Mechanistic Aspects of Shape Selection and Symmetry Breaking during Nanostructure Growth by Wet Chemical Methods

The control of shapes of nanocrystals is crucial for using them as building blocks for various applications. In this paper, we present a critical overview of the issues involved in shape-controlled synthesis of nanostructures. In particular, we focus on the mechanisms by which anisotropic structures of high-symmetry materials (fcc crystals, for instance) could be realized. Such structures require a symmetry-breaking mechanism to be operative that typically leads to selection of one of the facets/directions for growth over all the other symmetry-equivalent crystallographic facets. We show how this selection could arise for the growth of one-dimensional structures leading to ultrafine metal nanowires and for the case of two-dimensional nanostructures where the layer-by-layer growth takes place at low driving forces leading to plate-shaped structures. We illustrate morphology diagrams to predict the formation of two-dimensional structures during wet chemical synthesis. We show the generality of the method by extending it to predict the growth of plate-shaped inorganics produced by a precipitation reaction. Finally, we present the growth of crystals under high driving forces that can lead to the formation of porous structures with large surface areas.

Feature selection and the concept of immediate neighborhood in the context of clustering techniques

The concept of feature selection in a nonparametric unsupervised learning environment is practically undeveloped because no true measure for the effectiveness of a feature exists in such an environment. The lack of a feature selection phase preceding the clustering process seriously affects the reliability of such learning. New concepts such as significant features, level of significance of features, and immediate neighborhood are introduced which result in meeting implicitly the need for feature slection in the context of clustering techniques.

Evolution of Social Behavior Through Interpopulation Selection

Under certain special conditions natural selection can be effective at the level of local populations, or demes. Such interpopulation selection will favor genotypes that reduce the probability of extinction of their parent population even at the cost of a lowered inclusive fitness. Such genotypes may be characterized by altruistic traits only in a viscous population, i.e., in a population in which neighbors tend to be closely related. In a non-viscous population the interpopulation selection will instead favor spiteful traits when the populations are susceptible to extinction through the overutilization of the habitat, and cooperative traits when it is the newly established populations that are in the greatest danger of extinction.

Gene therapy: Principles, practice, problems and prospects

The remarkable advances made in recombinant DNA technology over the last two decades have paved way for the use of gene transfer to treat human diseases. Several protocols have been developed for the introduction and expression of genes in humans, but the clinical efficacy has not been conclusively demonstrated in any of them. The eventual success of gene therapy for genetic and acquired disorders depends on the development of better gene transfer vectors for sustained, long term expression of foreign genes as well as a better understanding of the pathophysiology of human diseases, it is heartening to note that some of the gene therapy protocols have found other applications such as the genetic immunization or DNA vaccines, which is being heralded as the third vaccine revolution, Gene therapy is yet to become a dream come true, but the light is seen at the end of the tunnel.

System integration design in MEMS - A case study of micromachined load cell

One of the critical issues in large scale commercial exploitation of MEMS technology is its system integration. In MEMS, a system design approach requires integration of varied and disparate subsystems with one of a kind interface. The physical scales as well as the magnitude of signals of various subsystems vary widely. Known and proven integration techniques often lead to considerable loss in advantages the tiny MEMS sensors have to offer. Therefore, it becomes imperative to think of the entire system at the outset, at least in terms of the concept design. Such design entails various aspects of the system ranging from selection of material, transduction mechanism, structural configuration, interface electronics, and packaging. One way of handling this problem is the system-in-package approach that uses optimized technology for each function using the concurrent hybrid engineering approach. The main strength of this design approach is the fast time to prototype development. In the present work, we pursue this approach for a MEMS load cell to complete the process of system integration for high capacity load sensing. The system includes; a micromachined sensing gauge, interface electronics and a packaging module representing a system-in-package ready for end characterization. The various subsystems are presented in a modular stacked form using hybrid technologies. The micromachined sensing subsystem works on principles of piezo-resistive sensing and is fabricated using CMOS compatible processes. The structural configuration of the sensing layer is designed to reduce the offset, temperature drift, and residual stress effects of the piezo-resistive sensor. ANSYS simulations are carried out to study the effect of substrate coupling on sensor structure and its sensitivity. The load cell system has built-in electronics for signal conditioning, processing, and communication, taking into consideration the issues associated with resolution of minimum detectable signal. The packaged system represents a compact and low cost solution for high capacity load sensing in the category of compressive type load sensor.

Mechanisms of molecular doping of graphene: A first-principles study

Doping graphene with electron donating or accepting molecules is an interesting approach to introduce carriers into it, analogous to electrochemical doping accomplished in graphene when used in a field-effect transistor. Here, we use first-principles density-functional theory to determine changes in the electronic-structure and vibrational properties of graphene that arise from the adsorption of aromatic molecules such as aniline and nitrobenzene. Identifying the roles of various mechanisms of chemical interaction between graphene and a molecule, we bring out the contrast between electrochemical and molecular doping of graphene. Our estimates of various contributions to shifts in the Raman-active modes of graphene with molecular doping are fundamental to the possible use of Raman spectroscopy in (a) characterization of the nature and concentration of carriers in graphene with molecular doping, and (b) graphene-based chemical sensors.

Random security scheme selection for mobile transactions

Security in a mobile communication environment is always a matter for concern, even after deploying many security techniques at device, network, and application levels. The end-to-end security for mobile applications can be made robust by developing dynamic schemes at application level which makes use of the existing security techniques varying in terms of space, time, and attacks complexities. In this paper we present a security techniques selection scheme for mobile transactions, called the Transactions-Based Security Scheme (TBSS). The TBSS uses intelligence to study, and analyzes the security implications of transactions under execution based on certain criterion such as user behaviors, transaction sensitivity levels, and credibility factors computed over the previous transactions by the users, network vulnerability, and device characteristics. The TBSS identifies a suitable level of security techniques from the repository, which consists of symmetric, and asymmetric types of security algorithms arranged in three complexity levels, covering various encryption/decryption techniques, digital signature schemes, andhashing techniques. From this identified level, one of the techniques is deployed randomly. The results shows that, there is a considerable reduction in security cost compared to static schemes, which employ pre-fixed security techniques to secure the transactions data.

Splitting Algorithms for Fast Relay Selection: Generalizations, Analysis, and a Unified View

Relay selection for cooperative communications promises significant performance improvements, and is, therefore, attracting considerable attention. While several criteria have been proposed for selecting one or more relays, distributed mechanisms that perform the selection have received relatively less attention. In this paper, we develop a novel, yet simple, asymptotic analysis of a splitting-based multiple access selection algorithm to find the single best relay. The analysis leads to simpler and alternate expressions for the average number of slots required to find the best user. By introducing a new contention load' parameter, the analysis shows that the parameter settings used in the existing literature can be improved upon. New and simple bounds are also derived. Furthermore, we propose a new algorithm that addresses the general problem of selecting the best Q >= 1 relays, and analyze and optimize it. Even for a large number of relays, the scalable algorithm selects the best two relays within 4.406 slots and the best three within 6.491 slots, on average. We also propose a new and simple scheme for the practically relevant case of discrete metrics. Altogether, our results develop a unifying perspective about the general problem of distributed selection in cooperative systems and several other multi-node systems.

Selection of intrusion detection system threshold bounds for effective sensor fusion

The motivation behind the fusion of Intrusion Detection Systems was the realization that with the increasing traffic and increasing complexity of attacks, none of the present day stand-alone Intrusion Detection Systems can meet the high demand for a very high detection rate and an extremely low false positive rate. Multi-sensor fusion can be used to meet these requirements by a refinement of the combined response of different Intrusion Detection Systems. In this paper, we show the design technique of sensor fusion to best utilize the useful response from multiple sensors by an appropriate adjustment of the fusion threshold. The threshold is generally chosen according to the past experiences or by an expert system. In this paper, we show that the choice of the threshold bounds according to the Chebyshev inequality principle performs better. This approach also helps to solve the problem of scalability and has the advantage of failsafe capability. This paper theoretically models the fusion of Intrusion Detection Systems for the purpose of proving the improvement in performance, supplemented with the empirical evaluation. The combination of complementary sensors is shown to detect more attacks than the individual components. Since the individual sensors chosen detect sufficiently different attacks, their result can be merged for improved performance. The combination is done in different ways like (i) taking all the alarms from each system and avoiding duplications, (ii) taking alarms from each system by fixing threshold bounds, and (iii) rule-based fusion with a priori knowledge of the individual sensor performance. A number of evaluation metrics are used, and the results indicate that there is an overall enhancement in the performance of the combined detector using sensor fusion incorporating the threshold bounds and significantly better performance using simple rule-based fusion.

On Guo and Nixon's Criterion for Feature Subset Selection: Assumptions, Implications,and Alternative Options

Guo and Nixon proposed a feature selection method based on maximizing I(x; Y),the multidimensional mutual information between feature vector x and class variable Y. Because computing I(x; Y) can be difficult in practice, Guo and Nixon proposed an approximation of I(x; Y) as the criterion for feature selection. We show that Guo and Nixon's criterion originates from approximating the joint probability distributions in I(x; Y) by second-order product distributions. We remark on the limitations of the approximation and discuss computationally attractive alternatives to compute I(x; Y).

Structure of Ce1-xSnxO2 and its relation to oxygen storage property from first-principles analysis

CeO2-SnO2 solid solution has been reported to possess high oxygen storage/release property which possibly originates from local structural distortion. We have performed first-principles based density functional calculations of Ce1-xSnxO2 structure (x=0, 0.25, 0.5, 1) to understand its structural stability in fluorite in comparison to rutile structure of the other end-member SnO2, and studied the local structural distortion induced by the dopant Sn ion. Analysis of relative energies of fluorite and rutile phases of CeO2, SnO2, and Ce1-xSnxO2 indicates that fluorite structure is the most stable for Ce1-xSnxO2 solid solution. An analysis of local structural distortions reflected in phonon dispersion show that SnO2 in fluorite structure is highly unstable while CeO2 in rutile structure is only weakly unstable. Thus, Sn in Ce1-xSnxO2-fluorite structure is associated with high local structural distortion whereas Ce in Ce1-xSnxO2-rutile structure, if formed, will show only marginal local distortion. Determination of M-O (M=Ce or Sn) bond lengths and analysis of Born effective charges for the optimized structure of Ce1-xSnxO2 show that local coordination of these cations changes from ideal eightfold coordination expected of fluorite lattice to 4+4 coordination, leading to generation of long and short Ce-O and Sn-O bonds in the doped structure. Bond valence analyses for all ions show the presence of oxygen with bond valence similar to 1.84. These weakly bonded oxygen ions are relevant for enhanced oxygen storage/release properties observed in Ce1-xSnxO2 solid solution. (C) 2010 American Institute of Physics.

Anisotropy of the Stone-Wales defect and warping of graphene nanoribbons: A first-principles analysis

Stone-Wales (SW) defects, analogous to dislocations in crystals, play an important role in mechanical behavior of sp(2)-bonded carbon based materials. Here, we show using first-principles calculations that a marked anisotropy in the interaction among the SW defects has interesting consequences when such defects are present near the edges of a graphene nanoribbon: depending on their orientation with respect to edge, they result in compressive or tensile stress, and the former is responsible to depression or warping of the graphene nanoribbon. Such warping results in delocalization of electrons in the defect states.

Moment-Matched Lognormal Modeling of Uplink Interference with Power Control and Cell Selection

We develop an alternate characterization of the statistical distribution of the inter-cell interference power observed in the uplink of CDMA systems. We show that the lognormal distribution better matches the cumulative distribution and complementary cumulative distribution functions of the uplink interference than the conventionally assumed Gaussian distribution and variants based on it. This is in spite of the fact that many users together contribute to uplink interference, with the number of users and their locations both being random. Our observations hold even in the presence of power control and cell selection, which have hitherto been used to justify the Gaussian distribution approximation. The parameters of the lognormal are obtained by matching moments, for which detailed analytical expressions that incorporate wireless propagation, cellular layout, power control, and cell selection parameters are developed. The moment-matched lognormal model, while not perfect, is an order of magnitude better in modeling the interference power distribution.

Relay Selection and Data Transmission Throughput Tradeoff in Cooperative Systems

A common and practical paradigm in cooperative communications is the use of a dynamically selected 'best' relay to decode and forward information from a source to a destination. Such a system consists of two core phases: a relay selection phase, in which the system expends resources to select the best relay, and a data transmission phase, in which it uses the selected relay to forward data to the destination. In this paper, we study and optimize the trade-off between the selection and data transmission phase durations. We derive closed-form expressions for the overall throughput of a non-adaptive system that includes the selection phase overhead, and then optimize the selection and data transmission phase durations. Corresponding results are also derived for an adaptive system in which the relays can vary their transmission rates. Our results show that the optimal selection phase overhead can be significant even for fast selection algorithms. Furthermore, the optimal selection phase duration depends on the number of relays and whether adaptation is used.

Optimal Weighted Antenna Selection For Imperfect Channel Knowledge From Training

Receive antenna selection (AS) reduces the hardware complexity of multi-antenna receivers by dynamically connecting an instantaneously best antenna element to the available radio frequency (RF) chain. Due to the hardware constraints, the channels at various antenna elements have to be sounded sequentially to obtain estimates that are required for selecting the ``best'' antenna and for coherently demodulating data. Consequently, the channel state information at different antennas is outdated by different amounts. We show that, for this reason, simply selecting the antenna with the highest estimated channel gain is not optimum. Rather, the channel estimates of different antennas should be weighted differently, depending on the training scheme. We derive closed-form expressions for the symbol error probability (SEP) of AS for MPSK and MQAM in time-varying Rayleigh fading channels for arbitrary selection weights, and validate them with simulations. We then derive an explicit formula for the optimal selection weights that minimize the SEP. We find that when selection weights are not used, the SEP need not improve as the number of antenna elements increases, which is in contrast to the ideal channel estimation case. However, the optimal selection weights remedy this situation and significantly improve performance.