Multiple transmitter localization and communication footprint identification using sparse reconstruction techniques

This paper considers the problem of identifying the footprints of communication of multiple transmitters in a given geographical area. To do this, a number of sensors are deployed at arbitrary but known locations in the area, and their individual decisions regarding the presence or absence of the transmitters' signal are combined at a fusion center to reconstruct the spatial spectral usage map. One straightforward scheme to construct this map is to query each of the sensors and cluster the sensors that detect the primary's signal. However, using the fact that a typical transmitter footprint map is a sparse image, two novel compressive sensing based schemes are proposed, which require significantly fewer number of transmissions compared to the querying scheme. A key feature of the proposed schemes is that the measurement matrix is constructed from a pseudo-random binary phase shift applied to the decision of each sensor prior to transmission. The measurement matrix is thus a binary ensemble which satisfies the restricted isometry property. The number of measurements needed for accurate footprint reconstruction is determined using compressive sampling theory. The three schemes are compared through simulations in terms of a performance measure that quantifies the accuracy of the reconstructed spatial spectral usage map. It is found that the proposed sparse reconstruction technique-based schemes significantly outperform the round-robin scheme.

Accelerating Reduction for Enabling Fast Multiplication over Large Binary Fields

In this paper we present a hardware-software hybrid technique for modular multiplication over large binary fields. The technique involves application of Karatsuba-Ofman algorithm for polynomial multiplication and a novel technique for reduction. The proposed reduction technique is based on the popular repeated multiplication technique and Barrett reduction. We propose a new design of a parallel polynomial multiplier that serves as a hardware accelerator for large field multiplications. We show that the proposed reduction technique, accelerated using the modified polynomial multiplier, achieves significantly higher performance compared to a purely software technique and other hybrid techniques. We also show that the hybrid accelerated approach to modular field multiplication is significantly faster than the Montgomery algorithm based integrated multiplication approach.

Feature selection for unsupervised learning

In this paper, we present a methodology for identifying best features from a large feature space. In high dimensional feature space nearest neighbor search is meaningless. In this feature space we see quality and performance issue with nearest neighbor search. Many data mining algorithms use nearest neighbor search. So instead of doing nearest neighbor search using all the features we need to select relevant features. We propose feature selection using Non-negative Matrix Factorization(NMF) and its application to nearest neighbor search. Recent clustering algorithm based on Locally Consistent Concept Factorization(LCCF) shows better quality of document clustering by using local geometrical and discriminating structure of the data. By using our feature selection method we have shown further improvement of performance in the clustering.

Feature weighting for clustering by particle swarm optimization

Clustering has been the most popular method for data exploration. Clustering is partitioning the data set into sub-partitions based on some measures say the distance measure, each partition has its own significant information. There are a number of algorithms explored for this purpose, one such algorithm is the Particle Swarm Optimization(PSO) which is a population based heuristic search technique derived from swarm intelligence. In this paper we present an improved version of the Particle Swarm Optimization where, each feature of the data set is given significance accordingly by adding some random weights, which also minimizes the distortions in the dataset if any. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The experimental results shows that our proposed methodology performs significantly better than the previously performed experiments.

Topology optimization-based design of a compliant aircraft compliant aircraft wing for morphing leading and trailing edges

In this paper, we present a methodology for designing a compliant aircraft wing, which can morph from a given airfoil shape to another given shape under the actuation of internal forces and can offer sufficient stiffness in both configurations under the respective aerodynamic loads. The least square error in displacements, Fourier descriptors, geometric moments, and moment invariants are studied to compare candidate shapes and to pose the optimization problem. Their relative merits and demerits are discussed in this paper. The `frame finite element ground structure' approach is used for topology optimization and the resulting solutions are converted to continuum solutions. The introduction of a notch-like feature is the key to the success of the design. It not only gives a good match for the target morphed shape for the leading and trailing edges but also minimizes the extension of the flexible skin that is to be put on the airfoil frame. Even though linear small-displacement elastic analysis is used in optimization, the obtained designs are analysed for large displacement behavior. The methodology developed here is not restricted to aircraft wings; it can be used to solve any shape-morphing requirement in flexible structures and compliant mechanisms.

Remoulded shear strength at plastic and semi-solid states

The undrained shear strength of remoulded soils is of great concern in geotechnical engineering applications. This study aims to develop a reliable approach for determining the undrained shear strength of remoulded fine-grained soils, through the use of index test results, at both the plastic and semi-solid states of consistency. Experimental investigation and subsequent analysis involving a number of fine-grained soils of widely varying plasticity and geological origin have led to a two-parameter linear model of the relationship between logarithm of remoulded undrained shear strength and liquidity index. The numerical values of the parameters are found to be dependent to a lesser extent on the soil group and to a greater extent on the soil state. Based on the values of regression coefficient, ranking index and ranking distance, it seems that the relationship represents the experimental results well. It may be pointed out that the possibility of such a relationship in the semi-solid state of a soil has not been explored in the past. It is also shown that the shear strength at the plastic limit is about 32-34 times that at the liquid limit.

Experimental determination and activity coefficient based models for mixture solubilities of nitrophenol isomers in supercritical carbon dioxide

The experimental solubilities of the mixture of nitrophenol (m- and p-) isomers were determined at 308, 318 and 328 K over a pressure range of 10-17.55 MPa. Compared to the binary solubilities, the ternary solubilities of m-nitrophenol increased at 308, 318 and 328 K. The ternary solubilities of p-nitrophenol increased at 308 K, while the ternary solubilities decreased at lower pressures and increased at higher pressure at 318 and 328 K. The solubilities of the solid mixtures in supercritical carbon dioxide (SCCO2) were correlated with solution models by incorporating the non-idealities using activity coefficient based models. The Wilson and NRTL activity coefficient models were applied to determine the nature of the interactions between the molecules. The equation developed by using the NRTL model has three parameters and correlates mixture solubilities of solid solutes in terms of temperature and cosolute composition. The equation derived from the Wilson model contains five parameters and correlates solubilities in terms of temperature, density and cosolute composition. These two new equations developed in this work were used to correlate the solubilities of 25 binary solid mixtures including the current data. The average AARDs of the model equations derived using the NRTL and Wilson models for the solid mixtures were found to be 7% and 4%, respectively. (C) 2012 Elsevier B.V. All rights reserved.

An equivalent undercooling model to account for flow effect on binary alloy dendrite growth rate

A theoretical analysis is carried out to observe the influence of important flow parameters such as Nusselt number and Sherwood number on the tip speed of an equiaxed dendrite growing in a convecting alloy melt. The effect of thermal and solutal transfer at the interface due to convection is equated to an undercooling of the melt, and an expression is derived for this equivalent undercooling in terms of the flow Nusselt number and Sherwood number. Results for the equivalent undercooling are compared with corresponding numerical values obtained by performing simulations based on the enthalpy method. This method represents a relatively simple procedure to analyze the effects of melt convection on the growth rate of dendrites. (C) 2013 Elsevier Ltd. All rights reserved.

Discussion: Remoulded shear strength at plastic and semi-solid states

The undrained shear strength of remoulded soils is of great concern in geotechnical engineering applications. This study aims to develop a reliable approach for determining the undrained shear strength of remoulded fine-grained soils, through the use of index test results, at both the plastic and semi-solid states of consistency. Experimental investigation and subsequent analysis involving a number of fine-grained soils of widely varying plasticity and geological origin have led to a two-parameter linear model of the relationship between logarithm of remoulded undrained shear strength and liquidity index. The numerical values of the parameters are found to be dependent to a lesser extent on the soil group and to a greater extent on the soil state. Based on the values of regression coefficient, ranking index and ranking distance, it seems that the relationship represents the experimental results well. It may be pointed out that the possibility of such a relationship in the semi-solid state of a soil has not been explored in the past. It is also shown that the shear strength at the plastic limit is about 32–34 times that at the liquid limit.

Call intensity is a repeatable and dominant acoustic feature determining male call attractiveness in a field cricket

Acoustic signal variation and female preference for different signal components constitute the prerequisite framework to study the mechanisms of sexual selection that shape acoustic communication. Despite several studies of acoustic communication in crickets, information on both male calling song variation in the field and female preference in the same system is lacking for most species. Previous studies on acoustic signal variation either were carried out on populations maintained in the laboratory or did not investigate signal repeatability. We therefore used repeatability analysis to quantify variation in the spectral, temporal and amplitudinal characteristics of the male calling song of the field cricket Plebeiogryllus guttiventris in a wild population, at two temporal scales, within and across nights. Carrier frequency (CF) was the most repeatable character across nights, whereas chirp period (CP) had low repeatability across nights. We investigated whether female preferences were more likely to be based on features with high (CF) or low (CP) repeatability. Females showed no consistent preferences for CF but were significantly more attracted towards signals with short CPs. The attractiveness of lower CP calls disappeared, however, when traded off with sound pressure level (SPL). SPL was the only acoustic feature that was significantly positively correlated with male body size. Since relative SPL affects female phonotaxis strongly and can vary unpredictably based on male spacing, our results suggest that even strong female preferences for acoustic features may not necessarily translate into greater advantage for males possessing these features in the field. (C) 2013 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Bit Error Probability Analysis of SSK in DF Relaying with Threshold-Based Best Relay Selection and Selection Combining

In this letter, we analyze the end-to-end average bit error probability (ABEP) of space shift keying (SSK) in cooperative relaying with decode-and-forward (DF) protocol, considering multiple relays with a threshold based best relay selection, and selection combining of direct and relayed paths at the destination. We derive an exact analytical expression for the end-to-end ABEP in closed-form for binary SSK, where analytical results agree with simulation results. For non-binary SSK, approximate analytical and simulation results are presented.

Generative Maximum Entropy Learning for Multiclass Classification

Maximum entropy approach to classification is very well studied in applied statistics and machine learning and almost all the methods that exists in literature are discriminative in nature. In this paper, we introduce a maximum entropy classification method with feature selection for large dimensional data such as text datasets that is generative in nature. To tackle the curse of dimensionality of large data sets, we employ conditional independence assumption (Naive Bayes) and we perform feature selection simultaneously, by enforcing a `maximum discrimination' between estimated class conditional densities. For two class problems, in the proposed method, we use Jeffreys (J) divergence to discriminate the class conditional densities. To extend our method to the multi-class case, we propose a completely new approach by considering a multi-distribution divergence: we replace Jeffreys divergence by Jensen-Shannon (JS) divergence to discriminate conditional densities of multiple classes. In order to reduce computational complexity, we employ a modified Jensen-Shannon divergence (JS(GM)), based on AM-GM inequality. We show that the resulting divergence is a natural generalization of Jeffreys divergence to a multiple distributions case. As far as the theoretical justifications are concerned we show that when one intends to select the best features in a generative maximum entropy approach, maximum discrimination using J-divergence emerges naturally in binary classification. Performance and comparative study of the proposed algorithms have been demonstrated on large dimensional text and gene expression datasets that show our methods scale up very well with large dimensional datasets.

Accurate point matching based on multi-objective Genetic Algorithm for multi-sensor satellite imagery

This paper investigates a novel approach for point matching of multi-sensor satellite imagery. The feature (corner) points extracted using an improved version of the Harris Corner Detector (HCD) is matched using multi-objective optimization based on a Genetic Algorithm (GA). An objective switching approach to optimization that incorporates an angle criterion, distance condition and point matching condition in the multi-objective fitness function is applied to match corresponding corner-points between the reference image and the sensed image. The matched points obtained in this way are used to align the sensed image with a reference image by applying an affine transformation. From the results obtained, the performance of the image registration is evaluated and compared with existing methods, namely Nearest Neighbor-Random SAmple Consensus (NN-Ran-SAC) and multi-objective Discrete Particle Swarm Optimization (DPSO). From the performed experiments it can be concluded that the proposed approach is an accurate method for registration of multi-sensor satellite imagery. (C) 2014 Elsevier Inc. All rights reserved.

Effect of shrinkage induced flow on binary alloy dendrite growth: An equivalent undercooling model

This paper presents a theoretical model for studying the effects of shrinkage induced flow on the growth rate of binary alloy dendrites. An equivalent undercooling of the melt is defined in terms of ratio of the phase densities to represent the change in dendrite growth rate due to variation in solutal and thermal transport resulting from shrinkage induced flow. Subsequently, results for dendrite growth rate predicted by the equivalent undercooling model is compared with the corresponding predictions obtained using an enthalpy based numerical method for dendrite growth with shrinkage. The agreement is found to be good. Published by Elsevier Ltd.

VISUAL OBJECT TRACKING VIA RANDOM FERNS BASED CLASSIFICATION

Designing a robust algorithm for visual object tracking has been a challenging task since many years. There are trackers in the literature that are reasonably accurate for many tracking scenarios but most of them are computationally expensive. This narrows down their applicability as many tracking applications demand real time response. In this paper, we present a tracker based on random ferns. Tracking is posed as a classification problem and classification is done using ferns. We used ferns as they rely on binary features and are extremely fast at both training and classification as compared to other classification algorithms. Our experiments show that the proposed tracker performs well on some of the most challenging tracking datasets and executes much faster than one of the state-of-the-art trackers, without much difference in tracking accuracy.