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We have home brewed a perfect recipe for Arduino automated home utility monitoring and correction system. In this paper, we propose a smart system that could easily be built using off-the-shelf modules and could be programmed to fulfill utility power quality requirements using existing smart utility meters and non-intrusive appliance monitoring and correction system. We have included the hardware design, software, wireless topology system used, experiment results and implemented real hardware load modeling. Our utility monitoring and correction system can be used for detecting energy expenditure breakdown per appliance, detecting power hungry faulty appliance, solving the power quality problem at the root and recognition of occupant activity. The system also able to correct each appliances power factor and suppress its harmonics, resulting in more efficiently reducing of electricity bill of building owners.

It's obvious we love Arduino due to its simplicity but it does not lack in handling complexity. We have put together a list of ideas using Arduino as utility metering. We have included the hardware design, software and wireless topology system used. Our design is capable to preform multi-utility-metering, utility real time load profiling and a novel utility waveform capture logging. In this paper we present off-the-shelf modules that could easily build and be programmed to fulfill utility metering transformation and integration with the existing utility meters. The same hardware can be used as multi-utility-meter, load profiling or waveform capture with just a program flicker. Our utility meter can be used for detecting energy expenditure breakdown per appliance, power hungry faulty appliance and recognition of occupant activity. It also helps empower building owners of energy awareness.

We attempt here to quantify and model physiographic features off the central west coast of India in terms of power spectral exponent, amplitude parameter. We demonstrate that statistical analysis of multi-beam echo-sounder grid bathymetry data is able to characterise the outer shelf, upper slope, shelf margin basin and several structural rises in the region. A scatter diagram analysis shows that the seafloor data can be grouped into two distinct clusters. Distinctly different clustering patterns are observed over the structural rises, compared to the shelf, slope and basinal areas. This suggests different modes of formation for the members of these two clusters. In fact, the steep structural rises appear to be part of the NW–SE-trending coast-parallel Mid-Shelf Basement and Prathap ridges. These ridges are rift-induced volcanic emplacements on a stretched and thinned continental crust which probably formed during mid-Cretaceous times.

A prime concern for turfgrass manangers using bermudagrass [Cynodon dactylon (L.) Pers.] in temperate regions is its winter survival. Because bermudagrasses do not reach maximum hardiness until midwinter, they are susceptible to cold temperatures that may occur during early winter. Management strategies to reduce winter injury and hasten post-dormancy growth include the use of protective winter covers. A 3-year field study was initiated to test the effect of spun-bonded winter covers applied in early December and removed in late May on bermudagrass cold acclimation and crown moisture content, and to assess the variation in the cold acclimation of eight selected cultivars (.)

Although the effect of cold winters on the severity of bermudagrass (Cynodon dactylon Pers.) spring dead spot (SDS) has been studied, information is needed concerning the effect of infection by fungi associated with SDS on the host's freezing resistance. A-22 bermudagrass was inoculated with Leptosphaeria korrae J. Walker & A.M. Smith and Ophiospharella herpotricha (Fr.) J. Walker & A.M Smith. Differential thermal analysis was used to monitor exotherm temperatures of healthy and O. herpotricha- and L. korrae-infected A-22 bermudagrass at 10-day intervals during 90 days of acclimation in cold chambers. Healthy bermudagrass crowns supercooled to an average of -6.7C and fungi-infected crowns supercooled to an average of -4.8 and -4.4C, respectively. Healthy crown exotherm temperatures were significantly lower than those of fungi-infected bermudagrass crowns on all nine sampling dates. This result indicates that fungi-infected plants are more susceptible to cold damage.