Efficient clustering algorithms for self-organizing wireless sensor networks

Self-organization of wireless sensor networks, which involves network decomposition into connected clusters, is a challenging task because of the limited bandwidth and energy resources available in these networks. In this paper, we make contributions towards improving the efficiency of self-organization in wireless sensor networks. We first present a novel approach for message-efficient clustering, in which nodes allocate local “growth budgets” to neighbors. We introduce two algorithms that make use of this approach. We analyze the message complexity of these algorithms and provide performance results from simulations. The algorithms produce clusters of bounded size and low diameter, using significantly fewer messages than the earlier, commonly used, Expanding Ring approach. Next, we present a new randomized methodology for designing the timers of cluster initiators. This methodology provides a probabilistic guarantee that initiators will not interfere with each other. We derive an upper bound on the expected time for network decomposition that is logarithmic in the number of nodes in the network. We also present a variant that optimistically allows more concurrency among initiators and significantly reduces the network decomposition time. However, it produces slightly more clusters than the first method. Extensive simulations over different topologies confirm the analytical results and demonstrate that our proposed methodology scales to large networks.     

Instrumental Learning Within the Rat Spinal Cord: Localization of the Essential Neural Circuit.

Following spinal transection of the upper thoracic spinal cord, male Sprague-Dawley rats given legshock whenever a hindlimb is extended learn to maintain the leg in a flexed position. The region of the cord that mediates this instrumental learning was isolated using neuroanatomical tracing, localized infusion of lidocaine, and surgical transections. DiI and Fluoro-Gold microinjection at the site of shock application labeled motor neuron bodies of lamina IX in the lower lumbar region. Local application of the Na++ channel blocker lidocaine disrupted learning when it was applied over a region extending from the lower lumbar (L3) to upper sacral (S2) cord. The drug had no effect rostral or caudal to this region. Surgical transections as low as L4 had no effect on learning. Learning also survived a dual transection at L4 and S3, but not L4 and S2. The results suggest that the essential neural circuit lies between L4 and S3. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Lessons and Challenges from Mining Retail E-Commerce Data

The architecture of Blue Martini Software's e-commerce suite has supported data collection, data transformation, and data mining since its inception. With clickstreams being collected at the application-server layer, high-level events being logged, and data automatically transformed into a data warehouse using meta-data, common problems plaguing data mining using weblogs (e.g., sessionization and conflating multi-sourced data) were obviated, thus allowing us to concentrate on actual data mining goals. The paper briefly reviews the architecture and discusses many lessons learned over the last four years and the challenges that still need to be addressed. The lessons and challenges are presented across two dimensions: business-level vs. technical, and throughout the data mining lifecycle stages of data collection, data warehouse construction, business intelligence, and deployment. The lessons and challenges are also widely applicable to data mining domains outside retail e-commerce.     

Truncated O-Glycan-Bearing MUC16 Enhances Pancreatic Cancer Cells Aggressiveness via α4β1 Integrin Complexes and FAK Signaling

Elevated levels of Mucin-16 (MUC16) in conjunction with a high expression of truncated O-glycans is implicated in playing crucial roles in the malignancy of pancreatic ductal adenocarcinoma (PDAC). However, the mechanisms by which such aberrant glycoforms present on MUC16 itself promote an increased disease burden in PDAC are yet to be elucidated. This study demonstrates that the CRISPR/Cas9-mediated genetic deletion of MUC16 in PDAC cells decreases tumor cell migration. We found that MUC16 enhances tumor malignancy by activating the integrin-linked kinase and focal adhesion kinase (ILK/FAK)-signaling axis. These findings are especially noteworthy in truncated O-glycan (Tn and STn antigen)-expressing PDAC cells. Activation of these oncogenic-signaling pathways resulted in part from interactions between MUC16 and integrin complexes (α4β1), which showed a stronger association with aberrant glycoforms of MUC16. Using a monoclonal antibody to functionally hinder MUC16 significantly reduced the migratory cascades in our model. Together, these findings suggest that truncated O-glycan containing MUC16 exacerbates malignancy in PDAC by activating FAK signaling through specific interactions with α4 and β1 integrin complexes on cancer cell membranes. Targeting these aberrant glycoforms of MUC16 can aid in the development of a novel platform to study and treat metastatic pancreatic cancer.     

DSP-based voice activity detection and background noise reduction

These days’ speech processing devices like voice-controlled devices, radio, and cell phones have gained more popularity in the area of military, audio forensics, speech recognition, education and health sectors. In the real world, speech signal during communication always contains background noise. The main task of speech related applications is voice activity detection (VAD) which include speech communication, speech recognition, and speech coding. Noise-reduction schemes for speech communication may increase the quality of speech and improve working efficiency in military aviation. Most of the developed algorithms can improve the quality of speech but unable to remove the background noise from the speech. This study provides researchers with a summary of the challenges in speech communication with background noise and provides research directions in the area of military personnel and workforces who work in noisy environments. Results of the study reveal that the DSP-based voice activity detection and background noise reduction algorithm reduced the spurious values of the speech signal.     

Numerical evaluation of the Hankel transform by using linear Legendre multi-wavelets

An efficient algorithm for evaluating the Hankel transform Fn(p) of order n of a function f(r) is given. As the continuous Legendre multi-wavelets forms an orthonormal basis for L²(R); we expand the part rf(r) of the integrand in its wavelet series reducing the Hankel transform integral as a series of Bessel functions multiplied by the wavelet coefficients of the input function. Numerical examples are given to illustrate the efficiency of the proposed method.     

Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

Accurate node localization in wireless sensor networks (WSNs) is an essential for many networking protocols like clustering, routing, and network map building. The classical localization techniques such as multilateration and optimization‐based least square localization (OLSL) techniques estimate position of unknown node (UN) from the distance measured between all anchor nodes (ANs) and UNs. On the other hand, node localization using fixed terrestrial ANs suffers from poor localization accuracy because the ground to ground (GG) channel link is not reliable. By contrast, the mobile anchor deployed in unmanned aerial vehicle (UAV) provides high localization accuracy through reliable air to ground (AG) channel link. Still, the nonlinear distortion introduced in the wireless channel makes the distance measurement noisy. This noisy distance measurement also limits localization accuracy of classical localization techniques. Hence, the highly nonlinear artificial neural network (ANN) models such as multilayer perceptron (MLP) models can be applied effectively for node localization in UAV‐assisted WSNs. However, the MLP suffers from slow training speed, which limits its usage in real‐time applications. So, the extreme learning machine (ELM) is found to be a better alternative because it works on empirical error minimization theory, and its learning process requires only a single iteration. The detailed simulation analysis supports the proposed ELM localization scheme in terms of both localization accuracy and computational complexity.     

Analysis of LDPC with optimum combining

Low density parity check (LDPC) codes have shown exceptionally good performance for single antenna systems over a wide class of channels. LDPC when implemented with a single input multiple output system with maximum ratio combining is optimum from the standpoint of maximising signal-to-noise ratio at combiner output without the presence of interferer. Optimum combining outperforms maximal ratio combining (MRC) in the presence of interferer(s). In this article, the performance of the LDPC codes with multiple receiver antennas with optimum combining in the presence of single interferer is investigated. The simulation results showed that LDPC codes of irregular construction are able to give high coding and diversity gain with optimum combining. The proposed LDPC optimum combined (LDPC–OC) system in Rayleigh fading channel in the presence of a single interferer improves the signal to interferer plus noise ratio by 2.62 dB with four receiver antennas and by 1.98 dB when the number of receiver antennas is three.     

Experimental investigation of low-velocity impact characteristics of woven glass fiber epoxy matrix composite laminates of EP3 grade

This work presents the results of an experimental investigation concerning the low-velocity impact behavior of woven glass fiber epoxy matrix composite laminates. Experimental test were performed according to ASTM standards using an instrumented falling weight impact testing machine. Impact test were conducted to characterize the type and extent of the damage observed in laminate for range of thickness subjected to different impact velocities. Correlation of the residual indentation of the impacted specimens provides a criterion for the extent of the damage. As the impact energy was increased, the samples experienced one of two types of damages: a crack from the center of the laminate to the edge, or significant damage consisting of a dent localized in the region of impact. The history of relevant kinematical, dynamic and energetic quantities, both to synthesize the dependency of the energy parameters and force threshold values on the impact velocity are discussed.     

Catalytic Vicinal Diacylation of Epoxidized Triglycerides in Canola Oil

The epoxy ring opening and vicinal diacylation of fatty acids in vegetable oils was found to be promising reaction to synthesize stable biolubricants and bioplasticizers. The current research investigation is emphasized on the synthesis of a value added product vicinally diacylated canola oil by sulfated‐ZrO₂. The two‐step research approach employed includes: (i) epoxidation, and (ii) epoxy ring opening and vicinal diacylation of epoxidized triglycerides in the canola oil. Sulfated‐ZrO₂ was prepared and characterized to measure the physico‐chemical properties required for the effective catalysis. The Taguchi (L16 orthogonal array) statistical design method was employed to optimize the process conditions for the maximum formation of diacylated canola oil. Sulfated‐ZrO₂ demonstrated promising activity for the epoxy ring opening and vicinal diacylation of canola oil, and 99 % conversion was achieved at the optimum process conditions of temperature 130 °C, epoxy to acetic anhydride molar ratio (1:1.25), 16 wt% of catalyst loading and reaction time of 1 h which were inferred from the Taguchi analyses. The products were characterized and confirmed with FT‐IR, ¹H NMR and sodium spray mass spectroscopy. Spectroscopic analysis also confirmed the absence of intermediate products. The statistical analyses was undertaken to determine the order, rank and interactions among the process variables. The reaction followed Langmuir–Hinshelwood–Hougen–Watson type mechanism and the kinetic data was fitted in overall second order equation. Calculated apparent activation energy was 23.1 kcal/mol.