MFIS and MFS structures using SrBi2Ta2O9 thin films for the FRAM applications

Recently there is an increasing demand and extensive research on high density memories, in particular to the ferroelectric random access memory composed of 1T/1C (1 transistor/1 capacitor) or 2T/2C. FRAM's exhibit fast random acess in read/write mode, non - volatility and low power for good performance. An integration of the ferroelectric on Si is the key importance and in this regard, there had been various models proposed like MFS, MFIS, MFMIS structure etc., Choosing the proper insulator is very essential for the better performance of the device and to exhibit excellent electrical characteristics. ZrTiO4 is a potential candidate because of its excellent thermal stability and lattice match on the Si substrate. SrBi2Ta2O9 and ZrTiO4 thin films were prepared on p - type Si substrate by pulsed excimer laser ablation technique. Optimization of both ZT and SBT thin films in MFS and MFIS structure had been done based on the annealing, oxygen partial pressures and substrate temperatures to have proper texture of the thin films. The dc leakage current, P - E hysteresis, capacitance - voltage and conductance - voltage measurement were carried out. The effect of the frequency dependence on MFIS structure was observed in the C – V curve. It displays a transition of C - V curve from high frequency to low frequency curve on subjection to varied frequencies. Density of interface states has been calculated using Terman and high - low frequency C - V curve. The effect of memory window in the C - V hysteresis were analysed in terms of film thickness and annealing temperatures. DC conduction mechanism were analysed in terms of poole - frenkel, Schottky and space charge limited conduction separately on MFS, MIS structure.

Optical and Electronic Properties of Conjugated Polymer -Nanocluster Semiconductor Hybrid Systems

Conjugated polymers are intensively pursued as candidate materials for emission and detection devices with the optical range of interest determined by the chemical structure. On the other hand the optical range for emission and detection can also be tuned by size selection in semiconductor nanoclusters. The mechanisms for charge generation and separation upon optical excitation, and light emission are different for these systems. Hybrid systems based on these different class of materials reveal interesting electronic and optical properties and add further insight into the individual characteristics of the different components. Multilayer structures and blends of these materials on different substrates were prepared for absorption, photocurrent (Iph), photoluminescence (PL) and electroluminscence (EL) studies. Polymers chosen were derivatives of polythiophene (PT) and polyparaphenylenevinylene (PPV) along with nanoclusters of cadmium sulphide of average size 4.4 nm (CdS-44). The photocurrent spectral response in these systems followed the absorption response around the band edges for each of the components and revealed additional features, which depended on bias voltage, thickness of the layers and interfacial effects. The current-voltage curves showed multi-component features with emission varying for different regimes of voltage. The emission spectral response revealed additive features and is discussed in terms of excitonic mechanisms.

Solving a part classification problem using simulated annealing-like hybrid

Part classification and coding is still considered as laborious and time-consuming exercise. Keeping in view, the crucial role, which it plays, in developing automated CAPP systems, the attempts have been made in this article to automate a few elements of this exercise using a shape analysis model. In this study, a 24-vector directional template is contemplated to represent the feature elements of the parts (candidate and prototype). Various transformation processes such as deformation, straightening, bypassing, insertion and deletion are embedded in the proposed simulated annealing (SA)-like hybrid algorithm to match the candidate part with their prototype. For a candidate part, searching its matching prototype from the information data is computationally expensive and requires large search space. However, the proposed SA-like hybrid algorithm for solving the part classification problem considerably minimizes the search space and ensures early convergence of the solution. The application of the proposed approach is illustrated by an example part. The proposed approach is applied for the classification of 100 candidate parts and their prototypes to demonstrate the effectiveness of the algorithm. (C) 2003 Elsevier Science Ltd. All rights reserved.

The promise of fuel cell-based automobiles

Fuel cell-based automobiles have gained attention in the last few years due to growing public concern about urban air pollution and consequent environmental problems. From an analysis of the power and energy requirements of a modern car, it is estimated that a base sustainable power of ca. 50 kW supplemented with short bursts up to 80 kW will suffice in most driving requirements. The energy demand depends greatly on driving characteristics but under normal usage is expected to be 200 Wh/km. The advantages and disadvantages of candidate fuel-cell systems and various fuels are considered together with the issue of whether the fuel should be converted directly in the fuel cell or should be reformed to hydrogen onboard the vehicle. For fuel cell vehicles to compete successfully with conventional internal-combustion engine vehicles, it appears that direct conversion fuel cells using probably hydrogen, but possibly methanol, are the only realistic contenders for road transportation applications. Among the available fuel cell technologies, polymer-electrolyte fuel cells directly fueled with hydrogen appear to be the best option for powering fuel cell vehicles as there is every prospect that these will exceed the performance of the internal-combustion engine vehicles but for their first cost. A target cost of $ 50/kW would be mandatory to make polymer-electrolyte fuel cells competitive with the internal combustion engines and can only be achieved with design changes that would substantially reduce the quantity of materials used. At present, prominent car manufacturers are deploying important research and development efforts to develop fuel cell vehicles and are projecting to start production by 2005.

Solution processed reduced graphene oxide ultraviolet detector

Electronic properties of graphene have been studied more extensively than its photonic applications, in spite of its exciting optical properties. Recent results on solar cells, light emitting diodes and photodetectors show its true potential in photonics and optoelectronics. Here, we have explored the use of reduced graphene oxide as a candidate for solution processed ultraviolet photodetectors. UV detection is demonstrated by reduced graphene oxide in terms of time resolved photocurrent as well as photoresponse. The responsivity of the detectors is found to be 0.12 A/W with an external quantum efficiency of 40%. (C) 2011 American Institute of Physics. [doi:10.1063/1.3640222]

Modeling the Field Emission Current Fluctuation in Carbon Nanotube Thin Films

Owing to their distinct properties, carbon nanotubes (CNTs) have emerged as promising candidate for field emission devices. It has been found experimentally that the results related to the field emission performance show variability. The design of an efficient field emitting device requires the analysis of the variabilities with a systematic and multiphysics based modeling approach. In this paper, we develop a model of randomly oriented CNTs in a thin film by coupling the field emission phenomena, the electron-phonon transport and the mechanics of single isolated CNT. A computational scheme is developed by which the states of CNTs are updated in time incremental manner. The device current is calculated by using Fowler-Nordheim equation for field emission to study the performance at the device scale.

Mutations in WDR62, encoding a centrosomal and nuclear protein, in Indian primary microcephaly families with cortical malformations

Primary microcephaly is an autosomal recessive disorder characterized by smaller than normal brain size and mental retardation. It is genetically heterogeneous with seven loci: MCPH1-MCPH7. We have previously reported genetic analysis of 35 families, including the identification of the MCPH7 gene STIL. Of the 35 families, three families showed linkage to the MCPH2 locus. Recent whole-exome sequencing studies have shown that the WDR62 gene, located in the MCPH2 candidate region, is mutated in patients with severe brain malformations. We therefore sequenced the WDR62 gene in our MCPH2 families and identified two novel homozygous protein truncating mutations in two families. Affected individuals in the two families had pachygyria, microlissencephaly, band heterotopias, gyral thickening, and dysplastic cortex. Using immunofluorescence study, we showed that, as with other MCPH proteins, WDR62 localizes to centrosomes in A549, HepG2, and HaCaT cells. In addition, WDR62 was also localized to nucleoli. Bioinformatics analysis predicted two overlapping nuclear localization signals and multiple WD-40 repeats in WDR62. Two other groups have also recently identified WDR62 mutations in MCPH2 families. Our results therefore add further evidence that WDR62 is the MCPH2 gene. The present findings will be helpful in genetic diagnosis of patients linked to the MCPH2 locus.

Robust Observer Design with Application to Fault Detection

A new scheme for robust estimation of the partial state of linear time-invariant multivariable systems is presented, and it is shown how this may be used for the detection of sensor faults in such systems. We consider an observer to be robust if it generates a faithful estimate of the plant state in the face of modelling uncertainty or plant perturbations. Using the Stable Factorization approach we formulate the problem of optimal robust observer design by minimizing an appropriate norm on the estimation error. A logical candidate is the 2-norm, corresponding to an H�¿ optimization problem, for which solutions are readily available. In the special case of a stable plant, the optimal fault diagnosis scheme reduces to an internal model control architecture.

Design, synthesis and docking studies of new quinoline-3-carbohydrazide derivatives as antitubercular agents

Three new series of 4-hydroxy-8-trifluoromethyl-quinoline derivatives were synthesized through multi step reactions. All the newly synthesized compounds were characterized by spectral and elemental analyses. The structure of 5j was evidenced by X-ray crystallographic study. The newly synthesized title compounds were evaluated for their antimicrobial activities including antimycobacterial activity. Amongst the tested compounds, 5b, 5e, 5h, 5j, 6c and 7c displayed promising antimicrobial activity. The mode of action of these active compounds was carried out by docking of receptor enoyl-ACP reductase with newly synthesized candidate ligands, 5b, 5e, 5h, 5j and 6c. (C) 2011 Elsevier Masson SAS. All rights reserved.

Anisotropic electrical transport properties of poly(methyl methacrylate) infiltrated aligned carbon nanotube mats

We report the electrical anisotropic transport properties of poly(methyl methacrylate) infiltrated aligned carbon nanotube mats. The anisotropy in the resistivity increases with decreasing temperature and the conduction mechanism in the parallel and perpendicular direction is different. Magnetoresistance (MR) studies also suggest anisotropic behavior of the infiltrated mats. Though MR is negative, an upturn is observed when the magnetic field is increased. This is due to the interplay of electron weak localization and electron-electron interactions mechanisms. Overall, infiltrated carbon nanotube mat is a good candidate for anisotropically conductive polymer composite and a simple fabrication method has been reported. (C) 2012 American Institute of Physics. [doi: 10.1063/1.3675873]

A unified view of the Apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discoverymethods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies.Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.

System and Method for Temporal Datamining

A system for temporal data mining includes a computer readable medium having an application configured to receive at an input module a temporal data series and a threshold frequency. The system is further configured to identify, using a candidate identification and tracking module, one or more occurrences in the temporal data series of a candidate episode and increment a count for each identified occurrence. The system is also configured to produce at an output module an output for those episodes whose count of occurrences results in a frequency exceeding the threshold frequency.

System and Method for mining of Temporal Data

A system for temporal data mining includes a computer readable medium having an application configured to receive at an input module a temporal data series having events with start times and end times, a set of allowed dwelling times and a threshold frequency. The system is further configured to identify, using a candidate identification and tracking module, one or more occurrences in the temporal data series of a candidate episode and increment a count for each identified occurrence. The system is also configured to produce at an output module an output for those episodes whose count of occurrences results in a frequency exceeding the threshold frequency.

Fast algorithms for frequent episode discovery in event sequences

In this paper we consider the process of discovering frequent episodes in event sequences. The most computationally intensive part of this process is that of counting the frequencies of a set of candidate episodes. We present two new frequency counting algorithms for speeding up this part. These, referred to as non-overlapping and non-inteleaved frequency counts, are based on directly counting suitable subsets of the occurrences of an episode. Hence they are different from the frequency counts of Mannila et al [1], where they count the number of windows in which the episode occurs. Our new frequency counts offer a speed-up factor of 7 or more on real and synthetic datasets. We also show how the new frequency counts can be used when the events in episodes have time-durations as well.

A unified view of the apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discovery methods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies. Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.