Delay Optimal Event Detection on Ad Hoc Wireless Sensor Networks

We consider a small extent sensor network for event detection, in which nodes take samples periodically and then contend over a {\em random access network} to transmit their measurement packets to the fusion center. We consider two procedures at the fusion center to process the measurements. The Bayesian setting is assumed; i.e., the fusion center has a prior distribution on the change time. In the first procedure, the decision algorithm at the fusion center is \emph{network-oblivious} and makes a decision only when a complete vector of measurements taken at a sampling instant is available. In the second procedure, the decision algorithm at the fusion center is \emph{network-aware} and processes measurements as they arrive, but in a time causal order. In this case, the decision statistic depends on the network delays as well, whereas in the network-oblivious case, the decision statistic does not depend on the network delays. This yields a Bayesian change detection problem with a tradeoff between the random network delay and the decision delay; a higher sampling rate reduces the decision delay but increases the random access delay. Under periodic sampling, in the network--oblivious case, the structure of the optimal stopping rule is the same as that without the network, and the optimal change detection delay decouples into the network delay and the optimal decision delay without the network. In the network--aware case, the optimal stopping problem is analysed as a partially observable Markov decision process, in which the states of the queues and delays in the network need to be maintained. A sufficient statistic for decision is found to be the network-state and the posterior probability of change having occurred given the measurements received and the state of the network. The optimal regimes are studied using simulation.Comment: To appear in ACM Transactions on Sensor Networks. A part of this work was presented in IEEE SECON 2006, and Allerton 201

A study on lipoprotein(a) in health and type-2 diabetes mellitus

Background: Increased lipoprotein (a) [Lp (a)] concentrations are predictive of coronary artery disease (CAD). Type 2 diabetes mellitus also leads to dyslipidemia, which are known risk factors for CAD. This study was designed to investigate the levels of Lp (a) in type 2 diabetic patients and their association with healthy controls and glycemic control.Methods: The study included 87 subjects out of which 20 were healthy volunteers. The remaining 67 were patients with type 2 diabetes from which 3 groups were formed 23 formed newly diagnosed group while those on treatment for diabetes were 44 out of which 22 were type 2 diabetics on oral hypoglycemic agents and the other 22 were type 2 diabetics on insulin. Individuals suffering from HT, renal disease, liver disease, thyroid dysfunction, nephrotic syndrome & cardiac disease, alcoholics, smokers or on lipid lowering drugs were excluded. Statistical analysis was done using the pearsons correlation.Results: Lp(a) levels were found to be significantly increased in the diabetic group irrespective of whether newly diagnosed not on treatment or old cases on treatment with oral hypoglycemic agents or insulin. Lp(a) levels showed no correlation to the degree of glycemic control in these patients. Lp(a) positively correlates with total cholesterol, LDLc and negatively with TGL and VLDLc in diabetics while it does not correlate with any of the lipid parameters in controlsConclusions: The results of the present study suggest that Lp(a) levels are increased in type 2 diabetic patients. The elevated Lp(a) levels do not reflect the glycemic status and correlates with increase in total cholesterol and LDLc suggesting similar metabolic pathways and the genetic connection for LDL and Lp(a)

Some results on a graph associated with a non-quasi-local atomic domain

Let R be an atomic domain which admits at least two maximal ideals. Let Irr(R) denote the set of all irreducible elements of R and let A(R) = {Rπ | π ∈ Irr(R)}. Let I(R) denote the subset of A(R) consisting of all Rπ ∈ A(R) such that π does not belong to the Jacobson radical of R. With R, we associate an undirected graph denoted by G(R) whose vertex set is I(R) and distinct vertices Rπ1 and Rπ2 are adjacent if and only if Rπ1 ∩ Rπ2 = Rπ1π2. The aim of this article is to discuss some results on the connectedness of G(R) and on the girth of G(R)

Transport properties of polydisperse hard sphere fluid: Effect of distribution shape and mass scaling

A model polydisperse fluid represents many real fluids such as colloidal suspensions and polymer solutions. In this study, considering a concentrated size-polydisperse hard sphere fluid with size derived from two different distribution functions, namely, uniform and Gaussian and explore the effect of polydispersity and mass scaling on the transport properties in general. A simple analytical solution based on the Boltzmann transport equation is also presented (together with the solution using Chapman-Enskog (CE) method) using which various transport coefficients are obtained. The central idea of our approach is the realization that, in polydisperse system, the collision scattering cross section is proportional to a random variable \textit{z} which is equal to the sum of two random variables $\sigma_i$ and $\sigma_j$ (representing particle diameters), and the distribution of \textit{z} can be written as the convolution of the two distributions $P(\sigma_i)$ and $P(\sigma_j)$. The obtained transport coefficients are expressed as explicit function of polydispersity index, $\delta$, and their dependence on the nature of particle size distribution is explored. It is observed that in the low polydispersity limit, the transport coefficients are found to be insensitive to the type of size distribution functions considered. The analytical results (for diffusion coefficients and thermal conductivity) obtained using Chapman-Enskog method and our simple analytical approach agrees well with the simulation. However, for shear viscosity, our analyical approach agress for $\delta \le 20\%$, while it agrees upto $\delta \approx 40\%$ with the result obtained using CE-method (in the limit $\delta \rightarrow 0$). Interestingly, the effect of scaling mass (i.e., mass proportional to the particle size and thus a random variable) produces no significant qualitative difference.Comment: 10 pages, 7 figure

Adjunctive quetiapine for serotonin reuptake inhibitor-resistant obsessive-compulsive disorder: A meta-analysis of randomised controlled treatment trials

Small studies have shown positive effects from adding a variety of antipsychotic agents in patients with obsessive–compulsive disorder who are unresponsive to treatment with serotonin reuptake inhibitors. The evidence, however, is contradictory. This paper reports a meta-analysis of existing double-blind randomized placebo-controlled studies looking at the addition of the second-generation antipsychotic quetiapine in such cases. Three studies fulfilled the inclusion criteria. Altogether 102 individuals were subjected to analysis using Review Manager (4.2.7). The results showed evidence of efficacy for adjunctive quetiapine (< 400 mg/day) on the primary efficacy criterion, measured as changes from baseline in total Yale–Brown Obsessive Compulsive Scale scores (P = 0.008), the clinical significance of which was limited by between-study heterogeneity. The mechanism underlying the effect may involve serotonin and/or dopamine neurotransmission

Synthesis and Spectroscopic, Thermal and Crystal Structure Studies of Hydrazinium Hydrogensuccinate

The mono-hydrazinium salt of succinic acid (hydrazinium hydrogensuccinate salt) was prepared by reacting hydrazine hydrate and succinic acid. Its spectral, thermal and structural properties have been determined. The salt, N2H5+.C4H5O4–, is a molecular salt containing discrete hydrazinium cations and hydrogen  succinate anions. The crystal packing consists of infinite chains of anions and cations that are alternatively linked throughO–H···N and bifurcatedN–H···O bonds and this chain is connected to adjacent antiparallel chains by N–H···O hydrogen bonding. The adjacent chains are cross linked by N–H···O bonds from the donor N2H5+ ions, thus forming a three-dimensional network. The salt undergoes melting followed by decomposition to give gaseous products.KEYWORDS: Hydrazine, succinic acid, hydrazinium hydrogensuccinate, crystal structure, thermal studies

An End-to-End Automated License Plate Recognition System Using YOLO Based Vehicle and License Plate Detection with Vehicle Classification

An accurate and robust Automatic License Plate Recognition (ALPR) method proves surprising versatility in an Intelligent Transportation and Surveillance (ITS) system. However, most of the existing approaches often use prior knowledge or fixed pre-and-post processing rules and are thus limited by poor generalization in complex real-life conditions. In this paper, we leverage a YOLO-based end-to-end generic ALPR pipeline for vehicle detection (VD), license plate (LP) detection and recognition without exploiting prior knowledge or additional steps in inference. We assess the whole ALPR pipeline, starting from vehicle detection to the LP recognition stage, including a vehicle classifier for emergency vehicles and heavy trucks. We used YOLO v2 in the initial stage of the pipeline and remaining stages are based on the state-of-the-art YOLO v4 detector with various data augmentation and generation techniques to obtain LP recognition accuracy on par with current proposed methods. To evaluate our approach, we used five public datasets from different regions, and we achieved an average recognition accuracy of 90.3% while maintaining an acceptable frames per second (FPS) on a low-end GPU