Joseph C. Dagher

A Theory for Maximizing the Lifetime of Sensor Networks

An important issue in wireless sensor networks is the limited energy and bandwidth resources. A novel theory is developed here for maximizing the lifetime of unicast multihop wireless sensor networks. An optimal centralized solution is presented in the form of an iterative algorithm. The algorithm attempts to find a Pareto-optimal (PO) solution. In the first iteration, the minimum lifetime of the network is maximized. If the solution is not PO, a second iteration is performed which maximizes the second minimum lifetime, subject to the minimum lifetime being maximum. At the nth iteration, the algorithm maximizes the nth minimum lifetime subject to the (n-1) the minimum lifetime being maximum, subject to the (n-2) the minimum lifetime being maximum, etc. The algorithm can be stopped at any iteration n. The presented solution assumes static network conditions

A method for coordinating the distributed transmission of imagery

Distributed imaging using sensor arrays is gaining popularity among various research and development communities. A common bottleneck within such an imaging sensor network is the large resulting data load. In applications for which transmission power and/or bandwidth are constrained, this can drastically decrease the sensor network lifetime. We present an algorithm that efficiently exploits inter- and intrasensor correlation for the purpose of power-constrained distributed transmission of sensor-network imagery. Gains in network lifetime up to 114% are obtained when using the suggested algorithm with lossless compression. Our results also demonstrate that when lossy compression is employed, much larger gains are achieved. For example, when a normalized root-mean-squared error of 0.78% can be tolerated in the received measurements, the network lifetime increases by a factor of 2.8, as compared to the (optimized) lossless case.

Resource-constrained rate control for Motion JPEG2000

With the increasing importance of heterogeneous networks and time-varying communication channels, fine scalability has become a highly desirable feature in both image and video coders. A single highly scalable bitstream can provide precise rate control for constant bitrate (CBR) traffic and accurate quality control for variable bitrate (VBR) traffic. We first propose two leaky-bucket rate allocation methods that provide constant quality video under buffer constraints. These methods can be used with all scalable coders. Moreover, we make use of one of these methods (DBRC) and extend it so that it can be used when multiple sequences are multiplexed over a single communications channel. The goal is to allocate the capacity of the channel between sequences to achieve constant quality across all sequences. Experimental results using Motion JPEG2000 demonstrate substantial benefits.

Compression of multislice CT: 2D vs. 3D JPEG2000 and effects of slice thickness

The widespread use of multi-detector CT scanners has been associated with a remarkable increase in the number of CT slices as well as a substantial decrease in the average thickness of individual slices. This increased number of thinner slices has created a marked increase in archival and network bandwidth requirements associated with storage and transmission of these studies. We demonstrate that although compression can be used to decrease the size of these image files, thinner CT slices are less compressible than thicker slices when measured by either a visual discrimination model (VDM) or the more traditional peak signal to noise ratio. The former technique (VDM) suggests that the discrepancy in compressibility between thin and thick slices becomes greater at greater compression levels while the latter technique (PSNR), suggests that this is not the case. Previous studies that we and others have performed suggest that the VDM model probably corresponds more closely with human observers than does the PSNR model. Additionally we demonstrated that the poor relative compressibility of thin sections can be substantially negated by the use of JPEG 2000 3D compression which yields superior image quality at a given level of compression in comparison with 2D compression. Additionally, thin and thick sections are approximately equally compressible for 3D compression with little change with increasing levels of compression.

MAGPI: A framework for maximum likelihood MR phase imaging using multiple receive coils.

Combining MR phase images from multiple receive coils is a challenging problem, complicated by ambiguities introduced by phase wrapping, noise, and the unknown phase‐offset between the coils. Various techniques have been proposed to mitigate the effect of these ambiguities but most of the existing methods require additional reference scans and/or use ad hoc post‐processing techniques that do not guarantee any optimality.

Efficient storage and transmission of ladar imagery

We develop novel methods for compressing volumetric imagery that has been generated by single-platform (mobile) range sensors. We exploit the correlation structure inherent in multiple views in order to improve compression efficiency. We show that, for lossless compression, three-dimensional volumes compress more efficiently than two-dimensional (2D) images by a factor of 60%. Furthermore, our error metric for lossy compression suggests that accumulating more than nine range images in one volume before compression yields as much as a 99% improvement in compression performance over 2D compression.

Reduced memory multi-layer multi-component rate allocation for JPEG2000

Remote sensing images are often multispectral in nature and are acquired by on-board sensors in a “push-broom” fashion. These images are compressed and transmitted to ground stations for further analysis. Since they are extremely large, buffering all acquired data before encoding requires huge amounts of memory and introduces latency. Incremental compression schemes work on small chunks of raw data as soon as they are acquired and help reduce buffer memory requirements. However, incremental processing leads to large variations in quality across the reconstructed image. We propose two “leaky bucket” rate control algorithms that can be employed for incrementally compressing hyperspectral images using JPEG2000. Both schemes perform rate control using the fine granularity afforded by JPEG2000. The proposed algorithms have low memory requirements and enable SNR scalability through the use of quality layers. Experiments show that the proposed schemes provide significant reduction in quality variation with no loss in mean overall PSNR performance.

A Joint Acquisition-Estimation Framework for MR Phase Imaging

Measuring the phase of the MR signal is faced with fundamental challenges such as phase aliasing, noise and unknown offsets of the coil array. There is a paucity of acquisition, reconstruction and estimation methods that rigorously address these challenges. This reduces the reliability of information processing in phase domain. We propose a joint acquisition-processing framework that addresses the challenges of MR phase imaging using a rigorous theoretical treatment. Our proposed solution acquires the multi-coil complex data without any increase in acquisition time. Our corresponding estimation algorithm is applied optimally voxel-per-voxel. Results show that our framework achieves performance gains up to an order of magnitude compared to existing methods.

Collaborative multihop transmission of distributed sensor imagery

We consider a network of imaging sensors. We address the problem of energy-efficient communication of the measurements of the sensors. A novel algorithm is presented for the purpose of exploiting intersensor and intrasensor correlation, which is inherent in a network of imaging sensors. The collaborative algorithm is used in conjunction with a cooperative multihop routing strategy to maximize the lifetime of the network. The algorithm is demonstrated to achieve an average gain in the lifetime as high as 3.2 over previous methods.

MR phase imaging with bipolar acquisition

We have previously proposed a novel magnetic resonance (MR) phase imaging framework (MAGPI) based on a three‐echo sequence that demonstrated substantial gains in phase signal‐to‐noise ratio (SNR). We improve upon the performance of MAGPI by extending the formulation to handle (i) an alternating gradient polarity (bipolar) readout scheme and (ii) an arbitrary number of echoes. We formulate the phase‐imaging problem using maximum‐likelihood (ML) estimation. The acquisition uses an optimized multi‐echo gradient echo (MEGE) sequence. The tissue‐phase estimation algorithm is a voxel‐per‐voxel approach, which requires no reference scans, no phase unwrapping and no spatial denoising. Unlike other methods, our bipolar readout model is general and does not make simplifying assumptions about the even–odd echo phase errors. The results show that (a) our proposed bipolar MAGPI approach improves on the phase SNR gains achieved with monopolar MAGPI and (b) the phase SNR converges with the number of echoes more rapidly with bipolar MAGPI. Importantly, bipolar MAGPI enables phase imaging in severely SNR‐constrained scenarios, where monopolar MAGPI is unable to find solutions. The substantial phase SNR gains achieved with our framework are used here to (a) accelerate acquisitions (full brain 0.89 mm in‐plane resolution in 2 min 30 sec) and (b) enable high‐contrast high‐resolution phase imaging (310 µm in‐plane resolution) at clinical field strengths. Copyright