Accurate Modelling of IoT Data Traffic Based on Weighted Sum of Distributions

This work proposes a novel mathematical approach to accurately model data traffic for the Internet of Things (IoT). Most of the conventional results on statistical data traffic models for IoT are based on the underlying assumption that the data generation follows standard Poisson or Exponential distribution which lacks experimental validation. However, in some of the use case applications a single statistical distribution is not adequate to provide the best fit for the inter-arrival time of the data packets generation. Based on the real data collected for over 10 weeks using our customized experimental IoT prototype for smart home application, in this paper we have established this very fact, citing barometric air pressure as an example. The statistical distribution of the inter-arrival time between the data packets for a specified barometric pressure fluctuation threshold is initially determined by approximating the best-fit with a set of standard classical distributions. The goodness-of-fit with the empirical data is numerically quantified using Kolmogorov-Smirnov (KS) Test. Furthermore, it is observed that any single standard distribution is unable to provide a good fit which is at least less than 10%. Therefore, a novel weighted distribution scheme is proposed that could provide an acceptable fit. The weighing factor including the location, scaling and weighing parameters of the best fitting distribution are estimated and analyzed. The distribution parameters are finally expressed as a function of the differential pressure value that can be used for different theoretical analysis and network optimization. © 2019 IEEE

Detectable abundance of cyanoacetylene (hc3n) predicted on reduced nitrogen-rich super-earth atmospheres

Abstract: We predict that cyanoacetylene (HC3N) is produced photochemically in the atmosphere of GJ 1132 b in abundances detectable by the James Webb Space Telescope (JWST), assuming that the atmosphere is hydrogen dominated and rich in molecular nitrogen (N2), methane (CH4), and hydrogen cyanide (HCN), as described by Swain et al. First, we construct line lists and cross sections for HC3N. Then we apply these cross sections and the model atmosphere of Swain et al. to a radiative transfer model in order to simulate the transmission spectrum of GJ 1132 b as it would be seen by JWST, accounting for the uncertainty in the retrieved abundances. We predict that cyanoacetylene features at various wavelengths, with a clear lone feature at 4.5 μm, observable by JWST after one transit. This feature persists within the 1σ uncertainty of the retrieved abundances of HCN and CH4. The signal is detectable for stratospheric temperatures ≲600 K and moderate stratospheric mixing (106 cm2 s−1 ≲ K zz ≲ 108 cm2 s−1). Our results also indicate that HC3N is an important source of opacity that future retrieval models should consider

Packet Size Optimization for Topology Aware Cognitive Radio Sensor Networks

In this paper, we propose a framework to optimize the packet length and modulation level to determine the optimal packet size (OPS) for topology aware cognitive radio sensor networks (CRSNs) using a variable rate modulation scheme. A generalized network topology with specific node density of the Primary Users (PUs) is accounted to estimate the OPS. Based on stochastic geometry and non-linear optimization techniques, a joint multivariate optimization problem is formulated to determine the OPS for the topology dependent CRSNs

Laminar-turbulent transition in Raman fiber lasers:a first passage statistics based analysis

Loss of coherence with increasing excitation amplitudes and spatial size modulation is a fundamental problem in designing Raman fiber lasers. While it is known that ramping up laser pump power increases the amplitude of stochastic excitations, such higher energy inputs can also lead to a transition from a linearly stable coherent laminar regime to a non-desirable disordered turbulent state. This report presents a new statistical methodology, based on first passage statistics, that classifies lasing regimes in Raman fiber lasers, thereby leading to a fast and highly accurate identification of a strong instability leading to a laminar-turbulent phase transition through a self-consistently defined order parameter. The results have been consistent across a wide range of pump power values, heralding a breakthrough in the non-invasive analysis of fiber laser dynamics

The Wasteland of Random Supergravities

We show that in a general \cal{N} = 1 supergravity with N \gg 1 scalar fields, an exponentially small fraction of the de Sitter critical points are metastable vacua. Taking the superpotential and Kahler potential to be random functions, we construct a random matrix model for the Hessian matrix, which is well-approximated by the sum of a Wigner matrix and two Wishart matrices. We compute the eigenvalue spectrum analytically from the free convolution of the constituent spectra and find that in typical configurations, a significant fraction of the eigenvalues are negative. Building on the Tracy-Widom law governing fluctuations of extreme eigenvalues, we determine the probability P of a large fluctuation in which all the eigenvalues become positive. Strong eigenvalue repulsion makes this extremely unlikely: we find P \propto exp(-c N^p), with c, p being constants. For generic critical points we find p \approx 1.5, while for approximately-supersymmetric critical points, p \approx 1.3. Our results have significant implications for the counting of de Sitter vacua in string theory, but the number of vacua remains vast.Comment: 39 pages, 9 figures; v2: fixed typos, added refs and clarification

Approaching criticality via the zero dissipation limit in the abelian avalanche model

The discrete height abelian sandpile model was introduced by Bak, Tang & Wiesenfeld and Dhar as an example for the concept of self-organized criticality. When the model is modified to allow grains to disappear on each toppling, it is called bulk-dissipative. We provide a detailed study of a continuous height version of the abelian sandpile model, called the abelian avalanche model, which allows an arbitrarily small amount of dissipation to take place on every toppling. We prove that for non-zero dissipation, the infinite volume limit of the stationary measure of the abelian avalanche model exists and can be obtained via a weighted spanning tree measure. We show that in the whole non-zero dissipation regime, the model is not critical, i.e., spatial covariances of local observables decay exponentially. We then study the zero dissipation limit and prove that the self-organized critical model is recovered, both for the stationary measure and for the dynamics. We obtain rigorous bounds on toppling probabilities and introduce an exponent describing their scaling at criticality. We rigorously establish the mean-field value of this exponent for $d > 4$.Comment: 46 pages, substantially revised 4th version, title has been changed. The main new material is Section 6 on toppling probabilities and the toppling probability exponen

Discussion on a possible neutrino detector located in India

We have identified some important and worthwhile physics opportunitites with a possible neutrino detector located in India. Particular emphasis is placed on the geographical advantage with a stress on the complimentary aspects with respect to other neutrino detectors already in operation.Comment: 9 pages; arXiv copy of published proceedings contributio

The diagnosis of colorectal cancer in patients with symptoms: finding a needle in a haystack

Patients often see primary care physicians with symptoms that might signal colorectal cancer but are also common in adults without cancer. Physicians and patients must then make a difficult decision about whether and how aggressively to evaluate the symptom. Favoring referral is that missed diagnoses lead to unnecessary testing, prolonged uncertainty, and continuing symptoms; also, the physician will suffer chagrin. It is not clear that diagnostic delay leads to progression to a more advanced stage. Against referral is that proper evaluation includes colonoscopy, with attendant inconvenience, discomfort, cost, and risk. The article by Hamilton et al, published this month in BMC Medicine, provides strong estimates of the predictive value of the various symptoms and signs of colorectal cancer and show how much higher predictive values are with increasing age and male sex. Unfortunately, their results also make clear that most colorectal cancers present with symptoms with low predictive values, < 1.2%. Models that include a set of predictive variables, that is, risk factors, age, sex, screening history, and symptoms, have been developed to guide primary prevention and clinical decision-making and are more powerful than individual symptoms and signs alone. Although screening for colorectal cancer is increasing in many countries, cancers will still be found outside screening programs so primary care physicians will remain at the front line in the difficult task of distinguishing everyday symptoms from life-threatening cancer