A Modified Sum-Product Algorithm over Graphs with Isolated Short Cycles

We investigate into the limitations of the sum-product algorithm in the probability domain over graphs with isolated short cycles. By considering the statistical dependency of messages passed in a cycle of length 4, we modify the update equations for the beliefs at the variable and check nodes. We highlight an approximate log domain algebra for the modified variable node update to ensure numerical stability. At higher signal-to-noise ratios (SNR), the performance of decoding over graphs with isolated short cycles using the modified algorithm is improved compared to the original message passing algorithm (MPA).

Effects of refractive index mismatch in optical CT imaging of polymer gel dosimeters

Purpose: Proposing an image reconstruction technique, algebraic reconstruction technique-refraction correction (ART-rc). The proposed method takes care of refractive index mismatches present in gel dosimeter scanner at the boundary, and also corrects for the interior ray refraction. Polymer gel dosimeters with high dose regions have higher refractive index and optical density compared to the background medium, these changes in refractive index at high dose results in interior ray bending. Methods: The inclusion of the effects of refraction is an important step in reconstruction of optical density in gel dosimeters. The proposed ray tracing algorithm models the interior multiple refraction at the inhomogeneities. Jacob's ray tracing algorithm has been modified to calculate the pathlengths of the ray that traverses through the higher dose regions. The algorithm computes the length of the ray in each pixel along its path and is used as the weight matrix. Algebraic reconstruction technique and pixel based reconstruction algorithms are used for solving the reconstruction problem. The proposed method is tested with numerical phantoms for various noise levels. The experimental dosimetric results are also presented. Results: The results show that the proposed scheme ART-rc is able to reconstruct optical density inside the dosimeter better than the results obtained using filtered backprojection and conventional algebraic reconstruction approaches. The quantitative improvement using ART-rc is evaluated using gamma-index. The refraction errors due to regions of different refractive indices are discussed. The effects of modeling of interior refraction in the dose region are presented. Conclusions: The errors propagated due to multiple refraction effects have been modeled and the improvements in reconstruction using proposed model is presented. The refractive index of the dosimeter has a mismatch with the surrounding medium (for dry air or water scanning). The algorithm reconstructs the dose profiles by estimating refractive indices of multiple inhomogeneities having different refractive indices and optical densities embedded in the dosimeter. This is achieved by tracking the path of the ray that traverses through the dosimeter. Extensive simulation studies have been carried out and results are found to be matching that of experimental results. (C) 2015 American Association of Physicists in Medicine.

Electrospun SnSb Crystalline Nanoparticles inside Porous Carbon Fibers as a High Stability and Rate Capability Anode for Rechargeable Batteries

In an electrochemical alloying reaction, the electroactive particles become mechanically unstable owing to large volume changes occurring as a result of high amounts of lithium intake. This is detrimental for long-term battery performance. Herein, a novel synthesis approach to minimize such mechanical instabilities in tin particles is presented. An optimal one-dimensional assembly of crystalline single-phase tin-antimony (SnSb) alloy nanoparticles inside porous carbon fibers (abbreviated SnSb-C) is synthesized for the first time by using the electrospinning technique (employing non-oxide precursors) in combination with a sintering protocol. The ability of antimony to alloy independently with lithium is beneficial as it buffers the unfavorable volume changes occurring during successive alloying/dealloying cycles in Sn. The SnSb-C assembly provides nontortuous (tortuosity coefficient, =1) fast conducting pathways for both electrons and ions. The presence of carbon in SnSb-C completely nullifies the conventional requirement of other carbon forms during cell electrode assembly. The SnSb-C exhibited remarkably high electrochemical lithium stability and high specific capacities over a wide range of currents (0.2-5Ag(-1)). In addition to lithium-ion batteries, it is envisaged that SnSb-C also has potential as a noncarbonaceous anode for other battery chemistries, such as sodium-ion batteries.

A Distributed TDMA Slot Scheduling Algorithm for Spatially Correlated Contention in WSNs

In WSNs the communication traffic is often time and space correlated, where multiple nodes in a proximity start transmitting simultaneously. Such a situation is known as spatially correlated contention. The random access method to resolve such contention suffers from high collision rate, whereas the traditional distributed TDMA scheduling techniques primarily try to improve the network capacity by reducing the schedule length. Usually, the situation of spatially correlated contention persists only for a short duration, and therefore generating an optimal or suboptimal schedule is not very useful. Additionally, if an algorithm takes very long time to schedule, it will not only introduce additional delay in the data transfer but also consume more energy. In this paper, we present a distributed TDMA slot scheduling (DTSS) algorithm, which considerably reduces the time required to perform scheduling, while restricting the schedule length to the maximum degree of interference graph. The DTSS algorithm supports unicast, multicast, and broadcast scheduling, simultaneously without any modification in the protocol. We have analyzed the protocol for average case performance and also simulated it using Castalia simulator to evaluate its runtime performance. Both analytical and simulation results show that our protocol is able to considerably reduce the time required for scheduling.

A provably tight delay-driven concurrently congestion mitigating global routing algorithm

Routing is a very important step in VLSI physical design. A set of nets are routed under delay and resource constraints in multi-net global routing. In this paper a delay-driven congestion-aware global routing algorithm is developed, which is a heuristic based method to solve a multi-objective NP-hard optimization problem. The proposed delay-driven Steiner tree construction method is of O(n(2) log n) complexity, where n is the number of terminal points and it provides n-approximation solution of the critical time minimization problem for a certain class of grid graphs. The existing timing-driven method (Hu and Sapatnekar, 2002) has a complexity O(n(4)) and is implemented on nets with small number of sinks. Next we propose a FPTAS Gradient algorithm for minimizing the total overflow. This is a concurrent approach considering all the nets simultaneously contrary to the existing approaches of sequential rip-up and reroute. The algorithms are implemented on ISPD98 derived benchmarks and the drastic reduction of overflow is observed. (C) 2014 Elsevier Inc. All rights reserved.

Looking beyond parks: the conservation value of unprotected areas for hornbills in Arunachal Pradesh, Eastern Himalaya

The loss of tropical forests and associated biodiversity is a global concern. Conservation efforts in tropical countries such as India have mostly focused on state-administered protected areas despite the existence of vast tracts of forest outside these areas. We studied hornbills (Bucerotidae), an ecologically important vertebrate group and a flagship for tropical forest conservation, to assess the importance of forests outside protected areas in Arunachal Pradesh, north-east India. We conducted a state-wide survey to record encounters with hornbills in seven protected areas, six state-managed reserved forests and six community-managed unclassed forests. We estimated the density of hornbills in one protected area, four reserved forests and two unclassed forests in eastern Arunachal Pradesh. The state-wide survey showed that the mean rate of encounter of rufous-necked hornbills Aceros nipalensis was four times higher in protected areas than in reserved forests and 22 times higher in protected areas than in unclassed forests. The mean rate of encounter of wreathed hornbills Rhyticeros undulatus was twice as high in protected areas as in reserved forests and eight times higher in protected areas than in unclassed forests. The densities of rufous-necked hornbill were higher inside protected areas, whereas the densities of great hornbill Buceros bicornis and wreathed hornbill were similar inside and outside protected areas. Key informant surveys revealed possible extirpation of some hornbill species at sites in two protected areas and three unclassed forests. These results highlight a paradoxical situation where individual populations of hornbills are being lost even in some legally protected habitat, whereas they continue to persist over most of the landscape. Better protection within protected areas and creative community-based conservation efforts elsewhere are necessary to maintain hornbill populations in this biodiversity-rich region.

A faster algorithm for testing polynomial representability of functions over finite integer rings

Given a function from Z(n) to itself one can determine its polynomial representability by using Kempner function. In this paper we present an alternative characterization of polynomial functions over Z(n) by constructing a generating set for the Z(n)-module of polynomial functions. This characterization results in an algorithm that is faster on average in deciding polynomial representability. We also extend the characterization to functions in several variables. (C) 2015 Elsevier B.V. All rights reserved.

A New Successive Displacement Type Load Flow Algorithm and its Application to Radial Systems

A new successive displacement type load flow method is developed in this paper. This algorithm differs from the conventional Y-Bus based Gauss Seidel load flow in that the voltages at each bus is updated in every iteration based on the exact solution of the power balance equation at that node instead of an approximate solution used by the Gauss Seidel method. It turns out that this modified implementation translates into only a marginal improvement in convergence behaviour for obtaining load flow solutions of interconnected systems. However it is demonstrated that the new approach can be adapted with some additional refinements in order to develop an effective load flow solution technique for radial systems. Numerical results considering a number of systems-both interconnected and radial, are provided to validate the proposed approach.

Robust design of multiple trailing edge flaps for helicopter vibration reduction: A multi-objective bat algorithm approach

The objective of this study is to determine an optimal trailing edge flap configuration and flap location to achieve minimum hub vibration levels and flap actuation power simultaneously. An aeroelastic analysis of a soft in-plane four-bladed rotor is performed in conjunction with optimal control. A second-order polynomial response surface based on an orthogonal array (OA) with 3-level design describes both the objectives adequately. Two new orthogonal arrays called MGB2P-OA and MGB4P-OA are proposed to generate nonlinear response surfaces with all interaction terms for two and four parameters, respectively. A multi-objective bat algorithm (MOBA) approach is used to obtain the optimal design point for the mutually conflicting objectives. MOBA is a recently developed nature-inspired metaheuristic optimization algorithm that is based on the echolocation behaviour of bats. It is found that MOBA inspired Pareto optimal trailing edge flap design reduces vibration levels by 73% and flap actuation power by 27% in comparison with the baseline design.

A New Algorithm for Nonparametric Sequential Detection

We consider nonparametric sequential hypothesis testing problem when the distribution under the null hypothesis is fully known but the alternate hypothesis corresponds to a general family of distributions. We propose a simple algorithm to address the problem. Its performance is analysed and asymptotic properties are proved. The simulated and analysed performance of the algorithm is compared with an earlier algorithm addressing the same problem with similar assumptions. Finally, we provide a justification for our model motivated by a Cognitive Radio scenario and modify the algorithm for optimizing performance when information about the prior probabilities of occurrence of the two hypotheses is available.

Distribution, relative abundance, and conservation status of Asian elephants in Karnataka, southern India

Karnataka state in southern India supports a globally significant and the country's largest population of the Asian elephant Elephas maximus. A reliable map of Asian elephant distribution and measures of spatial variation in their abundance, both vital needs for conservation and management action, are unavailable not only in Karnataka, but across its global range. Here, we use various data gathered between 2000 and 2015 to map the distribution of elephants in Karnataka at the scale of the smallest forest management unit, the `beat', while also presenting data on elephant dung density for a subset of `elephant beats.' Elephants occurred in 972 out of 2855 forest beats of Karnataka. Sixty percent of these 972 beats and 55% of the forest habitat lay outside notified protected areas (PM), and included lands designated for agricultural production and human dwelling. While median elephant dung density inside protected areas was nearly thrice as much as outside, elephants routinely occurred in or used habitats outside PM where human density, land fraction under cultivation, and the interface between human-dominated areas and forests were greater. Based on our data, it is clear that India's framework for elephant conservation which legally protects the species wherever it occurs, but protects only some of its habitats while being appropriate in furthering their conservation within PM, seriously falters in situations where elephants reside in and/or seasonally use areas outside PAs. Attempts to further elephant conservation in production and dwelling areas have extracted high costs in human, elephant, material and monetary terms in Karnataka. In such settings, conservation planning exercises are necessary to determine where the needs of elephants or humans must take priority over the other, and to achieve that in a manner that is based not only on reliable scientific data but also on a process of public reasoning. (C) 2015 Elsevier Ltd. All rights reserved.

Algorithm for Autonomous Reorganization of Mobile Wireless Camera Sensor Networks to Improve Coverage

In this paper, sensing coverage by wireless camera-embedded sensor networks (WCSNs), a class of directional sensors is studied. The proposed work facilitates the autonomous tuning of orientation parameters and displacement of camera-sensor nodes in the bounded field of interest (FoI), where the network coverage in terms of every point in the FoI is important. The proposed work is first of its kind to study the problem of maximizing coverage of randomly deployed mobile WCSNs which exploits their mobility. We propose an algorithm uncovered region exploration algorithm (UREA-CS) that can be executed in centralized and distributed modes. Further, the work is extended for two special scenarios: 1) to suit autonomous combing operations after initial random WCSN deployments and 2) to improve the network coverage with occlusions in the FoI. The extensive simulation results show that the performance of UREA-CS is consistent, robust, and versatile to achieve maximum coverage, both in centralized and distributed modes. The centralized and distributed modes are further analyzed with respect to the computational and communicational overheads.

Secure Routing with an Integrated Localized Key Management Protocol in MANETs

A routing protocol in a mobile ad hoc network (MANET) should be secure against both the outside attackers which do not hold valid security credentials and the inside attackers which are the compromised nodes in the network. The outside attackers can be prevented with the help of an efficient key management protocol and cryptography. However, to prevent inside attackers, it should be accompanied with an intrusion detection system (IDS). In this paper, we propose a novel secure routing with an integrated localized key management (SR-LKM) protocol, which is aimed to prevent both inside and outside attackers. The localized key management mechanism is not dependent on any routing protocol. Thus, unlike many other existing schemes, the protocol does not suffer from the key management - secure routing interdependency problem. The key management mechanism is lightweight as it optimizes the use of public key cryptography with the help of a novel neighbor based handshaking and Least Common Multiple (LCM) based broadcast key distribution mechanism. The protocol is storage scalable and its efficiency is confirmed by the results obtained from simulation experiments.

An Alternating l(p) - l(2) Projections Algorithm (ALPA) for Speech Modeling using Sparsity Constraints

We address the problem of separating a speech signal into its excitation and vocal-tract filter components, which falls within the framework of blind deconvolution. Typically, the excitation in case of voiced speech is assumed to be sparse and the vocal-tract filter stable. We develop an alternating l(p) - l(2) projections algorithm (ALPA) to perform deconvolution taking into account these constraints. The algorithm is iterative, and alternates between two solution spaces. The initialization is based on the standard linear prediction decomposition of a speech signal into an autoregressive filter and prediction residue. In every iteration, a sparse excitation is estimated by optimizing an l(p)-norm-based cost and the vocal-tract filter is derived as a solution to a standard least-squares minimization problem. We validate the algorithm on voiced segments of natural speech signals and show applications to epoch estimation. We also present comparisons with state-of-the-art techniques and show that ALPA gives a sparser impulse-like excitation, where the impulses directly denote the epochs or instants of significant excitation.

A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation: Application to Image Denoising

In big data image/video analytics, we encounter the problem of learning an over-complete dictionary for sparse representation from a large training dataset, which cannot be processed at once because of storage and computational constraints. To tackle the problem of dictionary learning in such scenarios, we propose an algorithm that exploits the inherent clustered structure of the training data and make use of a divide-and-conquer approach. The fundamental idea behind the algorithm is to partition the training dataset into smaller clusters, and learn local dictionaries for each cluster. Subsequently, the local dictionaries are merged to form a global dictionary. Merging is done by solving another dictionary learning problem on the atoms of the locally trained dictionaries. This algorithm is referred to as the split-and-merge algorithm. We show that the proposed algorithm is efficient in its usage of memory and computational complexity, and performs on par with the standard learning strategy, which operates on the entire data at a time. As an application, we consider the problem of image denoising. We present a comparative analysis of our algorithm with the standard learning techniques that use the entire database at a time, in terms of training and denoising performance. We observe that the split-and-merge algorithm results in a remarkable reduction of training time, without significantly affecting the denoising performance.