15209820
Real-time digital signal processing of component-oriented phased array radars
With the advance of hardware and software technology, modern phased array radars are now built with commercial off-the-shelf (COTS) components, and it opens up a new era in real-time resource scheduling of digital signal processing. This paper targets the essential issues in building a component-oriented signal processor (SP), which is one of the two major modules in modern phased array radars. We propose a simple but effective task allocation policy and a real-time scheduling algorithm to address the design objectives of SPs. We are able to bound the number of processing units needed for a component-oriented SP in the design time, while everything was done empirically in the past. A series of experiments were done to demonstrate the strength of our methodology.
1767384
Real-Time Dwell Scheduling of Component-Oriented Phased Array Radars
A multifunction phased array radar must search and track suspicious targets in its surveillance space in a real-time fashion. With inefficient scheduling implementations in many traditional systems, much radar resource is wasted with a very limited performance gain. This paper targets one of the most important issues in the design of modern phased array radars: real-time dwell scheduling. We formalize the typical workload of a modern phased array radar and propose a rate-based approach to schedule radar dwells in a real-time fashion. We show how to reserve radar resources to guarantee the minimum radar operation without sacrificing the stability of the system. The strength of our approach is verified by a series of simulation experiments based on a real phased array radar for air defense frigates [A. G. Huizing et al. (1996)]. A significant improvement in the performance of phased array radars was shown.
10631202
Real-time scheduling in a programmable radar signal processor
The new trend of designing radar digital signal processors is not only to improve their processing speed by adopting the parallel multi-processor architecture but also to make their infrastructure flexible so that one generic digital signal processor framework can be applied to a variety of different radar systems. The key technology to make such radar digital signal processor architecture programmable, or flexible, is the scheduler which manages resources of digital signal processing in real-time according to system requirements. In this paper, the architecture of the programmable radar signal processor (PRSP) is presented, and a real-time scheduling algorithm used in PRSP is thoroughly discussed.
1776041
The design of distributed real-time video analytic system
Video has become an indispensable carrier of information and requires extremely computation expensive analysis to understand high level semantics. The computational difficulties in extracting embedded information and bridging the semantic gap present the major challenges in interoperability, scalability and real-time response of video analytic systems. In this paper, we propose a distributed scalable infrastructure VAP (Video Analytic Platform) for supporting real-time video stream analysis. In VAP, the application requirements are represented as a Directed Acrylic Graph (DAG), where nodes stand for video analysis computation modules and links show data flow and dependencies between nodes. VAP leverages UIMA (Unstructured Information Management Architecture) framework as the data flow control engine and multiple commodity databases as the storage and computation resources. The actual executions of video analysis computation modules have been pushed down into database engines to minimize the data movement cost. We initially choose video surveillance in retail store as a representative application domain. As a case study, a prototype system has been developed to achieve fundamental functions of real-time video surveillance, including video capture/store, human detection/tracking and customer shopping trajectory analysis.
18226410
Real-time tracking with multiple cues by set theoretic random search
Conventional treatment of visual tracking has been to optimize an objective function in a probabilistic framework. In this formulation, efficient algorithms employing simple prior distributions are usually insufficient to handle clutters (e.g., Kalman filter). On the other hand, distributions that are complex enough to incorporate all a priori knowledge can make the problem computationally intractable (e.g., particle filters (PF)). This paper proposes a new formulation of visual tracking where every piece of information, be it from a priori knowledge or observed data, is represented by a set in the solution space and the intersection of these sets, the feasibility set, represents all acceptable solutions. Based on this formulation, we propose an algorithm whose objective is to find a solution in the feasibility set. We show that this set theoretic tracking algorithm performs effective face tracking and is computationally more efficient than standard PF-based tracking.
18355867
Multi-granularity Visualization of Trajectory Clusters Using Sub-trajectory Clustering
With the surging of the requirements of location-based services, mining various interesting patterns from the spatial data becomes more and more important. In this paper, we propose an approach for visualizing the trajectory clustering results based on sub-trajectory clusters discovered from large-scale trajectory data. At first, we segment each trajectory into a set of sub-trajectories by detecting its corner points. And then, we choose Fréchet distance to compute the similarity between sub-trajectories, and use a density-based clustering method to cluster sub-trajectories and get an augmented order of the sub-trajectories. The visualization method can support multi-granularity views of the generated sub-trajectory clusters. Experiments have demonstrated the applicability and benefits of the proposed approach.
3263120
The price of ignorance: The impact of side-information on delay for lossless source-coding
Inspired by the context of compressing encrypted sources, this paper considers the general tradeoff between rate, end-to-end delay, and probability of error for lossless sou rce coding with side-information. The notion of end-to-end delay is made precise by considering a sequential setting in which source symbols are revealed in real time and need to be reconstructed at the decoder within a certain fixed latency requirement. Upper bounds are derived on the reliability functions with delay when side-informatio n is known only to the decoder as well as when it is also known at the encoder. When the encoder is not ignorant of the side-information (including the trivial case when there is no sideinformation), it is possible to have substantially better t radeoffs between delay and probability of error at all rates . This shows that there is a fundamental price of ignorance in terms of end-to-end delay when the encoder is not aware of the side information. This effect is not visible if o nly fixed-block-length codes are considered. In this way, side-information in source-coding plays a role analogous t o that of feedback in channel coding. While the theorems in this paper are asymptotic in terms of long delays and low probabilities of error, an example is used to show that the qualitative effects describ ed here are significant even at short and moderate delays.
30762676
Kernel particle filter: iterative sampling for efficient visual tracking
Particle filter has recently received attention in computer vision applications due to attributes such as its ability to carry multiple hypotheses and its relaxation of the linearity assumption. Its shortcoming is increase in complexity with state dimension. We present kernel particle filter as a variation of particle filter with improved sampling efficiency and performance in visual tracking. Unlike existing methods that use stochastic or deterministic optimization procedures to find the modes in a likelihood function, we redistribute particles by invoking kernel-based representation of densities and introducing mean shift as an iterative mode-seeking procedure, in which particles move towards dominant modes while still maintaining as fair samples from the posterior. Experiments on face and limb tracking show that the algorithm is superior to conventional particle filter in handling weak dynamic models and occlusions with 60% fewer particles in 3-9 dimensional spaces.