Pipelined VLSI Architecture using CORDIC for Transform Domain Equalizer

In this paper, a pipelined architecture using CORDIC for realization of transform domain equalizer is presented. Transform domain equalizer has much faster convergence than its time domain counterpart for practical hardware realization having nonzero adaptation delay. Here running DFT is employed as the transform, and CORDIC is used for realization of running DFT. Pipelining is applied throughout the architecture, thus limiting the critical path delay to the propagation delay of a single 16 bit adder for 16 bit arithmetic. For N tap equalizer, primary clock speed is N times of the sample clock speed, so that on arrival of each sample, the computation of whole transform and weight update is possible. In the proposed architecture, hardware complexity is reduced by fully utilizing the pipeline without using parallel structures. The adaptation delay is only 2 sample clock periods resulting in fast convergence. The proposed architecture is suitable for VLSI implementation with primary clock speed limited by the binary adder propagation delay which could be as low as 2 ns in the present state-of-the-art technology.     

Integrated Computational Materials Engineering (ICME) Approach to Design of Novel Microstructures for Ti-Alloys

In this overview, we present integrated CAPHAD and phase-field modeling with critical experiments to explore a newly discovered, nonconventional, solid–solid phase transformation pathway based on the so-called pseudo-spinodal mechanism. We show that this new transformation pathway offers a new design strategy for Ti alloys with extremely fine and uniform α + β microstructures that could potentially have highly attractive balances of mechanical properties. To broaden the processing window for such a mechanism to operate, we also explore a different nonconventional transformation pathway that involves precursory phase separation. In addition, the variant selection process during the β → α transformation leading to macrozones is investigated and the results could shed light on how to control processing conditions to avoid or reduce microtexture at both the individual β grain level and the overall polycrystalline sample level.     

On Security and Reliability Using Cooperative Transmissions in Sensor Networks

Cooperative transmissions have received recent attention and research papers have demonstrated their benefits for wireless networks. Such benefits include improving the reliability of links through diversity and/or increasing the reach of a link compared to a single transmitter transmitting to a single receiver (single-input single-output or SISO). In one form of cooperative transmissions, multiple nodes can act as virtual antenna elements and provide diversity gain or range improvement using space-time coding. In a multi-hop ad hoc or sensor network, a source node can make use of its neighbors as relays with itself to reach an intermediate node with greater reliability or at a larger distance than otherwise possible. The intermediate node will use its neighbors in a similar manner and this process continues till the destination is reached. Thus, for the same reliability of a link as SISO, the number of hops between a source and destination may be reduced using cooperative transmissions as each hop spans a larger distance. However, the presence of malicious or compromised nodes in the network impacts the benefits obtained with cooperative transmissions. Using more relays can increase the reach of a link, but if one or more relays are malicious, the transmission may fail. However, the relationships between the number of relays, the number of hops, and success probabilities are not trivial to determine. In this paper, we analyze this problem to understand the conditions under which cooperative transmissions fare better or worse than SISO transmissions. We take into consideration additional parameters such as the path-loss exponent and provide a framework that allows us to evaluate the conditions when cooperative transmissions are better than SISO transmissions. This analysis provides insights that can be employed before resorting to simulations or experimentation.     

Control of a robotic orthosis for gait rehabilitation

Robot assisted gait training may help in producing rapid improvements in functional gait parameters. This paper presents new experimental results with an intrinsically compliant robotic gait training orthosis. The newly developed robotic orthosis has 6 degrees of freedom (DOFs). A trajectory tracking controller based on the boundary layer augmented sliding control (BASMC) law was implemented to guide the subject’s limbs on physiological gait trajectories. The compliance of the robotic orthosis sagittal plane hip and knee joints was also controlled, independently of the trajectory tracking control. The robotic orthosis and the control scheme were evaluated on three neurologically intact subjects walking on a treadmill. A maximum trajectory tracking error of 10° was recorded at the hip and knee sagittal plane joints. The results showed that subjects can walk in the robotic orthosis with comfort and the BASMC law was able to guide the subject’s limbs on reference physiological trajectories.     

Fraudulent Firm Classification: A Case Study of an External Audit

This paper is a case study of visiting an external audit company to explore the usefulness of machine learning algorithms for improving the quality of an audit work. Annual data of 777 firms from 14 different sectors are collected. The Particle Swarm Optimization (PSO) algorithm is used as a feature selection method. Ten different state-of-the-art classification models are compared in terms of their accuracy, error rate, sensitivity, specificity, F measures, Mathew’s Correlation Coefficient (MCC), Type-I error, Type-II error, and Area Under the Curve (AUC) using Multi-Criteria Decision-Making methods like Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results of Bayes Net and J48 demonstrate an accuracy of 93% for suspicious firm classification. With the appearance of tremendous growth of financial fraud cases, machine learning will play a big part in improving the quality of an audit field work in the future.     

On the Role of C Addition on α Precipitation in a β Titanium Alloy

The onset of α nucleation in a carbon containing β-titanium alloy has been investigated by coupling atom probe tomography (APT) with transmission electron microscopy. The analysis of the APT results indicates that in addition to ω precipitates that can act as potential α nucleation sites, carbon atoms tend to form clusters within the β-matrix, which in turn give rise to additional nucleation sites for α, resulting in finer scale α precipitates due to increased nucleation density.     

Effect of NOx on AgI formation in Ag-mordenite

Ag loaded mordenite can be used as a trap for radio-iodine arising from nuclear reprocessing operations. Typically, iodine is trapped in the Ag loaded mordenite by the formation of AgI in the pores of the mordenite, through a solid-vapour reaction. In the presence of NO_x and water vapour, AgI is most likely formed by liquid-vapour reaction between AgNO₃ and I₂. This reaction results in the formation of large aggregates of AgI crystals on the surface of the mordenite, also leading to a yellow colouration of the mordenite, which is not observed when NO_x and water vapour is absent.     

Development of real-time reference evapotranspiration at the regional scale using satellite-based observations

Real-time data of reference evapotranspiration (ET₀) at different space-time scales are essential to regional agricultural drought assessment, water accounting at the watershed to basin scale, and provide irrigation advisory to farmers. Here, we present a data-fusion approach that integrates satellite-based insolation product (8 km) from an Indian geostationary satellite (Kalpana-1) sensor (VHRR; Very High Resolution Radiometer) and high-resolution (~ 5 km) short-range weather forecast into an FAO56 model based on the classical Penman–Monteith (P-M) formulation. Five year (2009–2013) mean monthly estimates from the daily ET₀ product over the Indian landmass were found to vary between 10 and 350 mm. It increased from January to May (70–350 mm), followed by a decrease to reach the lowest in November (10–140 mm), thus typically showing unimodal distribution. The comparison of daily space-based and station-based estimates (at six ground stations) produced a root mean square deviation (RMSD) ranging from 21% to 38% for 977 paired data sets with the correlation coefficient (r) varying from 0.32 to 0.82. The error was reduced from 25% to 10% with an increase in ‘r’ from 0.43 to 0.98 for daily to 10 day summation period. Spatial grid-to-grid comparison of monthly ET₀ estimates with Global Data Assimilation System (GDAS) potential evapotranspiration (PET) showed RMSD within a range of 1.4–18.4% for most of the months, except for two. Further ET₀ analysis over normal and drought years showed that it could be used for comprehensive drought assessment with other existing indicators.