fewer triangles in the latter case to accommodate the channel coding bits. When the proposed method is used to transmit a typical model over a channel with a 10% packet-loss rate, the distortion (measured using the Hausdorff distance between the original and the decoded models) is reduced by 50% compared to the case when no error protection is applied.

Network Lifetime Maximization for Estimation in Multihop Wireless Sensor Networks

We consider the distributed estimation by a network consisting of a fusion center and a set of sensor nodes, where the goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional. In energy-limited wireless sensor networks, both local quantization and multihop transmission are essential to save transmission energy and thus prolong the network lifetime. The network lifetime optimization problem includes three components: i) optimizing source coding at each sensor node, ii) optimizing source throughput of each sensor node, and iii) optimizing multihop routing path. Fortunately, source coding optimization can be decoupled from source throughput and multihop routing path optimization, and is solved by introducing a concept of equivalent 1-bit MSE function. Based on the optimal source coding, the source throughput and multihop routing path optimization is formulated as a linear programming (LP) problem, which suggests a new notion of character-based routing. The proposed algorithm is optimal and the simulation results show that a significant gain is achieved by the proposed algorithm compared with heuristic methods.

An unequal error protection method for packet loss resilient 3D mesh transmission

A packet-loss resilient, bandwidth-scalable 3D graphics streaming system is proposed. It uses the compressed progressive mesh (CPM) algorithm (see Pajarola, R. and Rossignac, J., IEEE Trans. on Visualization and Computer Graphics, vol.6, no.1, p.79-93, 2000) to generate a hierarchical bit-stream to represent different levels of details (LODs). We assign forward error correction (FEC) codes to each layer in the encoded bit-stream according to its importance. To this end, we develop a new distortion measure that quantifies the distortion in the reconstructed mesh when part of the bit-stream is lost. Experimental results with a collection of 3D meshes have been conducted to demonstrate the efficacy of our solution. They indicate that with our proposed unequal error protection (UEP), the decoded mesh quality degrades more gracefully, compared to either no error protection (NEP) or equal error protection (EEP) methods, as the packet loss rate increases.

Optimal packet scheduling for wireless video streaming with error-prone feedback

In wireless video transmission, burst packet errors generally produce more catastrophic results than equal number of isolated errors. To miniimize the playback distortion it is crucial for the sender to know the packet errors at the receiver and then optimally schedule next transmissions. Unfortunately, in practice, feedback errors result in inaccurate observations of the receiving status. In this paper, we develop an optimal scheduling framework to minimize the expected distortion by first estimating the receiving status. Then, we jointly consider the source and channel characteristics and optimally choose the packets to transmit. The optimal transmission strategy is computed through a partially observable Markov decision process. The experimental results show that the proposed framework improves the average peak signal-to-noise ratio (PSNR) by 0.6-1.3 dB upon using a traditional system without packet scheduling. Moreover, we show that the proposed method smoothes out the bursty distortion periods and results in less fluctuating PSNR values.

An unequal error protection method for progressively transmitted 3D models

In this paper, we present a packet-loss resilient system for the transmission of progressively compressed three-dimensional (3D) models. It is based on a joint source and channel coding approach that trades off geometry precision for increased error resiliency to optimize the decoded model quality on the client side. We derive a theoretical framework for the overall system by which the channel packet loss behavior and the channel bandwidth can be directly related to the decoded model quality at the receiver. First, the 3D model is progressively compressed into a base mesh and a number of refinement layers. Then, we assign optimal forward error correction code rates to protect these layers according to their importance to the decoded model quality. Experimental results show that with the proposed unequal error protection approach, the decoded model quality degrades more gracefully (compared to either no error protection or equal error protection methods) as the packet-loss rate increases.

Feature Processing and Modeling for 6D Motion Gesture Recognition

A 6D motion gesture is represented by a 3D spatial trajectory and augmented by another three dimensions of orientation. Using different tracking technologies, the motion can be tracked explicitly with the position and orientation or implicitly with the acceleration and angular speed. In this work, we address the problem of motion gesture recognition for command-and-control applications. Our main contribution is to investigate the relative effectiveness of various feature dimensions for motion gesture recognition in both user-dependent and user-independent cases. We introduce a statistical feature-based classifier as the baseline and propose an HMM-based recognizer, which offers more flexibility in feature selection and achieves better performance in recognition accuracy than the baseline system. Our motion gesture database which contains both explicit and implicit motion information allows us to compare the recognition performance of different tracking signals on a common ground. This study also gives an insight into the attainable recognition rate with different tracking devices, which is valuable for the system designer to choose the proper tracking technology.