Embedded Network Test-Bed for Validating Real-Time Control Algorithms to Ensure Optimal Time Domain Performance

The paper presents a Stateflow based network test-bed to validate real-time optimal control algorithms. Genetic Algorithm (GA) based time domain performance index minimization is attempted for tuning of PI controller to handle a balanced lag and delay type First Order Plus Time Delay (FOPTD) process over network. The tuning performance is validated on a real-time communication network with artificially simulated stochastic delay, packet loss and out-of order packets characterizing the network.Comment: 6 pages, 12 figure

A rare case report of a cyclopian malformation

A rare form of median faciocerebral dysplasia, characterized by a single central orbital fossa with a tubular nose-like appendage above the orbit is known as cyclopian malformation/monster. It is the most severe form of alobar holoprosencephaly. Since most of these cases are sporadic, incompatible with life, and due to the limited literature knowledge, the exact etiology of this condition remains undetermined. However, various risk factors implicated include genetic factors and chromosomal anomalies (mostly trisomy D). Here we present a case of stillborn male cyclopian fetus born to a 34 year old 3rd gravida by caesarean section. There was no history of any drugs or alternative medicine intake (except iron-folic acid, calcium, thyroxin), radiation exposure, or a significant family history or consanguinity. Her only 33-week scan (done at a peripheral center) failed to identify any fetal abnormality. This case is reported because cyclopia is a rare/uncommon developmental anomaly especially with the advancement in antenatal ultrasonography to identify malformed fetuses early in pregnancy

A Simple Flood Forecasting Scheme Using Wireless Sensor Networks

This paper presents a forecasting model designed using WSNs (Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.Comment: 16 pages, 4 figures, published in International Journal Of Ad-Hoc, Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.201

Identification of Nonlinear Systems From the Knowledge Around Different Operating Conditions: A Feed-Forward Multi-Layer ANN Based Approach

The paper investigates nonlinear system identification using system output data at various linearized operating points. A feed-forward multi-layer Artificial Neural Network (ANN) based approach is used for this purpose and tested for two target applications i.e. nuclear reactor power level monitoring and an AC servo position control system. Various configurations of ANN using different activation functions, number of hidden layers and neurons in each layer are trained and tested to find out the best configuration. The training is carried out multiple times to check for consistency and the mean and standard deviation of the root mean square errors (RMSE) are reported for each configuration.Comment: "6 pages, 9 figures; The Second IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC-2012), December 2012, Solan

Polymorphism in biomineral nanoparticles

Biomineralisation is the process by which living things produce hard mineral tissues with unique physical properties. The study of this process can help us produce biomimetic materials, reproducing such properties, with the study of nucleation and crystallisation of the materials being particularly important. I have used molecular simulation techniques to help gain a greater understanding of these processes, focussing particularly on identifying the conformations and solid phases available to nanoparticles of two biomineral compounds. The bones and teeth of mammals are made largely of calcium phosphates. I have used metadynamics to study nanoparticles of tricalcium phosphate (TCP) and have identified high and lower order configurations. To facilitate this work I reviewed the extant empirical potentials for calcium phosphate systems, selecting the most appropriate for TCP. Calcium carbonate, found in examples throughout the animal kingdom, has three crystalline polymorphs relevant to biomineralisation: calcite, aragonite and vaterite. While nanoparticles of calcite have been extensively studied the other polymorphs have been neglected to date. In this work I present a technique for predicting crystalline morphologies for all three polymorphs across a range of sizes, and compare the energetic ordering. In water the energetic ordering of the nanoparticles is heavily dependent on nanoparticle size. Furthermore, I present work calculating the surface enthalpies of a variety of calcium carbonate surfaces, many of which are negative. It appears that entropic penalty of ordered water is key to understanding the stability of nanocrystals. Also presented is an application of the nudged elastic band method to study transitions between nanoparticle crystal conformations. Between all three crystal polymorphs the nanoparticles passed through an amorphous region of phase space. These results have also been used to evaluate order parameters for use in metadynamics simulations.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An Optimal Station Allocation Policy for Tree Local Area Networks

This paper reports on the simulation results of a heuristic solution to the station allocation problem in a tree topology Local Area Network (LAN). A local network is a data communication network where communication remains confined within a moderate sized area, such as a plant site, an office building or a university campus. Tree LANs with collision avoidance switches and multiple broadcast facility have, recently, become popular due to their suitability for high speed light wave communications. Given a tree LAN with fanout F and given the total number of stations N to be connected, a combinatorial optimization problem arises regarding how to allocate the stations to the leaf nodes so that the total system availability (a network performance criteria) is maximized. This is known as the optimal station assignment problem. In this paper, it is formulated as a non-linear optimization problem which can be solved by the Lagrangean relaxation and the subgradient optimization techniques. A simple heuristic is developed based on these techniques. The simulation studies show that the proposed heuristic is relatively fast operating only in a subspace of the complete solution space