Ankur Jain

Policy controlled self-configuration in unattended wireless sensor networks

With the decreasing cost and size of sensors, their importance has grown rapidly in the field of unattended network systems, which have a significant and vital role in military and security applications where human access is difficult once the nodes are deployed. A major issue in wireless sensor networks (WSN) is the limited availability of energy supply. The nodes have to cooperate and self-organize to provide an active backbone, making optimal use of energy by putting off the radio of the nodes which are not required. We propose the Policy Controlled Self-Configuration for Unattended Wireless Sensor Networks (PCSSN) scheme for self-organization in WSN. The algorithm consists of two phases: topology discovery and topology maintenance. Based on the concepts of Markov Decision Processes (MDP) we design a policy to activate the optimum number of nodes such that the application fidelity is not affected. The policy is based on the distance between neighboring nodes, the residual energy, and the neighbor count, which are indicators of the hop count, the network lifetime, and the state of connectivity of the network, respectively. We also consider the case of node failures, since failures are quite common in harsh unattended environments. In this paper, we show that our proposed scheme increases the utility function ranging from about 7% to 50% as the number of nodes increases. Further, we show that despite the overhead involved in the proposed scheme, the energy spent is much less than the active case, in which all the nodes stay active forever, without going to sleep.

A Distributed Self-Stabilizing Algorithm for Finding a Connected Dominating Set in a Graph

A connected dominating set of a graph G is a set of nodes of G such that every node in G is either in the set or is adjacent to some node in the set, and the graph induced by the elements of the set is connected. Connected dominating sets have major applications in routing in wireless ad-hoc networks. In this paper, we present a distributed self-stabilizing algorithm for finding a connected dominating set of a graph. Starting from an arbitrary initial state, the algorithm finds a connected dominating set in O(N^2) time, where N is the number of nodes. We also show detailed simulation results to indicate that in practice, the algorithm finds small-sized connected dominating sets in a short time.

Content-based adaptive compression of educational videos using phase correlation techniques

A serious bottleneck towards multimedia e-learning is the non-availability of required bandwidth to view the lecture videos at good resolution because of large size of lecture videos. Content-based compression of video data can greatly enhance the bandwidth utilization over scarce resource networks. In this paper, an educational video compression technique is presented that dynamically allocates the space according to the content importance of each video segment in the educational videos. We present a phase-correlation-based motion estimation and compensation algorithm that assists in the compression of important moving objects in an efficient manner. Temporal coherence is exploited in a two-phase manner. First, the frames with high similarity are categorized and encoded efficiently. Second, the compression ratio is adapted according to the frame content. Shots that are of high importance are stored at a higher bit rate as compared to the frames of relatively low importance. The importance and priority of the frames is computed automatically by our algorithm. Results over several hours of educational videos and comparison with the state-of-the-art compression algorithms illustrate the high compression performance of our technique.

On converting OSTC scheme from non-full rate to full-rate with better error performance

In the paper, ldquoImproved Transmission Scheme for Orthogonal Space Time Codesrdquo, a new scheme is proposed that uses an iterative Expectation Maximization (EM) algorithm for decoding and provides full rate and full diversity. For full rate, some of the codeword symbols are not transmitted but rather estimated at the receiver using the expectation maximization (EM) algorithm. In this paper, we prove analytically that the EM algorithm converges exponentially and unconditionally to the least squares (LS) estimate and the rate of convergence depends on the channel parameters and not on the initial vector. We also propose a new very low complexity decoding for the aforementioned transmission scheme with identical error performance.

Adaptive connected dominating set – an exercise in distributed output switching

Switching between protocols based on environment is an elegant idea of enabling adaptation in distributed systems. In this paper, we give one approach of switching between two connected dominating set (CDS) construction protocols, one suitable for low load and the other suitable for higher load. In this method, the two connected dominating sets are computed in advance and switching is done between the two precomputed outputs. In addition, some CDS of the network is always maintained when switching is in progress.

Purification, crystallization and X-ray diffraction analysis of pavine N-methyltransferase from Thalictrum flavum.

A cDNA from the plant Thalictrum flavum encoding pavine N-methyltransferase, an enzyme belonging to a novel class of S-adenosylmethionine-dependent N-methyltransferases specific for benzylisoquinoline alkaloids, has been heterologously expressed in Escherichia coli. The enzyme was purified using affinity and gel-filtration chromatography and was crystallized in space group P2(1). The structure was solved at 2.0 A resolution using a xenon derivative and the single isomorphous replacement with anomalous scattering method.

Audio---Video based People Counting and Security Framework for Traffic Crossings

In this paper, we propose a traffic management system which uses both audio and video data at intersections to prevent traffic congestion. A novel scheme of counting and tracking crowd with a single overhead camera is proposed for the purpose of real-time traffic control at crossings as well as for adjusting the carrier frequency using the video data. The system recognizes people movement along various directions estimating the possibility of traffic congestion. To carry out the tracking procedure, various temporal and spatial features of the images are used to identify the people in the crowd in order to predict the position of the objects in the current frame. Several issues such as emergence of people, departure of people, occlusions, and de-occlusions are resolved through interactions between regions and objects. With the help of audio data at counting region, we identify children by their voice using pitch analysis. The database constitutes of voices of speakers of all ages and of both genders. The paper also includes automated accident detection at the traffic intersections through use of audio data at intersections. It can classify the sounds into “crash”, “power brake” and “normal traffic” sounds. The experimental results show the effectiveness of our framework.

High quality compression of educational videos using content-adaptive framework

With multimedia E-learning becoming popular, better compression algorithms are required that are space efficient but maintain the quality of video data, particularly slide and blackboard text. In this paper, an educational video compression technique is presented that dynamically allocates the space according to the content importance of each video segment in the educational videos. We present a phase correlation based motion estimation and compensation algorithm to encode important moving objects in efficient manner. Temporal coherence is exploited in a two phase manner. First, the frames with high similarity are categorized and encoded efficiently. Secondly, the compression ratio is adapted according to the frame content. The algorithm is compared with the state-of-the-art standards such as H.261, MPEG-4, etc. on large database. The comparison shows that for similar bit rates, the video quality for our algorithm is significantly better than the other methods.

Probabilistic Approach to Modeling of 3D Objects using Silhouettes

Recently, 3D model construction from 2D images using an uncalibrated camera has attracted significant attention in the research community. Most of the algorithms for 3D model construction suffer from problems such as inefficiency, irregular construction, and necessity of camera calibration. In this paper, a novel algorithm is presented that uses the silhouette images obtained from the object to construct the 3D model. To carry out the 3D modeling, multiple views of the object are taken from different angles. Then using a silhouette based technique, new silhouettes are constructed and feature points are derived from them. These feature points are then used to construct the triangular meshes, which in turn construct the whole surface of the 3D model. The noise in the silhouette images is dealt with a probabilistic framework. In addition, a faster technique is presented to reduce the time and space complexity of this algorithm making it feasible for most commercial applications. The algorithm has been successfully tested on several objects. The experimental results and comparison with a voxelization technique over several sequences shows the superiority and the effectiveness of our technique.

Fluorescence Lifetime Spectroscopy and Imaging of Visible Fluorescent Proteins

Publisher Summary The excited state lifetime of a fluorophore is a fundamental parameter that reports sensitively on the fluorophore nanoenvironment and on interactions between the fluorophore and other molecules. When coupled with the spatial resolution inherent to imaging techniques, fluorescence lifetime spectroscopy provides information about the spatial distributions of the molecules and the dynamics of related processes. In combination with genetically encodable visible fluorescent protein fluorophores, fluorescence lifetime imaging enables the real-time visualization of dynamic biological interactions in the complex context of the living cell. This chapter provides an introduction to fluorescence spectroscopy with an emphasis on fluorescence lifetimes and to the use of visible fluorescent proteins and fluorescence lifetime microspectroscopy for biophotonics applications. The discovery and development of fluorescent proteins as molecular tags in the recent years has led to a revolution by allowing complex biochemical processes to be correlated with the functioning of proteins in living cells. Recent advances like the development and use of new fluorescent tools such as semiconductor nanocrystals (quantum dots), multi-photon excitation, single molecule detection and development of better fluorescent proteins have opened up newer dimensions for the application of fluorescence lifetime for studying biological systems.