Smart Meter Privacy: A Utility-Privacy Framework

End-user privacy in smart meter measurements is a well-known challenge in the smart grid. The solutions offered thus far have been tied to specific technologies such as batteries or assumptions on data usage. Existing solutions have also not quantified the loss of benefit (utility) that results from any such privacy-preserving approach. Using tools from information theory, a new framework is presented that abstracts both the privacy and the utility requirements of smart meter data. This leads to a novel privacy-utility tradeoff problem with minimal assumptions that is tractable. Specifically for a stationary Gaussian Markov model of the electricity load, it is shown that the optimal utility-and-privacy preserving solution requires filtering out frequency components that are low in power, and this approach appears to encompass most of the proposed privacy approaches.Comment: Accepted for publication and presentation at the IEEE SmartGridComm. 201

Seminar on Potential Marine Fishery Resources - Proceedings and Recommendations

The seminar was organized by the Central Marine Fisheries Research Institute at Cochin on 23rd April 1986, marking the occasion of the Institute's moving into its own permanent building. The objective of the seminar was to find ways and means of bridging the gap between the estimated potential marine fishery resources of the country and the present level of actual yield from the exploited stocks. Considering the increasing demand for marine fish and the potential for export of marine products, critical information on the presently exploited stocks and those identified as under-exploited or unexploited resources is vitally essential for suggesting management and other measures to obtain optimum yields from these resources

Local polynomial method for ensemble forecast of time series

We present a nonparametric approach based on local polynomial regression for ensemble forecast of time series. The state space is first reconstructed by embedding the univariate time series of the response variable in a space of dimension (<i>D</i>) with a delay time (&tau;). To obtain a forecast from a given time point <i>t</i>, three steps are involved: (i) the current state of the system is mapped on to the state space, known as the feature vector, (ii) a small number (<i>K</i>=&alpha;*<i>n</i>, &alpha;=fraction (0,1] of the data, <i>n</i>=data length) of neighbors (and their future evolution) to the feature vector are identified in the state space, and (iii) a polynomial of order <i>p</i> is fitted to the identified neighbors, which is then used for prediction. A suite of parameter combinations (<i>D</i>, &tau;, &alpha;, <i>p</i>) is selected based on an objective criterion, called the Generalized Cross Validation (GCV). All of the selected parameter combinations are then used to issue a T-step iterated forecast starting from the current time <i>t</i>, thus generating an ensemble forecast which can be used to obtain the forecast probability density function (PDF). The ensemble approach improves upon the traditional method of providing a single mean forecast by providing the forecast uncertainty. Further, for short noisy data it can provide better forecasts. We demonstrate the utility of this approach on two synthetic (Henon and Lorenz attractors) and two real data sets (Great Salt Lake bi-weekly volume and NINO3 index). This framework can also be used to forecast a vector of response variables based on a vector of predictors

Utility of B-type natriuretic peptide in predicting medium-term mortality in patients undergoing major non-cardiac surgery

We assessed the ability of pre-operative B-type natriuretic peptide (BNP) levels to predict medium-term mortality in patients undergoing major noncardiac surgery. During a median 654 days follow-up 33 patients from a total cohort of 204 patients (16%) died. The optimal cut-off in this cohort, determined using a receiver operating characteristic curve, was >35pg.mL-1. This was associated with a 3.47-fold increase in the hazard of death (p=0.001) and had a sensitivity of 70% and a specificity of 68% for this outcome. These findings extend recent work demonstrating that BNP levels obtained before major noncardiac surgery can be used to predict peri-operative morbidity, and indicate that they also forecast medium-term mortality.This work was supported by a grant from TENOVUS Scotland. The Health Services Research Unit is core-funded by the Chief Scientists Office of the Scottish Executive Health Department.Peer reviewedAuthor versio

Strandings of whales along Gulf of Mannar and Palk Bay

Whale strandings occurred frequently along the southeast coasts of India along Gulf of Mannar and Palk bay during Dec 2005 and August 2006

Underwater acoustic instrumentation for investigating deep scattering layer

The instruments used for the DSL studies were scientific echo-sounder for detection, Isacc's Kid Midwater trawl for sampling mesopelagics and the trawl sonde to lower the IKMT to the exact depth at which the DSL appeared. Opening of the IKMT and the temperature of the seawater at which the DSL appeared was also recorded on the trawl sonde system. The echoscope connected to the echosounder showed the DSL in different colours depending upon its density. The continuous monitoring on echogram revealed that the DSL observed at surface during night and at 600 m depth during day