Ashfaq A. Khokhar

Heterogeneous computing: challenges and opportunities

The issues and problems posed by heterogeneous computing are discussed. They include design of algorithms for applications, partitioning and mapping of application tasks, interconnection requirements, and the design of programming environments. The use of heterogeneous computing in image understanding is reviewed. An example vision task is presented, and the different types of parallelism used in the example are identified.<<ETX>>

A survey of secure mobile Ad Hoc routing protocols

Several routing protocols have been proposed in recent years for possible deployment of mobile ad hoc networks (MANETs) in military, government and commercial applications. In this paper, we review these protocols with a particular focus on security aspects. The protocols differ in terms of routing methodologies and the information used to make routing decisions. Four representative routing protocols are chosen for analysis and evaluation including: Ad Hoc on demand Distance Vector routing (AODV), Dynamic Source Routing (DSR), Optimized Link State Routing (OLSR) and Temporally Ordered Routing Algorithm (TORA). Secure ad hoc networks have to meet five security requirements: confidentiality, integrity, authentication, non-repudiation and availability. The analyses of the secure versions of the proposed protocols are discussed with respect to the above security requirements.

Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models

Motion trajectories provide rich spatiotemporal information about an objects activity. This paper presents novel classification algorithms for recognizing object activity using object motion trajectory. In the proposed classification system, trajectories are segmented at points of change in curvature, and the subtrajectories are represented by their principal component analysis (PCA) coefficients. We first present a framework to robustly estimate the multivariate probability density function based on PCA coefficients of the subtrajectories using Gaussian mixture models (GMMs). We show that GMM-based modeling alone cannot capture the temporal relations and ordering between underlying entities. To address this issue, we use hidden Markov models (HMMs) with a data-driven design in terms of number of states and topology (e.g., left-right versus ergodic). Experiments using a database of over 5700 complex trajectories (obtained from UCI-KDD data archives and Columbia University Multimedia Group) subdivided into 85 different classes demonstrate the superiority of our proposed HMM-based scheme using PCA coefficients of subtrajectories in comparison with other techniques in the literature.

Object-oriented conceptual modeling of video data

We propose a graphical data model for specifying spatio-temporal semantics of video data. The proposed model segments a video clip into subsegments consisting of objects. Each object is detected and recognized, and the relevant information of each object is recorded. The motions of objects are modeled through their relative spatial relationships as time evolves. Based on the semantics provided by this model, a user can create his/her own, object-oriented view of the video database. Using the propositional logic, we describe a methodology for specifying conceptual queries involving spatio-temporal semantics and expressing views for retrieving various video clips. Alternatively, a user can sketch the query, by exemplifying the concept. The proposed methodology can be used to specify spatio-temporal concepts at various levels of information granularity.<<ETX>>

Real-Time Motion Trajectory-Based Indexing and Retrieval of Video Sequences

This paper presents a novel motion trajectory-based compact indexing and efficient retrieval mechanism for video sequences. Assuming trajectory information is already available, we represent trajectories as temporal ordering of subtrajectories. This approach solves the problem of trajectory representation when only partial trajectory information is available due to occlusion. It is achieved by a hypothesis testing-based method applied to curvature data computed from trajectories. The subtrajectories are then represented by their principal component analysis (PCA) coefficients for optimally compact representation. Different techniques are integrated to index and retrieve subtrajectories, including PCA, spectral clustering, and string matching. We assume a query by example mechanism where an example trajectory is presented to the system and the search system returns a ranked list of most similar items in the dataset. Experiments based on datasets obtained from University of California at Irvines KDD archives and Columbia Universitys DVMM group demonstrate the superiority of our proposed PCA-based approaches in terms of indexing and retrieval times and precision recall ratios, when compared to other techniques in the literature

Self organization and energy efficient TDMA MAC protocol by wake up for wireless sensor networks

This paper proposes novel energy efficient self-organization and medium access control (MAC) protocols for wireless sensor networks. The proposed protocol is based on time division multiple access (TDMA) principle and are referred to as TDMA. In the proposed protocol, a sensor utilizes its assigned slot only when it is sending or receiving information, otherwise its receiver and transmitter are turned off to avoid unnecessary neighbor listening. In order to accelerate the receive response, wake-up packets are used to activate a sleeping node. When compared with the existing energy efficient MAC protocols, such as 10% S-MAC, the performance results show that the proposed TDMA-W protocol consumes only 1.5% to 15% of the S-MAC power, yielding 6 to 67 folds enhancement in battery life.

Segmented trajectory based indexing and retrieval of video data

In this paper, we present a novel principal component analysis (PCA) based approach towards modeling the object trajectory in a video clip. An eigenspace decomposition of high-dimensional trajectory data leads to very compact representation, which is then used as indexing structure. To cutback on PCA computation during indexing, we first segment the trajectories into atomic subtrajectories using a curvature zero-crossing based approach followed by clustering of these subtrajectories. A two-level PCA operation with coarse-to-fine retrieval for query trajectory is then performed to generate retrieval results. Our experimental results show that our global PCA based approach performs better when input query trajectory is of similar length compared to the matching trajectories in the database. However, when partial trajectories are posed as queries our segmented trajectory based approach provides superior results for all precision-recall ratios.

Hierarchical Data Aggregation Using Compressive Sensing (HDACS) in WSNs

Energy efficiency is one of the key objectives in data gathering in wireless sensor networks (WSNs). Recent research on energy-efficient data gathering in WSNs has explored the use of Compressive Sensing (CS) to parsimoniously represent the data. However, the performance of CS-based data gathering methods has been limited since the approaches failed to take advantage of judicious network configurations and effective CS-based data aggregation procedures. In this article, a novel Hierarchical Data Aggregation method using Compressive Sensing (HDACS) is presented, which combines a hierarchical network configuration with CS. Our key idea is to set multiple compression thresholds adaptively based on cluster sizes at different levels of the data aggregation tree to optimize the amount of data transmitted. The advantages of the proposed model in terms of the total amount of data transmitted and data compression ratio are analytically verified. Moreover, we formulate a new energy model by factoring in both processor and radio energy consumption into the cost, especially the computation cost incurred in relatively complex algorithms. We also show that communication cost remains dominant in data aggregation in the practical applications of large-scale networks. We use both the real-world data and synthetic datasets to test CS-based data aggregation schemes on the SIDnet-SWANS simulation platform. The simulation results demonstrate that the proposed HDACS model guarantees accurate signal recovery performance. It also provides substantial energy savings compared with existing methods.

Multiple object tracking with kernel particle filter

A new particle filter, kernel particle filter (KPF), is proposed for visual tracking for multiple objects in image sequences. The KPF invokes kernels to form a continuous estimate of the posterior density function and allocates particles based on the gradient derived from the kernel density estimate. A data association technique is also proposed to resolve the motion correspondence ambiguities that arise when multiple objects are present. The data association technique introduces minimal amount of computation by making use of the intermediate results obtained in particle allocation. We show that KPF performs robust multiple object tracking with improved sampling efficiency.

Frequent Pattern Mining on Message Passing Multiprocessor Systems

Extraction of frequent patterns in transaction-oriented database is crucial to several data mining tasks such as association rule generation, time series analysis, classification, etc. Most of these mining tasks require multiple passes over the database and if the database size is large, which is usually the case, scalable high performance solutions involving multiple processors are required. This paper presents an efficient scalable parallel algorithm for mining frequent patterns on parallel shared nothing platforms. The proposed algorithm is based on one of the best known sequential techniques referred to as Frequent Pattern (FP) Growth algorithm. Unlike most of the earlier parallel approaches based on different variants of the Apriori Algorithm, the algorithm presented in this paper does not explicitly result in having entire counting data structure duplicated on each processor. Furthermore, the proposed algorithm introduces minimum communication (and hence synchronization) overheads by efficiently partitioning the list of frequent elements list over processors. The experimental results show scalable performance over different machine and problem sizes. The comparison of implementation results with existing parallel approaches show significant gains in the speedup. On an 8-processor machine, we report an average speedup of 6 for different problem sizes.