Nonparametric distributed sequential detection via universal source coding

We consider nonparametric or universal sequential hypothesis testing when the distribution under the null hypothesis is fully known but the alternate hypothesis corresponds to some other unknown distribution. These algorithms are primarily motivated from spectrum sensing in Cognitive Radios and intruder detection in wireless sensor networks. We use easily implementable universal lossless source codes to propose simple algorithms for such a setup. The algorithms are first proposed for discrete alphabet. Their performance and asymptotic properties are studied theoretically. Later these are extended to continuous alphabets. Their performance with two well known universal source codes, Lempel-Ziv code and KT-estimator with Arithmetic Encoder are compared. These algorithms are also compared with the tests using various other nonparametric estimators. Finally a decentralized version utilizing spatial diversity is also proposed and analysed.

Fragment-based real-time object tracking: a sparse representation approach

Real-time object tracking is a critical task in many computer vision applications. Achieving rapid and robust tracking while handling changes in object pose and size, varying illumination and partial occlusion, is a challenging task given the limited amount of computational resources. In this paper we propose a real-time object tracker in l(1) framework addressing these issues. In the proposed approach, dictionaries containing templates of overlapping object fragments are created. The candidate fragments are sparsely represented in the dictionary fragment space by solving the l(1) regularized least squares problem. The non zero coefficients indicate the relative motion between the target and candidate fragments along with a fidelity measure. The final object motion is obtained by fusing the reliable motion information. The dictionary is updated based on the object likelihood map. The proposed tracking algorithm is tested on various challenging videos and found to outperform earlier approach.

Space vector-based analysis of overmodulation in triangle-comparison based PWM for voltage source inverter

The equivalence of triangle-comparison-based pulse width modulation (TCPWM) and space vector based PWM (SVPWM) during linear modulation is well-known. This paper analyses triangle-comparison based PWM techniques (TCPWM) such as sine-triangle PWM (SPWM) and common-mode voltage injection PWM during overmodulation from a space vector point of view. The average voltage vector produced by TCPWM during overmodulation is studied in the stationary (a-b) reference frame. This is compared and contrasted with the average voltage vector corresponding to the well-known standard two-zone algorithm for space vector modulated inverters. It is shown that the two-zone overmodulation algorithm itself can be derived from the variation of average voltage vector with TCPWM. The average voltage vector is further studied in a synchronously revolving (d-q) reference frame. The RMS value of low-order voltage ripple can be estimated, and can be used to compare harmonic distortion due to different PWM methods during overmodulation. The measured values of the total harmonic distortion (THD) in the line currents are presented at various fundamental frequencies. The relative values of measured current THD pertaining to different PWM methods tally with those of analytically evaluated RMS voltage ripple.

A Hybrid Parallel Algorithm for Computing and Tracking Level Set Topology

The contour tree is a topological abstraction of a scalar field that captures evolution in level set connectivity. It is an effective representation for visual exploration and analysis of scientific data. We describe a work-efficient, output sensitive, and scalable parallel algorithm for computing the contour tree of a scalar field defined on a domain that is represented using either an unstructured mesh or a structured grid. A hybrid implementation of the algorithm using the GPU and multi-core CPU can compute the contour tree of an input containing 16 million vertices in less than ten seconds with a speedup factor of upto 13. Experiments based on an implementation in a multi-core CPU environment show near-linear speedup for large data sets.

En Masse relay selection algorithms for multi-source, multi-relay, decode-and-forward cooperative systems

Opportunistic relay selection in a multiple source-destination (MSD) cooperative system requires quickly allocating to each source-destination (SD) pair a suitable relay based on channel gains. Since the channel knowledge is available only locally at a relay and not globally, efficient relay selection algorithms are needed. For an MSD system, in which the SD pairs communicate in a time-orthogonal manner with the help of decode-and-forward relays, we propose three novel relay selection algorithms, namely, contention-free en masse assignment (CFEA), contention-based en masse assignment (CBEA), and a hybrid algorithm that combines the best features of CFEA and CBEA. En masse assignment exploits the fact that a relay can often aid not one but multiple SD pairs, and, therefore, can be assigned to multiple SD pairs. This drastically reduces the average time required to allocate an SD pair when compared to allocating the SD pairs one by one. We show that the algorithms are much faster than other selection schemes proposed in the literature and yield significantly higher net system throughputs. Interestingly, CFEA is as effective as CBEA over a wider range of system parameters than in single SD pair systems.

Device Fabrication of insoluble donor-acceptor-donor structured molecule by pulsed laser deposition- A comparative study using different laser source

Many of the conducting polymers though having good material property are not solution processable. Hence an alternate method of fabrication of film by pulsed laser deposition, was explored in this work. PDTCPA, a donor-acceptor-donor type of polymer having absorption from 900 nm to 300 nm was deposited by both UV and IR laser to understand the effect of deposition parameters on the film quality. It was observed that the laser ablation of PDTCPA doesn't alter its chemical structure hence retaining the chemical integrity of the polymer. Microscopic studies of the ablated film shows that the IR laser ablated films were particulate in nature while UV laser ablated films are deposited as smooth continuous layer. The morphology of the film influences its electrical characteristics as current-voltage characteristic of these films shows that films deposited by UV laser are p rectifying while those by IR laser are more of resistor in nature.

Facile synthesis of carbon doped TiO2 nanowires without an external carbon source and their opto-electronic properties

The present study demonstrates a simple protocol for the preparation of one dimensional (1D) oxidized titanium carbide nanowires and their opto-electronic properties. The oxidized titanium carbide nanowires (Ox-TiC-NW) are prepared from TiC nanowires (TiC-NW) that are in turn synthesized from micron sized TiC particles using the solvothermal technique. The Ox-TiC-NW is characterized by X-ray diffraction, UV-Vis spectroscopy, transmission electron microscopy (TEM), scanning electron microscopy (SEM) and Raman spectroscopy. Thermal oxidation of TiC-NW yields carbon doped TiO2-NW (C-TiO2-NW), a simple methodology to obtain 1D C-TiO2-NW. Temperature dependent Raman spectra reveal characteristic bands for TiO2-NW. Electrical characterization of individual C-TiO2-NW is performed by fabricating a device structure using the focused ion beam deposition technique. The opto-electronic properties of individual C-TiO2-NW demonstrate visible light activity and the parameters obtained from photoconductivity measurements reveal very good sensitivity. This methodology opens up the possibility of using C-TiO2-NW in electronic and opto-electronic device applications.

Eigen-profiles of spatio-temporal fragments for adaptive region-based tracking

We propose a novel space-time descriptor for region-based tracking which is very concise and efficient. The regions represented by covariance matrices within a temporal fragment, are used to estimate this space-time descriptor which we call the Eigenprofiles(EP). EP so obtained is used in estimating the Covariance Matrix of features over spatio-temporal fragments. The Second Order Statistics of spatio-temporal fragments form our target model which can be adapted for variations across the video. The model being concise also allows the use of multiple spatially overlapping fragments to represent the target. We demonstrate good tracking results on very challenging datasets, shot under insufficient illumination conditions.

On the feasibility of network alignment for three-source three-destination Multiple Unicast networks with delays

A transform approach to network coding was in-troduced by Bavirisetti et al. (arXiv:1103.3882v3 [cs.IT]) as a tool to view wireline networks with delays as k-instantaneous networks (for some large k). When the local encoding kernels (LEKs) of the network are varied with every time block of length k >1, the network is said to use block time varying LEKs. In this work, we propose a Precoding Based Network Alignment (PBNA) scheme based on transform approach and block time varying LEKs for three-source three-destination multiple unicast network with delays (3-S3-D MUN-D). In a recent work, Menget al. (arXiv:1202.3405v1 [cs.IT]) reduced the infinite set of sufficient conditions for feasibility of PBNA in a three-source three-destination instantaneous multiple unicast network as given by Das et al. (arXiv:1008.0235v1 [cs.IT]) to a finite set and also showed that the conditions are necessary. We show that the conditions of Meng et al. are also necessary and sufficient conditions for feasibility of PBNA based on transform approach and block time varying LEKs for 3-S3-D MUN-D.

Spectrum sensing via universal source coding

We consider nonparametric sequential hypothesis testing when the distribution under null hypothesis is fully known and the alternate hypothesis corresponds to some other unknown distribution. We use easily implementable universal lossless source codes to propose simple algorithms for such a setup. These algorithms are motivated from spectrum sensing application in Cognitive Radios. Universal sequential hypothesis testing using Lempel Ziv codes and Krichevsky-Trofimov estimator with Arithmetic Encoder are considered and compared for different distributions. Cooperative spectrum sensing with multiple Cognitive Radios using universal codes is also considered.

Simultaneous attitude control and trajectory tracking of a micro quadrotor: a SNAC aided nonlinear dynamic inversion approach

This paper presents an advanced single network adaptive critic (SNAC) aided nonlinear dynamic inversion (NDI) approach for simultaneous attitude control and trajectory tracking of a micro-quadrotor. Control of micro-quadrotors is a challenging problem due to its small size, strong coupling in pitch-yaw-roll and aerodynamic effects that often need to be ignored in the control design process to avoid mathematical complexities. In the proposed SNAC aided NDI approach, the gains of the dynamic inversion design are selected in such a way that the resulting controller behaves closely to a pre-synthesized SNAC controller for the output regulation problem. However, since SNAC is based on optimal control theory, it makes the dynamic inversion controller to operate near optimal and enhances its robustness property as well. More important, it retains two major benefits of dynamic inversion: (i) closed form expression of the controller and (ii) easy scalability to command tracking application even without any apriori knowledge of the reference command. Effectiveness of the proposed controller is demonstrated from six degree-of-freedom simulation studies of a micro-quadrotor. It has also been observed that the proposed SNAC aided NDI approach is more robust to modeling inaccuracies, as compared to the NDI controller designed independently from time domain specifications.

A Reduced Device-Count Hybrid Multilevel Inverter Topology with single DC Source and Improved Fault Tolerance

A new hybrid multilevel power converter topology is presented in this paper. The proposed power converter topology uses only one DC source and floating capacitors charged to asymmetrical voltage levels, are used for generating different voltage levels. The SVPWM based control strategy used in this converter maintains the capacitor voltages at the required levels in the entire modulation range including the over-modulation region. For the voltage levels: nine and above, the number of components required in the proposed topology is significantly lower, compared to the conventional multilevel inverter topologies. The number of capacitors required in this topology reduces drastically compared to the conventional flying capacitor topology, when the number of levels in the inverter output increases. This topology has better fault tolerance, as it is capable of operating with reduced number of levels, in the entire modulation range, in the event of any failure in the H-bridges. The transient as well as the steady state performance of the nine-level version of the proposed topology is experimentally verified in the entire modulation range including the over-modulation region.

Optimum steering input determination and path-tracking of all-wheel steer vehicles on uneven terrains based on constrained optimization

In this paper we propose a framework for optimum steering input determination of all-wheel steer vehicles (AWSV) on rough terrains. The framework computes the steering input which minimizes the tracking error for a given trajectory. Unlike previous methodologies of computing steering inputs of car-like vehicles, the proposed methodology depends explicitly on the vehicle dynamics and can be extended to vehicle having arbitrary number of steering inputs. A fully generic framework has been used to derive the vehicle dynamics and a non-linear programming based constrained optimization approach has been used to compute the steering input considering the instantaneous vehicle dynamics, no-slip and contact constraints of the vehicle. All Wheel steer Vehicles have a special parallel steering ability where the instantaneous centre of rotation (ICR) is at infinity. The proposed framework automatically enables the vehicle to choose between parallel steer and normal operation depending on the error with respect to the desired trajectory. The efficacy of the proposed framework is proved by extensive uneven terrain simulations, for trajectories with continuous or discontinuous velocity profile.

Simultaneous source localization and boundary mapping for contaminants

This paper addresses the problem of localizing the sources of contaminants spread in the environment, and mapping the boundary of the affected region using an innovative swarm intelligence based technique. Unlike most work in this area the algorithm is capable of localizing multiple sources simultaneously while also mapping the boundary of the contaminant spread. At the same time the algorithm is suitable for implementation using a mobile robotic sensor network. Two types of agents, called the source localization agents (or S-agents) and boundary mapping agents (or B-agents) are used for this purpose. The paper uses the basic glowworm swarm optimization (GSO) algorithm, which has been used only for multiple signal source localization, and modifies it considerably to make it suitable for both these tasks. This requires the definition of new behaviour patterns for the agents based on their terminal performance as well as interactions between them that helps the swarm to split into subgroups easily and identify contaminant sources as well as spread along the boundary to map its full length. Simulations results are given to demonstrate the efficacy of the algorithm.

A Diffusion-Based Processor Reallocation Strategy for Tracking Multiple Dynamically Varying Weather Phenomena

Many meteorological phenomena occur at different locations simultaneously. These phenomena vary temporally and spatially. It is essential to track these multiple phenomena for accurate weather prediction. Efficient analysis require high-resolution simulations which can be conducted by introducing finer resolution nested simulations, nests at the locations of these phenomena. Simultaneous tracking of these multiple weather phenomena requires simultaneous execution of the nests on different subsets of the maximum number of processors for the main weather simulation. Dynamic variation in the number of these nests require efficient processor reallocation strategies. In this paper, we have developed strategies for efficient partitioning and repartitioning of the nests among the processors. As a case study, we consider an application of tracking multiple organized cloud clusters in tropical weather systems. We first present a parallel data analysis algorithm to detect such clouds. We have developed a tree-based hierarchical diffusion method which reallocates processors for the nests such that the redistribution cost is less. We achieve this by a novel tree reorganization approach. We show that our approach exhibits up to 25% lower redistribution cost and 53% lesser hop-bytes than the processor reallocation strategy that does not consider the existing processor allocation.