Gunasekaran Seetharaman

Contemporary concerns in Geographical/Geospatial Information Systems (GIS) processing

With the advent of advances in Geospatial Information Systems (GIS); there is a need to determine the areas of research and new tools available for GIS systems. GIS consists of the collection, integration, storage, exploitation, and visualization of geographic and contextual data and spatial information. Future GIS needs, techniques, models, and standards should be shared openly among developers for future instantiation of products. The summary of selected areas include (1) support for large-data formats including meta-data transparency, (2) adherence to open standards, (3) generation of extensible architectures, and (4) development of a consistent set of metrics for analysis. The future of GIS products will include non-spatial as well as spatial data which requires information fusion, management functions from machine-processed data to user-defined actionable information, and use-case challenge problems for comparison.

PC-DUOS: Fast TCAM lookup and update for packet classifiers

We propose algorithms for distributing the classifier rules to two TCAMs (ternary content addressable memories) and for incrementally updating the TCAMs. The performance of our scheme is compared against the prevalent scheme of storing classifier rules in a single TCAM in priority order. Our scheme results in an improvement in average lookup speed by up to 48% and our experiments demonstrate an improvement in update performance by up to 2.8 times in terms of the number of TCAM writes.

PC-TRIO: A Power Efficient TCAM Architecture for Packet Classifiers

PC-TRIO is an indexed TCAM architecture for packet classification. In addition to index TCAMs, PC-TRIO uses wide SRAM words. On our packet classifier data sets, PC-TRIO reduced TCAM power by 96 percent and lookup time by 98 percent on an average, compared to PC-DUOS+ [28] that does not use indexing or wide SRAMs. PC-DUOS+ was shown to be better than STCAM, which is a single TCAM architecture conventionally used for packet classification [28]. In this paper, we also extend PC-DUOS+ by augmenting it with wide SRAMs and index TCAMs using the same methodology as used in PC-TRIO, to obtain PC-DUOS+W. On ACL data sets, PC-DUOS+W reduced TCAM power by 86 percent and lookup time by 98 percent, compared to PC-DUOS+, which demonstrates the effectiveness of indexing and usage of wide SRAMs in reducing power and lookup time for packet classifiers.

Scalable representation of dataflow graph structures using topological patterns

Tools for designing signal processing systems with their semantic foundation in dataflow modeling often use high-level graphical user interface (GUI) or text based languages that allow specifying applications as directed graphs. Such graphical representations serve as an initial reference point for further analysis and optimizations that lead to platform-specific implementations. For large-scale applications, the underlying graphs often consist of smaller substructures that repeat multiple times. To enable more concise representation and direct analysis of such substructures in the context of high level DSP specification languages and design tools, we develop the modeling concept of topological patterns, and propose ways for supporting this concept in a high-level language. We augment the DIF language — a language for specifying DSP-oriented dataflow graphs — with constructs for supporting topological patterns, and we show how topological patterns can be effective in various aspects of embedded signal processing design flows using specific application examples.

Topological Patterns for Scalable Representation and Analysis of Dataflow Graphs

Tools for designing signal processing systems with their semantic foundation in dataflow modeling often use high-level graphical user interfaces (GUIs) or text based languages that allow specifying applications as directed graphs. Such graphical representations serve as an initial reference point for further analysis and optimizations that lead to platform-specific implementations. For large-scale applications, the underlying graphs often consist of smaller substructures that repeat multiple times. To enable more concise representation and direct analysis of such substructures in the context of high level DSP specification languages and design tools, we develop the modeling concept of topological patterns, and propose ways for supporting this concept in a high-level language. We augment the dataflow interchange format (DIF) language—a language for specifying DSP-oriented dataflow graphs—with constructs for supporting topological patterns, and we show how topological patterns can be effective in various aspects of embedded signal processing design flows using specific application examples.

PC-DUOS+: A TCAM Architecture for Packet Classifiers

We propose algorithms for distributing the classifier rules to two ternary content addressable memories (TCAMs) and for incrementally updating the TCAMs. The performance of our scheme is compared against the prevalent scheme of storing classifier rules in a single TCAM in priority order. Our scheme results in an improvement in average lookup speed by up to 49% and an improvement in update performance by up to 3.84 times in terms of the number of TCAM writes.

Vehicle detection and orientation estimation using the radon transform

Determining the location and orientation of vehicles in satellite and airborne imagery is a challenging task given the density of cars and other vehicles and complexity of the environment in urban scenes almost anywhere in the world. We have developed a robust and accurate method for detecting vehicles using a template-based directional chamfer matching, combined with vehicle orientation estimation based on a refined segmentation, followed by a Radon transform based profile variance peak analysis approach. The same algorithm was applied to both high resolution satellite imagery and wide area aerial imagery and initial results show robustness to illumination changes and geometric appearance distortions. Nearly 80% of the orientation angle estimates for 1585 vehicles across both satellite and aerial imagery were accurate to within 15◦ of the ground truth. In the case of satellite imagery alone, nearly 90% of the objects have an estimated error within ±1.0° of the ground truth.

Sub-pixel registration of moving objects in visible and thermal imagery with adaptive segmentation

Sub-pixel registration is critical in object tracking and image super-resolution. Motion segmentation algorithms using the gradient can be applied prior to image registration to improve its accuracy and computational runtime. This paper proposes a new segmentation method that is adaptive variation segmentation in the form of local variances taken at different block sizes to be applied to the sum of absolute image differences. In this paper, two motion segmentation and four image registration methods are tested to optimize the registration accuracy in visible and thermal imagery. Two motion segmentation methods, flux tensor and adaptive variation segmentation, are quantitatively tested by comparing calculated regions of movement with accepted areas of motion. Four image registration methods, including two optical flow, feature correspondence, and correlation methods, are tested in two steps: gross shift and sub-pixel shift estimations. Gross shift estimation accuracy is assessed by comparing estimated shifts against a ground truth. Sub-pixel shift estimation accuracy is assessed by simulated, down-sampled images. Evaluations show that the best segmentation results are achieved using either the flux tensor or adaptive segmentation methods. For well-defined objects, feature correspondence and correlation registration produce the most accurate gross shift registrations. For not well-defined objects, the correlation method produces the most accurate gross and sub-pixel shift registration.

The effect of state dependent probability of detection in multitarget tracking applications

Through its ability to create situation awareness, multi-target target tracking is an extremely important capability for almost any kind of surveillance and tracking system. Many approaches have been proposed to address its inherent challenges. However, the majority of these approaches make two assumptions: the probability of detection and the clutter rate are constant. However, neither are likely to be true in practice. For example, as the projected size of a target becomes smaller as it moves further from the sensor, the probability of detection will decline. When target detection is carried out using templates, clutter rate will depend on how much the environment resembles the current target of interest. In this paper, we begin to investigate the impacts on these effects. Using a simulation environment inspired by the challenges of Wide Area Surveillance (WAS), we develop a state dependent formulation for probability of detection and clutter. The impacts of these models are compared in a simulated urban environment populated by multiple vehicles and cursed with occlusions. The results show that accurate modelling the effects of occlusion and degradation in detection, significant improvements in performance can be obtained.

Multidimensional Dataflow Graph Modeling and Mapping for Efficient GPU Implementation

Multidimensional synchronous dataflow (MDSDF) provides an effective model of computation for a variety of multidimensional DSP systems that have static dataflow structures. In this paper, we develop new methods for optimized implementation of MDSDF graphs on embedded platforms that employ multiple levels of parallelism to enhance performance at different levels of granularity. Our approach allows designers to systematically represent and transform multi-level parallelism specifications from a common, MDSDF-based application level model. We demonstrate our methods with a case study of image histogram implementation on a graphics processing unit (GPU). Experimental results from this study show that our approach can be used to derive fast GPU implementations, and enhance trade-off analysis during design space exploration.