Homography-Based Passive Vehicle Speed Measuring

An apparatus for passively measuring vehicle speed includes at least one video camera or acquiring images of a roadway upon which at least one moving vehicle travels upon, each of the images comprising a plurality of pixels. A computer processes pixel data associated with the plurality of pixels, including using a adaptive background subtraction model to perform background subtraction on the pixel data to identify a plurality of foreground pixels, extracting a plurality of blobs from the foreground pixels, and rectifying the blobs to form a plurality of rectified blobs using a homography matrix. The homography matrix is obtained by comparing at least one known distance in the roadway with distances between the pixels. Using a planar homography transform, the moving vehicle is identified from the plurality of rectified blobs, wherein the respective ones of the plurality of rectified blobs include vehicle data associated with the moving vehicle.The speed of the moving vehicle is computed from the vehicle data

Non-intrusive load monitoring under residential solar power influx

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method for a consumer premises with a residentially installed solar plant. This method simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels. Further, it works effectively with a single active power measurement taken at the total power entry point with a sampling rate of 1 Hz. First, a unique set of appliance and solar signatures were constructed using a high-resolution implementation of Karhunen Loéve expansion (KLE). Then, different operating modes of multi-state appliances were automatically classified utilizing a spectral clustering based method. Finally, using the total power demand profile, through a subspace component power level matching algorithm, the turned ON appliances along with their operating modes and power levels as well as the solar influx amount were found at each time point. The proposed NILM method was first successfully validated on six synthetically generated houses (with solar units) using real household data taken from the Reference Energy Disaggregation Dataset (REDD) - USA. Then, in order to demonstrate the scalability of the proposed NILM method, it was employed on a set of 400 individual households. From that, reliable estimations were obtained for the total residential solar generation and for the total load that can be shed to provide reserve services. Finally, through a developed prediction technique, NILM results observed from 400 households during four days in the recent past were utilized to predict the next day’s total load that can be shed

MedZIM: Mediation analysis for Zero-Inflated Mediators with applications to microbiome data

The human microbiome can contribute to the pathogenesis of many complex diseases such as cancer and Alzheimer's disease by mediating disease-leading causal pathways. However, standard mediation analysis is not adequate in the context of microbiome data due to the excessive number of zero values in the data. Zero-valued sequencing reads, commonly observed in microbiome studies, arise for technical and/or biological reasons. Mediation analysis approaches for analyzing zero-inflated mediators are still lacking largely because of challenges raised by the zero-inflated data structure: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are actually not zero (i.e., false zeros). We develop a novel mediation analysis method under the potential-outcomes framework to fill this gap. We show that the mediation effect of the microbiome can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. The first component corresponds to the mediation effect attributable to a unit-change over the positive relative abundance and the second component corresponds to the mediation effect attributable to discrete binary change of the mediator from zero to a non-zero state. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the applications in two real microbiome studies demonstrate that our approach outperforms existing mediation analysis approaches.Comment: Corresponding: Zhigang L

A Versatile and Efficient Novel Approach for Mendelian Randomization Analysis with Application to Assess the Causal Effect of Fetal Hemoglobin on Anemia in Sickle Cell Anemia

Mendelian randomization (MR) is increasingly employed as a technique to assess the causation of a risk factor on an outcome using observational data. The two-stage least-squares (2SLS) procedure is commonly used to examine the causation using genetic variants as the instrument variables. The validity of 2SLS relies on a representative sample randomly selected from a study cohort or a population for genome-wide association study (GWAS), which is not always true in practice. For example, the extreme phenotype sequencing (EPS) design is widely used to investigate genetic determinants of an outcome in GWAS as it bears many advantages such as efficiency, low sequencing or genotyping cost, and large power in detecting the involvement of rare genetic variants in disease etiology. In this paper, we develop a novel, versatile, and efficient approach, namely MR analysis under Extreme or random Phenotype Sampling (MREPS), for one-sample MR analysis based on samples drawn through either the random sampling design or the nonrandom EPS design. In simulations, MREPS provides unbiased estimates for causal effects, correct type I errors for causal effect testing. Furthermore, it is robust under different study designs and has high power. These results demonstrate the superiority of MREPS over the widely used standard 2SLS approach. We applied MREPS to assess and highlight the causal effect of total fetal hemoglobin on anemia risk in patients with sickle cell anemia using two independent cohort studies. A user-friendly Shiny app web interface was implemented for professionals to easily explore the MREPS

Threshold Selection for High Dimensional Covariance Estimation

Thresholding is a regularization method commonly used for covariance estimation (Bickel and Levina, 2008, Cai and Liu, 2011), which provides consistent estimators in high-dimensional settings if the population covariance satisfies certain sparsity conditions. However, the performance of those estimators heavily depends on the threshold level. By minimizing the Frobenius risk of the adaptive thresholding covariance estimator, we conduct a theoretical study for the optimal threshold level, and obtain its analytical expression under a general setting of n and p. A consistent estimator based on this expression is proposed for the optimal threshold level, which is easy to implement in practice and efficient in computation. Numerical simulations and a case study on gene expression data are conducted to illustrate the proposed method. Based on the concepts developed in the theoretical study, another two efficient numerical methods are proposed for estimating the threshold level. These methods are more flexible and precise. As a result, they provide more precise and stable threshold levels by correctly adjusting to the true covariance structure, which enhances applicability in practice. Additional numerical simulations and a case study on different gene expression data are conducted to compare all proposed methods

The elephant at the dump: how does garbage consumption impact Asian elephants?

We studied garbage consumption by Asian elephants at the Uddakandara garbage dump in southern Sri Lanka. Garbage at the dump was classified under six categories and quantified using a grid overlay. Elephants visiting the dump were individually identified by morphological criteria and items and quantities consumed by them were determined by focal animal sampling. Dung of elephants that did not consume garbage and those from the dump were compared quantitatively and dung constituents assessed by washing through three layered sieves. A total of 17 individual elephants visited the garbage dump during the study period, all of who were males. The observed sexual bias could be related to behavioural differences between the sexes. Elephants mostly consumed ‘fruits and vegetables’ and ‘prepared food’, possibly due to their higher palatability and nutritional value. Ingestion of polythene was incidental and associated with consuming prepared food. Proportions of the six categories in elephant diet and garbage piles were significantly different, indicating that elephants were highly selective when feeding. Elephant arrivals increased in response to unloading of garbage, suggesting attraction to fresh garbage. Dung analysis found that garbage consumption did not change the quantity and constituents of dung, except for the presence of anthropogenic items. As consumed anthropogenic items were regularly excreted, retention and obstruction of the alimentary tract are unlikely in elephants. Elephants feeding on garbage had better body condition than non-garbage consuming elephants, indicating that garbage provided better nutrition than natural food and was not detrimental to their health

Utilization of a blockchainized reputation management service for performance enhancement of Smart Grid 2.0 applications

Blockchain has become the technology enabler in delivering modern Smart Grid 2.0 functionalities. Many services including Peer-to-Peer energy trading, distribution network management, financial settlements, and energy data management are catered through blockchain-enabled platforms. However, areas such as service quality-based pricing strategies, supply–demand balancing in distribution system to attain enhanced reliability and consumption-oriented rewarding mechanisms need improving in order to achieve the full benefits of the envisaged grid architecture. In response, this study proposes a novel Blockchain-as-a-Service for Energy Trading (BaaSET) platform, which offers reputation-based services, executed through smart contracts for smart grid applications. Reputation-based grid operations are automatically executed through smart contracts deployed onto a blockchain. The reputation is estimated using power quality and reliability indices, obtained through grid measurements. Further, tests have been conducted to evaluate the associated latency and the implementation cost of the proposed blockchainized service architecture. Test results signify the performance to be comparatively better considering the state-of-the-art. The results further suggest alternatives to improve the scalability of the architecture, to cater the increasing number of stakeholders in the SG 2.0 environment

Accelerating Matlab Image Processing Toolbox Functions On Gpus

In this paper, we present our effort in developing an open-source GPU (graphics processing units) code library for the MATLAB Image Processing Toolbox (IPT). We ported a dozen of representative functions from IPT and based on their inherent characteristics, we grouped these functions into four categories: data independent, data sharing, algorithm dependent and data dependent. For each category, we present a detailed case study, which reveals interesting insights on how to efficiently optimize the code for GPUs and highlight performance-critical hardware features, some of which have not been well explored in existing literature. Our results show drastic speedups for the functions in the data-independent or data-sharing category by leveraging hardware support judiciously; and moderate speedups for those in the algorithm-dependent category by careful algorithm selection and parallelization. For the functions in the last category, fine-grain synchronization and data-dependency requirements are the main obstacles to an efficient implementation on GPUs. Copyright© 2010 ACM

Survey on blockchain for future smart grids:technical aspects, applications, integration challenges and future research

Abstract Smart Grid 2.0 is envisaged to automate the operations of the intelligent electricity grid. Blockchain and smart contracts are integrated to facilitate the transformation from DSO-centric operations to consumer-oriented, distributed electricity grid management. The envisaged smart grids, integrated with blockchain would provoke challenges, which would hinder the maximum utilization of Distribute Energy Resources (DERs). This comprehensive review aims at analyzing the applicability of blockchain technology in Smart Grid 2.0, which would facilitate a seamless decentralization process. Further, the paper elaborates the blockchain-based applications of future smart grid operations and the role of blockchain in each scenario. The paper further provides a concise analysis on the blockchain integration challenges, thereby ensure secure and scalable, decentralized operations of future, autonomous electricity networks

Real-time non-intrusive appliance load monitoring under supply voltage fluctuations

This paper presents a complete real-time implementation of a Non-Intrusive Appliance Load Monitoring (NIALM) system that, is robust under residential voltage level fluctuations. Existing NIALM techniques rely on multiple measurements taken at high sampling rates, but, only have been proven in simulated environments without even considering the effect of residential voltage level fluctuations - which is a severe problem in power systems of most developing countries like Sri Lanka. In contrast, through the NIALM method proposed in this paper, accurate load monitoring results were obtained in realtime using only smart meter measurements taken at a low sampling rate from a real appliance setup under residential voltage level fluctuations. In the proposed NIALM method, initially in the learning phase, a properly constructed MATLABTM Graphical User Interface (GUI) was used to acquire signals of each appliance active power consumption and voltage levels. Then, obtained active power measurements were separated into subspace components (SCs) via the Karhunen Loeve' Expansion (KLE) while also taking the voltage variations into account. Using those SCs, a unique information rich appliance level signature database was constructed and it was then used to obtain the signatures for all possible device combinations. Next, a separate GUI was designed to identify the turned ON appliance combination in the current time window using the pre-constructed signature databases, after reading the total residential active power consumption and the supply voltage. To validate the proposed real-time NIALM implementation, data from a laboratory arrangement consisting of ten household appliances was used. From the results, it was found that the proposed method is capable of accurately identifying the turned on appliances even under severe residential supply voltage level fluctuations