Donald A. Adjeroh

Priority-based rate control for service differentiation and congestion control in wireless multimedia sensor networks

In wireless multimedia sensor networks (WMSNs) a sensor node may have different types of sensor which gather different kinds of data. To support quality of service (QoS) requirements for multimedia applications having a reliable and fair transport protocol is necessary. One of the main objectives of the transport layer in WMSNs is congestion control. We observe that the information provided may have different levels of importance and argue that sensor networks should be willing to spend more resources in disseminating packets carrying more important information. Some applications of WMSNs may need to send real time traffic toward the sink node. This real time traffic requires low latency and high reliability so that immediate remedial and defensive actions can be taken when needed. Therefore, similar to wired networks, service differentiation in wireless sensor networks is also an important issue. We present a priority-based rate control mechanism for congestion control and service differentiation in WMSNs. We distinguish high priority real time traffic from low priority non-real time traffic, and service the input traffic based on its priority. Simulation results confirm the superior performance of the proposed model with respect to delays, delay variation and loss probability.

The Burrows-Wheeler Transform: Data Compression, Suffix Arrays, and Pattern Matching

The Burrows-Wheeler Transform is a text transformation scheme that has found applications in different aspects of the data explosion problem, from data compression to index structures and search. The BWT belongs to a new class of compression algorithms, distinguished by its ability to perform compression by sorted contexts. More recently, the BWT has also found various applications in addition to text data compression, such as in lossless and lossy image compression, tree-source identification, bioinformatics, machine translation, shape matching, and test data compression. This book will serve as a reference for seasoned professionals or researchers in the area, while providing a gentle introduction, making it accessible for senior undergraduate students or first year graduate students embarking upon research in compression, pattern matching, full text retrieval, compressed index structures, or other areas related to the BWT. Key Features Comprehensive resource for information related to different aspects of the Burrows-Wheeler Transform including: Gentle introduction to the BWT History of the development of the BWT Detailed theoretical analysis of algorithmic issues and performance limits Searching on BWT compressed data Hardware architectures for the BWT Explores non-traditional applications of the BWT in areas such as: Bioinformatics Joint source-channel coding Modern information retrieval Machine translation Test data compression for systems-on-chip Teaching materials ideal for classroom use on courses in: Data Compression and Source Coding Modern Information Retrieval Information Science Digital Libraries

A new priority based congestion control protocol for Wireless Multimedia Sensor Networks

New applications made possible by the rapid improvements and miniaturization in hardware has motivated recent developments in wireless multimedia sensor networks (WMSNs). As multimedia applications produce high volumes of data which require high transmission rates, multimedia traffic is usually high speed. This may cause congestion in the sensor nodes, leading to impairments in the quality of service (QoS) of multimedia applications. Thus, to meet the QoS requirements of multimedia applications, a reliable and fair transport protocol is mandatory. An important function of the transport layer in WMSNs is congestion control. In this paper, we present a new queue based congestion control protocol with priority support (QCCP-PS), using the queue length as an indication of congestion degree. The rate assignment to each traffic source is based on its priority index as well as its current congestion degree. Simulation results show that the proposed QCCP-PS protocol can detect congestion better than previous mechanisms. Furthermore it has a good achieved priority close to the ideal and near-zero packet loss probability, which make it an efficient congestion control protocol for multimedia traffic in WMSNs. As congestion wastes the scarce energy due to a large number of retransmissions and packet drops, the proposed QCCP-PS protocol can save energy at each node, given the reduced number of retransmissions and packet losses.

Efficient texture analysis of SAR imagery

We address the problem of efficiency in texture analysis for synthetic aperture radar (SAR) imagery. Motivated by the statistical occupancy model, we introduce the notion of patch reoccurrences. Using the reoccurrences, we propose the use of approximate textural features in analysis of SAR images. We describe how the proposed approximate features can be extracted for two popular texture analysis methods-the gray-level cooccurrence matrix and Gabor wavelets. Results on image texture classification show that the proposed method can provide an improved efficiency in the analysis of SAR imagery, without introducing any significant degradation in the classification results.

A distance measure for video sequences

Video is a unique multimedia data type, in that it comes with distinguished spatio-temporal constraints. Content-based video retrieval thus requires methods for video sequence-to-sequence matching, incorporating the temporal ordering inherent in a video sequence, without losing sight of the visual nature of the information in the sequence. Such methods will require reliable measures of similarity between the video sequences. In this paper, we formulate the problem of video sequence-to-sequence matching as a pattern-matching problem and propose the vstring edit distance as a suitable distance measure for video sequences.

A novel tracing algorithm for high throughput imaging Screening of neuron-based assays.

High throughput neuron image processing is an important method for drug screening and quantitative neurobiological studies. The method usually includes detection of neurite structures, feature extraction, quantification, and statistical analysis. In this paper, we present a new algorithm for fast and automatic extraction of neurite structures in microscopy neuron images. The algorithm is based on novel methods for soma segmentation, seed point detection, recursive center-line detection, and 2D curve smoothing. The algorithm is fully automatic without any human interaction, and robust enough for usage on images with poor quality, such as those with low contrast or low signal-to-noise ratio. It is able to completely and accurately extract neurite segments in neuron images with highly complicated neurite structures. Robustness comes from the use of 2D smoothening techniques and the idea of center-line extraction by estimating the surrounding edges. Efficiency is achieved by processing only pixels that are close enough to the line structures, and by carefully chosen stopping conditions. These make the proposed approach suitable for demanding image processing tasks in high throughput screening of neuron-based assays. Detailed results on experimental validation of the proposed method and on its comparative performance with other proposed schemes are included.

Adaptive edge-oriented shot boundary detection

We study the problem of video shot boundary detection using an adaptive edge-oriented framework. Our approach is distinct in its use of multiple multilevel features in the required processing. Adaptation is provided by a careful analysis of these multilevel features, based on shot variability. We consider three levels of adaptation: at the feature extraction stage using locally-adaptive edge maps, at the video sequence level, and at the individual shot level. We show how to provide adaptive parameters for the multilevel edge-based approach, and how to determine adaptive thresholds for the shot boundaries based on the characteristics of the particular shot being indexed. The result is a fast adaptive scheme that provides a slightly better performance in terms of robustness, and a five fold efficiency improvement in shot characterization and classification. The reported work has applications beyond direct video indexing, and could be used in real-time applications, such as in dynamic monitoring and modeling of video data traffic in multimedia communications, and in real-time video surveillance. Experimental results are included.

RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information

RNA-protein complexes are essential in mediating important fundamental cellular processes, such as transport and localization. In particular, ncRNA-protein interactions play an important role in post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ∼94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ∼83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at: http://ctsb.is.wfubmc.edu/projects/rpi-pred.

Dendritic spine detection using curvilinear structure detector and LDA classifier

Dendritic spines are small, bulbous cellular compartments that carry synapses. Biologists have been studying the biochemical pathways by examining the morphological and statistical changes of the dendritic spines at the intracellular level. In this paper a novel approach is presented for automated detection of dendritic spines in neuron images. The dendritic spines are recognized as small objects of variable shape attached or detached to multiple dendritic backbones in the 2D projection of the image stack along the optical direction. We extend the curvilinear structure detector to extract the boundaries as well as the centerlines for the dendritic backbones and spines. We further build a classifier using Linear Discriminate Analysis (LDA) to classify the attached spines into valid and invalid types to improve the accuracy of the spine detection. We evaluate the proposed approach by comparing with the manual results in terms of backbone length, spine number, spine length, and spine density.

A distance measure for video sequence similarity matching

Contrary to current approaches which generally treat the video data as a random collection of static images, content-based video retrieval requires methods for video sequence-to-sequence matching incorporating the temporal order inherent in video data. We formulate the problem of video sequence-to-sequence matching as a pattern matching problem. New string edit operations required for the special characteristics of video sequences and the unique features of the vstring representation are introduced. Based on the edit operations, the vstring edit distance is proposed as a new similarity measure for video sequence matching.