Formation of a geometric pattern with a mobile wireless sensor network

Mobile wireless sensor networks (MWSNs) will enable information systems to gather detailed information about the environment on an unprecedented scale. These self‐organizing, distributed networks of sensors, processors, and actuators that are capable of movement have a broad range of potential applications, including military reconnaissance, surveillance, planetary exploration, and geophysical mapping. In many of the foreseen applications, the MWSN will need to form a geometric pattern without assistance from the user. In military reconnaissance, for example, the nodes will be dropped onto the battlefield from a plane and land at random positions. The nodes will be expected to arrange themselves into a predetermined formation in order to perform a specific task. Thus, we present algorithms for forming a line, circle, and regular polygon from a given set of random positions. The algorithms are distributed and use no communication between the nodes to minimize energy consumption. Unlike past studies of geometric problems where algorithms are either tested in simulations where each node has global knowledge of all the other nodes or implemented on a small number of robots, the robustness of our algorithms has been studied with simulations that model the sensor system in detail. The simulations demonstrate that the algorithms are robust against random errors in the sensors and actuators. © 2004 Wiley Periodicals, Inc.     

Solubilization of Glibenclamide with β-Cyclodextrin & its Derivatives

Gilbenclamide, a widely used potent hypoglycaemic agent was solubllized using β -Cyclodextrin and β -Cyclodextrin derivatives. Complexes were prepared by kneading method in a molar ratio of 1:1 of the drug and the cyclodextrlns respectively. The Glibenclamide β -Cyelocextrin complex was characterized and evaluated by I.R. studies, Differential Scanning Calorimotry 6 X-ray diffractometry. The in-vitro dissolution rates of drug from inclusion complexes of β Cyclodextrins and its derivatives were compared. A significant Improvement In dissolution lor, rates of Gllbenclamide was observed with Inclusion complexes of all the Cyclodextrins. However, the solubilizing effect was more in case of β-Cyclodextrin derivatives.     

Unit commitment ? a fuzzy mixed integer Linear Programming solution

Unit commitment (UC) of a large system is a complex puzzle with integer/continuous variables and numerous inter-temporal constraints. After deregulation, price offers submitted by GenCos are predominantly in the form of linear price quantity (PQ) pairs. A fuzzy UC formulation that uses price offers modeled as PQ pairs. This fuzzy linear optimisation formulation of UC is solved using a mixed integer linear programming (MILP) routine. In this formulation, start up cost is modelled using linear variables. The fuzzy formulation provides modeling flexibility, relaxation in constraint enforcement and allows the method to seek a practical solution. The use of MILP technique makes the proposed solution method rigorous and fast. The method is tested on a 24 h, 104-generator system demonstrating its speed and robustness gained by using the LP technique. A five-generator system is additionally used to create a see-through example demonstrating advantages of using the fuzzy optimisation model.     

Heat and Fluid Flow in an Optical Switch Bubble

The Agilent all-optical bubble switch uses bubbles in an organic fluid index matched to a silica planar lightwave circuit. The bubble is created and sustained by heaters that are deposited on an attached silicon substrate. Testing of the bubble shows how heater power and ambient pressure affect bubble shape, size, and optical reflection characteristics. Heat and fluid flow in the bubble were modeled in 2D and 3D using the homogeneous bubble model in the Flow3D modeling software. Fluid condensing on the trench wall causes a dimple on the bubble and hence nonoptimum optical reflection. To aid understanding, the bubble, silica walls, and heaters were also modeled as a thermal resistance network. Because the pressure drop across the bubble wall is fixed, the bubble size is determined by P_res/DeltaT_t , where P_res is the heater power and DeltaT_t is the temperature difference between the bubble and the substrate. Heating the trench walls beyond the bubble temperature with heaters located underneath the trench wall will dry out the trench wall and give a stable optical reflection. As DeltaT_t approaches zero, a bubble is sustained without any heater power and with zero fluid flow. This "static" bubble provides for a very stable optical reflection     

IEEE-PES Transmission and Distribution Conference and Exposition Call for Papers

Static network-related system voltage stability margin (VSM) depends on the availability of reactive power to support the transport of real power from sources to sinks. Based on this premise, the total VAr loss is minimized in the unified OPF framework considering real and reactive power controllers, and its effect on VSM is studied. Studies are conducted on a three-bus system, the IEEE 30-bus system, and a 191-bus Indian electric power system, and their results are reported.     

Triple Metal Surrounding Gate Junctionless Tunnel FET Based 6T SRAM Design for Low Leakage Memory System

Silicon - The promising capability of Triple Material Surrounding Gate Junctionless Tunnel FET (TMSG – JL – TFET) based 6 T SRAM structure is demonstrated by employing...     

Automatic classification of computed tomography brain images using ANN,k-NN and SVM

Computed tomography images are widely used in the diagnosis of intracranial hematoma and hemorrhage. This paper presents a new approach for automated diagnosis based on classification of the normal and abnormal images of computed tomography. The computed tomography images used in the classification consists of non-enhanced computed tomography images. The proposed method consists of four stages namely pre-processing, feature extraction, feature reduction and classification. The discrete wavelet transform coefficients are the features extracted in this method. The essential coefficients are selected by the principal component analysis. The features derived are used to train the binary classifier, which infer automatically whether the image is that of a normal brain or a pathological brain, suffering from brain lesion. The proposed method has been evaluated on a dataset of 80 images. A classification with a success of 92, 97 and 98 % has been obtained by artificial neural network, k-nearest neighbor and support vector machine, respectively. This result shows that the proposed technique is robust and effective.     

Mood sensing from social media texts and its applications

We present a large-scale mood analysis in social media texts. We organise the paper in three parts: (1) addressing the problem of feature selection and classification of mood in blogosphere, (2) we extract global mood patterns at different level of aggregation from a large-scale data set of approximately 18 millions documents (3) and finally, we extract mood trajectory for an egocentric user and study how it can be used to detect subtle emotion signals in a user-centric manner, supporting discovery of hyper-groups of communities based on sentiment information. For mood classification, two feature sets proposed in psychology are used, showing that these features are efficient, do not require a training phase and yield classification results comparable to state of the art, supervised feature selection schemes; on mood patterns, empirical results for mood organisation in the blogosphere are provided, analogous to the structure of human emotion proposed independently in the psychology literature; and on community structure discovery, sentiment-based approach can yield useful insights into community formation.