Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia

An operational global soil moisture data product is currently generated from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite using the retrieval procedure described in Njoku and Chan [Njoku, E.G. and Chan, S.K., 2006. Vegetation and surface roughness effects on AMSR-E land observations, remote sensing environment, 100(2), 190-199]. We have generated another soil moisture dataset from the same AMSR-E observed brightness temperature data using the Land Surface Microwave Emission Model (LSMEM) adopting a different estimation method. This paper focuses on a comparison study of soil moisture estimates from the above two methods. The soil moisture data from current AMSR-E product and LSMEM are compared with the in-situ measured soil moisture datasets over the Little River Experimental Watershed (LREW), Georgia, USA for the year 2003. The comparison study was carried out separately for the AMSR-E daytime and night time overpasses. The LSMEM method performed better than the current operational AMSR-E retrieval algorithm in this study. The differences between the AMSR-E and LSMEM results are mostly due to differences in various simplifications and assumptions made for variables in the radiative transfer equations and the soil and vegetation based physical models and the accuracy of the input surface temperature datasets for the LSMEM forward model approach. This study confirms that remote sensing data have the potential to provide useful hydrologic information, but the accuracy of the geophysical parameters could vary depending on the estimation methods. It cannot be concluded from this study whether the soil moisture estimation by the LSMEM approach will perform better in other geographic, climatic or topographic conditions. Nevertheless, this study sheds light on the effects of different approaches for the estimation of geophysical parameters, which may be useful for current and future satellite missions.     

FSE2R: An Improved Collision-Avoidance-based Energy Efficient Route Selection Protocol in USN

The 3D Underwater Sensor Network (USNs) has become the most optimistic medium for tracking and monitoring underwater environment. Energy and collision are two most critical factors in USNs for both sparse and dense regions. Due to harsh ocean environment, it is a challenge to design a reliable energy efficient with collision free protocol. Diversity in link qualities may cause collision and frequent communication lead to energy loss; that effects the network performance. To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing (FSE2R) is proposed. Our proposal’s key idea is based on computation of node distance from the sink, Residual Energy (RE) of each node and Signal to Interference Noise Ratio (SINR). The node distance from sink and RE is computed for reliable forwarder node selection and SINR is used for analysis of collision. The novel proposal compares with existing protocols like H2AB, DEEP, and E2LR to achieve Quality of Service (QoS) in terms of throughput, packet delivery ratio and energy consumption. The comparative analysis shows that FSE2R gives on an average 30% less energy consumption, 24.62% better PDR and 48.31% less end-to-end delay compared to other protocols.     

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Twitter is a radiant platform with a quick and effective technique to analyze users’ perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare, behaviour estimation, etc. In addition, the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive, negative and neutral tweets. In this work, we obligated Twitter Application Programming Interface (API) account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor. To distinguish the results in terms of the performance evaluation, an error analysis investigates the features of various stakeholders comprising social media analytics researchers, Natural Language Processing (NLP) developers, engineering managers and experts involved to have a decision-making approach.     

Optimization of annealing cycle parameters of dual phase and interstitial free steels by multiobjective genetic algorithms

Data-driven models have been constructed for Dual Phase (DP) and Interstitials Free (IF) steels using an evolutionary approach. DP steel data are utilized from an existing database, while for the IF steels, data generated at an integrated steel plant have been used. The objective function for Ultimate Tensile Strength (UTS) and % elongation, created as data-driven models, is simultaneously optimized for an optimum strength-ductility balance and the results indicate the possibilities of developing steels with better mechanical properties than what are known to have been existing so far.     

CFD study of forced convective heat transfer enhancement in a 90° bend duct of square cross section using nanofluid

In this paper, the forced convective heat transfer enhancement with nanofluids in a 90° pipe bend has been presented. Numerical investigation is carried out for the turbulent flow through the pipe employing finite volume method. The governing differential equations are discretized using hexahedral cells, and the resulting algebraic equations are solved using Commercial solver Fluent 6.3. In order to close the time averaged Navier–Stokes equations, the two-equation k–? turbulence model with a standard wall function have been used. The duct Reynolds number is varied in the range of 2,500–6,000. It is observed that the heat transfer is enhanced significantly by varying the volume fraction of the nanofluid. It is also found that the heat transfer is increased with Reynolds number. A strong secondary flow is observed due to the presence of the wall. Turbulent kinetic energy near outer wall is found to be higher than the inner wall of the bend. A comparative assessment for the heat transfer enhancement with different types of nanofluids is also presented. The computed results of area weighted average Nusselt numbers are validated with some of the existing literature.     

The optimal control approach to generalized multiprocessor scheduling

In this paper we present several new results in the theory of homogeneous multiprocessor scheduling. We start with some assumptions about the behavior of tasks, with associated precedence constraints, as processor power is applied. We assume that as more processors are applied to a task, the time taken to compute it decreases, yielding some speedup. Because of communication, synchronization, and task scheduling overhead, this speedup increases less than linearly with the number of processors applied. We also assume that the number of processors which can be assigned to a task is a continuous variable, with a view to exploiting continuous mathematics. The optimal scheduling problem is to determine the number of processors assigned to each task, and task sequencing, to minimize the finishing time.These assumptions allow us to recast the optimal scheduling problem in a form which can be addressed by optimal control theory. Various theorems can be proven which characterize the optimal scheduling solution. Most importantly, for the special case where the speedup function of each task isp , wherep is the amount of processing power applied to the task, we can directly solve our equations for the optimal solution. In this case, for task graphs formed from parallel and series connections, the solution can be derived by inspection. The solution can also be shown to be shortest path from the initial to the final state, as measured by anl _1/ distance metric, subject to obstacle constraints imposed by the precedence constraints.This research has been funded in part by the Advanced Research Project Agency monitored by ONR under Grant No. N00014-89-J-1489, in part by Draper Laboratory, in part by DARPA Contract No. N00014-87-K-0825, and in part by NSF Grant No. MIP-9012773. The first author is now with AT&T Bell Laboratories and the second author is with BBN Incorporated.     

Subsegmental,segmental and suprasegmental processing of linear prediction residual for speaker information

This work processes linear prediction (LP) residual in the time domain at three different levels, extracts speaker information, and demonstrates their significance and also different nature for text-independent speaker recognition. The subsegmental analysis considers LP residual in blocks of 5 msec with shift of 2.5 msec to extract speaker information. The segmental analysis extracts speaker information by processing in blocks of 20 msec with shift of 2.5 msec. The suprasegmental speaker information is extracted by viewing in blocks of 250 msec with shift of 6.25 msec. The speaker identification and verification studies performed using NIST-99 and NIST-03 databases demonstrate that the segmental analysis provides best performance followed by subsegmental analysis. The suprasegmental analysis gives the least performance. However, the evidences from all the three levels of processing seem to be different and combine well to provide improved performance, demonstrating different speaker information captured at each level of processing. Finally, the combined evidence from all the three levels of processing together with vocal tract information further improves the speaker recognition performance.     

Recognition of consonant-vowel (CV) units under background noise using combined temporal and spectral preprocessing

This paper proposes hybrid classification models and preprocessing methods for enhancing the consonant-vowel (CV) recognition in the presence of background noise. Background Noise is one of the major degradation in real-time environments which strongly effects the performance of speech recognition system. In this work, combined temporal and spectral processing (TSP) methods are explored for preprocessing to improve CV recognition performance. Proposed CV recognition method is carried out in two levels to reduce the similarity among large number of CV classes. In the first level vowel category of CV unit will be recognized, and in the second level consonant category will be recognized. At each level complementary evidences from hybrid models consisting of support vector machine (SVM) and hidden Markov models (HMM) are combined for enhancing the recognition performance. Performance of the proposed CV recognition system is evaluated on Telugu broadcast database for white and vehicle noise. The proposed preprocessing methods and hybrid classification models have improved the recognition performance compared to existed methods.     

Robust resource allocation in a cluster based imaging system

Recently there has been an increased demand for imaging systems in support of high-speed digital printing. The required increase in performance in support of such systems can be accomplished through an effective parallel execution of image processing applications in a distributed cluster computing environment. The output of the system must be presented to a raster based display at regular intervals, effectively establishing a hard deadline for the production of each image. Failure to complete a rasterization task before its deadline will result in an interruption of service that is unacceptable. The goal of this research was to derive a metric for measuring robustness in this environment and to design a resource allocation heuristic capable of completing each rasterization task before its assigned deadline, thus, preventing any service interruptions. We present a mathematical model of such a cluster based raster imaging system, derive a robustness metric for evaluating heuristics in this environment, and demonstrate using the metric to make resource allocation decisions. The heuristics are evaluated within a simulation of the studied raster imaging system. We clearly demonstrate the effectiveness of the heuristics by comparing their results with the results of a resource allocation heuristic commonly used in this type of system.     

Single-channel speech separation using empirical mode decomposition and multi pitch information with estimation of number of speakers

Speech separation is an essential part of any voice recognition system like speaker recognition, speech recognition and hearing aids etc. When speech separation is applied at the front-end of any voice recognition system increases the performance efficiency of that particular system. In this paper we propose a system for single channel speech separation by combining empirical mode decomposition (EMD) and multi pitch information. The proposed method is completely unsupervised and requires no knowledge of the underlying speakers. In this method we apply EMD to short frames of the mixed speech for better estimation of the speech specific information. Speech specific information is derived through multi pitch tracking. To track multi pitch information from the mixed signal we apply simple-inverse filtering tracking and histogram based pitch estimation to excitation source information along with estimating the number of speakers present in the mixed signal.