A Centralized SDN Architecture for the 5G Cellular Network

In order to meet the increasing demands of high data rate and low latency cellular broadband applications, plans are underway to roll out the Fifth Generation (5G) cellular wireless system by the year 2020. This paper proposes a novel method for adapting the Third Generation Partnership Project (3GPP)'s 5G architecture to the principles of Software Defined Networking (SDN). We propose to have centralized network functions in the 5G network core to control the network, end-to-end. This is achieved by relocating the control functionality present in the 5G Radio Access Network (RAN) to the network core, resulting in the conversion of the base station known as the gNB into a pure data plane node. This brings about a significant reduction in signaling costs between the RAN and the core network. It also results in improved system performance. The merits of our proposal have been illustrated by evaluating the Key Performance Indicators (KPIs) of the 5G network, such as network attach (registration) time and handover time. We have also demonstrated improvements in attach time and system throughput due to the use of centralized algorithms for mobility management with the help of ns-3 simulations

Convergence of Batch Asynchronous Stochastic Approximation With Applications to Reinforcement Learning

The stochastic approximation algorithm is a widely used probabilistic method for finding a zero of a vector-valued funtion, when only noisy measurements of the function are available. In the literature to date, one can make a distinction between "synchronous" updating, whereby every component of the current guess is updated at each time, and `"synchronous" updating, whereby only one component is updated. In principle, it is also possible to update, at each time instant, some but not all components of $\theta_t$, which might be termed as "batch asynchronous stochastic approximation" (BASA). Also, one can also make a distinction between using a "local" clock versus a "global" clock. In this paper, we propose a unified formulation of batch asynchronous stochastic approximation (BASA) algorithms, and develop a general methodology for proving that such algorithms converge, irrespective of whether global or local clocks are used. These convergence proofs make use of weaker hypotheses than existing results. For example: existing convergence proofs when a local clock is used require that the measurement noise is an i.i.d sequence. Here, it is assumed that the measurement errors form a martingale difference sequence. Also, all results to date assume that the stochastic step sizes satisfy a probabilistic analog of the Robbins-Monro conditions. We replace this by a purely deterministic condition on the irreducibility of the underlying Markov processes. As specific applications to Reinforcement Learning, we introduce ``batch'' versions of the temporal difference algorithm $TD(0)$ for value iteration, and the $Q$-learning algorithm for finding the optimal action-value function, and also permit the use of local clocks instead of a global clock. In all cases, we establish the convergence of these algorithms, under milder conditions than in the existing literature.Comment: 27 page

Measure free martingales

We give a necessary and sufficient condition on a sequence of functions on a set Ω under which there is a measure on Ω which renders the given sequence of functions a martingale. Further such a measure is unique if we impose a natural maximum entropy condition on the conditional probabilities

The impact of insulin resistance, dyslipidemia and high sensitivity C-reactivity protein on carotid intima-media thickness in metabolic syndrome

Background: Carotid intima-media thickness (CIMT) is a strong predictor of cardiovascular events and associated with metabolic syndrome (MetS). The CIMT has been widely used as one of the parameters of atherosclerosis. The aim of the study was to evaluate the impact of insulin resistance, dyslipidemia and high sensitivity C-reactive protein on carotid intima-media thickness in metabolic syndrome patients of Western Maharashtra as very sparse data is available.Methods: It was a cross-sectional study of 400 adults (200 cases and 200 control), 18-50 years of age, both the sexes randomly selected from diabetes and obesity OPD at tertiary care hospital. Diagnosis of metabolic syndrome was done according to modified NCEP adult treatment panel III criteria. CIMT was measured by B mode ultrasound (Philips HT-11, Color Doppler), hs-CRP by ELISA method (Cal biotech). Insulin resistance by HOMA-IR (Homeostatic model assessment of insulin resistance). The predictors of CIMT with various variables were studied by multiple linear regression analysis.Results: We found significant increase in CIMT (0.7895±0.110, p<0.001) in MetS and a positive correlation of CIMT with age, waist to hip ratio, triglyceride levels and systolic blood pressure (p<0.001).Conclusions: Increased carotid intima-media thickness in metabolic syndrome may increase the risk of having a stroke and cardiovascular mortality. It was considered an early deterioration in the arterial intima and is a preclinical stage of atherosclerosis. Early diagnosis and prevention may help to reduce the risk of stroke and cardiovascular mortality.

Prediction of Heart Disease Using Machine Learning Algorithms

The successful experiment of data mining in highly visible fields like marketing, e-business, and retail has led to its application in other sectors and industries. Healthcare is being discovered among these areas. There is an opulence of data available within the healthcare systems. However, there is a scarcity of useful analysis tool to find hidden relationships in data. This research intends to provide a detailed description of Naïve Bayes and decision tree classifier that are applied in our research particularly in the prediction of Heart Disease. Some experiment has been conducted to compare the execution of predictive data mining technique on the same dataset, and the consequence reveals that Decision Tree outperforms over Bayesian classification

Suitability of cellulose ester derivatives in hot melt extrusion.Thermal, rheological and thermodynamic approaches used in the characterization of cellulose ester derivatives for their suitability in pharmaceutical hot melt extrusion

Applications of Hot Melt Extrusion (HME) in pharmaceuticals have become increasingly popular over the years but nonetheless a few obstacles still remain before wide scale implementation. In many instances these improvements are related to both processing and product performance. It is observed that HME process optimisation is majorly focused on the active pharmaceutical ingredient's (API) properties. Characterising polymeric properties for their suitability in HME should be equally studied since the impact of excipients on both product and process performance is just as vital. In this work, two well-established cellulose ester derivatives: Hydroxy Propyl Methyl Cellulose Acetate Succinate (HPMCAS) and Hydroxy Propyl Methyl Cellulose Phthalate (HPMCP) are studied for their HME suitability. Their thermal, thermodynamic, rheological, thermo-chemical and degradation kinetic properties were evaluated with model plasticisers and APIs. It was found the thermal properties of HPMCP are severely compromised whereas HPMCAS is more stable in the processing zone of 150 to 200 °C. Thermodynamic properties revealed that both polymers share an important solubility parameter range (20-30 MPa P1/2P) where the majority of plasticisers and BCS class II APIs lie. Thus, greater miscibility/solubility can be expected. Further, the processability of these two polymers investigated by rheometric measurements showed HPMCAS possesses better flow properties than HPMCP because HPMCP forms a weak network of chain interactions at a molecular level. However, adding plasticisers such as PEG and TEC the flow properties of HPMCP can be tailored. The study also showed that plasticisers have a major influence on thermo-chemical and kinetic properties of polymers. For instance, PEG reduced polymer degradation with reversal in kinetic parameters whereas blends of CA produced detrimental effects and increased polymer degradation with reduction in onset degradation temperatures. Further, both polymers are observed to be chemically reactive with the APIs containing free -OH, -SOR2RN- and -NH2 groups. Finally, these properties prove that suitability of HPMCP is highly debated for HME and demands great care in use while that of HPMCAS is relatively better than HPMCP in many instances

Enhancement of reactivity and increased usage of low lime class -F-fly ash-possible avenues

The low lime class-F fly ash available in the country shows high degree of variability in the quality, higher content of crystallites , lower glassy phase which accounts for lower of usage in cement and concrete . The time reactivity test used for assessing the pozzolanicity of fly ash did not always correlate with its observed reactivity in Blended cements . An alternative rapid alkali reactivity rest developed at the authors ' laboratory is illustrated in the paper. The paper also discusses the possibility of increasing the reactivity of fly ash and effect of the reactive fly ash on characteristics of PPC and concrete. The paper further discusses other avenues of fly ash utilisation, which could be categorised as low, medium and high value applications. One of such applications developed at the authors ' laboratory that merits special interest, is the Hydrogel process of clinkerisation , which has a potential for utilisation of 20-30% fly ash as a raw material in cement manufacture

Stochastic integration based on simple, symmetric random walks

A new approach to stochastic integration is described, which is based on an a.s. pathwise approximation of the integrator by simple, symmetric random walks. Hopefully, this method is didactically more advantageous, more transparent, and technically less demanding than other existing ones. In a large part of the theory one has a.s. uniform convergence on compacts. In particular, it gives a.s. convergence for the stochastic integral of a finite variation function of the integrator, which is not c\`adl\`ag in general.Comment: 16 pages, some typos correcte

The Parametric Ordinal-Recursive Complexity of Post Embedding Problems

Post Embedding Problems are a family of decision problems based on the interaction of a rational relation with the subword embedding ordering, and are used in the literature to prove non multiply-recursive complexity lower bounds. We refine the construction of Chambart and Schnoebelen (LICS 2008) and prove parametric lower bounds depending on the size of the alphabet.Comment: 16 + vii page