Gene expression programming-fuzzy logic method for crop type classification

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.

A peer-peer particle swarm optimizer

Particle Swarm Optimization is a parallel algorithm that spawns particles across a search space searching for an optimized solution. Though inherently parallel, they have distinct synchronizations points which stumbles attempts to create completely distributed versions of it. In this paper, we attempt to create a completely distributed peer-peer particle swarm optimization in a cluster of heterogeneous nodes. Since, the original algorithm requires explicit synchronization points we modified the algorithm in multiple ways to support a peer-peer system of nodes. We also modify certain aspect of the basic PSO algorithm and show how certain numerical problems can take advantage of the same thereby yielding fast convergence.

Feature weighting for clustering by particle swarm optimization

Clustering has been the most popular method for data exploration. Clustering is partitioning the data set into sub-partitions based on some measures say the distance measure, each partition has its own significant information. There are a number of algorithms explored for this purpose, one such algorithm is the Particle Swarm Optimization(PSO) which is a population based heuristic search technique derived from swarm intelligence. In this paper we present an improved version of the Particle Swarm Optimization where, each feature of the data set is given significance accordingly by adding some random weights, which also minimizes the distortions in the dataset if any. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The experimental results shows that our proposed methodology performs significantly better than the previously performed experiments.

Initiation with Elongator tRNAs

In all domains of life, initiator tRNA functions exclusively at the first step of protein synthesis while elongator tRNAs extend the polypeptide chain. Unique features of initiator tRNA enable it to preferentially bind the ribosomal P site and initiate translation. Recently, we showed that the abundance of initiator tRNA also contributes to its specialized role. This motivates the question, can a cell also use elongator tRNA to initiate translation under certain conditions? To address this, we introduced non-AUG initiation codons CCC (Pro), GAG (Glu), GGU (Gly), UCU (Ser), UGU (Cys), ACG (Thr), AAU (Asn), and AGA (Arg) into the uracil DNA glycosylase gene (ung) used as a reporter gene. Enzyme assays from log-phase cells revealed initiation from non-AUG codons when intracellular initiator tRNA levels were reduced. The activity increased significantly in stationary phase. Further increases in initiation from non-AUG codons occurred in both growth phases upon introduction of plasmid-borne genes of cognate elongator tRNAs. Since purine-rich Shine-Dalgarno sequences occur frequently on mRNAs (in places other than the canonical AUG codon initiation contexts), initiation with elongator tRNAs from the alternate contexts may generate proteome diversity under stress without compromising genomic integrity. Thus, by changing the relative amounts of initiator and elongator tRNAs within the cell, we have blurred the distinction between the two classes of tRNAs thought to be frozen through years of evolution.

A strategy-proof and budget-balanced mechanism for carbon footprint reduction by global companies

The problem addressed in this paper is concerned with an important issue faced by any green aware global company to keep its emissions within a prescribed cap. The specific problem is to allocate carbon reductions to its different divisions and supply chain partners in achieving a required target of reductions in its carbon reduction program. The problem becomes a challenging one since the divisions and supply chain partners, being autonomous, may exhibit strategic behavior. We use a standard mechanism design approach to solve this problem. While designing a mechanism for the emission reduction allocation problem, the key properties that need to be satisfied are dominant strategy incentive compatibility (DSIC) (also called strategy-proofness), strict budget balance (SBB), and allocative efficiency (AE). Mechanism design theory has shown that it is not possible to achieve the above three properties simultaneously. In the literature, a mechanism that satisfies DSIC and AE has recently been proposed in this context, keeping the budget imbalance minimal. Motivated by the observation that SBB is an important requirement, in this paper, we propose a mechanism that satisfies DSIC and SBB with slight compromise in allocative efficiency. Our experimentation with a stylized case study shows that the proposed mechanism performs satisfactorily and provides an attractive alternative mechanism for carbon footprint reduction by global companies.

Mechanism design for green, truthful procurement auctions

Auction based mechanisms have become popular in industrial procurement settings. These mechanisms minimize the cost of procurement and at the same time achieve desirable properties such as truthful bidding by the suppliers. In this paper, we investigate the design of truthful procurement auctions taking into account an additional important issue namely carbon emissions. In particular, we focus on the following procurement problem: A buyer wishes to source multiple units of a homogeneous item from several competing suppliers who offer volume discount bids and who also provide emission curves that specify the cost of emissions as a function of volume of supply. We assume that emission curves are reported truthfully since that information is easily verifiable through standard sources. First we formulate the volume discount procurement auction problem with emission constraints under the assumption that the suppliers are honest (that is they report production costs truthfully). Next we describe a mechanism design formulation for green procurement with strategic suppliers. Our numerical experimentation shows that emission constraints can significantly alter sourcing decisions and affect the procurement costs dramatically. To the best of our knowledge, this is the first effort in explicitly taking into account carbon emissions in planning procurement auctions.

Compact polarization dependent EBG surface with fractal boundary patches

Patches with variants of fractal Minkowski curves as boundaries are used here to design a polarization dependent electromagnetic bandgap surface. Reflection phases of the proposed structure depends upon the polarization state of the incident wave and frequency. The phase difference between the x-polarized and y-polarized components of the reflected wave can be as high as 200 degrees and this is achieved without excessive increase in unit cell dimensions and vias. The performance of the surface is analyzed numerically using CST microwave studio. The potential applications of the surface are in polarization conversion surfaces, polarimetric radar calibration, and RCS reduction.

Multi-port network modeling of stub loaded Microstrip ring antenna for dual-band operations

A square ring microstrip antenna can be modified for dual-band operations by appropriately attaching an open ended stub. The input impedance of this antenna is analyzed here using multi-port network modeling (MNM) approach. The coupled feed is included by defining additional terms in the model. A prototype antenna is fabricated and tested to validate these computations.

Large linear magnetoresistance in a GaAs/AlGaAs heterostructure

We report non-saturating linear magnetoresistance (MR) in a two-dimensional electron system (2DES) at a GaAs/AlGaAs heterointerface in the strongly insulating regime. We achieve this by driving the gate voltage below the pinch-off point of the device and operating it in the non-equilibrium regime with high source-drain bias. Remarkably, the magnitude of MR is as large as 500% per Tesla with respect to resistance at zero magnetic field, thus dwarfing most non-magnetic materials which exhibit this linearity. Its primary advantage over most other materials is that both linearity and the enormous magnitude are retained over a broad temperature range (0.3 K to 10 K), thus making it an attractive candidate for cryogenic sensor applications.

Electrostatic modulation of periodic potentials in a two-dimensional electron gas: from antidot lattice to quantum dot lattice

We use a dual gated device structure to introduce a gate-tuneable periodic potential in a GaAs/AlGaAs two dimensional electron gas (2DEG). Using only a suitable choice of gate voltages we can controllably alter the potential landscape of the bare 2DEG, inducing either a periodic array of antidots or quantum dots. Antidots are artificial scattering centers, and therefore allow for a study of electron dynamics. In particular, we show that the thermovoltage of an antidot lattice is particularly sensitive to the relative positions of the Fermi level and the antidot potential. A quantum dot lattice, on the other hand, provides the opportunity to study correlated electron physics. We find that its current-voltage characteristics display a voltage threshold, as well as a power law scaling, indicative of collective Coulomb blockade in a disordered background.

Topological Excitations in Semiconductor Heterostructures

Topological defects play an important role in the melting phenomena in two-dimensions. In this work, we report experimental observation of topological defect induced melting in two-dimensional electron systems (2DES) in the presence of strong Coulomb interaction and disorder. The phenomenon is characterised by measurement of conductivity which goes to zero in a Berezinskii-Kosterlitz-Thouless like transition. Further evidence is provided via low-frequency conductivity noise measurements.

Observation of localized states in atomically thin MoS2 field effect transistor

We present electrical transport arid low frequency (1/f) noise measurements on mechanically exfoliated single, In and triLayer MoS2-based FPI devices on Si/SiO2 substrate. We find that tie electronic states hi MoS2 are localized at low temperatures (T) and conduction happens through variable range hopping (VRH). A steep increase of 1/f noise with decreasing T, typical for localized regime was observed in all of our devices. From gate voltage dependence of noise, we find that the noise power is inversely proportional to square of the number density (proportional to 1/n(2)) for a wide range of T, indicating number density fluctuations to be the dominant source of 1/f noise in these MoS2 FETs.

Origin of noise in two dimensionally doped Silicon and Germanium

We present the study of low-frequency noise, or 1/f noise, in degenerately doped Si: P and Ge: P delta-layers at low temperatures. For the Si: P d-layers we find that the noise is several orders of magnitude lower than that of bulk Si: P systems in the metallic regime and is one of the lowest values reported for doped semiconductors. Ge: P d-layers as a function of perpendicular magnetic field, shows a factor of two reduction in noise magnitude at the scale of B-phi, where B-phi is phase breaking field. We show that this is a characteristic feature of universal conductance fluctuations.